diff --git a/python/ClipDetection/COPYING b/python/ClipDetection/COPYING
index 19dc35b2..c33078f3 100644
--- a/python/ClipDetection/COPYING
+++ b/python/ClipDetection/COPYING
@@ -1,175 +1,175 @@
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+ http://www.apache.org/licenses/
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+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+ "You" (or "Your") shall mean an individual or Legal Entity
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+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
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+ "Work" shall mean the work of authorship, whether in Source or
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+ 4. Redistribution. You may reproduce and distribute copies of the
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+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
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+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
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+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/.gitignore b/python/ClipDetection/CoOp/.gitignore
new file mode 100644
index 00000000..ef81d188
--- /dev/null
+++ b/python/ClipDetection/CoOp/.gitignore
@@ -0,0 +1,133 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Custom
+# output/
+debug.sh
diff --git a/python/ClipDetection/CoOp/LICENSE b/python/ClipDetection/CoOp/LICENSE
new file mode 100644
index 00000000..26d793c7
--- /dev/null
+++ b/python/ClipDetection/CoOp/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Kaiyang Zhou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/python/ClipDetection/CoOp/MODIFICATIONS b/python/ClipDetection/CoOp/MODIFICATIONS
new file mode 100644
index 00000000..d0dc8356
--- /dev/null
+++ b/python/ClipDetection/CoOp/MODIFICATIONS
@@ -0,0 +1,20 @@
+The files in the following directories have been created for use in the ClipDetection repo:
+
+./configs/CoOp/vit_l14_ep50.yaml
+
+
+The files in the following directories have been modified (see top of file for description of changes):
+
+./train.py
+./trainers/coop.py
+./clip/clip.py
+
+
+The files in the following directories CAN PROBABLY BE DELETED:
+
+./configs/* (except for files listed above)
+./datasets/*
+./lpclip/*
+./output/* (once trained model files are saved in Docker)
+./saved_outputs/*
+./scripts/*
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/README.md b/python/ClipDetection/CoOp/README.md
new file mode 100644
index 00000000..31ee299c
--- /dev/null
+++ b/python/ClipDetection/CoOp/README.md
@@ -0,0 +1,64 @@
+# Prompt Learning for Vision-Language Models
+
+This repo contains the codebase of a series of research projects focused on adapting vision-language models like [CLIP](https://arxiv.org/abs/2103.00020) to downstream datasets via *prompt learning*:
+
+* [Conditional Prompt Learning for Vision-Language Models](https://arxiv.org/abs/2203.05557), in CVPR, 2022.
+* [Learning to Prompt for Vision-Language Models](https://arxiv.org/abs/2109.01134), IJCV, 2022.
+
+## Updates
+
+- **07.10.2022**: Just added to both [CoOp](https://arxiv.org/abs/2109.01134) and [CoCoOp](https://arxiv.org/abs/2203.05557) (in their appendices) the results on the newly proposed DOSCO (DOmain Shift in COntext) benchmark, which focuses on contextual domain shift and covers a diverse set of classification problems. (The paper about DOSCO is [here](https://arxiv.org/abs/2209.07521) and the code for running CoOp/CoCoOp on DOSCO is [here](https://github.com/KaiyangZhou/on-device-dg).)
+
+- **17.09.2022**: [Call for Papers](https://kaiyangzhou.github.io/assets/cfp_ijcv_lvms.html): IJCV Special Issue on *The Promises and Dangers of Large Vision Models*.
+
+- **16.07.2022**: CoOp has been accepted to IJCV for publication!
+
+- **10.06.2022**: Our latest work, [Neural Prompt Search](https://arxiv.org/abs/2206.04673), has just been released on arxiv. It provides a novel perspective for fine-tuning large vision models like [ViT](https://arxiv.org/abs/2010.11929), so please check it out if you're interested in parameter-efficient fine-tuning/transfer learning. The code is also made public [here](https://github.com/Davidzhangyuanhan/NOAH).
+
+- **08.06.2022**: If you're looking for the code to draw the few-shot performance curves (like the ones we show in the CoOp's paper), see `draw_curves.py`.
+
+- **09.04.2022**: The pre-trained weights of CoOp on ImageNet are released [here](#pre-trained-models).
+
+- **11.03.2022**: The code of our CVPR'22 paper, "[Conditional Prompt Learning for Vision-Language Models](https://arxiv.org/abs/2203.05557)," is released.
+
+- **15.10.2021**: We find that the `best_val` model and the `last_step` model achieve similar performance, so we set `TEST.FINAL_MODEL = "last_step"` for all datasets to save training time. Why we used `best_val`: the ([tiny](https://github.com/KaiyangZhou/CoOp/blob/main/datasets/oxford_pets.py#L32)) validation set was designed for the linear probe approach, which requires extensive tuning for its hyperparameters, so we used the `best_val` model for CoOp as well for fair comparison (in this way, both approaches have access to the validation set).
+
+- **09.10.2021**: Important changes are made to Dassl's transforms.py. Please pull the latest commits from https://github.com/KaiyangZhou/Dassl.pytorch and this repo to make sure the code works properly. In particular, 1) `center_crop` now becomes a default transform in testing (applied after resizing the smaller edge to a certain size to keep the image aspect ratio), and 2) for training, `Resize(cfg.INPUT.SIZE)` is deactivated when `random_crop` or `random_resized_crop` is used. Please read this [issue](https://github.com/KaiyangZhou/CoOp/issues/8) on how these changes might affect the performance.
+
+- **18.09.2021**: We have fixed an error in Dassl which could cause a training data loader to have zero length (so no training will be performed) when the dataset size is smaller than the batch size (due to `drop_last=True`). Please pull the latest commit for Dassl (>= `8eecc3c`). This error led to lower results for CoOp in EuroSAT's 1- and 2-shot settings (others are all correct). We will update the paper on arxiv to fix this error.
+
+## How to Install
+This code is built on top of the awesome toolbox [Dassl.pytorch](https://github.com/KaiyangZhou/Dassl.pytorch) so you need to install the `dassl` environment first. Simply follow the instructions described [here](https://github.com/KaiyangZhou/Dassl.pytorch#installation) to install `dassl` as well as PyTorch. After that, run `pip install -r requirements.txt` under `CoOp/` to install a few more packages required by [CLIP](https://github.com/openai/CLIP) (this should be done when `dassl` is activated). Then, you are ready to go.
+
+Follow [DATASETS.md](DATASETS.md) to install the datasets.
+
+## How to Run
+
+Click a paper below to see the detailed instructions on how to run the code to reproduce the results.
+
+* [Learning to Prompt for Vision-Language Models](COOP.md)
+* [Conditional Prompt Learning for Vision-Language Models](COCOOP.md)
+
+## Models and Results
+
+- The pre-trained weights of CoOp (both M=16 & M=4) on ImageNet based on RN50, RN101, ViT-B/16 and ViT-B/32 can be downloaded altogether via this [link](https://drive.google.com/file/d/18ypxfd82RR0pizc5MM1ZWDYDk4j0BtPF/view?usp=sharing). The weights can be used to reproduce the results in Table 1 of CoOp's paper (i.e., the results on ImageNet and its four variants with domain shift). To load the weights and run the evaluation code, you will need to specify `--model-dir` and `--load-epoch` (see this [script](https://github.com/KaiyangZhou/CoOp/blob/main/scripts/eval.sh) for example).
+- The raw numerical results can be found at this [google drive link](https://docs.google.com/spreadsheets/d/12_kaFdD0nct9aUIrDoreY0qDunQ9q9tv/edit?usp=sharing&ouid=100312610418109826457&rtpof=true&sd=true).
+
+## Citation
+If you use this code in your research, please kindly cite the following papers
+
+```bash
+@inproceedings{zhou2022cocoop,
+ title={Conditional Prompt Learning for Vision-Language Models},
+ author={Zhou, Kaiyang and Yang, Jingkang and Loy, Chen Change and Liu, Ziwei},
+ booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+ year={2022}
+}
+
+@article{zhou2022coop,
+ title={Learning to Prompt for Vision-Language Models},
+ author={Zhou, Kaiyang and Yang, Jingkang and Loy, Chen Change and Liu, Ziwei},
+ journal={International Journal of Computer Vision (IJCV)},
+ year={2022}
+}
+```
diff --git a/python/ClipDetection/CoOp/clip/__init__.py b/python/ClipDetection/CoOp/clip/__init__.py
new file mode 100644
index 00000000..dcc56195
--- /dev/null
+++ b/python/ClipDetection/CoOp/clip/__init__.py
@@ -0,0 +1 @@
+from .clip import *
diff --git a/python/ClipDetection/CoOp/clip/bpe_simple_vocab_16e6.txt.gz b/python/ClipDetection/CoOp/clip/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
index 00000000..7b5088a5
Binary files /dev/null and b/python/ClipDetection/CoOp/clip/bpe_simple_vocab_16e6.txt.gz differ
diff --git a/python/ClipDetection/CoOp/clip/clip.py b/python/ClipDetection/CoOp/clip/clip.py
new file mode 100644
index 00000000..7cfaf724
--- /dev/null
+++ b/python/ClipDetection/CoOp/clip/clip.py
@@ -0,0 +1,253 @@
+################################################################
+# CHANGES MADE TO FILE #
+# ------------------------------------------------------------ #
+# Modified CLIP files to support ViT-L/14 model #
+# - From OpenAI source code for CLIP #
+# #
+################################################################
+
+import hashlib
+import os
+import urllib
+import warnings
+from typing import Any, Union, List
+from pkg_resources import packaging
+
+import torch
+from PIL import Image
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from tqdm import tqdm
+
+from .model import build_model
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+try:
+ from torchvision.transforms import InterpolationMode
+ BICUBIC = InterpolationMode.BICUBIC
+except ImportError:
+ BICUBIC = Image.BICUBIC
+
+
+if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
+ warnings.warn("PyTorch version 1.7.1 or higher is recommended")
+
+
+__all__ = ["available_models", "load", "tokenize"]
+_tokenizer = _Tokenizer()
+
+_MODELS = {
+ "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
+ "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
+ "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
+ "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
+ "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
+ "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
+ "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
+ "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
+ "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
+}
+
+
+def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
+ os.makedirs(root, exist_ok=True)
+ filename = os.path.basename(url)
+
+ expected_sha256 = url.split("/")[-2]
+ download_target = os.path.join(root, filename)
+
+ if os.path.exists(download_target) and not os.path.isfile(download_target):
+ raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+ if os.path.isfile(download_target):
+ if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
+ return download_target
+ else:
+ warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+ with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+ with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+ while True:
+ buffer = source.read(8192)
+ if not buffer:
+ break
+
+ output.write(buffer)
+ loop.update(len(buffer))
+
+ if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
+ raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
+
+ return download_target
+
+
+def _convert_image_to_rgb(image):
+ return image.convert("RGB")
+
+
+def _transform(n_px):
+ return Compose([
+ Resize(n_px, interpolation=BICUBIC),
+ CenterCrop(n_px),
+ _convert_image_to_rgb,
+ ToTensor(),
+ Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+ ])
+
+
+def available_models() -> List[str]:
+ """Returns the names of available CLIP models"""
+ return list(_MODELS.keys())
+
+
+def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
+ """Load a CLIP model
+
+ Parameters
+ ----------
+ name : str
+ A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
+
+ device : Union[str, torch.device]
+ The device to put the loaded model
+
+ jit : bool
+ Whether to load the optimized JIT model or more hackable non-JIT model (default).
+
+ download_root: str
+ path to download the model files; by default, it uses "~/.cache/clip"
+
+ Returns
+ -------
+ model : torch.nn.Module
+ The CLIP model
+
+ preprocess : Callable[[PIL.Image], torch.Tensor]
+ A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
+ """
+ if name in _MODELS:
+ model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
+ elif os.path.isfile(name):
+ model_path = name
+ else:
+ raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+ with open(model_path, 'rb') as opened_file:
+ try:
+ # loading JIT archive
+ model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
+ state_dict = None
+ except RuntimeError:
+ # loading saved state dict
+ if jit:
+ warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
+ jit = False
+ state_dict = torch.load(opened_file, map_location="cpu")
+
+ if not jit:
+ model = build_model(state_dict or model.state_dict()).to(device)
+ if str(device) == "cpu":
+ model.float()
+ return model, _transform(model.visual.input_resolution)
+
+ # patch the device names
+ device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
+ device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
+
+ def _node_get(node: torch._C.Node, key: str):
+ """Gets attributes of a node which is polymorphic over return type.
+
+ From https://github.com/pytorch/pytorch/pull/82628
+ """
+ sel = node.kindOf(key)
+ return getattr(node, sel)(key)
+
+ def patch_device(module):
+ try:
+ graphs = [module.graph] if hasattr(module, "graph") else []
+ except RuntimeError:
+ graphs = []
+
+ if hasattr(module, "forward1"):
+ graphs.append(module.forward1.graph)
+
+ for graph in graphs:
+ for node in graph.findAllNodes("prim::Constant"):
+ if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
+ node.copyAttributes(device_node)
+
+ model.apply(patch_device)
+ patch_device(model.encode_image)
+ patch_device(model.encode_text)
+
+ # patch dtype to float32 on CPU
+ if str(device) == "cpu":
+ float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
+ float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
+ float_node = float_input.node()
+
+ def patch_float(module):
+ try:
+ graphs = [module.graph] if hasattr(module, "graph") else []
+ except RuntimeError:
+ graphs = []
+
+ if hasattr(module, "forward1"):
+ graphs.append(module.forward1.graph)
+
+ for graph in graphs:
+ for node in graph.findAllNodes("aten::to"):
+ inputs = list(node.inputs())
+ for i in [1, 2]: # dtype can be the second or third argument to aten::to()
+ if _node_get(inputs[i].node(), "value") == 5:
+ inputs[i].node().copyAttributes(float_node)
+
+ model.apply(patch_float)
+ patch_float(model.encode_image)
+ patch_float(model.encode_text)
+
+ model.float()
+
+ return model, _transform(model.input_resolution.item())
+
+
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
+ """
+ Returns the tokenized representation of given input string(s)
+
+ Parameters
+ ----------
+ texts : Union[str, List[str]]
+ An input string or a list of input strings to tokenize
+
+ context_length : int
+ The context length to use; all CLIP models use 77 as the context length
+
+ truncate: bool
+ Whether to truncate the text in case its encoding is longer than the context length
+
+ Returns
+ -------
+ A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
+ We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
+ """
+ if isinstance(texts, str):
+ texts = [texts]
+
+ sot_token = _tokenizer.encoder["<|startoftext|>"]
+ eot_token = _tokenizer.encoder["<|endoftext|>"]
+ all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+ if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
+ result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+ else:
+ result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
+
+ for i, tokens in enumerate(all_tokens):
+ if len(tokens) > context_length:
+ if truncate:
+ tokens = tokens[:context_length]
+ tokens[-1] = eot_token
+ else:
+ raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+ result[i, :len(tokens)] = torch.tensor(tokens)
+
+ return result
diff --git a/python/ClipDetection/CoOp/clip/model.py b/python/ClipDetection/CoOp/clip/model.py
new file mode 100644
index 00000000..232b7792
--- /dev/null
+++ b/python/ClipDetection/CoOp/clip/model.py
@@ -0,0 +1,436 @@
+from collections import OrderedDict
+from typing import Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1):
+ super().__init__()
+
+ # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
+ self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.relu1 = nn.ReLU(inplace=True)
+
+ self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.relu2 = nn.ReLU(inplace=True)
+
+ self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
+
+ self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
+ self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+ self.relu3 = nn.ReLU(inplace=True)
+
+ self.downsample = None
+ self.stride = stride
+
+ if stride > 1 or inplanes != planes * Bottleneck.expansion:
+ # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
+ self.downsample = nn.Sequential(OrderedDict([
+ ("-1", nn.AvgPool2d(stride)),
+ ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
+ ("1", nn.BatchNorm2d(planes * self.expansion))
+ ]))
+
+ def forward(self, x: torch.Tensor):
+ identity = x
+
+ out = self.relu1(self.bn1(self.conv1(x)))
+ out = self.relu2(self.bn2(self.conv2(out)))
+ out = self.avgpool(out)
+ out = self.bn3(self.conv3(out))
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu3(out)
+ return out
+
+
+class AttentionPool2d(nn.Module):
+ def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
+ self.k_proj = nn.Linear(embed_dim, embed_dim)
+ self.q_proj = nn.Linear(embed_dim, embed_dim)
+ self.v_proj = nn.Linear(embed_dim, embed_dim)
+ self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
+ self.num_heads = num_heads
+
+ def forward(self, x):
+ x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC
+ x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
+ x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
+ x, _ = F.multi_head_attention_forward(
+ query=x[:1], key=x, value=x,
+ embed_dim_to_check=x.shape[-1],
+ num_heads=self.num_heads,
+ q_proj_weight=self.q_proj.weight,
+ k_proj_weight=self.k_proj.weight,
+ v_proj_weight=self.v_proj.weight,
+ in_proj_weight=None,
+ in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+ bias_k=None,
+ bias_v=None,
+ add_zero_attn=False,
+ dropout_p=0,
+ out_proj_weight=self.c_proj.weight,
+ out_proj_bias=self.c_proj.bias,
+ use_separate_proj_weight=True,
+ training=self.training,
+ need_weights=False
+ )
+ return x.squeeze(0)
+
+
+class ModifiedResNet(nn.Module):
+ """
+ A ResNet class that is similar to torchvision's but contains the following changes:
+ - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
+ - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
+ - The final pooling layer is a QKV attention instead of an average pool
+ """
+
+ def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
+ super().__init__()
+ self.output_dim = output_dim
+ self.input_resolution = input_resolution
+
+ # the 3-layer stem
+ self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
+ self.bn1 = nn.BatchNorm2d(width // 2)
+ self.relu1 = nn.ReLU(inplace=True)
+ self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
+ self.bn2 = nn.BatchNorm2d(width // 2)
+ self.relu2 = nn.ReLU(inplace=True)
+ self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
+ self.bn3 = nn.BatchNorm2d(width)
+ self.relu3 = nn.ReLU(inplace=True)
+ self.avgpool = nn.AvgPool2d(2)
+
+ # residual layers
+ self._inplanes = width # this is a *mutable* variable used during construction
+ self.layer1 = self._make_layer(width, layers[0])
+ self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
+ self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
+ self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
+
+ embed_dim = width * 32 # the ResNet feature dimension
+ self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
+
+ def _make_layer(self, planes, blocks, stride=1):
+ layers = [Bottleneck(self._inplanes, planes, stride)]
+
+ self._inplanes = planes * Bottleneck.expansion
+ for _ in range(1, blocks):
+ layers.append(Bottleneck(self._inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ def stem(x):
+ x = self.relu1(self.bn1(self.conv1(x)))
+ x = self.relu2(self.bn2(self.conv2(x)))
+ x = self.relu3(self.bn3(self.conv3(x)))
+ x = self.avgpool(x)
+ return x
+
+ x = x.type(self.conv1.weight.dtype)
+ x = stem(x)
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+ x = self.layer4(x)
+ x = self.attnpool(x)
+
+ return x
+
+
+class LayerNorm(nn.LayerNorm):
+ """Subclass torch's LayerNorm to handle fp16."""
+
+ def forward(self, x: torch.Tensor):
+ orig_type = x.dtype
+ ret = super().forward(x.type(torch.float32))
+ return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+ def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+ super().__init__()
+
+ self.attn = nn.MultiheadAttention(d_model, n_head)
+ self.ln_1 = LayerNorm(d_model)
+ self.mlp = nn.Sequential(OrderedDict([
+ ("c_fc", nn.Linear(d_model, d_model * 4)),
+ ("gelu", QuickGELU()),
+ ("c_proj", nn.Linear(d_model * 4, d_model))
+ ]))
+ self.ln_2 = LayerNorm(d_model)
+ self.attn_mask = attn_mask
+
+ def attention(self, x: torch.Tensor):
+ self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+ return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+ def forward(self, x: torch.Tensor):
+ x = x + self.attention(self.ln_1(x))
+ x = x + self.mlp(self.ln_2(x))
+ return x
+
+
+class Transformer(nn.Module):
+ def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
+ super().__init__()
+ self.width = width
+ self.layers = layers
+ self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
+
+ def forward(self, x: torch.Tensor):
+ return self.resblocks(x)
+
+
+class VisionTransformer(nn.Module):
+ def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.output_dim = output_dim
+ self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
+
+ scale = width ** -0.5
+ self.class_embedding = nn.Parameter(scale * torch.randn(width))
+ self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
+ self.ln_pre = LayerNorm(width)
+
+ self.transformer = Transformer(width, layers, heads)
+
+ self.ln_post = LayerNorm(width)
+ self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
+
+ def forward(self, x: torch.Tensor):
+ x = self.conv1(x) # shape = [*, width, grid, grid]
+ x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
+ x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
+ x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
+ x = x + self.positional_embedding.to(x.dtype)
+ x = self.ln_pre(x)
+
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ x = self.ln_post(x[:, 0, :])
+
+ if self.proj is not None:
+ x = x @ self.proj
+
+ return x
+
+
+class CLIP(nn.Module):
+ def __init__(self,
+ embed_dim: int,
+ # vision
+ image_resolution: int,
+ vision_layers: Union[Tuple[int, int, int, int], int],
+ vision_width: int,
+ vision_patch_size: int,
+ # text
+ context_length: int,
+ vocab_size: int,
+ transformer_width: int,
+ transformer_heads: int,
+ transformer_layers: int
+ ):
+ super().__init__()
+
+ self.context_length = context_length
+
+ if isinstance(vision_layers, (tuple, list)):
+ vision_heads = vision_width * 32 // 64
+ self.visual = ModifiedResNet(
+ layers=vision_layers,
+ output_dim=embed_dim,
+ heads=vision_heads,
+ input_resolution=image_resolution,
+ width=vision_width
+ )
+ else:
+ vision_heads = vision_width // 64
+ self.visual = VisionTransformer(
+ input_resolution=image_resolution,
+ patch_size=vision_patch_size,
+ width=vision_width,
+ layers=vision_layers,
+ heads=vision_heads,
+ output_dim=embed_dim
+ )
+
+ self.transformer = Transformer(
+ width=transformer_width,
+ layers=transformer_layers,
+ heads=transformer_heads,
+ attn_mask=self.build_attention_mask()
+ )
+
+ self.vocab_size = vocab_size
+ self.token_embedding = nn.Embedding(vocab_size, transformer_width)
+ self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
+ self.ln_final = LayerNorm(transformer_width)
+
+ self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
+ self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+ self.initialize_parameters()
+
+ def initialize_parameters(self):
+ nn.init.normal_(self.token_embedding.weight, std=0.02)
+ nn.init.normal_(self.positional_embedding, std=0.01)
+
+ if isinstance(self.visual, ModifiedResNet):
+ if self.visual.attnpool is not None:
+ std = self.visual.attnpool.c_proj.in_features ** -0.5
+ nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
+ nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
+ nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
+ nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
+
+ for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
+ for name, param in resnet_block.named_parameters():
+ if name.endswith("bn3.weight"):
+ nn.init.zeros_(param)
+
+ proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
+ attn_std = self.transformer.width ** -0.5
+ fc_std = (2 * self.transformer.width) ** -0.5
+ for block in self.transformer.resblocks:
+ nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
+ nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
+ nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
+ nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
+
+ if self.text_projection is not None:
+ nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
+
+ def build_attention_mask(self):
+ # lazily create causal attention mask, with full attention between the vision tokens
+ # pytorch uses additive attention mask; fill with -inf
+ mask = torch.empty(self.context_length, self.context_length)
+ mask.fill_(float("-inf"))
+ mask.triu_(1) # zero out the lower diagonal
+ return mask
+
+ @property
+ def dtype(self):
+ return self.visual.conv1.weight.dtype
+
+ def encode_image(self, image):
+ return self.visual(image.type(self.dtype))
+
+ def encode_text(self, text):
+ x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
+
+ x = x + self.positional_embedding.type(self.dtype)
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.ln_final(x).type(self.dtype)
+
+ # x.shape = [batch_size, n_ctx, transformer.width]
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+
+ return x
+
+ def forward(self, image, text):
+ image_features = self.encode_image(image)
+ text_features = self.encode_text(text)
+
+ # normalized features
+ image_features = image_features / image_features.norm(dim=1, keepdim=True)
+ text_features = text_features / text_features.norm(dim=1, keepdim=True)
+
+ # cosine similarity as logits
+ logit_scale = self.logit_scale.exp()
+ logits_per_image = logit_scale * image_features @ text_features.t()
+ logits_per_text = logits_per_image.t()
+
+ # shape = [global_batch_size, global_batch_size]
+ return logits_per_image, logits_per_text
+
+
+def convert_weights(model: nn.Module):
+ """Convert applicable model parameters to fp16"""
+
+ def _convert_weights_to_fp16(l):
+ if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+ l.weight.data = l.weight.data.half()
+ if l.bias is not None:
+ l.bias.data = l.bias.data.half()
+
+ if isinstance(l, nn.MultiheadAttention):
+ for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+ tensor = getattr(l, attr)
+ if tensor is not None:
+ tensor.data = tensor.data.half()
+
+ for name in ["text_projection", "proj"]:
+ if hasattr(l, name):
+ attr = getattr(l, name)
+ if attr is not None:
+ attr.data = attr.data.half()
+
+ model.apply(_convert_weights_to_fp16)
+
+
+def build_model(state_dict: dict):
+ vit = "visual.proj" in state_dict
+
+ if vit:
+ vision_width = state_dict["visual.conv1.weight"].shape[0]
+ vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
+ vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+ grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
+ image_resolution = vision_patch_size * grid_size
+ else:
+ counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
+ vision_layers = tuple(counts)
+ vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
+ output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
+ vision_patch_size = None
+ assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
+ image_resolution = output_width * 32
+
+ embed_dim = state_dict["text_projection"].shape[1]
+ context_length = state_dict["positional_embedding"].shape[0]
+ vocab_size = state_dict["token_embedding.weight"].shape[0]
+ transformer_width = state_dict["ln_final.weight"].shape[0]
+ transformer_heads = transformer_width // 64
+ transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
+
+ model = CLIP(
+ embed_dim,
+ image_resolution, vision_layers, vision_width, vision_patch_size,
+ context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
+ )
+
+ for key in ["input_resolution", "context_length", "vocab_size"]:
+ if key in state_dict:
+ del state_dict[key]
+
+ convert_weights(model)
+ model.load_state_dict(state_dict)
+ return model.eval()
diff --git a/python/ClipDetection/CoOp/clip/simple_tokenizer.py b/python/ClipDetection/CoOp/clip/simple_tokenizer.py
new file mode 100644
index 00000000..0a66286b
--- /dev/null
+++ b/python/ClipDetection/CoOp/clip/simple_tokenizer.py
@@ -0,0 +1,132 @@
+import gzip
+import html
+import os
+from functools import lru_cache
+
+import ftfy
+import regex as re
+
+
+@lru_cache()
+def default_bpe():
+ return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
+
+
+@lru_cache()
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a corresponding list of unicode strings.
+ The reversible bpe codes work on unicode strings.
+ This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+ When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+ This is a signficant percentage of your normal, say, 32K bpe vocab.
+ To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8+n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """Return set of symbol pairs in a word.
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+def basic_clean(text):
+ text = ftfy.fix_text(text)
+ text = html.unescape(html.unescape(text))
+ return text.strip()
+
+
+def whitespace_clean(text):
+ text = re.sub(r'\s+', ' ', text)
+ text = text.strip()
+ return text
+
+
+class SimpleTokenizer(object):
+ def __init__(self, bpe_path: str = default_bpe()):
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
+ merges = merges[1:49152-256-2+1]
+ merges = [tuple(merge.split()) for merge in merges]
+ vocab = list(bytes_to_unicode().values())
+ vocab = vocab + [v+'' for v in vocab]
+ for merge in merges:
+ vocab.append(''.join(merge))
+ vocab.extend(['<|startoftext|>', '<|endoftext|>'])
+ self.encoder = dict(zip(vocab, range(len(vocab))))
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.bpe_ranks = dict(zip(merges, range(len(merges))))
+ self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
+ self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token[:-1]) + ( token[-1] + '',)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token+''
+
+ while True:
+ bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ new_word.extend(word[i:j])
+ i = j
+ except:
+ new_word.extend(word[i:])
+ break
+
+ if word[i] == first and i < len(word)-1 and word[i+1] == second:
+ new_word.append(first+second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = ' '.join(word)
+ self.cache[token] = word
+ return word
+
+ def encode(self, text):
+ bpe_tokens = []
+ text = whitespace_clean(basic_clean(text)).lower()
+ for token in re.findall(self.pat, text):
+ token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+ bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+ return bpe_tokens
+
+ def decode(self, tokens):
+ text = ''.join([self.decoder[token] for token in tokens])
+ text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ')
+ return text
diff --git a/python/ClipDetection/CoOp/configs/trainers/CoOp/vit_l14_ep50.yaml b/python/ClipDetection/CoOp/configs/trainers/CoOp/vit_l14_ep50.yaml
new file mode 100644
index 00000000..2319b286
--- /dev/null
+++ b/python/ClipDetection/CoOp/configs/trainers/CoOp/vit_l14_ep50.yaml
@@ -0,0 +1,29 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 32
+ TEST:
+ BATCH_SIZE: 100
+ NUM_WORKERS: 8
+
+INPUT:
+ SIZE: (224, 224)
+ INTERPOLATION: "bicubic"
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"]
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ MAX_EPOCH: 50
+ LR_SCHEDULER: "cosine"
+ WARMUP_EPOCH: 1
+ WARMUP_TYPE: "constant"
+ WARMUP_CONS_LR: 1e-5
+
+TRAIN:
+ PRINT_FREQ: 5
+
+MODEL:
+ BACKBONE:
+ NAME: "ViT-L/14"
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/coop_test_results.txt b/python/ClipDetection/CoOp/coop_test_results.txt
new file mode 100644
index 00000000..e3ef2a5b
--- /dev/null
+++ b/python/ClipDetection/CoOp/coop_test_results.txt
@@ -0,0 +1,1008 @@
+CoOp Results:
+
+tench: 1/50
+goldfish: 36/50
+great white shark: 45/50
+tiger shark: 13/50
+hammerhead shark: 23/50
+electric ray: 0/50
+stingray: 0/50
+rooster: 0/50
+hen: 25/50
+ostrich: 2/50
+brambling: 16/50
+goldfinch: 5/50
+house finch: 48/50
+junco: 40/50
+indigo bunting: 49/50
+American robin: 49/50
+bulbul: 10/50
+jay: 40/50
+magpie: 25/50
+chickadee: 47/50
+American dipper: 14/50
+kite (bird of prey): 48/50
+bald eagle: 48/50
+vulture: 17/50
+great grey owl: 49/50
+fire salamander: 7/50
+smooth newt: 0/50
+newt: 0/50
+spotted salamander: 49/50
+axolotl: 42/50
+American bullfrog: 22/50
+tree frog: 12/50
+tailed frog: 9/50
+loggerhead sea turtle: 50/50
+leatherback sea turtle: 9/50
+mud turtle: 1/50
+terrapin: 4/50
+box turtle: 19/50
+banded gecko: 22/50
+green iguana: 7/50
+Carolina anole: 28/50
+desert grassland whiptail lizard: 46/50
+agama: 0/50
+frilled-necked lizard: 42/50
+alligator lizard: 13/50
+Gila monster: 24/50
+European green lizard: 0/50
+chameleon: 0/50
+Komodo dragon: 1/50
+Nile crocodile: 17/50
+American alligator: 0/50
+triceratops: 50/50
+worm snake: 1/50
+ring-necked snake: 49/50
+eastern hog-nosed snake: 0/50
+smooth green snake: 5/50
+kingsnake: 0/50
+garter snake: 0/50
+water snake: 0/50
+vine snake: 1/50
+night snake: 0/50
+boa constrictor: 2/50
+African rock python: 22/50
+Indian cobra: 7/50
+green mamba: 0/50
+sea snake: 0/50
+Saharan horned viper: 4/50
+eastern diamondback rattlesnake: 50/50
+sidewinder rattlesnake: 0/50
+trilobite: 5/50
+harvestman: 6/50
+scorpion: 0/50
+yellow garden spider: 5/50
+barn spider: 1/50
+European garden spider: 49/50
+southern black widow: 0/50
+tarantula: 37/50
+wolf spider: 2/50
+tick: 0/50
+centipede: 0/50
+black grouse: 19/50
+ptarmigan: 20/50
+ruffed grouse: 46/50
+prairie grouse: 11/50
+peafowl: 31/50
+quail: 45/50
+partridge: 0/50
+african grey parrot: 48/50
+macaw: 49/50
+sulphur-crested cockatoo: 44/50
+lorikeet: 49/50
+coucal: 39/50
+bee eater: 46/50
+hornbill: 34/50
+hummingbird: 18/50
+jacamar: 44/50
+toucan: 49/50
+duck: 21/50
+red-breasted merganser: 47/50
+goose: 19/50
+black swan: 2/50
+tusker: 29/50
+echidna: 41/50
+platypus: 2/50
+wallaby: 41/50
+koala: 1/50
+wombat: 30/50
+jellyfish: 0/50
+sea anemone: 0/50
+brain coral: 38/50
+flatworm: 25/50
+nematode: 0/50
+conch: 15/50
+snail: 42/50
+slug: 12/50
+sea slug: 9/50
+chiton: 0/50
+chambered nautilus: 7/50
+Dungeness crab: 46/50
+rock crab: 6/50
+fiddler crab: 4/50
+red king crab: 3/50
+American lobster: 43/50
+spiny lobster: 44/50
+crayfish: 13/50
+hermit crab: 39/50
+isopod: 33/50
+white stork: 18/50
+black stork: 6/50
+spoonbill: 39/50
+flamingo: 0/50
+little blue heron: 29/50
+great egret: 47/50
+bittern bird: 8/50
+crane bird: 5/50
+limpkin: 48/50
+common gallinule: 20/50
+American coot: 20/50
+bustard: 0/50
+ruddy turnstone: 50/50
+dunlin: 25/50
+common redshank: 0/50
+dowitcher: 39/50
+oystercatcher: 28/50
+pelican: 0/50
+king penguin: 50/50
+albatross: 31/50
+grey whale: 2/50
+killer whale: 46/50
+dugong: 47/50
+sea lion: 44/50
+Chihuahua: 5/50
+Japanese Chin: 35/50
+Maltese: 0/50
+Pekingese: 32/50
+Shih Tzu: 40/50
+King Charles Spaniel: 43/50
+Papillon: 0/50
+toy terrier: 23/50
+Rhodesian Ridgeback: 40/50
+Afghan Hound: 21/50
+Basset Hound: 26/50
+Beagle: 2/50
+Bloodhound: 12/50
+Bluetick Coonhound: 37/50
+Black and Tan Coonhound: 35/50
+Treeing Walker Coonhound: 48/50
+English foxhound: 16/50
+Redbone Coonhound: 27/50
+borzoi: 44/50
+Irish Wolfhound: 21/50
+Italian Greyhound: 44/50
+Whippet: 21/50
+Ibizan Hound: 40/50
+Norwegian Elkhound: 39/50
+Otterhound: 21/50
+Saluki: 28/50
+Scottish Deerhound: 46/50
+Weimaraner: 46/50
+Staffordshire Bull Terrier: 38/50
+American Staffordshire Terrier: 36/50
+Bedlington Terrier: 37/50
+Border Terrier: 41/50
+Kerry Blue Terrier: 5/50
+Irish Terrier: 33/50
+Norfolk Terrier: 34/50
+Norwich Terrier: 0/50
+Yorkshire Terrier: 33/50
+Wire Fox Terrier: 34/50
+Lakeland Terrier: 16/50
+Sealyham Terrier: 43/50
+Airedale Terrier: 26/50
+Cairn Terrier: 31/50
+Australian Terrier: 7/50
+Dandie Dinmont Terrier: 8/50
+Boston Terrier: 41/50
+Miniature Schnauzer: 25/50
+Giant Schnauzer: 27/50
+Standard Schnauzer: 33/50
+Scottish Terrier: 36/50
+Tibetan Terrier: 33/50
+Australian Silky Terrier: 9/50
+Soft-coated Wheaten Terrier: 42/50
+West Highland White Terrier: 48/50
+Lhasa Apso: 0/50
+Flat-Coated Retriever: 45/50
+Curly-coated Retriever: 16/50
+Golden Retriever: 33/50
+Labrador Retriever: 38/50
+Chesapeake Bay Retriever: 37/50
+German Shorthaired Pointer: 41/50
+Vizsla: 44/50
+English Setter: 28/50
+Irish Setter: 25/50
+Gordon Setter: 34/50
+Brittany dog: 0/50
+Clumber Spaniel: 44/50
+English Springer Spaniel: 41/50
+Welsh Springer Spaniel: 44/50
+Cocker Spaniel: 0/50
+Sussex Spaniel: 21/50
+Irish Water Spaniel: 41/50
+Kuvasz: 0/50
+Schipperke: 33/50
+Groenendael dog: 5/50
+Malinois: 36/50
+Briard: 0/50
+Australian Kelpie: 33/50
+Komondor: 26/50
+Old English Sheepdog: 33/50
+Shetland Sheepdog: 34/50
+collie: 1/50
+Border Collie: 27/50
+Bouvier des Flandres dog: 27/50
+Rottweiler: 42/50
+German Shepherd Dog: 48/50
+Dobermann: 31/50
+Miniature Pinscher: 45/50
+Greater Swiss Mountain Dog: 18/50
+Bernese Mountain Dog: 41/50
+Appenzeller Sennenhund: 0/50
+Entlebucher Sennenhund: 44/50
+Boxer: 27/50
+Bullmastiff: 42/50
+Tibetan Mastiff: 44/50
+French Bulldog: 44/50
+Great Dane: 30/50
+St. Bernard: 0/50
+husky: 0/50
+Alaskan Malamute: 31/50
+Siberian Husky: 28/50
+Dalmatian: 29/50
+Affenpinscher: 34/50
+Basenji: 0/50
+pug: 11/50
+Leonberger: 31/50
+Newfoundland dog: 42/50
+Great Pyrenees dog: 45/50
+Samoyed: 48/50
+Pomeranian: 30/50
+Chow Chow: 0/50
+Keeshond: 46/50
+brussels griffon: 29/50
+Pembroke Welsh Corgi: 44/50
+Cardigan Welsh Corgi: 25/50
+Toy Poodle: 35/50
+Miniature Poodle: 14/50
+Standard Poodle: 26/50
+Mexican hairless dog (xoloitzcuintli): 49/50
+grey wolf: 0/50
+Alaskan tundra wolf: 42/50
+red wolf or maned wolf: 36/50
+coyote: 0/50
+dingo: 0/50
+dhole: 7/50
+African wild dog: 48/50
+hyena: 33/50
+red fox: 0/50
+kit fox: 0/50
+Arctic fox: 3/50
+grey fox: 4/50
+tabby cat: 49/50
+tiger cat: 7/50
+Persian cat: 42/50
+Siamese cat: 50/50
+Egyptian Mau: 8/50
+cougar: 18/50
+lynx: 0/50
+leopard: 0/50
+snow leopard: 37/50
+jaguar: 15/50
+lion: 2/50
+tiger: 0/50
+cheetah: 0/50
+brown bear: 17/50
+American black bear: 42/50
+polar bear: 43/50
+sloth bear: 36/50
+mongoose: 0/50
+meerkat: 11/50
+tiger beetle: 1/50
+ladybug: 16/50
+ground beetle: 20/50
+longhorn beetle: 28/50
+leaf beetle: 2/50
+dung beetle: 2/50
+rhinoceros beetle: 49/50
+weevil: 1/50
+fly: 0/50
+bee: 25/50
+ant: 0/50
+grasshopper: 0/50
+cricket insect: 0/50
+stick insect: 38/50
+cockroach: 6/50
+praying mantis: 15/50
+cicada: 6/50
+leafhopper: 50/50
+lacewing: 20/50
+dragonfly: 0/50
+damselfly: 50/50
+red admiral butterfly: 17/50
+ringlet butterfly: 39/50
+monarch butterfly: 38/50
+small white butterfly: 29/50
+sulphur butterfly: 47/50
+gossamer-winged butterfly: 4/50
+starfish: 13/50
+sea urchin: 2/50
+sea cucumber: 0/50
+cottontail rabbit: 44/50
+hare: 0/50
+Angora rabbit: 48/50
+hamster: 50/50
+porcupine: 11/50
+fox squirrel: 1/50
+marmot: 35/50
+beaver: 0/50
+guinea pig: 36/50
+common sorrel horse: 0/50
+zebra: 0/50
+pig: 35/50
+wild boar: 0/50
+warthog: 44/50
+hippopotamus: 45/50
+ox: 0/50
+water buffalo: 0/50
+bison: 25/50
+ram (adult male sheep): 24/50
+bighorn sheep: 48/50
+Alpine ibex: 31/50
+hartebeest: 0/50
+impala (antelope): 6/50
+gazelle: 1/50
+arabian camel: 40/50
+llama: 22/50
+weasel: 0/50
+mink: 0/50
+European polecat: 13/50
+black-footed ferret: 45/50
+otter: 46/50
+skunk: 0/50
+badger: 1/50
+armadillo: 6/50
+three-toed sloth: 50/50
+orangutan: 46/50
+gorilla: 0/50
+chimpanzee: 28/50
+gibbon: 0/50
+siamang: 0/50
+guenon: 0/50
+patas monkey: 0/50
+baboon: 30/50
+macaque: 21/50
+langur: 41/50
+black-and-white colobus: 49/50
+proboscis monkey: 48/50
+marmoset: 0/50
+white-headed capuchin: 20/50
+howler monkey: 3/50
+titi monkey: 0/50
+Geoffroy's spider monkey: 0/50
+common squirrel monkey: 18/50
+ring-tailed lemur: 48/50
+indri: 0/50
+Asian elephant: 5/50
+African bush elephant: 48/50
+red panda: 47/50
+giant panda: 49/50
+snoek fish: 8/50
+eel: 0/50
+silver salmon: 0/50
+rock beauty fish: 0/50
+clownfish: 50/50
+sturgeon: 0/50
+gar fish: 0/50
+lionfish: 44/50
+pufferfish: 46/50
+abacus: 2/50
+abaya: 46/50
+academic gown: 50/50
+accordion: 0/50
+acoustic guitar: 43/50
+aircraft carrier: 47/50
+airliner: 22/50
+airship: 16/50
+altar: 10/50
+ambulance: 43/50
+amphibious vehicle: 33/50
+analog clock: 1/50
+apiary: 45/50
+apron: 20/50
+trash can: 1/50
+assault rifle: 18/50
+backpack: 21/50
+bakery: 3/50
+balance beam: 0/50
+balloon: 1/50
+ballpoint pen: 32/50
+Band-Aid: 43/50
+banjo: 0/50
+baluster / handrail: 40/50
+barbell: 13/50
+barber chair: 33/50
+barbershop: 4/50
+barn: 0/50
+barometer: 0/50
+barrel: 2/50
+wheelbarrow: 30/50
+baseball: 32/50
+basketball: 49/50
+bassinet: 0/50
+bassoon: 45/50
+swimming cap: 29/50
+bath towel: 15/50
+bathtub: 2/50
+station wagon: 30/50
+lighthouse: 0/50
+beaker: 10/50
+military hat (bearskin or shako): 42/50
+beer bottle: 36/50
+beer glass: 35/50
+bell tower: 9/50
+baby bib: 40/50
+tandem bicycle: 50/50
+bikini: 28/50
+ring binder: 3/50
+binoculars: 43/50
+birdhouse: 40/50
+boathouse: 0/50
+bobsleigh: 25/50
+bolo tie: 49/50
+poke bonnet: 0/50
+bookcase: 31/50
+bookstore: 31/50
+bottle cap: 2/50
+hunting bow: 25/50
+bow tie: 38/50
+brass memorial plaque: 33/50
+bra: 32/50
+breakwater: 0/50
+breastplate: 2/50
+broom: 0/50
+bucket: 0/50
+buckle: 8/50
+bulletproof vest: 29/50
+high-speed train: 46/50
+butcher shop: 5/50
+taxicab: 35/50
+cauldron: 12/50
+candle: 10/50
+cannon: 10/50
+canoe: 2/50
+can opener: 14/50
+cardigan: 41/50
+car mirror: 4/50
+carousel: 1/50
+tool kit: 31/50
+cardboard box / carton: 12/50
+car wheel: 12/50
+automated teller machine: 45/50
+cassette: 0/50
+cassette player: 9/50
+castle: 0/50
+catamaran: 0/50
+CD player: 19/50
+cello: 0/50
+mobile phone: 19/50
+chain: 0/50
+chain-link fence: 17/50
+chain mail: 30/50
+chainsaw: 36/50
+storage chest: 2/50
+chiffonier: 6/50
+bell or wind chime: 15/50
+china cabinet: 38/50
+Christmas stocking: 47/50
+church: 0/50
+movie theater: 49/50
+cleaver: 0/50
+cliff dwelling: 40/50
+cloak: 0/50
+clogs: 21/50
+cocktail shaker: 4/50
+coffee mug: 30/50
+coffeemaker: 8/50
+spiral or coil: 2/50
+combination lock: 38/50
+computer keyboard: 1/50
+candy store: 24/50
+container ship: 50/50
+convertible: 0/50
+corkscrew: 23/50
+cornet: 0/50
+cowboy boot: 41/50
+cowboy hat: 34/50
+cradle: 0/50
+construction crane: 20/50
+crash helmet: 41/50
+crate: 0/50
+infant bed: 48/50
+Crock Pot: 45/50
+croquet ball: 44/50
+crutch: 13/50
+cuirass: 50/50
+dam: 0/50
+desk: 13/50
+desktop computer: 0/50
+rotary dial telephone: 48/50
+diaper: 23/50
+digital clock: 24/50
+digital watch: 28/50
+dining table: 31/50
+dishcloth: 45/50
+dishwasher: 0/50
+disc brake: 12/50
+dock: 0/50
+dog sled: 49/50
+dome: 0/50
+doormat: 20/50
+drilling rig: 39/50
+drum: 0/50
+drumstick: 15/50
+dumbbell: 1/50
+Dutch oven: 1/50
+electric fan: 3/50
+electric guitar: 40/50
+electric locomotive: 48/50
+entertainment center: 0/50
+envelope: 0/50
+espresso machine: 8/50
+face powder: 34/50
+feather boa: 22/50
+filing cabinet: 45/50
+fireboat: 50/50
+fire truck: 48/50
+fire screen: 4/50
+flagpole: 26/50
+flute: 0/50
+folding chair: 37/50
+football helmet: 47/50
+forklift: 35/50
+fountain: 0/50
+fountain pen: 28/50
+four-poster bed: 42/50
+freight car: 47/50
+French horn: 37/50
+frying pan: 38/50
+fur coat: 42/50
+garbage truck: 41/50
+gas mask or respirator: 48/50
+gas pump: 47/50
+goblet: 9/50
+go-kart: 47/50
+golf ball: 23/50
+golf cart: 37/50
+gondola: 3/50
+gong: 0/50
+gown: 8/50
+grand piano: 1/50
+greenhouse: 0/50
+radiator grille: 37/50
+grocery store: 7/50
+guillotine: 20/50
+hair clip: 13/50
+hair spray: 12/50
+half-track: 6/50
+hammer: 0/50
+hamper: 26/50
+hair dryer: 11/50
+hand-held computer: 0/50
+handkerchief: 0/50
+hard disk drive: 50/50
+harmonica: 6/50
+harp: 0/50
+combine harvester: 14/50
+hatchet: 27/50
+holster: 35/50
+home theater: 18/50
+honeycomb: 0/50
+hook: 0/50
+hoop skirt: 46/50
+gymnastic horizontal bar: 45/50
+horse-drawn vehicle: 46/50
+hourglass: 1/50
+iPod: 0/50
+clothes iron: 45/50
+carved pumpkin: 49/50
+jeans: 32/50
+jeep: 2/50
+T-shirt: 16/50
+jigsaw puzzle: 49/50
+rickshaw: 7/50
+joystick: 31/50
+kimono: 47/50
+knee pad: 26/50
+knot: 0/50
+lab coat: 33/50
+ladle: 2/50
+lampshade: 4/50
+laptop computer: 4/50
+lawn mower: 0/50
+lens cap: 7/50
+letter opener: 19/50
+library: 0/50
+lifeboat: 5/50
+lighter: 2/50
+limousine: 34/50
+ocean liner: 0/50
+lipstick: 23/50
+slip-on shoe: 45/50
+lotion: 19/50
+music speaker: 29/50
+loupe magnifying glass: 7/50
+sawmill: 11/50
+magnetic compass: 16/50
+messenger bag: 22/50
+mailbox: 6/50
+tights: 0/50
+one-piece bathing suit: 40/50
+manhole cover: 48/50
+maraca: 10/50
+marimba: 0/50
+mask: 11/50
+matchstick: 12/50
+maypole: 8/50
+maze: 0/50
+measuring cup: 34/50
+medicine cabinet: 30/50
+megalith: 9/50
+microphone: 14/50
+microwave oven: 39/50
+military uniform: 19/50
+milk can: 12/50
+minibus: 32/50
+miniskirt: 7/50
+minivan: 17/50
+missile: 37/50
+mitten: 27/50
+mixing bowl: 1/50
+mobile home: 8/50
+ford model t: 22/50
+modem: 1/50
+monastery: 0/50
+monitor: 0/50
+moped: 27/50
+mortar and pestle: 38/50
+graduation cap: 8/50
+mosque: 19/50
+mosquito net: 20/50
+vespa: 13/50
+mountain bike: 39/50
+tent: 1/50
+computer mouse: 4/50
+mousetrap: 27/50
+moving van: 5/50
+muzzle: 3/50
+metal nail: 0/50
+neck brace: 39/50
+necklace: 14/50
+baby pacifier: 33/50
+notebook computer: 0/50
+obelisk: 10/50
+oboe: 0/50
+ocarina: 16/50
+odometer: 49/50
+oil filter: 15/50
+pipe organ: 49/50
+oscilloscope: 45/50
+overskirt: 0/50
+bullock cart: 40/50
+oxygen mask: 10/50
+product packet / packaging: 0/50
+paddle: 0/50
+paddle wheel: 31/50
+padlock: 13/50
+paintbrush: 10/50
+pajamas: 34/50
+palace: 0/50
+pan flute: 40/50
+paper towel: 0/50
+parachute: 0/50
+parallel bars: 1/50
+park bench: 30/50
+parking meter: 16/50
+railroad car: 0/50
+patio: 0/50
+payphone: 38/50
+pedestal: 0/50
+pencil case: 23/50
+pencil sharpener: 27/50
+perfume: 38/50
+Petri dish: 2/50
+photocopier: 11/50
+plectrum: 0/50
+Pickelhaube: 27/50
+picket fence: 30/50
+pickup truck: 35/50
+pier: 0/50
+piggy bank: 47/50
+pill bottle: 31/50
+pillow: 1/50
+ping-pong ball: 41/50
+pinwheel: 17/50
+pirate ship: 28/50
+drink pitcher: 19/50
+block plane: 44/50
+planetarium: 35/50
+plastic bag: 6/50
+plate rack: 35/50
+farm plow: 0/50
+plunger: 14/50
+Polaroid camera: 9/50
+pole: 3/50
+police van: 45/50
+poncho: 8/50
+pool table: 37/50
+soda bottle: 6/50
+plant pot: 2/50
+potter's wheel: 14/50
+power drill: 29/50
+prayer rug: 40/50
+printer: 6/50
+prison: 36/50
+missile: 32/50
+projector: 0/50
+hockey puck: 48/50
+punching bag: 37/50
+purse: 32/50
+quill: 4/50
+quilt: 2/50
+race car: 44/50
+racket: 0/50
+radiator: 0/50
+radio: 9/50
+radio telescope: 38/50
+rain barrel: 40/50
+recreational vehicle: 45/50
+fishing casting reel: 48/50
+reflex camera: 0/50
+refrigerator: 0/50
+remote control: 0/50
+restaurant: 1/50
+revolver: 0/50
+rifle: 38/50
+rocking chair: 22/50
+rotisserie: 25/50
+eraser: 0/50
+rugby ball: 49/50
+ruler measuring stick: 37/50
+sneaker: 5/50
+safe: 13/50
+safety pin: 15/50
+salt shaker: 11/50
+sandal: 4/50
+sarong: 35/50
+saxophone: 30/50
+scabbard: 0/50
+weighing scale: 27/50
+school bus: 49/50
+schooner: 0/50
+scoreboard: 36/50
+CRT monitor: 0/50
+screw: 0/50
+screwdriver: 1/50
+seat belt: 18/50
+sewing machine: 40/50
+shield: 0/50
+shoe store: 13/50
+shoji screen / room divider: 10/50
+shopping basket: 3/50
+shopping cart: 9/50
+shovel: 1/50
+shower cap: 30/50
+shower curtain: 31/50
+ski: 22/50
+balaclava ski mask: 46/50
+sleeping bag: 36/50
+slide rule: 35/50
+sliding door: 6/50
+slot machine: 49/50
+snorkel: 18/50
+snowmobile: 50/50
+snowplow: 46/50
+soap dispenser: 21/50
+soccer ball: 37/50
+sock: 19/50
+solar thermal collector: 22/50
+sombrero: 22/50
+soup bowl: 29/50
+keyboard space bar: 14/50
+space heater: 11/50
+space shuttle: 2/50
+spatula: 0/50
+motorboat: 0/50
+spider web: 0/50
+spindle: 1/50
+sports car: 26/50
+spotlight: 0/50
+stage: 0/50
+steam locomotive: 47/50
+through arch bridge: 0/50
+steel drum: 9/50
+stethoscope: 18/50
+scarf: 39/50
+stone wall: 11/50
+stopwatch: 34/50
+stove: 9/50
+strainer: 0/50
+tram: 29/50
+stretcher: 1/50
+couch: 29/50
+stupa: 41/50
+submarine: 0/50
+suit: 0/50
+sundial: 6/50
+sunglasses: 16/50
+sunglasses: 13/50
+sunscreen: 6/50
+suspension bridge: 23/50
+mop: 0/50
+sweatshirt: 13/50
+swim trunks / shorts: 32/50
+swing: 7/50
+electrical switch: 1/50
+syringe: 1/50
+table lamp: 32/50
+tank: 0/50
+tape player: 21/50
+teapot: 47/50
+teddy bear: 31/50
+television: 3/50
+tennis ball: 45/50
+thatched roof: 39/50
+front curtain: 0/50
+thimble: 0/50
+threshing machine: 37/50
+throne: 35/50
+tile roof: 25/50
+toaster: 28/50
+tobacco shop: 16/50
+toilet seat: 17/50
+torch: 1/50
+totem pole: 44/50
+tow truck: 25/50
+toy store: 32/50
+tractor: 0/50
+semi-trailer truck: 47/50
+tray: 2/50
+trench coat: 39/50
+tricycle: 15/50
+trimaran: 44/50
+tripod: 0/50
+triumphal arch: 36/50
+trolleybus: 11/50
+trombone: 6/50
+hot tub: 8/50
+turnstile: 14/50
+typewriter keyboard: 50/50
+umbrella: 8/50
+unicycle: 10/50
+upright piano: 8/50
+vacuum cleaner: 44/50
+vase: 29/50
+vaulted or arched ceiling: 12/50
+velvet fabric: 1/50
+vending machine: 10/50
+vestment: 0/50
+viaduct: 24/50
+violin: 8/50
+volleyball: 45/50
+waffle iron: 40/50
+wall clock: 39/50
+wallet: 8/50
+wardrobe: 4/50
+military aircraft: 2/50
+sink: 0/50
+washing machine: 21/50
+water bottle: 15/50
+water jug: 1/50
+water tower: 20/50
+whiskey jug: 29/50
+whistle: 0/50
+hair wig: 34/50
+window screen: 6/50
+window shade: 28/50
+Windsor tie: 15/50
+wine bottle: 28/50
+airplane wing: 4/50
+wok: 14/50
+wooden spoon: 23/50
+wool: 13/50
+split-rail fence: 26/50
+shipwreck: 0/50
+sailboat: 26/50
+yurt: 46/50
+website: 2/50
+comic book: 42/50
+crossword: 44/50
+traffic or street sign: 21/50
+traffic light: 0/50
+dust jacket: 0/50
+menu: 1/50
+plate: 0/50
+guacamole: 40/50
+consomme: 0/50
+hot pot: 41/50
+trifle: 35/50
+ice cream: 19/50
+popsicle: 7/50
+baguette: 0/50
+bagel: 45/50
+pretzel: 48/50
+cheeseburger: 44/50
+hot dog: 50/50
+mashed potatoes: 43/50
+cabbage: 29/50
+broccoli: 44/50
+cauliflower: 40/50
+zucchini: 35/50
+spaghetti squash: 35/50
+acorn squash: 28/50
+butternut squash: 35/50
+cucumber: 9/50
+artichoke: 16/50
+bell pepper: 39/50
+cardoon: 36/50
+mushroom: 0/50
+Granny Smith apple: 46/50
+strawberry: 0/50
+orange: 0/50
+lemon: 6/50
+fig: 22/50
+pineapple: 26/50
+banana: 1/50
+jackfruit: 16/50
+cherimoya (custard apple): 47/50
+pomegranate: 11/50
+hay: 1/50
+carbonara: 39/50
+chocolate syrup: 31/50
+dough: 18/50
+meatloaf: 48/50
+pizza: 38/50
+pot pie: 47/50
+burrito: 50/50
+red wine: 9/50
+espresso: 0/50
+tea cup: 20/50
+eggnog: 27/50
+mountain: 0/50
+bubble: 0/50
+cliff: 0/50
+coral reef: 0/50
+geyser: 1/50
+lakeshore: 0/50
+promontory: 0/50
+sandbar: 0/50
+beach: 0/50
+valley: 0/50
+volcano: 0/50
+baseball player: 38/50
+bridegroom: 21/50
+scuba diver: 22/50
+rapeseed: 45/50
+daisy: 10/50
+yellow lady's slipper: 50/50
+corn: 0/50
+acorn: 0/50
+rose hip: 21/50
+horse chestnut seed: 40/50
+coral fungus: 41/50
+agaric: 8/50
+gyromitra: 1/50
+stinkhorn mushroom: 43/50
+earth star fungus: 0/50
+hen of the woods mushroom: 49/50
+bolete: 30/50
+corn cob: 35/50
+toilet paper: 37/50
+
+
+Accuracy Results:
+Total: 50,000
+Correct: 20,771
+Accuracy: 41.5%
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.ipynb b/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.ipynb
new file mode 100644
index 00000000..d82ad482
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.ipynb
@@ -0,0 +1,171 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/ckb-nfs/home/zcafego/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "import clip\n",
+ "from PIL import Image\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "device = 'cuda:2'\n",
+ "\n",
+ "vitl14_path = './vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50'\n",
+ "vitb32_path = './vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50'\n",
+ "\n",
+ "vitl14_check = torch.load(vitl14_path, map_location='cpu')\n",
+ "vitb32_check = torch.load(vitb32_path, map_location='cpu')\n",
+ "\n",
+ "vitl14_state_dict = vitl14_check['state_dict']\n",
+ "vitl14_tnsr = vitl14_state_dict['ctx']\n",
+ "\n",
+ "vitb32_state_dict = vitb32_check['state_dict']\n",
+ "vitb32_tnsr = vitb32_state_dict['ctx']\n",
+ "\n",
+ "img = Image.open('/ckb-nfs/home/zcafego/test_images/sturgeon.JPEG')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l14_model, l14_preprocessor = clip.load('ViT-L/14', device=device)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "_IncompatibleKeys(missing_keys=['positional_embedding', 'text_projection', 'logit_scale', 'visual.class_embedding', 'visual.positional_embedding', 'visual.proj', 'visual.conv1.weight', 'visual.ln_pre.weight', 'visual.ln_pre.bias', 'visual.transformer.resblocks.0.attn.in_proj_weight', 'visual.transformer.resblocks.0.attn.in_proj_bias', 'visual.transformer.resblocks.0.attn.out_proj.weight', 'visual.transformer.resblocks.0.attn.out_proj.bias', 'visual.transformer.resblocks.0.ln_1.weight', 'visual.transformer.resblocks.0.ln_1.bias', 'visual.transformer.resblocks.0.mlp.c_fc.weight', 'visual.transformer.resblocks.0.mlp.c_fc.bias', 'visual.transformer.resblocks.0.mlp.c_proj.weight', 'visual.transformer.resblocks.0.mlp.c_proj.bias', 'visual.transformer.resblocks.0.ln_2.weight', 'visual.transformer.resblocks.0.ln_2.bias', 'visual.transformer.resblocks.1.attn.in_proj_weight', 'visual.transformer.resblocks.1.attn.in_proj_bias', 'visual.transformer.resblocks.1.attn.out_proj.weight', 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+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "l14_model.load_state_dict(vitl14_state_dict, strict=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "categories = []\n",
+ "with open('/ckb-nfs/home/zcafego/imagenet_labels/synset_words.txt') as f:\n",
+ " for line in f.readlines():\n",
+ " line = line.strip()\n",
+ " categories.append(' '.join(line.split(' ')[1:]).split(', ')[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_classifications_l14(img):\n",
+ " preproc_img = l14_preprocessor(img).unsqueeze(0).to(device)\n",
+ " tokens = clip.tokenize([f\"a photo of a {category}\" for category in categories]).to(device)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " image_features = l14_model.encode_image(preproc_img)\n",
+ " text_features = l14_model.encode_text(tokens)\n",
+ "\n",
+ " logits_per_image, _ = l14_model(image_features, text_features)\n",
+ " probs = logits_per_image.softmax(dim=-1).cpu().numpy()\n",
+ " \n",
+ " print(\"Label probs: \", probs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "RuntimeError",
+ "evalue": "Expected 4-dimensional input for 4-dimensional weight [1024, 3, 14, 14], but got 2-dimensional input of size [1, 768] instead",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_classifications_l14\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Cell \u001b[0;32mIn[6], line 9\u001b[0m, in \u001b[0;36mget_classifications_l14\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m 6\u001b[0m image_features \u001b[38;5;241m=\u001b[39m l14_model\u001b[38;5;241m.\u001b[39mencode_image(preproc_img)\n\u001b[1;32m 7\u001b[0m text_features \u001b[38;5;241m=\u001b[39m l14_model\u001b[38;5;241m.\u001b[39mencode_text(tokens)\n\u001b[0;32m----> 9\u001b[0m logits_per_image, _ \u001b[38;5;241m=\u001b[39m \u001b[43ml14_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m probs \u001b[38;5;241m=\u001b[39m logits_per_image\u001b[38;5;241m.\u001b[39msoftmax(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLabel probs: \u001b[39m\u001b[38;5;124m\"\u001b[39m, probs)\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/torch/nn/modules/module.py:727\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 725\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slow_forward(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m itertools\u001b[38;5;241m.\u001b[39mchain(\n\u001b[1;32m 729\u001b[0m _global_forward_hooks\u001b[38;5;241m.\u001b[39mvalues(),\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 731\u001b[0m hook_result \u001b[38;5;241m=\u001b[39m hook(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, result)\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/clip/model.py:359\u001b[0m, in \u001b[0;36mCLIP.forward\u001b[0;34m(self, image, text)\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, image, text):\n\u001b[0;32m--> 359\u001b[0m image_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 360\u001b[0m text_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode_text(text)\n\u001b[1;32m 362\u001b[0m \u001b[38;5;66;03m# normalized features\u001b[39;00m\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/clip/model.py:341\u001b[0m, in \u001b[0;36mCLIP.encode_image\u001b[0;34m(self, image)\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mencode_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image):\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisual\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/torch/nn/modules/module.py:727\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 725\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slow_forward(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m itertools\u001b[38;5;241m.\u001b[39mchain(\n\u001b[1;32m 729\u001b[0m _global_forward_hooks\u001b[38;5;241m.\u001b[39mvalues(),\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 731\u001b[0m hook_result \u001b[38;5;241m=\u001b[39m hook(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, result)\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/clip/model.py:224\u001b[0m, in \u001b[0;36mVisionTransformer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m--> 224\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# shape = [*, width, grid, grid]\u001b[39;00m\n\u001b[1;32m 225\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mreshape(x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# shape = [*, width, grid ** 2]\u001b[39;00m\n\u001b[1;32m 226\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# shape = [*, grid ** 2, width]\u001b[39;00m\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/torch/nn/modules/module.py:727\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 725\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slow_forward(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 727\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m itertools\u001b[38;5;241m.\u001b[39mchain(\n\u001b[1;32m 729\u001b[0m _global_forward_hooks\u001b[38;5;241m.\u001b[39mvalues(),\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 731\u001b[0m hook_result \u001b[38;5;241m=\u001b[39m hook(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, result)\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py:423\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/git/openmpf-projects/openmpf-components/python/ClipDetection/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py:419\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight)\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 417\u001b[0m weight, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 418\u001b[0m _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: Expected 4-dimensional input for 4-dimensional weight [1024, 3, 14, 14], but got 2-dimensional input of size [1, 768] instead"
+ ]
+ }
+ ],
+ "source": [
+ "get_classifications_l14(img)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print(sorted(likenesses, key=lambda x: x[1], reverse=True))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.py b/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.py
new file mode 100644
index 00000000..86d41a91
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/explore_outputs.py
@@ -0,0 +1,62 @@
+import torch
+import clip
+from PIL import Image
+import torch
+import os
+
+import trainers
+
+device = 'cuda:1'
+
+vitl14_path = os.path.join(os.getcwd(), 'vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50')
+vitb32_path = os.path.join(os.getcwd(), 'vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50')
+
+vitl14_check = torch.load(vitl14_path, map_location=None)
+
+vitl14_state_dict = vitl14_check['state_dict']
+vitl14_tnsr = vitl14_state_dict['ctx']
+
+images = [
+ Image.open('/ckb-nfs/home/zcafego/test_images/sturgeon.JPEG'),
+ Image.open('/ckb-nfs/home/zcafego/test_images/val2017/000000000139.jpg'),
+ Image.open('/ckb-nfs/home/zcafego/test_images/val2017/000000000285.jpg'),
+ ]
+
+# l14_model, l14_preprocessor = clip.load('ViT-L/14', device=device)
+
+categories = []
+with open('/ckb-nfs/home/zcafego/imagenet_labels/synset_words.txt') as f:
+ for line in f.readlines():
+ line = line.strip()
+ categories.append(' '.join(line.split(' ')[1:]).split(', ')[0])
+
+tokens = torch.cat([clip.tokenize(f"a photo of a {c}") for c in categories]).to(device)
+
+print(vitl14_check.keys())
+
+def get_classifications_l14(img):
+ # for img in images:
+ preproc_img = l14_preprocessor(img).unsqueeze(0).to(device)
+ return l14_model(preproc_img)
+
+ # with torch.no_grad():
+ # image_features = l14_model.encode_image(preproc_img)
+ # text_features = l14_model.encode_text(tokens)
+
+
+ # image_features /= image_features.norm(dim=-1, keepdim=True)
+ # text_features /= text_features.norm(dim=-1, keepdim=True)
+ # similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+ # values, indices = similarity[0].topk(5)
+
+ # print("\nTop predictions:\n")
+ # for value, index in zip(values, indices):
+ # print(f"{categories[index]:>20s}: {100 * value.item():.2f}%")
+
+# print("WITHOUT STATE DICT:\n")
+# get_classifications_l14(images)
+
+# l14_model.load_state_dict(vitl14_state_dict, strict=False)
+# print(get_classifications_l14(images[0]))
+# print("\nWITH STATE DICT:\n")
+# get_classifications_l14(images[0])
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/output_1.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/output_1.txt
new file mode 100644
index 00000000..aaf53968
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/output_1.txt
@@ -0,0 +1,449 @@
+CLIP(
+ (visual): VisionTransformer(
+ (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
+ (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (transformer): Transformer(
+ (resblocks): Sequential(
+ (0): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (1): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (2): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (3): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (4): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (5): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (6): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (7): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (8): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (9): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (10): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (11): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (12): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (13): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (14): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (15): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (16): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (17): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (18): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (19): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (20): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (21): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (22): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (23): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ )
+ )
+ (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (transformer): Transformer(
+ (resblocks): Sequential(
+ (0): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (1): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (2): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (3): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (4): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (5): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (6): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (7): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (8): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (9): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (10): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (11): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ )
+ )
+ (token_embedding): Embedding(49408, 768)
+ (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+)
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/output_2.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/output_2.txt
new file mode 100644
index 00000000..b657d773
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/output_2.txt
@@ -0,0 +1,449 @@
+ CLIP(
+ (visual): VisionTransformer(
+ (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
+ (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (transformer): Transformer(
+ (resblocks): Sequential(
+ (0): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (1): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (2): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (3): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (4): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (5): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (6): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (7): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (8): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (9): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (10): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (11): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (12): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (13): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (14): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (15): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (16): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (17): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (18): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (19): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (20): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (21): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (22): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (23): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=1024, out_features=1024, bias=True)
+ )
+ (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=1024, out_features=4096, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=4096, out_features=1024, bias=True)
+ )
+ (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ )
+ )
+ (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+ )
+ (transformer): Transformer(
+ (resblocks): Sequential(
+ (0): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (1): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (2): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (3): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (4): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (5): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (6): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (7): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (8): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (9): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (10): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ (11): ResidualAttentionBlock(
+ (attn): MultiheadAttention(
+ (out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
+ )
+ (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ (mlp): Sequential(
+ (c_fc): Linear(in_features=768, out_features=3072, bias=True)
+ (gelu): QuickGELU()
+ (c_proj): Linear(in_features=3072, out_features=768, bias=True)
+ )
+ (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+ )
+ )
+ )
+ (token_embedding): Embedding(49408, 768)
+ (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+)
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..cf1a0bec
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,1539 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 200
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1687.224
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/200] batch [5/31] time 0.711 (1.572) data 0.000 (0.178) loss 4.3398 (4.6867) acc 25.0000 (18.1250) lr 1.0000e-05 eta 2:42:17
+epoch [1/200] batch [10/31] time 0.712 (1.143) data 0.000 (0.089) loss 3.7773 (4.3557) acc 21.8750 (21.8750) lr 1.0000e-05 eta 1:57:56
+epoch [1/200] batch [15/31] time 0.709 (0.998) data 0.000 (0.059) loss 3.6875 (4.2160) acc 28.1250 (23.5417) lr 1.0000e-05 eta 1:42:52
+epoch [1/200] batch [20/31] time 0.710 (0.926) data 0.000 (0.045) loss 3.5723 (4.0763) acc 25.0000 (24.5312) lr 1.0000e-05 eta 1:35:20
+epoch [1/200] batch [25/31] time 0.720 (0.883) data 0.000 (0.036) loss 2.6855 (3.8645) acc 43.7500 (27.5000) lr 1.0000e-05 eta 1:30:50
+epoch [1/200] batch [30/31] time 0.709 (0.854) data 0.000 (0.030) loss 3.4629 (3.7541) acc 31.2500 (28.7500) lr 1.0000e-05 eta 1:27:49
+epoch [2/200] batch [5/31] time 0.712 (0.912) data 0.000 (0.184) loss 1.8252 (2.5494) acc 59.3750 (42.5000) lr 2.0000e-03 eta 1:33:43
+epoch [2/200] batch [10/31] time 0.711 (0.813) data 0.000 (0.092) loss 2.0430 (2.4092) acc 50.0000 (46.5625) lr 2.0000e-03 eta 1:23:27
+epoch [2/200] batch [15/31] time 0.722 (0.782) data 0.000 (0.062) loss 2.0762 (2.2622) acc 43.7500 (48.9583) lr 2.0000e-03 eta 1:20:12
+epoch [2/200] batch [20/31] time 0.720 (0.764) data 0.000 (0.046) loss 1.6934 (2.2380) acc 56.2500 (50.1562) lr 2.0000e-03 eta 1:18:17
+epoch [2/200] batch [25/31] time 0.713 (0.753) data 0.000 (0.037) loss 2.3320 (2.1624) acc 40.6250 (51.7500) lr 2.0000e-03 eta 1:17:07
+epoch [2/200] batch [30/31] time 0.743 (0.748) data 0.000 (0.031) loss 2.1250 (2.1692) acc 40.6250 (50.3125) lr 2.0000e-03 eta 1:16:29
+epoch [3/200] batch [5/31] time 0.713 (0.890) data 0.000 (0.171) loss 1.3066 (1.8510) acc 68.7500 (55.0000) lr 1.9999e-03 eta 1:31:00
+epoch [3/200] batch [10/31] time 0.706 (0.813) data 0.000 (0.086) loss 2.2305 (1.9200) acc 56.2500 (55.0000) lr 1.9999e-03 eta 1:23:02
+epoch [3/200] batch [15/31] time 0.704 (0.779) data 0.000 (0.057) loss 2.0352 (1.9470) acc 56.2500 (54.1667) lr 1.9999e-03 eta 1:19:30
+epoch [3/200] batch [20/31] time 0.705 (0.761) data 0.000 (0.043) loss 1.6963 (1.8799) acc 56.2500 (55.1562) lr 1.9999e-03 eta 1:17:37
+epoch [3/200] batch [25/31] time 0.717 (0.751) data 0.000 (0.034) loss 1.8242 (1.9009) acc 68.7500 (55.0000) lr 1.9999e-03 eta 1:16:32
+epoch [3/200] batch [30/31] time 0.710 (0.744) data 0.000 (0.029) loss 2.3730 (1.8986) acc 53.1250 (54.8958) lr 1.9999e-03 eta 1:15:42
+epoch [4/200] batch [5/31] time 0.711 (0.891) data 0.000 (0.172) loss 1.6074 (1.9096) acc 53.1250 (48.1250) lr 1.9995e-03 eta 1:30:36
+epoch [4/200] batch [10/31] time 0.709 (0.802) data 0.000 (0.086) loss 1.7686 (1.7490) acc 59.3750 (54.3750) lr 1.9995e-03 eta 1:21:30
+epoch [4/200] batch [15/31] time 0.705 (0.772) data 0.000 (0.058) loss 1.9619 (1.7015) acc 59.3750 (56.8750) lr 1.9995e-03 eta 1:18:22
+epoch [4/200] batch [20/31] time 0.708 (0.756) data 0.000 (0.043) loss 1.8096 (1.7618) acc 62.5000 (57.8125) lr 1.9995e-03 eta 1:16:42
+epoch [4/200] batch [25/31] time 0.708 (0.747) data 0.000 (0.035) loss 2.4961 (1.7508) acc 62.5000 (57.8750) lr 1.9995e-03 eta 1:15:45
+epoch [4/200] batch [30/31] time 0.708 (0.741) data 0.000 (0.029) loss 2.0410 (1.7606) acc 53.1250 (58.2292) lr 1.9995e-03 eta 1:15:01
+epoch [5/200] batch [5/31] time 0.711 (0.892) data 0.000 (0.172) loss 1.6562 (1.8258) acc 59.3750 (60.0000) lr 1.9989e-03 eta 1:30:14
+epoch [5/200] batch [10/31] time 0.715 (0.803) data 0.000 (0.086) loss 1.2363 (1.7505) acc 75.0000 (59.6875) lr 1.9989e-03 eta 1:21:12
+epoch [5/200] batch [15/31] time 0.701 (0.774) data 0.000 (0.058) loss 2.6504 (1.8702) acc 34.3750 (57.5000) lr 1.9989e-03 eta 1:18:09
+epoch [5/200] batch [20/31] time 0.725 (0.758) data 0.000 (0.043) loss 1.0527 (1.7463) acc 65.6250 (58.4375) lr 1.9989e-03 eta 1:16:31
+epoch [5/200] batch [25/31] time 0.707 (0.748) data 0.000 (0.035) loss 1.4512 (1.7601) acc 62.5000 (58.1250) lr 1.9989e-03 eta 1:15:25
+epoch [5/200] batch [30/31] time 0.705 (0.741) data 0.000 (0.029) loss 2.1270 (1.7994) acc 43.7500 (57.1875) lr 1.9989e-03 eta 1:14:42
+epoch [6/200] batch [5/31] time 0.709 (0.883) data 0.000 (0.159) loss 1.5957 (1.8076) acc 59.3750 (55.6250) lr 1.9980e-03 eta 1:28:50
+epoch [6/200] batch [10/31] time 0.740 (0.801) data 0.000 (0.080) loss 1.8115 (1.9626) acc 56.2500 (55.9375) lr 1.9980e-03 eta 1:20:31
+epoch [6/200] batch [15/31] time 0.723 (0.771) data 0.000 (0.053) loss 1.9209 (1.8530) acc 62.5000 (58.7500) lr 1.9980e-03 eta 1:17:32
+epoch [6/200] batch [20/31] time 0.707 (0.756) data 0.000 (0.040) loss 1.6807 (1.8609) acc 65.6250 (58.1250) lr 1.9980e-03 eta 1:15:53
+epoch [6/200] batch [25/31] time 0.708 (0.746) data 0.000 (0.032) loss 1.9551 (1.8631) acc 46.8750 (57.6250) lr 1.9980e-03 eta 1:14:51
+epoch [6/200] batch [30/31] time 0.709 (0.739) data 0.000 (0.027) loss 2.0332 (1.8683) acc 56.2500 (57.6042) lr 1.9980e-03 eta 1:14:07
+epoch [7/200] batch [5/31] time 0.711 (0.909) data 0.000 (0.186) loss 1.4756 (1.7725) acc 59.3750 (56.2500) lr 1.9969e-03 eta 1:31:01
+epoch [7/200] batch [10/31] time 0.707 (0.812) data 0.000 (0.093) loss 1.9033 (1.7049) acc 65.6250 (59.0625) lr 1.9969e-03 eta 1:21:12
+epoch [7/200] batch [15/31] time 0.714 (0.781) data 0.000 (0.062) loss 1.6963 (1.7694) acc 65.6250 (58.5417) lr 1.9969e-03 eta 1:18:02
+epoch [7/200] batch [20/31] time 0.708 (0.763) data 0.000 (0.047) loss 2.5566 (1.7866) acc 43.7500 (58.7500) lr 1.9969e-03 eta 1:16:11
+epoch [7/200] batch [25/31] time 0.727 (0.754) data 0.000 (0.037) loss 1.5820 (1.7530) acc 65.6250 (59.3750) lr 1.9969e-03 eta 1:15:14
+epoch [7/200] batch [30/31] time 0.701 (0.746) data 0.000 (0.031) loss 2.1797 (1.7648) acc 53.1250 (59.0625) lr 1.9969e-03 eta 1:14:24
+epoch [8/200] batch [5/31] time 0.723 (0.891) data 0.000 (0.164) loss 1.4297 (1.6346) acc 62.5000 (56.8750) lr 1.9956e-03 eta 1:28:47
+epoch [8/200] batch [10/31] time 0.709 (0.802) data 0.000 (0.082) loss 1.6357 (1.7118) acc 68.7500 (58.1250) lr 1.9956e-03 eta 1:19:47
+epoch [8/200] batch [15/31] time 0.727 (0.772) data 0.000 (0.055) loss 1.1309 (1.6551) acc 71.8750 (60.0000) lr 1.9956e-03 eta 1:16:48
+epoch [8/200] batch [20/31] time 0.708 (0.765) data 0.000 (0.041) loss 1.4277 (1.6852) acc 50.0000 (59.0625) lr 1.9956e-03 eta 1:16:03
+epoch [8/200] batch [25/31] time 0.711 (0.755) data 0.000 (0.033) loss 2.0273 (1.7451) acc 46.8750 (57.5000) lr 1.9956e-03 eta 1:14:57
+epoch [8/200] batch [30/31] time 0.711 (0.748) data 0.000 (0.028) loss 2.1055 (1.7774) acc 59.3750 (57.6042) lr 1.9956e-03 eta 1:14:15
+epoch [9/200] batch [5/31] time 0.724 (0.893) data 0.000 (0.171) loss 1.6455 (1.8209) acc 62.5000 (58.1250) lr 1.9940e-03 eta 1:28:32
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+epoch [195/200] batch [15/31] time 0.713 (0.778) data 0.001 (0.059) loss 0.4456 (0.6377) acc 84.3750 (85.6250) lr 6.0390e-06 eta 0:02:13
+epoch [195/200] batch [20/31] time 0.711 (0.761) data 0.000 (0.045) loss 1.2979 (0.6597) acc 87.5000 (85.9375) lr 6.0390e-06 eta 0:02:06
+epoch [195/200] batch [25/31] time 0.713 (0.752) data 0.000 (0.036) loss 1.0176 (0.6794) acc 78.1250 (85.5000) lr 6.0390e-06 eta 0:02:01
+epoch [195/200] batch [30/31] time 0.725 (0.746) data 0.000 (0.030) loss 1.0342 (0.7042) acc 71.8750 (84.4792) lr 6.0390e-06 eta 0:01:56
+epoch [196/200] batch [5/31] time 0.715 (0.886) data 0.000 (0.167) loss 0.6230 (0.7889) acc 81.2500 (82.5000) lr 4.4380e-06 eta 0:02:12
+epoch [196/200] batch [10/31] time 0.711 (0.798) data 0.000 (0.084) loss 0.6069 (0.7774) acc 81.2500 (82.5000) lr 4.4380e-06 eta 0:01:55
+epoch [196/200] batch [15/31] time 0.709 (0.769) data 0.000 (0.056) loss 1.0127 (0.7665) acc 87.5000 (83.7500) lr 4.4380e-06 eta 0:01:47
+epoch [196/200] batch [20/31] time 0.702 (0.754) data 0.000 (0.042) loss 0.5303 (0.7511) acc 84.3750 (82.8125) lr 4.4380e-06 eta 0:01:41
+epoch [196/200] batch [25/31] time 0.711 (0.745) data 0.000 (0.034) loss 0.5986 (0.7430) acc 78.1250 (83.0000) lr 4.4380e-06 eta 0:01:36
+epoch [196/200] batch [30/31] time 0.705 (0.738) data 0.000 (0.028) loss 0.6211 (0.7341) acc 81.2500 (83.2292) lr 4.4380e-06 eta 0:01:32
+epoch [197/200] batch [5/31] time 0.708 (0.893) data 0.000 (0.172) loss 0.5566 (0.6500) acc 84.3750 (87.5000) lr 3.0827e-06 eta 0:01:46
+epoch [197/200] batch [10/31] time 0.711 (0.804) data 0.000 (0.086) loss 1.0439 (0.6165) acc 78.1250 (87.5000) lr 3.0827e-06 eta 0:01:31
+epoch [197/200] batch [15/31] time 0.705 (0.783) data 0.000 (0.057) loss 0.3193 (0.6039) acc 93.7500 (87.5000) lr 3.0827e-06 eta 0:01:25
+epoch [197/200] batch [20/31] time 0.714 (0.765) data 0.000 (0.043) loss 0.4773 (0.6542) acc 84.3750 (86.0938) lr 3.0827e-06 eta 0:01:19
+epoch [197/200] batch [25/31] time 0.722 (0.753) data 0.000 (0.035) loss 0.6729 (0.6991) acc 87.5000 (85.7500) lr 3.0827e-06 eta 0:01:14
+epoch [197/200] batch [30/31] time 0.708 (0.746) data 0.000 (0.029) loss 0.5718 (0.6882) acc 87.5000 (85.5208) lr 3.0827e-06 eta 0:01:10
+epoch [198/200] batch [5/31] time 0.715 (0.889) data 0.000 (0.166) loss 1.0898 (0.6305) acc 84.3750 (86.8750) lr 1.9733e-06 eta 0:01:18
+epoch [198/200] batch [10/31] time 0.715 (0.800) data 0.000 (0.083) loss 0.6802 (0.6902) acc 84.3750 (85.0000) lr 1.9733e-06 eta 0:01:06
+epoch [198/200] batch [15/31] time 0.712 (0.771) data 0.000 (0.056) loss 0.7754 (0.7208) acc 87.5000 (84.7917) lr 1.9733e-06 eta 0:01:00
+epoch [198/200] batch [20/31] time 0.715 (0.756) data 0.000 (0.042) loss 0.9370 (0.7311) acc 84.3750 (85.6250) lr 1.9733e-06 eta 0:00:55
+epoch [198/200] batch [25/31] time 0.722 (0.748) data 0.000 (0.033) loss 0.6304 (0.7143) acc 87.5000 (85.2500) lr 1.9733e-06 eta 0:00:50
+epoch [198/200] batch [30/31] time 0.715 (0.742) data 0.000 (0.028) loss 0.8320 (0.6965) acc 90.6250 (85.4167) lr 1.9733e-06 eta 0:00:46
+epoch [199/200] batch [5/31] time 0.724 (0.894) data 0.000 (0.175) loss 0.5249 (0.6249) acc 90.6250 (86.2500) lr 1.1101e-06 eta 0:00:50
+epoch [199/200] batch [10/31] time 0.707 (0.801) data 0.000 (0.088) loss 0.5200 (0.6340) acc 78.1250 (85.0000) lr 1.1101e-06 eta 0:00:41
+epoch [199/200] batch [15/31] time 0.721 (0.772) data 0.000 (0.059) loss 1.0742 (0.7109) acc 78.1250 (83.5417) lr 1.1101e-06 eta 0:00:36
+epoch [199/200] batch [20/31] time 0.708 (0.757) data 0.000 (0.044) loss 0.7495 (0.6770) acc 78.1250 (83.7500) lr 1.1101e-06 eta 0:00:31
+epoch [199/200] batch [25/31] time 0.713 (0.747) data 0.000 (0.035) loss 0.6880 (0.7011) acc 90.6250 (83.6250) lr 1.1101e-06 eta 0:00:27
+epoch [199/200] batch [30/31] time 0.710 (0.742) data 0.000 (0.029) loss 0.5269 (0.6959) acc 81.2500 (84.2708) lr 1.1101e-06 eta 0:00:23
+epoch [200/200] batch [5/31] time 0.712 (0.896) data 0.000 (0.174) loss 0.5693 (0.7124) acc 78.1250 (83.7500) lr 4.9344e-07 eta 0:00:23
+epoch [200/200] batch [10/31] time 0.717 (0.803) data 0.000 (0.087) loss 1.0322 (0.7587) acc 71.8750 (83.7500) lr 4.9344e-07 eta 0:00:16
+epoch [200/200] batch [15/31] time 0.712 (0.772) data 0.000 (0.058) loss 0.4985 (0.7351) acc 87.5000 (83.5417) lr 4.9344e-07 eta 0:00:12
+epoch [200/200] batch [20/31] time 0.707 (0.756) data 0.000 (0.044) loss 0.4678 (0.7178) acc 87.5000 (83.7500) lr 4.9344e-07 eta 0:00:08
+epoch [200/200] batch [25/31] time 0.717 (0.747) data 0.000 (0.035) loss 0.7026 (0.7015) acc 81.2500 (83.8750) lr 4.9344e-07 eta 0:00:04
+epoch [200/200] batch [30/31] time 0.704 (0.741) data 0.000 (0.029) loss 0.7373 (0.6828) acc 81.2500 (84.1667) lr 4.9344e-07 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-200
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 26,715
+* accuracy: 53.4%
+* error: 46.6%
+* macro_f1: 51.9%
+Elapsed: 1:20:05
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..b2929f7c
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-200
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-200 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-200
new file mode 100644
index 00000000..fe2bf7b5
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-200 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698072893.ckb-gpu-lambda.1310895.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698072893.ckb-gpu-lambda.1310895.0
new file mode 100644
index 00000000..b400e30a
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698072893.ckb-gpu-lambda.1310895.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..3e5e85c3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,1539 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 200
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.083
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/200] batch [5/31] time 0.710 (1.596) data 0.000 (0.178) loss 3.5605 (3.5969) acc 25.0000 (32.5000) lr 1.0000e-05 eta 2:44:44
+epoch [1/200] batch [10/31] time 0.716 (1.153) data 0.000 (0.089) loss 3.8535 (3.4861) acc 18.7500 (34.0625) lr 1.0000e-05 eta 1:58:54
+epoch [1/200] batch [15/31] time 0.707 (1.003) data 0.000 (0.060) loss 3.4395 (3.3546) acc 43.7500 (35.6250) lr 1.0000e-05 eta 1:43:26
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+epoch [188/200] batch [10/31] time 0.727 (0.816) data 0.000 (0.093) loss 0.4399 (0.6442) acc 90.6250 (87.1875) lr 2.4083e-05 eta 0:05:20
+epoch [188/200] batch [15/31] time 0.711 (0.781) data 0.000 (0.062) loss 0.3320 (0.5493) acc 96.8750 (89.3750) lr 2.4083e-05 eta 0:05:03
+epoch [188/200] batch [20/31] time 0.715 (0.765) data 0.000 (0.047) loss 0.6611 (0.5645) acc 84.3750 (88.9062) lr 2.4083e-05 eta 0:04:52
+epoch [188/200] batch [25/31] time 0.705 (0.754) data 0.000 (0.037) loss 0.7095 (0.5775) acc 90.6250 (89.0000) lr 2.4083e-05 eta 0:04:45
+epoch [188/200] batch [30/31] time 0.716 (0.746) data 0.000 (0.031) loss 0.2886 (0.5689) acc 93.7500 (88.6458) lr 2.4083e-05 eta 0:04:38
+epoch [189/200] batch [5/31] time 0.711 (0.887) data 0.000 (0.162) loss 0.3789 (0.6108) acc 90.6250 (86.8750) lr 2.0777e-05 eta 0:05:25
+epoch [189/200] batch [10/31] time 0.709 (0.799) data 0.000 (0.081) loss 0.5737 (0.6161) acc 87.5000 (87.5000) lr 2.0777e-05 eta 0:04:49
+epoch [189/200] batch [15/31] time 0.710 (0.770) data 0.000 (0.054) loss 0.5146 (0.6103) acc 90.6250 (88.5417) lr 2.0777e-05 eta 0:04:34
+epoch [189/200] batch [20/31] time 0.721 (0.755) data 0.000 (0.041) loss 0.7349 (0.5909) acc 84.3750 (87.8125) lr 2.0777e-05 eta 0:04:25
+epoch [189/200] batch [25/31] time 0.702 (0.747) data 0.000 (0.033) loss 0.9917 (0.5817) acc 81.2500 (87.5000) lr 2.0777e-05 eta 0:04:19
+epoch [189/200] batch [30/31] time 0.709 (0.740) data 0.000 (0.027) loss 0.9937 (0.5987) acc 81.2500 (87.1875) lr 2.0777e-05 eta 0:04:13
+epoch [190/200] batch [5/31] time 0.718 (0.899) data 0.000 (0.175) loss 0.7642 (0.6048) acc 84.3750 (86.2500) lr 1.7713e-05 eta 0:05:02
+epoch [190/200] batch [10/31] time 0.705 (0.806) data 0.000 (0.088) loss 0.5015 (0.5355) acc 90.6250 (89.6875) lr 1.7713e-05 eta 0:04:26
+epoch [190/200] batch [15/31] time 0.714 (0.786) data 0.000 (0.058) loss 0.8770 (0.5124) acc 78.1250 (88.9583) lr 1.7713e-05 eta 0:04:16
+epoch [190/200] batch [20/31] time 0.710 (0.768) data 0.000 (0.044) loss 0.6099 (0.5617) acc 81.2500 (88.5938) lr 1.7713e-05 eta 0:04:06
+epoch [190/200] batch [25/31] time 0.704 (0.757) data 0.000 (0.035) loss 0.4763 (0.5519) acc 90.6250 (88.7500) lr 1.7713e-05 eta 0:03:59
+epoch [190/200] batch [30/31] time 0.712 (0.749) data 0.000 (0.029) loss 0.7725 (0.5718) acc 87.5000 (88.4375) lr 1.7713e-05 eta 0:03:53
+epoch [191/200] batch [5/31] time 0.708 (0.890) data 0.000 (0.172) loss 0.5405 (0.4627) acc 87.5000 (86.2500) lr 1.4891e-05 eta 0:04:31
+epoch [191/200] batch [10/31] time 0.723 (0.803) data 0.000 (0.086) loss 0.7803 (0.5803) acc 81.2500 (85.3125) lr 1.4891e-05 eta 0:04:00
+epoch [191/200] batch [15/31] time 0.706 (0.773) data 0.000 (0.058) loss 0.7739 (0.5909) acc 87.5000 (86.6667) lr 1.4891e-05 eta 0:03:47
+epoch [191/200] batch [20/31] time 0.714 (0.757) data 0.000 (0.043) loss 0.3147 (0.5663) acc 90.6250 (87.0312) lr 1.4891e-05 eta 0:03:39
+epoch [191/200] batch [25/31] time 0.709 (0.747) data 0.000 (0.035) loss 0.5723 (0.5360) acc 81.2500 (87.5000) lr 1.4891e-05 eta 0:03:32
+epoch [191/200] batch [30/31] time 0.714 (0.741) data 0.000 (0.029) loss 0.4197 (0.5261) acc 87.5000 (87.8125) lr 1.4891e-05 eta 0:03:27
+epoch [192/200] batch [5/31] time 0.713 (0.889) data 0.000 (0.172) loss 0.5356 (0.5300) acc 87.5000 (87.5000) lr 1.2312e-05 eta 0:04:03
+epoch [192/200] batch [10/31] time 0.708 (0.804) data 0.000 (0.086) loss 0.6499 (0.5417) acc 84.3750 (87.1875) lr 1.2312e-05 eta 0:03:36
+epoch [192/200] batch [15/31] time 0.702 (0.774) data 0.000 (0.058) loss 0.3933 (0.5610) acc 87.5000 (87.2917) lr 1.2312e-05 eta 0:03:24
+epoch [192/200] batch [20/31] time 0.709 (0.758) data 0.000 (0.043) loss 1.1787 (0.6332) acc 78.1250 (86.4062) lr 1.2312e-05 eta 0:03:16
+epoch [192/200] batch [25/31] time 0.706 (0.749) data 0.000 (0.035) loss 0.2935 (0.6070) acc 90.6250 (86.5000) lr 1.2312e-05 eta 0:03:10
+epoch [192/200] batch [30/31] time 0.707 (0.743) data 0.000 (0.029) loss 1.1133 (0.5975) acc 84.3750 (87.2917) lr 1.2312e-05 eta 0:03:04
+epoch [193/200] batch [5/31] time 0.713 (0.909) data 0.000 (0.155) loss 0.4404 (0.4575) acc 87.5000 (90.6250) lr 9.9763e-06 eta 0:03:40
+epoch [193/200] batch [10/31] time 0.709 (0.810) data 0.000 (0.078) loss 0.3950 (0.4993) acc 93.7500 (90.0000) lr 9.9763e-06 eta 0:03:12
+epoch [193/200] batch [15/31] time 0.712 (0.779) data 0.000 (0.052) loss 0.4463 (0.5383) acc 90.6250 (88.5417) lr 9.9763e-06 eta 0:03:01
+epoch [193/200] batch [20/31] time 0.706 (0.762) data 0.000 (0.039) loss 0.4087 (0.5476) acc 90.6250 (88.2812) lr 9.9763e-06 eta 0:02:53
+epoch [193/200] batch [25/31] time 0.720 (0.752) data 0.000 (0.031) loss 0.4824 (0.5555) acc 87.5000 (87.8750) lr 9.9763e-06 eta 0:02:47
+epoch [193/200] batch [30/31] time 0.717 (0.745) data 0.000 (0.026) loss 0.5425 (0.5620) acc 90.6250 (88.1250) lr 9.9763e-06 eta 0:02:42
+epoch [194/200] batch [5/31] time 0.712 (0.888) data 0.000 (0.166) loss 0.5293 (0.4829) acc 87.5000 (89.3750) lr 7.8853e-06 eta 0:03:08
+epoch [194/200] batch [10/31] time 0.723 (0.802) data 0.000 (0.083) loss 0.5112 (0.5417) acc 90.6250 (86.8750) lr 7.8853e-06 eta 0:02:46
+epoch [194/200] batch [15/31] time 0.708 (0.774) data 0.000 (0.055) loss 0.6260 (0.5466) acc 84.3750 (87.5000) lr 7.8853e-06 eta 0:02:36
+epoch [194/200] batch [20/31] time 0.727 (0.759) data 0.000 (0.042) loss 0.5225 (0.5578) acc 90.6250 (87.6562) lr 7.8853e-06 eta 0:02:29
+epoch [194/200] batch [25/31] time 0.706 (0.754) data 0.000 (0.033) loss 0.1947 (0.5088) acc 96.8750 (89.0000) lr 7.8853e-06 eta 0:02:24
+epoch [194/200] batch [30/31] time 0.709 (0.747) data 0.000 (0.028) loss 0.8818 (0.5127) acc 78.1250 (88.6458) lr 7.8853e-06 eta 0:02:19
+epoch [195/200] batch [5/31] time 0.713 (0.878) data 0.000 (0.157) loss 0.4041 (0.4655) acc 90.6250 (88.1250) lr 6.0390e-06 eta 0:02:38
+epoch [195/200] batch [10/31] time 0.715 (0.794) data 0.000 (0.079) loss 0.7734 (0.5737) acc 81.2500 (85.9375) lr 6.0390e-06 eta 0:02:19
+epoch [195/200] batch [15/31] time 0.720 (0.768) data 0.001 (0.052) loss 0.8638 (0.6191) acc 84.3750 (85.8333) lr 6.0390e-06 eta 0:02:11
+epoch [195/200] batch [20/31] time 0.706 (0.753) data 0.000 (0.039) loss 0.4734 (0.5753) acc 90.6250 (86.5625) lr 6.0390e-06 eta 0:02:04
+epoch [195/200] batch [25/31] time 0.707 (0.743) data 0.000 (0.032) loss 0.4275 (0.5761) acc 87.5000 (86.5000) lr 6.0390e-06 eta 0:01:59
+epoch [195/200] batch [30/31] time 0.729 (0.738) data 0.000 (0.026) loss 0.7178 (0.5929) acc 81.2500 (85.7292) lr 6.0390e-06 eta 0:01:55
+epoch [196/200] batch [5/31] time 0.711 (0.890) data 0.000 (0.170) loss 0.7964 (0.6464) acc 84.3750 (86.8750) lr 4.4380e-06 eta 0:02:13
+epoch [196/200] batch [10/31] time 0.731 (0.803) data 0.000 (0.085) loss 0.3560 (0.5503) acc 93.7500 (88.4375) lr 4.4380e-06 eta 0:01:56
+epoch [196/200] batch [15/31] time 0.716 (0.772) data 0.000 (0.057) loss 0.9194 (0.5799) acc 87.5000 (88.3333) lr 4.4380e-06 eta 0:01:48
+epoch [196/200] batch [20/31] time 0.710 (0.764) data 0.000 (0.043) loss 0.3303 (0.5499) acc 96.8750 (89.0625) lr 4.4380e-06 eta 0:01:43
+epoch [196/200] batch [25/31] time 0.725 (0.755) data 0.000 (0.034) loss 0.3518 (0.5533) acc 96.8750 (89.0000) lr 4.4380e-06 eta 0:01:38
+epoch [196/200] batch [30/31] time 0.707 (0.747) data 0.000 (0.029) loss 0.6284 (0.5708) acc 90.6250 (88.6458) lr 4.4380e-06 eta 0:01:33
+epoch [197/200] batch [5/31] time 0.718 (0.888) data 0.000 (0.168) loss 0.7339 (0.5548) acc 81.2500 (87.5000) lr 3.0827e-06 eta 0:01:45
+epoch [197/200] batch [10/31] time 0.733 (0.801) data 0.000 (0.084) loss 0.6167 (0.5221) acc 81.2500 (88.1250) lr 3.0827e-06 eta 0:01:31
+epoch [197/200] batch [15/31] time 0.727 (0.772) data 0.000 (0.056) loss 0.4768 (0.5226) acc 84.3750 (88.3333) lr 3.0827e-06 eta 0:01:24
+epoch [197/200] batch [20/31] time 0.713 (0.756) data 0.000 (0.042) loss 0.8550 (0.5702) acc 84.3750 (87.1875) lr 3.0827e-06 eta 0:01:18
+epoch [197/200] batch [25/31] time 0.708 (0.747) data 0.000 (0.034) loss 0.5371 (0.5292) acc 87.5000 (88.6250) lr 3.0827e-06 eta 0:01:13
+epoch [197/200] batch [30/31] time 0.723 (0.742) data 0.000 (0.028) loss 0.5938 (0.5348) acc 90.6250 (88.6458) lr 3.0827e-06 eta 0:01:09
+epoch [198/200] batch [5/31] time 0.718 (0.886) data 0.000 (0.163) loss 0.6973 (0.5996) acc 78.1250 (86.8750) lr 1.9733e-06 eta 0:01:17
+epoch [198/200] batch [10/31] time 0.712 (0.800) data 0.000 (0.082) loss 0.8091 (0.6579) acc 84.3750 (86.2500) lr 1.9733e-06 eta 0:01:06
+epoch [198/200] batch [15/31] time 0.710 (0.770) data 0.000 (0.055) loss 0.5361 (0.6286) acc 87.5000 (87.0833) lr 1.9733e-06 eta 0:01:00
+epoch [198/200] batch [20/31] time 0.723 (0.758) data 0.000 (0.041) loss 0.3972 (0.5813) acc 90.6250 (87.9688) lr 1.9733e-06 eta 0:00:55
+epoch [198/200] batch [25/31] time 0.713 (0.748) data 0.000 (0.033) loss 0.6602 (0.6342) acc 81.2500 (86.8750) lr 1.9733e-06 eta 0:00:50
+epoch [198/200] batch [30/31] time 0.714 (0.743) data 0.000 (0.027) loss 0.5698 (0.6117) acc 84.3750 (86.8750) lr 1.9733e-06 eta 0:00:46
+epoch [199/200] batch [5/31] time 0.717 (0.892) data 0.000 (0.163) loss 0.1622 (0.4615) acc 100.0000 (90.6250) lr 1.1101e-06 eta 0:00:50
+epoch [199/200] batch [10/31] time 0.706 (0.802) data 0.000 (0.082) loss 0.8735 (0.5222) acc 81.2500 (89.3750) lr 1.1101e-06 eta 0:00:41
+epoch [199/200] batch [15/31] time 0.715 (0.781) data 0.000 (0.055) loss 0.8413 (0.5243) acc 87.5000 (89.7917) lr 1.1101e-06 eta 0:00:36
+epoch [199/200] batch [20/31] time 0.701 (0.763) data 0.000 (0.041) loss 0.4297 (0.5435) acc 96.8750 (89.2188) lr 1.1101e-06 eta 0:00:32
+epoch [199/200] batch [25/31] time 0.706 (0.752) data 0.000 (0.033) loss 0.6406 (0.5718) acc 90.6250 (88.5000) lr 1.1101e-06 eta 0:00:27
+epoch [199/200] batch [30/31] time 0.706 (0.746) data 0.000 (0.027) loss 0.1531 (0.5661) acc 100.0000 (88.4375) lr 1.1101e-06 eta 0:00:23
+epoch [200/200] batch [5/31] time 0.709 (0.896) data 0.000 (0.176) loss 0.4402 (0.4069) acc 96.8750 (91.8750) lr 4.9344e-07 eta 0:00:23
+epoch [200/200] batch [10/31] time 0.709 (0.805) data 0.000 (0.088) loss 0.3171 (0.4749) acc 96.8750 (90.9375) lr 4.9344e-07 eta 0:00:16
+epoch [200/200] batch [15/31] time 0.713 (0.774) data 0.000 (0.059) loss 0.5254 (0.5168) acc 90.6250 (90.2083) lr 4.9344e-07 eta 0:00:12
+epoch [200/200] batch [20/31] time 0.714 (0.759) data 0.000 (0.044) loss 0.5879 (0.5126) acc 90.6250 (90.0000) lr 4.9344e-07 eta 0:00:08
+epoch [200/200] batch [25/31] time 0.711 (0.750) data 0.000 (0.035) loss 0.8896 (0.5643) acc 78.1250 (88.2500) lr 4.9344e-07 eta 0:00:04
+epoch [200/200] batch [30/31] time 0.711 (0.743) data 0.000 (0.030) loss 0.8833 (0.5708) acc 84.3750 (88.1250) lr 4.9344e-07 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-200
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 25,605
+* accuracy: 51.2%
+* error: 48.8%
+* macro_f1: 50.0%
+Elapsed: 1:20:00
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
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index 00000000..b2929f7c
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-200
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-200 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-200
new file mode 100644
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698077718.ckb-gpu-lambda.1396520.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698077718.ckb-gpu-lambda.1396520.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
@@ -0,0 +1,1539 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 200
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1590.721
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/200] batch [5/31] time 0.709 (1.654) data 0.000 (0.209) loss 3.8047 (3.6379) acc 34.3750 (34.3750) lr 1.0000e-05 eta 2:50:44
+epoch [1/200] batch [10/31] time 0.711 (1.184) data 0.000 (0.105) loss 2.9688 (3.6266) acc 53.1250 (35.6250) lr 1.0000e-05 eta 2:02:09
+epoch [1/200] batch [15/31] time 0.729 (1.028) data 0.001 (0.070) loss 2.9219 (3.4673) acc 43.7500 (37.9167) lr 1.0000e-05 eta 1:45:57
+epoch [1/200] batch [20/31] time 0.716 (0.951) data 0.000 (0.053) loss 2.0703 (3.3063) acc 59.3750 (39.8438) lr 1.0000e-05 eta 1:37:55
+epoch [1/200] batch [25/31] time 0.712 (0.903) data 0.000 (0.042) loss 2.7500 (3.2285) acc 31.2500 (38.1250) lr 1.0000e-05 eta 1:32:54
+epoch [1/200] batch [30/31] time 0.716 (0.871) data 0.000 (0.035) loss 2.1719 (3.1133) acc 43.7500 (39.7917) lr 1.0000e-05 eta 1:29:33
+epoch [2/200] batch [5/31] time 0.707 (0.893) data 0.000 (0.169) loss 2.0625 (2.5371) acc 59.3750 (48.1250) lr 2.0000e-03 eta 1:31:44
+epoch [2/200] batch [10/31] time 0.724 (0.805) data 0.001 (0.085) loss 1.9395 (2.3554) acc 53.1250 (50.6250) lr 2.0000e-03 eta 1:22:36
+epoch [2/200] batch [15/31] time 0.716 (0.773) data 0.000 (0.057) loss 1.4062 (2.2290) acc 65.6250 (51.4583) lr 2.0000e-03 eta 1:19:19
+epoch [2/200] batch [20/31] time 0.719 (0.758) data 0.000 (0.043) loss 1.9258 (2.2071) acc 53.1250 (50.7812) lr 2.0000e-03 eta 1:17:43
+epoch [2/200] batch [25/31] time 0.709 (0.750) data 0.000 (0.034) loss 2.2695 (2.1543) acc 53.1250 (52.2500) lr 2.0000e-03 eta 1:16:45
+epoch [2/200] batch [30/31] time 0.715 (0.744) data 0.000 (0.028) loss 2.0996 (2.1356) acc 43.7500 (51.3542) lr 2.0000e-03 eta 1:16:05
+epoch [3/200] batch [5/31] time 0.711 (0.890) data 0.000 (0.165) loss 2.0098 (2.1277) acc 50.0000 (53.1250) lr 1.9999e-03 eta 1:30:56
+epoch [3/200] batch [10/31] time 0.727 (0.804) data 0.001 (0.083) loss 2.2578 (2.0598) acc 46.8750 (54.6875) lr 1.9999e-03 eta 1:22:06
+epoch [3/200] batch [15/31] time 0.713 (0.774) data 0.000 (0.055) loss 1.7539 (2.0201) acc 56.2500 (55.0000) lr 1.9999e-03 eta 1:18:58
+epoch [3/200] batch [20/31] time 0.705 (0.758) data 0.000 (0.041) loss 1.1543 (1.9505) acc 68.7500 (55.7812) lr 1.9999e-03 eta 1:17:17
+epoch [3/200] batch [25/31] time 0.725 (0.749) data 0.000 (0.033) loss 1.8516 (1.9237) acc 53.1250 (56.5000) lr 1.9999e-03 eta 1:16:16
+epoch [3/200] batch [30/31] time 0.726 (0.743) data 0.000 (0.028) loss 1.8340 (1.9159) acc 53.1250 (55.7292) lr 1.9999e-03 eta 1:15:37
+epoch [4/200] batch [5/31] time 0.709 (0.896) data 0.001 (0.176) loss 1.6562 (1.5807) acc 53.1250 (61.2500) lr 1.9995e-03 eta 1:31:06
+epoch [4/200] batch [10/31] time 0.706 (0.804) data 0.000 (0.088) loss 1.5430 (1.7313) acc 65.6250 (58.7500) lr 1.9995e-03 eta 1:21:40
+epoch [4/200] batch [15/31] time 0.709 (0.773) data 0.000 (0.059) loss 1.2949 (1.7861) acc 65.6250 (55.8333) lr 1.9995e-03 eta 1:18:28
+epoch [4/200] batch [20/31] time 0.717 (0.757) data 0.000 (0.044) loss 1.5830 (1.7467) acc 56.2500 (56.0938) lr 1.9995e-03 eta 1:16:47
+epoch [4/200] batch [25/31] time 0.726 (0.748) data 0.000 (0.036) loss 2.2402 (1.8125) acc 56.2500 (55.3750) lr 1.9995e-03 eta 1:15:50
+epoch [4/200] batch [30/31] time 0.710 (0.743) data 0.000 (0.030) loss 1.4385 (1.7637) acc 78.1250 (56.3542) lr 1.9995e-03 eta 1:15:15
+epoch [5/200] batch [5/31] time 0.723 (0.897) data 0.000 (0.179) loss 1.4658 (1.7893) acc 62.5000 (61.8750) lr 1.9989e-03 eta 1:30:45
+epoch [5/200] batch [10/31] time 0.713 (0.804) data 0.000 (0.090) loss 1.7021 (1.7466) acc 56.2500 (60.3125) lr 1.9989e-03 eta 1:21:15
+epoch [5/200] batch [15/31] time 0.710 (0.773) data 0.000 (0.060) loss 1.8281 (1.8020) acc 50.0000 (57.7083) lr 1.9989e-03 eta 1:18:05
+epoch [5/200] batch [20/31] time 0.712 (0.758) data 0.000 (0.045) loss 1.1445 (1.8159) acc 78.1250 (57.3438) lr 1.9989e-03 eta 1:16:28
+epoch [5/200] batch [25/31] time 0.703 (0.748) data 0.000 (0.036) loss 1.5664 (1.8088) acc 53.1250 (56.2500) lr 1.9989e-03 eta 1:15:24
+epoch [5/200] batch [30/31] time 0.727 (0.742) data 0.000 (0.030) loss 1.5967 (1.7736) acc 65.6250 (57.0833) lr 1.9989e-03 eta 1:14:45
+epoch [6/200] batch [5/31] time 0.719 (0.905) data 0.000 (0.173) loss 2.1777 (1.8941) acc 53.1250 (54.3750) lr 1.9980e-03 eta 1:31:03
+epoch [6/200] batch [10/31] time 0.709 (0.809) data 0.000 (0.087) loss 1.5176 (1.8242) acc 46.8750 (53.4375) lr 1.9980e-03 eta 1:21:25
+epoch [6/200] batch [15/31] time 0.705 (0.777) data 0.000 (0.058) loss 2.5938 (1.8923) acc 50.0000 (53.9583) lr 1.9980e-03 eta 1:18:03
+epoch [6/200] batch [20/31] time 0.703 (0.759) data 0.000 (0.044) loss 1.7578 (1.8480) acc 62.5000 (55.0000) lr 1.9980e-03 eta 1:16:13
+epoch [6/200] batch [25/31] time 0.709 (0.750) data 0.000 (0.035) loss 1.7266 (1.7841) acc 65.6250 (56.7500) lr 1.9980e-03 eta 1:15:16
+epoch [6/200] batch [30/31] time 0.704 (0.743) data 0.000 (0.029) loss 1.8564 (1.7594) acc 53.1250 (57.3958) lr 1.9980e-03 eta 1:14:30
+epoch [7/200] batch [5/31] time 0.728 (0.887) data 0.000 (0.159) loss 1.8535 (1.7396) acc 56.2500 (61.2500) lr 1.9969e-03 eta 1:28:49
+epoch [7/200] batch [10/31] time 0.725 (0.800) data 0.001 (0.080) loss 2.1523 (1.7740) acc 46.8750 (56.2500) lr 1.9969e-03 eta 1:20:04
+epoch [7/200] batch [15/31] time 0.716 (0.771) data 0.000 (0.053) loss 1.6943 (1.8325) acc 50.0000 (54.7917) lr 1.9969e-03 eta 1:17:03
+epoch [7/200] batch [20/31] time 0.710 (0.756) data 0.000 (0.040) loss 0.9897 (1.8052) acc 71.8750 (55.3125) lr 1.9969e-03 eta 1:15:30
+epoch [7/200] batch [25/31] time 0.712 (0.747) data 0.000 (0.032) loss 1.7158 (1.8285) acc 50.0000 (55.2500) lr 1.9969e-03 eta 1:14:34
+epoch [7/200] batch [30/31] time 0.708 (0.741) data 0.000 (0.027) loss 2.7559 (1.8856) acc 43.7500 (55.0000) lr 1.9969e-03 eta 1:13:55
+epoch [8/200] batch [5/31] time 0.712 (0.892) data 0.000 (0.167) loss 2.5781 (2.0729) acc 43.7500 (51.2500) lr 1.9956e-03 eta 1:28:52
+epoch [8/200] batch [10/31] time 0.718 (0.805) data 0.000 (0.084) loss 1.4668 (1.8531) acc 62.5000 (55.3125) lr 1.9956e-03 eta 1:20:09
+epoch [8/200] batch [15/31] time 0.719 (0.775) data 0.001 (0.056) loss 1.5166 (1.8248) acc 65.6250 (55.2083) lr 1.9956e-03 eta 1:17:08
+epoch [8/200] batch [20/31] time 0.723 (0.760) data 0.000 (0.042) loss 1.5000 (1.8031) acc 68.7500 (56.4062) lr 1.9956e-03 eta 1:15:32
+epoch [8/200] batch [25/31] time 0.708 (0.750) data 0.000 (0.034) loss 1.2197 (1.7323) acc 71.8750 (57.1250) lr 1.9956e-03 eta 1:14:28
+epoch [8/200] batch [30/31] time 0.724 (0.750) data 0.000 (0.028) loss 1.7861 (1.6822) acc 65.6250 (58.6458) lr 1.9956e-03 eta 1:14:23
+epoch [9/200] batch [5/31] time 0.726 (0.902) data 0.000 (0.176) loss 1.8379 (1.9916) acc 50.0000 (53.1250) lr 1.9940e-03 eta 1:29:26
+epoch [9/200] batch [10/31] time 0.711 (0.808) data 0.000 (0.088) loss 1.4307 (1.8373) acc 56.2500 (56.2500) lr 1.9940e-03 eta 1:20:00
+epoch [9/200] batch [15/31] time 0.715 (0.775) data 0.000 (0.059) loss 2.0996 (1.8068) acc 40.6250 (54.5833) lr 1.9940e-03 eta 1:16:43
+epoch [9/200] batch [20/31] time 0.705 (0.759) data 0.000 (0.044) loss 2.5938 (1.7920) acc 43.7500 (55.4688) lr 1.9940e-03 eta 1:15:01
+epoch [9/200] batch [25/31] time 0.709 (0.749) data 0.000 (0.035) loss 1.5518 (1.7948) acc 62.5000 (56.2500) lr 1.9940e-03 eta 1:13:58
+epoch [9/200] batch [30/31] time 0.711 (0.743) data 0.000 (0.030) loss 1.2998 (1.7383) acc 65.6250 (57.1875) lr 1.9940e-03 eta 1:13:22
+epoch [10/200] batch [5/31] time 0.711 (0.914) data 0.000 (0.188) loss 1.5527 (1.7641) acc 59.3750 (58.1250) lr 1.9921e-03 eta 1:30:08
+epoch [10/200] batch [10/31] time 0.714 (0.814) data 0.000 (0.094) loss 1.8535 (1.8920) acc 56.2500 (56.5625) lr 1.9921e-03 eta 1:20:14
+epoch [10/200] batch [15/31] time 0.716 (0.780) data 0.000 (0.063) loss 1.5098 (1.8217) acc 59.3750 (56.4583) lr 1.9921e-03 eta 1:16:48
+epoch [10/200] batch [20/31] time 0.713 (0.763) data 0.000 (0.047) loss 1.4785 (1.8319) acc 59.3750 (56.0938) lr 1.9921e-03 eta 1:15:01
+epoch [10/200] batch [25/31] time 0.710 (0.752) data 0.000 (0.038) loss 2.0527 (1.7996) acc 56.2500 (56.3750) lr 1.9921e-03 eta 1:13:52
+epoch [10/200] batch [30/31] time 0.725 (0.746) data 0.000 (0.032) loss 1.8027 (1.7868) acc 53.1250 (55.9375) lr 1.9921e-03 eta 1:13:13
+epoch [11/200] batch [5/31] time 0.714 (0.902) data 0.000 (0.171) loss 1.4980 (1.8707) acc 62.5000 (56.2500) lr 1.9900e-03 eta 1:28:25
+epoch [11/200] batch [10/31] time 0.714 (0.807) data 0.000 (0.086) loss 1.5693 (1.7632) acc 65.6250 (59.3750) lr 1.9900e-03 eta 1:19:02
+epoch [11/200] batch [15/31] time 0.711 (0.775) data 0.000 (0.057) loss 1.3018 (1.6317) acc 59.3750 (61.2500) lr 1.9900e-03 eta 1:15:51
+epoch [11/200] batch [20/31] time 0.721 (0.759) data 0.000 (0.043) loss 1.5176 (1.6302) acc 62.5000 (60.3125) lr 1.9900e-03 eta 1:14:13
+epoch [11/200] batch [25/31] time 0.709 (0.749) data 0.000 (0.034) loss 1.1523 (1.6337) acc 75.0000 (61.0000) lr 1.9900e-03 eta 1:13:14
+epoch [11/200] batch [30/31] time 0.704 (0.742) data 0.000 (0.029) loss 2.3574 (1.6860) acc 43.7500 (59.2708) lr 1.9900e-03 eta 1:12:28
+epoch [12/200] batch [5/31] time 0.709 (0.910) data 0.000 (0.179) loss 1.6855 (1.6955) acc 50.0000 (56.8750) lr 1.9877e-03 eta 1:28:44
+epoch [12/200] batch [10/31] time 0.726 (0.827) data 0.000 (0.090) loss 1.3057 (1.5716) acc 65.6250 (59.0625) lr 1.9877e-03 eta 1:20:36
+epoch [12/200] batch [15/31] time 0.716 (0.790) data 0.000 (0.060) loss 1.2666 (1.5922) acc 71.8750 (59.5833) lr 1.9877e-03 eta 1:16:56
+epoch [12/200] batch [20/31] time 0.710 (0.771) data 0.000 (0.045) loss 1.4961 (1.6080) acc 53.1250 (59.2188) lr 1.9877e-03 eta 1:14:59
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+epoch [199/200] batch [10/31] time 0.726 (0.812) data 0.000 (0.089) loss 0.2791 (0.6614) acc 93.7500 (86.2500) lr 1.1101e-06 eta 0:00:42
+epoch [199/200] batch [15/31] time 0.730 (0.780) data 0.000 (0.059) loss 0.6978 (0.6577) acc 81.2500 (85.6250) lr 1.1101e-06 eta 0:00:36
+epoch [199/200] batch [20/31] time 0.711 (0.763) data 0.000 (0.045) loss 1.0977 (0.7065) acc 81.2500 (84.8438) lr 1.1101e-06 eta 0:00:32
+epoch [199/200] batch [25/31] time 0.714 (0.752) data 0.000 (0.036) loss 1.0938 (0.7501) acc 71.8750 (84.0000) lr 1.1101e-06 eta 0:00:27
+epoch [199/200] batch [30/31] time 0.707 (0.745) data 0.000 (0.030) loss 0.7710 (0.7527) acc 84.3750 (84.1667) lr 1.1101e-06 eta 0:00:23
+epoch [200/200] batch [5/31] time 0.718 (0.889) data 0.000 (0.161) loss 0.8462 (0.6117) acc 84.3750 (85.0000) lr 4.9344e-07 eta 0:00:23
+epoch [200/200] batch [10/31] time 0.712 (0.800) data 0.000 (0.081) loss 0.9668 (0.6253) acc 90.6250 (86.5625) lr 4.9344e-07 eta 0:00:16
+epoch [200/200] batch [15/31] time 0.713 (0.772) data 0.000 (0.054) loss 0.3579 (0.6491) acc 90.6250 (86.2500) lr 4.9344e-07 eta 0:00:12
+epoch [200/200] batch [20/31] time 0.707 (0.757) data 0.000 (0.041) loss 0.6587 (0.6775) acc 90.6250 (85.9375) lr 4.9344e-07 eta 0:00:08
+epoch [200/200] batch [25/31] time 0.708 (0.748) data 0.000 (0.033) loss 0.6250 (0.6682) acc 78.1250 (85.5000) lr 4.9344e-07 eta 0:00:04
+epoch [200/200] batch [30/31] time 0.712 (0.743) data 0.000 (0.027) loss 0.6016 (0.6683) acc 84.3750 (85.5208) lr 4.9344e-07 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-200
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 26,204
+* accuracy: 52.4%
+* error: 47.6%
+* macro_f1: 51.0%
+Elapsed: 1:20:09
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
new file mode 100644
index 00000000..b2929f7c
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-200
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-200 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-200
new file mode 100644
index 00000000..d68ba04a
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698082537.ckb-gpu-lambda.1480462.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698082537.ckb-gpu-lambda.1480462.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..1767c7e2
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.126
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/31] time 0.736 (1.619) data 0.000 (0.155) loss 4.3398 (4.6867) acc 25.0000 (18.1250) lr 1.0000e-05 eta 0:41:40
+epoch [1/50] batch [10/31] time 0.722 (1.171) data 0.000 (0.078) loss 3.7773 (4.3557) acc 21.8750 (21.8750) lr 1.0000e-05 eta 0:30:04
+epoch [1/50] batch [15/31] time 0.721 (1.022) data 0.000 (0.052) loss 3.6875 (4.2160) acc 28.1250 (23.5417) lr 1.0000e-05 eta 0:26:09
+epoch [1/50] batch [20/31] time 0.734 (0.952) data 0.000 (0.039) loss 3.5723 (4.0763) acc 25.0000 (24.5312) lr 1.0000e-05 eta 0:24:17
+epoch [1/50] batch [25/31] time 0.725 (0.909) data 0.000 (0.031) loss 2.6855 (3.8645) acc 43.7500 (27.5000) lr 1.0000e-05 eta 0:23:06
+epoch [1/50] batch [30/31] time 0.712 (0.880) data 0.000 (0.026) loss 3.4629 (3.7541) acc 31.2500 (28.7500) lr 1.0000e-05 eta 0:22:17
+epoch [2/50] batch [5/31] time 0.733 (0.897) data 0.000 (0.154) loss 1.8252 (2.5494) acc 59.3750 (42.5000) lr 2.0000e-03 eta 0:22:38
+epoch [2/50] batch [10/31] time 0.723 (0.809) data 0.000 (0.077) loss 2.0430 (2.4092) acc 50.0000 (46.5625) lr 2.0000e-03 eta 0:20:21
+epoch [2/50] batch [15/31] time 0.733 (0.783) data 0.000 (0.052) loss 2.0762 (2.2622) acc 43.7500 (48.9583) lr 2.0000e-03 eta 0:19:37
+epoch [2/50] batch [20/31] time 0.733 (0.769) data 0.000 (0.039) loss 1.6934 (2.2380) acc 56.2500 (50.1562) lr 2.0000e-03 eta 0:19:13
+epoch [2/50] batch [25/31] time 0.766 (0.762) data 0.000 (0.031) loss 2.3320 (2.1624) acc 40.6250 (51.7500) lr 2.0000e-03 eta 0:18:58
+epoch [2/50] batch [30/31] time 0.723 (0.756) data 0.000 (0.026) loss 2.1250 (2.1692) acc 40.6250 (50.3125) lr 2.0000e-03 eta 0:18:45
+epoch [3/50] batch [5/31] time 0.756 (0.903) data 0.000 (0.151) loss 1.3076 (1.8510) acc 68.7500 (55.0000) lr 1.9980e-03 eta 0:22:18
+epoch [3/50] batch [10/31] time 0.741 (0.817) data 0.000 (0.076) loss 2.2324 (1.9201) acc 59.3750 (55.0000) lr 1.9980e-03 eta 0:20:07
+epoch [3/50] batch [15/31] time 0.727 (0.792) data 0.000 (0.051) loss 2.0391 (1.9473) acc 56.2500 (54.1667) lr 1.9980e-03 eta 0:19:26
+epoch [3/50] batch [20/31] time 0.736 (0.778) data 0.000 (0.038) loss 1.6973 (1.8804) acc 56.2500 (55.1562) lr 1.9980e-03 eta 0:19:01
+epoch [3/50] batch [25/31] time 0.725 (0.768) data 0.000 (0.031) loss 1.8252 (1.9014) acc 68.7500 (55.0000) lr 1.9980e-03 eta 0:18:44
+epoch [3/50] batch [30/31] time 0.727 (0.762) data 0.000 (0.026) loss 2.3770 (1.8992) acc 53.1250 (54.8958) lr 1.9980e-03 eta 0:18:31
+epoch [4/50] batch [5/31] time 0.749 (0.879) data 0.000 (0.140) loss 1.6064 (1.9088) acc 53.1250 (48.1250) lr 1.9921e-03 eta 0:21:16
+epoch [4/50] batch [10/31] time 0.720 (0.805) data 0.000 (0.070) loss 1.7705 (1.7486) acc 59.3750 (54.3750) lr 1.9921e-03 eta 0:19:25
+epoch [4/50] batch [15/31] time 0.722 (0.781) data 0.000 (0.047) loss 1.9619 (1.7015) acc 59.3750 (56.8750) lr 1.9921e-03 eta 0:18:46
+epoch [4/50] batch [20/31] time 0.761 (0.769) data 0.000 (0.035) loss 1.8125 (1.7617) acc 62.5000 (57.8125) lr 1.9921e-03 eta 0:18:25
+epoch [4/50] batch [25/31] time 0.738 (0.763) data 0.000 (0.028) loss 2.4980 (1.7508) acc 62.5000 (57.8750) lr 1.9921e-03 eta 0:18:12
+epoch [4/50] batch [30/31] time 0.744 (0.757) data 0.000 (0.024) loss 2.0391 (1.7604) acc 53.1250 (58.2292) lr 1.9921e-03 eta 0:17:59
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+epoch [5/50] batch [10/31] time 0.720 (0.806) data 0.000 (0.073) loss 1.2393 (1.7511) acc 75.0000 (59.6875) lr 1.9823e-03 eta 0:19:00
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+epoch [42/50] batch [10/31] time 0.728 (0.812) data 0.000 (0.073) loss 1.6162 (1.3337) acc 59.3750 (64.6875) lr 1.9098e-04 eta 0:03:38
+epoch [42/50] batch [15/31] time 0.742 (0.787) data 0.000 (0.048) loss 1.4580 (1.3781) acc 62.5000 (64.5833) lr 1.9098e-04 eta 0:03:27
+epoch [42/50] batch [20/31] time 0.725 (0.773) data 0.000 (0.036) loss 1.5098 (1.3544) acc 65.6250 (65.4688) lr 1.9098e-04 eta 0:03:20
+epoch [42/50] batch [25/31] time 0.718 (0.765) data 0.000 (0.029) loss 1.0391 (1.3333) acc 81.2500 (66.7500) lr 1.9098e-04 eta 0:03:14
+epoch [42/50] batch [30/31] time 0.745 (0.762) data 0.000 (0.024) loss 1.5986 (1.3479) acc 59.3750 (66.3542) lr 1.9098e-04 eta 0:03:09
+epoch [43/50] batch [5/31] time 0.760 (0.888) data 0.000 (0.143) loss 1.6934 (1.2563) acc 65.6250 (68.1250) lr 1.5567e-04 eta 0:03:35
+epoch [43/50] batch [10/31] time 0.728 (0.823) data 0.001 (0.072) loss 1.7900 (1.3118) acc 50.0000 (64.3750) lr 1.5567e-04 eta 0:03:15
+epoch [43/50] batch [15/31] time 0.753 (0.795) data 0.000 (0.048) loss 1.7500 (1.4312) acc 59.3750 (62.7083) lr 1.5567e-04 eta 0:03:05
+epoch [43/50] batch [20/31] time 0.729 (0.777) data 0.000 (0.036) loss 1.4404 (1.4677) acc 65.6250 (62.9688) lr 1.5567e-04 eta 0:02:57
+epoch [43/50] batch [25/31] time 0.741 (0.770) data 0.000 (0.029) loss 1.1084 (1.4239) acc 62.5000 (64.2500) lr 1.5567e-04 eta 0:02:51
+epoch [43/50] batch [30/31] time 0.736 (0.764) data 0.000 (0.024) loss 2.2480 (1.4388) acc 50.0000 (63.5417) lr 1.5567e-04 eta 0:02:46
+epoch [44/50] batch [5/31] time 0.726 (0.877) data 0.001 (0.138) loss 1.2227 (1.5900) acc 71.8750 (65.6250) lr 1.2369e-04 eta 0:03:05
+epoch [44/50] batch [10/31] time 0.726 (0.807) data 0.000 (0.069) loss 1.8057 (1.4959) acc 65.6250 (65.0000) lr 1.2369e-04 eta 0:02:46
+epoch [44/50] batch [15/31] time 0.730 (0.784) data 0.001 (0.046) loss 0.9814 (1.4033) acc 75.0000 (66.2500) lr 1.2369e-04 eta 0:02:38
+epoch [44/50] batch [20/31] time 0.736 (0.773) data 0.000 (0.035) loss 2.0352 (1.4160) acc 59.3750 (66.4062) lr 1.2369e-04 eta 0:02:32
+epoch [44/50] batch [25/31] time 0.733 (0.765) data 0.000 (0.028) loss 1.2451 (1.3965) acc 78.1250 (67.1250) lr 1.2369e-04 eta 0:02:26
+epoch [44/50] batch [30/31] time 0.726 (0.759) data 0.000 (0.023) loss 1.4102 (1.4282) acc 65.6250 (66.9792) lr 1.2369e-04 eta 0:02:21
+epoch [45/50] batch [5/31] time 0.733 (0.890) data 0.000 (0.150) loss 1.6064 (1.3188) acc 56.2500 (69.3750) lr 9.5173e-05 eta 0:02:41
+epoch [45/50] batch [10/31] time 0.724 (0.806) data 0.000 (0.075) loss 1.5996 (1.2697) acc 59.3750 (67.1875) lr 9.5173e-05 eta 0:02:21
+epoch [45/50] batch [15/31] time 0.719 (0.783) data 0.000 (0.050) loss 1.6396 (1.3646) acc 62.5000 (67.0833) lr 9.5173e-05 eta 0:02:13
+epoch [45/50] batch [20/31] time 0.720 (0.766) data 0.000 (0.038) loss 1.5332 (1.3892) acc 65.6250 (65.9375) lr 9.5173e-05 eta 0:02:07
+epoch [45/50] batch [25/31] time 0.725 (0.757) data 0.000 (0.030) loss 1.6221 (1.4027) acc 50.0000 (65.1250) lr 9.5173e-05 eta 0:02:01
+epoch [45/50] batch [30/31] time 0.741 (0.752) data 0.000 (0.025) loss 1.2979 (1.3923) acc 62.5000 (65.8333) lr 9.5173e-05 eta 0:01:57
+epoch [46/50] batch [5/31] time 0.729 (0.874) data 0.000 (0.142) loss 1.0176 (1.2252) acc 71.8750 (72.5000) lr 7.0224e-05 eta 0:02:11
+epoch [46/50] batch [10/31] time 0.736 (0.799) data 0.000 (0.071) loss 1.2402 (1.2223) acc 75.0000 (69.6875) lr 7.0224e-05 eta 0:01:55
+epoch [46/50] batch [15/31] time 0.728 (0.777) data 0.000 (0.048) loss 0.9253 (1.2210) acc 78.1250 (70.6250) lr 7.0224e-05 eta 0:01:48
+epoch [46/50] batch [20/31] time 0.718 (0.763) data 0.000 (0.036) loss 1.3721 (1.2589) acc 62.5000 (70.0000) lr 7.0224e-05 eta 0:01:43
+epoch [46/50] batch [25/31] time 0.745 (0.760) data 0.000 (0.029) loss 2.1504 (1.3328) acc 50.0000 (67.5000) lr 7.0224e-05 eta 0:01:38
+epoch [46/50] batch [30/31] time 0.734 (0.756) data 0.000 (0.024) loss 0.9854 (1.3487) acc 68.7500 (67.1875) lr 7.0224e-05 eta 0:01:34
+epoch [47/50] batch [5/31] time 0.747 (0.892) data 0.000 (0.142) loss 1.4736 (1.4928) acc 62.5000 (61.2500) lr 4.8943e-05 eta 0:01:46
+epoch [47/50] batch [10/31] time 0.734 (0.817) data 0.000 (0.071) loss 1.4746 (1.4506) acc 56.2500 (63.7500) lr 4.8943e-05 eta 0:01:33
+epoch [47/50] batch [15/31] time 0.717 (0.788) data 0.000 (0.048) loss 1.4756 (1.4164) acc 62.5000 (65.6250) lr 4.8943e-05 eta 0:01:25
+epoch [47/50] batch [20/31] time 0.732 (0.775) data 0.000 (0.036) loss 1.2275 (1.4059) acc 75.0000 (66.8750) lr 4.8943e-05 eta 0:01:20
+epoch [47/50] batch [25/31] time 0.722 (0.766) data 0.001 (0.029) loss 0.5596 (1.3811) acc 84.3750 (67.7500) lr 4.8943e-05 eta 0:01:15
+epoch [47/50] batch [30/31] time 0.732 (0.760) data 0.000 (0.024) loss 1.0039 (1.3337) acc 62.5000 (68.0208) lr 4.8943e-05 eta 0:01:11
+epoch [48/50] batch [5/31] time 0.725 (0.896) data 0.000 (0.148) loss 1.4316 (1.3472) acc 62.5000 (65.6250) lr 3.1417e-05 eta 0:01:18
+epoch [48/50] batch [10/31] time 0.730 (0.813) data 0.000 (0.074) loss 1.2852 (1.5045) acc 81.2500 (65.9375) lr 3.1417e-05 eta 0:01:07
+epoch [48/50] batch [15/31] time 0.735 (0.789) data 0.001 (0.050) loss 1.2705 (1.4290) acc 62.5000 (66.8750) lr 3.1417e-05 eta 0:01:01
+epoch [48/50] batch [20/31] time 0.741 (0.775) data 0.000 (0.037) loss 1.4619 (1.4861) acc 65.6250 (65.1562) lr 3.1417e-05 eta 0:00:56
+epoch [48/50] batch [25/31] time 0.720 (0.766) data 0.000 (0.030) loss 1.3750 (1.5050) acc 75.0000 (65.1250) lr 3.1417e-05 eta 0:00:52
+epoch [48/50] batch [30/31] time 0.730 (0.761) data 0.000 (0.025) loss 1.6787 (1.4763) acc 68.7500 (66.2500) lr 3.1417e-05 eta 0:00:47
+epoch [49/50] batch [5/31] time 0.741 (0.892) data 0.000 (0.143) loss 1.1025 (1.4008) acc 68.7500 (67.5000) lr 1.7713e-05 eta 0:00:50
+epoch [49/50] batch [10/31] time 0.741 (0.826) data 0.000 (0.072) loss 1.4150 (1.3645) acc 62.5000 (67.5000) lr 1.7713e-05 eta 0:00:42
+epoch [49/50] batch [15/31] time 0.730 (0.795) data 0.000 (0.048) loss 1.5713 (1.4048) acc 56.2500 (66.6667) lr 1.7713e-05 eta 0:00:37
+epoch [49/50] batch [20/31] time 0.750 (0.780) data 0.000 (0.036) loss 1.2402 (1.5054) acc 68.7500 (65.7812) lr 1.7713e-05 eta 0:00:32
+epoch [49/50] batch [25/31] time 0.727 (0.771) data 0.000 (0.029) loss 0.8521 (1.4586) acc 78.1250 (66.2500) lr 1.7713e-05 eta 0:00:28
+epoch [49/50] batch [30/31] time 0.735 (0.764) data 0.000 (0.024) loss 1.4336 (1.4798) acc 62.5000 (65.5208) lr 1.7713e-05 eta 0:00:24
+epoch [50/50] batch [5/31] time 0.716 (0.890) data 0.000 (0.156) loss 1.8701 (1.5762) acc 50.0000 (60.0000) lr 7.8853e-06 eta 0:00:23
+epoch [50/50] batch [10/31] time 0.728 (0.810) data 0.000 (0.078) loss 1.3701 (1.4328) acc 71.8750 (63.7500) lr 7.8853e-06 eta 0:00:17
+epoch [50/50] batch [15/31] time 0.723 (0.782) data 0.000 (0.052) loss 1.6875 (1.6109) acc 68.7500 (61.2500) lr 7.8853e-06 eta 0:00:12
+epoch [50/50] batch [20/31] time 0.718 (0.770) data 0.000 (0.039) loss 1.1250 (1.5353) acc 65.6250 (62.1875) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [25/31] time 0.729 (0.762) data 0.000 (0.032) loss 1.4551 (1.4917) acc 59.3750 (62.8750) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [30/31] time 0.722 (0.760) data 0.000 (0.026) loss 1.2148 (1.4424) acc 75.0000 (64.7917) lr 7.8853e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 30,777
+* accuracy: 61.6%
+* error: 38.4%
+* macro_f1: 60.5%
+Elapsed: 0:22:36
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..a34d1f44
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1697829628.ckb-gpu-lambda.2064170.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1697829628.ckb-gpu-lambda.2064170.0
new file mode 100644
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1697829628.ckb-gpu-lambda.2064170.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..60032aa3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.103
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/31] time 0.744 (1.596) data 0.000 (0.145) loss 3.5605 (3.5969) acc 25.0000 (32.5000) lr 1.0000e-05 eta 0:41:05
+epoch [1/50] batch [10/31] time 0.728 (1.160) data 0.000 (0.073) loss 3.8535 (3.4861) acc 18.7500 (34.0625) lr 1.0000e-05 eta 0:29:45
+epoch [1/50] batch [15/31] time 0.729 (1.015) data 0.001 (0.049) loss 3.4395 (3.3546) acc 43.7500 (35.6250) lr 1.0000e-05 eta 0:25:57
+epoch [1/50] batch [20/31] time 0.743 (0.943) data 0.000 (0.037) loss 3.0215 (3.2204) acc 34.3750 (37.3438) lr 1.0000e-05 eta 0:24:02
+epoch [1/50] batch [25/31] time 0.726 (0.898) data 0.000 (0.029) loss 2.7930 (3.1371) acc 40.6250 (37.3750) lr 1.0000e-05 eta 0:22:49
+epoch [1/50] batch [30/31] time 0.711 (0.867) data 0.000 (0.024) loss 2.6113 (3.0720) acc 43.7500 (37.7083) lr 1.0000e-05 eta 0:21:58
+epoch [2/50] batch [5/31] time 0.745 (0.879) data 0.000 (0.141) loss 2.0879 (2.5738) acc 56.2500 (46.8750) lr 2.0000e-03 eta 0:22:11
+epoch [2/50] batch [10/31] time 0.752 (0.804) data 0.000 (0.071) loss 1.0957 (2.2741) acc 59.3750 (50.0000) lr 2.0000e-03 eta 0:20:13
+epoch [2/50] batch [15/31] time 0.737 (0.779) data 0.000 (0.047) loss 2.2617 (2.2068) acc 46.8750 (51.8750) lr 2.0000e-03 eta 0:19:31
+epoch [2/50] batch [20/31] time 0.720 (0.764) data 0.000 (0.036) loss 1.9941 (2.1865) acc 56.2500 (52.1875) lr 2.0000e-03 eta 0:19:05
+epoch [2/50] batch [25/31] time 0.713 (0.758) data 0.000 (0.029) loss 2.1230 (2.1198) acc 46.8750 (52.6250) lr 2.0000e-03 eta 0:18:52
+epoch [2/50] batch [30/31] time 0.719 (0.752) data 0.000 (0.024) loss 1.9346 (2.0827) acc 53.1250 (52.7083) lr 2.0000e-03 eta 0:18:39
+epoch [3/50] batch [5/31] time 0.727 (0.888) data 0.000 (0.148) loss 2.2754 (2.2193) acc 46.8750 (50.6250) lr 1.9980e-03 eta 0:21:56
+epoch [3/50] batch [10/31] time 0.718 (0.806) data 0.000 (0.074) loss 1.9541 (2.2126) acc 59.3750 (52.1875) lr 1.9980e-03 eta 0:19:51
+epoch [3/50] batch [15/31] time 0.722 (0.780) data 0.000 (0.050) loss 1.6699 (2.1538) acc 59.3750 (53.9583) lr 1.9980e-03 eta 0:19:09
+epoch [3/50] batch [20/31] time 0.701 (0.766) data 0.000 (0.037) loss 1.6592 (2.0384) acc 65.6250 (56.0938) lr 1.9980e-03 eta 0:18:45
+epoch [3/50] batch [25/31] time 0.727 (0.759) data 0.000 (0.030) loss 1.4814 (1.9636) acc 68.7500 (56.8750) lr 1.9980e-03 eta 0:18:29
+epoch [3/50] batch [30/31] time 0.710 (0.753) data 0.000 (0.025) loss 1.9062 (1.9651) acc 46.8750 (56.1458) lr 1.9980e-03 eta 0:18:17
+epoch [4/50] batch [5/31] time 0.718 (0.876) data 0.001 (0.132) loss 1.8096 (1.7613) acc 53.1250 (55.0000) lr 1.9921e-03 eta 0:21:11
+epoch [4/50] batch [10/31] time 0.732 (0.802) data 0.000 (0.066) loss 1.9268 (1.8851) acc 68.7500 (55.6250) lr 1.9921e-03 eta 0:19:20
+epoch [4/50] batch [15/31] time 0.749 (0.775) data 0.000 (0.044) loss 2.2266 (1.8395) acc 53.1250 (56.8750) lr 1.9921e-03 eta 0:18:37
+epoch [4/50] batch [20/31] time 0.727 (0.762) data 0.000 (0.033) loss 1.3496 (1.8689) acc 68.7500 (56.4062) lr 1.9921e-03 eta 0:18:15
+epoch [4/50] batch [25/31] time 0.716 (0.754) data 0.000 (0.027) loss 1.8672 (1.8460) acc 53.1250 (57.0000) lr 1.9921e-03 eta 0:17:59
+epoch [4/50] batch [30/31] time 0.718 (0.748) data 0.000 (0.022) loss 1.8730 (1.8520) acc 59.3750 (55.8333) lr 1.9921e-03 eta 0:17:48
+epoch [5/50] batch [5/31] time 0.722 (0.849) data 0.000 (0.127) loss 1.9121 (1.9563) acc 59.3750 (56.2500) lr 1.9823e-03 eta 0:20:06
+epoch [5/50] batch [10/31] time 0.731 (0.796) data 0.000 (0.064) loss 2.3223 (1.7925) acc 46.8750 (59.0625) lr 1.9823e-03 eta 0:18:46
+epoch [5/50] batch [15/31] time 0.731 (0.772) data 0.000 (0.042) loss 1.8213 (1.8116) acc 53.1250 (57.9167) lr 1.9823e-03 eta 0:18:09
+epoch [5/50] batch [20/31] time 0.722 (0.759) data 0.000 (0.032) loss 1.7676 (1.7863) acc 62.5000 (58.4375) lr 1.9823e-03 eta 0:17:47
+epoch [5/50] batch [25/31] time 0.735 (0.753) data 0.000 (0.026) loss 1.5215 (1.7683) acc 62.5000 (57.7500) lr 1.9823e-03 eta 0:17:34
+epoch [5/50] batch [30/31] time 0.723 (0.747) data 0.000 (0.021) loss 1.9707 (1.8178) acc 53.1250 (56.8750) lr 1.9823e-03 eta 0:17:22
+epoch [6/50] batch [5/31] time 0.734 (0.877) data 0.000 (0.141) loss 2.3691 (1.8631) acc 53.1250 (56.8750) lr 1.9686e-03 eta 0:20:19
+epoch [6/50] batch [10/31] time 0.734 (0.804) data 0.000 (0.072) loss 2.0332 (1.7026) acc 43.7500 (58.7500) lr 1.9686e-03 eta 0:18:33
+epoch [6/50] batch [15/31] time 0.715 (0.778) data 0.000 (0.048) loss 1.4961 (1.6917) acc 62.5000 (57.9167) lr 1.9686e-03 eta 0:17:54
+epoch [6/50] batch [20/31] time 0.713 (0.765) data 0.000 (0.036) loss 2.0117 (1.6800) acc 53.1250 (57.6562) lr 1.9686e-03 eta 0:17:32
+epoch [6/50] batch [25/31] time 0.721 (0.757) data 0.000 (0.029) loss 2.4688 (1.7189) acc 50.0000 (58.2500) lr 1.9686e-03 eta 0:17:16
+epoch [6/50] batch [30/31] time 0.733 (0.751) data 0.000 (0.024) loss 1.7646 (1.7213) acc 50.0000 (57.9167) lr 1.9686e-03 eta 0:17:04
+epoch [7/50] batch [5/31] time 0.725 (0.886) data 0.000 (0.142) loss 1.3223 (1.4674) acc 68.7500 (65.6250) lr 1.9511e-03 eta 0:20:03
+epoch [7/50] batch [10/31] time 0.733 (0.803) data 0.001 (0.071) loss 1.0420 (1.5184) acc 75.0000 (62.5000) lr 1.9511e-03 eta 0:18:06
+epoch [7/50] batch [15/31] time 0.718 (0.779) data 0.000 (0.048) loss 1.6641 (1.5729) acc 53.1250 (62.9167) lr 1.9511e-03 eta 0:17:30
+epoch [7/50] batch [20/31] time 0.718 (0.765) data 0.000 (0.036) loss 1.7910 (1.6291) acc 50.0000 (60.0000) lr 1.9511e-03 eta 0:17:08
+epoch [7/50] batch [25/31] time 0.707 (0.756) data 0.000 (0.029) loss 3.2559 (1.7070) acc 53.1250 (59.7500) lr 1.9511e-03 eta 0:16:52
+epoch [7/50] batch [30/31] time 0.718 (0.750) data 0.000 (0.024) loss 1.9922 (1.7209) acc 56.2500 (59.7917) lr 1.9511e-03 eta 0:16:40
+epoch [8/50] batch [5/31] time 0.756 (0.897) data 0.000 (0.156) loss 1.7803 (1.6650) acc 62.5000 (61.8750) lr 1.9298e-03 eta 0:19:50
+epoch [8/50] batch [10/31] time 0.731 (0.814) data 0.000 (0.078) loss 1.6670 (1.7481) acc 68.7500 (58.4375) lr 1.9298e-03 eta 0:17:56
+epoch [8/50] batch [15/31] time 0.727 (0.792) data 0.000 (0.052) loss 1.3877 (1.7196) acc 68.7500 (60.4167) lr 1.9298e-03 eta 0:17:23
+epoch [8/50] batch [20/31] time 0.748 (0.780) data 0.001 (0.039) loss 1.1709 (1.6853) acc 75.0000 (60.3125) lr 1.9298e-03 eta 0:17:04
+epoch [8/50] batch [25/31] time 0.716 (0.768) data 0.000 (0.031) loss 1.3311 (1.6531) acc 62.5000 (60.5000) lr 1.9298e-03 eta 0:16:44
+epoch [8/50] batch [30/31] time 0.721 (0.762) data 0.000 (0.026) loss 1.6582 (1.6616) acc 68.7500 (60.5208) lr 1.9298e-03 eta 0:16:32
+epoch [9/50] batch [5/31] time 0.744 (0.912) data 0.002 (0.169) loss 1.8535 (2.0027) acc 53.1250 (51.8750) lr 1.9048e-03 eta 0:19:43
+epoch [9/50] batch [10/31] time 0.731 (0.823) data 0.001 (0.085) loss 1.9072 (1.7606) acc 50.0000 (57.1875) lr 1.9048e-03 eta 0:17:43
+epoch [9/50] batch [15/31] time 0.716 (0.789) data 0.000 (0.057) loss 1.1660 (1.7206) acc 71.8750 (58.5417) lr 1.9048e-03 eta 0:16:55
+epoch [9/50] batch [20/31] time 0.748 (0.777) data 0.000 (0.043) loss 1.8662 (1.7536) acc 53.1250 (57.9688) lr 1.9048e-03 eta 0:16:35
+epoch [9/50] batch [25/31] time 0.725 (0.768) data 0.000 (0.034) loss 0.9844 (1.7092) acc 68.7500 (58.6250) lr 1.9048e-03 eta 0:16:20
+epoch [9/50] batch [30/31] time 0.723 (0.762) data 0.000 (0.029) loss 1.0918 (1.6839) acc 75.0000 (59.5833) lr 1.9048e-03 eta 0:16:09
+epoch [10/50] batch [5/31] time 0.714 (0.896) data 0.000 (0.141) loss 0.8774 (1.4388) acc 78.1250 (63.1250) lr 1.8763e-03 eta 0:18:54
+epoch [10/50] batch [10/31] time 0.708 (0.807) data 0.001 (0.071) loss 1.6494 (1.6143) acc 56.2500 (57.1875) lr 1.8763e-03 eta 0:16:57
+epoch [10/50] batch [15/31] time 0.755 (0.782) data 0.000 (0.047) loss 1.9668 (1.6659) acc 56.2500 (57.0833) lr 1.8763e-03 eta 0:16:21
+epoch [10/50] batch [20/31] time 0.714 (0.766) data 0.000 (0.036) loss 1.2910 (1.6744) acc 62.5000 (58.4375) lr 1.8763e-03 eta 0:15:57
+epoch [10/50] batch [25/31] time 0.713 (0.756) data 0.000 (0.029) loss 2.4766 (1.7025) acc 53.1250 (58.5000) lr 1.8763e-03 eta 0:15:42
+epoch [10/50] batch [30/31] time 0.719 (0.751) data 0.000 (0.024) loss 1.7588 (1.7114) acc 65.6250 (58.8542) lr 1.8763e-03 eta 0:15:31
+epoch [11/50] batch [5/31] time 0.725 (0.867) data 0.000 (0.133) loss 1.7334 (1.5055) acc 68.7500 (65.0000) lr 1.8443e-03 eta 0:17:50
+epoch [11/50] batch [10/31] time 0.717 (0.800) data 0.000 (0.067) loss 2.3652 (1.6203) acc 56.2500 (65.6250) lr 1.8443e-03 eta 0:16:23
+epoch [11/50] batch [15/31] time 0.722 (0.775) data 0.000 (0.045) loss 1.5303 (1.6292) acc 65.6250 (63.5417) lr 1.8443e-03 eta 0:15:49
+epoch [11/50] batch [20/31] time 0.723 (0.763) data 0.000 (0.034) loss 1.6963 (1.6538) acc 71.8750 (63.1250) lr 1.8443e-03 eta 0:15:30
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+epoch [12/50] batch [25/31] time 0.717 (0.762) data 0.000 (0.029) loss 2.3496 (1.5637) acc 53.1250 (63.5000) lr 1.8090e-03 eta 0:15:01
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+epoch [13/50] batch [10/31] time 0.735 (0.808) data 0.001 (0.070) loss 2.0215 (1.5420) acc 50.0000 (65.6250) lr 1.7705e-03 eta 0:15:43
+epoch [13/50] batch [15/31] time 0.721 (0.783) data 0.000 (0.047) loss 1.2812 (1.5355) acc 46.8750 (63.3333) lr 1.7705e-03 eta 0:15:10
+epoch [13/50] batch [20/31] time 0.735 (0.770) data 0.000 (0.035) loss 1.3457 (1.5146) acc 71.8750 (63.4375) lr 1.7705e-03 eta 0:14:51
+epoch [13/50] batch [25/31] time 0.706 (0.761) data 0.000 (0.028) loss 2.4258 (1.6273) acc 56.2500 (61.7500) lr 1.7705e-03 eta 0:14:37
+epoch [13/50] batch [30/31] time 0.721 (0.756) data 0.000 (0.023) loss 1.7217 (1.6521) acc 62.5000 (61.5625) lr 1.7705e-03 eta 0:14:28
+epoch [14/50] batch [5/31] time 0.713 (0.876) data 0.000 (0.146) loss 1.0156 (1.3631) acc 75.0000 (68.1250) lr 1.7290e-03 eta 0:16:40
+epoch [14/50] batch [10/31] time 0.715 (0.802) data 0.000 (0.073) loss 1.6777 (1.4973) acc 62.5000 (65.9375) lr 1.7290e-03 eta 0:15:11
+epoch [14/50] batch [15/31] time 0.717 (0.777) data 0.000 (0.049) loss 1.2773 (1.5543) acc 78.1250 (64.1667) lr 1.7290e-03 eta 0:14:40
+epoch [14/50] batch [20/31] time 0.734 (0.765) data 0.000 (0.037) loss 1.2764 (1.5383) acc 53.1250 (63.2812) lr 1.7290e-03 eta 0:14:22
+epoch [14/50] batch [25/31] time 0.720 (0.756) data 0.000 (0.030) loss 1.8125 (1.5907) acc 65.6250 (62.2500) lr 1.7290e-03 eta 0:14:08
+epoch [14/50] batch [30/31] time 0.724 (0.751) data 0.000 (0.025) loss 1.4014 (1.5722) acc 65.6250 (62.8125) lr 1.7290e-03 eta 0:13:58
+epoch [15/50] batch [5/31] time 0.742 (0.872) data 0.000 (0.133) loss 2.0273 (1.6936) acc 59.3750 (58.7500) lr 1.6845e-03 eta 0:16:08
+epoch [15/50] batch [10/31] time 0.714 (0.798) data 0.000 (0.067) loss 1.2549 (1.5649) acc 68.7500 (62.1875) lr 1.6845e-03 eta 0:14:42
+epoch [15/50] batch [15/31] time 0.705 (0.774) data 0.000 (0.045) loss 1.7324 (1.5818) acc 56.2500 (61.4583) lr 1.6845e-03 eta 0:14:11
+epoch [15/50] batch [20/31] time 0.759 (0.763) data 0.000 (0.034) loss 1.2412 (1.4862) acc 59.3750 (62.3438) lr 1.6845e-03 eta 0:13:56
+epoch [15/50] batch [25/31] time 0.741 (0.758) data 0.000 (0.027) loss 1.2305 (1.4680) acc 71.8750 (63.0000) lr 1.6845e-03 eta 0:13:47
+epoch [15/50] batch [30/31] time 0.722 (0.754) data 0.000 (0.022) loss 1.6230 (1.5143) acc 65.6250 (62.8125) lr 1.6845e-03 eta 0:13:38
+epoch [16/50] batch [5/31] time 0.721 (0.858) data 0.000 (0.128) loss 1.9316 (1.6373) acc 56.2500 (57.5000) lr 1.6374e-03 eta 0:15:26
+epoch [16/50] batch [10/31] time 0.739 (0.789) data 0.000 (0.064) loss 1.4121 (1.4872) acc 65.6250 (61.8750) lr 1.6374e-03 eta 0:14:08
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+epoch [35/50] batch [15/31] time 0.727 (0.779) data 0.000 (0.049) loss 1.0439 (1.1299) acc 68.7500 (72.5000) lr 5.1825e-04 eta 0:06:14
+epoch [35/50] batch [20/31] time 0.735 (0.767) data 0.000 (0.037) loss 2.1270 (1.2122) acc 65.6250 (70.6250) lr 5.1825e-04 eta 0:06:05
+epoch [35/50] batch [25/31] time 0.715 (0.757) data 0.000 (0.030) loss 1.1777 (1.2107) acc 71.8750 (70.3750) lr 5.1825e-04 eta 0:05:56
+epoch [35/50] batch [30/31] time 0.707 (0.751) data 0.000 (0.025) loss 0.8657 (1.1820) acc 84.3750 (71.1458) lr 5.1825e-04 eta 0:05:49
+epoch [36/50] batch [5/31] time 0.716 (0.879) data 0.000 (0.140) loss 1.0488 (1.2559) acc 75.0000 (68.1250) lr 4.6417e-04 eta 0:06:44
+epoch [36/50] batch [10/31] time 0.726 (0.802) data 0.000 (0.070) loss 0.8877 (1.1361) acc 68.7500 (70.6250) lr 4.6417e-04 eta 0:06:04
+epoch [36/50] batch [15/31] time 0.747 (0.777) data 0.000 (0.047) loss 1.0771 (1.1012) acc 75.0000 (71.8750) lr 4.6417e-04 eta 0:05:49
+epoch [36/50] batch [20/31] time 0.735 (0.771) data 0.000 (0.035) loss 1.5693 (1.1261) acc 62.5000 (70.9375) lr 4.6417e-04 eta 0:05:43
+epoch [36/50] batch [25/31] time 0.727 (0.762) data 0.000 (0.028) loss 1.8057 (1.1817) acc 59.3750 (71.0000) lr 4.6417e-04 eta 0:05:35
+epoch [36/50] batch [30/31] time 0.722 (0.756) data 0.000 (0.024) loss 1.0264 (1.2024) acc 78.1250 (70.4167) lr 4.6417e-04 eta 0:05:28
+epoch [37/50] batch [5/31] time 0.729 (0.884) data 0.002 (0.149) loss 1.0107 (1.1337) acc 65.6250 (69.3750) lr 4.1221e-04 eta 0:06:19
+epoch [37/50] batch [10/31] time 0.718 (0.803) data 0.001 (0.075) loss 0.4521 (1.0836) acc 96.8750 (74.3750) lr 4.1221e-04 eta 0:05:40
+epoch [37/50] batch [15/31] time 0.739 (0.779) data 0.000 (0.050) loss 0.4690 (1.0958) acc 87.5000 (74.5833) lr 4.1221e-04 eta 0:05:26
+epoch [37/50] batch [20/31] time 0.704 (0.766) data 0.000 (0.038) loss 1.0850 (1.0876) acc 78.1250 (75.0000) lr 4.1221e-04 eta 0:05:17
+epoch [37/50] batch [25/31] time 0.720 (0.756) data 0.000 (0.030) loss 1.5088 (1.1106) acc 65.6250 (73.5000) lr 4.1221e-04 eta 0:05:09
+epoch [37/50] batch [30/31] time 0.727 (0.750) data 0.000 (0.025) loss 1.3252 (1.1237) acc 65.6250 (73.2292) lr 4.1221e-04 eta 0:05:03
+epoch [38/50] batch [5/31] time 0.777 (0.884) data 0.001 (0.130) loss 1.0127 (1.2063) acc 75.0000 (72.5000) lr 3.6258e-04 eta 0:05:51
+epoch [38/50] batch [10/31] time 0.732 (0.807) data 0.001 (0.065) loss 1.9443 (1.3479) acc 68.7500 (70.9375) lr 3.6258e-04 eta 0:05:17
+epoch [38/50] batch [15/31] time 0.739 (0.781) data 0.001 (0.044) loss 1.1787 (1.3493) acc 68.7500 (71.2500) lr 3.6258e-04 eta 0:05:03
+epoch [38/50] batch [20/31] time 0.711 (0.769) data 0.000 (0.033) loss 1.0098 (1.2838) acc 81.2500 (72.3438) lr 3.6258e-04 eta 0:04:54
+epoch [38/50] batch [25/31] time 0.715 (0.760) data 0.000 (0.026) loss 1.5742 (1.2915) acc 71.8750 (72.2500) lr 3.6258e-04 eta 0:04:47
+epoch [38/50] batch [30/31] time 0.718 (0.753) data 0.000 (0.022) loss 1.7168 (1.2972) acc 62.5000 (72.1875) lr 3.6258e-04 eta 0:04:40
+epoch [39/50] batch [5/31] time 0.735 (0.902) data 0.001 (0.148) loss 0.9028 (0.9735) acc 87.5000 (80.0000) lr 3.1545e-04 eta 0:05:31
+epoch [39/50] batch [10/31] time 0.730 (0.818) data 0.000 (0.074) loss 0.9497 (1.1544) acc 75.0000 (74.3750) lr 3.1545e-04 eta 0:04:56
+epoch [39/50] batch [15/31] time 0.753 (0.805) data 0.001 (0.050) loss 0.9351 (1.1617) acc 81.2500 (73.7500) lr 3.1545e-04 eta 0:04:47
+epoch [39/50] batch [20/31] time 0.725 (0.784) data 0.000 (0.037) loss 1.2432 (1.1104) acc 75.0000 (74.8438) lr 3.1545e-04 eta 0:04:36
+epoch [39/50] batch [25/31] time 0.727 (0.772) data 0.000 (0.030) loss 1.0293 (1.1575) acc 75.0000 (74.3750) lr 3.1545e-04 eta 0:04:27
+epoch [39/50] batch [30/31] time 0.726 (0.764) data 0.000 (0.025) loss 0.9058 (1.1349) acc 75.0000 (74.4792) lr 3.1545e-04 eta 0:04:21
+epoch [40/50] batch [5/31] time 0.738 (0.896) data 0.000 (0.152) loss 1.1230 (1.2922) acc 75.0000 (71.8750) lr 2.7103e-04 eta 0:05:01
+epoch [40/50] batch [10/31] time 0.751 (0.816) data 0.000 (0.076) loss 1.3340 (1.0914) acc 81.2500 (75.3125) lr 2.7103e-04 eta 0:04:30
+epoch [40/50] batch [15/31] time 0.739 (0.790) data 0.000 (0.051) loss 1.4502 (1.1591) acc 71.8750 (73.3333) lr 2.7103e-04 eta 0:04:17
+epoch [40/50] batch [20/31] time 0.717 (0.773) data 0.000 (0.038) loss 1.1797 (1.1588) acc 75.0000 (73.5938) lr 2.7103e-04 eta 0:04:08
+epoch [40/50] batch [25/31] time 0.782 (0.769) data 0.000 (0.031) loss 1.0732 (1.1318) acc 65.6250 (73.3750) lr 2.7103e-04 eta 0:04:02
+epoch [40/50] batch [30/31] time 0.733 (0.762) data 0.000 (0.026) loss 0.9785 (1.1510) acc 78.1250 (72.5000) lr 2.7103e-04 eta 0:03:56
+epoch [41/50] batch [5/31] time 0.722 (0.929) data 0.001 (0.154) loss 0.9302 (1.0750) acc 81.2500 (76.2500) lr 2.2949e-04 eta 0:04:43
+epoch [41/50] batch [10/31] time 0.732 (0.834) data 0.001 (0.077) loss 0.9922 (1.0893) acc 71.8750 (75.3125) lr 2.2949e-04 eta 0:04:10
+epoch [41/50] batch [15/31] time 0.723 (0.797) data 0.000 (0.052) loss 0.6318 (1.0942) acc 81.2500 (75.0000) lr 2.2949e-04 eta 0:03:55
+epoch [41/50] batch [20/31] time 0.723 (0.779) data 0.000 (0.039) loss 1.5518 (1.1214) acc 56.2500 (74.3750) lr 2.2949e-04 eta 0:03:45
+epoch [41/50] batch [25/31] time 0.725 (0.767) data 0.000 (0.031) loss 1.2510 (1.1181) acc 75.0000 (74.6250) lr 2.2949e-04 eta 0:03:38
+epoch [41/50] batch [30/31] time 0.712 (0.758) data 0.000 (0.026) loss 1.6875 (1.1575) acc 56.2500 (74.0625) lr 2.2949e-04 eta 0:03:32
+epoch [42/50] batch [5/31] time 0.741 (0.878) data 0.000 (0.142) loss 0.9155 (1.2512) acc 81.2500 (73.1250) lr 1.9098e-04 eta 0:04:00
+epoch [42/50] batch [10/31] time 0.739 (0.808) data 0.000 (0.071) loss 0.8833 (1.2255) acc 75.0000 (72.5000) lr 1.9098e-04 eta 0:03:37
+epoch [42/50] batch [15/31] time 0.709 (0.780) data 0.000 (0.047) loss 1.3262 (1.2412) acc 71.8750 (72.5000) lr 1.9098e-04 eta 0:03:25
+epoch [42/50] batch [20/31] time 0.728 (0.767) data 0.000 (0.036) loss 1.0449 (1.1684) acc 78.1250 (73.7500) lr 1.9098e-04 eta 0:03:18
+epoch [42/50] batch [25/31] time 0.715 (0.756) data 0.000 (0.029) loss 1.1914 (1.1492) acc 68.7500 (73.8750) lr 1.9098e-04 eta 0:03:12
+epoch [42/50] batch [30/31] time 0.734 (0.752) data 0.000 (0.024) loss 1.3770 (1.1182) acc 65.6250 (74.1667) lr 1.9098e-04 eta 0:03:07
+epoch [43/50] batch [5/31] time 0.716 (0.877) data 0.000 (0.144) loss 0.6709 (1.2892) acc 84.3750 (70.0000) lr 1.5567e-04 eta 0:03:33
+epoch [43/50] batch [10/31] time 0.720 (0.815) data 0.000 (0.072) loss 0.9087 (1.2473) acc 78.1250 (70.3125) lr 1.5567e-04 eta 0:03:13
+epoch [43/50] batch [15/31] time 0.710 (0.784) data 0.000 (0.048) loss 0.9614 (1.1530) acc 78.1250 (72.2917) lr 1.5567e-04 eta 0:03:02
+epoch [43/50] batch [20/31] time 0.725 (0.767) data 0.000 (0.036) loss 0.8843 (1.1657) acc 68.7500 (71.7188) lr 1.5567e-04 eta 0:02:54
+epoch [43/50] batch [25/31] time 0.715 (0.757) data 0.000 (0.029) loss 1.2881 (1.2128) acc 81.2500 (70.3750) lr 1.5567e-04 eta 0:02:48
+epoch [43/50] batch [30/31] time 0.713 (0.751) data 0.000 (0.024) loss 1.0928 (1.2049) acc 84.3750 (71.5625) lr 1.5567e-04 eta 0:02:43
+epoch [44/50] batch [5/31] time 0.758 (0.868) data 0.000 (0.130) loss 0.9248 (1.1054) acc 75.0000 (75.0000) lr 1.2369e-04 eta 0:03:03
+epoch [44/50] batch [10/31] time 0.725 (0.799) data 0.001 (0.065) loss 0.9395 (1.0227) acc 81.2500 (76.5625) lr 1.2369e-04 eta 0:02:45
+epoch [44/50] batch [15/31] time 0.716 (0.775) data 0.000 (0.044) loss 0.9077 (1.0438) acc 78.1250 (76.0417) lr 1.2369e-04 eta 0:02:36
+epoch [44/50] batch [20/31] time 0.712 (0.761) data 0.000 (0.033) loss 0.9248 (1.0976) acc 71.8750 (74.5312) lr 1.2369e-04 eta 0:02:29
+epoch [44/50] batch [25/31] time 0.709 (0.752) data 0.000 (0.026) loss 2.4297 (1.1417) acc 53.1250 (73.5000) lr 1.2369e-04 eta 0:02:24
+epoch [44/50] batch [30/31] time 0.712 (0.747) data 0.000 (0.022) loss 1.1543 (1.1745) acc 71.8750 (72.6042) lr 1.2369e-04 eta 0:02:19
+epoch [45/50] batch [5/31] time 0.709 (0.858) data 0.000 (0.131) loss 0.8179 (0.9809) acc 75.0000 (75.6250) lr 9.5173e-05 eta 0:02:35
+epoch [45/50] batch [10/31] time 0.728 (0.797) data 0.000 (0.066) loss 1.5811 (1.0124) acc 65.6250 (74.0625) lr 9.5173e-05 eta 0:02:20
+epoch [45/50] batch [15/31] time 0.740 (0.773) data 0.000 (0.044) loss 0.8193 (0.9762) acc 75.0000 (75.6250) lr 9.5173e-05 eta 0:02:12
+epoch [45/50] batch [20/31] time 0.732 (0.761) data 0.000 (0.033) loss 1.0312 (1.0317) acc 75.0000 (75.4688) lr 9.5173e-05 eta 0:02:06
+epoch [45/50] batch [25/31] time 0.705 (0.752) data 0.000 (0.026) loss 1.7295 (1.0725) acc 68.7500 (75.3750) lr 9.5173e-05 eta 0:02:01
+epoch [45/50] batch [30/31] time 0.706 (0.747) data 0.000 (0.022) loss 1.3398 (1.1198) acc 75.0000 (75.4167) lr 9.5173e-05 eta 0:01:56
+epoch [46/50] batch [5/31] time 0.724 (0.872) data 0.000 (0.138) loss 1.5244 (1.2507) acc 62.5000 (68.7500) lr 7.0224e-05 eta 0:02:10
+epoch [46/50] batch [10/31] time 0.733 (0.798) data 0.001 (0.069) loss 0.6855 (1.0826) acc 84.3750 (73.4375) lr 7.0224e-05 eta 0:01:55
+epoch [46/50] batch [15/31] time 0.723 (0.773) data 0.000 (0.046) loss 0.8613 (1.0256) acc 78.1250 (74.7917) lr 7.0224e-05 eta 0:01:48
+epoch [46/50] batch [20/31] time 0.751 (0.761) data 0.000 (0.035) loss 0.8389 (0.9915) acc 81.2500 (75.3125) lr 7.0224e-05 eta 0:01:42
+epoch [46/50] batch [25/31] time 0.721 (0.751) data 0.000 (0.028) loss 1.0332 (1.0185) acc 78.1250 (75.2500) lr 7.0224e-05 eta 0:01:37
+epoch [46/50] batch [30/31] time 0.721 (0.747) data 0.000 (0.023) loss 1.5195 (1.0605) acc 62.5000 (74.6875) lr 7.0224e-05 eta 0:01:33
+epoch [47/50] batch [5/31] time 0.729 (0.910) data 0.001 (0.172) loss 0.7959 (1.1036) acc 81.2500 (73.7500) lr 4.8943e-05 eta 0:01:48
+epoch [47/50] batch [10/31] time 0.716 (0.818) data 0.000 (0.086) loss 2.3203 (1.1669) acc 65.6250 (74.0625) lr 4.8943e-05 eta 0:01:33
+epoch [47/50] batch [15/31] time 0.763 (0.788) data 0.001 (0.058) loss 1.0830 (1.1063) acc 65.6250 (73.7500) lr 4.8943e-05 eta 0:01:25
+epoch [47/50] batch [20/31] time 0.726 (0.774) data 0.000 (0.043) loss 0.7446 (1.1035) acc 78.1250 (74.8438) lr 4.8943e-05 eta 0:01:20
+epoch [47/50] batch [25/31] time 0.720 (0.764) data 0.000 (0.035) loss 0.7915 (1.0645) acc 71.8750 (74.8750) lr 4.8943e-05 eta 0:01:15
+epoch [47/50] batch [30/31] time 0.749 (0.759) data 0.000 (0.029) loss 1.1504 (1.0510) acc 75.0000 (75.3125) lr 4.8943e-05 eta 0:01:11
+epoch [48/50] batch [5/31] time 0.716 (0.865) data 0.000 (0.132) loss 0.7554 (1.2302) acc 81.2500 (71.8750) lr 3.1417e-05 eta 0:01:16
+epoch [48/50] batch [10/31] time 0.719 (0.793) data 0.000 (0.066) loss 0.7261 (1.0709) acc 75.0000 (75.9375) lr 3.1417e-05 eta 0:01:05
+epoch [48/50] batch [15/31] time 0.720 (0.770) data 0.000 (0.044) loss 0.8545 (1.0399) acc 81.2500 (76.4583) lr 3.1417e-05 eta 0:01:00
+epoch [48/50] batch [20/31] time 0.725 (0.758) data 0.000 (0.033) loss 1.1396 (1.0664) acc 81.2500 (76.2500) lr 3.1417e-05 eta 0:00:55
+epoch [48/50] batch [25/31] time 0.740 (0.751) data 0.000 (0.027) loss 0.7456 (1.0921) acc 84.3750 (76.0000) lr 3.1417e-05 eta 0:00:51
+epoch [48/50] batch [30/31] time 0.716 (0.748) data 0.000 (0.022) loss 1.1191 (1.0939) acc 75.0000 (75.7292) lr 3.1417e-05 eta 0:00:47
+epoch [49/50] batch [5/31] time 0.727 (0.886) data 0.000 (0.153) loss 0.5942 (1.0597) acc 90.6250 (74.3750) lr 1.7713e-05 eta 0:00:50
+epoch [49/50] batch [10/31] time 0.715 (0.819) data 0.000 (0.076) loss 1.3730 (1.0991) acc 71.8750 (72.8125) lr 1.7713e-05 eta 0:00:42
+epoch [49/50] batch [15/31] time 0.716 (0.793) data 0.000 (0.051) loss 0.9316 (1.1173) acc 81.2500 (72.7083) lr 1.7713e-05 eta 0:00:37
+epoch [49/50] batch [20/31] time 0.728 (0.776) data 0.000 (0.038) loss 1.1768 (1.1434) acc 81.2500 (73.4375) lr 1.7713e-05 eta 0:00:32
+epoch [49/50] batch [25/31] time 0.745 (0.768) data 0.000 (0.031) loss 1.1123 (1.1660) acc 59.3750 (72.1250) lr 1.7713e-05 eta 0:00:28
+epoch [49/50] batch [30/31] time 0.717 (0.761) data 0.000 (0.026) loss 0.8979 (1.1324) acc 75.0000 (72.9167) lr 1.7713e-05 eta 0:00:24
+epoch [50/50] batch [5/31] time 0.735 (0.872) data 0.000 (0.134) loss 1.5400 (1.2837) acc 68.7500 (71.2500) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [10/31] time 0.738 (0.802) data 0.000 (0.067) loss 0.8896 (1.1411) acc 87.5000 (74.6875) lr 7.8853e-06 eta 0:00:16
+epoch [50/50] batch [15/31] time 0.726 (0.778) data 0.000 (0.045) loss 1.1006 (1.1269) acc 75.0000 (73.7500) lr 7.8853e-06 eta 0:00:12
+epoch [50/50] batch [20/31] time 0.771 (0.767) data 0.000 (0.034) loss 1.2803 (1.1313) acc 81.2500 (73.9062) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [25/31] time 0.727 (0.758) data 0.000 (0.027) loss 1.4160 (1.1408) acc 75.0000 (74.2500) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [30/31] time 0.842 (0.756) data 0.000 (0.023) loss 1.6436 (1.1501) acc 53.1250 (73.9583) lr 7.8853e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 29,372
+* accuracy: 58.7%
+* error: 41.3%
+* macro_f1: 57.8%
+Elapsed: 0:22:25
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1697831001.ckb-gpu-lambda.2099160.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1697831001.ckb-gpu-lambda.2099160.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_b32_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-B/32
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1199.999
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-B/32)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/50] batch [5/31] time 0.724 (1.638) data 0.001 (0.182) loss 3.8047 (3.6379) acc 34.3750 (34.3750) lr 1.0000e-05 eta 0:42:10
+epoch [1/50] batch [10/31] time 0.739 (1.186) data 0.000 (0.091) loss 2.9688 (3.6266) acc 53.1250 (35.6250) lr 1.0000e-05 eta 0:30:26
+epoch [1/50] batch [15/31] time 0.741 (1.037) data 0.000 (0.061) loss 2.9219 (3.4673) acc 43.7500 (37.9167) lr 1.0000e-05 eta 0:26:31
+epoch [1/50] batch [20/31] time 0.741 (0.958) data 0.000 (0.046) loss 2.0703 (3.3063) acc 59.3750 (39.8438) lr 1.0000e-05 eta 0:24:26
+epoch [1/50] batch [25/31] time 0.742 (0.914) data 0.000 (0.037) loss 2.7500 (3.2285) acc 31.2500 (38.1250) lr 1.0000e-05 eta 0:23:14
+epoch [1/50] batch [30/31] time 0.741 (0.885) data 0.000 (0.031) loss 2.1719 (3.1133) acc 43.7500 (39.7917) lr 1.0000e-05 eta 0:22:25
+epoch [2/50] batch [5/31] time 0.725 (0.885) data 0.000 (0.154) loss 2.0625 (2.5371) acc 59.3750 (48.1250) lr 2.0000e-03 eta 0:22:19
+epoch [2/50] batch [10/31] time 0.746 (0.808) data 0.000 (0.077) loss 1.9395 (2.3554) acc 53.1250 (50.6250) lr 2.0000e-03 eta 0:20:18
+epoch [2/50] batch [15/31] time 0.720 (0.780) data 0.000 (0.051) loss 1.4062 (2.2290) acc 65.6250 (51.4583) lr 2.0000e-03 eta 0:19:33
+epoch [2/50] batch [20/31] time 0.720 (0.766) data 0.000 (0.039) loss 1.9258 (2.2071) acc 53.1250 (50.7812) lr 2.0000e-03 eta 0:19:08
+epoch [2/50] batch [25/31] time 0.725 (0.756) data 0.000 (0.031) loss 2.2695 (2.1543) acc 53.1250 (52.2500) lr 2.0000e-03 eta 0:18:49
+epoch [2/50] batch [30/31] time 0.739 (0.750) data 0.000 (0.026) loss 2.0996 (2.1356) acc 43.7500 (51.3542) lr 2.0000e-03 eta 0:18:37
+epoch [3/50] batch [5/31] time 0.711 (0.870) data 0.000 (0.142) loss 2.0098 (2.1279) acc 50.0000 (53.1250) lr 1.9980e-03 eta 0:21:29
+epoch [3/50] batch [10/31] time 0.745 (0.798) data 0.001 (0.071) loss 2.2578 (2.0594) acc 46.8750 (54.6875) lr 1.9980e-03 eta 0:19:38
+epoch [3/50] batch [15/31] time 0.718 (0.772) data 0.000 (0.048) loss 1.7539 (2.0196) acc 56.2500 (55.0000) lr 1.9980e-03 eta 0:18:56
+epoch [3/50] batch [20/31] time 0.714 (0.761) data 0.001 (0.036) loss 1.1533 (1.9502) acc 68.7500 (55.7812) lr 1.9980e-03 eta 0:18:36
+epoch [3/50] batch [25/31] time 0.719 (0.754) data 0.000 (0.029) loss 1.8496 (1.9234) acc 53.1250 (56.5000) lr 1.9980e-03 eta 0:18:22
+epoch [3/50] batch [30/31] time 0.731 (0.748) data 0.000 (0.024) loss 1.8340 (1.9156) acc 53.1250 (55.7292) lr 1.9980e-03 eta 0:18:11
+epoch [4/50] batch [5/31] time 0.720 (0.899) data 0.001 (0.149) loss 1.6562 (1.5801) acc 53.1250 (60.6250) lr 1.9921e-03 eta 0:21:45
+epoch [4/50] batch [10/31] time 0.736 (0.815) data 0.000 (0.075) loss 1.5469 (1.7316) acc 65.6250 (58.4375) lr 1.9921e-03 eta 0:19:38
+epoch [4/50] batch [15/31] time 0.730 (0.785) data 0.000 (0.050) loss 1.2969 (1.7867) acc 65.6250 (55.6250) lr 1.9921e-03 eta 0:18:51
+epoch [4/50] batch [20/31] time 0.722 (0.769) data 0.000 (0.038) loss 1.5811 (1.7468) acc 56.2500 (55.9375) lr 1.9921e-03 eta 0:18:25
+epoch [4/50] batch [25/31] time 0.730 (0.760) data 0.000 (0.030) loss 2.2422 (1.8128) acc 56.2500 (55.2500) lr 1.9921e-03 eta 0:18:07
+epoch [4/50] batch [30/31] time 0.736 (0.754) data 0.000 (0.025) loss 1.4375 (1.7640) acc 78.1250 (56.2500) lr 1.9921e-03 eta 0:17:55
+epoch [5/50] batch [5/31] time 0.728 (0.905) data 0.000 (0.166) loss 1.4658 (1.7898) acc 62.5000 (61.2500) lr 1.9823e-03 eta 0:21:26
+epoch [5/50] batch [10/31] time 0.741 (0.815) data 0.000 (0.083) loss 1.6992 (1.7464) acc 56.2500 (60.3125) lr 1.9823e-03 eta 0:19:13
+epoch [5/50] batch [15/31] time 0.711 (0.786) data 0.000 (0.055) loss 1.8320 (1.8018) acc 50.0000 (57.7083) lr 1.9823e-03 eta 0:18:29
+epoch [5/50] batch [20/31] time 0.711 (0.770) data 0.000 (0.042) loss 1.1455 (1.8159) acc 78.1250 (57.3438) lr 1.9823e-03 eta 0:18:03
+epoch [5/50] batch [25/31] time 0.716 (0.759) data 0.000 (0.033) loss 1.5684 (1.8089) acc 50.0000 (56.2500) lr 1.9823e-03 eta 0:17:43
+epoch [5/50] batch [30/31] time 0.723 (0.753) data 0.000 (0.028) loss 1.5957 (1.7737) acc 65.6250 (57.0833) lr 1.9823e-03 eta 0:17:31
+epoch [6/50] batch [5/31] time 0.722 (0.910) data 0.000 (0.173) loss 2.1797 (1.8949) acc 50.0000 (53.7500) lr 1.9686e-03 eta 0:21:04
+epoch [6/50] batch [10/31] time 0.715 (0.820) data 0.000 (0.087) loss 1.5176 (1.8247) acc 46.8750 (53.4375) lr 1.9686e-03 eta 0:18:55
+epoch [6/50] batch [15/31] time 0.737 (0.788) data 0.001 (0.058) loss 2.5957 (1.8934) acc 50.0000 (53.9583) lr 1.9686e-03 eta 0:18:07
+epoch [6/50] batch [20/31] time 0.720 (0.771) data 0.000 (0.044) loss 1.7568 (1.8487) acc 65.6250 (55.3125) lr 1.9686e-03 eta 0:17:40
+epoch [6/50] batch [25/31] time 0.713 (0.762) data 0.000 (0.035) loss 1.7275 (1.7846) acc 65.6250 (57.0000) lr 1.9686e-03 eta 0:17:24
+epoch [6/50] batch [30/31] time 0.712 (0.756) data 0.000 (0.029) loss 1.8555 (1.7599) acc 53.1250 (57.6042) lr 1.9686e-03 eta 0:17:11
+epoch [7/50] batch [5/31] time 0.718 (0.883) data 0.000 (0.143) loss 1.8506 (1.7391) acc 56.2500 (61.2500) lr 1.9511e-03 eta 0:20:00
+epoch [7/50] batch [10/31] time 0.736 (0.803) data 0.000 (0.072) loss 2.1523 (1.7735) acc 46.8750 (56.2500) lr 1.9511e-03 eta 0:18:07
+epoch [7/50] batch [15/31] time 0.723 (0.777) data 0.000 (0.048) loss 1.6973 (1.8325) acc 50.0000 (54.7917) lr 1.9511e-03 eta 0:17:27
+epoch [7/50] batch [20/31] time 0.708 (0.763) data 0.000 (0.036) loss 0.9883 (1.8048) acc 71.8750 (55.3125) lr 1.9511e-03 eta 0:17:05
+epoch [7/50] batch [25/31] time 0.739 (0.757) data 0.000 (0.029) loss 1.7148 (1.8280) acc 50.0000 (55.2500) lr 1.9511e-03 eta 0:16:54
+epoch [7/50] batch [30/31] time 0.738 (0.756) data 0.000 (0.024) loss 2.7559 (1.8851) acc 43.7500 (55.0000) lr 1.9511e-03 eta 0:16:47
+epoch [8/50] batch [5/31] time 0.719 (0.874) data 0.001 (0.135) loss 2.5820 (2.0729) acc 43.7500 (51.2500) lr 1.9298e-03 eta 0:19:20
+epoch [8/50] batch [10/31] time 0.718 (0.794) data 0.000 (0.068) loss 1.4648 (1.8527) acc 62.5000 (55.6250) lr 1.9298e-03 eta 0:17:30
+epoch [8/50] batch [15/31] time 0.779 (0.783) data 0.001 (0.045) loss 1.5137 (1.8245) acc 65.6250 (55.2083) lr 1.9298e-03 eta 0:17:11
+epoch [8/50] batch [20/31] time 0.721 (0.767) data 0.000 (0.034) loss 1.5020 (1.8030) acc 68.7500 (56.4062) lr 1.9298e-03 eta 0:16:46
+epoch [8/50] batch [25/31] time 0.710 (0.757) data 0.000 (0.027) loss 1.2197 (1.7323) acc 71.8750 (57.3750) lr 1.9298e-03 eta 0:16:29
+epoch [8/50] batch [30/31] time 0.710 (0.751) data 0.000 (0.023) loss 1.7822 (1.6820) acc 65.6250 (58.8542) lr 1.9298e-03 eta 0:16:18
+epoch [9/50] batch [5/31] time 0.714 (0.871) data 0.000 (0.142) loss 1.8418 (1.9926) acc 50.0000 (53.1250) lr 1.9048e-03 eta 0:18:49
+epoch [9/50] batch [10/31] time 0.722 (0.795) data 0.000 (0.071) loss 1.4336 (1.8394) acc 56.2500 (56.2500) lr 1.9048e-03 eta 0:17:07
+epoch [9/50] batch [15/31] time 0.725 (0.771) data 0.000 (0.048) loss 2.0957 (1.8074) acc 40.6250 (54.5833) lr 1.9048e-03 eta 0:16:32
+epoch [9/50] batch [20/31] time 0.717 (0.760) data 0.000 (0.036) loss 2.5938 (1.7923) acc 40.6250 (55.3125) lr 1.9048e-03 eta 0:16:14
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+epoch [46/50] batch [30/31] time 0.737 (0.748) data 0.000 (0.023) loss 1.3330 (1.2746) acc 68.7500 (66.0417) lr 7.0224e-05 eta 0:01:33
+epoch [47/50] batch [5/31] time 0.722 (0.904) data 0.000 (0.165) loss 1.3525 (1.0114) acc 71.8750 (73.1250) lr 4.8943e-05 eta 0:01:47
+epoch [47/50] batch [10/31] time 0.734 (0.814) data 0.001 (0.082) loss 1.4824 (1.2396) acc 56.2500 (68.4375) lr 4.8943e-05 eta 0:01:32
+epoch [47/50] batch [15/31] time 0.714 (0.785) data 0.000 (0.055) loss 1.4648 (1.2509) acc 71.8750 (69.5833) lr 4.8943e-05 eta 0:01:25
+epoch [47/50] batch [20/31] time 0.721 (0.771) data 0.000 (0.041) loss 1.3281 (1.2389) acc 65.6250 (69.6875) lr 4.8943e-05 eta 0:01:20
+epoch [47/50] batch [25/31] time 0.722 (0.762) data 0.001 (0.033) loss 1.5146 (1.2389) acc 59.3750 (69.2500) lr 4.8943e-05 eta 0:01:15
+epoch [47/50] batch [30/31] time 0.730 (0.755) data 0.000 (0.028) loss 1.6680 (1.3007) acc 62.5000 (68.1250) lr 4.8943e-05 eta 0:01:11
+epoch [48/50] batch [5/31] time 0.747 (0.891) data 0.000 (0.155) loss 1.4346 (1.5248) acc 62.5000 (65.0000) lr 3.1417e-05 eta 0:01:18
+epoch [48/50] batch [10/31] time 0.718 (0.806) data 0.000 (0.078) loss 0.9692 (1.3942) acc 78.1250 (67.8125) lr 3.1417e-05 eta 0:01:06
+epoch [48/50] batch [15/31] time 0.713 (0.780) data 0.000 (0.052) loss 1.2432 (1.3733) acc 62.5000 (66.0417) lr 3.1417e-05 eta 0:01:00
+epoch [48/50] batch [20/31] time 0.710 (0.764) data 0.000 (0.039) loss 1.7451 (1.3773) acc 56.2500 (66.0938) lr 3.1417e-05 eta 0:00:55
+epoch [48/50] batch [25/31] time 0.718 (0.755) data 0.000 (0.031) loss 0.8774 (1.3236) acc 78.1250 (68.5000) lr 3.1417e-05 eta 0:00:51
+epoch [48/50] batch [30/31] time 0.724 (0.749) data 0.000 (0.026) loss 1.2100 (1.3621) acc 75.0000 (68.0208) lr 3.1417e-05 eta 0:00:47
+epoch [49/50] batch [5/31] time 0.725 (0.889) data 0.000 (0.150) loss 1.5684 (1.2110) acc 68.7500 (70.6250) lr 1.7713e-05 eta 0:00:50
+epoch [49/50] batch [10/31] time 0.727 (0.804) data 0.000 (0.075) loss 0.9038 (1.2394) acc 87.5000 (70.3125) lr 1.7713e-05 eta 0:00:41
+epoch [49/50] batch [15/31] time 0.717 (0.782) data 0.000 (0.050) loss 1.8975 (1.2426) acc 62.5000 (72.2917) lr 1.7713e-05 eta 0:00:36
+epoch [49/50] batch [20/31] time 0.733 (0.768) data 0.000 (0.038) loss 1.8271 (1.3422) acc 53.1250 (68.4375) lr 1.7713e-05 eta 0:00:32
+epoch [49/50] batch [25/31] time 0.734 (0.760) data 0.000 (0.030) loss 0.9790 (1.3547) acc 68.7500 (68.2500) lr 1.7713e-05 eta 0:00:28
+epoch [49/50] batch [30/31] time 0.718 (0.754) data 0.000 (0.025) loss 2.4453 (1.3583) acc 53.1250 (67.6042) lr 1.7713e-05 eta 0:00:24
+epoch [50/50] batch [5/31] time 0.747 (0.910) data 0.000 (0.156) loss 1.4131 (1.3474) acc 65.6250 (67.5000) lr 7.8853e-06 eta 0:00:23
+epoch [50/50] batch [10/31] time 0.733 (0.823) data 0.000 (0.078) loss 1.1445 (1.3498) acc 71.8750 (67.1875) lr 7.8853e-06 eta 0:00:17
+epoch [50/50] batch [15/31] time 0.713 (0.796) data 0.001 (0.052) loss 1.5879 (1.3459) acc 71.8750 (67.7083) lr 7.8853e-06 eta 0:00:12
+epoch [50/50] batch [20/31] time 0.724 (0.777) data 0.000 (0.039) loss 1.2510 (1.3591) acc 65.6250 (67.3438) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [25/31] time 0.726 (0.768) data 0.000 (0.032) loss 1.1650 (1.3461) acc 59.3750 (67.1250) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [30/31] time 0.739 (0.762) data 0.000 (0.026) loss 1.1816 (1.3125) acc 78.1250 (67.9167) lr 7.8853e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 30,681
+* accuracy: 61.4%
+* error: 38.6%
+* macro_f1: 60.3%
+Elapsed: 0:22:24
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..1ca4bb2e
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1697832363.ckb-gpu-lambda.2133735.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_b32_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1697832363.ckb-gpu-lambda.2133735.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..cdb1be0c
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,582 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.077
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/500] time 1.548 (2.654) data 0.000 (0.196) loss 2.5957 (3.2949) acc 37.5000 (35.6250) lr 1.0000e-05 eta 18:25:26
+epoch [1/50] batch [10/500] time 1.552 (2.102) data 0.000 (0.098) loss 2.7363 (3.0840) acc 43.7500 (40.0000) lr 1.0000e-05 eta 14:35:25
+epoch [1/50] batch [15/500] time 1.516 (1.918) data 0.000 (0.066) loss 2.2109 (2.7854) acc 50.0000 (44.5833) lr 1.0000e-05 eta 13:18:34
+epoch [1/50] batch [20/500] time 1.534 (1.824) data 0.001 (0.049) loss 2.5312 (2.6285) acc 50.0000 (48.4375) lr 1.0000e-05 eta 12:39:17
+epoch [1/50] batch [25/500] time 1.548 (1.767) data 0.000 (0.040) loss 1.8896 (2.5264) acc 56.2500 (49.1250) lr 1.0000e-05 eta 12:15:39
+epoch [1/50] batch [30/500] time 1.540 (1.730) data 0.000 (0.033) loss 1.5879 (2.4069) acc 65.6250 (51.1458) lr 1.0000e-05 eta 12:00:09
+epoch [1/50] batch [35/500] time 1.575 (1.706) data 0.000 (0.028) loss 1.3916 (2.3061) acc 59.3750 (51.7857) lr 1.0000e-05 eta 11:49:50
+epoch [1/50] batch [40/500] time 1.542 (1.687) data 0.001 (0.025) loss 2.0996 (2.2664) acc 56.2500 (52.4219) lr 1.0000e-05 eta 11:41:43
+epoch [1/50] batch [45/500] time 1.544 (1.673) data 0.000 (0.022) loss 2.7988 (2.2370) acc 43.7500 (52.7778) lr 1.0000e-05 eta 11:35:46
+epoch [1/50] batch [50/500] time 1.534 (1.662) data 0.000 (0.020) loss 1.8828 (2.1898) acc 59.3750 (53.0625) lr 1.0000e-05 eta 11:30:56
+epoch [1/50] batch [55/500] time 1.554 (1.652) data 0.000 (0.018) loss 1.3057 (2.1259) acc 78.1250 (54.3182) lr 1.0000e-05 eta 11:26:50
+epoch [1/50] batch [60/500] time 1.550 (1.643) data 0.000 (0.017) loss 1.0781 (2.0803) acc 68.7500 (54.7396) lr 1.0000e-05 eta 11:23:05
+epoch [1/50] batch [65/500] time 1.566 (1.637) data 0.000 (0.015) loss 1.3174 (2.0519) acc 59.3750 (54.9519) lr 1.0000e-05 eta 11:20:14
+epoch [1/50] batch [70/500] time 1.551 (1.632) data 0.000 (0.014) loss 2.0801 (2.0319) acc 62.5000 (55.1786) lr 1.0000e-05 eta 11:17:54
+epoch [1/50] batch [75/500] time 1.552 (1.626) data 0.000 (0.013) loss 1.3330 (1.9988) acc 65.6250 (55.7083) lr 1.0000e-05 eta 11:15:39
+epoch [1/50] batch [80/500] time 1.559 (1.623) data 0.000 (0.013) loss 1.0049 (1.9743) acc 68.7500 (56.4062) lr 1.0000e-05 eta 11:13:56
+epoch [1/50] batch [85/500] time 1.563 (1.619) data 0.000 (0.012) loss 1.5674 (1.9595) acc 71.8750 (56.8015) lr 1.0000e-05 eta 11:12:27
+epoch [1/50] batch [90/500] time 1.582 (1.617) data 0.000 (0.011) loss 2.5020 (1.9502) acc 50.0000 (56.7708) lr 1.0000e-05 eta 11:11:08
+epoch [1/50] batch [95/500] time 1.569 (1.614) data 0.000 (0.011) loss 1.5625 (1.9372) acc 65.6250 (57.2368) lr 1.0000e-05 eta 11:10:04
+epoch [1/50] batch [100/500] time 1.546 (1.612) data 0.001 (0.010) loss 1.6943 (1.9139) acc 62.5000 (57.6562) lr 1.0000e-05 eta 11:08:51
+epoch [1/50] batch [105/500] time 1.563 (1.609) data 0.000 (0.010) loss 1.8271 (1.9145) acc 68.7500 (57.5595) lr 1.0000e-05 eta 11:07:46
+epoch [1/50] batch [110/500] time 1.546 (1.607) data 0.000 (0.009) loss 1.5078 (1.8877) acc 68.7500 (58.3239) lr 1.0000e-05 eta 11:06:35
+epoch [1/50] batch [115/500] time 1.555 (1.605) data 0.000 (0.009) loss 1.9453 (1.8722) acc 53.1250 (58.5598) lr 1.0000e-05 eta 11:05:32
+epoch [1/50] batch [120/500] time 1.548 (1.603) data 0.000 (0.009) loss 2.4883 (1.8671) acc 40.6250 (58.5677) lr 1.0000e-05 eta 11:04:33
+epoch [1/50] batch [125/500] time 1.566 (1.601) data 0.000 (0.008) loss 2.1230 (1.8599) acc 46.8750 (58.7000) lr 1.0000e-05 eta 11:03:43
+epoch [1/50] batch [130/500] time 1.553 (1.599) data 0.000 (0.008) loss 1.8857 (1.8599) acc 62.5000 (58.6298) lr 1.0000e-05 eta 11:02:56
+epoch [1/50] batch [135/500] time 1.557 (1.598) data 0.000 (0.008) loss 1.8574 (1.8511) acc 56.2500 (58.7037) lr 1.0000e-05 eta 11:02:14
+epoch [1/50] batch [140/500] time 1.552 (1.597) data 0.000 (0.007) loss 1.6787 (1.8529) acc 56.2500 (58.5268) lr 1.0000e-05 eta 11:01:34
+epoch [1/50] batch [145/500] time 1.561 (1.595) data 0.000 (0.007) loss 1.5439 (1.8515) acc 62.5000 (58.4052) lr 1.0000e-05 eta 11:00:50
+epoch [1/50] batch [150/500] time 1.597 (1.594) data 0.000 (0.007) loss 2.3809 (1.8468) acc 43.7500 (58.5625) lr 1.0000e-05 eta 11:00:08
+epoch [1/50] batch [155/500] time 1.551 (1.593) data 0.000 (0.007) loss 1.3672 (1.8343) acc 59.3750 (58.6492) lr 1.0000e-05 eta 10:59:34
+epoch [1/50] batch [160/500] time 1.545 (1.593) data 0.000 (0.007) loss 0.8252 (1.8258) acc 81.2500 (58.7305) lr 1.0000e-05 eta 10:59:22
+epoch [1/50] batch [165/500] time 1.558 (1.591) data 0.000 (0.006) loss 1.6670 (1.8149) acc 53.1250 (58.8826) lr 1.0000e-05 eta 10:58:39
+epoch [1/50] batch [170/500] time 1.559 (1.590) data 0.000 (0.006) loss 1.0439 (1.8017) acc 71.8750 (59.1360) lr 1.0000e-05 eta 10:58:05
+epoch [1/50] batch [175/500] time 1.574 (1.589) data 0.000 (0.006) loss 1.9434 (1.7958) acc 59.3750 (59.1786) lr 1.0000e-05 eta 10:57:35
+epoch [1/50] batch [180/500] time 1.557 (1.589) data 0.001 (0.006) loss 1.8701 (1.7892) acc 59.3750 (59.1840) lr 1.0000e-05 eta 10:57:12
+epoch [1/50] batch [185/500] time 1.540 (1.588) data 0.000 (0.006) loss 1.3232 (1.7843) acc 65.6250 (59.2568) lr 1.0000e-05 eta 10:56:47
+epoch [1/50] batch [190/500] time 1.560 (1.587) data 0.000 (0.006) loss 1.3926 (1.7733) acc 68.7500 (59.5230) lr 1.0000e-05 eta 10:56:19
+epoch [1/50] batch [195/500] time 1.550 (1.586) data 0.001 (0.005) loss 1.6104 (1.7688) acc 59.3750 (59.6314) lr 1.0000e-05 eta 10:55:52
+epoch [1/50] batch [200/500] time 1.574 (1.586) data 0.000 (0.005) loss 1.1621 (1.7606) acc 68.7500 (59.7656) lr 1.0000e-05 eta 10:55:24
+epoch [1/50] batch [205/500] time 1.554 (1.586) data 0.000 (0.005) loss 1.0078 (1.7479) acc 68.7500 (59.9390) lr 1.0000e-05 eta 10:55:17
+epoch [1/50] batch [210/500] time 1.561 (1.585) data 0.001 (0.005) loss 1.6758 (1.7415) acc 68.7500 (60.1190) lr 1.0000e-05 eta 10:54:56
+epoch [1/50] batch [215/500] time 1.560 (1.585) data 0.001 (0.005) loss 1.3613 (1.7380) acc 75.0000 (60.2471) lr 1.0000e-05 eta 10:54:36
+epoch [1/50] batch [220/500] time 1.572 (1.584) data 0.000 (0.005) loss 1.2627 (1.7310) acc 65.6250 (60.4261) lr 1.0000e-05 eta 10:54:18
+epoch [1/50] batch [225/500] time 1.576 (1.584) data 0.000 (0.005) loss 2.0684 (1.7332) acc 59.3750 (60.4167) lr 1.0000e-05 eta 10:54:02
+epoch [1/50] batch [230/500] time 1.539 (1.583) data 0.000 (0.005) loss 1.6084 (1.7276) acc 56.2500 (60.4348) lr 1.0000e-05 eta 10:53:42
+epoch [1/50] batch [235/500] time 1.555 (1.583) data 0.000 (0.005) loss 1.5430 (1.7261) acc 62.5000 (60.4654) lr 1.0000e-05 eta 10:53:20
+epoch [1/50] batch [240/500] time 1.565 (1.582) data 0.000 (0.005) loss 1.5811 (1.7237) acc 65.6250 (60.5859) lr 1.0000e-05 eta 10:52:55
+epoch [1/50] batch [245/500] time 1.566 (1.582) data 0.000 (0.004) loss 1.4033 (1.7219) acc 68.7500 (60.6378) lr 1.0000e-05 eta 10:52:36
+epoch [1/50] batch [250/500] time 1.541 (1.581) data 0.000 (0.004) loss 1.4551 (1.7234) acc 68.7500 (60.6125) lr 1.0000e-05 eta 10:52:14
+epoch [1/50] batch [255/500] time 1.581 (1.581) data 0.000 (0.004) loss 1.5400 (1.7234) acc 59.3750 (60.6618) lr 1.0000e-05 eta 10:51:58
+epoch [1/50] batch [260/500] time 1.560 (1.581) data 0.000 (0.004) loss 2.3906 (1.7175) acc 53.1250 (60.7933) lr 1.0000e-05 eta 10:51:41
+epoch [1/50] batch [265/500] time 1.531 (1.580) data 0.000 (0.004) loss 1.7559 (1.7127) acc 65.6250 (60.9552) lr 1.0000e-05 eta 10:51:24
+epoch [1/50] batch [270/500] time 1.551 (1.579) data 0.000 (0.004) loss 1.3486 (1.7093) acc 65.6250 (61.0301) lr 1.0000e-05 eta 10:51:00
+epoch [1/50] batch [275/500] time 1.583 (1.579) data 0.000 (0.004) loss 1.8271 (1.7078) acc 71.8750 (61.0682) lr 1.0000e-05 eta 10:50:47
+epoch [1/50] batch [280/500] time 1.556 (1.579) data 0.000 (0.004) loss 2.8652 (1.7116) acc 46.8750 (61.0491) lr 1.0000e-05 eta 10:50:32
+epoch [1/50] batch [285/500] time 1.598 (1.579) data 0.000 (0.004) loss 1.1494 (1.7097) acc 75.0000 (61.1294) lr 1.0000e-05 eta 10:50:14
+epoch [1/50] batch [290/500] time 1.685 (1.580) data 0.000 (0.004) loss 2.1191 (1.7120) acc 59.3750 (61.1422) lr 1.0000e-05 eta 10:50:37
+epoch [1/50] batch [295/500] time 1.569 (1.580) data 0.000 (0.004) loss 2.1055 (1.7135) acc 46.8750 (61.1017) lr 1.0000e-05 eta 10:50:36
+epoch [1/50] batch [300/500] time 1.565 (1.580) data 0.000 (0.004) loss 1.2188 (1.7096) acc 68.7500 (61.1354) lr 1.0000e-05 eta 10:50:20
+epoch [1/50] batch [305/500] time 1.555 (1.580) data 0.000 (0.004) loss 1.2275 (1.7028) acc 68.7500 (61.2500) lr 1.0000e-05 eta 10:50:11
+epoch [1/50] batch [310/500] time 1.556 (1.580) data 0.000 (0.004) loss 1.5947 (1.6971) acc 65.6250 (61.3105) lr 1.0000e-05 eta 10:49:58
+epoch [1/50] batch [315/500] time 1.566 (1.579) data 0.000 (0.004) loss 2.0039 (1.6965) acc 59.3750 (61.3591) lr 1.0000e-05 eta 10:49:41
+epoch [1/50] batch [320/500] time 1.541 (1.579) data 0.000 (0.003) loss 1.1045 (1.6944) acc 78.1250 (61.4453) lr 1.0000e-05 eta 10:49:19
+epoch [1/50] batch [325/500] time 1.542 (1.578) data 0.000 (0.003) loss 1.3682 (1.6912) acc 71.8750 (61.5192) lr 1.0000e-05 eta 10:48:59
+epoch [1/50] batch [330/500] time 1.562 (1.578) data 0.000 (0.003) loss 2.4453 (1.6857) acc 46.8750 (61.6383) lr 1.0000e-05 eta 10:48:44
+epoch [1/50] batch [335/500] time 1.552 (1.577) data 0.000 (0.003) loss 1.8232 (1.6861) acc 65.6250 (61.6698) lr 1.0000e-05 eta 10:48:28
+epoch [1/50] batch [340/500] time 1.561 (1.577) data 0.000 (0.003) loss 0.9780 (1.6801) acc 78.1250 (61.7647) lr 1.0000e-05 eta 10:48:15
+epoch [1/50] batch [345/500] time 1.581 (1.577) data 0.000 (0.003) loss 1.6279 (1.6789) acc 59.3750 (61.7663) lr 1.0000e-05 eta 10:48:05
+epoch [1/50] batch [350/500] time 1.534 (1.577) data 0.000 (0.003) loss 1.7861 (1.6800) acc 65.6250 (61.8036) lr 1.0000e-05 eta 10:47:55
+epoch [1/50] batch [355/500] time 1.567 (1.577) data 0.000 (0.003) loss 1.4678 (1.6768) acc 62.5000 (61.8310) lr 1.0000e-05 eta 10:47:39
+epoch [1/50] batch [360/500] time 1.546 (1.577) data 0.000 (0.003) loss 1.8447 (1.6742) acc 56.2500 (61.8576) lr 1.0000e-05 eta 10:47:25
+epoch [1/50] batch [365/500] time 1.539 (1.576) data 0.000 (0.003) loss 1.0889 (1.6704) acc 68.7500 (61.8921) lr 1.0000e-05 eta 10:47:08
+epoch [1/50] batch [370/500] time 1.558 (1.576) data 0.000 (0.003) loss 1.4863 (1.6713) acc 59.3750 (61.8666) lr 1.0000e-05 eta 10:46:53
+epoch [1/50] batch [375/500] time 1.555 (1.576) data 0.000 (0.003) loss 1.3115 (1.6675) acc 65.6250 (61.9250) lr 1.0000e-05 eta 10:46:42
+epoch [1/50] batch [380/500] time 1.542 (1.575) data 0.000 (0.003) loss 1.2100 (1.6659) acc 71.8750 (61.8914) lr 1.0000e-05 eta 10:46:26
+epoch [1/50] batch [385/500] time 1.555 (1.575) data 0.000 (0.003) loss 1.1992 (1.6654) acc 62.5000 (61.9075) lr 1.0000e-05 eta 10:46:11
+epoch [1/50] batch [390/500] time 1.550 (1.575) data 0.000 (0.003) loss 2.3633 (1.6621) acc 53.1250 (62.0353) lr 1.0000e-05 eta 10:45:58
+epoch [1/50] batch [395/500] time 1.565 (1.575) data 0.000 (0.003) loss 0.9912 (1.6581) acc 75.0000 (62.1282) lr 1.0000e-05 eta 10:45:44
+epoch [1/50] batch [400/500] time 1.584 (1.575) data 0.000 (0.003) loss 1.9746 (1.6596) acc 46.8750 (62.0938) lr 1.0000e-05 eta 10:45:36
+epoch [1/50] batch [405/500] time 1.594 (1.575) data 0.000 (0.003) loss 1.0908 (1.6587) acc 68.7500 (62.0988) lr 1.0000e-05 eta 10:45:26
+epoch [1/50] batch [410/500] time 1.564 (1.574) data 0.000 (0.003) loss 1.1006 (1.6530) acc 75.0000 (62.2332) lr 1.0000e-05 eta 10:45:12
+epoch [1/50] batch [415/500] time 1.539 (1.574) data 0.000 (0.003) loss 1.3232 (1.6466) acc 65.6250 (62.3494) lr 1.0000e-05 eta 10:45:01
+epoch [1/50] batch [420/500] time 1.545 (1.574) data 0.000 (0.003) loss 2.1602 (1.6463) acc 56.2500 (62.3438) lr 1.0000e-05 eta 10:44:47
+epoch [1/50] batch [425/500] time 1.567 (1.574) data 0.000 (0.003) loss 1.2725 (1.6437) acc 75.0000 (62.3897) lr 1.0000e-05 eta 10:44:40
+epoch [1/50] batch [430/500] time 1.576 (1.574) data 0.000 (0.003) loss 1.1377 (1.6406) acc 53.1250 (62.4273) lr 1.0000e-05 eta 10:44:32
+epoch [1/50] batch [435/500] time 1.564 (1.574) data 0.000 (0.003) loss 2.1758 (1.6393) acc 59.3750 (62.4641) lr 1.0000e-05 eta 10:44:21
+epoch [1/50] batch [440/500] time 1.560 (1.574) data 0.000 (0.003) loss 1.5996 (1.6367) acc 71.8750 (62.5213) lr 1.0000e-05 eta 10:44:10
+epoch [1/50] batch [445/500] time 1.673 (1.574) data 0.000 (0.003) loss 1.3076 (1.6349) acc 71.8750 (62.5351) lr 1.0000e-05 eta 10:44:04
+epoch [1/50] batch [450/500] time 1.563 (1.574) data 0.000 (0.003) loss 2.0469 (1.6356) acc 68.7500 (62.5556) lr 1.0000e-05 eta 10:43:54
+epoch [1/50] batch [455/500] time 1.576 (1.574) data 0.000 (0.003) loss 2.0859 (1.6344) acc 56.2500 (62.5618) lr 1.0000e-05 eta 10:43:43
+epoch [1/50] batch [460/500] time 1.601 (1.574) data 0.000 (0.003) loss 1.6621 (1.6325) acc 71.8750 (62.6155) lr 1.0000e-05 eta 10:43:34
+epoch [1/50] batch [465/500] time 1.566 (1.573) data 0.000 (0.003) loss 1.7051 (1.6333) acc 65.6250 (62.6142) lr 1.0000e-05 eta 10:43:25
+epoch [1/50] batch [470/500] time 1.538 (1.573) data 0.000 (0.002) loss 1.7656 (1.6313) acc 71.8750 (62.6662) lr 1.0000e-05 eta 10:43:15
+epoch [1/50] batch [475/500] time 1.555 (1.573) data 0.001 (0.002) loss 1.2695 (1.6325) acc 62.5000 (62.6447) lr 1.0000e-05 eta 10:43:05
+epoch [1/50] batch [480/500] time 1.583 (1.573) data 0.000 (0.002) loss 1.6318 (1.6317) acc 62.5000 (62.6432) lr 1.0000e-05 eta 10:42:56
+epoch [1/50] batch [485/500] time 1.550 (1.573) data 0.001 (0.002) loss 1.5332 (1.6318) acc 53.1250 (62.6289) lr 1.0000e-05 eta 10:42:45
+epoch [1/50] batch [490/500] time 1.597 (1.573) data 0.000 (0.002) loss 1.7109 (1.6316) acc 62.5000 (62.6722) lr 1.0000e-05 eta 10:42:41
+epoch [1/50] batch [495/500] time 1.581 (1.573) data 0.000 (0.002) loss 1.1582 (1.6314) acc 75.0000 (62.6957) lr 1.0000e-05 eta 10:42:31
+epoch [1/50] batch [500/500] time 1.538 (1.573) data 0.000 (0.002) loss 1.7373 (1.6320) acc 59.3750 (62.6812) lr 2.0000e-03 eta 10:42:18
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,378
+* accuracy: 72.8%
+* error: 27.2%
+* macro_f1: 71.6%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/500] time 1.541 (1.700) data 0.000 (0.170) loss 1.3867 (1.4484) acc 68.7500 (68.1250) lr 2.0000e-03 eta 11:33:58
+epoch [2/50] batch [10/500] time 1.553 (1.625) data 0.001 (0.085) loss 1.6123 (1.6553) acc 75.0000 (65.3125) lr 2.0000e-03 eta 11:03:19
+epoch [2/50] batch [15/500] time 1.557 (1.605) data 0.001 (0.057) loss 1.6123 (1.6129) acc 59.3750 (63.9583) lr 2.0000e-03 eta 10:54:57
+epoch [2/50] batch [20/500] time 1.559 (1.594) data 0.001 (0.043) loss 1.9463 (1.5927) acc 50.0000 (63.1250) lr 2.0000e-03 eta 10:50:21
+epoch [2/50] batch [25/500] time 1.569 (1.588) data 0.001 (0.034) loss 1.4678 (1.5658) acc 59.3750 (62.8750) lr 2.0000e-03 eta 10:47:40
+epoch [2/50] batch [30/500] time 1.562 (1.584) data 0.000 (0.029) loss 1.5146 (1.5066) acc 62.5000 (63.7500) lr 2.0000e-03 eta 10:45:59
+epoch [2/50] batch [35/500] time 1.559 (1.581) data 0.000 (0.025) loss 1.6230 (1.4516) acc 65.6250 (65.0000) lr 2.0000e-03 eta 10:44:30
+epoch [2/50] batch [40/500] time 1.568 (1.578) data 0.000 (0.022) loss 1.9307 (1.4441) acc 62.5000 (65.4688) lr 2.0000e-03 eta 10:43:06
+epoch [2/50] batch [45/500] time 1.550 (1.575) data 0.000 (0.019) loss 1.2168 (1.4396) acc 71.8750 (65.1389) lr 2.0000e-03 eta 10:42:00
+epoch [2/50] batch [50/500] time 1.560 (1.573) data 0.000 (0.017) loss 1.5312 (1.4308) acc 65.6250 (65.3750) lr 2.0000e-03 eta 10:40:56
+epoch [2/50] batch [55/500] time 1.576 (1.572) data 0.001 (0.016) loss 1.3662 (1.4116) acc 59.3750 (65.6250) lr 2.0000e-03 eta 10:40:33
+epoch [2/50] batch [60/500] time 1.572 (1.571) data 0.000 (0.015) loss 1.2666 (1.3945) acc 65.6250 (65.8854) lr 2.0000e-03 eta 10:39:55
+epoch [2/50] batch [65/500] time 1.544 (1.569) data 0.000 (0.013) loss 1.3574 (1.4251) acc 65.6250 (65.1923) lr 2.0000e-03 eta 10:39:02
+epoch [2/50] batch [70/500] time 1.529 (1.568) data 0.000 (0.013) loss 1.7812 (1.4317) acc 62.5000 (65.3571) lr 2.0000e-03 eta 10:38:22
+epoch [2/50] batch [75/500] time 1.534 (1.567) data 0.001 (0.012) loss 1.1631 (1.4245) acc 65.6250 (65.5833) lr 2.0000e-03 eta 10:37:59
+epoch [2/50] batch [80/500] time 1.577 (1.566) data 0.000 (0.011) loss 1.2891 (1.4228) acc 78.1250 (65.9766) lr 2.0000e-03 eta 10:37:33
+epoch [2/50] batch [85/500] time 1.570 (1.566) data 0.000 (0.010) loss 0.7935 (1.4097) acc 84.3750 (66.0662) lr 2.0000e-03 eta 10:37:12
+epoch [2/50] batch [90/500] time 1.564 (1.565) data 0.001 (0.010) loss 1.0361 (1.4035) acc 75.0000 (66.1806) lr 2.0000e-03 eta 10:36:50
+epoch [2/50] batch [95/500] time 1.574 (1.565) data 0.000 (0.009) loss 1.7119 (1.4033) acc 59.3750 (66.0197) lr 2.0000e-03 eta 10:36:26
+epoch [2/50] batch [100/500] time 1.572 (1.565) data 0.001 (0.009) loss 0.5449 (1.3834) acc 90.6250 (66.4062) lr 2.0000e-03 eta 10:36:37
+epoch [2/50] batch [105/500] time 1.553 (1.565) data 0.001 (0.009) loss 0.6543 (1.3699) acc 75.0000 (66.5179) lr 2.0000e-03 eta 10:36:15
+epoch [2/50] batch [110/500] time 1.559 (1.565) data 0.000 (0.008) loss 2.1133 (1.3812) acc 53.1250 (66.5341) lr 2.0000e-03 eta 10:36:01
+epoch [2/50] batch [115/500] time 1.552 (1.564) data 0.000 (0.008) loss 1.4795 (1.3909) acc 71.8750 (66.4402) lr 2.0000e-03 eta 10:35:43
+epoch [2/50] batch [120/500] time 1.550 (1.563) data 0.000 (0.007) loss 1.0488 (1.3895) acc 75.0000 (66.4583) lr 2.0000e-03 eta 10:35:17
+epoch [2/50] batch [125/500] time 1.558 (1.563) data 0.000 (0.007) loss 1.5322 (1.3804) acc 59.3750 (66.5500) lr 2.0000e-03 eta 10:34:58
+epoch [2/50] batch [130/500] time 1.536 (1.563) data 0.000 (0.007) loss 1.3447 (1.3729) acc 68.7500 (66.6587) lr 2.0000e-03 eta 10:34:47
+epoch [2/50] batch [135/500] time 1.552 (1.563) data 0.000 (0.007) loss 1.1836 (1.3687) acc 65.6250 (66.6898) lr 2.0000e-03 eta 10:34:30
+epoch [2/50] batch [140/500] time 1.548 (1.562) data 0.000 (0.006) loss 1.6953 (1.3809) acc 59.3750 (66.5402) lr 2.0000e-03 eta 10:34:13
+epoch [2/50] batch [145/500] time 1.555 (1.563) data 0.001 (0.006) loss 1.3867 (1.3854) acc 59.3750 (66.3793) lr 2.0000e-03 eta 10:34:18
+epoch [2/50] batch [150/500] time 1.561 (1.562) data 0.000 (0.006) loss 1.5205 (1.3895) acc 56.2500 (66.2917) lr 2.0000e-03 eta 10:34:01
+epoch [2/50] batch [155/500] time 1.563 (1.562) data 0.000 (0.006) loss 1.0938 (1.3817) acc 62.5000 (66.4315) lr 2.0000e-03 eta 10:33:44
+epoch [2/50] batch [160/500] time 1.575 (1.562) data 0.000 (0.006) loss 1.6445 (1.3812) acc 59.3750 (66.4062) lr 2.0000e-03 eta 10:33:32
+epoch [2/50] batch [165/500] time 1.550 (1.562) data 0.000 (0.006) loss 1.8525 (1.3759) acc 59.3750 (66.6477) lr 2.0000e-03 eta 10:33:25
+epoch [2/50] batch [170/500] time 1.552 (1.561) data 0.001 (0.005) loss 1.5947 (1.3772) acc 56.2500 (66.6912) lr 2.0000e-03 eta 10:33:09
+epoch [2/50] batch [175/500] time 1.554 (1.561) data 0.000 (0.005) loss 1.7363 (1.3792) acc 62.5000 (66.5357) lr 2.0000e-03 eta 10:32:59
+epoch [2/50] batch [180/500] time 1.588 (1.562) data 0.001 (0.005) loss 1.5449 (1.3739) acc 71.8750 (66.7361) lr 2.0000e-03 eta 10:32:59
+epoch [2/50] batch [185/500] time 1.556 (1.561) data 0.000 (0.005) loss 1.0918 (1.3670) acc 71.8750 (66.7905) lr 2.0000e-03 eta 10:32:40
+epoch [2/50] batch [190/500] time 1.568 (1.561) data 0.000 (0.005) loss 1.3027 (1.3661) acc 78.1250 (66.7763) lr 2.0000e-03 eta 10:32:36
+epoch [2/50] batch [195/500] time 1.553 (1.561) data 0.000 (0.005) loss 1.3584 (1.3647) acc 68.7500 (66.7308) lr 2.0000e-03 eta 10:32:25
+epoch [2/50] batch [200/500] time 1.540 (1.561) data 0.000 (0.005) loss 2.5137 (1.3639) acc 50.0000 (66.7500) lr 2.0000e-03 eta 10:32:10
+epoch [2/50] batch [205/500] time 1.536 (1.561) data 0.000 (0.005) loss 1.8623 (1.3661) acc 50.0000 (66.7073) lr 2.0000e-03 eta 10:31:55
+epoch [2/50] batch [210/500] time 1.566 (1.561) data 0.000 (0.004) loss 1.2871 (1.3612) acc 65.6250 (66.7262) lr 2.0000e-03 eta 10:31:51
+epoch [2/50] batch [215/500] time 1.533 (1.561) data 0.000 (0.004) loss 0.6880 (1.3559) acc 87.5000 (66.8605) lr 2.0000e-03 eta 10:31:43
+epoch [2/50] batch [220/500] time 1.555 (1.560) data 0.000 (0.004) loss 1.2783 (1.3520) acc 62.5000 (66.9602) lr 2.0000e-03 eta 10:31:26
+epoch [2/50] batch [225/500] time 1.542 (1.561) data 0.000 (0.004) loss 1.0176 (1.3480) acc 71.8750 (67.0139) lr 2.0000e-03 eta 10:31:21
+epoch [2/50] batch [230/500] time 1.545 (1.560) data 0.000 (0.004) loss 1.1494 (1.3513) acc 71.8750 (66.9837) lr 2.0000e-03 eta 10:31:01
+epoch [2/50] batch [235/500] time 1.574 (1.560) data 0.000 (0.004) loss 1.3701 (1.3494) acc 71.8750 (67.1011) lr 2.0000e-03 eta 10:30:55
+epoch [2/50] batch [240/500] time 1.545 (1.560) data 0.000 (0.004) loss 1.2910 (1.3506) acc 62.5000 (66.9922) lr 2.0000e-03 eta 10:30:54
+epoch [2/50] batch [245/500] time 1.560 (1.561) data 0.001 (0.004) loss 2.0156 (1.3481) acc 43.7500 (67.0153) lr 2.0000e-03 eta 10:30:56
+epoch [2/50] batch [250/500] time 1.544 (1.561) data 0.000 (0.004) loss 0.8706 (1.3490) acc 81.2500 (66.9500) lr 2.0000e-03 eta 10:30:42
+epoch [2/50] batch [255/500] time 1.548 (1.560) data 0.000 (0.004) loss 1.1543 (1.3473) acc 65.6250 (66.9730) lr 2.0000e-03 eta 10:30:23
+epoch [2/50] batch [260/500] time 1.567 (1.560) data 0.000 (0.004) loss 1.2666 (1.3454) acc 65.6250 (66.9231) lr 2.0000e-03 eta 10:30:15
+epoch [2/50] batch [265/500] time 1.562 (1.560) data 0.000 (0.004) loss 1.6523 (1.3472) acc 59.3750 (66.8868) lr 2.0000e-03 eta 10:30:04
+epoch [2/50] batch [270/500] time 1.555 (1.560) data 0.000 (0.004) loss 1.4375 (1.3450) acc 59.3750 (66.9213) lr 2.0000e-03 eta 10:29:55
+epoch [2/50] batch [275/500] time 1.568 (1.560) data 0.000 (0.003) loss 1.0410 (1.3423) acc 78.1250 (67.0227) lr 2.0000e-03 eta 10:29:45
+epoch [2/50] batch [280/500] time 1.553 (1.560) data 0.000 (0.003) loss 1.3076 (1.3415) acc 68.7500 (66.9866) lr 2.0000e-03 eta 10:29:40
+epoch [2/50] batch [285/500] time 1.548 (1.560) data 0.000 (0.003) loss 1.1865 (1.3402) acc 71.8750 (67.0395) lr 2.0000e-03 eta 10:29:28
+epoch [2/50] batch [290/500] time 1.549 (1.560) data 0.000 (0.003) loss 0.7808 (1.3359) acc 81.2500 (67.1336) lr 2.0000e-03 eta 10:29:20
+epoch [2/50] batch [295/500] time 1.552 (1.560) data 0.000 (0.003) loss 1.2275 (1.3338) acc 71.8750 (67.1928) lr 2.0000e-03 eta 10:29:08
+epoch [2/50] batch [300/500] time 1.574 (1.559) data 0.000 (0.003) loss 1.1143 (1.3328) acc 65.6250 (67.1667) lr 2.0000e-03 eta 10:28:56
+epoch [2/50] batch [305/500] time 1.534 (1.559) data 0.001 (0.003) loss 1.2148 (1.3365) acc 75.0000 (67.1721) lr 2.0000e-03 eta 10:28:44
+epoch [2/50] batch [310/500] time 1.527 (1.559) data 0.000 (0.003) loss 1.4824 (1.3344) acc 65.6250 (67.2581) lr 2.0000e-03 eta 10:28:32
+epoch [2/50] batch [315/500] time 1.548 (1.559) data 0.000 (0.003) loss 1.0566 (1.3325) acc 65.6250 (67.2718) lr 2.0000e-03 eta 10:28:23
+epoch [2/50] batch [320/500] time 1.589 (1.559) data 0.001 (0.003) loss 1.0850 (1.3319) acc 71.8750 (67.2754) lr 2.0000e-03 eta 10:28:16
+epoch [2/50] batch [325/500] time 1.572 (1.559) data 0.000 (0.003) loss 2.1191 (1.3308) acc 56.2500 (67.3365) lr 2.0000e-03 eta 10:28:07
+epoch [2/50] batch [330/500] time 1.578 (1.559) data 0.000 (0.003) loss 1.5439 (1.3299) acc 71.8750 (67.3864) lr 2.0000e-03 eta 10:27:58
+epoch [2/50] batch [335/500] time 1.548 (1.559) data 0.000 (0.003) loss 1.3525 (1.3271) acc 78.1250 (67.4627) lr 2.0000e-03 eta 10:27:49
+epoch [2/50] batch [340/500] time 1.560 (1.559) data 0.000 (0.003) loss 0.9536 (1.3258) acc 81.2500 (67.5368) lr 2.0000e-03 eta 10:27:44
+epoch [2/50] batch [345/500] time 1.562 (1.559) data 0.000 (0.003) loss 1.1748 (1.3237) acc 71.8750 (67.5815) lr 2.0000e-03 eta 10:27:37
+epoch [2/50] batch [350/500] time 1.572 (1.559) data 0.000 (0.003) loss 1.5605 (1.3274) acc 62.5000 (67.5536) lr 2.0000e-03 eta 10:27:30
+epoch [2/50] batch [355/500] time 1.547 (1.559) data 0.001 (0.003) loss 1.6318 (1.3277) acc 65.6250 (67.5880) lr 2.0000e-03 eta 10:27:24
+epoch [2/50] batch [360/500] time 1.544 (1.559) data 0.000 (0.003) loss 0.8418 (1.3256) acc 81.2500 (67.6562) lr 2.0000e-03 eta 10:27:11
+epoch [2/50] batch [365/500] time 1.566 (1.559) data 0.000 (0.003) loss 1.0469 (1.3255) acc 78.1250 (67.6969) lr 2.0000e-03 eta 10:27:05
+epoch [2/50] batch [370/500] time 1.556 (1.559) data 0.000 (0.003) loss 1.4307 (1.3218) acc 71.8750 (67.8463) lr 2.0000e-03 eta 10:26:54
+epoch [2/50] batch [375/500] time 1.573 (1.559) data 0.000 (0.003) loss 0.7222 (1.3212) acc 81.2500 (67.8667) lr 2.0000e-03 eta 10:26:46
+epoch [2/50] batch [380/500] time 1.606 (1.559) data 0.001 (0.003) loss 0.8188 (1.3172) acc 81.2500 (67.9770) lr 2.0000e-03 eta 10:26:39
+epoch [2/50] batch [385/500] time 1.988 (1.562) data 0.001 (0.003) loss 1.2891 (1.3153) acc 68.7500 (68.0276) lr 2.0000e-03 eta 10:27:49
+epoch [2/50] batch [390/500] time 1.712 (1.564) data 0.001 (0.003) loss 1.3574 (1.3195) acc 68.7500 (67.9567) lr 2.0000e-03 eta 10:28:39
+epoch [2/50] batch [395/500] time 1.775 (1.566) data 0.000 (0.003) loss 1.4707 (1.3208) acc 62.5000 (67.9193) lr 2.0000e-03 eta 10:29:17
+epoch [2/50] batch [400/500] time 1.768 (1.569) data 0.002 (0.003) loss 1.4375 (1.3192) acc 59.3750 (67.8984) lr 2.0000e-03 eta 10:30:07
+epoch [2/50] batch [405/500] time 1.782 (1.571) data 0.001 (0.003) loss 0.9619 (1.3185) acc 81.2500 (67.8935) lr 2.0000e-03 eta 10:30:46
+epoch [2/50] batch [410/500] time 1.741 (1.573) data 0.000 (0.003) loss 1.0957 (1.3163) acc 75.0000 (67.9345) lr 2.0000e-03 eta 10:31:28
+epoch [2/50] batch [415/500] time 1.827 (1.575) data 0.000 (0.003) loss 1.0439 (1.3161) acc 75.0000 (67.9066) lr 2.0000e-03 eta 10:32:10
+epoch [2/50] batch [420/500] time 1.888 (1.577) data 0.001 (0.002) loss 1.0957 (1.3136) acc 71.8750 (67.9762) lr 2.0000e-03 eta 10:33:00
+epoch [2/50] batch [425/500] time 1.736 (1.579) data 0.002 (0.002) loss 0.9883 (1.3155) acc 84.3750 (67.9706) lr 2.0000e-03 eta 10:33:34
+epoch [2/50] batch [430/500] time 1.678 (1.581) data 0.001 (0.002) loss 0.6963 (1.3140) acc 81.2500 (68.0451) lr 2.0000e-03 eta 10:34:21
+epoch [2/50] batch [435/500] time 1.574 (1.583) data 0.001 (0.002) loss 0.8965 (1.3135) acc 84.3750 (68.0675) lr 2.0000e-03 eta 10:34:49
+epoch [2/50] batch [440/500] time 1.757 (1.585) data 0.001 (0.002) loss 1.2578 (1.3147) acc 71.8750 (68.0398) lr 2.0000e-03 eta 10:35:24
+epoch [2/50] batch [445/500] time 1.778 (1.586) data 0.001 (0.002) loss 1.0576 (1.3137) acc 68.7500 (68.0548) lr 2.0000e-03 eta 10:36:01
+epoch [2/50] batch [450/500] time 1.738 (1.588) data 0.001 (0.002) loss 1.2119 (1.3132) acc 78.1250 (68.0764) lr 2.0000e-03 eta 10:36:43
+epoch [2/50] batch [455/500] time 1.719 (1.590) data 0.001 (0.002) loss 1.1318 (1.3164) acc 71.8750 (68.0563) lr 2.0000e-03 eta 10:37:10
+epoch [2/50] batch [460/500] time 1.766 (1.592) data 0.001 (0.002) loss 1.4941 (1.3176) acc 68.7500 (67.9688) lr 2.0000e-03 eta 10:37:46
+epoch [2/50] batch [465/500] time 1.814 (1.593) data 0.001 (0.002) loss 0.7646 (1.3186) acc 78.1250 (67.9167) lr 2.0000e-03 eta 10:38:14
+epoch [2/50] batch [470/500] time 1.616 (1.594) data 0.015 (0.002) loss 1.0859 (1.3178) acc 65.6250 (67.9521) lr 2.0000e-03 eta 10:38:29
+epoch [2/50] batch [475/500] time 1.674 (1.596) data 0.001 (0.002) loss 1.3477 (1.3176) acc 59.3750 (67.9276) lr 2.0000e-03 eta 10:38:55
+epoch [2/50] batch [480/500] time 1.605 (1.597) data 0.001 (0.002) loss 1.2148 (1.3185) acc 59.3750 (67.8971) lr 2.0000e-03 eta 10:39:12
+epoch [2/50] batch [485/500] time 1.741 (1.598) data 0.002 (0.002) loss 1.1455 (1.3175) acc 62.5000 (67.8479) lr 2.0000e-03 eta 10:39:36
+epoch [2/50] batch [490/500] time 1.776 (1.599) data 0.000 (0.002) loss 1.1348 (1.3155) acc 68.7500 (67.9018) lr 2.0000e-03 eta 10:39:49
+epoch [2/50] batch [495/500] time 1.683 (1.600) data 0.001 (0.002) loss 1.6367 (1.3167) acc 65.6250 (67.8598) lr 2.0000e-03 eta 10:40:00
+epoch [2/50] batch [500/500] time 1.616 (1.601) data 0.001 (0.002) loss 1.0479 (1.3164) acc 71.8750 (67.8812) lr 1.9980e-03 eta 10:40:13
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,397
+* accuracy: 76.8%
+* error: 23.2%
+* macro_f1: 76.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/500] time 1.573 (1.680) data 0.000 (0.162) loss 0.9067 (1.1451) acc 71.8750 (71.2500) lr 1.9980e-03 eta 11:11:39
+epoch [3/50] batch [10/500] time 1.551 (1.617) data 0.001 (0.081) loss 1.4072 (1.1780) acc 56.2500 (69.0625) lr 1.9980e-03 eta 10:46:36
+epoch [3/50] batch [15/500] time 1.554 (1.594) data 0.000 (0.054) loss 1.2217 (1.1547) acc 71.8750 (70.2083) lr 1.9980e-03 eta 10:37:22
+epoch [3/50] batch [20/500] time 1.544 (1.590) data 0.000 (0.041) loss 1.1904 (1.1639) acc 68.7500 (70.0000) lr 1.9980e-03 eta 10:35:17
+epoch [3/50] batch [25/500] time 1.562 (1.585) data 0.001 (0.033) loss 0.8750 (1.1539) acc 81.2500 (70.7500) lr 1.9980e-03 eta 10:33:24
+epoch [3/50] batch [30/500] time 1.564 (1.584) data 0.001 (0.027) loss 1.3848 (1.1704) acc 62.5000 (70.3125) lr 1.9980e-03 eta 10:32:44
+epoch [3/50] batch [35/500] time 1.574 (1.586) data 0.001 (0.024) loss 1.4150 (1.1980) acc 62.5000 (69.8214) lr 1.9980e-03 eta 10:33:34
+epoch [3/50] batch [40/500] time 1.577 (1.584) data 0.001 (0.021) loss 1.6670 (1.2277) acc 71.8750 (69.2969) lr 1.9980e-03 eta 10:32:41
+epoch [3/50] batch [45/500] time 1.548 (1.581) data 0.000 (0.018) loss 1.1328 (1.2451) acc 75.0000 (69.1667) lr 1.9980e-03 eta 10:31:13
+epoch [3/50] batch [50/500] time 1.538 (1.579) data 0.001 (0.017) loss 1.4072 (1.2453) acc 59.3750 (68.4375) lr 1.9980e-03 eta 10:30:22
+epoch [3/50] batch [55/500] time 1.556 (1.577) data 0.000 (0.015) loss 1.3545 (1.2553) acc 68.7500 (68.3523) lr 1.9980e-03 eta 10:29:20
+epoch [3/50] batch [60/500] time 1.541 (1.575) data 0.000 (0.014) loss 1.4473 (1.2722) acc 59.3750 (68.3854) lr 1.9980e-03 eta 10:28:28
+epoch [3/50] batch [65/500] time 1.580 (1.574) data 0.001 (0.013) loss 1.4658 (1.2867) acc 65.6250 (68.1731) lr 1.9980e-03 eta 10:27:55
+epoch [3/50] batch [70/500] time 1.526 (1.573) data 0.000 (0.012) loss 1.1016 (1.2943) acc 75.0000 (68.3036) lr 1.9980e-03 eta 10:27:21
+epoch [3/50] batch [75/500] time 1.569 (1.572) data 0.001 (0.011) loss 1.6680 (1.3023) acc 50.0000 (68.0833) lr 1.9980e-03 eta 10:26:41
+epoch [3/50] batch [80/500] time 1.558 (1.572) data 0.000 (0.011) loss 1.0234 (1.3105) acc 71.8750 (68.1250) lr 1.9980e-03 eta 10:26:30
+epoch [3/50] batch [85/500] time 1.534 (1.571) data 0.000 (0.010) loss 1.0527 (1.3199) acc 62.5000 (67.9412) lr 1.9980e-03 eta 10:26:01
+epoch [3/50] batch [90/500] time 1.580 (1.571) data 0.000 (0.009) loss 0.6431 (1.3157) acc 75.0000 (68.0903) lr 1.9980e-03 eta 10:25:59
+epoch [3/50] batch [95/500] time 1.591 (1.571) data 0.000 (0.009) loss 1.6377 (1.3152) acc 59.3750 (68.0592) lr 1.9980e-03 eta 10:26:00
+epoch [3/50] batch [100/500] time 1.554 (1.571) data 0.000 (0.009) loss 1.7949 (1.3186) acc 62.5000 (68.0625) lr 1.9980e-03 eta 10:25:37
+epoch [3/50] batch [105/500] time 1.572 (1.570) data 0.000 (0.008) loss 1.1875 (1.3059) acc 68.7500 (68.4226) lr 1.9980e-03 eta 10:25:17
+epoch [3/50] batch [110/500] time 1.568 (1.570) data 0.000 (0.008) loss 0.9106 (1.2969) acc 75.0000 (68.6648) lr 1.9980e-03 eta 10:25:06
+epoch [3/50] batch [115/500] time 1.560 (1.570) data 0.000 (0.007) loss 1.0967 (1.2977) acc 78.1250 (68.6413) lr 1.9980e-03 eta 10:24:56
+epoch [3/50] batch [120/500] time 1.529 (1.569) data 0.000 (0.007) loss 0.8037 (1.2939) acc 78.1250 (68.7500) lr 1.9980e-03 eta 10:24:29
+epoch [3/50] batch [125/500] time 1.563 (1.569) data 0.000 (0.007) loss 0.9609 (1.3017) acc 68.7500 (68.5000) lr 1.9980e-03 eta 10:24:18
+epoch [3/50] batch [130/500] time 1.707 (1.573) data 0.000 (0.007) loss 0.7139 (1.2872) acc 87.5000 (68.8702) lr 1.9980e-03 eta 10:25:41
+epoch [3/50] batch [135/500] time 1.583 (1.574) data 0.001 (0.006) loss 1.1924 (1.2873) acc 78.1250 (68.9352) lr 1.9980e-03 eta 10:26:08
+epoch [3/50] batch [140/500] time 1.563 (1.574) data 0.001 (0.006) loss 1.3086 (1.2778) acc 68.7500 (69.1518) lr 1.9980e-03 eta 10:25:47
+epoch [3/50] batch [145/500] time 1.571 (1.573) data 0.000 (0.006) loss 1.1006 (1.2739) acc 62.5000 (69.1595) lr 1.9980e-03 eta 10:25:33
+epoch [3/50] batch [150/500] time 1.564 (1.573) data 0.001 (0.006) loss 1.4229 (1.2761) acc 59.3750 (69.0417) lr 1.9980e-03 eta 10:25:25
+epoch [3/50] batch [155/500] time 1.569 (1.573) data 0.000 (0.006) loss 0.9771 (1.2671) acc 68.7500 (69.2137) lr 1.9980e-03 eta 10:25:09
+epoch [3/50] batch [160/500] time 1.569 (1.573) data 0.001 (0.006) loss 0.6021 (1.2571) acc 71.8750 (69.3555) lr 1.9980e-03 eta 10:24:57
+epoch [3/50] batch [165/500] time 1.593 (1.573) data 0.000 (0.005) loss 0.8345 (1.2536) acc 81.2500 (69.5265) lr 1.9980e-03 eta 10:24:44
+epoch [3/50] batch [170/500] time 1.562 (1.572) data 0.000 (0.005) loss 1.3096 (1.2554) acc 65.6250 (69.4301) lr 1.9980e-03 eta 10:24:32
+epoch [3/50] batch [175/500] time 1.572 (1.573) data 0.000 (0.005) loss 1.0371 (1.2549) acc 78.1250 (69.3571) lr 1.9980e-03 eta 10:24:32
+epoch [3/50] batch [180/500] time 1.559 (1.572) data 0.000 (0.005) loss 1.2119 (1.2604) acc 78.1250 (69.2361) lr 1.9980e-03 eta 10:24:13
+epoch [3/50] batch [185/500] time 1.551 (1.572) data 0.001 (0.005) loss 1.6846 (1.2541) acc 56.2500 (69.3412) lr 1.9980e-03 eta 10:24:06
+epoch [3/50] batch [190/500] time 1.579 (1.572) data 0.000 (0.005) loss 0.9927 (1.2481) acc 84.3750 (69.4572) lr 1.9980e-03 eta 10:23:52
+epoch [3/50] batch [195/500] time 1.555 (1.572) data 0.001 (0.005) loss 0.8354 (1.2516) acc 75.0000 (69.3590) lr 1.9980e-03 eta 10:23:33
+epoch [3/50] batch [200/500] time 1.550 (1.572) data 0.000 (0.005) loss 1.9736 (1.2583) acc 43.7500 (69.1875) lr 1.9980e-03 eta 10:23:22
+epoch [3/50] batch [205/500] time 1.553 (1.571) data 0.000 (0.004) loss 0.6851 (1.2489) acc 90.6250 (69.4970) lr 1.9980e-03 eta 10:23:03
+epoch [3/50] batch [210/500] time 1.547 (1.571) data 0.001 (0.004) loss 1.2510 (1.2436) acc 71.8750 (69.7470) lr 1.9980e-03 eta 10:22:47
+epoch [3/50] batch [215/500] time 1.581 (1.570) data 0.000 (0.004) loss 0.9111 (1.2438) acc 78.1250 (69.7820) lr 1.9980e-03 eta 10:22:33
+epoch [3/50] batch [220/500] time 1.607 (1.571) data 0.000 (0.004) loss 1.3145 (1.2434) acc 75.0000 (69.8438) lr 1.9980e-03 eta 10:22:38
+epoch [3/50] batch [225/500] time 1.629 (1.573) data 0.001 (0.004) loss 1.7656 (1.2480) acc 53.1250 (69.7083) lr 1.9980e-03 eta 10:23:13
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..22cb2ffb
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model-best.pth.tar
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
new file mode 100644
index 00000000..acd01576
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699474665.ckb-gpu-v.mitre.org.3629295.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699474665.ckb-gpu-v.mitre.org.3629295.0
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699474665.ckb-gpu-v.mitre.org.3629295.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..f1ae6541
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,5697 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.579
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/500] time 1.526 (2.729) data 0.000 (0.198) loss 2.6680 (3.0004) acc 46.8750 (43.1250) lr 1.0000e-05 eta 18:56:52
+epoch [1/50] batch [10/500] time 1.572 (2.149) data 0.001 (0.099) loss 2.1602 (2.6768) acc 53.1250 (46.8750) lr 1.0000e-05 eta 14:54:53
+epoch [1/50] batch [15/500] time 1.655 (1.964) data 0.000 (0.066) loss 2.3926 (2.6326) acc 53.1250 (47.0833) lr 1.0000e-05 eta 13:37:47
+epoch [1/50] batch [20/500] time 1.600 (1.880) data 0.000 (0.050) loss 2.6055 (2.5407) acc 40.6250 (48.1250) lr 1.0000e-05 eta 13:02:32
+epoch [1/50] batch [25/500] time 1.661 (1.826) data 0.000 (0.040) loss 2.2500 (2.4505) acc 46.8750 (49.3750) lr 1.0000e-05 eta 12:39:59
+epoch [1/50] batch [30/500] time 1.566 (1.781) data 0.000 (0.033) loss 1.9404 (2.4199) acc 56.2500 (49.6875) lr 1.0000e-05 eta 12:21:15
+epoch [1/50] batch [35/500] time 1.553 (1.749) data 0.000 (0.029) loss 1.7510 (2.3313) acc 56.2500 (51.0714) lr 1.0000e-05 eta 12:07:55
+epoch [1/50] batch [40/500] time 1.572 (1.727) data 0.000 (0.025) loss 2.2148 (2.2764) acc 50.0000 (51.7188) lr 1.0000e-05 eta 11:58:27
+epoch [1/50] batch [45/500] time 1.547 (1.710) data 0.000 (0.023) loss 1.5869 (2.2606) acc 59.3750 (52.1528) lr 1.0000e-05 eta 11:51:04
+epoch [1/50] batch [50/500] time 1.555 (1.695) data 0.000 (0.020) loss 1.3623 (2.2209) acc 71.8750 (53.0000) lr 1.0000e-05 eta 11:44:40
+epoch [1/50] batch [55/500] time 1.582 (1.683) data 0.001 (0.019) loss 2.5352 (2.1800) acc 40.6250 (53.6932) lr 1.0000e-05 eta 11:39:34
+epoch [1/50] batch [60/500] time 1.559 (1.673) data 0.001 (0.017) loss 1.6846 (2.1436) acc 59.3750 (54.3750) lr 1.0000e-05 eta 11:35:16
+epoch [1/50] batch [65/500] time 1.546 (1.665) data 0.001 (0.016) loss 1.3691 (2.1060) acc 68.7500 (55.0962) lr 1.0000e-05 eta 11:31:46
+epoch [1/50] batch [70/500] time 1.592 (1.658) data 0.000 (0.015) loss 1.3213 (2.0912) acc 68.7500 (55.4464) lr 1.0000e-05 eta 11:28:50
+epoch [1/50] batch [75/500] time 1.555 (1.651) data 0.000 (0.014) loss 1.7529 (2.0628) acc 56.2500 (55.6667) lr 1.0000e-05 eta 11:25:57
+epoch [1/50] batch [80/500] time 1.640 (1.648) data 0.000 (0.013) loss 1.4932 (2.0367) acc 68.7500 (56.3281) lr 1.0000e-05 eta 11:24:32
+epoch [1/50] batch [85/500] time 1.695 (1.651) data 0.000 (0.012) loss 2.0371 (2.0133) acc 40.6250 (56.4706) lr 1.0000e-05 eta 11:25:24
+epoch [1/50] batch [90/500] time 1.617 (1.653) data 0.001 (0.011) loss 1.4316 (2.0048) acc 59.3750 (56.3194) lr 1.0000e-05 eta 11:26:11
+epoch [1/50] batch [95/500] time 1.571 (1.649) data 0.000 (0.011) loss 0.9624 (1.9725) acc 75.0000 (56.8092) lr 1.0000e-05 eta 11:24:25
+epoch [1/50] batch [100/500] time 1.569 (1.645) data 0.000 (0.010) loss 1.8203 (1.9715) acc 59.3750 (56.9688) lr 1.0000e-05 eta 11:22:40
+epoch [1/50] batch [105/500] time 1.574 (1.641) data 0.000 (0.010) loss 1.8516 (1.9651) acc 59.3750 (57.3214) lr 1.0000e-05 eta 11:20:56
+epoch [1/50] batch [110/500] time 1.551 (1.638) data 0.000 (0.009) loss 1.3242 (1.9543) acc 75.0000 (57.5000) lr 1.0000e-05 eta 11:19:20
+epoch [1/50] batch [115/500] time 1.568 (1.634) data 0.000 (0.009) loss 2.3379 (1.9418) acc 56.2500 (57.6902) lr 1.0000e-05 eta 11:17:36
+epoch [1/50] batch [120/500] time 1.537 (1.631) data 0.000 (0.009) loss 2.0215 (1.9298) acc 46.8750 (57.9688) lr 1.0000e-05 eta 11:16:11
+epoch [1/50] batch [125/500] time 1.532 (1.628) data 0.000 (0.008) loss 1.2373 (1.9107) acc 71.8750 (58.3500) lr 1.0000e-05 eta 11:14:47
+epoch [1/50] batch [130/500] time 1.547 (1.625) data 0.001 (0.008) loss 1.9961 (1.9038) acc 59.3750 (58.5096) lr 1.0000e-05 eta 11:13:33
+epoch [1/50] batch [135/500] time 1.559 (1.623) data 0.001 (0.008) loss 1.7256 (1.8903) acc 56.2500 (58.7269) lr 1.0000e-05 eta 11:12:38
+epoch [1/50] batch [140/500] time 1.551 (1.621) data 0.001 (0.008) loss 2.3574 (1.8775) acc 53.1250 (58.8839) lr 1.0000e-05 eta 11:11:33
+epoch [1/50] batch [145/500] time 1.556 (1.619) data 0.001 (0.007) loss 1.8555 (1.8715) acc 56.2500 (58.8362) lr 1.0000e-05 eta 11:10:35
+epoch [1/50] batch [150/500] time 1.564 (1.617) data 0.001 (0.007) loss 1.9990 (1.8628) acc 53.1250 (59.1458) lr 1.0000e-05 eta 11:09:39
+epoch [1/50] batch [155/500] time 1.595 (1.615) data 0.001 (0.007) loss 2.0996 (1.8558) acc 59.3750 (59.3347) lr 1.0000e-05 eta 11:08:50
+epoch [1/50] batch [160/500] time 1.559 (1.613) data 0.001 (0.007) loss 1.4707 (1.8541) acc 56.2500 (59.1797) lr 1.0000e-05 eta 11:07:56
+epoch [1/50] batch [165/500] time 1.552 (1.611) data 0.001 (0.006) loss 1.7148 (1.8492) acc 53.1250 (59.1856) lr 1.0000e-05 eta 11:06:58
+epoch [1/50] batch [170/500] time 1.591 (1.611) data 0.000 (0.006) loss 1.7578 (1.8375) acc 62.5000 (59.3566) lr 1.0000e-05 eta 11:06:35
+epoch [1/50] batch [175/500] time 1.562 (1.610) data 0.000 (0.006) loss 2.0742 (1.8386) acc 59.3750 (59.4107) lr 1.0000e-05 eta 11:05:59
+epoch [1/50] batch [180/500] time 1.557 (1.608) data 0.001 (0.006) loss 0.9971 (1.8301) acc 75.0000 (59.5486) lr 1.0000e-05 eta 11:05:20
+epoch [1/50] batch [185/500] time 1.573 (1.607) data 0.000 (0.006) loss 1.1113 (1.8254) acc 65.6250 (59.6284) lr 1.0000e-05 eta 11:04:44
+epoch [1/50] batch [190/500] time 1.547 (1.606) data 0.000 (0.006) loss 1.1191 (1.8157) acc 71.8750 (59.7862) lr 1.0000e-05 eta 11:04:05
+epoch [1/50] batch [195/500] time 1.544 (1.605) data 0.001 (0.006) loss 2.0312 (1.8152) acc 62.5000 (59.8397) lr 1.0000e-05 eta 11:03:26
+epoch [1/50] batch [200/500] time 1.565 (1.604) data 0.001 (0.005) loss 1.7812 (1.8054) acc 65.6250 (60.0156) lr 1.0000e-05 eta 11:02:59
+epoch [1/50] batch [205/500] time 1.580 (1.603) data 0.000 (0.005) loss 1.7959 (1.7951) acc 62.5000 (60.1067) lr 1.0000e-05 eta 11:02:24
+epoch [1/50] batch [210/500] time 1.563 (1.602) data 0.000 (0.005) loss 1.6230 (1.7956) acc 62.5000 (60.0744) lr 1.0000e-05 eta 11:01:59
+epoch [1/50] batch [215/500] time 1.546 (1.601) data 0.000 (0.005) loss 1.2314 (1.7844) acc 65.6250 (60.2907) lr 1.0000e-05 eta 11:01:21
+epoch [1/50] batch [220/500] time 1.555 (1.600) data 0.000 (0.005) loss 2.3105 (1.7787) acc 56.2500 (60.4545) lr 1.0000e-05 eta 11:00:47
+epoch [1/50] batch [225/500] time 1.553 (1.599) data 0.000 (0.005) loss 1.5068 (1.7711) acc 65.6250 (60.5694) lr 1.0000e-05 eta 11:00:11
+epoch [1/50] batch [230/500] time 1.524 (1.598) data 0.000 (0.005) loss 1.1777 (1.7646) acc 75.0000 (60.7473) lr 1.0000e-05 eta 10:59:37
+epoch [1/50] batch [235/500] time 1.553 (1.597) data 0.001 (0.005) loss 1.4883 (1.7569) acc 62.5000 (60.8245) lr 1.0000e-05 eta 10:59:07
+epoch [1/50] batch [240/500] time 1.540 (1.596) data 0.000 (0.005) loss 1.3818 (1.7569) acc 71.8750 (60.9245) lr 1.0000e-05 eta 10:58:37
+epoch [1/50] batch [245/500] time 1.538 (1.595) data 0.000 (0.005) loss 1.0498 (1.7544) acc 75.0000 (60.9566) lr 1.0000e-05 eta 10:58:04
+epoch [1/50] batch [250/500] time 1.549 (1.594) data 0.000 (0.004) loss 1.2861 (1.7467) acc 59.3750 (61.0375) lr 1.0000e-05 eta 10:57:42
+epoch [1/50] batch [255/500] time 1.573 (1.594) data 0.000 (0.004) loss 1.6689 (1.7407) acc 68.7500 (61.1275) lr 1.0000e-05 eta 10:57:19
+epoch [1/50] batch [260/500] time 1.569 (1.593) data 0.000 (0.004) loss 1.8477 (1.7376) acc 68.7500 (61.2620) lr 1.0000e-05 eta 10:56:49
+epoch [1/50] batch [265/500] time 1.545 (1.592) data 0.000 (0.004) loss 2.2305 (1.7313) acc 46.8750 (61.4033) lr 1.0000e-05 eta 10:56:23
+epoch [1/50] batch [270/500] time 1.543 (1.592) data 0.000 (0.004) loss 1.3428 (1.7195) acc 71.8750 (61.6551) lr 1.0000e-05 eta 10:56:01
+epoch [1/50] batch [275/500] time 1.559 (1.591) data 0.000 (0.004) loss 1.4639 (1.7159) acc 68.7500 (61.6932) lr 1.0000e-05 eta 10:55:36
+epoch [1/50] batch [280/500] time 1.572 (1.590) data 0.000 (0.004) loss 1.2197 (1.7089) acc 65.6250 (61.8304) lr 1.0000e-05 eta 10:55:15
+epoch [1/50] batch [285/500] time 1.575 (1.590) data 0.000 (0.004) loss 1.1084 (1.7031) acc 71.8750 (61.9737) lr 1.0000e-05 eta 10:55:02
+epoch [1/50] batch [290/500] time 1.579 (1.590) data 0.000 (0.004) loss 1.0654 (1.6992) acc 68.7500 (62.0043) lr 1.0000e-05 eta 10:54:45
+epoch [1/50] batch [295/500] time 1.587 (1.589) data 0.000 (0.004) loss 1.1455 (1.6939) acc 65.6250 (62.0763) lr 1.0000e-05 eta 10:54:23
+epoch [1/50] batch [300/500] time 1.576 (1.589) data 0.000 (0.004) loss 1.6162 (1.6871) acc 56.2500 (62.0625) lr 1.0000e-05 eta 10:54:01
+epoch [1/50] batch [305/500] time 1.549 (1.588) data 0.000 (0.004) loss 1.3760 (1.6856) acc 71.8750 (62.0902) lr 1.0000e-05 eta 10:53:41
+epoch [1/50] batch [310/500] time 1.559 (1.588) data 0.000 (0.004) loss 0.8809 (1.6797) acc 78.1250 (62.2177) lr 1.0000e-05 eta 10:53:25
+epoch [1/50] batch [315/500] time 1.567 (1.587) data 0.001 (0.004) loss 1.0000 (1.6795) acc 65.6250 (62.2123) lr 1.0000e-05 eta 10:53:06
+epoch [1/50] batch [320/500] time 1.531 (1.587) data 0.000 (0.004) loss 1.0859 (1.6703) acc 65.6250 (62.3633) lr 1.0000e-05 eta 10:52:46
+epoch [1/50] batch [325/500] time 1.572 (1.587) data 0.000 (0.004) loss 1.5293 (1.6671) acc 65.6250 (62.4327) lr 1.0000e-05 eta 10:52:32
+epoch [1/50] batch [330/500] time 1.541 (1.586) data 0.001 (0.003) loss 1.6631 (1.6666) acc 59.3750 (62.4053) lr 1.0000e-05 eta 10:52:12
+epoch [1/50] batch [335/500] time 1.545 (1.586) data 0.000 (0.003) loss 1.8867 (1.6611) acc 65.6250 (62.4720) lr 1.0000e-05 eta 10:51:54
+epoch [1/50] batch [340/500] time 1.552 (1.585) data 0.000 (0.003) loss 0.6108 (1.6590) acc 87.5000 (62.5551) lr 1.0000e-05 eta 10:51:35
+epoch [1/50] batch [345/500] time 1.592 (1.585) data 0.000 (0.003) loss 1.0752 (1.6564) acc 71.8750 (62.5996) lr 1.0000e-05 eta 10:51:19
+epoch [1/50] batch [350/500] time 1.683 (1.585) data 0.000 (0.003) loss 1.5283 (1.6548) acc 65.6250 (62.6250) lr 1.0000e-05 eta 10:51:12
+epoch [1/50] batch [355/500] time 1.585 (1.585) data 0.000 (0.003) loss 1.1992 (1.6517) acc 71.8750 (62.6673) lr 1.0000e-05 eta 10:51:03
+epoch [1/50] batch [360/500] time 1.573 (1.585) data 0.000 (0.003) loss 1.1680 (1.6477) acc 71.8750 (62.7083) lr 1.0000e-05 eta 10:50:50
+epoch [1/50] batch [365/500] time 1.557 (1.584) data 0.000 (0.003) loss 1.4873 (1.6436) acc 59.3750 (62.7140) lr 1.0000e-05 eta 10:50:33
+epoch [1/50] batch [370/500] time 1.554 (1.584) data 0.000 (0.003) loss 1.4668 (1.6414) acc 59.3750 (62.7703) lr 1.0000e-05 eta 10:50:14
+epoch [1/50] batch [375/500] time 1.532 (1.584) data 0.000 (0.003) loss 2.1230 (1.6374) acc 53.1250 (62.8000) lr 1.0000e-05 eta 10:49:55
+epoch [1/50] batch [380/500] time 1.540 (1.583) data 0.000 (0.003) loss 1.3418 (1.6347) acc 75.0000 (62.8125) lr 1.0000e-05 eta 10:49:39
+epoch [1/50] batch [385/500] time 1.573 (1.583) data 0.001 (0.003) loss 1.7363 (1.6326) acc 62.5000 (62.8571) lr 1.0000e-05 eta 10:49:27
+epoch [1/50] batch [390/500] time 1.541 (1.583) data 0.000 (0.003) loss 1.1152 (1.6298) acc 71.8750 (62.9808) lr 1.0000e-05 eta 10:49:13
+epoch [1/50] batch [395/500] time 1.555 (1.583) data 0.000 (0.003) loss 0.9424 (1.6234) acc 71.8750 (63.1013) lr 1.0000e-05 eta 10:48:57
+epoch [1/50] batch [400/500] time 1.572 (1.582) data 0.000 (0.003) loss 1.6758 (1.6199) acc 59.3750 (63.2188) lr 1.0000e-05 eta 10:48:42
+epoch [1/50] batch [405/500] time 1.572 (1.582) data 0.000 (0.003) loss 0.9937 (1.6166) acc 59.3750 (63.2330) lr 1.0000e-05 eta 10:48:24
+epoch [1/50] batch [410/500] time 1.567 (1.582) data 0.000 (0.003) loss 1.7041 (1.6145) acc 62.5000 (63.2470) lr 1.0000e-05 eta 10:48:09
+epoch [1/50] batch [415/500] time 1.549 (1.581) data 0.000 (0.003) loss 1.7549 (1.6136) acc 62.5000 (63.3133) lr 1.0000e-05 eta 10:47:55
+epoch [1/50] batch [420/500] time 1.602 (1.581) data 0.000 (0.003) loss 1.8330 (1.6125) acc 53.1250 (63.3185) lr 1.0000e-05 eta 10:47:49
+epoch [1/50] batch [425/500] time 1.530 (1.581) data 0.000 (0.003) loss 1.9277 (1.6128) acc 62.5000 (63.3235) lr 1.0000e-05 eta 10:47:35
+epoch [1/50] batch [430/500] time 1.557 (1.581) data 0.000 (0.003) loss 1.2998 (1.6113) acc 75.0000 (63.3648) lr 1.0000e-05 eta 10:47:23
+epoch [1/50] batch [435/500] time 1.534 (1.581) data 0.000 (0.003) loss 1.1543 (1.6075) acc 71.8750 (63.4483) lr 1.0000e-05 eta 10:47:06
+epoch [1/50] batch [440/500] time 1.597 (1.580) data 0.001 (0.003) loss 1.1318 (1.6046) acc 71.8750 (63.4943) lr 1.0000e-05 eta 10:46:54
+epoch [1/50] batch [445/500] time 1.570 (1.580) data 0.000 (0.003) loss 1.1436 (1.6021) acc 75.0000 (63.5604) lr 1.0000e-05 eta 10:46:41
+epoch [1/50] batch [450/500] time 1.548 (1.580) data 0.000 (0.003) loss 2.3691 (1.6010) acc 53.1250 (63.5556) lr 1.0000e-05 eta 10:46:31
+epoch [1/50] batch [455/500] time 1.570 (1.580) data 0.000 (0.003) loss 1.4902 (1.5982) acc 56.2500 (63.6195) lr 1.0000e-05 eta 10:46:18
+epoch [1/50] batch [460/500] time 1.554 (1.580) data 0.000 (0.003) loss 1.8545 (1.5993) acc 56.2500 (63.6209) lr 1.0000e-05 eta 10:46:03
+epoch [1/50] batch [465/500] time 1.578 (1.579) data 0.000 (0.003) loss 0.8286 (1.5967) acc 75.0000 (63.6492) lr 1.0000e-05 eta 10:45:49
+epoch [1/50] batch [470/500] time 1.534 (1.579) data 0.000 (0.003) loss 1.4180 (1.5934) acc 71.8750 (63.6968) lr 1.0000e-05 eta 10:45:32
+epoch [1/50] batch [475/500] time 1.553 (1.579) data 0.000 (0.003) loss 1.5449 (1.5898) acc 71.8750 (63.7763) lr 1.0000e-05 eta 10:45:18
+epoch [1/50] batch [480/500] time 1.562 (1.579) data 0.000 (0.002) loss 2.2305 (1.5881) acc 56.2500 (63.8021) lr 1.0000e-05 eta 10:45:05
+epoch [1/50] batch [485/500] time 1.562 (1.578) data 0.001 (0.002) loss 1.3672 (1.5830) acc 78.1250 (63.8982) lr 1.0000e-05 eta 10:44:52
+epoch [1/50] batch [490/500] time 1.550 (1.578) data 0.000 (0.002) loss 1.2090 (1.5812) acc 68.7500 (63.9158) lr 1.0000e-05 eta 10:44:36
+epoch [1/50] batch [495/500] time 1.549 (1.578) data 0.000 (0.002) loss 1.5117 (1.5812) acc 65.6250 (63.8699) lr 1.0000e-05 eta 10:44:29
+epoch [1/50] batch [500/500] time 1.578 (1.578) data 0.000 (0.002) loss 1.5713 (1.5795) acc 65.6250 (63.8937) lr 2.0000e-03 eta 10:44:14
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,807
+* accuracy: 73.6%
+* error: 26.4%
+* macro_f1: 72.6%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/500] time 1.574 (1.689) data 0.000 (0.159) loss 0.8926 (1.5412) acc 78.1250 (65.6250) lr 2.0000e-03 eta 11:29:43
+epoch [2/50] batch [10/500] time 1.558 (1.625) data 0.000 (0.080) loss 1.4668 (1.4212) acc 62.5000 (65.9375) lr 2.0000e-03 eta 11:03:21
+epoch [2/50] batch [15/500] time 1.583 (1.605) data 0.000 (0.053) loss 1.8105 (1.3924) acc 59.3750 (66.4583) lr 2.0000e-03 eta 10:54:59
+epoch [2/50] batch [20/500] time 1.561 (1.598) data 0.000 (0.040) loss 1.8594 (1.4065) acc 56.2500 (66.7188) lr 2.0000e-03 eta 10:52:10
+epoch [2/50] batch [25/500] time 1.547 (1.591) data 0.000 (0.032) loss 1.5322 (1.4153) acc 68.7500 (65.7500) lr 2.0000e-03 eta 10:48:51
+epoch [2/50] batch [30/500] time 1.571 (1.585) data 0.000 (0.027) loss 1.1768 (1.4120) acc 62.5000 (66.2500) lr 2.0000e-03 eta 10:46:30
+epoch [2/50] batch [35/500] time 1.567 (1.582) data 0.000 (0.023) loss 0.9868 (1.3767) acc 78.1250 (67.3214) lr 2.0000e-03 eta 10:44:57
+epoch [2/50] batch [40/500] time 1.547 (1.579) data 0.000 (0.020) loss 1.2715 (1.3468) acc 65.6250 (67.5000) lr 2.0000e-03 eta 10:43:31
+epoch [2/50] batch [45/500] time 1.542 (1.576) data 0.000 (0.018) loss 1.1768 (1.3403) acc 71.8750 (67.6389) lr 2.0000e-03 eta 10:42:22
+epoch [2/50] batch [50/500] time 1.566 (1.574) data 0.000 (0.016) loss 0.7129 (1.3346) acc 78.1250 (67.4375) lr 2.0000e-03 eta 10:41:24
+epoch [2/50] batch [55/500] time 1.543 (1.573) data 0.000 (0.015) loss 1.4150 (1.3184) acc 68.7500 (68.0114) lr 2.0000e-03 eta 10:40:57
+epoch [2/50] batch [60/500] time 1.581 (1.572) data 0.001 (0.014) loss 1.5693 (1.3319) acc 53.1250 (67.7083) lr 2.0000e-03 eta 10:40:08
+epoch [2/50] batch [65/500] time 1.554 (1.570) data 0.000 (0.013) loss 1.4238 (1.3278) acc 71.8750 (67.5962) lr 2.0000e-03 eta 10:39:22
+epoch [2/50] batch [70/500] time 1.567 (1.570) data 0.001 (0.012) loss 1.4346 (1.3550) acc 65.6250 (67.1875) lr 2.0000e-03 eta 10:39:05
+epoch [2/50] batch [75/500] time 1.576 (1.569) data 0.001 (0.011) loss 1.2705 (1.3523) acc 81.2500 (67.5000) lr 2.0000e-03 eta 10:38:40
+epoch [2/50] batch [80/500] time 1.551 (1.569) data 0.000 (0.010) loss 1.0664 (1.3410) acc 71.8750 (67.6953) lr 2.0000e-03 eta 10:38:22
+epoch [2/50] batch [85/500] time 1.550 (1.568) data 0.000 (0.010) loss 0.9814 (1.3386) acc 78.1250 (67.7206) lr 2.0000e-03 eta 10:37:58
+epoch [2/50] batch [90/500] time 1.525 (1.567) data 0.000 (0.009) loss 1.0654 (1.3369) acc 75.0000 (67.8819) lr 2.0000e-03 eta 10:37:33
+epoch [2/50] batch [95/500] time 1.561 (1.567) data 0.000 (0.009) loss 1.2188 (1.3364) acc 65.6250 (67.7961) lr 2.0000e-03 eta 10:37:24
+epoch [2/50] batch [100/500] time 1.540 (1.566) data 0.000 (0.008) loss 1.6260 (1.3385) acc 65.6250 (67.7188) lr 2.0000e-03 eta 10:36:57
+epoch [2/50] batch [105/500] time 1.562 (1.567) data 0.000 (0.008) loss 0.7935 (1.3249) acc 81.2500 (67.8274) lr 2.0000e-03 eta 10:37:08
+epoch [2/50] batch [110/500] time 1.569 (1.567) data 0.000 (0.008) loss 1.5908 (1.3312) acc 59.3750 (67.5568) lr 2.0000e-03 eta 10:36:56
+epoch [2/50] batch [115/500] time 1.574 (1.567) data 0.000 (0.007) loss 0.9521 (1.3371) acc 68.7500 (67.4185) lr 2.0000e-03 eta 10:36:56
+epoch [2/50] batch [120/500] time 1.572 (1.567) data 0.000 (0.007) loss 1.3838 (1.3426) acc 59.3750 (67.3177) lr 2.0000e-03 eta 10:36:46
+epoch [2/50] batch [125/500] time 1.583 (1.567) data 0.000 (0.007) loss 1.0908 (1.3338) acc 71.8750 (67.4500) lr 2.0000e-03 eta 10:36:41
+epoch [2/50] batch [130/500] time 1.557 (1.567) data 0.001 (0.007) loss 1.5820 (1.3318) acc 62.5000 (67.4760) lr 2.0000e-03 eta 10:36:29
+epoch [2/50] batch [135/500] time 1.563 (1.567) data 0.000 (0.006) loss 1.2168 (1.3331) acc 71.8750 (67.3380) lr 2.0000e-03 eta 10:36:19
+epoch [2/50] batch [140/500] time 1.570 (1.567) data 0.000 (0.006) loss 1.4570 (1.3298) acc 59.3750 (67.3884) lr 2.0000e-03 eta 10:36:10
+epoch [2/50] batch [145/500] time 1.560 (1.567) data 0.000 (0.006) loss 1.3965 (1.3301) acc 62.5000 (67.4569) lr 2.0000e-03 eta 10:36:12
+epoch [2/50] batch [150/500] time 1.547 (1.568) data 0.000 (0.006) loss 0.8350 (1.3281) acc 78.1250 (67.4792) lr 2.0000e-03 eta 10:36:13
+epoch [2/50] batch [155/500] time 1.553 (1.567) data 0.001 (0.006) loss 1.4609 (1.3193) acc 62.5000 (67.5806) lr 2.0000e-03 eta 10:35:55
+epoch [2/50] batch [160/500] time 1.542 (1.567) data 0.000 (0.005) loss 0.9316 (1.3172) acc 78.1250 (67.6172) lr 2.0000e-03 eta 10:35:38
+epoch [2/50] batch [165/500] time 1.568 (1.567) data 0.000 (0.005) loss 1.2754 (1.3211) acc 71.8750 (67.6326) lr 2.0000e-03 eta 10:35:31
+epoch [2/50] batch [170/500] time 1.555 (1.567) data 0.000 (0.005) loss 1.0801 (1.3219) acc 71.8750 (67.6103) lr 2.0000e-03 eta 10:35:17
+epoch [2/50] batch [175/500] time 1.552 (1.566) data 0.001 (0.005) loss 1.7803 (1.3283) acc 59.3750 (67.5357) lr 2.0000e-03 eta 10:35:01
+epoch [2/50] batch [180/500] time 1.577 (1.566) data 0.001 (0.005) loss 0.9062 (1.3284) acc 78.1250 (67.6389) lr 2.0000e-03 eta 10:34:49
+epoch [2/50] batch [185/500] time 1.536 (1.566) data 0.001 (0.005) loss 1.2637 (1.3290) acc 62.5000 (67.5845) lr 2.0000e-03 eta 10:34:39
+epoch [2/50] batch [190/500] time 1.541 (1.566) data 0.000 (0.005) loss 1.8135 (1.3286) acc 59.3750 (67.6316) lr 2.0000e-03 eta 10:34:20
+epoch [2/50] batch [195/500] time 1.537 (1.565) data 0.000 (0.004) loss 1.4922 (1.3350) acc 56.2500 (67.4840) lr 2.0000e-03 eta 10:34:02
+epoch [2/50] batch [200/500] time 1.557 (1.565) data 0.000 (0.004) loss 0.6753 (1.3341) acc 81.2500 (67.5625) lr 2.0000e-03 eta 10:33:51
+epoch [2/50] batch [205/500] time 1.590 (1.565) data 0.000 (0.004) loss 1.2021 (1.3319) acc 68.7500 (67.5762) lr 2.0000e-03 eta 10:33:45
+epoch [2/50] batch [210/500] time 1.562 (1.565) data 0.000 (0.004) loss 1.6230 (1.3349) acc 53.1250 (67.5298) lr 2.0000e-03 eta 10:33:36
+epoch [2/50] batch [215/500] time 1.557 (1.565) data 0.000 (0.004) loss 0.9067 (1.3347) acc 75.0000 (67.6308) lr 2.0000e-03 eta 10:33:27
+epoch [2/50] batch [220/500] time 1.570 (1.565) data 0.000 (0.004) loss 1.2646 (1.3329) acc 65.6250 (67.6705) lr 2.0000e-03 eta 10:33:24
+epoch [2/50] batch [225/500] time 1.553 (1.565) data 0.000 (0.004) loss 1.1807 (1.3324) acc 71.8750 (67.7222) lr 2.0000e-03 eta 10:33:13
+epoch [2/50] batch [230/500] time 1.587 (1.565) data 0.000 (0.004) loss 0.6294 (1.3270) acc 81.2500 (67.8397) lr 2.0000e-03 eta 10:33:04
+epoch [2/50] batch [235/500] time 1.569 (1.565) data 0.001 (0.004) loss 1.7725 (1.3291) acc 56.2500 (67.7926) lr 2.0000e-03 eta 10:32:54
+epoch [2/50] batch [240/500] time 1.566 (1.565) data 0.001 (0.004) loss 2.5840 (1.3297) acc 46.8750 (67.7734) lr 2.0000e-03 eta 10:32:44
+epoch [2/50] batch [245/500] time 1.571 (1.565) data 0.000 (0.004) loss 1.8184 (1.3283) acc 53.1250 (67.7806) lr 2.0000e-03 eta 10:32:39
+epoch [2/50] batch [250/500] time 1.568 (1.565) data 0.001 (0.004) loss 1.5049 (1.3301) acc 62.5000 (67.7375) lr 2.0000e-03 eta 10:32:35
+epoch [2/50] batch [255/500] time 1.554 (1.565) data 0.001 (0.004) loss 1.8916 (1.3319) acc 56.2500 (67.6716) lr 2.0000e-03 eta 10:32:25
+epoch [2/50] batch [260/500] time 1.600 (1.565) data 0.000 (0.003) loss 1.5449 (1.3284) acc 71.8750 (67.8125) lr 2.0000e-03 eta 10:32:21
+epoch [2/50] batch [265/500] time 1.565 (1.565) data 0.000 (0.003) loss 1.6748 (1.3271) acc 59.3750 (67.8656) lr 2.0000e-03 eta 10:32:16
+epoch [2/50] batch [270/500] time 1.574 (1.565) data 0.000 (0.003) loss 1.1953 (1.3214) acc 62.5000 (67.9167) lr 2.0000e-03 eta 10:32:04
+epoch [2/50] batch [275/500] time 1.529 (1.565) data 0.000 (0.003) loss 0.9761 (1.3169) acc 75.0000 (68.0114) lr 2.0000e-03 eta 10:31:53
+epoch [2/50] batch [280/500] time 1.537 (1.565) data 0.000 (0.003) loss 1.4150 (1.3160) acc 62.5000 (68.0246) lr 2.0000e-03 eta 10:31:40
+epoch [2/50] batch [285/500] time 1.543 (1.565) data 0.001 (0.003) loss 1.2051 (1.3106) acc 68.7500 (68.1250) lr 2.0000e-03 eta 10:31:28
+epoch [2/50] batch [290/500] time 1.688 (1.565) data 0.001 (0.003) loss 1.1045 (1.3092) acc 68.7500 (68.1250) lr 2.0000e-03 eta 10:31:33
+epoch [2/50] batch [295/500] time 1.547 (1.565) data 0.000 (0.003) loss 1.3945 (1.3094) acc 56.2500 (68.1462) lr 2.0000e-03 eta 10:31:24
+epoch [2/50] batch [300/500] time 1.540 (1.565) data 0.000 (0.003) loss 2.0840 (1.3116) acc 53.1250 (68.0938) lr 2.0000e-03 eta 10:31:12
+epoch [2/50] batch [305/500] time 1.560 (1.565) data 0.000 (0.003) loss 0.6509 (1.3058) acc 75.0000 (68.1762) lr 2.0000e-03 eta 10:31:00
+epoch [2/50] batch [310/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.8008 (1.3080) acc 65.6250 (68.1048) lr 2.0000e-03 eta 10:30:51
+epoch [2/50] batch [315/500] time 1.555 (1.565) data 0.000 (0.003) loss 1.7734 (1.3092) acc 62.5000 (68.0556) lr 2.0000e-03 eta 10:30:43
+epoch [2/50] batch [320/500] time 1.559 (1.565) data 0.001 (0.003) loss 1.4893 (1.3052) acc 75.0000 (68.2031) lr 2.0000e-03 eta 10:30:36
+epoch [2/50] batch [325/500] time 1.562 (1.565) data 0.000 (0.003) loss 0.9800 (1.3026) acc 75.0000 (68.2212) lr 2.0000e-03 eta 10:30:27
+epoch [2/50] batch [330/500] time 1.544 (1.565) data 0.000 (0.003) loss 1.5420 (1.2990) acc 65.6250 (68.2955) lr 2.0000e-03 eta 10:30:16
+epoch [2/50] batch [335/500] time 1.557 (1.564) data 0.000 (0.003) loss 1.4570 (1.2995) acc 62.5000 (68.3396) lr 2.0000e-03 eta 10:30:05
+epoch [2/50] batch [340/500] time 1.572 (1.565) data 0.000 (0.003) loss 1.2188 (1.3039) acc 75.0000 (68.2996) lr 2.0000e-03 eta 10:29:58
+epoch [2/50] batch [345/500] time 1.573 (1.564) data 0.000 (0.003) loss 1.4551 (1.2997) acc 71.8750 (68.3514) lr 2.0000e-03 eta 10:29:49
+epoch [2/50] batch [350/500] time 1.550 (1.564) data 0.000 (0.003) loss 0.7661 (1.3018) acc 78.1250 (68.2679) lr 2.0000e-03 eta 10:29:42
+epoch [2/50] batch [355/500] time 1.534 (1.564) data 0.000 (0.003) loss 1.2217 (1.3073) acc 59.3750 (68.1250) lr 2.0000e-03 eta 10:29:31
+epoch [2/50] batch [360/500] time 1.581 (1.564) data 0.000 (0.003) loss 1.4404 (1.3069) acc 65.6250 (68.1250) lr 2.0000e-03 eta 10:29:26
+epoch [2/50] batch [365/500] time 1.555 (1.564) data 0.000 (0.003) loss 1.6787 (1.3076) acc 71.8750 (68.1849) lr 2.0000e-03 eta 10:29:15
+epoch [2/50] batch [370/500] time 1.559 (1.564) data 0.000 (0.003) loss 0.7705 (1.3095) acc 81.2500 (68.1672) lr 2.0000e-03 eta 10:29:05
+epoch [2/50] batch [375/500] time 1.660 (1.565) data 0.000 (0.003) loss 1.0947 (1.3086) acc 81.2500 (68.1750) lr 2.0000e-03 eta 10:29:12
+epoch [2/50] batch [380/500] time 1.578 (1.566) data 0.001 (0.003) loss 1.3682 (1.3086) acc 75.0000 (68.2484) lr 2.0000e-03 eta 10:29:34
+epoch [2/50] batch [385/500] time 1.548 (1.566) data 0.000 (0.002) loss 1.6162 (1.3093) acc 65.6250 (68.2386) lr 2.0000e-03 eta 10:29:28
+epoch [2/50] batch [390/500] time 1.560 (1.566) data 0.001 (0.002) loss 0.5020 (1.3070) acc 90.6250 (68.3093) lr 2.0000e-03 eta 10:29:23
+epoch [2/50] batch [395/500] time 1.547 (1.566) data 0.000 (0.002) loss 2.4316 (1.3069) acc 43.7500 (68.3070) lr 2.0000e-03 eta 10:29:12
+epoch [2/50] batch [400/500] time 1.552 (1.566) data 0.000 (0.002) loss 1.1543 (1.3074) acc 75.0000 (68.3672) lr 2.0000e-03 eta 10:29:01
+epoch [2/50] batch [405/500] time 1.529 (1.566) data 0.001 (0.002) loss 1.0859 (1.3060) acc 78.1250 (68.4259) lr 2.0000e-03 eta 10:28:50
+epoch [2/50] batch [410/500] time 1.559 (1.566) data 0.001 (0.002) loss 1.0137 (1.3050) acc 71.8750 (68.4299) lr 2.0000e-03 eta 10:28:41
+epoch [2/50] batch [415/500] time 1.537 (1.566) data 0.000 (0.002) loss 1.0488 (1.3045) acc 75.0000 (68.4337) lr 2.0000e-03 eta 10:28:29
+epoch [2/50] batch [420/500] time 1.560 (1.566) data 0.001 (0.002) loss 1.2471 (1.3037) acc 68.7500 (68.4747) lr 2.0000e-03 eta 10:28:19
+epoch [2/50] batch [425/500] time 1.576 (1.566) data 0.001 (0.002) loss 0.7466 (1.3027) acc 78.1250 (68.4485) lr 2.0000e-03 eta 10:28:11
+epoch [2/50] batch [430/500] time 1.555 (1.566) data 0.000 (0.002) loss 1.0918 (1.3041) acc 84.3750 (68.4375) lr 2.0000e-03 eta 10:28:02
+epoch [2/50] batch [435/500] time 1.578 (1.566) data 0.000 (0.002) loss 1.7021 (1.3054) acc 59.3750 (68.4195) lr 2.0000e-03 eta 10:28:02
+epoch [2/50] batch [440/500] time 1.531 (1.566) data 0.000 (0.002) loss 1.5176 (1.3074) acc 65.6250 (68.3523) lr 2.0000e-03 eta 10:27:50
+epoch [2/50] batch [445/500] time 1.551 (1.566) data 0.000 (0.002) loss 1.3896 (1.3077) acc 65.6250 (68.3497) lr 2.0000e-03 eta 10:27:41
+epoch [2/50] batch [450/500] time 1.539 (1.566) data 0.000 (0.002) loss 1.3564 (1.3112) acc 65.6250 (68.2708) lr 2.0000e-03 eta 10:27:31
+epoch [2/50] batch [455/500] time 1.545 (1.565) data 0.000 (0.002) loss 1.4131 (1.3092) acc 59.3750 (68.2967) lr 2.0000e-03 eta 10:27:18
+epoch [2/50] batch [460/500] time 1.569 (1.565) data 0.000 (0.002) loss 0.8896 (1.3068) acc 65.6250 (68.3016) lr 2.0000e-03 eta 10:27:08
+epoch [2/50] batch [465/500] time 1.555 (1.565) data 0.000 (0.002) loss 0.8228 (1.3071) acc 75.0000 (68.2796) lr 2.0000e-03 eta 10:27:00
+epoch [2/50] batch [470/500] time 1.622 (1.566) data 0.000 (0.002) loss 1.7109 (1.3087) acc 59.3750 (68.2846) lr 2.0000e-03 eta 10:27:01
+epoch [2/50] batch [475/500] time 1.693 (1.566) data 0.000 (0.002) loss 1.0137 (1.3076) acc 68.7500 (68.2566) lr 2.0000e-03 eta 10:27:14
+epoch [2/50] batch [480/500] time 1.582 (1.567) data 0.001 (0.002) loss 1.3545 (1.3066) acc 75.0000 (68.3333) lr 2.0000e-03 eta 10:27:15
+epoch [2/50] batch [485/500] time 1.586 (1.567) data 0.001 (0.002) loss 1.2471 (1.3057) acc 68.7500 (68.3247) lr 2.0000e-03 eta 10:27:10
+epoch [2/50] batch [490/500] time 1.562 (1.567) data 0.000 (0.002) loss 1.6396 (1.3040) acc 59.3750 (68.3610) lr 2.0000e-03 eta 10:26:59
+epoch [2/50] batch [495/500] time 1.576 (1.567) data 0.001 (0.002) loss 1.1729 (1.3034) acc 71.8750 (68.3649) lr 2.0000e-03 eta 10:26:52
+epoch [2/50] batch [500/500] time 1.575 (1.567) data 0.000 (0.002) loss 0.9146 (1.3015) acc 68.7500 (68.4000) lr 1.9980e-03 eta 10:26:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,278
+* accuracy: 76.6%
+* error: 23.4%
+* macro_f1: 75.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/500] time 1.555 (1.655) data 0.000 (0.161) loss 2.0117 (1.6623) acc 46.8750 (58.7500) lr 1.9980e-03 eta 11:01:49
+epoch [3/50] batch [10/500] time 1.555 (1.606) data 0.000 (0.081) loss 1.0332 (1.4162) acc 78.1250 (63.4375) lr 1.9980e-03 eta 10:42:01
+epoch [3/50] batch [15/500] time 1.558 (1.589) data 0.000 (0.054) loss 1.4023 (1.3310) acc 68.7500 (65.0000) lr 1.9980e-03 eta 10:35:06
+epoch [3/50] batch [20/500] time 1.567 (1.584) data 0.000 (0.041) loss 1.1230 (1.3204) acc 75.0000 (66.8750) lr 1.9980e-03 eta 10:32:53
+epoch [3/50] batch [25/500] time 1.550 (1.578) data 0.000 (0.033) loss 1.0381 (1.2748) acc 65.6250 (67.5000) lr 1.9980e-03 eta 10:30:24
+epoch [3/50] batch [30/500] time 1.561 (1.574) data 0.000 (0.027) loss 1.9795 (1.2768) acc 59.3750 (67.8125) lr 1.9980e-03 eta 10:28:49
+epoch [3/50] batch [35/500] time 1.696 (1.576) data 0.001 (0.023) loss 0.8716 (1.2351) acc 84.3750 (68.6607) lr 1.9980e-03 eta 10:29:20
+epoch [3/50] batch [40/500] time 1.605 (1.578) data 0.001 (0.021) loss 1.5859 (1.2416) acc 53.1250 (68.1250) lr 1.9980e-03 eta 10:30:02
+epoch [3/50] batch [45/500] time 1.687 (1.588) data 0.001 (0.018) loss 1.1816 (1.2484) acc 75.0000 (68.4028) lr 1.9980e-03 eta 10:33:58
+epoch [3/50] batch [50/500] time 1.564 (1.586) data 0.000 (0.017) loss 1.1445 (1.2576) acc 75.0000 (68.4375) lr 1.9980e-03 eta 10:32:59
+epoch [3/50] batch [55/500] time 1.565 (1.583) data 0.001 (0.015) loss 1.5146 (1.2569) acc 59.3750 (68.1818) lr 1.9980e-03 eta 10:31:50
+epoch [3/50] batch [60/500] time 1.550 (1.582) data 0.000 (0.014) loss 1.1328 (1.2719) acc 75.0000 (68.1771) lr 1.9980e-03 eta 10:31:04
+epoch [3/50] batch [65/500] time 1.634 (1.583) data 0.000 (0.013) loss 1.7070 (1.2751) acc 59.3750 (68.0769) lr 1.9980e-03 eta 10:31:21
+epoch [3/50] batch [70/500] time 1.582 (1.585) data 0.000 (0.012) loss 1.3145 (1.2746) acc 59.3750 (67.8571) lr 1.9980e-03 eta 10:32:09
+epoch [3/50] batch [75/500] time 1.585 (1.588) data 0.000 (0.011) loss 1.1006 (1.2799) acc 75.0000 (68.0417) lr 1.9980e-03 eta 10:33:10
+epoch [3/50] batch [80/500] time 1.576 (1.589) data 0.001 (0.011) loss 1.2227 (1.2814) acc 62.5000 (68.1250) lr 1.9980e-03 eta 10:33:34
+epoch [3/50] batch [85/500] time 1.544 (1.587) data 0.000 (0.010) loss 1.3154 (1.2786) acc 71.8750 (68.3824) lr 1.9980e-03 eta 10:32:42
+epoch [3/50] batch [90/500] time 1.589 (1.586) data 0.000 (0.009) loss 1.3203 (1.2642) acc 71.8750 (68.8542) lr 1.9980e-03 eta 10:32:09
+epoch [3/50] batch [95/500] time 1.535 (1.584) data 0.000 (0.009) loss 1.2959 (1.2522) acc 71.8750 (69.1118) lr 1.9980e-03 eta 10:31:14
+epoch [3/50] batch [100/500] time 1.564 (1.584) data 0.001 (0.009) loss 1.3018 (1.2459) acc 59.3750 (69.2188) lr 1.9980e-03 eta 10:30:47
+epoch [3/50] batch [105/500] time 1.545 (1.582) data 0.000 (0.008) loss 1.2100 (1.2501) acc 71.8750 (69.4048) lr 1.9980e-03 eta 10:30:01
+epoch [3/50] batch [110/500] time 1.558 (1.581) data 0.000 (0.008) loss 0.8018 (1.2406) acc 78.1250 (69.5739) lr 1.9980e-03 eta 10:29:21
+epoch [3/50] batch [115/500] time 1.562 (1.580) data 0.000 (0.008) loss 1.1914 (1.2411) acc 62.5000 (69.5924) lr 1.9980e-03 eta 10:28:53
+epoch [3/50] batch [120/500] time 1.552 (1.579) data 0.001 (0.007) loss 1.0977 (1.2373) acc 75.0000 (69.6094) lr 1.9980e-03 eta 10:28:27
+epoch [3/50] batch [125/500] time 1.612 (1.580) data 0.000 (0.007) loss 1.7559 (1.2450) acc 65.6250 (69.5500) lr 1.9980e-03 eta 10:28:32
+epoch [3/50] batch [130/500] time 1.657 (1.582) data 0.000 (0.007) loss 1.1387 (1.2410) acc 75.0000 (69.6635) lr 1.9980e-03 eta 10:29:18
+epoch [3/50] batch [135/500] time 1.560 (1.583) data 0.000 (0.006) loss 1.3643 (1.2332) acc 68.7500 (69.8611) lr 1.9980e-03 eta 10:29:27
+epoch [3/50] batch [140/500] time 1.581 (1.582) data 0.001 (0.006) loss 0.9819 (1.2339) acc 71.8750 (69.8884) lr 1.9980e-03 eta 10:29:07
+epoch [3/50] batch [145/500] time 1.566 (1.582) data 0.001 (0.006) loss 0.9292 (1.2302) acc 84.3750 (70.0431) lr 1.9980e-03 eta 10:28:57
+epoch [3/50] batch [150/500] time 1.576 (1.581) data 0.000 (0.006) loss 1.8154 (1.2310) acc 65.6250 (70.2500) lr 1.9980e-03 eta 10:28:29
+epoch [3/50] batch [155/500] time 1.550 (1.580) data 0.000 (0.006) loss 1.3721 (1.2265) acc 65.6250 (70.2419) lr 1.9980e-03 eta 10:28:02
+epoch [3/50] batch [160/500] time 1.540 (1.579) data 0.000 (0.006) loss 0.9526 (1.2252) acc 75.0000 (70.1953) lr 1.9980e-03 eta 10:27:34
+epoch [3/50] batch [165/500] time 1.558 (1.579) data 0.001 (0.005) loss 1.3564 (1.2263) acc 59.3750 (69.9432) lr 1.9980e-03 eta 10:27:14
+epoch [3/50] batch [170/500] time 1.573 (1.578) data 0.000 (0.005) loss 1.9707 (1.2291) acc 56.2500 (69.9081) lr 1.9980e-03 eta 10:26:54
+epoch [3/50] batch [175/500] time 1.560 (1.578) data 0.001 (0.005) loss 1.3447 (1.2270) acc 59.3750 (69.9286) lr 1.9980e-03 eta 10:26:35
+epoch [3/50] batch [180/500] time 1.553 (1.578) data 0.000 (0.005) loss 0.7598 (1.2340) acc 81.2500 (69.8611) lr 1.9980e-03 eta 10:26:35
+epoch [3/50] batch [185/500] time 1.567 (1.578) data 0.000 (0.005) loss 1.7090 (1.2367) acc 65.6250 (69.8649) lr 1.9980e-03 eta 10:26:13
+epoch [3/50] batch [190/500] time 1.558 (1.577) data 0.000 (0.005) loss 1.0781 (1.2362) acc 71.8750 (69.8520) lr 1.9980e-03 eta 10:25:54
+epoch [3/50] batch [195/500] time 1.570 (1.577) data 0.001 (0.005) loss 1.3027 (1.2314) acc 75.0000 (70.0000) lr 1.9980e-03 eta 10:25:39
+epoch [3/50] batch [200/500] time 1.547 (1.577) data 0.000 (0.005) loss 1.3936 (1.2267) acc 71.8750 (70.0938) lr 1.9980e-03 eta 10:25:21
+epoch [3/50] batch [205/500] time 1.607 (1.577) data 0.000 (0.004) loss 1.0244 (1.2241) acc 75.0000 (70.1220) lr 1.9980e-03 eta 10:25:22
+epoch [3/50] batch [210/500] time 1.606 (1.579) data 0.000 (0.004) loss 1.4238 (1.2288) acc 65.6250 (70.1339) lr 1.9980e-03 eta 10:26:03
+epoch [3/50] batch [215/500] time 1.524 (1.580) data 0.000 (0.004) loss 1.2236 (1.2328) acc 68.7500 (70.1308) lr 1.9980e-03 eta 10:26:13
+epoch [3/50] batch [220/500] time 1.550 (1.579) data 0.000 (0.004) loss 1.3301 (1.2364) acc 71.8750 (69.9858) lr 1.9980e-03 eta 10:25:57
+epoch [3/50] batch [225/500] time 1.580 (1.579) data 0.000 (0.004) loss 1.2734 (1.2434) acc 62.5000 (69.8750) lr 1.9980e-03 eta 10:25:42
+epoch [3/50] batch [230/500] time 1.579 (1.579) data 0.000 (0.004) loss 1.8096 (1.2460) acc 62.5000 (69.7826) lr 1.9980e-03 eta 10:25:32
+epoch [3/50] batch [235/500] time 1.588 (1.579) data 0.000 (0.004) loss 1.3721 (1.2463) acc 56.2500 (69.7606) lr 1.9980e-03 eta 10:25:20
+epoch [3/50] batch [240/500] time 1.582 (1.578) data 0.000 (0.004) loss 1.4688 (1.2462) acc 65.6250 (69.7266) lr 1.9980e-03 eta 10:25:03
+epoch [3/50] batch [245/500] time 1.561 (1.578) data 0.000 (0.004) loss 1.4209 (1.2476) acc 62.5000 (69.6556) lr 1.9980e-03 eta 10:24:51
+epoch [3/50] batch [250/500] time 1.617 (1.579) data 0.000 (0.004) loss 0.8022 (1.2458) acc 81.2500 (69.7125) lr 1.9980e-03 eta 10:24:50
+epoch [3/50] batch [255/500] time 1.619 (1.580) data 0.000 (0.004) loss 1.2803 (1.2434) acc 68.7500 (69.7059) lr 1.9980e-03 eta 10:25:06
+epoch [3/50] batch [260/500] time 1.559 (1.580) data 0.000 (0.004) loss 0.9209 (1.2409) acc 81.2500 (69.6995) lr 1.9980e-03 eta 10:25:11
+epoch [3/50] batch [265/500] time 1.563 (1.580) data 0.000 (0.003) loss 1.1641 (1.2419) acc 62.5000 (69.6344) lr 1.9980e-03 eta 10:24:54
+epoch [3/50] batch [270/500] time 1.560 (1.579) data 0.000 (0.003) loss 1.5391 (1.2430) acc 65.6250 (69.5833) lr 1.9980e-03 eta 10:24:39
+epoch [3/50] batch [275/500] time 1.533 (1.579) data 0.000 (0.003) loss 1.3955 (1.2429) acc 65.6250 (69.5568) lr 1.9980e-03 eta 10:24:19
+epoch [3/50] batch [280/500] time 1.547 (1.579) data 0.000 (0.003) loss 0.5273 (1.2387) acc 90.6250 (69.6652) lr 1.9980e-03 eta 10:24:09
+epoch [3/50] batch [285/500] time 1.645 (1.579) data 0.000 (0.003) loss 1.4336 (1.2400) acc 59.3750 (69.6601) lr 1.9980e-03 eta 10:24:11
+epoch [3/50] batch [290/500] time 1.673 (1.581) data 0.000 (0.003) loss 1.1748 (1.2422) acc 68.7500 (69.5690) lr 1.9980e-03 eta 10:24:39
+epoch [3/50] batch [295/500] time 1.571 (1.581) data 0.000 (0.003) loss 1.1289 (1.2415) acc 71.8750 (69.5339) lr 1.9980e-03 eta 10:24:34
+epoch [3/50] batch [300/500] time 1.551 (1.580) data 0.000 (0.003) loss 0.9497 (1.2412) acc 71.8750 (69.5208) lr 1.9980e-03 eta 10:24:16
+epoch [3/50] batch [305/500] time 1.542 (1.580) data 0.000 (0.003) loss 1.5264 (1.2457) acc 62.5000 (69.4160) lr 1.9980e-03 eta 10:24:02
+epoch [3/50] batch [310/500] time 1.586 (1.580) data 0.001 (0.003) loss 0.6758 (1.2386) acc 78.1250 (69.5968) lr 1.9980e-03 eta 10:23:51
+epoch [3/50] batch [315/500] time 1.579 (1.580) data 0.001 (0.003) loss 1.9258 (1.2429) acc 50.0000 (69.5139) lr 1.9980e-03 eta 10:23:40
+epoch [3/50] batch [320/500] time 1.572 (1.580) data 0.000 (0.003) loss 1.3369 (1.2450) acc 68.7500 (69.4824) lr 1.9980e-03 eta 10:23:26
+epoch [3/50] batch [325/500] time 1.568 (1.580) data 0.001 (0.003) loss 1.1367 (1.2425) acc 75.0000 (69.5673) lr 1.9980e-03 eta 10:23:19
+epoch [3/50] batch [330/500] time 1.567 (1.579) data 0.001 (0.003) loss 1.8594 (1.2457) acc 56.2500 (69.4792) lr 1.9980e-03 eta 10:23:01
+epoch [3/50] batch [335/500] time 1.564 (1.579) data 0.000 (0.003) loss 0.7114 (1.2422) acc 78.1250 (69.5149) lr 1.9980e-03 eta 10:22:46
+epoch [3/50] batch [340/500] time 1.536 (1.579) data 0.000 (0.003) loss 0.7476 (1.2405) acc 84.3750 (69.5864) lr 1.9980e-03 eta 10:22:28
+epoch [3/50] batch [345/500] time 1.590 (1.578) data 0.000 (0.003) loss 1.3877 (1.2426) acc 68.7500 (69.5290) lr 1.9980e-03 eta 10:22:14
+epoch [3/50] batch [350/500] time 1.564 (1.578) data 0.000 (0.003) loss 1.6250 (1.2422) acc 59.3750 (69.5625) lr 1.9980e-03 eta 10:21:59
+epoch [3/50] batch [355/500] time 1.551 (1.578) data 0.001 (0.003) loss 1.1211 (1.2442) acc 59.3750 (69.4806) lr 1.9980e-03 eta 10:21:46
+epoch [3/50] batch [360/500] time 1.550 (1.578) data 0.000 (0.003) loss 1.6533 (1.2463) acc 65.6250 (69.3750) lr 1.9980e-03 eta 10:21:32
+epoch [3/50] batch [365/500] time 1.564 (1.577) data 0.000 (0.003) loss 1.1748 (1.2465) acc 68.7500 (69.3921) lr 1.9980e-03 eta 10:21:21
+epoch [3/50] batch [370/500] time 1.578 (1.577) data 0.000 (0.003) loss 1.2334 (1.2454) acc 78.1250 (69.3750) lr 1.9980e-03 eta 10:21:09
+epoch [3/50] batch [375/500] time 1.561 (1.577) data 0.000 (0.003) loss 1.1953 (1.2447) acc 68.7500 (69.4083) lr 1.9980e-03 eta 10:20:55
+epoch [3/50] batch [380/500] time 1.569 (1.577) data 0.000 (0.003) loss 1.4150 (1.2461) acc 75.0000 (69.3832) lr 1.9980e-03 eta 10:20:44
+epoch [3/50] batch [385/500] time 1.580 (1.577) data 0.000 (0.003) loss 0.7090 (1.2436) acc 81.2500 (69.4075) lr 1.9980e-03 eta 10:20:34
+epoch [3/50] batch [390/500] time 1.574 (1.577) data 0.000 (0.003) loss 1.4736 (1.2430) acc 65.6250 (69.4631) lr 1.9980e-03 eta 10:20:22
+epoch [3/50] batch [395/500] time 1.582 (1.576) data 0.000 (0.002) loss 1.4951 (1.2438) acc 75.0000 (69.5095) lr 1.9980e-03 eta 10:20:12
+epoch [3/50] batch [400/500] time 1.567 (1.577) data 0.000 (0.002) loss 1.3945 (1.2459) acc 65.6250 (69.4766) lr 1.9980e-03 eta 10:20:05
+epoch [3/50] batch [405/500] time 1.559 (1.576) data 0.000 (0.002) loss 0.8706 (1.2433) acc 78.1250 (69.5293) lr 1.9980e-03 eta 10:19:57
+epoch [3/50] batch [410/500] time 1.569 (1.576) data 0.001 (0.002) loss 1.4961 (1.2434) acc 65.6250 (69.5808) lr 1.9980e-03 eta 10:19:42
+epoch [3/50] batch [415/500] time 1.577 (1.576) data 0.000 (0.002) loss 2.0527 (1.2469) acc 53.1250 (69.4880) lr 1.9980e-03 eta 10:19:30
+epoch [3/50] batch [420/500] time 1.675 (1.576) data 0.001 (0.002) loss 1.3770 (1.2465) acc 65.6250 (69.4345) lr 1.9980e-03 eta 10:19:24
+epoch [3/50] batch [425/500] time 1.584 (1.576) data 0.000 (0.002) loss 1.4131 (1.2470) acc 68.7500 (69.4191) lr 1.9980e-03 eta 10:19:10
+epoch [3/50] batch [430/500] time 1.555 (1.576) data 0.001 (0.002) loss 1.7168 (1.2459) acc 62.5000 (69.4913) lr 1.9980e-03 eta 10:18:59
+epoch [3/50] batch [435/500] time 1.558 (1.576) data 0.000 (0.002) loss 1.4170 (1.2459) acc 56.2500 (69.4684) lr 1.9980e-03 eta 10:18:48
+epoch [3/50] batch [440/500] time 1.530 (1.575) data 0.000 (0.002) loss 1.1709 (1.2458) acc 68.7500 (69.4318) lr 1.9980e-03 eta 10:18:36
+epoch [3/50] batch [445/500] time 1.563 (1.575) data 0.001 (0.002) loss 0.8955 (1.2464) acc 78.1250 (69.4312) lr 1.9980e-03 eta 10:18:24
+epoch [3/50] batch [450/500] time 1.551 (1.575) data 0.000 (0.002) loss 0.9526 (1.2455) acc 81.2500 (69.4583) lr 1.9980e-03 eta 10:18:10
+epoch [3/50] batch [455/500] time 1.560 (1.575) data 0.000 (0.002) loss 0.9551 (1.2462) acc 75.0000 (69.4780) lr 1.9980e-03 eta 10:17:58
+epoch [3/50] batch [460/500] time 1.551 (1.575) data 0.001 (0.002) loss 0.9155 (1.2448) acc 78.1250 (69.5041) lr 1.9980e-03 eta 10:17:47
+epoch [3/50] batch [465/500] time 1.585 (1.575) data 0.001 (0.002) loss 1.2549 (1.2459) acc 68.7500 (69.5027) lr 1.9980e-03 eta 10:17:43
+epoch [3/50] batch [470/500] time 1.547 (1.575) data 0.000 (0.002) loss 1.1553 (1.2429) acc 71.8750 (69.5479) lr 1.9980e-03 eta 10:17:31
+epoch [3/50] batch [475/500] time 1.546 (1.575) data 0.001 (0.002) loss 1.4355 (1.2431) acc 68.7500 (69.5724) lr 1.9980e-03 eta 10:17:22
+epoch [3/50] batch [480/500] time 1.568 (1.574) data 0.000 (0.002) loss 1.4873 (1.2438) acc 62.5000 (69.5768) lr 1.9980e-03 eta 10:17:11
+epoch [3/50] batch [485/500] time 1.559 (1.574) data 0.001 (0.002) loss 1.0420 (1.2426) acc 71.8750 (69.5747) lr 1.9980e-03 eta 10:16:59
+epoch [3/50] batch [490/500] time 1.565 (1.574) data 0.000 (0.002) loss 1.1377 (1.2422) acc 71.8750 (69.5855) lr 1.9980e-03 eta 10:16:46
+epoch [3/50] batch [495/500] time 1.576 (1.574) data 0.000 (0.002) loss 1.2266 (1.2433) acc 65.6250 (69.5265) lr 1.9980e-03 eta 10:16:36
+epoch [3/50] batch [500/500] time 1.551 (1.574) data 0.000 (0.002) loss 1.4248 (1.2423) acc 68.7500 (69.5438) lr 1.9921e-03 eta 10:16:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,505
+* accuracy: 77.0%
+* error: 23.0%
+* macro_f1: 76.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [4/50] batch [5/500] time 1.579 (1.704) data 0.000 (0.190) loss 1.0117 (1.0842) acc 68.7500 (70.0000) lr 1.9921e-03 eta 11:07:12
+epoch [4/50] batch [10/500] time 1.570 (1.634) data 0.001 (0.095) loss 1.2666 (1.0937) acc 59.3750 (70.6250) lr 1.9921e-03 eta 10:39:46
+epoch [4/50] batch [15/500] time 1.574 (1.609) data 0.000 (0.064) loss 1.4385 (1.1573) acc 65.6250 (70.0000) lr 1.9921e-03 eta 10:29:56
+epoch [4/50] batch [20/500] time 1.568 (1.596) data 0.000 (0.048) loss 1.1191 (1.1961) acc 75.0000 (69.3750) lr 1.9921e-03 eta 10:24:22
+epoch [4/50] batch [25/500] time 1.565 (1.590) data 0.001 (0.038) loss 0.9971 (1.1626) acc 65.6250 (69.8750) lr 1.9921e-03 eta 10:22:04
+epoch [4/50] batch [30/500] time 1.555 (1.587) data 0.000 (0.032) loss 1.2852 (1.1376) acc 68.7500 (70.4167) lr 1.9921e-03 eta 10:20:44
+epoch [4/50] batch [35/500] time 1.544 (1.582) data 0.001 (0.027) loss 1.3730 (1.1701) acc 65.6250 (69.8214) lr 1.9921e-03 eta 10:18:33
+epoch [4/50] batch [40/500] time 1.566 (1.580) data 0.001 (0.024) loss 1.6885 (1.1932) acc 75.0000 (69.8438) lr 1.9921e-03 eta 10:17:58
+epoch [4/50] batch [45/500] time 1.568 (1.579) data 0.000 (0.021) loss 1.0000 (1.1524) acc 78.1250 (70.9722) lr 1.9921e-03 eta 10:17:11
+epoch [4/50] batch [50/500] time 1.561 (1.577) data 0.000 (0.019) loss 1.0430 (1.1627) acc 71.8750 (70.3125) lr 1.9921e-03 eta 10:16:25
+epoch [4/50] batch [55/500] time 1.550 (1.575) data 0.000 (0.018) loss 1.0234 (1.1496) acc 78.1250 (70.8523) lr 1.9921e-03 eta 10:15:29
+epoch [4/50] batch [60/500] time 1.564 (1.574) data 0.001 (0.016) loss 1.4580 (1.1664) acc 71.8750 (70.6771) lr 1.9921e-03 eta 10:14:57
+epoch [4/50] batch [65/500] time 1.557 (1.573) data 0.000 (0.015) loss 1.1709 (1.1770) acc 75.0000 (70.5288) lr 1.9921e-03 eta 10:14:22
+epoch [4/50] batch [70/500] time 1.571 (1.571) data 0.001 (0.014) loss 1.4639 (1.1951) acc 56.2500 (70.2232) lr 1.9921e-03 eta 10:13:38
+epoch [4/50] batch [75/500] time 1.561 (1.571) data 0.001 (0.013) loss 1.4580 (1.2051) acc 68.7500 (70.0833) lr 1.9921e-03 eta 10:13:30
+epoch [4/50] batch [80/500] time 1.565 (1.571) data 0.000 (0.012) loss 1.2051 (1.2118) acc 75.0000 (69.9609) lr 1.9921e-03 eta 10:13:05
+epoch [4/50] batch [85/500] time 1.552 (1.570) data 0.000 (0.012) loss 1.1973 (1.2102) acc 71.8750 (69.9632) lr 1.9921e-03 eta 10:12:42
+epoch [4/50] batch [90/500] time 1.565 (1.570) data 0.000 (0.011) loss 1.6445 (1.2122) acc 71.8750 (70.1042) lr 1.9921e-03 eta 10:12:32
+epoch [4/50] batch [95/500] time 1.545 (1.570) data 0.000 (0.010) loss 1.9326 (1.2112) acc 62.5000 (70.0329) lr 1.9921e-03 eta 10:12:24
+epoch [4/50] batch [100/500] time 1.535 (1.569) data 0.000 (0.010) loss 1.1172 (1.2098) acc 78.1250 (69.9688) lr 1.9921e-03 eta 10:12:00
+epoch [4/50] batch [105/500] time 1.545 (1.569) data 0.000 (0.009) loss 1.0703 (1.2214) acc 75.0000 (69.7619) lr 1.9921e-03 eta 10:11:41
+epoch [4/50] batch [110/500] time 1.562 (1.568) data 0.000 (0.009) loss 1.5508 (1.2382) acc 65.6250 (69.3750) lr 1.9921e-03 eta 10:11:19
+epoch [4/50] batch [115/500] time 1.562 (1.568) data 0.000 (0.009) loss 1.5566 (1.2333) acc 59.3750 (69.4293) lr 1.9921e-03 eta 10:11:01
+epoch [4/50] batch [120/500] time 1.556 (1.567) data 0.001 (0.008) loss 1.4326 (1.2462) acc 68.7500 (69.3490) lr 1.9921e-03 eta 10:10:45
+epoch [4/50] batch [125/500] time 1.569 (1.567) data 0.000 (0.008) loss 1.2109 (1.2422) acc 71.8750 (69.3500) lr 1.9921e-03 eta 10:10:38
+epoch [4/50] batch [130/500] time 1.566 (1.567) data 0.000 (0.008) loss 0.9985 (1.2418) acc 81.2500 (69.5673) lr 1.9921e-03 eta 10:10:22
+epoch [4/50] batch [135/500] time 1.549 (1.567) data 0.000 (0.007) loss 0.7749 (1.2329) acc 84.3750 (69.7222) lr 1.9921e-03 eta 10:10:04
+epoch [4/50] batch [140/500] time 1.560 (1.567) data 0.000 (0.007) loss 0.9810 (1.2294) acc 71.8750 (69.7991) lr 1.9921e-03 eta 10:09:54
+epoch [4/50] batch [145/500] time 1.558 (1.566) data 0.000 (0.007) loss 0.9033 (1.2265) acc 75.0000 (69.6767) lr 1.9921e-03 eta 10:09:37
+epoch [4/50] batch [150/500] time 1.595 (1.566) data 0.000 (0.007) loss 1.0654 (1.2325) acc 68.7500 (69.5208) lr 1.9921e-03 eta 10:09:29
+epoch [4/50] batch [155/500] time 1.561 (1.566) data 0.000 (0.007) loss 1.7422 (1.2398) acc 65.6250 (69.3952) lr 1.9921e-03 eta 10:09:16
+epoch [4/50] batch [160/500] time 1.572 (1.566) data 0.000 (0.006) loss 1.3193 (1.2409) acc 71.8750 (69.3555) lr 1.9921e-03 eta 10:09:09
+epoch [4/50] batch [165/500] time 1.588 (1.566) data 0.001 (0.006) loss 1.0908 (1.2354) acc 65.6250 (69.4129) lr 1.9921e-03 eta 10:09:05
+epoch [4/50] batch [170/500] time 1.577 (1.566) data 0.001 (0.006) loss 1.6221 (1.2378) acc 62.5000 (69.4853) lr 1.9921e-03 eta 10:08:54
+epoch [4/50] batch [175/500] time 1.587 (1.566) data 0.000 (0.006) loss 0.6177 (1.2329) acc 81.2500 (69.5000) lr 1.9921e-03 eta 10:08:52
+epoch [4/50] batch [180/500] time 1.551 (1.566) data 0.000 (0.006) loss 1.5752 (1.2312) acc 65.6250 (69.4618) lr 1.9921e-03 eta 10:08:46
+epoch [4/50] batch [185/500] time 1.584 (1.567) data 0.000 (0.006) loss 1.7217 (1.2269) acc 62.5000 (69.6284) lr 1.9921e-03 eta 10:08:45
+epoch [4/50] batch [190/500] time 1.597 (1.567) data 0.000 (0.005) loss 1.2354 (1.2381) acc 62.5000 (69.5066) lr 1.9921e-03 eta 10:08:38
+epoch [4/50] batch [195/500] time 1.561 (1.567) data 0.001 (0.005) loss 1.0664 (1.2369) acc 78.1250 (69.5994) lr 1.9921e-03 eta 10:08:44
+epoch [4/50] batch [200/500] time 1.562 (1.567) data 0.000 (0.005) loss 0.9712 (1.2392) acc 65.6250 (69.5625) lr 1.9921e-03 eta 10:08:33
+epoch [4/50] batch [205/500] time 1.562 (1.567) data 0.000 (0.005) loss 1.1055 (1.2396) acc 65.6250 (69.6189) lr 1.9921e-03 eta 10:08:14
+epoch [4/50] batch [210/500] time 1.581 (1.566) data 0.000 (0.005) loss 1.1250 (1.2449) acc 68.7500 (69.3750) lr 1.9921e-03 eta 10:07:59
+epoch [4/50] batch [215/500] time 1.568 (1.566) data 0.000 (0.005) loss 1.5010 (1.2452) acc 59.3750 (69.3750) lr 1.9921e-03 eta 10:07:49
+epoch [4/50] batch [220/500] time 1.567 (1.566) data 0.000 (0.005) loss 1.8496 (1.2496) acc 50.0000 (69.2045) lr 1.9921e-03 eta 10:07:41
+epoch [4/50] batch [225/500] time 1.564 (1.566) data 0.000 (0.005) loss 0.8960 (1.2456) acc 71.8750 (69.1667) lr 1.9921e-03 eta 10:07:37
+epoch [4/50] batch [230/500] time 1.570 (1.566) data 0.001 (0.005) loss 1.9600 (1.2480) acc 62.5000 (69.1712) lr 1.9921e-03 eta 10:07:23
+epoch [4/50] batch [235/500] time 1.566 (1.566) data 0.000 (0.004) loss 1.4814 (1.2479) acc 62.5000 (69.1755) lr 1.9921e-03 eta 10:07:05
+epoch [4/50] batch [240/500] time 1.566 (1.566) data 0.000 (0.004) loss 1.2100 (1.2486) acc 68.7500 (69.1536) lr 1.9921e-03 eta 10:06:59
+epoch [4/50] batch [245/500] time 1.532 (1.565) data 0.000 (0.004) loss 1.2988 (1.2456) acc 71.8750 (69.2857) lr 1.9921e-03 eta 10:06:42
+epoch [4/50] batch [250/500] time 1.567 (1.565) data 0.000 (0.004) loss 1.3262 (1.2420) acc 65.6250 (69.3375) lr 1.9921e-03 eta 10:06:31
+epoch [4/50] batch [255/500] time 1.547 (1.565) data 0.000 (0.004) loss 1.1543 (1.2448) acc 78.1250 (69.3137) lr 1.9921e-03 eta 10:06:21
+epoch [4/50] batch [260/500] time 1.550 (1.565) data 0.000 (0.004) loss 1.4512 (1.2440) acc 68.7500 (69.3269) lr 1.9921e-03 eta 10:06:07
+epoch [4/50] batch [265/500] time 1.565 (1.565) data 0.000 (0.004) loss 2.0156 (1.2491) acc 65.6250 (69.2453) lr 1.9921e-03 eta 10:05:55
+epoch [4/50] batch [270/500] time 1.564 (1.565) data 0.000 (0.004) loss 0.8750 (1.2496) acc 65.6250 (69.1551) lr 1.9921e-03 eta 10:05:46
+epoch [4/50] batch [275/500] time 1.563 (1.565) data 0.000 (0.004) loss 1.8525 (1.2486) acc 46.8750 (69.0909) lr 1.9921e-03 eta 10:05:40
+epoch [4/50] batch [280/500] time 1.556 (1.565) data 0.000 (0.004) loss 1.4209 (1.2503) acc 59.3750 (69.1071) lr 1.9921e-03 eta 10:05:32
+epoch [4/50] batch [285/500] time 1.539 (1.565) data 0.000 (0.004) loss 1.7041 (1.2529) acc 62.5000 (69.0570) lr 1.9921e-03 eta 10:05:20
+epoch [4/50] batch [290/500] time 1.562 (1.565) data 0.000 (0.004) loss 1.1855 (1.2579) acc 68.7500 (69.0517) lr 1.9921e-03 eta 10:05:14
+epoch [4/50] batch [295/500] time 1.559 (1.565) data 0.001 (0.004) loss 1.2637 (1.2580) acc 68.7500 (69.0360) lr 1.9921e-03 eta 10:05:07
+epoch [4/50] batch [300/500] time 1.548 (1.564) data 0.000 (0.004) loss 1.1660 (1.2598) acc 71.8750 (69.0208) lr 1.9921e-03 eta 10:04:55
+epoch [4/50] batch [305/500] time 1.542 (1.564) data 0.000 (0.004) loss 1.2051 (1.2588) acc 68.7500 (69.0369) lr 1.9921e-03 eta 10:04:39
+epoch [4/50] batch [310/500] time 1.558 (1.564) data 0.000 (0.003) loss 1.7178 (1.2602) acc 65.6250 (69.0524) lr 1.9921e-03 eta 10:04:27
+epoch [4/50] batch [315/500] time 1.563 (1.564) data 0.000 (0.003) loss 1.4180 (1.2618) acc 62.5000 (69.0079) lr 1.9921e-03 eta 10:04:17
+epoch [4/50] batch [320/500] time 1.580 (1.564) data 0.000 (0.003) loss 0.6138 (1.2596) acc 84.3750 (69.0625) lr 1.9921e-03 eta 10:04:09
+epoch [4/50] batch [325/500] time 1.534 (1.564) data 0.000 (0.003) loss 0.8281 (1.2554) acc 78.1250 (69.0865) lr 1.9921e-03 eta 10:03:58
+epoch [4/50] batch [330/500] time 1.536 (1.563) data 0.000 (0.003) loss 1.7598 (1.2550) acc 68.7500 (69.1098) lr 1.9921e-03 eta 10:03:46
+epoch [4/50] batch [335/500] time 1.571 (1.563) data 0.000 (0.003) loss 1.1611 (1.2548) acc 68.7500 (69.0951) lr 1.9921e-03 eta 10:03:37
+epoch [4/50] batch [340/500] time 1.554 (1.564) data 0.000 (0.003) loss 1.7158 (1.2602) acc 65.6250 (69.0257) lr 1.9921e-03 eta 10:03:31
+epoch [4/50] batch [345/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.6377 (1.2625) acc 50.0000 (68.9130) lr 1.9921e-03 eta 10:03:21
+epoch [4/50] batch [350/500] time 1.561 (1.563) data 0.000 (0.003) loss 1.1768 (1.2657) acc 68.7500 (68.8304) lr 1.9921e-03 eta 10:03:13
+epoch [4/50] batch [355/500] time 1.573 (1.564) data 0.000 (0.003) loss 1.1328 (1.2642) acc 71.8750 (68.8644) lr 1.9921e-03 eta 10:03:07
+epoch [4/50] batch [360/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.5342 (1.2629) acc 68.7500 (68.8628) lr 1.9921e-03 eta 10:03:02
+epoch [4/50] batch [365/500] time 1.557 (1.564) data 0.000 (0.003) loss 0.9419 (1.2591) acc 75.0000 (68.9298) lr 1.9921e-03 eta 10:02:53
+epoch [4/50] batch [370/500] time 1.571 (1.564) data 0.000 (0.003) loss 1.5430 (1.2614) acc 68.7500 (68.9527) lr 1.9921e-03 eta 10:02:45
+epoch [4/50] batch [375/500] time 1.559 (1.564) data 0.000 (0.003) loss 1.5049 (1.2603) acc 68.7500 (68.9917) lr 1.9921e-03 eta 10:02:36
+epoch [4/50] batch [380/500] time 1.658 (1.564) data 0.000 (0.003) loss 1.0547 (1.2558) acc 78.1250 (69.1447) lr 1.9921e-03 eta 10:02:36
+epoch [4/50] batch [385/500] time 1.567 (1.564) data 0.000 (0.003) loss 1.1650 (1.2533) acc 71.8750 (69.2127) lr 1.9921e-03 eta 10:02:28
+epoch [4/50] batch [390/500] time 1.575 (1.564) data 0.000 (0.003) loss 0.7842 (1.2510) acc 81.2500 (69.2548) lr 1.9921e-03 eta 10:02:21
+epoch [4/50] batch [395/500] time 1.542 (1.564) data 0.000 (0.003) loss 0.6729 (1.2513) acc 84.3750 (69.2722) lr 1.9921e-03 eta 10:02:12
+epoch [4/50] batch [400/500] time 1.549 (1.564) data 0.000 (0.003) loss 1.5723 (1.2535) acc 65.6250 (69.2266) lr 1.9921e-03 eta 10:02:01
+epoch [4/50] batch [405/500] time 1.556 (1.564) data 0.001 (0.003) loss 1.0869 (1.2527) acc 78.1250 (69.2284) lr 1.9921e-03 eta 10:01:55
+epoch [4/50] batch [410/500] time 1.545 (1.564) data 0.001 (0.003) loss 0.9116 (1.2521) acc 75.0000 (69.1845) lr 1.9921e-03 eta 10:01:44
+epoch [4/50] batch [415/500] time 1.569 (1.564) data 0.000 (0.003) loss 1.0850 (1.2500) acc 71.8750 (69.2470) lr 1.9921e-03 eta 10:01:37
+epoch [4/50] batch [420/500] time 1.566 (1.564) data 0.000 (0.003) loss 1.1279 (1.2513) acc 65.6250 (69.2113) lr 1.9921e-03 eta 10:01:29
+epoch [4/50] batch [425/500] time 1.578 (1.564) data 0.000 (0.003) loss 1.4443 (1.2499) acc 62.5000 (69.2647) lr 1.9921e-03 eta 10:01:21
+epoch [4/50] batch [430/500] time 1.553 (1.564) data 0.000 (0.003) loss 1.2422 (1.2486) acc 78.1250 (69.2805) lr 1.9921e-03 eta 10:01:12
+epoch [4/50] batch [435/500] time 1.552 (1.563) data 0.001 (0.003) loss 1.2695 (1.2494) acc 62.5000 (69.2601) lr 1.9921e-03 eta 10:01:01
+epoch [4/50] batch [440/500] time 1.526 (1.563) data 0.000 (0.003) loss 1.3135 (1.2484) acc 71.8750 (69.2756) lr 1.9921e-03 eta 10:00:47
+epoch [4/50] batch [445/500] time 1.542 (1.563) data 0.000 (0.003) loss 1.0078 (1.2468) acc 62.5000 (69.3048) lr 1.9921e-03 eta 10:00:39
+epoch [4/50] batch [450/500] time 1.571 (1.563) data 0.000 (0.003) loss 1.3154 (1.2476) acc 62.5000 (69.2917) lr 1.9921e-03 eta 10:00:31
+epoch [4/50] batch [455/500] time 1.545 (1.563) data 0.000 (0.002) loss 1.8447 (1.2491) acc 65.6250 (69.2514) lr 1.9921e-03 eta 10:00:24
+epoch [4/50] batch [460/500] time 1.571 (1.563) data 0.000 (0.002) loss 2.0859 (1.2496) acc 59.3750 (69.2323) lr 1.9921e-03 eta 10:00:15
+epoch [4/50] batch [465/500] time 1.544 (1.563) data 0.000 (0.002) loss 1.3457 (1.2475) acc 62.5000 (69.2608) lr 1.9921e-03 eta 10:00:05
+epoch [4/50] batch [470/500] time 1.591 (1.563) data 0.000 (0.002) loss 1.3936 (1.2486) acc 62.5000 (69.2553) lr 1.9921e-03 eta 9:59:59
+epoch [4/50] batch [475/500] time 1.576 (1.563) data 0.000 (0.002) loss 0.9556 (1.2460) acc 75.0000 (69.3026) lr 1.9921e-03 eta 9:59:51
+epoch [4/50] batch [480/500] time 1.602 (1.563) data 0.000 (0.002) loss 0.5884 (1.2435) acc 78.1250 (69.3490) lr 1.9921e-03 eta 9:59:50
+epoch [4/50] batch [485/500] time 1.554 (1.564) data 0.001 (0.002) loss 0.9624 (1.2406) acc 75.0000 (69.4137) lr 1.9921e-03 eta 9:59:44
+epoch [4/50] batch [490/500] time 1.567 (1.563) data 0.000 (0.002) loss 0.9971 (1.2381) acc 81.2500 (69.4770) lr 1.9921e-03 eta 9:59:35
+epoch [4/50] batch [495/500] time 1.544 (1.563) data 0.001 (0.002) loss 1.2695 (1.2374) acc 71.8750 (69.4760) lr 1.9921e-03 eta 9:59:23
+epoch [4/50] batch [500/500] time 1.541 (1.563) data 0.000 (0.002) loss 0.9971 (1.2377) acc 71.8750 (69.4813) lr 1.9823e-03 eta 9:59:13
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,742
+* accuracy: 77.5%
+* error: 22.5%
+* macro_f1: 76.8%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [5/50] batch [5/500] time 1.559 (1.647) data 0.001 (0.148) loss 0.9780 (1.2434) acc 78.1250 (68.1250) lr 1.9823e-03 eta 10:31:12
+epoch [5/50] batch [10/500] time 1.575 (1.606) data 0.001 (0.074) loss 1.3682 (1.2698) acc 68.7500 (67.8125) lr 1.9823e-03 eta 10:15:25
+epoch [5/50] batch [15/500] time 1.562 (1.592) data 0.000 (0.050) loss 1.2891 (1.2788) acc 71.8750 (68.5417) lr 1.9823e-03 eta 10:10:01
+epoch [5/50] batch [20/500] time 1.548 (1.586) data 0.000 (0.037) loss 1.2607 (1.2253) acc 71.8750 (69.2188) lr 1.9823e-03 eta 10:07:18
+epoch [5/50] batch [25/500] time 1.557 (1.581) data 0.001 (0.030) loss 0.7227 (1.1727) acc 81.2500 (70.5000) lr 1.9823e-03 eta 10:05:29
+epoch [5/50] batch [30/500] time 1.561 (1.582) data 0.000 (0.025) loss 0.6631 (1.1491) acc 84.3750 (71.4583) lr 1.9823e-03 eta 10:05:47
+epoch [5/50] batch [35/500] time 1.547 (1.578) data 0.000 (0.022) loss 1.0410 (1.1458) acc 75.0000 (71.3393) lr 1.9823e-03 eta 10:04:04
+epoch [5/50] batch [40/500] time 1.574 (1.577) data 0.001 (0.019) loss 2.0527 (1.1837) acc 68.7500 (71.0938) lr 1.9823e-03 eta 10:03:22
+epoch [5/50] batch [45/500] time 1.542 (1.575) data 0.000 (0.017) loss 1.1201 (1.1862) acc 81.2500 (71.0417) lr 1.9823e-03 eta 10:02:23
+epoch [5/50] batch [50/500] time 1.560 (1.573) data 0.000 (0.015) loss 1.5674 (1.1940) acc 68.7500 (70.9375) lr 1.9823e-03 eta 10:01:36
+epoch [5/50] batch [55/500] time 1.553 (1.571) data 0.000 (0.014) loss 1.1074 (1.2061) acc 78.1250 (70.3977) lr 1.9823e-03 eta 10:00:45
+epoch [5/50] batch [60/500] time 1.565 (1.570) data 0.000 (0.013) loss 1.1904 (1.2185) acc 65.6250 (69.8438) lr 1.9823e-03 eta 10:00:23
+epoch [5/50] batch [65/500] time 1.548 (1.569) data 0.000 (0.012) loss 1.4883 (1.2387) acc 62.5000 (69.4712) lr 1.9823e-03 eta 9:59:51
+epoch [5/50] batch [70/500] time 1.556 (1.568) data 0.000 (0.011) loss 1.1182 (1.2517) acc 50.0000 (68.6607) lr 1.9823e-03 eta 9:59:08
+epoch [5/50] batch [75/500] time 1.574 (1.568) data 0.001 (0.010) loss 1.4619 (1.2601) acc 68.7500 (68.6250) lr 1.9823e-03 eta 9:59:10
+epoch [5/50] batch [80/500] time 1.576 (1.568) data 0.001 (0.010) loss 0.7012 (1.2414) acc 84.3750 (69.0234) lr 1.9823e-03 eta 9:59:07
+epoch [5/50] batch [85/500] time 1.544 (1.567) data 0.000 (0.009) loss 1.0088 (1.2397) acc 68.7500 (69.0441) lr 1.9823e-03 eta 9:58:34
+epoch [5/50] batch [90/500] time 1.558 (1.567) data 0.000 (0.009) loss 1.6299 (1.2374) acc 59.3750 (69.1319) lr 1.9823e-03 eta 9:58:26
+epoch [5/50] batch [95/500] time 1.543 (1.567) data 0.000 (0.008) loss 1.2256 (1.2449) acc 78.1250 (69.2763) lr 1.9823e-03 eta 9:58:17
+epoch [5/50] batch [100/500] time 1.582 (1.567) data 0.000 (0.008) loss 1.4834 (1.2443) acc 65.6250 (69.4062) lr 1.9823e-03 eta 9:58:12
+epoch [5/50] batch [105/500] time 1.560 (1.567) data 0.000 (0.007) loss 1.1270 (1.2385) acc 71.8750 (69.4940) lr 1.9823e-03 eta 9:57:55
+epoch [5/50] batch [110/500] time 1.543 (1.566) data 0.000 (0.007) loss 1.3105 (1.2316) acc 62.5000 (69.6591) lr 1.9823e-03 eta 9:57:33
+epoch [5/50] batch [115/500] time 1.574 (1.566) data 0.000 (0.007) loss 0.8174 (1.2217) acc 78.1250 (70.0815) lr 1.9823e-03 eta 9:57:17
+epoch [5/50] batch [120/500] time 1.573 (1.566) data 0.000 (0.007) loss 1.5967 (1.2267) acc 65.6250 (69.9479) lr 1.9823e-03 eta 9:57:11
+epoch [5/50] batch [125/500] time 1.659 (1.567) data 0.000 (0.006) loss 0.5195 (1.2201) acc 84.3750 (69.9750) lr 1.9823e-03 eta 9:57:24
+epoch [5/50] batch [130/500] time 1.562 (1.567) data 0.000 (0.006) loss 1.9971 (1.2283) acc 53.1250 (69.6875) lr 1.9823e-03 eta 9:57:10
+epoch [5/50] batch [135/500] time 1.578 (1.567) data 0.000 (0.006) loss 0.8267 (1.2271) acc 81.2500 (69.7454) lr 1.9823e-03 eta 9:57:08
+epoch [5/50] batch [140/500] time 1.573 (1.567) data 0.000 (0.006) loss 1.1572 (1.2247) acc 71.8750 (69.7991) lr 1.9823e-03 eta 9:57:04
+epoch [5/50] batch [145/500] time 1.574 (1.567) data 0.000 (0.006) loss 0.8613 (1.2155) acc 71.8750 (69.9569) lr 1.9823e-03 eta 9:57:04
+epoch [5/50] batch [150/500] time 1.562 (1.567) data 0.000 (0.005) loss 0.5791 (1.2093) acc 87.5000 (70.0208) lr 1.9823e-03 eta 9:56:56
+epoch [5/50] batch [155/500] time 1.558 (1.567) data 0.000 (0.005) loss 1.7275 (1.2122) acc 56.2500 (70.0806) lr 1.9823e-03 eta 9:56:41
+epoch [5/50] batch [160/500] time 1.579 (1.567) data 0.000 (0.005) loss 1.1113 (1.2098) acc 62.5000 (70.0000) lr 1.9823e-03 eta 9:56:26
+epoch [5/50] batch [165/500] time 1.582 (1.567) data 0.000 (0.005) loss 1.0322 (1.2088) acc 78.1250 (70.0758) lr 1.9823e-03 eta 9:56:23
+epoch [5/50] batch [170/500] time 1.570 (1.568) data 0.000 (0.005) loss 1.1416 (1.2049) acc 59.3750 (70.0735) lr 1.9823e-03 eta 9:56:29
+epoch [5/50] batch [175/500] time 1.579 (1.567) data 0.000 (0.005) loss 1.4033 (1.2039) acc 65.6250 (70.0536) lr 1.9823e-03 eta 9:56:17
+epoch [5/50] batch [180/500] time 1.564 (1.567) data 0.000 (0.005) loss 1.1465 (1.2006) acc 75.0000 (70.0694) lr 1.9823e-03 eta 9:56:05
+epoch [5/50] batch [185/500] time 1.567 (1.567) data 0.000 (0.004) loss 1.0615 (1.1945) acc 75.0000 (70.2534) lr 1.9823e-03 eta 9:55:56
+epoch [5/50] batch [190/500] time 1.556 (1.567) data 0.000 (0.004) loss 1.2832 (1.2018) acc 71.8750 (70.0658) lr 1.9823e-03 eta 9:55:45
+epoch [5/50] batch [195/500] time 1.557 (1.567) data 0.000 (0.004) loss 0.9360 (1.2083) acc 81.2500 (70.1122) lr 1.9823e-03 eta 9:55:38
+epoch [5/50] batch [200/500] time 1.549 (1.567) data 0.000 (0.004) loss 1.1768 (1.2101) acc 75.0000 (70.0469) lr 1.9823e-03 eta 9:55:25
+epoch [5/50] batch [205/500] time 1.557 (1.567) data 0.000 (0.004) loss 2.2168 (1.2166) acc 56.2500 (69.9390) lr 1.9823e-03 eta 9:55:13
+epoch [5/50] batch [210/500] time 1.560 (1.566) data 0.000 (0.004) loss 1.2607 (1.2198) acc 65.6250 (69.8363) lr 1.9823e-03 eta 9:54:58
+epoch [5/50] batch [215/500] time 1.570 (1.567) data 0.000 (0.004) loss 0.6343 (1.2169) acc 84.3750 (69.9419) lr 1.9823e-03 eta 9:54:55
+epoch [5/50] batch [220/500] time 1.561 (1.567) data 0.000 (0.004) loss 1.3848 (1.2217) acc 78.1250 (69.8864) lr 1.9823e-03 eta 9:54:47
+epoch [5/50] batch [225/500] time 1.590 (1.567) data 0.001 (0.004) loss 0.8379 (1.2153) acc 81.2500 (70.0278) lr 1.9823e-03 eta 9:54:41
+epoch [5/50] batch [230/500] time 1.563 (1.566) data 0.000 (0.004) loss 0.9448 (1.2136) acc 78.1250 (70.1223) lr 1.9823e-03 eta 9:54:28
+epoch [5/50] batch [235/500] time 1.555 (1.566) data 0.000 (0.004) loss 1.3779 (1.2107) acc 65.6250 (70.1064) lr 1.9823e-03 eta 9:54:11
+epoch [5/50] batch [240/500] time 1.602 (1.566) data 0.000 (0.004) loss 1.7461 (1.2081) acc 62.5000 (70.1172) lr 1.9823e-03 eta 9:54:02
+epoch [5/50] batch [245/500] time 1.558 (1.566) data 0.000 (0.003) loss 1.2070 (1.2149) acc 71.8750 (70.0510) lr 1.9823e-03 eta 9:53:53
+epoch [5/50] batch [250/500] time 1.590 (1.566) data 0.000 (0.003) loss 1.2881 (1.2182) acc 62.5000 (69.9750) lr 1.9823e-03 eta 9:53:50
+epoch [5/50] batch [255/500] time 1.560 (1.566) data 0.000 (0.003) loss 1.0889 (1.2152) acc 71.8750 (70.0000) lr 1.9823e-03 eta 9:53:41
+epoch [5/50] batch [260/500] time 1.575 (1.566) data 0.000 (0.003) loss 1.3086 (1.2186) acc 71.8750 (70.0120) lr 1.9823e-03 eta 9:53:30
+epoch [5/50] batch [265/500] time 1.558 (1.566) data 0.001 (0.003) loss 0.8726 (1.2148) acc 81.2500 (70.1061) lr 1.9823e-03 eta 9:53:24
+epoch [5/50] batch [270/500] time 1.556 (1.566) data 0.000 (0.003) loss 1.2109 (1.2180) acc 68.7500 (70.1389) lr 1.9823e-03 eta 9:53:21
+epoch [5/50] batch [275/500] time 1.552 (1.566) data 0.000 (0.003) loss 1.2227 (1.2146) acc 62.5000 (70.1932) lr 1.9823e-03 eta 9:53:11
+epoch [5/50] batch [280/500] time 1.549 (1.566) data 0.000 (0.003) loss 1.1543 (1.2136) acc 65.6250 (70.1786) lr 1.9823e-03 eta 9:52:58
+epoch [5/50] batch [285/500] time 1.557 (1.566) data 0.000 (0.003) loss 0.9629 (1.2156) acc 65.6250 (70.1535) lr 1.9823e-03 eta 9:52:51
+epoch [5/50] batch [290/500] time 1.544 (1.566) data 0.000 (0.003) loss 1.2158 (1.2116) acc 71.8750 (70.1832) lr 1.9823e-03 eta 9:52:38
+epoch [5/50] batch [295/500] time 1.562 (1.566) data 0.000 (0.003) loss 0.7954 (1.2078) acc 84.3750 (70.2860) lr 1.9823e-03 eta 9:52:26
+epoch [5/50] batch [300/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.0654 (1.2073) acc 68.7500 (70.3125) lr 1.9823e-03 eta 9:52:16
+epoch [5/50] batch [305/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.1387 (1.2023) acc 71.8750 (70.4098) lr 1.9823e-03 eta 9:52:04
+epoch [5/50] batch [310/500] time 1.551 (1.565) data 0.000 (0.003) loss 1.2041 (1.2025) acc 71.8750 (70.3528) lr 1.9823e-03 eta 9:51:53
+epoch [5/50] batch [315/500] time 1.534 (1.566) data 0.001 (0.003) loss 0.8281 (1.1986) acc 78.1250 (70.4365) lr 1.9823e-03 eta 9:51:54
+epoch [5/50] batch [320/500] time 1.546 (1.565) data 0.000 (0.003) loss 0.7910 (1.1967) acc 81.2500 (70.4883) lr 1.9823e-03 eta 9:51:43
+epoch [5/50] batch [325/500] time 1.540 (1.565) data 0.000 (0.003) loss 1.7041 (1.1961) acc 53.1250 (70.5000) lr 1.9823e-03 eta 9:51:29
+epoch [5/50] batch [330/500] time 1.572 (1.565) data 0.000 (0.003) loss 1.5176 (1.1947) acc 68.7500 (70.5492) lr 1.9823e-03 eta 9:51:25
+epoch [5/50] batch [335/500] time 1.551 (1.565) data 0.000 (0.003) loss 1.9004 (1.1977) acc 59.3750 (70.5224) lr 1.9823e-03 eta 9:51:17
+epoch [5/50] batch [340/500] time 1.559 (1.565) data 0.001 (0.003) loss 1.3467 (1.2001) acc 65.6250 (70.4228) lr 1.9823e-03 eta 9:51:06
+epoch [5/50] batch [345/500] time 1.563 (1.565) data 0.000 (0.003) loss 1.0195 (1.2019) acc 81.2500 (70.4076) lr 1.9823e-03 eta 9:50:59
+epoch [5/50] batch [350/500] time 1.549 (1.565) data 0.000 (0.003) loss 1.4463 (1.1999) acc 75.0000 (70.4821) lr 1.9823e-03 eta 9:50:48
+epoch [5/50] batch [355/500] time 1.588 (1.565) data 0.000 (0.002) loss 0.9028 (1.1983) acc 71.8750 (70.4754) lr 1.9823e-03 eta 9:50:43
+epoch [5/50] batch [360/500] time 1.577 (1.565) data 0.001 (0.002) loss 1.8008 (1.2005) acc 53.1250 (70.3906) lr 1.9823e-03 eta 9:50:37
+epoch [5/50] batch [365/500] time 1.572 (1.565) data 0.000 (0.002) loss 0.9731 (1.1967) acc 75.0000 (70.4966) lr 1.9823e-03 eta 9:50:32
+epoch [5/50] batch [370/500] time 1.559 (1.565) data 0.000 (0.002) loss 0.9214 (1.1966) acc 81.2500 (70.4983) lr 1.9823e-03 eta 9:50:25
+epoch [5/50] batch [375/500] time 1.558 (1.565) data 0.001 (0.002) loss 1.2393 (1.1972) acc 68.7500 (70.5250) lr 1.9823e-03 eta 9:50:16
+epoch [5/50] batch [380/500] time 1.585 (1.565) data 0.000 (0.002) loss 1.2891 (1.2005) acc 56.2500 (70.4112) lr 1.9823e-03 eta 9:50:09
+epoch [5/50] batch [385/500] time 1.595 (1.566) data 0.000 (0.002) loss 1.0703 (1.2003) acc 78.1250 (70.4302) lr 1.9823e-03 eta 9:50:04
+epoch [5/50] batch [390/500] time 1.566 (1.566) data 0.001 (0.002) loss 1.1191 (1.2011) acc 62.5000 (70.4247) lr 1.9823e-03 eta 9:49:59
+epoch [5/50] batch [395/500] time 1.580 (1.566) data 0.000 (0.002) loss 0.5508 (1.1987) acc 84.3750 (70.4589) lr 1.9823e-03 eta 9:49:58
+epoch [5/50] batch [400/500] time 1.548 (1.566) data 0.000 (0.002) loss 0.8955 (1.1995) acc 75.0000 (70.3906) lr 1.9823e-03 eta 9:49:46
+epoch [5/50] batch [405/500] time 1.569 (1.566) data 0.001 (0.002) loss 1.6846 (1.2019) acc 59.3750 (70.3318) lr 1.9823e-03 eta 9:49:37
+epoch [5/50] batch [410/500] time 1.561 (1.566) data 0.000 (0.002) loss 0.8418 (1.2021) acc 78.1250 (70.3582) lr 1.9823e-03 eta 9:49:26
+epoch [5/50] batch [415/500] time 1.554 (1.566) data 0.000 (0.002) loss 0.7163 (1.2005) acc 75.0000 (70.3916) lr 1.9823e-03 eta 9:49:29
+epoch [5/50] batch [420/500] time 1.542 (1.566) data 0.000 (0.002) loss 1.3398 (1.2012) acc 59.3750 (70.3795) lr 1.9823e-03 eta 9:49:17
+epoch [5/50] batch [425/500] time 1.573 (1.566) data 0.000 (0.002) loss 0.7524 (1.2015) acc 65.6250 (70.3603) lr 1.9823e-03 eta 9:49:09
+epoch [5/50] batch [430/500] time 1.548 (1.566) data 0.000 (0.002) loss 1.2852 (1.2044) acc 62.5000 (70.2834) lr 1.9823e-03 eta 9:49:02
+epoch [5/50] batch [435/500] time 1.562 (1.566) data 0.000 (0.002) loss 0.8398 (1.2028) acc 78.1250 (70.3017) lr 1.9823e-03 eta 9:48:50
+epoch [5/50] batch [440/500] time 1.568 (1.566) data 0.000 (0.002) loss 1.2236 (1.2037) acc 68.7500 (70.2770) lr 1.9823e-03 eta 9:48:43
+epoch [5/50] batch [445/500] time 1.581 (1.566) data 0.001 (0.002) loss 0.9819 (1.2003) acc 78.1250 (70.3581) lr 1.9823e-03 eta 9:48:35
+epoch [5/50] batch [450/500] time 1.557 (1.566) data 0.000 (0.002) loss 1.1445 (1.1989) acc 68.7500 (70.3750) lr 1.9823e-03 eta 9:48:28
+epoch [5/50] batch [455/500] time 1.662 (1.566) data 0.001 (0.002) loss 1.3564 (1.1982) acc 65.6250 (70.4190) lr 1.9823e-03 eta 9:48:25
+epoch [5/50] batch [460/500] time 1.559 (1.566) data 0.000 (0.002) loss 0.5566 (1.1991) acc 84.3750 (70.4008) lr 1.9823e-03 eta 9:48:17
+epoch [5/50] batch [465/500] time 1.558 (1.566) data 0.001 (0.002) loss 1.0459 (1.1997) acc 78.1250 (70.3965) lr 1.9823e-03 eta 9:48:09
+epoch [5/50] batch [470/500] time 1.576 (1.566) data 0.000 (0.002) loss 0.9663 (1.1989) acc 71.8750 (70.3923) lr 1.9823e-03 eta 9:48:02
+epoch [5/50] batch [475/500] time 1.555 (1.566) data 0.000 (0.002) loss 1.6660 (1.2013) acc 59.3750 (70.3355) lr 1.9823e-03 eta 9:47:52
+epoch [5/50] batch [480/500] time 1.551 (1.566) data 0.000 (0.002) loss 0.8594 (1.2000) acc 75.0000 (70.3255) lr 1.9823e-03 eta 9:47:42
+epoch [5/50] batch [485/500] time 1.585 (1.566) data 0.001 (0.002) loss 1.4746 (1.1987) acc 62.5000 (70.3415) lr 1.9823e-03 eta 9:47:36
+epoch [5/50] batch [490/500] time 1.548 (1.566) data 0.000 (0.002) loss 1.5742 (1.1972) acc 62.5000 (70.3890) lr 1.9823e-03 eta 9:47:27
+epoch [5/50] batch [495/500] time 1.564 (1.566) data 0.000 (0.002) loss 1.3584 (1.1981) acc 59.3750 (70.3409) lr 1.9823e-03 eta 9:47:20
+epoch [5/50] batch [500/500] time 1.570 (1.566) data 0.000 (0.002) loss 0.4722 (1.1967) acc 93.7500 (70.3937) lr 1.9686e-03 eta 9:47:09
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,806
+* accuracy: 77.6%
+* error: 22.4%
+* macro_f1: 77.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [6/50] batch [5/500] time 1.554 (1.704) data 0.001 (0.164) loss 0.9009 (1.1271) acc 75.0000 (70.0000) lr 1.9686e-03 eta 10:38:55
+epoch [6/50] batch [10/500] time 1.578 (1.627) data 0.000 (0.082) loss 0.9814 (1.1522) acc 65.6250 (70.3125) lr 1.9686e-03 eta 10:10:01
+epoch [6/50] batch [15/500] time 1.562 (1.606) data 0.001 (0.055) loss 1.1875 (1.1706) acc 59.3750 (70.0000) lr 1.9686e-03 eta 10:01:59
+epoch [6/50] batch [20/500] time 1.550 (1.595) data 0.001 (0.041) loss 0.6875 (1.1511) acc 81.2500 (70.3125) lr 1.9686e-03 eta 9:57:35
+epoch [6/50] batch [25/500] time 1.554 (1.590) data 0.001 (0.033) loss 1.4238 (1.1739) acc 71.8750 (70.1250) lr 1.9686e-03 eta 9:55:43
+epoch [6/50] batch [30/500] time 1.579 (1.587) data 0.000 (0.028) loss 1.7061 (1.1952) acc 56.2500 (69.8958) lr 1.9686e-03 eta 9:54:30
+epoch [6/50] batch [35/500] time 1.564 (1.584) data 0.000 (0.024) loss 1.0088 (1.2089) acc 71.8750 (69.5536) lr 1.9686e-03 eta 9:53:05
+epoch [6/50] batch [40/500] time 1.548 (1.581) data 0.000 (0.021) loss 1.6221 (1.2377) acc 62.5000 (69.3750) lr 1.9686e-03 eta 9:51:55
+epoch [6/50] batch [45/500] time 1.555 (1.579) data 0.000 (0.019) loss 1.2324 (1.2621) acc 62.5000 (68.8194) lr 1.9686e-03 eta 9:50:54
+epoch [6/50] batch [50/500] time 1.558 (1.577) data 0.000 (0.017) loss 1.2383 (1.2481) acc 68.7500 (68.8125) lr 1.9686e-03 eta 9:49:58
+epoch [6/50] batch [55/500] time 1.572 (1.577) data 0.000 (0.015) loss 0.9521 (1.2302) acc 78.1250 (69.4318) lr 1.9686e-03 eta 9:49:46
+epoch [6/50] batch [60/500] time 1.544 (1.576) data 0.000 (0.014) loss 1.5342 (1.2341) acc 59.3750 (69.3229) lr 1.9686e-03 eta 9:49:17
+epoch [6/50] batch [65/500] time 1.576 (1.577) data 0.001 (0.013) loss 1.1934 (1.2054) acc 68.7500 (70.0000) lr 1.9686e-03 eta 9:49:31
+epoch [6/50] batch [70/500] time 1.586 (1.576) data 0.000 (0.012) loss 1.1270 (1.1953) acc 71.8750 (70.0000) lr 1.9686e-03 eta 9:49:08
+epoch [6/50] batch [75/500] time 1.569 (1.575) data 0.000 (0.011) loss 1.5439 (1.2123) acc 65.6250 (70.0417) lr 1.9686e-03 eta 9:48:28
+epoch [6/50] batch [80/500] time 1.559 (1.574) data 0.000 (0.011) loss 1.2109 (1.2147) acc 68.7500 (70.1172) lr 1.9686e-03 eta 9:48:15
+epoch [6/50] batch [85/500] time 1.553 (1.573) data 0.000 (0.010) loss 1.7070 (1.2116) acc 59.3750 (70.1103) lr 1.9686e-03 eta 9:47:44
+epoch [6/50] batch [90/500] time 1.549 (1.572) data 0.000 (0.010) loss 0.7505 (1.2095) acc 78.1250 (70.0347) lr 1.9686e-03 eta 9:47:13
+epoch [6/50] batch [95/500] time 1.550 (1.571) data 0.001 (0.009) loss 1.4316 (1.2142) acc 62.5000 (69.8684) lr 1.9686e-03 eta 9:46:43
+epoch [6/50] batch [100/500] time 1.565 (1.571) data 0.001 (0.009) loss 1.4668 (1.2135) acc 62.5000 (69.9688) lr 1.9686e-03 eta 9:46:28
+epoch [6/50] batch [105/500] time 1.661 (1.571) data 0.000 (0.008) loss 0.9951 (1.2194) acc 71.8750 (69.8512) lr 1.9686e-03 eta 9:46:29
+epoch [6/50] batch [110/500] time 1.566 (1.571) data 0.001 (0.008) loss 0.8232 (1.2216) acc 71.8750 (69.6591) lr 1.9686e-03 eta 9:46:07
+epoch [6/50] batch [115/500] time 1.530 (1.570) data 0.000 (0.008) loss 1.2314 (1.2171) acc 75.0000 (69.7826) lr 1.9686e-03 eta 9:45:34
+epoch [6/50] batch [120/500] time 1.549 (1.569) data 0.001 (0.007) loss 1.3457 (1.2147) acc 62.5000 (69.8698) lr 1.9686e-03 eta 9:45:15
+epoch [6/50] batch [125/500] time 1.583 (1.569) data 0.000 (0.007) loss 1.2744 (1.2120) acc 71.8750 (69.9250) lr 1.9686e-03 eta 9:45:01
+epoch [6/50] batch [130/500] time 1.584 (1.569) data 0.001 (0.007) loss 1.0781 (1.2074) acc 68.7500 (69.9519) lr 1.9686e-03 eta 9:44:55
+epoch [6/50] batch [135/500] time 1.572 (1.569) data 0.000 (0.007) loss 1.0859 (1.2076) acc 75.0000 (70.0231) lr 1.9686e-03 eta 9:44:45
+epoch [6/50] batch [140/500] time 1.550 (1.568) data 0.000 (0.006) loss 1.8311 (1.2064) acc 53.1250 (70.0670) lr 1.9686e-03 eta 9:44:24
+epoch [6/50] batch [145/500] time 1.548 (1.567) data 0.001 (0.006) loss 1.2236 (1.2043) acc 75.0000 (70.0647) lr 1.9686e-03 eta 9:43:57
+epoch [6/50] batch [150/500] time 1.581 (1.568) data 0.000 (0.006) loss 1.0088 (1.2103) acc 65.6250 (69.7917) lr 1.9686e-03 eta 9:44:04
+epoch [6/50] batch [155/500] time 1.567 (1.568) data 0.001 (0.006) loss 1.6396 (1.2161) acc 62.5000 (69.7379) lr 1.9686e-03 eta 9:43:49
+epoch [6/50] batch [160/500] time 1.575 (1.568) data 0.000 (0.006) loss 1.4385 (1.2115) acc 62.5000 (69.8438) lr 1.9686e-03 eta 9:43:39
+epoch [6/50] batch [165/500] time 1.562 (1.568) data 0.000 (0.005) loss 0.9961 (1.2092) acc 81.2500 (69.9242) lr 1.9686e-03 eta 9:43:31
+epoch [6/50] batch [170/500] time 1.579 (1.568) data 0.000 (0.005) loss 1.0068 (1.2049) acc 78.1250 (69.9816) lr 1.9686e-03 eta 9:43:27
+epoch [6/50] batch [175/500] time 1.533 (1.567) data 0.001 (0.005) loss 0.8379 (1.2021) acc 81.2500 (70.1250) lr 1.9686e-03 eta 9:43:06
+epoch [6/50] batch [180/500] time 1.549 (1.567) data 0.000 (0.005) loss 1.6650 (1.2061) acc 65.6250 (70.0694) lr 1.9686e-03 eta 9:43:00
+epoch [6/50] batch [185/500] time 1.575 (1.567) data 0.001 (0.005) loss 1.3867 (1.2053) acc 65.6250 (70.1014) lr 1.9686e-03 eta 9:42:46
+epoch [6/50] batch [190/500] time 1.568 (1.567) data 0.000 (0.005) loss 1.4990 (1.2038) acc 71.8750 (70.1809) lr 1.9686e-03 eta 9:42:36
+epoch [6/50] batch [195/500] time 1.586 (1.567) data 0.000 (0.005) loss 1.4102 (1.2067) acc 56.2500 (70.0000) lr 1.9686e-03 eta 9:42:29
+epoch [6/50] batch [200/500] time 1.579 (1.567) data 0.000 (0.005) loss 1.5850 (1.2094) acc 59.3750 (70.0000) lr 1.9686e-03 eta 9:42:23
+epoch [6/50] batch [205/500] time 1.580 (1.568) data 0.000 (0.004) loss 1.8174 (1.2129) acc 59.3750 (69.9848) lr 1.9686e-03 eta 9:42:32
+epoch [6/50] batch [210/500] time 1.570 (1.568) data 0.001 (0.004) loss 1.7471 (1.2147) acc 50.0000 (69.8512) lr 1.9686e-03 eta 9:42:26
+epoch [6/50] batch [215/500] time 1.562 (1.568) data 0.000 (0.004) loss 1.4385 (1.2133) acc 59.3750 (69.9128) lr 1.9686e-03 eta 9:42:18
+epoch [6/50] batch [220/500] time 1.565 (1.567) data 0.000 (0.004) loss 1.2598 (1.2104) acc 68.7500 (70.0000) lr 1.9686e-03 eta 9:42:01
+epoch [6/50] batch [225/500] time 1.554 (1.567) data 0.000 (0.004) loss 1.3613 (1.2145) acc 78.1250 (69.9722) lr 1.9686e-03 eta 9:41:54
+epoch [6/50] batch [230/500] time 1.533 (1.567) data 0.001 (0.004) loss 1.7021 (1.2082) acc 62.5000 (70.1223) lr 1.9686e-03 eta 9:41:43
+epoch [6/50] batch [235/500] time 1.530 (1.567) data 0.000 (0.004) loss 1.0986 (1.2037) acc 68.7500 (70.1729) lr 1.9686e-03 eta 9:41:30
+epoch [6/50] batch [240/500] time 1.580 (1.567) data 0.000 (0.004) loss 1.1553 (1.2039) acc 59.3750 (70.1302) lr 1.9686e-03 eta 9:41:19
+epoch [6/50] batch [245/500] time 1.565 (1.567) data 0.000 (0.004) loss 1.0684 (1.1996) acc 71.8750 (70.2296) lr 1.9686e-03 eta 9:41:10
+epoch [6/50] batch [250/500] time 1.560 (1.567) data 0.000 (0.004) loss 1.4941 (1.1962) acc 65.6250 (70.3250) lr 1.9686e-03 eta 9:41:05
+epoch [6/50] batch [255/500] time 1.551 (1.567) data 0.000 (0.004) loss 0.9658 (1.1918) acc 71.8750 (70.3554) lr 1.9686e-03 eta 9:40:52
+epoch [6/50] batch [260/500] time 1.573 (1.567) data 0.000 (0.004) loss 1.6387 (1.1952) acc 59.3750 (70.2764) lr 1.9686e-03 eta 9:40:45
+epoch [6/50] batch [265/500] time 1.575 (1.567) data 0.000 (0.004) loss 1.4268 (1.1949) acc 71.8750 (70.3184) lr 1.9686e-03 eta 9:40:37
+epoch [6/50] batch [270/500] time 1.570 (1.567) data 0.000 (0.003) loss 1.0752 (1.1959) acc 59.3750 (70.2431) lr 1.9686e-03 eta 9:40:26
+epoch [6/50] batch [275/500] time 1.531 (1.566) data 0.000 (0.003) loss 1.0078 (1.1941) acc 78.1250 (70.3409) lr 1.9686e-03 eta 9:40:10
+epoch [6/50] batch [280/500] time 1.557 (1.566) data 0.000 (0.003) loss 1.1855 (1.1906) acc 68.7500 (70.4129) lr 1.9686e-03 eta 9:39:58
+epoch [6/50] batch [285/500] time 1.557 (1.566) data 0.000 (0.003) loss 1.6602 (1.1910) acc 62.5000 (70.3947) lr 1.9686e-03 eta 9:39:44
+epoch [6/50] batch [290/500] time 1.558 (1.566) data 0.000 (0.003) loss 1.4316 (1.1923) acc 65.6250 (70.3233) lr 1.9686e-03 eta 9:39:34
+epoch [6/50] batch [295/500] time 1.550 (1.566) data 0.001 (0.003) loss 1.0049 (1.1888) acc 78.1250 (70.4025) lr 1.9686e-03 eta 9:39:26
+epoch [6/50] batch [300/500] time 1.572 (1.566) data 0.000 (0.003) loss 1.4717 (1.1858) acc 68.7500 (70.4688) lr 1.9686e-03 eta 9:39:15
+epoch [6/50] batch [305/500] time 1.555 (1.566) data 0.000 (0.003) loss 1.2871 (1.1859) acc 71.8750 (70.5430) lr 1.9686e-03 eta 9:39:09
+epoch [6/50] batch [310/500] time 1.585 (1.566) data 0.001 (0.003) loss 1.5361 (1.1877) acc 65.6250 (70.5544) lr 1.9686e-03 eta 9:39:01
+epoch [6/50] batch [315/500] time 1.550 (1.565) data 0.000 (0.003) loss 1.4736 (1.1871) acc 59.3750 (70.5456) lr 1.9686e-03 eta 9:38:50
+epoch [6/50] batch [320/500] time 1.572 (1.565) data 0.000 (0.003) loss 1.3867 (1.1892) acc 78.1250 (70.5762) lr 1.9686e-03 eta 9:38:38
+epoch [6/50] batch [325/500] time 1.609 (1.566) data 0.000 (0.003) loss 1.0498 (1.1928) acc 87.5000 (70.5481) lr 1.9686e-03 eta 9:38:35
+epoch [6/50] batch [330/500] time 1.560 (1.566) data 0.000 (0.003) loss 0.8828 (1.1924) acc 84.3750 (70.5777) lr 1.9686e-03 eta 9:38:29
+epoch [6/50] batch [335/500] time 1.568 (1.566) data 0.000 (0.003) loss 1.4990 (1.1936) acc 59.3750 (70.5597) lr 1.9686e-03 eta 9:38:21
+epoch [6/50] batch [340/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.7637 (1.1959) acc 53.1250 (70.4871) lr 1.9686e-03 eta 9:38:10
+epoch [6/50] batch [345/500] time 1.554 (1.565) data 0.001 (0.003) loss 1.5225 (1.1986) acc 62.5000 (70.4348) lr 1.9686e-03 eta 9:37:59
+epoch [6/50] batch [350/500] time 1.547 (1.565) data 0.000 (0.003) loss 1.2051 (1.1948) acc 78.1250 (70.5089) lr 1.9686e-03 eta 9:37:51
+epoch [6/50] batch [355/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.1807 (1.1955) acc 71.8750 (70.5018) lr 1.9686e-03 eta 9:37:42
+epoch [6/50] batch [360/500] time 1.555 (1.565) data 0.001 (0.003) loss 0.8398 (1.1962) acc 87.5000 (70.4948) lr 1.9686e-03 eta 9:37:33
+epoch [6/50] batch [365/500] time 1.572 (1.565) data 0.000 (0.003) loss 1.8545 (1.1973) acc 62.5000 (70.4709) lr 1.9686e-03 eta 9:37:25
+epoch [6/50] batch [370/500] time 1.576 (1.565) data 0.000 (0.003) loss 1.1240 (1.1951) acc 71.8750 (70.5405) lr 1.9686e-03 eta 9:37:17
+epoch [6/50] batch [375/500] time 1.555 (1.565) data 0.000 (0.003) loss 0.9224 (1.1920) acc 78.1250 (70.5667) lr 1.9686e-03 eta 9:37:06
+epoch [6/50] batch [380/500] time 1.564 (1.565) data 0.001 (0.003) loss 0.7495 (1.1894) acc 90.6250 (70.6414) lr 1.9686e-03 eta 9:36:56
+epoch [6/50] batch [385/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.2881 (1.1881) acc 75.0000 (70.7143) lr 1.9686e-03 eta 9:36:47
+epoch [6/50] batch [390/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.7227 (1.1938) acc 50.0000 (70.6170) lr 1.9686e-03 eta 9:36:34
+epoch [6/50] batch [395/500] time 1.547 (1.565) data 0.000 (0.003) loss 1.2656 (1.1940) acc 65.6250 (70.6013) lr 1.9686e-03 eta 9:36:27
+epoch [6/50] batch [400/500] time 1.550 (1.565) data 0.000 (0.002) loss 1.3955 (1.1961) acc 62.5000 (70.5391) lr 1.9686e-03 eta 9:36:16
+epoch [6/50] batch [405/500] time 1.558 (1.564) data 0.000 (0.002) loss 1.0078 (1.1956) acc 75.0000 (70.5633) lr 1.9686e-03 eta 9:36:07
+epoch [6/50] batch [410/500] time 1.579 (1.564) data 0.001 (0.002) loss 1.2900 (1.1946) acc 71.8750 (70.5869) lr 1.9686e-03 eta 9:35:58
+epoch [6/50] batch [415/500] time 1.583 (1.565) data 0.001 (0.002) loss 0.8096 (1.1952) acc 75.0000 (70.5045) lr 1.9686e-03 eta 9:35:53
+epoch [6/50] batch [420/500] time 1.562 (1.565) data 0.001 (0.002) loss 1.5107 (1.1957) acc 62.5000 (70.5134) lr 1.9686e-03 eta 9:35:44
+epoch [6/50] batch [425/500] time 1.570 (1.564) data 0.000 (0.002) loss 0.9902 (1.1938) acc 71.8750 (70.5368) lr 1.9686e-03 eta 9:35:34
+epoch [6/50] batch [430/500] time 1.532 (1.564) data 0.000 (0.002) loss 1.2705 (1.1970) acc 71.8750 (70.4288) lr 1.9686e-03 eta 9:35:24
+epoch [6/50] batch [435/500] time 1.559 (1.564) data 0.000 (0.002) loss 1.2627 (1.2017) acc 68.7500 (70.3592) lr 1.9686e-03 eta 9:35:11
+epoch [6/50] batch [440/500] time 1.552 (1.564) data 0.001 (0.002) loss 1.5059 (1.2017) acc 56.2500 (70.3196) lr 1.9686e-03 eta 9:35:00
+epoch [6/50] batch [445/500] time 1.541 (1.564) data 0.000 (0.002) loss 0.9238 (1.2011) acc 71.8750 (70.3301) lr 1.9686e-03 eta 9:34:49
+epoch [6/50] batch [450/500] time 1.554 (1.564) data 0.000 (0.002) loss 0.6357 (1.2007) acc 75.0000 (70.3125) lr 1.9686e-03 eta 9:34:39
+epoch [6/50] batch [455/500] time 1.559 (1.564) data 0.000 (0.002) loss 0.8726 (1.2018) acc 84.3750 (70.3297) lr 1.9686e-03 eta 9:34:31
+epoch [6/50] batch [460/500] time 1.554 (1.564) data 0.000 (0.002) loss 0.8228 (1.2001) acc 84.3750 (70.3533) lr 1.9686e-03 eta 9:34:21
+epoch [6/50] batch [465/500] time 1.546 (1.564) data 0.000 (0.002) loss 1.4023 (1.2019) acc 62.5000 (70.3024) lr 1.9686e-03 eta 9:34:13
+epoch [6/50] batch [470/500] time 1.577 (1.564) data 0.000 (0.002) loss 0.4924 (1.2006) acc 87.5000 (70.3059) lr 1.9686e-03 eta 9:34:05
+epoch [6/50] batch [475/500] time 1.546 (1.564) data 0.000 (0.002) loss 0.6514 (1.1981) acc 81.2500 (70.3355) lr 1.9686e-03 eta 9:33:57
+epoch [6/50] batch [480/500] time 1.562 (1.563) data 0.000 (0.002) loss 1.1143 (1.1954) acc 75.0000 (70.3776) lr 1.9686e-03 eta 9:33:46
+epoch [6/50] batch [485/500] time 1.545 (1.563) data 0.001 (0.002) loss 1.0840 (1.1966) acc 71.8750 (70.3737) lr 1.9686e-03 eta 9:33:34
+epoch [6/50] batch [490/500] time 1.643 (1.563) data 0.000 (0.002) loss 1.2070 (1.1966) acc 78.1250 (70.4018) lr 1.9686e-03 eta 9:33:29
+epoch [6/50] batch [495/500] time 1.545 (1.563) data 0.000 (0.002) loss 1.1699 (1.1969) acc 71.8750 (70.4040) lr 1.9686e-03 eta 9:33:19
+epoch [6/50] batch [500/500] time 1.542 (1.563) data 0.000 (0.002) loss 1.1074 (1.1961) acc 62.5000 (70.3937) lr 1.9511e-03 eta 9:33:08
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,751
+* accuracy: 77.5%
+* error: 22.5%
+* macro_f1: 76.9%
+epoch [7/50] batch [5/500] time 1.535 (1.678) data 0.000 (0.188) loss 0.7231 (0.8938) acc 81.2500 (78.7500) lr 1.9511e-03 eta 10:15:02
+epoch [7/50] batch [10/500] time 1.570 (1.630) data 0.000 (0.094) loss 1.1719 (0.9581) acc 75.0000 (76.2500) lr 1.9511e-03 eta 9:57:18
+epoch [7/50] batch [15/500] time 1.535 (1.605) data 0.000 (0.063) loss 1.1143 (0.9807) acc 68.7500 (75.2083) lr 1.9511e-03 eta 9:47:58
+epoch [7/50] batch [20/500] time 1.539 (1.593) data 0.001 (0.047) loss 0.9385 (0.9950) acc 78.1250 (74.2188) lr 1.9511e-03 eta 9:43:43
+epoch [7/50] batch [25/500] time 1.566 (1.588) data 0.000 (0.038) loss 0.7515 (1.0485) acc 84.3750 (72.6250) lr 1.9511e-03 eta 9:41:26
+epoch [7/50] batch [30/500] time 1.605 (1.585) data 0.001 (0.032) loss 0.8882 (1.0608) acc 78.1250 (72.2917) lr 1.9511e-03 eta 9:40:17
+epoch [7/50] batch [35/500] time 1.565 (1.581) data 0.000 (0.027) loss 1.3145 (1.0521) acc 59.3750 (71.9643) lr 1.9511e-03 eta 9:38:42
+epoch [7/50] batch [40/500] time 1.571 (1.579) data 0.000 (0.024) loss 0.9487 (1.0401) acc 75.0000 (72.4219) lr 1.9511e-03 eta 9:38:05
+epoch [7/50] batch [45/500] time 1.559 (1.576) data 0.000 (0.021) loss 1.3281 (1.0617) acc 65.6250 (71.8056) lr 1.9511e-03 eta 9:36:44
+epoch [7/50] batch [50/500] time 1.583 (1.575) data 0.002 (0.019) loss 1.2070 (1.0854) acc 68.7500 (71.0625) lr 1.9511e-03 eta 9:36:01
+epoch [7/50] batch [55/500] time 1.551 (1.573) data 0.000 (0.017) loss 1.1943 (1.1038) acc 68.7500 (70.7955) lr 1.9511e-03 eta 9:35:12
+epoch [7/50] batch [60/500] time 1.555 (1.571) data 0.000 (0.016) loss 0.9805 (1.1114) acc 71.8750 (70.7812) lr 1.9511e-03 eta 9:34:27
+epoch [7/50] batch [65/500] time 1.564 (1.571) data 0.000 (0.015) loss 1.5898 (1.1351) acc 62.5000 (70.5769) lr 1.9511e-03 eta 9:34:09
+epoch [7/50] batch [70/500] time 1.570 (1.571) data 0.000 (0.014) loss 1.5986 (1.1523) acc 62.5000 (70.3125) lr 1.9511e-03 eta 9:34:03
+epoch [7/50] batch [75/500] time 1.553 (1.569) data 0.000 (0.013) loss 1.0342 (1.1526) acc 65.6250 (70.2917) lr 1.9511e-03 eta 9:33:26
+epoch [7/50] batch [80/500] time 1.564 (1.568) data 0.000 (0.012) loss 1.1963 (1.1814) acc 65.6250 (69.9219) lr 1.9511e-03 eta 9:32:58
+epoch [7/50] batch [85/500] time 1.543 (1.567) data 0.000 (0.011) loss 1.7773 (1.1878) acc 62.5000 (69.7426) lr 1.9511e-03 eta 9:32:28
+epoch [7/50] batch [90/500] time 1.567 (1.567) data 0.000 (0.011) loss 1.5322 (1.1906) acc 62.5000 (69.5833) lr 1.9511e-03 eta 9:32:06
+epoch [7/50] batch [95/500] time 1.571 (1.566) data 0.000 (0.010) loss 1.0957 (1.1931) acc 75.0000 (69.6382) lr 1.9511e-03 eta 9:31:52
+epoch [7/50] batch [100/500] time 1.557 (1.567) data 0.000 (0.010) loss 1.3877 (1.1932) acc 65.6250 (69.5625) lr 1.9511e-03 eta 9:31:46
+epoch [7/50] batch [105/500] time 1.552 (1.567) data 0.001 (0.009) loss 1.5986 (1.1966) acc 53.1250 (69.4048) lr 1.9511e-03 eta 9:31:49
+epoch [7/50] batch [110/500] time 1.569 (1.567) data 0.001 (0.009) loss 0.8413 (1.1818) acc 81.2500 (69.7727) lr 1.9511e-03 eta 9:31:36
+epoch [7/50] batch [115/500] time 1.525 (1.566) data 0.000 (0.009) loss 1.1846 (1.1809) acc 68.7500 (69.9728) lr 1.9511e-03 eta 9:31:21
+epoch [7/50] batch [120/500] time 1.548 (1.566) data 0.000 (0.008) loss 0.7422 (1.1729) acc 78.1250 (70.1562) lr 1.9511e-03 eta 9:31:03
+epoch [7/50] batch [125/500] time 1.550 (1.566) data 0.000 (0.008) loss 1.5508 (1.1830) acc 78.1250 (70.0750) lr 1.9511e-03 eta 9:30:57
+epoch [7/50] batch [130/500] time 1.550 (1.566) data 0.000 (0.008) loss 0.9263 (1.1801) acc 71.8750 (70.1923) lr 1.9511e-03 eta 9:30:42
+epoch [7/50] batch [135/500] time 1.558 (1.565) data 0.000 (0.007) loss 1.2852 (1.1787) acc 71.8750 (70.2546) lr 1.9511e-03 eta 9:30:27
+epoch [7/50] batch [140/500] time 1.549 (1.565) data 0.000 (0.007) loss 2.0469 (1.1876) acc 59.3750 (70.1786) lr 1.9511e-03 eta 9:30:10
+epoch [7/50] batch [145/500] time 1.552 (1.565) data 0.001 (0.007) loss 1.1992 (1.1824) acc 68.7500 (70.2155) lr 1.9511e-03 eta 9:30:00
+epoch [7/50] batch [150/500] time 1.556 (1.565) data 0.000 (0.007) loss 0.7559 (1.1873) acc 78.1250 (70.0417) lr 1.9511e-03 eta 9:30:02
+epoch [7/50] batch [155/500] time 1.558 (1.565) data 0.001 (0.006) loss 0.9155 (1.1755) acc 75.0000 (70.4032) lr 1.9511e-03 eta 9:29:46
+epoch [7/50] batch [160/500] time 1.555 (1.564) data 0.000 (0.006) loss 0.8452 (1.1766) acc 78.1250 (70.5273) lr 1.9511e-03 eta 9:29:27
+epoch [7/50] batch [165/500] time 1.559 (1.564) data 0.000 (0.006) loss 1.5098 (1.1841) acc 68.7500 (70.4545) lr 1.9511e-03 eta 9:29:19
+epoch [7/50] batch [170/500] time 1.559 (1.564) data 0.000 (0.006) loss 1.3408 (1.1795) acc 62.5000 (70.5515) lr 1.9511e-03 eta 9:29:07
+epoch [7/50] batch [175/500] time 1.536 (1.564) data 0.000 (0.006) loss 1.3516 (1.1813) acc 59.3750 (70.4643) lr 1.9511e-03 eta 9:28:50
+epoch [7/50] batch [180/500] time 1.568 (1.564) data 0.000 (0.006) loss 1.3594 (1.1789) acc 65.6250 (70.5035) lr 1.9511e-03 eta 9:28:36
+epoch [7/50] batch [185/500] time 1.555 (1.563) data 0.000 (0.005) loss 1.3486 (1.1811) acc 75.0000 (70.5405) lr 1.9511e-03 eta 9:28:23
+epoch [7/50] batch [190/500] time 1.543 (1.563) data 0.001 (0.005) loss 1.3262 (1.1846) acc 75.0000 (70.5757) lr 1.9511e-03 eta 9:28:10
+epoch [7/50] batch [195/500] time 1.562 (1.563) data 0.000 (0.005) loss 1.3633 (1.1874) acc 59.3750 (70.4327) lr 1.9511e-03 eta 9:27:56
+epoch [7/50] batch [200/500] time 1.556 (1.563) data 0.001 (0.005) loss 0.8330 (1.1868) acc 78.1250 (70.5156) lr 1.9511e-03 eta 9:27:45
+epoch [7/50] batch [205/500] time 1.578 (1.562) data 0.000 (0.005) loss 1.0850 (1.1840) acc 65.6250 (70.4878) lr 1.9511e-03 eta 9:27:34
+epoch [7/50] batch [210/500] time 1.540 (1.562) data 0.001 (0.005) loss 1.7578 (1.1869) acc 62.5000 (70.5208) lr 1.9511e-03 eta 9:27:20
+epoch [7/50] batch [215/500] time 1.571 (1.562) data 0.000 (0.005) loss 1.4404 (1.1933) acc 53.1250 (70.4070) lr 1.9511e-03 eta 9:27:13
+epoch [7/50] batch [220/500] time 1.547 (1.562) data 0.000 (0.005) loss 0.8252 (1.1933) acc 71.8750 (70.2983) lr 1.9511e-03 eta 9:26:58
+epoch [7/50] batch [225/500] time 1.581 (1.562) data 0.000 (0.005) loss 1.3223 (1.1942) acc 71.8750 (70.3333) lr 1.9511e-03 eta 9:26:51
+epoch [7/50] batch [230/500] time 1.538 (1.562) data 0.000 (0.004) loss 1.1719 (1.1930) acc 68.7500 (70.2174) lr 1.9511e-03 eta 9:26:38
+epoch [7/50] batch [235/500] time 1.548 (1.562) data 0.000 (0.004) loss 0.7725 (1.1954) acc 81.2500 (70.2128) lr 1.9511e-03 eta 9:26:30
+epoch [7/50] batch [240/500] time 1.543 (1.561) data 0.000 (0.004) loss 1.2705 (1.1988) acc 78.1250 (70.1432) lr 1.9511e-03 eta 9:26:16
+epoch [7/50] batch [245/500] time 1.570 (1.561) data 0.001 (0.004) loss 1.9219 (1.2065) acc 53.1250 (70.0128) lr 1.9511e-03 eta 9:26:02
+epoch [7/50] batch [250/500] time 1.561 (1.561) data 0.000 (0.004) loss 0.4814 (1.2043) acc 84.3750 (70.0625) lr 1.9511e-03 eta 9:25:58
+epoch [7/50] batch [255/500] time 1.569 (1.561) data 0.000 (0.004) loss 1.4385 (1.2021) acc 56.2500 (70.0735) lr 1.9511e-03 eta 9:25:50
+epoch [7/50] batch [260/500] time 1.558 (1.561) data 0.000 (0.004) loss 1.0811 (1.1993) acc 75.0000 (70.1442) lr 1.9511e-03 eta 9:25:37
+epoch [7/50] batch [265/500] time 1.567 (1.561) data 0.000 (0.004) loss 1.4561 (1.2013) acc 59.3750 (70.0236) lr 1.9511e-03 eta 9:25:27
+epoch [7/50] batch [270/500] time 1.574 (1.561) data 0.000 (0.004) loss 1.0713 (1.1971) acc 71.8750 (70.1273) lr 1.9511e-03 eta 9:25:19
+epoch [7/50] batch [275/500] time 1.579 (1.561) data 0.001 (0.004) loss 1.1533 (1.2000) acc 68.7500 (70.1023) lr 1.9511e-03 eta 9:25:13
+epoch [7/50] batch [280/500] time 1.552 (1.561) data 0.001 (0.004) loss 0.7900 (1.1978) acc 75.0000 (70.0893) lr 1.9511e-03 eta 9:25:06
+epoch [7/50] batch [285/500] time 1.578 (1.561) data 0.000 (0.004) loss 1.1543 (1.1964) acc 65.6250 (70.1096) lr 1.9511e-03 eta 9:25:00
+epoch [7/50] batch [290/500] time 1.561 (1.561) data 0.000 (0.004) loss 1.1289 (1.1954) acc 71.8750 (70.1401) lr 1.9511e-03 eta 9:24:55
+epoch [7/50] batch [295/500] time 1.561 (1.562) data 0.000 (0.004) loss 0.9463 (1.1939) acc 78.1250 (70.1695) lr 1.9511e-03 eta 9:24:55
+epoch [7/50] batch [300/500] time 1.568 (1.562) data 0.000 (0.004) loss 0.5400 (1.1919) acc 84.3750 (70.2083) lr 1.9511e-03 eta 9:24:49
+epoch [7/50] batch [305/500] time 1.567 (1.562) data 0.000 (0.003) loss 1.1562 (1.1926) acc 78.1250 (70.2561) lr 1.9511e-03 eta 9:24:44
+epoch [7/50] batch [310/500] time 1.567 (1.562) data 0.001 (0.003) loss 1.1660 (1.1921) acc 68.7500 (70.2319) lr 1.9511e-03 eta 9:24:39
+epoch [7/50] batch [315/500] time 1.568 (1.562) data 0.000 (0.003) loss 1.8740 (1.1968) acc 59.3750 (70.1786) lr 1.9511e-03 eta 9:24:28
+epoch [7/50] batch [320/500] time 1.556 (1.562) data 0.000 (0.003) loss 1.0518 (1.1929) acc 75.0000 (70.2930) lr 1.9511e-03 eta 9:24:20
+epoch [7/50] batch [325/500] time 1.572 (1.562) data 0.001 (0.003) loss 0.6011 (1.1913) acc 84.3750 (70.3462) lr 1.9511e-03 eta 9:24:16
+epoch [7/50] batch [330/500] time 1.551 (1.562) data 0.001 (0.003) loss 0.7905 (1.1870) acc 78.1250 (70.4356) lr 1.9511e-03 eta 9:24:08
+epoch [7/50] batch [335/500] time 1.557 (1.562) data 0.000 (0.003) loss 0.9556 (1.1858) acc 71.8750 (70.5037) lr 1.9511e-03 eta 9:24:02
+epoch [7/50] batch [340/500] time 1.573 (1.562) data 0.000 (0.003) loss 0.9961 (1.1869) acc 75.0000 (70.4136) lr 1.9511e-03 eta 9:24:00
+epoch [7/50] batch [345/500] time 1.582 (1.563) data 0.000 (0.003) loss 1.5830 (1.1872) acc 65.6250 (70.4529) lr 1.9511e-03 eta 9:23:56
+epoch [7/50] batch [350/500] time 1.593 (1.563) data 0.000 (0.003) loss 1.6982 (1.1880) acc 59.3750 (70.4375) lr 1.9511e-03 eta 9:23:49
+epoch [7/50] batch [355/500] time 1.545 (1.562) data 0.000 (0.003) loss 0.8428 (1.1865) acc 78.1250 (70.4577) lr 1.9511e-03 eta 9:23:40
+epoch [7/50] batch [360/500] time 1.549 (1.563) data 0.000 (0.003) loss 1.1895 (1.1875) acc 71.8750 (70.3906) lr 1.9511e-03 eta 9:23:32
+epoch [7/50] batch [365/500] time 1.563 (1.562) data 0.000 (0.003) loss 0.9121 (1.1897) acc 71.8750 (70.3425) lr 1.9511e-03 eta 9:23:24
+epoch [7/50] batch [370/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.6772 (1.1882) acc 84.3750 (70.3716) lr 1.9511e-03 eta 9:23:19
+epoch [7/50] batch [375/500] time 1.553 (1.562) data 0.000 (0.003) loss 0.7891 (1.1880) acc 75.0000 (70.3417) lr 1.9511e-03 eta 9:23:08
+epoch [7/50] batch [380/500] time 1.529 (1.562) data 0.000 (0.003) loss 1.7412 (1.1900) acc 65.6250 (70.3289) lr 1.9511e-03 eta 9:22:59
+epoch [7/50] batch [385/500] time 1.563 (1.562) data 0.000 (0.003) loss 1.1777 (1.1893) acc 62.5000 (70.3571) lr 1.9511e-03 eta 9:22:51
+epoch [7/50] batch [390/500] time 1.691 (1.563) data 0.000 (0.003) loss 0.4980 (1.1920) acc 87.5000 (70.2804) lr 1.9511e-03 eta 9:22:53
+epoch [7/50] batch [395/500] time 1.579 (1.563) data 0.000 (0.003) loss 1.2520 (1.1898) acc 71.8750 (70.3481) lr 1.9511e-03 eta 9:22:46
+epoch [7/50] batch [400/500] time 1.569 (1.563) data 0.000 (0.003) loss 1.4287 (1.1891) acc 68.7500 (70.3203) lr 1.9511e-03 eta 9:22:40
+epoch [7/50] batch [405/500] time 1.565 (1.563) data 0.000 (0.003) loss 0.9854 (1.1858) acc 71.8750 (70.4090) lr 1.9511e-03 eta 9:22:33
+epoch [7/50] batch [410/500] time 1.560 (1.563) data 0.000 (0.003) loss 0.9419 (1.1850) acc 78.1250 (70.4345) lr 1.9511e-03 eta 9:22:26
+epoch [7/50] batch [415/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.1680 (1.1831) acc 78.1250 (70.4970) lr 1.9511e-03 eta 9:22:15
+epoch [7/50] batch [420/500] time 1.579 (1.563) data 0.000 (0.003) loss 0.8491 (1.1864) acc 81.2500 (70.4464) lr 1.9511e-03 eta 9:22:08
+epoch [7/50] batch [425/500] time 1.580 (1.563) data 0.000 (0.003) loss 0.8730 (1.1854) acc 78.1250 (70.4706) lr 1.9511e-03 eta 9:22:02
+epoch [7/50] batch [430/500] time 1.559 (1.563) data 0.000 (0.003) loss 0.6416 (1.1842) acc 81.2500 (70.5523) lr 1.9511e-03 eta 9:21:53
+epoch [7/50] batch [435/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.1348 (1.1835) acc 68.7500 (70.5460) lr 1.9511e-03 eta 9:21:50
+epoch [7/50] batch [440/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.1055 (1.1829) acc 75.0000 (70.6037) lr 1.9511e-03 eta 9:21:41
+epoch [7/50] batch [445/500] time 1.569 (1.563) data 0.000 (0.003) loss 0.7974 (1.1805) acc 78.1250 (70.6531) lr 1.9511e-03 eta 9:21:36
+epoch [7/50] batch [450/500] time 1.608 (1.563) data 0.000 (0.002) loss 1.2334 (1.1812) acc 65.6250 (70.5972) lr 1.9511e-03 eta 9:21:26
+epoch [7/50] batch [455/500] time 1.551 (1.563) data 0.000 (0.002) loss 1.2725 (1.1807) acc 75.0000 (70.6662) lr 1.9511e-03 eta 9:21:15
+epoch [7/50] batch [460/500] time 1.568 (1.563) data 0.000 (0.002) loss 0.5776 (1.1802) acc 81.2500 (70.7201) lr 1.9511e-03 eta 9:21:06
+epoch [7/50] batch [465/500] time 1.548 (1.563) data 0.000 (0.002) loss 0.7881 (1.1798) acc 78.1250 (70.7460) lr 1.9511e-03 eta 9:20:55
+epoch [7/50] batch [470/500] time 1.563 (1.563) data 0.000 (0.002) loss 0.9546 (1.1800) acc 75.0000 (70.7447) lr 1.9511e-03 eta 9:20:47
+epoch [7/50] batch [475/500] time 1.527 (1.563) data 0.000 (0.002) loss 1.5938 (1.1822) acc 65.6250 (70.7303) lr 1.9511e-03 eta 9:20:36
+epoch [7/50] batch [480/500] time 1.560 (1.563) data 0.000 (0.002) loss 0.6245 (1.1799) acc 90.6250 (70.7943) lr 1.9511e-03 eta 9:20:28
+epoch [7/50] batch [485/500] time 1.544 (1.563) data 0.001 (0.002) loss 1.1689 (1.1804) acc 68.7500 (70.7539) lr 1.9511e-03 eta 9:20:18
+epoch [7/50] batch [490/500] time 1.553 (1.562) data 0.000 (0.002) loss 1.3359 (1.1825) acc 56.2500 (70.7143) lr 1.9511e-03 eta 9:20:08
+epoch [7/50] batch [495/500] time 1.548 (1.562) data 0.000 (0.002) loss 1.1377 (1.1800) acc 71.8750 (70.7513) lr 1.9511e-03 eta 9:19:57
+epoch [7/50] batch [500/500] time 1.542 (1.562) data 0.000 (0.002) loss 0.9746 (1.1807) acc 75.0000 (70.7812) lr 1.9298e-03 eta 9:19:46
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,847
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [8/50] batch [5/500] time 1.543 (1.651) data 0.001 (0.158) loss 2.2266 (1.4221) acc 46.8750 (66.2500) lr 1.9298e-03 eta 9:51:36
+epoch [8/50] batch [10/500] time 1.564 (1.604) data 0.001 (0.079) loss 1.5420 (1.3735) acc 62.5000 (68.4375) lr 1.9298e-03 eta 9:34:20
+epoch [8/50] batch [15/500] time 1.530 (1.584) data 0.000 (0.053) loss 1.4883 (1.3611) acc 53.1250 (65.8333) lr 1.9298e-03 eta 9:27:20
+epoch [8/50] batch [20/500] time 1.588 (1.577) data 0.000 (0.040) loss 1.1045 (1.2959) acc 75.0000 (67.6562) lr 1.9298e-03 eta 9:24:39
+epoch [8/50] batch [25/500] time 1.540 (1.572) data 0.000 (0.032) loss 1.8818 (1.3143) acc 50.0000 (68.1250) lr 1.9298e-03 eta 9:22:49
+epoch [8/50] batch [30/500] time 1.558 (1.575) data 0.001 (0.027) loss 1.0225 (1.2820) acc 75.0000 (68.9583) lr 1.9298e-03 eta 9:23:42
+epoch [8/50] batch [35/500] time 1.563 (1.572) data 0.000 (0.023) loss 0.7915 (1.2578) acc 75.0000 (69.5536) lr 1.9298e-03 eta 9:22:19
+epoch [8/50] batch [40/500] time 1.551 (1.569) data 0.000 (0.020) loss 1.4990 (1.2798) acc 62.5000 (69.2969) lr 1.9298e-03 eta 9:21:13
+epoch [8/50] batch [45/500] time 1.558 (1.567) data 0.000 (0.018) loss 1.7832 (1.2660) acc 65.6250 (69.7917) lr 1.9298e-03 eta 9:20:29
+epoch [8/50] batch [50/500] time 1.565 (1.567) data 0.000 (0.016) loss 1.1807 (1.2503) acc 65.6250 (70.2500) lr 1.9298e-03 eta 9:20:14
+epoch [8/50] batch [55/500] time 1.576 (1.566) data 0.000 (0.015) loss 1.3438 (1.2310) acc 62.5000 (70.3977) lr 1.9298e-03 eta 9:19:43
+epoch [8/50] batch [60/500] time 1.569 (1.566) data 0.000 (0.014) loss 0.9614 (1.2343) acc 75.0000 (70.6250) lr 1.9298e-03 eta 9:19:29
+epoch [8/50] batch [65/500] time 1.596 (1.565) data 0.001 (0.013) loss 1.3096 (1.2598) acc 65.6250 (70.0962) lr 1.9298e-03 eta 9:19:10
+epoch [8/50] batch [70/500] time 1.641 (1.566) data 0.000 (0.012) loss 0.7632 (1.2497) acc 81.2500 (70.1786) lr 1.9298e-03 eta 9:19:21
+epoch [8/50] batch [75/500] time 1.560 (1.566) data 0.000 (0.011) loss 1.3164 (1.2444) acc 65.6250 (70.1250) lr 1.9298e-03 eta 9:19:14
+epoch [8/50] batch [80/500] time 1.545 (1.565) data 0.000 (0.010) loss 1.1807 (1.2406) acc 65.6250 (70.2734) lr 1.9298e-03 eta 9:18:44
+epoch [8/50] batch [85/500] time 1.557 (1.564) data 0.000 (0.010) loss 0.7422 (1.2327) acc 75.0000 (70.2941) lr 1.9298e-03 eta 9:18:13
+epoch [8/50] batch [90/500] time 1.542 (1.563) data 0.000 (0.009) loss 1.7041 (1.2344) acc 59.3750 (70.0694) lr 1.9298e-03 eta 9:17:41
+epoch [8/50] batch [95/500] time 1.564 (1.563) data 0.000 (0.009) loss 1.4297 (1.2399) acc 71.8750 (70.0658) lr 1.9298e-03 eta 9:17:27
+epoch [8/50] batch [100/500] time 1.572 (1.563) data 0.000 (0.008) loss 1.6309 (1.2533) acc 59.3750 (69.8125) lr 1.9298e-03 eta 9:17:21
+epoch [8/50] batch [105/500] time 1.538 (1.563) data 0.000 (0.008) loss 0.9141 (1.2481) acc 78.1250 (69.7917) lr 1.9298e-03 eta 9:17:11
+epoch [8/50] batch [110/500] time 1.553 (1.562) data 0.000 (0.008) loss 1.0918 (1.2418) acc 71.8750 (70.0284) lr 1.9298e-03 eta 9:17:00
+epoch [8/50] batch [115/500] time 1.543 (1.562) data 0.000 (0.007) loss 1.4209 (1.2312) acc 59.3750 (69.9185) lr 1.9298e-03 eta 9:16:46
+epoch [8/50] batch [120/500] time 1.554 (1.562) data 0.000 (0.007) loss 1.8340 (1.2367) acc 56.2500 (69.7656) lr 1.9298e-03 eta 9:16:39
+epoch [8/50] batch [125/500] time 1.555 (1.562) data 0.000 (0.007) loss 1.1924 (1.2402) acc 75.0000 (69.8250) lr 1.9298e-03 eta 9:16:22
+epoch [8/50] batch [130/500] time 1.529 (1.561) data 0.000 (0.006) loss 0.8999 (1.2332) acc 87.5000 (70.0962) lr 1.9298e-03 eta 9:16:06
+epoch [8/50] batch [135/500] time 1.562 (1.561) data 0.000 (0.006) loss 1.0596 (1.2274) acc 71.8750 (70.1157) lr 1.9298e-03 eta 9:15:52
+epoch [8/50] batch [140/500] time 1.562 (1.561) data 0.001 (0.006) loss 0.8955 (1.2265) acc 71.8750 (70.0223) lr 1.9298e-03 eta 9:15:40
+epoch [8/50] batch [145/500] time 1.555 (1.561) data 0.001 (0.006) loss 1.0254 (1.2169) acc 71.8750 (70.1078) lr 1.9298e-03 eta 9:15:31
+epoch [8/50] batch [150/500] time 1.597 (1.561) data 0.001 (0.006) loss 1.2100 (1.2129) acc 65.6250 (70.1875) lr 1.9298e-03 eta 9:15:35
+epoch [8/50] batch [155/500] time 1.554 (1.561) data 0.001 (0.006) loss 0.8013 (1.2071) acc 78.1250 (70.2218) lr 1.9298e-03 eta 9:15:26
+epoch [8/50] batch [160/500] time 1.541 (1.561) data 0.000 (0.005) loss 0.8101 (1.2048) acc 71.8750 (70.2539) lr 1.9298e-03 eta 9:15:11
+epoch [8/50] batch [165/500] time 1.546 (1.561) data 0.000 (0.005) loss 1.6738 (1.2066) acc 65.6250 (70.2652) lr 1.9298e-03 eta 9:14:53
+epoch [8/50] batch [170/500] time 1.535 (1.561) data 0.000 (0.005) loss 1.3477 (1.2123) acc 62.5000 (70.1654) lr 1.9298e-03 eta 9:14:49
+epoch [8/50] batch [175/500] time 1.554 (1.561) data 0.001 (0.005) loss 0.5005 (1.2161) acc 84.3750 (70.0536) lr 1.9298e-03 eta 9:14:40
+epoch [8/50] batch [180/500] time 1.561 (1.560) data 0.000 (0.005) loss 1.0176 (1.2108) acc 84.3750 (70.2083) lr 1.9298e-03 eta 9:14:26
+epoch [8/50] batch [185/500] time 1.554 (1.560) data 0.000 (0.005) loss 1.1191 (1.2091) acc 75.0000 (70.2534) lr 1.9298e-03 eta 9:14:19
+epoch [8/50] batch [190/500] time 1.544 (1.560) data 0.000 (0.005) loss 0.8467 (1.2112) acc 78.1250 (70.2961) lr 1.9298e-03 eta 9:14:07
+epoch [8/50] batch [195/500] time 1.537 (1.560) data 0.000 (0.004) loss 1.0986 (1.2127) acc 68.7500 (70.1923) lr 1.9298e-03 eta 9:14:02
+epoch [8/50] batch [200/500] time 1.564 (1.560) data 0.000 (0.004) loss 1.9697 (1.2166) acc 62.5000 (70.1875) lr 1.9298e-03 eta 9:13:56
+epoch [8/50] batch [205/500] time 1.546 (1.560) data 0.000 (0.004) loss 1.1855 (1.2166) acc 71.8750 (70.1982) lr 1.9298e-03 eta 9:13:48
+epoch [8/50] batch [210/500] time 1.538 (1.560) data 0.000 (0.004) loss 1.2207 (1.2134) acc 65.6250 (70.2083) lr 1.9298e-03 eta 9:13:38
+epoch [8/50] batch [215/500] time 1.569 (1.561) data 0.000 (0.004) loss 1.4951 (1.2167) acc 68.7500 (70.1163) lr 1.9298e-03 eta 9:13:38
+epoch [8/50] batch [220/500] time 1.553 (1.561) data 0.000 (0.004) loss 1.2080 (1.2180) acc 71.8750 (70.0142) lr 1.9298e-03 eta 9:13:33
+epoch [8/50] batch [225/500] time 1.553 (1.561) data 0.000 (0.004) loss 0.8125 (1.2135) acc 75.0000 (70.0833) lr 1.9298e-03 eta 9:13:23
+epoch [8/50] batch [230/500] time 1.544 (1.560) data 0.000 (0.004) loss 0.4666 (1.2170) acc 87.5000 (70.0408) lr 1.9298e-03 eta 9:13:11
+epoch [8/50] batch [235/500] time 1.576 (1.560) data 0.000 (0.004) loss 1.3047 (1.2156) acc 62.5000 (70.0133) lr 1.9298e-03 eta 9:13:03
+epoch [8/50] batch [240/500] time 1.546 (1.560) data 0.000 (0.004) loss 1.0908 (1.2121) acc 81.2500 (70.1302) lr 1.9298e-03 eta 9:12:51
+epoch [8/50] batch [245/500] time 1.556 (1.560) data 0.000 (0.004) loss 0.9551 (1.2156) acc 78.1250 (70.1148) lr 1.9298e-03 eta 9:12:39
+epoch [8/50] batch [250/500] time 1.549 (1.560) data 0.000 (0.004) loss 0.6680 (1.2141) acc 84.3750 (70.2250) lr 1.9298e-03 eta 9:12:29
+epoch [8/50] batch [255/500] time 1.578 (1.560) data 0.000 (0.004) loss 1.4229 (1.2117) acc 65.6250 (70.1838) lr 1.9298e-03 eta 9:12:21
+epoch [8/50] batch [260/500] time 1.565 (1.560) data 0.000 (0.003) loss 1.4824 (1.2138) acc 68.7500 (70.1322) lr 1.9298e-03 eta 9:12:17
+epoch [8/50] batch [265/500] time 1.568 (1.560) data 0.000 (0.003) loss 1.2031 (1.2129) acc 78.1250 (70.1415) lr 1.9298e-03 eta 9:12:11
+epoch [8/50] batch [270/500] time 1.563 (1.560) data 0.000 (0.003) loss 1.5635 (1.2168) acc 56.2500 (70.0810) lr 1.9298e-03 eta 9:12:08
+epoch [8/50] batch [275/500] time 1.587 (1.560) data 0.000 (0.003) loss 1.4590 (1.2176) acc 62.5000 (70.0455) lr 1.9298e-03 eta 9:12:00
+epoch [8/50] batch [280/500] time 1.552 (1.560) data 0.000 (0.003) loss 1.2012 (1.2144) acc 78.1250 (70.1228) lr 1.9298e-03 eta 9:11:49
+epoch [8/50] batch [285/500] time 1.593 (1.560) data 0.000 (0.003) loss 0.7852 (1.2094) acc 75.0000 (70.1974) lr 1.9298e-03 eta 9:11:43
+epoch [8/50] batch [290/500] time 1.561 (1.560) data 0.000 (0.003) loss 0.7939 (1.2087) acc 78.1250 (70.2263) lr 1.9298e-03 eta 9:11:32
+epoch [8/50] batch [295/500] time 1.557 (1.560) data 0.000 (0.003) loss 0.9507 (1.2081) acc 81.2500 (70.2225) lr 1.9298e-03 eta 9:11:24
+epoch [8/50] batch [300/500] time 1.573 (1.560) data 0.000 (0.003) loss 1.0801 (1.2065) acc 75.0000 (70.2708) lr 1.9298e-03 eta 9:11:17
+epoch [8/50] batch [305/500] time 1.571 (1.560) data 0.000 (0.003) loss 1.0254 (1.2062) acc 75.0000 (70.2357) lr 1.9298e-03 eta 9:11:12
+epoch [8/50] batch [310/500] time 1.652 (1.561) data 0.000 (0.003) loss 1.0879 (1.2016) acc 75.0000 (70.3629) lr 1.9298e-03 eta 9:11:10
+epoch [8/50] batch [315/500] time 1.568 (1.561) data 0.000 (0.003) loss 1.2666 (1.2020) acc 68.7500 (70.3770) lr 1.9298e-03 eta 9:11:04
+epoch [8/50] batch [320/500] time 1.558 (1.561) data 0.000 (0.003) loss 1.0137 (1.2018) acc 78.1250 (70.4102) lr 1.9298e-03 eta 9:10:53
+epoch [8/50] batch [325/500] time 1.576 (1.561) data 0.000 (0.003) loss 1.1201 (1.2044) acc 78.1250 (70.3750) lr 1.9298e-03 eta 9:10:47
+epoch [8/50] batch [330/500] time 1.575 (1.561) data 0.000 (0.003) loss 1.6084 (1.2064) acc 59.3750 (70.3220) lr 1.9298e-03 eta 9:10:40
+epoch [8/50] batch [335/500] time 1.553 (1.561) data 0.000 (0.003) loss 0.9233 (1.2057) acc 75.0000 (70.2985) lr 1.9298e-03 eta 9:10:35
+epoch [8/50] batch [340/500] time 1.575 (1.561) data 0.000 (0.003) loss 1.0215 (1.2030) acc 71.8750 (70.3493) lr 1.9298e-03 eta 9:10:30
+epoch [8/50] batch [345/500] time 1.556 (1.561) data 0.000 (0.003) loss 0.9780 (1.1994) acc 87.5000 (70.4348) lr 1.9298e-03 eta 9:10:21
+epoch [8/50] batch [350/500] time 1.567 (1.561) data 0.000 (0.003) loss 1.3682 (1.1960) acc 65.6250 (70.5179) lr 1.9298e-03 eta 9:10:13
+epoch [8/50] batch [355/500] time 1.560 (1.561) data 0.000 (0.003) loss 0.8555 (1.1957) acc 81.2500 (70.5282) lr 1.9298e-03 eta 9:10:11
+epoch [8/50] batch [360/500] time 1.537 (1.561) data 0.000 (0.003) loss 1.3398 (1.1979) acc 68.7500 (70.4774) lr 1.9298e-03 eta 9:10:02
+epoch [8/50] batch [365/500] time 1.545 (1.561) data 0.000 (0.003) loss 0.9971 (1.1983) acc 78.1250 (70.5137) lr 1.9298e-03 eta 9:09:52
+epoch [8/50] batch [370/500] time 1.558 (1.561) data 0.000 (0.003) loss 1.7100 (1.2007) acc 65.6250 (70.4814) lr 1.9298e-03 eta 9:09:42
+epoch [8/50] batch [375/500] time 1.552 (1.561) data 0.000 (0.003) loss 0.9482 (1.2007) acc 65.6250 (70.4833) lr 1.9298e-03 eta 9:09:36
+epoch [8/50] batch [380/500] time 1.563 (1.561) data 0.000 (0.002) loss 1.3936 (1.2008) acc 68.7500 (70.4441) lr 1.9298e-03 eta 9:09:29
+epoch [8/50] batch [385/500] time 1.564 (1.561) data 0.000 (0.002) loss 1.5234 (1.1980) acc 75.0000 (70.4870) lr 1.9298e-03 eta 9:09:21
+epoch [8/50] batch [390/500] time 1.567 (1.561) data 0.000 (0.002) loss 1.1934 (1.1948) acc 62.5000 (70.5208) lr 1.9298e-03 eta 9:09:14
+epoch [8/50] batch [395/500] time 1.537 (1.561) data 0.000 (0.002) loss 1.1562 (1.1931) acc 75.0000 (70.5696) lr 1.9298e-03 eta 9:09:05
+epoch [8/50] batch [400/500] time 1.556 (1.561) data 0.000 (0.002) loss 1.4746 (1.1894) acc 65.6250 (70.6562) lr 1.9298e-03 eta 9:08:57
+epoch [8/50] batch [405/500] time 1.547 (1.561) data 0.000 (0.002) loss 1.2236 (1.1901) acc 75.0000 (70.6636) lr 1.9298e-03 eta 9:08:46
+epoch [8/50] batch [410/500] time 1.573 (1.561) data 0.000 (0.002) loss 1.7041 (1.1959) acc 53.1250 (70.5640) lr 1.9298e-03 eta 9:08:38
+epoch [8/50] batch [415/500] time 1.562 (1.561) data 0.000 (0.002) loss 0.7910 (1.1942) acc 87.5000 (70.5798) lr 1.9298e-03 eta 9:08:29
+epoch [8/50] batch [420/500] time 1.536 (1.561) data 0.000 (0.002) loss 0.5635 (1.1951) acc 81.2500 (70.6027) lr 1.9298e-03 eta 9:08:18
+epoch [8/50] batch [425/500] time 1.540 (1.561) data 0.001 (0.002) loss 1.3027 (1.1957) acc 75.0000 (70.6029) lr 1.9298e-03 eta 9:08:10
+epoch [8/50] batch [430/500] time 1.567 (1.561) data 0.000 (0.002) loss 1.7803 (1.1978) acc 65.6250 (70.5596) lr 1.9298e-03 eta 9:08:01
+epoch [8/50] batch [435/500] time 1.554 (1.560) data 0.000 (0.002) loss 0.8750 (1.1979) acc 75.0000 (70.5675) lr 1.9298e-03 eta 9:07:51
+epoch [8/50] batch [440/500] time 1.529 (1.560) data 0.000 (0.002) loss 0.6616 (1.1963) acc 81.2500 (70.6392) lr 1.9298e-03 eta 9:07:40
+epoch [8/50] batch [445/500] time 1.557 (1.560) data 0.000 (0.002) loss 1.2090 (1.1966) acc 65.6250 (70.6039) lr 1.9298e-03 eta 9:07:31
+epoch [8/50] batch [450/500] time 1.525 (1.560) data 0.000 (0.002) loss 1.5986 (1.1985) acc 62.5000 (70.5833) lr 1.9298e-03 eta 9:07:21
+epoch [8/50] batch [455/500] time 1.546 (1.560) data 0.000 (0.002) loss 1.0439 (1.1963) acc 81.2500 (70.6387) lr 1.9298e-03 eta 9:07:18
+epoch [8/50] batch [460/500] time 1.543 (1.560) data 0.000 (0.002) loss 0.9673 (1.1950) acc 81.2500 (70.6861) lr 1.9298e-03 eta 9:07:11
+epoch [8/50] batch [465/500] time 1.557 (1.560) data 0.000 (0.002) loss 0.8887 (1.1916) acc 75.0000 (70.7392) lr 1.9298e-03 eta 9:07:02
+epoch [8/50] batch [470/500] time 1.557 (1.560) data 0.000 (0.002) loss 0.6357 (1.1906) acc 84.3750 (70.7447) lr 1.9298e-03 eta 9:06:54
+epoch [8/50] batch [475/500] time 1.553 (1.560) data 0.000 (0.002) loss 1.0703 (1.1878) acc 81.2500 (70.8224) lr 1.9298e-03 eta 9:06:44
+epoch [8/50] batch [480/500] time 1.573 (1.560) data 0.000 (0.002) loss 0.9653 (1.1861) acc 71.8750 (70.8268) lr 1.9298e-03 eta 9:06:38
+epoch [8/50] batch [485/500] time 1.547 (1.560) data 0.001 (0.002) loss 0.4692 (1.1858) acc 90.6250 (70.8376) lr 1.9298e-03 eta 9:06:29
+epoch [8/50] batch [490/500] time 1.578 (1.560) data 0.000 (0.002) loss 1.3926 (1.1879) acc 59.3750 (70.7781) lr 1.9298e-03 eta 9:06:22
+epoch [8/50] batch [495/500] time 1.548 (1.560) data 0.000 (0.002) loss 1.9434 (1.1875) acc 40.6250 (70.7765) lr 1.9298e-03 eta 9:06:14
+epoch [8/50] batch [500/500] time 1.571 (1.560) data 0.000 (0.002) loss 0.6724 (1.1865) acc 78.1250 (70.7625) lr 1.9048e-03 eta 9:06:09
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,946
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [9/50] batch [5/500] time 1.549 (1.663) data 0.000 (0.161) loss 1.1377 (1.3512) acc 71.8750 (65.6250) lr 1.9048e-03 eta 9:42:00
+epoch [9/50] batch [10/500] time 1.560 (1.624) data 0.000 (0.081) loss 1.7568 (1.2298) acc 53.1250 (67.5000) lr 1.9048e-03 eta 9:28:12
+epoch [9/50] batch [15/500] time 1.552 (1.604) data 0.000 (0.054) loss 1.2002 (1.2139) acc 75.0000 (68.5417) lr 1.9048e-03 eta 9:21:08
+epoch [9/50] batch [20/500] time 1.563 (1.595) data 0.001 (0.041) loss 1.4658 (1.2248) acc 65.6250 (69.2188) lr 1.9048e-03 eta 9:17:35
+epoch [9/50] batch [25/500] time 1.567 (1.587) data 0.000 (0.033) loss 1.2207 (1.2206) acc 68.7500 (69.5000) lr 1.9048e-03 eta 9:14:50
+epoch [9/50] batch [30/500] time 1.536 (1.580) data 0.000 (0.027) loss 1.0703 (1.2153) acc 84.3750 (69.7917) lr 1.9048e-03 eta 9:12:18
+epoch [9/50] batch [35/500] time 1.563 (1.577) data 0.000 (0.023) loss 1.6113 (1.2279) acc 62.5000 (69.4643) lr 1.9048e-03 eta 9:11:00
+epoch [9/50] batch [40/500] time 1.560 (1.575) data 0.000 (0.021) loss 1.2568 (1.2345) acc 75.0000 (69.6094) lr 1.9048e-03 eta 9:10:08
+epoch [9/50] batch [45/500] time 1.542 (1.572) data 0.000 (0.018) loss 0.8345 (1.1850) acc 75.0000 (70.4861) lr 1.9048e-03 eta 9:09:01
+epoch [9/50] batch [50/500] time 1.569 (1.571) data 0.000 (0.016) loss 0.5737 (1.1827) acc 84.3750 (70.6875) lr 1.9048e-03 eta 9:08:25
+epoch [9/50] batch [55/500] time 1.567 (1.570) data 0.001 (0.015) loss 0.9893 (1.1588) acc 68.7500 (70.9091) lr 1.9048e-03 eta 9:08:10
+epoch [9/50] batch [60/500] time 1.577 (1.570) data 0.001 (0.014) loss 1.3350 (1.1596) acc 62.5000 (70.7812) lr 1.9048e-03 eta 9:07:56
+epoch [9/50] batch [65/500] time 1.570 (1.569) data 0.000 (0.013) loss 0.8618 (1.1329) acc 75.0000 (71.0577) lr 1.9048e-03 eta 9:07:29
+epoch [9/50] batch [70/500] time 1.562 (1.568) data 0.000 (0.012) loss 1.3535 (1.1303) acc 71.8750 (71.2500) lr 1.9048e-03 eta 9:07:06
+epoch [9/50] batch [75/500] time 1.544 (1.567) data 0.000 (0.011) loss 1.2773 (1.1289) acc 68.7500 (71.4583) lr 1.9048e-03 eta 9:06:38
+epoch [9/50] batch [80/500] time 1.561 (1.567) data 0.000 (0.010) loss 1.4863 (1.1261) acc 65.6250 (71.5234) lr 1.9048e-03 eta 9:06:26
+epoch [9/50] batch [85/500] time 1.561 (1.567) data 0.000 (0.010) loss 0.8701 (1.1199) acc 78.1250 (71.5441) lr 1.9048e-03 eta 9:06:03
+epoch [9/50] batch [90/500] time 1.561 (1.566) data 0.000 (0.009) loss 0.8413 (1.1322) acc 75.0000 (71.1806) lr 1.9048e-03 eta 9:05:37
+epoch [9/50] batch [95/500] time 1.568 (1.565) data 0.001 (0.009) loss 0.5962 (1.1317) acc 78.1250 (71.2829) lr 1.9048e-03 eta 9:05:21
+epoch [9/50] batch [100/500] time 1.561 (1.565) data 0.000 (0.008) loss 1.1885 (1.1350) acc 65.6250 (71.3750) lr 1.9048e-03 eta 9:05:05
+epoch [9/50] batch [105/500] time 1.538 (1.564) data 0.000 (0.008) loss 0.9712 (1.1460) acc 71.8750 (71.2500) lr 1.9048e-03 eta 9:04:38
+epoch [9/50] batch [110/500] time 1.535 (1.564) data 0.000 (0.008) loss 1.2939 (1.1504) acc 65.6250 (71.1364) lr 1.9048e-03 eta 9:04:35
+epoch [9/50] batch [115/500] time 1.541 (1.564) data 0.000 (0.007) loss 0.8149 (1.1451) acc 68.7500 (71.2500) lr 1.9048e-03 eta 9:04:18
+epoch [9/50] batch [120/500] time 1.552 (1.563) data 0.001 (0.007) loss 0.9014 (1.1482) acc 68.7500 (71.0938) lr 1.9048e-03 eta 9:03:57
+epoch [9/50] batch [125/500] time 1.544 (1.562) data 0.000 (0.007) loss 0.8237 (1.1399) acc 78.1250 (71.2500) lr 1.9048e-03 eta 9:03:33
+epoch [9/50] batch [130/500] time 1.554 (1.562) data 0.000 (0.007) loss 1.4062 (1.1468) acc 59.3750 (71.0096) lr 1.9048e-03 eta 9:03:19
+epoch [9/50] batch [135/500] time 1.550 (1.562) data 0.000 (0.006) loss 1.9814 (1.1602) acc 65.6250 (70.9028) lr 1.9048e-03 eta 9:03:10
+epoch [9/50] batch [140/500] time 1.536 (1.562) data 0.000 (0.006) loss 0.7036 (1.1502) acc 81.2500 (71.0491) lr 1.9048e-03 eta 9:02:52
+epoch [9/50] batch [145/500] time 1.552 (1.562) data 0.000 (0.006) loss 1.1582 (1.1436) acc 56.2500 (70.9914) lr 1.9048e-03 eta 9:02:47
+epoch [9/50] batch [150/500] time 1.544 (1.562) data 0.001 (0.006) loss 0.8896 (1.1484) acc 75.0000 (70.9167) lr 1.9048e-03 eta 9:02:38
+epoch [9/50] batch [155/500] time 1.575 (1.562) data 0.000 (0.006) loss 1.2734 (1.1392) acc 65.6250 (71.0685) lr 1.9048e-03 eta 9:02:43
+epoch [9/50] batch [160/500] time 1.560 (1.562) data 0.000 (0.005) loss 1.1143 (1.1496) acc 78.1250 (70.8789) lr 1.9048e-03 eta 9:02:30
+epoch [9/50] batch [165/500] time 1.561 (1.562) data 0.000 (0.005) loss 1.6855 (1.1468) acc 68.7500 (70.9848) lr 1.9048e-03 eta 9:02:17
+epoch [9/50] batch [170/500] time 1.564 (1.561) data 0.000 (0.005) loss 1.5117 (1.1470) acc 65.6250 (70.9559) lr 1.9048e-03 eta 9:02:05
+epoch [9/50] batch [175/500] time 1.556 (1.561) data 0.000 (0.005) loss 1.5176 (1.1449) acc 68.7500 (71.0357) lr 1.9048e-03 eta 9:01:54
+epoch [9/50] batch [180/500] time 1.545 (1.561) data 0.000 (0.005) loss 1.7783 (1.1403) acc 59.3750 (71.1458) lr 1.9048e-03 eta 9:01:43
+epoch [9/50] batch [185/500] time 1.565 (1.561) data 0.001 (0.005) loss 0.8037 (1.1424) acc 84.3750 (71.1824) lr 1.9048e-03 eta 9:01:38
+epoch [9/50] batch [190/500] time 1.548 (1.561) data 0.000 (0.005) loss 1.4609 (1.1444) acc 56.2500 (71.0526) lr 1.9048e-03 eta 9:01:30
+epoch [9/50] batch [195/500] time 1.576 (1.561) data 0.000 (0.005) loss 1.0107 (1.1371) acc 78.1250 (71.2179) lr 1.9048e-03 eta 9:01:26
+epoch [9/50] batch [200/500] time 1.554 (1.562) data 0.000 (0.004) loss 0.8711 (1.1419) acc 75.0000 (71.1406) lr 1.9048e-03 eta 9:01:19
+epoch [9/50] batch [205/500] time 1.575 (1.562) data 0.000 (0.004) loss 0.5874 (1.1429) acc 84.3750 (71.1738) lr 1.9048e-03 eta 9:01:18
+epoch [9/50] batch [210/500] time 1.552 (1.562) data 0.001 (0.004) loss 0.8794 (1.1361) acc 75.0000 (71.3095) lr 1.9048e-03 eta 9:01:12
+epoch [9/50] batch [215/500] time 1.585 (1.562) data 0.000 (0.004) loss 0.5527 (1.1298) acc 81.2500 (71.4535) lr 1.9048e-03 eta 9:01:05
+epoch [9/50] batch [220/500] time 1.575 (1.562) data 0.000 (0.004) loss 1.0010 (1.1315) acc 78.1250 (71.5057) lr 1.9048e-03 eta 9:00:56
+epoch [9/50] batch [225/500] time 1.550 (1.562) data 0.000 (0.004) loss 1.8730 (1.1356) acc 56.2500 (71.4722) lr 1.9048e-03 eta 9:00:50
+epoch [9/50] batch [230/500] time 1.587 (1.562) data 0.000 (0.004) loss 1.7617 (1.1391) acc 68.7500 (71.3859) lr 1.9048e-03 eta 9:00:46
+epoch [9/50] batch [235/500] time 1.558 (1.562) data 0.000 (0.004) loss 1.0361 (1.1381) acc 78.1250 (71.4495) lr 1.9048e-03 eta 9:00:40
+epoch [9/50] batch [240/500] time 1.539 (1.562) data 0.000 (0.004) loss 1.1875 (1.1379) acc 68.7500 (71.4453) lr 1.9048e-03 eta 9:00:31
+epoch [9/50] batch [245/500] time 1.549 (1.562) data 0.000 (0.004) loss 0.9189 (1.1408) acc 78.1250 (71.4158) lr 1.9048e-03 eta 9:00:17
+epoch [9/50] batch [250/500] time 1.656 (1.562) data 0.000 (0.004) loss 0.9956 (1.1431) acc 75.0000 (71.3750) lr 1.9048e-03 eta 9:00:15
+epoch [9/50] batch [255/500] time 1.554 (1.562) data 0.000 (0.004) loss 0.4658 (1.1427) acc 87.5000 (71.3113) lr 1.9048e-03 eta 9:00:06
+epoch [9/50] batch [260/500] time 1.548 (1.562) data 0.000 (0.004) loss 0.8447 (1.1423) acc 68.7500 (71.2260) lr 1.9048e-03 eta 8:59:50
+epoch [9/50] batch [265/500] time 1.584 (1.562) data 0.001 (0.003) loss 1.0293 (1.1420) acc 68.7500 (71.2618) lr 1.9048e-03 eta 8:59:43
+epoch [9/50] batch [270/500] time 1.535 (1.561) data 0.000 (0.003) loss 1.3867 (1.1441) acc 68.7500 (71.2384) lr 1.9048e-03 eta 8:59:26
+epoch [9/50] batch [275/500] time 1.549 (1.561) data 0.000 (0.003) loss 1.5010 (1.1458) acc 71.8750 (71.2955) lr 1.9048e-03 eta 8:59:15
+epoch [9/50] batch [280/500] time 1.562 (1.561) data 0.000 (0.003) loss 1.1416 (1.1415) acc 78.1250 (71.4174) lr 1.9048e-03 eta 8:59:06
+epoch [9/50] batch [285/500] time 1.542 (1.561) data 0.000 (0.003) loss 2.1230 (1.1449) acc 53.1250 (71.3816) lr 1.9048e-03 eta 8:58:56
+epoch [9/50] batch [290/500] time 1.545 (1.561) data 0.000 (0.003) loss 1.0547 (1.1455) acc 71.8750 (71.3254) lr 1.9048e-03 eta 8:58:45
+epoch [9/50] batch [295/500] time 1.527 (1.561) data 0.000 (0.003) loss 0.9248 (1.1445) acc 78.1250 (71.3877) lr 1.9048e-03 eta 8:58:42
+epoch [9/50] batch [300/500] time 1.544 (1.561) data 0.000 (0.003) loss 0.7822 (1.1447) acc 78.1250 (71.3229) lr 1.9048e-03 eta 8:58:33
+epoch [9/50] batch [305/500] time 1.553 (1.561) data 0.001 (0.003) loss 1.2197 (1.1478) acc 68.7500 (71.2705) lr 1.9048e-03 eta 8:58:25
+epoch [9/50] batch [310/500] time 1.558 (1.561) data 0.001 (0.003) loss 0.9517 (1.1474) acc 75.0000 (71.1996) lr 1.9048e-03 eta 8:58:19
+epoch [9/50] batch [315/500] time 1.556 (1.561) data 0.000 (0.003) loss 1.1572 (1.1513) acc 62.5000 (71.0813) lr 1.9048e-03 eta 8:58:12
+epoch [9/50] batch [320/500] time 1.583 (1.562) data 0.000 (0.003) loss 0.5933 (1.1535) acc 90.6250 (71.0449) lr 1.9048e-03 eta 8:58:12
+epoch [9/50] batch [325/500] time 1.551 (1.562) data 0.000 (0.003) loss 0.6792 (1.1532) acc 78.1250 (71.0673) lr 1.9048e-03 eta 8:58:04
+epoch [9/50] batch [330/500] time 1.543 (1.562) data 0.000 (0.003) loss 1.5371 (1.1538) acc 68.7500 (71.1080) lr 1.9048e-03 eta 8:57:57
+epoch [9/50] batch [335/500] time 1.540 (1.562) data 0.000 (0.003) loss 1.4668 (1.1543) acc 68.7500 (71.0634) lr 1.9048e-03 eta 8:57:50
+epoch [9/50] batch [340/500] time 1.567 (1.562) data 0.000 (0.003) loss 0.9434 (1.1553) acc 68.7500 (71.0478) lr 1.9048e-03 eta 8:57:43
+epoch [9/50] batch [345/500] time 1.574 (1.562) data 0.000 (0.003) loss 1.3730 (1.1580) acc 68.7500 (71.0236) lr 1.9048e-03 eta 8:57:36
+epoch [9/50] batch [350/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.1572 (1.1591) acc 68.7500 (70.9643) lr 1.9048e-03 eta 8:57:30
+epoch [9/50] batch [355/500] time 1.548 (1.562) data 0.000 (0.003) loss 1.4199 (1.1567) acc 68.7500 (71.0123) lr 1.9048e-03 eta 8:57:23
+epoch [9/50] batch [360/500] time 1.562 (1.562) data 0.000 (0.003) loss 0.9326 (1.1570) acc 84.3750 (71.0851) lr 1.9048e-03 eta 8:57:17
+epoch [9/50] batch [365/500] time 1.574 (1.562) data 0.000 (0.003) loss 1.3096 (1.1574) acc 65.6250 (71.0017) lr 1.9048e-03 eta 8:57:08
+epoch [9/50] batch [370/500] time 1.549 (1.562) data 0.000 (0.003) loss 1.5723 (1.1604) acc 62.5000 (70.9544) lr 1.9048e-03 eta 8:56:59
+epoch [9/50] batch [375/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.1836 (1.1619) acc 59.3750 (70.9000) lr 1.9048e-03 eta 8:56:48
+epoch [9/50] batch [380/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.3457 (1.1633) acc 65.6250 (70.8964) lr 1.9048e-03 eta 8:56:39
+epoch [9/50] batch [385/500] time 1.531 (1.561) data 0.000 (0.003) loss 1.7959 (1.1638) acc 59.3750 (70.8442) lr 1.9048e-03 eta 8:56:27
+epoch [9/50] batch [390/500] time 1.559 (1.561) data 0.000 (0.002) loss 1.0449 (1.1628) acc 71.8750 (70.8253) lr 1.9048e-03 eta 8:56:19
+epoch [9/50] batch [395/500] time 1.540 (1.562) data 0.000 (0.002) loss 1.3037 (1.1641) acc 71.8750 (70.8386) lr 1.9048e-03 eta 8:56:15
+epoch [9/50] batch [400/500] time 1.564 (1.562) data 0.000 (0.002) loss 1.0195 (1.1623) acc 84.3750 (70.9141) lr 1.9048e-03 eta 8:56:06
+epoch [9/50] batch [405/500] time 1.566 (1.561) data 0.000 (0.002) loss 0.8501 (1.1641) acc 81.2500 (70.8951) lr 1.9048e-03 eta 8:55:57
+epoch [9/50] batch [410/500] time 1.558 (1.561) data 0.000 (0.002) loss 0.6714 (1.1614) acc 84.3750 (70.9756) lr 1.9048e-03 eta 8:55:48
+epoch [9/50] batch [415/500] time 1.573 (1.561) data 0.000 (0.002) loss 1.0186 (1.1597) acc 75.0000 (71.0241) lr 1.9048e-03 eta 8:55:39
+epoch [9/50] batch [420/500] time 1.563 (1.561) data 0.000 (0.002) loss 1.0693 (1.1587) acc 78.1250 (71.0268) lr 1.9048e-03 eta 8:55:30
+epoch [9/50] batch [425/500] time 1.585 (1.561) data 0.000 (0.002) loss 0.6245 (1.1567) acc 87.5000 (71.0515) lr 1.9048e-03 eta 8:55:26
+epoch [9/50] batch [430/500] time 1.583 (1.562) data 0.000 (0.002) loss 1.0762 (1.1565) acc 71.8750 (71.0828) lr 1.9048e-03 eta 8:55:22
+epoch [9/50] batch [435/500] time 1.547 (1.562) data 0.000 (0.002) loss 1.0000 (1.1590) acc 71.8750 (71.0417) lr 1.9048e-03 eta 8:55:16
+epoch [9/50] batch [440/500] time 1.568 (1.562) data 0.000 (0.002) loss 0.7715 (1.1574) acc 75.0000 (71.0440) lr 1.9048e-03 eta 8:55:14
+epoch [9/50] batch [445/500] time 1.532 (1.562) data 0.001 (0.002) loss 1.4971 (1.1580) acc 65.6250 (70.9761) lr 1.9048e-03 eta 8:55:03
+epoch [9/50] batch [450/500] time 1.540 (1.562) data 0.000 (0.002) loss 0.9263 (1.1563) acc 81.2500 (71.0069) lr 1.9048e-03 eta 8:54:53
+epoch [9/50] batch [455/500] time 1.579 (1.562) data 0.000 (0.002) loss 1.1631 (1.1550) acc 75.0000 (71.0234) lr 1.9048e-03 eta 8:54:44
+epoch [9/50] batch [460/500] time 1.558 (1.562) data 0.000 (0.002) loss 0.9458 (1.1555) acc 68.7500 (71.0122) lr 1.9048e-03 eta 8:54:36
+epoch [9/50] batch [465/500] time 1.568 (1.562) data 0.000 (0.002) loss 1.4121 (1.1548) acc 71.8750 (71.0685) lr 1.9048e-03 eta 8:54:29
+epoch [9/50] batch [470/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.3652 (1.1539) acc 59.3750 (71.0904) lr 1.9048e-03 eta 8:54:21
+epoch [9/50] batch [475/500] time 1.548 (1.562) data 0.000 (0.002) loss 0.9604 (1.1538) acc 75.0000 (71.1118) lr 1.9048e-03 eta 8:54:12
+epoch [9/50] batch [480/500] time 1.554 (1.562) data 0.000 (0.002) loss 1.2822 (1.1539) acc 75.0000 (71.1654) lr 1.9048e-03 eta 8:54:03
+epoch [9/50] batch [485/500] time 1.534 (1.561) data 0.001 (0.002) loss 1.0576 (1.1559) acc 84.3750 (71.1856) lr 1.9048e-03 eta 8:53:51
+epoch [9/50] batch [490/500] time 1.546 (1.561) data 0.000 (0.002) loss 1.9336 (1.1585) acc 56.2500 (71.1288) lr 1.9048e-03 eta 8:53:41
+epoch [9/50] batch [495/500] time 1.552 (1.561) data 0.000 (0.002) loss 1.3652 (1.1586) acc 71.8750 (71.1301) lr 1.9048e-03 eta 8:53:31
+epoch [9/50] batch [500/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.6602 (1.1582) acc 87.5000 (71.1562) lr 1.8763e-03 eta 8:53:20
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,876
+* accuracy: 77.8%
+* error: 22.2%
+* macro_f1: 77.2%
+epoch [10/50] batch [5/500] time 1.557 (1.684) data 0.000 (0.181) loss 1.0049 (1.0425) acc 78.1250 (71.8750) lr 1.8763e-03 eta 9:35:21
+epoch [10/50] batch [10/500] time 1.570 (1.629) data 0.000 (0.091) loss 1.6309 (1.2458) acc 62.5000 (67.1875) lr 1.8763e-03 eta 9:16:12
+epoch [10/50] batch [15/500] time 1.544 (1.608) data 0.000 (0.061) loss 0.9360 (1.1974) acc 78.1250 (68.9583) lr 1.8763e-03 eta 9:08:56
+epoch [10/50] batch [20/500] time 1.585 (1.598) data 0.000 (0.046) loss 1.6816 (1.1740) acc 65.6250 (70.3125) lr 1.8763e-03 eta 9:05:30
+epoch [10/50] batch [25/500] time 1.567 (1.591) data 0.000 (0.037) loss 1.2861 (1.1744) acc 68.7500 (70.8750) lr 1.8763e-03 eta 9:03:01
+epoch [10/50] batch [30/500] time 1.579 (1.585) data 0.000 (0.031) loss 1.1572 (1.1629) acc 75.0000 (71.2500) lr 1.8763e-03 eta 9:00:38
+epoch [10/50] batch [35/500] time 1.572 (1.581) data 0.000 (0.026) loss 0.3308 (1.1419) acc 90.6250 (71.6071) lr 1.8763e-03 eta 8:59:08
+epoch [10/50] batch [40/500] time 1.564 (1.582) data 0.000 (0.023) loss 0.8428 (1.1288) acc 81.2500 (71.7969) lr 1.8763e-03 eta 8:59:21
+epoch [10/50] batch [45/500] time 1.564 (1.579) data 0.000 (0.021) loss 1.0547 (1.1230) acc 75.0000 (71.6667) lr 1.8763e-03 eta 8:58:27
+epoch [10/50] batch [50/500] time 1.555 (1.578) data 0.000 (0.019) loss 1.3477 (1.1156) acc 62.5000 (71.5000) lr 1.8763e-03 eta 8:57:40
+epoch [10/50] batch [55/500] time 1.548 (1.576) data 0.000 (0.017) loss 0.8716 (1.1069) acc 68.7500 (71.0795) lr 1.8763e-03 eta 8:57:03
+epoch [10/50] batch [60/500] time 1.563 (1.575) data 0.000 (0.015) loss 1.6787 (1.1198) acc 62.5000 (70.6771) lr 1.8763e-03 eta 8:56:35
+epoch [10/50] batch [65/500] time 1.575 (1.575) data 0.001 (0.014) loss 1.1924 (1.1250) acc 71.8750 (70.5769) lr 1.8763e-03 eta 8:56:28
+epoch [10/50] batch [70/500] time 1.590 (1.574) data 0.001 (0.013) loss 0.6768 (1.1176) acc 81.2500 (70.9375) lr 1.8763e-03 eta 8:56:01
+epoch [10/50] batch [75/500] time 1.584 (1.574) data 0.000 (0.013) loss 1.1641 (1.1304) acc 78.1250 (70.7917) lr 1.8763e-03 eta 8:55:43
+epoch [10/50] batch [80/500] time 1.585 (1.575) data 0.000 (0.012) loss 0.7695 (1.1330) acc 87.5000 (70.7812) lr 1.8763e-03 eta 8:55:58
+epoch [10/50] batch [85/500] time 1.559 (1.574) data 0.002 (0.011) loss 1.2852 (1.1293) acc 65.6250 (70.9559) lr 1.8763e-03 eta 8:55:28
+epoch [10/50] batch [90/500] time 1.579 (1.574) data 0.001 (0.011) loss 0.7041 (1.1323) acc 78.1250 (70.5903) lr 1.8763e-03 eta 8:55:15
+epoch [10/50] batch [95/500] time 1.582 (1.574) data 0.000 (0.010) loss 1.2041 (1.1376) acc 62.5000 (70.4605) lr 1.8763e-03 eta 8:55:09
+epoch [10/50] batch [100/500] time 1.569 (1.573) data 0.000 (0.009) loss 1.0645 (1.1353) acc 81.2500 (70.7812) lr 1.8763e-03 eta 8:54:50
+epoch [10/50] batch [105/500] time 1.559 (1.572) data 0.001 (0.009) loss 1.6094 (1.1412) acc 59.3750 (70.7143) lr 1.8763e-03 eta 8:54:29
+epoch [10/50] batch [110/500] time 1.546 (1.571) data 0.000 (0.009) loss 1.1250 (1.1405) acc 56.2500 (70.5966) lr 1.8763e-03 eta 8:53:56
+epoch [10/50] batch [115/500] time 1.519 (1.571) data 0.000 (0.008) loss 1.0283 (1.1280) acc 68.7500 (70.8152) lr 1.8763e-03 eta 8:53:38
+epoch [10/50] batch [120/500] time 1.566 (1.570) data 0.000 (0.008) loss 1.0215 (1.1176) acc 71.8750 (70.9375) lr 1.8763e-03 eta 8:53:23
+epoch [10/50] batch [125/500] time 1.585 (1.570) data 0.001 (0.008) loss 1.0771 (1.1138) acc 78.1250 (71.0750) lr 1.8763e-03 eta 8:53:13
+epoch [10/50] batch [130/500] time 1.560 (1.570) data 0.001 (0.007) loss 0.6831 (1.1105) acc 81.2500 (71.1058) lr 1.8763e-03 eta 8:52:51
+epoch [10/50] batch [135/500] time 1.539 (1.569) data 0.000 (0.007) loss 0.8530 (1.1056) acc 68.7500 (71.1111) lr 1.8763e-03 eta 8:52:29
+epoch [10/50] batch [140/500] time 1.566 (1.569) data 0.000 (0.007) loss 1.4141 (1.1181) acc 59.3750 (70.9375) lr 1.8763e-03 eta 8:52:21
+epoch [10/50] batch [145/500] time 1.570 (1.568) data 0.000 (0.007) loss 1.4209 (1.1213) acc 75.0000 (70.9267) lr 1.8763e-03 eta 8:52:06
+epoch [10/50] batch [150/500] time 1.554 (1.568) data 0.000 (0.006) loss 1.3789 (1.1205) acc 65.6250 (70.8750) lr 1.8763e-03 eta 8:51:57
+epoch [10/50] batch [155/500] time 1.570 (1.568) data 0.000 (0.006) loss 1.2061 (1.1309) acc 75.0000 (70.8468) lr 1.8763e-03 eta 8:51:47
+epoch [10/50] batch [160/500] time 1.544 (1.568) data 0.000 (0.006) loss 0.5586 (1.1204) acc 87.5000 (71.0938) lr 1.8763e-03 eta 8:51:28
+epoch [10/50] batch [165/500] time 1.555 (1.567) data 0.001 (0.006) loss 1.5303 (1.1269) acc 71.8750 (71.0038) lr 1.8763e-03 eta 8:51:10
+epoch [10/50] batch [170/500] time 1.562 (1.567) data 0.000 (0.006) loss 1.5195 (1.1215) acc 62.5000 (71.0478) lr 1.8763e-03 eta 8:51:02
+epoch [10/50] batch [175/500] time 1.560 (1.567) data 0.000 (0.006) loss 0.9473 (1.1193) acc 81.2500 (71.1429) lr 1.8763e-03 eta 8:50:48
+epoch [10/50] batch [180/500] time 1.570 (1.566) data 0.000 (0.005) loss 0.4827 (1.1165) acc 78.1250 (71.1285) lr 1.8763e-03 eta 8:50:29
+epoch [10/50] batch [185/500] time 1.556 (1.567) data 0.000 (0.005) loss 1.2627 (1.1200) acc 62.5000 (71.0304) lr 1.8763e-03 eta 8:50:30
+epoch [10/50] batch [190/500] time 1.572 (1.566) data 0.001 (0.005) loss 1.4248 (1.1211) acc 65.6250 (71.0855) lr 1.8763e-03 eta 8:50:14
+epoch [10/50] batch [195/500] time 1.552 (1.566) data 0.000 (0.005) loss 1.3945 (1.1253) acc 59.3750 (70.9936) lr 1.8763e-03 eta 8:49:59
+epoch [10/50] batch [200/500] time 1.555 (1.566) data 0.000 (0.005) loss 0.9141 (1.1228) acc 81.2500 (71.0625) lr 1.8763e-03 eta 8:49:46
+epoch [10/50] batch [205/500] time 1.543 (1.566) data 0.000 (0.005) loss 0.7109 (1.1251) acc 84.3750 (71.0518) lr 1.8763e-03 eta 8:49:35
+epoch [10/50] batch [210/500] time 1.550 (1.565) data 0.001 (0.005) loss 0.9575 (1.1293) acc 71.8750 (70.9970) lr 1.8763e-03 eta 8:49:21
+epoch [10/50] batch [215/500] time 1.545 (1.565) data 0.001 (0.005) loss 0.7744 (1.1306) acc 81.2500 (71.0465) lr 1.8763e-03 eta 8:49:09
+epoch [10/50] batch [220/500] time 1.571 (1.565) data 0.000 (0.005) loss 1.0547 (1.1350) acc 71.8750 (70.9375) lr 1.8763e-03 eta 8:48:56
+epoch [10/50] batch [225/500] time 1.550 (1.565) data 0.000 (0.004) loss 1.7314 (1.1400) acc 68.7500 (70.9306) lr 1.8763e-03 eta 8:48:45
+epoch [10/50] batch [230/500] time 1.579 (1.565) data 0.000 (0.004) loss 1.5264 (1.1436) acc 62.5000 (70.7880) lr 1.8763e-03 eta 8:48:36
+epoch [10/50] batch [235/500] time 1.565 (1.565) data 0.000 (0.004) loss 1.2129 (1.1457) acc 71.8750 (70.8378) lr 1.8763e-03 eta 8:48:28
+epoch [10/50] batch [240/500] time 1.563 (1.565) data 0.000 (0.004) loss 0.6650 (1.1447) acc 78.1250 (70.8203) lr 1.8763e-03 eta 8:48:23
+epoch [10/50] batch [245/500] time 1.554 (1.565) data 0.000 (0.004) loss 1.0811 (1.1415) acc 75.0000 (71.0204) lr 1.8763e-03 eta 8:48:18
+epoch [10/50] batch [250/500] time 1.541 (1.565) data 0.000 (0.004) loss 1.4297 (1.1425) acc 62.5000 (70.9750) lr 1.8763e-03 eta 8:48:02
+epoch [10/50] batch [255/500] time 1.542 (1.564) data 0.000 (0.004) loss 1.4922 (1.1385) acc 59.3750 (71.0417) lr 1.8763e-03 eta 8:47:49
+epoch [10/50] batch [260/500] time 1.565 (1.564) data 0.001 (0.004) loss 0.7651 (1.1375) acc 81.2500 (71.0216) lr 1.8763e-03 eta 8:47:38
+epoch [10/50] batch [265/500] time 1.544 (1.564) data 0.000 (0.004) loss 1.2041 (1.1400) acc 75.0000 (71.0613) lr 1.8763e-03 eta 8:47:22
+epoch [10/50] batch [270/500] time 1.554 (1.564) data 0.000 (0.004) loss 1.0859 (1.1392) acc 65.6250 (71.0532) lr 1.8763e-03 eta 8:47:13
+epoch [10/50] batch [275/500] time 1.556 (1.564) data 0.000 (0.004) loss 1.6455 (1.1438) acc 56.2500 (70.9886) lr 1.8763e-03 eta 8:47:03
+epoch [10/50] batch [280/500] time 1.567 (1.564) data 0.000 (0.004) loss 0.8105 (1.1408) acc 75.0000 (71.0268) lr 1.8763e-03 eta 8:47:00
+epoch [10/50] batch [285/500] time 1.574 (1.564) data 0.000 (0.004) loss 1.6260 (1.1432) acc 62.5000 (70.9759) lr 1.8763e-03 eta 8:46:59
+epoch [10/50] batch [290/500] time 1.548 (1.564) data 0.000 (0.004) loss 1.2197 (1.1416) acc 65.6250 (70.9483) lr 1.8763e-03 eta 8:46:48
+epoch [10/50] batch [295/500] time 1.544 (1.564) data 0.000 (0.004) loss 1.3027 (1.1451) acc 65.6250 (70.8792) lr 1.8763e-03 eta 8:46:40
+epoch [10/50] batch [300/500] time 1.563 (1.564) data 0.000 (0.003) loss 1.4209 (1.1437) acc 62.5000 (70.9375) lr 1.8763e-03 eta 8:46:28
+epoch [10/50] batch [305/500] time 1.549 (1.564) data 0.000 (0.003) loss 1.0234 (1.1433) acc 62.5000 (70.9016) lr 1.8763e-03 eta 8:46:19
+epoch [10/50] batch [310/500] time 1.574 (1.564) data 0.000 (0.003) loss 1.8398 (1.1447) acc 62.5000 (70.9274) lr 1.8763e-03 eta 8:46:11
+epoch [10/50] batch [315/500] time 1.547 (1.564) data 0.001 (0.003) loss 1.6357 (1.1455) acc 59.3750 (70.9325) lr 1.8763e-03 eta 8:46:02
+epoch [10/50] batch [320/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.2510 (1.1473) acc 71.8750 (70.8887) lr 1.8763e-03 eta 8:45:50
+epoch [10/50] batch [325/500] time 1.673 (1.564) data 0.000 (0.003) loss 1.2510 (1.1454) acc 68.7500 (70.8750) lr 1.8763e-03 eta 8:45:47
+epoch [10/50] batch [330/500] time 1.550 (1.564) data 0.000 (0.003) loss 1.0820 (1.1475) acc 65.6250 (70.8523) lr 1.8763e-03 eta 8:45:40
+epoch [10/50] batch [335/500] time 1.564 (1.564) data 0.000 (0.003) loss 1.0146 (1.1486) acc 71.8750 (70.8955) lr 1.8763e-03 eta 8:45:34
+epoch [10/50] batch [340/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.1777 (1.1474) acc 68.7500 (70.9007) lr 1.8763e-03 eta 8:45:27
+epoch [10/50] batch [345/500] time 1.558 (1.564) data 0.000 (0.003) loss 1.2754 (1.1459) acc 65.6250 (70.9058) lr 1.8763e-03 eta 8:45:17
+epoch [10/50] batch [350/500] time 1.576 (1.564) data 0.000 (0.003) loss 1.1865 (1.1429) acc 68.7500 (70.9821) lr 1.8763e-03 eta 8:45:09
+epoch [10/50] batch [355/500] time 1.533 (1.564) data 0.000 (0.003) loss 1.0391 (1.1420) acc 71.8750 (70.9947) lr 1.8763e-03 eta 8:44:59
+epoch [10/50] batch [360/500] time 1.578 (1.564) data 0.000 (0.003) loss 1.2490 (1.1393) acc 68.7500 (71.0764) lr 1.8763e-03 eta 8:44:49
+epoch [10/50] batch [365/500] time 1.577 (1.564) data 0.000 (0.003) loss 1.6650 (1.1420) acc 62.5000 (71.0531) lr 1.8763e-03 eta 8:44:41
+epoch [10/50] batch [370/500] time 1.571 (1.563) data 0.000 (0.003) loss 0.7930 (1.1422) acc 81.2500 (71.0389) lr 1.8763e-03 eta 8:44:33
+epoch [10/50] batch [375/500] time 1.565 (1.563) data 0.000 (0.003) loss 1.2852 (1.1423) acc 75.0000 (71.0917) lr 1.8763e-03 eta 8:44:22
+epoch [10/50] batch [380/500] time 1.553 (1.563) data 0.000 (0.003) loss 1.3105 (1.1431) acc 68.7500 (71.0855) lr 1.8763e-03 eta 8:44:12
+epoch [10/50] batch [385/500] time 1.538 (1.563) data 0.000 (0.003) loss 1.4707 (1.1444) acc 62.5000 (71.0714) lr 1.8763e-03 eta 8:44:01
+epoch [10/50] batch [390/500] time 1.567 (1.563) data 0.000 (0.003) loss 1.0254 (1.1441) acc 78.1250 (71.1218) lr 1.8763e-03 eta 8:43:52
+epoch [10/50] batch [395/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.1582 (1.1450) acc 56.2500 (71.1076) lr 1.8763e-03 eta 8:43:44
+epoch [10/50] batch [400/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.8784 (1.1465) acc 71.8750 (71.0938) lr 1.8763e-03 eta 8:43:35
+epoch [10/50] batch [405/500] time 1.574 (1.563) data 0.000 (0.003) loss 1.3936 (1.1483) acc 59.3750 (71.0340) lr 1.8763e-03 eta 8:43:28
+epoch [10/50] batch [410/500] time 1.560 (1.563) data 0.001 (0.003) loss 1.3457 (1.1470) acc 75.0000 (71.1052) lr 1.8763e-03 eta 8:43:20
+epoch [10/50] batch [415/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.0801 (1.1462) acc 78.1250 (71.1446) lr 1.8763e-03 eta 8:43:14
+epoch [10/50] batch [420/500] time 1.581 (1.563) data 0.000 (0.003) loss 1.4336 (1.1445) acc 65.6250 (71.1682) lr 1.8763e-03 eta 8:43:07
+epoch [10/50] batch [425/500] time 1.595 (1.563) data 0.000 (0.003) loss 1.2969 (1.1455) acc 75.0000 (71.1250) lr 1.8763e-03 eta 8:43:04
+epoch [10/50] batch [430/500] time 1.559 (1.563) data 0.000 (0.003) loss 1.0811 (1.1441) acc 71.8750 (71.1628) lr 1.8763e-03 eta 8:42:56
+epoch [10/50] batch [435/500] time 1.556 (1.563) data 0.000 (0.002) loss 0.8174 (1.1449) acc 65.6250 (71.1279) lr 1.8763e-03 eta 8:42:47
+epoch [10/50] batch [440/500] time 1.592 (1.563) data 0.000 (0.002) loss 1.3262 (1.1458) acc 65.6250 (71.1080) lr 1.8763e-03 eta 8:42:42
+epoch [10/50] batch [445/500] time 1.563 (1.563) data 0.000 (0.002) loss 1.8271 (1.1494) acc 59.3750 (71.0253) lr 1.8763e-03 eta 8:42:32
+epoch [10/50] batch [450/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.5723 (1.1518) acc 62.5000 (71.0069) lr 1.8763e-03 eta 8:42:26
+epoch [10/50] batch [455/500] time 1.560 (1.563) data 0.001 (0.002) loss 1.7559 (1.1510) acc 68.7500 (71.0234) lr 1.8763e-03 eta 8:42:18
+epoch [10/50] batch [460/500] time 1.554 (1.563) data 0.000 (0.002) loss 1.1807 (1.1509) acc 65.6250 (70.9783) lr 1.8763e-03 eta 8:42:08
+epoch [10/50] batch [465/500] time 1.586 (1.563) data 0.000 (0.002) loss 2.2109 (1.1515) acc 56.2500 (70.9812) lr 1.8763e-03 eta 8:42:00
+epoch [10/50] batch [470/500] time 1.549 (1.563) data 0.000 (0.002) loss 1.1523 (1.1481) acc 62.5000 (71.0439) lr 1.8763e-03 eta 8:41:56
+epoch [10/50] batch [475/500] time 1.564 (1.563) data 0.001 (0.002) loss 1.9941 (1.1490) acc 56.2500 (71.0263) lr 1.8763e-03 eta 8:41:46
+epoch [10/50] batch [480/500] time 1.544 (1.563) data 0.000 (0.002) loss 0.9736 (1.1499) acc 78.1250 (71.0286) lr 1.8763e-03 eta 8:41:37
+epoch [10/50] batch [485/500] time 1.560 (1.563) data 0.001 (0.002) loss 1.6484 (1.1500) acc 68.7500 (71.0631) lr 1.8763e-03 eta 8:41:29
+epoch [10/50] batch [490/500] time 1.569 (1.563) data 0.000 (0.002) loss 0.8760 (1.1499) acc 81.2500 (71.0651) lr 1.8763e-03 eta 8:41:20
+epoch [10/50] batch [495/500] time 1.526 (1.563) data 0.000 (0.002) loss 0.7622 (1.1482) acc 78.1250 (71.0922) lr 1.8763e-03 eta 8:41:10
+epoch [10/50] batch [500/500] time 1.551 (1.563) data 0.000 (0.002) loss 1.5322 (1.1499) acc 71.8750 (71.0875) lr 1.8443e-03 eta 8:41:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,866
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.2%
+epoch [11/50] batch [5/500] time 1.531 (1.647) data 0.000 (0.158) loss 1.2617 (1.3807) acc 65.6250 (66.8750) lr 1.8443e-03 eta 9:08:44
+epoch [11/50] batch [10/500] time 1.581 (1.605) data 0.000 (0.079) loss 1.1992 (1.2677) acc 62.5000 (67.5000) lr 1.8443e-03 eta 8:54:44
+epoch [11/50] batch [15/500] time 1.597 (1.596) data 0.000 (0.053) loss 1.4199 (1.2301) acc 71.8750 (68.7500) lr 1.8443e-03 eta 8:51:41
+epoch [11/50] batch [20/500] time 1.555 (1.589) data 0.001 (0.040) loss 1.3447 (1.2435) acc 59.3750 (69.5312) lr 1.8443e-03 eta 8:49:03
+epoch [11/50] batch [25/500] time 1.552 (1.583) data 0.000 (0.032) loss 1.5156 (1.2126) acc 68.7500 (69.6250) lr 1.8443e-03 eta 8:46:53
+epoch [11/50] batch [30/500] time 1.569 (1.580) data 0.000 (0.027) loss 0.6602 (1.1862) acc 75.0000 (69.0625) lr 1.8443e-03 eta 8:45:52
+epoch [11/50] batch [35/500] time 1.568 (1.580) data 0.001 (0.023) loss 0.9351 (1.1939) acc 68.7500 (69.1071) lr 1.8443e-03 eta 8:45:37
+epoch [11/50] batch [40/500] time 1.567 (1.577) data 0.000 (0.020) loss 0.6821 (1.1574) acc 75.0000 (69.4531) lr 1.8443e-03 eta 8:44:42
+epoch [11/50] batch [45/500] time 1.559 (1.576) data 0.000 (0.018) loss 0.9819 (1.1488) acc 71.8750 (69.7222) lr 1.8443e-03 eta 8:43:59
+epoch [11/50] batch [50/500] time 1.553 (1.575) data 0.001 (0.016) loss 0.7866 (1.1512) acc 75.0000 (70.0000) lr 1.8443e-03 eta 8:43:36
+epoch [11/50] batch [55/500] time 1.557 (1.573) data 0.001 (0.015) loss 1.8008 (1.1491) acc 62.5000 (70.2841) lr 1.8443e-03 eta 8:42:58
+epoch [11/50] batch [60/500] time 1.563 (1.575) data 0.000 (0.014) loss 1.3223 (1.1579) acc 68.7500 (70.1042) lr 1.8443e-03 eta 8:43:15
+epoch [11/50] batch [65/500] time 1.535 (1.573) data 0.000 (0.013) loss 1.1377 (1.1519) acc 65.6250 (70.3365) lr 1.8443e-03 eta 8:42:35
+epoch [11/50] batch [70/500] time 1.565 (1.572) data 0.000 (0.012) loss 0.8003 (1.1460) acc 90.6250 (70.8036) lr 1.8443e-03 eta 8:42:13
+epoch [11/50] batch [75/500] time 1.551 (1.571) data 0.000 (0.011) loss 0.7349 (1.1259) acc 81.2500 (71.3750) lr 1.8443e-03 eta 8:41:33
+epoch [11/50] batch [80/500] time 1.543 (1.570) data 0.000 (0.010) loss 1.2314 (1.1241) acc 75.0000 (71.4844) lr 1.8443e-03 eta 8:41:16
+epoch [11/50] batch [85/500] time 1.556 (1.569) data 0.000 (0.010) loss 1.2002 (1.1190) acc 71.8750 (71.4706) lr 1.8443e-03 eta 8:40:45
+epoch [11/50] batch [90/500] time 1.565 (1.568) data 0.001 (0.009) loss 1.6182 (1.1236) acc 56.2500 (71.3194) lr 1.8443e-03 eta 8:40:25
+epoch [11/50] batch [95/500] time 1.575 (1.567) data 0.000 (0.009) loss 0.7324 (1.1265) acc 84.3750 (71.3816) lr 1.8443e-03 eta 8:39:47
+epoch [11/50] batch [100/500] time 1.555 (1.566) data 0.000 (0.008) loss 1.3447 (1.1229) acc 65.6250 (71.3750) lr 1.8443e-03 eta 8:39:30
+epoch [11/50] batch [105/500] time 1.526 (1.567) data 0.000 (0.008) loss 1.0332 (1.1300) acc 75.0000 (71.3690) lr 1.8443e-03 eta 8:39:26
+epoch [11/50] batch [110/500] time 1.561 (1.566) data 0.000 (0.008) loss 1.0742 (1.1309) acc 71.8750 (71.4205) lr 1.8443e-03 eta 8:39:07
+epoch [11/50] batch [115/500] time 1.548 (1.565) data 0.000 (0.007) loss 1.0352 (1.1322) acc 71.8750 (71.1957) lr 1.8443e-03 eta 8:38:43
+epoch [11/50] batch [120/500] time 1.538 (1.565) data 0.000 (0.007) loss 0.8257 (1.1227) acc 78.1250 (71.4583) lr 1.8443e-03 eta 8:38:30
+epoch [11/50] batch [125/500] time 1.569 (1.565) data 0.001 (0.007) loss 1.3213 (1.1266) acc 71.8750 (71.4250) lr 1.8443e-03 eta 8:38:18
+epoch [11/50] batch [130/500] time 1.554 (1.564) data 0.000 (0.007) loss 1.2891 (1.1349) acc 65.6250 (71.3702) lr 1.8443e-03 eta 8:38:01
+epoch [11/50] batch [135/500] time 1.556 (1.564) data 0.000 (0.006) loss 0.6377 (1.1321) acc 84.3750 (71.3657) lr 1.8443e-03 eta 8:37:54
+epoch [11/50] batch [140/500] time 1.566 (1.564) data 0.001 (0.006) loss 0.9077 (1.1371) acc 75.0000 (71.2054) lr 1.8443e-03 eta 8:37:42
+epoch [11/50] batch [145/500] time 1.553 (1.564) data 0.000 (0.006) loss 1.2998 (1.1425) acc 71.8750 (71.2069) lr 1.8443e-03 eta 8:37:29
+epoch [11/50] batch [150/500] time 1.561 (1.564) data 0.000 (0.006) loss 1.2471 (1.1436) acc 65.6250 (71.0833) lr 1.8443e-03 eta 8:37:21
+epoch [11/50] batch [155/500] time 1.572 (1.564) data 0.001 (0.006) loss 1.6406 (1.1470) acc 65.6250 (71.0282) lr 1.8443e-03 eta 8:37:10
+epoch [11/50] batch [160/500] time 1.572 (1.563) data 0.001 (0.005) loss 1.4229 (1.1530) acc 59.3750 (70.9375) lr 1.8443e-03 eta 8:36:57
+epoch [11/50] batch [165/500] time 1.551 (1.563) data 0.000 (0.005) loss 1.4922 (1.1609) acc 62.5000 (70.9470) lr 1.8443e-03 eta 8:36:47
+epoch [11/50] batch [170/500] time 1.551 (1.563) data 0.001 (0.005) loss 1.3623 (1.1635) acc 56.2500 (70.8272) lr 1.8443e-03 eta 8:36:36
+epoch [11/50] batch [175/500] time 1.562 (1.563) data 0.001 (0.005) loss 1.3711 (1.1649) acc 65.6250 (70.7500) lr 1.8443e-03 eta 8:36:31
+epoch [11/50] batch [180/500] time 1.552 (1.563) data 0.001 (0.005) loss 0.8203 (1.1635) acc 71.8750 (70.7986) lr 1.8443e-03 eta 8:36:21
+epoch [11/50] batch [185/500] time 1.528 (1.563) data 0.000 (0.005) loss 0.9575 (1.1640) acc 71.8750 (70.6588) lr 1.8443e-03 eta 8:36:03
+epoch [11/50] batch [190/500] time 1.578 (1.562) data 0.000 (0.005) loss 0.7744 (1.1597) acc 75.0000 (70.6743) lr 1.8443e-03 eta 8:35:52
+epoch [11/50] batch [195/500] time 1.565 (1.563) data 0.000 (0.005) loss 1.0449 (1.1630) acc 68.7500 (70.5288) lr 1.8443e-03 eta 8:35:47
+epoch [11/50] batch [200/500] time 1.547 (1.562) data 0.001 (0.004) loss 1.3193 (1.1620) acc 71.8750 (70.4531) lr 1.8443e-03 eta 8:35:36
+epoch [11/50] batch [205/500] time 1.547 (1.563) data 0.000 (0.004) loss 1.0752 (1.1636) acc 68.7500 (70.4268) lr 1.8443e-03 eta 8:35:35
+epoch [11/50] batch [210/500] time 1.537 (1.562) data 0.001 (0.004) loss 1.3789 (1.1654) acc 59.3750 (70.3869) lr 1.8443e-03 eta 8:35:17
+epoch [11/50] batch [215/500] time 1.558 (1.562) data 0.000 (0.004) loss 0.8921 (1.1662) acc 65.6250 (70.2471) lr 1.8443e-03 eta 8:35:06
+epoch [11/50] batch [220/500] time 1.546 (1.562) data 0.000 (0.004) loss 1.0693 (1.1676) acc 65.6250 (70.2131) lr 1.8443e-03 eta 8:34:49
+epoch [11/50] batch [225/500] time 1.556 (1.562) data 0.000 (0.004) loss 0.8726 (1.1621) acc 75.0000 (70.2917) lr 1.8443e-03 eta 8:34:40
+epoch [11/50] batch [230/500] time 1.587 (1.562) data 0.001 (0.004) loss 0.8315 (1.1570) acc 81.2500 (70.4484) lr 1.8443e-03 eta 8:34:31
+epoch [11/50] batch [235/500] time 1.559 (1.562) data 0.000 (0.004) loss 1.1816 (1.1625) acc 75.0000 (70.3590) lr 1.8443e-03 eta 8:34:24
+epoch [11/50] batch [240/500] time 1.554 (1.561) data 0.000 (0.004) loss 1.6484 (1.1648) acc 62.5000 (70.3776) lr 1.8443e-03 eta 8:34:14
+epoch [11/50] batch [245/500] time 1.689 (1.562) data 0.000 (0.004) loss 1.4531 (1.1658) acc 68.7500 (70.3827) lr 1.8443e-03 eta 8:34:15
+epoch [11/50] batch [250/500] time 1.531 (1.562) data 0.001 (0.004) loss 0.8540 (1.1625) acc 81.2500 (70.4750) lr 1.8443e-03 eta 8:34:04
+epoch [11/50] batch [255/500] time 1.546 (1.562) data 0.000 (0.004) loss 0.9536 (1.1660) acc 81.2500 (70.4657) lr 1.8443e-03 eta 8:33:51
+epoch [11/50] batch [260/500] time 1.577 (1.561) data 0.000 (0.004) loss 0.8813 (1.1653) acc 68.7500 (70.4447) lr 1.8443e-03 eta 8:33:39
+epoch [11/50] batch [265/500] time 1.565 (1.561) data 0.000 (0.003) loss 1.2578 (1.1668) acc 71.8750 (70.4245) lr 1.8443e-03 eta 8:33:29
+epoch [11/50] batch [270/500] time 1.581 (1.561) data 0.000 (0.003) loss 0.6055 (1.1627) acc 84.3750 (70.5208) lr 1.8443e-03 eta 8:33:24
+epoch [11/50] batch [275/500] time 1.567 (1.561) data 0.000 (0.003) loss 0.9272 (1.1644) acc 81.2500 (70.5114) lr 1.8443e-03 eta 8:33:17
+epoch [11/50] batch [280/500] time 1.567 (1.561) data 0.000 (0.003) loss 1.4541 (1.1646) acc 68.7500 (70.4799) lr 1.8443e-03 eta 8:33:10
+epoch [11/50] batch [285/500] time 1.559 (1.561) data 0.000 (0.003) loss 1.2666 (1.1636) acc 75.0000 (70.5263) lr 1.8443e-03 eta 8:32:59
+epoch [11/50] batch [290/500] time 1.555 (1.561) data 0.000 (0.003) loss 1.1943 (1.1639) acc 71.8750 (70.4957) lr 1.8443e-03 eta 8:32:49
+epoch [11/50] batch [295/500] time 1.574 (1.561) data 0.000 (0.003) loss 1.4609 (1.1615) acc 65.6250 (70.5403) lr 1.8443e-03 eta 8:32:45
+epoch [11/50] batch [300/500] time 1.552 (1.561) data 0.000 (0.003) loss 1.4658 (1.1605) acc 59.3750 (70.5417) lr 1.8443e-03 eta 8:32:35
+epoch [11/50] batch [305/500] time 1.536 (1.561) data 0.001 (0.003) loss 0.8521 (1.1572) acc 71.8750 (70.6045) lr 1.8443e-03 eta 8:32:25
+epoch [11/50] batch [310/500] time 1.565 (1.561) data 0.000 (0.003) loss 1.5068 (1.1601) acc 71.8750 (70.5847) lr 1.8443e-03 eta 8:32:16
+epoch [11/50] batch [315/500] time 1.568 (1.561) data 0.000 (0.003) loss 1.0537 (1.1616) acc 75.0000 (70.5952) lr 1.8443e-03 eta 8:32:08
+epoch [11/50] batch [320/500] time 1.551 (1.561) data 0.000 (0.003) loss 1.7627 (1.1573) acc 59.3750 (70.6836) lr 1.8443e-03 eta 8:31:59
+epoch [11/50] batch [325/500] time 1.556 (1.561) data 0.001 (0.003) loss 0.4792 (1.1563) acc 87.5000 (70.7212) lr 1.8443e-03 eta 8:31:48
+epoch [11/50] batch [330/500] time 1.577 (1.561) data 0.000 (0.003) loss 0.9131 (1.1561) acc 68.7500 (70.6723) lr 1.8443e-03 eta 8:31:41
+epoch [11/50] batch [335/500] time 1.571 (1.561) data 0.000 (0.003) loss 0.8164 (1.1572) acc 87.5000 (70.6810) lr 1.8443e-03 eta 8:31:33
+epoch [11/50] batch [340/500] time 1.567 (1.561) data 0.000 (0.003) loss 1.7617 (1.1577) acc 65.6250 (70.6985) lr 1.8443e-03 eta 8:31:25
+epoch [11/50] batch [345/500] time 1.555 (1.561) data 0.001 (0.003) loss 1.2119 (1.1565) acc 78.1250 (70.7065) lr 1.8443e-03 eta 8:31:20
+epoch [11/50] batch [350/500] time 1.566 (1.561) data 0.000 (0.003) loss 1.6035 (1.1567) acc 65.6250 (70.7232) lr 1.8443e-03 eta 8:31:12
+epoch [11/50] batch [355/500] time 1.545 (1.561) data 0.000 (0.003) loss 1.8604 (1.1579) acc 53.1250 (70.6778) lr 1.8443e-03 eta 8:31:06
+epoch [11/50] batch [360/500] time 1.552 (1.561) data 0.000 (0.003) loss 1.2119 (1.1584) acc 62.5000 (70.6424) lr 1.8443e-03 eta 8:30:58
+epoch [11/50] batch [365/500] time 1.570 (1.561) data 0.000 (0.003) loss 0.9155 (1.1586) acc 84.3750 (70.7192) lr 1.8443e-03 eta 8:30:49
+epoch [11/50] batch [370/500] time 1.561 (1.561) data 0.000 (0.003) loss 0.9204 (1.1571) acc 68.7500 (70.6926) lr 1.8443e-03 eta 8:30:41
+epoch [11/50] batch [375/500] time 1.561 (1.561) data 0.000 (0.003) loss 1.0439 (1.1581) acc 71.8750 (70.6750) lr 1.8443e-03 eta 8:30:32
+epoch [11/50] batch [380/500] time 1.554 (1.561) data 0.000 (0.003) loss 0.8442 (1.1553) acc 81.2500 (70.7648) lr 1.8443e-03 eta 8:30:24
+epoch [11/50] batch [385/500] time 1.563 (1.561) data 0.000 (0.003) loss 0.9883 (1.1561) acc 78.1250 (70.7955) lr 1.8443e-03 eta 8:30:11
+epoch [11/50] batch [390/500] time 1.563 (1.561) data 0.000 (0.002) loss 0.6997 (1.1573) acc 84.3750 (70.8494) lr 1.8443e-03 eta 8:30:10
+epoch [11/50] batch [395/500] time 1.547 (1.561) data 0.001 (0.002) loss 1.0479 (1.1578) acc 68.7500 (70.8307) lr 1.8443e-03 eta 8:30:01
+epoch [11/50] batch [400/500] time 1.554 (1.561) data 0.000 (0.002) loss 0.9951 (1.1569) acc 65.6250 (70.8438) lr 1.8443e-03 eta 8:29:50
+epoch [11/50] batch [405/500] time 1.598 (1.561) data 0.000 (0.002) loss 1.6904 (1.1578) acc 68.7500 (70.8488) lr 1.8443e-03 eta 8:29:44
+epoch [11/50] batch [410/500] time 1.555 (1.561) data 0.001 (0.002) loss 1.4199 (1.1590) acc 65.6250 (70.8155) lr 1.8443e-03 eta 8:29:38
+epoch [11/50] batch [415/500] time 1.549 (1.561) data 0.000 (0.002) loss 0.7964 (1.1576) acc 78.1250 (70.8057) lr 1.8443e-03 eta 8:29:31
+epoch [11/50] batch [420/500] time 1.565 (1.561) data 0.000 (0.002) loss 1.2646 (1.1583) acc 71.8750 (70.8408) lr 1.8443e-03 eta 8:29:22
+epoch [11/50] batch [425/500] time 1.588 (1.561) data 0.001 (0.002) loss 0.9937 (1.1547) acc 68.7500 (70.9191) lr 1.8443e-03 eta 8:29:15
+epoch [11/50] batch [430/500] time 1.563 (1.561) data 0.000 (0.002) loss 0.9995 (1.1529) acc 75.0000 (70.9448) lr 1.8443e-03 eta 8:29:06
+epoch [11/50] batch [435/500] time 1.564 (1.561) data 0.000 (0.002) loss 0.7612 (1.1532) acc 71.8750 (70.9052) lr 1.8443e-03 eta 8:28:56
+epoch [11/50] batch [440/500] time 1.565 (1.561) data 0.000 (0.002) loss 1.4268 (1.1543) acc 65.6250 (70.8665) lr 1.8443e-03 eta 8:28:49
+epoch [11/50] batch [445/500] time 1.556 (1.561) data 0.001 (0.002) loss 0.7598 (1.1544) acc 81.2500 (70.9129) lr 1.8443e-03 eta 8:28:43
+epoch [11/50] batch [450/500] time 1.557 (1.561) data 0.000 (0.002) loss 1.3574 (1.1532) acc 78.1250 (70.9722) lr 1.8443e-03 eta 8:28:35
+epoch [11/50] batch [455/500] time 1.551 (1.561) data 0.000 (0.002) loss 1.1406 (1.1536) acc 71.8750 (70.9890) lr 1.8443e-03 eta 8:28:26
+epoch [11/50] batch [460/500] time 1.555 (1.561) data 0.000 (0.002) loss 2.0723 (1.1575) acc 46.8750 (70.9103) lr 1.8443e-03 eta 8:28:16
+epoch [11/50] batch [465/500] time 1.556 (1.561) data 0.001 (0.002) loss 0.8853 (1.1568) acc 78.1250 (70.9341) lr 1.8443e-03 eta 8:28:08
+epoch [11/50] batch [470/500] time 1.542 (1.561) data 0.000 (0.002) loss 1.4092 (1.1590) acc 59.3750 (70.8777) lr 1.8443e-03 eta 8:28:01
+epoch [11/50] batch [475/500] time 1.542 (1.561) data 0.000 (0.002) loss 1.8789 (1.1594) acc 53.1250 (70.8684) lr 1.8443e-03 eta 8:27:51
+epoch [11/50] batch [480/500] time 1.564 (1.560) data 0.000 (0.002) loss 1.1367 (1.1612) acc 71.8750 (70.8464) lr 1.8443e-03 eta 8:27:40
+epoch [11/50] batch [485/500] time 1.542 (1.560) data 0.001 (0.002) loss 1.2646 (1.1610) acc 75.0000 (70.8634) lr 1.8443e-03 eta 8:27:31
+epoch [11/50] batch [490/500] time 1.539 (1.561) data 0.000 (0.002) loss 1.1279 (1.1592) acc 65.6250 (70.8865) lr 1.8443e-03 eta 8:27:26
+epoch [11/50] batch [495/500] time 1.546 (1.560) data 0.000 (0.002) loss 1.1406 (1.1573) acc 78.1250 (70.9280) lr 1.8443e-03 eta 8:27:17
+epoch [11/50] batch [500/500] time 1.564 (1.560) data 0.000 (0.002) loss 1.6260 (1.1565) acc 65.6250 (70.9437) lr 1.8090e-03 eta 8:27:09
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,840
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.1%
+epoch [12/50] batch [5/500] time 1.541 (1.671) data 0.000 (0.162) loss 1.3418 (1.1914) acc 75.0000 (70.0000) lr 1.8090e-03 eta 9:02:59
+epoch [12/50] batch [10/500] time 1.575 (1.614) data 0.001 (0.081) loss 0.9624 (1.1471) acc 81.2500 (71.5625) lr 1.8090e-03 eta 8:44:11
+epoch [12/50] batch [15/500] time 1.566 (1.595) data 0.001 (0.054) loss 0.6597 (1.1317) acc 75.0000 (71.2500) lr 1.8090e-03 eta 8:37:55
+epoch [12/50] batch [20/500] time 1.570 (1.588) data 0.000 (0.041) loss 1.0889 (1.0639) acc 65.6250 (72.3438) lr 1.8090e-03 eta 8:35:27
+epoch [12/50] batch [25/500] time 1.565 (1.584) data 0.001 (0.033) loss 1.1943 (1.1051) acc 71.8750 (71.6250) lr 1.8090e-03 eta 8:34:08
+epoch [12/50] batch [30/500] time 1.556 (1.580) data 0.000 (0.027) loss 1.3223 (1.1496) acc 59.3750 (70.6250) lr 1.8090e-03 eta 8:32:38
+epoch [12/50] batch [35/500] time 1.542 (1.581) data 0.001 (0.024) loss 1.2314 (1.1492) acc 78.1250 (70.8929) lr 1.8090e-03 eta 8:32:49
+epoch [12/50] batch [40/500] time 1.533 (1.579) data 0.001 (0.021) loss 1.3164 (1.1487) acc 75.0000 (71.0156) lr 1.8090e-03 eta 8:32:08
+epoch [12/50] batch [45/500] time 1.561 (1.577) data 0.000 (0.018) loss 1.5527 (1.1423) acc 62.5000 (70.9722) lr 1.8090e-03 eta 8:31:20
+epoch [12/50] batch [50/500] time 1.558 (1.574) data 0.001 (0.017) loss 0.5581 (1.1039) acc 84.3750 (71.8750) lr 1.8090e-03 eta 8:30:06
+epoch [12/50] batch [55/500] time 1.534 (1.572) data 0.001 (0.015) loss 1.3262 (1.1008) acc 68.7500 (72.0455) lr 1.8090e-03 eta 8:29:18
+epoch [12/50] batch [60/500] time 1.566 (1.571) data 0.001 (0.014) loss 0.4045 (1.0862) acc 90.6250 (72.6042) lr 1.8090e-03 eta 8:29:07
+epoch [12/50] batch [65/500] time 1.554 (1.570) data 0.000 (0.013) loss 1.0020 (1.0842) acc 78.1250 (72.8365) lr 1.8090e-03 eta 8:28:42
+epoch [12/50] batch [70/500] time 1.566 (1.570) data 0.000 (0.012) loss 1.4414 (1.0980) acc 62.5000 (72.4554) lr 1.8090e-03 eta 8:28:17
+epoch [12/50] batch [75/500] time 1.562 (1.569) data 0.001 (0.011) loss 1.4023 (1.1102) acc 62.5000 (72.0000) lr 1.8090e-03 eta 8:27:58
+epoch [12/50] batch [80/500] time 1.551 (1.568) data 0.001 (0.011) loss 0.6592 (1.1086) acc 81.2500 (71.9531) lr 1.8090e-03 eta 8:27:27
+epoch [12/50] batch [85/500] time 1.555 (1.568) data 0.000 (0.010) loss 1.3672 (1.1111) acc 65.6250 (72.0956) lr 1.8090e-03 eta 8:27:13
+epoch [12/50] batch [90/500] time 1.561 (1.568) data 0.000 (0.009) loss 1.2559 (1.0972) acc 71.8750 (72.3264) lr 1.8090e-03 eta 8:27:10
+epoch [12/50] batch [95/500] time 1.577 (1.568) data 0.000 (0.009) loss 0.9419 (1.0888) acc 68.7500 (72.4342) lr 1.8090e-03 eta 8:27:05
+epoch [12/50] batch [100/500] time 1.541 (1.567) data 0.000 (0.009) loss 1.8037 (1.0977) acc 68.7500 (72.5938) lr 1.8090e-03 eta 8:26:41
+epoch [12/50] batch [105/500] time 1.580 (1.567) data 0.000 (0.008) loss 1.0518 (1.0928) acc 75.0000 (72.6488) lr 1.8090e-03 eta 8:26:40
+epoch [12/50] batch [110/500] time 1.553 (1.568) data 0.000 (0.008) loss 1.3008 (1.0982) acc 75.0000 (72.6420) lr 1.8090e-03 eta 8:26:33
+epoch [12/50] batch [115/500] time 1.539 (1.567) data 0.000 (0.007) loss 1.3467 (1.0960) acc 62.5000 (72.7717) lr 1.8090e-03 eta 8:26:10
+epoch [12/50] batch [120/500] time 1.564 (1.567) data 0.001 (0.007) loss 1.0801 (1.0898) acc 68.7500 (72.7083) lr 1.8090e-03 eta 8:25:59
+epoch [12/50] batch [125/500] time 1.577 (1.566) data 0.000 (0.007) loss 1.5293 (1.0967) acc 62.5000 (72.5750) lr 1.8090e-03 eta 8:25:46
+epoch [12/50] batch [130/500] time 1.569 (1.566) data 0.000 (0.007) loss 0.9038 (1.0962) acc 71.8750 (72.5721) lr 1.8090e-03 eta 8:25:35
+epoch [12/50] batch [135/500] time 1.562 (1.567) data 0.001 (0.006) loss 1.8877 (1.0997) acc 62.5000 (72.4769) lr 1.8090e-03 eta 8:25:41
+epoch [12/50] batch [140/500] time 1.560 (1.567) data 0.000 (0.006) loss 0.5303 (1.0928) acc 87.5000 (72.8795) lr 1.8090e-03 eta 8:25:31
+epoch [12/50] batch [145/500] time 1.607 (1.567) data 0.000 (0.006) loss 0.6987 (1.0887) acc 75.0000 (72.8448) lr 1.8090e-03 eta 8:25:21
+epoch [12/50] batch [150/500] time 1.564 (1.566) data 0.000 (0.006) loss 0.9683 (1.0933) acc 75.0000 (72.7292) lr 1.8090e-03 eta 8:25:09
+epoch [12/50] batch [155/500] time 1.535 (1.566) data 0.000 (0.006) loss 1.0684 (1.0999) acc 75.0000 (72.6210) lr 1.8090e-03 eta 8:24:55
+epoch [12/50] batch [160/500] time 1.604 (1.566) data 0.000 (0.006) loss 0.6919 (1.0971) acc 81.2500 (72.5781) lr 1.8090e-03 eta 8:24:40
+epoch [12/50] batch [165/500] time 1.569 (1.566) data 0.000 (0.005) loss 0.9839 (1.0922) acc 78.1250 (72.6515) lr 1.8090e-03 eta 8:24:29
+epoch [12/50] batch [170/500] time 1.578 (1.565) data 0.000 (0.005) loss 0.8433 (1.0848) acc 71.8750 (72.7941) lr 1.8090e-03 eta 8:24:20
+epoch [12/50] batch [175/500] time 1.535 (1.565) data 0.000 (0.005) loss 1.3418 (1.0881) acc 65.6250 (72.6786) lr 1.8090e-03 eta 8:24:09
+epoch [12/50] batch [180/500] time 1.555 (1.566) data 0.000 (0.005) loss 0.9819 (1.0870) acc 65.6250 (72.6042) lr 1.8090e-03 eta 8:24:08
+epoch [12/50] batch [185/500] time 1.558 (1.566) data 0.000 (0.005) loss 0.6860 (1.0868) acc 78.1250 (72.6520) lr 1.8090e-03 eta 8:23:58
+epoch [12/50] batch [190/500] time 1.574 (1.566) data 0.000 (0.005) loss 1.2197 (1.0922) acc 71.8750 (72.5329) lr 1.8090e-03 eta 8:23:50
+epoch [12/50] batch [195/500] time 1.532 (1.565) data 0.000 (0.005) loss 1.1260 (1.0931) acc 75.0000 (72.4679) lr 1.8090e-03 eta 8:23:28
+epoch [12/50] batch [200/500] time 1.551 (1.564) data 0.000 (0.004) loss 0.8516 (1.0932) acc 71.8750 (72.4688) lr 1.8090e-03 eta 8:23:13
+epoch [12/50] batch [205/500] time 1.542 (1.564) data 0.000 (0.004) loss 1.6348 (1.0978) acc 65.6250 (72.3933) lr 1.8090e-03 eta 8:23:04
+epoch [12/50] batch [210/500] time 1.548 (1.564) data 0.000 (0.004) loss 1.3682 (1.0981) acc 71.8750 (72.4107) lr 1.8090e-03 eta 8:22:50
+epoch [12/50] batch [215/500] time 1.558 (1.564) data 0.000 (0.004) loss 1.0234 (1.0945) acc 62.5000 (72.3692) lr 1.8090e-03 eta 8:22:35
+epoch [12/50] batch [220/500] time 1.572 (1.564) data 0.000 (0.004) loss 1.2090 (1.0962) acc 75.0000 (72.3438) lr 1.8090e-03 eta 8:22:28
+epoch [12/50] batch [225/500] time 1.553 (1.564) data 0.000 (0.004) loss 1.3418 (1.1024) acc 68.7500 (72.2222) lr 1.8090e-03 eta 8:22:23
+epoch [12/50] batch [230/500] time 1.561 (1.564) data 0.000 (0.004) loss 1.3428 (1.1030) acc 62.5000 (72.2283) lr 1.8090e-03 eta 8:22:16
+epoch [12/50] batch [235/500] time 1.563 (1.564) data 0.000 (0.004) loss 1.3438 (1.1051) acc 56.2500 (72.1543) lr 1.8090e-03 eta 8:22:07
+epoch [12/50] batch [240/500] time 1.544 (1.564) data 0.000 (0.004) loss 1.4297 (1.1079) acc 56.2500 (72.0182) lr 1.8090e-03 eta 8:21:57
+epoch [12/50] batch [245/500] time 1.532 (1.563) data 0.000 (0.004) loss 1.0449 (1.1064) acc 75.0000 (72.0026) lr 1.8090e-03 eta 8:21:44
+epoch [12/50] batch [250/500] time 1.582 (1.564) data 0.000 (0.004) loss 1.6553 (1.1069) acc 62.5000 (72.0125) lr 1.8090e-03 eta 8:21:42
+epoch [12/50] batch [255/500] time 1.555 (1.564) data 0.000 (0.004) loss 0.9102 (1.1123) acc 71.8750 (71.8505) lr 1.8090e-03 eta 8:21:30
+epoch [12/50] batch [260/500] time 1.540 (1.563) data 0.000 (0.004) loss 1.7080 (1.1159) acc 56.2500 (71.7668) lr 1.8090e-03 eta 8:21:18
+epoch [12/50] batch [265/500] time 1.556 (1.563) data 0.000 (0.003) loss 1.0078 (1.1131) acc 75.0000 (71.8514) lr 1.8090e-03 eta 8:21:07
+epoch [12/50] batch [270/500] time 1.556 (1.563) data 0.001 (0.003) loss 0.9292 (1.1119) acc 68.7500 (71.7940) lr 1.8090e-03 eta 8:20:59
+epoch [12/50] batch [275/500] time 1.573 (1.563) data 0.001 (0.003) loss 1.5391 (1.1136) acc 71.8750 (71.7727) lr 1.8090e-03 eta 8:20:52
+epoch [12/50] batch [280/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.2041 (1.1121) acc 71.8750 (71.7969) lr 1.8090e-03 eta 8:20:54
+epoch [12/50] batch [285/500] time 1.538 (1.564) data 0.000 (0.003) loss 1.1875 (1.1149) acc 71.8750 (71.7325) lr 1.8090e-03 eta 8:20:45
+epoch [12/50] batch [290/500] time 1.564 (1.564) data 0.001 (0.003) loss 1.4727 (1.1174) acc 68.7500 (71.6595) lr 1.8090e-03 eta 8:20:39
+epoch [12/50] batch [295/500] time 1.555 (1.564) data 0.001 (0.003) loss 1.2803 (1.1197) acc 59.3750 (71.6314) lr 1.8090e-03 eta 8:20:32
+epoch [12/50] batch [300/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.6328 (1.1216) acc 59.3750 (71.5833) lr 1.8090e-03 eta 8:20:20
+epoch [12/50] batch [305/500] time 1.551 (1.563) data 0.000 (0.003) loss 1.1992 (1.1221) acc 68.7500 (71.5984) lr 1.8090e-03 eta 8:20:10
+epoch [12/50] batch [310/500] time 1.536 (1.563) data 0.001 (0.003) loss 1.1035 (1.1265) acc 68.7500 (71.5020) lr 1.8090e-03 eta 8:19:59
+epoch [12/50] batch [315/500] time 1.559 (1.563) data 0.000 (0.003) loss 1.6729 (1.1268) acc 62.5000 (71.5179) lr 1.8090e-03 eta 8:19:51
+epoch [12/50] batch [320/500] time 1.660 (1.564) data 0.000 (0.003) loss 1.1211 (1.1274) acc 68.7500 (71.5039) lr 1.8090e-03 eta 8:19:48
+epoch [12/50] batch [325/500] time 1.579 (1.564) data 0.000 (0.003) loss 0.8096 (1.1264) acc 78.1250 (71.5096) lr 1.8090e-03 eta 8:19:40
+epoch [12/50] batch [330/500] time 1.575 (1.564) data 0.001 (0.003) loss 1.1035 (1.1250) acc 65.6250 (71.4678) lr 1.8090e-03 eta 8:19:34
+epoch [12/50] batch [335/500] time 1.567 (1.564) data 0.000 (0.003) loss 1.7539 (1.1289) acc 59.3750 (71.3713) lr 1.8090e-03 eta 8:19:27
+epoch [12/50] batch [340/500] time 1.572 (1.564) data 0.000 (0.003) loss 0.9707 (1.1304) acc 75.0000 (71.3511) lr 1.8090e-03 eta 8:19:22
+epoch [12/50] batch [345/500] time 1.573 (1.564) data 0.000 (0.003) loss 1.7676 (1.1313) acc 56.2500 (71.3134) lr 1.8090e-03 eta 8:19:13
+epoch [12/50] batch [350/500] time 1.551 (1.564) data 0.001 (0.003) loss 1.4014 (1.1317) acc 65.6250 (71.3304) lr 1.8090e-03 eta 8:19:03
+epoch [12/50] batch [355/500] time 1.573 (1.564) data 0.001 (0.003) loss 1.4932 (1.1336) acc 68.7500 (71.3468) lr 1.8090e-03 eta 8:18:54
+epoch [12/50] batch [360/500] time 1.538 (1.563) data 0.000 (0.003) loss 1.0156 (1.1338) acc 68.7500 (71.3368) lr 1.8090e-03 eta 8:18:42
+epoch [12/50] batch [365/500] time 1.570 (1.563) data 0.000 (0.003) loss 0.6875 (1.1317) acc 81.2500 (71.3613) lr 1.8090e-03 eta 8:18:31
+epoch [12/50] batch [370/500] time 1.540 (1.563) data 0.001 (0.003) loss 1.2656 (1.1332) acc 68.7500 (71.3176) lr 1.8090e-03 eta 8:18:19
+epoch [12/50] batch [375/500] time 1.547 (1.563) data 0.001 (0.003) loss 0.7466 (1.1319) acc 75.0000 (71.3583) lr 1.8090e-03 eta 8:18:08
+epoch [12/50] batch [380/500] time 1.572 (1.563) data 0.000 (0.003) loss 0.4326 (1.1283) acc 90.6250 (71.4638) lr 1.8090e-03 eta 8:17:59
+epoch [12/50] batch [385/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.3477 (1.1282) acc 71.8750 (71.4692) lr 1.8090e-03 eta 8:17:51
+epoch [12/50] batch [390/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.2705 (1.1296) acc 65.6250 (71.4423) lr 1.8090e-03 eta 8:17:41
+epoch [12/50] batch [395/500] time 1.543 (1.563) data 0.000 (0.002) loss 0.9048 (1.1305) acc 65.6250 (71.3924) lr 1.8090e-03 eta 8:17:34
+epoch [12/50] batch [400/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.2520 (1.1331) acc 68.7500 (71.3828) lr 1.8090e-03 eta 8:17:23
+epoch [12/50] batch [405/500] time 1.543 (1.562) data 0.000 (0.002) loss 1.6963 (1.1332) acc 56.2500 (71.3812) lr 1.8090e-03 eta 8:17:12
+epoch [12/50] batch [410/500] time 1.574 (1.562) data 0.001 (0.002) loss 0.7632 (1.1300) acc 84.3750 (71.4253) lr 1.8090e-03 eta 8:17:04
+epoch [12/50] batch [415/500] time 1.570 (1.562) data 0.000 (0.002) loss 1.3242 (1.1322) acc 71.8750 (71.3780) lr 1.8090e-03 eta 8:16:53
+epoch [12/50] batch [420/500] time 1.573 (1.562) data 0.000 (0.002) loss 0.9707 (1.1307) acc 71.8750 (71.4360) lr 1.8090e-03 eta 8:16:49
+epoch [12/50] batch [425/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.6396 (1.1308) acc 62.5000 (71.4485) lr 1.8090e-03 eta 8:16:40
+epoch [12/50] batch [430/500] time 1.537 (1.562) data 0.000 (0.002) loss 1.7334 (1.1340) acc 65.6250 (71.4099) lr 1.8090e-03 eta 8:16:32
+epoch [12/50] batch [435/500] time 1.552 (1.562) data 0.000 (0.002) loss 1.3271 (1.1349) acc 71.8750 (71.4152) lr 1.8090e-03 eta 8:16:26
+epoch [12/50] batch [440/500] time 1.566 (1.562) data 0.000 (0.002) loss 1.1855 (1.1356) acc 68.7500 (71.3423) lr 1.8090e-03 eta 8:16:18
+epoch [12/50] batch [445/500] time 1.569 (1.562) data 0.000 (0.002) loss 1.3721 (1.1370) acc 62.5000 (71.3062) lr 1.8090e-03 eta 8:16:12
+epoch [12/50] batch [450/500] time 1.577 (1.563) data 0.000 (0.002) loss 1.0215 (1.1355) acc 75.0000 (71.3403) lr 1.8090e-03 eta 8:16:07
+epoch [12/50] batch [455/500] time 1.541 (1.563) data 0.000 (0.002) loss 1.2568 (1.1374) acc 62.5000 (71.2912) lr 1.8090e-03 eta 8:16:00
+epoch [12/50] batch [460/500] time 1.548 (1.563) data 0.000 (0.002) loss 1.6602 (1.1427) acc 56.2500 (71.2092) lr 1.8090e-03 eta 8:15:54
+epoch [12/50] batch [465/500] time 1.569 (1.563) data 0.000 (0.002) loss 1.1523 (1.1436) acc 65.6250 (71.1694) lr 1.8090e-03 eta 8:15:52
+epoch [12/50] batch [470/500] time 1.569 (1.563) data 0.000 (0.002) loss 1.0234 (1.1427) acc 65.6250 (71.1569) lr 1.8090e-03 eta 8:15:42
+epoch [12/50] batch [475/500] time 1.570 (1.563) data 0.000 (0.002) loss 0.5332 (1.1422) acc 84.3750 (71.1842) lr 1.8090e-03 eta 8:15:33
+epoch [12/50] batch [480/500] time 1.566 (1.563) data 0.000 (0.002) loss 0.9316 (1.1387) acc 75.0000 (71.2760) lr 1.8090e-03 eta 8:15:27
+epoch [12/50] batch [485/500] time 1.574 (1.563) data 0.001 (0.002) loss 1.4268 (1.1375) acc 62.5000 (71.2822) lr 1.8090e-03 eta 8:15:19
+epoch [12/50] batch [490/500] time 1.543 (1.563) data 0.000 (0.002) loss 1.3701 (1.1388) acc 75.0000 (71.2755) lr 1.8090e-03 eta 8:15:08
+epoch [12/50] batch [495/500] time 1.554 (1.563) data 0.000 (0.002) loss 0.7710 (1.1378) acc 78.1250 (71.2689) lr 1.8090e-03 eta 8:14:59
+epoch [12/50] batch [500/500] time 1.544 (1.563) data 0.000 (0.002) loss 0.8726 (1.1376) acc 65.6250 (71.2250) lr 1.7705e-03 eta 8:14:48
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,836
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.2%
+epoch [13/50] batch [5/500] time 1.540 (1.642) data 0.000 (0.146) loss 1.5537 (1.2555) acc 65.6250 (68.1250) lr 1.7705e-03 eta 8:39:41
+epoch [13/50] batch [10/500] time 1.558 (1.597) data 0.000 (0.073) loss 0.7593 (1.1264) acc 75.0000 (71.8750) lr 1.7705e-03 eta 8:25:28
+epoch [13/50] batch [15/500] time 1.541 (1.584) data 0.000 (0.049) loss 0.8159 (1.1257) acc 81.2500 (71.8750) lr 1.7705e-03 eta 8:21:08
+epoch [13/50] batch [20/500] time 1.543 (1.576) data 0.000 (0.037) loss 1.3799 (1.1493) acc 65.6250 (71.8750) lr 1.7705e-03 eta 8:18:41
+epoch [13/50] batch [25/500] time 1.555 (1.579) data 0.001 (0.029) loss 1.4561 (1.1832) acc 68.7500 (71.0000) lr 1.7705e-03 eta 8:19:29
+epoch [13/50] batch [30/500] time 1.574 (1.577) data 0.000 (0.025) loss 0.7671 (1.1530) acc 78.1250 (71.9792) lr 1.7705e-03 eta 8:18:42
+epoch [13/50] batch [35/500] time 1.586 (1.576) data 0.001 (0.021) loss 0.9263 (1.1301) acc 78.1250 (72.4107) lr 1.7705e-03 eta 8:18:10
+epoch [13/50] batch [40/500] time 1.569 (1.574) data 0.000 (0.019) loss 1.6221 (1.1521) acc 65.6250 (72.2656) lr 1.7705e-03 eta 8:17:29
+epoch [13/50] batch [45/500] time 1.573 (1.573) data 0.000 (0.017) loss 0.4153 (1.1509) acc 81.2500 (72.2222) lr 1.7705e-03 eta 8:16:50
+epoch [13/50] batch [50/500] time 1.572 (1.572) data 0.000 (0.015) loss 1.1602 (1.1336) acc 71.8750 (72.6250) lr 1.7705e-03 eta 8:16:36
+epoch [13/50] batch [55/500] time 1.559 (1.572) data 0.000 (0.014) loss 0.8091 (1.1304) acc 71.8750 (72.4432) lr 1.7705e-03 eta 8:16:22
+epoch [13/50] batch [60/500] time 1.555 (1.571) data 0.001 (0.013) loss 0.9800 (1.1136) acc 75.0000 (72.6042) lr 1.7705e-03 eta 8:15:46
+epoch [13/50] batch [65/500] time 1.577 (1.571) data 0.000 (0.012) loss 0.4883 (1.1077) acc 84.3750 (72.6442) lr 1.7705e-03 eta 8:15:42
+epoch [13/50] batch [70/500] time 1.559 (1.570) data 0.000 (0.011) loss 0.6631 (1.0919) acc 90.6250 (73.3482) lr 1.7705e-03 eta 8:15:22
+epoch [13/50] batch [75/500] time 1.560 (1.570) data 0.001 (0.010) loss 1.1436 (1.0845) acc 65.6250 (73.2917) lr 1.7705e-03 eta 8:15:08
+epoch [13/50] batch [80/500] time 1.571 (1.570) data 0.001 (0.010) loss 1.2705 (1.0937) acc 78.1250 (73.1641) lr 1.7705e-03 eta 8:14:56
+epoch [13/50] batch [85/500] time 1.565 (1.570) data 0.001 (0.009) loss 1.0986 (1.0867) acc 84.3750 (73.4191) lr 1.7705e-03 eta 8:15:00
+epoch [13/50] batch [90/500] time 1.551 (1.569) data 0.001 (0.009) loss 1.0684 (1.0911) acc 78.1250 (73.1944) lr 1.7705e-03 eta 8:14:35
+epoch [13/50] batch [95/500] time 1.581 (1.569) data 0.001 (0.008) loss 0.8657 (1.0864) acc 71.8750 (73.2237) lr 1.7705e-03 eta 8:14:24
+epoch [13/50] batch [100/500] time 1.529 (1.568) data 0.000 (0.008) loss 1.0186 (1.0914) acc 81.2500 (73.1250) lr 1.7705e-03 eta 8:13:52
+epoch [13/50] batch [105/500] time 1.545 (1.568) data 0.000 (0.007) loss 1.1445 (1.0985) acc 68.7500 (72.8869) lr 1.7705e-03 eta 8:13:40
+epoch [13/50] batch [110/500] time 1.544 (1.567) data 0.000 (0.007) loss 1.2588 (1.0995) acc 68.7500 (72.8125) lr 1.7705e-03 eta 8:13:25
+epoch [13/50] batch [115/500] time 1.548 (1.566) data 0.000 (0.007) loss 1.5605 (1.0987) acc 62.5000 (72.8804) lr 1.7705e-03 eta 8:13:01
+epoch [13/50] batch [120/500] time 1.560 (1.566) data 0.000 (0.007) loss 1.3242 (1.1039) acc 75.0000 (72.8646) lr 1.7705e-03 eta 8:12:50
+epoch [13/50] batch [125/500] time 1.654 (1.567) data 0.001 (0.006) loss 0.9854 (1.1118) acc 75.0000 (72.6500) lr 1.7705e-03 eta 8:12:48
+epoch [13/50] batch [130/500] time 1.573 (1.566) data 0.001 (0.006) loss 1.2266 (1.1030) acc 68.7500 (72.7644) lr 1.7705e-03 eta 8:12:33
+epoch [13/50] batch [135/500] time 1.535 (1.565) data 0.001 (0.006) loss 0.8745 (1.0895) acc 81.2500 (73.0093) lr 1.7705e-03 eta 8:12:12
+epoch [13/50] batch [140/500] time 1.594 (1.566) data 0.000 (0.006) loss 1.2764 (1.0909) acc 65.6250 (72.9911) lr 1.7705e-03 eta 8:12:05
+epoch [13/50] batch [145/500] time 1.570 (1.565) data 0.000 (0.005) loss 1.4238 (1.0850) acc 59.3750 (73.0172) lr 1.7705e-03 eta 8:11:48
+epoch [13/50] batch [150/500] time 1.565 (1.565) data 0.000 (0.005) loss 1.3564 (1.0839) acc 62.5000 (72.9375) lr 1.7705e-03 eta 8:11:36
+epoch [13/50] batch [155/500] time 1.565 (1.565) data 0.000 (0.005) loss 0.8750 (1.0816) acc 68.7500 (72.9839) lr 1.7705e-03 eta 8:11:29
+epoch [13/50] batch [160/500] time 1.525 (1.564) data 0.000 (0.005) loss 0.8755 (1.0856) acc 78.1250 (72.9297) lr 1.7705e-03 eta 8:11:10
+epoch [13/50] batch [165/500] time 1.571 (1.564) data 0.000 (0.005) loss 0.8662 (1.0844) acc 81.2500 (72.8409) lr 1.7705e-03 eta 8:10:54
+epoch [13/50] batch [170/500] time 1.561 (1.563) data 0.001 (0.005) loss 0.7271 (1.0843) acc 75.0000 (72.7757) lr 1.7705e-03 eta 8:10:39
+epoch [13/50] batch [175/500] time 1.553 (1.563) data 0.000 (0.005) loss 0.5771 (1.0837) acc 78.1250 (72.7679) lr 1.7705e-03 eta 8:10:28
+epoch [13/50] batch [180/500] time 1.543 (1.563) data 0.000 (0.004) loss 1.4434 (1.0892) acc 59.3750 (72.6042) lr 1.7705e-03 eta 8:10:18
+epoch [13/50] batch [185/500] time 1.562 (1.563) data 0.001 (0.004) loss 1.0332 (1.0947) acc 71.8750 (72.4662) lr 1.7705e-03 eta 8:10:11
+epoch [13/50] batch [190/500] time 1.549 (1.563) data 0.000 (0.004) loss 1.5420 (1.0976) acc 62.5000 (72.5000) lr 1.7705e-03 eta 8:10:01
+epoch [13/50] batch [195/500] time 1.540 (1.563) data 0.000 (0.004) loss 1.1221 (1.0908) acc 75.0000 (72.6923) lr 1.7705e-03 eta 8:09:48
+epoch [13/50] batch [200/500] time 1.570 (1.563) data 0.000 (0.004) loss 0.9048 (1.0872) acc 75.0000 (72.7500) lr 1.7705e-03 eta 8:09:42
+epoch [13/50] batch [205/500] time 1.555 (1.563) data 0.000 (0.004) loss 1.0068 (1.0876) acc 81.2500 (72.8811) lr 1.7705e-03 eta 8:09:33
+epoch [13/50] batch [210/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.1211 (1.0859) acc 75.0000 (72.9315) lr 1.7705e-03 eta 8:09:19
+epoch [13/50] batch [215/500] time 1.574 (1.562) data 0.000 (0.004) loss 1.6572 (1.0901) acc 65.6250 (72.8779) lr 1.7705e-03 eta 8:09:09
+epoch [13/50] batch [220/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.1748 (1.0900) acc 75.0000 (72.8977) lr 1.7705e-03 eta 8:08:58
+epoch [13/50] batch [225/500] time 1.538 (1.562) data 0.000 (0.004) loss 1.2568 (1.0903) acc 68.7500 (72.8611) lr 1.7705e-03 eta 8:08:55
+epoch [13/50] batch [230/500] time 1.547 (1.562) data 0.001 (0.004) loss 0.7739 (1.0970) acc 78.1250 (72.7310) lr 1.7705e-03 eta 8:08:40
+epoch [13/50] batch [235/500] time 1.562 (1.562) data 0.000 (0.004) loss 1.1377 (1.0965) acc 62.5000 (72.6463) lr 1.7705e-03 eta 8:08:29
+epoch [13/50] batch [240/500] time 1.552 (1.562) data 0.001 (0.003) loss 1.1260 (1.0983) acc 68.7500 (72.5911) lr 1.7705e-03 eta 8:08:20
+epoch [13/50] batch [245/500] time 1.591 (1.562) data 0.000 (0.003) loss 1.4814 (1.0991) acc 56.2500 (72.6403) lr 1.7705e-03 eta 8:08:11
+epoch [13/50] batch [250/500] time 1.566 (1.562) data 0.001 (0.003) loss 1.2178 (1.0943) acc 62.5000 (72.6500) lr 1.7705e-03 eta 8:08:03
+epoch [13/50] batch [255/500] time 1.536 (1.562) data 0.000 (0.003) loss 1.7275 (1.0971) acc 56.2500 (72.5858) lr 1.7705e-03 eta 8:07:53
+epoch [13/50] batch [260/500] time 1.547 (1.562) data 0.000 (0.003) loss 1.2412 (1.0975) acc 75.0000 (72.6683) lr 1.7705e-03 eta 8:07:45
+epoch [13/50] batch [265/500] time 1.582 (1.562) data 0.000 (0.003) loss 1.0586 (1.0999) acc 65.6250 (72.5590) lr 1.7705e-03 eta 8:07:40
+epoch [13/50] batch [270/500] time 1.579 (1.562) data 0.000 (0.003) loss 1.1670 (1.0975) acc 78.1250 (72.6389) lr 1.7705e-03 eta 8:07:44
+epoch [13/50] batch [275/500] time 1.552 (1.562) data 0.001 (0.003) loss 0.5547 (1.1052) acc 84.3750 (72.4545) lr 1.7705e-03 eta 8:07:36
+epoch [13/50] batch [280/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.5234 (1.1085) acc 62.5000 (72.4330) lr 1.7705e-03 eta 8:07:26
+epoch [13/50] batch [285/500] time 1.552 (1.562) data 0.001 (0.003) loss 0.7832 (1.1053) acc 71.8750 (72.4232) lr 1.7705e-03 eta 8:07:16
+epoch [13/50] batch [290/500] time 1.546 (1.562) data 0.000 (0.003) loss 1.1387 (1.1059) acc 71.8750 (72.4569) lr 1.7705e-03 eta 8:07:06
+epoch [13/50] batch [295/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.4219 (1.1059) acc 62.5000 (72.4894) lr 1.7705e-03 eta 8:06:54
+epoch [13/50] batch [300/500] time 1.572 (1.562) data 0.001 (0.003) loss 1.7227 (1.1076) acc 50.0000 (72.4375) lr 1.7705e-03 eta 8:06:50
+epoch [13/50] batch [305/500] time 1.538 (1.562) data 0.000 (0.003) loss 1.3721 (1.1066) acc 68.7500 (72.4078) lr 1.7705e-03 eta 8:06:38
+epoch [13/50] batch [310/500] time 1.563 (1.562) data 0.001 (0.003) loss 1.2559 (1.1107) acc 65.6250 (72.4093) lr 1.7705e-03 eta 8:06:31
+epoch [13/50] batch [315/500] time 1.555 (1.562) data 0.000 (0.003) loss 1.4131 (1.1111) acc 68.7500 (72.4206) lr 1.7705e-03 eta 8:06:19
+epoch [13/50] batch [320/500] time 1.562 (1.562) data 0.000 (0.003) loss 1.1396 (1.1132) acc 71.8750 (72.3730) lr 1.7705e-03 eta 8:06:11
+epoch [13/50] batch [325/500] time 1.565 (1.562) data 0.001 (0.003) loss 1.3232 (1.1164) acc 59.3750 (72.3077) lr 1.7705e-03 eta 8:06:04
+epoch [13/50] batch [330/500] time 1.575 (1.562) data 0.000 (0.003) loss 1.1914 (1.1178) acc 68.7500 (72.2538) lr 1.7705e-03 eta 8:05:58
+epoch [13/50] batch [335/500] time 1.534 (1.562) data 0.000 (0.003) loss 1.2148 (1.1207) acc 71.8750 (72.1642) lr 1.7705e-03 eta 8:05:53
+epoch [13/50] batch [340/500] time 1.598 (1.562) data 0.001 (0.003) loss 1.0801 (1.1233) acc 75.0000 (72.1140) lr 1.7705e-03 eta 8:05:46
+epoch [13/50] batch [345/500] time 1.585 (1.562) data 0.000 (0.003) loss 1.4824 (1.1238) acc 68.7500 (72.1467) lr 1.7705e-03 eta 8:05:42
+epoch [13/50] batch [350/500] time 1.546 (1.562) data 0.001 (0.003) loss 0.8257 (1.1229) acc 78.1250 (72.1786) lr 1.7705e-03 eta 8:05:34
+epoch [13/50] batch [355/500] time 1.533 (1.562) data 0.000 (0.002) loss 1.5898 (1.1232) acc 71.8750 (72.1831) lr 1.7705e-03 eta 8:05:25
+epoch [13/50] batch [360/500] time 1.600 (1.562) data 0.000 (0.002) loss 0.9292 (1.1233) acc 78.1250 (72.1788) lr 1.7705e-03 eta 8:05:22
+epoch [13/50] batch [365/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.8848 (1.1243) acc 81.2500 (72.1747) lr 1.7705e-03 eta 8:05:14
+epoch [13/50] batch [370/500] time 1.538 (1.563) data 0.001 (0.002) loss 1.3818 (1.1243) acc 68.7500 (72.1622) lr 1.7705e-03 eta 8:05:10
+epoch [13/50] batch [375/500] time 1.566 (1.563) data 0.001 (0.002) loss 1.2715 (1.1224) acc 71.8750 (72.2417) lr 1.7705e-03 eta 8:05:03
+epoch [13/50] batch [380/500] time 1.580 (1.563) data 0.000 (0.002) loss 1.6631 (1.1241) acc 65.6250 (72.2039) lr 1.7705e-03 eta 8:04:55
+epoch [13/50] batch [385/500] time 1.574 (1.563) data 0.000 (0.002) loss 0.8389 (1.1235) acc 78.1250 (72.1347) lr 1.7705e-03 eta 8:04:48
+epoch [13/50] batch [390/500] time 1.554 (1.563) data 0.000 (0.002) loss 1.0771 (1.1274) acc 71.8750 (72.0833) lr 1.7705e-03 eta 8:04:38
+epoch [13/50] batch [395/500] time 1.547 (1.562) data 0.000 (0.002) loss 0.7031 (1.1263) acc 78.1250 (72.1361) lr 1.7705e-03 eta 8:04:29
+epoch [13/50] batch [400/500] time 1.560 (1.562) data 0.000 (0.002) loss 1.1973 (1.1262) acc 65.6250 (72.1328) lr 1.7705e-03 eta 8:04:22
+epoch [13/50] batch [405/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.8442 (1.1312) acc 71.8750 (72.0602) lr 1.7705e-03 eta 8:04:14
+epoch [13/50] batch [410/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.3027 (1.1318) acc 68.7500 (72.0198) lr 1.7705e-03 eta 8:04:05
+epoch [13/50] batch [415/500] time 1.526 (1.563) data 0.000 (0.002) loss 1.3945 (1.1329) acc 62.5000 (72.0181) lr 1.7705e-03 eta 8:03:59
+epoch [13/50] batch [420/500] time 1.550 (1.563) data 0.000 (0.002) loss 0.9829 (1.1339) acc 75.0000 (72.0610) lr 1.7705e-03 eta 8:03:51
+epoch [13/50] batch [425/500] time 1.552 (1.562) data 0.000 (0.002) loss 1.5527 (1.1348) acc 62.5000 (72.0147) lr 1.7705e-03 eta 8:03:40
+epoch [13/50] batch [430/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.1191 (1.1371) acc 78.1250 (72.0131) lr 1.7705e-03 eta 8:03:33
+epoch [13/50] batch [435/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.7090 (1.1364) acc 75.0000 (71.9971) lr 1.7705e-03 eta 8:03:25
+epoch [13/50] batch [440/500] time 1.533 (1.562) data 0.000 (0.002) loss 1.9336 (1.1376) acc 59.3750 (71.9531) lr 1.7705e-03 eta 8:03:14
+epoch [13/50] batch [445/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.2471 (1.1393) acc 68.7500 (71.9031) lr 1.7705e-03 eta 8:03:04
+epoch [13/50] batch [450/500] time 1.549 (1.562) data 0.001 (0.002) loss 1.2383 (1.1396) acc 68.7500 (71.9028) lr 1.7705e-03 eta 8:02:59
+epoch [13/50] batch [455/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.0674 (1.1375) acc 71.8750 (71.9368) lr 1.7705e-03 eta 8:02:51
+epoch [13/50] batch [460/500] time 1.561 (1.562) data 0.002 (0.002) loss 1.1426 (1.1378) acc 68.7500 (71.9226) lr 1.7705e-03 eta 8:02:44
+epoch [13/50] batch [465/500] time 1.571 (1.562) data 0.000 (0.002) loss 1.3672 (1.1391) acc 71.8750 (71.9019) lr 1.7705e-03 eta 8:02:37
+epoch [13/50] batch [470/500] time 1.550 (1.562) data 0.002 (0.002) loss 0.9819 (1.1401) acc 71.8750 (71.8617) lr 1.7705e-03 eta 8:02:28
+epoch [13/50] batch [475/500] time 1.573 (1.562) data 0.000 (0.002) loss 1.0547 (1.1379) acc 71.8750 (71.8816) lr 1.7705e-03 eta 8:02:19
+epoch [13/50] batch [480/500] time 1.560 (1.562) data 0.000 (0.002) loss 1.3242 (1.1381) acc 68.7500 (71.8555) lr 1.7705e-03 eta 8:02:10
+epoch [13/50] batch [485/500] time 1.582 (1.562) data 0.001 (0.002) loss 1.1387 (1.1390) acc 71.8750 (71.8686) lr 1.7705e-03 eta 8:02:02
+epoch [13/50] batch [490/500] time 1.594 (1.562) data 0.000 (0.002) loss 1.0713 (1.1391) acc 78.1250 (71.8431) lr 1.7705e-03 eta 8:01:56
+epoch [13/50] batch [495/500] time 1.575 (1.562) data 0.000 (0.002) loss 0.8530 (1.1376) acc 71.8750 (71.8371) lr 1.7705e-03 eta 8:01:49
+epoch [13/50] batch [500/500] time 1.572 (1.562) data 0.000 (0.002) loss 1.2891 (1.1379) acc 65.6250 (71.8187) lr 1.7290e-03 eta 8:01:42
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,978
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [14/50] batch [5/500] time 1.567 (1.662) data 0.000 (0.165) loss 0.7349 (1.1411) acc 84.3750 (78.1250) lr 1.7290e-03 eta 8:32:12
+epoch [14/50] batch [10/500] time 1.568 (1.613) data 0.000 (0.083) loss 0.7437 (1.0822) acc 81.2500 (76.2500) lr 1.7290e-03 eta 8:16:56
+epoch [14/50] batch [15/500] time 1.552 (1.591) data 0.001 (0.055) loss 0.7139 (1.0884) acc 84.3750 (74.3750) lr 1.7290e-03 eta 8:10:16
+epoch [14/50] batch [20/500] time 1.572 (1.591) data 0.001 (0.041) loss 1.0918 (1.0852) acc 68.7500 (73.5938) lr 1.7290e-03 eta 8:10:00
+epoch [14/50] batch [25/500] time 1.554 (1.584) data 0.000 (0.033) loss 1.6953 (1.0643) acc 65.6250 (74.1250) lr 1.7290e-03 eta 8:07:41
+epoch [14/50] batch [30/500] time 1.551 (1.578) data 0.000 (0.028) loss 1.1006 (1.0622) acc 75.0000 (73.8542) lr 1.7290e-03 eta 8:05:49
+epoch [14/50] batch [35/500] time 1.565 (1.577) data 0.000 (0.024) loss 1.3223 (1.0786) acc 53.1250 (72.3214) lr 1.7290e-03 eta 8:05:15
+epoch [14/50] batch [40/500] time 1.566 (1.573) data 0.000 (0.021) loss 0.3613 (1.0661) acc 93.7500 (73.1250) lr 1.7290e-03 eta 8:04:04
+epoch [14/50] batch [45/500] time 1.550 (1.572) data 0.000 (0.019) loss 1.1299 (1.0500) acc 75.0000 (73.6111) lr 1.7290e-03 eta 8:03:23
+epoch [14/50] batch [50/500] time 1.552 (1.571) data 0.001 (0.017) loss 1.0117 (1.0533) acc 68.7500 (73.2500) lr 1.7290e-03 eta 8:03:00
+epoch [14/50] batch [55/500] time 1.567 (1.570) data 0.000 (0.015) loss 1.3643 (1.0546) acc 59.3750 (72.9545) lr 1.7290e-03 eta 8:02:46
+epoch [14/50] batch [60/500] time 1.551 (1.570) data 0.001 (0.014) loss 0.6152 (1.0450) acc 78.1250 (73.3854) lr 1.7290e-03 eta 8:02:27
+epoch [14/50] batch [65/500] time 1.537 (1.569) data 0.001 (0.013) loss 0.6206 (1.0586) acc 78.1250 (72.9808) lr 1.7290e-03 eta 8:02:07
+epoch [14/50] batch [70/500] time 1.557 (1.569) data 0.000 (0.012) loss 1.1885 (1.0617) acc 62.5000 (72.9911) lr 1.7290e-03 eta 8:01:51
+epoch [14/50] batch [75/500] time 1.545 (1.567) data 0.001 (0.011) loss 1.3408 (1.0715) acc 68.7500 (72.6667) lr 1.7290e-03 eta 8:01:18
+epoch [14/50] batch [80/500] time 1.571 (1.568) data 0.000 (0.011) loss 1.2910 (1.0736) acc 68.7500 (72.5781) lr 1.7290e-03 eta 8:01:16
+epoch [14/50] batch [85/500] time 1.575 (1.568) data 0.000 (0.010) loss 1.7168 (1.0776) acc 53.1250 (72.5368) lr 1.7290e-03 eta 8:01:07
+epoch [14/50] batch [90/500] time 1.601 (1.568) data 0.001 (0.010) loss 1.4199 (1.0813) acc 68.7500 (72.3958) lr 1.7290e-03 eta 8:01:01
+epoch [14/50] batch [95/500] time 1.537 (1.567) data 0.001 (0.009) loss 1.3232 (1.0883) acc 62.5000 (72.2368) lr 1.7290e-03 eta 8:00:36
+epoch [14/50] batch [100/500] time 1.558 (1.567) data 0.000 (0.009) loss 0.8955 (1.0882) acc 68.7500 (72.0625) lr 1.7290e-03 eta 8:00:27
+epoch [14/50] batch [105/500] time 1.548 (1.566) data 0.000 (0.008) loss 1.4023 (1.1037) acc 62.5000 (71.8750) lr 1.7290e-03 eta 8:00:14
+epoch [14/50] batch [110/500] time 1.546 (1.566) data 0.000 (0.008) loss 0.9868 (1.0950) acc 71.8750 (72.1591) lr 1.7290e-03 eta 7:59:55
+epoch [14/50] batch [115/500] time 1.579 (1.566) data 0.000 (0.008) loss 1.2939 (1.0842) acc 75.0000 (72.4728) lr 1.7290e-03 eta 7:59:47
+epoch [14/50] batch [120/500] time 1.556 (1.567) data 0.001 (0.007) loss 1.2998 (1.0829) acc 78.1250 (72.6562) lr 1.7290e-03 eta 7:59:55
+epoch [14/50] batch [125/500] time 1.562 (1.566) data 0.001 (0.007) loss 1.5791 (1.0852) acc 65.6250 (72.6500) lr 1.7290e-03 eta 7:59:38
+epoch [14/50] batch [130/500] time 1.533 (1.566) data 0.000 (0.007) loss 1.1338 (1.0946) acc 68.7500 (72.4519) lr 1.7290e-03 eta 7:59:24
+epoch [14/50] batch [135/500] time 1.517 (1.565) data 0.000 (0.007) loss 1.4326 (1.0959) acc 62.5000 (72.2454) lr 1.7290e-03 eta 7:59:03
+epoch [14/50] batch [140/500] time 1.551 (1.565) data 0.000 (0.006) loss 0.6421 (1.0882) acc 87.5000 (72.4777) lr 1.7290e-03 eta 7:58:53
+epoch [14/50] batch [145/500] time 1.571 (1.565) data 0.000 (0.006) loss 1.2783 (1.0920) acc 65.6250 (72.3060) lr 1.7290e-03 eta 7:58:41
+epoch [14/50] batch [150/500] time 1.569 (1.564) data 0.000 (0.006) loss 1.3711 (1.1045) acc 75.0000 (72.2083) lr 1.7290e-03 eta 7:58:27
+epoch [14/50] batch [155/500] time 1.594 (1.564) data 0.000 (0.006) loss 0.8457 (1.1106) acc 75.0000 (72.0766) lr 1.7290e-03 eta 7:58:15
+epoch [14/50] batch [160/500] time 1.661 (1.564) data 0.000 (0.006) loss 0.9048 (1.1085) acc 75.0000 (72.1289) lr 1.7290e-03 eta 7:58:12
+epoch [14/50] batch [165/500] time 1.542 (1.564) data 0.000 (0.005) loss 0.9966 (1.1102) acc 78.1250 (71.9697) lr 1.7290e-03 eta 7:57:54
+epoch [14/50] batch [170/500] time 1.562 (1.564) data 0.000 (0.005) loss 0.8462 (1.1124) acc 71.8750 (71.9853) lr 1.7290e-03 eta 7:57:40
+epoch [14/50] batch [175/500] time 1.572 (1.564) data 0.000 (0.005) loss 1.1055 (1.1122) acc 65.6250 (71.9821) lr 1.7290e-03 eta 7:57:33
+epoch [14/50] batch [180/500] time 1.569 (1.563) data 0.000 (0.005) loss 0.9175 (1.1138) acc 71.8750 (71.8403) lr 1.7290e-03 eta 7:57:22
+epoch [14/50] batch [185/500] time 1.567 (1.564) data 0.000 (0.005) loss 1.2969 (1.1105) acc 71.8750 (71.9932) lr 1.7290e-03 eta 7:57:19
+epoch [14/50] batch [190/500] time 1.548 (1.564) data 0.000 (0.005) loss 0.9512 (1.1118) acc 81.2500 (72.0888) lr 1.7290e-03 eta 7:57:09
+epoch [14/50] batch [195/500] time 1.592 (1.564) data 0.000 (0.005) loss 1.0791 (1.1154) acc 71.8750 (72.0032) lr 1.7290e-03 eta 7:57:06
+epoch [14/50] batch [200/500] time 1.569 (1.564) data 0.000 (0.005) loss 1.2520 (1.1123) acc 65.6250 (72.1094) lr 1.7290e-03 eta 7:56:57
+epoch [14/50] batch [205/500] time 1.576 (1.564) data 0.000 (0.004) loss 0.6758 (1.1105) acc 81.2500 (72.1951) lr 1.7290e-03 eta 7:56:55
+epoch [14/50] batch [210/500] time 1.563 (1.564) data 0.000 (0.004) loss 1.2646 (1.1112) acc 68.7500 (72.2024) lr 1.7290e-03 eta 7:56:46
+epoch [14/50] batch [215/500] time 1.551 (1.564) data 0.001 (0.004) loss 0.8462 (1.1122) acc 87.5000 (72.2965) lr 1.7290e-03 eta 7:56:37
+epoch [14/50] batch [220/500] time 1.571 (1.564) data 0.000 (0.004) loss 0.5454 (1.1087) acc 84.3750 (72.4148) lr 1.7290e-03 eta 7:56:27
+epoch [14/50] batch [225/500] time 1.568 (1.564) data 0.000 (0.004) loss 1.2998 (1.1045) acc 56.2500 (72.4306) lr 1.7290e-03 eta 7:56:21
+epoch [14/50] batch [230/500] time 1.574 (1.564) data 0.000 (0.004) loss 1.0830 (1.1076) acc 71.8750 (72.3370) lr 1.7290e-03 eta 7:56:19
+epoch [14/50] batch [235/500] time 1.536 (1.564) data 0.001 (0.004) loss 1.8691 (1.1110) acc 62.5000 (72.3271) lr 1.7290e-03 eta 7:56:10
+epoch [14/50] batch [240/500] time 1.556 (1.564) data 0.000 (0.004) loss 1.7012 (1.1167) acc 59.3750 (72.2656) lr 1.7290e-03 eta 7:56:02
+epoch [14/50] batch [245/500] time 1.564 (1.564) data 0.000 (0.004) loss 1.6855 (1.1179) acc 50.0000 (72.2577) lr 1.7290e-03 eta 7:55:51
+epoch [14/50] batch [250/500] time 1.556 (1.564) data 0.000 (0.004) loss 1.3047 (1.1157) acc 62.5000 (72.3000) lr 1.7290e-03 eta 7:55:39
+epoch [14/50] batch [255/500] time 1.535 (1.563) data 0.000 (0.004) loss 1.1104 (1.1152) acc 71.8750 (72.3162) lr 1.7290e-03 eta 7:55:25
+epoch [14/50] batch [260/500] time 1.526 (1.564) data 0.000 (0.004) loss 1.1084 (1.1144) acc 81.2500 (72.3558) lr 1.7290e-03 eta 7:55:19
+epoch [14/50] batch [265/500] time 1.536 (1.563) data 0.000 (0.004) loss 1.1719 (1.1151) acc 59.3750 (72.2642) lr 1.7290e-03 eta 7:55:06
+epoch [14/50] batch [270/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.7646 (1.1139) acc 81.2500 (72.3380) lr 1.7290e-03 eta 7:54:49
+epoch [14/50] batch [275/500] time 1.562 (1.562) data 0.000 (0.003) loss 0.6343 (1.1177) acc 87.5000 (72.2727) lr 1.7290e-03 eta 7:54:35
+epoch [14/50] batch [280/500] time 1.557 (1.562) data 0.000 (0.003) loss 0.8662 (1.1155) acc 84.3750 (72.3549) lr 1.7290e-03 eta 7:54:27
+epoch [14/50] batch [285/500] time 1.572 (1.562) data 0.000 (0.003) loss 1.1670 (1.1137) acc 75.0000 (72.4232) lr 1.7290e-03 eta 7:54:19
+epoch [14/50] batch [290/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.4121 (1.1131) acc 59.3750 (72.4138) lr 1.7290e-03 eta 7:54:04
+epoch [14/50] batch [295/500] time 1.554 (1.562) data 0.000 (0.003) loss 1.0459 (1.1105) acc 75.0000 (72.4470) lr 1.7290e-03 eta 7:53:58
+epoch [14/50] batch [300/500] time 1.568 (1.562) data 0.001 (0.003) loss 1.6309 (1.1089) acc 75.0000 (72.4792) lr 1.7290e-03 eta 7:53:48
+epoch [14/50] batch [305/500] time 1.580 (1.562) data 0.001 (0.003) loss 0.5347 (1.1058) acc 78.1250 (72.5307) lr 1.7290e-03 eta 7:53:47
+epoch [14/50] batch [310/500] time 1.567 (1.562) data 0.000 (0.003) loss 1.0732 (1.1056) acc 78.1250 (72.6008) lr 1.7290e-03 eta 7:53:37
+epoch [14/50] batch [315/500] time 1.593 (1.562) data 0.001 (0.003) loss 0.5425 (1.1020) acc 81.2500 (72.6587) lr 1.7290e-03 eta 7:53:27
+epoch [14/50] batch [320/500] time 1.558 (1.562) data 0.000 (0.003) loss 1.1543 (1.1060) acc 68.7500 (72.5879) lr 1.7290e-03 eta 7:53:16
+epoch [14/50] batch [325/500] time 1.570 (1.562) data 0.000 (0.003) loss 2.1387 (1.1138) acc 68.7500 (72.4808) lr 1.7290e-03 eta 7:53:08
+epoch [14/50] batch [330/500] time 1.552 (1.562) data 0.000 (0.003) loss 0.6982 (1.1122) acc 81.2500 (72.4716) lr 1.7290e-03 eta 7:52:59
+epoch [14/50] batch [335/500] time 1.560 (1.562) data 0.000 (0.003) loss 1.5850 (1.1134) acc 62.5000 (72.4067) lr 1.7290e-03 eta 7:52:51
+epoch [14/50] batch [340/500] time 1.547 (1.562) data 0.000 (0.003) loss 0.6851 (1.1129) acc 90.6250 (72.4265) lr 1.7290e-03 eta 7:52:40
+epoch [14/50] batch [345/500] time 1.532 (1.561) data 0.000 (0.003) loss 1.1143 (1.1161) acc 75.0000 (72.4004) lr 1.7290e-03 eta 7:52:28
+epoch [14/50] batch [350/500] time 1.554 (1.561) data 0.000 (0.003) loss 0.9282 (1.1156) acc 65.6250 (72.4196) lr 1.7290e-03 eta 7:52:16
+epoch [14/50] batch [355/500] time 1.570 (1.561) data 0.000 (0.003) loss 1.0547 (1.1186) acc 71.8750 (72.3592) lr 1.7290e-03 eta 7:52:08
+epoch [14/50] batch [360/500] time 1.563 (1.561) data 0.000 (0.003) loss 1.0771 (1.1186) acc 68.7500 (72.2917) lr 1.7290e-03 eta 7:52:04
+epoch [14/50] batch [365/500] time 1.580 (1.561) data 0.000 (0.003) loss 0.7915 (1.1195) acc 81.2500 (72.3288) lr 1.7290e-03 eta 7:51:57
+epoch [14/50] batch [370/500] time 1.544 (1.562) data 0.000 (0.003) loss 1.0977 (1.1222) acc 68.7500 (72.2213) lr 1.7290e-03 eta 7:51:50
+epoch [14/50] batch [375/500] time 1.544 (1.561) data 0.000 (0.003) loss 1.3711 (1.1248) acc 78.1250 (72.1500) lr 1.7290e-03 eta 7:51:40
+epoch [14/50] batch [380/500] time 1.560 (1.561) data 0.001 (0.003) loss 0.9546 (1.1230) acc 75.0000 (72.1628) lr 1.7290e-03 eta 7:51:30
+epoch [14/50] batch [385/500] time 1.561 (1.561) data 0.000 (0.003) loss 0.7144 (1.1239) acc 87.5000 (72.1834) lr 1.7290e-03 eta 7:51:20
+epoch [14/50] batch [390/500] time 1.566 (1.561) data 0.001 (0.003) loss 1.0850 (1.1235) acc 71.8750 (72.1715) lr 1.7290e-03 eta 7:51:13
+epoch [14/50] batch [395/500] time 1.559 (1.561) data 0.000 (0.002) loss 0.5503 (1.1208) acc 87.5000 (72.2706) lr 1.7290e-03 eta 7:51:05
+epoch [14/50] batch [400/500] time 1.559 (1.561) data 0.000 (0.002) loss 0.8125 (1.1209) acc 75.0000 (72.2500) lr 1.7290e-03 eta 7:50:57
+epoch [14/50] batch [405/500] time 1.567 (1.562) data 0.000 (0.002) loss 1.4502 (1.1231) acc 65.6250 (72.2068) lr 1.7290e-03 eta 7:50:56
+epoch [14/50] batch [410/500] time 1.555 (1.561) data 0.000 (0.002) loss 1.4658 (1.1263) acc 56.2500 (72.1341) lr 1.7290e-03 eta 7:50:47
+epoch [14/50] batch [415/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.8579 (1.1257) acc 81.2500 (72.1687) lr 1.7290e-03 eta 7:50:38
+epoch [14/50] batch [420/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.6748 (1.1257) acc 81.2500 (72.1652) lr 1.7290e-03 eta 7:50:30
+epoch [14/50] batch [425/500] time 1.567 (1.561) data 0.000 (0.002) loss 1.7080 (1.1265) acc 68.7500 (72.1691) lr 1.7290e-03 eta 7:50:21
+epoch [14/50] batch [430/500] time 1.569 (1.561) data 0.000 (0.002) loss 0.7896 (1.1273) acc 78.1250 (72.2020) lr 1.7290e-03 eta 7:50:16
+epoch [14/50] batch [435/500] time 1.563 (1.561) data 0.000 (0.002) loss 1.2305 (1.1269) acc 65.6250 (72.1911) lr 1.7290e-03 eta 7:50:07
+epoch [14/50] batch [440/500] time 1.537 (1.561) data 0.000 (0.002) loss 0.9150 (1.1276) acc 78.1250 (72.1804) lr 1.7290e-03 eta 7:50:00
+epoch [14/50] batch [445/500] time 1.604 (1.562) data 0.000 (0.002) loss 1.2773 (1.1301) acc 71.8750 (72.1489) lr 1.7290e-03 eta 7:49:53
+epoch [14/50] batch [450/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.7402 (1.1326) acc 53.1250 (72.0764) lr 1.7290e-03 eta 7:49:47
+epoch [14/50] batch [455/500] time 1.555 (1.562) data 0.000 (0.002) loss 1.7109 (1.1326) acc 56.2500 (72.0398) lr 1.7290e-03 eta 7:49:38
+epoch [14/50] batch [460/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.2100 (1.1342) acc 75.0000 (72.0380) lr 1.7290e-03 eta 7:49:30
+epoch [14/50] batch [465/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.8213 (1.1362) acc 56.2500 (72.0094) lr 1.7290e-03 eta 7:49:22
+epoch [14/50] batch [470/500] time 1.570 (1.562) data 0.000 (0.002) loss 1.8418 (1.1380) acc 65.6250 (71.9681) lr 1.7290e-03 eta 7:49:16
+epoch [14/50] batch [475/500] time 1.554 (1.562) data 0.001 (0.002) loss 0.8364 (1.1391) acc 78.1250 (71.9211) lr 1.7290e-03 eta 7:49:07
+epoch [14/50] batch [480/500] time 1.553 (1.562) data 0.001 (0.002) loss 1.0469 (1.1390) acc 71.8750 (71.8750) lr 1.7290e-03 eta 7:48:58
+epoch [14/50] batch [485/500] time 1.527 (1.561) data 0.001 (0.002) loss 1.1172 (1.1396) acc 71.8750 (71.8621) lr 1.7290e-03 eta 7:48:48
+epoch [14/50] batch [490/500] time 1.550 (1.561) data 0.000 (0.002) loss 0.5771 (1.1390) acc 84.3750 (71.8814) lr 1.7290e-03 eta 7:48:40
+epoch [14/50] batch [495/500] time 1.540 (1.561) data 0.000 (0.002) loss 1.2256 (1.1373) acc 65.6250 (71.8750) lr 1.7290e-03 eta 7:48:31
+epoch [14/50] batch [500/500] time 1.562 (1.561) data 0.000 (0.002) loss 1.2100 (1.1384) acc 68.7500 (71.8563) lr 1.6845e-03 eta 7:48:21
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,917
+* accuracy: 77.8%
+* error: 22.2%
+* macro_f1: 77.3%
+epoch [15/50] batch [5/500] time 1.529 (1.674) data 0.000 (0.174) loss 1.6348 (1.2069) acc 59.3750 (70.0000) lr 1.6845e-03 eta 8:22:01
+epoch [15/50] batch [10/500] time 1.536 (1.613) data 0.000 (0.087) loss 0.6860 (1.1134) acc 81.2500 (70.9375) lr 1.6845e-03 eta 8:03:32
+epoch [15/50] batch [15/500] time 1.564 (1.598) data 0.001 (0.058) loss 1.1777 (1.0796) acc 75.0000 (72.0833) lr 1.6845e-03 eta 7:58:58
+epoch [15/50] batch [20/500] time 1.539 (1.586) data 0.001 (0.044) loss 1.1162 (1.0834) acc 75.0000 (72.3438) lr 1.6845e-03 eta 7:55:16
+epoch [15/50] batch [25/500] time 1.541 (1.580) data 0.000 (0.035) loss 1.4609 (1.0973) acc 62.5000 (71.1250) lr 1.6845e-03 eta 7:53:19
+epoch [15/50] batch [30/500] time 1.574 (1.579) data 0.000 (0.029) loss 1.0068 (1.0550) acc 75.0000 (72.1875) lr 1.6845e-03 eta 7:52:49
+epoch [15/50] batch [35/500] time 1.564 (1.576) data 0.000 (0.025) loss 1.2783 (1.0491) acc 71.8750 (72.6786) lr 1.6845e-03 eta 7:51:49
+epoch [15/50] batch [40/500] time 1.567 (1.578) data 0.000 (0.022) loss 1.2725 (1.0589) acc 62.5000 (72.3438) lr 1.6845e-03 eta 7:52:12
+epoch [15/50] batch [45/500] time 1.566 (1.576) data 0.000 (0.020) loss 1.3291 (1.0554) acc 65.6250 (72.5000) lr 1.6845e-03 eta 7:51:32
+epoch [15/50] batch [50/500] time 1.547 (1.573) data 0.000 (0.018) loss 0.7280 (1.0515) acc 78.1250 (72.6250) lr 1.6845e-03 eta 7:50:36
+epoch [15/50] batch [55/500] time 1.532 (1.572) data 0.000 (0.016) loss 0.9946 (1.0502) acc 68.7500 (72.3295) lr 1.6845e-03 eta 7:50:09
+epoch [15/50] batch [60/500] time 1.540 (1.570) data 0.000 (0.015) loss 0.7212 (1.0447) acc 78.1250 (72.5521) lr 1.6845e-03 eta 7:49:31
+epoch [15/50] batch [65/500] time 1.541 (1.568) data 0.000 (0.014) loss 0.9312 (1.0487) acc 81.2500 (72.7885) lr 1.6845e-03 eta 7:48:49
+epoch [15/50] batch [70/500] time 1.569 (1.568) data 0.000 (0.013) loss 0.9375 (1.0565) acc 75.0000 (72.7679) lr 1.6845e-03 eta 7:48:36
+epoch [15/50] batch [75/500] time 1.541 (1.567) data 0.001 (0.012) loss 1.3750 (1.0538) acc 65.6250 (72.9167) lr 1.6845e-03 eta 7:48:01
+epoch [15/50] batch [80/500] time 1.625 (1.566) data 0.000 (0.011) loss 1.3027 (1.0658) acc 68.7500 (72.6953) lr 1.6845e-03 eta 7:47:48
+epoch [15/50] batch [85/500] time 1.576 (1.565) data 0.000 (0.011) loss 1.0635 (1.0664) acc 68.7500 (72.7206) lr 1.6845e-03 eta 7:47:25
+epoch [15/50] batch [90/500] time 1.563 (1.564) data 0.000 (0.010) loss 0.7568 (1.0632) acc 78.1250 (72.7778) lr 1.6845e-03 eta 7:46:57
+epoch [15/50] batch [95/500] time 1.539 (1.564) data 0.000 (0.010) loss 1.0439 (1.0534) acc 75.0000 (72.9605) lr 1.6845e-03 eta 7:46:39
+epoch [15/50] batch [100/500] time 1.548 (1.563) data 0.000 (0.009) loss 1.0469 (1.0661) acc 71.8750 (72.6562) lr 1.6845e-03 eta 7:46:21
+epoch [15/50] batch [105/500] time 1.524 (1.562) data 0.000 (0.009) loss 0.7856 (1.0722) acc 81.2500 (72.6190) lr 1.6845e-03 eta 7:45:46
+epoch [15/50] batch [110/500] time 1.562 (1.562) data 0.001 (0.008) loss 0.9976 (1.0772) acc 84.3750 (72.6705) lr 1.6845e-03 eta 7:45:46
+epoch [15/50] batch [115/500] time 1.571 (1.562) data 0.000 (0.008) loss 0.7456 (1.0753) acc 71.8750 (72.6902) lr 1.6845e-03 eta 7:45:35
+epoch [15/50] batch [120/500] time 1.568 (1.562) data 0.000 (0.008) loss 0.7759 (1.0767) acc 81.2500 (72.7604) lr 1.6845e-03 eta 7:45:24
+epoch [15/50] batch [125/500] time 1.548 (1.562) data 0.000 (0.007) loss 0.9468 (1.0753) acc 81.2500 (72.8250) lr 1.6845e-03 eta 7:45:13
+epoch [15/50] batch [130/500] time 1.569 (1.561) data 0.000 (0.007) loss 0.9575 (1.0758) acc 75.0000 (72.8846) lr 1.6845e-03 eta 7:45:02
+epoch [15/50] batch [135/500] time 1.564 (1.562) data 0.000 (0.007) loss 1.5117 (1.0814) acc 71.8750 (72.8472) lr 1.6845e-03 eta 7:44:56
+epoch [15/50] batch [140/500] time 1.540 (1.561) data 0.001 (0.007) loss 1.9619 (1.0895) acc 46.8750 (72.6116) lr 1.6845e-03 eta 7:44:44
+epoch [15/50] batch [145/500] time 1.549 (1.561) data 0.001 (0.006) loss 0.8462 (1.0905) acc 68.7500 (72.3922) lr 1.6845e-03 eta 7:44:30
+epoch [15/50] batch [150/500] time 1.579 (1.561) data 0.000 (0.006) loss 1.2266 (1.0859) acc 65.6250 (72.4375) lr 1.6845e-03 eta 7:44:21
+epoch [15/50] batch [155/500] time 1.551 (1.561) data 0.000 (0.006) loss 1.1289 (1.0856) acc 62.5000 (72.3589) lr 1.6845e-03 eta 7:44:09
+epoch [15/50] batch [160/500] time 1.560 (1.561) data 0.001 (0.006) loss 1.0820 (1.0857) acc 71.8750 (72.3047) lr 1.6845e-03 eta 7:44:00
+epoch [15/50] batch [165/500] time 1.555 (1.560) data 0.000 (0.006) loss 1.2451 (1.0835) acc 65.6250 (72.3485) lr 1.6845e-03 eta 7:43:49
+epoch [15/50] batch [170/500] time 1.551 (1.560) data 0.001 (0.006) loss 1.1064 (1.0901) acc 68.7500 (72.1691) lr 1.6845e-03 eta 7:43:40
+epoch [15/50] batch [175/500] time 1.593 (1.560) data 0.000 (0.005) loss 0.5601 (1.0815) acc 81.2500 (72.3929) lr 1.6845e-03 eta 7:43:35
+epoch [15/50] batch [180/500] time 1.533 (1.560) data 0.000 (0.005) loss 0.9272 (1.0775) acc 71.8750 (72.5347) lr 1.6845e-03 eta 7:43:27
+epoch [15/50] batch [185/500] time 1.536 (1.560) data 0.000 (0.005) loss 1.0039 (1.0770) acc 81.2500 (72.5507) lr 1.6845e-03 eta 7:43:19
+epoch [15/50] batch [190/500] time 1.541 (1.560) data 0.000 (0.005) loss 1.2686 (1.0786) acc 75.0000 (72.4836) lr 1.6845e-03 eta 7:43:07
+epoch [15/50] batch [195/500] time 1.525 (1.560) data 0.001 (0.005) loss 1.7139 (1.0883) acc 59.3750 (72.3237) lr 1.6845e-03 eta 7:42:52
+epoch [15/50] batch [200/500] time 1.571 (1.560) data 0.001 (0.005) loss 0.9814 (1.0857) acc 71.8750 (72.3438) lr 1.6845e-03 eta 7:42:46
+epoch [15/50] batch [205/500] time 1.585 (1.560) data 0.000 (0.005) loss 1.5420 (1.0843) acc 59.3750 (72.4085) lr 1.6845e-03 eta 7:42:44
+epoch [15/50] batch [210/500] time 1.557 (1.560) data 0.000 (0.005) loss 1.4268 (1.0886) acc 62.5000 (72.2470) lr 1.6845e-03 eta 7:42:37
+epoch [15/50] batch [215/500] time 1.545 (1.560) data 0.001 (0.004) loss 1.0811 (1.0839) acc 68.7500 (72.4128) lr 1.6845e-03 eta 7:42:25
+epoch [15/50] batch [220/500] time 1.552 (1.560) data 0.001 (0.004) loss 0.9849 (1.0912) acc 75.0000 (72.3438) lr 1.6845e-03 eta 7:42:13
+epoch [15/50] batch [225/500] time 1.548 (1.560) data 0.001 (0.004) loss 1.2510 (1.0957) acc 62.5000 (72.2500) lr 1.6845e-03 eta 7:42:11
+epoch [15/50] batch [230/500] time 1.536 (1.560) data 0.001 (0.004) loss 1.2920 (1.0973) acc 75.0000 (72.2826) lr 1.6845e-03 eta 7:41:59
+epoch [15/50] batch [235/500] time 1.546 (1.560) data 0.001 (0.004) loss 0.7256 (1.0927) acc 81.2500 (72.3803) lr 1.6845e-03 eta 7:41:45
+epoch [15/50] batch [240/500] time 1.539 (1.559) data 0.000 (0.004) loss 0.8672 (1.0924) acc 71.8750 (72.3438) lr 1.6845e-03 eta 7:41:35
+epoch [15/50] batch [245/500] time 1.555 (1.559) data 0.000 (0.004) loss 1.5586 (1.0928) acc 65.6250 (72.3597) lr 1.6845e-03 eta 7:41:25
+epoch [15/50] batch [250/500] time 1.550 (1.559) data 0.000 (0.004) loss 1.5684 (1.0961) acc 62.5000 (72.2875) lr 1.6845e-03 eta 7:41:11
+epoch [15/50] batch [255/500] time 1.555 (1.559) data 0.000 (0.004) loss 1.1387 (1.0960) acc 81.2500 (72.3407) lr 1.6845e-03 eta 7:41:02
+epoch [15/50] batch [260/500] time 1.538 (1.559) data 0.000 (0.004) loss 1.1865 (1.0961) acc 71.8750 (72.2957) lr 1.6845e-03 eta 7:40:52
+epoch [15/50] batch [265/500] time 1.572 (1.559) data 0.000 (0.004) loss 0.6162 (1.0939) acc 78.1250 (72.2995) lr 1.6845e-03 eta 7:40:46
+epoch [15/50] batch [270/500] time 1.571 (1.559) data 0.000 (0.004) loss 1.1807 (1.0949) acc 78.1250 (72.2801) lr 1.6845e-03 eta 7:40:36
+epoch [15/50] batch [275/500] time 1.563 (1.559) data 0.000 (0.004) loss 1.1064 (1.0955) acc 65.6250 (72.2955) lr 1.6845e-03 eta 7:40:29
+epoch [15/50] batch [280/500] time 1.573 (1.559) data 0.000 (0.004) loss 0.9585 (1.0933) acc 81.2500 (72.3661) lr 1.6845e-03 eta 7:40:22
+epoch [15/50] batch [285/500] time 1.567 (1.559) data 0.000 (0.003) loss 1.4072 (1.0948) acc 59.3750 (72.3575) lr 1.6845e-03 eta 7:40:14
+epoch [15/50] batch [290/500] time 1.554 (1.559) data 0.000 (0.003) loss 1.3887 (1.0985) acc 65.6250 (72.3599) lr 1.6845e-03 eta 7:40:05
+epoch [15/50] batch [295/500] time 1.573 (1.559) data 0.000 (0.003) loss 1.2656 (1.0993) acc 78.1250 (72.3411) lr 1.6845e-03 eta 7:39:56
+epoch [15/50] batch [300/500] time 1.577 (1.559) data 0.001 (0.003) loss 0.8843 (1.0976) acc 81.2500 (72.3542) lr 1.6845e-03 eta 7:39:52
+epoch [15/50] batch [305/500] time 1.560 (1.559) data 0.000 (0.003) loss 0.5117 (1.0934) acc 84.3750 (72.4180) lr 1.6845e-03 eta 7:39:46
+epoch [15/50] batch [310/500] time 1.561 (1.559) data 0.000 (0.003) loss 0.9146 (1.0990) acc 84.3750 (72.3690) lr 1.6845e-03 eta 7:39:39
+epoch [15/50] batch [315/500] time 1.560 (1.559) data 0.000 (0.003) loss 1.4961 (1.0997) acc 65.6250 (72.3115) lr 1.6845e-03 eta 7:39:34
+epoch [15/50] batch [320/500] time 1.558 (1.559) data 0.000 (0.003) loss 1.1289 (1.1006) acc 71.8750 (72.2754) lr 1.6845e-03 eta 7:39:26
+epoch [15/50] batch [325/500] time 1.534 (1.559) data 0.000 (0.003) loss 1.0117 (1.0999) acc 68.7500 (72.3462) lr 1.6845e-03 eta 7:39:22
+epoch [15/50] batch [330/500] time 1.552 (1.559) data 0.000 (0.003) loss 0.9619 (1.0986) acc 78.1250 (72.3580) lr 1.6845e-03 eta 7:39:14
+epoch [15/50] batch [335/500] time 1.559 (1.559) data 0.000 (0.003) loss 1.1523 (1.0981) acc 68.7500 (72.3321) lr 1.6845e-03 eta 7:39:06
+epoch [15/50] batch [340/500] time 1.544 (1.559) data 0.000 (0.003) loss 0.8740 (1.0978) acc 81.2500 (72.3162) lr 1.6845e-03 eta 7:38:57
+epoch [15/50] batch [345/500] time 1.560 (1.559) data 0.001 (0.003) loss 1.1445 (1.0985) acc 71.8750 (72.3098) lr 1.6845e-03 eta 7:38:49
+epoch [15/50] batch [350/500] time 1.573 (1.559) data 0.000 (0.003) loss 0.8530 (1.0972) acc 81.2500 (72.3214) lr 1.6845e-03 eta 7:38:43
+epoch [15/50] batch [355/500] time 1.561 (1.559) data 0.000 (0.003) loss 1.2207 (1.0970) acc 75.0000 (72.3239) lr 1.6845e-03 eta 7:38:34
+epoch [15/50] batch [360/500] time 1.561 (1.559) data 0.000 (0.003) loss 1.0391 (1.1004) acc 71.8750 (72.2656) lr 1.6845e-03 eta 7:38:26
+epoch [15/50] batch [365/500] time 1.579 (1.559) data 0.000 (0.003) loss 0.9058 (1.1028) acc 65.6250 (72.1832) lr 1.6845e-03 eta 7:38:18
+epoch [15/50] batch [370/500] time 1.582 (1.560) data 0.000 (0.003) loss 1.5664 (1.1023) acc 59.3750 (72.1706) lr 1.6845e-03 eta 7:38:18
+epoch [15/50] batch [375/500] time 1.567 (1.560) data 0.000 (0.003) loss 0.6729 (1.1006) acc 78.1250 (72.2250) lr 1.6845e-03 eta 7:38:10
+epoch [15/50] batch [380/500] time 1.561 (1.560) data 0.000 (0.003) loss 1.2178 (1.1002) acc 65.6250 (72.2039) lr 1.6845e-03 eta 7:38:02
+epoch [15/50] batch [385/500] time 1.557 (1.560) data 0.001 (0.003) loss 1.4326 (1.1024) acc 65.6250 (72.1916) lr 1.6845e-03 eta 7:37:54
+epoch [15/50] batch [390/500] time 1.558 (1.560) data 0.000 (0.003) loss 1.3047 (1.1022) acc 71.8750 (72.1875) lr 1.6845e-03 eta 7:37:46
+epoch [15/50] batch [395/500] time 1.558 (1.560) data 0.000 (0.003) loss 1.2295 (1.1010) acc 65.6250 (72.1835) lr 1.6845e-03 eta 7:37:39
+epoch [15/50] batch [400/500] time 1.557 (1.560) data 0.000 (0.003) loss 0.8838 (1.0990) acc 78.1250 (72.2344) lr 1.6845e-03 eta 7:37:32
+epoch [15/50] batch [405/500] time 1.549 (1.560) data 0.000 (0.003) loss 0.6621 (1.0982) acc 90.6250 (72.2685) lr 1.6845e-03 eta 7:37:24
+epoch [15/50] batch [410/500] time 1.561 (1.560) data 0.000 (0.003) loss 1.2500 (1.0998) acc 59.3750 (72.1951) lr 1.6845e-03 eta 7:37:17
+epoch [15/50] batch [415/500] time 1.560 (1.560) data 0.000 (0.003) loss 1.4619 (1.1005) acc 62.5000 (72.1913) lr 1.6845e-03 eta 7:37:10
+epoch [15/50] batch [420/500] time 1.567 (1.560) data 0.000 (0.003) loss 1.3906 (1.1024) acc 71.8750 (72.1205) lr 1.6845e-03 eta 7:37:04
+epoch [15/50] batch [425/500] time 1.569 (1.560) data 0.000 (0.002) loss 1.2275 (1.1053) acc 65.6250 (72.0735) lr 1.6845e-03 eta 7:36:58
+epoch [15/50] batch [430/500] time 1.573 (1.560) data 0.000 (0.002) loss 1.0605 (1.1055) acc 68.7500 (72.0640) lr 1.6845e-03 eta 7:36:50
+epoch [15/50] batch [435/500] time 1.551 (1.560) data 0.000 (0.002) loss 0.9507 (1.1063) acc 78.1250 (72.0618) lr 1.6845e-03 eta 7:36:43
+epoch [15/50] batch [440/500] time 1.559 (1.560) data 0.000 (0.002) loss 0.9360 (1.1041) acc 78.1250 (72.1378) lr 1.6845e-03 eta 7:36:35
+epoch [15/50] batch [445/500] time 1.560 (1.560) data 0.000 (0.002) loss 1.4473 (1.1058) acc 78.1250 (72.1348) lr 1.6845e-03 eta 7:36:29
+epoch [15/50] batch [450/500] time 1.578 (1.560) data 0.000 (0.002) loss 1.2764 (1.1048) acc 65.6250 (72.1111) lr 1.6845e-03 eta 7:36:21
+epoch [15/50] batch [455/500] time 1.547 (1.560) data 0.000 (0.002) loss 0.8813 (1.1070) acc 71.8750 (72.0398) lr 1.6845e-03 eta 7:36:12
+epoch [15/50] batch [460/500] time 1.568 (1.560) data 0.000 (0.002) loss 1.3047 (1.1081) acc 68.7500 (72.0109) lr 1.6845e-03 eta 7:36:03
+epoch [15/50] batch [465/500] time 1.645 (1.560) data 0.000 (0.002) loss 1.3184 (1.1098) acc 65.6250 (71.9960) lr 1.6845e-03 eta 7:35:59
+epoch [15/50] batch [470/500] time 1.539 (1.560) data 0.000 (0.002) loss 1.0498 (1.1099) acc 75.0000 (71.9814) lr 1.6845e-03 eta 7:35:49
+epoch [15/50] batch [475/500] time 1.552 (1.560) data 0.000 (0.002) loss 1.7334 (1.1110) acc 59.3750 (71.9145) lr 1.6845e-03 eta 7:35:41
+epoch [15/50] batch [480/500] time 1.565 (1.560) data 0.000 (0.002) loss 1.3320 (1.1106) acc 65.6250 (71.9141) lr 1.6845e-03 eta 7:35:32
+epoch [15/50] batch [485/500] time 1.549 (1.560) data 0.001 (0.002) loss 1.0244 (1.1116) acc 78.1250 (71.9072) lr 1.6845e-03 eta 7:35:24
+epoch [15/50] batch [490/500] time 1.555 (1.560) data 0.000 (0.002) loss 1.7363 (1.1167) acc 65.6250 (71.8367) lr 1.6845e-03 eta 7:35:15
+epoch [15/50] batch [495/500] time 1.537 (1.560) data 0.000 (0.002) loss 1.5918 (1.1186) acc 56.2500 (71.7677) lr 1.6845e-03 eta 7:35:06
+epoch [15/50] batch [500/500] time 1.529 (1.560) data 0.000 (0.002) loss 1.7139 (1.1202) acc 56.2500 (71.7313) lr 1.6374e-03 eta 7:34:56
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,882
+* accuracy: 77.8%
+* error: 22.2%
+* macro_f1: 77.2%
+epoch [16/50] batch [5/500] time 1.540 (1.717) data 0.000 (0.212) loss 0.9033 (1.3578) acc 71.8750 (64.3750) lr 1.6374e-03 eta 8:20:36
+epoch [16/50] batch [10/500] time 1.564 (1.637) data 0.000 (0.106) loss 1.6211 (1.2714) acc 68.7500 (65.3125) lr 1.6374e-03 eta 7:57:10
+epoch [16/50] batch [15/500] time 1.570 (1.616) data 0.001 (0.071) loss 1.3447 (1.1962) acc 62.5000 (66.8750) lr 1.6374e-03 eta 7:50:47
+epoch [16/50] batch [20/500] time 1.562 (1.607) data 0.000 (0.053) loss 0.6968 (1.1239) acc 84.3750 (68.5938) lr 1.6374e-03 eta 7:48:14
+epoch [16/50] batch [25/500] time 1.556 (1.598) data 0.000 (0.043) loss 1.1416 (1.1286) acc 78.1250 (69.3750) lr 1.6374e-03 eta 7:45:25
+epoch [16/50] batch [30/500] time 1.558 (1.592) data 0.000 (0.036) loss 1.5693 (1.1378) acc 68.7500 (70.1042) lr 1.6374e-03 eta 7:43:28
+epoch [16/50] batch [35/500] time 1.548 (1.587) data 0.000 (0.031) loss 0.6387 (1.0902) acc 75.0000 (70.8929) lr 1.6374e-03 eta 7:42:04
+epoch [16/50] batch [40/500] time 1.549 (1.585) data 0.000 (0.027) loss 1.1973 (1.0668) acc 65.6250 (71.3281) lr 1.6374e-03 eta 7:41:06
+epoch [16/50] batch [45/500] time 1.571 (1.584) data 0.000 (0.024) loss 1.7637 (1.0641) acc 56.2500 (71.5278) lr 1.6374e-03 eta 7:40:41
+epoch [16/50] batch [50/500] time 1.544 (1.581) data 0.001 (0.022) loss 0.9302 (1.0646) acc 81.2500 (71.8125) lr 1.6374e-03 eta 7:39:55
+epoch [16/50] batch [55/500] time 1.574 (1.579) data 0.000 (0.020) loss 0.5962 (1.0631) acc 81.2500 (71.9886) lr 1.6374e-03 eta 7:39:11
+epoch [16/50] batch [60/500] time 1.569 (1.578) data 0.001 (0.018) loss 1.3516 (1.0617) acc 65.6250 (72.2396) lr 1.6374e-03 eta 7:38:39
+epoch [16/50] batch [65/500] time 1.544 (1.577) data 0.000 (0.017) loss 1.1289 (1.0570) acc 75.0000 (72.2596) lr 1.6374e-03 eta 7:38:12
+epoch [16/50] batch [70/500] time 1.563 (1.575) data 0.000 (0.016) loss 0.9043 (1.0553) acc 81.2500 (72.3661) lr 1.6374e-03 eta 7:37:36
+epoch [16/50] batch [75/500] time 1.565 (1.575) data 0.001 (0.015) loss 0.9272 (1.0562) acc 75.0000 (72.3333) lr 1.6374e-03 eta 7:37:17
+epoch [16/50] batch [80/500] time 1.553 (1.573) data 0.000 (0.014) loss 0.7446 (1.0548) acc 81.2500 (72.2656) lr 1.6374e-03 eta 7:36:44
+epoch [16/50] batch [85/500] time 1.543 (1.572) data 0.000 (0.013) loss 1.4072 (1.0629) acc 62.5000 (72.2426) lr 1.6374e-03 eta 7:36:10
+epoch [16/50] batch [90/500] time 1.538 (1.570) data 0.000 (0.012) loss 0.9653 (1.0577) acc 78.1250 (72.2917) lr 1.6374e-03 eta 7:35:38
+epoch [16/50] batch [95/500] time 1.565 (1.570) data 0.000 (0.012) loss 1.0869 (1.0638) acc 71.8750 (72.3684) lr 1.6374e-03 eta 7:35:19
+epoch [16/50] batch [100/500] time 1.574 (1.570) data 0.000 (0.011) loss 0.8623 (1.0670) acc 81.2500 (72.4688) lr 1.6374e-03 eta 7:35:10
+epoch [16/50] batch [105/500] time 1.576 (1.569) data 0.000 (0.011) loss 1.3105 (1.0685) acc 62.5000 (72.4107) lr 1.6374e-03 eta 7:34:56
+epoch [16/50] batch [110/500] time 1.570 (1.569) data 0.000 (0.010) loss 0.9619 (1.0740) acc 75.0000 (72.1023) lr 1.6374e-03 eta 7:34:50
+epoch [16/50] batch [115/500] time 1.697 (1.570) data 0.000 (0.010) loss 0.7505 (1.0695) acc 78.1250 (72.3098) lr 1.6374e-03 eta 7:34:51
+epoch [16/50] batch [120/500] time 1.580 (1.569) data 0.001 (0.009) loss 1.3750 (1.0750) acc 71.8750 (72.3177) lr 1.6374e-03 eta 7:34:33
+epoch [16/50] batch [125/500] time 1.550 (1.569) data 0.000 (0.009) loss 0.6514 (1.0805) acc 78.1250 (72.2000) lr 1.6374e-03 eta 7:34:27
+epoch [16/50] batch [130/500] time 1.556 (1.569) data 0.000 (0.009) loss 0.8989 (1.0768) acc 75.0000 (72.2596) lr 1.6374e-03 eta 7:34:13
+epoch [16/50] batch [135/500] time 1.550 (1.568) data 0.000 (0.008) loss 0.9595 (1.0807) acc 71.8750 (72.0139) lr 1.6374e-03 eta 7:33:53
+epoch [16/50] batch [140/500] time 1.561 (1.568) data 0.000 (0.008) loss 1.6035 (1.0832) acc 68.7500 (72.0536) lr 1.6374e-03 eta 7:33:46
+epoch [16/50] batch [145/500] time 1.572 (1.568) data 0.000 (0.008) loss 1.0117 (1.0872) acc 75.0000 (72.0474) lr 1.6374e-03 eta 7:33:36
+epoch [16/50] batch [150/500] time 1.580 (1.569) data 0.000 (0.007) loss 1.5391 (1.0875) acc 65.6250 (72.1042) lr 1.6374e-03 eta 7:33:33
+epoch [16/50] batch [155/500] time 1.561 (1.568) data 0.000 (0.007) loss 1.3154 (1.0848) acc 71.8750 (72.1774) lr 1.6374e-03 eta 7:33:25
+epoch [16/50] batch [160/500] time 1.581 (1.569) data 0.001 (0.007) loss 1.1260 (1.0883) acc 75.0000 (72.1289) lr 1.6374e-03 eta 7:33:28
+epoch [16/50] batch [165/500] time 1.557 (1.569) data 0.000 (0.007) loss 2.1406 (1.0976) acc 53.1250 (72.0265) lr 1.6374e-03 eta 7:33:21
+epoch [16/50] batch [170/500] time 1.570 (1.569) data 0.001 (0.007) loss 0.9556 (1.0953) acc 68.7500 (72.0037) lr 1.6374e-03 eta 7:33:13
+epoch [16/50] batch [175/500] time 1.546 (1.569) data 0.000 (0.006) loss 0.8828 (1.0883) acc 75.0000 (72.1071) lr 1.6374e-03 eta 7:33:00
+epoch [16/50] batch [180/500] time 1.561 (1.569) data 0.000 (0.006) loss 0.9404 (1.0860) acc 78.1250 (72.2049) lr 1.6374e-03 eta 7:32:53
+epoch [16/50] batch [185/500] time 1.559 (1.569) data 0.001 (0.006) loss 0.9678 (1.0860) acc 75.0000 (72.1959) lr 1.6374e-03 eta 7:32:44
+epoch [16/50] batch [190/500] time 1.568 (1.569) data 0.001 (0.006) loss 0.8794 (1.0898) acc 71.8750 (72.1546) lr 1.6374e-03 eta 7:32:38
+epoch [16/50] batch [195/500] time 1.569 (1.569) data 0.000 (0.006) loss 0.9053 (1.0872) acc 71.8750 (72.1474) lr 1.6374e-03 eta 7:32:29
+epoch [16/50] batch [200/500] time 1.569 (1.569) data 0.000 (0.006) loss 0.4844 (1.0851) acc 87.5000 (72.2031) lr 1.6374e-03 eta 7:32:17
+epoch [16/50] batch [205/500] time 1.556 (1.568) data 0.000 (0.006) loss 1.0664 (1.0873) acc 75.0000 (72.2256) lr 1.6374e-03 eta 7:32:05
+epoch [16/50] batch [210/500] time 1.544 (1.568) data 0.000 (0.005) loss 1.3223 (1.0893) acc 59.3750 (72.1726) lr 1.6374e-03 eta 7:31:50
+epoch [16/50] batch [215/500] time 1.540 (1.568) data 0.000 (0.005) loss 1.9189 (1.0947) acc 59.3750 (72.0785) lr 1.6374e-03 eta 7:31:36
+epoch [16/50] batch [220/500] time 1.563 (1.567) data 0.000 (0.005) loss 1.2031 (1.1005) acc 62.5000 (72.0028) lr 1.6374e-03 eta 7:31:20
+epoch [16/50] batch [225/500] time 1.563 (1.567) data 0.000 (0.005) loss 1.5049 (1.1018) acc 62.5000 (71.9861) lr 1.6374e-03 eta 7:31:10
+epoch [16/50] batch [230/500] time 1.580 (1.567) data 0.000 (0.005) loss 0.5078 (1.0993) acc 87.5000 (72.0516) lr 1.6374e-03 eta 7:30:57
+epoch [16/50] batch [235/500] time 1.556 (1.567) data 0.000 (0.005) loss 1.2363 (1.0990) acc 75.0000 (72.1144) lr 1.6374e-03 eta 7:30:46
+epoch [16/50] batch [240/500] time 1.538 (1.566) data 0.000 (0.005) loss 0.5117 (1.0949) acc 87.5000 (72.1745) lr 1.6374e-03 eta 7:30:31
+epoch [16/50] batch [245/500] time 1.554 (1.566) data 0.001 (0.005) loss 0.9775 (1.0946) acc 75.0000 (72.2577) lr 1.6374e-03 eta 7:30:19
+epoch [16/50] batch [250/500] time 1.555 (1.566) data 0.000 (0.005) loss 0.9458 (1.0931) acc 71.8750 (72.2625) lr 1.6374e-03 eta 7:30:07
+epoch [16/50] batch [255/500] time 1.543 (1.565) data 0.000 (0.005) loss 1.0723 (1.0974) acc 65.6250 (72.1078) lr 1.6374e-03 eta 7:29:55
+epoch [16/50] batch [260/500] time 1.563 (1.566) data 0.000 (0.004) loss 0.5669 (1.0952) acc 84.3750 (72.1274) lr 1.6374e-03 eta 7:29:53
+epoch [16/50] batch [265/500] time 1.582 (1.566) data 0.000 (0.004) loss 1.4102 (1.0957) acc 65.6250 (72.0991) lr 1.6374e-03 eta 7:29:47
+epoch [16/50] batch [270/500] time 1.561 (1.566) data 0.000 (0.004) loss 0.8208 (1.0963) acc 84.3750 (72.1412) lr 1.6374e-03 eta 7:29:34
+epoch [16/50] batch [275/500] time 1.569 (1.565) data 0.000 (0.004) loss 0.9370 (1.0963) acc 78.1250 (72.1932) lr 1.6374e-03 eta 7:29:22
+epoch [16/50] batch [280/500] time 1.556 (1.565) data 0.000 (0.004) loss 1.2266 (1.0947) acc 68.7500 (72.2321) lr 1.6374e-03 eta 7:29:12
+epoch [16/50] batch [285/500] time 1.565 (1.565) data 0.000 (0.004) loss 1.4961 (1.0975) acc 68.7500 (72.1162) lr 1.6374e-03 eta 7:29:02
+epoch [16/50] batch [290/500] time 1.543 (1.565) data 0.000 (0.004) loss 0.9873 (1.0978) acc 75.0000 (72.1228) lr 1.6374e-03 eta 7:28:49
+epoch [16/50] batch [295/500] time 1.550 (1.565) data 0.000 (0.004) loss 1.1924 (1.0966) acc 71.8750 (72.1822) lr 1.6374e-03 eta 7:28:40
+epoch [16/50] batch [300/500] time 1.544 (1.565) data 0.000 (0.004) loss 1.1787 (1.0974) acc 68.7500 (72.2083) lr 1.6374e-03 eta 7:28:30
+epoch [16/50] batch [305/500] time 1.558 (1.565) data 0.000 (0.004) loss 1.0361 (1.1004) acc 75.0000 (72.2131) lr 1.6374e-03 eta 7:28:23
+epoch [16/50] batch [310/500] time 1.543 (1.565) data 0.000 (0.004) loss 0.7246 (1.1005) acc 78.1250 (72.2077) lr 1.6374e-03 eta 7:28:14
+epoch [16/50] batch [315/500] time 1.562 (1.564) data 0.000 (0.004) loss 1.5303 (1.1054) acc 68.7500 (72.1726) lr 1.6374e-03 eta 7:28:04
+epoch [16/50] batch [320/500] time 1.558 (1.564) data 0.000 (0.004) loss 1.5078 (1.1085) acc 71.8750 (72.1191) lr 1.6374e-03 eta 7:27:54
+epoch [16/50] batch [325/500] time 1.580 (1.565) data 0.001 (0.004) loss 1.7061 (1.1144) acc 62.5000 (71.9904) lr 1.6374e-03 eta 7:27:50
+epoch [16/50] batch [330/500] time 1.579 (1.565) data 0.000 (0.004) loss 0.7954 (1.1178) acc 81.2500 (71.8939) lr 1.6374e-03 eta 7:27:42
+epoch [16/50] batch [335/500] time 1.566 (1.565) data 0.000 (0.004) loss 0.9502 (1.1188) acc 71.8750 (71.8843) lr 1.6374e-03 eta 7:27:35
+epoch [16/50] batch [340/500] time 1.576 (1.565) data 0.000 (0.004) loss 1.5127 (1.1190) acc 59.3750 (71.8842) lr 1.6374e-03 eta 7:27:29
+epoch [16/50] batch [345/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.1855 (1.1210) acc 68.7500 (71.8297) lr 1.6374e-03 eta 7:27:19
+epoch [16/50] batch [350/500] time 1.579 (1.565) data 0.000 (0.003) loss 0.6055 (1.1202) acc 75.0000 (71.7857) lr 1.6374e-03 eta 7:27:12
+epoch [16/50] batch [355/500] time 1.571 (1.564) data 0.000 (0.003) loss 0.9023 (1.1245) acc 78.1250 (71.6989) lr 1.6374e-03 eta 7:27:02
+epoch [16/50] batch [360/500] time 1.546 (1.564) data 0.000 (0.003) loss 1.3740 (1.1243) acc 71.8750 (71.7188) lr 1.6374e-03 eta 7:26:49
+epoch [16/50] batch [365/500] time 1.546 (1.564) data 0.000 (0.003) loss 1.0352 (1.1215) acc 65.6250 (71.7723) lr 1.6374e-03 eta 7:26:38
+epoch [16/50] batch [370/500] time 1.574 (1.564) data 0.000 (0.003) loss 1.5225 (1.1246) acc 59.3750 (71.7399) lr 1.6374e-03 eta 7:26:30
+epoch [16/50] batch [375/500] time 1.573 (1.564) data 0.000 (0.003) loss 0.5283 (1.1247) acc 87.5000 (71.7667) lr 1.6374e-03 eta 7:26:19
+epoch [16/50] batch [380/500] time 1.550 (1.564) data 0.000 (0.003) loss 1.1484 (1.1243) acc 75.0000 (71.7516) lr 1.6374e-03 eta 7:26:09
+epoch [16/50] batch [385/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.0664 (1.1231) acc 84.3750 (71.8263) lr 1.6374e-03 eta 7:26:01
+epoch [16/50] batch [390/500] time 1.556 (1.563) data 0.000 (0.003) loss 0.9863 (1.1230) acc 68.7500 (71.8510) lr 1.6374e-03 eta 7:25:51
+epoch [16/50] batch [395/500] time 1.560 (1.564) data 0.000 (0.003) loss 1.9600 (1.1240) acc 62.5000 (71.8196) lr 1.6374e-03 eta 7:25:44
+epoch [16/50] batch [400/500] time 1.575 (1.564) data 0.000 (0.003) loss 0.8433 (1.1238) acc 78.1250 (71.8750) lr 1.6374e-03 eta 7:25:36
+epoch [16/50] batch [405/500] time 1.541 (1.564) data 0.001 (0.003) loss 1.6895 (1.1256) acc 59.3750 (71.8441) lr 1.6374e-03 eta 7:25:32
+epoch [16/50] batch [410/500] time 1.563 (1.564) data 0.000 (0.003) loss 0.9702 (1.1242) acc 81.2500 (71.8674) lr 1.6374e-03 eta 7:25:24
+epoch [16/50] batch [415/500] time 1.576 (1.564) data 0.000 (0.003) loss 0.8809 (1.1221) acc 68.7500 (71.8976) lr 1.6374e-03 eta 7:25:16
+epoch [16/50] batch [420/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.0693 (1.1187) acc 75.0000 (71.9717) lr 1.6374e-03 eta 7:25:07
+epoch [16/50] batch [425/500] time 1.542 (1.564) data 0.000 (0.003) loss 0.7485 (1.1177) acc 84.3750 (72.0441) lr 1.6374e-03 eta 7:24:58
+epoch [16/50] batch [430/500] time 1.550 (1.563) data 0.000 (0.003) loss 1.6943 (1.1215) acc 53.1250 (71.9622) lr 1.6374e-03 eta 7:24:48
+epoch [16/50] batch [435/500] time 1.542 (1.563) data 0.000 (0.003) loss 0.9194 (1.1201) acc 81.2500 (72.0043) lr 1.6374e-03 eta 7:24:39
+epoch [16/50] batch [440/500] time 1.543 (1.563) data 0.000 (0.003) loss 0.7651 (1.1195) acc 81.2500 (71.9957) lr 1.6374e-03 eta 7:24:29
+epoch [16/50] batch [445/500] time 1.653 (1.564) data 0.000 (0.003) loss 0.9902 (1.1178) acc 75.0000 (72.0014) lr 1.6374e-03 eta 7:24:25
+epoch [16/50] batch [450/500] time 1.542 (1.563) data 0.000 (0.003) loss 1.2334 (1.1194) acc 65.6250 (71.9444) lr 1.6374e-03 eta 7:24:16
+epoch [16/50] batch [455/500] time 1.556 (1.563) data 0.000 (0.003) loss 1.1250 (1.1202) acc 65.6250 (71.9093) lr 1.6374e-03 eta 7:24:08
+epoch [16/50] batch [460/500] time 1.558 (1.563) data 0.000 (0.003) loss 0.9736 (1.1204) acc 78.1250 (71.9565) lr 1.6374e-03 eta 7:23:57
+epoch [16/50] batch [465/500] time 1.542 (1.563) data 0.000 (0.003) loss 1.1348 (1.1200) acc 62.5000 (71.9153) lr 1.6374e-03 eta 7:23:49
+epoch [16/50] batch [470/500] time 1.544 (1.563) data 0.000 (0.003) loss 1.3398 (1.1232) acc 62.5000 (71.8551) lr 1.6374e-03 eta 7:23:38
+epoch [16/50] batch [475/500] time 1.565 (1.563) data 0.000 (0.003) loss 1.1152 (1.1218) acc 78.1250 (71.8816) lr 1.6374e-03 eta 7:23:30
+epoch [16/50] batch [480/500] time 1.538 (1.563) data 0.000 (0.003) loss 0.9019 (1.1187) acc 78.1250 (71.9271) lr 1.6374e-03 eta 7:23:20
+epoch [16/50] batch [485/500] time 1.531 (1.563) data 0.001 (0.003) loss 1.1387 (1.1191) acc 75.0000 (71.9008) lr 1.6374e-03 eta 7:23:10
+epoch [16/50] batch [490/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.1377 (1.1186) acc 71.8750 (71.9069) lr 1.6374e-03 eta 7:23:00
+epoch [16/50] batch [495/500] time 1.539 (1.563) data 0.000 (0.003) loss 1.8232 (1.1190) acc 68.7500 (71.9192) lr 1.6374e-03 eta 7:22:51
+epoch [16/50] batch [500/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.5898 (1.1208) acc 65.6250 (71.8875) lr 1.5878e-03 eta 7:22:42
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,859
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.2%
+epoch [17/50] batch [5/500] time 1.544 (1.733) data 0.001 (0.192) loss 1.1123 (1.2357) acc 62.5000 (68.1250) lr 1.5878e-03 eta 8:10:44
+epoch [17/50] batch [10/500] time 1.554 (1.643) data 0.000 (0.096) loss 1.7197 (1.1653) acc 68.7500 (70.6250) lr 1.5878e-03 eta 7:45:09
+epoch [17/50] batch [15/500] time 1.573 (1.616) data 0.000 (0.064) loss 1.1719 (1.2168) acc 71.8750 (69.7917) lr 1.5878e-03 eta 7:37:21
+epoch [17/50] batch [20/500] time 1.557 (1.601) data 0.000 (0.048) loss 0.9111 (1.1754) acc 78.1250 (70.0000) lr 1.5878e-03 eta 7:33:00
+epoch [17/50] batch [25/500] time 1.542 (1.591) data 0.000 (0.039) loss 0.9136 (1.1717) acc 81.2500 (70.3750) lr 1.5878e-03 eta 7:30:00
+epoch [17/50] batch [30/500] time 1.524 (1.583) data 0.000 (0.032) loss 0.6201 (1.1398) acc 84.3750 (71.1458) lr 1.5878e-03 eta 7:27:46
+epoch [17/50] batch [35/500] time 1.563 (1.579) data 0.000 (0.028) loss 1.0059 (1.1173) acc 78.1250 (71.6071) lr 1.5878e-03 eta 7:26:24
+epoch [17/50] batch [40/500] time 1.561 (1.576) data 0.000 (0.024) loss 1.4502 (1.1210) acc 65.6250 (71.7188) lr 1.5878e-03 eta 7:25:25
+epoch [17/50] batch [45/500] time 1.544 (1.575) data 0.001 (0.022) loss 1.6621 (1.1355) acc 65.6250 (71.7361) lr 1.5878e-03 eta 7:25:05
+epoch [17/50] batch [50/500] time 1.568 (1.574) data 0.000 (0.020) loss 1.2109 (1.1292) acc 71.8750 (72.1250) lr 1.5878e-03 eta 7:24:34
+epoch [17/50] batch [55/500] time 1.554 (1.572) data 0.000 (0.018) loss 1.3984 (1.1258) acc 56.2500 (71.9886) lr 1.5878e-03 eta 7:24:05
+epoch [17/50] batch [60/500] time 1.584 (1.572) data 0.000 (0.016) loss 1.0068 (1.1165) acc 68.7500 (72.2917) lr 1.5878e-03 eta 7:23:45
+epoch [17/50] batch [65/500] time 1.530 (1.570) data 0.000 (0.015) loss 1.7275 (1.1152) acc 71.8750 (72.3558) lr 1.5878e-03 eta 7:23:10
+epoch [17/50] batch [70/500] time 1.554 (1.569) data 0.000 (0.014) loss 1.3398 (1.1167) acc 62.5000 (72.0982) lr 1.5878e-03 eta 7:22:47
+epoch [17/50] batch [75/500] time 1.547 (1.568) data 0.001 (0.013) loss 0.9204 (1.1118) acc 81.2500 (72.0417) lr 1.5878e-03 eta 7:22:21
+epoch [17/50] batch [80/500] time 1.557 (1.567) data 0.000 (0.012) loss 1.1064 (1.1244) acc 65.6250 (71.6797) lr 1.5878e-03 eta 7:21:57
+epoch [17/50] batch [85/500] time 1.569 (1.567) data 0.000 (0.012) loss 1.2607 (1.1267) acc 71.8750 (71.6176) lr 1.5878e-03 eta 7:21:48
+epoch [17/50] batch [90/500] time 1.545 (1.567) data 0.000 (0.011) loss 1.2461 (1.1293) acc 71.8750 (71.5278) lr 1.5878e-03 eta 7:21:31
+epoch [17/50] batch [95/500] time 1.575 (1.567) data 0.000 (0.011) loss 1.1406 (1.1324) acc 68.7500 (71.4145) lr 1.5878e-03 eta 7:21:25
+epoch [17/50] batch [100/500] time 1.570 (1.567) data 0.000 (0.010) loss 1.2227 (1.1288) acc 68.7500 (71.5000) lr 1.5878e-03 eta 7:21:22
+epoch [17/50] batch [105/500] time 1.542 (1.566) data 0.000 (0.010) loss 1.1670 (1.1206) acc 68.7500 (71.5476) lr 1.5878e-03 eta 7:21:04
+epoch [17/50] batch [110/500] time 1.569 (1.566) data 0.001 (0.009) loss 0.9272 (1.1106) acc 84.3750 (71.9034) lr 1.5878e-03 eta 7:20:56
+epoch [17/50] batch [115/500] time 1.558 (1.566) data 0.000 (0.009) loss 1.8252 (1.1152) acc 53.1250 (71.9565) lr 1.5878e-03 eta 7:20:42
+epoch [17/50] batch [120/500] time 1.533 (1.565) data 0.000 (0.008) loss 1.1406 (1.1248) acc 65.6250 (71.6927) lr 1.5878e-03 eta 7:20:21
+epoch [17/50] batch [125/500] time 1.565 (1.565) data 0.000 (0.008) loss 1.2119 (1.1197) acc 71.8750 (71.8500) lr 1.5878e-03 eta 7:20:12
+epoch [17/50] batch [130/500] time 1.550 (1.565) data 0.001 (0.008) loss 0.9868 (1.1302) acc 78.1250 (71.7548) lr 1.5878e-03 eta 7:20:07
+epoch [17/50] batch [135/500] time 1.568 (1.566) data 0.000 (0.008) loss 0.8403 (1.1246) acc 78.1250 (71.9907) lr 1.5878e-03 eta 7:20:08
+epoch [17/50] batch [140/500] time 1.587 (1.566) data 0.000 (0.007) loss 1.1055 (1.1216) acc 68.7500 (71.9866) lr 1.5878e-03 eta 7:19:57
+epoch [17/50] batch [145/500] time 1.564 (1.566) data 0.000 (0.007) loss 0.7563 (1.1170) acc 75.0000 (72.1552) lr 1.5878e-03 eta 7:19:56
+epoch [17/50] batch [150/500] time 1.569 (1.566) data 0.000 (0.007) loss 1.3066 (1.1177) acc 68.7500 (72.0625) lr 1.5878e-03 eta 7:19:42
+epoch [17/50] batch [155/500] time 1.531 (1.565) data 0.000 (0.007) loss 0.6372 (1.1183) acc 87.5000 (72.1371) lr 1.5878e-03 eta 7:19:29
+epoch [17/50] batch [160/500] time 1.570 (1.565) data 0.000 (0.006) loss 1.7969 (1.1288) acc 53.1250 (71.8359) lr 1.5878e-03 eta 7:19:16
+epoch [17/50] batch [165/500] time 1.550 (1.565) data 0.000 (0.006) loss 0.8662 (1.1253) acc 78.1250 (71.8939) lr 1.5878e-03 eta 7:19:01
+epoch [17/50] batch [170/500] time 1.557 (1.564) data 0.000 (0.006) loss 1.1045 (1.1296) acc 71.8750 (71.8566) lr 1.5878e-03 eta 7:18:50
+epoch [17/50] batch [175/500] time 1.555 (1.564) data 0.000 (0.006) loss 0.7544 (1.1281) acc 81.2500 (71.8571) lr 1.5878e-03 eta 7:18:36
+epoch [17/50] batch [180/500] time 1.580 (1.565) data 0.000 (0.006) loss 1.0381 (1.1265) acc 75.0000 (71.9965) lr 1.5878e-03 eta 7:18:36
+epoch [17/50] batch [185/500] time 1.571 (1.564) data 0.000 (0.006) loss 0.9146 (1.1334) acc 78.1250 (71.9257) lr 1.5878e-03 eta 7:18:26
+epoch [17/50] batch [190/500] time 1.573 (1.565) data 0.000 (0.005) loss 0.9702 (1.1397) acc 75.0000 (71.8586) lr 1.5878e-03 eta 7:18:21
+epoch [17/50] batch [195/500] time 1.558 (1.564) data 0.000 (0.005) loss 0.8682 (1.1349) acc 68.7500 (71.9551) lr 1.5878e-03 eta 7:18:10
+epoch [17/50] batch [200/500] time 1.548 (1.564) data 0.000 (0.005) loss 1.0977 (1.1351) acc 71.8750 (72.0156) lr 1.5878e-03 eta 7:18:02
+epoch [17/50] batch [205/500] time 1.540 (1.564) data 0.001 (0.005) loss 1.0439 (1.1344) acc 71.8750 (72.0274) lr 1.5878e-03 eta 7:17:53
+epoch [17/50] batch [210/500] time 1.578 (1.564) data 0.000 (0.005) loss 1.1953 (1.1329) acc 71.8750 (71.9940) lr 1.5878e-03 eta 7:17:45
+epoch [17/50] batch [215/500] time 1.546 (1.564) data 0.000 (0.005) loss 0.9517 (1.1281) acc 65.6250 (72.1512) lr 1.5878e-03 eta 7:17:35
+epoch [17/50] batch [220/500] time 1.566 (1.564) data 0.000 (0.005) loss 0.9927 (1.1236) acc 78.1250 (72.2443) lr 1.5878e-03 eta 7:17:30
+epoch [17/50] batch [225/500] time 1.547 (1.564) data 0.000 (0.005) loss 1.2334 (1.1233) acc 59.3750 (72.2222) lr 1.5878e-03 eta 7:17:17
+epoch [17/50] batch [230/500] time 1.560 (1.564) data 0.000 (0.005) loss 1.0195 (1.1284) acc 75.0000 (72.1467) lr 1.5878e-03 eta 7:17:08
+epoch [17/50] batch [235/500] time 1.562 (1.564) data 0.001 (0.004) loss 1.2598 (1.1346) acc 71.8750 (72.0080) lr 1.5878e-03 eta 7:16:56
+epoch [17/50] batch [240/500] time 1.535 (1.563) data 0.000 (0.004) loss 0.7441 (1.1351) acc 65.6250 (71.8750) lr 1.5878e-03 eta 7:16:43
+epoch [17/50] batch [245/500] time 1.558 (1.563) data 0.000 (0.004) loss 1.1221 (1.1351) acc 68.7500 (71.8495) lr 1.5878e-03 eta 7:16:33
+epoch [17/50] batch [250/500] time 1.586 (1.563) data 0.000 (0.004) loss 1.0596 (1.1320) acc 78.1250 (71.9000) lr 1.5878e-03 eta 7:16:26
+epoch [17/50] batch [255/500] time 1.569 (1.563) data 0.000 (0.004) loss 1.2725 (1.1330) acc 75.0000 (71.9363) lr 1.5878e-03 eta 7:16:18
+epoch [17/50] batch [260/500] time 1.567 (1.563) data 0.000 (0.004) loss 1.5303 (1.1384) acc 71.8750 (71.9111) lr 1.5878e-03 eta 7:16:08
+epoch [17/50] batch [265/500] time 1.577 (1.563) data 0.000 (0.004) loss 0.9297 (1.1344) acc 68.7500 (71.9929) lr 1.5878e-03 eta 7:16:04
+epoch [17/50] batch [270/500] time 1.544 (1.563) data 0.000 (0.004) loss 1.2773 (1.1366) acc 78.1250 (71.9907) lr 1.5878e-03 eta 7:15:49
+epoch [17/50] batch [275/500] time 1.550 (1.563) data 0.000 (0.004) loss 0.9521 (1.1364) acc 78.1250 (72.0341) lr 1.5878e-03 eta 7:15:39
+epoch [17/50] batch [280/500] time 1.572 (1.563) data 0.001 (0.004) loss 1.0566 (1.1393) acc 71.8750 (71.9754) lr 1.5878e-03 eta 7:15:33
+epoch [17/50] batch [285/500] time 1.635 (1.563) data 0.000 (0.004) loss 1.8955 (1.1430) acc 43.7500 (71.8421) lr 1.5878e-03 eta 7:15:28
+epoch [17/50] batch [290/500] time 1.546 (1.563) data 0.000 (0.004) loss 0.7144 (1.1415) acc 75.0000 (71.8534) lr 1.5878e-03 eta 7:15:19
+epoch [17/50] batch [295/500] time 1.553 (1.563) data 0.000 (0.004) loss 0.7998 (1.1409) acc 78.1250 (71.8644) lr 1.5878e-03 eta 7:15:11
+epoch [17/50] batch [300/500] time 1.543 (1.563) data 0.000 (0.004) loss 0.8379 (1.1393) acc 81.2500 (71.9271) lr 1.5878e-03 eta 7:15:00
+epoch [17/50] batch [305/500] time 1.589 (1.563) data 0.000 (0.004) loss 0.7285 (1.1406) acc 75.0000 (71.8648) lr 1.5878e-03 eta 7:14:54
+epoch [17/50] batch [310/500] time 1.565 (1.563) data 0.001 (0.003) loss 0.8237 (1.1378) acc 75.0000 (71.9355) lr 1.5878e-03 eta 7:14:44
+epoch [17/50] batch [315/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.8687 (1.1364) acc 87.5000 (72.0139) lr 1.5878e-03 eta 7:14:35
+epoch [17/50] batch [320/500] time 1.557 (1.563) data 0.000 (0.003) loss 0.5806 (1.1320) acc 87.5000 (72.1387) lr 1.5878e-03 eta 7:14:25
+epoch [17/50] batch [325/500] time 1.548 (1.563) data 0.000 (0.003) loss 0.8325 (1.1284) acc 81.2500 (72.1635) lr 1.5878e-03 eta 7:14:17
+epoch [17/50] batch [330/500] time 1.555 (1.563) data 0.000 (0.003) loss 0.8423 (1.1284) acc 81.2500 (72.1780) lr 1.5878e-03 eta 7:14:09
+epoch [17/50] batch [335/500] time 1.581 (1.563) data 0.000 (0.003) loss 1.3281 (1.1275) acc 75.0000 (72.2015) lr 1.5878e-03 eta 7:13:59
+epoch [17/50] batch [340/500] time 1.575 (1.563) data 0.001 (0.003) loss 1.0938 (1.1254) acc 75.0000 (72.2610) lr 1.5878e-03 eta 7:13:53
+epoch [17/50] batch [345/500] time 1.555 (1.563) data 0.000 (0.003) loss 0.8584 (1.1241) acc 81.2500 (72.2917) lr 1.5878e-03 eta 7:13:45
+epoch [17/50] batch [350/500] time 1.594 (1.563) data 0.000 (0.003) loss 0.8657 (1.1244) acc 75.0000 (72.3036) lr 1.5878e-03 eta 7:13:36
+epoch [17/50] batch [355/500] time 1.571 (1.563) data 0.000 (0.003) loss 0.9023 (1.1243) acc 75.0000 (72.3063) lr 1.5878e-03 eta 7:13:29
+epoch [17/50] batch [360/500] time 1.556 (1.563) data 0.000 (0.003) loss 1.0703 (1.1250) acc 68.7500 (72.2743) lr 1.5878e-03 eta 7:13:24
+epoch [17/50] batch [365/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.1094 (1.1268) acc 68.7500 (72.1747) lr 1.5878e-03 eta 7:13:17
+epoch [17/50] batch [370/500] time 1.545 (1.563) data 0.000 (0.003) loss 0.8804 (1.1290) acc 75.0000 (72.1368) lr 1.5878e-03 eta 7:13:10
+epoch [17/50] batch [375/500] time 1.570 (1.563) data 0.001 (0.003) loss 0.7354 (1.1269) acc 78.1250 (72.2000) lr 1.5878e-03 eta 7:13:05
+epoch [17/50] batch [380/500] time 1.570 (1.563) data 0.001 (0.003) loss 1.3564 (1.1255) acc 71.8750 (72.2451) lr 1.5878e-03 eta 7:12:57
+epoch [17/50] batch [385/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.2881 (1.1275) acc 68.7500 (72.2646) lr 1.5878e-03 eta 7:12:50
+epoch [17/50] batch [390/500] time 1.586 (1.563) data 0.001 (0.003) loss 0.9795 (1.1278) acc 68.7500 (72.2276) lr 1.5878e-03 eta 7:12:44
+epoch [17/50] batch [395/500] time 1.551 (1.563) data 0.000 (0.003) loss 1.5674 (1.1293) acc 59.3750 (72.1915) lr 1.5878e-03 eta 7:12:35
+epoch [17/50] batch [400/500] time 1.536 (1.563) data 0.000 (0.003) loss 0.9404 (1.1278) acc 84.3750 (72.2500) lr 1.5878e-03 eta 7:12:26
+epoch [17/50] batch [405/500] time 1.548 (1.563) data 0.000 (0.003) loss 0.6694 (1.1265) acc 90.6250 (72.2762) lr 1.5878e-03 eta 7:12:19
+epoch [17/50] batch [410/500] time 1.564 (1.563) data 0.001 (0.003) loss 1.2285 (1.1259) acc 71.8750 (72.3018) lr 1.5878e-03 eta 7:12:09
+epoch [17/50] batch [415/500] time 1.568 (1.563) data 0.000 (0.003) loss 1.4893 (1.1234) acc 71.8750 (72.3343) lr 1.5878e-03 eta 7:12:00
+epoch [17/50] batch [420/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.5908 (1.1237) acc 62.5000 (72.2768) lr 1.5878e-03 eta 7:11:54
+epoch [17/50] batch [425/500] time 1.569 (1.563) data 0.000 (0.003) loss 0.6689 (1.1239) acc 81.2500 (72.2794) lr 1.5878e-03 eta 7:11:49
+epoch [17/50] batch [430/500] time 1.572 (1.563) data 0.000 (0.003) loss 0.5356 (1.1238) acc 87.5000 (72.2602) lr 1.5878e-03 eta 7:11:45
+epoch [17/50] batch [435/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.2881 (1.1252) acc 71.8750 (72.2486) lr 1.5878e-03 eta 7:11:35
+epoch [17/50] batch [440/500] time 1.563 (1.563) data 0.000 (0.003) loss 1.1377 (1.1240) acc 62.5000 (72.2159) lr 1.5878e-03 eta 7:11:27
+epoch [17/50] batch [445/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.9409 (1.1229) acc 71.8750 (72.1980) lr 1.5878e-03 eta 7:11:21
+epoch [17/50] batch [450/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.4697 (1.1252) acc 62.5000 (72.1042) lr 1.5878e-03 eta 7:11:12
+epoch [17/50] batch [455/500] time 1.557 (1.563) data 0.001 (0.003) loss 0.8491 (1.1236) acc 75.0000 (72.1016) lr 1.5878e-03 eta 7:11:04
+epoch [17/50] batch [460/500] time 1.552 (1.563) data 0.001 (0.002) loss 1.2451 (1.1249) acc 59.3750 (72.0788) lr 1.5878e-03 eta 7:10:55
+epoch [17/50] batch [465/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.3848 (1.1236) acc 62.5000 (72.0632) lr 1.5878e-03 eta 7:10:46
+epoch [17/50] batch [470/500] time 1.560 (1.563) data 0.000 (0.002) loss 0.9834 (1.1214) acc 71.8750 (72.1210) lr 1.5878e-03 eta 7:10:37
+epoch [17/50] batch [475/500] time 1.572 (1.563) data 0.001 (0.002) loss 0.9624 (1.1236) acc 81.2500 (72.1118) lr 1.5878e-03 eta 7:10:32
+epoch [17/50] batch [480/500] time 1.596 (1.563) data 0.000 (0.002) loss 0.9639 (1.1259) acc 78.1250 (72.0703) lr 1.5878e-03 eta 7:10:25
+epoch [17/50] batch [485/500] time 1.554 (1.563) data 0.001 (0.002) loss 1.0957 (1.1263) acc 78.1250 (72.0490) lr 1.5878e-03 eta 7:10:16
+epoch [17/50] batch [490/500] time 1.577 (1.563) data 0.000 (0.002) loss 1.3652 (1.1263) acc 65.6250 (72.0344) lr 1.5878e-03 eta 7:10:10
+epoch [17/50] batch [495/500] time 1.578 (1.563) data 0.000 (0.002) loss 0.6069 (1.1236) acc 84.3750 (72.0960) lr 1.5878e-03 eta 7:10:03
+epoch [17/50] batch [500/500] time 1.587 (1.563) data 0.000 (0.002) loss 0.7563 (1.1229) acc 84.3750 (72.1562) lr 1.5358e-03 eta 7:09:55
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,968
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+epoch [18/50] batch [5/500] time 1.547 (1.651) data 0.000 (0.158) loss 0.6758 (0.9303) acc 75.0000 (72.5000) lr 1.5358e-03 eta 7:33:57
+epoch [18/50] batch [10/500] time 1.556 (1.600) data 0.000 (0.079) loss 1.1191 (1.0221) acc 68.7500 (71.8750) lr 1.5358e-03 eta 7:19:35
+epoch [18/50] batch [15/500] time 1.563 (1.585) data 0.001 (0.053) loss 0.9932 (1.0561) acc 81.2500 (72.9167) lr 1.5358e-03 eta 7:15:25
+epoch [18/50] batch [20/500] time 1.559 (1.580) data 0.001 (0.040) loss 0.8589 (1.0651) acc 75.0000 (72.6562) lr 1.5358e-03 eta 7:13:56
+epoch [18/50] batch [25/500] time 1.566 (1.574) data 0.000 (0.032) loss 1.3525 (1.1199) acc 78.1250 (72.0000) lr 1.5358e-03 eta 7:12:07
+epoch [18/50] batch [30/500] time 1.562 (1.571) data 0.000 (0.027) loss 1.3955 (1.1266) acc 68.7500 (71.8750) lr 1.5358e-03 eta 7:11:14
+epoch [18/50] batch [35/500] time 1.556 (1.570) data 0.001 (0.023) loss 0.6484 (1.1234) acc 75.0000 (71.9643) lr 1.5358e-03 eta 7:10:52
+epoch [18/50] batch [40/500] time 1.570 (1.569) data 0.000 (0.020) loss 0.8438 (1.1683) acc 78.1250 (71.3281) lr 1.5358e-03 eta 7:10:21
+epoch [18/50] batch [45/500] time 1.580 (1.569) data 0.001 (0.018) loss 0.9971 (1.1718) acc 78.1250 (71.0417) lr 1.5358e-03 eta 7:10:25
+epoch [18/50] batch [50/500] time 1.592 (1.569) data 0.000 (0.016) loss 1.2627 (1.1853) acc 68.7500 (70.6875) lr 1.5358e-03 eta 7:10:13
+epoch [18/50] batch [55/500] time 1.550 (1.568) data 0.000 (0.015) loss 1.1338 (1.1894) acc 62.5000 (70.5114) lr 1.5358e-03 eta 7:09:39
+epoch [18/50] batch [60/500] time 1.529 (1.566) data 0.000 (0.014) loss 1.1016 (1.1746) acc 68.7500 (71.0938) lr 1.5358e-03 eta 7:09:07
+epoch [18/50] batch [65/500] time 1.548 (1.568) data 0.000 (0.013) loss 1.3057 (1.1602) acc 68.7500 (71.2500) lr 1.5358e-03 eta 7:09:26
+epoch [18/50] batch [70/500] time 1.570 (1.568) data 0.000 (0.012) loss 1.0801 (1.1502) acc 78.1250 (71.6964) lr 1.5358e-03 eta 7:09:15
+epoch [18/50] batch [75/500] time 1.570 (1.568) data 0.000 (0.011) loss 0.8594 (1.1398) acc 68.7500 (71.7917) lr 1.5358e-03 eta 7:09:11
+epoch [18/50] batch [80/500] time 1.569 (1.568) data 0.000 (0.010) loss 1.7754 (1.1322) acc 56.2500 (71.9141) lr 1.5358e-03 eta 7:09:09
+epoch [18/50] batch [85/500] time 1.558 (1.568) data 0.001 (0.010) loss 0.6167 (1.1264) acc 84.3750 (71.9118) lr 1.5358e-03 eta 7:08:58
+epoch [18/50] batch [90/500] time 1.553 (1.568) data 0.000 (0.009) loss 1.2471 (1.1302) acc 68.7500 (71.9792) lr 1.5358e-03 eta 7:08:45
+epoch [18/50] batch [95/500] time 1.569 (1.568) data 0.000 (0.009) loss 0.8018 (1.1337) acc 84.3750 (71.8421) lr 1.5358e-03 eta 7:08:37
+epoch [18/50] batch [100/500] time 1.587 (1.567) data 0.000 (0.008) loss 1.0459 (1.1367) acc 75.0000 (71.8438) lr 1.5358e-03 eta 7:08:26
+epoch [18/50] batch [105/500] time 1.551 (1.567) data 0.001 (0.008) loss 0.6592 (1.1358) acc 84.3750 (71.7262) lr 1.5358e-03 eta 7:08:11
+epoch [18/50] batch [110/500] time 1.552 (1.568) data 0.000 (0.008) loss 0.6543 (1.1379) acc 78.1250 (71.6761) lr 1.5358e-03 eta 7:08:20
+epoch [18/50] batch [115/500] time 1.583 (1.568) data 0.000 (0.007) loss 1.1182 (1.1408) acc 78.1250 (71.7935) lr 1.5358e-03 eta 7:08:11
+epoch [18/50] batch [120/500] time 1.596 (1.568) data 0.001 (0.007) loss 1.1035 (1.1326) acc 68.7500 (71.8750) lr 1.5358e-03 eta 7:08:02
+epoch [18/50] batch [125/500] time 1.554 (1.567) data 0.001 (0.007) loss 1.0840 (1.1358) acc 78.1250 (71.9250) lr 1.5358e-03 eta 7:07:41
+epoch [18/50] batch [130/500] time 1.564 (1.567) data 0.000 (0.007) loss 1.2461 (1.1296) acc 62.5000 (71.9471) lr 1.5358e-03 eta 7:07:26
+epoch [18/50] batch [135/500] time 1.578 (1.567) data 0.000 (0.006) loss 0.8096 (1.1299) acc 81.2500 (72.0602) lr 1.5358e-03 eta 7:07:16
+epoch [18/50] batch [140/500] time 1.572 (1.566) data 0.000 (0.006) loss 1.1689 (1.1251) acc 68.7500 (72.0312) lr 1.5358e-03 eta 7:07:02
+epoch [18/50] batch [145/500] time 1.557 (1.566) data 0.000 (0.006) loss 1.4229 (1.1316) acc 65.6250 (71.8319) lr 1.5358e-03 eta 7:06:51
+epoch [18/50] batch [150/500] time 1.578 (1.566) data 0.001 (0.006) loss 1.2432 (1.1309) acc 75.0000 (71.8125) lr 1.5358e-03 eta 7:06:40
+epoch [18/50] batch [155/500] time 1.560 (1.565) data 0.001 (0.006) loss 1.5107 (1.1307) acc 65.6250 (71.8952) lr 1.5358e-03 eta 7:06:26
+epoch [18/50] batch [160/500] time 1.541 (1.565) data 0.000 (0.005) loss 0.8667 (1.1341) acc 68.7500 (71.7383) lr 1.5358e-03 eta 7:06:11
+epoch [18/50] batch [165/500] time 1.554 (1.565) data 0.000 (0.005) loss 1.0361 (1.1356) acc 68.7500 (71.7992) lr 1.5358e-03 eta 7:06:03
+epoch [18/50] batch [170/500] time 1.572 (1.565) data 0.000 (0.005) loss 0.9390 (1.1297) acc 81.2500 (71.9301) lr 1.5358e-03 eta 7:05:52
+epoch [18/50] batch [175/500] time 1.556 (1.565) data 0.001 (0.005) loss 0.6533 (1.1252) acc 78.1250 (72.0536) lr 1.5358e-03 eta 7:05:43
+epoch [18/50] batch [180/500] time 1.576 (1.565) data 0.000 (0.005) loss 0.5225 (1.1149) acc 90.6250 (72.2917) lr 1.5358e-03 eta 7:05:36
+epoch [18/50] batch [185/500] time 1.557 (1.565) data 0.000 (0.005) loss 0.8311 (1.1143) acc 78.1250 (72.2297) lr 1.5358e-03 eta 7:05:27
+epoch [18/50] batch [190/500] time 1.569 (1.565) data 0.000 (0.005) loss 0.6631 (1.1110) acc 81.2500 (72.2697) lr 1.5358e-03 eta 7:05:21
+epoch [18/50] batch [195/500] time 1.561 (1.565) data 0.000 (0.004) loss 1.5469 (1.1123) acc 68.7500 (72.2115) lr 1.5358e-03 eta 7:05:12
+epoch [18/50] batch [200/500] time 1.552 (1.564) data 0.001 (0.004) loss 0.9775 (1.1118) acc 71.8750 (72.1562) lr 1.5358e-03 eta 7:04:59
+epoch [18/50] batch [205/500] time 1.649 (1.565) data 0.001 (0.004) loss 1.1572 (1.1110) acc 65.6250 (72.1646) lr 1.5358e-03 eta 7:04:57
+epoch [18/50] batch [210/500] time 1.572 (1.565) data 0.000 (0.004) loss 0.7817 (1.1065) acc 75.0000 (72.2470) lr 1.5358e-03 eta 7:04:48
+epoch [18/50] batch [215/500] time 1.573 (1.565) data 0.000 (0.004) loss 0.9507 (1.1046) acc 78.1250 (72.3256) lr 1.5358e-03 eta 7:04:41
+epoch [18/50] batch [220/500] time 1.549 (1.565) data 0.000 (0.004) loss 0.7520 (1.1036) acc 78.1250 (72.3011) lr 1.5358e-03 eta 7:04:30
+epoch [18/50] batch [225/500] time 1.551 (1.564) data 0.000 (0.004) loss 1.8828 (1.1080) acc 56.2500 (72.2083) lr 1.5358e-03 eta 7:04:20
+epoch [18/50] batch [230/500] time 1.571 (1.564) data 0.000 (0.004) loss 0.9883 (1.1075) acc 71.8750 (72.1603) lr 1.5358e-03 eta 7:04:11
+epoch [18/50] batch [235/500] time 1.566 (1.564) data 0.000 (0.004) loss 1.0605 (1.1085) acc 68.7500 (72.2074) lr 1.5358e-03 eta 7:04:00
+epoch [18/50] batch [240/500] time 1.550 (1.564) data 0.000 (0.004) loss 1.0352 (1.1079) acc 75.0000 (72.2917) lr 1.5358e-03 eta 7:03:49
+epoch [18/50] batch [245/500] time 1.547 (1.564) data 0.000 (0.004) loss 0.9736 (1.1062) acc 87.5000 (72.4235) lr 1.5358e-03 eta 7:03:39
+epoch [18/50] batch [250/500] time 1.535 (1.564) data 0.000 (0.004) loss 0.6528 (1.1046) acc 78.1250 (72.4250) lr 1.5358e-03 eta 7:03:31
+epoch [18/50] batch [255/500] time 1.556 (1.564) data 0.000 (0.004) loss 0.6055 (1.0996) acc 78.1250 (72.4755) lr 1.5358e-03 eta 7:03:21
+epoch [18/50] batch [260/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.6045 (1.1048) acc 62.5000 (72.3918) lr 1.5358e-03 eta 7:03:08
+epoch [18/50] batch [265/500] time 1.532 (1.563) data 0.000 (0.003) loss 1.0830 (1.1032) acc 78.1250 (72.4528) lr 1.5358e-03 eta 7:02:59
+epoch [18/50] batch [270/500] time 1.542 (1.563) data 0.000 (0.003) loss 0.7207 (1.1011) acc 84.3750 (72.5116) lr 1.5358e-03 eta 7:02:48
+epoch [18/50] batch [275/500] time 1.571 (1.563) data 0.001 (0.003) loss 1.4658 (1.1024) acc 71.8750 (72.4545) lr 1.5358e-03 eta 7:02:40
+epoch [18/50] batch [280/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.6650 (1.1065) acc 75.0000 (72.4219) lr 1.5358e-03 eta 7:02:33
+epoch [18/50] batch [285/500] time 1.554 (1.563) data 0.000 (0.003) loss 1.2178 (1.1071) acc 62.5000 (72.4232) lr 1.5358e-03 eta 7:02:24
+epoch [18/50] batch [290/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.1123 (1.1080) acc 75.0000 (72.3599) lr 1.5358e-03 eta 7:02:14
+epoch [18/50] batch [295/500] time 1.537 (1.563) data 0.000 (0.003) loss 0.7446 (1.1048) acc 81.2500 (72.3941) lr 1.5358e-03 eta 7:02:05
+epoch [18/50] batch [300/500] time 1.574 (1.563) data 0.000 (0.003) loss 0.7012 (1.1001) acc 81.2500 (72.4583) lr 1.5358e-03 eta 7:01:55
+epoch [18/50] batch [305/500] time 1.565 (1.563) data 0.000 (0.003) loss 1.0566 (1.1036) acc 78.1250 (72.4488) lr 1.5358e-03 eta 7:01:46
+epoch [18/50] batch [310/500] time 1.560 (1.563) data 0.000 (0.003) loss 1.3438 (1.1029) acc 65.6250 (72.4395) lr 1.5358e-03 eta 7:01:38
+epoch [18/50] batch [315/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.0918 (1.1065) acc 75.0000 (72.3710) lr 1.5358e-03 eta 7:01:29
+epoch [18/50] batch [320/500] time 1.555 (1.562) data 0.000 (0.003) loss 0.7876 (1.1097) acc 81.2500 (72.2949) lr 1.5358e-03 eta 7:01:20
+epoch [18/50] batch [325/500] time 1.560 (1.562) data 0.000 (0.003) loss 0.9990 (1.1129) acc 81.2500 (72.2404) lr 1.5358e-03 eta 7:01:13
+epoch [18/50] batch [330/500] time 1.574 (1.562) data 0.000 (0.003) loss 1.5518 (1.1154) acc 71.8750 (72.2064) lr 1.5358e-03 eta 7:01:05
+epoch [18/50] batch [335/500] time 1.571 (1.562) data 0.000 (0.003) loss 1.8096 (1.1189) acc 50.0000 (72.1642) lr 1.5358e-03 eta 7:00:56
+epoch [18/50] batch [340/500] time 1.541 (1.562) data 0.000 (0.003) loss 1.0840 (1.1199) acc 71.8750 (72.1324) lr 1.5358e-03 eta 7:00:49
+epoch [18/50] batch [345/500] time 1.568 (1.562) data 0.000 (0.003) loss 0.9561 (1.1200) acc 68.7500 (72.0833) lr 1.5358e-03 eta 7:00:41
+epoch [18/50] batch [350/500] time 1.568 (1.563) data 0.000 (0.003) loss 0.8853 (1.1211) acc 81.2500 (72.0536) lr 1.5358e-03 eta 7:00:36
+epoch [18/50] batch [355/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.0371 (1.1201) acc 75.0000 (72.0687) lr 1.5358e-03 eta 7:00:29
+epoch [18/50] batch [360/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.0361 (1.1190) acc 71.8750 (72.1354) lr 1.5358e-03 eta 7:00:20
+epoch [18/50] batch [365/500] time 1.566 (1.563) data 0.000 (0.003) loss 0.9805 (1.1175) acc 71.8750 (72.1575) lr 1.5358e-03 eta 7:00:11
+epoch [18/50] batch [370/500] time 1.562 (1.562) data 0.000 (0.003) loss 0.9810 (1.1225) acc 75.0000 (72.0355) lr 1.5358e-03 eta 7:00:02
+epoch [18/50] batch [375/500] time 1.586 (1.563) data 0.000 (0.003) loss 0.7661 (1.1202) acc 87.5000 (72.0917) lr 1.5358e-03 eta 6:59:55
+epoch [18/50] batch [380/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.6211 (1.1189) acc 81.2500 (72.0888) lr 1.5358e-03 eta 6:59:47
+epoch [18/50] batch [385/500] time 1.571 (1.563) data 0.000 (0.002) loss 0.9189 (1.1193) acc 71.8750 (72.0211) lr 1.5358e-03 eta 6:59:40
+epoch [18/50] batch [390/500] time 1.578 (1.563) data 0.000 (0.002) loss 1.0967 (1.1191) acc 78.1250 (72.0673) lr 1.5358e-03 eta 6:59:34
+epoch [18/50] batch [395/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.0439 (1.1188) acc 75.0000 (72.0886) lr 1.5358e-03 eta 6:59:29
+epoch [18/50] batch [400/500] time 1.592 (1.563) data 0.001 (0.002) loss 1.4072 (1.1181) acc 71.8750 (72.1250) lr 1.5358e-03 eta 6:59:20
+epoch [18/50] batch [405/500] time 1.545 (1.563) data 0.000 (0.002) loss 0.9575 (1.1205) acc 71.8750 (72.0910) lr 1.5358e-03 eta 6:59:13
+epoch [18/50] batch [410/500] time 1.600 (1.563) data 0.001 (0.002) loss 1.1729 (1.1201) acc 75.0000 (72.1037) lr 1.5358e-03 eta 6:59:05
+epoch [18/50] batch [415/500] time 1.566 (1.563) data 0.001 (0.002) loss 0.7344 (1.1188) acc 87.5000 (72.1461) lr 1.5358e-03 eta 6:58:58
+epoch [18/50] batch [420/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.1670 (1.1183) acc 71.8750 (72.1280) lr 1.5358e-03 eta 6:58:51
+epoch [18/50] batch [425/500] time 1.568 (1.563) data 0.000 (0.002) loss 0.9346 (1.1212) acc 71.8750 (72.0515) lr 1.5358e-03 eta 6:58:46
+epoch [18/50] batch [430/500] time 1.571 (1.563) data 0.001 (0.002) loss 0.7925 (1.1179) acc 81.2500 (72.1294) lr 1.5358e-03 eta 6:58:37
+epoch [18/50] batch [435/500] time 1.575 (1.563) data 0.000 (0.002) loss 1.2793 (1.1184) acc 68.7500 (72.1408) lr 1.5358e-03 eta 6:58:30
+epoch [18/50] batch [440/500] time 1.577 (1.563) data 0.000 (0.002) loss 0.6792 (1.1161) acc 81.2500 (72.1733) lr 1.5358e-03 eta 6:58:23
+epoch [18/50] batch [445/500] time 1.586 (1.563) data 0.000 (0.002) loss 1.2334 (1.1145) acc 68.7500 (72.2121) lr 1.5358e-03 eta 6:58:15
+epoch [18/50] batch [450/500] time 1.577 (1.563) data 0.000 (0.002) loss 1.1475 (1.1140) acc 71.8750 (72.2153) lr 1.5358e-03 eta 6:58:05
+epoch [18/50] batch [455/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.1064 (1.1163) acc 68.7500 (72.1566) lr 1.5358e-03 eta 6:57:57
+epoch [18/50] batch [460/500] time 1.571 (1.563) data 0.001 (0.002) loss 0.8760 (1.1167) acc 81.2500 (72.2079) lr 1.5358e-03 eta 6:57:46
+epoch [18/50] batch [465/500] time 1.572 (1.563) data 0.001 (0.002) loss 1.0615 (1.1188) acc 71.8750 (72.1371) lr 1.5358e-03 eta 6:57:38
+epoch [18/50] batch [470/500] time 1.549 (1.563) data 0.000 (0.002) loss 0.6748 (1.1186) acc 81.2500 (72.1809) lr 1.5358e-03 eta 6:57:28
+epoch [18/50] batch [475/500] time 1.571 (1.563) data 0.000 (0.002) loss 1.4893 (1.1175) acc 65.6250 (72.2171) lr 1.5358e-03 eta 6:57:19
+epoch [18/50] batch [480/500] time 1.557 (1.562) data 0.000 (0.002) loss 1.1875 (1.1168) acc 75.0000 (72.2266) lr 1.5358e-03 eta 6:57:09
+epoch [18/50] batch [485/500] time 1.534 (1.562) data 0.001 (0.002) loss 1.3408 (1.1166) acc 71.8750 (72.2165) lr 1.5358e-03 eta 6:56:59
+epoch [18/50] batch [490/500] time 1.528 (1.562) data 0.000 (0.002) loss 1.2812 (1.1177) acc 65.6250 (72.1811) lr 1.5358e-03 eta 6:56:49
+epoch [18/50] batch [495/500] time 1.526 (1.562) data 0.000 (0.002) loss 1.0635 (1.1159) acc 84.3750 (72.2601) lr 1.5358e-03 eta 6:56:41
+epoch [18/50] batch [500/500] time 1.544 (1.562) data 0.000 (0.002) loss 0.9624 (1.1152) acc 78.1250 (72.2313) lr 1.4818e-03 eta 6:56:31
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,987
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [19/50] batch [5/500] time 1.549 (1.671) data 0.000 (0.168) loss 0.8496 (1.2457) acc 71.8750 (65.6250) lr 1.4818e-03 eta 7:25:28
+epoch [19/50] batch [10/500] time 1.556 (1.618) data 0.000 (0.084) loss 0.7856 (1.1403) acc 81.2500 (68.7500) lr 1.4818e-03 eta 7:11:05
+epoch [19/50] batch [15/500] time 1.554 (1.596) data 0.000 (0.056) loss 1.3994 (1.1925) acc 68.7500 (69.5833) lr 1.4818e-03 eta 7:05:06
+epoch [19/50] batch [20/500] time 1.566 (1.588) data 0.000 (0.042) loss 0.9438 (1.1386) acc 75.0000 (70.7812) lr 1.4818e-03 eta 7:02:48
+epoch [19/50] batch [25/500] time 1.567 (1.582) data 0.000 (0.034) loss 1.0029 (1.1531) acc 78.1250 (70.2500) lr 1.4818e-03 eta 7:01:11
+epoch [19/50] batch [30/500] time 1.587 (1.581) data 0.000 (0.028) loss 0.6016 (1.1156) acc 78.1250 (71.0417) lr 1.4818e-03 eta 7:00:44
+epoch [19/50] batch [35/500] time 1.574 (1.580) data 0.000 (0.024) loss 0.3547 (1.0906) acc 93.7500 (72.0536) lr 1.4818e-03 eta 7:00:27
+epoch [19/50] batch [40/500] time 1.573 (1.583) data 0.000 (0.021) loss 1.7275 (1.1554) acc 68.7500 (71.1719) lr 1.4818e-03 eta 7:00:57
+epoch [19/50] batch [45/500] time 1.578 (1.581) data 0.001 (0.019) loss 1.1250 (1.1459) acc 71.8750 (71.7361) lr 1.4818e-03 eta 7:00:29
+epoch [19/50] batch [50/500] time 1.588 (1.582) data 0.000 (0.017) loss 0.9639 (1.1094) acc 75.0000 (72.6250) lr 1.4818e-03 eta 7:00:30
+epoch [19/50] batch [55/500] time 1.579 (1.581) data 0.001 (0.016) loss 1.0820 (1.0930) acc 68.7500 (72.7841) lr 1.4818e-03 eta 7:00:09
+epoch [19/50] batch [60/500] time 1.563 (1.580) data 0.000 (0.014) loss 1.3232 (1.1039) acc 62.5000 (72.2917) lr 1.4818e-03 eta 6:59:46
+epoch [19/50] batch [65/500] time 1.577 (1.580) data 0.000 (0.013) loss 1.0547 (1.1083) acc 75.0000 (72.2596) lr 1.4818e-03 eta 6:59:32
+epoch [19/50] batch [70/500] time 1.545 (1.578) data 0.001 (0.012) loss 0.7002 (1.1006) acc 78.1250 (72.4107) lr 1.4818e-03 eta 6:59:01
+epoch [19/50] batch [75/500] time 1.541 (1.577) data 0.000 (0.012) loss 0.6987 (1.0867) acc 78.1250 (72.6667) lr 1.4818e-03 eta 6:58:30
+epoch [19/50] batch [80/500] time 1.542 (1.577) data 0.000 (0.011) loss 0.8994 (1.0936) acc 75.0000 (72.6172) lr 1.4818e-03 eta 6:58:24
+epoch [19/50] batch [85/500] time 1.579 (1.577) data 0.000 (0.010) loss 1.3145 (1.0985) acc 68.7500 (72.2794) lr 1.4818e-03 eta 6:58:10
+epoch [19/50] batch [90/500] time 1.552 (1.575) data 0.000 (0.010) loss 1.6055 (1.1117) acc 65.6250 (72.0139) lr 1.4818e-03 eta 6:57:39
+epoch [19/50] batch [95/500] time 1.558 (1.575) data 0.000 (0.009) loss 0.9209 (1.1063) acc 81.2500 (72.1382) lr 1.4818e-03 eta 6:57:27
+epoch [19/50] batch [100/500] time 1.544 (1.573) data 0.001 (0.009) loss 1.1953 (1.1101) acc 71.8750 (72.0312) lr 1.4818e-03 eta 6:56:58
+epoch [19/50] batch [105/500] time 1.557 (1.573) data 0.001 (0.008) loss 0.9639 (1.1070) acc 78.1250 (72.1726) lr 1.4818e-03 eta 6:56:37
+epoch [19/50] batch [110/500] time 1.528 (1.571) data 0.000 (0.008) loss 1.2803 (1.1113) acc 71.8750 (72.3580) lr 1.4818e-03 eta 6:56:05
+epoch [19/50] batch [115/500] time 1.548 (1.570) data 0.000 (0.008) loss 0.7803 (1.1065) acc 68.7500 (72.3641) lr 1.4818e-03 eta 6:55:43
+epoch [19/50] batch [120/500] time 1.548 (1.570) data 0.001 (0.007) loss 1.0469 (1.1110) acc 78.1250 (72.2656) lr 1.4818e-03 eta 6:55:29
+epoch [19/50] batch [125/500] time 1.584 (1.570) data 0.001 (0.007) loss 0.9849 (1.1106) acc 78.1250 (72.4000) lr 1.4818e-03 eta 6:55:16
+epoch [19/50] batch [130/500] time 1.580 (1.570) data 0.000 (0.007) loss 0.9487 (1.1088) acc 68.7500 (72.3558) lr 1.4818e-03 eta 6:55:09
+epoch [19/50] batch [135/500] time 1.584 (1.570) data 0.000 (0.007) loss 1.2842 (1.1042) acc 68.7500 (72.4769) lr 1.4818e-03 eta 6:55:01
+epoch [19/50] batch [140/500] time 1.564 (1.570) data 0.000 (0.006) loss 0.6572 (1.1003) acc 78.1250 (72.5446) lr 1.4818e-03 eta 6:54:59
+epoch [19/50] batch [145/500] time 1.565 (1.569) data 0.000 (0.006) loss 0.5039 (1.1007) acc 84.3750 (72.6078) lr 1.4818e-03 eta 6:54:43
+epoch [19/50] batch [150/500] time 1.560 (1.569) data 0.000 (0.006) loss 0.9131 (1.0931) acc 81.2500 (72.7083) lr 1.4818e-03 eta 6:54:31
+epoch [19/50] batch [155/500] time 1.563 (1.569) data 0.001 (0.006) loss 2.4180 (1.1057) acc 56.2500 (72.4395) lr 1.4818e-03 eta 6:54:22
+epoch [19/50] batch [160/500] time 1.553 (1.569) data 0.000 (0.006) loss 0.8838 (1.1019) acc 78.1250 (72.4023) lr 1.4818e-03 eta 6:54:08
+epoch [19/50] batch [165/500] time 1.534 (1.568) data 0.000 (0.006) loss 1.7236 (1.1076) acc 65.6250 (72.4621) lr 1.4818e-03 eta 6:53:56
+epoch [19/50] batch [170/500] time 1.576 (1.568) data 0.001 (0.005) loss 0.5205 (1.1041) acc 84.3750 (72.4265) lr 1.4818e-03 eta 6:53:45
+epoch [19/50] batch [175/500] time 1.554 (1.568) data 0.000 (0.005) loss 0.7949 (1.1009) acc 71.8750 (72.4107) lr 1.4818e-03 eta 6:53:30
+epoch [19/50] batch [180/500] time 1.549 (1.568) data 0.000 (0.005) loss 0.6836 (1.0990) acc 84.3750 (72.5521) lr 1.4818e-03 eta 6:53:19
+epoch [19/50] batch [185/500] time 1.565 (1.568) data 0.001 (0.005) loss 0.8906 (1.0945) acc 78.1250 (72.6014) lr 1.4818e-03 eta 6:53:15
+epoch [19/50] batch [190/500] time 1.553 (1.568) data 0.001 (0.005) loss 1.2305 (1.0947) acc 71.8750 (72.6151) lr 1.4818e-03 eta 6:53:06
+epoch [19/50] batch [195/500] time 1.553 (1.568) data 0.000 (0.005) loss 1.1709 (1.0898) acc 62.5000 (72.6923) lr 1.4818e-03 eta 6:52:56
+epoch [19/50] batch [200/500] time 1.552 (1.568) data 0.000 (0.005) loss 0.7783 (1.0883) acc 71.8750 (72.7188) lr 1.4818e-03 eta 6:52:46
+epoch [19/50] batch [205/500] time 1.572 (1.567) data 0.000 (0.005) loss 0.8237 (1.0859) acc 75.0000 (72.8354) lr 1.4818e-03 eta 6:52:33
+epoch [19/50] batch [210/500] time 1.552 (1.567) data 0.000 (0.004) loss 1.4609 (1.0970) acc 68.7500 (72.6637) lr 1.4818e-03 eta 6:52:22
+epoch [19/50] batch [215/500] time 1.569 (1.567) data 0.001 (0.004) loss 1.2031 (1.0988) acc 78.1250 (72.6744) lr 1.4818e-03 eta 6:52:11
+epoch [19/50] batch [220/500] time 1.543 (1.567) data 0.000 (0.004) loss 0.6113 (1.0973) acc 84.3750 (72.5994) lr 1.4818e-03 eta 6:52:00
+epoch [19/50] batch [225/500] time 1.556 (1.566) data 0.000 (0.004) loss 1.0596 (1.0997) acc 68.7500 (72.5000) lr 1.4818e-03 eta 6:51:49
+epoch [19/50] batch [230/500] time 1.574 (1.566) data 0.000 (0.004) loss 0.8120 (1.1007) acc 75.0000 (72.4457) lr 1.4818e-03 eta 6:51:41
+epoch [19/50] batch [235/500] time 1.554 (1.566) data 0.000 (0.004) loss 0.9028 (1.1015) acc 78.1250 (72.4734) lr 1.4818e-03 eta 6:51:31
+epoch [19/50] batch [240/500] time 1.557 (1.566) data 0.000 (0.004) loss 0.7842 (1.0998) acc 78.1250 (72.4609) lr 1.4818e-03 eta 6:51:21
+epoch [19/50] batch [245/500] time 1.558 (1.566) data 0.001 (0.004) loss 0.7471 (1.0970) acc 84.3750 (72.5128) lr 1.4818e-03 eta 6:51:09
+epoch [19/50] batch [250/500] time 1.564 (1.566) data 0.000 (0.004) loss 1.4043 (1.0945) acc 65.6250 (72.5750) lr 1.4818e-03 eta 6:51:00
+epoch [19/50] batch [255/500] time 1.551 (1.566) data 0.000 (0.004) loss 1.4199 (1.0977) acc 62.5000 (72.5490) lr 1.4818e-03 eta 6:50:54
+epoch [19/50] batch [260/500] time 1.541 (1.566) data 0.000 (0.004) loss 0.7236 (1.0955) acc 81.2500 (72.5481) lr 1.4818e-03 eta 6:50:43
+epoch [19/50] batch [265/500] time 1.563 (1.566) data 0.000 (0.004) loss 0.9692 (1.0955) acc 71.8750 (72.5118) lr 1.4818e-03 eta 6:50:34
+epoch [19/50] batch [270/500] time 1.568 (1.566) data 0.001 (0.004) loss 1.2295 (1.0948) acc 62.5000 (72.4537) lr 1.4818e-03 eta 6:50:25
+epoch [19/50] batch [275/500] time 1.545 (1.565) data 0.000 (0.003) loss 1.1973 (1.0943) acc 75.0000 (72.4773) lr 1.4818e-03 eta 6:50:13
+epoch [19/50] batch [280/500] time 1.638 (1.565) data 0.000 (0.003) loss 0.9780 (1.0936) acc 71.8750 (72.5000) lr 1.4818e-03 eta 6:50:05
+epoch [19/50] batch [285/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.4961 (1.0957) acc 68.7500 (72.4781) lr 1.4818e-03 eta 6:49:55
+epoch [19/50] batch [290/500] time 1.567 (1.565) data 0.000 (0.003) loss 2.1523 (1.1009) acc 59.3750 (72.4461) lr 1.4818e-03 eta 6:49:48
+epoch [19/50] batch [295/500] time 1.573 (1.565) data 0.001 (0.003) loss 1.0791 (1.1008) acc 78.1250 (72.4682) lr 1.4818e-03 eta 6:49:38
+epoch [19/50] batch [300/500] time 1.552 (1.565) data 0.001 (0.003) loss 1.5049 (1.1021) acc 62.5000 (72.4479) lr 1.4818e-03 eta 6:49:28
+epoch [19/50] batch [305/500] time 1.550 (1.565) data 0.000 (0.003) loss 1.9014 (1.1036) acc 62.5000 (72.4590) lr 1.4818e-03 eta 6:49:18
+epoch [19/50] batch [310/500] time 1.561 (1.565) data 0.000 (0.003) loss 1.2178 (1.1055) acc 71.8750 (72.3790) lr 1.4818e-03 eta 6:49:09
+epoch [19/50] batch [315/500] time 1.554 (1.564) data 0.000 (0.003) loss 1.5459 (1.1086) acc 62.5000 (72.3413) lr 1.4818e-03 eta 6:48:57
+epoch [19/50] batch [320/500] time 1.522 (1.564) data 0.001 (0.003) loss 1.2500 (1.1109) acc 71.8750 (72.2852) lr 1.4818e-03 eta 6:48:46
+epoch [19/50] batch [325/500] time 1.563 (1.564) data 0.000 (0.003) loss 1.2812 (1.1129) acc 65.6250 (72.2788) lr 1.4818e-03 eta 6:48:41
+epoch [19/50] batch [330/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.2822 (1.1132) acc 68.7500 (72.2633) lr 1.4818e-03 eta 6:48:32
+epoch [19/50] batch [335/500] time 1.560 (1.564) data 0.000 (0.003) loss 0.8135 (1.1155) acc 81.2500 (72.2481) lr 1.4818e-03 eta 6:48:21
+epoch [19/50] batch [340/500] time 1.561 (1.564) data 0.000 (0.003) loss 0.8706 (1.1155) acc 75.0000 (72.2518) lr 1.4818e-03 eta 6:48:10
+epoch [19/50] batch [345/500] time 1.535 (1.564) data 0.000 (0.003) loss 1.0215 (1.1142) acc 62.5000 (72.2192) lr 1.4818e-03 eta 6:48:00
+epoch [19/50] batch [350/500] time 1.564 (1.564) data 0.000 (0.003) loss 1.6641 (1.1143) acc 56.2500 (72.2143) lr 1.4818e-03 eta 6:47:51
+epoch [19/50] batch [355/500] time 1.527 (1.563) data 0.000 (0.003) loss 1.4824 (1.1178) acc 62.5000 (72.1039) lr 1.4818e-03 eta 6:47:40
+epoch [19/50] batch [360/500] time 1.541 (1.563) data 0.000 (0.003) loss 2.2109 (1.1204) acc 50.0000 (72.0660) lr 1.4818e-03 eta 6:47:28
+epoch [19/50] batch [365/500] time 1.571 (1.563) data 0.001 (0.003) loss 1.2852 (1.1188) acc 78.1250 (72.1832) lr 1.4818e-03 eta 6:47:20
+epoch [19/50] batch [370/500] time 1.573 (1.563) data 0.000 (0.003) loss 1.5049 (1.1197) acc 65.6250 (72.1791) lr 1.4818e-03 eta 6:47:11
+epoch [19/50] batch [375/500] time 1.574 (1.563) data 0.000 (0.003) loss 0.7837 (1.1167) acc 84.3750 (72.2417) lr 1.4818e-03 eta 6:47:03
+epoch [19/50] batch [380/500] time 1.577 (1.563) data 0.000 (0.003) loss 0.9136 (1.1162) acc 78.1250 (72.2615) lr 1.4818e-03 eta 6:46:54
+epoch [19/50] batch [385/500] time 1.560 (1.563) data 0.000 (0.003) loss 1.3428 (1.1158) acc 71.8750 (72.3295) lr 1.4818e-03 eta 6:46:48
+epoch [19/50] batch [390/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.3721 (1.1202) acc 65.6250 (72.2356) lr 1.4818e-03 eta 6:46:40
+epoch [19/50] batch [395/500] time 1.548 (1.563) data 0.000 (0.003) loss 0.8911 (1.1232) acc 75.0000 (72.2231) lr 1.4818e-03 eta 6:46:29
+epoch [19/50] batch [400/500] time 1.563 (1.563) data 0.000 (0.003) loss 0.9229 (1.1261) acc 68.7500 (72.1797) lr 1.4818e-03 eta 6:46:22
+epoch [19/50] batch [405/500] time 1.555 (1.563) data 0.000 (0.002) loss 0.8955 (1.1239) acc 71.8750 (72.1836) lr 1.4818e-03 eta 6:46:13
+epoch [19/50] batch [410/500] time 1.566 (1.563) data 0.000 (0.002) loss 0.8145 (1.1212) acc 71.8750 (72.2027) lr 1.4818e-03 eta 6:46:06
+epoch [19/50] batch [415/500] time 1.577 (1.563) data 0.000 (0.002) loss 0.8091 (1.1184) acc 75.0000 (72.2364) lr 1.4818e-03 eta 6:46:00
+epoch [19/50] batch [420/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.0479 (1.1178) acc 62.5000 (72.2024) lr 1.4818e-03 eta 6:45:51
+epoch [19/50] batch [425/500] time 1.548 (1.563) data 0.000 (0.002) loss 0.9819 (1.1188) acc 71.8750 (72.1838) lr 1.4818e-03 eta 6:45:45
+epoch [19/50] batch [430/500] time 1.576 (1.563) data 0.001 (0.002) loss 2.0977 (1.1213) acc 59.3750 (72.1148) lr 1.4818e-03 eta 6:45:36
+epoch [19/50] batch [435/500] time 1.546 (1.563) data 0.000 (0.002) loss 0.8560 (1.1217) acc 75.0000 (72.0905) lr 1.4818e-03 eta 6:45:28
+epoch [19/50] batch [440/500] time 1.557 (1.563) data 0.000 (0.002) loss 0.7354 (1.1197) acc 78.1250 (72.1307) lr 1.4818e-03 eta 6:45:20
+epoch [19/50] batch [445/500] time 1.567 (1.563) data 0.000 (0.002) loss 1.1729 (1.1215) acc 78.1250 (72.0927) lr 1.4818e-03 eta 6:45:13
+epoch [19/50] batch [450/500] time 1.578 (1.563) data 0.000 (0.002) loss 0.8994 (1.1183) acc 78.1250 (72.1667) lr 1.4818e-03 eta 6:45:04
+epoch [19/50] batch [455/500] time 1.572 (1.563) data 0.000 (0.002) loss 1.2236 (1.1189) acc 68.7500 (72.1223) lr 1.4818e-03 eta 6:44:57
+epoch [19/50] batch [460/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.2568 (1.1201) acc 68.7500 (72.0720) lr 1.4818e-03 eta 6:44:49
+epoch [19/50] batch [465/500] time 1.529 (1.563) data 0.000 (0.002) loss 1.5146 (1.1201) acc 65.6250 (72.0833) lr 1.4818e-03 eta 6:44:41
+epoch [19/50] batch [470/500] time 1.550 (1.563) data 0.001 (0.002) loss 1.4785 (1.1199) acc 75.0000 (72.1410) lr 1.4818e-03 eta 6:44:35
+epoch [19/50] batch [475/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.7231 (1.1181) acc 84.3750 (72.1908) lr 1.4818e-03 eta 6:44:28
+epoch [19/50] batch [480/500] time 1.542 (1.563) data 0.000 (0.002) loss 0.6470 (1.1151) acc 78.1250 (72.2201) lr 1.4818e-03 eta 6:44:18
+epoch [19/50] batch [485/500] time 1.541 (1.563) data 0.001 (0.002) loss 1.0820 (1.1145) acc 75.0000 (72.2487) lr 1.4818e-03 eta 6:44:11
+epoch [19/50] batch [490/500] time 1.565 (1.563) data 0.000 (0.002) loss 1.0576 (1.1133) acc 75.0000 (72.2513) lr 1.4818e-03 eta 6:44:01
+epoch [19/50] batch [495/500] time 1.573 (1.563) data 0.000 (0.002) loss 0.7896 (1.1120) acc 87.5000 (72.2854) lr 1.4818e-03 eta 6:43:53
+epoch [19/50] batch [500/500] time 1.575 (1.563) data 0.000 (0.002) loss 1.5312 (1.1123) acc 59.3750 (72.3000) lr 1.4258e-03 eta 6:43:45
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,984
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+epoch [20/50] batch [5/500] time 1.536 (1.714) data 0.000 (0.214) loss 1.3018 (1.2977) acc 65.6250 (68.1250) lr 1.4258e-03 eta 7:22:36
+epoch [20/50] batch [10/500] time 1.551 (1.633) data 0.000 (0.107) loss 1.5371 (1.1150) acc 68.7500 (72.1875) lr 1.4258e-03 eta 7:01:35
+epoch [20/50] batch [15/500] time 1.567 (1.611) data 0.000 (0.072) loss 1.2793 (1.0517) acc 71.8750 (73.5417) lr 1.4258e-03 eta 6:55:49
+epoch [20/50] batch [20/500] time 1.554 (1.595) data 0.001 (0.054) loss 1.0234 (1.0387) acc 75.0000 (74.2188) lr 1.4258e-03 eta 6:51:33
+epoch [20/50] batch [25/500] time 1.723 (1.594) data 0.000 (0.043) loss 1.1572 (1.0395) acc 62.5000 (73.5000) lr 1.4258e-03 eta 6:51:07
+epoch [20/50] batch [30/500] time 1.582 (1.590) data 0.001 (0.036) loss 0.7954 (1.0005) acc 75.0000 (74.2708) lr 1.4258e-03 eta 6:50:03
+epoch [20/50] batch [35/500] time 1.546 (1.586) data 0.000 (0.031) loss 0.9839 (1.0185) acc 81.2500 (74.1071) lr 1.4258e-03 eta 6:48:45
+epoch [20/50] batch [40/500] time 1.561 (1.583) data 0.001 (0.027) loss 1.0439 (1.0485) acc 84.3750 (73.8281) lr 1.4258e-03 eta 6:47:55
+epoch [20/50] batch [45/500] time 1.572 (1.581) data 0.000 (0.024) loss 0.7373 (1.0373) acc 81.2500 (74.2361) lr 1.4258e-03 eta 6:47:20
+epoch [20/50] batch [50/500] time 1.543 (1.579) data 0.001 (0.022) loss 1.3682 (1.0553) acc 65.6250 (73.5625) lr 1.4258e-03 eta 6:46:33
+epoch [20/50] batch [55/500] time 1.560 (1.577) data 0.001 (0.020) loss 1.5107 (1.0697) acc 75.0000 (73.4659) lr 1.4258e-03 eta 6:46:03
+epoch [20/50] batch [60/500] time 1.546 (1.576) data 0.000 (0.018) loss 1.5703 (1.0924) acc 71.8750 (73.1771) lr 1.4258e-03 eta 6:45:37
+epoch [20/50] batch [65/500] time 1.561 (1.575) data 0.001 (0.017) loss 1.0400 (1.1057) acc 78.1250 (73.2212) lr 1.4258e-03 eta 6:45:12
+epoch [20/50] batch [70/500] time 1.568 (1.574) data 0.001 (0.016) loss 1.9287 (1.1217) acc 62.5000 (72.8571) lr 1.4258e-03 eta 6:44:45
+epoch [20/50] batch [75/500] time 1.560 (1.573) data 0.000 (0.015) loss 1.1143 (1.1335) acc 71.8750 (72.7500) lr 1.4258e-03 eta 6:44:29
+epoch [20/50] batch [80/500] time 1.556 (1.572) data 0.000 (0.014) loss 1.2949 (1.1210) acc 71.8750 (72.9688) lr 1.4258e-03 eta 6:44:07
+epoch [20/50] batch [85/500] time 1.633 (1.573) data 0.000 (0.013) loss 0.5850 (1.1186) acc 87.5000 (73.0147) lr 1.4258e-03 eta 6:44:06
+epoch [20/50] batch [90/500] time 1.568 (1.572) data 0.000 (0.012) loss 1.0684 (1.1252) acc 68.7500 (72.8472) lr 1.4258e-03 eta 6:43:48
+epoch [20/50] batch [95/500] time 1.610 (1.572) data 0.000 (0.012) loss 0.7832 (1.1276) acc 81.2500 (72.6974) lr 1.4258e-03 eta 6:43:42
+epoch [20/50] batch [100/500] time 1.572 (1.572) data 0.000 (0.011) loss 0.8672 (1.1314) acc 84.3750 (72.6875) lr 1.4258e-03 eta 6:43:32
+epoch [20/50] batch [105/500] time 1.589 (1.572) data 0.000 (0.011) loss 1.9316 (1.1272) acc 62.5000 (72.7381) lr 1.4258e-03 eta 6:43:28
+epoch [20/50] batch [110/500] time 1.547 (1.572) data 0.000 (0.010) loss 0.7661 (1.1156) acc 75.0000 (73.0114) lr 1.4258e-03 eta 6:43:12
+epoch [20/50] batch [115/500] time 1.555 (1.571) data 0.001 (0.010) loss 0.7012 (1.1099) acc 78.1250 (73.0978) lr 1.4258e-03 eta 6:42:57
+epoch [20/50] batch [120/500] time 1.593 (1.571) data 0.000 (0.009) loss 0.6943 (1.1009) acc 84.3750 (73.1510) lr 1.4258e-03 eta 6:42:49
+epoch [20/50] batch [125/500] time 1.544 (1.571) data 0.000 (0.009) loss 1.4639 (1.1005) acc 53.1250 (73.0250) lr 1.4258e-03 eta 6:42:31
+epoch [20/50] batch [130/500] time 1.540 (1.571) data 0.000 (0.009) loss 0.9121 (1.0979) acc 71.8750 (73.1250) lr 1.4258e-03 eta 6:42:22
+epoch [20/50] batch [135/500] time 1.549 (1.571) data 0.000 (0.008) loss 0.6685 (1.0951) acc 75.0000 (72.9630) lr 1.4258e-03 eta 6:42:10
+epoch [20/50] batch [140/500] time 1.577 (1.570) data 0.000 (0.008) loss 1.6113 (1.0983) acc 62.5000 (72.8125) lr 1.4258e-03 eta 6:42:01
+epoch [20/50] batch [145/500] time 1.554 (1.570) data 0.001 (0.008) loss 1.2461 (1.1000) acc 68.7500 (72.7371) lr 1.4258e-03 eta 6:41:46
+epoch [20/50] batch [150/500] time 1.540 (1.570) data 0.000 (0.008) loss 0.9897 (1.0979) acc 68.7500 (72.7917) lr 1.4258e-03 eta 6:41:32
+epoch [20/50] batch [155/500] time 1.534 (1.569) data 0.000 (0.007) loss 1.0049 (1.1001) acc 68.7500 (72.7419) lr 1.4258e-03 eta 6:41:19
+epoch [20/50] batch [160/500] time 1.538 (1.568) data 0.000 (0.007) loss 1.0342 (1.0965) acc 71.8750 (72.8906) lr 1.4258e-03 eta 6:40:59
+epoch [20/50] batch [165/500] time 1.551 (1.568) data 0.000 (0.007) loss 0.9453 (1.0920) acc 71.8750 (72.9924) lr 1.4258e-03 eta 6:40:43
+epoch [20/50] batch [170/500] time 1.549 (1.568) data 0.000 (0.007) loss 1.4482 (1.0966) acc 65.6250 (72.9963) lr 1.4258e-03 eta 6:40:30
+epoch [20/50] batch [175/500] time 1.550 (1.567) data 0.000 (0.007) loss 1.3193 (1.0997) acc 68.7500 (72.9286) lr 1.4258e-03 eta 6:40:15
+epoch [20/50] batch [180/500] time 1.565 (1.567) data 0.000 (0.006) loss 0.8901 (1.0987) acc 78.1250 (72.8993) lr 1.4258e-03 eta 6:40:07
+epoch [20/50] batch [185/500] time 1.566 (1.567) data 0.000 (0.006) loss 1.3066 (1.1008) acc 68.7500 (72.8378) lr 1.4258e-03 eta 6:39:59
+epoch [20/50] batch [190/500] time 1.541 (1.567) data 0.000 (0.006) loss 0.7046 (1.0982) acc 81.2500 (72.9276) lr 1.4258e-03 eta 6:39:48
+epoch [20/50] batch [195/500] time 1.576 (1.567) data 0.000 (0.006) loss 0.8584 (1.0989) acc 68.7500 (72.9167) lr 1.4258e-03 eta 6:39:40
+epoch [20/50] batch [200/500] time 1.557 (1.566) data 0.000 (0.006) loss 1.0908 (1.0984) acc 62.5000 (72.8438) lr 1.4258e-03 eta 6:39:25
+epoch [20/50] batch [205/500] time 1.532 (1.566) data 0.000 (0.006) loss 0.7866 (1.0943) acc 78.1250 (72.8963) lr 1.4258e-03 eta 6:39:11
+epoch [20/50] batch [210/500] time 1.549 (1.566) data 0.000 (0.006) loss 0.7007 (1.0949) acc 71.8750 (72.9167) lr 1.4258e-03 eta 6:39:00
+epoch [20/50] batch [215/500] time 1.568 (1.566) data 0.000 (0.005) loss 1.0469 (1.0901) acc 71.8750 (73.0378) lr 1.4258e-03 eta 6:38:51
+epoch [20/50] batch [220/500] time 1.558 (1.566) data 0.000 (0.005) loss 1.0225 (1.0905) acc 75.0000 (73.0256) lr 1.4258e-03 eta 6:38:42
+epoch [20/50] batch [225/500] time 1.558 (1.566) data 0.000 (0.005) loss 1.0820 (1.0950) acc 65.6250 (72.8889) lr 1.4258e-03 eta 6:38:34
+epoch [20/50] batch [230/500] time 1.574 (1.566) data 0.000 (0.005) loss 0.5239 (1.0973) acc 78.1250 (72.7853) lr 1.4258e-03 eta 6:38:32
+epoch [20/50] batch [235/500] time 1.553 (1.566) data 0.001 (0.005) loss 1.5703 (1.0975) acc 65.6250 (72.7926) lr 1.4258e-03 eta 6:38:22
+epoch [20/50] batch [240/500] time 1.579 (1.566) data 0.000 (0.005) loss 1.5107 (1.1020) acc 71.8750 (72.7604) lr 1.4258e-03 eta 6:38:13
+epoch [20/50] batch [245/500] time 1.599 (1.566) data 0.000 (0.005) loss 0.8867 (1.1033) acc 75.0000 (72.7041) lr 1.4258e-03 eta 6:38:08
+epoch [20/50] batch [250/500] time 1.591 (1.566) data 0.000 (0.005) loss 1.3369 (1.1071) acc 78.1250 (72.6375) lr 1.4258e-03 eta 6:38:01
+epoch [20/50] batch [255/500] time 1.568 (1.566) data 0.000 (0.005) loss 1.6455 (1.1094) acc 65.6250 (72.6471) lr 1.4258e-03 eta 6:37:51
+epoch [20/50] batch [260/500] time 1.553 (1.566) data 0.000 (0.005) loss 0.9546 (1.1075) acc 71.8750 (72.5841) lr 1.4258e-03 eta 6:37:38
+epoch [20/50] batch [265/500] time 1.557 (1.565) data 0.000 (0.004) loss 0.9829 (1.1083) acc 71.8750 (72.5472) lr 1.4258e-03 eta 6:37:30
+epoch [20/50] batch [270/500] time 1.575 (1.565) data 0.000 (0.004) loss 0.8560 (1.1070) acc 65.6250 (72.5116) lr 1.4258e-03 eta 6:37:21
+epoch [20/50] batch [275/500] time 1.589 (1.566) data 0.000 (0.004) loss 0.9268 (1.1089) acc 65.6250 (72.4205) lr 1.4258e-03 eta 6:37:18
+epoch [20/50] batch [280/500] time 1.566 (1.566) data 0.000 (0.004) loss 0.8169 (1.1098) acc 75.0000 (72.4107) lr 1.4258e-03 eta 6:37:12
+epoch [20/50] batch [285/500] time 1.581 (1.566) data 0.000 (0.004) loss 1.1953 (1.1136) acc 62.5000 (72.2807) lr 1.4258e-03 eta 6:37:05
+epoch [20/50] batch [290/500] time 1.565 (1.566) data 0.001 (0.004) loss 1.7520 (1.1169) acc 65.6250 (72.2414) lr 1.4258e-03 eta 6:36:56
+epoch [20/50] batch [295/500] time 1.542 (1.566) data 0.000 (0.004) loss 0.4651 (1.1155) acc 87.5000 (72.3093) lr 1.4258e-03 eta 6:36:47
+epoch [20/50] batch [300/500] time 1.555 (1.566) data 0.000 (0.004) loss 0.4658 (1.1126) acc 84.3750 (72.3333) lr 1.4258e-03 eta 6:36:40
+epoch [20/50] batch [305/500] time 1.550 (1.566) data 0.000 (0.004) loss 1.2900 (1.1165) acc 65.6250 (72.2541) lr 1.4258e-03 eta 6:36:30
+epoch [20/50] batch [310/500] time 1.536 (1.565) data 0.000 (0.004) loss 1.1104 (1.1181) acc 75.0000 (72.2480) lr 1.4258e-03 eta 6:36:19
+epoch [20/50] batch [315/500] time 1.585 (1.565) data 0.001 (0.004) loss 1.2529 (1.1155) acc 68.7500 (72.3016) lr 1.4258e-03 eta 6:36:10
+epoch [20/50] batch [320/500] time 1.541 (1.565) data 0.001 (0.004) loss 0.8091 (1.1122) acc 75.0000 (72.4023) lr 1.4258e-03 eta 6:36:02
+epoch [20/50] batch [325/500] time 1.563 (1.565) data 0.000 (0.004) loss 0.9248 (1.1097) acc 81.2500 (72.4231) lr 1.4258e-03 eta 6:35:54
+epoch [20/50] batch [330/500] time 1.555 (1.565) data 0.001 (0.004) loss 1.5225 (1.1126) acc 65.6250 (72.3958) lr 1.4258e-03 eta 6:35:48
+epoch [20/50] batch [335/500] time 1.562 (1.566) data 0.001 (0.004) loss 1.4600 (1.1140) acc 65.6250 (72.3507) lr 1.4258e-03 eta 6:35:41
+epoch [20/50] batch [340/500] time 1.537 (1.565) data 0.000 (0.004) loss 0.6689 (1.1121) acc 78.1250 (72.3529) lr 1.4258e-03 eta 6:35:31
+epoch [20/50] batch [345/500] time 1.554 (1.565) data 0.000 (0.004) loss 0.9985 (1.1114) acc 71.8750 (72.3551) lr 1.4258e-03 eta 6:35:20
+epoch [20/50] batch [350/500] time 1.559 (1.565) data 0.000 (0.004) loss 0.8374 (1.1094) acc 78.1250 (72.3661) lr 1.4258e-03 eta 6:35:10
+epoch [20/50] batch [355/500] time 1.580 (1.565) data 0.001 (0.003) loss 0.9453 (1.1075) acc 75.0000 (72.4032) lr 1.4258e-03 eta 6:35:02
+epoch [20/50] batch [360/500] time 1.568 (1.565) data 0.000 (0.003) loss 1.3506 (1.1074) acc 75.0000 (72.4392) lr 1.4258e-03 eta 6:34:52
+epoch [20/50] batch [365/500] time 1.535 (1.565) data 0.000 (0.003) loss 1.2266 (1.1053) acc 71.8750 (72.4572) lr 1.4258e-03 eta 6:34:42
+epoch [20/50] batch [370/500] time 1.575 (1.565) data 0.000 (0.003) loss 0.9653 (1.1043) acc 71.8750 (72.4662) lr 1.4258e-03 eta 6:34:33
+epoch [20/50] batch [375/500] time 1.541 (1.565) data 0.000 (0.003) loss 1.0469 (1.1051) acc 71.8750 (72.4583) lr 1.4258e-03 eta 6:34:27
+epoch [20/50] batch [380/500] time 1.550 (1.565) data 0.001 (0.003) loss 0.7026 (1.1043) acc 84.3750 (72.5329) lr 1.4258e-03 eta 6:34:18
+epoch [20/50] batch [385/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.1641 (1.1054) acc 71.8750 (72.4675) lr 1.4258e-03 eta 6:34:09
+epoch [20/50] batch [390/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.6787 (1.1046) acc 68.7500 (72.5000) lr 1.4258e-03 eta 6:34:03
+epoch [20/50] batch [395/500] time 1.554 (1.565) data 0.000 (0.003) loss 1.1201 (1.1036) acc 68.7500 (72.4842) lr 1.4258e-03 eta 6:33:53
+epoch [20/50] batch [400/500] time 1.554 (1.565) data 0.000 (0.003) loss 0.7944 (1.1035) acc 71.8750 (72.4062) lr 1.4258e-03 eta 6:33:45
+epoch [20/50] batch [405/500] time 1.549 (1.565) data 0.000 (0.003) loss 1.0703 (1.1024) acc 75.0000 (72.4769) lr 1.4258e-03 eta 6:33:36
+epoch [20/50] batch [410/500] time 1.575 (1.565) data 0.000 (0.003) loss 1.5410 (1.1023) acc 65.6250 (72.4695) lr 1.4258e-03 eta 6:33:29
+epoch [20/50] batch [415/500] time 1.664 (1.565) data 0.000 (0.003) loss 0.8945 (1.1021) acc 78.1250 (72.5075) lr 1.4258e-03 eta 6:33:24
+epoch [20/50] batch [420/500] time 1.542 (1.565) data 0.000 (0.003) loss 1.1807 (1.1018) acc 68.7500 (72.5446) lr 1.4258e-03 eta 6:33:16
+epoch [20/50] batch [425/500] time 1.541 (1.565) data 0.001 (0.003) loss 1.1641 (1.1016) acc 68.7500 (72.5221) lr 1.4258e-03 eta 6:33:06
+epoch [20/50] batch [430/500] time 1.567 (1.565) data 0.001 (0.003) loss 0.5742 (1.0988) acc 87.5000 (72.5945) lr 1.4258e-03 eta 6:32:57
+epoch [20/50] batch [435/500] time 1.565 (1.565) data 0.000 (0.003) loss 0.8242 (1.0956) acc 84.3750 (72.6580) lr 1.4258e-03 eta 6:32:51
+epoch [20/50] batch [440/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.2334 (1.0988) acc 75.0000 (72.5710) lr 1.4258e-03 eta 6:32:42
+epoch [20/50] batch [445/500] time 1.556 (1.565) data 0.001 (0.003) loss 1.0908 (1.1006) acc 75.0000 (72.5351) lr 1.4258e-03 eta 6:32:34
+epoch [20/50] batch [450/500] time 1.585 (1.565) data 0.000 (0.003) loss 0.9580 (1.1007) acc 78.1250 (72.5556) lr 1.4258e-03 eta 6:32:29
+epoch [20/50] batch [455/500] time 1.587 (1.565) data 0.000 (0.003) loss 1.7207 (1.1016) acc 65.6250 (72.5549) lr 1.4258e-03 eta 6:32:23
+epoch [20/50] batch [460/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.1318 (1.1028) acc 68.7500 (72.5204) lr 1.4258e-03 eta 6:32:13
+epoch [20/50] batch [465/500] time 1.570 (1.565) data 0.000 (0.003) loss 1.4512 (1.1059) acc 71.8750 (72.5000) lr 1.4258e-03 eta 6:32:06
+epoch [20/50] batch [470/500] time 1.567 (1.565) data 0.000 (0.003) loss 0.6133 (1.1047) acc 84.3750 (72.4867) lr 1.4258e-03 eta 6:32:00
+epoch [20/50] batch [475/500] time 1.541 (1.565) data 0.001 (0.003) loss 0.8716 (1.1042) acc 87.5000 (72.5658) lr 1.4258e-03 eta 6:31:49
+epoch [20/50] batch [480/500] time 1.569 (1.565) data 0.000 (0.003) loss 0.9834 (1.1029) acc 71.8750 (72.5716) lr 1.4258e-03 eta 6:31:41
+epoch [20/50] batch [485/500] time 1.552 (1.565) data 0.001 (0.003) loss 1.8643 (1.1069) acc 62.5000 (72.4936) lr 1.4258e-03 eta 6:31:33
+epoch [20/50] batch [490/500] time 1.562 (1.565) data 0.000 (0.003) loss 0.9976 (1.1054) acc 68.7500 (72.5255) lr 1.4258e-03 eta 6:31:23
+epoch [20/50] batch [495/500] time 1.536 (1.564) data 0.000 (0.003) loss 1.6113 (1.1082) acc 56.2500 (72.4684) lr 1.4258e-03 eta 6:31:13
+epoch [20/50] batch [500/500] time 1.558 (1.564) data 0.000 (0.003) loss 0.6240 (1.1078) acc 87.5000 (72.4875) lr 1.3681e-03 eta 6:31:03
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,041
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [21/50] batch [5/500] time 1.547 (1.644) data 0.000 (0.152) loss 1.0645 (1.1066) acc 71.8750 (70.0000) lr 1.3681e-03 eta 6:50:59
+epoch [21/50] batch [10/500] time 1.565 (1.607) data 0.000 (0.076) loss 0.9150 (1.0889) acc 75.0000 (70.6250) lr 1.3681e-03 eta 6:41:29
+epoch [21/50] batch [15/500] time 1.562 (1.596) data 0.000 (0.051) loss 0.7744 (1.1385) acc 84.3750 (71.6667) lr 1.3681e-03 eta 6:38:28
+epoch [21/50] batch [20/500] time 1.571 (1.587) data 0.000 (0.038) loss 1.5928 (1.1478) acc 68.7500 (71.2500) lr 1.3681e-03 eta 6:36:15
+epoch [21/50] batch [25/500] time 1.563 (1.588) data 0.000 (0.031) loss 1.8770 (1.1002) acc 65.6250 (73.0000) lr 1.3681e-03 eta 6:36:24
+epoch [21/50] batch [30/500] time 1.572 (1.585) data 0.001 (0.026) loss 1.3857 (1.1252) acc 71.8750 (71.9792) lr 1.3681e-03 eta 6:35:25
+epoch [21/50] batch [35/500] time 1.561 (1.581) data 0.000 (0.022) loss 1.0742 (1.1556) acc 71.8750 (71.4286) lr 1.3681e-03 eta 6:34:21
+epoch [21/50] batch [40/500] time 1.573 (1.578) data 0.001 (0.019) loss 1.0166 (1.1547) acc 68.7500 (71.2500) lr 1.3681e-03 eta 6:33:24
+epoch [21/50] batch [45/500] time 1.578 (1.577) data 0.000 (0.017) loss 1.7529 (1.1788) acc 62.5000 (70.8333) lr 1.3681e-03 eta 6:33:08
+epoch [21/50] batch [50/500] time 1.559 (1.576) data 0.000 (0.016) loss 1.3145 (1.1869) acc 65.6250 (70.5625) lr 1.3681e-03 eta 6:32:35
+epoch [21/50] batch [55/500] time 1.539 (1.573) data 0.000 (0.014) loss 1.1504 (1.1719) acc 68.7500 (70.5114) lr 1.3681e-03 eta 6:31:52
+epoch [21/50] batch [60/500] time 1.570 (1.573) data 0.000 (0.013) loss 0.7568 (1.1468) acc 81.2500 (71.1979) lr 1.3681e-03 eta 6:31:33
+epoch [21/50] batch [65/500] time 1.553 (1.572) data 0.000 (0.012) loss 1.2139 (1.1395) acc 65.6250 (71.2981) lr 1.3681e-03 eta 6:31:21
+epoch [21/50] batch [70/500] time 1.567 (1.571) data 0.001 (0.011) loss 0.6182 (1.1292) acc 78.1250 (71.4732) lr 1.3681e-03 eta 6:30:56
+epoch [21/50] batch [75/500] time 1.609 (1.571) data 0.000 (0.011) loss 1.4199 (1.1312) acc 59.3750 (71.5000) lr 1.3681e-03 eta 6:30:50
+epoch [21/50] batch [80/500] time 1.548 (1.571) data 0.000 (0.010) loss 0.7578 (1.1174) acc 75.0000 (71.5234) lr 1.3681e-03 eta 6:30:34
+epoch [21/50] batch [85/500] time 1.540 (1.570) data 0.000 (0.009) loss 0.9639 (1.1153) acc 78.1250 (71.8382) lr 1.3681e-03 eta 6:30:15
+epoch [21/50] batch [90/500] time 1.572 (1.570) data 0.000 (0.009) loss 1.0605 (1.1166) acc 78.1250 (71.8750) lr 1.3681e-03 eta 6:30:05
+epoch [21/50] batch [95/500] time 1.536 (1.569) data 0.001 (0.008) loss 0.9185 (1.1244) acc 71.8750 (71.7763) lr 1.3681e-03 eta 6:29:45
+epoch [21/50] batch [100/500] time 1.552 (1.569) data 0.001 (0.008) loss 1.6543 (1.1190) acc 59.3750 (71.8750) lr 1.3681e-03 eta 6:29:37
+epoch [21/50] batch [105/500] time 1.551 (1.569) data 0.000 (0.008) loss 0.9473 (1.1096) acc 78.1250 (72.0833) lr 1.3681e-03 eta 6:29:24
+epoch [21/50] batch [110/500] time 1.553 (1.568) data 0.001 (0.007) loss 1.1777 (1.1022) acc 71.8750 (72.2443) lr 1.3681e-03 eta 6:29:10
+epoch [21/50] batch [115/500] time 1.554 (1.568) data 0.000 (0.007) loss 1.0713 (1.0979) acc 62.5000 (72.2283) lr 1.3681e-03 eta 6:28:56
+epoch [21/50] batch [120/500] time 1.640 (1.569) data 0.000 (0.007) loss 1.4824 (1.1016) acc 75.0000 (72.1615) lr 1.3681e-03 eta 6:29:05
+epoch [21/50] batch [125/500] time 1.546 (1.569) data 0.000 (0.007) loss 1.1709 (1.1115) acc 78.1250 (72.1000) lr 1.3681e-03 eta 6:28:52
+epoch [21/50] batch [130/500] time 1.553 (1.568) data 0.000 (0.006) loss 1.0811 (1.1095) acc 68.7500 (71.9712) lr 1.3681e-03 eta 6:28:35
+epoch [21/50] batch [135/500] time 1.534 (1.567) data 0.000 (0.006) loss 1.1123 (1.1102) acc 65.6250 (71.9444) lr 1.3681e-03 eta 6:28:17
+epoch [21/50] batch [140/500] time 1.542 (1.566) data 0.000 (0.006) loss 0.7632 (1.1126) acc 87.5000 (72.1205) lr 1.3681e-03 eta 6:27:57
+epoch [21/50] batch [145/500] time 1.568 (1.566) data 0.000 (0.006) loss 1.3154 (1.1065) acc 62.5000 (72.2629) lr 1.3681e-03 eta 6:27:39
+epoch [21/50] batch [150/500] time 1.541 (1.565) data 0.000 (0.006) loss 1.0049 (1.1086) acc 68.7500 (72.2292) lr 1.3681e-03 eta 6:27:22
+epoch [21/50] batch [155/500] time 1.535 (1.565) data 0.000 (0.005) loss 1.1230 (1.1113) acc 81.2500 (72.2581) lr 1.3681e-03 eta 6:27:11
+epoch [21/50] batch [160/500] time 1.556 (1.565) data 0.000 (0.005) loss 1.6973 (1.1108) acc 78.1250 (72.3242) lr 1.3681e-03 eta 6:26:59
+epoch [21/50] batch [165/500] time 1.556 (1.565) data 0.000 (0.005) loss 1.1455 (1.1126) acc 75.0000 (72.2917) lr 1.3681e-03 eta 6:26:54
+epoch [21/50] batch [170/500] time 1.557 (1.565) data 0.000 (0.005) loss 1.3281 (1.1072) acc 68.7500 (72.3897) lr 1.3681e-03 eta 6:26:43
+epoch [21/50] batch [175/500] time 1.566 (1.564) data 0.001 (0.005) loss 1.3027 (1.1090) acc 71.8750 (72.4286) lr 1.3681e-03 eta 6:26:33
+epoch [21/50] batch [180/500] time 1.575 (1.564) data 0.000 (0.005) loss 0.6489 (1.1106) acc 84.3750 (72.4653) lr 1.3681e-03 eta 6:26:23
+epoch [21/50] batch [185/500] time 1.544 (1.564) data 0.000 (0.005) loss 1.2480 (1.1071) acc 71.8750 (72.5169) lr 1.3681e-03 eta 6:26:05
+epoch [21/50] batch [190/500] time 1.562 (1.563) data 0.000 (0.004) loss 0.9507 (1.1057) acc 78.1250 (72.5987) lr 1.3681e-03 eta 6:25:50
+epoch [21/50] batch [195/500] time 1.581 (1.563) data 0.000 (0.004) loss 0.8091 (1.0995) acc 78.1250 (72.7083) lr 1.3681e-03 eta 6:25:42
+epoch [21/50] batch [200/500] time 1.541 (1.563) data 0.000 (0.004) loss 0.6416 (1.0999) acc 84.3750 (72.7031) lr 1.3681e-03 eta 6:25:33
+epoch [21/50] batch [205/500] time 1.559 (1.563) data 0.000 (0.004) loss 1.2598 (1.0997) acc 68.7500 (72.7134) lr 1.3681e-03 eta 6:25:26
+epoch [21/50] batch [210/500] time 1.575 (1.563) data 0.000 (0.004) loss 0.8882 (1.0956) acc 84.3750 (72.8125) lr 1.3681e-03 eta 6:25:16
+epoch [21/50] batch [215/500] time 1.564 (1.563) data 0.000 (0.004) loss 0.4175 (1.0950) acc 93.7500 (72.8924) lr 1.3681e-03 eta 6:25:13
+epoch [21/50] batch [220/500] time 1.558 (1.563) data 0.000 (0.004) loss 1.2949 (1.0947) acc 68.7500 (72.9119) lr 1.3681e-03 eta 6:25:02
+epoch [21/50] batch [225/500] time 1.547 (1.563) data 0.000 (0.004) loss 0.9907 (1.0928) acc 71.8750 (72.9444) lr 1.3681e-03 eta 6:24:48
+epoch [21/50] batch [230/500] time 1.571 (1.563) data 0.000 (0.004) loss 0.6875 (1.0932) acc 78.1250 (72.9212) lr 1.3681e-03 eta 6:24:41
+epoch [21/50] batch [235/500] time 1.574 (1.563) data 0.000 (0.004) loss 1.2119 (1.0947) acc 78.1250 (72.9787) lr 1.3681e-03 eta 6:24:31
+epoch [21/50] batch [240/500] time 1.601 (1.563) data 0.001 (0.004) loss 1.0957 (1.0963) acc 75.0000 (72.9948) lr 1.3681e-03 eta 6:24:25
+epoch [21/50] batch [245/500] time 1.559 (1.563) data 0.000 (0.004) loss 1.0654 (1.0962) acc 65.6250 (72.9719) lr 1.3681e-03 eta 6:24:19
+epoch [21/50] batch [250/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.5713 (1.0963) acc 68.7500 (72.9375) lr 1.3681e-03 eta 6:24:11
+epoch [21/50] batch [255/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.3701 (1.0976) acc 62.5000 (72.9167) lr 1.3681e-03 eta 6:24:02
+epoch [21/50] batch [260/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.6182 (1.0954) acc 71.8750 (72.9808) lr 1.3681e-03 eta 6:23:50
+epoch [21/50] batch [265/500] time 1.561 (1.563) data 0.000 (0.003) loss 1.0527 (1.0965) acc 65.6250 (72.8892) lr 1.3681e-03 eta 6:23:46
+epoch [21/50] batch [270/500] time 1.541 (1.562) data 0.000 (0.003) loss 0.8481 (1.0943) acc 78.1250 (72.8819) lr 1.3681e-03 eta 6:23:32
+epoch [21/50] batch [275/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.3867 (1.0938) acc 62.5000 (72.7841) lr 1.3681e-03 eta 6:23:25
+epoch [21/50] batch [280/500] time 1.572 (1.562) data 0.001 (0.003) loss 1.0918 (1.0900) acc 75.0000 (72.9353) lr 1.3681e-03 eta 6:23:16
+epoch [21/50] batch [285/500] time 1.573 (1.562) data 0.001 (0.003) loss 1.7207 (1.0945) acc 65.6250 (72.8289) lr 1.3681e-03 eta 6:23:09
+epoch [21/50] batch [290/500] time 1.581 (1.562) data 0.000 (0.003) loss 0.9648 (1.0902) acc 68.7500 (72.8987) lr 1.3681e-03 eta 6:23:03
+epoch [21/50] batch [295/500] time 1.548 (1.562) data 0.000 (0.003) loss 0.6851 (1.0910) acc 87.5000 (72.8708) lr 1.3681e-03 eta 6:22:55
+epoch [21/50] batch [300/500] time 1.551 (1.562) data 0.000 (0.003) loss 0.8018 (1.0911) acc 78.1250 (72.8125) lr 1.3681e-03 eta 6:22:45
+epoch [21/50] batch [305/500] time 1.554 (1.562) data 0.000 (0.003) loss 0.8052 (1.0940) acc 84.3750 (72.7254) lr 1.3681e-03 eta 6:22:37
+epoch [21/50] batch [310/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.3691 (1.0941) acc 68.7500 (72.7621) lr 1.3681e-03 eta 6:22:34
+epoch [21/50] batch [315/500] time 1.551 (1.562) data 0.000 (0.003) loss 0.7363 (1.0939) acc 84.3750 (72.7778) lr 1.3681e-03 eta 6:22:24
+epoch [21/50] batch [320/500] time 1.572 (1.563) data 0.000 (0.003) loss 0.5889 (1.0941) acc 87.5000 (72.7930) lr 1.3681e-03 eta 6:22:18
+epoch [21/50] batch [325/500] time 1.556 (1.563) data 0.000 (0.003) loss 0.7197 (1.0937) acc 81.2500 (72.7692) lr 1.3681e-03 eta 6:22:11
+epoch [21/50] batch [330/500] time 1.567 (1.563) data 0.000 (0.003) loss 1.2432 (1.0956) acc 65.6250 (72.7083) lr 1.3681e-03 eta 6:22:04
+epoch [21/50] batch [335/500] time 1.539 (1.563) data 0.000 (0.003) loss 1.4326 (1.0972) acc 62.5000 (72.6772) lr 1.3681e-03 eta 6:21:55
+epoch [21/50] batch [340/500] time 1.563 (1.562) data 0.000 (0.003) loss 1.1680 (1.0978) acc 71.8750 (72.5827) lr 1.3681e-03 eta 6:21:46
+epoch [21/50] batch [345/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.2783 (1.1012) acc 62.5000 (72.4728) lr 1.3681e-03 eta 6:21:37
+epoch [21/50] batch [350/500] time 1.570 (1.562) data 0.000 (0.003) loss 0.9468 (1.1011) acc 71.8750 (72.4286) lr 1.3681e-03 eta 6:21:27
+epoch [21/50] batch [355/500] time 1.572 (1.562) data 0.000 (0.003) loss 1.4971 (1.1021) acc 65.6250 (72.3944) lr 1.3681e-03 eta 6:21:21
+epoch [21/50] batch [360/500] time 1.551 (1.562) data 0.000 (0.003) loss 0.4651 (1.0997) acc 87.5000 (72.4132) lr 1.3681e-03 eta 6:21:13
+epoch [21/50] batch [365/500] time 1.583 (1.562) data 0.000 (0.002) loss 0.9751 (1.1015) acc 65.6250 (72.3716) lr 1.3681e-03 eta 6:21:06
+epoch [21/50] batch [370/500] time 1.582 (1.562) data 0.001 (0.002) loss 1.3057 (1.0994) acc 68.7500 (72.4155) lr 1.3681e-03 eta 6:20:57
+epoch [21/50] batch [375/500] time 1.557 (1.562) data 0.000 (0.002) loss 1.0938 (1.0983) acc 65.6250 (72.3750) lr 1.3681e-03 eta 6:20:47
+epoch [21/50] batch [380/500] time 1.548 (1.562) data 0.000 (0.002) loss 1.6816 (1.0999) acc 62.5000 (72.3684) lr 1.3681e-03 eta 6:20:37
+epoch [21/50] batch [385/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.1953 (1.0994) acc 78.1250 (72.3620) lr 1.3681e-03 eta 6:20:27
+epoch [21/50] batch [390/500] time 1.550 (1.562) data 0.000 (0.002) loss 1.5527 (1.1009) acc 62.5000 (72.3558) lr 1.3681e-03 eta 6:20:20
+epoch [21/50] batch [395/500] time 1.553 (1.562) data 0.001 (0.002) loss 1.3330 (1.1005) acc 65.6250 (72.3418) lr 1.3681e-03 eta 6:20:14
+epoch [21/50] batch [400/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.1084 (1.1011) acc 71.8750 (72.3516) lr 1.3681e-03 eta 6:20:04
+epoch [21/50] batch [405/500] time 1.552 (1.562) data 0.000 (0.002) loss 0.6821 (1.0982) acc 90.6250 (72.4460) lr 1.3681e-03 eta 6:19:55
+epoch [21/50] batch [410/500] time 1.551 (1.562) data 0.000 (0.002) loss 1.0303 (1.0972) acc 68.7500 (72.4695) lr 1.3681e-03 eta 6:19:51
+epoch [21/50] batch [415/500] time 1.577 (1.562) data 0.000 (0.002) loss 0.8965 (1.0953) acc 81.2500 (72.5226) lr 1.3681e-03 eta 6:19:43
+epoch [21/50] batch [420/500] time 1.541 (1.562) data 0.001 (0.002) loss 1.3857 (1.0957) acc 65.6250 (72.5149) lr 1.3681e-03 eta 6:19:34
+epoch [21/50] batch [425/500] time 1.562 (1.562) data 0.000 (0.002) loss 0.8965 (1.0976) acc 78.1250 (72.4853) lr 1.3681e-03 eta 6:19:25
+epoch [21/50] batch [430/500] time 1.563 (1.562) data 0.000 (0.002) loss 0.8716 (1.0973) acc 78.1250 (72.5000) lr 1.3681e-03 eta 6:19:16
+epoch [21/50] batch [435/500] time 1.568 (1.562) data 0.000 (0.002) loss 1.3145 (1.0984) acc 65.6250 (72.4425) lr 1.3681e-03 eta 6:19:07
+epoch [21/50] batch [440/500] time 1.592 (1.562) data 0.000 (0.002) loss 1.1816 (1.0974) acc 78.1250 (72.4432) lr 1.3681e-03 eta 6:18:59
+epoch [21/50] batch [445/500] time 1.547 (1.562) data 0.000 (0.002) loss 1.4023 (1.0964) acc 53.1250 (72.4438) lr 1.3681e-03 eta 6:18:51
+epoch [21/50] batch [450/500] time 1.663 (1.562) data 0.000 (0.002) loss 1.4189 (1.0958) acc 62.5000 (72.4653) lr 1.3681e-03 eta 6:18:46
+epoch [21/50] batch [455/500] time 1.554 (1.562) data 0.000 (0.002) loss 0.7065 (1.0961) acc 81.2500 (72.4519) lr 1.3681e-03 eta 6:18:37
+epoch [21/50] batch [460/500] time 1.573 (1.562) data 0.000 (0.002) loss 0.7012 (1.0947) acc 81.2500 (72.5272) lr 1.3681e-03 eta 6:18:29
+epoch [21/50] batch [465/500] time 1.569 (1.562) data 0.000 (0.002) loss 1.2734 (1.0934) acc 62.5000 (72.5538) lr 1.3681e-03 eta 6:18:22
+epoch [21/50] batch [470/500] time 1.582 (1.562) data 0.001 (0.002) loss 0.8501 (1.0909) acc 78.1250 (72.6263) lr 1.3681e-03 eta 6:18:15
+epoch [21/50] batch [475/500] time 1.578 (1.562) data 0.001 (0.002) loss 1.2275 (1.0912) acc 65.6250 (72.6382) lr 1.3681e-03 eta 6:18:06
+epoch [21/50] batch [480/500] time 1.564 (1.562) data 0.000 (0.002) loss 1.0254 (1.0924) acc 71.8750 (72.6172) lr 1.3681e-03 eta 6:17:59
+epoch [21/50] batch [485/500] time 1.542 (1.562) data 0.001 (0.002) loss 1.0898 (1.0913) acc 75.0000 (72.6353) lr 1.3681e-03 eta 6:17:50
+epoch [21/50] batch [490/500] time 1.555 (1.562) data 0.000 (0.002) loss 1.0439 (1.0935) acc 71.8750 (72.6148) lr 1.3681e-03 eta 6:17:41
+epoch [21/50] batch [495/500] time 1.541 (1.562) data 0.000 (0.002) loss 1.5928 (1.0948) acc 65.6250 (72.5568) lr 1.3681e-03 eta 6:17:31
+epoch [21/50] batch [500/500] time 1.566 (1.562) data 0.000 (0.002) loss 1.4590 (1.0961) acc 62.5000 (72.5438) lr 1.3090e-03 eta 6:17:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,982
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+epoch [22/50] batch [5/500] time 1.550 (1.659) data 0.000 (0.151) loss 0.8403 (0.9158) acc 81.2500 (76.8750) lr 1.3090e-03 eta 6:40:46
+epoch [22/50] batch [10/500] time 1.543 (1.604) data 0.000 (0.076) loss 0.7544 (0.9626) acc 84.3750 (74.3750) lr 1.3090e-03 eta 6:27:22
+epoch [22/50] batch [15/500] time 1.546 (1.589) data 0.000 (0.051) loss 0.6270 (0.9099) acc 81.2500 (75.0000) lr 1.3090e-03 eta 6:23:43
+epoch [22/50] batch [20/500] time 1.522 (1.576) data 0.000 (0.038) loss 0.6055 (0.9514) acc 84.3750 (74.0625) lr 1.3090e-03 eta 6:20:20
+epoch [22/50] batch [25/500] time 1.542 (1.568) data 0.000 (0.031) loss 1.3516 (1.0503) acc 59.3750 (72.1250) lr 1.3090e-03 eta 6:18:19
+epoch [22/50] batch [30/500] time 1.582 (1.565) data 0.001 (0.026) loss 1.1299 (1.0113) acc 62.5000 (73.0208) lr 1.3090e-03 eta 6:17:31
+epoch [22/50] batch [35/500] time 1.549 (1.565) data 0.001 (0.022) loss 1.7559 (0.9967) acc 56.2500 (73.7500) lr 1.3090e-03 eta 6:17:12
+epoch [22/50] batch [40/500] time 1.562 (1.568) data 0.001 (0.019) loss 0.7158 (0.9986) acc 81.2500 (74.1406) lr 1.3090e-03 eta 6:17:53
+epoch [22/50] batch [45/500] time 1.547 (1.567) data 0.001 (0.017) loss 1.1699 (0.9893) acc 62.5000 (73.8194) lr 1.3090e-03 eta 6:17:28
+epoch [22/50] batch [50/500] time 1.571 (1.565) data 0.000 (0.016) loss 0.9746 (0.9798) acc 68.7500 (73.8750) lr 1.3090e-03 eta 6:17:00
+epoch [22/50] batch [55/500] time 1.566 (1.565) data 0.000 (0.014) loss 1.3486 (0.9824) acc 75.0000 (74.1477) lr 1.3090e-03 eta 6:16:47
+epoch [22/50] batch [60/500] time 1.557 (1.565) data 0.001 (0.013) loss 0.8633 (0.9895) acc 68.7500 (74.0104) lr 1.3090e-03 eta 6:16:41
+epoch [22/50] batch [65/500] time 1.551 (1.565) data 0.001 (0.012) loss 1.4961 (1.0174) acc 50.0000 (73.4135) lr 1.3090e-03 eta 6:16:23
+epoch [22/50] batch [70/500] time 1.566 (1.565) data 0.001 (0.011) loss 1.6973 (1.0408) acc 65.6250 (72.9911) lr 1.3090e-03 eta 6:16:23
+epoch [22/50] batch [75/500] time 1.579 (1.565) data 0.000 (0.011) loss 0.8369 (1.0308) acc 75.0000 (73.1667) lr 1.3090e-03 eta 6:16:22
+epoch [22/50] batch [80/500] time 1.561 (1.565) data 0.000 (0.010) loss 0.7969 (1.0267) acc 84.3750 (73.3984) lr 1.3090e-03 eta 6:16:03
+epoch [22/50] batch [85/500] time 1.575 (1.566) data 0.000 (0.009) loss 1.0400 (1.0150) acc 75.0000 (73.5294) lr 1.3090e-03 eta 6:16:09
+epoch [22/50] batch [90/500] time 1.554 (1.565) data 0.001 (0.009) loss 1.7744 (1.0332) acc 65.6250 (73.2986) lr 1.3090e-03 eta 6:15:52
+epoch [22/50] batch [95/500] time 1.562 (1.565) data 0.000 (0.008) loss 1.2529 (1.0286) acc 81.2500 (73.5526) lr 1.3090e-03 eta 6:15:42
+epoch [22/50] batch [100/500] time 1.581 (1.565) data 0.000 (0.008) loss 0.8403 (1.0366) acc 84.3750 (73.5625) lr 1.3090e-03 eta 6:15:32
+epoch [22/50] batch [105/500] time 1.564 (1.565) data 0.000 (0.008) loss 0.9219 (1.0533) acc 71.8750 (73.5417) lr 1.3090e-03 eta 6:15:25
+epoch [22/50] batch [110/500] time 1.561 (1.565) data 0.000 (0.007) loss 1.8096 (1.0674) acc 53.1250 (73.3523) lr 1.3090e-03 eta 6:15:16
+epoch [22/50] batch [115/500] time 1.577 (1.565) data 0.000 (0.007) loss 1.3633 (1.0842) acc 68.7500 (72.9620) lr 1.3090e-03 eta 6:15:10
+epoch [22/50] batch [120/500] time 1.579 (1.565) data 0.000 (0.007) loss 0.8433 (1.0818) acc 78.1250 (72.9427) lr 1.3090e-03 eta 6:15:04
+epoch [22/50] batch [125/500] time 1.560 (1.565) data 0.000 (0.006) loss 1.5117 (1.0846) acc 68.7500 (72.9500) lr 1.3090e-03 eta 6:15:02
+epoch [22/50] batch [130/500] time 1.578 (1.566) data 0.001 (0.006) loss 1.0146 (1.0792) acc 68.7500 (72.8606) lr 1.3090e-03 eta 6:14:57
+epoch [22/50] batch [135/500] time 1.571 (1.565) data 0.000 (0.006) loss 0.8369 (1.0792) acc 75.0000 (72.8704) lr 1.3090e-03 eta 6:14:43
+epoch [22/50] batch [140/500] time 1.564 (1.565) data 0.000 (0.006) loss 0.5479 (1.0677) acc 87.5000 (73.1250) lr 1.3090e-03 eta 6:14:31
+epoch [22/50] batch [145/500] time 1.572 (1.565) data 0.000 (0.006) loss 1.1826 (1.0698) acc 71.8750 (73.1681) lr 1.3090e-03 eta 6:14:25
+epoch [22/50] batch [150/500] time 1.542 (1.565) data 0.000 (0.005) loss 1.0029 (1.0656) acc 65.6250 (73.3125) lr 1.3090e-03 eta 6:14:16
+epoch [22/50] batch [155/500] time 1.573 (1.565) data 0.000 (0.005) loss 1.1523 (1.0704) acc 65.6250 (73.1048) lr 1.3090e-03 eta 6:14:08
+epoch [22/50] batch [160/500] time 1.564 (1.565) data 0.000 (0.005) loss 1.5615 (1.0731) acc 75.0000 (73.1641) lr 1.3090e-03 eta 6:14:00
+epoch [22/50] batch [165/500] time 1.564 (1.565) data 0.000 (0.005) loss 1.3066 (1.0710) acc 56.2500 (73.1061) lr 1.3090e-03 eta 6:13:51
+epoch [22/50] batch [170/500] time 1.547 (1.565) data 0.001 (0.005) loss 0.8018 (1.0698) acc 78.1250 (73.0515) lr 1.3090e-03 eta 6:13:39
+epoch [22/50] batch [175/500] time 1.561 (1.564) data 0.000 (0.005) loss 1.0068 (1.0747) acc 71.8750 (73.0357) lr 1.3090e-03 eta 6:13:29
+epoch [22/50] batch [180/500] time 1.555 (1.565) data 0.000 (0.005) loss 1.2666 (1.0778) acc 62.5000 (72.9340) lr 1.3090e-03 eta 6:13:26
+epoch [22/50] batch [185/500] time 1.538 (1.564) data 0.000 (0.005) loss 2.1895 (1.0862) acc 56.2500 (72.7534) lr 1.3090e-03 eta 6:13:14
+epoch [22/50] batch [190/500] time 1.559 (1.564) data 0.000 (0.004) loss 1.1494 (1.0901) acc 68.7500 (72.6316) lr 1.3090e-03 eta 6:13:04
+epoch [22/50] batch [195/500] time 1.599 (1.565) data 0.000 (0.004) loss 1.5303 (1.0884) acc 56.2500 (72.6442) lr 1.3090e-03 eta 6:13:02
+epoch [22/50] batch [200/500] time 1.566 (1.565) data 0.000 (0.004) loss 1.5859 (1.0937) acc 59.3750 (72.5000) lr 1.3090e-03 eta 6:12:55
+epoch [22/50] batch [205/500] time 1.547 (1.565) data 0.000 (0.004) loss 1.4199 (1.0950) acc 68.7500 (72.5152) lr 1.3090e-03 eta 6:12:45
+epoch [22/50] batch [210/500] time 1.548 (1.564) data 0.000 (0.004) loss 1.2783 (1.0955) acc 71.8750 (72.5298) lr 1.3090e-03 eta 6:12:34
+epoch [22/50] batch [215/500] time 1.577 (1.564) data 0.000 (0.004) loss 1.1055 (1.0987) acc 75.0000 (72.4855) lr 1.3090e-03 eta 6:12:27
+epoch [22/50] batch [220/500] time 1.550 (1.564) data 0.001 (0.004) loss 1.6416 (1.1038) acc 53.1250 (72.4006) lr 1.3090e-03 eta 6:12:14
+epoch [22/50] batch [225/500] time 1.569 (1.564) data 0.000 (0.004) loss 0.8882 (1.1019) acc 78.1250 (72.3750) lr 1.3090e-03 eta 6:12:09
+epoch [22/50] batch [230/500] time 1.568 (1.564) data 0.000 (0.004) loss 0.8579 (1.1075) acc 81.2500 (72.2418) lr 1.3090e-03 eta 6:12:02
+epoch [22/50] batch [235/500] time 1.541 (1.564) data 0.001 (0.004) loss 0.7988 (1.1080) acc 75.0000 (72.2739) lr 1.3090e-03 eta 6:11:53
+epoch [22/50] batch [240/500] time 1.544 (1.564) data 0.001 (0.004) loss 1.5879 (1.1085) acc 62.5000 (72.2656) lr 1.3090e-03 eta 6:11:42
+epoch [22/50] batch [245/500] time 1.571 (1.564) data 0.000 (0.004) loss 1.5742 (1.1082) acc 68.7500 (72.2832) lr 1.3090e-03 eta 6:11:31
+epoch [22/50] batch [250/500] time 1.561 (1.564) data 0.000 (0.003) loss 0.8379 (1.1064) acc 81.2500 (72.3500) lr 1.3090e-03 eta 6:11:24
+epoch [22/50] batch [255/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.0625 (1.1067) acc 78.1250 (72.4142) lr 1.3090e-03 eta 6:11:15
+epoch [22/50] batch [260/500] time 1.565 (1.564) data 0.001 (0.003) loss 1.6328 (1.1121) acc 65.6250 (72.2115) lr 1.3090e-03 eta 6:11:05
+epoch [22/50] batch [265/500] time 1.553 (1.563) data 0.000 (0.003) loss 1.1348 (1.1108) acc 71.8750 (72.2406) lr 1.3090e-03 eta 6:10:54
+epoch [22/50] batch [270/500] time 1.561 (1.563) data 0.000 (0.003) loss 0.8896 (1.1095) acc 81.2500 (72.2454) lr 1.3090e-03 eta 6:10:43
+epoch [22/50] batch [275/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.9404 (1.1080) acc 75.0000 (72.2955) lr 1.3090e-03 eta 6:10:34
+epoch [22/50] batch [280/500] time 1.573 (1.563) data 0.001 (0.003) loss 0.6382 (1.1056) acc 78.1250 (72.3884) lr 1.3090e-03 eta 6:10:26
+epoch [22/50] batch [285/500] time 1.557 (1.563) data 0.000 (0.003) loss 0.7793 (1.1016) acc 81.2500 (72.4561) lr 1.3090e-03 eta 6:10:19
+epoch [22/50] batch [290/500] time 1.559 (1.563) data 0.000 (0.003) loss 1.2871 (1.1026) acc 81.2500 (72.4246) lr 1.3090e-03 eta 6:10:13
+epoch [22/50] batch [295/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.1572 (1.0995) acc 71.8750 (72.4894) lr 1.3090e-03 eta 6:10:06
+epoch [22/50] batch [300/500] time 1.546 (1.563) data 0.000 (0.003) loss 0.9819 (1.0993) acc 68.7500 (72.5000) lr 1.3090e-03 eta 6:09:59
+epoch [22/50] batch [305/500] time 1.564 (1.563) data 0.000 (0.003) loss 0.9351 (1.0953) acc 81.2500 (72.5922) lr 1.3090e-03 eta 6:09:50
+epoch [22/50] batch [310/500] time 1.563 (1.563) data 0.001 (0.003) loss 0.5645 (1.0919) acc 90.6250 (72.6512) lr 1.3090e-03 eta 6:09:43
+epoch [22/50] batch [315/500] time 1.552 (1.563) data 0.000 (0.003) loss 1.0068 (1.0901) acc 68.7500 (72.6687) lr 1.3090e-03 eta 6:09:34
+epoch [22/50] batch [320/500] time 1.581 (1.563) data 0.000 (0.003) loss 0.9536 (1.0881) acc 75.0000 (72.7246) lr 1.3090e-03 eta 6:09:26
+epoch [22/50] batch [325/500] time 1.581 (1.564) data 0.000 (0.003) loss 0.8638 (1.0864) acc 75.0000 (72.7308) lr 1.3090e-03 eta 6:09:24
+epoch [22/50] batch [330/500] time 1.544 (1.564) data 0.000 (0.003) loss 1.0410 (1.0844) acc 75.0000 (72.7273) lr 1.3090e-03 eta 6:09:15
+epoch [22/50] batch [335/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.1240 (1.0876) acc 71.8750 (72.6399) lr 1.3090e-03 eta 6:09:04
+epoch [22/50] batch [340/500] time 1.541 (1.563) data 0.001 (0.003) loss 1.4648 (1.0900) acc 68.7500 (72.5919) lr 1.3090e-03 eta 6:08:55
+epoch [22/50] batch [345/500] time 1.549 (1.563) data 0.000 (0.003) loss 1.4072 (1.0937) acc 62.5000 (72.4547) lr 1.3090e-03 eta 6:08:46
+epoch [22/50] batch [350/500] time 1.575 (1.563) data 0.000 (0.003) loss 1.0049 (1.0921) acc 75.0000 (72.4732) lr 1.3090e-03 eta 6:08:39
+epoch [22/50] batch [355/500] time 1.559 (1.563) data 0.000 (0.003) loss 1.7021 (1.0903) acc 68.7500 (72.5352) lr 1.3090e-03 eta 6:08:30
+epoch [22/50] batch [360/500] time 1.571 (1.563) data 0.001 (0.003) loss 1.0205 (1.0876) acc 68.7500 (72.5694) lr 1.3090e-03 eta 6:08:21
+epoch [22/50] batch [365/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.2070 (1.0853) acc 75.0000 (72.6627) lr 1.3090e-03 eta 6:08:11
+epoch [22/50] batch [370/500] time 1.571 (1.563) data 0.000 (0.002) loss 1.6025 (1.0858) acc 68.7500 (72.6605) lr 1.3090e-03 eta 6:08:05
+epoch [22/50] batch [375/500] time 1.570 (1.563) data 0.000 (0.002) loss 1.1406 (1.0886) acc 68.7500 (72.5750) lr 1.3090e-03 eta 6:07:56
+epoch [22/50] batch [380/500] time 1.530 (1.563) data 0.000 (0.002) loss 0.8994 (1.0867) acc 75.0000 (72.5905) lr 1.3090e-03 eta 6:07:46
+epoch [22/50] batch [385/500] time 1.555 (1.563) data 0.001 (0.002) loss 1.1299 (1.0860) acc 62.5000 (72.6136) lr 1.3090e-03 eta 6:07:37
+epoch [22/50] batch [390/500] time 1.563 (1.563) data 0.000 (0.002) loss 1.5527 (1.0857) acc 68.7500 (72.6282) lr 1.3090e-03 eta 6:07:28
+epoch [22/50] batch [395/500] time 1.545 (1.563) data 0.001 (0.002) loss 0.7490 (1.0833) acc 78.1250 (72.6820) lr 1.3090e-03 eta 6:07:19
+epoch [22/50] batch [400/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.2070 (1.0839) acc 68.7500 (72.6797) lr 1.3090e-03 eta 6:07:09
+epoch [22/50] batch [405/500] time 1.551 (1.562) data 0.000 (0.002) loss 1.4277 (1.0860) acc 65.6250 (72.6698) lr 1.3090e-03 eta 6:07:00
+epoch [22/50] batch [410/500] time 1.572 (1.562) data 0.000 (0.002) loss 1.5479 (1.0886) acc 53.1250 (72.5610) lr 1.3090e-03 eta 6:06:52
+epoch [22/50] batch [415/500] time 1.554 (1.562) data 0.000 (0.002) loss 1.0947 (1.0892) acc 71.8750 (72.5527) lr 1.3090e-03 eta 6:06:44
+epoch [22/50] batch [420/500] time 1.564 (1.562) data 0.000 (0.002) loss 0.8589 (1.0921) acc 81.2500 (72.5074) lr 1.3090e-03 eta 6:06:36
+epoch [22/50] batch [425/500] time 1.550 (1.562) data 0.000 (0.002) loss 0.9839 (1.0917) acc 71.8750 (72.5294) lr 1.3090e-03 eta 6:06:28
+epoch [22/50] batch [430/500] time 1.551 (1.562) data 0.000 (0.002) loss 0.8066 (1.0896) acc 81.2500 (72.5509) lr 1.3090e-03 eta 6:06:19
+epoch [22/50] batch [435/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.0771 (1.0897) acc 62.5000 (72.5216) lr 1.3090e-03 eta 6:06:09
+epoch [22/50] batch [440/500] time 1.597 (1.562) data 0.001 (0.002) loss 0.9722 (1.0872) acc 71.8750 (72.5071) lr 1.3090e-03 eta 6:06:01
+epoch [22/50] batch [445/500] time 1.553 (1.562) data 0.000 (0.002) loss 0.9824 (1.0850) acc 84.3750 (72.5632) lr 1.3090e-03 eta 6:05:54
+epoch [22/50] batch [450/500] time 1.542 (1.562) data 0.000 (0.002) loss 0.8828 (1.0846) acc 81.2500 (72.6111) lr 1.3090e-03 eta 6:05:46
+epoch [22/50] batch [455/500] time 1.544 (1.562) data 0.000 (0.002) loss 0.7896 (1.0828) acc 75.0000 (72.6305) lr 1.3090e-03 eta 6:05:36
+epoch [22/50] batch [460/500] time 1.594 (1.562) data 0.001 (0.002) loss 1.1934 (1.0818) acc 68.7500 (72.6630) lr 1.3090e-03 eta 6:05:30
+epoch [22/50] batch [465/500] time 1.646 (1.562) data 0.001 (0.002) loss 1.3174 (1.0811) acc 68.7500 (72.7083) lr 1.3090e-03 eta 6:05:23
+epoch [22/50] batch [470/500] time 1.550 (1.562) data 0.000 (0.002) loss 1.0986 (1.0807) acc 71.8750 (72.6862) lr 1.3090e-03 eta 6:05:16
+epoch [22/50] batch [475/500] time 1.550 (1.562) data 0.000 (0.002) loss 0.8862 (1.0777) acc 75.0000 (72.6974) lr 1.3090e-03 eta 6:05:07
+epoch [22/50] batch [480/500] time 1.561 (1.562) data 0.000 (0.002) loss 0.5645 (1.0768) acc 84.3750 (72.7344) lr 1.3090e-03 eta 6:04:59
+epoch [22/50] batch [485/500] time 1.541 (1.562) data 0.001 (0.002) loss 1.3789 (1.0788) acc 65.6250 (72.6933) lr 1.3090e-03 eta 6:04:51
+epoch [22/50] batch [490/500] time 1.578 (1.562) data 0.000 (0.002) loss 1.0098 (1.0777) acc 71.8750 (72.7232) lr 1.3090e-03 eta 6:04:43
+epoch [22/50] batch [495/500] time 1.554 (1.562) data 0.000 (0.002) loss 0.9741 (1.0766) acc 68.7500 (72.7462) lr 1.3090e-03 eta 6:04:35
+epoch [22/50] batch [500/500] time 1.555 (1.562) data 0.000 (0.002) loss 0.7769 (1.0764) acc 75.0000 (72.7562) lr 1.2487e-03 eta 6:04:27
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,053
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [23/50] batch [5/500] time 1.532 (1.664) data 0.000 (0.160) loss 1.1143 (1.0252) acc 75.0000 (75.0000) lr 1.2487e-03 eta 6:28:05
+epoch [23/50] batch [10/500] time 1.544 (1.614) data 0.000 (0.080) loss 0.7446 (1.0323) acc 84.3750 (74.0625) lr 1.2487e-03 eta 6:16:20
+epoch [23/50] batch [15/500] time 1.534 (1.593) data 0.001 (0.054) loss 0.9395 (1.0508) acc 68.7500 (73.3333) lr 1.2487e-03 eta 6:11:21
+epoch [23/50] batch [20/500] time 1.563 (1.590) data 0.001 (0.040) loss 1.3574 (1.0665) acc 75.0000 (73.4375) lr 1.2487e-03 eta 6:10:34
+epoch [23/50] batch [25/500] time 1.565 (1.584) data 0.000 (0.032) loss 0.7012 (1.0445) acc 78.1250 (74.0000) lr 1.2487e-03 eta 6:09:01
+epoch [23/50] batch [30/500] time 1.564 (1.580) data 0.001 (0.027) loss 1.3467 (1.0660) acc 59.3750 (73.2292) lr 1.2487e-03 eta 6:07:56
+epoch [23/50] batch [35/500] time 1.555 (1.576) data 0.001 (0.023) loss 1.1641 (1.0544) acc 59.3750 (72.8571) lr 1.2487e-03 eta 6:06:50
+epoch [23/50] batch [40/500] time 1.550 (1.574) data 0.000 (0.020) loss 1.2061 (1.0850) acc 68.7500 (72.2656) lr 1.2487e-03 eta 6:06:13
+epoch [23/50] batch [45/500] time 1.559 (1.573) data 0.001 (0.018) loss 0.8066 (1.1002) acc 78.1250 (71.9444) lr 1.2487e-03 eta 6:05:46
+epoch [23/50] batch [50/500] time 1.546 (1.570) data 0.000 (0.016) loss 1.6123 (1.1020) acc 62.5000 (72.3125) lr 1.2487e-03 eta 6:05:03
+epoch [23/50] batch [55/500] time 1.557 (1.569) data 0.000 (0.015) loss 0.5884 (1.0914) acc 81.2500 (72.2727) lr 1.2487e-03 eta 6:04:39
+epoch [23/50] batch [60/500] time 1.552 (1.568) data 0.000 (0.014) loss 1.6201 (1.1029) acc 59.3750 (72.0833) lr 1.2487e-03 eta 6:04:18
+epoch [23/50] batch [65/500] time 1.571 (1.568) data 0.000 (0.013) loss 1.2832 (1.1096) acc 75.0000 (72.1154) lr 1.2487e-03 eta 6:04:09
+epoch [23/50] batch [70/500] time 1.562 (1.567) data 0.000 (0.012) loss 0.7466 (1.1092) acc 81.2500 (72.2321) lr 1.2487e-03 eta 6:03:55
+epoch [23/50] batch [75/500] time 1.592 (1.567) data 0.000 (0.011) loss 0.9614 (1.1185) acc 78.1250 (72.1667) lr 1.2487e-03 eta 6:03:47
+epoch [23/50] batch [80/500] time 1.540 (1.567) data 0.000 (0.010) loss 0.8613 (1.1166) acc 68.7500 (72.0312) lr 1.2487e-03 eta 6:03:35
+epoch [23/50] batch [85/500] time 1.542 (1.567) data 0.001 (0.010) loss 1.0771 (1.1080) acc 75.0000 (72.2426) lr 1.2487e-03 eta 6:03:18
+epoch [23/50] batch [90/500] time 1.567 (1.567) data 0.001 (0.009) loss 1.0186 (1.1063) acc 78.1250 (72.1181) lr 1.2487e-03 eta 6:03:10
+epoch [23/50] batch [95/500] time 1.553 (1.566) data 0.001 (0.009) loss 1.1621 (1.1059) acc 78.1250 (72.3026) lr 1.2487e-03 eta 6:02:57
+epoch [23/50] batch [100/500] time 1.544 (1.565) data 0.000 (0.008) loss 1.0137 (1.0988) acc 65.6250 (72.4688) lr 1.2487e-03 eta 6:02:39
+epoch [23/50] batch [105/500] time 1.569 (1.565) data 0.000 (0.008) loss 0.7051 (1.0943) acc 81.2500 (72.5000) lr 1.2487e-03 eta 6:02:28
+epoch [23/50] batch [110/500] time 1.565 (1.565) data 0.001 (0.008) loss 1.1250 (1.0938) acc 71.8750 (72.3864) lr 1.2487e-03 eta 6:02:22
+epoch [23/50] batch [115/500] time 1.649 (1.566) data 0.001 (0.007) loss 0.8867 (1.0896) acc 75.0000 (72.5000) lr 1.2487e-03 eta 6:02:19
+epoch [23/50] batch [120/500] time 1.542 (1.565) data 0.000 (0.007) loss 1.5537 (1.0938) acc 68.7500 (72.4479) lr 1.2487e-03 eta 6:02:04
+epoch [23/50] batch [125/500] time 1.538 (1.564) data 0.000 (0.007) loss 0.7017 (1.0900) acc 90.6250 (72.5750) lr 1.2487e-03 eta 6:01:43
+epoch [23/50] batch [130/500] time 1.551 (1.564) data 0.000 (0.007) loss 1.5527 (1.0929) acc 65.6250 (72.5721) lr 1.2487e-03 eta 6:01:33
+epoch [23/50] batch [135/500] time 1.536 (1.563) data 0.000 (0.006) loss 0.5537 (1.0834) acc 81.2500 (72.6620) lr 1.2487e-03 eta 6:01:16
+epoch [23/50] batch [140/500] time 1.569 (1.563) data 0.001 (0.006) loss 0.8579 (1.0883) acc 81.2500 (72.6562) lr 1.2487e-03 eta 6:01:02
+epoch [23/50] batch [145/500] time 1.542 (1.562) data 0.000 (0.006) loss 0.7998 (1.0867) acc 81.2500 (72.6724) lr 1.2487e-03 eta 6:00:46
+epoch [23/50] batch [150/500] time 1.601 (1.562) data 0.001 (0.006) loss 0.3425 (1.0773) acc 90.6250 (72.8958) lr 1.2487e-03 eta 6:00:35
+epoch [23/50] batch [155/500] time 1.555 (1.562) data 0.000 (0.006) loss 1.5127 (1.0804) acc 56.2500 (72.8831) lr 1.2487e-03 eta 6:00:29
+epoch [23/50] batch [160/500] time 1.550 (1.562) data 0.000 (0.005) loss 0.8413 (1.0738) acc 75.0000 (72.9688) lr 1.2487e-03 eta 6:00:21
+epoch [23/50] batch [165/500] time 1.544 (1.562) data 0.001 (0.005) loss 1.2432 (1.0695) acc 68.7500 (73.0303) lr 1.2487e-03 eta 6:00:12
+epoch [23/50] batch [170/500] time 1.556 (1.562) data 0.001 (0.005) loss 0.8301 (1.0720) acc 71.8750 (72.9228) lr 1.2487e-03 eta 5:59:58
+epoch [23/50] batch [175/500] time 1.556 (1.562) data 0.001 (0.005) loss 1.0898 (1.0728) acc 71.8750 (72.9286) lr 1.2487e-03 eta 5:59:48
+epoch [23/50] batch [180/500] time 1.562 (1.562) data 0.000 (0.005) loss 1.1074 (1.0716) acc 75.0000 (73.0382) lr 1.2487e-03 eta 5:59:41
+epoch [23/50] batch [185/500] time 1.570 (1.562) data 0.001 (0.005) loss 1.2549 (1.0717) acc 68.7500 (73.0743) lr 1.2487e-03 eta 5:59:32
+epoch [23/50] batch [190/500] time 1.544 (1.561) data 0.001 (0.005) loss 0.9932 (1.0738) acc 75.0000 (73.0428) lr 1.2487e-03 eta 5:59:21
+epoch [23/50] batch [195/500] time 1.570 (1.561) data 0.001 (0.005) loss 1.0303 (1.0709) acc 71.8750 (73.1250) lr 1.2487e-03 eta 5:59:13
+epoch [23/50] batch [200/500] time 1.544 (1.561) data 0.000 (0.004) loss 0.4429 (1.0684) acc 93.7500 (73.3125) lr 1.2487e-03 eta 5:59:03
+epoch [23/50] batch [205/500] time 1.569 (1.561) data 0.001 (0.004) loss 0.8330 (1.0656) acc 78.1250 (73.2470) lr 1.2487e-03 eta 5:58:54
+epoch [23/50] batch [210/500] time 1.575 (1.561) data 0.001 (0.004) loss 0.5850 (1.0613) acc 81.2500 (73.2887) lr 1.2487e-03 eta 5:58:46
+epoch [23/50] batch [215/500] time 1.583 (1.561) data 0.001 (0.004) loss 1.2510 (1.0663) acc 78.1250 (73.2413) lr 1.2487e-03 eta 5:58:37
+epoch [23/50] batch [220/500] time 1.564 (1.561) data 0.001 (0.004) loss 1.0801 (1.0676) acc 78.1250 (73.2812) lr 1.2487e-03 eta 5:58:25
+epoch [23/50] batch [225/500] time 1.554 (1.560) data 0.001 (0.004) loss 0.9468 (1.0709) acc 75.0000 (73.1944) lr 1.2487e-03 eta 5:58:12
+epoch [23/50] batch [230/500] time 1.563 (1.560) data 0.000 (0.004) loss 1.2188 (1.0746) acc 75.0000 (73.1929) lr 1.2487e-03 eta 5:58:03
+epoch [23/50] batch [235/500] time 1.564 (1.560) data 0.000 (0.004) loss 0.6997 (1.0725) acc 81.2500 (73.2181) lr 1.2487e-03 eta 5:57:53
+epoch [23/50] batch [240/500] time 1.578 (1.560) data 0.000 (0.004) loss 0.8716 (1.0727) acc 68.7500 (73.1510) lr 1.2487e-03 eta 5:57:46
+epoch [23/50] batch [245/500] time 1.559 (1.560) data 0.001 (0.004) loss 1.3906 (1.0775) acc 71.8750 (72.9847) lr 1.2487e-03 eta 5:57:36
+epoch [23/50] batch [250/500] time 1.563 (1.560) data 0.000 (0.004) loss 1.0127 (1.0784) acc 78.1250 (73.0125) lr 1.2487e-03 eta 5:57:25
+epoch [23/50] batch [255/500] time 1.533 (1.559) data 0.001 (0.004) loss 0.8960 (1.0785) acc 84.3750 (73.0882) lr 1.2487e-03 eta 5:57:14
+epoch [23/50] batch [260/500] time 1.548 (1.560) data 0.000 (0.004) loss 1.1250 (1.0813) acc 62.5000 (73.0168) lr 1.2487e-03 eta 5:57:11
+epoch [23/50] batch [265/500] time 1.555 (1.560) data 0.000 (0.004) loss 0.6743 (1.0774) acc 84.3750 (73.1250) lr 1.2487e-03 eta 5:57:01
+epoch [23/50] batch [270/500] time 1.585 (1.560) data 0.000 (0.003) loss 1.1377 (1.0771) acc 75.0000 (73.1250) lr 1.2487e-03 eta 5:56:55
+epoch [23/50] batch [275/500] time 1.563 (1.560) data 0.000 (0.003) loss 1.3115 (1.0817) acc 71.8750 (73.0568) lr 1.2487e-03 eta 5:56:51
+epoch [23/50] batch [280/500] time 1.588 (1.560) data 0.001 (0.003) loss 1.1436 (1.0810) acc 71.8750 (73.0357) lr 1.2487e-03 eta 5:56:45
+epoch [23/50] batch [285/500] time 1.586 (1.560) data 0.001 (0.003) loss 0.9653 (1.0798) acc 84.3750 (73.0154) lr 1.2487e-03 eta 5:56:39
+epoch [23/50] batch [290/500] time 1.528 (1.560) data 0.001 (0.003) loss 1.4482 (1.0794) acc 65.6250 (73.0496) lr 1.2487e-03 eta 5:56:31
+epoch [23/50] batch [295/500] time 1.550 (1.560) data 0.000 (0.003) loss 1.4307 (1.0837) acc 56.2500 (72.9661) lr 1.2487e-03 eta 5:56:24
+epoch [23/50] batch [300/500] time 1.570 (1.560) data 0.000 (0.003) loss 0.7959 (1.0849) acc 81.2500 (72.9479) lr 1.2487e-03 eta 5:56:16
+epoch [23/50] batch [305/500] time 1.571 (1.561) data 0.000 (0.003) loss 0.9673 (1.0877) acc 75.0000 (72.8996) lr 1.2487e-03 eta 5:56:16
+epoch [23/50] batch [310/500] time 1.564 (1.561) data 0.000 (0.003) loss 0.9980 (1.0876) acc 68.7500 (72.9234) lr 1.2487e-03 eta 5:56:09
+epoch [23/50] batch [315/500] time 1.589 (1.561) data 0.000 (0.003) loss 0.6787 (1.0876) acc 81.2500 (72.9067) lr 1.2487e-03 eta 5:56:01
+epoch [23/50] batch [320/500] time 1.571 (1.561) data 0.000 (0.003) loss 1.5146 (1.0876) acc 62.5000 (72.8906) lr 1.2487e-03 eta 5:55:54
+epoch [23/50] batch [325/500] time 1.569 (1.561) data 0.000 (0.003) loss 1.0420 (1.0875) acc 65.6250 (72.8654) lr 1.2487e-03 eta 5:55:47
+epoch [23/50] batch [330/500] time 1.554 (1.561) data 0.001 (0.003) loss 0.8599 (1.0865) acc 71.8750 (72.9356) lr 1.2487e-03 eta 5:55:38
+epoch [23/50] batch [335/500] time 1.567 (1.561) data 0.000 (0.003) loss 1.1084 (1.0861) acc 71.8750 (72.9011) lr 1.2487e-03 eta 5:55:29
+epoch [23/50] batch [340/500] time 1.559 (1.561) data 0.000 (0.003) loss 0.7344 (1.0865) acc 78.1250 (72.8860) lr 1.2487e-03 eta 5:55:20
+epoch [23/50] batch [345/500] time 1.552 (1.561) data 0.000 (0.003) loss 1.4053 (1.0838) acc 71.8750 (72.9529) lr 1.2487e-03 eta 5:55:12
+epoch [23/50] batch [350/500] time 1.568 (1.561) data 0.000 (0.003) loss 0.8760 (1.0814) acc 71.8750 (72.9911) lr 1.2487e-03 eta 5:55:06
+epoch [23/50] batch [355/500] time 1.578 (1.561) data 0.000 (0.003) loss 0.9639 (1.0806) acc 81.2500 (73.0194) lr 1.2487e-03 eta 5:54:57
+epoch [23/50] batch [360/500] time 1.546 (1.561) data 0.000 (0.003) loss 2.0898 (1.0833) acc 59.3750 (72.9688) lr 1.2487e-03 eta 5:54:47
+epoch [23/50] batch [365/500] time 1.536 (1.561) data 0.000 (0.003) loss 0.5498 (1.0854) acc 84.3750 (72.9195) lr 1.2487e-03 eta 5:54:38
+epoch [23/50] batch [370/500] time 1.528 (1.561) data 0.000 (0.003) loss 0.8999 (1.0834) acc 71.8750 (72.9645) lr 1.2487e-03 eta 5:54:29
+epoch [23/50] batch [375/500] time 1.556 (1.560) data 0.000 (0.003) loss 1.0342 (1.0850) acc 68.7500 (72.9167) lr 1.2487e-03 eta 5:54:19
+epoch [23/50] batch [380/500] time 1.562 (1.560) data 0.000 (0.003) loss 0.6108 (1.0845) acc 84.3750 (72.9276) lr 1.2487e-03 eta 5:54:11
+epoch [23/50] batch [385/500] time 1.546 (1.560) data 0.000 (0.003) loss 0.8779 (1.0846) acc 81.2500 (72.9383) lr 1.2487e-03 eta 5:53:59
+epoch [23/50] batch [390/500] time 1.564 (1.560) data 0.000 (0.003) loss 0.8145 (1.0847) acc 75.0000 (72.9247) lr 1.2487e-03 eta 5:53:52
+epoch [23/50] batch [395/500] time 1.588 (1.560) data 0.000 (0.002) loss 1.3320 (1.0850) acc 65.6250 (72.9430) lr 1.2487e-03 eta 5:53:45
+epoch [23/50] batch [400/500] time 1.559 (1.560) data 0.000 (0.002) loss 1.5283 (1.0860) acc 62.5000 (72.8906) lr 1.2487e-03 eta 5:53:36
+epoch [23/50] batch [405/500] time 1.549 (1.560) data 0.000 (0.002) loss 0.9307 (1.0848) acc 81.2500 (72.9552) lr 1.2487e-03 eta 5:53:31
+epoch [23/50] batch [410/500] time 1.571 (1.560) data 0.000 (0.002) loss 1.6309 (1.0848) acc 65.6250 (72.9649) lr 1.2487e-03 eta 5:53:24
+epoch [23/50] batch [415/500] time 1.570 (1.560) data 0.001 (0.002) loss 0.9111 (1.0826) acc 68.7500 (72.9669) lr 1.2487e-03 eta 5:53:15
+epoch [23/50] batch [420/500] time 1.545 (1.560) data 0.000 (0.002) loss 1.3818 (1.0837) acc 68.7500 (72.9911) lr 1.2487e-03 eta 5:53:07
+epoch [23/50] batch [425/500] time 1.539 (1.560) data 0.000 (0.002) loss 0.6890 (1.0829) acc 81.2500 (73.0294) lr 1.2487e-03 eta 5:52:56
+epoch [23/50] batch [430/500] time 1.572 (1.560) data 0.000 (0.002) loss 0.6982 (1.0832) acc 75.0000 (72.9942) lr 1.2487e-03 eta 5:52:47
+epoch [23/50] batch [435/500] time 1.551 (1.560) data 0.000 (0.002) loss 1.2354 (1.0825) acc 75.0000 (72.9957) lr 1.2487e-03 eta 5:52:39
+epoch [23/50] batch [440/500] time 1.554 (1.560) data 0.000 (0.002) loss 1.1992 (1.0855) acc 75.0000 (72.9830) lr 1.2487e-03 eta 5:52:31
+epoch [23/50] batch [445/500] time 1.649 (1.560) data 0.000 (0.002) loss 1.2939 (1.0889) acc 65.6250 (72.9073) lr 1.2487e-03 eta 5:52:28
+epoch [23/50] batch [450/500] time 1.596 (1.560) data 0.000 (0.002) loss 0.8813 (1.0926) acc 78.1250 (72.8472) lr 1.2487e-03 eta 5:52:22
+epoch [23/50] batch [455/500] time 1.599 (1.560) data 0.000 (0.002) loss 1.3623 (1.0955) acc 65.6250 (72.8091) lr 1.2487e-03 eta 5:52:15
+epoch [23/50] batch [460/500] time 1.544 (1.560) data 0.001 (0.002) loss 0.7642 (1.0947) acc 81.2500 (72.8601) lr 1.2487e-03 eta 5:52:06
+epoch [23/50] batch [465/500] time 1.588 (1.560) data 0.000 (0.002) loss 1.4287 (1.0955) acc 71.8750 (72.8360) lr 1.2487e-03 eta 5:51:59
+epoch [23/50] batch [470/500] time 1.564 (1.560) data 0.000 (0.002) loss 0.9688 (1.0947) acc 78.1250 (72.7926) lr 1.2487e-03 eta 5:51:51
+epoch [23/50] batch [475/500] time 1.548 (1.560) data 0.000 (0.002) loss 1.0518 (1.0963) acc 65.6250 (72.7566) lr 1.2487e-03 eta 5:51:41
+epoch [23/50] batch [480/500] time 1.562 (1.560) data 0.000 (0.002) loss 1.5547 (1.0946) acc 68.7500 (72.8190) lr 1.2487e-03 eta 5:51:31
+epoch [23/50] batch [485/500] time 1.552 (1.560) data 0.001 (0.002) loss 1.1592 (1.0935) acc 78.1250 (72.8673) lr 1.2487e-03 eta 5:51:22
+epoch [23/50] batch [490/500] time 1.557 (1.560) data 0.000 (0.002) loss 1.2598 (1.0930) acc 65.6250 (72.8763) lr 1.2487e-03 eta 5:51:14
+epoch [23/50] batch [495/500] time 1.546 (1.560) data 0.000 (0.002) loss 1.2002 (1.0925) acc 71.8750 (72.8788) lr 1.2487e-03 eta 5:51:05
+epoch [23/50] batch [500/500] time 1.535 (1.560) data 0.000 (0.002) loss 0.9116 (1.0927) acc 78.1250 (72.9062) lr 1.1874e-03 eta 5:50:55
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,947
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+epoch [24/50] batch [5/500] time 1.544 (1.728) data 0.000 (0.202) loss 1.2100 (1.2296) acc 71.8750 (69.3750) lr 1.1874e-03 eta 6:28:45
+epoch [24/50] batch [10/500] time 1.553 (1.643) data 0.001 (0.101) loss 0.9985 (1.1294) acc 75.0000 (71.2500) lr 1.1874e-03 eta 6:09:30
+epoch [24/50] batch [15/500] time 1.555 (1.617) data 0.001 (0.068) loss 0.4429 (1.0226) acc 84.3750 (73.1250) lr 1.1874e-03 eta 6:03:21
+epoch [24/50] batch [20/500] time 1.535 (1.598) data 0.000 (0.051) loss 1.9072 (1.0740) acc 59.3750 (72.3438) lr 1.1874e-03 eta 5:59:06
+epoch [24/50] batch [25/500] time 1.557 (1.590) data 0.000 (0.041) loss 1.0566 (1.0590) acc 71.8750 (72.7500) lr 1.1874e-03 eta 5:57:03
+epoch [24/50] batch [30/500] time 1.570 (1.586) data 0.001 (0.034) loss 0.8350 (1.0584) acc 75.0000 (72.2917) lr 1.1874e-03 eta 5:56:04
+epoch [24/50] batch [35/500] time 1.571 (1.583) data 0.000 (0.029) loss 0.3411 (1.0336) acc 87.5000 (73.3036) lr 1.1874e-03 eta 5:55:11
+epoch [24/50] batch [40/500] time 1.533 (1.579) data 0.000 (0.026) loss 0.7666 (1.0215) acc 78.1250 (72.8906) lr 1.1874e-03 eta 5:54:16
+epoch [24/50] batch [45/500] time 1.568 (1.578) data 0.000 (0.023) loss 0.9771 (1.0156) acc 75.0000 (72.9167) lr 1.1874e-03 eta 5:53:54
+epoch [24/50] batch [50/500] time 1.572 (1.578) data 0.000 (0.021) loss 0.6255 (1.0208) acc 81.2500 (73.0625) lr 1.1874e-03 eta 5:53:41
+epoch [24/50] batch [55/500] time 1.573 (1.576) data 0.001 (0.019) loss 0.5908 (1.0201) acc 81.2500 (73.0114) lr 1.1874e-03 eta 5:53:15
+epoch [24/50] batch [60/500] time 1.579 (1.575) data 0.000 (0.017) loss 1.6836 (1.0230) acc 71.8750 (73.3854) lr 1.1874e-03 eta 5:52:54
+epoch [24/50] batch [65/500] time 1.564 (1.575) data 0.000 (0.016) loss 1.2842 (1.0404) acc 65.6250 (72.9327) lr 1.1874e-03 eta 5:52:37
+epoch [24/50] batch [70/500] time 1.567 (1.574) data 0.001 (0.015) loss 0.8384 (1.0432) acc 84.3750 (73.3482) lr 1.1874e-03 eta 5:52:14
+epoch [24/50] batch [75/500] time 1.559 (1.572) data 0.001 (0.014) loss 0.7246 (1.0391) acc 75.0000 (73.2500) lr 1.1874e-03 eta 5:51:46
+epoch [24/50] batch [80/500] time 1.522 (1.571) data 0.001 (0.013) loss 1.0234 (1.0380) acc 68.7500 (73.3203) lr 1.1874e-03 eta 5:51:19
+epoch [24/50] batch [85/500] time 1.566 (1.570) data 0.000 (0.012) loss 1.1045 (1.0506) acc 68.7500 (73.1985) lr 1.1874e-03 eta 5:50:58
+epoch [24/50] batch [90/500] time 1.564 (1.569) data 0.001 (0.012) loss 1.7607 (1.0592) acc 56.2500 (73.0208) lr 1.1874e-03 eta 5:50:43
+epoch [24/50] batch [95/500] time 1.570 (1.570) data 0.000 (0.011) loss 0.7896 (1.0500) acc 75.0000 (73.1579) lr 1.1874e-03 eta 5:50:44
+epoch [24/50] batch [100/500] time 1.575 (1.569) data 0.000 (0.011) loss 1.1562 (1.0550) acc 71.8750 (73.0312) lr 1.1874e-03 eta 5:50:28
+epoch [24/50] batch [105/500] time 1.549 (1.569) data 0.001 (0.010) loss 0.9624 (1.0584) acc 68.7500 (73.0060) lr 1.1874e-03 eta 5:50:19
+epoch [24/50] batch [110/500] time 1.560 (1.569) data 0.000 (0.010) loss 0.7817 (1.0481) acc 78.1250 (73.3239) lr 1.1874e-03 eta 5:50:02
+epoch [24/50] batch [115/500] time 1.587 (1.568) data 0.001 (0.009) loss 0.9980 (1.0561) acc 84.3750 (73.2337) lr 1.1874e-03 eta 5:49:53
+epoch [24/50] batch [120/500] time 1.537 (1.568) data 0.000 (0.009) loss 0.9878 (1.0581) acc 78.1250 (72.9948) lr 1.1874e-03 eta 5:49:38
+epoch [24/50] batch [125/500] time 1.544 (1.567) data 0.001 (0.009) loss 0.8569 (1.0571) acc 78.1250 (72.9250) lr 1.1874e-03 eta 5:49:22
+epoch [24/50] batch [130/500] time 1.556 (1.567) data 0.000 (0.008) loss 1.4844 (1.0651) acc 68.7500 (72.7163) lr 1.1874e-03 eta 5:49:14
+epoch [24/50] batch [135/500] time 1.562 (1.567) data 0.000 (0.008) loss 1.2764 (1.0702) acc 65.6250 (72.6620) lr 1.1874e-03 eta 5:48:58
+epoch [24/50] batch [140/500] time 1.547 (1.566) data 0.000 (0.008) loss 1.1875 (1.0732) acc 75.0000 (72.5000) lr 1.1874e-03 eta 5:48:44
+epoch [24/50] batch [145/500] time 1.560 (1.567) data 0.000 (0.007) loss 1.2266 (1.0694) acc 62.5000 (72.6078) lr 1.1874e-03 eta 5:48:48
+epoch [24/50] batch [150/500] time 1.584 (1.567) data 0.001 (0.007) loss 1.3584 (1.0733) acc 71.8750 (72.5833) lr 1.1874e-03 eta 5:48:44
+epoch [24/50] batch [155/500] time 1.567 (1.568) data 0.000 (0.007) loss 0.9009 (1.0726) acc 78.1250 (72.7218) lr 1.1874e-03 eta 5:48:42
+epoch [24/50] batch [160/500] time 1.578 (1.568) data 0.000 (0.007) loss 0.9609 (1.0724) acc 75.0000 (72.8125) lr 1.1874e-03 eta 5:48:36
+epoch [24/50] batch [165/500] time 1.565 (1.568) data 0.000 (0.007) loss 1.5791 (1.0724) acc 62.5000 (72.7273) lr 1.1874e-03 eta 5:48:25
+epoch [24/50] batch [170/500] time 1.570 (1.568) data 0.001 (0.006) loss 1.4102 (1.0785) acc 62.5000 (72.5184) lr 1.1874e-03 eta 5:48:16
+epoch [24/50] batch [175/500] time 1.557 (1.567) data 0.000 (0.006) loss 1.5527 (1.0797) acc 65.6250 (72.5000) lr 1.1874e-03 eta 5:48:03
+epoch [24/50] batch [180/500] time 1.574 (1.567) data 0.001 (0.006) loss 1.5312 (1.0788) acc 59.3750 (72.5000) lr 1.1874e-03 eta 5:47:52
+epoch [24/50] batch [185/500] time 1.552 (1.567) data 0.001 (0.006) loss 1.1816 (1.0868) acc 75.0000 (72.4493) lr 1.1874e-03 eta 5:47:39
+epoch [24/50] batch [190/500] time 1.562 (1.567) data 0.000 (0.006) loss 1.2656 (1.0878) acc 78.1250 (72.5329) lr 1.1874e-03 eta 5:47:33
+epoch [24/50] batch [195/500] time 1.576 (1.567) data 0.000 (0.006) loss 1.1309 (1.0827) acc 68.7500 (72.5481) lr 1.1874e-03 eta 5:47:27
+epoch [24/50] batch [200/500] time 1.572 (1.567) data 0.001 (0.006) loss 0.8765 (1.0846) acc 78.1250 (72.5625) lr 1.1874e-03 eta 5:47:16
+epoch [24/50] batch [205/500] time 1.585 (1.567) data 0.000 (0.005) loss 0.5068 (1.0841) acc 87.5000 (72.6372) lr 1.1874e-03 eta 5:47:11
+epoch [24/50] batch [210/500] time 1.558 (1.567) data 0.001 (0.005) loss 1.6826 (1.0885) acc 65.6250 (72.6190) lr 1.1874e-03 eta 5:47:03
+epoch [24/50] batch [215/500] time 1.574 (1.567) data 0.001 (0.005) loss 1.5303 (1.0913) acc 68.7500 (72.4855) lr 1.1874e-03 eta 5:46:53
+epoch [24/50] batch [220/500] time 1.554 (1.566) data 0.001 (0.005) loss 1.2178 (1.0906) acc 75.0000 (72.5284) lr 1.1874e-03 eta 5:46:42
+epoch [24/50] batch [225/500] time 1.551 (1.566) data 0.001 (0.005) loss 1.1348 (1.0965) acc 68.7500 (72.4583) lr 1.1874e-03 eta 5:46:30
+epoch [24/50] batch [230/500] time 1.569 (1.566) data 0.001 (0.005) loss 1.4346 (1.0942) acc 65.6250 (72.5815) lr 1.1874e-03 eta 5:46:22
+epoch [24/50] batch [235/500] time 1.572 (1.566) data 0.001 (0.005) loss 1.2100 (1.0920) acc 65.6250 (72.5000) lr 1.1874e-03 eta 5:46:14
+epoch [24/50] batch [240/500] time 1.550 (1.566) data 0.001 (0.005) loss 0.9404 (1.0942) acc 71.8750 (72.5130) lr 1.1874e-03 eta 5:46:06
+epoch [24/50] batch [245/500] time 1.592 (1.566) data 0.000 (0.005) loss 1.1270 (1.0939) acc 68.7500 (72.5000) lr 1.1874e-03 eta 5:46:00
+epoch [24/50] batch [250/500] time 1.551 (1.566) data 0.000 (0.005) loss 1.1416 (1.0940) acc 71.8750 (72.4875) lr 1.1874e-03 eta 5:45:51
+epoch [24/50] batch [255/500] time 1.574 (1.566) data 0.000 (0.004) loss 1.5908 (1.0977) acc 71.8750 (72.4632) lr 1.1874e-03 eta 5:45:42
+epoch [24/50] batch [260/500] time 1.567 (1.566) data 0.000 (0.004) loss 1.1562 (1.0994) acc 71.8750 (72.4519) lr 1.1874e-03 eta 5:45:32
+epoch [24/50] batch [265/500] time 1.560 (1.566) data 0.000 (0.004) loss 1.0967 (1.0971) acc 71.8750 (72.5118) lr 1.1874e-03 eta 5:45:24
+epoch [24/50] batch [270/500] time 1.559 (1.566) data 0.000 (0.004) loss 1.0439 (1.0969) acc 68.7500 (72.4537) lr 1.1874e-03 eta 5:45:12
+epoch [24/50] batch [275/500] time 1.569 (1.565) data 0.001 (0.004) loss 0.8862 (1.0971) acc 81.2500 (72.5227) lr 1.1874e-03 eta 5:45:03
+epoch [24/50] batch [280/500] time 1.558 (1.565) data 0.000 (0.004) loss 1.2168 (1.0980) acc 81.2500 (72.5446) lr 1.1874e-03 eta 5:44:54
+epoch [24/50] batch [285/500] time 1.682 (1.566) data 0.000 (0.004) loss 0.8110 (1.0966) acc 75.0000 (72.5329) lr 1.1874e-03 eta 5:44:48
+epoch [24/50] batch [290/500] time 1.569 (1.565) data 0.000 (0.004) loss 0.9463 (1.0942) acc 71.8750 (72.5754) lr 1.1874e-03 eta 5:44:38
+epoch [24/50] batch [295/500] time 1.566 (1.565) data 0.001 (0.004) loss 1.5771 (1.0987) acc 56.2500 (72.4682) lr 1.1874e-03 eta 5:44:32
+epoch [24/50] batch [300/500] time 1.551 (1.565) data 0.000 (0.004) loss 1.4062 (1.1015) acc 59.3750 (72.4062) lr 1.1874e-03 eta 5:44:20
+epoch [24/50] batch [305/500] time 1.554 (1.565) data 0.001 (0.004) loss 1.0420 (1.1049) acc 68.7500 (72.3053) lr 1.1874e-03 eta 5:44:09
+epoch [24/50] batch [310/500] time 1.561 (1.565) data 0.000 (0.004) loss 0.6094 (1.1024) acc 84.3750 (72.3690) lr 1.1874e-03 eta 5:44:00
+epoch [24/50] batch [315/500] time 1.549 (1.565) data 0.000 (0.004) loss 0.8857 (1.1034) acc 81.2500 (72.3413) lr 1.1874e-03 eta 5:43:51
+epoch [24/50] batch [320/500] time 1.577 (1.565) data 0.000 (0.004) loss 0.8984 (1.1053) acc 78.1250 (72.3340) lr 1.1874e-03 eta 5:43:45
+epoch [24/50] batch [325/500] time 1.556 (1.565) data 0.000 (0.004) loss 1.2607 (1.1033) acc 78.1250 (72.4135) lr 1.1874e-03 eta 5:43:35
+epoch [24/50] batch [330/500] time 1.541 (1.565) data 0.000 (0.004) loss 0.9932 (1.1062) acc 78.1250 (72.3958) lr 1.1874e-03 eta 5:43:30
+epoch [24/50] batch [335/500] time 1.541 (1.565) data 0.000 (0.003) loss 1.0967 (1.1048) acc 71.8750 (72.4160) lr 1.1874e-03 eta 5:43:20
+epoch [24/50] batch [340/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.0166 (1.1038) acc 68.7500 (72.4632) lr 1.1874e-03 eta 5:43:10
+epoch [24/50] batch [345/500] time 1.589 (1.565) data 0.000 (0.003) loss 1.1289 (1.1042) acc 59.3750 (72.4275) lr 1.1874e-03 eta 5:43:03
+epoch [24/50] batch [350/500] time 1.583 (1.565) data 0.000 (0.003) loss 0.7114 (1.1042) acc 71.8750 (72.3750) lr 1.1874e-03 eta 5:42:56
+epoch [24/50] batch [355/500] time 1.559 (1.565) data 0.001 (0.003) loss 1.1465 (1.1031) acc 71.8750 (72.3415) lr 1.1874e-03 eta 5:42:47
+epoch [24/50] batch [360/500] time 1.558 (1.564) data 0.000 (0.003) loss 0.8066 (1.1020) acc 84.3750 (72.3785) lr 1.1874e-03 eta 5:42:37
+epoch [24/50] batch [365/500] time 1.578 (1.564) data 0.000 (0.003) loss 1.0400 (1.0984) acc 75.0000 (72.4743) lr 1.1874e-03 eta 5:42:28
+epoch [24/50] batch [370/500] time 1.552 (1.564) data 0.000 (0.003) loss 1.4678 (1.1006) acc 65.6250 (72.3986) lr 1.1874e-03 eta 5:42:19
+epoch [24/50] batch [375/500] time 1.558 (1.564) data 0.001 (0.003) loss 0.7036 (1.0990) acc 90.6250 (72.4917) lr 1.1874e-03 eta 5:42:09
+epoch [24/50] batch [380/500] time 1.571 (1.564) data 0.000 (0.003) loss 0.9243 (1.0975) acc 75.0000 (72.5411) lr 1.1874e-03 eta 5:42:00
+epoch [24/50] batch [385/500] time 1.595 (1.564) data 0.001 (0.003) loss 1.1152 (1.0976) acc 68.7500 (72.5649) lr 1.1874e-03 eta 5:41:53
+epoch [24/50] batch [390/500] time 1.581 (1.564) data 0.000 (0.003) loss 0.8145 (1.0962) acc 75.0000 (72.5641) lr 1.1874e-03 eta 5:41:44
+epoch [24/50] batch [395/500] time 1.575 (1.564) data 0.001 (0.003) loss 1.0283 (1.0935) acc 75.0000 (72.6661) lr 1.1874e-03 eta 5:41:38
+epoch [24/50] batch [400/500] time 1.556 (1.564) data 0.000 (0.003) loss 1.0430 (1.0980) acc 62.5000 (72.5625) lr 1.1874e-03 eta 5:41:30
+epoch [24/50] batch [405/500] time 1.550 (1.564) data 0.000 (0.003) loss 1.2012 (1.1008) acc 65.6250 (72.4923) lr 1.1874e-03 eta 5:41:19
+epoch [24/50] batch [410/500] time 1.533 (1.564) data 0.000 (0.003) loss 1.4316 (1.1022) acc 59.3750 (72.4085) lr 1.1874e-03 eta 5:41:11
+epoch [24/50] batch [415/500] time 1.534 (1.564) data 0.000 (0.003) loss 1.5088 (1.1027) acc 62.5000 (72.3870) lr 1.1874e-03 eta 5:41:01
+epoch [24/50] batch [420/500] time 1.528 (1.563) data 0.000 (0.003) loss 1.3428 (1.1011) acc 71.8750 (72.4479) lr 1.1874e-03 eta 5:40:49
+epoch [24/50] batch [425/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.1006 (1.0990) acc 71.8750 (72.5147) lr 1.1874e-03 eta 5:40:37
+epoch [24/50] batch [430/500] time 1.559 (1.563) data 0.000 (0.003) loss 0.8452 (1.0987) acc 84.3750 (72.5000) lr 1.1874e-03 eta 5:40:31
+epoch [24/50] batch [435/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.6357 (1.0983) acc 59.3750 (72.4856) lr 1.1874e-03 eta 5:40:20
+epoch [24/50] batch [440/500] time 1.534 (1.563) data 0.000 (0.003) loss 1.4268 (1.0993) acc 68.7500 (72.4716) lr 1.1874e-03 eta 5:40:10
+epoch [24/50] batch [445/500] time 1.546 (1.563) data 0.000 (0.003) loss 1.2930 (1.0999) acc 81.2500 (72.5140) lr 1.1874e-03 eta 5:40:00
+epoch [24/50] batch [450/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.1396 (1.1010) acc 71.8750 (72.4653) lr 1.1874e-03 eta 5:39:51
+epoch [24/50] batch [455/500] time 1.554 (1.563) data 0.000 (0.003) loss 0.9312 (1.0993) acc 78.1250 (72.4931) lr 1.1874e-03 eta 5:39:42
+epoch [24/50] batch [460/500] time 1.569 (1.562) data 0.000 (0.003) loss 1.1318 (1.1026) acc 62.5000 (72.4253) lr 1.1874e-03 eta 5:39:34
+epoch [24/50] batch [465/500] time 1.564 (1.563) data 0.001 (0.003) loss 0.6411 (1.1029) acc 78.1250 (72.3992) lr 1.1874e-03 eta 5:39:29
+epoch [24/50] batch [470/500] time 1.575 (1.563) data 0.000 (0.003) loss 0.4072 (1.0996) acc 87.5000 (72.4601) lr 1.1874e-03 eta 5:39:21
+epoch [24/50] batch [475/500] time 1.566 (1.563) data 0.000 (0.003) loss 0.5239 (1.0995) acc 90.6250 (72.4342) lr 1.1874e-03 eta 5:39:18
+epoch [24/50] batch [480/500] time 1.549 (1.563) data 0.000 (0.003) loss 0.8066 (1.0983) acc 81.2500 (72.4479) lr 1.1874e-03 eta 5:39:10
+epoch [24/50] batch [485/500] time 1.548 (1.563) data 0.001 (0.003) loss 1.0801 (1.0986) acc 68.7500 (72.4098) lr 1.1874e-03 eta 5:39:02
+epoch [24/50] batch [490/500] time 1.570 (1.563) data 0.000 (0.003) loss 1.1797 (1.1009) acc 65.6250 (72.3469) lr 1.1874e-03 eta 5:38:53
+epoch [24/50] batch [495/500] time 1.561 (1.563) data 0.000 (0.002) loss 1.4678 (1.1033) acc 56.2500 (72.2854) lr 1.1874e-03 eta 5:38:44
+epoch [24/50] batch [500/500] time 1.555 (1.563) data 0.000 (0.002) loss 0.6470 (1.1017) acc 75.0000 (72.3000) lr 1.1253e-03 eta 5:38:34
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,024
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+epoch [25/50] batch [5/500] time 1.556 (1.673) data 0.000 (0.177) loss 1.0693 (1.1984) acc 75.0000 (70.6250) lr 1.1253e-03 eta 6:02:26
+epoch [25/50] batch [10/500] time 1.558 (1.611) data 0.000 (0.089) loss 0.9302 (1.1119) acc 71.8750 (71.8750) lr 1.1253e-03 eta 5:48:51
+epoch [25/50] batch [15/500] time 1.569 (1.594) data 0.001 (0.059) loss 1.4326 (1.0871) acc 68.7500 (72.9167) lr 1.1253e-03 eta 5:45:04
+epoch [25/50] batch [20/500] time 1.561 (1.587) data 0.000 (0.045) loss 1.7627 (1.1357) acc 68.7500 (71.7188) lr 1.1253e-03 eta 5:43:16
+epoch [25/50] batch [25/500] time 1.567 (1.582) data 0.000 (0.036) loss 0.8867 (1.1252) acc 78.1250 (72.5000) lr 1.1253e-03 eta 5:42:11
+epoch [25/50] batch [30/500] time 1.576 (1.579) data 0.000 (0.030) loss 1.1396 (1.1418) acc 71.8750 (72.3958) lr 1.1253e-03 eta 5:41:19
+epoch [25/50] batch [35/500] time 1.556 (1.576) data 0.001 (0.026) loss 1.1201 (1.1179) acc 78.1250 (72.5893) lr 1.1253e-03 eta 5:40:37
+epoch [25/50] batch [40/500] time 1.554 (1.574) data 0.000 (0.023) loss 0.9365 (1.1119) acc 75.0000 (72.8906) lr 1.1253e-03 eta 5:40:04
+epoch [25/50] batch [45/500] time 1.585 (1.574) data 0.000 (0.020) loss 0.8379 (1.1117) acc 75.0000 (72.4306) lr 1.1253e-03 eta 5:39:48
+epoch [25/50] batch [50/500] time 1.571 (1.574) data 0.000 (0.018) loss 1.5078 (1.1305) acc 71.8750 (72.0000) lr 1.1253e-03 eta 5:39:38
+epoch [25/50] batch [55/500] time 1.578 (1.573) data 0.000 (0.016) loss 1.1611 (1.1360) acc 78.1250 (71.8182) lr 1.1253e-03 eta 5:39:17
+epoch [25/50] batch [60/500] time 1.566 (1.572) data 0.001 (0.015) loss 1.1270 (1.1387) acc 78.1250 (71.9271) lr 1.1253e-03 eta 5:39:00
+epoch [25/50] batch [65/500] time 1.552 (1.573) data 0.000 (0.014) loss 1.2500 (1.1389) acc 59.3750 (71.6346) lr 1.1253e-03 eta 5:39:04
+epoch [25/50] batch [70/500] time 1.563 (1.572) data 0.000 (0.013) loss 1.5352 (1.1370) acc 68.7500 (71.7411) lr 1.1253e-03 eta 5:38:50
+epoch [25/50] batch [75/500] time 1.560 (1.571) data 0.001 (0.012) loss 1.2158 (1.1337) acc 62.5000 (71.8333) lr 1.1253e-03 eta 5:38:28
+epoch [25/50] batch [80/500] time 1.572 (1.571) data 0.000 (0.011) loss 1.1885 (1.1254) acc 71.8750 (71.9141) lr 1.1253e-03 eta 5:38:20
+epoch [25/50] batch [85/500] time 1.547 (1.571) data 0.000 (0.011) loss 0.6738 (1.1228) acc 78.1250 (71.9485) lr 1.1253e-03 eta 5:38:06
+epoch [25/50] batch [90/500] time 1.597 (1.571) data 0.000 (0.010) loss 1.3604 (1.1229) acc 59.3750 (71.8056) lr 1.1253e-03 eta 5:37:59
+epoch [25/50] batch [95/500] time 1.567 (1.570) data 0.000 (0.010) loss 1.3262 (1.1255) acc 68.7500 (71.8092) lr 1.1253e-03 eta 5:37:46
+epoch [25/50] batch [100/500] time 1.566 (1.570) data 0.000 (0.009) loss 0.8784 (1.1271) acc 62.5000 (71.6250) lr 1.1253e-03 eta 5:37:28
+epoch [25/50] batch [105/500] time 1.558 (1.569) data 0.000 (0.009) loss 1.0742 (1.1261) acc 71.8750 (71.7560) lr 1.1253e-03 eta 5:37:10
+epoch [25/50] batch [110/500] time 1.575 (1.570) data 0.000 (0.008) loss 1.2207 (1.1166) acc 68.7500 (72.0455) lr 1.1253e-03 eta 5:37:10
+epoch [25/50] batch [115/500] time 1.576 (1.570) data 0.000 (0.008) loss 1.8809 (1.1184) acc 62.5000 (72.0109) lr 1.1253e-03 eta 5:37:04
+epoch [25/50] batch [120/500] time 1.557 (1.570) data 0.000 (0.008) loss 1.3701 (1.1165) acc 78.1250 (72.1615) lr 1.1253e-03 eta 5:36:58
+epoch [25/50] batch [125/500] time 1.570 (1.570) data 0.000 (0.007) loss 1.4873 (1.1203) acc 59.3750 (72.0500) lr 1.1253e-03 eta 5:36:50
+epoch [25/50] batch [130/500] time 1.541 (1.569) data 0.000 (0.007) loss 1.3770 (1.1276) acc 59.3750 (71.9231) lr 1.1253e-03 eta 5:36:38
+epoch [25/50] batch [135/500] time 1.577 (1.569) data 0.000 (0.007) loss 1.3262 (1.1219) acc 59.3750 (72.0370) lr 1.1253e-03 eta 5:36:29
+epoch [25/50] batch [140/500] time 1.553 (1.569) data 0.000 (0.007) loss 1.0811 (1.1199) acc 68.7500 (72.1875) lr 1.1253e-03 eta 5:36:19
+epoch [25/50] batch [145/500] time 1.536 (1.569) data 0.001 (0.007) loss 1.7246 (1.1206) acc 53.1250 (72.0905) lr 1.1253e-03 eta 5:36:03
+epoch [25/50] batch [150/500] time 1.556 (1.568) data 0.000 (0.006) loss 0.8438 (1.1117) acc 81.2500 (72.1875) lr 1.1253e-03 eta 5:35:47
+epoch [25/50] batch [155/500] time 1.568 (1.568) data 0.001 (0.006) loss 1.2393 (1.1100) acc 68.7500 (72.1774) lr 1.1253e-03 eta 5:35:40
+epoch [25/50] batch [160/500] time 1.578 (1.568) data 0.000 (0.006) loss 1.2676 (1.1092) acc 56.2500 (72.0703) lr 1.1253e-03 eta 5:35:34
+epoch [25/50] batch [165/500] time 1.553 (1.568) data 0.000 (0.006) loss 1.6250 (1.1177) acc 62.5000 (71.8371) lr 1.1253e-03 eta 5:35:23
+epoch [25/50] batch [170/500] time 1.560 (1.568) data 0.000 (0.006) loss 1.0869 (1.1192) acc 71.8750 (71.8199) lr 1.1253e-03 eta 5:35:14
+epoch [25/50] batch [175/500] time 1.547 (1.568) data 0.000 (0.005) loss 0.5811 (1.1129) acc 84.3750 (71.8750) lr 1.1253e-03 eta 5:35:03
+epoch [25/50] batch [180/500] time 1.566 (1.568) data 0.000 (0.005) loss 0.6968 (1.1135) acc 81.2500 (71.9271) lr 1.1253e-03 eta 5:35:00
+epoch [25/50] batch [185/500] time 1.559 (1.568) data 0.000 (0.005) loss 1.1699 (1.1109) acc 68.7500 (71.9764) lr 1.1253e-03 eta 5:34:53
+epoch [25/50] batch [190/500] time 1.557 (1.568) data 0.000 (0.005) loss 1.6338 (1.1172) acc 75.0000 (71.9079) lr 1.1253e-03 eta 5:34:45
+epoch [25/50] batch [195/500] time 1.567 (1.568) data 0.000 (0.005) loss 1.4346 (1.1196) acc 68.7500 (71.8750) lr 1.1253e-03 eta 5:34:36
+epoch [25/50] batch [200/500] time 1.569 (1.568) data 0.000 (0.005) loss 1.0654 (1.1171) acc 75.0000 (71.9219) lr 1.1253e-03 eta 5:34:27
+epoch [25/50] batch [205/500] time 1.662 (1.568) data 0.000 (0.005) loss 1.8359 (1.1151) acc 65.6250 (72.0579) lr 1.1253e-03 eta 5:34:24
+epoch [25/50] batch [210/500] time 1.616 (1.568) data 0.000 (0.005) loss 1.2861 (1.1179) acc 68.7500 (72.0238) lr 1.1253e-03 eta 5:34:18
+epoch [25/50] batch [215/500] time 1.576 (1.568) data 0.000 (0.005) loss 1.2461 (1.1162) acc 75.0000 (72.1512) lr 1.1253e-03 eta 5:34:09
+epoch [25/50] batch [220/500] time 1.554 (1.568) data 0.000 (0.004) loss 0.7056 (1.1099) acc 84.3750 (72.3722) lr 1.1253e-03 eta 5:33:59
+epoch [25/50] batch [225/500] time 1.552 (1.568) data 0.001 (0.004) loss 1.2842 (1.1094) acc 78.1250 (72.4028) lr 1.1253e-03 eta 5:33:49
+epoch [25/50] batch [230/500] time 1.559 (1.568) data 0.000 (0.004) loss 1.0967 (1.1094) acc 68.7500 (72.3641) lr 1.1253e-03 eta 5:33:38
+epoch [25/50] batch [235/500] time 1.535 (1.567) data 0.000 (0.004) loss 1.6641 (1.1077) acc 62.5000 (72.3670) lr 1.1253e-03 eta 5:33:27
+epoch [25/50] batch [240/500] time 1.547 (1.567) data 0.000 (0.004) loss 1.4053 (1.1053) acc 65.6250 (72.4479) lr 1.1253e-03 eta 5:33:16
+epoch [25/50] batch [245/500] time 1.567 (1.567) data 0.000 (0.004) loss 0.7690 (1.1039) acc 84.3750 (72.4490) lr 1.1253e-03 eta 5:33:08
+epoch [25/50] batch [250/500] time 1.532 (1.567) data 0.000 (0.004) loss 0.7998 (1.1000) acc 78.1250 (72.5000) lr 1.1253e-03 eta 5:33:04
+epoch [25/50] batch [255/500] time 1.557 (1.567) data 0.000 (0.004) loss 0.7871 (1.1008) acc 71.8750 (72.4877) lr 1.1253e-03 eta 5:32:52
+epoch [25/50] batch [260/500] time 1.550 (1.567) data 0.000 (0.004) loss 0.5142 (1.0984) acc 90.6250 (72.5240) lr 1.1253e-03 eta 5:32:42
+epoch [25/50] batch [265/500] time 1.547 (1.567) data 0.000 (0.004) loss 0.6714 (1.0972) acc 81.2500 (72.5236) lr 1.1253e-03 eta 5:32:30
+epoch [25/50] batch [270/500] time 1.553 (1.566) data 0.000 (0.004) loss 0.8218 (1.0967) acc 87.5000 (72.5926) lr 1.1253e-03 eta 5:32:20
+epoch [25/50] batch [275/500] time 1.562 (1.566) data 0.000 (0.004) loss 1.1426 (1.0985) acc 78.1250 (72.5909) lr 1.1253e-03 eta 5:32:10
+epoch [25/50] batch [280/500] time 1.542 (1.566) data 0.000 (0.004) loss 1.1270 (1.0965) acc 65.6250 (72.6562) lr 1.1253e-03 eta 5:32:02
+epoch [25/50] batch [285/500] time 1.556 (1.566) data 0.000 (0.004) loss 0.7373 (1.0946) acc 81.2500 (72.6754) lr 1.1253e-03 eta 5:31:51
+epoch [25/50] batch [290/500] time 1.560 (1.566) data 0.000 (0.003) loss 0.9673 (1.0971) acc 65.6250 (72.5539) lr 1.1253e-03 eta 5:31:42
+epoch [25/50] batch [295/500] time 1.561 (1.566) data 0.000 (0.003) loss 1.6191 (1.1017) acc 53.1250 (72.3835) lr 1.1253e-03 eta 5:31:31
+epoch [25/50] batch [300/500] time 1.548 (1.566) data 0.000 (0.003) loss 1.0381 (1.0988) acc 75.0000 (72.4792) lr 1.1253e-03 eta 5:31:22
+epoch [25/50] batch [305/500] time 1.549 (1.565) data 0.000 (0.003) loss 1.7236 (1.1001) acc 62.5000 (72.4590) lr 1.1253e-03 eta 5:31:10
+epoch [25/50] batch [310/500] time 1.562 (1.565) data 0.000 (0.003) loss 1.0654 (1.0989) acc 75.0000 (72.4395) lr 1.1253e-03 eta 5:30:58
+epoch [25/50] batch [315/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.4463 (1.1008) acc 78.1250 (72.4702) lr 1.1253e-03 eta 5:30:49
+epoch [25/50] batch [320/500] time 1.556 (1.565) data 0.000 (0.003) loss 0.6069 (1.0956) acc 90.6250 (72.6465) lr 1.1253e-03 eta 5:30:40
+epoch [25/50] batch [325/500] time 1.527 (1.565) data 0.000 (0.003) loss 1.1191 (1.0987) acc 71.8750 (72.6058) lr 1.1253e-03 eta 5:30:31
+epoch [25/50] batch [330/500] time 1.578 (1.565) data 0.000 (0.003) loss 1.1318 (1.0966) acc 78.1250 (72.6610) lr 1.1253e-03 eta 5:30:23
+epoch [25/50] batch [335/500] time 1.532 (1.564) data 0.000 (0.003) loss 0.8955 (1.0965) acc 71.8750 (72.6119) lr 1.1253e-03 eta 5:30:12
+epoch [25/50] batch [340/500] time 1.538 (1.564) data 0.000 (0.003) loss 1.3105 (1.0980) acc 78.1250 (72.5735) lr 1.1253e-03 eta 5:30:02
+epoch [25/50] batch [345/500] time 1.553 (1.564) data 0.000 (0.003) loss 0.9717 (1.0975) acc 65.6250 (72.5543) lr 1.1253e-03 eta 5:29:52
+epoch [25/50] batch [350/500] time 1.541 (1.564) data 0.000 (0.003) loss 1.3936 (1.0984) acc 68.7500 (72.5536) lr 1.1253e-03 eta 5:29:47
+epoch [25/50] batch [355/500] time 1.548 (1.564) data 0.000 (0.003) loss 0.8892 (1.0979) acc 75.0000 (72.5616) lr 1.1253e-03 eta 5:29:38
+epoch [25/50] batch [360/500] time 1.571 (1.564) data 0.000 (0.003) loss 1.2832 (1.1015) acc 68.7500 (72.4306) lr 1.1253e-03 eta 5:29:29
+epoch [25/50] batch [365/500] time 1.540 (1.564) data 0.000 (0.003) loss 0.7544 (1.0977) acc 87.5000 (72.5086) lr 1.1253e-03 eta 5:29:19
+epoch [25/50] batch [370/500] time 1.562 (1.564) data 0.000 (0.003) loss 0.4934 (1.0953) acc 84.3750 (72.5253) lr 1.1253e-03 eta 5:29:11
+epoch [25/50] batch [375/500] time 1.589 (1.564) data 0.000 (0.003) loss 1.0508 (1.0942) acc 71.8750 (72.5750) lr 1.1253e-03 eta 5:29:02
+epoch [25/50] batch [380/500] time 1.586 (1.564) data 0.000 (0.003) loss 0.9336 (1.0946) acc 68.7500 (72.5658) lr 1.1253e-03 eta 5:28:56
+epoch [25/50] batch [385/500] time 1.532 (1.564) data 0.000 (0.003) loss 0.9385 (1.0942) acc 75.0000 (72.5325) lr 1.1253e-03 eta 5:28:45
+epoch [25/50] batch [390/500] time 1.568 (1.564) data 0.001 (0.003) loss 1.0635 (1.0973) acc 68.7500 (72.4599) lr 1.1253e-03 eta 5:28:36
+epoch [25/50] batch [395/500] time 1.529 (1.564) data 0.000 (0.003) loss 0.8564 (1.0949) acc 78.1250 (72.5158) lr 1.1253e-03 eta 5:28:29
+epoch [25/50] batch [400/500] time 1.560 (1.564) data 0.000 (0.003) loss 1.5420 (1.0938) acc 71.8750 (72.5859) lr 1.1253e-03 eta 5:28:22
+epoch [25/50] batch [405/500] time 1.576 (1.564) data 0.000 (0.003) loss 0.8716 (1.0928) acc 78.1250 (72.5926) lr 1.1253e-03 eta 5:28:14
+epoch [25/50] batch [410/500] time 1.564 (1.564) data 0.000 (0.003) loss 1.1572 (1.0942) acc 68.7500 (72.5762) lr 1.1253e-03 eta 5:28:07
+epoch [25/50] batch [415/500] time 1.542 (1.564) data 0.000 (0.003) loss 1.2881 (1.0940) acc 71.8750 (72.5828) lr 1.1253e-03 eta 5:28:00
+epoch [25/50] batch [420/500] time 1.577 (1.564) data 0.000 (0.003) loss 0.9233 (1.0943) acc 71.8750 (72.5670) lr 1.1253e-03 eta 5:27:51
+epoch [25/50] batch [425/500] time 1.544 (1.563) data 0.001 (0.002) loss 1.5449 (1.0958) acc 62.5000 (72.5441) lr 1.1253e-03 eta 5:27:40
+epoch [25/50] batch [430/500] time 1.572 (1.563) data 0.000 (0.002) loss 1.3105 (1.0943) acc 71.8750 (72.6017) lr 1.1253e-03 eta 5:27:32
+epoch [25/50] batch [435/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.9155 (1.0955) acc 68.7500 (72.5503) lr 1.1253e-03 eta 5:27:24
+epoch [25/50] batch [440/500] time 1.589 (1.563) data 0.000 (0.002) loss 1.4893 (1.0934) acc 53.1250 (72.5639) lr 1.1253e-03 eta 5:27:16
+epoch [25/50] batch [445/500] time 1.573 (1.563) data 0.000 (0.002) loss 1.2754 (1.0934) acc 71.8750 (72.5492) lr 1.1253e-03 eta 5:27:09
+epoch [25/50] batch [450/500] time 1.542 (1.563) data 0.000 (0.002) loss 0.8560 (1.0941) acc 78.1250 (72.5347) lr 1.1253e-03 eta 5:26:59
+epoch [25/50] batch [455/500] time 1.572 (1.563) data 0.000 (0.002) loss 0.8901 (1.0951) acc 87.5000 (72.5755) lr 1.1253e-03 eta 5:26:51
+epoch [25/50] batch [460/500] time 1.577 (1.563) data 0.000 (0.002) loss 0.5586 (1.0959) acc 87.5000 (72.5883) lr 1.1253e-03 eta 5:26:43
+epoch [25/50] batch [465/500] time 1.556 (1.563) data 0.001 (0.002) loss 1.1406 (1.0972) acc 71.8750 (72.5941) lr 1.1253e-03 eta 5:26:35
+epoch [25/50] batch [470/500] time 1.572 (1.563) data 0.000 (0.002) loss 1.0215 (1.0966) acc 78.1250 (72.6064) lr 1.1253e-03 eta 5:26:26
+epoch [25/50] batch [475/500] time 1.547 (1.563) data 0.000 (0.002) loss 1.9531 (1.0978) acc 62.5000 (72.5789) lr 1.1253e-03 eta 5:26:18
+epoch [25/50] batch [480/500] time 1.561 (1.563) data 0.000 (0.002) loss 1.0020 (1.0957) acc 75.0000 (72.6107) lr 1.1253e-03 eta 5:26:08
+epoch [25/50] batch [485/500] time 1.550 (1.563) data 0.001 (0.002) loss 0.6602 (1.0950) acc 75.0000 (72.6353) lr 1.1253e-03 eta 5:25:59
+epoch [25/50] batch [490/500] time 1.570 (1.563) data 0.000 (0.002) loss 1.4121 (1.0940) acc 71.8750 (72.6786) lr 1.1253e-03 eta 5:25:52
+epoch [25/50] batch [495/500] time 1.547 (1.563) data 0.000 (0.002) loss 1.2188 (1.0951) acc 78.1250 (72.6705) lr 1.1253e-03 eta 5:25:46
+epoch [25/50] batch [500/500] time 1.550 (1.563) data 0.000 (0.002) loss 1.0244 (1.0960) acc 71.8750 (72.6562) lr 1.0628e-03 eta 5:25:36
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,964
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+epoch [26/50] batch [5/500] time 1.549 (1.653) data 0.000 (0.153) loss 1.3701 (0.9959) acc 75.0000 (75.6250) lr 1.0628e-03 eta 5:44:13
+epoch [26/50] batch [10/500] time 1.572 (1.608) data 0.000 (0.077) loss 0.9077 (1.0585) acc 81.2500 (74.0625) lr 1.0628e-03 eta 5:34:43
+epoch [26/50] batch [15/500] time 1.538 (1.588) data 0.000 (0.051) loss 1.1318 (1.1010) acc 75.0000 (72.9167) lr 1.0628e-03 eta 5:30:29
+epoch [26/50] batch [20/500] time 1.548 (1.580) data 0.000 (0.039) loss 1.3789 (1.1268) acc 71.8750 (72.5000) lr 1.0628e-03 eta 5:28:44
+epoch [26/50] batch [25/500] time 1.557 (1.576) data 0.000 (0.031) loss 2.1582 (1.1294) acc 62.5000 (73.1250) lr 1.0628e-03 eta 5:27:41
+epoch [26/50] batch [30/500] time 1.556 (1.574) data 0.000 (0.026) loss 1.0947 (1.1174) acc 75.0000 (72.6042) lr 1.0628e-03 eta 5:27:11
+epoch [26/50] batch [35/500] time 1.568 (1.574) data 0.001 (0.022) loss 1.5146 (1.1124) acc 65.6250 (72.5893) lr 1.0628e-03 eta 5:26:55
+epoch [26/50] batch [40/500] time 1.533 (1.575) data 0.001 (0.020) loss 1.6357 (1.1326) acc 65.6250 (72.0312) lr 1.0628e-03 eta 5:26:59
+epoch [26/50] batch [45/500] time 1.556 (1.573) data 0.001 (0.018) loss 1.2002 (1.1271) acc 65.6250 (71.8056) lr 1.0628e-03 eta 5:26:35
+epoch [26/50] batch [50/500] time 1.567 (1.573) data 0.000 (0.016) loss 0.4143 (1.1057) acc 87.5000 (72.4375) lr 1.0628e-03 eta 5:26:24
+epoch [26/50] batch [55/500] time 1.569 (1.573) data 0.000 (0.014) loss 1.4277 (1.1251) acc 65.6250 (72.1591) lr 1.0628e-03 eta 5:26:10
+epoch [26/50] batch [60/500] time 1.558 (1.571) data 0.001 (0.013) loss 1.0283 (1.1172) acc 78.1250 (72.3438) lr 1.0628e-03 eta 5:25:48
+epoch [26/50] batch [65/500] time 1.578 (1.571) data 0.000 (0.012) loss 0.5723 (1.1105) acc 90.6250 (72.5481) lr 1.0628e-03 eta 5:25:32
+epoch [26/50] batch [70/500] time 1.536 (1.570) data 0.000 (0.011) loss 1.1338 (1.1216) acc 75.0000 (72.6339) lr 1.0628e-03 eta 5:25:12
+epoch [26/50] batch [75/500] time 1.552 (1.569) data 0.001 (0.011) loss 1.1182 (1.0982) acc 75.0000 (73.1667) lr 1.0628e-03 eta 5:24:58
+epoch [26/50] batch [80/500] time 1.571 (1.570) data 0.000 (0.010) loss 1.0010 (1.0987) acc 78.1250 (73.1250) lr 1.0628e-03 eta 5:25:02
+epoch [26/50] batch [85/500] time 1.556 (1.569) data 0.000 (0.009) loss 1.3457 (1.1040) acc 62.5000 (72.9412) lr 1.0628e-03 eta 5:24:42
+epoch [26/50] batch [90/500] time 1.527 (1.568) data 0.000 (0.009) loss 1.3525 (1.1057) acc 75.0000 (73.0556) lr 1.0628e-03 eta 5:24:19
+epoch [26/50] batch [95/500] time 1.546 (1.568) data 0.000 (0.009) loss 0.9971 (1.1002) acc 65.6250 (73.1908) lr 1.0628e-03 eta 5:24:07
+epoch [26/50] batch [100/500] time 1.591 (1.568) data 0.001 (0.008) loss 1.7256 (1.1002) acc 65.6250 (73.3125) lr 1.0628e-03 eta 5:23:59
+epoch [26/50] batch [105/500] time 1.556 (1.568) data 0.000 (0.008) loss 1.8398 (1.1129) acc 65.6250 (73.1548) lr 1.0628e-03 eta 5:23:53
+epoch [26/50] batch [110/500] time 1.571 (1.567) data 0.000 (0.007) loss 1.4961 (1.1269) acc 68.7500 (72.8409) lr 1.0628e-03 eta 5:23:37
+epoch [26/50] batch [115/500] time 1.539 (1.567) data 0.001 (0.007) loss 0.8740 (1.1201) acc 78.1250 (72.9348) lr 1.0628e-03 eta 5:23:21
+epoch [26/50] batch [120/500] time 1.558 (1.566) data 0.000 (0.007) loss 0.8540 (1.1103) acc 75.0000 (72.9948) lr 1.0628e-03 eta 5:23:08
+epoch [26/50] batch [125/500] time 1.548 (1.566) data 0.000 (0.007) loss 1.0967 (1.1159) acc 68.7500 (72.7750) lr 1.0628e-03 eta 5:22:58
+epoch [26/50] batch [130/500] time 1.565 (1.566) data 0.000 (0.006) loss 0.6572 (1.1064) acc 81.2500 (72.9087) lr 1.0628e-03 eta 5:22:49
+epoch [26/50] batch [135/500] time 1.584 (1.566) data 0.000 (0.006) loss 1.0117 (1.1094) acc 81.2500 (72.9630) lr 1.0628e-03 eta 5:22:39
+epoch [26/50] batch [140/500] time 1.557 (1.566) data 0.001 (0.006) loss 1.0010 (1.1156) acc 65.6250 (72.7232) lr 1.0628e-03 eta 5:22:40
+epoch [26/50] batch [145/500] time 1.553 (1.566) data 0.001 (0.006) loss 1.3086 (1.1217) acc 68.7500 (72.5431) lr 1.0628e-03 eta 5:22:29
+epoch [26/50] batch [150/500] time 1.548 (1.566) data 0.001 (0.006) loss 1.0674 (1.1142) acc 78.1250 (72.7292) lr 1.0628e-03 eta 5:22:20
+epoch [26/50] batch [155/500] time 1.548 (1.565) data 0.000 (0.005) loss 0.9580 (1.1158) acc 87.5000 (72.7419) lr 1.0628e-03 eta 5:22:05
+epoch [26/50] batch [160/500] time 1.555 (1.565) data 0.000 (0.005) loss 0.6685 (1.1097) acc 84.3750 (72.9297) lr 1.0628e-03 eta 5:21:52
+epoch [26/50] batch [165/500] time 1.558 (1.565) data 0.000 (0.005) loss 0.9746 (1.1088) acc 78.1250 (72.8788) lr 1.0628e-03 eta 5:21:40
+epoch [26/50] batch [170/500] time 1.542 (1.564) data 0.001 (0.005) loss 1.4688 (1.1103) acc 62.5000 (72.8125) lr 1.0628e-03 eta 5:21:27
+epoch [26/50] batch [175/500] time 1.547 (1.564) data 0.001 (0.005) loss 1.5928 (1.1095) acc 56.2500 (72.7321) lr 1.0628e-03 eta 5:21:16
+epoch [26/50] batch [180/500] time 1.569 (1.564) data 0.001 (0.005) loss 1.2178 (1.1095) acc 65.6250 (72.7778) lr 1.0628e-03 eta 5:21:06
+epoch [26/50] batch [185/500] time 1.564 (1.564) data 0.000 (0.005) loss 0.9980 (1.1088) acc 81.2500 (72.8547) lr 1.0628e-03 eta 5:21:02
+epoch [26/50] batch [190/500] time 1.574 (1.564) data 0.000 (0.005) loss 1.0615 (1.1012) acc 78.1250 (73.0099) lr 1.0628e-03 eta 5:20:51
+epoch [26/50] batch [195/500] time 1.566 (1.564) data 0.001 (0.004) loss 1.1895 (1.0998) acc 65.6250 (72.9647) lr 1.0628e-03 eta 5:20:43
+epoch [26/50] batch [200/500] time 1.564 (1.564) data 0.000 (0.004) loss 1.0371 (1.0983) acc 75.0000 (72.9688) lr 1.0628e-03 eta 5:20:36
+epoch [26/50] batch [205/500] time 1.537 (1.564) data 0.000 (0.004) loss 1.5068 (1.1024) acc 71.8750 (72.9116) lr 1.0628e-03 eta 5:20:27
+epoch [26/50] batch [210/500] time 1.565 (1.564) data 0.001 (0.004) loss 1.8047 (1.1052) acc 62.5000 (72.9464) lr 1.0628e-03 eta 5:20:18
+epoch [26/50] batch [215/500] time 1.555 (1.563) data 0.001 (0.004) loss 1.5049 (1.1077) acc 75.0000 (72.9651) lr 1.0628e-03 eta 5:20:06
+epoch [26/50] batch [220/500] time 1.546 (1.563) data 0.000 (0.004) loss 0.8472 (1.1088) acc 65.6250 (72.8693) lr 1.0628e-03 eta 5:19:57
+epoch [26/50] batch [225/500] time 1.565 (1.563) data 0.000 (0.004) loss 0.7358 (1.1098) acc 75.0000 (72.7639) lr 1.0628e-03 eta 5:19:49
+epoch [26/50] batch [230/500] time 1.561 (1.563) data 0.000 (0.004) loss 0.4685 (1.1105) acc 93.7500 (72.7446) lr 1.0628e-03 eta 5:19:40
+epoch [26/50] batch [235/500] time 1.543 (1.563) data 0.001 (0.004) loss 1.1367 (1.1057) acc 71.8750 (72.9122) lr 1.0628e-03 eta 5:19:28
+epoch [26/50] batch [240/500] time 1.553 (1.563) data 0.001 (0.004) loss 0.8716 (1.1038) acc 81.2500 (72.9818) lr 1.0628e-03 eta 5:19:19
+epoch [26/50] batch [245/500] time 1.567 (1.562) data 0.001 (0.004) loss 1.0547 (1.0992) acc 81.2500 (73.1378) lr 1.0628e-03 eta 5:19:08
+epoch [26/50] batch [250/500] time 1.556 (1.562) data 0.000 (0.004) loss 0.9502 (1.1002) acc 84.3750 (73.2625) lr 1.0628e-03 eta 5:18:59
+epoch [26/50] batch [255/500] time 1.556 (1.562) data 0.001 (0.003) loss 1.1797 (1.0998) acc 65.6250 (73.1863) lr 1.0628e-03 eta 5:18:49
+epoch [26/50] batch [260/500] time 1.587 (1.562) data 0.000 (0.003) loss 0.9487 (1.1007) acc 78.1250 (73.1851) lr 1.0628e-03 eta 5:18:40
+epoch [26/50] batch [265/500] time 1.574 (1.562) data 0.001 (0.003) loss 1.0576 (1.0988) acc 75.0000 (73.2429) lr 1.0628e-03 eta 5:18:34
+epoch [26/50] batch [270/500] time 1.579 (1.562) data 0.000 (0.003) loss 1.3535 (1.1005) acc 68.7500 (73.2060) lr 1.0628e-03 eta 5:18:27
+epoch [26/50] batch [275/500] time 1.574 (1.563) data 0.000 (0.003) loss 1.3916 (1.0990) acc 62.5000 (73.1705) lr 1.0628e-03 eta 5:18:21
+epoch [26/50] batch [280/500] time 1.641 (1.563) data 0.000 (0.003) loss 0.9424 (1.0951) acc 68.7500 (73.2254) lr 1.0628e-03 eta 5:18:17
+epoch [26/50] batch [285/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.1914 (1.0936) acc 75.0000 (73.2456) lr 1.0628e-03 eta 5:18:09
+epoch [26/50] batch [290/500] time 1.575 (1.563) data 0.000 (0.003) loss 1.2256 (1.0914) acc 68.7500 (73.3190) lr 1.0628e-03 eta 5:18:02
+epoch [26/50] batch [295/500] time 1.567 (1.563) data 0.000 (0.003) loss 1.1084 (1.0880) acc 65.6250 (73.3369) lr 1.0628e-03 eta 5:17:53
+epoch [26/50] batch [300/500] time 1.559 (1.563) data 0.000 (0.003) loss 1.3623 (1.0880) acc 71.8750 (73.3646) lr 1.0628e-03 eta 5:17:43
+epoch [26/50] batch [305/500] time 1.554 (1.563) data 0.000 (0.003) loss 1.1143 (1.0868) acc 78.1250 (73.4631) lr 1.0628e-03 eta 5:17:37
+epoch [26/50] batch [310/500] time 1.568 (1.563) data 0.000 (0.003) loss 1.5439 (1.0897) acc 75.0000 (73.4879) lr 1.0628e-03 eta 5:17:28
+epoch [26/50] batch [315/500] time 1.566 (1.563) data 0.001 (0.003) loss 0.9438 (1.0835) acc 75.0000 (73.6012) lr 1.0628e-03 eta 5:17:20
+epoch [26/50] batch [320/500] time 1.584 (1.563) data 0.000 (0.003) loss 1.1387 (1.0896) acc 81.2500 (73.5156) lr 1.0628e-03 eta 5:17:13
+epoch [26/50] batch [325/500] time 1.543 (1.563) data 0.000 (0.003) loss 1.0234 (1.0921) acc 71.8750 (73.4615) lr 1.0628e-03 eta 5:17:07
+epoch [26/50] batch [330/500] time 1.560 (1.563) data 0.001 (0.003) loss 1.1396 (1.0925) acc 68.7500 (73.4564) lr 1.0628e-03 eta 5:16:58
+epoch [26/50] batch [335/500] time 1.558 (1.563) data 0.000 (0.003) loss 0.9204 (1.0922) acc 81.2500 (73.4515) lr 1.0628e-03 eta 5:16:50
+epoch [26/50] batch [340/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.2207 (1.0905) acc 68.7500 (73.4926) lr 1.0628e-03 eta 5:16:41
+epoch [26/50] batch [345/500] time 1.537 (1.562) data 0.000 (0.003) loss 1.2197 (1.0887) acc 71.8750 (73.5145) lr 1.0628e-03 eta 5:16:32
+epoch [26/50] batch [350/500] time 1.555 (1.562) data 0.000 (0.003) loss 0.9106 (1.0926) acc 75.0000 (73.3839) lr 1.0628e-03 eta 5:16:23
+epoch [26/50] batch [355/500] time 1.539 (1.562) data 0.000 (0.003) loss 0.9336 (1.0937) acc 71.8750 (73.3451) lr 1.0628e-03 eta 5:16:11
+epoch [26/50] batch [360/500] time 1.552 (1.562) data 0.000 (0.003) loss 0.8413 (1.0900) acc 71.8750 (73.3854) lr 1.0628e-03 eta 5:16:02
+epoch [26/50] batch [365/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.3672 (1.0918) acc 71.8750 (73.3904) lr 1.0628e-03 eta 5:15:53
+epoch [26/50] batch [370/500] time 1.540 (1.562) data 0.000 (0.003) loss 0.8906 (1.0911) acc 75.0000 (73.4037) lr 1.0628e-03 eta 5:15:44
+epoch [26/50] batch [375/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.2910 (1.0921) acc 71.8750 (73.3833) lr 1.0628e-03 eta 5:15:34
+epoch [26/50] batch [380/500] time 1.558 (1.562) data 0.000 (0.002) loss 0.9106 (1.0916) acc 81.2500 (73.3799) lr 1.0628e-03 eta 5:15:27
+epoch [26/50] batch [385/500] time 1.577 (1.562) data 0.001 (0.002) loss 0.9512 (1.0913) acc 75.0000 (73.3766) lr 1.0628e-03 eta 5:15:18
+epoch [26/50] batch [390/500] time 1.573 (1.562) data 0.000 (0.002) loss 0.7480 (1.0906) acc 84.3750 (73.4295) lr 1.0628e-03 eta 5:15:11
+epoch [26/50] batch [395/500] time 1.545 (1.562) data 0.001 (0.002) loss 1.1055 (1.0921) acc 65.6250 (73.4256) lr 1.0628e-03 eta 5:15:02
+epoch [26/50] batch [400/500] time 1.554 (1.561) data 0.000 (0.002) loss 1.0361 (1.0930) acc 75.0000 (73.3672) lr 1.0628e-03 eta 5:14:53
+epoch [26/50] batch [405/500] time 1.547 (1.561) data 0.000 (0.002) loss 0.9609 (1.0931) acc 78.1250 (73.3488) lr 1.0628e-03 eta 5:14:44
+epoch [26/50] batch [410/500] time 1.542 (1.561) data 0.000 (0.002) loss 1.1934 (1.0910) acc 65.6250 (73.3765) lr 1.0628e-03 eta 5:14:36
+epoch [26/50] batch [415/500] time 1.543 (1.561) data 0.000 (0.002) loss 1.0732 (1.0922) acc 68.7500 (73.3509) lr 1.0628e-03 eta 5:14:26
+epoch [26/50] batch [420/500] time 1.545 (1.561) data 0.000 (0.002) loss 1.2822 (1.0945) acc 68.7500 (73.2292) lr 1.0628e-03 eta 5:14:17
+epoch [26/50] batch [425/500] time 1.573 (1.561) data 0.000 (0.002) loss 1.0078 (1.0931) acc 81.2500 (73.2279) lr 1.0628e-03 eta 5:14:11
+epoch [26/50] batch [430/500] time 1.543 (1.561) data 0.000 (0.002) loss 0.7070 (1.0914) acc 84.3750 (73.2776) lr 1.0628e-03 eta 5:14:01
+epoch [26/50] batch [435/500] time 1.571 (1.561) data 0.000 (0.002) loss 1.1348 (1.0929) acc 81.2500 (73.2543) lr 1.0628e-03 eta 5:13:52
+epoch [26/50] batch [440/500] time 1.572 (1.561) data 0.000 (0.002) loss 0.8853 (1.0933) acc 75.0000 (73.2031) lr 1.0628e-03 eta 5:13:44
+epoch [26/50] batch [445/500] time 1.555 (1.561) data 0.000 (0.002) loss 0.6899 (1.0912) acc 84.3750 (73.2163) lr 1.0628e-03 eta 5:13:35
+epoch [26/50] batch [450/500] time 1.563 (1.561) data 0.001 (0.002) loss 1.0166 (1.0925) acc 71.8750 (73.1458) lr 1.0628e-03 eta 5:13:29
+epoch [26/50] batch [455/500] time 1.558 (1.561) data 0.000 (0.002) loss 0.9355 (1.0928) acc 71.8750 (73.1319) lr 1.0628e-03 eta 5:13:19
+epoch [26/50] batch [460/500] time 1.539 (1.561) data 0.001 (0.002) loss 1.1533 (1.0943) acc 65.6250 (73.1046) lr 1.0628e-03 eta 5:13:10
+epoch [26/50] batch [465/500] time 1.556 (1.560) data 0.000 (0.002) loss 1.4102 (1.0944) acc 65.6250 (73.0645) lr 1.0628e-03 eta 5:13:00
+epoch [26/50] batch [470/500] time 1.538 (1.561) data 0.000 (0.002) loss 1.7607 (1.0954) acc 62.5000 (73.0319) lr 1.0628e-03 eta 5:12:53
+epoch [26/50] batch [475/500] time 1.531 (1.560) data 0.000 (0.002) loss 0.6455 (1.0946) acc 84.3750 (73.0592) lr 1.0628e-03 eta 5:12:43
+epoch [26/50] batch [480/500] time 1.559 (1.560) data 0.000 (0.002) loss 1.0361 (1.0931) acc 71.8750 (73.0794) lr 1.0628e-03 eta 5:12:35
+epoch [26/50] batch [485/500] time 1.544 (1.560) data 0.001 (0.002) loss 1.1562 (1.0941) acc 78.1250 (73.0477) lr 1.0628e-03 eta 5:12:26
+epoch [26/50] batch [490/500] time 1.526 (1.560) data 0.000 (0.002) loss 1.3232 (1.0943) acc 65.6250 (73.0357) lr 1.0628e-03 eta 5:12:16
+epoch [26/50] batch [495/500] time 1.550 (1.560) data 0.000 (0.002) loss 1.8838 (1.0958) acc 59.3750 (72.9861) lr 1.0628e-03 eta 5:12:07
+epoch [26/50] batch [500/500] time 1.539 (1.560) data 0.000 (0.002) loss 0.6860 (1.0946) acc 78.1250 (73.0062) lr 1.0000e-03 eta 5:11:59
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,042
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [27/50] batch [5/500] time 1.564 (1.651) data 0.000 (0.148) loss 1.0791 (1.1414) acc 68.7500 (68.7500) lr 1.0000e-03 eta 5:30:05
+epoch [27/50] batch [10/500] time 1.570 (1.608) data 0.000 (0.074) loss 0.7207 (1.0937) acc 87.5000 (71.5625) lr 1.0000e-03 eta 5:21:21
+epoch [27/50] batch [15/500] time 1.553 (1.590) data 0.000 (0.050) loss 1.0498 (1.1743) acc 71.8750 (70.8333) lr 1.0000e-03 eta 5:17:32
+epoch [27/50] batch [20/500] time 1.548 (1.583) data 0.001 (0.037) loss 1.1299 (1.1269) acc 75.0000 (72.0312) lr 1.0000e-03 eta 5:16:03
+epoch [27/50] batch [25/500] time 1.710 (1.585) data 0.000 (0.030) loss 1.0566 (1.1730) acc 71.8750 (71.2500) lr 1.0000e-03 eta 5:16:18
+epoch [27/50] batch [30/500] time 1.551 (1.582) data 0.001 (0.025) loss 1.3955 (1.1415) acc 68.7500 (72.6042) lr 1.0000e-03 eta 5:15:35
+epoch [27/50] batch [35/500] time 1.585 (1.578) data 0.001 (0.022) loss 1.5830 (1.1942) acc 56.2500 (71.9643) lr 1.0000e-03 eta 5:14:35
+epoch [27/50] batch [40/500] time 1.569 (1.576) data 0.000 (0.019) loss 1.3379 (1.1788) acc 68.7500 (71.8750) lr 1.0000e-03 eta 5:14:04
+epoch [27/50] batch [45/500] time 1.523 (1.572) data 0.001 (0.017) loss 1.4990 (1.1833) acc 71.8750 (71.6667) lr 1.0000e-03 eta 5:13:17
+epoch [27/50] batch [50/500] time 1.558 (1.570) data 0.000 (0.015) loss 0.6191 (1.1708) acc 81.2500 (71.3125) lr 1.0000e-03 eta 5:12:35
+epoch [27/50] batch [55/500] time 1.563 (1.569) data 0.000 (0.014) loss 1.0332 (1.1554) acc 71.8750 (71.3068) lr 1.0000e-03 eta 5:12:17
+epoch [27/50] batch [60/500] time 1.555 (1.567) data 0.000 (0.013) loss 0.9819 (1.1535) acc 65.6250 (71.0417) lr 1.0000e-03 eta 5:11:53
+epoch [27/50] batch [65/500] time 1.551 (1.567) data 0.000 (0.012) loss 1.4248 (1.1520) acc 59.3750 (70.7212) lr 1.0000e-03 eta 5:11:41
+epoch [27/50] batch [70/500] time 1.552 (1.566) data 0.000 (0.011) loss 0.9473 (1.1460) acc 75.0000 (70.7143) lr 1.0000e-03 eta 5:11:26
+epoch [27/50] batch [75/500] time 1.525 (1.565) data 0.000 (0.010) loss 0.9741 (1.1313) acc 65.6250 (70.7917) lr 1.0000e-03 eta 5:11:03
+epoch [27/50] batch [80/500] time 1.542 (1.564) data 0.000 (0.010) loss 0.7505 (1.1245) acc 78.1250 (70.7812) lr 1.0000e-03 eta 5:10:46
+epoch [27/50] batch [85/500] time 1.662 (1.564) data 0.001 (0.009) loss 1.0215 (1.1229) acc 65.6250 (70.5882) lr 1.0000e-03 eta 5:10:40
+epoch [27/50] batch [90/500] time 1.559 (1.564) data 0.000 (0.009) loss 0.8540 (1.1281) acc 78.1250 (70.6250) lr 1.0000e-03 eta 5:10:30
+epoch [27/50] batch [95/500] time 1.527 (1.564) data 0.001 (0.008) loss 0.6553 (1.1178) acc 84.3750 (70.8224) lr 1.0000e-03 eta 5:10:18
+epoch [27/50] batch [100/500] time 1.570 (1.564) data 0.000 (0.008) loss 0.7451 (1.1134) acc 71.8750 (70.8125) lr 1.0000e-03 eta 5:10:14
+epoch [27/50] batch [105/500] time 1.564 (1.564) data 0.000 (0.007) loss 0.9482 (1.1107) acc 78.1250 (71.0714) lr 1.0000e-03 eta 5:10:06
+epoch [27/50] batch [110/500] time 1.565 (1.565) data 0.000 (0.007) loss 1.1475 (1.1040) acc 71.8750 (71.0227) lr 1.0000e-03 eta 5:10:03
+epoch [27/50] batch [115/500] time 1.548 (1.564) data 0.000 (0.007) loss 1.1875 (1.1020) acc 68.7500 (71.1413) lr 1.0000e-03 eta 5:09:49
+epoch [27/50] batch [120/500] time 1.582 (1.564) data 0.000 (0.007) loss 1.0361 (1.0992) acc 75.0000 (71.2240) lr 1.0000e-03 eta 5:09:40
+epoch [27/50] batch [125/500] time 1.565 (1.564) data 0.001 (0.006) loss 0.9761 (1.1006) acc 78.1250 (71.2250) lr 1.0000e-03 eta 5:09:38
+epoch [27/50] batch [130/500] time 1.572 (1.565) data 0.001 (0.006) loss 0.8975 (1.1075) acc 84.3750 (71.3702) lr 1.0000e-03 eta 5:09:40
+epoch [27/50] batch [135/500] time 1.574 (1.565) data 0.000 (0.006) loss 0.8052 (1.1081) acc 75.0000 (71.3657) lr 1.0000e-03 eta 5:09:32
+epoch [27/50] batch [140/500] time 1.575 (1.565) data 0.000 (0.006) loss 0.8560 (1.1066) acc 84.3750 (71.4062) lr 1.0000e-03 eta 5:09:20
+epoch [27/50] batch [145/500] time 1.559 (1.565) data 0.001 (0.006) loss 1.3701 (1.1110) acc 68.7500 (71.4224) lr 1.0000e-03 eta 5:09:07
+epoch [27/50] batch [150/500] time 1.580 (1.565) data 0.001 (0.005) loss 1.4326 (1.1122) acc 62.5000 (71.3333) lr 1.0000e-03 eta 5:09:03
+epoch [27/50] batch [155/500] time 1.544 (1.565) data 0.000 (0.005) loss 1.4590 (1.1195) acc 59.3750 (71.1895) lr 1.0000e-03 eta 5:08:52
+epoch [27/50] batch [160/500] time 1.555 (1.565) data 0.001 (0.005) loss 0.9565 (1.1178) acc 78.1250 (71.3086) lr 1.0000e-03 eta 5:08:43
+epoch [27/50] batch [165/500] time 1.577 (1.564) data 0.000 (0.005) loss 1.0781 (1.1136) acc 68.7500 (71.4205) lr 1.0000e-03 eta 5:08:33
+epoch [27/50] batch [170/500] time 1.567 (1.564) data 0.000 (0.005) loss 0.4539 (1.1062) acc 84.3750 (71.5993) lr 1.0000e-03 eta 5:08:24
+epoch [27/50] batch [175/500] time 1.548 (1.564) data 0.000 (0.005) loss 1.2852 (1.1041) acc 78.1250 (71.7857) lr 1.0000e-03 eta 5:08:18
+epoch [27/50] batch [180/500] time 1.606 (1.565) data 0.000 (0.005) loss 1.1270 (1.1093) acc 71.8750 (71.6493) lr 1.0000e-03 eta 5:08:13
+epoch [27/50] batch [185/500] time 1.558 (1.565) data 0.000 (0.004) loss 1.3525 (1.1046) acc 78.1250 (71.9088) lr 1.0000e-03 eta 5:08:10
+epoch [27/50] batch [190/500] time 1.561 (1.565) data 0.000 (0.004) loss 1.3330 (1.1033) acc 71.8750 (71.8586) lr 1.0000e-03 eta 5:08:03
+epoch [27/50] batch [195/500] time 1.571 (1.565) data 0.000 (0.004) loss 0.8633 (1.1052) acc 81.2500 (71.8910) lr 1.0000e-03 eta 5:07:53
+epoch [27/50] batch [200/500] time 1.547 (1.565) data 0.000 (0.004) loss 1.4062 (1.1114) acc 62.5000 (71.8125) lr 1.0000e-03 eta 5:07:44
+epoch [27/50] batch [205/500] time 1.573 (1.565) data 0.000 (0.004) loss 0.4807 (1.1112) acc 87.5000 (71.8293) lr 1.0000e-03 eta 5:07:34
+epoch [27/50] batch [210/500] time 1.580 (1.564) data 0.000 (0.004) loss 1.0996 (1.1168) acc 78.1250 (71.8006) lr 1.0000e-03 eta 5:07:24
+epoch [27/50] batch [215/500] time 1.580 (1.564) data 0.000 (0.004) loss 1.0430 (1.1111) acc 81.2500 (71.8750) lr 1.0000e-03 eta 5:07:16
+epoch [27/50] batch [220/500] time 1.552 (1.564) data 0.000 (0.004) loss 1.5293 (1.1143) acc 53.1250 (71.8040) lr 1.0000e-03 eta 5:07:05
+epoch [27/50] batch [225/500] time 1.544 (1.564) data 0.000 (0.004) loss 1.6230 (1.1206) acc 62.5000 (71.7361) lr 1.0000e-03 eta 5:06:55
+epoch [27/50] batch [230/500] time 1.551 (1.564) data 0.001 (0.004) loss 0.7124 (1.1141) acc 81.2500 (71.8478) lr 1.0000e-03 eta 5:06:50
+epoch [27/50] batch [235/500] time 1.588 (1.564) data 0.001 (0.004) loss 0.6279 (1.1119) acc 84.3750 (71.9415) lr 1.0000e-03 eta 5:06:43
+epoch [27/50] batch [240/500] time 1.559 (1.564) data 0.000 (0.004) loss 1.5312 (1.1157) acc 62.5000 (71.8750) lr 1.0000e-03 eta 5:06:34
+epoch [27/50] batch [245/500] time 1.574 (1.564) data 0.000 (0.003) loss 0.8374 (1.1163) acc 81.2500 (71.7857) lr 1.0000e-03 eta 5:06:23
+epoch [27/50] batch [250/500] time 1.535 (1.564) data 0.000 (0.003) loss 0.3015 (1.1100) acc 90.6250 (71.9000) lr 1.0000e-03 eta 5:06:14
+epoch [27/50] batch [255/500] time 1.569 (1.564) data 0.000 (0.003) loss 0.8013 (1.1042) acc 68.7500 (71.9853) lr 1.0000e-03 eta 5:06:04
+epoch [27/50] batch [260/500] time 1.540 (1.563) data 0.000 (0.003) loss 1.4766 (1.1068) acc 75.0000 (72.0433) lr 1.0000e-03 eta 5:05:55
+epoch [27/50] batch [265/500] time 1.541 (1.563) data 0.000 (0.003) loss 0.7612 (1.1046) acc 81.2500 (72.1462) lr 1.0000e-03 eta 5:05:47
+epoch [27/50] batch [270/500] time 1.523 (1.563) data 0.000 (0.003) loss 0.6729 (1.1021) acc 84.3750 (72.1875) lr 1.0000e-03 eta 5:05:37
+epoch [27/50] batch [275/500] time 1.567 (1.564) data 0.000 (0.003) loss 1.5820 (1.1018) acc 65.6250 (72.2045) lr 1.0000e-03 eta 5:05:32
+epoch [27/50] batch [280/500] time 1.536 (1.563) data 0.000 (0.003) loss 1.0020 (1.1053) acc 78.1250 (72.1540) lr 1.0000e-03 eta 5:05:22
+epoch [27/50] batch [285/500] time 1.530 (1.563) data 0.000 (0.003) loss 0.8218 (1.1057) acc 68.7500 (72.1711) lr 1.0000e-03 eta 5:05:12
+epoch [27/50] batch [290/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.1650 (1.1056) acc 62.5000 (72.1659) lr 1.0000e-03 eta 5:05:03
+epoch [27/50] batch [295/500] time 1.592 (1.563) data 0.000 (0.003) loss 0.8511 (1.1062) acc 81.2500 (72.1292) lr 1.0000e-03 eta 5:04:57
+epoch [27/50] batch [300/500] time 1.565 (1.563) data 0.000 (0.003) loss 0.5869 (1.1040) acc 87.5000 (72.2500) lr 1.0000e-03 eta 5:04:48
+epoch [27/50] batch [305/500] time 1.571 (1.563) data 0.001 (0.003) loss 0.6025 (1.1025) acc 84.3750 (72.2439) lr 1.0000e-03 eta 5:04:40
+epoch [27/50] batch [310/500] time 1.549 (1.563) data 0.000 (0.003) loss 2.0762 (1.1054) acc 62.5000 (72.1976) lr 1.0000e-03 eta 5:04:34
+epoch [27/50] batch [315/500] time 1.556 (1.563) data 0.000 (0.003) loss 1.1621 (1.1035) acc 81.2500 (72.2619) lr 1.0000e-03 eta 5:04:26
+epoch [27/50] batch [320/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.0176 (1.1021) acc 68.7500 (72.2461) lr 1.0000e-03 eta 5:04:17
+epoch [27/50] batch [325/500] time 1.554 (1.563) data 0.000 (0.003) loss 1.2988 (1.1017) acc 56.2500 (72.1538) lr 1.0000e-03 eta 5:04:07
+epoch [27/50] batch [330/500] time 1.542 (1.563) data 0.001 (0.003) loss 1.5654 (1.1032) acc 68.7500 (72.1402) lr 1.0000e-03 eta 5:03:58
+epoch [27/50] batch [335/500] time 1.568 (1.563) data 0.000 (0.003) loss 1.1123 (1.1023) acc 68.7500 (72.1828) lr 1.0000e-03 eta 5:03:49
+epoch [27/50] batch [340/500] time 1.557 (1.563) data 0.001 (0.003) loss 1.3770 (1.1002) acc 56.2500 (72.2059) lr 1.0000e-03 eta 5:03:42
+epoch [27/50] batch [345/500] time 1.549 (1.563) data 0.001 (0.003) loss 1.6006 (1.1020) acc 59.3750 (72.1467) lr 1.0000e-03 eta 5:03:33
+epoch [27/50] batch [350/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.7197 (1.1006) acc 78.1250 (72.2143) lr 1.0000e-03 eta 5:03:23
+epoch [27/50] batch [355/500] time 1.528 (1.562) data 0.000 (0.003) loss 0.5913 (1.1003) acc 81.2500 (72.2535) lr 1.0000e-03 eta 5:03:13
+epoch [27/50] batch [360/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.3242 (1.0998) acc 68.7500 (72.2917) lr 1.0000e-03 eta 5:03:04
+epoch [27/50] batch [365/500] time 1.530 (1.562) data 0.000 (0.002) loss 0.9141 (1.1004) acc 78.1250 (72.3031) lr 1.0000e-03 eta 5:02:54
+epoch [27/50] batch [370/500] time 1.572 (1.562) data 0.000 (0.002) loss 1.4600 (1.1021) acc 71.8750 (72.3311) lr 1.0000e-03 eta 5:02:47
+epoch [27/50] batch [375/500] time 1.556 (1.562) data 0.000 (0.002) loss 0.5405 (1.0992) acc 81.2500 (72.4000) lr 1.0000e-03 eta 5:02:41
+epoch [27/50] batch [380/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.4277 (1.1013) acc 62.5000 (72.3438) lr 1.0000e-03 eta 5:02:34
+epoch [27/50] batch [385/500] time 1.561 (1.562) data 0.000 (0.002) loss 0.6938 (1.0982) acc 84.3750 (72.3945) lr 1.0000e-03 eta 5:02:26
+epoch [27/50] batch [390/500] time 1.552 (1.562) data 0.001 (0.002) loss 1.0713 (1.0984) acc 68.7500 (72.3958) lr 1.0000e-03 eta 5:02:18
+epoch [27/50] batch [395/500] time 1.568 (1.562) data 0.000 (0.002) loss 1.4844 (1.1023) acc 59.3750 (72.3022) lr 1.0000e-03 eta 5:02:10
+epoch [27/50] batch [400/500] time 1.580 (1.562) data 0.001 (0.002) loss 0.6489 (1.1039) acc 81.2500 (72.2734) lr 1.0000e-03 eta 5:02:02
+epoch [27/50] batch [405/500] time 1.564 (1.562) data 0.000 (0.002) loss 1.1406 (1.1045) acc 75.0000 (72.2377) lr 1.0000e-03 eta 5:01:55
+epoch [27/50] batch [410/500] time 1.576 (1.562) data 0.000 (0.002) loss 1.1914 (1.1076) acc 68.7500 (72.1570) lr 1.0000e-03 eta 5:01:47
+epoch [27/50] batch [415/500] time 1.624 (1.562) data 0.000 (0.002) loss 0.3599 (1.1036) acc 90.6250 (72.2967) lr 1.0000e-03 eta 5:01:40
+epoch [27/50] batch [420/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.2090 (1.1038) acc 56.2500 (72.2396) lr 1.0000e-03 eta 5:01:31
+epoch [27/50] batch [425/500] time 1.542 (1.562) data 0.000 (0.002) loss 1.0801 (1.1006) acc 71.8750 (72.3382) lr 1.0000e-03 eta 5:01:22
+epoch [27/50] batch [430/500] time 1.580 (1.562) data 0.000 (0.002) loss 1.6543 (1.1015) acc 59.3750 (72.3256) lr 1.0000e-03 eta 5:01:14
+epoch [27/50] batch [435/500] time 1.570 (1.562) data 0.000 (0.002) loss 1.3379 (1.1008) acc 59.3750 (72.2773) lr 1.0000e-03 eta 5:01:06
+epoch [27/50] batch [440/500] time 1.560 (1.562) data 0.001 (0.002) loss 0.8091 (1.1003) acc 84.3750 (72.3509) lr 1.0000e-03 eta 5:00:58
+epoch [27/50] batch [445/500] time 1.536 (1.562) data 0.000 (0.002) loss 0.8506 (1.1003) acc 81.2500 (72.3876) lr 1.0000e-03 eta 5:00:49
+epoch [27/50] batch [450/500] time 1.600 (1.562) data 0.000 (0.002) loss 0.8428 (1.0981) acc 71.8750 (72.4236) lr 1.0000e-03 eta 5:00:43
+epoch [27/50] batch [455/500] time 1.563 (1.562) data 0.000 (0.002) loss 0.7319 (1.0971) acc 75.0000 (72.4107) lr 1.0000e-03 eta 5:00:35
+epoch [27/50] batch [460/500] time 1.534 (1.562) data 0.000 (0.002) loss 0.9067 (1.0995) acc 84.3750 (72.3913) lr 1.0000e-03 eta 5:00:26
+epoch [27/50] batch [465/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.0762 (1.0981) acc 81.2500 (72.4261) lr 1.0000e-03 eta 5:00:17
+epoch [27/50] batch [470/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.3975 (1.0979) acc 68.7500 (72.4069) lr 1.0000e-03 eta 5:00:09
+epoch [27/50] batch [475/500] time 1.545 (1.562) data 0.000 (0.002) loss 1.0381 (1.0969) acc 75.0000 (72.4408) lr 1.0000e-03 eta 5:00:01
+epoch [27/50] batch [480/500] time 1.546 (1.562) data 0.001 (0.002) loss 0.9902 (1.0969) acc 75.0000 (72.4544) lr 1.0000e-03 eta 4:59:51
+epoch [27/50] batch [485/500] time 1.551 (1.562) data 0.001 (0.002) loss 0.8315 (1.0951) acc 78.1250 (72.5000) lr 1.0000e-03 eta 4:59:42
+epoch [27/50] batch [490/500] time 1.580 (1.562) data 0.000 (0.002) loss 0.7510 (1.0946) acc 81.2500 (72.5128) lr 1.0000e-03 eta 4:59:33
+epoch [27/50] batch [495/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.8794 (1.0932) acc 81.2500 (72.5189) lr 1.0000e-03 eta 4:59:25
+epoch [27/50] batch [500/500] time 1.540 (1.561) data 0.000 (0.002) loss 0.8550 (1.0947) acc 75.0000 (72.4750) lr 9.3721e-04 eta 4:59:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,018
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+epoch [28/50] batch [5/500] time 1.532 (1.689) data 0.001 (0.173) loss 0.5068 (1.0148) acc 87.5000 (71.8750) lr 9.3721e-04 eta 5:23:39
+epoch [28/50] batch [10/500] time 1.566 (1.621) data 0.000 (0.087) loss 0.7607 (1.0102) acc 75.0000 (74.0625) lr 9.3721e-04 eta 5:10:19
+epoch [28/50] batch [15/500] time 1.552 (1.602) data 0.000 (0.058) loss 0.7119 (1.0230) acc 84.3750 (74.5833) lr 9.3721e-04 eta 5:06:42
+epoch [28/50] batch [20/500] time 1.556 (1.589) data 0.000 (0.043) loss 0.9072 (1.0121) acc 71.8750 (74.2188) lr 9.3721e-04 eta 5:04:05
+epoch [28/50] batch [25/500] time 1.572 (1.589) data 0.000 (0.035) loss 1.3262 (1.0288) acc 78.1250 (73.8750) lr 9.3721e-04 eta 5:03:51
+epoch [28/50] batch [30/500] time 1.531 (1.585) data 0.000 (0.029) loss 0.7266 (1.0227) acc 78.1250 (74.1667) lr 9.3721e-04 eta 5:03:01
+epoch [28/50] batch [35/500] time 1.550 (1.582) data 0.000 (0.025) loss 0.8218 (1.0672) acc 78.1250 (73.4821) lr 9.3721e-04 eta 5:02:19
+epoch [28/50] batch [40/500] time 1.578 (1.581) data 0.000 (0.022) loss 0.8916 (1.0374) acc 81.2500 (74.0625) lr 9.3721e-04 eta 5:01:56
+epoch [28/50] batch [45/500] time 1.576 (1.580) data 0.000 (0.020) loss 1.2324 (1.0525) acc 71.8750 (73.9583) lr 9.3721e-04 eta 5:01:35
+epoch [28/50] batch [50/500] time 1.558 (1.577) data 0.000 (0.018) loss 0.8364 (1.0494) acc 75.0000 (73.9375) lr 9.3721e-04 eta 5:00:55
+epoch [28/50] batch [55/500] time 1.565 (1.576) data 0.000 (0.016) loss 1.0576 (1.0489) acc 71.8750 (74.0341) lr 9.3721e-04 eta 5:00:32
+epoch [28/50] batch [60/500] time 1.577 (1.574) data 0.001 (0.015) loss 1.1562 (1.0524) acc 68.7500 (73.3333) lr 9.3721e-04 eta 5:00:08
+epoch [28/50] batch [65/500] time 1.576 (1.574) data 0.000 (0.014) loss 1.5078 (1.0401) acc 56.2500 (73.6058) lr 9.3721e-04 eta 4:59:54
+epoch [28/50] batch [70/500] time 1.567 (1.573) data 0.000 (0.013) loss 1.5215 (1.0388) acc 71.8750 (73.6161) lr 9.3721e-04 eta 4:59:41
+epoch [28/50] batch [75/500] time 1.570 (1.573) data 0.001 (0.012) loss 0.6753 (1.0355) acc 81.2500 (73.4583) lr 9.3721e-04 eta 4:59:34
+epoch [28/50] batch [80/500] time 1.571 (1.572) data 0.000 (0.011) loss 1.1533 (1.0587) acc 71.8750 (73.0859) lr 9.3721e-04 eta 4:59:17
+epoch [28/50] batch [85/500] time 1.582 (1.572) data 0.000 (0.011) loss 1.0713 (1.0505) acc 75.0000 (73.1985) lr 9.3721e-04 eta 4:59:07
+epoch [28/50] batch [90/500] time 1.550 (1.571) data 0.001 (0.010) loss 0.8667 (1.0432) acc 75.0000 (73.3681) lr 9.3721e-04 eta 4:58:45
+epoch [28/50] batch [95/500] time 1.552 (1.570) data 0.000 (0.009) loss 1.1514 (1.0288) acc 71.8750 (73.5526) lr 9.3721e-04 eta 4:58:27
+epoch [28/50] batch [100/500] time 1.587 (1.570) data 0.000 (0.009) loss 1.0391 (1.0271) acc 65.6250 (73.3438) lr 9.3721e-04 eta 4:58:14
+epoch [28/50] batch [105/500] time 1.536 (1.569) data 0.000 (0.009) loss 0.9619 (1.0309) acc 75.0000 (73.2738) lr 9.3721e-04 eta 4:58:02
+epoch [28/50] batch [110/500] time 1.560 (1.569) data 0.000 (0.008) loss 0.9268 (1.0374) acc 75.0000 (73.1818) lr 9.3721e-04 eta 4:57:45
+epoch [28/50] batch [115/500] time 1.562 (1.569) data 0.000 (0.008) loss 1.3828 (1.0399) acc 71.8750 (73.3696) lr 9.3721e-04 eta 4:57:37
+epoch [28/50] batch [120/500] time 1.673 (1.569) data 0.000 (0.008) loss 1.7168 (1.0454) acc 65.6250 (73.3854) lr 9.3721e-04 eta 4:57:39
+epoch [28/50] batch [125/500] time 1.553 (1.569) data 0.000 (0.007) loss 1.0381 (1.0457) acc 75.0000 (73.5000) lr 9.3721e-04 eta 4:57:25
+epoch [28/50] batch [130/500] time 1.558 (1.568) data 0.000 (0.007) loss 0.5400 (1.0559) acc 90.6250 (73.5096) lr 9.3721e-04 eta 4:57:12
+epoch [28/50] batch [135/500] time 1.572 (1.568) data 0.000 (0.007) loss 1.3887 (1.0544) acc 65.6250 (73.5417) lr 9.3721e-04 eta 4:56:59
+epoch [28/50] batch [140/500] time 1.559 (1.568) data 0.000 (0.007) loss 1.2129 (1.0509) acc 68.7500 (73.5938) lr 9.3721e-04 eta 4:56:48
+epoch [28/50] batch [145/500] time 1.557 (1.567) data 0.001 (0.006) loss 0.8921 (1.0502) acc 71.8750 (73.6207) lr 9.3721e-04 eta 4:56:35
+epoch [28/50] batch [150/500] time 1.560 (1.567) data 0.000 (0.006) loss 1.2002 (1.0542) acc 71.8750 (73.5417) lr 9.3721e-04 eta 4:56:23
+epoch [28/50] batch [155/500] time 1.570 (1.567) data 0.000 (0.006) loss 1.1719 (1.0570) acc 75.0000 (73.5484) lr 9.3721e-04 eta 4:56:15
+epoch [28/50] batch [160/500] time 1.559 (1.567) data 0.001 (0.006) loss 0.9536 (1.0582) acc 68.7500 (73.5156) lr 9.3721e-04 eta 4:56:09
+epoch [28/50] batch [165/500] time 1.580 (1.568) data 0.000 (0.006) loss 0.7793 (1.0533) acc 71.8750 (73.4848) lr 9.3721e-04 eta 4:56:16
+epoch [28/50] batch [170/500] time 1.569 (1.568) data 0.000 (0.006) loss 0.6084 (1.0541) acc 81.2500 (73.4375) lr 9.3721e-04 eta 4:56:09
+epoch [28/50] batch [175/500] time 1.573 (1.568) data 0.000 (0.005) loss 1.2188 (1.0611) acc 75.0000 (73.4107) lr 9.3721e-04 eta 4:56:02
+epoch [28/50] batch [180/500] time 1.545 (1.568) data 0.000 (0.005) loss 1.0273 (1.0612) acc 68.7500 (73.3681) lr 9.3721e-04 eta 4:55:50
+epoch [28/50] batch [185/500] time 1.536 (1.568) data 0.000 (0.005) loss 1.1855 (1.0627) acc 75.0000 (73.3615) lr 9.3721e-04 eta 4:55:39
+epoch [28/50] batch [190/500] time 1.548 (1.568) data 0.001 (0.005) loss 0.8501 (1.0557) acc 81.2500 (73.4868) lr 9.3721e-04 eta 4:55:30
+epoch [28/50] batch [195/500] time 1.575 (1.568) data 0.000 (0.005) loss 1.0840 (1.0556) acc 78.1250 (73.6058) lr 9.3721e-04 eta 4:55:24
+epoch [28/50] batch [200/500] time 1.539 (1.568) data 0.000 (0.005) loss 0.7510 (1.0587) acc 75.0000 (73.4219) lr 9.3721e-04 eta 4:55:12
+epoch [28/50] batch [205/500] time 1.566 (1.567) data 0.000 (0.005) loss 0.8970 (1.0628) acc 75.0000 (73.3384) lr 9.3721e-04 eta 4:55:04
+epoch [28/50] batch [210/500] time 1.541 (1.567) data 0.000 (0.005) loss 0.6240 (1.0534) acc 75.0000 (73.5268) lr 9.3721e-04 eta 4:54:56
+epoch [28/50] batch [215/500] time 1.540 (1.567) data 0.000 (0.004) loss 1.1465 (1.0577) acc 68.7500 (73.4738) lr 9.3721e-04 eta 4:54:47
+epoch [28/50] batch [220/500] time 1.542 (1.567) data 0.000 (0.004) loss 1.6914 (1.0579) acc 59.3750 (73.4659) lr 9.3721e-04 eta 4:54:35
+epoch [28/50] batch [225/500] time 1.547 (1.567) data 0.000 (0.004) loss 0.8418 (1.0580) acc 81.2500 (73.4028) lr 9.3721e-04 eta 4:54:22
+epoch [28/50] batch [230/500] time 1.537 (1.566) data 0.000 (0.004) loss 0.7915 (1.0575) acc 81.2500 (73.4783) lr 9.3721e-04 eta 4:54:11
+epoch [28/50] batch [235/500] time 1.539 (1.566) data 0.000 (0.004) loss 1.3018 (1.0620) acc 59.3750 (73.3910) lr 9.3721e-04 eta 4:53:59
+epoch [28/50] batch [240/500] time 1.534 (1.566) data 0.000 (0.004) loss 1.2627 (1.0655) acc 71.8750 (73.2943) lr 9.3721e-04 eta 4:53:49
+epoch [28/50] batch [245/500] time 1.552 (1.565) data 0.000 (0.004) loss 1.4844 (1.0660) acc 71.8750 (73.3291) lr 9.3721e-04 eta 4:53:39
+epoch [28/50] batch [250/500] time 1.578 (1.565) data 0.000 (0.004) loss 0.8926 (1.0668) acc 71.8750 (73.3500) lr 9.3721e-04 eta 4:53:30
+epoch [28/50] batch [255/500] time 1.570 (1.565) data 0.001 (0.004) loss 0.6333 (1.0647) acc 87.5000 (73.3824) lr 9.3721e-04 eta 4:53:23
+epoch [28/50] batch [260/500] time 1.578 (1.565) data 0.000 (0.004) loss 1.5586 (1.0649) acc 62.5000 (73.3293) lr 9.3721e-04 eta 4:53:14
+epoch [28/50] batch [265/500] time 1.559 (1.565) data 0.000 (0.004) loss 1.5879 (1.0696) acc 62.5000 (73.2783) lr 9.3721e-04 eta 4:53:06
+epoch [28/50] batch [270/500] time 1.541 (1.565) data 0.000 (0.004) loss 1.4678 (1.0721) acc 59.3750 (73.2639) lr 9.3721e-04 eta 4:52:55
+epoch [28/50] batch [275/500] time 1.568 (1.565) data 0.000 (0.004) loss 0.8516 (1.0737) acc 78.1250 (73.2386) lr 9.3721e-04 eta 4:52:45
+epoch [28/50] batch [280/500] time 1.551 (1.565) data 0.000 (0.004) loss 0.2900 (1.0752) acc 87.5000 (73.2366) lr 9.3721e-04 eta 4:52:33
+epoch [28/50] batch [285/500] time 1.553 (1.564) data 0.000 (0.003) loss 0.9893 (1.0754) acc 81.2500 (73.2456) lr 9.3721e-04 eta 4:52:25
+epoch [28/50] batch [290/500] time 1.582 (1.564) data 0.000 (0.003) loss 1.1807 (1.0748) acc 59.3750 (73.2543) lr 9.3721e-04 eta 4:52:16
+epoch [28/50] batch [295/500] time 1.551 (1.564) data 0.000 (0.003) loss 1.0996 (1.0762) acc 71.8750 (73.2203) lr 9.3721e-04 eta 4:52:09
+epoch [28/50] batch [300/500] time 1.573 (1.564) data 0.000 (0.003) loss 1.1270 (1.0794) acc 68.7500 (73.2396) lr 9.3721e-04 eta 4:52:00
+epoch [28/50] batch [305/500] time 1.539 (1.564) data 0.000 (0.003) loss 0.9995 (1.0825) acc 75.0000 (73.1967) lr 9.3721e-04 eta 4:51:49
+epoch [28/50] batch [310/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.2373 (1.0858) acc 65.6250 (73.1250) lr 9.3721e-04 eta 4:51:45
+epoch [28/50] batch [315/500] time 1.544 (1.564) data 0.000 (0.003) loss 1.5283 (1.0863) acc 56.2500 (73.1845) lr 9.3721e-04 eta 4:51:35
+epoch [28/50] batch [320/500] time 1.568 (1.564) data 0.000 (0.003) loss 0.6084 (1.0849) acc 78.1250 (73.2227) lr 9.3721e-04 eta 4:51:27
+epoch [28/50] batch [325/500] time 1.558 (1.564) data 0.001 (0.003) loss 0.6104 (1.0826) acc 87.5000 (73.2692) lr 9.3721e-04 eta 4:51:18
+epoch [28/50] batch [330/500] time 1.561 (1.564) data 0.001 (0.003) loss 0.7949 (1.0834) acc 78.1250 (73.1534) lr 9.3721e-04 eta 4:51:11
+epoch [28/50] batch [335/500] time 1.554 (1.564) data 0.001 (0.003) loss 0.8672 (1.0857) acc 81.2500 (73.0877) lr 9.3721e-04 eta 4:51:01
+epoch [28/50] batch [340/500] time 1.550 (1.564) data 0.001 (0.003) loss 1.0400 (1.0849) acc 71.8750 (73.0790) lr 9.3721e-04 eta 4:50:50
+epoch [28/50] batch [345/500] time 1.554 (1.563) data 0.001 (0.003) loss 0.8135 (1.0859) acc 81.2500 (73.0978) lr 9.3721e-04 eta 4:50:39
+epoch [28/50] batch [350/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.3125 (1.0839) acc 62.5000 (73.0893) lr 9.3721e-04 eta 4:50:28
+epoch [28/50] batch [355/500] time 1.566 (1.563) data 0.000 (0.003) loss 0.8091 (1.0827) acc 75.0000 (73.0810) lr 9.3721e-04 eta 4:50:18
+epoch [28/50] batch [360/500] time 1.563 (1.563) data 0.000 (0.003) loss 1.5605 (1.0816) acc 65.6250 (73.0903) lr 9.3721e-04 eta 4:50:10
+epoch [28/50] batch [365/500] time 1.535 (1.563) data 0.000 (0.003) loss 1.0117 (1.0833) acc 68.7500 (73.0479) lr 9.3721e-04 eta 4:50:00
+epoch [28/50] batch [370/500] time 1.563 (1.562) data 0.000 (0.003) loss 0.9131 (1.0841) acc 75.0000 (73.0068) lr 9.3721e-04 eta 4:49:50
+epoch [28/50] batch [375/500] time 1.548 (1.562) data 0.000 (0.003) loss 1.3447 (1.0837) acc 75.0000 (72.9750) lr 9.3721e-04 eta 4:49:39
+epoch [28/50] batch [380/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.2383 (1.0866) acc 62.5000 (72.9030) lr 9.3721e-04 eta 4:49:31
+epoch [28/50] batch [385/500] time 1.559 (1.562) data 0.001 (0.003) loss 0.8418 (1.0891) acc 68.7500 (72.8490) lr 9.3721e-04 eta 4:49:23
+epoch [28/50] batch [390/500] time 1.554 (1.562) data 0.000 (0.003) loss 0.9062 (1.0889) acc 71.8750 (72.8446) lr 9.3721e-04 eta 4:49:15
+epoch [28/50] batch [395/500] time 1.559 (1.562) data 0.000 (0.003) loss 1.2334 (1.0899) acc 68.7500 (72.8323) lr 9.3721e-04 eta 4:49:06
+epoch [28/50] batch [400/500] time 1.530 (1.562) data 0.000 (0.003) loss 0.5264 (1.0875) acc 90.6250 (72.8750) lr 9.3721e-04 eta 4:48:56
+epoch [28/50] batch [405/500] time 1.533 (1.562) data 0.001 (0.003) loss 1.2139 (1.0888) acc 75.0000 (72.8704) lr 9.3721e-04 eta 4:48:46
+epoch [28/50] batch [410/500] time 1.543 (1.562) data 0.000 (0.003) loss 1.0664 (1.0877) acc 65.6250 (72.8659) lr 9.3721e-04 eta 4:48:39
+epoch [28/50] batch [415/500] time 1.551 (1.562) data 0.000 (0.002) loss 1.3789 (1.0890) acc 75.0000 (72.7937) lr 9.3721e-04 eta 4:48:30
+epoch [28/50] batch [420/500] time 1.526 (1.561) data 0.000 (0.002) loss 1.0449 (1.0869) acc 75.0000 (72.8125) lr 9.3721e-04 eta 4:48:20
+epoch [28/50] batch [425/500] time 1.556 (1.561) data 0.000 (0.002) loss 1.3965 (1.0856) acc 65.6250 (72.8529) lr 9.3721e-04 eta 4:48:13
+epoch [28/50] batch [430/500] time 1.566 (1.561) data 0.001 (0.002) loss 1.0381 (1.0859) acc 81.2500 (72.8997) lr 9.3721e-04 eta 4:48:03
+epoch [28/50] batch [435/500] time 1.565 (1.561) data 0.000 (0.002) loss 1.3867 (1.0868) acc 59.3750 (72.8233) lr 9.3721e-04 eta 4:47:55
+epoch [28/50] batch [440/500] time 1.551 (1.561) data 0.000 (0.002) loss 0.8457 (1.0856) acc 78.1250 (72.8764) lr 9.3721e-04 eta 4:47:47
+epoch [28/50] batch [445/500] time 1.545 (1.561) data 0.000 (0.002) loss 0.9385 (1.0849) acc 78.1250 (72.8722) lr 9.3721e-04 eta 4:47:37
+epoch [28/50] batch [450/500] time 1.650 (1.561) data 0.000 (0.002) loss 0.6875 (1.0834) acc 90.6250 (72.8958) lr 9.3721e-04 eta 4:47:30
+epoch [28/50] batch [455/500] time 1.565 (1.561) data 0.000 (0.002) loss 0.4670 (1.0820) acc 87.5000 (72.9052) lr 9.3721e-04 eta 4:47:21
+epoch [28/50] batch [460/500] time 1.566 (1.561) data 0.000 (0.002) loss 1.9707 (1.0841) acc 50.0000 (72.8804) lr 9.3721e-04 eta 4:47:14
+epoch [28/50] batch [465/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.9614 (1.0848) acc 75.0000 (72.8898) lr 9.3721e-04 eta 4:47:06
+epoch [28/50] batch [470/500] time 1.530 (1.561) data 0.000 (0.002) loss 1.1855 (1.0852) acc 65.6250 (72.8457) lr 9.3721e-04 eta 4:46:55
+epoch [28/50] batch [475/500] time 1.544 (1.561) data 0.000 (0.002) loss 0.9258 (1.0861) acc 75.0000 (72.8487) lr 9.3721e-04 eta 4:46:46
+epoch [28/50] batch [480/500] time 1.548 (1.561) data 0.000 (0.002) loss 1.1416 (1.0880) acc 71.8750 (72.7539) lr 9.3721e-04 eta 4:46:37
+epoch [28/50] batch [485/500] time 1.552 (1.561) data 0.001 (0.002) loss 0.8257 (1.0863) acc 75.0000 (72.7577) lr 9.3721e-04 eta 4:46:30
+epoch [28/50] batch [490/500] time 1.538 (1.560) data 0.000 (0.002) loss 0.9048 (1.0861) acc 71.8750 (72.7615) lr 9.3721e-04 eta 4:46:19
+epoch [28/50] batch [495/500] time 1.534 (1.560) data 0.000 (0.002) loss 1.6582 (1.0858) acc 68.7500 (72.7525) lr 9.3721e-04 eta 4:46:09
+epoch [28/50] batch [500/500] time 1.556 (1.560) data 0.000 (0.002) loss 1.1943 (1.0857) acc 71.8750 (72.7812) lr 8.7467e-04 eta 4:46:01
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,065
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [29/50] batch [5/500] time 1.554 (1.661) data 0.000 (0.159) loss 1.0869 (0.9994) acc 78.1250 (71.8750) lr 8.7467e-04 eta 5:04:21
+epoch [29/50] batch [10/500] time 1.550 (1.608) data 0.000 (0.080) loss 1.7656 (1.1471) acc 50.0000 (68.1250) lr 8.7467e-04 eta 4:54:32
+epoch [29/50] batch [15/500] time 1.545 (1.588) data 0.000 (0.053) loss 0.6743 (1.1446) acc 84.3750 (70.6250) lr 8.7467e-04 eta 4:50:46
+epoch [29/50] batch [20/500] time 1.560 (1.580) data 0.000 (0.040) loss 0.6372 (1.1028) acc 81.2500 (70.6250) lr 8.7467e-04 eta 4:49:08
+epoch [29/50] batch [25/500] time 1.591 (1.578) data 0.001 (0.032) loss 1.1729 (1.0950) acc 68.7500 (71.0000) lr 8.7467e-04 eta 4:48:33
+epoch [29/50] batch [30/500] time 1.545 (1.574) data 0.001 (0.027) loss 0.9458 (1.0865) acc 65.6250 (71.4583) lr 8.7467e-04 eta 4:47:51
+epoch [29/50] batch [35/500] time 1.567 (1.573) data 0.000 (0.023) loss 0.9517 (1.0908) acc 75.0000 (72.1429) lr 8.7467e-04 eta 4:47:32
+epoch [29/50] batch [40/500] time 1.541 (1.574) data 0.001 (0.020) loss 1.1016 (1.0963) acc 71.8750 (72.3438) lr 8.7467e-04 eta 4:47:34
+epoch [29/50] batch [45/500] time 1.556 (1.573) data 0.000 (0.018) loss 0.6533 (1.1119) acc 87.5000 (72.2917) lr 8.7467e-04 eta 4:47:09
+epoch [29/50] batch [50/500] time 1.544 (1.571) data 0.000 (0.016) loss 1.0225 (1.0912) acc 71.8750 (72.5000) lr 8.7467e-04 eta 4:46:41
+epoch [29/50] batch [55/500] time 1.566 (1.570) data 0.000 (0.015) loss 1.5498 (1.0941) acc 78.1250 (72.8409) lr 8.7467e-04 eta 4:46:19
+epoch [29/50] batch [60/500] time 1.576 (1.569) data 0.001 (0.014) loss 1.2109 (1.0733) acc 68.7500 (73.3854) lr 8.7467e-04 eta 4:46:09
+epoch [29/50] batch [65/500] time 1.537 (1.568) data 0.001 (0.013) loss 1.2900 (1.0818) acc 65.6250 (72.9327) lr 8.7467e-04 eta 4:45:50
+epoch [29/50] batch [70/500] time 1.550 (1.567) data 0.000 (0.012) loss 0.9736 (1.0714) acc 78.1250 (73.1250) lr 8.7467e-04 eta 4:45:27
+epoch [29/50] batch [75/500] time 1.560 (1.566) data 0.000 (0.011) loss 0.9272 (1.0684) acc 68.7500 (72.9583) lr 8.7467e-04 eta 4:45:12
+epoch [29/50] batch [80/500] time 1.555 (1.566) data 0.000 (0.010) loss 1.3418 (1.0721) acc 68.7500 (72.6562) lr 8.7467e-04 eta 4:45:01
+epoch [29/50] batch [85/500] time 1.569 (1.567) data 0.000 (0.010) loss 0.8042 (1.0763) acc 78.1250 (72.4265) lr 8.7467e-04 eta 4:45:01
+epoch [29/50] batch [90/500] time 1.545 (1.566) data 0.000 (0.009) loss 1.7451 (1.0897) acc 59.3750 (72.0139) lr 8.7467e-04 eta 4:44:40
+epoch [29/50] batch [95/500] time 1.573 (1.566) data 0.000 (0.009) loss 1.1826 (1.0837) acc 78.1250 (72.3355) lr 8.7467e-04 eta 4:44:34
+epoch [29/50] batch [100/500] time 1.580 (1.565) data 0.000 (0.008) loss 0.4702 (1.0696) acc 84.3750 (72.7188) lr 8.7467e-04 eta 4:44:19
+epoch [29/50] batch [105/500] time 1.586 (1.565) data 0.001 (0.008) loss 0.8184 (1.0643) acc 84.3750 (72.8571) lr 8.7467e-04 eta 4:44:15
+epoch [29/50] batch [110/500] time 1.554 (1.565) data 0.000 (0.008) loss 1.4902 (1.0733) acc 68.7500 (72.7273) lr 8.7467e-04 eta 4:44:05
+epoch [29/50] batch [115/500] time 1.571 (1.565) data 0.001 (0.007) loss 0.7109 (1.0692) acc 75.0000 (72.6630) lr 8.7467e-04 eta 4:43:58
+epoch [29/50] batch [120/500] time 1.580 (1.566) data 0.000 (0.007) loss 1.3818 (1.0751) acc 71.8750 (72.7083) lr 8.7467e-04 eta 4:43:56
+epoch [29/50] batch [125/500] time 1.589 (1.566) data 0.000 (0.007) loss 1.0664 (1.0785) acc 68.7500 (72.6000) lr 8.7467e-04 eta 4:43:49
+epoch [29/50] batch [130/500] time 1.584 (1.566) data 0.000 (0.007) loss 0.5098 (1.0727) acc 84.3750 (72.7644) lr 8.7467e-04 eta 4:43:45
+epoch [29/50] batch [135/500] time 1.569 (1.566) data 0.000 (0.006) loss 1.1514 (1.0710) acc 68.7500 (72.8472) lr 8.7467e-04 eta 4:43:38
+epoch [29/50] batch [140/500] time 1.559 (1.566) data 0.000 (0.006) loss 0.8120 (1.0730) acc 78.1250 (72.7232) lr 8.7467e-04 eta 4:43:31
+epoch [29/50] batch [145/500] time 1.560 (1.566) data 0.000 (0.006) loss 1.1729 (1.0711) acc 71.8750 (72.7802) lr 8.7467e-04 eta 4:43:23
+epoch [29/50] batch [150/500] time 1.562 (1.566) data 0.000 (0.006) loss 0.7808 (1.0709) acc 75.0000 (72.8542) lr 8.7467e-04 eta 4:43:14
+epoch [29/50] batch [155/500] time 1.551 (1.566) data 0.000 (0.006) loss 1.1631 (1.0747) acc 78.1250 (72.8024) lr 8.7467e-04 eta 4:43:00
+epoch [29/50] batch [160/500] time 1.556 (1.566) data 0.001 (0.005) loss 0.9575 (1.0713) acc 78.1250 (72.8125) lr 8.7467e-04 eta 4:42:52
+epoch [29/50] batch [165/500] time 1.569 (1.566) data 0.000 (0.005) loss 0.8135 (1.0739) acc 84.3750 (72.7273) lr 8.7467e-04 eta 4:42:45
+epoch [29/50] batch [170/500] time 1.570 (1.566) data 0.001 (0.005) loss 0.5005 (1.0682) acc 81.2500 (72.6838) lr 8.7467e-04 eta 4:42:35
+epoch [29/50] batch [175/500] time 1.574 (1.565) data 0.001 (0.005) loss 1.1523 (1.0764) acc 75.0000 (72.7321) lr 8.7467e-04 eta 4:42:25
+epoch [29/50] batch [180/500] time 1.559 (1.566) data 0.000 (0.005) loss 1.0439 (1.0745) acc 71.8750 (72.7604) lr 8.7467e-04 eta 4:42:21
+epoch [29/50] batch [185/500] time 1.556 (1.566) data 0.001 (0.005) loss 0.8589 (1.0762) acc 71.8750 (72.6520) lr 8.7467e-04 eta 4:42:11
+epoch [29/50] batch [190/500] time 1.585 (1.566) data 0.000 (0.005) loss 1.1279 (1.0829) acc 68.7500 (72.5658) lr 8.7467e-04 eta 4:42:04
+epoch [29/50] batch [195/500] time 1.547 (1.566) data 0.000 (0.004) loss 1.4434 (1.0858) acc 65.6250 (72.5481) lr 8.7467e-04 eta 4:41:57
+epoch [29/50] batch [200/500] time 1.554 (1.566) data 0.000 (0.004) loss 1.2891 (1.0858) acc 65.6250 (72.5156) lr 8.7467e-04 eta 4:41:48
+epoch [29/50] batch [205/500] time 1.562 (1.565) data 0.000 (0.004) loss 0.8394 (1.0821) acc 78.1250 (72.5915) lr 8.7467e-04 eta 4:41:37
+epoch [29/50] batch [210/500] time 1.552 (1.565) data 0.000 (0.004) loss 0.9321 (1.0773) acc 75.0000 (72.6935) lr 8.7467e-04 eta 4:41:27
+epoch [29/50] batch [215/500] time 1.555 (1.565) data 0.000 (0.004) loss 1.4707 (1.0785) acc 65.6250 (72.6890) lr 8.7467e-04 eta 4:41:16
+epoch [29/50] batch [220/500] time 1.562 (1.565) data 0.001 (0.004) loss 1.2383 (1.0780) acc 62.5000 (72.5852) lr 8.7467e-04 eta 4:41:06
+epoch [29/50] batch [225/500] time 1.573 (1.565) data 0.000 (0.004) loss 1.3242 (1.0784) acc 68.7500 (72.5556) lr 8.7467e-04 eta 4:41:03
+epoch [29/50] batch [230/500] time 1.567 (1.565) data 0.000 (0.004) loss 0.7915 (1.0764) acc 78.1250 (72.5951) lr 8.7467e-04 eta 4:40:52
+epoch [29/50] batch [235/500] time 1.601 (1.565) data 0.000 (0.004) loss 1.1357 (1.0751) acc 65.6250 (72.5399) lr 8.7467e-04 eta 4:40:46
+epoch [29/50] batch [240/500] time 1.573 (1.565) data 0.001 (0.004) loss 1.0635 (1.0726) acc 68.7500 (72.5521) lr 8.7467e-04 eta 4:40:39
+epoch [29/50] batch [245/500] time 1.545 (1.565) data 0.000 (0.004) loss 0.9487 (1.0717) acc 68.7500 (72.5765) lr 8.7467e-04 eta 4:40:29
+epoch [29/50] batch [250/500] time 1.546 (1.565) data 0.000 (0.004) loss 1.0449 (1.0722) acc 75.0000 (72.5375) lr 8.7467e-04 eta 4:40:22
+epoch [29/50] batch [255/500] time 1.548 (1.565) data 0.000 (0.004) loss 0.8853 (1.0734) acc 68.7500 (72.4632) lr 8.7467e-04 eta 4:40:13
+epoch [29/50] batch [260/500] time 1.596 (1.565) data 0.000 (0.003) loss 1.0977 (1.0733) acc 68.7500 (72.4519) lr 8.7467e-04 eta 4:40:05
+epoch [29/50] batch [265/500] time 1.591 (1.565) data 0.001 (0.003) loss 1.5137 (1.0728) acc 65.6250 (72.5236) lr 8.7467e-04 eta 4:39:57
+epoch [29/50] batch [270/500] time 1.575 (1.565) data 0.000 (0.003) loss 0.7812 (1.0716) acc 81.2500 (72.5463) lr 8.7467e-04 eta 4:39:49
+epoch [29/50] batch [275/500] time 1.578 (1.565) data 0.000 (0.003) loss 1.5254 (1.0723) acc 65.6250 (72.5227) lr 8.7467e-04 eta 4:39:40
+epoch [29/50] batch [280/500] time 1.542 (1.565) data 0.000 (0.003) loss 0.7803 (1.0757) acc 71.8750 (72.4107) lr 8.7467e-04 eta 4:39:32
+epoch [29/50] batch [285/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.1836 (1.0778) acc 65.6250 (72.3904) lr 8.7467e-04 eta 4:39:28
+epoch [29/50] batch [290/500] time 1.585 (1.565) data 0.001 (0.003) loss 1.4229 (1.0789) acc 65.6250 (72.3384) lr 8.7467e-04 eta 4:39:22
+epoch [29/50] batch [295/500] time 1.573 (1.565) data 0.000 (0.003) loss 0.8442 (1.0777) acc 75.0000 (72.3517) lr 8.7467e-04 eta 4:39:14
+epoch [29/50] batch [300/500] time 1.561 (1.565) data 0.000 (0.003) loss 0.6836 (1.0765) acc 75.0000 (72.3229) lr 8.7467e-04 eta 4:39:07
+epoch [29/50] batch [305/500] time 1.526 (1.565) data 0.000 (0.003) loss 2.1660 (1.0844) acc 62.5000 (72.2643) lr 8.7467e-04 eta 4:38:57
+epoch [29/50] batch [310/500] time 1.547 (1.565) data 0.000 (0.003) loss 1.3135 (1.0825) acc 65.6250 (72.3185) lr 8.7467e-04 eta 4:38:47
+epoch [29/50] batch [315/500] time 1.538 (1.564) data 0.000 (0.003) loss 1.3721 (1.0843) acc 75.0000 (72.3115) lr 8.7467e-04 eta 4:38:36
+epoch [29/50] batch [320/500] time 1.550 (1.564) data 0.000 (0.003) loss 0.8877 (1.0830) acc 71.8750 (72.3535) lr 8.7467e-04 eta 4:38:27
+epoch [29/50] batch [325/500] time 1.549 (1.565) data 0.000 (0.003) loss 0.8931 (1.0799) acc 81.2500 (72.4423) lr 8.7467e-04 eta 4:38:22
+epoch [29/50] batch [330/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.5361 (1.0809) acc 68.7500 (72.4148) lr 8.7467e-04 eta 4:38:14
+epoch [29/50] batch [335/500] time 1.564 (1.565) data 0.000 (0.003) loss 1.0000 (1.0793) acc 78.1250 (72.5000) lr 8.7467e-04 eta 4:38:05
+epoch [29/50] batch [340/500] time 1.576 (1.564) data 0.000 (0.003) loss 0.9521 (1.0804) acc 75.0000 (72.4449) lr 8.7467e-04 eta 4:37:57
+epoch [29/50] batch [345/500] time 1.587 (1.564) data 0.000 (0.003) loss 1.1553 (1.0793) acc 75.0000 (72.4819) lr 8.7467e-04 eta 4:37:49
+epoch [29/50] batch [350/500] time 1.583 (1.565) data 0.001 (0.003) loss 2.1387 (1.0819) acc 62.5000 (72.5000) lr 8.7467e-04 eta 4:37:42
+epoch [29/50] batch [355/500] time 1.554 (1.565) data 0.001 (0.003) loss 1.3486 (1.0828) acc 75.0000 (72.5000) lr 8.7467e-04 eta 4:37:34
+epoch [29/50] batch [360/500] time 1.549 (1.565) data 0.000 (0.003) loss 1.3320 (1.0850) acc 71.8750 (72.4653) lr 8.7467e-04 eta 4:37:27
+epoch [29/50] batch [365/500] time 1.556 (1.565) data 0.000 (0.003) loss 0.6582 (1.0855) acc 71.8750 (72.4229) lr 8.7467e-04 eta 4:37:19
+epoch [29/50] batch [370/500] time 1.566 (1.565) data 0.001 (0.003) loss 1.1611 (1.0868) acc 62.5000 (72.3226) lr 8.7467e-04 eta 4:37:16
+epoch [29/50] batch [375/500] time 1.592 (1.565) data 0.000 (0.003) loss 1.0596 (1.0859) acc 81.2500 (72.3833) lr 8.7467e-04 eta 4:37:08
+epoch [29/50] batch [380/500] time 1.560 (1.565) data 0.000 (0.002) loss 0.9458 (1.0866) acc 71.8750 (72.4013) lr 8.7467e-04 eta 4:37:00
+epoch [29/50] batch [385/500] time 1.553 (1.565) data 0.001 (0.002) loss 1.1074 (1.0868) acc 71.8750 (72.4026) lr 8.7467e-04 eta 4:36:49
+epoch [29/50] batch [390/500] time 1.548 (1.565) data 0.001 (0.002) loss 0.9038 (1.0880) acc 78.1250 (72.3638) lr 8.7467e-04 eta 4:36:39
+epoch [29/50] batch [395/500] time 1.545 (1.564) data 0.001 (0.002) loss 0.6099 (1.0858) acc 81.2500 (72.4367) lr 8.7467e-04 eta 4:36:30
+epoch [29/50] batch [400/500] time 1.558 (1.564) data 0.000 (0.002) loss 1.0586 (1.0852) acc 78.1250 (72.4688) lr 8.7467e-04 eta 4:36:21
+epoch [29/50] batch [405/500] time 1.562 (1.564) data 0.001 (0.002) loss 1.3232 (1.0843) acc 78.1250 (72.5000) lr 8.7467e-04 eta 4:36:12
+epoch [29/50] batch [410/500] time 1.569 (1.564) data 0.000 (0.002) loss 0.7681 (1.0824) acc 84.3750 (72.5762) lr 8.7467e-04 eta 4:36:03
+epoch [29/50] batch [415/500] time 1.558 (1.564) data 0.000 (0.002) loss 0.8213 (1.0832) acc 75.0000 (72.5979) lr 8.7467e-04 eta 4:35:56
+epoch [29/50] batch [420/500] time 1.575 (1.564) data 0.000 (0.002) loss 1.5830 (1.0839) acc 62.5000 (72.5818) lr 8.7467e-04 eta 4:35:47
+epoch [29/50] batch [425/500] time 1.554 (1.564) data 0.000 (0.002) loss 1.3418 (1.0840) acc 65.6250 (72.5588) lr 8.7467e-04 eta 4:35:37
+epoch [29/50] batch [430/500] time 1.526 (1.564) data 0.000 (0.002) loss 0.6816 (1.0816) acc 78.1250 (72.5654) lr 8.7467e-04 eta 4:35:28
+epoch [29/50] batch [435/500] time 1.566 (1.564) data 0.000 (0.002) loss 1.0381 (1.0804) acc 75.0000 (72.6365) lr 8.7467e-04 eta 4:35:19
+epoch [29/50] batch [440/500] time 1.545 (1.564) data 0.001 (0.002) loss 1.1348 (1.0832) acc 71.8750 (72.5710) lr 8.7467e-04 eta 4:35:11
+epoch [29/50] batch [445/500] time 1.541 (1.564) data 0.001 (0.002) loss 1.7549 (1.0885) acc 62.5000 (72.5000) lr 8.7467e-04 eta 4:35:02
+epoch [29/50] batch [450/500] time 1.568 (1.564) data 0.000 (0.002) loss 0.7939 (1.0895) acc 81.2500 (72.5000) lr 8.7467e-04 eta 4:34:55
+epoch [29/50] batch [455/500] time 1.552 (1.563) data 0.000 (0.002) loss 1.2109 (1.0877) acc 68.7500 (72.5137) lr 8.7467e-04 eta 4:34:45
+epoch [29/50] batch [460/500] time 1.549 (1.563) data 0.000 (0.002) loss 1.2148 (1.0885) acc 75.0000 (72.4864) lr 8.7467e-04 eta 4:34:37
+epoch [29/50] batch [465/500] time 1.653 (1.563) data 0.000 (0.002) loss 1.2744 (1.0861) acc 71.8750 (72.4933) lr 8.7467e-04 eta 4:34:30
+epoch [29/50] batch [470/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.4268 (1.0861) acc 75.0000 (72.5000) lr 8.7467e-04 eta 4:34:22
+epoch [29/50] batch [475/500] time 1.555 (1.563) data 0.000 (0.002) loss 1.1621 (1.0867) acc 62.5000 (72.4934) lr 8.7467e-04 eta 4:34:13
+epoch [29/50] batch [480/500] time 1.541 (1.563) data 0.000 (0.002) loss 0.8716 (1.0873) acc 75.0000 (72.4740) lr 8.7467e-04 eta 4:34:04
+epoch [29/50] batch [485/500] time 1.537 (1.563) data 0.001 (0.002) loss 1.1348 (1.0846) acc 71.8750 (72.5580) lr 8.7467e-04 eta 4:33:55
+epoch [29/50] batch [490/500] time 1.555 (1.563) data 0.000 (0.002) loss 1.2275 (1.0847) acc 71.8750 (72.5702) lr 8.7467e-04 eta 4:33:48
+epoch [29/50] batch [495/500] time 1.549 (1.563) data 0.000 (0.002) loss 1.0039 (1.0855) acc 71.8750 (72.4937) lr 8.7467e-04 eta 4:33:39
+epoch [29/50] batch [500/500] time 1.533 (1.563) data 0.000 (0.002) loss 0.9341 (1.0856) acc 68.7500 (72.4562) lr 8.1262e-04 eta 4:33:30
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,062
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.7%
+epoch [30/50] batch [5/500] time 1.551 (1.654) data 0.001 (0.161) loss 0.7310 (1.0721) acc 78.1250 (75.0000) lr 8.1262e-04 eta 4:49:15
+epoch [30/50] batch [10/500] time 1.574 (1.611) data 0.001 (0.081) loss 0.8491 (1.0624) acc 71.8750 (72.5000) lr 8.1262e-04 eta 4:41:37
+epoch [30/50] batch [15/500] time 1.578 (1.596) data 0.000 (0.054) loss 0.9766 (1.0934) acc 75.0000 (71.4583) lr 8.1262e-04 eta 4:38:57
+epoch [30/50] batch [20/500] time 1.561 (1.594) data 0.000 (0.041) loss 0.9316 (1.0319) acc 75.0000 (73.2812) lr 8.1262e-04 eta 4:38:26
+epoch [30/50] batch [25/500] time 1.582 (1.590) data 0.000 (0.033) loss 0.8945 (1.0216) acc 75.0000 (73.1250) lr 8.1262e-04 eta 4:37:39
+epoch [30/50] batch [30/500] time 1.584 (1.587) data 0.001 (0.027) loss 1.0635 (1.0608) acc 65.6250 (72.8125) lr 8.1262e-04 eta 4:36:54
+epoch [30/50] batch [35/500] time 1.555 (1.582) data 0.000 (0.023) loss 0.7773 (1.0572) acc 71.8750 (72.9464) lr 8.1262e-04 eta 4:35:59
+epoch [30/50] batch [40/500] time 1.567 (1.579) data 0.000 (0.021) loss 1.1738 (1.0431) acc 65.6250 (73.3594) lr 8.1262e-04 eta 4:35:18
+epoch [30/50] batch [45/500] time 1.555 (1.576) data 0.000 (0.018) loss 1.3389 (1.0544) acc 59.3750 (73.3333) lr 8.1262e-04 eta 4:34:38
+epoch [30/50] batch [50/500] time 1.571 (1.574) data 0.000 (0.017) loss 1.0977 (1.0535) acc 78.1250 (73.3125) lr 8.1262e-04 eta 4:34:11
+epoch [30/50] batch [55/500] time 1.558 (1.573) data 0.000 (0.015) loss 0.8735 (1.0553) acc 75.0000 (73.2955) lr 8.1262e-04 eta 4:33:46
+epoch [30/50] batch [60/500] time 1.586 (1.571) data 0.000 (0.014) loss 1.1523 (1.0429) acc 71.8750 (73.6458) lr 8.1262e-04 eta 4:33:26
+epoch [30/50] batch [65/500] time 1.565 (1.571) data 0.000 (0.013) loss 1.7676 (1.0556) acc 59.3750 (73.1250) lr 8.1262e-04 eta 4:33:11
+epoch [30/50] batch [70/500] time 1.558 (1.570) data 0.001 (0.012) loss 0.9297 (1.0540) acc 71.8750 (73.0804) lr 8.1262e-04 eta 4:32:51
+epoch [30/50] batch [75/500] time 1.553 (1.568) data 0.001 (0.011) loss 1.2627 (1.0613) acc 75.0000 (72.6667) lr 8.1262e-04 eta 4:32:29
+epoch [30/50] batch [80/500] time 1.542 (1.567) data 0.000 (0.010) loss 1.1963 (1.0637) acc 68.7500 (72.7344) lr 8.1262e-04 eta 4:32:10
+epoch [30/50] batch [85/500] time 1.571 (1.567) data 0.000 (0.010) loss 0.8706 (1.0585) acc 65.6250 (72.9412) lr 8.1262e-04 eta 4:32:01
+epoch [30/50] batch [90/500] time 1.561 (1.567) data 0.001 (0.009) loss 1.2500 (1.0680) acc 68.7500 (72.8472) lr 8.1262e-04 eta 4:31:50
+epoch [30/50] batch [95/500] time 1.565 (1.567) data 0.000 (0.009) loss 0.8013 (1.0660) acc 81.2500 (73.0921) lr 8.1262e-04 eta 4:31:41
+epoch [30/50] batch [100/500] time 1.570 (1.566) data 0.000 (0.008) loss 1.3779 (1.0650) acc 75.0000 (73.3125) lr 8.1262e-04 eta 4:31:30
+epoch [30/50] batch [105/500] time 1.579 (1.566) data 0.000 (0.008) loss 1.1172 (1.0599) acc 75.0000 (73.2738) lr 8.1262e-04 eta 4:31:19
+epoch [30/50] batch [110/500] time 1.559 (1.566) data 0.000 (0.008) loss 1.2402 (1.0688) acc 62.5000 (72.9261) lr 8.1262e-04 eta 4:31:11
+epoch [30/50] batch [115/500] time 1.661 (1.566) data 0.001 (0.007) loss 1.3193 (1.0770) acc 62.5000 (72.7446) lr 8.1262e-04 eta 4:31:04
+epoch [30/50] batch [120/500] time 1.564 (1.566) data 0.001 (0.007) loss 1.0605 (1.0809) acc 78.1250 (72.6042) lr 8.1262e-04 eta 4:30:58
+epoch [30/50] batch [125/500] time 1.556 (1.566) data 0.000 (0.007) loss 1.1543 (1.0831) acc 68.7500 (72.4000) lr 8.1262e-04 eta 4:30:46
+epoch [30/50] batch [130/500] time 1.552 (1.566) data 0.000 (0.007) loss 0.7612 (1.0777) acc 78.1250 (72.5721) lr 8.1262e-04 eta 4:30:39
+epoch [30/50] batch [135/500] time 1.536 (1.566) data 0.000 (0.006) loss 1.7158 (1.0809) acc 65.6250 (72.5000) lr 8.1262e-04 eta 4:30:30
+epoch [30/50] batch [140/500] time 1.553 (1.566) data 0.000 (0.006) loss 0.9443 (1.0796) acc 78.1250 (72.5670) lr 8.1262e-04 eta 4:30:18
+epoch [30/50] batch [145/500] time 1.543 (1.565) data 0.000 (0.006) loss 0.8057 (1.0777) acc 71.8750 (72.5216) lr 8.1262e-04 eta 4:30:09
+epoch [30/50] batch [150/500] time 1.581 (1.566) data 0.000 (0.006) loss 1.2832 (1.0722) acc 65.6250 (72.5833) lr 8.1262e-04 eta 4:30:07
+epoch [30/50] batch [155/500] time 1.563 (1.566) data 0.000 (0.006) loss 1.2881 (1.0756) acc 71.8750 (72.5605) lr 8.1262e-04 eta 4:29:57
+epoch [30/50] batch [160/500] time 1.558 (1.566) data 0.000 (0.005) loss 1.2822 (1.0774) acc 75.0000 (72.5781) lr 8.1262e-04 eta 4:29:52
+epoch [30/50] batch [165/500] time 1.536 (1.565) data 0.000 (0.005) loss 1.3760 (1.0769) acc 68.7500 (72.6326) lr 8.1262e-04 eta 4:29:36
+epoch [30/50] batch [170/500] time 1.540 (1.565) data 0.000 (0.005) loss 1.0244 (1.0756) acc 75.0000 (72.5735) lr 8.1262e-04 eta 4:29:25
+epoch [30/50] batch [175/500] time 1.557 (1.564) data 0.000 (0.005) loss 0.9644 (1.0756) acc 71.8750 (72.5893) lr 8.1262e-04 eta 4:29:13
+epoch [30/50] batch [180/500] time 1.544 (1.564) data 0.000 (0.005) loss 1.2588 (1.0742) acc 68.7500 (72.5694) lr 8.1262e-04 eta 4:29:02
+epoch [30/50] batch [185/500] time 1.554 (1.564) data 0.000 (0.005) loss 0.8804 (1.0782) acc 78.1250 (72.4493) lr 8.1262e-04 eta 4:28:53
+epoch [30/50] batch [190/500] time 1.567 (1.564) data 0.000 (0.005) loss 0.8491 (1.0747) acc 78.1250 (72.5493) lr 8.1262e-04 eta 4:28:42
+epoch [30/50] batch [195/500] time 1.550 (1.564) data 0.000 (0.005) loss 1.6152 (1.0798) acc 68.7500 (72.4519) lr 8.1262e-04 eta 4:28:35
+epoch [30/50] batch [200/500] time 1.579 (1.564) data 0.001 (0.004) loss 0.7485 (1.0736) acc 75.0000 (72.5625) lr 8.1262e-04 eta 4:28:28
+epoch [30/50] batch [205/500] time 1.567 (1.564) data 0.001 (0.004) loss 1.0791 (1.0750) acc 68.7500 (72.5610) lr 8.1262e-04 eta 4:28:22
+epoch [30/50] batch [210/500] time 1.544 (1.564) data 0.000 (0.004) loss 1.0684 (1.0772) acc 71.8750 (72.4851) lr 8.1262e-04 eta 4:28:13
+epoch [30/50] batch [215/500] time 1.570 (1.564) data 0.000 (0.004) loss 1.4434 (1.0761) acc 62.5000 (72.5000) lr 8.1262e-04 eta 4:28:06
+epoch [30/50] batch [220/500] time 1.542 (1.564) data 0.001 (0.004) loss 1.8086 (1.0809) acc 50.0000 (72.3295) lr 8.1262e-04 eta 4:27:55
+epoch [30/50] batch [225/500] time 1.569 (1.564) data 0.001 (0.004) loss 0.8931 (1.0759) acc 81.2500 (72.4722) lr 8.1262e-04 eta 4:27:46
+epoch [30/50] batch [230/500] time 1.578 (1.564) data 0.001 (0.004) loss 0.7769 (1.0722) acc 87.5000 (72.5951) lr 8.1262e-04 eta 4:27:38
+epoch [30/50] batch [235/500] time 1.549 (1.563) data 0.000 (0.004) loss 1.1367 (1.0697) acc 71.8750 (72.6596) lr 8.1262e-04 eta 4:27:27
+epoch [30/50] batch [240/500] time 1.561 (1.563) data 0.000 (0.004) loss 1.0127 (1.0728) acc 71.8750 (72.5651) lr 8.1262e-04 eta 4:27:19
+epoch [30/50] batch [245/500] time 1.563 (1.563) data 0.000 (0.004) loss 0.5098 (1.0716) acc 87.5000 (72.6276) lr 8.1262e-04 eta 4:27:12
+epoch [30/50] batch [250/500] time 1.545 (1.563) data 0.000 (0.004) loss 1.2080 (1.0696) acc 71.8750 (72.7000) lr 8.1262e-04 eta 4:27:03
+epoch [30/50] batch [255/500] time 1.557 (1.563) data 0.000 (0.004) loss 0.7935 (1.0706) acc 65.6250 (72.5980) lr 8.1262e-04 eta 4:26:54
+epoch [30/50] batch [260/500] time 1.554 (1.563) data 0.000 (0.004) loss 0.7715 (1.0691) acc 87.5000 (72.7163) lr 8.1262e-04 eta 4:26:49
+epoch [30/50] batch [265/500] time 1.578 (1.563) data 0.000 (0.003) loss 1.3457 (1.0734) acc 59.3750 (72.6297) lr 8.1262e-04 eta 4:26:41
+epoch [30/50] batch [270/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.6855 (1.0744) acc 71.8750 (72.6505) lr 8.1262e-04 eta 4:26:34
+epoch [30/50] batch [275/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.2246 (1.0750) acc 75.0000 (72.6136) lr 8.1262e-04 eta 4:26:26
+epoch [30/50] batch [280/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.0381 (1.0760) acc 75.0000 (72.5781) lr 8.1262e-04 eta 4:26:16
+epoch [30/50] batch [285/500] time 1.531 (1.563) data 0.000 (0.003) loss 1.2539 (1.0751) acc 68.7500 (72.5768) lr 8.1262e-04 eta 4:26:05
+epoch [30/50] batch [290/500] time 1.569 (1.563) data 0.000 (0.003) loss 1.0576 (1.0775) acc 68.7500 (72.5647) lr 8.1262e-04 eta 4:25:57
+epoch [30/50] batch [295/500] time 1.538 (1.563) data 0.000 (0.003) loss 1.9863 (1.0828) acc 59.3750 (72.5318) lr 8.1262e-04 eta 4:25:47
+epoch [30/50] batch [300/500] time 1.590 (1.563) data 0.000 (0.003) loss 1.1123 (1.0845) acc 56.2500 (72.3854) lr 8.1262e-04 eta 4:25:41
+epoch [30/50] batch [305/500] time 1.554 (1.563) data 0.000 (0.003) loss 0.9424 (1.0828) acc 78.1250 (72.4283) lr 8.1262e-04 eta 4:25:34
+epoch [30/50] batch [310/500] time 1.601 (1.563) data 0.001 (0.003) loss 0.9609 (1.0825) acc 78.1250 (72.4395) lr 8.1262e-04 eta 4:25:28
+epoch [30/50] batch [315/500] time 1.560 (1.563) data 0.000 (0.003) loss 1.5283 (1.0851) acc 59.3750 (72.4107) lr 8.1262e-04 eta 4:25:20
+epoch [30/50] batch [320/500] time 1.592 (1.563) data 0.001 (0.003) loss 1.0488 (1.0839) acc 75.0000 (72.4805) lr 8.1262e-04 eta 4:25:13
+epoch [30/50] batch [325/500] time 1.567 (1.563) data 0.000 (0.003) loss 0.4131 (1.0837) acc 87.5000 (72.4808) lr 8.1262e-04 eta 4:25:07
+epoch [30/50] batch [330/500] time 1.566 (1.563) data 0.001 (0.003) loss 1.1152 (1.0869) acc 75.0000 (72.4242) lr 8.1262e-04 eta 4:25:00
+epoch [30/50] batch [335/500] time 1.543 (1.563) data 0.000 (0.003) loss 1.0615 (1.0880) acc 75.0000 (72.4534) lr 8.1262e-04 eta 4:24:51
+epoch [30/50] batch [340/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.0615 (1.0886) acc 68.7500 (72.4265) lr 8.1262e-04 eta 4:24:41
+epoch [30/50] batch [345/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.7632 (1.0873) acc 78.1250 (72.5000) lr 8.1262e-04 eta 4:24:34
+epoch [30/50] batch [350/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.0723 (1.0850) acc 78.1250 (72.5536) lr 8.1262e-04 eta 4:24:24
+epoch [30/50] batch [355/500] time 1.581 (1.563) data 0.001 (0.003) loss 1.4307 (1.0854) acc 59.3750 (72.5440) lr 8.1262e-04 eta 4:24:17
+epoch [30/50] batch [360/500] time 1.548 (1.563) data 0.001 (0.003) loss 0.8296 (1.0850) acc 84.3750 (72.5955) lr 8.1262e-04 eta 4:24:08
+epoch [30/50] batch [365/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.8916 (1.0875) acc 71.8750 (72.5342) lr 8.1262e-04 eta 4:23:59
+epoch [30/50] batch [370/500] time 1.549 (1.563) data 0.000 (0.003) loss 0.7930 (1.0832) acc 75.0000 (72.6182) lr 8.1262e-04 eta 4:23:49
+epoch [30/50] batch [375/500] time 1.551 (1.562) data 0.000 (0.003) loss 1.3467 (1.0853) acc 68.7500 (72.6000) lr 8.1262e-04 eta 4:23:39
+epoch [30/50] batch [380/500] time 1.560 (1.562) data 0.001 (0.003) loss 1.0781 (1.0852) acc 65.6250 (72.5822) lr 8.1262e-04 eta 4:23:30
+epoch [30/50] batch [385/500] time 1.560 (1.562) data 0.000 (0.003) loss 0.8867 (1.0827) acc 75.0000 (72.5893) lr 8.1262e-04 eta 4:23:23
+epoch [30/50] batch [390/500] time 1.558 (1.562) data 0.000 (0.002) loss 1.3682 (1.0818) acc 62.5000 (72.6122) lr 8.1262e-04 eta 4:23:15
+epoch [30/50] batch [395/500] time 1.528 (1.562) data 0.000 (0.002) loss 1.0605 (1.0823) acc 68.7500 (72.6187) lr 8.1262e-04 eta 4:23:06
+epoch [30/50] batch [400/500] time 1.578 (1.562) data 0.000 (0.002) loss 0.9365 (1.0790) acc 75.0000 (72.6797) lr 8.1262e-04 eta 4:22:58
+epoch [30/50] batch [405/500] time 1.540 (1.562) data 0.000 (0.002) loss 1.5039 (1.0790) acc 68.7500 (72.6389) lr 8.1262e-04 eta 4:22:53
+epoch [30/50] batch [410/500] time 1.596 (1.563) data 0.000 (0.002) loss 0.9634 (1.0787) acc 75.0000 (72.6220) lr 8.1262e-04 eta 4:22:47
+epoch [30/50] batch [415/500] time 1.555 (1.563) data 0.000 (0.002) loss 1.1318 (1.0793) acc 68.7500 (72.6054) lr 8.1262e-04 eta 4:22:39
+epoch [30/50] batch [420/500] time 1.571 (1.563) data 0.000 (0.002) loss 1.0127 (1.0801) acc 81.2500 (72.6339) lr 8.1262e-04 eta 4:22:32
+epoch [30/50] batch [425/500] time 1.559 (1.563) data 0.000 (0.002) loss 0.8848 (1.0789) acc 78.1250 (72.6765) lr 8.1262e-04 eta 4:22:24
+epoch [30/50] batch [430/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.2461 (1.0808) acc 65.6250 (72.6453) lr 8.1262e-04 eta 4:22:18
+epoch [30/50] batch [435/500] time 1.534 (1.563) data 0.001 (0.002) loss 1.1621 (1.0794) acc 75.0000 (72.6796) lr 8.1262e-04 eta 4:22:07
+epoch [30/50] batch [440/500] time 1.555 (1.563) data 0.000 (0.002) loss 2.1270 (1.0797) acc 46.8750 (72.6420) lr 8.1262e-04 eta 4:21:59
+epoch [30/50] batch [445/500] time 1.683 (1.563) data 0.000 (0.002) loss 1.0283 (1.0778) acc 81.2500 (72.7247) lr 8.1262e-04 eta 4:21:54
+epoch [30/50] batch [450/500] time 1.582 (1.563) data 0.000 (0.002) loss 1.2617 (1.0794) acc 71.8750 (72.6944) lr 8.1262e-04 eta 4:21:47
+epoch [30/50] batch [455/500] time 1.565 (1.563) data 0.000 (0.002) loss 0.9663 (1.0770) acc 68.7500 (72.7610) lr 8.1262e-04 eta 4:21:40
+epoch [30/50] batch [460/500] time 1.567 (1.563) data 0.000 (0.002) loss 1.0615 (1.0781) acc 68.7500 (72.7174) lr 8.1262e-04 eta 4:21:33
+epoch [30/50] batch [465/500] time 1.573 (1.563) data 0.001 (0.002) loss 1.3408 (1.0790) acc 65.6250 (72.6680) lr 8.1262e-04 eta 4:21:27
+epoch [30/50] batch [470/500] time 1.550 (1.563) data 0.000 (0.002) loss 0.9590 (1.0787) acc 68.7500 (72.6928) lr 8.1262e-04 eta 4:21:19
+epoch [30/50] batch [475/500] time 1.546 (1.563) data 0.000 (0.002) loss 1.3135 (1.0788) acc 68.7500 (72.6711) lr 8.1262e-04 eta 4:21:10
+epoch [30/50] batch [480/500] time 1.566 (1.563) data 0.000 (0.002) loss 0.3689 (1.0796) acc 90.6250 (72.6823) lr 8.1262e-04 eta 4:21:03
+epoch [30/50] batch [485/500] time 1.559 (1.563) data 0.000 (0.002) loss 1.1143 (1.0798) acc 65.6250 (72.6546) lr 8.1262e-04 eta 4:20:54
+epoch [30/50] batch [490/500] time 1.578 (1.563) data 0.000 (0.002) loss 1.7549 (1.0829) acc 68.7500 (72.6467) lr 8.1262e-04 eta 4:20:47
+epoch [30/50] batch [495/500] time 1.539 (1.563) data 0.000 (0.002) loss 1.4170 (1.0837) acc 68.7500 (72.6263) lr 8.1262e-04 eta 4:20:38
+epoch [30/50] batch [500/500] time 1.546 (1.563) data 0.000 (0.002) loss 1.1084 (1.0840) acc 65.6250 (72.6125) lr 7.5131e-04 eta 4:20:30
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,949
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+epoch [31/50] batch [5/500] time 1.534 (1.717) data 0.000 (0.199) loss 1.3662 (1.2356) acc 68.7500 (74.3750) lr 7.5131e-04 eta 4:46:05
+epoch [31/50] batch [10/500] time 1.568 (1.639) data 0.000 (0.100) loss 0.9072 (1.0597) acc 68.7500 (73.1250) lr 7.5131e-04 eta 4:32:52
+epoch [31/50] batch [15/500] time 1.591 (1.616) data 0.000 (0.067) loss 0.9956 (1.0345) acc 68.7500 (73.1250) lr 7.5131e-04 eta 4:28:58
+epoch [31/50] batch [20/500] time 1.559 (1.602) data 0.001 (0.050) loss 1.2285 (1.0872) acc 65.6250 (73.1250) lr 7.5131e-04 eta 4:26:28
+epoch [31/50] batch [25/500] time 1.564 (1.595) data 0.000 (0.040) loss 0.9561 (1.0935) acc 81.2500 (72.5000) lr 7.5131e-04 eta 4:25:09
+epoch [31/50] batch [30/500] time 1.570 (1.590) data 0.001 (0.034) loss 1.2725 (1.0880) acc 75.0000 (72.3958) lr 7.5131e-04 eta 4:24:13
+epoch [31/50] batch [35/500] time 1.581 (1.587) data 0.001 (0.029) loss 1.2676 (1.0960) acc 65.6250 (72.2321) lr 7.5131e-04 eta 4:23:36
+epoch [31/50] batch [40/500] time 1.543 (1.583) data 0.000 (0.025) loss 0.9839 (1.0733) acc 65.6250 (72.1094) lr 7.5131e-04 eta 4:22:50
+epoch [31/50] batch [45/500] time 1.567 (1.583) data 0.000 (0.023) loss 0.7832 (1.0665) acc 84.3750 (72.8472) lr 7.5131e-04 eta 4:22:40
+epoch [31/50] batch [50/500] time 1.538 (1.580) data 0.000 (0.020) loss 0.7363 (1.0648) acc 84.3750 (72.9375) lr 7.5131e-04 eta 4:22:02
+epoch [31/50] batch [55/500] time 1.544 (1.578) data 0.001 (0.019) loss 1.1377 (1.0712) acc 68.7500 (72.5000) lr 7.5131e-04 eta 4:21:29
+epoch [31/50] batch [60/500] time 1.536 (1.575) data 0.001 (0.017) loss 0.9365 (1.0653) acc 81.2500 (72.5521) lr 7.5131e-04 eta 4:20:57
+epoch [31/50] batch [65/500] time 1.573 (1.574) data 0.000 (0.016) loss 1.2891 (1.0667) acc 71.8750 (72.5962) lr 7.5131e-04 eta 4:20:41
+epoch [31/50] batch [70/500] time 1.545 (1.573) data 0.000 (0.015) loss 1.4375 (1.0733) acc 68.7500 (72.3661) lr 7.5131e-04 eta 4:20:20
+epoch [31/50] batch [75/500] time 1.576 (1.572) data 0.000 (0.014) loss 0.9917 (1.0786) acc 78.1250 (72.3750) lr 7.5131e-04 eta 4:20:03
+epoch [31/50] batch [80/500] time 1.568 (1.571) data 0.001 (0.013) loss 0.7886 (1.0711) acc 71.8750 (72.3828) lr 7.5131e-04 eta 4:19:49
+epoch [31/50] batch [85/500] time 1.569 (1.572) data 0.000 (0.012) loss 0.7642 (1.0756) acc 78.1250 (72.3529) lr 7.5131e-04 eta 4:19:43
+epoch [31/50] batch [90/500] time 1.575 (1.571) data 0.000 (0.011) loss 1.1602 (1.0634) acc 68.7500 (72.5694) lr 7.5131e-04 eta 4:19:32
+epoch [31/50] batch [95/500] time 1.573 (1.572) data 0.000 (0.011) loss 1.1172 (1.0556) acc 75.0000 (72.8618) lr 7.5131e-04 eta 4:19:26
+epoch [31/50] batch [100/500] time 1.568 (1.572) data 0.000 (0.010) loss 0.9990 (1.0641) acc 75.0000 (72.9375) lr 7.5131e-04 eta 4:19:17
+epoch [31/50] batch [105/500] time 1.551 (1.571) data 0.001 (0.010) loss 1.1445 (1.0563) acc 71.8750 (73.0655) lr 7.5131e-04 eta 4:19:06
+epoch [31/50] batch [110/500] time 1.538 (1.571) data 0.000 (0.009) loss 0.8965 (1.0567) acc 71.8750 (73.0114) lr 7.5131e-04 eta 4:18:52
+epoch [31/50] batch [115/500] time 1.565 (1.571) data 0.001 (0.009) loss 0.6372 (1.0582) acc 78.1250 (72.9620) lr 7.5131e-04 eta 4:18:45
+epoch [31/50] batch [120/500] time 1.565 (1.571) data 0.001 (0.009) loss 1.0859 (1.0606) acc 78.1250 (73.0208) lr 7.5131e-04 eta 4:18:37
+epoch [31/50] batch [125/500] time 1.567 (1.570) data 0.000 (0.008) loss 1.0029 (1.0623) acc 71.8750 (72.8750) lr 7.5131e-04 eta 4:18:27
+epoch [31/50] batch [130/500] time 1.567 (1.570) data 0.001 (0.008) loss 0.5430 (1.0616) acc 81.2500 (73.0529) lr 7.5131e-04 eta 4:18:16
+epoch [31/50] batch [135/500] time 1.581 (1.570) data 0.000 (0.008) loss 1.2949 (1.0609) acc 62.5000 (73.0324) lr 7.5131e-04 eta 4:18:06
+epoch [31/50] batch [140/500] time 1.563 (1.570) data 0.001 (0.008) loss 0.6826 (1.0573) acc 75.0000 (73.0357) lr 7.5131e-04 eta 4:17:59
+epoch [31/50] batch [145/500] time 1.594 (1.571) data 0.000 (0.007) loss 0.9526 (1.0540) acc 75.0000 (73.1034) lr 7.5131e-04 eta 4:17:57
+epoch [31/50] batch [150/500] time 1.562 (1.571) data 0.000 (0.007) loss 0.8545 (1.0538) acc 84.3750 (73.1042) lr 7.5131e-04 eta 4:17:51
+epoch [31/50] batch [155/500] time 1.543 (1.570) data 0.000 (0.007) loss 1.1816 (1.0517) acc 68.7500 (73.1250) lr 7.5131e-04 eta 4:17:38
+epoch [31/50] batch [160/500] time 1.566 (1.570) data 0.000 (0.007) loss 0.5420 (1.0494) acc 87.5000 (73.3594) lr 7.5131e-04 eta 4:17:25
+epoch [31/50] batch [165/500] time 1.555 (1.569) data 0.000 (0.006) loss 1.3574 (1.0465) acc 68.7500 (73.4470) lr 7.5131e-04 eta 4:17:12
+epoch [31/50] batch [170/500] time 1.560 (1.569) data 0.001 (0.006) loss 1.2178 (1.0415) acc 68.7500 (73.4926) lr 7.5131e-04 eta 4:17:01
+epoch [31/50] batch [175/500] time 1.530 (1.568) data 0.000 (0.006) loss 1.2197 (1.0455) acc 71.8750 (73.4821) lr 7.5131e-04 eta 4:16:46
+epoch [31/50] batch [180/500] time 1.555 (1.568) data 0.000 (0.006) loss 0.7422 (1.0465) acc 81.2500 (73.5069) lr 7.5131e-04 eta 4:16:34
+epoch [31/50] batch [185/500] time 1.541 (1.567) data 0.001 (0.006) loss 1.0361 (1.0460) acc 75.0000 (73.4628) lr 7.5131e-04 eta 4:16:22
+epoch [31/50] batch [190/500] time 1.533 (1.567) data 0.000 (0.006) loss 0.7202 (1.0479) acc 78.1250 (73.4211) lr 7.5131e-04 eta 4:16:16
+epoch [31/50] batch [195/500] time 1.563 (1.567) data 0.000 (0.006) loss 1.4141 (1.0529) acc 62.5000 (73.2692) lr 7.5131e-04 eta 4:16:07
+epoch [31/50] batch [200/500] time 1.563 (1.567) data 0.001 (0.005) loss 0.8257 (1.0480) acc 81.2500 (73.3906) lr 7.5131e-04 eta 4:15:55
+epoch [31/50] batch [205/500] time 1.569 (1.567) data 0.001 (0.005) loss 0.9805 (1.0520) acc 71.8750 (73.2622) lr 7.5131e-04 eta 4:15:47
+epoch [31/50] batch [210/500] time 1.590 (1.567) data 0.000 (0.005) loss 0.8857 (1.0484) acc 81.2500 (73.4375) lr 7.5131e-04 eta 4:15:40
+epoch [31/50] batch [215/500] time 1.561 (1.567) data 0.001 (0.005) loss 1.8506 (1.0525) acc 65.6250 (73.4012) lr 7.5131e-04 eta 4:15:31
+epoch [31/50] batch [220/500] time 1.562 (1.567) data 0.000 (0.005) loss 0.8125 (1.0529) acc 81.2500 (73.3239) lr 7.5131e-04 eta 4:15:22
+epoch [31/50] batch [225/500] time 1.560 (1.567) data 0.000 (0.005) loss 0.5845 (1.0534) acc 84.3750 (73.3194) lr 7.5131e-04 eta 4:15:13
+epoch [31/50] batch [230/500] time 1.567 (1.566) data 0.000 (0.005) loss 1.0322 (1.0535) acc 65.6250 (73.2065) lr 7.5131e-04 eta 4:15:03
+epoch [31/50] batch [235/500] time 1.556 (1.566) data 0.000 (0.005) loss 1.3350 (1.0613) acc 68.7500 (73.0053) lr 7.5131e-04 eta 4:14:55
+epoch [31/50] batch [240/500] time 1.554 (1.566) data 0.001 (0.005) loss 1.1133 (1.0620) acc 71.8750 (72.9818) lr 7.5131e-04 eta 4:14:47
+epoch [31/50] batch [245/500] time 1.569 (1.566) data 0.001 (0.005) loss 1.3506 (1.0640) acc 71.8750 (73.0612) lr 7.5131e-04 eta 4:14:39
+epoch [31/50] batch [250/500] time 1.556 (1.566) data 0.000 (0.004) loss 1.3164 (1.0668) acc 59.3750 (72.9750) lr 7.5131e-04 eta 4:14:30
+epoch [31/50] batch [255/500] time 1.550 (1.566) data 0.000 (0.004) loss 1.1865 (1.0705) acc 68.7500 (72.9167) lr 7.5131e-04 eta 4:14:20
+epoch [31/50] batch [260/500] time 1.567 (1.566) data 0.000 (0.004) loss 1.2021 (1.0733) acc 68.7500 (72.8726) lr 7.5131e-04 eta 4:14:12
+epoch [31/50] batch [265/500] time 1.585 (1.566) data 0.001 (0.004) loss 0.7690 (1.0709) acc 81.2500 (72.9363) lr 7.5131e-04 eta 4:14:03
+epoch [31/50] batch [270/500] time 1.584 (1.566) data 0.001 (0.004) loss 1.6240 (1.0740) acc 62.5000 (72.8819) lr 7.5131e-04 eta 4:13:57
+epoch [31/50] batch [275/500] time 1.550 (1.566) data 0.000 (0.004) loss 1.1562 (1.0733) acc 71.8750 (72.9432) lr 7.5131e-04 eta 4:13:48
+epoch [31/50] batch [280/500] time 1.542 (1.566) data 0.000 (0.004) loss 0.8774 (1.0729) acc 71.8750 (72.9241) lr 7.5131e-04 eta 4:13:40
+epoch [31/50] batch [285/500] time 1.666 (1.566) data 0.000 (0.004) loss 2.0078 (1.0756) acc 56.2500 (72.8180) lr 7.5131e-04 eta 4:13:36
+epoch [31/50] batch [290/500] time 1.561 (1.566) data 0.001 (0.004) loss 2.0020 (1.0797) acc 62.5000 (72.7694) lr 7.5131e-04 eta 4:13:28
+epoch [31/50] batch [295/500] time 1.541 (1.566) data 0.001 (0.004) loss 1.0049 (1.0769) acc 87.5000 (72.8496) lr 7.5131e-04 eta 4:13:19
+epoch [31/50] batch [300/500] time 1.564 (1.566) data 0.000 (0.004) loss 1.0449 (1.0744) acc 71.8750 (72.9167) lr 7.5131e-04 eta 4:13:09
+epoch [31/50] batch [305/500] time 1.539 (1.566) data 0.000 (0.004) loss 1.3564 (1.0790) acc 59.3750 (72.7766) lr 7.5131e-04 eta 4:13:03
+epoch [31/50] batch [310/500] time 1.581 (1.566) data 0.001 (0.004) loss 0.9863 (1.0797) acc 81.2500 (72.7823) lr 7.5131e-04 eta 4:12:54
+epoch [31/50] batch [315/500] time 1.539 (1.566) data 0.000 (0.004) loss 1.7324 (1.0864) acc 62.5000 (72.6786) lr 7.5131e-04 eta 4:12:44
+epoch [31/50] batch [320/500] time 1.578 (1.566) data 0.000 (0.004) loss 1.0010 (1.0844) acc 78.1250 (72.7051) lr 7.5131e-04 eta 4:12:35
+epoch [31/50] batch [325/500] time 1.558 (1.566) data 0.000 (0.004) loss 0.7427 (1.0852) acc 75.0000 (72.7019) lr 7.5131e-04 eta 4:12:26
+epoch [31/50] batch [330/500] time 1.558 (1.566) data 0.000 (0.003) loss 0.5664 (1.0840) acc 84.3750 (72.7746) lr 7.5131e-04 eta 4:12:21
+epoch [31/50] batch [335/500] time 1.558 (1.566) data 0.001 (0.003) loss 1.0371 (1.0838) acc 68.7500 (72.7425) lr 7.5131e-04 eta 4:12:14
+epoch [31/50] batch [340/500] time 1.528 (1.566) data 0.000 (0.003) loss 1.4902 (1.0861) acc 62.5000 (72.6930) lr 7.5131e-04 eta 4:12:04
+epoch [31/50] batch [345/500] time 1.552 (1.565) data 0.001 (0.003) loss 2.1699 (1.0867) acc 53.1250 (72.6902) lr 7.5131e-04 eta 4:11:54
+epoch [31/50] batch [350/500] time 1.554 (1.565) data 0.000 (0.003) loss 1.0479 (1.0870) acc 75.0000 (72.7232) lr 7.5131e-04 eta 4:11:45
+epoch [31/50] batch [355/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.6484 (1.0848) acc 50.0000 (72.7377) lr 7.5131e-04 eta 4:11:37
+epoch [31/50] batch [360/500] time 1.587 (1.566) data 0.000 (0.003) loss 0.9194 (1.0827) acc 71.8750 (72.7604) lr 7.5131e-04 eta 4:11:32
+epoch [31/50] batch [365/500] time 1.569 (1.566) data 0.000 (0.003) loss 1.6650 (1.0864) acc 68.7500 (72.6969) lr 7.5131e-04 eta 4:11:24
+epoch [31/50] batch [370/500] time 1.566 (1.566) data 0.000 (0.003) loss 1.0010 (1.0858) acc 78.1250 (72.7196) lr 7.5131e-04 eta 4:11:16
+epoch [31/50] batch [375/500] time 1.577 (1.566) data 0.000 (0.003) loss 1.4561 (1.0881) acc 62.5000 (72.7250) lr 7.5131e-04 eta 4:11:09
+epoch [31/50] batch [380/500] time 1.571 (1.566) data 0.000 (0.003) loss 1.0811 (1.0896) acc 65.6250 (72.6562) lr 7.5131e-04 eta 4:11:02
+epoch [31/50] batch [385/500] time 1.565 (1.566) data 0.000 (0.003) loss 1.1455 (1.0888) acc 75.0000 (72.6948) lr 7.5131e-04 eta 4:10:54
+epoch [31/50] batch [390/500] time 1.558 (1.566) data 0.001 (0.003) loss 1.5352 (1.0919) acc 65.6250 (72.6042) lr 7.5131e-04 eta 4:10:45
+epoch [31/50] batch [395/500] time 1.555 (1.565) data 0.000 (0.003) loss 0.6509 (1.0895) acc 78.1250 (72.6661) lr 7.5131e-04 eta 4:10:35
+epoch [31/50] batch [400/500] time 1.568 (1.565) data 0.000 (0.003) loss 0.8467 (1.0906) acc 78.1250 (72.5938) lr 7.5131e-04 eta 4:10:26
+epoch [31/50] batch [405/500] time 1.550 (1.565) data 0.000 (0.003) loss 1.1855 (1.0904) acc 68.7500 (72.6080) lr 7.5131e-04 eta 4:10:16
+epoch [31/50] batch [410/500] time 1.546 (1.565) data 0.000 (0.003) loss 0.9297 (1.0928) acc 78.1250 (72.5838) lr 7.5131e-04 eta 4:10:08
+epoch [31/50] batch [415/500] time 1.553 (1.565) data 0.000 (0.003) loss 0.8501 (1.0936) acc 71.8750 (72.5678) lr 7.5131e-04 eta 4:09:58
+epoch [31/50] batch [420/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.2217 (1.0933) acc 65.6250 (72.5893) lr 7.5131e-04 eta 4:09:51
+epoch [31/50] batch [425/500] time 1.583 (1.565) data 0.000 (0.003) loss 1.2686 (1.0956) acc 75.0000 (72.5809) lr 7.5131e-04 eta 4:09:44
+epoch [31/50] batch [430/500] time 1.571 (1.565) data 0.000 (0.003) loss 1.1582 (1.0955) acc 65.6250 (72.6163) lr 7.5131e-04 eta 4:09:39
+epoch [31/50] batch [435/500] time 1.526 (1.565) data 0.000 (0.003) loss 0.8076 (1.0943) acc 78.1250 (72.6365) lr 7.5131e-04 eta 4:09:30
+epoch [31/50] batch [440/500] time 1.560 (1.565) data 0.001 (0.003) loss 1.4521 (1.0968) acc 59.3750 (72.5639) lr 7.5131e-04 eta 4:09:21
+epoch [31/50] batch [445/500] time 1.573 (1.565) data 0.000 (0.003) loss 1.1904 (1.0962) acc 68.7500 (72.5281) lr 7.5131e-04 eta 4:09:11
+epoch [31/50] batch [450/500] time 1.545 (1.565) data 0.000 (0.003) loss 0.9077 (1.0948) acc 78.1250 (72.5556) lr 7.5131e-04 eta 4:09:03
+epoch [31/50] batch [455/500] time 1.539 (1.565) data 0.000 (0.003) loss 0.8770 (1.0952) acc 75.0000 (72.5412) lr 7.5131e-04 eta 4:08:54
+epoch [31/50] batch [460/500] time 1.563 (1.564) data 0.000 (0.003) loss 1.2051 (1.0955) acc 71.8750 (72.5272) lr 7.5131e-04 eta 4:08:45
+epoch [31/50] batch [465/500] time 1.561 (1.564) data 0.000 (0.003) loss 0.8052 (1.0957) acc 87.5000 (72.5470) lr 7.5131e-04 eta 4:08:36
+epoch [31/50] batch [470/500] time 1.598 (1.565) data 0.000 (0.003) loss 0.5508 (1.0940) acc 84.3750 (72.6330) lr 7.5131e-04 eta 4:08:29
+epoch [31/50] batch [475/500] time 1.566 (1.565) data 0.000 (0.003) loss 0.6470 (1.0922) acc 78.1250 (72.6842) lr 7.5131e-04 eta 4:08:24
+epoch [31/50] batch [480/500] time 1.555 (1.565) data 0.000 (0.003) loss 1.4639 (1.0942) acc 59.3750 (72.6432) lr 7.5131e-04 eta 4:08:15
+epoch [31/50] batch [485/500] time 1.579 (1.565) data 0.001 (0.002) loss 0.7729 (1.0940) acc 81.2500 (72.6482) lr 7.5131e-04 eta 4:08:08
+epoch [31/50] batch [490/500] time 1.554 (1.565) data 0.000 (0.002) loss 0.8037 (1.0935) acc 68.7500 (72.6403) lr 7.5131e-04 eta 4:07:59
+epoch [31/50] batch [495/500] time 1.540 (1.565) data 0.000 (0.002) loss 1.4971 (1.0957) acc 50.0000 (72.5253) lr 7.5131e-04 eta 4:07:50
+epoch [31/50] batch [500/500] time 1.566 (1.564) data 0.000 (0.002) loss 1.0469 (1.0947) acc 81.2500 (72.5625) lr 6.9098e-04 eta 4:07:41
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,010
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [32/50] batch [5/500] time 1.564 (1.670) data 0.000 (0.167) loss 1.1035 (1.2072) acc 68.7500 (70.0000) lr 6.9098e-04 eta 4:24:16
+epoch [32/50] batch [10/500] time 1.539 (1.609) data 0.000 (0.084) loss 1.0869 (1.1386) acc 62.5000 (69.6875) lr 6.9098e-04 eta 4:14:32
+epoch [32/50] batch [15/500] time 1.554 (1.592) data 0.001 (0.056) loss 0.5576 (1.1084) acc 78.1250 (71.0417) lr 6.9098e-04 eta 4:11:35
+epoch [32/50] batch [20/500] time 1.562 (1.585) data 0.001 (0.042) loss 1.1738 (1.0792) acc 78.1250 (72.3438) lr 6.9098e-04 eta 4:10:30
+epoch [32/50] batch [25/500] time 1.545 (1.579) data 0.001 (0.034) loss 1.0342 (1.0576) acc 68.7500 (72.0000) lr 6.9098e-04 eta 4:09:16
+epoch [32/50] batch [30/500] time 1.594 (1.577) data 0.000 (0.028) loss 1.1230 (1.0566) acc 68.7500 (72.1875) lr 6.9098e-04 eta 4:08:52
+epoch [32/50] batch [35/500] time 1.563 (1.574) data 0.001 (0.024) loss 0.9331 (1.0694) acc 78.1250 (72.4107) lr 6.9098e-04 eta 4:08:22
+epoch [32/50] batch [40/500] time 1.571 (1.574) data 0.000 (0.021) loss 0.9502 (1.0842) acc 68.7500 (72.1875) lr 6.9098e-04 eta 4:08:13
+epoch [32/50] batch [45/500] time 1.532 (1.573) data 0.001 (0.019) loss 1.2002 (1.0832) acc 71.8750 (72.4306) lr 6.9098e-04 eta 4:07:53
+epoch [32/50] batch [50/500] time 1.532 (1.571) data 0.001 (0.017) loss 1.2539 (1.0746) acc 68.7500 (72.5625) lr 6.9098e-04 eta 4:07:29
+epoch [32/50] batch [55/500] time 1.540 (1.570) data 0.000 (0.016) loss 0.4421 (1.0478) acc 87.5000 (73.0682) lr 6.9098e-04 eta 4:07:06
+epoch [32/50] batch [60/500] time 1.574 (1.568) data 0.001 (0.014) loss 0.7520 (1.0745) acc 81.2500 (72.5521) lr 6.9098e-04 eta 4:06:45
+epoch [32/50] batch [65/500] time 1.537 (1.569) data 0.001 (0.013) loss 1.1152 (1.0725) acc 65.6250 (72.4519) lr 6.9098e-04 eta 4:06:41
+epoch [32/50] batch [70/500] time 1.562 (1.568) data 0.000 (0.012) loss 0.7729 (1.0601) acc 78.1250 (72.7232) lr 6.9098e-04 eta 4:06:25
+epoch [32/50] batch [75/500] time 1.560 (1.567) data 0.000 (0.012) loss 1.4795 (1.0519) acc 68.7500 (73.0833) lr 6.9098e-04 eta 4:06:08
+epoch [32/50] batch [80/500] time 1.540 (1.566) data 0.000 (0.011) loss 0.6650 (1.0481) acc 78.1250 (73.0078) lr 6.9098e-04 eta 4:05:53
+epoch [32/50] batch [85/500] time 1.559 (1.565) data 0.000 (0.010) loss 0.8198 (1.0471) acc 75.0000 (72.8676) lr 6.9098e-04 eta 4:05:36
+epoch [32/50] batch [90/500] time 1.575 (1.565) data 0.000 (0.010) loss 0.9888 (1.0423) acc 78.1250 (73.0903) lr 6.9098e-04 eta 4:05:26
+epoch [32/50] batch [95/500] time 1.550 (1.565) data 0.001 (0.009) loss 0.6250 (1.0483) acc 87.5000 (73.0592) lr 6.9098e-04 eta 4:05:14
+epoch [32/50] batch [100/500] time 1.599 (1.565) data 0.000 (0.009) loss 1.2715 (1.0483) acc 78.1250 (73.1562) lr 6.9098e-04 eta 4:05:11
+epoch [32/50] batch [105/500] time 1.574 (1.565) data 0.000 (0.008) loss 0.9346 (1.0507) acc 71.8750 (73.0655) lr 6.9098e-04 eta 4:05:05
+epoch [32/50] batch [110/500] time 1.524 (1.566) data 0.000 (0.008) loss 0.4937 (1.0434) acc 87.5000 (73.2955) lr 6.9098e-04 eta 4:05:00
+epoch [32/50] batch [115/500] time 1.571 (1.565) data 0.000 (0.008) loss 1.4893 (1.0494) acc 68.7500 (73.1793) lr 6.9098e-04 eta 4:04:47
+epoch [32/50] batch [120/500] time 1.534 (1.565) data 0.001 (0.007) loss 1.2080 (1.0535) acc 68.7500 (73.0990) lr 6.9098e-04 eta 4:04:36
+epoch [32/50] batch [125/500] time 1.555 (1.565) data 0.000 (0.007) loss 1.2969 (1.0482) acc 68.7500 (73.3000) lr 6.9098e-04 eta 4:04:27
+epoch [32/50] batch [130/500] time 1.551 (1.565) data 0.000 (0.007) loss 0.8281 (1.0387) acc 78.1250 (73.5337) lr 6.9098e-04 eta 4:04:20
+epoch [32/50] batch [135/500] time 1.552 (1.564) data 0.000 (0.007) loss 1.1426 (1.0393) acc 65.6250 (73.4491) lr 6.9098e-04 eta 4:04:10
+epoch [32/50] batch [140/500] time 1.560 (1.564) data 0.000 (0.006) loss 1.1064 (1.0462) acc 75.0000 (73.2812) lr 6.9098e-04 eta 4:03:56
+epoch [32/50] batch [145/500] time 1.578 (1.564) data 0.001 (0.006) loss 1.5098 (1.0499) acc 62.5000 (73.2328) lr 6.9098e-04 eta 4:03:52
+epoch [32/50] batch [150/500] time 1.543 (1.564) data 0.000 (0.006) loss 0.5737 (1.0487) acc 78.1250 (73.2083) lr 6.9098e-04 eta 4:03:43
+epoch [32/50] batch [155/500] time 1.569 (1.564) data 0.000 (0.006) loss 0.8960 (1.0470) acc 84.3750 (73.3065) lr 6.9098e-04 eta 4:03:34
+epoch [32/50] batch [160/500] time 1.573 (1.564) data 0.000 (0.006) loss 1.5518 (1.0488) acc 68.7500 (73.2812) lr 6.9098e-04 eta 4:03:26
+epoch [32/50] batch [165/500] time 1.550 (1.564) data 0.001 (0.006) loss 1.0605 (1.0435) acc 62.5000 (73.3523) lr 6.9098e-04 eta 4:03:16
+epoch [32/50] batch [170/500] time 1.541 (1.563) data 0.001 (0.005) loss 1.6152 (1.0475) acc 59.3750 (73.2904) lr 6.9098e-04 eta 4:03:03
+epoch [32/50] batch [175/500] time 1.555 (1.563) data 0.000 (0.005) loss 1.4658 (1.0546) acc 59.3750 (73.0357) lr 6.9098e-04 eta 4:02:50
+epoch [32/50] batch [180/500] time 1.578 (1.563) data 0.000 (0.005) loss 1.2754 (1.0541) acc 62.5000 (72.8646) lr 6.9098e-04 eta 4:02:43
+epoch [32/50] batch [185/500] time 1.569 (1.563) data 0.000 (0.005) loss 1.4736 (1.0549) acc 68.7500 (72.8885) lr 6.9098e-04 eta 4:02:34
+epoch [32/50] batch [190/500] time 1.597 (1.563) data 0.000 (0.005) loss 0.9092 (1.0509) acc 71.8750 (72.9770) lr 6.9098e-04 eta 4:02:27
+epoch [32/50] batch [195/500] time 1.563 (1.563) data 0.000 (0.005) loss 0.7148 (1.0499) acc 84.3750 (72.9487) lr 6.9098e-04 eta 4:02:20
+epoch [32/50] batch [200/500] time 1.545 (1.562) data 0.000 (0.005) loss 1.3418 (1.0598) acc 75.0000 (72.8438) lr 6.9098e-04 eta 4:02:11
+epoch [32/50] batch [205/500] time 1.622 (1.563) data 0.000 (0.005) loss 1.4316 (1.0641) acc 68.7500 (72.8506) lr 6.9098e-04 eta 4:02:05
+epoch [32/50] batch [210/500] time 1.567 (1.563) data 0.000 (0.004) loss 1.3291 (1.0692) acc 62.5000 (72.7381) lr 6.9098e-04 eta 4:01:59
+epoch [32/50] batch [215/500] time 1.587 (1.563) data 0.000 (0.004) loss 0.6045 (1.0670) acc 84.3750 (72.8052) lr 6.9098e-04 eta 4:01:54
+epoch [32/50] batch [220/500] time 1.560 (1.563) data 0.000 (0.004) loss 0.6895 (1.0644) acc 81.2500 (72.7841) lr 6.9098e-04 eta 4:01:47
+epoch [32/50] batch [225/500] time 1.558 (1.563) data 0.000 (0.004) loss 1.2783 (1.0674) acc 71.8750 (72.7639) lr 6.9098e-04 eta 4:01:40
+epoch [32/50] batch [230/500] time 1.586 (1.564) data 0.001 (0.004) loss 0.9053 (1.0655) acc 68.7500 (72.7989) lr 6.9098e-04 eta 4:01:34
+epoch [32/50] batch [235/500] time 1.566 (1.564) data 0.000 (0.004) loss 0.8257 (1.0656) acc 75.0000 (72.6995) lr 6.9098e-04 eta 4:01:26
+epoch [32/50] batch [240/500] time 1.565 (1.563) data 0.000 (0.004) loss 1.0273 (1.0618) acc 78.1250 (72.8516) lr 6.9098e-04 eta 4:01:15
+epoch [32/50] batch [245/500] time 1.583 (1.564) data 0.000 (0.004) loss 1.3994 (1.0677) acc 68.7500 (72.7679) lr 6.9098e-04 eta 4:01:10
+epoch [32/50] batch [250/500] time 1.586 (1.564) data 0.000 (0.004) loss 1.0703 (1.0664) acc 71.8750 (72.8500) lr 6.9098e-04 eta 4:01:06
+epoch [32/50] batch [255/500] time 1.573 (1.564) data 0.000 (0.004) loss 1.1455 (1.0663) acc 68.7500 (72.7819) lr 6.9098e-04 eta 4:00:57
+epoch [32/50] batch [260/500] time 1.583 (1.564) data 0.000 (0.004) loss 0.7144 (1.0699) acc 75.0000 (72.7524) lr 6.9098e-04 eta 4:00:51
+epoch [32/50] batch [265/500] time 1.555 (1.564) data 0.000 (0.004) loss 1.0947 (1.0694) acc 78.1250 (72.8420) lr 6.9098e-04 eta 4:00:43
+epoch [32/50] batch [270/500] time 1.571 (1.564) data 0.000 (0.004) loss 1.3057 (1.0713) acc 68.7500 (72.8009) lr 6.9098e-04 eta 4:00:35
+epoch [32/50] batch [275/500] time 1.567 (1.564) data 0.000 (0.003) loss 1.1611 (1.0705) acc 62.5000 (72.7159) lr 6.9098e-04 eta 4:00:29
+epoch [32/50] batch [280/500] time 1.560 (1.564) data 0.000 (0.003) loss 1.1748 (1.0684) acc 75.0000 (72.7567) lr 6.9098e-04 eta 4:00:20
+epoch [32/50] batch [285/500] time 1.561 (1.564) data 0.000 (0.003) loss 0.6763 (1.0661) acc 87.5000 (72.7961) lr 6.9098e-04 eta 4:00:12
+epoch [32/50] batch [290/500] time 1.564 (1.564) data 0.000 (0.003) loss 0.7354 (1.0667) acc 78.1250 (72.7478) lr 6.9098e-04 eta 4:00:03
+epoch [32/50] batch [295/500] time 1.539 (1.564) data 0.000 (0.003) loss 1.2578 (1.0684) acc 65.6250 (72.6589) lr 6.9098e-04 eta 3:59:52
+epoch [32/50] batch [300/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.3301 (1.0715) acc 62.5000 (72.6458) lr 6.9098e-04 eta 3:59:44
+epoch [32/50] batch [305/500] time 1.570 (1.563) data 0.000 (0.003) loss 0.7007 (1.0747) acc 81.2500 (72.6127) lr 6.9098e-04 eta 3:59:34
+epoch [32/50] batch [310/500] time 1.562 (1.563) data 0.000 (0.003) loss 1.1445 (1.0741) acc 65.6250 (72.6210) lr 6.9098e-04 eta 3:59:26
+epoch [32/50] batch [315/500] time 1.561 (1.563) data 0.000 (0.003) loss 1.3711 (1.0753) acc 75.0000 (72.6786) lr 6.9098e-04 eta 3:59:19
+epoch [32/50] batch [320/500] time 1.554 (1.563) data 0.000 (0.003) loss 1.5781 (1.0751) acc 65.6250 (72.6758) lr 6.9098e-04 eta 3:59:09
+epoch [32/50] batch [325/500] time 1.575 (1.563) data 0.000 (0.003) loss 1.0322 (1.0779) acc 78.1250 (72.6250) lr 6.9098e-04 eta 3:59:01
+epoch [32/50] batch [330/500] time 1.561 (1.563) data 0.000 (0.003) loss 1.1045 (1.0764) acc 75.0000 (72.6799) lr 6.9098e-04 eta 3:58:54
+epoch [32/50] batch [335/500] time 1.560 (1.563) data 0.000 (0.003) loss 0.7959 (1.0759) acc 78.1250 (72.6772) lr 6.9098e-04 eta 3:58:45
+epoch [32/50] batch [340/500] time 1.575 (1.563) data 0.000 (0.003) loss 1.1387 (1.0778) acc 75.0000 (72.6379) lr 6.9098e-04 eta 3:58:37
+epoch [32/50] batch [345/500] time 1.558 (1.563) data 0.000 (0.003) loss 1.7734 (1.0790) acc 59.3750 (72.6178) lr 6.9098e-04 eta 3:58:28
+epoch [32/50] batch [350/500] time 1.574 (1.563) data 0.000 (0.003) loss 0.8022 (1.0796) acc 87.5000 (72.6786) lr 6.9098e-04 eta 3:58:23
+epoch [32/50] batch [355/500] time 1.578 (1.563) data 0.000 (0.003) loss 0.9707 (1.0813) acc 81.2500 (72.7113) lr 6.9098e-04 eta 3:58:17
+epoch [32/50] batch [360/500] time 1.577 (1.563) data 0.000 (0.003) loss 1.2305 (1.0774) acc 68.7500 (72.7517) lr 6.9098e-04 eta 3:58:09
+epoch [32/50] batch [365/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.0488 (1.0805) acc 75.0000 (72.6798) lr 6.9098e-04 eta 3:58:00
+epoch [32/50] batch [370/500] time 1.531 (1.563) data 0.001 (0.003) loss 1.0488 (1.0805) acc 71.8750 (72.6943) lr 6.9098e-04 eta 3:57:52
+epoch [32/50] batch [375/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.2422 (1.0800) acc 65.6250 (72.7083) lr 6.9098e-04 eta 3:57:44
+epoch [32/50] batch [380/500] time 1.546 (1.563) data 0.000 (0.003) loss 0.9365 (1.0782) acc 81.2500 (72.7467) lr 6.9098e-04 eta 3:57:36
+epoch [32/50] batch [385/500] time 1.556 (1.563) data 0.000 (0.003) loss 1.5156 (1.0803) acc 59.3750 (72.6867) lr 6.9098e-04 eta 3:57:28
+epoch [32/50] batch [390/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.2129 (1.0818) acc 71.8750 (72.6603) lr 6.9098e-04 eta 3:57:19
+epoch [32/50] batch [395/500] time 1.562 (1.563) data 0.000 (0.002) loss 0.6182 (1.0808) acc 84.3750 (72.7215) lr 6.9098e-04 eta 3:57:14
+epoch [32/50] batch [400/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.8521 (1.0789) acc 81.2500 (72.7656) lr 6.9098e-04 eta 3:57:04
+epoch [32/50] batch [405/500] time 1.548 (1.563) data 0.000 (0.002) loss 0.6426 (1.0781) acc 75.0000 (72.7855) lr 6.9098e-04 eta 3:56:57
+epoch [32/50] batch [410/500] time 1.546 (1.563) data 0.000 (0.002) loss 0.9868 (1.0751) acc 71.8750 (72.8277) lr 6.9098e-04 eta 3:56:49
+epoch [32/50] batch [415/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.7832 (1.0726) acc 75.0000 (72.8690) lr 6.9098e-04 eta 3:56:41
+epoch [32/50] batch [420/500] time 1.557 (1.563) data 0.000 (0.002) loss 0.7656 (1.0703) acc 84.3750 (72.9241) lr 6.9098e-04 eta 3:56:33
+epoch [32/50] batch [425/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.8447 (1.0710) acc 59.3750 (72.8971) lr 6.9098e-04 eta 3:56:23
+epoch [32/50] batch [430/500] time 1.559 (1.563) data 0.000 (0.002) loss 1.2119 (1.0722) acc 71.8750 (72.8488) lr 6.9098e-04 eta 3:56:15
+epoch [32/50] batch [435/500] time 1.545 (1.563) data 0.000 (0.002) loss 1.4707 (1.0744) acc 68.7500 (72.8161) lr 6.9098e-04 eta 3:56:07
+epoch [32/50] batch [440/500] time 1.558 (1.563) data 0.000 (0.002) loss 0.6177 (1.0754) acc 81.2500 (72.7912) lr 6.9098e-04 eta 3:55:59
+epoch [32/50] batch [445/500] time 1.591 (1.563) data 0.000 (0.002) loss 1.4678 (1.0776) acc 71.8750 (72.7177) lr 6.9098e-04 eta 3:55:50
+epoch [32/50] batch [450/500] time 1.554 (1.563) data 0.000 (0.002) loss 1.2158 (1.0769) acc 71.8750 (72.7639) lr 6.9098e-04 eta 3:55:43
+epoch [32/50] batch [455/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.4434 (1.0775) acc 65.6250 (72.7473) lr 6.9098e-04 eta 3:55:35
+epoch [32/50] batch [460/500] time 1.566 (1.563) data 0.000 (0.002) loss 1.6025 (1.0790) acc 50.0000 (72.6902) lr 6.9098e-04 eta 3:55:27
+epoch [32/50] batch [465/500] time 1.568 (1.563) data 0.000 (0.002) loss 1.0557 (1.0765) acc 75.0000 (72.7755) lr 6.9098e-04 eta 3:55:19
+epoch [32/50] batch [470/500] time 1.555 (1.563) data 0.000 (0.002) loss 1.2363 (1.0790) acc 68.7500 (72.7527) lr 6.9098e-04 eta 3:55:11
+epoch [32/50] batch [475/500] time 1.557 (1.563) data 0.000 (0.002) loss 0.9082 (1.0814) acc 75.0000 (72.7237) lr 6.9098e-04 eta 3:55:03
+epoch [32/50] batch [480/500] time 1.586 (1.563) data 0.000 (0.002) loss 1.6289 (1.0806) acc 78.1250 (72.7734) lr 6.9098e-04 eta 3:54:55
+epoch [32/50] batch [485/500] time 1.544 (1.563) data 0.001 (0.002) loss 0.8623 (1.0790) acc 75.0000 (72.8028) lr 6.9098e-04 eta 3:54:45
+epoch [32/50] batch [490/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.8257 (1.0785) acc 78.1250 (72.8125) lr 6.9098e-04 eta 3:54:37
+epoch [32/50] batch [495/500] time 1.586 (1.563) data 0.000 (0.002) loss 1.0889 (1.0788) acc 71.8750 (72.7967) lr 6.9098e-04 eta 3:54:31
+epoch [32/50] batch [500/500] time 1.557 (1.563) data 0.000 (0.002) loss 1.4375 (1.0793) acc 71.8750 (72.7812) lr 6.3188e-04 eta 3:54:22
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,083
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [33/50] batch [5/500] time 1.539 (1.643) data 0.000 (0.156) loss 0.9487 (1.1862) acc 78.1250 (70.0000) lr 6.3188e-04 eta 4:06:20
+epoch [33/50] batch [10/500] time 1.562 (1.600) data 0.001 (0.078) loss 0.8979 (1.1542) acc 81.2500 (71.5625) lr 6.3188e-04 eta 3:59:39
+epoch [33/50] batch [15/500] time 1.564 (1.586) data 0.000 (0.052) loss 1.0078 (1.0964) acc 68.7500 (72.5000) lr 6.3188e-04 eta 3:57:27
+epoch [33/50] batch [20/500] time 1.562 (1.581) data 0.000 (0.039) loss 1.4004 (1.1304) acc 56.2500 (71.2500) lr 6.3188e-04 eta 3:56:40
+epoch [33/50] batch [25/500] time 1.570 (1.579) data 0.000 (0.032) loss 0.5640 (1.0452) acc 84.3750 (72.8750) lr 6.3188e-04 eta 3:56:11
+epoch [33/50] batch [30/500] time 1.583 (1.577) data 0.000 (0.026) loss 1.0566 (1.0753) acc 78.1250 (72.1875) lr 6.3188e-04 eta 3:55:47
+epoch [33/50] batch [35/500] time 1.551 (1.575) data 0.000 (0.023) loss 1.5713 (1.1019) acc 56.2500 (72.1429) lr 6.3188e-04 eta 3:55:22
+epoch [33/50] batch [40/500] time 1.559 (1.577) data 0.000 (0.020) loss 0.9424 (1.0596) acc 75.0000 (73.2031) lr 6.3188e-04 eta 3:55:32
+epoch [33/50] batch [45/500] time 1.575 (1.577) data 0.000 (0.018) loss 0.5483 (1.0471) acc 87.5000 (73.2639) lr 6.3188e-04 eta 3:55:18
+epoch [33/50] batch [50/500] time 1.541 (1.575) data 0.000 (0.016) loss 1.0830 (1.0400) acc 65.6250 (73.5000) lr 6.3188e-04 eta 3:54:55
+epoch [33/50] batch [55/500] time 1.543 (1.573) data 0.001 (0.015) loss 1.0986 (1.0487) acc 78.1250 (73.3523) lr 6.3188e-04 eta 3:54:27
+epoch [33/50] batch [60/500] time 1.573 (1.571) data 0.000 (0.013) loss 0.8994 (1.0457) acc 75.0000 (73.3333) lr 6.3188e-04 eta 3:54:08
+epoch [33/50] batch [65/500] time 1.551 (1.570) data 0.000 (0.012) loss 0.8994 (1.0542) acc 78.1250 (73.2692) lr 6.3188e-04 eta 3:53:50
+epoch [33/50] batch [70/500] time 1.557 (1.569) data 0.001 (0.012) loss 1.2129 (1.0565) acc 68.7500 (73.1696) lr 6.3188e-04 eta 3:53:33
+epoch [33/50] batch [75/500] time 1.563 (1.569) data 0.000 (0.011) loss 0.9077 (1.0394) acc 84.3750 (73.8333) lr 6.3188e-04 eta 3:53:25
+epoch [33/50] batch [80/500] time 1.585 (1.570) data 0.000 (0.010) loss 1.4443 (1.0334) acc 68.7500 (73.8672) lr 6.3188e-04 eta 3:53:26
+epoch [33/50] batch [85/500] time 1.554 (1.570) data 0.001 (0.010) loss 0.6646 (1.0334) acc 81.2500 (73.8603) lr 6.3188e-04 eta 3:53:12
+epoch [33/50] batch [90/500] time 1.565 (1.569) data 0.001 (0.009) loss 1.2695 (1.0331) acc 65.6250 (73.8542) lr 6.3188e-04 eta 3:53:02
+epoch [33/50] batch [95/500] time 1.560 (1.569) data 0.001 (0.009) loss 1.6455 (1.0386) acc 71.8750 (73.8816) lr 6.3188e-04 eta 3:52:52
+epoch [33/50] batch [100/500] time 1.572 (1.569) data 0.000 (0.008) loss 0.8813 (1.0413) acc 75.0000 (73.6875) lr 6.3188e-04 eta 3:52:44
+epoch [33/50] batch [105/500] time 1.549 (1.569) data 0.000 (0.008) loss 1.6006 (1.0385) acc 56.2500 (73.7798) lr 6.3188e-04 eta 3:52:32
+epoch [33/50] batch [110/500] time 1.554 (1.568) data 0.000 (0.008) loss 1.2148 (1.0453) acc 71.8750 (73.7216) lr 6.3188e-04 eta 3:52:21
+epoch [33/50] batch [115/500] time 1.569 (1.568) data 0.000 (0.007) loss 0.9287 (1.0461) acc 75.0000 (73.8315) lr 6.3188e-04 eta 3:52:11
+epoch [33/50] batch [120/500] time 1.561 (1.568) data 0.001 (0.007) loss 0.9839 (1.0469) acc 71.8750 (73.8281) lr 6.3188e-04 eta 3:52:01
+epoch [33/50] batch [125/500] time 1.535 (1.567) data 0.001 (0.007) loss 1.3623 (1.0452) acc 68.7500 (73.9000) lr 6.3188e-04 eta 3:51:48
+epoch [33/50] batch [130/500] time 1.570 (1.567) data 0.001 (0.006) loss 1.1924 (1.0522) acc 81.2500 (73.8462) lr 6.3188e-04 eta 3:51:41
+epoch [33/50] batch [135/500] time 1.560 (1.567) data 0.000 (0.006) loss 0.5439 (1.0400) acc 81.2500 (74.0046) lr 6.3188e-04 eta 3:51:30
+epoch [33/50] batch [140/500] time 1.573 (1.568) data 0.000 (0.006) loss 0.8921 (1.0310) acc 75.0000 (74.1964) lr 6.3188e-04 eta 3:51:28
+epoch [33/50] batch [145/500] time 1.564 (1.567) data 0.001 (0.006) loss 0.6802 (1.0276) acc 87.5000 (74.3750) lr 6.3188e-04 eta 3:51:16
+epoch [33/50] batch [150/500] time 1.581 (1.567) data 0.000 (0.006) loss 0.2839 (1.0245) acc 93.7500 (74.5208) lr 6.3188e-04 eta 3:51:05
+epoch [33/50] batch [155/500] time 1.560 (1.567) data 0.000 (0.005) loss 1.1416 (1.0210) acc 62.5000 (74.6573) lr 6.3188e-04 eta 3:50:56
+epoch [33/50] batch [160/500] time 1.567 (1.567) data 0.001 (0.005) loss 1.2266 (1.0200) acc 65.6250 (74.6484) lr 6.3188e-04 eta 3:50:50
+epoch [33/50] batch [165/500] time 1.558 (1.567) data 0.000 (0.005) loss 0.9702 (1.0224) acc 78.1250 (74.6023) lr 6.3188e-04 eta 3:50:40
+epoch [33/50] batch [170/500] time 1.562 (1.567) data 0.000 (0.005) loss 1.2959 (1.0236) acc 59.3750 (74.6140) lr 6.3188e-04 eta 3:50:33
+epoch [33/50] batch [175/500] time 1.546 (1.566) data 0.001 (0.005) loss 1.1787 (1.0282) acc 71.8750 (74.4643) lr 6.3188e-04 eta 3:50:24
+epoch [33/50] batch [180/500] time 1.565 (1.566) data 0.000 (0.005) loss 1.3896 (1.0333) acc 68.7500 (74.3056) lr 6.3188e-04 eta 3:50:13
+epoch [33/50] batch [185/500] time 1.539 (1.566) data 0.001 (0.005) loss 1.0332 (1.0326) acc 75.0000 (74.3412) lr 6.3188e-04 eta 3:50:04
+epoch [33/50] batch [190/500] time 1.573 (1.566) data 0.001 (0.005) loss 2.2070 (1.0370) acc 46.8750 (74.1941) lr 6.3188e-04 eta 3:49:55
+epoch [33/50] batch [195/500] time 1.554 (1.566) data 0.001 (0.004) loss 1.1318 (1.0412) acc 68.7500 (74.0865) lr 6.3188e-04 eta 3:49:47
+epoch [33/50] batch [200/500] time 1.573 (1.566) data 0.000 (0.004) loss 0.9995 (1.0411) acc 71.8750 (74.0000) lr 6.3188e-04 eta 3:49:39
+epoch [33/50] batch [205/500] time 1.551 (1.566) data 0.001 (0.004) loss 0.7979 (1.0408) acc 75.0000 (73.9177) lr 6.3188e-04 eta 3:49:31
+epoch [33/50] batch [210/500] time 1.557 (1.566) data 0.000 (0.004) loss 1.0195 (1.0434) acc 78.1250 (73.8988) lr 6.3188e-04 eta 3:49:21
+epoch [33/50] batch [215/500] time 1.561 (1.566) data 0.000 (0.004) loss 1.1533 (1.0487) acc 71.8750 (73.7064) lr 6.3188e-04 eta 3:49:13
+epoch [33/50] batch [220/500] time 1.556 (1.566) data 0.000 (0.004) loss 0.9238 (1.0497) acc 71.8750 (73.7358) lr 6.3188e-04 eta 3:49:05
+epoch [33/50] batch [225/500] time 1.551 (1.566) data 0.000 (0.004) loss 1.1680 (1.0490) acc 65.6250 (73.7639) lr 6.3188e-04 eta 3:48:57
+epoch [33/50] batch [230/500] time 1.548 (1.565) data 0.000 (0.004) loss 1.3447 (1.0479) acc 62.5000 (73.7772) lr 6.3188e-04 eta 3:48:47
+epoch [33/50] batch [235/500] time 1.547 (1.565) data 0.000 (0.004) loss 1.0615 (1.0492) acc 78.1250 (73.7633) lr 6.3188e-04 eta 3:48:37
+epoch [33/50] batch [240/500] time 1.550 (1.565) data 0.000 (0.004) loss 1.1250 (1.0457) acc 75.0000 (73.8802) lr 6.3188e-04 eta 3:48:27
+epoch [33/50] batch [245/500] time 1.560 (1.565) data 0.000 (0.004) loss 1.1631 (1.0455) acc 59.3750 (73.8648) lr 6.3188e-04 eta 3:48:17
+epoch [33/50] batch [250/500] time 1.581 (1.564) data 0.000 (0.004) loss 0.5503 (1.0472) acc 71.8750 (73.8375) lr 6.3188e-04 eta 3:48:08
+epoch [33/50] batch [255/500] time 1.578 (1.564) data 0.000 (0.004) loss 1.1250 (1.0471) acc 71.8750 (73.8725) lr 6.3188e-04 eta 3:48:00
+epoch [33/50] batch [260/500] time 1.584 (1.564) data 0.000 (0.003) loss 1.2559 (1.0480) acc 65.6250 (73.8822) lr 6.3188e-04 eta 3:47:53
+epoch [33/50] batch [265/500] time 1.568 (1.564) data 0.001 (0.003) loss 1.0762 (1.0450) acc 62.5000 (73.8443) lr 6.3188e-04 eta 3:47:45
+epoch [33/50] batch [270/500] time 1.556 (1.564) data 0.000 (0.003) loss 1.1191 (1.0468) acc 81.2500 (73.7847) lr 6.3188e-04 eta 3:47:36
+epoch [33/50] batch [275/500] time 1.551 (1.564) data 0.001 (0.003) loss 1.2666 (1.0486) acc 78.1250 (73.8182) lr 6.3188e-04 eta 3:47:28
+epoch [33/50] batch [280/500] time 1.729 (1.565) data 0.000 (0.003) loss 0.8574 (1.0498) acc 81.2500 (73.7835) lr 6.3188e-04 eta 3:47:26
+epoch [33/50] batch [285/500] time 1.575 (1.565) data 0.000 (0.003) loss 1.3857 (1.0524) acc 71.8750 (73.7829) lr 6.3188e-04 eta 3:47:18
+epoch [33/50] batch [290/500] time 1.561 (1.565) data 0.001 (0.003) loss 0.9912 (1.0517) acc 78.1250 (73.8470) lr 6.3188e-04 eta 3:47:11
+epoch [33/50] batch [295/500] time 1.592 (1.565) data 0.001 (0.003) loss 0.7354 (1.0502) acc 84.3750 (73.8983) lr 6.3188e-04 eta 3:47:02
+epoch [33/50] batch [300/500] time 1.561 (1.565) data 0.000 (0.003) loss 1.4404 (1.0515) acc 68.7500 (73.9167) lr 6.3188e-04 eta 3:46:53
+epoch [33/50] batch [305/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.4131 (1.0534) acc 68.7500 (73.8934) lr 6.3188e-04 eta 3:46:44
+epoch [33/50] batch [310/500] time 1.532 (1.565) data 0.000 (0.003) loss 0.8159 (1.0513) acc 71.8750 (73.8911) lr 6.3188e-04 eta 3:46:35
+epoch [33/50] batch [315/500] time 1.579 (1.564) data 0.000 (0.003) loss 0.8784 (1.0495) acc 81.2500 (73.9286) lr 6.3188e-04 eta 3:46:27
+epoch [33/50] batch [320/500] time 1.560 (1.564) data 0.000 (0.003) loss 1.3525 (1.0481) acc 65.6250 (73.9062) lr 6.3188e-04 eta 3:46:18
+epoch [33/50] batch [325/500] time 1.564 (1.565) data 0.001 (0.003) loss 0.3372 (1.0466) acc 93.7500 (73.9038) lr 6.3188e-04 eta 3:46:15
+epoch [33/50] batch [330/500] time 1.557 (1.565) data 0.001 (0.003) loss 0.6543 (1.0464) acc 78.1250 (73.9394) lr 6.3188e-04 eta 3:46:05
+epoch [33/50] batch [335/500] time 1.578 (1.565) data 0.000 (0.003) loss 0.8633 (1.0448) acc 78.1250 (73.9832) lr 6.3188e-04 eta 3:45:59
+epoch [33/50] batch [340/500] time 1.576 (1.565) data 0.000 (0.003) loss 0.5996 (1.0425) acc 81.2500 (73.9982) lr 6.3188e-04 eta 3:45:51
+epoch [33/50] batch [345/500] time 1.552 (1.565) data 0.000 (0.003) loss 1.0469 (1.0466) acc 68.7500 (73.9221) lr 6.3188e-04 eta 3:45:42
+epoch [33/50] batch [350/500] time 1.559 (1.565) data 0.000 (0.003) loss 0.6206 (1.0464) acc 78.1250 (73.8750) lr 6.3188e-04 eta 3:45:34
+epoch [33/50] batch [355/500] time 1.576 (1.564) data 0.000 (0.003) loss 0.9937 (1.0500) acc 75.0000 (73.7764) lr 6.3188e-04 eta 3:45:24
+epoch [33/50] batch [360/500] time 1.541 (1.564) data 0.000 (0.003) loss 1.0225 (1.0523) acc 68.7500 (73.6979) lr 6.3188e-04 eta 3:45:16
+epoch [33/50] batch [365/500] time 1.574 (1.565) data 0.000 (0.003) loss 1.0283 (1.0485) acc 71.8750 (73.8014) lr 6.3188e-04 eta 3:45:09
+epoch [33/50] batch [370/500] time 1.563 (1.565) data 0.000 (0.003) loss 1.2324 (1.0521) acc 65.6250 (73.6993) lr 6.3188e-04 eta 3:45:01
+epoch [33/50] batch [375/500] time 1.561 (1.564) data 0.000 (0.003) loss 0.4458 (1.0524) acc 84.3750 (73.6667) lr 6.3188e-04 eta 3:44:52
+epoch [33/50] batch [380/500] time 1.556 (1.564) data 0.000 (0.003) loss 1.1914 (1.0515) acc 71.8750 (73.6760) lr 6.3188e-04 eta 3:44:43
+epoch [33/50] batch [385/500] time 1.597 (1.564) data 0.000 (0.002) loss 0.8804 (1.0489) acc 81.2500 (73.7175) lr 6.3188e-04 eta 3:44:35
+epoch [33/50] batch [390/500] time 1.562 (1.564) data 0.000 (0.002) loss 1.2354 (1.0479) acc 68.7500 (73.7019) lr 6.3188e-04 eta 3:44:27
+epoch [33/50] batch [395/500] time 1.558 (1.564) data 0.001 (0.002) loss 0.8027 (1.0468) acc 78.1250 (73.6946) lr 6.3188e-04 eta 3:44:19
+epoch [33/50] batch [400/500] time 1.556 (1.564) data 0.000 (0.002) loss 0.9331 (1.0479) acc 75.0000 (73.6719) lr 6.3188e-04 eta 3:44:10
+epoch [33/50] batch [405/500] time 1.551 (1.564) data 0.000 (0.002) loss 1.2744 (1.0511) acc 71.8750 (73.6034) lr 6.3188e-04 eta 3:44:02
+epoch [33/50] batch [410/500] time 1.567 (1.564) data 0.001 (0.002) loss 0.9155 (1.0511) acc 78.1250 (73.6280) lr 6.3188e-04 eta 3:43:53
+epoch [33/50] batch [415/500] time 1.544 (1.564) data 0.000 (0.002) loss 1.1963 (1.0517) acc 75.0000 (73.5994) lr 6.3188e-04 eta 3:43:45
+epoch [33/50] batch [420/500] time 1.570 (1.564) data 0.000 (0.002) loss 0.8618 (1.0514) acc 75.0000 (73.5789) lr 6.3188e-04 eta 3:43:36
+epoch [33/50] batch [425/500] time 1.550 (1.564) data 0.000 (0.002) loss 0.9150 (1.0503) acc 84.3750 (73.5956) lr 6.3188e-04 eta 3:43:30
+epoch [33/50] batch [430/500] time 1.554 (1.564) data 0.000 (0.002) loss 1.0527 (1.0479) acc 78.1250 (73.6773) lr 6.3188e-04 eta 3:43:21
+epoch [33/50] batch [435/500] time 1.559 (1.564) data 0.001 (0.002) loss 1.2070 (1.0480) acc 75.0000 (73.6997) lr 6.3188e-04 eta 3:43:13
+epoch [33/50] batch [440/500] time 1.564 (1.564) data 0.000 (0.002) loss 0.9976 (1.0494) acc 78.1250 (73.6932) lr 6.3188e-04 eta 3:43:04
+epoch [33/50] batch [445/500] time 1.547 (1.564) data 0.000 (0.002) loss 1.7061 (1.0515) acc 59.3750 (73.6587) lr 6.3188e-04 eta 3:42:55
+epoch [33/50] batch [450/500] time 1.563 (1.564) data 0.000 (0.002) loss 1.2041 (1.0509) acc 75.0000 (73.6736) lr 6.3188e-04 eta 3:42:48
+epoch [33/50] batch [455/500] time 1.557 (1.564) data 0.001 (0.002) loss 0.9946 (1.0522) acc 75.0000 (73.6058) lr 6.3188e-04 eta 3:42:41
+epoch [33/50] batch [460/500] time 1.555 (1.564) data 0.000 (0.002) loss 1.5566 (1.0532) acc 75.0000 (73.5870) lr 6.3188e-04 eta 3:42:32
+epoch [33/50] batch [465/500] time 1.590 (1.564) data 0.000 (0.002) loss 1.2793 (1.0556) acc 71.8750 (73.5484) lr 6.3188e-04 eta 3:42:25
+epoch [33/50] batch [470/500] time 1.532 (1.564) data 0.000 (0.002) loss 1.0703 (1.0550) acc 71.8750 (73.5505) lr 6.3188e-04 eta 3:42:18
+epoch [33/50] batch [475/500] time 1.555 (1.564) data 0.000 (0.002) loss 1.3086 (1.0534) acc 62.5000 (73.5855) lr 6.3188e-04 eta 3:42:10
+epoch [33/50] batch [480/500] time 1.584 (1.564) data 0.000 (0.002) loss 0.8750 (1.0515) acc 75.0000 (73.6393) lr 6.3188e-04 eta 3:42:03
+epoch [33/50] batch [485/500] time 1.563 (1.564) data 0.001 (0.002) loss 0.9619 (1.0518) acc 68.7500 (73.6340) lr 6.3188e-04 eta 3:41:55
+epoch [33/50] batch [490/500] time 1.569 (1.564) data 0.000 (0.002) loss 1.2100 (1.0537) acc 84.3750 (73.5842) lr 6.3188e-04 eta 3:41:46
+epoch [33/50] batch [495/500] time 1.561 (1.564) data 0.000 (0.002) loss 1.3584 (1.0525) acc 71.8750 (73.6048) lr 6.3188e-04 eta 3:41:38
+epoch [33/50] batch [500/500] time 1.548 (1.564) data 0.000 (0.002) loss 1.2656 (1.0543) acc 62.5000 (73.5687) lr 5.7422e-04 eta 3:41:29
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,057
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [34/50] batch [5/500] time 1.541 (1.668) data 0.001 (0.162) loss 1.5850 (1.0038) acc 65.6250 (72.5000) lr 5.7422e-04 eta 3:56:07
+epoch [34/50] batch [10/500] time 1.543 (1.614) data 0.000 (0.081) loss 0.7798 (0.9412) acc 75.0000 (74.0625) lr 5.7422e-04 eta 3:48:20
+epoch [34/50] batch [15/500] time 1.558 (1.596) data 0.000 (0.054) loss 0.7817 (0.9425) acc 81.2500 (74.5833) lr 5.7422e-04 eta 3:45:42
+epoch [34/50] batch [20/500] time 1.562 (1.590) data 0.000 (0.041) loss 0.9648 (0.9363) acc 71.8750 (74.5312) lr 5.7422e-04 eta 3:44:44
+epoch [34/50] batch [25/500] time 1.690 (1.590) data 0.000 (0.033) loss 0.9634 (0.9486) acc 75.0000 (74.2500) lr 5.7422e-04 eta 3:44:31
+epoch [34/50] batch [30/500] time 1.568 (1.585) data 0.000 (0.027) loss 1.4287 (0.9438) acc 62.5000 (74.7917) lr 5.7422e-04 eta 3:43:44
+epoch [34/50] batch [35/500] time 1.564 (1.582) data 0.001 (0.023) loss 1.1309 (0.9537) acc 78.1250 (74.6429) lr 5.7422e-04 eta 3:43:13
+epoch [34/50] batch [40/500] time 1.558 (1.579) data 0.001 (0.021) loss 1.3174 (0.9618) acc 68.7500 (74.6094) lr 5.7422e-04 eta 3:42:34
+epoch [34/50] batch [45/500] time 1.568 (1.577) data 0.001 (0.018) loss 0.9731 (0.9613) acc 81.2500 (74.3056) lr 5.7422e-04 eta 3:42:13
+epoch [34/50] batch [50/500] time 1.569 (1.576) data 0.000 (0.017) loss 1.4121 (0.9673) acc 68.7500 (74.5625) lr 5.7422e-04 eta 3:42:00
+epoch [34/50] batch [55/500] time 1.581 (1.576) data 0.000 (0.015) loss 1.0010 (0.9683) acc 75.0000 (74.8864) lr 5.7422e-04 eta 3:41:45
+epoch [34/50] batch [60/500] time 1.554 (1.574) data 0.000 (0.014) loss 0.7666 (0.9507) acc 81.2500 (75.1042) lr 5.7422e-04 eta 3:41:21
+epoch [34/50] batch [65/500] time 1.567 (1.573) data 0.000 (0.013) loss 1.4307 (0.9726) acc 71.8750 (74.9519) lr 5.7422e-04 eta 3:41:08
+epoch [34/50] batch [70/500] time 1.591 (1.573) data 0.000 (0.012) loss 0.7568 (0.9812) acc 78.1250 (74.9554) lr 5.7422e-04 eta 3:41:00
+epoch [34/50] batch [75/500] time 1.552 (1.572) data 0.000 (0.011) loss 1.2461 (0.9835) acc 71.8750 (74.9583) lr 5.7422e-04 eta 3:40:43
+epoch [34/50] batch [80/500] time 1.572 (1.571) data 0.000 (0.011) loss 1.0996 (0.9970) acc 68.7500 (74.7656) lr 5.7422e-04 eta 3:40:27
+epoch [34/50] batch [85/500] time 1.648 (1.572) data 0.000 (0.010) loss 0.8271 (0.9864) acc 71.8750 (74.8897) lr 5.7422e-04 eta 3:40:24
+epoch [34/50] batch [90/500] time 1.565 (1.571) data 0.000 (0.009) loss 1.3682 (0.9983) acc 65.6250 (74.5833) lr 5.7422e-04 eta 3:40:14
+epoch [34/50] batch [95/500] time 1.573 (1.571) data 0.000 (0.009) loss 1.3525 (0.9971) acc 68.7500 (74.7697) lr 5.7422e-04 eta 3:40:00
+epoch [34/50] batch [100/500] time 1.566 (1.570) data 0.000 (0.008) loss 0.9541 (1.0034) acc 71.8750 (74.5000) lr 5.7422e-04 eta 3:39:50
+epoch [34/50] batch [105/500] time 1.580 (1.570) data 0.000 (0.008) loss 1.0303 (1.0050) acc 78.1250 (74.4643) lr 5.7422e-04 eta 3:39:39
+epoch [34/50] batch [110/500] time 1.559 (1.570) data 0.000 (0.008) loss 0.8721 (0.9973) acc 81.2500 (74.6307) lr 5.7422e-04 eta 3:39:28
+epoch [34/50] batch [115/500] time 1.558 (1.569) data 0.001 (0.007) loss 0.5776 (0.9946) acc 81.2500 (74.4565) lr 5.7422e-04 eta 3:39:18
+epoch [34/50] batch [120/500] time 1.580 (1.569) data 0.001 (0.007) loss 0.7573 (0.9862) acc 81.2500 (74.5573) lr 5.7422e-04 eta 3:39:08
+epoch [34/50] batch [125/500] time 1.556 (1.569) data 0.000 (0.007) loss 0.7935 (0.9947) acc 84.3750 (74.3500) lr 5.7422e-04 eta 3:39:01
+epoch [34/50] batch [130/500] time 1.573 (1.570) data 0.000 (0.007) loss 1.0225 (0.9894) acc 78.1250 (74.5673) lr 5.7422e-04 eta 3:38:56
+epoch [34/50] batch [135/500] time 1.569 (1.570) data 0.000 (0.006) loss 1.4160 (0.9864) acc 68.7500 (74.5370) lr 5.7422e-04 eta 3:38:49
+epoch [34/50] batch [140/500] time 1.568 (1.569) data 0.000 (0.006) loss 1.5947 (0.9847) acc 62.5000 (74.5089) lr 5.7422e-04 eta 3:38:40
+epoch [34/50] batch [145/500] time 1.548 (1.570) data 0.000 (0.006) loss 0.6997 (0.9943) acc 81.2500 (74.2241) lr 5.7422e-04 eta 3:38:33
+epoch [34/50] batch [150/500] time 1.544 (1.569) data 0.000 (0.006) loss 0.7456 (0.9873) acc 71.8750 (74.4167) lr 5.7422e-04 eta 3:38:21
+epoch [34/50] batch [155/500] time 1.566 (1.569) data 0.000 (0.006) loss 1.3721 (0.9862) acc 59.3750 (74.3750) lr 5.7422e-04 eta 3:38:10
+epoch [34/50] batch [160/500] time 1.564 (1.568) data 0.001 (0.005) loss 0.7637 (0.9846) acc 84.3750 (74.3750) lr 5.7422e-04 eta 3:37:58
+epoch [34/50] batch [165/500] time 1.541 (1.568) data 0.001 (0.005) loss 0.9067 (0.9815) acc 81.2500 (74.4508) lr 5.7422e-04 eta 3:37:48
+epoch [34/50] batch [170/500] time 1.553 (1.568) data 0.000 (0.005) loss 1.1514 (0.9872) acc 71.8750 (74.3934) lr 5.7422e-04 eta 3:37:38
+epoch [34/50] batch [175/500] time 1.530 (1.567) data 0.000 (0.005) loss 1.1865 (0.9924) acc 62.5000 (74.4286) lr 5.7422e-04 eta 3:37:26
+epoch [34/50] batch [180/500] time 1.585 (1.567) data 0.000 (0.005) loss 1.0088 (0.9917) acc 78.1250 (74.4271) lr 5.7422e-04 eta 3:37:15
+epoch [34/50] batch [185/500] time 1.557 (1.566) data 0.001 (0.005) loss 1.3369 (0.9905) acc 62.5000 (74.4426) lr 5.7422e-04 eta 3:37:04
+epoch [34/50] batch [190/500] time 1.537 (1.566) data 0.000 (0.005) loss 1.2314 (0.9959) acc 71.8750 (74.3586) lr 5.7422e-04 eta 3:36:51
+epoch [34/50] batch [195/500] time 1.586 (1.566) data 0.001 (0.005) loss 1.0371 (0.9961) acc 71.8750 (74.2949) lr 5.7422e-04 eta 3:36:42
+epoch [34/50] batch [200/500] time 1.553 (1.565) data 0.000 (0.004) loss 1.1143 (0.9954) acc 71.8750 (74.3125) lr 5.7422e-04 eta 3:36:32
+epoch [34/50] batch [205/500] time 1.548 (1.565) data 0.000 (0.004) loss 0.7285 (0.9950) acc 81.2500 (74.3445) lr 5.7422e-04 eta 3:36:21
+epoch [34/50] batch [210/500] time 1.549 (1.565) data 0.001 (0.004) loss 1.2002 (0.9962) acc 65.6250 (74.2411) lr 5.7422e-04 eta 3:36:11
+epoch [34/50] batch [215/500] time 1.560 (1.565) data 0.000 (0.004) loss 0.6938 (0.9958) acc 78.1250 (74.3169) lr 5.7422e-04 eta 3:36:05
+epoch [34/50] batch [220/500] time 1.578 (1.565) data 0.001 (0.004) loss 1.2598 (0.9966) acc 68.7500 (74.3040) lr 5.7422e-04 eta 3:35:57
+epoch [34/50] batch [225/500] time 1.549 (1.565) data 0.000 (0.004) loss 1.5156 (0.9979) acc 65.6250 (74.2500) lr 5.7422e-04 eta 3:35:49
+epoch [34/50] batch [230/500] time 1.558 (1.565) data 0.000 (0.004) loss 0.9336 (1.0017) acc 71.8750 (74.1033) lr 5.7422e-04 eta 3:35:43
+epoch [34/50] batch [235/500] time 1.599 (1.565) data 0.001 (0.004) loss 1.3496 (1.0041) acc 65.6250 (74.0957) lr 5.7422e-04 eta 3:35:35
+epoch [34/50] batch [240/500] time 1.552 (1.565) data 0.000 (0.004) loss 0.9805 (1.0011) acc 65.6250 (74.1276) lr 5.7422e-04 eta 3:35:28
+epoch [34/50] batch [245/500] time 1.575 (1.565) data 0.000 (0.004) loss 1.0693 (1.0021) acc 68.7500 (74.0306) lr 5.7422e-04 eta 3:35:20
+epoch [34/50] batch [250/500] time 1.572 (1.565) data 0.000 (0.004) loss 0.6890 (1.0003) acc 90.6250 (74.1250) lr 5.7422e-04 eta 3:35:11
+epoch [34/50] batch [255/500] time 1.554 (1.565) data 0.001 (0.004) loss 1.1299 (0.9998) acc 75.0000 (74.0931) lr 5.7422e-04 eta 3:35:02
+epoch [34/50] batch [260/500] time 1.545 (1.565) data 0.001 (0.004) loss 1.1484 (0.9991) acc 78.1250 (74.1106) lr 5.7422e-04 eta 3:34:52
+epoch [34/50] batch [265/500] time 1.542 (1.565) data 0.000 (0.003) loss 0.8208 (0.9988) acc 78.1250 (74.1274) lr 5.7422e-04 eta 3:34:46
+epoch [34/50] batch [270/500] time 1.569 (1.565) data 0.001 (0.003) loss 1.4736 (1.0026) acc 65.6250 (74.0856) lr 5.7422e-04 eta 3:34:39
+epoch [34/50] batch [275/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.7598 (1.0103) acc 56.2500 (73.9659) lr 5.7422e-04 eta 3:34:33
+epoch [34/50] batch [280/500] time 1.545 (1.565) data 0.000 (0.003) loss 1.0283 (1.0118) acc 75.0000 (73.9174) lr 5.7422e-04 eta 3:34:23
+epoch [34/50] batch [285/500] time 1.562 (1.565) data 0.000 (0.003) loss 0.5034 (1.0087) acc 81.2500 (73.9693) lr 5.7422e-04 eta 3:34:17
+epoch [34/50] batch [290/500] time 1.565 (1.565) data 0.000 (0.003) loss 0.6670 (1.0111) acc 71.8750 (73.9440) lr 5.7422e-04 eta 3:34:10
+epoch [34/50] batch [295/500] time 1.549 (1.565) data 0.000 (0.003) loss 0.6831 (1.0108) acc 81.2500 (73.9301) lr 5.7422e-04 eta 3:34:01
+epoch [34/50] batch [300/500] time 1.599 (1.565) data 0.000 (0.003) loss 1.5264 (1.0114) acc 65.6250 (73.9062) lr 5.7422e-04 eta 3:33:54
+epoch [34/50] batch [305/500] time 1.558 (1.565) data 0.000 (0.003) loss 1.8564 (1.0141) acc 56.2500 (73.8320) lr 5.7422e-04 eta 3:33:47
+epoch [34/50] batch [310/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.0977 (1.0168) acc 75.0000 (73.7500) lr 5.7422e-04 eta 3:33:39
+epoch [34/50] batch [315/500] time 1.588 (1.565) data 0.000 (0.003) loss 1.1660 (1.0182) acc 62.5000 (73.7302) lr 5.7422e-04 eta 3:33:32
+epoch [34/50] batch [320/500] time 1.567 (1.565) data 0.001 (0.003) loss 0.7905 (1.0196) acc 71.8750 (73.7500) lr 5.7422e-04 eta 3:33:23
+epoch [34/50] batch [325/500] time 1.553 (1.565) data 0.001 (0.003) loss 1.2383 (1.0203) acc 68.7500 (73.7404) lr 5.7422e-04 eta 3:33:14
+epoch [34/50] batch [330/500] time 1.555 (1.565) data 0.000 (0.003) loss 1.5781 (1.0244) acc 50.0000 (73.5795) lr 5.7422e-04 eta 3:33:04
+epoch [34/50] batch [335/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.2891 (1.0282) acc 65.6250 (73.4981) lr 5.7422e-04 eta 3:32:56
+epoch [34/50] batch [340/500] time 1.540 (1.565) data 0.000 (0.003) loss 1.5332 (1.0282) acc 68.7500 (73.5294) lr 5.7422e-04 eta 3:32:47
+epoch [34/50] batch [345/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.1475 (1.0267) acc 65.6250 (73.5779) lr 5.7422e-04 eta 3:32:38
+epoch [34/50] batch [350/500] time 1.557 (1.564) data 0.000 (0.003) loss 0.6973 (1.0286) acc 71.8750 (73.5357) lr 5.7422e-04 eta 3:32:30
+epoch [34/50] batch [355/500] time 1.575 (1.564) data 0.000 (0.003) loss 0.7944 (1.0277) acc 75.0000 (73.5475) lr 5.7422e-04 eta 3:32:22
+epoch [34/50] batch [360/500] time 1.557 (1.564) data 0.000 (0.003) loss 1.3174 (1.0276) acc 68.7500 (73.5590) lr 5.7422e-04 eta 3:32:13
+epoch [34/50] batch [365/500] time 1.549 (1.564) data 0.000 (0.003) loss 1.3994 (1.0298) acc 75.0000 (73.5188) lr 5.7422e-04 eta 3:32:04
+epoch [34/50] batch [370/500] time 1.540 (1.564) data 0.000 (0.003) loss 0.6704 (1.0280) acc 75.0000 (73.6064) lr 5.7422e-04 eta 3:31:55
+epoch [34/50] batch [375/500] time 1.570 (1.564) data 0.000 (0.003) loss 1.5107 (1.0310) acc 71.8750 (73.5417) lr 5.7422e-04 eta 3:31:50
+epoch [34/50] batch [380/500] time 1.546 (1.564) data 0.000 (0.003) loss 0.8677 (1.0333) acc 78.1250 (73.4868) lr 5.7422e-04 eta 3:31:39
+epoch [34/50] batch [385/500] time 1.570 (1.564) data 0.000 (0.003) loss 0.7627 (1.0353) acc 81.2500 (73.4821) lr 5.7422e-04 eta 3:31:30
+epoch [34/50] batch [390/500] time 1.549 (1.564) data 0.000 (0.002) loss 0.9668 (1.0331) acc 78.1250 (73.5096) lr 5.7422e-04 eta 3:31:20
+epoch [34/50] batch [395/500] time 1.557 (1.564) data 0.000 (0.002) loss 1.7881 (1.0335) acc 53.1250 (73.4573) lr 5.7422e-04 eta 3:31:12
+epoch [34/50] batch [400/500] time 1.572 (1.564) data 0.001 (0.002) loss 0.9873 (1.0342) acc 81.2500 (73.4453) lr 5.7422e-04 eta 3:31:05
+epoch [34/50] batch [405/500] time 1.551 (1.564) data 0.000 (0.002) loss 0.8076 (1.0345) acc 71.8750 (73.4336) lr 5.7422e-04 eta 3:30:56
+epoch [34/50] batch [410/500] time 1.545 (1.563) data 0.000 (0.002) loss 1.2783 (1.0354) acc 68.7500 (73.4223) lr 5.7422e-04 eta 3:30:48
+epoch [34/50] batch [415/500] time 1.647 (1.564) data 0.000 (0.002) loss 1.3984 (1.0339) acc 65.6250 (73.4639) lr 5.7422e-04 eta 3:30:42
+epoch [34/50] batch [420/500] time 1.581 (1.564) data 0.000 (0.002) loss 0.7407 (1.0365) acc 75.0000 (73.4003) lr 5.7422e-04 eta 3:30:34
+epoch [34/50] batch [425/500] time 1.558 (1.564) data 0.000 (0.002) loss 0.4021 (1.0339) acc 84.3750 (73.4412) lr 5.7422e-04 eta 3:30:26
+epoch [34/50] batch [430/500] time 1.545 (1.564) data 0.000 (0.002) loss 1.3135 (1.0350) acc 62.5000 (73.4230) lr 5.7422e-04 eta 3:30:17
+epoch [34/50] batch [435/500] time 1.587 (1.564) data 0.000 (0.002) loss 0.8818 (1.0337) acc 84.3750 (73.4770) lr 5.7422e-04 eta 3:30:10
+epoch [34/50] batch [440/500] time 1.580 (1.564) data 0.000 (0.002) loss 0.8262 (1.0336) acc 71.8750 (73.4659) lr 5.7422e-04 eta 3:30:03
+epoch [34/50] batch [445/500] time 1.552 (1.564) data 0.000 (0.002) loss 0.9028 (1.0339) acc 75.0000 (73.4410) lr 5.7422e-04 eta 3:29:54
+epoch [34/50] batch [450/500] time 1.545 (1.564) data 0.000 (0.002) loss 0.8291 (1.0354) acc 78.1250 (73.4028) lr 5.7422e-04 eta 3:29:46
+epoch [34/50] batch [455/500] time 1.567 (1.563) data 0.000 (0.002) loss 0.9312 (1.0379) acc 78.1250 (73.3791) lr 5.7422e-04 eta 3:29:37
+epoch [34/50] batch [460/500] time 1.569 (1.563) data 0.000 (0.002) loss 1.3232 (1.0388) acc 75.0000 (73.4035) lr 5.7422e-04 eta 3:29:29
+epoch [34/50] batch [465/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.2939 (1.0433) acc 56.2500 (73.2930) lr 5.7422e-04 eta 3:29:21
+epoch [34/50] batch [470/500] time 1.553 (1.563) data 0.000 (0.002) loss 1.1084 (1.0433) acc 65.6250 (73.3112) lr 5.7422e-04 eta 3:29:13
+epoch [34/50] batch [475/500] time 1.568 (1.563) data 0.000 (0.002) loss 0.6870 (1.0425) acc 78.1250 (73.3421) lr 5.7422e-04 eta 3:29:05
+epoch [34/50] batch [480/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.0801 (1.0454) acc 71.8750 (73.3138) lr 5.7422e-04 eta 3:28:56
+epoch [34/50] batch [485/500] time 1.580 (1.563) data 0.001 (0.002) loss 0.9585 (1.0455) acc 62.5000 (73.2796) lr 5.7422e-04 eta 3:28:49
+epoch [34/50] batch [490/500] time 1.557 (1.563) data 0.000 (0.002) loss 1.0469 (1.0477) acc 78.1250 (73.2781) lr 5.7422e-04 eta 3:28:40
+epoch [34/50] batch [495/500] time 1.554 (1.563) data 0.000 (0.002) loss 1.3867 (1.0490) acc 75.0000 (73.2449) lr 5.7422e-04 eta 3:28:31
+epoch [34/50] batch [500/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.3604 (1.0513) acc 78.1250 (73.2313) lr 5.1825e-04 eta 3:28:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,972
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.5%
+epoch [35/50] batch [5/500] time 1.552 (1.691) data 0.000 (0.174) loss 0.8682 (0.9563) acc 81.2500 (78.7500) lr 5.1825e-04 eta 3:45:17
+epoch [35/50] batch [10/500] time 1.538 (1.625) data 0.000 (0.087) loss 1.3291 (0.9780) acc 62.5000 (75.0000) lr 5.1825e-04 eta 3:36:22
+epoch [35/50] batch [15/500] time 1.547 (1.604) data 0.000 (0.058) loss 1.3350 (1.0960) acc 71.8750 (72.0833) lr 5.1825e-04 eta 3:33:25
+epoch [35/50] batch [20/500] time 1.573 (1.593) data 0.001 (0.044) loss 0.6309 (1.0545) acc 75.0000 (73.5938) lr 5.1825e-04 eta 3:31:54
+epoch [35/50] batch [25/500] time 1.571 (1.596) data 0.002 (0.035) loss 1.4561 (1.1026) acc 68.7500 (73.3750) lr 5.1825e-04 eta 3:32:08
+epoch [35/50] batch [30/500] time 1.547 (1.589) data 0.001 (0.030) loss 0.7998 (1.0922) acc 81.2500 (73.4375) lr 5.1825e-04 eta 3:31:04
+epoch [35/50] batch [35/500] time 1.582 (1.585) data 0.001 (0.025) loss 1.0322 (1.0660) acc 75.0000 (73.8393) lr 5.1825e-04 eta 3:30:23
+epoch [35/50] batch [40/500] time 1.562 (1.583) data 0.001 (0.022) loss 1.5498 (1.0858) acc 71.8750 (73.5156) lr 5.1825e-04 eta 3:29:56
+epoch [35/50] batch [45/500] time 1.578 (1.581) data 0.000 (0.020) loss 1.0508 (1.0599) acc 68.7500 (74.0972) lr 5.1825e-04 eta 3:29:40
+epoch [35/50] batch [50/500] time 1.568 (1.580) data 0.001 (0.018) loss 1.1475 (1.0573) acc 78.1250 (74.1875) lr 5.1825e-04 eta 3:29:23
+epoch [35/50] batch [55/500] time 1.603 (1.580) data 0.001 (0.016) loss 1.0869 (1.0521) acc 75.0000 (74.0909) lr 5.1825e-04 eta 3:29:15
+epoch [35/50] batch [60/500] time 1.575 (1.578) data 0.001 (0.015) loss 0.7627 (1.0245) acc 75.0000 (74.5312) lr 5.1825e-04 eta 3:28:52
+epoch [35/50] batch [65/500] time 1.556 (1.577) data 0.001 (0.014) loss 1.4023 (1.0335) acc 59.3750 (74.6154) lr 5.1825e-04 eta 3:28:35
+epoch [35/50] batch [70/500] time 1.589 (1.577) data 0.001 (0.013) loss 0.8633 (1.0128) acc 78.1250 (74.8214) lr 5.1825e-04 eta 3:28:27
+epoch [35/50] batch [75/500] time 1.561 (1.577) data 0.001 (0.012) loss 1.2568 (1.0044) acc 68.7500 (74.7917) lr 5.1825e-04 eta 3:28:15
+epoch [35/50] batch [80/500] time 1.590 (1.577) data 0.001 (0.011) loss 0.4397 (0.9982) acc 84.3750 (74.7266) lr 5.1825e-04 eta 3:28:10
+epoch [35/50] batch [85/500] time 1.559 (1.576) data 0.000 (0.011) loss 1.0996 (1.0041) acc 75.0000 (74.8897) lr 5.1825e-04 eta 3:27:57
+epoch [35/50] batch [90/500] time 1.582 (1.576) data 0.001 (0.010) loss 0.7383 (1.0026) acc 81.2500 (74.7917) lr 5.1825e-04 eta 3:27:43
+epoch [35/50] batch [95/500] time 1.561 (1.575) data 0.000 (0.010) loss 1.1074 (1.0076) acc 68.7500 (74.3750) lr 5.1825e-04 eta 3:27:31
+epoch [35/50] batch [100/500] time 1.572 (1.575) data 0.001 (0.009) loss 1.1592 (1.0192) acc 71.8750 (74.0938) lr 5.1825e-04 eta 3:27:20
+epoch [35/50] batch [105/500] time 1.568 (1.575) data 0.001 (0.009) loss 1.5146 (1.0186) acc 62.5000 (74.2560) lr 5.1825e-04 eta 3:27:12
+epoch [35/50] batch [110/500] time 1.561 (1.574) data 0.001 (0.008) loss 0.6895 (1.0144) acc 75.0000 (74.2330) lr 5.1825e-04 eta 3:27:01
+epoch [35/50] batch [115/500] time 1.564 (1.574) data 0.001 (0.008) loss 1.1836 (1.0167) acc 65.6250 (73.9946) lr 5.1825e-04 eta 3:26:52
+epoch [35/50] batch [120/500] time 1.695 (1.575) data 0.000 (0.008) loss 0.3455 (1.0085) acc 87.5000 (74.1146) lr 5.1825e-04 eta 3:26:51
+epoch [35/50] batch [125/500] time 1.549 (1.574) data 0.000 (0.008) loss 0.7480 (1.0147) acc 78.1250 (73.9500) lr 5.1825e-04 eta 3:26:32
+epoch [35/50] batch [130/500] time 1.561 (1.573) data 0.001 (0.007) loss 0.9004 (1.0096) acc 81.2500 (74.0865) lr 5.1825e-04 eta 3:26:20
+epoch [35/50] batch [135/500] time 1.575 (1.573) data 0.000 (0.007) loss 0.9492 (1.0050) acc 71.8750 (74.2130) lr 5.1825e-04 eta 3:26:08
+epoch [35/50] batch [140/500] time 1.574 (1.572) data 0.000 (0.007) loss 1.6455 (1.0032) acc 71.8750 (74.3080) lr 5.1825e-04 eta 3:25:56
+epoch [35/50] batch [145/500] time 1.573 (1.572) data 0.000 (0.007) loss 0.3672 (0.9965) acc 81.2500 (74.4181) lr 5.1825e-04 eta 3:25:48
+epoch [35/50] batch [150/500] time 1.535 (1.572) data 0.000 (0.006) loss 1.5879 (0.9945) acc 62.5000 (74.4375) lr 5.1825e-04 eta 3:25:37
+epoch [35/50] batch [155/500] time 1.563 (1.571) data 0.000 (0.006) loss 1.6973 (0.9995) acc 68.7500 (74.4960) lr 5.1825e-04 eta 3:25:26
+epoch [35/50] batch [160/500] time 1.544 (1.571) data 0.000 (0.006) loss 1.1348 (0.9950) acc 75.0000 (74.6094) lr 5.1825e-04 eta 3:25:14
+epoch [35/50] batch [165/500] time 1.570 (1.571) data 0.000 (0.006) loss 1.1348 (0.9969) acc 62.5000 (74.5644) lr 5.1825e-04 eta 3:25:08
+epoch [35/50] batch [170/500] time 1.559 (1.570) data 0.000 (0.006) loss 0.7627 (1.0003) acc 78.1250 (74.5404) lr 5.1825e-04 eta 3:24:54
+epoch [35/50] batch [175/500] time 1.560 (1.570) data 0.000 (0.006) loss 0.8506 (1.0040) acc 78.1250 (74.5893) lr 5.1825e-04 eta 3:24:44
+epoch [35/50] batch [180/500] time 1.547 (1.570) data 0.000 (0.005) loss 1.1611 (1.0042) acc 84.3750 (74.6007) lr 5.1825e-04 eta 3:24:33
+epoch [35/50] batch [185/500] time 1.534 (1.569) data 0.000 (0.005) loss 0.8828 (1.0097) acc 78.1250 (74.4932) lr 5.1825e-04 eta 3:24:21
+epoch [35/50] batch [190/500] time 1.548 (1.569) data 0.000 (0.005) loss 1.0107 (1.0106) acc 75.0000 (74.4079) lr 5.1825e-04 eta 3:24:10
+epoch [35/50] batch [195/500] time 1.559 (1.568) data 0.000 (0.005) loss 0.7554 (1.0111) acc 75.0000 (74.3750) lr 5.1825e-04 eta 3:23:59
+epoch [35/50] batch [200/500] time 1.548 (1.568) data 0.000 (0.005) loss 0.7749 (1.0106) acc 84.3750 (74.4688) lr 5.1825e-04 eta 3:23:49
+epoch [35/50] batch [205/500] time 1.534 (1.567) data 0.000 (0.005) loss 1.2598 (1.0069) acc 78.1250 (74.6494) lr 5.1825e-04 eta 3:23:38
+epoch [35/50] batch [210/500] time 1.557 (1.567) data 0.000 (0.005) loss 1.2510 (1.0097) acc 68.7500 (74.5536) lr 5.1825e-04 eta 3:23:26
+epoch [35/50] batch [215/500] time 1.547 (1.567) data 0.002 (0.005) loss 0.9170 (1.0121) acc 78.1250 (74.5203) lr 5.1825e-04 eta 3:23:17
+epoch [35/50] batch [220/500] time 1.536 (1.566) data 0.000 (0.004) loss 1.6133 (1.0152) acc 59.3750 (74.4176) lr 5.1825e-04 eta 3:23:06
+epoch [35/50] batch [225/500] time 1.545 (1.566) data 0.000 (0.004) loss 1.5879 (1.0200) acc 71.8750 (74.4306) lr 5.1825e-04 eta 3:22:55
+epoch [35/50] batch [230/500] time 1.548 (1.566) data 0.000 (0.004) loss 1.6846 (1.0232) acc 71.8750 (74.4022) lr 5.1825e-04 eta 3:22:47
+epoch [35/50] batch [235/500] time 1.556 (1.566) data 0.000 (0.004) loss 0.8711 (1.0230) acc 78.1250 (74.4149) lr 5.1825e-04 eta 3:22:38
+epoch [35/50] batch [240/500] time 1.541 (1.565) data 0.000 (0.004) loss 1.1113 (1.0289) acc 75.0000 (74.2448) lr 5.1825e-04 eta 3:22:27
+epoch [35/50] batch [245/500] time 1.554 (1.565) data 0.000 (0.004) loss 0.5581 (1.0249) acc 87.5000 (74.3878) lr 5.1825e-04 eta 3:22:18
+epoch [35/50] batch [250/500] time 1.557 (1.565) data 0.000 (0.004) loss 0.7476 (1.0238) acc 87.5000 (74.4000) lr 5.1825e-04 eta 3:22:09
+epoch [35/50] batch [255/500] time 1.547 (1.565) data 0.000 (0.004) loss 0.8374 (1.0246) acc 81.2500 (74.3505) lr 5.1825e-04 eta 3:22:00
+epoch [35/50] batch [260/500] time 1.574 (1.565) data 0.000 (0.004) loss 1.2500 (1.0282) acc 75.0000 (74.2668) lr 5.1825e-04 eta 3:21:52
+epoch [35/50] batch [265/500] time 1.564 (1.565) data 0.001 (0.004) loss 0.8555 (1.0269) acc 78.1250 (74.3160) lr 5.1825e-04 eta 3:21:46
+epoch [35/50] batch [270/500] time 1.546 (1.565) data 0.001 (0.004) loss 0.8857 (1.0264) acc 90.6250 (74.3403) lr 5.1825e-04 eta 3:21:36
+epoch [35/50] batch [275/500] time 1.536 (1.565) data 0.000 (0.004) loss 1.1387 (1.0283) acc 78.1250 (74.2727) lr 5.1825e-04 eta 3:21:26
+epoch [35/50] batch [280/500] time 1.530 (1.564) data 0.001 (0.004) loss 1.5479 (1.0288) acc 62.5000 (74.2634) lr 5.1825e-04 eta 3:21:15
+epoch [35/50] batch [285/500] time 1.571 (1.564) data 0.001 (0.004) loss 1.3242 (1.0343) acc 68.7500 (74.2105) lr 5.1825e-04 eta 3:21:05
+epoch [35/50] batch [290/500] time 1.546 (1.564) data 0.000 (0.003) loss 0.7144 (1.0358) acc 78.1250 (74.1918) lr 5.1825e-04 eta 3:20:55
+epoch [35/50] batch [295/500] time 1.578 (1.564) data 0.000 (0.003) loss 1.2637 (1.0375) acc 71.8750 (74.1843) lr 5.1825e-04 eta 3:20:47
+epoch [35/50] batch [300/500] time 1.550 (1.563) data 0.000 (0.003) loss 1.2402 (1.0406) acc 65.6250 (74.0729) lr 5.1825e-04 eta 3:20:38
+epoch [35/50] batch [305/500] time 1.545 (1.563) data 0.000 (0.003) loss 1.0889 (1.0423) acc 71.8750 (74.0676) lr 5.1825e-04 eta 3:20:28
+epoch [35/50] batch [310/500] time 1.554 (1.564) data 0.000 (0.003) loss 1.1807 (1.0464) acc 71.8750 (73.9617) lr 5.1825e-04 eta 3:20:23
+epoch [35/50] batch [315/500] time 1.544 (1.563) data 0.000 (0.003) loss 0.5723 (1.0452) acc 87.5000 (74.0079) lr 5.1825e-04 eta 3:20:15
+epoch [35/50] batch [320/500] time 1.532 (1.563) data 0.000 (0.003) loss 1.1396 (1.0438) acc 71.8750 (74.0332) lr 5.1825e-04 eta 3:20:05
+epoch [35/50] batch [325/500] time 1.548 (1.563) data 0.000 (0.003) loss 0.6875 (1.0415) acc 78.1250 (74.0385) lr 5.1825e-04 eta 3:19:56
+epoch [35/50] batch [330/500] time 1.532 (1.563) data 0.001 (0.003) loss 1.5391 (1.0422) acc 62.5000 (74.0057) lr 5.1825e-04 eta 3:19:47
+epoch [35/50] batch [335/500] time 1.611 (1.563) data 0.001 (0.003) loss 1.3584 (1.0474) acc 59.3750 (73.8806) lr 5.1825e-04 eta 3:19:40
+epoch [35/50] batch [340/500] time 1.551 (1.563) data 0.001 (0.003) loss 0.8667 (1.0465) acc 71.8750 (73.8235) lr 5.1825e-04 eta 3:19:32
+epoch [35/50] batch [345/500] time 1.561 (1.563) data 0.001 (0.003) loss 0.4517 (1.0454) acc 90.6250 (73.8949) lr 5.1825e-04 eta 3:19:24
+epoch [35/50] batch [350/500] time 1.540 (1.563) data 0.001 (0.003) loss 2.1211 (1.0502) acc 62.5000 (73.8482) lr 5.1825e-04 eta 3:19:14
+epoch [35/50] batch [355/500] time 1.537 (1.562) data 0.001 (0.003) loss 0.7725 (1.0519) acc 71.8750 (73.7940) lr 5.1825e-04 eta 3:19:04
+epoch [35/50] batch [360/500] time 1.568 (1.563) data 0.001 (0.003) loss 0.8877 (1.0489) acc 78.1250 (73.8542) lr 5.1825e-04 eta 3:18:57
+epoch [35/50] batch [365/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.1943 (1.0477) acc 71.8750 (73.8955) lr 5.1825e-04 eta 3:18:49
+epoch [35/50] batch [370/500] time 1.539 (1.562) data 0.001 (0.003) loss 0.8999 (1.0461) acc 71.8750 (73.9020) lr 5.1825e-04 eta 3:18:39
+epoch [35/50] batch [375/500] time 1.540 (1.562) data 0.001 (0.003) loss 0.9731 (1.0472) acc 62.5000 (73.8083) lr 5.1825e-04 eta 3:18:29
+epoch [35/50] batch [380/500] time 1.564 (1.562) data 0.000 (0.003) loss 0.9946 (1.0486) acc 75.0000 (73.8322) lr 5.1825e-04 eta 3:18:20
+epoch [35/50] batch [385/500] time 1.569 (1.562) data 0.000 (0.003) loss 0.9102 (1.0476) acc 71.8750 (73.8231) lr 5.1825e-04 eta 3:18:12
+epoch [35/50] batch [390/500] time 1.567 (1.562) data 0.000 (0.003) loss 1.0840 (1.0482) acc 68.7500 (73.8381) lr 5.1825e-04 eta 3:18:04
+epoch [35/50] batch [395/500] time 1.565 (1.562) data 0.001 (0.003) loss 1.4238 (1.0505) acc 68.7500 (73.8133) lr 5.1825e-04 eta 3:17:55
+epoch [35/50] batch [400/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.2520 (1.0544) acc 71.8750 (73.7578) lr 5.1825e-04 eta 3:17:47
+epoch [35/50] batch [405/500] time 1.561 (1.561) data 0.000 (0.003) loss 0.8726 (1.0572) acc 78.1250 (73.6497) lr 5.1825e-04 eta 3:17:38
+epoch [35/50] batch [410/500] time 1.533 (1.561) data 0.001 (0.003) loss 1.2021 (1.0590) acc 62.5000 (73.6128) lr 5.1825e-04 eta 3:17:30
+epoch [35/50] batch [415/500] time 1.576 (1.561) data 0.000 (0.003) loss 1.2607 (1.0597) acc 68.7500 (73.6069) lr 5.1825e-04 eta 3:17:21
+epoch [35/50] batch [420/500] time 1.577 (1.561) data 0.000 (0.003) loss 1.2734 (1.0614) acc 68.7500 (73.5193) lr 5.1825e-04 eta 3:17:14
+epoch [35/50] batch [425/500] time 1.557 (1.561) data 0.000 (0.003) loss 1.2119 (1.0617) acc 68.7500 (73.5515) lr 5.1825e-04 eta 3:17:05
+epoch [35/50] batch [430/500] time 1.573 (1.561) data 0.000 (0.003) loss 0.6143 (1.0615) acc 81.2500 (73.5610) lr 5.1825e-04 eta 3:16:56
+epoch [35/50] batch [435/500] time 1.547 (1.561) data 0.000 (0.002) loss 0.7769 (1.0630) acc 75.0000 (73.5345) lr 5.1825e-04 eta 3:16:47
+epoch [35/50] batch [440/500] time 1.547 (1.561) data 0.001 (0.002) loss 0.9062 (1.0609) acc 71.8750 (73.5298) lr 5.1825e-04 eta 3:16:39
+epoch [35/50] batch [445/500] time 1.578 (1.561) data 0.000 (0.002) loss 1.5283 (1.0605) acc 68.7500 (73.5674) lr 5.1825e-04 eta 3:16:30
+epoch [35/50] batch [450/500] time 1.653 (1.561) data 0.000 (0.002) loss 0.8232 (1.0607) acc 81.2500 (73.5694) lr 5.1825e-04 eta 3:16:23
+epoch [35/50] batch [455/500] time 1.552 (1.561) data 0.000 (0.002) loss 1.0166 (1.0599) acc 71.8750 (73.6264) lr 5.1825e-04 eta 3:16:14
+epoch [35/50] batch [460/500] time 1.560 (1.560) data 0.000 (0.002) loss 0.8159 (1.0574) acc 78.1250 (73.6413) lr 5.1825e-04 eta 3:16:06
+epoch [35/50] batch [465/500] time 1.565 (1.560) data 0.000 (0.002) loss 0.8955 (1.0566) acc 75.0000 (73.6358) lr 5.1825e-04 eta 3:15:56
+epoch [35/50] batch [470/500] time 1.568 (1.560) data 0.001 (0.002) loss 1.4355 (1.0578) acc 71.8750 (73.6503) lr 5.1825e-04 eta 3:15:48
+epoch [35/50] batch [475/500] time 1.542 (1.560) data 0.000 (0.002) loss 1.6367 (1.0580) acc 56.2500 (73.5987) lr 5.1825e-04 eta 3:15:39
+epoch [35/50] batch [480/500] time 1.562 (1.560) data 0.000 (0.002) loss 0.9146 (1.0592) acc 75.0000 (73.5547) lr 5.1825e-04 eta 3:15:31
+epoch [35/50] batch [485/500] time 1.581 (1.560) data 0.001 (0.002) loss 1.0898 (1.0591) acc 71.8750 (73.5438) lr 5.1825e-04 eta 3:15:23
+epoch [35/50] batch [490/500] time 1.545 (1.560) data 0.000 (0.002) loss 1.1475 (1.0609) acc 68.7500 (73.4885) lr 5.1825e-04 eta 3:15:15
+epoch [35/50] batch [495/500] time 1.549 (1.560) data 0.000 (0.002) loss 1.0068 (1.0614) acc 81.2500 (73.5101) lr 5.1825e-04 eta 3:15:07
+epoch [35/50] batch [500/500] time 1.573 (1.560) data 0.000 (0.002) loss 1.2979 (1.0624) acc 71.8750 (73.4875) lr 4.6417e-04 eta 3:15:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,027
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [36/50] batch [5/500] time 1.545 (1.664) data 0.000 (0.167) loss 0.4915 (0.8998) acc 84.3750 (76.2500) lr 4.6417e-04 eta 3:27:54
+epoch [36/50] batch [10/500] time 1.565 (1.611) data 0.000 (0.084) loss 1.4502 (1.0271) acc 65.6250 (72.5000) lr 4.6417e-04 eta 3:21:09
+epoch [36/50] batch [15/500] time 1.565 (1.592) data 0.000 (0.056) loss 0.6650 (1.0835) acc 84.3750 (71.4583) lr 4.6417e-04 eta 3:18:36
+epoch [36/50] batch [20/500] time 1.579 (1.585) data 0.000 (0.042) loss 0.7930 (1.0290) acc 81.2500 (73.5938) lr 4.6417e-04 eta 3:17:37
+epoch [36/50] batch [25/500] time 1.555 (1.580) data 0.000 (0.034) loss 1.0596 (1.0367) acc 68.7500 (73.0000) lr 4.6417e-04 eta 3:16:51
+epoch [36/50] batch [30/500] time 1.556 (1.577) data 0.000 (0.028) loss 1.4150 (1.0754) acc 65.6250 (72.3958) lr 4.6417e-04 eta 3:16:23
+epoch [36/50] batch [35/500] time 1.550 (1.575) data 0.000 (0.024) loss 1.1846 (1.0789) acc 75.0000 (72.2321) lr 4.6417e-04 eta 3:15:58
+epoch [36/50] batch [40/500] time 1.567 (1.576) data 0.000 (0.021) loss 0.5703 (1.0789) acc 78.1250 (71.8750) lr 4.6417e-04 eta 3:15:57
+epoch [36/50] batch [45/500] time 1.553 (1.573) data 0.000 (0.019) loss 0.9365 (1.0667) acc 68.7500 (72.0139) lr 4.6417e-04 eta 3:15:26
+epoch [36/50] batch [50/500] time 1.583 (1.572) data 0.000 (0.017) loss 1.0166 (1.0746) acc 65.6250 (71.3125) lr 4.6417e-04 eta 3:15:08
+epoch [36/50] batch [55/500] time 1.556 (1.570) data 0.000 (0.016) loss 0.9780 (1.0687) acc 71.8750 (71.4773) lr 4.6417e-04 eta 3:14:49
+epoch [36/50] batch [60/500] time 1.543 (1.570) data 0.000 (0.014) loss 1.1211 (1.0731) acc 75.0000 (71.4062) lr 4.6417e-04 eta 3:14:39
+epoch [36/50] batch [65/500] time 1.563 (1.570) data 0.000 (0.013) loss 1.6104 (1.0755) acc 59.3750 (71.2981) lr 4.6417e-04 eta 3:14:30
+epoch [36/50] batch [70/500] time 1.546 (1.568) data 0.000 (0.012) loss 1.0244 (1.0859) acc 75.0000 (71.3839) lr 4.6417e-04 eta 3:14:12
+epoch [36/50] batch [75/500] time 1.563 (1.569) data 0.000 (0.011) loss 0.7842 (1.0832) acc 81.2500 (71.5417) lr 4.6417e-04 eta 3:14:08
+epoch [36/50] batch [80/500] time 1.541 (1.568) data 0.000 (0.011) loss 1.0889 (1.0879) acc 84.3750 (71.6016) lr 4.6417e-04 eta 3:13:52
+epoch [36/50] batch [85/500] time 1.546 (1.568) data 0.000 (0.010) loss 0.7676 (1.0649) acc 78.1250 (72.2426) lr 4.6417e-04 eta 3:13:49
+epoch [36/50] batch [90/500] time 1.552 (1.568) data 0.000 (0.010) loss 1.0039 (1.0771) acc 87.5000 (72.1875) lr 4.6417e-04 eta 3:13:36
+epoch [36/50] batch [95/500] time 1.546 (1.567) data 0.000 (0.009) loss 1.1318 (1.0803) acc 78.1250 (72.2368) lr 4.6417e-04 eta 3:13:24
+epoch [36/50] batch [100/500] time 1.540 (1.566) data 0.000 (0.009) loss 0.6396 (1.0768) acc 78.1250 (72.2500) lr 4.6417e-04 eta 3:13:10
+epoch [36/50] batch [105/500] time 1.574 (1.566) data 0.001 (0.008) loss 0.3474 (1.0697) acc 93.7500 (72.5595) lr 4.6417e-04 eta 3:13:01
+epoch [36/50] batch [110/500] time 1.555 (1.565) data 0.000 (0.008) loss 0.8833 (1.0757) acc 78.1250 (72.4148) lr 4.6417e-04 eta 3:12:47
+epoch [36/50] batch [115/500] time 1.550 (1.565) data 0.000 (0.008) loss 1.0820 (1.0765) acc 62.5000 (72.3641) lr 4.6417e-04 eta 3:12:35
+epoch [36/50] batch [120/500] time 1.581 (1.565) data 0.001 (0.007) loss 1.2227 (1.0727) acc 65.6250 (72.3177) lr 4.6417e-04 eta 3:12:27
+epoch [36/50] batch [125/500] time 1.549 (1.565) data 0.000 (0.007) loss 1.9248 (1.0823) acc 65.6250 (72.4000) lr 4.6417e-04 eta 3:12:19
+epoch [36/50] batch [130/500] time 1.554 (1.564) data 0.001 (0.007) loss 1.0537 (1.0793) acc 75.0000 (72.4519) lr 4.6417e-04 eta 3:12:09
+epoch [36/50] batch [135/500] time 1.552 (1.564) data 0.000 (0.007) loss 1.0439 (1.0799) acc 68.7500 (72.4537) lr 4.6417e-04 eta 3:12:01
+epoch [36/50] batch [140/500] time 1.584 (1.565) data 0.000 (0.006) loss 1.2646 (1.0812) acc 71.8750 (72.6339) lr 4.6417e-04 eta 3:11:54
+epoch [36/50] batch [145/500] time 1.562 (1.564) data 0.000 (0.006) loss 1.1602 (1.0842) acc 71.8750 (72.5216) lr 4.6417e-04 eta 3:11:46
+epoch [36/50] batch [150/500] time 1.553 (1.564) data 0.000 (0.006) loss 1.0664 (1.0809) acc 78.1250 (72.6667) lr 4.6417e-04 eta 3:11:36
+epoch [36/50] batch [155/500] time 1.567 (1.564) data 0.000 (0.006) loss 0.4971 (1.0829) acc 84.3750 (72.6411) lr 4.6417e-04 eta 3:11:25
+epoch [36/50] batch [160/500] time 1.571 (1.564) data 0.001 (0.006) loss 1.1133 (1.0871) acc 78.1250 (72.6367) lr 4.6417e-04 eta 3:11:16
+epoch [36/50] batch [165/500] time 1.554 (1.564) data 0.000 (0.005) loss 0.7197 (1.0803) acc 84.3750 (72.6705) lr 4.6417e-04 eta 3:11:08
+epoch [36/50] batch [170/500] time 1.536 (1.563) data 0.000 (0.005) loss 1.0186 (1.0761) acc 78.1250 (72.7022) lr 4.6417e-04 eta 3:10:57
+epoch [36/50] batch [175/500] time 1.537 (1.563) data 0.000 (0.005) loss 0.9590 (1.0738) acc 68.7500 (72.6786) lr 4.6417e-04 eta 3:10:46
+epoch [36/50] batch [180/500] time 1.564 (1.564) data 0.000 (0.005) loss 1.2432 (1.0726) acc 71.8750 (72.6736) lr 4.6417e-04 eta 3:10:45
+epoch [36/50] batch [185/500] time 1.528 (1.563) data 0.000 (0.005) loss 1.6074 (1.0823) acc 62.5000 (72.5000) lr 4.6417e-04 eta 3:10:33
+epoch [36/50] batch [190/500] time 1.550 (1.563) data 0.000 (0.005) loss 0.7446 (1.0747) acc 78.1250 (72.6809) lr 4.6417e-04 eta 3:10:25
+epoch [36/50] batch [195/500] time 1.555 (1.563) data 0.000 (0.005) loss 1.2520 (1.0777) acc 75.0000 (72.6763) lr 4.6417e-04 eta 3:10:16
+epoch [36/50] batch [200/500] time 1.544 (1.563) data 0.000 (0.005) loss 1.0674 (1.0785) acc 78.1250 (72.7344) lr 4.6417e-04 eta 3:10:06
+epoch [36/50] batch [205/500] time 1.538 (1.563) data 0.000 (0.004) loss 0.4856 (1.0720) acc 84.3750 (72.9573) lr 4.6417e-04 eta 3:09:59
+epoch [36/50] batch [210/500] time 1.578 (1.563) data 0.000 (0.004) loss 1.1211 (1.0682) acc 62.5000 (73.0060) lr 4.6417e-04 eta 3:09:50
+epoch [36/50] batch [215/500] time 1.565 (1.563) data 0.000 (0.004) loss 1.4600 (1.0698) acc 59.3750 (72.9360) lr 4.6417e-04 eta 3:09:45
+epoch [36/50] batch [220/500] time 1.560 (1.563) data 0.000 (0.004) loss 1.0615 (1.0689) acc 71.8750 (72.9403) lr 4.6417e-04 eta 3:09:37
+epoch [36/50] batch [225/500] time 1.548 (1.563) data 0.000 (0.004) loss 1.0107 (1.0723) acc 68.7500 (72.8611) lr 4.6417e-04 eta 3:09:33
+epoch [36/50] batch [230/500] time 1.545 (1.563) data 0.000 (0.004) loss 0.7905 (1.0664) acc 78.1250 (72.9484) lr 4.6417e-04 eta 3:09:25
+epoch [36/50] batch [235/500] time 1.568 (1.563) data 0.000 (0.004) loss 1.2051 (1.0627) acc 75.0000 (73.0718) lr 4.6417e-04 eta 3:09:17
+epoch [36/50] batch [240/500] time 1.541 (1.563) data 0.000 (0.004) loss 1.5771 (1.0644) acc 65.6250 (73.0859) lr 4.6417e-04 eta 3:09:08
+epoch [36/50] batch [245/500] time 1.563 (1.563) data 0.000 (0.004) loss 0.9302 (1.0636) acc 81.2500 (73.1633) lr 4.6417e-04 eta 3:09:00
+epoch [36/50] batch [250/500] time 1.563 (1.563) data 0.000 (0.004) loss 0.9414 (1.0646) acc 68.7500 (73.1125) lr 4.6417e-04 eta 3:08:50
+epoch [36/50] batch [255/500] time 1.544 (1.563) data 0.000 (0.004) loss 1.3447 (1.0660) acc 68.7500 (73.1618) lr 4.6417e-04 eta 3:08:41
+epoch [36/50] batch [260/500] time 1.575 (1.563) data 0.000 (0.004) loss 0.9292 (1.0649) acc 71.8750 (73.1490) lr 4.6417e-04 eta 3:08:32
+epoch [36/50] batch [265/500] time 1.554 (1.563) data 0.000 (0.004) loss 0.6509 (1.0633) acc 81.2500 (73.1486) lr 4.6417e-04 eta 3:08:25
+epoch [36/50] batch [270/500] time 1.556 (1.562) data 0.001 (0.003) loss 1.0762 (1.0642) acc 68.7500 (73.1481) lr 4.6417e-04 eta 3:08:15
+epoch [36/50] batch [275/500] time 1.563 (1.562) data 0.001 (0.003) loss 1.4697 (1.0663) acc 68.7500 (73.1591) lr 4.6417e-04 eta 3:08:07
+epoch [36/50] batch [280/500] time 1.556 (1.562) data 0.001 (0.003) loss 1.0557 (1.0660) acc 68.7500 (73.1696) lr 4.6417e-04 eta 3:07:58
+epoch [36/50] batch [285/500] time 1.554 (1.562) data 0.000 (0.003) loss 1.0830 (1.0667) acc 75.0000 (73.1360) lr 4.6417e-04 eta 3:07:50
+epoch [36/50] batch [290/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.0928 (1.0681) acc 65.6250 (73.1573) lr 4.6417e-04 eta 3:07:42
+epoch [36/50] batch [295/500] time 1.588 (1.562) data 0.000 (0.003) loss 1.0918 (1.0648) acc 75.0000 (73.2309) lr 4.6417e-04 eta 3:07:34
+epoch [36/50] batch [300/500] time 1.534 (1.562) data 0.000 (0.003) loss 1.0195 (1.0626) acc 81.2500 (73.2917) lr 4.6417e-04 eta 3:07:27
+epoch [36/50] batch [305/500] time 1.550 (1.562) data 0.000 (0.003) loss 0.5649 (1.0653) acc 78.1250 (73.2582) lr 4.6417e-04 eta 3:07:19
+epoch [36/50] batch [310/500] time 1.578 (1.562) data 0.000 (0.003) loss 1.0762 (1.0651) acc 78.1250 (73.2661) lr 4.6417e-04 eta 3:07:09
+epoch [36/50] batch [315/500] time 1.557 (1.562) data 0.000 (0.003) loss 1.2139 (1.0670) acc 53.1250 (73.2639) lr 4.6417e-04 eta 3:07:01
+epoch [36/50] batch [320/500] time 1.553 (1.562) data 0.000 (0.003) loss 1.1094 (1.0638) acc 59.3750 (73.3398) lr 4.6417e-04 eta 3:06:54
+epoch [36/50] batch [325/500] time 1.567 (1.562) data 0.000 (0.003) loss 1.0850 (1.0666) acc 78.1250 (73.2692) lr 4.6417e-04 eta 3:06:48
+epoch [36/50] batch [330/500] time 1.535 (1.562) data 0.000 (0.003) loss 0.9062 (1.0630) acc 75.0000 (73.3523) lr 4.6417e-04 eta 3:06:40
+epoch [36/50] batch [335/500] time 1.544 (1.562) data 0.000 (0.003) loss 0.6934 (1.0602) acc 84.3750 (73.3862) lr 4.6417e-04 eta 3:06:31
+epoch [36/50] batch [340/500] time 1.548 (1.562) data 0.000 (0.003) loss 0.9473 (1.0627) acc 78.1250 (73.3732) lr 4.6417e-04 eta 3:06:22
+epoch [36/50] batch [345/500] time 1.558 (1.562) data 0.000 (0.003) loss 1.5459 (1.0659) acc 68.7500 (73.2880) lr 4.6417e-04 eta 3:06:14
+epoch [36/50] batch [350/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.0820 (1.0662) acc 75.0000 (73.2589) lr 4.6417e-04 eta 3:06:06
+epoch [36/50] batch [355/500] time 1.563 (1.562) data 0.000 (0.003) loss 0.8989 (1.0620) acc 84.3750 (73.3275) lr 4.6417e-04 eta 3:05:58
+epoch [36/50] batch [360/500] time 1.555 (1.562) data 0.000 (0.003) loss 1.2607 (1.0614) acc 78.1250 (73.3420) lr 4.6417e-04 eta 3:05:50
+epoch [36/50] batch [365/500] time 1.556 (1.562) data 0.001 (0.003) loss 0.9663 (1.0609) acc 71.8750 (73.3134) lr 4.6417e-04 eta 3:05:42
+epoch [36/50] batch [370/500] time 1.565 (1.562) data 0.000 (0.003) loss 1.7070 (1.0608) acc 65.6250 (73.3108) lr 4.6417e-04 eta 3:05:36
+epoch [36/50] batch [375/500] time 1.540 (1.562) data 0.000 (0.003) loss 1.6289 (1.0605) acc 56.2500 (73.2917) lr 4.6417e-04 eta 3:05:29
+epoch [36/50] batch [380/500] time 1.552 (1.562) data 0.000 (0.003) loss 0.8726 (1.0637) acc 75.0000 (73.1990) lr 4.6417e-04 eta 3:05:20
+epoch [36/50] batch [385/500] time 1.593 (1.562) data 0.000 (0.003) loss 0.7339 (1.0618) acc 84.3750 (73.2224) lr 4.6417e-04 eta 3:05:12
+epoch [36/50] batch [390/500] time 1.573 (1.562) data 0.000 (0.003) loss 0.9937 (1.0619) acc 78.1250 (73.2212) lr 4.6417e-04 eta 3:05:05
+epoch [36/50] batch [395/500] time 1.577 (1.562) data 0.000 (0.002) loss 1.4277 (1.0626) acc 62.5000 (73.1883) lr 4.6417e-04 eta 3:04:58
+epoch [36/50] batch [400/500] time 1.560 (1.562) data 0.000 (0.002) loss 0.8774 (1.0613) acc 75.0000 (73.2031) lr 4.6417e-04 eta 3:04:51
+epoch [36/50] batch [405/500] time 1.540 (1.562) data 0.000 (0.002) loss 0.9429 (1.0612) acc 75.0000 (73.1559) lr 4.6417e-04 eta 3:04:42
+epoch [36/50] batch [410/500] time 1.568 (1.562) data 0.000 (0.002) loss 1.2461 (1.0610) acc 75.0000 (73.1479) lr 4.6417e-04 eta 3:04:35
+epoch [36/50] batch [415/500] time 1.567 (1.562) data 0.001 (0.002) loss 0.3999 (1.0565) acc 87.5000 (73.2530) lr 4.6417e-04 eta 3:04:26
+epoch [36/50] batch [420/500] time 1.581 (1.562) data 0.001 (0.002) loss 0.6108 (1.0561) acc 78.1250 (73.2738) lr 4.6417e-04 eta 3:04:19
+epoch [36/50] batch [425/500] time 1.568 (1.562) data 0.000 (0.002) loss 0.6543 (1.0563) acc 81.2500 (73.2794) lr 4.6417e-04 eta 3:04:11
+epoch [36/50] batch [430/500] time 1.546 (1.562) data 0.000 (0.002) loss 1.0977 (1.0587) acc 71.8750 (73.2485) lr 4.6417e-04 eta 3:04:02
+epoch [36/50] batch [435/500] time 1.546 (1.562) data 0.000 (0.002) loss 1.1250 (1.0570) acc 68.7500 (73.2830) lr 4.6417e-04 eta 3:03:53
+epoch [36/50] batch [440/500] time 1.550 (1.562) data 0.000 (0.002) loss 1.1816 (1.0584) acc 71.8750 (73.2670) lr 4.6417e-04 eta 3:03:45
+epoch [36/50] batch [445/500] time 1.560 (1.561) data 0.000 (0.002) loss 1.4160 (1.0637) acc 65.6250 (73.1531) lr 4.6417e-04 eta 3:03:36
+epoch [36/50] batch [450/500] time 1.571 (1.562) data 0.000 (0.002) loss 0.6079 (1.0623) acc 81.2500 (73.1875) lr 4.6417e-04 eta 3:03:28
+epoch [36/50] batch [455/500] time 1.540 (1.562) data 0.000 (0.002) loss 1.3086 (1.0623) acc 65.6250 (73.1593) lr 4.6417e-04 eta 3:03:21
+epoch [36/50] batch [460/500] time 1.541 (1.562) data 0.000 (0.002) loss 0.9561 (1.0616) acc 71.8750 (73.1454) lr 4.6417e-04 eta 3:03:13
+epoch [36/50] batch [465/500] time 1.653 (1.562) data 0.000 (0.002) loss 0.7847 (1.0636) acc 71.8750 (73.1183) lr 4.6417e-04 eta 3:03:07
+epoch [36/50] batch [470/500] time 1.605 (1.562) data 0.000 (0.002) loss 0.5542 (1.0623) acc 87.5000 (73.1782) lr 4.6417e-04 eta 3:03:01
+epoch [36/50] batch [475/500] time 1.582 (1.562) data 0.000 (0.002) loss 0.6221 (1.0628) acc 81.2500 (73.1842) lr 4.6417e-04 eta 3:02:54
+epoch [36/50] batch [480/500] time 1.564 (1.562) data 0.000 (0.002) loss 0.7305 (1.0614) acc 84.3750 (73.2422) lr 4.6417e-04 eta 3:02:47
+epoch [36/50] batch [485/500] time 1.535 (1.562) data 0.001 (0.002) loss 0.9097 (1.0614) acc 78.1250 (73.2281) lr 4.6417e-04 eta 3:02:39
+epoch [36/50] batch [490/500] time 1.564 (1.562) data 0.000 (0.002) loss 1.0547 (1.0617) acc 78.1250 (73.2462) lr 4.6417e-04 eta 3:02:31
+epoch [36/50] batch [495/500] time 1.552 (1.562) data 0.000 (0.002) loss 1.0273 (1.0627) acc 78.1250 (73.2576) lr 4.6417e-04 eta 3:02:23
+epoch [36/50] batch [500/500] time 1.567 (1.562) data 0.000 (0.002) loss 1.0732 (1.0617) acc 71.8750 (73.2750) lr 4.1221e-04 eta 3:02:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,033
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [37/50] batch [5/500] time 1.577 (1.663) data 0.001 (0.145) loss 0.8506 (1.0982) acc 78.1250 (73.7500) lr 4.1221e-04 eta 3:13:50
+epoch [37/50] batch [10/500] time 1.532 (1.608) data 0.001 (0.073) loss 0.5952 (1.0958) acc 93.7500 (74.0625) lr 4.1221e-04 eta 3:07:18
+epoch [37/50] batch [15/500] time 1.540 (1.590) data 0.000 (0.049) loss 0.7021 (1.0501) acc 75.0000 (74.3750) lr 4.1221e-04 eta 3:05:08
+epoch [37/50] batch [20/500] time 1.530 (1.589) data 0.001 (0.037) loss 0.7085 (0.9700) acc 87.5000 (76.2500) lr 4.1221e-04 eta 3:04:52
+epoch [37/50] batch [25/500] time 1.554 (1.585) data 0.000 (0.029) loss 1.0176 (0.9620) acc 78.1250 (76.5000) lr 4.1221e-04 eta 3:04:14
+epoch [37/50] batch [30/500] time 1.576 (1.581) data 0.001 (0.025) loss 1.1113 (0.9409) acc 68.7500 (76.4583) lr 4.1221e-04 eta 3:03:40
+epoch [37/50] batch [35/500] time 1.569 (1.578) data 0.000 (0.021) loss 0.5474 (0.9629) acc 78.1250 (75.8036) lr 4.1221e-04 eta 3:03:07
+epoch [37/50] batch [40/500] time 1.555 (1.574) data 0.000 (0.019) loss 1.5645 (1.0165) acc 65.6250 (74.7656) lr 4.1221e-04 eta 3:02:36
+epoch [37/50] batch [45/500] time 1.564 (1.574) data 0.000 (0.017) loss 0.8950 (1.0550) acc 81.2500 (74.2361) lr 4.1221e-04 eta 3:02:25
+epoch [37/50] batch [50/500] time 1.543 (1.572) data 0.000 (0.015) loss 1.0732 (1.0440) acc 71.8750 (74.1875) lr 4.1221e-04 eta 3:02:04
+epoch [37/50] batch [55/500] time 1.566 (1.570) data 0.000 (0.014) loss 1.2275 (1.0543) acc 65.6250 (74.0909) lr 4.1221e-04 eta 3:01:42
+epoch [37/50] batch [60/500] time 1.580 (1.569) data 0.001 (0.013) loss 1.0049 (1.0434) acc 68.7500 (74.2708) lr 4.1221e-04 eta 3:01:31
+epoch [37/50] batch [65/500] time 1.561 (1.568) data 0.000 (0.012) loss 1.2168 (1.0500) acc 65.6250 (73.6538) lr 4.1221e-04 eta 3:01:16
+epoch [37/50] batch [70/500] time 1.571 (1.568) data 0.000 (0.011) loss 0.6606 (1.0266) acc 84.3750 (74.2857) lr 4.1221e-04 eta 3:01:08
+epoch [37/50] batch [75/500] time 1.559 (1.568) data 0.000 (0.010) loss 0.7202 (1.0226) acc 81.2500 (74.3750) lr 4.1221e-04 eta 3:00:55
+epoch [37/50] batch [80/500] time 1.553 (1.567) data 0.000 (0.009) loss 0.4463 (1.0093) acc 93.7500 (74.5312) lr 4.1221e-04 eta 3:00:43
+epoch [37/50] batch [85/500] time 1.550 (1.566) data 0.000 (0.009) loss 1.3213 (1.0182) acc 71.8750 (74.1912) lr 4.1221e-04 eta 3:00:31
+epoch [37/50] batch [90/500] time 1.547 (1.565) data 0.000 (0.008) loss 1.3311 (1.0166) acc 68.7500 (74.2014) lr 4.1221e-04 eta 3:00:13
+epoch [37/50] batch [95/500] time 1.551 (1.564) data 0.000 (0.008) loss 1.2256 (1.0219) acc 75.0000 (74.1447) lr 4.1221e-04 eta 2:59:59
+epoch [37/50] batch [100/500] time 1.578 (1.564) data 0.000 (0.008) loss 1.5107 (1.0212) acc 68.7500 (74.0000) lr 4.1221e-04 eta 2:59:54
+epoch [37/50] batch [105/500] time 1.576 (1.564) data 0.000 (0.007) loss 1.1279 (1.0285) acc 71.8750 (73.8393) lr 4.1221e-04 eta 2:59:47
+epoch [37/50] batch [110/500] time 1.571 (1.564) data 0.000 (0.007) loss 0.9307 (1.0250) acc 71.8750 (73.8068) lr 4.1221e-04 eta 2:59:37
+epoch [37/50] batch [115/500] time 1.665 (1.566) data 0.000 (0.007) loss 0.7739 (1.0207) acc 81.2500 (74.1033) lr 4.1221e-04 eta 2:59:39
+epoch [37/50] batch [120/500] time 1.549 (1.565) data 0.000 (0.006) loss 1.3574 (1.0306) acc 75.0000 (73.8281) lr 4.1221e-04 eta 2:59:26
+epoch [37/50] batch [125/500] time 1.559 (1.564) data 0.000 (0.006) loss 0.7617 (1.0216) acc 78.1250 (73.9500) lr 4.1221e-04 eta 2:59:13
+epoch [37/50] batch [130/500] time 1.554 (1.564) data 0.000 (0.006) loss 1.2979 (1.0296) acc 68.7500 (73.8221) lr 4.1221e-04 eta 2:59:05
+epoch [37/50] batch [135/500] time 1.582 (1.564) data 0.000 (0.006) loss 0.9102 (1.0244) acc 75.0000 (73.9815) lr 4.1221e-04 eta 2:58:58
+epoch [37/50] batch [140/500] time 1.557 (1.564) data 0.001 (0.006) loss 1.6572 (1.0267) acc 56.2500 (73.8839) lr 4.1221e-04 eta 2:58:51
+epoch [37/50] batch [145/500] time 1.537 (1.564) data 0.000 (0.005) loss 0.5703 (1.0255) acc 81.2500 (73.9655) lr 4.1221e-04 eta 2:58:39
+epoch [37/50] batch [150/500] time 1.548 (1.563) data 0.000 (0.005) loss 1.2119 (1.0404) acc 71.8750 (73.7917) lr 4.1221e-04 eta 2:58:27
+epoch [37/50] batch [155/500] time 1.556 (1.563) data 0.000 (0.005) loss 1.4072 (1.0467) acc 68.7500 (73.8306) lr 4.1221e-04 eta 2:58:17
+epoch [37/50] batch [160/500] time 1.561 (1.563) data 0.000 (0.005) loss 0.7109 (1.0441) acc 75.0000 (73.8477) lr 4.1221e-04 eta 2:58:13
+epoch [37/50] batch [165/500] time 1.551 (1.563) data 0.000 (0.005) loss 0.8042 (1.0395) acc 78.1250 (73.8447) lr 4.1221e-04 eta 2:58:04
+epoch [37/50] batch [170/500] time 1.552 (1.563) data 0.000 (0.005) loss 1.0020 (1.0354) acc 75.0000 (73.9706) lr 4.1221e-04 eta 2:57:57
+epoch [37/50] batch [175/500] time 1.525 (1.563) data 0.000 (0.005) loss 0.7856 (1.0331) acc 81.2500 (74.0179) lr 4.1221e-04 eta 2:57:46
+epoch [37/50] batch [180/500] time 1.561 (1.563) data 0.000 (0.004) loss 0.5566 (1.0284) acc 81.2500 (74.1493) lr 4.1221e-04 eta 2:57:37
+epoch [37/50] batch [185/500] time 1.570 (1.563) data 0.000 (0.004) loss 1.1777 (1.0315) acc 68.7500 (74.0709) lr 4.1221e-04 eta 2:57:30
+epoch [37/50] batch [190/500] time 1.578 (1.563) data 0.000 (0.004) loss 1.0371 (1.0363) acc 65.6250 (73.8980) lr 4.1221e-04 eta 2:57:21
+epoch [37/50] batch [195/500] time 1.539 (1.562) data 0.000 (0.004) loss 1.1426 (1.0340) acc 78.1250 (74.0385) lr 4.1221e-04 eta 2:57:12
+epoch [37/50] batch [200/500] time 1.577 (1.562) data 0.000 (0.004) loss 0.9321 (1.0339) acc 75.0000 (74.0156) lr 4.1221e-04 eta 2:57:04
+epoch [37/50] batch [205/500] time 1.549 (1.562) data 0.000 (0.004) loss 1.2959 (1.0257) acc 75.0000 (74.1921) lr 4.1221e-04 eta 2:56:55
+epoch [37/50] batch [210/500] time 1.545 (1.562) data 0.000 (0.004) loss 0.8442 (1.0226) acc 68.7500 (74.2411) lr 4.1221e-04 eta 2:56:44
+epoch [37/50] batch [215/500] time 1.552 (1.562) data 0.000 (0.004) loss 0.9956 (1.0221) acc 81.2500 (74.2442) lr 4.1221e-04 eta 2:56:37
+epoch [37/50] batch [220/500] time 1.537 (1.562) data 0.000 (0.004) loss 1.0361 (1.0191) acc 59.3750 (74.2330) lr 4.1221e-04 eta 2:56:27
+epoch [37/50] batch [225/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.0635 (1.0205) acc 78.1250 (74.1528) lr 4.1221e-04 eta 2:56:20
+epoch [37/50] batch [230/500] time 1.538 (1.561) data 0.000 (0.004) loss 1.2012 (1.0169) acc 75.0000 (74.1576) lr 4.1221e-04 eta 2:56:11
+epoch [37/50] batch [235/500] time 1.552 (1.561) data 0.000 (0.003) loss 1.0596 (1.0162) acc 78.1250 (74.1622) lr 4.1221e-04 eta 2:56:02
+epoch [37/50] batch [240/500] time 1.592 (1.561) data 0.000 (0.003) loss 0.7168 (1.0164) acc 78.1250 (74.1536) lr 4.1221e-04 eta 2:55:55
+epoch [37/50] batch [245/500] time 1.548 (1.561) data 0.000 (0.003) loss 0.8716 (1.0211) acc 75.0000 (74.1071) lr 4.1221e-04 eta 2:55:46
+epoch [37/50] batch [250/500] time 1.543 (1.561) data 0.001 (0.003) loss 0.6089 (1.0196) acc 78.1250 (74.1000) lr 4.1221e-04 eta 2:55:37
+epoch [37/50] batch [255/500] time 1.546 (1.561) data 0.000 (0.003) loss 1.1816 (1.0225) acc 62.5000 (74.0074) lr 4.1221e-04 eta 2:55:28
+epoch [37/50] batch [260/500] time 1.582 (1.561) data 0.000 (0.003) loss 0.9028 (1.0231) acc 71.8750 (73.9784) lr 4.1221e-04 eta 2:55:22
+epoch [37/50] batch [265/500] time 1.545 (1.561) data 0.000 (0.003) loss 0.5254 (1.0264) acc 78.1250 (73.9151) lr 4.1221e-04 eta 2:55:13
+epoch [37/50] batch [270/500] time 1.547 (1.561) data 0.000 (0.003) loss 1.4043 (1.0267) acc 71.8750 (73.9005) lr 4.1221e-04 eta 2:55:03
+epoch [37/50] batch [275/500] time 1.567 (1.561) data 0.000 (0.003) loss 1.2480 (1.0267) acc 75.0000 (73.9205) lr 4.1221e-04 eta 2:54:55
+epoch [37/50] batch [280/500] time 1.530 (1.560) data 0.000 (0.003) loss 1.1660 (1.0289) acc 68.7500 (73.8839) lr 4.1221e-04 eta 2:54:45
+epoch [37/50] batch [285/500] time 1.547 (1.560) data 0.000 (0.003) loss 0.4819 (1.0250) acc 81.2500 (73.9803) lr 4.1221e-04 eta 2:54:36
+epoch [37/50] batch [290/500] time 1.554 (1.560) data 0.000 (0.003) loss 0.7485 (1.0253) acc 75.0000 (73.9763) lr 4.1221e-04 eta 2:54:29
+epoch [37/50] batch [295/500] time 1.559 (1.560) data 0.000 (0.003) loss 0.7061 (1.0248) acc 87.5000 (74.0678) lr 4.1221e-04 eta 2:54:20
+epoch [37/50] batch [300/500] time 1.566 (1.560) data 0.001 (0.003) loss 2.3750 (1.0288) acc 59.3750 (74.0417) lr 4.1221e-04 eta 2:54:11
+epoch [37/50] batch [305/500] time 1.564 (1.560) data 0.000 (0.003) loss 1.0742 (1.0286) acc 75.0000 (74.0061) lr 4.1221e-04 eta 2:54:06
+epoch [37/50] batch [310/500] time 1.539 (1.560) data 0.000 (0.003) loss 0.5698 (1.0319) acc 87.5000 (73.9819) lr 4.1221e-04 eta 2:53:57
+epoch [37/50] batch [315/500] time 1.554 (1.560) data 0.000 (0.003) loss 0.7192 (1.0307) acc 84.3750 (74.0774) lr 4.1221e-04 eta 2:53:48
+epoch [37/50] batch [320/500] time 1.537 (1.560) data 0.000 (0.003) loss 1.5078 (1.0304) acc 65.6250 (74.0234) lr 4.1221e-04 eta 2:53:39
+epoch [37/50] batch [325/500] time 1.537 (1.560) data 0.000 (0.003) loss 0.9604 (1.0305) acc 75.0000 (73.9904) lr 4.1221e-04 eta 2:53:30
+epoch [37/50] batch [330/500] time 1.529 (1.559) data 0.000 (0.003) loss 0.6689 (1.0288) acc 81.2500 (74.0246) lr 4.1221e-04 eta 2:53:21
+epoch [37/50] batch [335/500] time 1.570 (1.559) data 0.000 (0.003) loss 1.1387 (1.0275) acc 68.7500 (74.0672) lr 4.1221e-04 eta 2:53:14
+epoch [37/50] batch [340/500] time 1.555 (1.559) data 0.000 (0.003) loss 1.2734 (1.0272) acc 78.1250 (74.0717) lr 4.1221e-04 eta 2:53:04
+epoch [37/50] batch [345/500] time 1.571 (1.559) data 0.000 (0.002) loss 1.0752 (1.0276) acc 78.1250 (74.0489) lr 4.1221e-04 eta 2:52:57
+epoch [37/50] batch [350/500] time 1.542 (1.559) data 0.000 (0.002) loss 1.9346 (1.0287) acc 53.1250 (73.9821) lr 4.1221e-04 eta 2:52:48
+epoch [37/50] batch [355/500] time 1.575 (1.559) data 0.000 (0.002) loss 1.3115 (1.0290) acc 75.0000 (73.9789) lr 4.1221e-04 eta 2:52:41
+epoch [37/50] batch [360/500] time 1.553 (1.559) data 0.000 (0.002) loss 1.0771 (1.0291) acc 68.7500 (73.9931) lr 4.1221e-04 eta 2:52:33
+epoch [37/50] batch [365/500] time 1.559 (1.559) data 0.000 (0.002) loss 1.4209 (1.0322) acc 62.5000 (73.9726) lr 4.1221e-04 eta 2:52:25
+epoch [37/50] batch [370/500] time 1.570 (1.559) data 0.000 (0.002) loss 1.2773 (1.0344) acc 71.8750 (73.9189) lr 4.1221e-04 eta 2:52:18
+epoch [37/50] batch [375/500] time 1.587 (1.560) data 0.000 (0.002) loss 0.9966 (1.0374) acc 71.8750 (73.8667) lr 4.1221e-04 eta 2:52:11
+epoch [37/50] batch [380/500] time 1.568 (1.560) data 0.001 (0.002) loss 0.6099 (1.0350) acc 78.1250 (73.8980) lr 4.1221e-04 eta 2:52:05
+epoch [37/50] batch [385/500] time 1.548 (1.559) data 0.000 (0.002) loss 1.0908 (1.0336) acc 62.5000 (73.8555) lr 4.1221e-04 eta 2:51:56
+epoch [37/50] batch [390/500] time 1.551 (1.559) data 0.000 (0.002) loss 1.2803 (1.0361) acc 71.8750 (73.8462) lr 4.1221e-04 eta 2:51:48
+epoch [37/50] batch [395/500] time 1.561 (1.559) data 0.000 (0.002) loss 0.9570 (1.0334) acc 84.3750 (73.9241) lr 4.1221e-04 eta 2:51:40
+epoch [37/50] batch [400/500] time 1.557 (1.559) data 0.001 (0.002) loss 0.8491 (1.0342) acc 68.7500 (73.8672) lr 4.1221e-04 eta 2:51:32
+epoch [37/50] batch [405/500] time 1.562 (1.560) data 0.000 (0.002) loss 0.8130 (1.0334) acc 68.7500 (73.8426) lr 4.1221e-04 eta 2:51:26
+epoch [37/50] batch [410/500] time 1.559 (1.560) data 0.000 (0.002) loss 1.2646 (1.0320) acc 62.5000 (73.8415) lr 4.1221e-04 eta 2:51:17
+epoch [37/50] batch [415/500] time 1.563 (1.560) data 0.001 (0.002) loss 2.3145 (1.0327) acc 50.0000 (73.8178) lr 4.1221e-04 eta 2:51:10
+epoch [37/50] batch [420/500] time 1.571 (1.560) data 0.001 (0.002) loss 0.7598 (1.0355) acc 78.1250 (73.7500) lr 4.1221e-04 eta 2:51:03
+epoch [37/50] batch [425/500] time 1.563 (1.560) data 0.000 (0.002) loss 1.2451 (1.0356) acc 68.7500 (73.7500) lr 4.1221e-04 eta 2:50:54
+epoch [37/50] batch [430/500] time 1.565 (1.560) data 0.001 (0.002) loss 0.7271 (1.0351) acc 75.0000 (73.7573) lr 4.1221e-04 eta 2:50:45
+epoch [37/50] batch [435/500] time 1.577 (1.560) data 0.000 (0.002) loss 1.0371 (1.0330) acc 65.6250 (73.7787) lr 4.1221e-04 eta 2:50:38
+epoch [37/50] batch [440/500] time 1.567 (1.560) data 0.000 (0.002) loss 1.3848 (1.0346) acc 71.8750 (73.7571) lr 4.1221e-04 eta 2:50:31
+epoch [37/50] batch [445/500] time 1.648 (1.560) data 0.001 (0.002) loss 1.1445 (1.0342) acc 71.8750 (73.7570) lr 4.1221e-04 eta 2:50:25
+epoch [37/50] batch [450/500] time 1.582 (1.560) data 0.001 (0.002) loss 1.1406 (1.0341) acc 68.7500 (73.7222) lr 4.1221e-04 eta 2:50:17
+epoch [37/50] batch [455/500] time 1.555 (1.560) data 0.000 (0.002) loss 0.6816 (1.0334) acc 78.1250 (73.7431) lr 4.1221e-04 eta 2:50:09
+epoch [37/50] batch [460/500] time 1.553 (1.560) data 0.000 (0.002) loss 1.2227 (1.0328) acc 62.5000 (73.7228) lr 4.1221e-04 eta 2:50:02
+epoch [37/50] batch [465/500] time 1.580 (1.560) data 0.001 (0.002) loss 0.9370 (1.0316) acc 68.7500 (73.7097) lr 4.1221e-04 eta 2:49:54
+epoch [37/50] batch [470/500] time 1.538 (1.560) data 0.001 (0.002) loss 1.0742 (1.0319) acc 78.1250 (73.7434) lr 4.1221e-04 eta 2:49:46
+epoch [37/50] batch [475/500] time 1.544 (1.560) data 0.000 (0.002) loss 1.3301 (1.0319) acc 71.8750 (73.7961) lr 4.1221e-04 eta 2:49:38
+epoch [37/50] batch [480/500] time 1.552 (1.560) data 0.000 (0.002) loss 1.0527 (1.0318) acc 81.2500 (73.8021) lr 4.1221e-04 eta 2:49:30
+epoch [37/50] batch [485/500] time 1.552 (1.560) data 0.001 (0.002) loss 2.1230 (1.0366) acc 59.3750 (73.7242) lr 4.1221e-04 eta 2:49:21
+epoch [37/50] batch [490/500] time 1.523 (1.560) data 0.000 (0.002) loss 0.8955 (1.0367) acc 71.8750 (73.6798) lr 4.1221e-04 eta 2:49:12
+epoch [37/50] batch [495/500] time 1.542 (1.559) data 0.000 (0.002) loss 0.6729 (1.0350) acc 75.0000 (73.6869) lr 4.1221e-04 eta 2:49:03
+epoch [37/50] batch [500/500] time 1.546 (1.559) data 0.000 (0.002) loss 1.1387 (1.0377) acc 65.6250 (73.6188) lr 3.6258e-04 eta 2:48:55
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,020
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [38/50] batch [5/500] time 1.531 (1.719) data 0.000 (0.200) loss 0.8770 (0.8992) acc 81.2500 (80.0000) lr 3.6258e-04 eta 3:06:05
+epoch [38/50] batch [10/500] time 1.543 (1.637) data 0.000 (0.100) loss 1.2969 (0.9837) acc 68.7500 (77.1875) lr 3.6258e-04 eta 2:57:06
+epoch [38/50] batch [15/500] time 1.556 (1.608) data 0.000 (0.067) loss 0.9521 (0.9674) acc 75.0000 (76.6667) lr 3.6258e-04 eta 2:53:46
+epoch [38/50] batch [20/500] time 1.542 (1.595) data 0.000 (0.050) loss 0.6992 (1.0143) acc 78.1250 (75.6250) lr 3.6258e-04 eta 2:52:13
+epoch [38/50] batch [25/500] time 1.564 (1.587) data 0.001 (0.040) loss 1.0713 (1.0393) acc 71.8750 (74.8750) lr 3.6258e-04 eta 2:51:15
+epoch [38/50] batch [30/500] time 1.548 (1.580) data 0.000 (0.034) loss 1.3330 (1.0667) acc 65.6250 (73.7500) lr 3.6258e-04 eta 2:50:24
+epoch [38/50] batch [35/500] time 1.555 (1.578) data 0.000 (0.029) loss 1.2881 (1.0721) acc 71.8750 (73.7500) lr 3.6258e-04 eta 2:49:59
+epoch [38/50] batch [40/500] time 1.529 (1.574) data 0.001 (0.025) loss 0.9824 (1.0729) acc 81.2500 (73.5156) lr 3.6258e-04 eta 2:49:27
+epoch [38/50] batch [45/500] time 1.553 (1.573) data 0.000 (0.023) loss 0.7388 (1.0479) acc 75.0000 (73.8194) lr 3.6258e-04 eta 2:49:16
+epoch [38/50] batch [50/500] time 1.565 (1.573) data 0.000 (0.020) loss 1.0791 (1.0578) acc 75.0000 (74.1250) lr 3.6258e-04 eta 2:49:03
+epoch [38/50] batch [55/500] time 1.577 (1.572) data 0.000 (0.019) loss 1.1719 (1.0575) acc 78.1250 (74.4886) lr 3.6258e-04 eta 2:48:54
+epoch [38/50] batch [60/500] time 1.557 (1.571) data 0.000 (0.017) loss 1.1328 (1.0440) acc 62.5000 (74.7396) lr 3.6258e-04 eta 2:48:35
+epoch [38/50] batch [65/500] time 1.564 (1.569) data 0.000 (0.016) loss 1.6172 (1.0464) acc 68.7500 (74.8077) lr 3.6258e-04 eta 2:48:18
+epoch [38/50] batch [70/500] time 1.575 (1.570) data 0.000 (0.015) loss 0.4858 (1.0433) acc 90.6250 (74.7768) lr 3.6258e-04 eta 2:48:11
+epoch [38/50] batch [75/500] time 1.570 (1.569) data 0.000 (0.014) loss 1.0098 (1.0312) acc 71.8750 (74.9167) lr 3.6258e-04 eta 2:48:01
+epoch [38/50] batch [80/500] time 1.555 (1.568) data 0.000 (0.013) loss 1.7510 (1.0457) acc 53.1250 (74.5703) lr 3.6258e-04 eta 2:47:46
+epoch [38/50] batch [85/500] time 1.541 (1.567) data 0.000 (0.012) loss 0.9053 (1.0418) acc 71.8750 (74.4485) lr 3.6258e-04 eta 2:47:32
+epoch [38/50] batch [90/500] time 1.552 (1.566) data 0.000 (0.012) loss 0.9819 (1.0479) acc 71.8750 (74.3750) lr 3.6258e-04 eta 2:47:18
+epoch [38/50] batch [95/500] time 1.568 (1.566) data 0.000 (0.011) loss 1.0986 (1.0496) acc 68.7500 (74.1447) lr 3.6258e-04 eta 2:47:10
+epoch [38/50] batch [100/500] time 1.559 (1.566) data 0.000 (0.010) loss 0.9526 (1.0606) acc 84.3750 (74.0000) lr 3.6258e-04 eta 2:47:00
+epoch [38/50] batch [105/500] time 1.565 (1.565) data 0.001 (0.010) loss 1.1016 (1.0674) acc 68.7500 (73.7798) lr 3.6258e-04 eta 2:46:51
+epoch [38/50] batch [110/500] time 1.543 (1.565) data 0.000 (0.010) loss 0.7969 (1.0592) acc 71.8750 (73.8352) lr 3.6258e-04 eta 2:46:42
+epoch [38/50] batch [115/500] time 1.554 (1.565) data 0.001 (0.009) loss 1.0898 (1.0508) acc 75.0000 (74.1304) lr 3.6258e-04 eta 2:46:31
+epoch [38/50] batch [120/500] time 1.556 (1.565) data 0.000 (0.009) loss 0.4893 (1.0385) acc 81.2500 (74.1927) lr 3.6258e-04 eta 2:46:22
+epoch [38/50] batch [125/500] time 1.556 (1.564) data 0.000 (0.008) loss 0.9507 (1.0504) acc 81.2500 (74.1000) lr 3.6258e-04 eta 2:46:11
+epoch [38/50] batch [130/500] time 1.553 (1.564) data 0.000 (0.008) loss 1.4746 (1.0550) acc 65.6250 (74.0865) lr 3.6258e-04 eta 2:46:00
+epoch [38/50] batch [135/500] time 1.554 (1.563) data 0.000 (0.008) loss 1.2705 (1.0615) acc 53.1250 (73.9120) lr 3.6258e-04 eta 2:45:51
+epoch [38/50] batch [140/500] time 1.560 (1.563) data 0.001 (0.008) loss 1.3281 (1.0610) acc 71.8750 (73.9286) lr 3.6258e-04 eta 2:45:43
+epoch [38/50] batch [145/500] time 1.561 (1.564) data 0.000 (0.007) loss 1.1543 (1.0555) acc 65.6250 (73.9009) lr 3.6258e-04 eta 2:45:40
+epoch [38/50] batch [150/500] time 1.560 (1.564) data 0.000 (0.007) loss 1.1973 (1.0536) acc 78.1250 (73.8958) lr 3.6258e-04 eta 2:45:30
+epoch [38/50] batch [155/500] time 1.568 (1.564) data 0.001 (0.007) loss 0.9268 (1.0565) acc 75.0000 (73.8306) lr 3.6258e-04 eta 2:45:21
+epoch [38/50] batch [160/500] time 1.554 (1.564) data 0.000 (0.007) loss 0.4839 (1.0504) acc 93.7500 (73.9453) lr 3.6258e-04 eta 2:45:14
+epoch [38/50] batch [165/500] time 1.578 (1.564) data 0.001 (0.007) loss 0.9185 (1.0481) acc 78.1250 (74.0720) lr 3.6258e-04 eta 2:45:07
+epoch [38/50] batch [170/500] time 1.584 (1.564) data 0.001 (0.006) loss 1.4873 (1.0543) acc 71.8750 (73.7684) lr 3.6258e-04 eta 2:45:00
+epoch [38/50] batch [175/500] time 1.570 (1.564) data 0.001 (0.006) loss 1.3154 (1.0500) acc 71.8750 (73.7500) lr 3.6258e-04 eta 2:44:53
+epoch [38/50] batch [180/500] time 1.569 (1.564) data 0.000 (0.006) loss 1.0869 (1.0506) acc 78.1250 (73.6458) lr 3.6258e-04 eta 2:44:44
+epoch [38/50] batch [185/500] time 1.543 (1.564) data 0.000 (0.006) loss 1.1904 (1.0546) acc 65.6250 (73.5811) lr 3.6258e-04 eta 2:44:34
+epoch [38/50] batch [190/500] time 1.554 (1.564) data 0.000 (0.006) loss 1.2236 (1.0562) acc 68.7500 (73.6020) lr 3.6258e-04 eta 2:44:29
+epoch [38/50] batch [195/500] time 1.569 (1.564) data 0.000 (0.006) loss 1.0381 (1.0570) acc 71.8750 (73.6378) lr 3.6258e-04 eta 2:44:19
+epoch [38/50] batch [200/500] time 1.552 (1.564) data 0.000 (0.005) loss 1.1035 (1.0581) acc 81.2500 (73.6875) lr 3.6258e-04 eta 2:44:10
+epoch [38/50] batch [205/500] time 1.545 (1.563) data 0.000 (0.005) loss 0.8579 (1.0567) acc 78.1250 (73.6890) lr 3.6258e-04 eta 2:44:01
+epoch [38/50] batch [210/500] time 1.551 (1.563) data 0.001 (0.005) loss 1.4102 (1.0564) acc 59.3750 (73.6607) lr 3.6258e-04 eta 2:43:50
+epoch [38/50] batch [215/500] time 1.557 (1.563) data 0.000 (0.005) loss 0.3906 (1.0502) acc 90.6250 (73.8227) lr 3.6258e-04 eta 2:43:42
+epoch [38/50] batch [220/500] time 1.563 (1.563) data 0.000 (0.005) loss 1.4297 (1.0550) acc 68.7500 (73.8210) lr 3.6258e-04 eta 2:43:33
+epoch [38/50] batch [225/500] time 1.570 (1.562) data 0.000 (0.005) loss 1.1523 (1.0581) acc 78.1250 (73.8333) lr 3.6258e-04 eta 2:43:23
+epoch [38/50] batch [230/500] time 1.548 (1.562) data 0.000 (0.005) loss 0.8179 (1.0531) acc 78.1250 (73.9266) lr 3.6258e-04 eta 2:43:14
+epoch [38/50] batch [235/500] time 1.581 (1.562) data 0.001 (0.005) loss 1.2793 (1.0568) acc 53.1250 (73.8165) lr 3.6258e-04 eta 2:43:08
+epoch [38/50] batch [240/500] time 1.554 (1.562) data 0.000 (0.005) loss 1.1445 (1.0595) acc 75.0000 (73.7760) lr 3.6258e-04 eta 2:42:59
+epoch [38/50] batch [245/500] time 1.545 (1.562) data 0.000 (0.005) loss 1.1387 (1.0589) acc 81.2500 (73.8903) lr 3.6258e-04 eta 2:42:51
+epoch [38/50] batch [250/500] time 1.546 (1.562) data 0.001 (0.004) loss 1.2793 (1.0605) acc 78.1250 (73.9250) lr 3.6258e-04 eta 2:42:42
+epoch [38/50] batch [255/500] time 1.560 (1.562) data 0.000 (0.004) loss 1.4551 (1.0611) acc 71.8750 (73.9461) lr 3.6258e-04 eta 2:42:33
+epoch [38/50] batch [260/500] time 1.574 (1.562) data 0.000 (0.004) loss 0.9248 (1.0612) acc 71.8750 (73.8702) lr 3.6258e-04 eta 2:42:25
+epoch [38/50] batch [265/500] time 1.552 (1.562) data 0.000 (0.004) loss 0.9106 (1.0611) acc 75.0000 (73.8797) lr 3.6258e-04 eta 2:42:17
+epoch [38/50] batch [270/500] time 1.553 (1.562) data 0.000 (0.004) loss 0.9634 (1.0606) acc 65.6250 (73.8310) lr 3.6258e-04 eta 2:42:09
+epoch [38/50] batch [275/500] time 1.572 (1.562) data 0.000 (0.004) loss 0.8711 (1.0611) acc 84.3750 (73.8864) lr 3.6258e-04 eta 2:42:01
+epoch [38/50] batch [280/500] time 1.556 (1.561) data 0.000 (0.004) loss 0.7520 (1.0607) acc 78.1250 (73.9174) lr 3.6258e-04 eta 2:41:52
+epoch [38/50] batch [285/500] time 1.674 (1.562) data 0.000 (0.004) loss 1.1982 (1.0587) acc 75.0000 (73.9693) lr 3.6258e-04 eta 2:41:47
+epoch [38/50] batch [290/500] time 1.549 (1.562) data 0.001 (0.004) loss 0.9844 (1.0610) acc 78.1250 (73.9763) lr 3.6258e-04 eta 2:41:38
+epoch [38/50] batch [295/500] time 1.564 (1.562) data 0.000 (0.004) loss 1.1494 (1.0615) acc 75.0000 (73.9936) lr 3.6258e-04 eta 2:41:30
+epoch [38/50] batch [300/500] time 1.550 (1.561) data 0.000 (0.004) loss 1.0020 (1.0592) acc 75.0000 (74.0729) lr 3.6258e-04 eta 2:41:20
+epoch [38/50] batch [305/500] time 1.573 (1.561) data 0.000 (0.004) loss 0.9541 (1.0573) acc 75.0000 (74.1086) lr 3.6258e-04 eta 2:41:13
+epoch [38/50] batch [310/500] time 1.568 (1.561) data 0.001 (0.004) loss 1.2334 (1.0622) acc 62.5000 (74.0121) lr 3.6258e-04 eta 2:41:05
+epoch [38/50] batch [315/500] time 1.538 (1.561) data 0.000 (0.004) loss 0.9292 (1.0610) acc 75.0000 (73.9782) lr 3.6258e-04 eta 2:40:56
+epoch [38/50] batch [320/500] time 1.561 (1.561) data 0.000 (0.004) loss 1.0186 (1.0610) acc 75.0000 (73.9453) lr 3.6258e-04 eta 2:40:48
+epoch [38/50] batch [325/500] time 1.543 (1.561) data 0.000 (0.004) loss 0.6670 (1.0573) acc 84.3750 (74.0192) lr 3.6258e-04 eta 2:40:39
+epoch [38/50] batch [330/500] time 1.563 (1.561) data 0.000 (0.003) loss 0.9927 (1.0556) acc 71.8750 (74.0246) lr 3.6258e-04 eta 2:40:32
+epoch [38/50] batch [335/500] time 1.538 (1.561) data 0.000 (0.003) loss 0.7129 (1.0538) acc 78.1250 (74.0112) lr 3.6258e-04 eta 2:40:24
+epoch [38/50] batch [340/500] time 1.550 (1.561) data 0.000 (0.003) loss 0.6914 (1.0540) acc 78.1250 (73.9982) lr 3.6258e-04 eta 2:40:16
+epoch [38/50] batch [345/500] time 1.558 (1.561) data 0.000 (0.003) loss 0.8975 (1.0525) acc 78.1250 (74.0308) lr 3.6258e-04 eta 2:40:08
+epoch [38/50] batch [350/500] time 1.575 (1.561) data 0.000 (0.003) loss 0.9321 (1.0533) acc 75.0000 (74.0268) lr 3.6258e-04 eta 2:40:00
+epoch [38/50] batch [355/500] time 1.543 (1.561) data 0.000 (0.003) loss 1.2441 (1.0551) acc 81.2500 (74.0493) lr 3.6258e-04 eta 2:39:52
+epoch [38/50] batch [360/500] time 1.559 (1.561) data 0.000 (0.003) loss 0.9917 (1.0562) acc 75.0000 (74.0451) lr 3.6258e-04 eta 2:39:44
+epoch [38/50] batch [365/500] time 1.546 (1.561) data 0.001 (0.003) loss 1.2363 (1.0545) acc 62.5000 (74.0668) lr 3.6258e-04 eta 2:39:37
+epoch [38/50] batch [370/500] time 1.559 (1.561) data 0.000 (0.003) loss 0.7881 (1.0535) acc 81.2500 (74.1216) lr 3.6258e-04 eta 2:39:29
+epoch [38/50] batch [375/500] time 1.556 (1.561) data 0.000 (0.003) loss 0.7910 (1.0514) acc 81.2500 (74.2167) lr 3.6258e-04 eta 2:39:21
+epoch [38/50] batch [380/500] time 1.540 (1.561) data 0.000 (0.003) loss 1.4541 (1.0511) acc 62.5000 (74.2352) lr 3.6258e-04 eta 2:39:13
+epoch [38/50] batch [385/500] time 1.533 (1.561) data 0.000 (0.003) loss 1.2930 (1.0490) acc 62.5000 (74.2614) lr 3.6258e-04 eta 2:39:04
+epoch [38/50] batch [390/500] time 1.587 (1.561) data 0.000 (0.003) loss 1.1592 (1.0522) acc 68.7500 (74.2228) lr 3.6258e-04 eta 2:38:57
+epoch [38/50] batch [395/500] time 1.546 (1.561) data 0.000 (0.003) loss 1.3955 (1.0519) acc 75.0000 (74.2484) lr 3.6258e-04 eta 2:38:48
+epoch [38/50] batch [400/500] time 1.548 (1.561) data 0.001 (0.003) loss 0.8086 (1.0501) acc 71.8750 (74.2500) lr 3.6258e-04 eta 2:38:40
+epoch [38/50] batch [405/500] time 1.566 (1.561) data 0.000 (0.003) loss 0.9429 (1.0473) acc 78.1250 (74.2747) lr 3.6258e-04 eta 2:38:32
+epoch [38/50] batch [410/500] time 1.564 (1.561) data 0.000 (0.003) loss 1.1162 (1.0487) acc 75.0000 (74.2302) lr 3.6258e-04 eta 2:38:23
+epoch [38/50] batch [415/500] time 1.561 (1.561) data 0.000 (0.003) loss 0.9019 (1.0486) acc 75.0000 (74.2018) lr 3.6258e-04 eta 2:38:15
+epoch [38/50] batch [420/500] time 1.568 (1.561) data 0.000 (0.003) loss 1.3486 (1.0495) acc 75.0000 (74.1964) lr 3.6258e-04 eta 2:38:08
+epoch [38/50] batch [425/500] time 1.566 (1.561) data 0.000 (0.003) loss 0.9590 (1.0487) acc 71.8750 (74.1691) lr 3.6258e-04 eta 2:38:00
+epoch [38/50] batch [430/500] time 1.557 (1.561) data 0.000 (0.003) loss 1.1396 (1.0499) acc 68.7500 (74.1134) lr 3.6258e-04 eta 2:37:54
+epoch [38/50] batch [435/500] time 1.574 (1.561) data 0.000 (0.003) loss 1.2432 (1.0507) acc 81.2500 (74.1236) lr 3.6258e-04 eta 2:37:46
+epoch [38/50] batch [440/500] time 1.541 (1.561) data 0.000 (0.003) loss 1.6016 (1.0520) acc 71.8750 (74.0767) lr 3.6258e-04 eta 2:37:38
+epoch [38/50] batch [445/500] time 1.560 (1.561) data 0.001 (0.003) loss 1.3652 (1.0514) acc 56.2500 (74.0379) lr 3.6258e-04 eta 2:37:29
+epoch [38/50] batch [450/500] time 1.547 (1.560) data 0.001 (0.003) loss 0.9365 (1.0534) acc 71.8750 (73.9931) lr 3.6258e-04 eta 2:37:20
+epoch [38/50] batch [455/500] time 1.561 (1.561) data 0.000 (0.003) loss 1.0020 (1.0525) acc 75.0000 (73.9973) lr 3.6258e-04 eta 2:37:13
+epoch [38/50] batch [460/500] time 1.577 (1.560) data 0.000 (0.003) loss 0.9756 (1.0521) acc 75.0000 (74.0149) lr 3.6258e-04 eta 2:37:05
+epoch [38/50] batch [465/500] time 1.546 (1.560) data 0.000 (0.003) loss 1.4795 (1.0524) acc 62.5000 (74.0390) lr 3.6258e-04 eta 2:36:56
+epoch [38/50] batch [470/500] time 1.537 (1.560) data 0.000 (0.003) loss 1.0732 (1.0536) acc 78.1250 (74.0426) lr 3.6258e-04 eta 2:36:48
+epoch [38/50] batch [475/500] time 1.588 (1.560) data 0.000 (0.003) loss 0.4607 (1.0527) acc 87.5000 (74.0395) lr 3.6258e-04 eta 2:36:41
+epoch [38/50] batch [480/500] time 1.547 (1.561) data 0.000 (0.003) loss 0.5444 (1.0514) acc 81.2500 (74.0690) lr 3.6258e-04 eta 2:36:34
+epoch [38/50] batch [485/500] time 1.560 (1.561) data 0.001 (0.002) loss 1.1992 (1.0508) acc 75.0000 (74.0786) lr 3.6258e-04 eta 2:36:27
+epoch [38/50] batch [490/500] time 1.547 (1.561) data 0.000 (0.002) loss 1.2129 (1.0513) acc 62.5000 (74.0434) lr 3.6258e-04 eta 2:36:18
+epoch [38/50] batch [495/500] time 1.563 (1.561) data 0.000 (0.002) loss 0.7827 (1.0510) acc 87.5000 (74.0657) lr 3.6258e-04 eta 2:36:11
+epoch [38/50] batch [500/500] time 1.572 (1.561) data 0.000 (0.002) loss 1.3350 (1.0502) acc 75.0000 (74.0875) lr 3.1545e-04 eta 2:36:03
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,086
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [39/50] batch [5/500] time 1.522 (1.650) data 0.001 (0.149) loss 1.3105 (1.1438) acc 71.8750 (75.6250) lr 3.1545e-04 eta 2:44:54
+epoch [39/50] batch [10/500] time 1.571 (1.604) data 0.000 (0.074) loss 0.8379 (1.0696) acc 78.1250 (76.5625) lr 3.1545e-04 eta 2:40:10
+epoch [39/50] batch [15/500] time 1.541 (1.589) data 0.000 (0.050) loss 1.0420 (1.0927) acc 81.2500 (75.2083) lr 3.1545e-04 eta 2:38:27
+epoch [39/50] batch [20/500] time 1.554 (1.581) data 0.001 (0.037) loss 0.6392 (0.9711) acc 84.3750 (77.3438) lr 3.1545e-04 eta 2:37:33
+epoch [39/50] batch [25/500] time 1.571 (1.577) data 0.000 (0.030) loss 1.3584 (0.9746) acc 68.7500 (76.7500) lr 3.1545e-04 eta 2:37:01
+epoch [39/50] batch [30/500] time 1.528 (1.573) data 0.000 (0.025) loss 0.6646 (0.9853) acc 84.3750 (76.2500) lr 3.1545e-04 eta 2:36:32
+epoch [39/50] batch [35/500] time 1.536 (1.571) data 0.000 (0.022) loss 1.0088 (0.9841) acc 71.8750 (75.5357) lr 3.1545e-04 eta 2:36:09
+epoch [39/50] batch [40/500] time 1.564 (1.569) data 0.000 (0.019) loss 0.9160 (0.9867) acc 71.8750 (75.4688) lr 3.1545e-04 eta 2:35:51
+epoch [39/50] batch [45/500] time 1.569 (1.568) data 0.000 (0.017) loss 0.7554 (0.9916) acc 78.1250 (75.2083) lr 3.1545e-04 eta 2:35:39
+epoch [39/50] batch [50/500] time 1.579 (1.569) data 0.000 (0.015) loss 1.2559 (0.9983) acc 75.0000 (75.0625) lr 3.1545e-04 eta 2:35:34
+epoch [39/50] batch [55/500] time 1.567 (1.567) data 0.000 (0.014) loss 0.6782 (0.9873) acc 75.0000 (75.0568) lr 3.1545e-04 eta 2:35:18
+epoch [39/50] batch [60/500] time 1.572 (1.568) data 0.001 (0.013) loss 1.0459 (0.9960) acc 65.6250 (74.7917) lr 3.1545e-04 eta 2:35:13
+epoch [39/50] batch [65/500] time 1.556 (1.569) data 0.001 (0.012) loss 1.5693 (1.0217) acc 53.1250 (74.0865) lr 3.1545e-04 eta 2:35:13
+epoch [39/50] batch [70/500] time 1.552 (1.568) data 0.000 (0.011) loss 1.8213 (1.0373) acc 56.2500 (73.6161) lr 3.1545e-04 eta 2:34:58
+epoch [39/50] batch [75/500] time 1.549 (1.567) data 0.000 (0.010) loss 0.4688 (1.0244) acc 81.2500 (73.7083) lr 3.1545e-04 eta 2:34:44
+epoch [39/50] batch [80/500] time 1.540 (1.567) data 0.000 (0.010) loss 1.0068 (1.0203) acc 78.1250 (73.7500) lr 3.1545e-04 eta 2:34:34
+epoch [39/50] batch [85/500] time 1.540 (1.566) data 0.000 (0.009) loss 1.1318 (1.0242) acc 75.0000 (73.6765) lr 3.1545e-04 eta 2:34:23
+epoch [39/50] batch [90/500] time 1.555 (1.566) data 0.001 (0.009) loss 0.8521 (1.0138) acc 78.1250 (73.8542) lr 3.1545e-04 eta 2:34:16
+epoch [39/50] batch [95/500] time 1.544 (1.566) data 0.000 (0.008) loss 1.2627 (1.0154) acc 62.5000 (73.6184) lr 3.1545e-04 eta 2:34:05
+epoch [39/50] batch [100/500] time 1.558 (1.565) data 0.000 (0.008) loss 1.5820 (1.0267) acc 62.5000 (73.3750) lr 3.1545e-04 eta 2:33:55
+epoch [39/50] batch [105/500] time 1.552 (1.565) data 0.000 (0.007) loss 1.1426 (1.0327) acc 75.0000 (73.3036) lr 3.1545e-04 eta 2:33:44
+epoch [39/50] batch [110/500] time 1.568 (1.565) data 0.001 (0.007) loss 0.8066 (1.0292) acc 71.8750 (73.3523) lr 3.1545e-04 eta 2:33:40
+epoch [39/50] batch [115/500] time 1.600 (1.565) data 0.001 (0.007) loss 0.9214 (1.0410) acc 78.1250 (73.1793) lr 3.1545e-04 eta 2:33:31
+epoch [39/50] batch [120/500] time 1.559 (1.565) data 0.001 (0.007) loss 0.6567 (1.0355) acc 84.3750 (73.4115) lr 3.1545e-04 eta 2:33:20
+epoch [39/50] batch [125/500] time 1.561 (1.565) data 0.001 (0.006) loss 0.7900 (1.0331) acc 75.0000 (73.4750) lr 3.1545e-04 eta 2:33:11
+epoch [39/50] batch [130/500] time 1.557 (1.564) data 0.000 (0.006) loss 1.0244 (1.0381) acc 68.7500 (73.2692) lr 3.1545e-04 eta 2:33:03
+epoch [39/50] batch [135/500] time 1.564 (1.564) data 0.001 (0.006) loss 0.6240 (1.0298) acc 84.3750 (73.5185) lr 3.1545e-04 eta 2:32:54
+epoch [39/50] batch [140/500] time 1.557 (1.564) data 0.001 (0.006) loss 0.9048 (1.0261) acc 81.2500 (73.7054) lr 3.1545e-04 eta 2:32:46
+epoch [39/50] batch [145/500] time 1.559 (1.564) data 0.000 (0.006) loss 0.7998 (1.0251) acc 78.1250 (73.6853) lr 3.1545e-04 eta 2:32:37
+epoch [39/50] batch [150/500] time 1.561 (1.564) data 0.000 (0.005) loss 1.4873 (1.0232) acc 65.6250 (73.8333) lr 3.1545e-04 eta 2:32:29
+epoch [39/50] batch [155/500] time 1.556 (1.564) data 0.001 (0.005) loss 0.6240 (1.0230) acc 78.1250 (73.8508) lr 3.1545e-04 eta 2:32:19
+epoch [39/50] batch [160/500] time 1.556 (1.563) data 0.000 (0.005) loss 0.9395 (1.0263) acc 75.0000 (73.7305) lr 3.1545e-04 eta 2:32:09
+epoch [39/50] batch [165/500] time 1.574 (1.563) data 0.000 (0.005) loss 1.1270 (1.0334) acc 71.8750 (73.7121) lr 3.1545e-04 eta 2:32:00
+epoch [39/50] batch [170/500] time 1.566 (1.563) data 0.000 (0.005) loss 0.6548 (1.0305) acc 75.0000 (73.7868) lr 3.1545e-04 eta 2:31:52
+epoch [39/50] batch [175/500] time 1.579 (1.563) data 0.001 (0.005) loss 1.1611 (1.0348) acc 65.6250 (73.7857) lr 3.1545e-04 eta 2:31:45
+epoch [39/50] batch [180/500] time 1.556 (1.563) data 0.001 (0.005) loss 0.7388 (1.0376) acc 75.0000 (73.6285) lr 3.1545e-04 eta 2:31:36
+epoch [39/50] batch [185/500] time 1.554 (1.563) data 0.000 (0.004) loss 0.9580 (1.0391) acc 81.2500 (73.6318) lr 3.1545e-04 eta 2:31:27
+epoch [39/50] batch [190/500] time 1.533 (1.563) data 0.000 (0.004) loss 1.1758 (1.0417) acc 71.8750 (73.5033) lr 3.1545e-04 eta 2:31:18
+epoch [39/50] batch [195/500] time 1.566 (1.563) data 0.001 (0.004) loss 1.0029 (1.0411) acc 71.8750 (73.5737) lr 3.1545e-04 eta 2:31:11
+epoch [39/50] batch [200/500] time 1.569 (1.563) data 0.000 (0.004) loss 1.0615 (1.0504) acc 71.8750 (73.3438) lr 3.1545e-04 eta 2:31:03
+epoch [39/50] batch [205/500] time 1.653 (1.563) data 0.000 (0.004) loss 0.5767 (1.0462) acc 90.6250 (73.5061) lr 3.1545e-04 eta 2:30:56
+epoch [39/50] batch [210/500] time 1.567 (1.563) data 0.000 (0.004) loss 1.0967 (1.0482) acc 81.2500 (73.4970) lr 3.1545e-04 eta 2:30:49
+epoch [39/50] batch [215/500] time 1.549 (1.563) data 0.000 (0.004) loss 0.9639 (1.0505) acc 71.8750 (73.4593) lr 3.1545e-04 eta 2:30:40
+epoch [39/50] batch [220/500] time 1.580 (1.562) data 0.000 (0.004) loss 0.8223 (1.0489) acc 71.8750 (73.4233) lr 3.1545e-04 eta 2:30:31
+epoch [39/50] batch [225/500] time 1.560 (1.562) data 0.000 (0.004) loss 1.1621 (1.0532) acc 71.8750 (73.2917) lr 3.1545e-04 eta 2:30:23
+epoch [39/50] batch [230/500] time 1.554 (1.562) data 0.000 (0.004) loss 1.3105 (1.0538) acc 62.5000 (73.2473) lr 3.1545e-04 eta 2:30:15
+epoch [39/50] batch [235/500] time 1.550 (1.562) data 0.000 (0.004) loss 1.2148 (1.0553) acc 65.6250 (73.2181) lr 3.1545e-04 eta 2:30:06
+epoch [39/50] batch [240/500] time 1.541 (1.562) data 0.000 (0.004) loss 0.8564 (1.0572) acc 84.3750 (73.2292) lr 3.1545e-04 eta 2:29:57
+epoch [39/50] batch [245/500] time 1.538 (1.562) data 0.000 (0.003) loss 0.5718 (1.0588) acc 87.5000 (73.2270) lr 3.1545e-04 eta 2:29:47
+epoch [39/50] batch [250/500] time 1.530 (1.562) data 0.000 (0.003) loss 0.5024 (1.0590) acc 81.2500 (73.2250) lr 3.1545e-04 eta 2:29:40
+epoch [39/50] batch [255/500] time 1.572 (1.562) data 0.000 (0.003) loss 0.8887 (1.0601) acc 75.0000 (73.1740) lr 3.1545e-04 eta 2:29:31
+epoch [39/50] batch [260/500] time 1.539 (1.562) data 0.000 (0.003) loss 1.1553 (1.0592) acc 68.7500 (73.2091) lr 3.1545e-04 eta 2:29:23
+epoch [39/50] batch [265/500] time 1.559 (1.562) data 0.000 (0.003) loss 1.1074 (1.0604) acc 78.1250 (73.1486) lr 3.1545e-04 eta 2:29:15
+epoch [39/50] batch [270/500] time 1.557 (1.562) data 0.000 (0.003) loss 0.9800 (1.0621) acc 68.7500 (73.0440) lr 3.1545e-04 eta 2:29:07
+epoch [39/50] batch [275/500] time 1.566 (1.562) data 0.000 (0.003) loss 1.1172 (1.0657) acc 68.7500 (72.9432) lr 3.1545e-04 eta 2:29:01
+epoch [39/50] batch [280/500] time 1.551 (1.562) data 0.001 (0.003) loss 1.3213 (1.0674) acc 62.5000 (72.8460) lr 3.1545e-04 eta 2:28:52
+epoch [39/50] batch [285/500] time 1.559 (1.562) data 0.000 (0.003) loss 1.0029 (1.0664) acc 71.8750 (72.8399) lr 3.1545e-04 eta 2:28:44
+epoch [39/50] batch [290/500] time 1.573 (1.562) data 0.001 (0.003) loss 0.5952 (1.0654) acc 81.2500 (72.8125) lr 3.1545e-04 eta 2:28:36
+epoch [39/50] batch [295/500] time 1.554 (1.562) data 0.000 (0.003) loss 1.1348 (1.0659) acc 68.7500 (72.7013) lr 3.1545e-04 eta 2:28:28
+epoch [39/50] batch [300/500] time 1.543 (1.561) data 0.000 (0.003) loss 1.4814 (1.0658) acc 75.0000 (72.7396) lr 3.1545e-04 eta 2:28:20
+epoch [39/50] batch [305/500] time 1.559 (1.561) data 0.001 (0.003) loss 0.6592 (1.0655) acc 71.8750 (72.6947) lr 3.1545e-04 eta 2:28:12
+epoch [39/50] batch [310/500] time 1.546 (1.562) data 0.000 (0.003) loss 0.6494 (1.0634) acc 78.1250 (72.7117) lr 3.1545e-04 eta 2:28:05
+epoch [39/50] batch [315/500] time 1.572 (1.562) data 0.000 (0.003) loss 1.2773 (1.0656) acc 68.7500 (72.7083) lr 3.1545e-04 eta 2:27:57
+epoch [39/50] batch [320/500] time 1.557 (1.561) data 0.000 (0.003) loss 1.2539 (1.0679) acc 65.6250 (72.6855) lr 3.1545e-04 eta 2:27:49
+epoch [39/50] batch [325/500] time 1.579 (1.562) data 0.000 (0.003) loss 1.4990 (1.0676) acc 65.6250 (72.7500) lr 3.1545e-04 eta 2:27:41
+epoch [39/50] batch [330/500] time 1.545 (1.561) data 0.000 (0.003) loss 1.4609 (1.0755) acc 62.5000 (72.6042) lr 3.1545e-04 eta 2:27:33
+epoch [39/50] batch [335/500] time 1.583 (1.561) data 0.000 (0.003) loss 1.1699 (1.0757) acc 65.6250 (72.5746) lr 3.1545e-04 eta 2:27:25
+epoch [39/50] batch [340/500] time 1.567 (1.562) data 0.000 (0.003) loss 0.5293 (1.0763) acc 84.3750 (72.6379) lr 3.1545e-04 eta 2:27:18
+epoch [39/50] batch [345/500] time 1.569 (1.562) data 0.000 (0.003) loss 0.8750 (1.0738) acc 81.2500 (72.7174) lr 3.1545e-04 eta 2:27:10
+epoch [39/50] batch [350/500] time 1.587 (1.562) data 0.000 (0.003) loss 1.1748 (1.0721) acc 71.8750 (72.7589) lr 3.1545e-04 eta 2:27:06
+epoch [39/50] batch [355/500] time 1.571 (1.562) data 0.000 (0.002) loss 1.1230 (1.0743) acc 78.1250 (72.6937) lr 3.1545e-04 eta 2:26:58
+epoch [39/50] batch [360/500] time 1.550 (1.562) data 0.000 (0.002) loss 0.8682 (1.0711) acc 68.7500 (72.7431) lr 3.1545e-04 eta 2:26:50
+epoch [39/50] batch [365/500] time 1.570 (1.562) data 0.000 (0.002) loss 1.2178 (1.0705) acc 78.1250 (72.7825) lr 3.1545e-04 eta 2:26:43
+epoch [39/50] batch [370/500] time 1.552 (1.562) data 0.000 (0.002) loss 1.0537 (1.0688) acc 65.6250 (72.8041) lr 3.1545e-04 eta 2:26:35
+epoch [39/50] batch [375/500] time 1.571 (1.562) data 0.000 (0.002) loss 0.8608 (1.0682) acc 87.5000 (72.8583) lr 3.1545e-04 eta 2:26:27
+epoch [39/50] batch [380/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.0596 (1.0682) acc 62.5000 (72.8536) lr 3.1545e-04 eta 2:26:20
+epoch [39/50] batch [385/500] time 1.564 (1.562) data 0.000 (0.002) loss 0.6797 (1.0656) acc 81.2500 (72.8977) lr 3.1545e-04 eta 2:26:11
+epoch [39/50] batch [390/500] time 1.557 (1.562) data 0.000 (0.002) loss 1.2188 (1.0645) acc 71.8750 (72.9327) lr 3.1545e-04 eta 2:26:04
+epoch [39/50] batch [395/500] time 1.546 (1.563) data 0.000 (0.002) loss 0.8887 (1.0636) acc 78.1250 (72.9905) lr 3.1545e-04 eta 2:25:58
+epoch [39/50] batch [400/500] time 1.585 (1.563) data 0.000 (0.002) loss 0.9790 (1.0614) acc 71.8750 (73.0156) lr 3.1545e-04 eta 2:25:50
+epoch [39/50] batch [405/500] time 1.576 (1.563) data 0.000 (0.002) loss 0.4990 (1.0583) acc 81.2500 (73.0864) lr 3.1545e-04 eta 2:25:42
+epoch [39/50] batch [410/500] time 1.553 (1.563) data 0.000 (0.002) loss 0.9131 (1.0557) acc 81.2500 (73.1402) lr 3.1545e-04 eta 2:25:34
+epoch [39/50] batch [415/500] time 1.552 (1.562) data 0.001 (0.002) loss 1.2578 (1.0605) acc 62.5000 (73.0723) lr 3.1545e-04 eta 2:25:26
+epoch [39/50] batch [420/500] time 1.542 (1.562) data 0.000 (0.002) loss 1.1016 (1.0606) acc 68.7500 (73.0580) lr 3.1545e-04 eta 2:25:17
+epoch [39/50] batch [425/500] time 1.551 (1.562) data 0.000 (0.002) loss 0.8545 (1.0586) acc 71.8750 (73.0956) lr 3.1545e-04 eta 2:25:10
+epoch [39/50] batch [430/500] time 1.565 (1.562) data 0.000 (0.002) loss 0.7280 (1.0586) acc 81.2500 (73.1323) lr 3.1545e-04 eta 2:25:02
+epoch [39/50] batch [435/500] time 1.578 (1.562) data 0.000 (0.002) loss 0.6582 (1.0547) acc 75.0000 (73.1537) lr 3.1545e-04 eta 2:24:55
+epoch [39/50] batch [440/500] time 1.553 (1.562) data 0.000 (0.002) loss 1.6885 (1.0566) acc 59.3750 (73.0895) lr 3.1545e-04 eta 2:24:47
+epoch [39/50] batch [445/500] time 1.558 (1.562) data 0.000 (0.002) loss 0.5952 (1.0552) acc 84.3750 (73.1039) lr 3.1545e-04 eta 2:24:38
+epoch [39/50] batch [450/500] time 1.533 (1.562) data 0.000 (0.002) loss 1.5596 (1.0556) acc 56.2500 (73.0903) lr 3.1545e-04 eta 2:24:30
+epoch [39/50] batch [455/500] time 1.567 (1.562) data 0.001 (0.002) loss 0.7710 (1.0533) acc 81.2500 (73.1593) lr 3.1545e-04 eta 2:24:22
+epoch [39/50] batch [460/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.3984 (1.0551) acc 68.7500 (73.1658) lr 3.1545e-04 eta 2:24:15
+epoch [39/50] batch [465/500] time 1.556 (1.562) data 0.000 (0.002) loss 0.6240 (1.0536) acc 90.6250 (73.2124) lr 3.1545e-04 eta 2:24:06
+epoch [39/50] batch [470/500] time 1.546 (1.562) data 0.000 (0.002) loss 1.2119 (1.0543) acc 59.3750 (73.1981) lr 3.1545e-04 eta 2:23:57
+epoch [39/50] batch [475/500] time 1.532 (1.562) data 0.000 (0.002) loss 0.9194 (1.0544) acc 75.0000 (73.2039) lr 3.1545e-04 eta 2:23:49
+epoch [39/50] batch [480/500] time 1.556 (1.562) data 0.000 (0.002) loss 0.5894 (1.0521) acc 78.1250 (73.2227) lr 3.1545e-04 eta 2:23:41
+epoch [39/50] batch [485/500] time 1.563 (1.562) data 0.001 (0.002) loss 1.4668 (1.0519) acc 65.6250 (73.2281) lr 3.1545e-04 eta 2:23:33
+epoch [39/50] batch [490/500] time 1.579 (1.562) data 0.000 (0.002) loss 1.4082 (1.0539) acc 75.0000 (73.1633) lr 3.1545e-04 eta 2:23:25
+epoch [39/50] batch [495/500] time 1.554 (1.562) data 0.000 (0.002) loss 1.0713 (1.0528) acc 68.7500 (73.2008) lr 3.1545e-04 eta 2:23:19
+epoch [39/50] batch [500/500] time 1.552 (1.562) data 0.000 (0.002) loss 0.7778 (1.0533) acc 75.0000 (73.2062) lr 2.7103e-04 eta 2:23:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,002
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+epoch [40/50] batch [5/500] time 1.552 (1.687) data 0.001 (0.186) loss 1.0938 (1.0329) acc 75.0000 (76.2500) lr 2.7103e-04 eta 2:34:31
+epoch [40/50] batch [10/500] time 1.555 (1.619) data 0.001 (0.093) loss 1.2939 (1.0709) acc 62.5000 (72.1875) lr 2.7103e-04 eta 2:28:08
+epoch [40/50] batch [15/500] time 1.598 (1.606) data 0.000 (0.062) loss 1.2393 (1.0038) acc 75.0000 (74.5833) lr 2.7103e-04 eta 2:26:51
+epoch [40/50] batch [20/500] time 1.549 (1.597) data 0.001 (0.047) loss 1.1543 (1.0953) acc 78.1250 (73.1250) lr 2.7103e-04 eta 2:25:48
+epoch [40/50] batch [25/500] time 1.563 (1.588) data 0.000 (0.038) loss 0.7290 (1.0694) acc 71.8750 (73.3750) lr 2.7103e-04 eta 2:24:54
+epoch [40/50] batch [30/500] time 1.583 (1.583) data 0.000 (0.031) loss 0.8276 (1.0680) acc 71.8750 (73.0208) lr 2.7103e-04 eta 2:24:21
+epoch [40/50] batch [35/500] time 1.549 (1.580) data 0.001 (0.027) loss 0.6924 (1.0583) acc 78.1250 (73.3036) lr 2.7103e-04 eta 2:23:52
+epoch [40/50] batch [40/500] time 1.569 (1.581) data 0.000 (0.024) loss 1.6201 (1.0481) acc 65.6250 (73.4375) lr 2.7103e-04 eta 2:23:52
+epoch [40/50] batch [45/500] time 1.557 (1.579) data 0.000 (0.021) loss 0.5327 (1.0620) acc 81.2500 (73.1250) lr 2.7103e-04 eta 2:23:33
+epoch [40/50] batch [50/500] time 1.547 (1.576) data 0.000 (0.019) loss 0.6729 (1.0469) acc 87.5000 (73.7500) lr 2.7103e-04 eta 2:23:11
+epoch [40/50] batch [55/500] time 1.556 (1.574) data 0.000 (0.017) loss 1.2236 (1.0453) acc 71.8750 (73.5227) lr 2.7103e-04 eta 2:22:51
+epoch [40/50] batch [60/500] time 1.544 (1.572) data 0.000 (0.016) loss 1.3232 (1.0483) acc 68.7500 (73.5938) lr 2.7103e-04 eta 2:22:31
+epoch [40/50] batch [65/500] time 1.552 (1.571) data 0.000 (0.015) loss 0.9966 (1.0453) acc 71.8750 (73.4135) lr 2.7103e-04 eta 2:22:16
+epoch [40/50] batch [70/500] time 1.553 (1.570) data 0.001 (0.014) loss 0.9028 (1.0462) acc 71.8750 (73.2589) lr 2.7103e-04 eta 2:22:02
+epoch [40/50] batch [75/500] time 1.530 (1.567) data 0.000 (0.013) loss 0.8921 (1.0544) acc 71.8750 (73.0833) lr 2.7103e-04 eta 2:21:42
+epoch [40/50] batch [80/500] time 1.558 (1.568) data 0.000 (0.012) loss 0.7983 (1.0540) acc 78.1250 (72.9297) lr 2.7103e-04 eta 2:21:39
+epoch [40/50] batch [85/500] time 1.539 (1.567) data 0.000 (0.011) loss 0.7451 (1.0410) acc 78.1250 (73.4559) lr 2.7103e-04 eta 2:21:26
+epoch [40/50] batch [90/500] time 1.546 (1.566) data 0.000 (0.011) loss 1.0195 (1.0443) acc 78.1250 (73.4722) lr 2.7103e-04 eta 2:21:14
+epoch [40/50] batch [95/500] time 1.572 (1.566) data 0.001 (0.010) loss 1.3125 (1.0401) acc 62.5000 (73.3882) lr 2.7103e-04 eta 2:21:05
+epoch [40/50] batch [100/500] time 1.559 (1.566) data 0.000 (0.010) loss 1.1406 (1.0468) acc 75.0000 (73.2188) lr 2.7103e-04 eta 2:20:55
+epoch [40/50] batch [105/500] time 1.580 (1.566) data 0.000 (0.009) loss 1.1084 (1.0438) acc 81.2500 (73.4226) lr 2.7103e-04 eta 2:20:47
+epoch [40/50] batch [110/500] time 1.560 (1.566) data 0.000 (0.009) loss 0.9814 (1.0423) acc 75.0000 (73.3239) lr 2.7103e-04 eta 2:20:39
+epoch [40/50] batch [115/500] time 1.560 (1.566) data 0.001 (0.009) loss 1.0723 (1.0495) acc 65.6250 (73.0435) lr 2.7103e-04 eta 2:20:31
+epoch [40/50] batch [120/500] time 1.581 (1.566) data 0.001 (0.008) loss 1.5508 (1.0522) acc 62.5000 (73.0208) lr 2.7103e-04 eta 2:20:22
+epoch [40/50] batch [125/500] time 1.543 (1.565) data 0.000 (0.008) loss 1.2588 (1.0597) acc 71.8750 (72.9000) lr 2.7103e-04 eta 2:20:10
+epoch [40/50] batch [130/500] time 1.567 (1.565) data 0.000 (0.008) loss 1.4561 (1.0590) acc 71.8750 (72.9087) lr 2.7103e-04 eta 2:20:02
+epoch [40/50] batch [135/500] time 1.565 (1.565) data 0.000 (0.007) loss 0.9873 (1.0583) acc 81.2500 (72.9630) lr 2.7103e-04 eta 2:19:55
+epoch [40/50] batch [140/500] time 1.565 (1.565) data 0.000 (0.007) loss 0.8301 (1.0609) acc 68.7500 (72.9464) lr 2.7103e-04 eta 2:19:49
+epoch [40/50] batch [145/500] time 1.535 (1.565) data 0.001 (0.007) loss 1.4297 (1.0657) acc 65.6250 (72.9095) lr 2.7103e-04 eta 2:19:38
+epoch [40/50] batch [150/500] time 1.574 (1.565) data 0.000 (0.007) loss 1.0078 (1.0702) acc 71.8750 (72.7708) lr 2.7103e-04 eta 2:19:30
+epoch [40/50] batch [155/500] time 1.570 (1.565) data 0.001 (0.006) loss 1.2168 (1.0696) acc 75.0000 (72.8024) lr 2.7103e-04 eta 2:19:23
+epoch [40/50] batch [160/500] time 1.558 (1.565) data 0.000 (0.006) loss 0.9048 (1.0731) acc 75.0000 (72.7344) lr 2.7103e-04 eta 2:19:14
+epoch [40/50] batch [165/500] time 1.563 (1.564) data 0.000 (0.006) loss 0.7866 (1.0781) acc 78.1250 (72.6136) lr 2.7103e-04 eta 2:19:04
+epoch [40/50] batch [170/500] time 1.565 (1.564) data 0.000 (0.006) loss 0.7734 (1.0731) acc 78.1250 (72.7206) lr 2.7103e-04 eta 2:18:53
+epoch [40/50] batch [175/500] time 1.550 (1.563) data 0.000 (0.006) loss 1.1201 (1.0700) acc 78.1250 (72.8750) lr 2.7103e-04 eta 2:18:44
+epoch [40/50] batch [180/500] time 1.614 (1.564) data 0.001 (0.006) loss 1.2549 (1.0729) acc 65.6250 (72.8125) lr 2.7103e-04 eta 2:18:38
+epoch [40/50] batch [185/500] time 1.564 (1.564) data 0.000 (0.005) loss 0.5723 (1.0655) acc 90.6250 (73.0743) lr 2.7103e-04 eta 2:18:34
+epoch [40/50] batch [190/500] time 1.543 (1.564) data 0.000 (0.005) loss 0.9058 (1.0668) acc 75.0000 (72.9770) lr 2.7103e-04 eta 2:18:25
+epoch [40/50] batch [195/500] time 1.582 (1.564) data 0.000 (0.005) loss 1.1504 (1.0666) acc 68.7500 (72.9167) lr 2.7103e-04 eta 2:18:17
+epoch [40/50] batch [200/500] time 1.578 (1.564) data 0.001 (0.005) loss 0.9736 (1.0674) acc 78.1250 (72.9844) lr 2.7103e-04 eta 2:18:08
+epoch [40/50] batch [205/500] time 1.579 (1.564) data 0.000 (0.005) loss 1.5518 (1.0701) acc 62.5000 (73.0488) lr 2.7103e-04 eta 2:18:00
+epoch [40/50] batch [210/500] time 1.577 (1.564) data 0.000 (0.005) loss 1.4189 (1.0706) acc 75.0000 (73.1696) lr 2.7103e-04 eta 2:17:51
+epoch [40/50] batch [215/500] time 1.571 (1.564) data 0.001 (0.005) loss 1.3789 (1.0688) acc 59.3750 (73.0814) lr 2.7103e-04 eta 2:17:43
+epoch [40/50] batch [220/500] time 1.556 (1.564) data 0.000 (0.005) loss 0.6826 (1.0642) acc 87.5000 (73.1960) lr 2.7103e-04 eta 2:17:36
+epoch [40/50] batch [225/500] time 1.565 (1.564) data 0.000 (0.005) loss 1.0459 (1.0642) acc 75.0000 (73.2500) lr 2.7103e-04 eta 2:17:28
+epoch [40/50] batch [230/500] time 1.574 (1.563) data 0.000 (0.004) loss 1.1855 (1.0665) acc 68.7500 (73.1522) lr 2.7103e-04 eta 2:17:19
+epoch [40/50] batch [235/500] time 1.542 (1.563) data 0.000 (0.004) loss 0.7812 (1.0638) acc 75.0000 (73.2979) lr 2.7103e-04 eta 2:17:10
+epoch [40/50] batch [240/500] time 1.552 (1.563) data 0.000 (0.004) loss 1.2324 (1.0644) acc 68.7500 (73.2812) lr 2.7103e-04 eta 2:17:03
+epoch [40/50] batch [245/500] time 1.541 (1.563) data 0.000 (0.004) loss 0.8291 (1.0626) acc 87.5000 (73.3546) lr 2.7103e-04 eta 2:16:55
+epoch [40/50] batch [250/500] time 1.533 (1.563) data 0.000 (0.004) loss 0.7871 (1.0643) acc 71.8750 (73.2500) lr 2.7103e-04 eta 2:16:45
+epoch [40/50] batch [255/500] time 1.547 (1.563) data 0.000 (0.004) loss 0.7603 (1.0613) acc 84.3750 (73.3456) lr 2.7103e-04 eta 2:16:37
+epoch [40/50] batch [260/500] time 1.558 (1.563) data 0.000 (0.004) loss 0.9829 (1.0596) acc 65.6250 (73.3293) lr 2.7103e-04 eta 2:16:29
+epoch [40/50] batch [265/500] time 1.537 (1.563) data 0.001 (0.004) loss 0.7856 (1.0594) acc 81.2500 (73.3373) lr 2.7103e-04 eta 2:16:20
+epoch [40/50] batch [270/500] time 1.559 (1.563) data 0.000 (0.004) loss 0.6699 (1.0581) acc 84.3750 (73.3912) lr 2.7103e-04 eta 2:16:11
+epoch [40/50] batch [275/500] time 1.591 (1.562) data 0.001 (0.004) loss 0.8418 (1.0558) acc 71.8750 (73.4318) lr 2.7103e-04 eta 2:16:03
+epoch [40/50] batch [280/500] time 1.668 (1.563) data 0.000 (0.004) loss 1.2490 (1.0568) acc 75.0000 (73.4821) lr 2.7103e-04 eta 2:15:57
+epoch [40/50] batch [285/500] time 1.568 (1.563) data 0.000 (0.004) loss 0.4954 (1.0545) acc 93.7500 (73.5307) lr 2.7103e-04 eta 2:15:49
+epoch [40/50] batch [290/500] time 1.540 (1.563) data 0.000 (0.004) loss 1.4043 (1.0559) acc 71.8750 (73.5453) lr 2.7103e-04 eta 2:15:41
+epoch [40/50] batch [295/500] time 1.556 (1.563) data 0.000 (0.004) loss 0.9082 (1.0561) acc 71.8750 (73.4852) lr 2.7103e-04 eta 2:15:33
+epoch [40/50] batch [300/500] time 1.531 (1.563) data 0.000 (0.004) loss 0.5693 (1.0543) acc 87.5000 (73.5000) lr 2.7103e-04 eta 2:15:25
+epoch [40/50] batch [305/500] time 1.567 (1.562) data 0.000 (0.003) loss 0.9600 (1.0534) acc 71.8750 (73.5143) lr 2.7103e-04 eta 2:15:16
+epoch [40/50] batch [310/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.0137 (1.0513) acc 71.8750 (73.5383) lr 2.7103e-04 eta 2:15:07
+epoch [40/50] batch [315/500] time 1.569 (1.562) data 0.000 (0.003) loss 1.0400 (1.0572) acc 68.7500 (73.4325) lr 2.7103e-04 eta 2:15:00
+epoch [40/50] batch [320/500] time 1.557 (1.562) data 0.000 (0.003) loss 0.5830 (1.0533) acc 81.2500 (73.4863) lr 2.7103e-04 eta 2:14:51
+epoch [40/50] batch [325/500] time 1.561 (1.562) data 0.000 (0.003) loss 0.8550 (1.0534) acc 75.0000 (73.5000) lr 2.7103e-04 eta 2:14:45
+epoch [40/50] batch [330/500] time 1.554 (1.562) data 0.001 (0.003) loss 0.8262 (1.0526) acc 75.0000 (73.5322) lr 2.7103e-04 eta 2:14:36
+epoch [40/50] batch [335/500] time 1.577 (1.562) data 0.000 (0.003) loss 1.0889 (1.0537) acc 75.0000 (73.5168) lr 2.7103e-04 eta 2:14:28
+epoch [40/50] batch [340/500] time 1.553 (1.562) data 0.000 (0.003) loss 1.0908 (1.0590) acc 71.8750 (73.4099) lr 2.7103e-04 eta 2:14:20
+epoch [40/50] batch [345/500] time 1.572 (1.562) data 0.000 (0.003) loss 1.0361 (1.0575) acc 75.0000 (73.4239) lr 2.7103e-04 eta 2:14:11
+epoch [40/50] batch [350/500] time 1.548 (1.562) data 0.000 (0.003) loss 1.0186 (1.0551) acc 75.0000 (73.4643) lr 2.7103e-04 eta 2:14:03
+epoch [40/50] batch [355/500] time 1.562 (1.562) data 0.000 (0.003) loss 1.4961 (1.0540) acc 62.5000 (73.4859) lr 2.7103e-04 eta 2:13:54
+epoch [40/50] batch [360/500] time 1.589 (1.562) data 0.001 (0.003) loss 1.2051 (1.0559) acc 68.7500 (73.4115) lr 2.7103e-04 eta 2:13:47
+epoch [40/50] batch [365/500] time 1.535 (1.562) data 0.000 (0.003) loss 0.4663 (1.0532) acc 84.3750 (73.4932) lr 2.7103e-04 eta 2:13:38
+epoch [40/50] batch [370/500] time 1.534 (1.561) data 0.000 (0.003) loss 1.0078 (1.0538) acc 78.1250 (73.4713) lr 2.7103e-04 eta 2:13:30
+epoch [40/50] batch [375/500] time 1.562 (1.561) data 0.000 (0.003) loss 1.2451 (1.0554) acc 65.6250 (73.4500) lr 2.7103e-04 eta 2:13:22
+epoch [40/50] batch [380/500] time 1.556 (1.562) data 0.000 (0.003) loss 1.0303 (1.0512) acc 81.2500 (73.5115) lr 2.7103e-04 eta 2:13:14
+epoch [40/50] batch [385/500] time 1.542 (1.561) data 0.000 (0.003) loss 1.0918 (1.0554) acc 75.0000 (73.4091) lr 2.7103e-04 eta 2:13:06
+epoch [40/50] batch [390/500] time 1.539 (1.561) data 0.000 (0.003) loss 1.1270 (1.0558) acc 71.8750 (73.4135) lr 2.7103e-04 eta 2:12:57
+epoch [40/50] batch [395/500] time 1.568 (1.561) data 0.000 (0.003) loss 1.4707 (1.0576) acc 65.6250 (73.3703) lr 2.7103e-04 eta 2:12:49
+epoch [40/50] batch [400/500] time 1.563 (1.561) data 0.000 (0.003) loss 1.0322 (1.0573) acc 65.6250 (73.3672) lr 2.7103e-04 eta 2:12:43
+epoch [40/50] batch [405/500] time 1.550 (1.561) data 0.000 (0.003) loss 1.3613 (1.0574) acc 62.5000 (73.3796) lr 2.7103e-04 eta 2:12:35
+epoch [40/50] batch [410/500] time 1.571 (1.561) data 0.000 (0.003) loss 0.9072 (1.0572) acc 62.5000 (73.3841) lr 2.7103e-04 eta 2:12:27
+epoch [40/50] batch [415/500] time 1.578 (1.561) data 0.000 (0.003) loss 1.1289 (1.0555) acc 71.8750 (73.4413) lr 2.7103e-04 eta 2:12:19
+epoch [40/50] batch [420/500] time 1.557 (1.561) data 0.000 (0.003) loss 1.0537 (1.0556) acc 78.1250 (73.4524) lr 2.7103e-04 eta 2:12:11
+epoch [40/50] batch [425/500] time 1.562 (1.561) data 0.000 (0.003) loss 1.1465 (1.0585) acc 71.8750 (73.3750) lr 2.7103e-04 eta 2:12:04
+epoch [40/50] batch [430/500] time 1.548 (1.561) data 0.000 (0.003) loss 0.8467 (1.0579) acc 71.8750 (73.3794) lr 2.7103e-04 eta 2:11:56
+epoch [40/50] batch [435/500] time 1.562 (1.561) data 0.000 (0.003) loss 0.7124 (1.0534) acc 87.5000 (73.4986) lr 2.7103e-04 eta 2:11:48
+epoch [40/50] batch [440/500] time 1.571 (1.561) data 0.000 (0.003) loss 0.6558 (1.0510) acc 87.5000 (73.5511) lr 2.7103e-04 eta 2:11:40
+epoch [40/50] batch [445/500] time 1.554 (1.561) data 0.001 (0.002) loss 1.1787 (1.0513) acc 75.0000 (73.5815) lr 2.7103e-04 eta 2:11:32
+epoch [40/50] batch [450/500] time 1.545 (1.561) data 0.000 (0.002) loss 0.9678 (1.0503) acc 78.1250 (73.6250) lr 2.7103e-04 eta 2:11:24
+epoch [40/50] batch [455/500] time 1.589 (1.561) data 0.000 (0.002) loss 0.9961 (1.0475) acc 81.2500 (73.6813) lr 2.7103e-04 eta 2:11:16
+epoch [40/50] batch [460/500] time 1.545 (1.561) data 0.000 (0.002) loss 1.4258 (1.0467) acc 71.8750 (73.6617) lr 2.7103e-04 eta 2:11:08
+epoch [40/50] batch [465/500] time 1.574 (1.561) data 0.000 (0.002) loss 1.3975 (1.0479) acc 78.1250 (73.6156) lr 2.7103e-04 eta 2:11:00
+epoch [40/50] batch [470/500] time 1.532 (1.561) data 0.000 (0.002) loss 1.2861 (1.0501) acc 53.1250 (73.5372) lr 2.7103e-04 eta 2:10:53
+epoch [40/50] batch [475/500] time 1.588 (1.561) data 0.000 (0.002) loss 0.3511 (1.0474) acc 87.5000 (73.6053) lr 2.7103e-04 eta 2:10:46
+epoch [40/50] batch [480/500] time 1.553 (1.561) data 0.000 (0.002) loss 0.9512 (1.0464) acc 68.7500 (73.6133) lr 2.7103e-04 eta 2:10:38
+epoch [40/50] batch [485/500] time 1.550 (1.561) data 0.001 (0.002) loss 0.2661 (1.0445) acc 93.7500 (73.6469) lr 2.7103e-04 eta 2:10:30
+epoch [40/50] batch [490/500] time 1.544 (1.561) data 0.000 (0.002) loss 1.2256 (1.0438) acc 71.8750 (73.6798) lr 2.7103e-04 eta 2:10:21
+epoch [40/50] batch [495/500] time 1.532 (1.561) data 0.000 (0.002) loss 1.1465 (1.0454) acc 68.7500 (73.6237) lr 2.7103e-04 eta 2:10:13
+epoch [40/50] batch [500/500] time 1.561 (1.561) data 0.000 (0.002) loss 0.7192 (1.0445) acc 78.1250 (73.6375) lr 2.2949e-04 eta 2:10:05
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,029
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [41/50] batch [5/500] time 1.560 (1.677) data 0.000 (0.174) loss 1.2871 (1.0147) acc 68.7500 (74.3750) lr 2.2949e-04 eta 2:19:34
+epoch [41/50] batch [10/500] time 1.551 (1.622) data 0.000 (0.087) loss 0.6543 (0.8895) acc 90.6250 (78.4375) lr 2.2949e-04 eta 2:14:51
+epoch [41/50] batch [15/500] time 1.554 (1.600) data 0.001 (0.058) loss 0.5225 (0.9221) acc 90.6250 (76.6667) lr 2.2949e-04 eta 2:12:57
+epoch [41/50] batch [20/500] time 1.563 (1.590) data 0.001 (0.044) loss 1.1514 (0.9344) acc 68.7500 (76.2500) lr 2.2949e-04 eta 2:11:58
+epoch [41/50] batch [25/500] time 1.648 (1.585) data 0.000 (0.035) loss 1.1104 (0.9791) acc 75.0000 (75.3750) lr 2.2949e-04 eta 2:11:26
+epoch [41/50] batch [30/500] time 1.534 (1.580) data 0.000 (0.029) loss 0.6982 (0.9972) acc 84.3750 (75.1042) lr 2.2949e-04 eta 2:10:52
+epoch [41/50] batch [35/500] time 1.552 (1.576) data 0.000 (0.025) loss 0.7124 (0.9657) acc 71.8750 (75.0000) lr 2.2949e-04 eta 2:10:27
+epoch [41/50] batch [40/500] time 1.562 (1.574) data 0.001 (0.022) loss 0.7407 (0.9631) acc 81.2500 (75.2344) lr 2.2949e-04 eta 2:10:08
+epoch [41/50] batch [45/500] time 1.548 (1.572) data 0.000 (0.020) loss 1.4189 (0.9959) acc 71.8750 (74.5833) lr 2.2949e-04 eta 2:09:50
+epoch [41/50] batch [50/500] time 1.562 (1.570) data 0.000 (0.018) loss 1.0342 (0.9960) acc 71.8750 (74.3750) lr 2.2949e-04 eta 2:09:32
+epoch [41/50] batch [55/500] time 1.558 (1.569) data 0.000 (0.016) loss 1.0381 (1.0057) acc 65.6250 (74.0341) lr 2.2949e-04 eta 2:09:20
+epoch [41/50] batch [60/500] time 1.545 (1.569) data 0.000 (0.015) loss 0.9604 (1.0037) acc 68.7500 (74.1146) lr 2.2949e-04 eta 2:09:08
+epoch [41/50] batch [65/500] time 1.555 (1.567) data 0.000 (0.014) loss 0.7935 (1.0209) acc 75.0000 (73.6538) lr 2.2949e-04 eta 2:08:53
+epoch [41/50] batch [70/500] time 1.561 (1.566) data 0.001 (0.013) loss 1.6553 (1.0341) acc 65.6250 (73.3929) lr 2.2949e-04 eta 2:08:42
+epoch [41/50] batch [75/500] time 1.557 (1.566) data 0.000 (0.012) loss 0.7173 (1.0254) acc 78.1250 (73.5417) lr 2.2949e-04 eta 2:08:30
+epoch [41/50] batch [80/500] time 1.556 (1.566) data 0.000 (0.011) loss 1.7207 (1.0433) acc 56.2500 (72.9297) lr 2.2949e-04 eta 2:08:22
+epoch [41/50] batch [85/500] time 1.663 (1.566) data 0.000 (0.011) loss 1.3672 (1.0469) acc 81.2500 (73.0515) lr 2.2949e-04 eta 2:08:17
+epoch [41/50] batch [90/500] time 1.581 (1.566) data 0.001 (0.010) loss 1.1689 (1.0505) acc 65.6250 (72.8472) lr 2.2949e-04 eta 2:08:09
+epoch [41/50] batch [95/500] time 1.559 (1.566) data 0.000 (0.010) loss 0.6523 (1.0602) acc 84.3750 (72.8947) lr 2.2949e-04 eta 2:07:58
+epoch [41/50] batch [100/500] time 1.545 (1.565) data 0.000 (0.009) loss 0.9526 (1.0618) acc 71.8750 (72.7812) lr 2.2949e-04 eta 2:07:49
+epoch [41/50] batch [105/500] time 1.562 (1.564) data 0.000 (0.009) loss 1.1426 (1.0722) acc 68.7500 (72.5595) lr 2.2949e-04 eta 2:07:36
+epoch [41/50] batch [110/500] time 1.539 (1.564) data 0.000 (0.008) loss 1.4248 (1.0728) acc 56.2500 (72.5568) lr 2.2949e-04 eta 2:07:26
+epoch [41/50] batch [115/500] time 1.561 (1.563) data 0.000 (0.008) loss 0.6235 (1.0725) acc 75.0000 (72.3370) lr 2.2949e-04 eta 2:07:16
+epoch [41/50] batch [120/500] time 1.561 (1.563) data 0.000 (0.008) loss 1.1270 (1.0678) acc 62.5000 (72.2656) lr 2.2949e-04 eta 2:07:09
+epoch [41/50] batch [125/500] time 1.552 (1.563) data 0.001 (0.007) loss 1.0107 (1.0729) acc 71.8750 (72.1000) lr 2.2949e-04 eta 2:06:59
+epoch [41/50] batch [130/500] time 1.595 (1.564) data 0.000 (0.007) loss 1.1250 (1.0752) acc 71.8750 (72.0192) lr 2.2949e-04 eta 2:06:57
+epoch [41/50] batch [135/500] time 1.585 (1.564) data 0.001 (0.007) loss 1.0049 (1.0693) acc 68.7500 (72.1759) lr 2.2949e-04 eta 2:06:49
+epoch [41/50] batch [140/500] time 1.543 (1.564) data 0.000 (0.007) loss 1.4854 (1.0727) acc 71.8750 (72.1875) lr 2.2949e-04 eta 2:06:38
+epoch [41/50] batch [145/500] time 1.555 (1.563) data 0.000 (0.006) loss 0.8540 (1.0711) acc 78.1250 (72.1767) lr 2.2949e-04 eta 2:06:30
+epoch [41/50] batch [150/500] time 1.555 (1.563) data 0.001 (0.006) loss 1.1006 (1.0734) acc 75.0000 (72.0833) lr 2.2949e-04 eta 2:06:22
+epoch [41/50] batch [155/500] time 1.543 (1.563) data 0.000 (0.006) loss 0.9458 (1.0750) acc 68.7500 (72.0565) lr 2.2949e-04 eta 2:06:13
+epoch [41/50] batch [160/500] time 1.562 (1.563) data 0.000 (0.006) loss 1.0225 (1.0783) acc 78.1250 (72.1094) lr 2.2949e-04 eta 2:06:04
+epoch [41/50] batch [165/500] time 1.553 (1.563) data 0.000 (0.006) loss 1.1484 (1.0788) acc 71.8750 (72.1970) lr 2.2949e-04 eta 2:05:56
+epoch [41/50] batch [170/500] time 1.583 (1.563) data 0.000 (0.006) loss 1.0020 (1.0755) acc 81.2500 (72.4081) lr 2.2949e-04 eta 2:05:48
+epoch [41/50] batch [175/500] time 1.579 (1.563) data 0.000 (0.005) loss 1.0352 (1.0732) acc 68.7500 (72.3929) lr 2.2949e-04 eta 2:05:41
+epoch [41/50] batch [180/500] time 1.549 (1.563) data 0.000 (0.005) loss 1.1143 (1.0696) acc 81.2500 (72.5000) lr 2.2949e-04 eta 2:05:32
+epoch [41/50] batch [185/500] time 1.564 (1.563) data 0.000 (0.005) loss 1.1260 (1.0725) acc 62.5000 (72.4155) lr 2.2949e-04 eta 2:05:23
+epoch [41/50] batch [190/500] time 1.572 (1.562) data 0.000 (0.005) loss 0.9868 (1.0684) acc 81.2500 (72.6151) lr 2.2949e-04 eta 2:05:15
+epoch [41/50] batch [195/500] time 1.556 (1.563) data 0.000 (0.005) loss 1.3232 (1.0734) acc 59.3750 (72.5641) lr 2.2949e-04 eta 2:05:07
+epoch [41/50] batch [200/500] time 1.594 (1.563) data 0.000 (0.005) loss 0.9927 (1.0761) acc 87.5000 (72.6406) lr 2.2949e-04 eta 2:05:01
+epoch [41/50] batch [205/500] time 1.554 (1.563) data 0.000 (0.005) loss 0.7852 (1.0742) acc 84.3750 (72.6829) lr 2.2949e-04 eta 2:04:52
+epoch [41/50] batch [210/500] time 1.557 (1.563) data 0.000 (0.005) loss 1.5303 (1.0824) acc 59.3750 (72.5893) lr 2.2949e-04 eta 2:04:46
+epoch [41/50] batch [215/500] time 1.545 (1.563) data 0.000 (0.004) loss 0.5220 (1.0823) acc 87.5000 (72.5872) lr 2.2949e-04 eta 2:04:37
+epoch [41/50] batch [220/500] time 1.547 (1.562) data 0.000 (0.004) loss 1.0381 (1.0802) acc 87.5000 (72.6705) lr 2.2949e-04 eta 2:04:28
+epoch [41/50] batch [225/500] time 1.585 (1.562) data 0.000 (0.004) loss 0.5005 (1.0813) acc 90.6250 (72.6528) lr 2.2949e-04 eta 2:04:19
+epoch [41/50] batch [230/500] time 1.543 (1.562) data 0.000 (0.004) loss 1.5898 (1.0816) acc 65.6250 (72.6902) lr 2.2949e-04 eta 2:04:12
+epoch [41/50] batch [235/500] time 1.568 (1.562) data 0.000 (0.004) loss 1.0605 (1.0853) acc 78.1250 (72.6463) lr 2.2949e-04 eta 2:04:04
+epoch [41/50] batch [240/500] time 1.560 (1.562) data 0.000 (0.004) loss 1.0576 (1.0851) acc 75.0000 (72.6042) lr 2.2949e-04 eta 2:03:55
+epoch [41/50] batch [245/500] time 1.568 (1.562) data 0.000 (0.004) loss 0.6665 (1.0801) acc 87.5000 (72.7296) lr 2.2949e-04 eta 2:03:47
+epoch [41/50] batch [250/500] time 1.547 (1.562) data 0.000 (0.004) loss 0.9683 (1.0799) acc 75.0000 (72.7875) lr 2.2949e-04 eta 2:03:40
+epoch [41/50] batch [255/500] time 1.562 (1.562) data 0.000 (0.004) loss 1.3057 (1.0794) acc 62.5000 (72.8309) lr 2.2949e-04 eta 2:03:31
+epoch [41/50] batch [260/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.1855 (1.0768) acc 81.2500 (72.9207) lr 2.2949e-04 eta 2:03:23
+epoch [41/50] batch [265/500] time 1.584 (1.562) data 0.000 (0.004) loss 1.0098 (1.0767) acc 81.2500 (72.9481) lr 2.2949e-04 eta 2:03:16
+epoch [41/50] batch [270/500] time 1.571 (1.562) data 0.001 (0.004) loss 1.0254 (1.0770) acc 68.7500 (72.8935) lr 2.2949e-04 eta 2:03:08
+epoch [41/50] batch [275/500] time 1.580 (1.562) data 0.000 (0.004) loss 0.9233 (1.0739) acc 71.8750 (72.8523) lr 2.2949e-04 eta 2:03:02
+epoch [41/50] batch [280/500] time 1.546 (1.563) data 0.001 (0.004) loss 1.1221 (1.0748) acc 68.7500 (72.8125) lr 2.2949e-04 eta 2:02:55
+epoch [41/50] batch [285/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.1309 (1.0757) acc 59.3750 (72.8289) lr 2.2949e-04 eta 2:02:46
+epoch [41/50] batch [290/500] time 1.577 (1.563) data 0.000 (0.003) loss 0.5171 (1.0697) acc 84.3750 (73.0172) lr 2.2949e-04 eta 2:02:39
+epoch [41/50] batch [295/500] time 1.571 (1.563) data 0.000 (0.003) loss 0.6455 (1.0655) acc 81.2500 (73.0720) lr 2.2949e-04 eta 2:02:31
+epoch [41/50] batch [300/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.0674 (1.0649) acc 71.8750 (73.0729) lr 2.2949e-04 eta 2:02:23
+epoch [41/50] batch [305/500] time 1.571 (1.563) data 0.000 (0.003) loss 0.9307 (1.0645) acc 84.3750 (73.1762) lr 2.2949e-04 eta 2:02:16
+epoch [41/50] batch [310/500] time 1.524 (1.562) data 0.000 (0.003) loss 0.7959 (1.0603) acc 81.2500 (73.2560) lr 2.2949e-04 eta 2:02:07
+epoch [41/50] batch [315/500] time 1.556 (1.562) data 0.001 (0.003) loss 1.2803 (1.0586) acc 68.7500 (73.2837) lr 2.2949e-04 eta 2:01:59
+epoch [41/50] batch [320/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.1445 (1.0572) acc 68.7500 (73.3203) lr 2.2949e-04 eta 2:01:51
+epoch [41/50] batch [325/500] time 1.574 (1.562) data 0.000 (0.003) loss 1.2188 (1.0576) acc 75.0000 (73.3077) lr 2.2949e-04 eta 2:01:43
+epoch [41/50] batch [330/500] time 1.563 (1.562) data 0.000 (0.003) loss 1.2549 (1.0610) acc 75.0000 (73.2670) lr 2.2949e-04 eta 2:01:35
+epoch [41/50] batch [335/500] time 1.551 (1.562) data 0.000 (0.003) loss 1.2090 (1.0594) acc 62.5000 (73.2836) lr 2.2949e-04 eta 2:01:27
+epoch [41/50] batch [340/500] time 1.601 (1.562) data 0.000 (0.003) loss 0.6685 (1.0552) acc 87.5000 (73.3915) lr 2.2949e-04 eta 2:01:20
+epoch [41/50] batch [345/500] time 1.560 (1.562) data 0.000 (0.003) loss 1.8613 (1.0556) acc 56.2500 (73.3696) lr 2.2949e-04 eta 2:01:12
+epoch [41/50] batch [350/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.3447 (1.0559) acc 68.7500 (73.3750) lr 2.2949e-04 eta 2:01:04
+epoch [41/50] batch [355/500] time 1.552 (1.562) data 0.000 (0.003) loss 1.3438 (1.0573) acc 68.7500 (73.3275) lr 2.2949e-04 eta 2:00:56
+epoch [41/50] batch [360/500] time 1.575 (1.562) data 0.000 (0.003) loss 1.4609 (1.0563) acc 65.6250 (73.2899) lr 2.2949e-04 eta 2:00:48
+epoch [41/50] batch [365/500] time 1.557 (1.562) data 0.001 (0.003) loss 1.4023 (1.0583) acc 71.8750 (73.2791) lr 2.2949e-04 eta 2:00:41
+epoch [41/50] batch [370/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.0410 (1.0586) acc 68.7500 (73.3277) lr 2.2949e-04 eta 2:00:34
+epoch [41/50] batch [375/500] time 1.537 (1.563) data 0.000 (0.003) loss 1.0801 (1.0601) acc 75.0000 (73.2667) lr 2.2949e-04 eta 2:00:27
+epoch [41/50] batch [380/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.2900 (1.0599) acc 78.1250 (73.3306) lr 2.2949e-04 eta 2:00:19
+epoch [41/50] batch [385/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.2656 (1.0577) acc 71.8750 (73.3604) lr 2.2949e-04 eta 2:00:11
+epoch [41/50] batch [390/500] time 1.539 (1.562) data 0.000 (0.003) loss 1.5391 (1.0578) acc 68.7500 (73.3494) lr 2.2949e-04 eta 2:00:02
+epoch [41/50] batch [395/500] time 1.553 (1.562) data 0.000 (0.003) loss 1.4277 (1.0581) acc 65.6250 (73.3465) lr 2.2949e-04 eta 1:59:54
+epoch [41/50] batch [400/500] time 1.560 (1.562) data 0.000 (0.003) loss 0.8184 (1.0566) acc 81.2500 (73.3750) lr 2.2949e-04 eta 1:59:47
+epoch [41/50] batch [405/500] time 1.565 (1.562) data 0.000 (0.003) loss 0.6611 (1.0534) acc 78.1250 (73.4028) lr 2.2949e-04 eta 1:59:38
+epoch [41/50] batch [410/500] time 1.587 (1.562) data 0.000 (0.003) loss 0.9424 (1.0547) acc 75.0000 (73.3460) lr 2.2949e-04 eta 1:59:31
+epoch [41/50] batch [415/500] time 1.671 (1.563) data 0.000 (0.003) loss 0.8184 (1.0527) acc 84.3750 (73.4337) lr 2.2949e-04 eta 1:59:25
+epoch [41/50] batch [420/500] time 1.552 (1.563) data 0.000 (0.002) loss 1.2920 (1.0547) acc 62.5000 (73.3780) lr 2.2949e-04 eta 1:59:17
+epoch [41/50] batch [425/500] time 1.582 (1.563) data 0.000 (0.002) loss 0.8960 (1.0556) acc 71.8750 (73.3824) lr 2.2949e-04 eta 1:59:09
+epoch [41/50] batch [430/500] time 1.571 (1.563) data 0.000 (0.002) loss 0.5317 (1.0529) acc 87.5000 (73.4302) lr 2.2949e-04 eta 1:59:01
+epoch [41/50] batch [435/500] time 1.554 (1.563) data 0.000 (0.002) loss 0.8877 (1.0529) acc 75.0000 (73.4339) lr 2.2949e-04 eta 1:58:54
+epoch [41/50] batch [440/500] time 1.561 (1.563) data 0.000 (0.002) loss 1.1611 (1.0536) acc 68.7500 (73.4517) lr 2.2949e-04 eta 1:58:46
+epoch [41/50] batch [445/500] time 1.554 (1.563) data 0.000 (0.002) loss 0.6479 (1.0529) acc 84.3750 (73.4480) lr 2.2949e-04 eta 1:58:38
+epoch [41/50] batch [450/500] time 1.561 (1.563) data 0.000 (0.002) loss 1.5498 (1.0535) acc 62.5000 (73.4653) lr 2.2949e-04 eta 1:58:29
+epoch [41/50] batch [455/500] time 1.550 (1.562) data 0.000 (0.002) loss 0.9985 (1.0520) acc 75.0000 (73.4890) lr 2.2949e-04 eta 1:58:21
+epoch [41/50] batch [460/500] time 1.558 (1.562) data 0.000 (0.002) loss 0.5708 (1.0506) acc 84.3750 (73.5258) lr 2.2949e-04 eta 1:58:13
+epoch [41/50] batch [465/500] time 1.568 (1.563) data 0.000 (0.002) loss 1.1484 (1.0496) acc 75.0000 (73.5349) lr 2.2949e-04 eta 1:58:06
+epoch [41/50] batch [470/500] time 1.562 (1.563) data 0.000 (0.002) loss 0.9126 (1.0486) acc 68.7500 (73.5306) lr 2.2949e-04 eta 1:57:58
+epoch [41/50] batch [475/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.1309 (1.0495) acc 75.0000 (73.5132) lr 2.2949e-04 eta 1:57:50
+epoch [41/50] batch [480/500] time 1.541 (1.562) data 0.000 (0.002) loss 1.2197 (1.0487) acc 62.5000 (73.5091) lr 2.2949e-04 eta 1:57:42
+epoch [41/50] batch [485/500] time 1.556 (1.562) data 0.001 (0.002) loss 0.8721 (1.0487) acc 84.3750 (73.5438) lr 2.2949e-04 eta 1:57:34
+epoch [41/50] batch [490/500] time 1.550 (1.562) data 0.000 (0.002) loss 1.0850 (1.0488) acc 75.0000 (73.5268) lr 2.2949e-04 eta 1:57:26
+epoch [41/50] batch [495/500] time 1.559 (1.562) data 0.000 (0.002) loss 0.7217 (1.0496) acc 81.2500 (73.5290) lr 2.2949e-04 eta 1:57:18
+epoch [41/50] batch [500/500] time 1.544 (1.562) data 0.000 (0.002) loss 0.8071 (1.0500) acc 87.5000 (73.5125) lr 1.9098e-04 eta 1:57:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,041
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [42/50] batch [5/500] time 1.521 (1.646) data 0.000 (0.148) loss 0.7954 (0.7874) acc 78.1250 (81.2500) lr 1.9098e-04 eta 2:03:18
+epoch [42/50] batch [10/500] time 1.534 (1.594) data 0.001 (0.074) loss 1.1807 (0.8896) acc 71.8750 (77.8125) lr 1.9098e-04 eta 1:59:16
+epoch [42/50] batch [15/500] time 1.551 (1.583) data 0.001 (0.050) loss 1.8350 (1.0346) acc 56.2500 (74.3750) lr 1.9098e-04 eta 1:58:20
+epoch [42/50] batch [20/500] time 1.562 (1.575) data 0.001 (0.037) loss 1.5752 (1.0465) acc 56.2500 (73.4375) lr 1.9098e-04 eta 1:57:37
+epoch [42/50] batch [25/500] time 1.574 (1.578) data 0.001 (0.030) loss 0.9531 (1.0641) acc 71.8750 (73.6250) lr 1.9098e-04 eta 1:57:43
+epoch [42/50] batch [30/500] time 1.558 (1.576) data 0.000 (0.025) loss 1.3652 (1.0668) acc 62.5000 (73.8542) lr 1.9098e-04 eta 1:57:25
+epoch [42/50] batch [35/500] time 1.559 (1.575) data 0.000 (0.021) loss 0.7163 (1.0637) acc 71.8750 (73.6607) lr 1.9098e-04 eta 1:57:10
+epoch [42/50] batch [40/500] time 1.576 (1.573) data 0.000 (0.019) loss 1.1064 (1.0460) acc 78.1250 (74.0625) lr 1.9098e-04 eta 1:56:55
+epoch [42/50] batch [45/500] time 1.567 (1.573) data 0.000 (0.017) loss 1.3262 (1.0453) acc 71.8750 (73.6111) lr 1.9098e-04 eta 1:56:46
+epoch [42/50] batch [50/500] time 1.568 (1.572) data 0.000 (0.015) loss 1.1572 (1.0423) acc 65.6250 (73.1875) lr 1.9098e-04 eta 1:56:33
+epoch [42/50] batch [55/500] time 1.567 (1.572) data 0.000 (0.014) loss 1.6631 (1.0258) acc 65.6250 (73.4091) lr 1.9098e-04 eta 1:56:25
+epoch [42/50] batch [60/500] time 1.550 (1.571) data 0.001 (0.013) loss 1.7891 (1.0470) acc 68.7500 (73.2812) lr 1.9098e-04 eta 1:56:13
+epoch [42/50] batch [65/500] time 1.554 (1.569) data 0.000 (0.012) loss 1.0410 (1.0307) acc 65.6250 (73.5577) lr 1.9098e-04 eta 1:55:59
+epoch [42/50] batch [70/500] time 1.600 (1.569) data 0.000 (0.011) loss 0.5620 (1.0181) acc 87.5000 (73.9732) lr 1.9098e-04 eta 1:55:51
+epoch [42/50] batch [75/500] time 1.539 (1.568) data 0.000 (0.010) loss 1.6357 (1.0288) acc 68.7500 (73.8750) lr 1.9098e-04 eta 1:55:38
+epoch [42/50] batch [80/500] time 1.571 (1.568) data 0.000 (0.010) loss 0.9644 (1.0215) acc 84.3750 (74.3359) lr 1.9098e-04 eta 1:55:28
+epoch [42/50] batch [85/500] time 1.558 (1.567) data 0.000 (0.009) loss 0.4973 (1.0165) acc 81.2500 (74.5221) lr 1.9098e-04 eta 1:55:18
+epoch [42/50] batch [90/500] time 1.559 (1.566) data 0.000 (0.009) loss 1.1162 (1.0172) acc 68.7500 (74.3750) lr 1.9098e-04 eta 1:55:07
+epoch [42/50] batch [95/500] time 1.525 (1.566) data 0.000 (0.008) loss 0.9351 (1.0063) acc 81.2500 (74.6053) lr 1.9098e-04 eta 1:54:56
+epoch [42/50] batch [100/500] time 1.550 (1.565) data 0.000 (0.008) loss 1.1836 (1.0007) acc 71.8750 (74.5938) lr 1.9098e-04 eta 1:54:45
+epoch [42/50] batch [105/500] time 1.553 (1.565) data 0.000 (0.007) loss 1.1387 (1.0143) acc 81.2500 (74.7321) lr 1.9098e-04 eta 1:54:37
+epoch [42/50] batch [110/500] time 1.571 (1.564) data 0.000 (0.007) loss 1.0645 (1.0169) acc 78.1250 (74.7443) lr 1.9098e-04 eta 1:54:25
+epoch [42/50] batch [115/500] time 1.542 (1.563) data 0.000 (0.007) loss 1.2490 (1.0195) acc 65.6250 (74.5924) lr 1.9098e-04 eta 1:54:14
+epoch [42/50] batch [120/500] time 1.659 (1.563) data 0.000 (0.007) loss 1.5244 (1.0261) acc 59.3750 (74.3229) lr 1.9098e-04 eta 1:54:07
+epoch [42/50] batch [125/500] time 1.534 (1.563) data 0.001 (0.006) loss 0.6064 (1.0275) acc 84.3750 (74.2250) lr 1.9098e-04 eta 1:53:57
+epoch [42/50] batch [130/500] time 1.577 (1.563) data 0.000 (0.006) loss 0.9551 (1.0314) acc 75.0000 (74.1346) lr 1.9098e-04 eta 1:53:48
+epoch [42/50] batch [135/500] time 1.540 (1.562) data 0.000 (0.006) loss 1.3516 (1.0326) acc 71.8750 (74.1204) lr 1.9098e-04 eta 1:53:39
+epoch [42/50] batch [140/500] time 1.560 (1.562) data 0.000 (0.006) loss 1.2021 (1.0379) acc 65.6250 (73.9732) lr 1.9098e-04 eta 1:53:32
+epoch [42/50] batch [145/500] time 1.559 (1.562) data 0.000 (0.005) loss 0.8696 (1.0424) acc 75.0000 (73.9009) lr 1.9098e-04 eta 1:53:24
+epoch [42/50] batch [150/500] time 1.573 (1.562) data 0.000 (0.005) loss 0.6162 (1.0403) acc 71.8750 (73.9167) lr 1.9098e-04 eta 1:53:16
+epoch [42/50] batch [155/500] time 1.538 (1.563) data 0.000 (0.005) loss 0.9575 (1.0420) acc 78.1250 (73.9113) lr 1.9098e-04 eta 1:53:09
+epoch [42/50] batch [160/500] time 1.557 (1.562) data 0.000 (0.005) loss 0.7407 (1.0460) acc 81.2500 (73.9453) lr 1.9098e-04 eta 1:53:01
+epoch [42/50] batch [165/500] time 1.570 (1.563) data 0.000 (0.005) loss 1.0977 (1.0468) acc 71.8750 (73.9205) lr 1.9098e-04 eta 1:52:56
+epoch [42/50] batch [170/500] time 1.563 (1.564) data 0.000 (0.005) loss 1.6914 (1.0507) acc 65.6250 (73.8419) lr 1.9098e-04 eta 1:52:50
+epoch [42/50] batch [175/500] time 1.561 (1.563) data 0.000 (0.005) loss 0.9873 (1.0470) acc 81.2500 (73.9821) lr 1.9098e-04 eta 1:52:41
+epoch [42/50] batch [180/500] time 1.563 (1.563) data 0.000 (0.004) loss 1.3477 (1.0470) acc 65.6250 (73.9236) lr 1.9098e-04 eta 1:52:33
+epoch [42/50] batch [185/500] time 1.544 (1.563) data 0.001 (0.004) loss 1.2773 (1.0516) acc 68.7500 (73.8345) lr 1.9098e-04 eta 1:52:24
+epoch [42/50] batch [190/500] time 1.561 (1.563) data 0.000 (0.004) loss 1.2559 (1.0493) acc 68.7500 (73.8651) lr 1.9098e-04 eta 1:52:16
+epoch [42/50] batch [195/500] time 1.562 (1.563) data 0.000 (0.004) loss 1.0498 (1.0529) acc 71.8750 (73.8462) lr 1.9098e-04 eta 1:52:09
+epoch [42/50] batch [200/500] time 1.588 (1.563) data 0.000 (0.004) loss 0.8003 (1.0484) acc 71.8750 (73.8594) lr 1.9098e-04 eta 1:52:02
+epoch [42/50] batch [205/500] time 1.574 (1.563) data 0.000 (0.004) loss 1.5352 (1.0466) acc 68.7500 (73.9329) lr 1.9098e-04 eta 1:51:53
+epoch [42/50] batch [210/500] time 1.557 (1.563) data 0.000 (0.004) loss 0.7178 (1.0431) acc 71.8750 (74.0179) lr 1.9098e-04 eta 1:51:45
+epoch [42/50] batch [215/500] time 1.549 (1.563) data 0.000 (0.004) loss 0.8237 (1.0434) acc 78.1250 (73.9971) lr 1.9098e-04 eta 1:51:37
+epoch [42/50] batch [220/500] time 1.564 (1.563) data 0.000 (0.004) loss 1.5166 (1.0464) acc 75.0000 (74.0483) lr 1.9098e-04 eta 1:51:30
+epoch [42/50] batch [225/500] time 1.569 (1.564) data 0.000 (0.004) loss 1.7969 (1.0489) acc 65.6250 (74.0417) lr 1.9098e-04 eta 1:51:24
+epoch [42/50] batch [230/500] time 1.572 (1.564) data 0.000 (0.004) loss 0.7393 (1.0449) acc 75.0000 (74.0761) lr 1.9098e-04 eta 1:51:17
+epoch [42/50] batch [235/500] time 1.572 (1.564) data 0.000 (0.004) loss 1.2910 (1.0471) acc 62.5000 (74.0426) lr 1.9098e-04 eta 1:51:09
+epoch [42/50] batch [240/500] time 1.551 (1.564) data 0.000 (0.003) loss 1.0762 (1.0445) acc 78.1250 (74.2057) lr 1.9098e-04 eta 1:51:00
+epoch [42/50] batch [245/500] time 1.542 (1.563) data 0.000 (0.003) loss 0.9175 (1.0426) acc 78.1250 (74.2730) lr 1.9098e-04 eta 1:50:52
+epoch [42/50] batch [250/500] time 1.579 (1.563) data 0.000 (0.003) loss 0.6436 (1.0453) acc 81.2500 (74.1750) lr 1.9098e-04 eta 1:50:44
+epoch [42/50] batch [255/500] time 1.565 (1.563) data 0.000 (0.003) loss 0.8516 (1.0441) acc 75.0000 (74.2157) lr 1.9098e-04 eta 1:50:36
+epoch [42/50] batch [260/500] time 1.537 (1.563) data 0.000 (0.003) loss 0.7969 (1.0450) acc 78.1250 (74.1947) lr 1.9098e-04 eta 1:50:27
+epoch [42/50] batch [265/500] time 1.535 (1.563) data 0.001 (0.003) loss 0.9351 (1.0446) acc 75.0000 (74.2335) lr 1.9098e-04 eta 1:50:20
+epoch [42/50] batch [270/500] time 1.560 (1.563) data 0.000 (0.003) loss 1.0127 (1.0480) acc 75.0000 (74.3171) lr 1.9098e-04 eta 1:50:12
+epoch [42/50] batch [275/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.4385 (1.0508) acc 65.6250 (74.2045) lr 1.9098e-04 eta 1:50:03
+epoch [42/50] batch [280/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.5000 (1.0545) acc 65.6250 (74.1183) lr 1.9098e-04 eta 1:49:54
+epoch [42/50] batch [285/500] time 1.555 (1.562) data 0.001 (0.003) loss 1.4043 (1.0541) acc 65.6250 (74.1009) lr 1.9098e-04 eta 1:49:45
+epoch [42/50] batch [290/500] time 1.548 (1.562) data 0.000 (0.003) loss 1.4707 (1.0520) acc 71.8750 (74.2026) lr 1.9098e-04 eta 1:49:36
+epoch [42/50] batch [295/500] time 1.557 (1.562) data 0.000 (0.003) loss 1.2266 (1.0497) acc 68.7500 (74.2267) lr 1.9098e-04 eta 1:49:28
+epoch [42/50] batch [300/500] time 1.546 (1.562) data 0.000 (0.003) loss 0.7500 (1.0475) acc 78.1250 (74.2188) lr 1.9098e-04 eta 1:49:20
+epoch [42/50] batch [305/500] time 1.602 (1.562) data 0.000 (0.003) loss 0.9521 (1.0462) acc 68.7500 (74.2316) lr 1.9098e-04 eta 1:49:14
+epoch [42/50] batch [310/500] time 1.575 (1.563) data 0.000 (0.003) loss 1.1152 (1.0487) acc 75.0000 (74.2641) lr 1.9098e-04 eta 1:49:07
+epoch [42/50] batch [315/500] time 1.555 (1.563) data 0.000 (0.003) loss 0.8198 (1.0475) acc 81.2500 (74.2956) lr 1.9098e-04 eta 1:49:00
+epoch [42/50] batch [320/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.7529 (1.0509) acc 46.8750 (74.1113) lr 1.9098e-04 eta 1:48:52
+epoch [42/50] batch [325/500] time 1.529 (1.563) data 0.000 (0.003) loss 1.1045 (1.0539) acc 71.8750 (74.0577) lr 1.9098e-04 eta 1:48:44
+epoch [42/50] batch [330/500] time 1.545 (1.563) data 0.000 (0.003) loss 1.4111 (1.0565) acc 56.2500 (73.9773) lr 1.9098e-04 eta 1:48:36
+epoch [42/50] batch [335/500] time 1.531 (1.562) data 0.000 (0.003) loss 1.2842 (1.0563) acc 65.6250 (73.9739) lr 1.9098e-04 eta 1:48:27
+epoch [42/50] batch [340/500] time 1.569 (1.562) data 0.000 (0.003) loss 1.0107 (1.0536) acc 71.8750 (73.9798) lr 1.9098e-04 eta 1:48:19
+epoch [42/50] batch [345/500] time 1.553 (1.563) data 0.001 (0.003) loss 1.1484 (1.0521) acc 65.6250 (73.9312) lr 1.9098e-04 eta 1:48:12
+epoch [42/50] batch [350/500] time 1.542 (1.563) data 0.000 (0.002) loss 1.5225 (1.0545) acc 68.7500 (73.8571) lr 1.9098e-04 eta 1:48:04
+epoch [42/50] batch [355/500] time 1.588 (1.563) data 0.000 (0.002) loss 1.2188 (1.0534) acc 68.7500 (73.8820) lr 1.9098e-04 eta 1:47:57
+epoch [42/50] batch [360/500] time 1.567 (1.563) data 0.000 (0.002) loss 1.7471 (1.0533) acc 56.2500 (73.8802) lr 1.9098e-04 eta 1:47:49
+epoch [42/50] batch [365/500] time 1.574 (1.563) data 0.000 (0.002) loss 1.3457 (1.0575) acc 68.7500 (73.8784) lr 1.9098e-04 eta 1:47:41
+epoch [42/50] batch [370/500] time 1.560 (1.563) data 0.000 (0.002) loss 0.8789 (1.0556) acc 75.0000 (73.9443) lr 1.9098e-04 eta 1:47:33
+epoch [42/50] batch [375/500] time 1.534 (1.563) data 0.000 (0.002) loss 0.6938 (1.0548) acc 71.8750 (73.9667) lr 1.9098e-04 eta 1:47:25
+epoch [42/50] batch [380/500] time 1.558 (1.563) data 0.001 (0.002) loss 1.2031 (1.0541) acc 65.6250 (73.9145) lr 1.9098e-04 eta 1:47:17
+epoch [42/50] batch [385/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.9487 (1.0551) acc 81.2500 (73.9529) lr 1.9098e-04 eta 1:47:09
+epoch [42/50] batch [390/500] time 1.568 (1.563) data 0.000 (0.002) loss 1.0312 (1.0574) acc 75.0000 (73.8782) lr 1.9098e-04 eta 1:47:01
+epoch [42/50] batch [395/500] time 1.541 (1.562) data 0.001 (0.002) loss 1.3457 (1.0568) acc 75.0000 (73.9241) lr 1.9098e-04 eta 1:46:53
+epoch [42/50] batch [400/500] time 1.547 (1.562) data 0.000 (0.002) loss 1.4404 (1.0578) acc 65.6250 (73.8438) lr 1.9098e-04 eta 1:46:45
+epoch [42/50] batch [405/500] time 1.561 (1.562) data 0.000 (0.002) loss 0.8135 (1.0554) acc 75.0000 (73.8812) lr 1.9098e-04 eta 1:46:37
+epoch [42/50] batch [410/500] time 1.563 (1.563) data 0.000 (0.002) loss 1.0459 (1.0538) acc 75.0000 (73.8796) lr 1.9098e-04 eta 1:46:30
+epoch [42/50] batch [415/500] time 1.560 (1.563) data 0.001 (0.002) loss 0.7300 (1.0506) acc 84.3750 (73.9458) lr 1.9098e-04 eta 1:46:23
+epoch [42/50] batch [420/500] time 1.577 (1.563) data 0.000 (0.002) loss 0.9160 (1.0489) acc 68.7500 (73.9658) lr 1.9098e-04 eta 1:46:15
+epoch [42/50] batch [425/500] time 1.543 (1.562) data 0.000 (0.002) loss 1.1289 (1.0505) acc 75.0000 (73.9632) lr 1.9098e-04 eta 1:46:06
+epoch [42/50] batch [430/500] time 1.571 (1.562) data 0.000 (0.002) loss 1.2363 (1.0506) acc 71.8750 (73.9680) lr 1.9098e-04 eta 1:45:59
+epoch [42/50] batch [435/500] time 1.542 (1.562) data 0.000 (0.002) loss 0.8892 (1.0503) acc 75.0000 (73.9871) lr 1.9098e-04 eta 1:45:50
+epoch [42/50] batch [440/500] time 1.552 (1.562) data 0.000 (0.002) loss 1.3555 (1.0501) acc 71.8750 (73.9702) lr 1.9098e-04 eta 1:45:43
+epoch [42/50] batch [445/500] time 1.593 (1.562) data 0.000 (0.002) loss 1.4619 (1.0515) acc 59.3750 (73.9045) lr 1.9098e-04 eta 1:45:34
+epoch [42/50] batch [450/500] time 1.666 (1.562) data 0.000 (0.002) loss 1.6006 (1.0534) acc 65.6250 (73.8958) lr 1.9098e-04 eta 1:45:27
+epoch [42/50] batch [455/500] time 1.567 (1.563) data 0.000 (0.002) loss 0.6885 (1.0544) acc 87.5000 (73.8599) lr 1.9098e-04 eta 1:45:20
+epoch [42/50] batch [460/500] time 1.585 (1.563) data 0.000 (0.002) loss 0.4204 (1.0518) acc 87.5000 (73.9198) lr 1.9098e-04 eta 1:45:13
+epoch [42/50] batch [465/500] time 1.543 (1.563) data 0.001 (0.002) loss 0.7202 (1.0524) acc 84.3750 (73.8777) lr 1.9098e-04 eta 1:45:05
+epoch [42/50] batch [470/500] time 1.564 (1.563) data 0.000 (0.002) loss 1.0703 (1.0509) acc 71.8750 (73.9029) lr 1.9098e-04 eta 1:44:57
+epoch [42/50] batch [475/500] time 1.551 (1.563) data 0.000 (0.002) loss 1.5908 (1.0504) acc 68.7500 (73.9276) lr 1.9098e-04 eta 1:44:49
+epoch [42/50] batch [480/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.4131 (1.0512) acc 65.6250 (73.9258) lr 1.9098e-04 eta 1:44:41
+epoch [42/50] batch [485/500] time 1.571 (1.563) data 0.001 (0.002) loss 0.7515 (1.0503) acc 75.0000 (73.8982) lr 1.9098e-04 eta 1:44:33
+epoch [42/50] batch [490/500] time 1.550 (1.563) data 0.000 (0.002) loss 1.2021 (1.0516) acc 75.0000 (73.8393) lr 1.9098e-04 eta 1:44:25
+epoch [42/50] batch [495/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.4443 (1.0524) acc 71.8750 (73.8068) lr 1.9098e-04 eta 1:44:17
+epoch [42/50] batch [500/500] time 1.577 (1.562) data 0.000 (0.002) loss 1.6924 (1.0522) acc 59.3750 (73.8187) lr 1.5567e-04 eta 1:44:09
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,035
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [43/50] batch [5/500] time 1.547 (1.660) data 0.000 (0.162) loss 0.9980 (1.0148) acc 71.8750 (77.5000) lr 1.5567e-04 eta 1:50:31
+epoch [43/50] batch [10/500] time 1.535 (1.607) data 0.000 (0.081) loss 0.6855 (0.9552) acc 78.1250 (77.5000) lr 1.5567e-04 eta 1:46:50
+epoch [43/50] batch [15/500] time 1.567 (1.590) data 0.000 (0.054) loss 0.7153 (0.9557) acc 84.3750 (76.8750) lr 1.5567e-04 eta 1:45:36
+epoch [43/50] batch [20/500] time 1.559 (1.582) data 0.000 (0.041) loss 0.8379 (0.9000) acc 78.1250 (78.7500) lr 1.5567e-04 eta 1:44:54
+epoch [43/50] batch [25/500] time 1.581 (1.577) data 0.000 (0.033) loss 1.6670 (0.9772) acc 59.3750 (77.2500) lr 1.5567e-04 eta 1:44:27
+epoch [43/50] batch [30/500] time 1.563 (1.572) data 0.000 (0.027) loss 1.1309 (1.0091) acc 78.1250 (76.2500) lr 1.5567e-04 eta 1:44:00
+epoch [43/50] batch [35/500] time 1.556 (1.571) data 0.000 (0.024) loss 1.3359 (1.0544) acc 78.1250 (75.7143) lr 1.5567e-04 eta 1:43:47
+epoch [43/50] batch [40/500] time 1.546 (1.571) data 0.000 (0.021) loss 1.1270 (1.0584) acc 71.8750 (75.6250) lr 1.5567e-04 eta 1:43:42
+epoch [43/50] batch [45/500] time 1.576 (1.571) data 0.000 (0.018) loss 0.9409 (1.0556) acc 75.0000 (75.2778) lr 1.5567e-04 eta 1:43:31
+epoch [43/50] batch [50/500] time 1.569 (1.571) data 0.000 (0.017) loss 1.0234 (1.0614) acc 68.7500 (74.7500) lr 1.5567e-04 eta 1:43:24
+epoch [43/50] batch [55/500] time 1.587 (1.571) data 0.000 (0.015) loss 1.1025 (1.0585) acc 71.8750 (75.0000) lr 1.5567e-04 eta 1:43:15
+epoch [43/50] batch [60/500] time 1.556 (1.571) data 0.001 (0.014) loss 1.1357 (1.0590) acc 75.0000 (75.0000) lr 1.5567e-04 eta 1:43:08
+epoch [43/50] batch [65/500] time 1.579 (1.570) data 0.000 (0.013) loss 0.9082 (1.0415) acc 68.7500 (75.0000) lr 1.5567e-04 eta 1:42:57
+epoch [43/50] batch [70/500] time 1.555 (1.569) data 0.000 (0.012) loss 1.0527 (1.0512) acc 71.8750 (74.7321) lr 1.5567e-04 eta 1:42:46
+epoch [43/50] batch [75/500] time 1.561 (1.568) data 0.000 (0.011) loss 0.7080 (1.0444) acc 78.1250 (74.7500) lr 1.5567e-04 eta 1:42:36
+epoch [43/50] batch [80/500] time 1.556 (1.568) data 0.000 (0.011) loss 1.2803 (1.0430) acc 65.6250 (74.7266) lr 1.5567e-04 eta 1:42:25
+epoch [43/50] batch [85/500] time 1.558 (1.568) data 0.000 (0.010) loss 1.2871 (1.0405) acc 71.8750 (74.7059) lr 1.5567e-04 eta 1:42:20
+epoch [43/50] batch [90/500] time 1.567 (1.568) data 0.001 (0.009) loss 1.1846 (1.0465) acc 65.6250 (74.4097) lr 1.5567e-04 eta 1:42:11
+epoch [43/50] batch [95/500] time 1.552 (1.567) data 0.000 (0.009) loss 0.9512 (1.0534) acc 75.0000 (74.2434) lr 1.5567e-04 eta 1:41:58
+epoch [43/50] batch [100/500] time 1.548 (1.566) data 0.000 (0.009) loss 0.9985 (1.0586) acc 71.8750 (74.0312) lr 1.5567e-04 eta 1:41:48
+epoch [43/50] batch [105/500] time 1.549 (1.566) data 0.000 (0.008) loss 0.5879 (1.0473) acc 87.5000 (74.1964) lr 1.5567e-04 eta 1:41:38
+epoch [43/50] batch [110/500] time 1.568 (1.565) data 0.000 (0.008) loss 0.7866 (1.0462) acc 78.1250 (74.3466) lr 1.5567e-04 eta 1:41:29
+epoch [43/50] batch [115/500] time 1.542 (1.565) data 0.000 (0.007) loss 0.8345 (1.0399) acc 81.2500 (74.4837) lr 1.5567e-04 eta 1:41:19
+epoch [43/50] batch [120/500] time 1.572 (1.565) data 0.000 (0.007) loss 0.5059 (1.0319) acc 87.5000 (74.7656) lr 1.5567e-04 eta 1:41:10
+epoch [43/50] batch [125/500] time 1.547 (1.565) data 0.000 (0.007) loss 0.9585 (1.0417) acc 75.0000 (74.5750) lr 1.5567e-04 eta 1:41:03
+epoch [43/50] batch [130/500] time 1.582 (1.565) data 0.000 (0.007) loss 0.4150 (1.0362) acc 90.6250 (74.6875) lr 1.5567e-04 eta 1:40:55
+epoch [43/50] batch [135/500] time 1.562 (1.565) data 0.000 (0.006) loss 1.6680 (1.0462) acc 62.5000 (74.4676) lr 1.5567e-04 eta 1:40:47
+epoch [43/50] batch [140/500] time 1.544 (1.564) data 0.000 (0.006) loss 0.7261 (1.0417) acc 71.8750 (74.4196) lr 1.5567e-04 eta 1:40:37
+epoch [43/50] batch [145/500] time 1.543 (1.564) data 0.000 (0.006) loss 0.9927 (1.0415) acc 71.8750 (74.4828) lr 1.5567e-04 eta 1:40:28
+epoch [43/50] batch [150/500] time 1.561 (1.564) data 0.000 (0.006) loss 0.8799 (1.0397) acc 78.1250 (74.3958) lr 1.5567e-04 eta 1:40:21
+epoch [43/50] batch [155/500] time 1.581 (1.564) data 0.000 (0.006) loss 0.6802 (1.0400) acc 84.3750 (74.4758) lr 1.5567e-04 eta 1:40:13
+epoch [43/50] batch [160/500] time 1.563 (1.564) data 0.001 (0.005) loss 1.0586 (1.0460) acc 75.0000 (74.3359) lr 1.5567e-04 eta 1:40:05
+epoch [43/50] batch [165/500] time 1.557 (1.564) data 0.001 (0.005) loss 1.0928 (1.0421) acc 81.2500 (74.3939) lr 1.5567e-04 eta 1:39:57
+epoch [43/50] batch [170/500] time 1.578 (1.564) data 0.000 (0.005) loss 1.2676 (1.0448) acc 68.7500 (74.2831) lr 1.5567e-04 eta 1:39:49
+epoch [43/50] batch [175/500] time 1.577 (1.564) data 0.000 (0.005) loss 1.3770 (1.0527) acc 62.5000 (74.0179) lr 1.5567e-04 eta 1:39:41
+epoch [43/50] batch [180/500] time 1.582 (1.565) data 0.000 (0.005) loss 0.7812 (1.0552) acc 75.0000 (73.9236) lr 1.5567e-04 eta 1:39:37
+epoch [43/50] batch [185/500] time 1.550 (1.565) data 0.001 (0.005) loss 1.5107 (1.0543) acc 65.6250 (73.9696) lr 1.5567e-04 eta 1:39:29
+epoch [43/50] batch [190/500] time 1.580 (1.565) data 0.000 (0.005) loss 0.9351 (1.0525) acc 71.8750 (73.9967) lr 1.5567e-04 eta 1:39:21
+epoch [43/50] batch [195/500] time 1.549 (1.565) data 0.000 (0.005) loss 0.7158 (1.0492) acc 75.0000 (74.1346) lr 1.5567e-04 eta 1:39:13
+epoch [43/50] batch [200/500] time 1.559 (1.564) data 0.000 (0.004) loss 1.0176 (1.0495) acc 68.7500 (74.0469) lr 1.5567e-04 eta 1:39:04
+epoch [43/50] batch [205/500] time 1.528 (1.564) data 0.000 (0.004) loss 1.1953 (1.0493) acc 78.1250 (74.0091) lr 1.5567e-04 eta 1:38:55
+epoch [43/50] batch [210/500] time 1.560 (1.564) data 0.000 (0.004) loss 0.7920 (1.0510) acc 75.0000 (73.9435) lr 1.5567e-04 eta 1:38:47
+epoch [43/50] batch [215/500] time 1.550 (1.564) data 0.000 (0.004) loss 1.1279 (1.0568) acc 68.7500 (73.8517) lr 1.5567e-04 eta 1:38:39
+epoch [43/50] batch [220/500] time 1.548 (1.564) data 0.000 (0.004) loss 1.3643 (1.0594) acc 65.6250 (73.6364) lr 1.5567e-04 eta 1:38:31
+epoch [43/50] batch [225/500] time 1.558 (1.564) data 0.000 (0.004) loss 0.6294 (1.0569) acc 71.8750 (73.6528) lr 1.5567e-04 eta 1:38:24
+epoch [43/50] batch [230/500] time 1.546 (1.564) data 0.000 (0.004) loss 0.7998 (1.0547) acc 81.2500 (73.6005) lr 1.5567e-04 eta 1:38:15
+epoch [43/50] batch [235/500] time 1.578 (1.564) data 0.001 (0.004) loss 0.6719 (1.0511) acc 81.2500 (73.6702) lr 1.5567e-04 eta 1:38:07
+epoch [43/50] batch [240/500] time 1.564 (1.564) data 0.000 (0.004) loss 1.2939 (1.0504) acc 75.0000 (73.7500) lr 1.5567e-04 eta 1:37:59
+epoch [43/50] batch [245/500] time 1.572 (1.564) data 0.001 (0.004) loss 1.0508 (1.0501) acc 84.3750 (73.8520) lr 1.5567e-04 eta 1:37:51
+epoch [43/50] batch [250/500] time 1.547 (1.564) data 0.000 (0.004) loss 1.1016 (1.0502) acc 71.8750 (73.8000) lr 1.5567e-04 eta 1:37:43
+epoch [43/50] batch [255/500] time 1.550 (1.563) data 0.000 (0.004) loss 0.9761 (1.0493) acc 75.0000 (73.7255) lr 1.5567e-04 eta 1:37:34
+epoch [43/50] batch [260/500] time 1.554 (1.563) data 0.000 (0.004) loss 0.7969 (1.0497) acc 81.2500 (73.6899) lr 1.5567e-04 eta 1:37:26
+epoch [43/50] batch [265/500] time 1.561 (1.563) data 0.000 (0.003) loss 0.9780 (1.0514) acc 68.7500 (73.5495) lr 1.5567e-04 eta 1:37:18
+epoch [43/50] batch [270/500] time 1.534 (1.563) data 0.000 (0.003) loss 1.0039 (1.0498) acc 68.7500 (73.5069) lr 1.5567e-04 eta 1:37:09
+epoch [43/50] batch [275/500] time 1.549 (1.563) data 0.001 (0.003) loss 2.0254 (1.0532) acc 56.2500 (73.4545) lr 1.5567e-04 eta 1:37:01
+epoch [43/50] batch [280/500] time 1.541 (1.563) data 0.001 (0.003) loss 0.6587 (1.0522) acc 78.1250 (73.4598) lr 1.5567e-04 eta 1:36:52
+epoch [43/50] batch [285/500] time 1.536 (1.562) data 0.000 (0.003) loss 0.5752 (1.0501) acc 78.1250 (73.4868) lr 1.5567e-04 eta 1:36:43
+epoch [43/50] batch [290/500] time 1.557 (1.562) data 0.000 (0.003) loss 1.5605 (1.0511) acc 68.7500 (73.4806) lr 1.5567e-04 eta 1:36:35
+epoch [43/50] batch [295/500] time 1.568 (1.562) data 0.000 (0.003) loss 0.8135 (1.0501) acc 87.5000 (73.5169) lr 1.5567e-04 eta 1:36:28
+epoch [43/50] batch [300/500] time 1.563 (1.562) data 0.000 (0.003) loss 0.4248 (1.0469) acc 84.3750 (73.5938) lr 1.5567e-04 eta 1:36:20
+epoch [43/50] batch [305/500] time 1.560 (1.562) data 0.000 (0.003) loss 0.8530 (1.0444) acc 81.2500 (73.6783) lr 1.5567e-04 eta 1:36:12
+epoch [43/50] batch [310/500] time 1.567 (1.562) data 0.000 (0.003) loss 0.6768 (1.0418) acc 81.2500 (73.7298) lr 1.5567e-04 eta 1:36:04
+epoch [43/50] batch [315/500] time 1.584 (1.562) data 0.000 (0.003) loss 1.6641 (1.0420) acc 65.6250 (73.6806) lr 1.5567e-04 eta 1:35:57
+epoch [43/50] batch [320/500] time 1.579 (1.562) data 0.000 (0.003) loss 1.3516 (1.0394) acc 62.5000 (73.7305) lr 1.5567e-04 eta 1:35:49
+epoch [43/50] batch [325/500] time 1.576 (1.563) data 0.000 (0.003) loss 0.8330 (1.0372) acc 81.2500 (73.7885) lr 1.5567e-04 eta 1:35:44
+epoch [43/50] batch [330/500] time 1.602 (1.563) data 0.000 (0.003) loss 1.1533 (1.0387) acc 75.0000 (73.7879) lr 1.5567e-04 eta 1:35:36
+epoch [43/50] batch [335/500] time 1.540 (1.563) data 0.001 (0.003) loss 1.9443 (1.0420) acc 53.1250 (73.7034) lr 1.5567e-04 eta 1:35:29
+epoch [43/50] batch [340/500] time 1.537 (1.563) data 0.000 (0.003) loss 1.1631 (1.0425) acc 75.0000 (73.6673) lr 1.5567e-04 eta 1:35:20
+epoch [43/50] batch [345/500] time 1.563 (1.563) data 0.000 (0.003) loss 0.9062 (1.0419) acc 78.1250 (73.6866) lr 1.5567e-04 eta 1:35:12
+epoch [43/50] batch [350/500] time 1.569 (1.563) data 0.001 (0.003) loss 0.5122 (1.0404) acc 87.5000 (73.7589) lr 1.5567e-04 eta 1:35:05
+epoch [43/50] batch [355/500] time 1.568 (1.563) data 0.000 (0.003) loss 0.6123 (1.0365) acc 84.3750 (73.8644) lr 1.5567e-04 eta 1:34:58
+epoch [43/50] batch [360/500] time 1.556 (1.563) data 0.000 (0.003) loss 0.7314 (1.0349) acc 78.1250 (73.8628) lr 1.5567e-04 eta 1:34:49
+epoch [43/50] batch [365/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.2891 (1.0351) acc 75.0000 (73.8870) lr 1.5567e-04 eta 1:34:41
+epoch [43/50] batch [370/500] time 1.551 (1.563) data 0.001 (0.003) loss 1.0967 (1.0371) acc 81.2500 (73.8345) lr 1.5567e-04 eta 1:34:34
+epoch [43/50] batch [375/500] time 1.587 (1.563) data 0.000 (0.003) loss 1.6904 (1.0360) acc 56.2500 (73.8583) lr 1.5567e-04 eta 1:34:26
+epoch [43/50] batch [380/500] time 1.562 (1.563) data 0.000 (0.003) loss 1.6084 (1.0386) acc 62.5000 (73.7993) lr 1.5567e-04 eta 1:34:18
+epoch [43/50] batch [385/500] time 1.551 (1.563) data 0.000 (0.003) loss 0.8530 (1.0420) acc 75.0000 (73.7581) lr 1.5567e-04 eta 1:34:10
+epoch [43/50] batch [390/500] time 1.568 (1.563) data 0.000 (0.002) loss 0.8599 (1.0413) acc 71.8750 (73.7660) lr 1.5567e-04 eta 1:34:02
+epoch [43/50] batch [395/500] time 1.555 (1.563) data 0.000 (0.002) loss 1.1025 (1.0406) acc 71.8750 (73.8212) lr 1.5567e-04 eta 1:33:54
+epoch [43/50] batch [400/500] time 1.573 (1.563) data 0.000 (0.002) loss 0.9902 (1.0416) acc 75.0000 (73.8672) lr 1.5567e-04 eta 1:33:46
+epoch [43/50] batch [405/500] time 1.543 (1.563) data 0.000 (0.002) loss 0.7646 (1.0401) acc 84.3750 (73.9660) lr 1.5567e-04 eta 1:33:37
+epoch [43/50] batch [410/500] time 1.570 (1.563) data 0.001 (0.002) loss 1.1289 (1.0398) acc 71.8750 (73.9787) lr 1.5567e-04 eta 1:33:30
+epoch [43/50] batch [415/500] time 1.571 (1.563) data 0.001 (0.002) loss 0.9150 (1.0420) acc 75.0000 (73.9307) lr 1.5567e-04 eta 1:33:22
+epoch [43/50] batch [420/500] time 1.545 (1.563) data 0.000 (0.002) loss 0.6929 (1.0400) acc 81.2500 (73.9732) lr 1.5567e-04 eta 1:33:14
+epoch [43/50] batch [425/500] time 1.545 (1.563) data 0.000 (0.002) loss 1.1641 (1.0422) acc 65.6250 (73.9044) lr 1.5567e-04 eta 1:33:06
+epoch [43/50] batch [430/500] time 1.552 (1.563) data 0.000 (0.002) loss 1.0400 (1.0430) acc 65.6250 (73.8445) lr 1.5567e-04 eta 1:32:58
+epoch [43/50] batch [435/500] time 1.554 (1.562) data 0.000 (0.002) loss 1.0410 (1.0426) acc 84.3750 (73.9009) lr 1.5567e-04 eta 1:32:50
+epoch [43/50] batch [440/500] time 1.546 (1.563) data 0.000 (0.002) loss 1.1387 (1.0419) acc 75.0000 (73.9205) lr 1.5567e-04 eta 1:32:42
+epoch [43/50] batch [445/500] time 1.581 (1.563) data 0.000 (0.002) loss 0.5620 (1.0404) acc 84.3750 (73.9466) lr 1.5567e-04 eta 1:32:35
+epoch [43/50] batch [450/500] time 1.580 (1.563) data 0.000 (0.002) loss 1.2910 (1.0390) acc 59.3750 (73.9444) lr 1.5567e-04 eta 1:32:28
+epoch [43/50] batch [455/500] time 1.560 (1.563) data 0.000 (0.002) loss 0.9893 (1.0398) acc 75.0000 (73.8805) lr 1.5567e-04 eta 1:32:20
+epoch [43/50] batch [460/500] time 1.564 (1.563) data 0.000 (0.002) loss 0.7778 (1.0397) acc 75.0000 (73.8587) lr 1.5567e-04 eta 1:32:12
+epoch [43/50] batch [465/500] time 1.668 (1.563) data 0.000 (0.002) loss 1.1914 (1.0405) acc 68.7500 (73.8710) lr 1.5567e-04 eta 1:32:04
+epoch [43/50] batch [470/500] time 1.570 (1.563) data 0.000 (0.002) loss 0.7417 (1.0390) acc 78.1250 (73.8830) lr 1.5567e-04 eta 1:31:56
+epoch [43/50] batch [475/500] time 1.569 (1.563) data 0.001 (0.002) loss 1.1816 (1.0396) acc 68.7500 (73.8487) lr 1.5567e-04 eta 1:31:48
+epoch [43/50] batch [480/500] time 1.553 (1.563) data 0.000 (0.002) loss 0.8169 (1.0376) acc 71.8750 (73.8542) lr 1.5567e-04 eta 1:31:40
+epoch [43/50] batch [485/500] time 1.551 (1.563) data 0.001 (0.002) loss 0.4236 (1.0359) acc 90.6250 (73.8724) lr 1.5567e-04 eta 1:31:32
+epoch [43/50] batch [490/500] time 1.525 (1.563) data 0.000 (0.002) loss 0.8315 (1.0367) acc 78.1250 (73.8712) lr 1.5567e-04 eta 1:31:24
+epoch [43/50] batch [495/500] time 1.569 (1.563) data 0.000 (0.002) loss 0.6655 (1.0355) acc 71.8750 (73.8510) lr 1.5567e-04 eta 1:31:16
+epoch [43/50] batch [500/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.8647 (1.0343) acc 78.1250 (73.8563) lr 1.2369e-04 eta 1:31:08
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,061
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.7%
+epoch [44/50] batch [5/500] time 1.538 (1.667) data 0.000 (0.168) loss 0.6187 (0.8603) acc 84.3750 (77.5000) lr 1.2369e-04 eta 1:37:04
+epoch [44/50] batch [10/500] time 1.561 (1.606) data 0.000 (0.084) loss 0.8901 (0.9103) acc 75.0000 (76.2500) lr 1.2369e-04 eta 1:33:25
+epoch [44/50] batch [15/500] time 1.560 (1.596) data 0.000 (0.056) loss 1.0420 (0.9664) acc 71.8750 (75.4167) lr 1.2369e-04 eta 1:32:40
+epoch [44/50] batch [20/500] time 1.576 (1.595) data 0.000 (0.042) loss 1.2334 (1.0615) acc 62.5000 (73.9062) lr 1.2369e-04 eta 1:32:31
+epoch [44/50] batch [25/500] time 1.543 (1.587) data 0.000 (0.034) loss 1.3252 (1.0790) acc 68.7500 (74.0000) lr 1.2369e-04 eta 1:31:55
+epoch [44/50] batch [30/500] time 1.531 (1.582) data 0.000 (0.028) loss 0.8945 (1.0950) acc 81.2500 (73.6458) lr 1.2369e-04 eta 1:31:28
+epoch [44/50] batch [35/500] time 1.568 (1.579) data 0.000 (0.024) loss 1.3760 (1.0898) acc 68.7500 (73.6607) lr 1.2369e-04 eta 1:31:09
+epoch [44/50] batch [40/500] time 1.573 (1.576) data 0.000 (0.021) loss 1.0811 (1.0944) acc 62.5000 (73.2812) lr 1.2369e-04 eta 1:30:52
+epoch [44/50] batch [45/500] time 1.561 (1.573) data 0.001 (0.019) loss 1.3916 (1.0985) acc 71.8750 (73.4722) lr 1.2369e-04 eta 1:30:35
+epoch [44/50] batch [50/500] time 1.576 (1.572) data 0.000 (0.017) loss 1.1953 (1.0813) acc 68.7500 (73.5000) lr 1.2369e-04 eta 1:30:22
+epoch [44/50] batch [55/500] time 1.577 (1.571) data 0.000 (0.016) loss 1.0908 (1.0693) acc 81.2500 (74.1477) lr 1.2369e-04 eta 1:30:13
+epoch [44/50] batch [60/500] time 1.566 (1.571) data 0.000 (0.014) loss 1.2578 (1.0711) acc 62.5000 (74.0625) lr 1.2369e-04 eta 1:30:03
+epoch [44/50] batch [65/500] time 1.553 (1.570) data 0.000 (0.013) loss 1.4619 (1.0815) acc 59.3750 (73.7500) lr 1.2369e-04 eta 1:29:53
+epoch [44/50] batch [70/500] time 1.555 (1.569) data 0.000 (0.012) loss 0.5898 (1.0654) acc 78.1250 (73.8393) lr 1.2369e-04 eta 1:29:41
+epoch [44/50] batch [75/500] time 1.566 (1.569) data 0.000 (0.012) loss 0.7563 (1.0602) acc 78.1250 (73.9583) lr 1.2369e-04 eta 1:29:32
+epoch [44/50] batch [80/500] time 1.565 (1.569) data 0.000 (0.011) loss 1.5322 (1.0549) acc 59.3750 (73.9844) lr 1.2369e-04 eta 1:29:25
+epoch [44/50] batch [85/500] time 1.570 (1.569) data 0.000 (0.010) loss 1.4580 (1.0524) acc 62.5000 (74.0441) lr 1.2369e-04 eta 1:29:18
+epoch [44/50] batch [90/500] time 1.563 (1.568) data 0.000 (0.010) loss 1.2998 (1.0563) acc 71.8750 (73.9236) lr 1.2369e-04 eta 1:29:08
+epoch [44/50] batch [95/500] time 1.564 (1.568) data 0.000 (0.009) loss 0.9536 (1.0594) acc 75.0000 (73.6842) lr 1.2369e-04 eta 1:29:00
+epoch [44/50] batch [100/500] time 1.552 (1.568) data 0.000 (0.009) loss 0.6338 (1.0429) acc 84.3750 (73.7812) lr 1.2369e-04 eta 1:28:51
+epoch [44/50] batch [105/500] time 1.570 (1.568) data 0.000 (0.008) loss 1.0850 (1.0607) acc 75.0000 (73.4524) lr 1.2369e-04 eta 1:28:43
+epoch [44/50] batch [110/500] time 1.559 (1.568) data 0.000 (0.008) loss 0.9517 (1.0535) acc 78.1250 (73.5795) lr 1.2369e-04 eta 1:28:34
+epoch [44/50] batch [115/500] time 1.662 (1.568) data 0.000 (0.008) loss 1.1787 (1.0644) acc 59.3750 (73.1793) lr 1.2369e-04 eta 1:28:28
+epoch [44/50] batch [120/500] time 1.550 (1.568) data 0.000 (0.007) loss 0.6069 (1.0648) acc 81.2500 (73.2292) lr 1.2369e-04 eta 1:28:18
+epoch [44/50] batch [125/500] time 1.569 (1.567) data 0.000 (0.007) loss 0.8735 (1.0647) acc 75.0000 (73.2750) lr 1.2369e-04 eta 1:28:09
+epoch [44/50] batch [130/500] time 1.556 (1.567) data 0.000 (0.007) loss 0.7563 (1.0554) acc 81.2500 (73.5577) lr 1.2369e-04 eta 1:28:00
+epoch [44/50] batch [135/500] time 1.569 (1.567) data 0.000 (0.007) loss 0.8340 (1.0585) acc 68.7500 (73.3796) lr 1.2369e-04 eta 1:27:52
+epoch [44/50] batch [140/500] time 1.559 (1.567) data 0.000 (0.006) loss 1.4375 (1.0585) acc 71.8750 (73.3482) lr 1.2369e-04 eta 1:27:45
+epoch [44/50] batch [145/500] time 1.561 (1.567) data 0.001 (0.006) loss 1.1348 (1.0577) acc 71.8750 (73.3405) lr 1.2369e-04 eta 1:27:38
+epoch [44/50] batch [150/500] time 1.572 (1.567) data 0.001 (0.006) loss 1.6592 (1.0603) acc 56.2500 (73.3958) lr 1.2369e-04 eta 1:27:29
+epoch [44/50] batch [155/500] time 1.548 (1.567) data 0.000 (0.006) loss 1.1680 (1.0560) acc 65.6250 (73.3669) lr 1.2369e-04 eta 1:27:21
+epoch [44/50] batch [160/500] time 1.570 (1.568) data 0.000 (0.006) loss 1.7422 (1.0559) acc 68.7500 (73.4180) lr 1.2369e-04 eta 1:27:15
+epoch [44/50] batch [165/500] time 1.560 (1.567) data 0.000 (0.005) loss 1.4434 (1.0615) acc 68.7500 (73.3333) lr 1.2369e-04 eta 1:27:07
+epoch [44/50] batch [170/500] time 1.581 (1.568) data 0.000 (0.005) loss 0.9321 (1.0608) acc 78.1250 (73.2904) lr 1.2369e-04 eta 1:26:59
+epoch [44/50] batch [175/500] time 1.541 (1.567) data 0.000 (0.005) loss 1.0430 (1.0650) acc 65.6250 (73.1607) lr 1.2369e-04 eta 1:26:51
+epoch [44/50] batch [180/500] time 1.532 (1.567) data 0.000 (0.005) loss 0.7695 (1.0608) acc 78.1250 (73.1771) lr 1.2369e-04 eta 1:26:42
+epoch [44/50] batch [185/500] time 1.564 (1.567) data 0.000 (0.005) loss 1.5352 (1.0679) acc 68.7500 (73.0743) lr 1.2369e-04 eta 1:26:34
+epoch [44/50] batch [190/500] time 1.553 (1.566) data 0.001 (0.005) loss 1.1348 (1.0691) acc 78.1250 (73.0099) lr 1.2369e-04 eta 1:26:24
+epoch [44/50] batch [195/500] time 1.569 (1.566) data 0.001 (0.005) loss 0.9136 (1.0696) acc 71.8750 (72.9808) lr 1.2369e-04 eta 1:26:16
+epoch [44/50] batch [200/500] time 1.550 (1.566) data 0.000 (0.005) loss 1.4297 (1.0718) acc 75.0000 (72.9844) lr 1.2369e-04 eta 1:26:09
+epoch [44/50] batch [205/500] time 1.573 (1.566) data 0.001 (0.004) loss 0.4404 (1.0711) acc 87.5000 (73.0488) lr 1.2369e-04 eta 1:26:00
+epoch [44/50] batch [210/500] time 1.554 (1.566) data 0.000 (0.004) loss 1.1475 (1.0724) acc 71.8750 (72.9762) lr 1.2369e-04 eta 1:25:52
+epoch [44/50] batch [215/500] time 1.557 (1.566) data 0.000 (0.004) loss 0.4087 (1.0671) acc 87.5000 (73.0523) lr 1.2369e-04 eta 1:25:44
+epoch [44/50] batch [220/500] time 1.555 (1.566) data 0.000 (0.004) loss 1.1338 (1.0689) acc 71.8750 (73.0114) lr 1.2369e-04 eta 1:25:35
+epoch [44/50] batch [225/500] time 1.550 (1.566) data 0.000 (0.004) loss 0.7480 (1.0703) acc 87.5000 (73.0000) lr 1.2369e-04 eta 1:25:27
+epoch [44/50] batch [230/500] time 1.587 (1.566) data 0.000 (0.004) loss 1.1680 (1.0714) acc 71.8750 (72.9620) lr 1.2369e-04 eta 1:25:19
+epoch [44/50] batch [235/500] time 1.564 (1.565) data 0.000 (0.004) loss 1.0283 (1.0724) acc 78.1250 (72.9654) lr 1.2369e-04 eta 1:25:11
+epoch [44/50] batch [240/500] time 1.551 (1.565) data 0.000 (0.004) loss 0.7354 (1.0703) acc 71.8750 (72.9948) lr 1.2369e-04 eta 1:25:03
+epoch [44/50] batch [245/500] time 1.527 (1.565) data 0.000 (0.004) loss 1.2441 (1.0648) acc 75.0000 (73.0740) lr 1.2369e-04 eta 1:24:54
+epoch [44/50] batch [250/500] time 1.568 (1.565) data 0.000 (0.004) loss 1.9082 (1.0688) acc 56.2500 (72.9000) lr 1.2369e-04 eta 1:24:46
+epoch [44/50] batch [255/500] time 1.559 (1.565) data 0.000 (0.004) loss 1.4023 (1.0668) acc 62.5000 (72.9289) lr 1.2369e-04 eta 1:24:37
+epoch [44/50] batch [260/500] time 1.571 (1.565) data 0.001 (0.004) loss 1.3262 (1.0655) acc 71.8750 (72.9567) lr 1.2369e-04 eta 1:24:31
+epoch [44/50] batch [265/500] time 1.537 (1.565) data 0.000 (0.004) loss 1.0576 (1.0654) acc 71.8750 (73.0071) lr 1.2369e-04 eta 1:24:22
+epoch [44/50] batch [270/500] time 1.548 (1.565) data 0.000 (0.004) loss 0.6523 (1.0635) acc 75.0000 (73.0093) lr 1.2369e-04 eta 1:24:13
+epoch [44/50] batch [275/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.5332 (1.0676) acc 65.6250 (72.9773) lr 1.2369e-04 eta 1:24:05
+epoch [44/50] batch [280/500] time 1.557 (1.564) data 0.000 (0.003) loss 0.9429 (1.0729) acc 71.8750 (72.8125) lr 1.2369e-04 eta 1:23:57
+epoch [44/50] batch [285/500] time 1.553 (1.564) data 0.000 (0.003) loss 0.7998 (1.0732) acc 75.0000 (72.8180) lr 1.2369e-04 eta 1:23:49
+epoch [44/50] batch [290/500] time 1.556 (1.564) data 0.000 (0.003) loss 0.9912 (1.0745) acc 68.7500 (72.7802) lr 1.2369e-04 eta 1:23:41
+epoch [44/50] batch [295/500] time 1.572 (1.564) data 0.001 (0.003) loss 1.0264 (1.0705) acc 75.0000 (72.8708) lr 1.2369e-04 eta 1:23:33
+epoch [44/50] batch [300/500] time 1.559 (1.564) data 0.001 (0.003) loss 0.8931 (1.0690) acc 71.8750 (72.8229) lr 1.2369e-04 eta 1:23:25
+epoch [44/50] batch [305/500] time 1.530 (1.564) data 0.000 (0.003) loss 0.8530 (1.0694) acc 65.6250 (72.8381) lr 1.2369e-04 eta 1:23:17
+epoch [44/50] batch [310/500] time 1.588 (1.564) data 0.000 (0.003) loss 1.4004 (1.0698) acc 65.6250 (72.8226) lr 1.2369e-04 eta 1:23:09
+epoch [44/50] batch [315/500] time 1.566 (1.564) data 0.000 (0.003) loss 0.4517 (1.0684) acc 93.7500 (72.8671) lr 1.2369e-04 eta 1:23:01
+epoch [44/50] batch [320/500] time 1.555 (1.564) data 0.000 (0.003) loss 0.9155 (1.0649) acc 75.0000 (72.9883) lr 1.2369e-04 eta 1:22:53
+epoch [44/50] batch [325/500] time 1.549 (1.564) data 0.000 (0.003) loss 0.6670 (1.0602) acc 81.2500 (73.0962) lr 1.2369e-04 eta 1:22:45
+epoch [44/50] batch [330/500] time 1.562 (1.564) data 0.001 (0.003) loss 0.4294 (1.0558) acc 84.3750 (73.2008) lr 1.2369e-04 eta 1:22:37
+epoch [44/50] batch [335/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.0137 (1.0538) acc 75.0000 (73.2183) lr 1.2369e-04 eta 1:22:29
+epoch [44/50] batch [340/500] time 1.562 (1.564) data 0.000 (0.003) loss 1.0488 (1.0532) acc 78.1250 (73.2445) lr 1.2369e-04 eta 1:22:21
+epoch [44/50] batch [345/500] time 1.548 (1.564) data 0.001 (0.003) loss 0.9629 (1.0534) acc 65.6250 (73.2246) lr 1.2369e-04 eta 1:22:13
+epoch [44/50] batch [350/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.2217 (1.0552) acc 56.2500 (73.1696) lr 1.2369e-04 eta 1:22:05
+epoch [44/50] batch [355/500] time 1.568 (1.564) data 0.000 (0.003) loss 0.8789 (1.0544) acc 75.0000 (73.1602) lr 1.2369e-04 eta 1:21:58
+epoch [44/50] batch [360/500] time 1.568 (1.564) data 0.000 (0.003) loss 0.9707 (1.0566) acc 75.0000 (73.1163) lr 1.2369e-04 eta 1:21:49
+epoch [44/50] batch [365/500] time 1.553 (1.564) data 0.000 (0.003) loss 0.8286 (1.0564) acc 78.1250 (73.0908) lr 1.2369e-04 eta 1:21:42
+epoch [44/50] batch [370/500] time 1.574 (1.564) data 0.000 (0.003) loss 1.1143 (1.0585) acc 78.1250 (73.0659) lr 1.2369e-04 eta 1:21:33
+epoch [44/50] batch [375/500] time 1.565 (1.564) data 0.000 (0.003) loss 0.8164 (1.0572) acc 71.8750 (73.1000) lr 1.2369e-04 eta 1:21:26
+epoch [44/50] batch [380/500] time 1.526 (1.564) data 0.000 (0.003) loss 0.9736 (1.0565) acc 81.2500 (73.1003) lr 1.2369e-04 eta 1:21:18
+epoch [44/50] batch [385/500] time 1.556 (1.564) data 0.000 (0.003) loss 0.8662 (1.0531) acc 78.1250 (73.1818) lr 1.2369e-04 eta 1:21:10
+epoch [44/50] batch [390/500] time 1.541 (1.563) data 0.000 (0.003) loss 0.7534 (1.0501) acc 84.3750 (73.2452) lr 1.2369e-04 eta 1:21:02
+epoch [44/50] batch [395/500] time 1.569 (1.563) data 0.000 (0.003) loss 1.0586 (1.0508) acc 68.7500 (73.2358) lr 1.2369e-04 eta 1:20:54
+epoch [44/50] batch [400/500] time 1.577 (1.563) data 0.000 (0.003) loss 0.6006 (1.0493) acc 87.5000 (73.2969) lr 1.2369e-04 eta 1:20:46
+epoch [44/50] batch [405/500] time 1.551 (1.563) data 0.000 (0.002) loss 0.9414 (1.0490) acc 78.1250 (73.3333) lr 1.2369e-04 eta 1:20:38
+epoch [44/50] batch [410/500] time 1.552 (1.563) data 0.001 (0.002) loss 0.9292 (1.0470) acc 87.5000 (73.4070) lr 1.2369e-04 eta 1:20:30
+epoch [44/50] batch [415/500] time 1.582 (1.563) data 0.000 (0.002) loss 1.3955 (1.0472) acc 62.5000 (73.4036) lr 1.2369e-04 eta 1:20:22
+epoch [44/50] batch [420/500] time 1.564 (1.563) data 0.000 (0.002) loss 0.9463 (1.0457) acc 75.0000 (73.4301) lr 1.2369e-04 eta 1:20:15
+epoch [44/50] batch [425/500] time 1.568 (1.563) data 0.001 (0.002) loss 0.6797 (1.0455) acc 78.1250 (73.4265) lr 1.2369e-04 eta 1:20:07
+epoch [44/50] batch [430/500] time 1.553 (1.563) data 0.000 (0.002) loss 1.1670 (1.0457) acc 65.6250 (73.4302) lr 1.2369e-04 eta 1:19:59
+epoch [44/50] batch [435/500] time 1.548 (1.563) data 0.000 (0.002) loss 0.7100 (1.0447) acc 78.1250 (73.4195) lr 1.2369e-04 eta 1:19:51
+epoch [44/50] batch [440/500] time 1.534 (1.563) data 0.000 (0.002) loss 1.6191 (1.0455) acc 65.6250 (73.4446) lr 1.2369e-04 eta 1:19:42
+epoch [44/50] batch [445/500] time 1.646 (1.563) data 0.000 (0.002) loss 1.2100 (1.0451) acc 71.8750 (73.4621) lr 1.2369e-04 eta 1:19:35
+epoch [44/50] batch [450/500] time 1.542 (1.563) data 0.000 (0.002) loss 0.9019 (1.0455) acc 78.1250 (73.4583) lr 1.2369e-04 eta 1:19:26
+epoch [44/50] batch [455/500] time 1.550 (1.563) data 0.000 (0.002) loss 0.7070 (1.0450) acc 81.2500 (73.4753) lr 1.2369e-04 eta 1:19:18
+epoch [44/50] batch [460/500] time 1.560 (1.563) data 0.000 (0.002) loss 1.2363 (1.0428) acc 68.7500 (73.4986) lr 1.2369e-04 eta 1:19:10
+epoch [44/50] batch [465/500] time 1.574 (1.563) data 0.000 (0.002) loss 0.9150 (1.0409) acc 71.8750 (73.5349) lr 1.2369e-04 eta 1:19:02
+epoch [44/50] batch [470/500] time 1.544 (1.563) data 0.000 (0.002) loss 2.2402 (1.0424) acc 56.2500 (73.5372) lr 1.2369e-04 eta 1:18:54
+epoch [44/50] batch [475/500] time 1.562 (1.563) data 0.000 (0.002) loss 0.8018 (1.0426) acc 78.1250 (73.5197) lr 1.2369e-04 eta 1:18:46
+epoch [44/50] batch [480/500] time 1.556 (1.563) data 0.000 (0.002) loss 0.4802 (1.0414) acc 87.5000 (73.5612) lr 1.2369e-04 eta 1:18:38
+epoch [44/50] batch [485/500] time 1.552 (1.563) data 0.001 (0.002) loss 0.8110 (1.0408) acc 78.1250 (73.5696) lr 1.2369e-04 eta 1:18:31
+epoch [44/50] batch [490/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.3740 (1.0429) acc 68.7500 (73.5140) lr 1.2369e-04 eta 1:18:23
+epoch [44/50] batch [495/500] time 1.536 (1.562) data 0.000 (0.002) loss 0.7944 (1.0419) acc 78.1250 (73.5354) lr 1.2369e-04 eta 1:18:15
+epoch [44/50] batch [500/500] time 1.544 (1.562) data 0.000 (0.002) loss 0.7769 (1.0402) acc 81.2500 (73.5750) lr 9.5173e-05 eta 1:18:06
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,021
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [45/50] batch [5/500] time 1.573 (1.725) data 0.000 (0.195) loss 1.1318 (1.0828) acc 75.0000 (75.6250) lr 9.5173e-05 eta 1:26:06
+epoch [45/50] batch [10/500] time 1.554 (1.639) data 0.001 (0.098) loss 1.0059 (1.0111) acc 75.0000 (75.9375) lr 9.5173e-05 eta 1:21:39
+epoch [45/50] batch [15/500] time 1.567 (1.613) data 0.001 (0.065) loss 0.9712 (0.9877) acc 71.8750 (74.3750) lr 9.5173e-05 eta 1:20:16
+epoch [45/50] batch [20/500] time 1.565 (1.600) data 0.001 (0.049) loss 1.4951 (1.0625) acc 62.5000 (72.8125) lr 9.5173e-05 eta 1:19:29
+epoch [45/50] batch [25/500] time 1.574 (1.593) data 0.000 (0.039) loss 1.0293 (1.0461) acc 68.7500 (73.6250) lr 9.5173e-05 eta 1:18:59
+epoch [45/50] batch [30/500] time 1.576 (1.588) data 0.000 (0.033) loss 1.3330 (1.0647) acc 62.5000 (72.9167) lr 9.5173e-05 eta 1:18:35
+epoch [45/50] batch [35/500] time 1.577 (1.586) data 0.000 (0.028) loss 0.8804 (1.0584) acc 78.1250 (73.3036) lr 9.5173e-05 eta 1:18:22
+epoch [45/50] batch [40/500] time 1.559 (1.583) data 0.000 (0.025) loss 0.5581 (1.0216) acc 81.2500 (74.4531) lr 9.5173e-05 eta 1:18:05
+epoch [45/50] batch [45/500] time 1.538 (1.581) data 0.000 (0.022) loss 1.4463 (1.0339) acc 68.7500 (74.0278) lr 9.5173e-05 eta 1:17:51
+epoch [45/50] batch [50/500] time 1.560 (1.578) data 0.001 (0.020) loss 0.4873 (1.0188) acc 87.5000 (74.5000) lr 9.5173e-05 eta 1:17:35
+epoch [45/50] batch [55/500] time 1.535 (1.575) data 0.000 (0.018) loss 1.3330 (1.0214) acc 71.8750 (74.4886) lr 9.5173e-05 eta 1:17:19
+epoch [45/50] batch [60/500] time 1.599 (1.575) data 0.001 (0.017) loss 0.8525 (1.0247) acc 84.3750 (74.7396) lr 9.5173e-05 eta 1:17:09
+epoch [45/50] batch [65/500] time 1.537 (1.573) data 0.000 (0.015) loss 0.9634 (1.0236) acc 75.0000 (74.8077) lr 9.5173e-05 eta 1:16:57
+epoch [45/50] batch [70/500] time 1.598 (1.573) data 0.000 (0.014) loss 1.2227 (1.0426) acc 68.7500 (74.4196) lr 9.5173e-05 eta 1:16:49
+epoch [45/50] batch [75/500] time 1.560 (1.573) data 0.001 (0.013) loss 1.0059 (1.0431) acc 65.6250 (74.2500) lr 9.5173e-05 eta 1:16:40
+epoch [45/50] batch [80/500] time 1.570 (1.573) data 0.000 (0.013) loss 0.7627 (1.0333) acc 87.5000 (74.6094) lr 9.5173e-05 eta 1:16:32
+epoch [45/50] batch [85/500] time 1.553 (1.572) data 0.000 (0.012) loss 0.5371 (1.0304) acc 75.0000 (74.4853) lr 9.5173e-05 eta 1:16:21
+epoch [45/50] batch [90/500] time 1.587 (1.572) data 0.000 (0.011) loss 0.9863 (1.0231) acc 84.3750 (74.6528) lr 9.5173e-05 eta 1:16:13
+epoch [45/50] batch [95/500] time 1.553 (1.571) data 0.000 (0.011) loss 0.4387 (1.0111) acc 90.6250 (74.9342) lr 9.5173e-05 eta 1:16:03
+epoch [45/50] batch [100/500] time 1.553 (1.571) data 0.001 (0.010) loss 0.6675 (1.0016) acc 81.2500 (75.0312) lr 9.5173e-05 eta 1:15:54
+epoch [45/50] batch [105/500] time 1.540 (1.570) data 0.000 (0.010) loss 1.1133 (1.0028) acc 75.0000 (74.9107) lr 9.5173e-05 eta 1:15:44
+epoch [45/50] batch [110/500] time 1.553 (1.569) data 0.000 (0.009) loss 0.6460 (1.0020) acc 87.5000 (74.9432) lr 9.5173e-05 eta 1:15:35
+epoch [45/50] batch [115/500] time 1.578 (1.569) data 0.000 (0.009) loss 1.1299 (1.0051) acc 65.6250 (74.7826) lr 9.5173e-05 eta 1:15:25
+epoch [45/50] batch [120/500] time 1.558 (1.568) data 0.000 (0.009) loss 1.2480 (1.0041) acc 65.6250 (74.7656) lr 9.5173e-05 eta 1:15:17
+epoch [45/50] batch [125/500] time 1.548 (1.568) data 0.000 (0.008) loss 0.7393 (0.9920) acc 65.6250 (74.9750) lr 9.5173e-05 eta 1:15:06
+epoch [45/50] batch [130/500] time 1.542 (1.567) data 0.000 (0.008) loss 1.1514 (0.9889) acc 75.0000 (75.2163) lr 9.5173e-05 eta 1:14:57
+epoch [45/50] batch [135/500] time 1.550 (1.567) data 0.001 (0.008) loss 0.9365 (0.9854) acc 78.1250 (75.2083) lr 9.5173e-05 eta 1:14:48
+epoch [45/50] batch [140/500] time 1.568 (1.566) data 0.000 (0.007) loss 0.7456 (0.9857) acc 75.0000 (75.1786) lr 9.5173e-05 eta 1:14:39
+epoch [45/50] batch [145/500] time 1.554 (1.567) data 0.000 (0.007) loss 1.1465 (0.9889) acc 68.7500 (75.0647) lr 9.5173e-05 eta 1:14:32
+epoch [45/50] batch [150/500] time 1.566 (1.566) data 0.000 (0.007) loss 1.0430 (0.9940) acc 68.7500 (74.9583) lr 9.5173e-05 eta 1:14:23
+epoch [45/50] batch [155/500] time 1.569 (1.566) data 0.000 (0.007) loss 0.9438 (0.9906) acc 65.6250 (74.9395) lr 9.5173e-05 eta 1:14:16
+epoch [45/50] batch [160/500] time 1.555 (1.566) data 0.000 (0.007) loss 1.0801 (0.9879) acc 68.7500 (75.0000) lr 9.5173e-05 eta 1:14:07
+epoch [45/50] batch [165/500] time 1.572 (1.566) data 0.000 (0.006) loss 1.3857 (0.9938) acc 75.0000 (75.0189) lr 9.5173e-05 eta 1:14:00
+epoch [45/50] batch [170/500] time 1.545 (1.566) data 0.000 (0.006) loss 1.1328 (0.9956) acc 78.1250 (74.8162) lr 9.5173e-05 eta 1:13:52
+epoch [45/50] batch [175/500] time 1.553 (1.566) data 0.000 (0.006) loss 0.9946 (0.9954) acc 71.8750 (74.7679) lr 9.5173e-05 eta 1:13:43
+epoch [45/50] batch [180/500] time 1.540 (1.566) data 0.000 (0.006) loss 0.9292 (0.9976) acc 81.2500 (74.7396) lr 9.5173e-05 eta 1:13:35
+epoch [45/50] batch [185/500] time 1.579 (1.566) data 0.001 (0.006) loss 0.9819 (0.9978) acc 75.0000 (74.7804) lr 9.5173e-05 eta 1:13:26
+epoch [45/50] batch [190/500] time 1.546 (1.566) data 0.000 (0.006) loss 0.8618 (0.9970) acc 75.0000 (74.7697) lr 9.5173e-05 eta 1:13:19
+epoch [45/50] batch [195/500] time 1.565 (1.566) data 0.000 (0.005) loss 0.7412 (0.9964) acc 84.3750 (74.8397) lr 9.5173e-05 eta 1:13:11
+epoch [45/50] batch [200/500] time 1.552 (1.566) data 0.000 (0.005) loss 1.0156 (0.9951) acc 71.8750 (74.8750) lr 9.5173e-05 eta 1:13:03
+epoch [45/50] batch [205/500] time 1.556 (1.566) data 0.000 (0.005) loss 0.7681 (0.9935) acc 78.1250 (74.8476) lr 9.5173e-05 eta 1:12:55
+epoch [45/50] batch [210/500] time 1.553 (1.565) data 0.000 (0.005) loss 0.7998 (0.9920) acc 84.3750 (74.9256) lr 9.5173e-05 eta 1:12:47
+epoch [45/50] batch [215/500] time 1.552 (1.565) data 0.000 (0.005) loss 0.9639 (0.9909) acc 78.1250 (75.0000) lr 9.5173e-05 eta 1:12:39
+epoch [45/50] batch [220/500] time 1.565 (1.566) data 0.000 (0.005) loss 1.1416 (0.9943) acc 78.1250 (74.8864) lr 9.5173e-05 eta 1:12:32
+epoch [45/50] batch [225/500] time 1.553 (1.565) data 0.001 (0.005) loss 1.3975 (0.9980) acc 59.3750 (74.8194) lr 9.5173e-05 eta 1:12:23
+epoch [45/50] batch [230/500] time 1.563 (1.565) data 0.000 (0.005) loss 1.1484 (0.9973) acc 68.7500 (74.8505) lr 9.5173e-05 eta 1:12:16
+epoch [45/50] batch [235/500] time 1.561 (1.565) data 0.000 (0.005) loss 0.6523 (0.9984) acc 81.2500 (74.7872) lr 9.5173e-05 eta 1:12:08
+epoch [45/50] batch [240/500] time 1.527 (1.565) data 0.000 (0.004) loss 1.2246 (0.9989) acc 71.8750 (74.7917) lr 9.5173e-05 eta 1:11:59
+epoch [45/50] batch [245/500] time 1.561 (1.565) data 0.000 (0.004) loss 0.7837 (0.9955) acc 84.3750 (74.8852) lr 9.5173e-05 eta 1:11:50
+epoch [45/50] batch [250/500] time 1.559 (1.564) data 0.000 (0.004) loss 0.4531 (0.9920) acc 84.3750 (74.9625) lr 9.5173e-05 eta 1:11:42
+epoch [45/50] batch [255/500] time 1.553 (1.564) data 0.000 (0.004) loss 0.9888 (0.9931) acc 71.8750 (74.9755) lr 9.5173e-05 eta 1:11:33
+epoch [45/50] batch [260/500] time 1.558 (1.564) data 0.000 (0.004) loss 0.7910 (0.9932) acc 81.2500 (75.0000) lr 9.5173e-05 eta 1:11:25
+epoch [45/50] batch [265/500] time 1.564 (1.564) data 0.000 (0.004) loss 0.8813 (0.9948) acc 75.0000 (75.0236) lr 9.5173e-05 eta 1:11:17
+epoch [45/50] batch [270/500] time 1.527 (1.564) data 0.000 (0.004) loss 0.7725 (0.9937) acc 75.0000 (75.0231) lr 9.5173e-05 eta 1:11:08
+epoch [45/50] batch [275/500] time 1.550 (1.563) data 0.000 (0.004) loss 1.2041 (0.9923) acc 75.0000 (75.0568) lr 9.5173e-05 eta 1:11:00
+epoch [45/50] batch [280/500] time 1.566 (1.564) data 0.000 (0.004) loss 0.5557 (0.9923) acc 84.3750 (75.0223) lr 9.5173e-05 eta 1:10:52
+epoch [45/50] batch [285/500] time 1.700 (1.564) data 0.000 (0.004) loss 1.1484 (0.9931) acc 68.7500 (74.9781) lr 9.5173e-05 eta 1:10:46
+epoch [45/50] batch [290/500] time 1.583 (1.564) data 0.000 (0.004) loss 1.1396 (0.9966) acc 65.6250 (74.8491) lr 9.5173e-05 eta 1:10:39
+epoch [45/50] batch [295/500] time 1.590 (1.564) data 0.001 (0.004) loss 0.7207 (0.9965) acc 78.1250 (74.8623) lr 9.5173e-05 eta 1:10:31
+epoch [45/50] batch [300/500] time 1.565 (1.564) data 0.000 (0.004) loss 1.3086 (0.9958) acc 68.7500 (74.8542) lr 9.5173e-05 eta 1:10:24
+epoch [45/50] batch [305/500] time 1.568 (1.565) data 0.000 (0.004) loss 0.9478 (0.9931) acc 68.7500 (74.8361) lr 9.5173e-05 eta 1:10:17
+epoch [45/50] batch [310/500] time 1.569 (1.565) data 0.001 (0.004) loss 0.9619 (0.9953) acc 78.1250 (74.8690) lr 9.5173e-05 eta 1:10:09
+epoch [45/50] batch [315/500] time 1.550 (1.565) data 0.000 (0.004) loss 1.6113 (0.9981) acc 68.7500 (74.8512) lr 9.5173e-05 eta 1:10:02
+epoch [45/50] batch [320/500] time 1.558 (1.565) data 0.000 (0.003) loss 0.9419 (0.9971) acc 75.0000 (74.8438) lr 9.5173e-05 eta 1:09:54
+epoch [45/50] batch [325/500] time 1.571 (1.565) data 0.000 (0.003) loss 1.1338 (0.9974) acc 81.2500 (74.8173) lr 9.5173e-05 eta 1:09:46
+epoch [45/50] batch [330/500] time 1.568 (1.565) data 0.000 (0.003) loss 0.6597 (0.9965) acc 84.3750 (74.8485) lr 9.5173e-05 eta 1:09:39
+epoch [45/50] batch [335/500] time 1.551 (1.565) data 0.000 (0.003) loss 1.3066 (0.9974) acc 65.6250 (74.8694) lr 9.5173e-05 eta 1:09:31
+epoch [45/50] batch [340/500] time 1.584 (1.565) data 0.000 (0.003) loss 1.3535 (0.9983) acc 75.0000 (74.8989) lr 9.5173e-05 eta 1:09:23
+epoch [45/50] batch [345/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.3447 (1.0042) acc 62.5000 (74.7917) lr 9.5173e-05 eta 1:09:15
+epoch [45/50] batch [350/500] time 1.576 (1.565) data 0.000 (0.003) loss 0.7725 (1.0063) acc 81.2500 (74.7857) lr 9.5173e-05 eta 1:09:07
+epoch [45/50] batch [355/500] time 1.580 (1.565) data 0.000 (0.003) loss 1.1045 (1.0085) acc 68.7500 (74.7095) lr 9.5173e-05 eta 1:09:00
+epoch [45/50] batch [360/500] time 1.577 (1.565) data 0.001 (0.003) loss 0.8955 (1.0094) acc 75.0000 (74.7049) lr 9.5173e-05 eta 1:08:52
+epoch [45/50] batch [365/500] time 1.544 (1.565) data 0.000 (0.003) loss 1.0508 (1.0088) acc 78.1250 (74.6661) lr 9.5173e-05 eta 1:08:44
+epoch [45/50] batch [370/500] time 1.528 (1.565) data 0.000 (0.003) loss 1.1836 (1.0104) acc 65.6250 (74.6284) lr 9.5173e-05 eta 1:08:36
+epoch [45/50] batch [375/500] time 1.553 (1.565) data 0.000 (0.003) loss 0.5913 (1.0071) acc 84.3750 (74.7083) lr 9.5173e-05 eta 1:08:27
+epoch [45/50] batch [380/500] time 1.537 (1.565) data 0.000 (0.003) loss 1.1182 (1.0091) acc 68.7500 (74.6382) lr 9.5173e-05 eta 1:08:19
+epoch [45/50] batch [385/500] time 1.568 (1.565) data 0.000 (0.003) loss 0.8037 (1.0107) acc 78.1250 (74.6510) lr 9.5173e-05 eta 1:08:11
+epoch [45/50] batch [390/500] time 1.581 (1.564) data 0.000 (0.003) loss 1.1611 (1.0119) acc 62.5000 (74.5833) lr 9.5173e-05 eta 1:08:03
+epoch [45/50] batch [395/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.2256 (1.0132) acc 75.0000 (74.5728) lr 9.5173e-05 eta 1:07:55
+epoch [45/50] batch [400/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.5664 (1.0137) acc 56.2500 (74.5391) lr 9.5173e-05 eta 1:07:48
+epoch [45/50] batch [405/500] time 1.573 (1.565) data 0.000 (0.003) loss 0.9370 (1.0135) acc 78.1250 (74.5525) lr 9.5173e-05 eta 1:07:40
+epoch [45/50] batch [410/500] time 1.579 (1.565) data 0.001 (0.003) loss 0.6333 (1.0143) acc 81.2500 (74.5503) lr 9.5173e-05 eta 1:07:32
+epoch [45/50] batch [415/500] time 1.561 (1.565) data 0.000 (0.003) loss 0.5518 (1.0118) acc 87.5000 (74.5482) lr 9.5173e-05 eta 1:07:25
+epoch [45/50] batch [420/500] time 1.572 (1.565) data 0.000 (0.003) loss 2.0508 (1.0142) acc 59.3750 (74.5164) lr 9.5173e-05 eta 1:07:17
+epoch [45/50] batch [425/500] time 1.535 (1.565) data 0.000 (0.003) loss 0.8545 (1.0155) acc 78.1250 (74.5147) lr 9.5173e-05 eta 1:07:09
+epoch [45/50] batch [430/500] time 1.539 (1.565) data 0.000 (0.003) loss 1.0361 (1.0152) acc 56.2500 (74.4404) lr 9.5173e-05 eta 1:07:01
+epoch [45/50] batch [435/500] time 1.551 (1.565) data 0.000 (0.003) loss 0.9995 (1.0160) acc 68.7500 (74.4181) lr 9.5173e-05 eta 1:06:53
+epoch [45/50] batch [440/500] time 1.562 (1.565) data 0.000 (0.003) loss 0.7173 (1.0171) acc 75.0000 (74.3892) lr 9.5173e-05 eta 1:06:45
+epoch [45/50] batch [445/500] time 1.530 (1.565) data 0.000 (0.003) loss 0.6011 (1.0148) acc 90.6250 (74.5084) lr 9.5173e-05 eta 1:06:37
+epoch [45/50] batch [450/500] time 1.555 (1.565) data 0.000 (0.003) loss 0.4243 (1.0123) acc 84.3750 (74.5486) lr 9.5173e-05 eta 1:06:29
+epoch [45/50] batch [455/500] time 1.546 (1.564) data 0.000 (0.003) loss 0.9375 (1.0155) acc 78.1250 (74.4986) lr 9.5173e-05 eta 1:06:21
+epoch [45/50] batch [460/500] time 1.570 (1.564) data 0.001 (0.003) loss 0.9351 (1.0162) acc 78.1250 (74.5177) lr 9.5173e-05 eta 1:06:13
+epoch [45/50] batch [465/500] time 1.575 (1.564) data 0.000 (0.003) loss 0.5449 (1.0145) acc 84.3750 (74.5430) lr 9.5173e-05 eta 1:06:05
+epoch [45/50] batch [470/500] time 1.557 (1.564) data 0.000 (0.002) loss 1.3135 (1.0149) acc 75.0000 (74.5878) lr 9.5173e-05 eta 1:05:57
+epoch [45/50] batch [475/500] time 1.546 (1.565) data 0.001 (0.002) loss 1.3330 (1.0159) acc 62.5000 (74.5395) lr 9.5173e-05 eta 1:05:50
+epoch [45/50] batch [480/500] time 1.549 (1.565) data 0.001 (0.002) loss 0.8979 (1.0169) acc 71.8750 (74.5182) lr 9.5173e-05 eta 1:05:42
+epoch [45/50] batch [485/500] time 1.579 (1.565) data 0.001 (0.002) loss 1.4746 (1.0180) acc 59.3750 (74.4845) lr 9.5173e-05 eta 1:05:34
+epoch [45/50] batch [490/500] time 1.562 (1.564) data 0.000 (0.002) loss 0.7666 (1.0160) acc 81.2500 (74.4898) lr 9.5173e-05 eta 1:05:26
+epoch [45/50] batch [495/500] time 1.558 (1.564) data 0.000 (0.002) loss 1.1797 (1.0185) acc 68.7500 (74.4318) lr 9.5173e-05 eta 1:05:18
+epoch [45/50] batch [500/500] time 1.550 (1.564) data 0.000 (0.002) loss 1.3389 (1.0190) acc 65.6250 (74.4313) lr 7.0224e-05 eta 1:05:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,016
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [46/50] batch [5/500] time 1.539 (1.694) data 0.000 (0.195) loss 1.0010 (1.0370) acc 75.0000 (75.0000) lr 7.0224e-05 eta 1:10:25
+epoch [46/50] batch [10/500] time 1.572 (1.628) data 0.000 (0.098) loss 0.8457 (1.1188) acc 81.2500 (73.7500) lr 7.0224e-05 eta 1:07:33
+epoch [46/50] batch [15/500] time 1.556 (1.606) data 0.001 (0.065) loss 0.9360 (1.0685) acc 75.0000 (73.9583) lr 7.0224e-05 eta 1:06:31
+epoch [46/50] batch [20/500] time 1.558 (1.592) data 0.000 (0.049) loss 0.6157 (1.0729) acc 87.5000 (74.6875) lr 7.0224e-05 eta 1:05:47
+epoch [46/50] batch [25/500] time 1.556 (1.584) data 0.000 (0.039) loss 1.1035 (1.0613) acc 78.1250 (74.8750) lr 7.0224e-05 eta 1:05:19
+epoch [46/50] batch [30/500] time 1.546 (1.581) data 0.000 (0.033) loss 1.1543 (1.0311) acc 68.7500 (75.1042) lr 7.0224e-05 eta 1:05:04
+epoch [46/50] batch [35/500] time 1.540 (1.577) data 0.000 (0.028) loss 1.0752 (1.0381) acc 75.0000 (75.0000) lr 7.0224e-05 eta 1:04:47
+epoch [46/50] batch [40/500] time 1.569 (1.576) data 0.000 (0.025) loss 1.2197 (1.0262) acc 71.8750 (75.1562) lr 7.0224e-05 eta 1:04:36
+epoch [46/50] batch [45/500] time 1.556 (1.575) data 0.000 (0.022) loss 1.0938 (1.0482) acc 78.1250 (74.1667) lr 7.0224e-05 eta 1:04:25
+epoch [46/50] batch [50/500] time 1.564 (1.573) data 0.000 (0.020) loss 0.9077 (1.0425) acc 71.8750 (74.5000) lr 7.0224e-05 eta 1:04:15
+epoch [46/50] batch [55/500] time 1.555 (1.571) data 0.000 (0.018) loss 1.3428 (1.0604) acc 71.8750 (74.2045) lr 7.0224e-05 eta 1:04:01
+epoch [46/50] batch [60/500] time 1.563 (1.571) data 0.000 (0.017) loss 0.8960 (1.0456) acc 68.7500 (74.4792) lr 7.0224e-05 eta 1:03:54
+epoch [46/50] batch [65/500] time 1.566 (1.573) data 0.000 (0.015) loss 0.6289 (1.0320) acc 84.3750 (74.5192) lr 7.0224e-05 eta 1:03:49
+epoch [46/50] batch [70/500] time 1.556 (1.572) data 0.000 (0.014) loss 1.0254 (1.0270) acc 84.3750 (74.5982) lr 7.0224e-05 eta 1:03:39
+epoch [46/50] batch [75/500] time 1.547 (1.570) data 0.000 (0.013) loss 1.1270 (1.0203) acc 71.8750 (74.5833) lr 7.0224e-05 eta 1:03:28
+epoch [46/50] batch [80/500] time 1.549 (1.571) data 0.000 (0.013) loss 0.8521 (1.0103) acc 75.0000 (74.9609) lr 7.0224e-05 eta 1:03:21
+epoch [46/50] batch [85/500] time 1.544 (1.569) data 0.001 (0.012) loss 0.9214 (1.0223) acc 71.8750 (74.7426) lr 7.0224e-05 eta 1:03:09
+epoch [46/50] batch [90/500] time 1.558 (1.569) data 0.000 (0.011) loss 1.0518 (1.0262) acc 75.0000 (74.4792) lr 7.0224e-05 eta 1:03:00
+epoch [46/50] batch [95/500] time 1.561 (1.568) data 0.000 (0.011) loss 1.1494 (1.0256) acc 71.8750 (74.4408) lr 7.0224e-05 eta 1:02:50
+epoch [46/50] batch [100/500] time 1.556 (1.567) data 0.000 (0.010) loss 0.8696 (1.0228) acc 84.3750 (74.3750) lr 7.0224e-05 eta 1:02:40
+epoch [46/50] batch [105/500] time 1.547 (1.566) data 0.000 (0.010) loss 0.7026 (1.0318) acc 84.3750 (74.4048) lr 7.0224e-05 eta 1:02:31
+epoch [46/50] batch [110/500] time 1.542 (1.566) data 0.000 (0.009) loss 0.7793 (1.0241) acc 84.3750 (74.6307) lr 7.0224e-05 eta 1:02:23
+epoch [46/50] batch [115/500] time 1.530 (1.565) data 0.000 (0.009) loss 1.0879 (1.0290) acc 78.1250 (74.6467) lr 7.0224e-05 eta 1:02:13
+epoch [46/50] batch [120/500] time 1.551 (1.565) data 0.000 (0.009) loss 1.2178 (1.0281) acc 62.5000 (74.5833) lr 7.0224e-05 eta 1:02:03
+epoch [46/50] batch [125/500] time 1.546 (1.564) data 0.000 (0.008) loss 1.0762 (1.0207) acc 81.2500 (74.8000) lr 7.0224e-05 eta 1:01:55
+epoch [46/50] batch [130/500] time 1.562 (1.564) data 0.000 (0.008) loss 0.4348 (1.0174) acc 87.5000 (74.8317) lr 7.0224e-05 eta 1:01:46
+epoch [46/50] batch [135/500] time 1.558 (1.564) data 0.001 (0.008) loss 0.8784 (1.0166) acc 75.0000 (74.8148) lr 7.0224e-05 eta 1:01:37
+epoch [46/50] batch [140/500] time 1.565 (1.564) data 0.000 (0.007) loss 0.7759 (1.0145) acc 81.2500 (74.7545) lr 7.0224e-05 eta 1:01:30
+epoch [46/50] batch [145/500] time 1.557 (1.564) data 0.000 (0.007) loss 1.7256 (1.0285) acc 65.6250 (74.6121) lr 7.0224e-05 eta 1:01:22
+epoch [46/50] batch [150/500] time 1.553 (1.563) data 0.001 (0.007) loss 1.6729 (1.0356) acc 65.6250 (74.3958) lr 7.0224e-05 eta 1:01:13
+epoch [46/50] batch [155/500] time 1.571 (1.563) data 0.001 (0.007) loss 0.9258 (1.0323) acc 78.1250 (74.3347) lr 7.0224e-05 eta 1:01:05
+epoch [46/50] batch [160/500] time 1.562 (1.563) data 0.001 (0.006) loss 1.7324 (1.0316) acc 65.6250 (74.4141) lr 7.0224e-05 eta 1:00:57
+epoch [46/50] batch [165/500] time 1.532 (1.563) data 0.000 (0.006) loss 0.6865 (1.0346) acc 84.3750 (74.3750) lr 7.0224e-05 eta 1:00:49
+epoch [46/50] batch [170/500] time 1.562 (1.563) data 0.000 (0.006) loss 0.6763 (1.0293) acc 81.2500 (74.5037) lr 7.0224e-05 eta 1:00:41
+epoch [46/50] batch [175/500] time 1.550 (1.563) data 0.000 (0.006) loss 1.5176 (1.0380) acc 68.7500 (74.4464) lr 7.0224e-05 eta 1:00:32
+epoch [46/50] batch [180/500] time 1.529 (1.562) data 0.000 (0.006) loss 0.7207 (1.0351) acc 84.3750 (74.4444) lr 7.0224e-05 eta 1:00:23
+epoch [46/50] batch [185/500] time 1.541 (1.562) data 0.000 (0.006) loss 1.0674 (1.0331) acc 68.7500 (74.4088) lr 7.0224e-05 eta 1:00:15
+epoch [46/50] batch [190/500] time 1.544 (1.561) data 0.000 (0.006) loss 0.8525 (1.0292) acc 68.7500 (74.2599) lr 7.0224e-05 eta 1:00:06
+epoch [46/50] batch [195/500] time 1.563 (1.561) data 0.000 (0.005) loss 1.6768 (1.0346) acc 56.2500 (74.0705) lr 7.0224e-05 eta 0:59:58
+epoch [46/50] batch [200/500] time 1.558 (1.561) data 0.000 (0.005) loss 1.2256 (1.0336) acc 71.8750 (74.1406) lr 7.0224e-05 eta 0:59:50
+epoch [46/50] batch [205/500] time 1.655 (1.561) data 0.000 (0.005) loss 0.9414 (1.0407) acc 81.2500 (74.0396) lr 7.0224e-05 eta 0:59:42
+epoch [46/50] batch [210/500] time 1.570 (1.561) data 0.000 (0.005) loss 0.9214 (1.0359) acc 78.1250 (74.1518) lr 7.0224e-05 eta 0:59:34
+epoch [46/50] batch [215/500] time 1.533 (1.561) data 0.000 (0.005) loss 1.1504 (1.0391) acc 68.7500 (74.0698) lr 7.0224e-05 eta 0:59:26
+epoch [46/50] batch [220/500] time 1.555 (1.561) data 0.000 (0.005) loss 1.3027 (1.0423) acc 65.6250 (74.0625) lr 7.0224e-05 eta 0:59:18
+epoch [46/50] batch [225/500] time 1.557 (1.561) data 0.000 (0.005) loss 1.2559 (1.0443) acc 65.6250 (74.0556) lr 7.0224e-05 eta 0:59:10
+epoch [46/50] batch [230/500] time 1.532 (1.560) data 0.001 (0.005) loss 1.7764 (1.0438) acc 59.3750 (74.0217) lr 7.0224e-05 eta 0:59:02
+epoch [46/50] batch [235/500] time 1.550 (1.561) data 0.000 (0.005) loss 1.0996 (1.0408) acc 78.1250 (74.1090) lr 7.0224e-05 eta 0:58:54
+epoch [46/50] batch [240/500] time 1.550 (1.560) data 0.000 (0.004) loss 0.6206 (1.0393) acc 84.3750 (74.2057) lr 7.0224e-05 eta 0:58:46
+epoch [46/50] batch [245/500] time 1.545 (1.560) data 0.000 (0.004) loss 2.0020 (1.0450) acc 62.5000 (74.1582) lr 7.0224e-05 eta 0:58:38
+epoch [46/50] batch [250/500] time 1.556 (1.561) data 0.000 (0.004) loss 1.5801 (1.0468) acc 75.0000 (74.1750) lr 7.0224e-05 eta 0:58:32
+epoch [46/50] batch [255/500] time 1.542 (1.561) data 0.000 (0.004) loss 0.7358 (1.0509) acc 84.3750 (74.1176) lr 7.0224e-05 eta 0:58:23
+epoch [46/50] batch [260/500] time 1.541 (1.560) data 0.000 (0.004) loss 1.6768 (1.0512) acc 65.6250 (74.1346) lr 7.0224e-05 eta 0:58:15
+epoch [46/50] batch [265/500] time 1.549 (1.560) data 0.000 (0.004) loss 1.0342 (1.0487) acc 68.7500 (74.1863) lr 7.0224e-05 eta 0:58:07
+epoch [46/50] batch [270/500] time 1.532 (1.560) data 0.000 (0.004) loss 1.2217 (1.0450) acc 68.7500 (74.1782) lr 7.0224e-05 eta 0:57:59
+epoch [46/50] batch [275/500] time 1.545 (1.560) data 0.000 (0.004) loss 1.5195 (1.0525) acc 68.7500 (74.0682) lr 7.0224e-05 eta 0:57:51
+epoch [46/50] batch [280/500] time 1.554 (1.560) data 0.000 (0.004) loss 0.7700 (1.0493) acc 81.2500 (74.1629) lr 7.0224e-05 eta 0:57:43
+epoch [46/50] batch [285/500] time 1.577 (1.560) data 0.000 (0.004) loss 1.3545 (1.0486) acc 62.5000 (74.1557) lr 7.0224e-05 eta 0:57:35
+epoch [46/50] batch [290/500] time 1.573 (1.560) data 0.000 (0.004) loss 1.0654 (1.0473) acc 75.0000 (74.1810) lr 7.0224e-05 eta 0:57:27
+epoch [46/50] batch [295/500] time 1.546 (1.560) data 0.000 (0.004) loss 1.1846 (1.0484) acc 71.8750 (74.0890) lr 7.0224e-05 eta 0:57:20
+epoch [46/50] batch [300/500] time 1.530 (1.560) data 0.000 (0.004) loss 1.4219 (1.0515) acc 68.7500 (74.0938) lr 7.0224e-05 eta 0:57:11
+epoch [46/50] batch [305/500] time 1.552 (1.560) data 0.001 (0.004) loss 0.9717 (1.0509) acc 65.6250 (74.0164) lr 7.0224e-05 eta 0:57:03
+epoch [46/50] batch [310/500] time 1.562 (1.560) data 0.001 (0.004) loss 1.1807 (1.0490) acc 71.8750 (74.1028) lr 7.0224e-05 eta 0:56:55
+epoch [46/50] batch [315/500] time 1.553 (1.560) data 0.000 (0.003) loss 0.5562 (1.0478) acc 84.3750 (74.1567) lr 7.0224e-05 eta 0:56:47
+epoch [46/50] batch [320/500] time 1.559 (1.560) data 0.000 (0.003) loss 1.2275 (1.0481) acc 65.6250 (74.1309) lr 7.0224e-05 eta 0:56:39
+epoch [46/50] batch [325/500] time 1.551 (1.559) data 0.000 (0.003) loss 0.5918 (1.0511) acc 78.1250 (74.0673) lr 7.0224e-05 eta 0:56:31
+epoch [46/50] batch [330/500] time 1.578 (1.560) data 0.000 (0.003) loss 0.7969 (1.0510) acc 81.2500 (74.0436) lr 7.0224e-05 eta 0:56:24
+epoch [46/50] batch [335/500] time 1.539 (1.560) data 0.000 (0.003) loss 1.0625 (1.0502) acc 75.0000 (74.0765) lr 7.0224e-05 eta 0:56:16
+epoch [46/50] batch [340/500] time 1.566 (1.559) data 0.000 (0.003) loss 0.7480 (1.0494) acc 71.8750 (74.1176) lr 7.0224e-05 eta 0:56:08
+epoch [46/50] batch [345/500] time 1.553 (1.560) data 0.000 (0.003) loss 1.3984 (1.0478) acc 68.7500 (74.1304) lr 7.0224e-05 eta 0:56:00
+epoch [46/50] batch [350/500] time 1.559 (1.560) data 0.000 (0.003) loss 1.2930 (1.0442) acc 68.7500 (74.2321) lr 7.0224e-05 eta 0:55:53
+epoch [46/50] batch [355/500] time 1.571 (1.560) data 0.001 (0.003) loss 1.0498 (1.0444) acc 75.0000 (74.2165) lr 7.0224e-05 eta 0:55:45
+epoch [46/50] batch [360/500] time 1.560 (1.560) data 0.000 (0.003) loss 1.3682 (1.0454) acc 68.7500 (74.2274) lr 7.0224e-05 eta 0:55:37
+epoch [46/50] batch [365/500] time 1.555 (1.560) data 0.000 (0.003) loss 0.8267 (1.0427) acc 71.8750 (74.2637) lr 7.0224e-05 eta 0:55:29
+epoch [46/50] batch [370/500] time 1.554 (1.560) data 0.000 (0.003) loss 1.1631 (1.0418) acc 71.8750 (74.2821) lr 7.0224e-05 eta 0:55:21
+epoch [46/50] batch [375/500] time 1.556 (1.560) data 0.000 (0.003) loss 0.8403 (1.0433) acc 65.6250 (74.2000) lr 7.0224e-05 eta 0:55:14
+epoch [46/50] batch [380/500] time 1.551 (1.559) data 0.000 (0.003) loss 1.5029 (1.0440) acc 59.3750 (74.1118) lr 7.0224e-05 eta 0:55:06
+epoch [46/50] batch [385/500] time 1.565 (1.560) data 0.000 (0.003) loss 0.9326 (1.0426) acc 71.8750 (74.1153) lr 7.0224e-05 eta 0:54:58
+epoch [46/50] batch [390/500] time 1.555 (1.559) data 0.000 (0.003) loss 0.8994 (1.0442) acc 71.8750 (74.0785) lr 7.0224e-05 eta 0:54:50
+epoch [46/50] batch [395/500] time 1.543 (1.560) data 0.000 (0.003) loss 1.0811 (1.0438) acc 75.0000 (74.1218) lr 7.0224e-05 eta 0:54:43
+epoch [46/50] batch [400/500] time 1.554 (1.560) data 0.000 (0.003) loss 1.0596 (1.0419) acc 68.7500 (74.1328) lr 7.0224e-05 eta 0:54:35
+epoch [46/50] batch [405/500] time 1.597 (1.560) data 0.000 (0.003) loss 1.0264 (1.0402) acc 78.1250 (74.1512) lr 7.0224e-05 eta 0:54:27
+epoch [46/50] batch [410/500] time 1.552 (1.560) data 0.000 (0.003) loss 0.5103 (1.0393) acc 84.3750 (74.1616) lr 7.0224e-05 eta 0:54:19
+epoch [46/50] batch [415/500] time 1.565 (1.560) data 0.000 (0.003) loss 0.5376 (1.0393) acc 93.7500 (74.1416) lr 7.0224e-05 eta 0:54:11
+epoch [46/50] batch [420/500] time 1.570 (1.559) data 0.000 (0.003) loss 0.9238 (1.0363) acc 75.0000 (74.1964) lr 7.0224e-05 eta 0:54:03
+epoch [46/50] batch [425/500] time 1.558 (1.560) data 0.000 (0.003) loss 0.8896 (1.0329) acc 81.2500 (74.2868) lr 7.0224e-05 eta 0:53:55
+epoch [46/50] batch [430/500] time 1.535 (1.559) data 0.000 (0.003) loss 0.4663 (1.0306) acc 75.0000 (74.2733) lr 7.0224e-05 eta 0:53:48
+epoch [46/50] batch [435/500] time 1.543 (1.559) data 0.000 (0.003) loss 0.9663 (1.0284) acc 62.5000 (74.2457) lr 7.0224e-05 eta 0:53:40
+epoch [46/50] batch [440/500] time 1.535 (1.559) data 0.000 (0.003) loss 0.7329 (1.0287) acc 81.2500 (74.2472) lr 7.0224e-05 eta 0:53:32
+epoch [46/50] batch [445/500] time 1.565 (1.559) data 0.001 (0.003) loss 1.2490 (1.0270) acc 68.7500 (74.2978) lr 7.0224e-05 eta 0:53:24
+epoch [46/50] batch [450/500] time 1.535 (1.559) data 0.001 (0.003) loss 1.2627 (1.0280) acc 75.0000 (74.2847) lr 7.0224e-05 eta 0:53:16
+epoch [46/50] batch [455/500] time 1.567 (1.559) data 0.000 (0.003) loss 1.2773 (1.0301) acc 71.8750 (74.2788) lr 7.0224e-05 eta 0:53:09
+epoch [46/50] batch [460/500] time 1.528 (1.559) data 0.001 (0.003) loss 0.3718 (1.0291) acc 90.6250 (74.2935) lr 7.0224e-05 eta 0:53:01
+epoch [46/50] batch [465/500] time 1.543 (1.559) data 0.000 (0.003) loss 0.8735 (1.0286) acc 68.7500 (74.2876) lr 7.0224e-05 eta 0:52:53
+epoch [46/50] batch [470/500] time 1.554 (1.559) data 0.000 (0.002) loss 1.0654 (1.0305) acc 68.7500 (74.2354) lr 7.0224e-05 eta 0:52:45
+epoch [46/50] batch [475/500] time 1.544 (1.559) data 0.000 (0.002) loss 0.4465 (1.0304) acc 84.3750 (74.2171) lr 7.0224e-05 eta 0:52:37
+epoch [46/50] batch [480/500] time 1.556 (1.559) data 0.000 (0.002) loss 1.0264 (1.0297) acc 71.8750 (74.2448) lr 7.0224e-05 eta 0:52:29
+epoch [46/50] batch [485/500] time 1.568 (1.559) data 0.001 (0.002) loss 1.0869 (1.0292) acc 75.0000 (74.2268) lr 7.0224e-05 eta 0:52:21
+epoch [46/50] batch [490/500] time 1.549 (1.559) data 0.000 (0.002) loss 0.7939 (1.0290) acc 75.0000 (74.1773) lr 7.0224e-05 eta 0:52:13
+epoch [46/50] batch [495/500] time 1.563 (1.559) data 0.000 (0.002) loss 1.3457 (1.0295) acc 68.7500 (74.1667) lr 7.0224e-05 eta 0:52:06
+epoch [46/50] batch [500/500] time 1.569 (1.559) data 0.000 (0.002) loss 0.8887 (1.0275) acc 81.2500 (74.2125) lr 4.8943e-05 eta 0:51:58
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,020
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [47/50] batch [5/500] time 1.540 (1.656) data 0.000 (0.161) loss 0.9849 (0.9564) acc 71.8750 (75.0000) lr 4.8943e-05 eta 0:55:04
+epoch [47/50] batch [10/500] time 1.581 (1.609) data 0.000 (0.081) loss 1.4580 (1.0336) acc 65.6250 (74.0625) lr 4.8943e-05 eta 0:53:22
+epoch [47/50] batch [15/500] time 1.557 (1.589) data 0.000 (0.054) loss 1.3105 (1.0239) acc 62.5000 (73.9583) lr 4.8943e-05 eta 0:52:33
+epoch [47/50] batch [20/500] time 1.553 (1.580) data 0.000 (0.041) loss 1.2715 (1.0800) acc 56.2500 (73.4375) lr 4.8943e-05 eta 0:52:07
+epoch [47/50] batch [25/500] time 1.573 (1.577) data 0.001 (0.033) loss 1.5186 (1.0676) acc 59.3750 (73.3750) lr 4.8943e-05 eta 0:51:55
+epoch [47/50] batch [30/500] time 1.556 (1.574) data 0.000 (0.027) loss 1.0732 (1.0557) acc 84.3750 (74.1667) lr 4.8943e-05 eta 0:51:41
+epoch [47/50] batch [35/500] time 1.557 (1.572) data 0.000 (0.023) loss 0.5747 (1.0511) acc 78.1250 (73.9286) lr 4.8943e-05 eta 0:51:28
+epoch [47/50] batch [40/500] time 1.559 (1.572) data 0.000 (0.021) loss 1.2666 (1.0726) acc 59.3750 (73.0469) lr 4.8943e-05 eta 0:51:21
+epoch [47/50] batch [45/500] time 1.575 (1.571) data 0.000 (0.018) loss 0.6787 (1.0451) acc 78.1250 (73.6806) lr 4.8943e-05 eta 0:51:10
+epoch [47/50] batch [50/500] time 1.547 (1.569) data 0.000 (0.017) loss 1.5146 (1.0637) acc 68.7500 (73.1875) lr 4.8943e-05 eta 0:50:59
+epoch [47/50] batch [55/500] time 1.576 (1.569) data 0.000 (0.015) loss 0.8569 (1.0563) acc 75.0000 (73.5227) lr 4.8943e-05 eta 0:50:52
+epoch [47/50] batch [60/500] time 1.561 (1.569) data 0.001 (0.014) loss 0.5273 (1.0334) acc 90.6250 (74.3229) lr 4.8943e-05 eta 0:50:44
+epoch [47/50] batch [65/500] time 1.528 (1.567) data 0.001 (0.013) loss 1.0527 (1.0460) acc 65.6250 (73.9423) lr 4.8943e-05 eta 0:50:32
+epoch [47/50] batch [70/500] time 1.557 (1.566) data 0.001 (0.012) loss 1.0547 (1.0331) acc 71.8750 (74.0625) lr 4.8943e-05 eta 0:50:21
+epoch [47/50] batch [75/500] time 1.595 (1.566) data 0.001 (0.011) loss 1.0430 (1.0425) acc 65.6250 (73.7083) lr 4.8943e-05 eta 0:50:14
+epoch [47/50] batch [80/500] time 1.573 (1.567) data 0.000 (0.011) loss 1.0664 (1.0439) acc 75.0000 (73.9453) lr 4.8943e-05 eta 0:50:08
+epoch [47/50] batch [85/500] time 1.558 (1.566) data 0.000 (0.010) loss 0.8818 (1.0397) acc 71.8750 (73.8603) lr 4.8943e-05 eta 0:49:58
+epoch [47/50] batch [90/500] time 1.555 (1.566) data 0.000 (0.009) loss 0.9932 (1.0235) acc 65.6250 (74.2361) lr 4.8943e-05 eta 0:49:50
+epoch [47/50] batch [95/500] time 1.541 (1.565) data 0.000 (0.009) loss 0.7124 (1.0318) acc 81.2500 (73.8816) lr 4.8943e-05 eta 0:49:41
+epoch [47/50] batch [100/500] time 1.549 (1.564) data 0.000 (0.009) loss 0.9116 (1.0259) acc 75.0000 (74.0625) lr 4.8943e-05 eta 0:49:32
+epoch [47/50] batch [105/500] time 1.562 (1.564) data 0.000 (0.008) loss 0.6050 (1.0407) acc 93.7500 (73.8095) lr 4.8943e-05 eta 0:49:24
+epoch [47/50] batch [110/500] time 1.559 (1.564) data 0.000 (0.008) loss 1.3955 (1.0471) acc 75.0000 (73.7216) lr 4.8943e-05 eta 0:49:15
+epoch [47/50] batch [115/500] time 1.568 (1.564) data 0.001 (0.007) loss 1.2510 (1.0540) acc 71.8750 (73.5326) lr 4.8943e-05 eta 0:49:08
+epoch [47/50] batch [120/500] time 1.597 (1.565) data 0.000 (0.007) loss 1.2656 (1.0503) acc 71.8750 (73.7760) lr 4.8943e-05 eta 0:49:02
+epoch [47/50] batch [125/500] time 1.560 (1.565) data 0.000 (0.007) loss 1.0801 (1.0582) acc 71.8750 (73.7750) lr 4.8943e-05 eta 0:48:54
+epoch [47/50] batch [130/500] time 1.572 (1.565) data 0.000 (0.007) loss 1.2920 (1.0594) acc 65.6250 (73.7500) lr 4.8943e-05 eta 0:48:46
+epoch [47/50] batch [135/500] time 1.565 (1.564) data 0.000 (0.006) loss 1.2080 (1.0630) acc 68.7500 (73.6806) lr 4.8943e-05 eta 0:48:37
+epoch [47/50] batch [140/500] time 1.559 (1.565) data 0.000 (0.006) loss 1.4727 (1.0603) acc 68.7500 (73.7277) lr 4.8943e-05 eta 0:48:30
+epoch [47/50] batch [145/500] time 1.542 (1.565) data 0.000 (0.006) loss 1.1387 (1.0635) acc 65.6250 (73.6207) lr 4.8943e-05 eta 0:48:22
+epoch [47/50] batch [150/500] time 1.530 (1.564) data 0.000 (0.006) loss 1.1279 (1.0631) acc 71.8750 (73.5417) lr 4.8943e-05 eta 0:48:13
+epoch [47/50] batch [155/500] time 1.545 (1.564) data 0.000 (0.006) loss 0.6382 (1.0577) acc 81.2500 (73.5081) lr 4.8943e-05 eta 0:48:04
+epoch [47/50] batch [160/500] time 1.558 (1.564) data 0.000 (0.005) loss 1.1123 (1.0566) acc 75.0000 (73.5938) lr 4.8943e-05 eta 0:47:57
+epoch [47/50] batch [165/500] time 1.558 (1.564) data 0.000 (0.005) loss 1.0137 (1.0585) acc 75.0000 (73.6553) lr 4.8943e-05 eta 0:47:50
+epoch [47/50] batch [170/500] time 1.581 (1.564) data 0.000 (0.005) loss 1.4102 (1.0576) acc 75.0000 (73.6949) lr 4.8943e-05 eta 0:47:42
+epoch [47/50] batch [175/500] time 1.553 (1.564) data 0.000 (0.005) loss 0.8066 (1.0533) acc 81.2500 (73.8036) lr 4.8943e-05 eta 0:47:34
+epoch [47/50] batch [180/500] time 1.563 (1.564) data 0.000 (0.005) loss 1.5566 (1.0546) acc 65.6250 (73.7153) lr 4.8943e-05 eta 0:47:26
+epoch [47/50] batch [185/500] time 1.569 (1.564) data 0.000 (0.005) loss 0.6045 (1.0485) acc 78.1250 (73.8007) lr 4.8943e-05 eta 0:47:18
+epoch [47/50] batch [190/500] time 1.554 (1.564) data 0.000 (0.005) loss 1.1436 (1.0578) acc 78.1250 (73.7171) lr 4.8943e-05 eta 0:47:10
+epoch [47/50] batch [195/500] time 1.584 (1.563) data 0.000 (0.005) loss 0.6978 (1.0551) acc 84.3750 (73.8141) lr 4.8943e-05 eta 0:47:02
+epoch [47/50] batch [200/500] time 1.562 (1.563) data 0.000 (0.004) loss 1.3213 (1.0561) acc 65.6250 (73.8281) lr 4.8943e-05 eta 0:46:53
+epoch [47/50] batch [205/500] time 1.548 (1.563) data 0.000 (0.004) loss 1.2930 (1.0610) acc 59.3750 (73.7043) lr 4.8943e-05 eta 0:46:45
+epoch [47/50] batch [210/500] time 1.563 (1.563) data 0.000 (0.004) loss 0.8477 (1.0555) acc 75.0000 (73.8393) lr 4.8943e-05 eta 0:46:37
+epoch [47/50] batch [215/500] time 1.559 (1.563) data 0.000 (0.004) loss 0.9019 (1.0516) acc 78.1250 (73.8953) lr 4.8943e-05 eta 0:46:29
+epoch [47/50] batch [220/500] time 1.580 (1.563) data 0.000 (0.004) loss 1.0635 (1.0503) acc 78.1250 (73.9347) lr 4.8943e-05 eta 0:46:21
+epoch [47/50] batch [225/500] time 1.589 (1.563) data 0.000 (0.004) loss 1.2119 (1.0545) acc 65.6250 (73.8750) lr 4.8943e-05 eta 0:46:13
+epoch [47/50] batch [230/500] time 1.547 (1.563) data 0.000 (0.004) loss 1.0752 (1.0560) acc 78.1250 (73.8995) lr 4.8943e-05 eta 0:46:05
+epoch [47/50] batch [235/500] time 1.542 (1.562) data 0.000 (0.004) loss 0.9624 (1.0567) acc 75.0000 (73.8963) lr 4.8943e-05 eta 0:45:57
+epoch [47/50] batch [240/500] time 1.541 (1.562) data 0.000 (0.004) loss 0.9478 (1.0581) acc 71.8750 (73.8802) lr 4.8943e-05 eta 0:45:49
+epoch [47/50] batch [245/500] time 1.559 (1.562) data 0.000 (0.004) loss 1.3926 (1.0593) acc 71.8750 (73.9158) lr 4.8943e-05 eta 0:45:41
+epoch [47/50] batch [250/500] time 1.550 (1.562) data 0.000 (0.004) loss 0.7192 (1.0601) acc 71.8750 (73.8125) lr 4.8943e-05 eta 0:45:33
+epoch [47/50] batch [255/500] time 1.574 (1.562) data 0.000 (0.004) loss 0.7505 (1.0607) acc 84.3750 (73.8113) lr 4.8943e-05 eta 0:45:25
+epoch [47/50] batch [260/500] time 1.565 (1.562) data 0.000 (0.003) loss 1.2871 (1.0632) acc 59.3750 (73.7620) lr 4.8943e-05 eta 0:45:17
+epoch [47/50] batch [265/500] time 1.573 (1.562) data 0.000 (0.003) loss 0.9927 (1.0623) acc 71.8750 (73.7146) lr 4.8943e-05 eta 0:45:10
+epoch [47/50] batch [270/500] time 1.560 (1.562) data 0.000 (0.003) loss 1.2715 (1.0649) acc 71.8750 (73.7037) lr 4.8943e-05 eta 0:45:02
+epoch [47/50] batch [275/500] time 1.580 (1.562) data 0.000 (0.003) loss 1.3994 (1.0670) acc 59.3750 (73.6591) lr 4.8943e-05 eta 0:44:54
+epoch [47/50] batch [280/500] time 1.661 (1.562) data 0.000 (0.003) loss 1.0889 (1.0683) acc 84.3750 (73.6496) lr 4.8943e-05 eta 0:44:47
+epoch [47/50] batch [285/500] time 1.557 (1.562) data 0.000 (0.003) loss 1.0205 (1.0691) acc 78.1250 (73.6184) lr 4.8943e-05 eta 0:44:39
+epoch [47/50] batch [290/500] time 1.560 (1.562) data 0.000 (0.003) loss 0.8013 (1.0714) acc 84.3750 (73.5560) lr 4.8943e-05 eta 0:44:31
+epoch [47/50] batch [295/500] time 1.559 (1.562) data 0.000 (0.003) loss 0.7505 (1.0678) acc 84.3750 (73.6547) lr 4.8943e-05 eta 0:44:23
+epoch [47/50] batch [300/500] time 1.568 (1.562) data 0.000 (0.003) loss 0.7812 (1.0713) acc 84.3750 (73.6042) lr 4.8943e-05 eta 0:44:15
+epoch [47/50] batch [305/500] time 1.551 (1.562) data 0.000 (0.003) loss 0.9370 (1.0698) acc 78.1250 (73.5758) lr 4.8943e-05 eta 0:44:07
+epoch [47/50] batch [310/500] time 1.568 (1.562) data 0.000 (0.003) loss 0.7661 (1.0671) acc 81.2500 (73.6794) lr 4.8943e-05 eta 0:43:59
+epoch [47/50] batch [315/500] time 1.559 (1.562) data 0.000 (0.003) loss 1.1230 (1.0662) acc 68.7500 (73.7103) lr 4.8943e-05 eta 0:43:52
+epoch [47/50] batch [320/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.1406 (1.0674) acc 68.7500 (73.6621) lr 4.8943e-05 eta 0:43:44
+epoch [47/50] batch [325/500] time 1.554 (1.562) data 0.000 (0.003) loss 0.8193 (1.0629) acc 71.8750 (73.7308) lr 4.8943e-05 eta 0:43:36
+epoch [47/50] batch [330/500] time 1.579 (1.562) data 0.000 (0.003) loss 0.6743 (1.0589) acc 81.2500 (73.7879) lr 4.8943e-05 eta 0:43:28
+epoch [47/50] batch [335/500] time 1.543 (1.562) data 0.000 (0.003) loss 1.1436 (1.0584) acc 68.7500 (73.7873) lr 4.8943e-05 eta 0:43:20
+epoch [47/50] batch [340/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.4170 (1.0574) acc 65.6250 (73.7684) lr 4.8943e-05 eta 0:43:12
+epoch [47/50] batch [345/500] time 1.554 (1.562) data 0.001 (0.003) loss 0.8525 (1.0541) acc 81.2500 (73.8315) lr 4.8943e-05 eta 0:43:04
+epoch [47/50] batch [350/500] time 1.543 (1.562) data 0.000 (0.003) loss 0.6016 (1.0541) acc 87.5000 (73.8571) lr 4.8943e-05 eta 0:42:57
+epoch [47/50] batch [355/500] time 1.543 (1.562) data 0.000 (0.003) loss 0.7339 (1.0547) acc 78.1250 (73.8380) lr 4.8943e-05 eta 0:42:48
+epoch [47/50] batch [360/500] time 1.554 (1.562) data 0.000 (0.003) loss 1.0342 (1.0530) acc 78.1250 (73.9149) lr 4.8943e-05 eta 0:42:41
+epoch [47/50] batch [365/500] time 1.572 (1.562) data 0.000 (0.003) loss 1.3262 (1.0514) acc 68.7500 (73.8699) lr 4.8943e-05 eta 0:42:33
+epoch [47/50] batch [370/500] time 1.587 (1.562) data 0.000 (0.003) loss 0.7432 (1.0483) acc 78.1250 (73.9358) lr 4.8943e-05 eta 0:42:25
+epoch [47/50] batch [375/500] time 1.545 (1.562) data 0.000 (0.002) loss 0.7964 (1.0473) acc 71.8750 (73.9167) lr 4.8943e-05 eta 0:42:17
+epoch [47/50] batch [380/500] time 1.552 (1.562) data 0.000 (0.002) loss 0.4971 (1.0445) acc 87.5000 (73.9474) lr 4.8943e-05 eta 0:42:09
+epoch [47/50] batch [385/500] time 1.561 (1.562) data 0.000 (0.002) loss 0.5488 (1.0470) acc 75.0000 (73.8880) lr 4.8943e-05 eta 0:42:01
+epoch [47/50] batch [390/500] time 1.541 (1.561) data 0.000 (0.002) loss 1.6504 (1.0503) acc 68.7500 (73.8381) lr 4.8943e-05 eta 0:41:53
+epoch [47/50] batch [395/500] time 1.562 (1.562) data 0.000 (0.002) loss 0.7808 (1.0481) acc 90.6250 (73.9320) lr 4.8943e-05 eta 0:41:46
+epoch [47/50] batch [400/500] time 1.565 (1.562) data 0.001 (0.002) loss 2.0859 (1.0493) acc 50.0000 (73.8984) lr 4.8943e-05 eta 0:41:38
+epoch [47/50] batch [405/500] time 1.560 (1.561) data 0.000 (0.002) loss 0.9985 (1.0496) acc 71.8750 (73.8889) lr 4.8943e-05 eta 0:41:30
+epoch [47/50] batch [410/500] time 1.565 (1.561) data 0.000 (0.002) loss 0.7505 (1.0470) acc 71.8750 (73.8948) lr 4.8943e-05 eta 0:41:22
+epoch [47/50] batch [415/500] time 1.546 (1.561) data 0.000 (0.002) loss 0.6152 (1.0479) acc 87.5000 (73.8780) lr 4.8943e-05 eta 0:41:14
+epoch [47/50] batch [420/500] time 1.588 (1.561) data 0.001 (0.002) loss 1.3125 (1.0454) acc 78.1250 (73.9509) lr 4.8943e-05 eta 0:41:07
+epoch [47/50] batch [425/500] time 1.577 (1.562) data 0.000 (0.002) loss 1.5059 (1.0446) acc 59.3750 (73.9559) lr 4.8943e-05 eta 0:40:59
+epoch [47/50] batch [430/500] time 1.545 (1.562) data 0.000 (0.002) loss 0.9985 (1.0462) acc 75.0000 (73.8953) lr 4.8943e-05 eta 0:40:52
+epoch [47/50] batch [435/500] time 1.557 (1.562) data 0.000 (0.002) loss 0.7993 (1.0446) acc 84.3750 (73.9440) lr 4.8943e-05 eta 0:40:44
+epoch [47/50] batch [440/500] time 1.560 (1.562) data 0.001 (0.002) loss 1.1240 (1.0461) acc 71.8750 (73.9560) lr 4.8943e-05 eta 0:40:36
+epoch [47/50] batch [445/500] time 1.551 (1.562) data 0.000 (0.002) loss 0.8901 (1.0446) acc 81.2500 (73.9537) lr 4.8943e-05 eta 0:40:28
+epoch [47/50] batch [450/500] time 1.545 (1.562) data 0.000 (0.002) loss 0.9292 (1.0425) acc 81.2500 (74.0069) lr 4.8943e-05 eta 0:40:20
+epoch [47/50] batch [455/500] time 1.548 (1.562) data 0.000 (0.002) loss 1.0635 (1.0433) acc 71.8750 (73.9835) lr 4.8943e-05 eta 0:40:12
+epoch [47/50] batch [460/500] time 1.562 (1.562) data 0.000 (0.002) loss 1.1992 (1.0440) acc 62.5000 (73.9810) lr 4.8943e-05 eta 0:40:04
+epoch [47/50] batch [465/500] time 1.575 (1.562) data 0.000 (0.002) loss 1.2402 (1.0427) acc 71.8750 (74.0457) lr 4.8943e-05 eta 0:39:57
+epoch [47/50] batch [470/500] time 1.533 (1.562) data 0.000 (0.002) loss 1.0928 (1.0434) acc 81.2500 (74.0359) lr 4.8943e-05 eta 0:39:49
+epoch [47/50] batch [475/500] time 1.581 (1.562) data 0.000 (0.002) loss 0.7490 (1.0397) acc 87.5000 (74.1250) lr 4.8943e-05 eta 0:39:41
+epoch [47/50] batch [480/500] time 1.567 (1.562) data 0.000 (0.002) loss 0.6436 (1.0371) acc 81.2500 (74.1927) lr 4.8943e-05 eta 0:39:34
+epoch [47/50] batch [485/500] time 1.565 (1.562) data 0.001 (0.002) loss 1.1885 (1.0370) acc 71.8750 (74.1624) lr 4.8943e-05 eta 0:39:26
+epoch [47/50] batch [490/500] time 1.552 (1.562) data 0.000 (0.002) loss 0.9917 (1.0365) acc 68.7500 (74.1327) lr 4.8943e-05 eta 0:39:18
+epoch [47/50] batch [495/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.1523 (1.0368) acc 78.1250 (74.1288) lr 4.8943e-05 eta 0:39:10
+epoch [47/50] batch [500/500] time 1.542 (1.562) data 0.000 (0.002) loss 0.7925 (1.0365) acc 78.1250 (74.1250) lr 3.1417e-05 eta 0:39:02
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,022
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+epoch [48/50] batch [5/500] time 1.541 (1.639) data 0.000 (0.143) loss 0.8838 (1.0408) acc 84.3750 (76.8750) lr 3.1417e-05 eta 0:40:50
+epoch [48/50] batch [10/500] time 1.571 (1.601) data 0.000 (0.072) loss 1.3477 (0.9808) acc 71.8750 (77.8125) lr 3.1417e-05 eta 0:39:45
+epoch [48/50] batch [15/500] time 1.573 (1.588) data 0.000 (0.048) loss 1.4424 (1.0341) acc 59.3750 (75.2083) lr 3.1417e-05 eta 0:39:17
+epoch [48/50] batch [20/500] time 1.544 (1.580) data 0.001 (0.036) loss 0.9644 (1.1244) acc 65.6250 (72.6562) lr 3.1417e-05 eta 0:38:59
+epoch [48/50] batch [25/500] time 1.693 (1.581) data 0.000 (0.029) loss 1.3691 (1.1977) acc 62.5000 (71.1250) lr 3.1417e-05 eta 0:38:51
+epoch [48/50] batch [30/500] time 1.546 (1.577) data 0.000 (0.024) loss 1.3125 (1.1949) acc 68.7500 (71.0417) lr 3.1417e-05 eta 0:38:38
+epoch [48/50] batch [35/500] time 1.553 (1.574) data 0.001 (0.021) loss 1.2178 (1.1658) acc 68.7500 (71.2500) lr 3.1417e-05 eta 0:38:26
+epoch [48/50] batch [40/500] time 1.569 (1.573) data 0.000 (0.018) loss 1.0322 (1.1922) acc 71.8750 (70.8594) lr 3.1417e-05 eta 0:38:17
+epoch [48/50] batch [45/500] time 1.568 (1.572) data 0.000 (0.016) loss 0.6929 (1.1606) acc 75.0000 (71.0417) lr 3.1417e-05 eta 0:38:07
+epoch [48/50] batch [50/500] time 1.529 (1.570) data 0.000 (0.015) loss 0.4404 (1.1335) acc 81.2500 (71.6875) lr 3.1417e-05 eta 0:37:57
+epoch [48/50] batch [55/500] time 1.559 (1.569) data 0.000 (0.013) loss 1.3506 (1.1356) acc 65.6250 (71.6477) lr 3.1417e-05 eta 0:37:47
+epoch [48/50] batch [60/500] time 1.565 (1.569) data 0.001 (0.012) loss 1.4102 (1.1266) acc 68.7500 (71.7188) lr 3.1417e-05 eta 0:37:38
+epoch [48/50] batch [65/500] time 1.542 (1.569) data 0.001 (0.011) loss 0.4600 (1.0938) acc 84.3750 (72.2596) lr 3.1417e-05 eta 0:37:30
+epoch [48/50] batch [70/500] time 1.537 (1.567) data 0.000 (0.011) loss 1.1504 (1.1035) acc 68.7500 (72.0089) lr 3.1417e-05 eta 0:37:21
+epoch [48/50] batch [75/500] time 1.550 (1.566) data 0.000 (0.010) loss 0.7847 (1.1001) acc 84.3750 (71.8750) lr 3.1417e-05 eta 0:37:11
+epoch [48/50] batch [80/500] time 1.556 (1.566) data 0.000 (0.009) loss 1.7236 (1.1119) acc 53.1250 (71.7188) lr 3.1417e-05 eta 0:37:03
+epoch [48/50] batch [85/500] time 1.664 (1.567) data 0.000 (0.009) loss 0.6113 (1.1156) acc 84.3750 (71.8382) lr 3.1417e-05 eta 0:36:56
+epoch [48/50] batch [90/500] time 1.542 (1.566) data 0.000 (0.008) loss 0.7515 (1.0921) acc 81.2500 (72.4306) lr 3.1417e-05 eta 0:36:48
+epoch [48/50] batch [95/500] time 1.532 (1.565) data 0.000 (0.008) loss 0.5693 (1.0890) acc 81.2500 (72.4342) lr 3.1417e-05 eta 0:36:38
+epoch [48/50] batch [100/500] time 1.548 (1.564) data 0.001 (0.008) loss 0.9985 (1.0887) acc 78.1250 (72.7188) lr 3.1417e-05 eta 0:36:30
+epoch [48/50] batch [105/500] time 1.555 (1.563) data 0.000 (0.007) loss 1.4863 (1.0857) acc 75.0000 (72.8274) lr 3.1417e-05 eta 0:36:20
+epoch [48/50] batch [110/500] time 1.539 (1.563) data 0.000 (0.007) loss 1.3193 (1.0966) acc 75.0000 (72.6420) lr 3.1417e-05 eta 0:36:12
+epoch [48/50] batch [115/500] time 1.578 (1.563) data 0.000 (0.007) loss 0.7642 (1.0870) acc 71.8750 (72.8261) lr 3.1417e-05 eta 0:36:04
+epoch [48/50] batch [120/500] time 1.562 (1.562) data 0.000 (0.006) loss 1.5625 (1.0872) acc 65.6250 (72.7344) lr 3.1417e-05 eta 0:35:55
+epoch [48/50] batch [125/500] time 1.553 (1.562) data 0.000 (0.006) loss 0.9785 (1.0825) acc 81.2500 (72.8750) lr 3.1417e-05 eta 0:35:47
+epoch [48/50] batch [130/500] time 1.554 (1.562) data 0.000 (0.006) loss 0.7578 (1.0781) acc 78.1250 (73.0769) lr 3.1417e-05 eta 0:35:39
+epoch [48/50] batch [135/500] time 1.583 (1.562) data 0.001 (0.006) loss 1.1904 (1.0797) acc 68.7500 (73.0787) lr 3.1417e-05 eta 0:35:32
+epoch [48/50] batch [140/500] time 1.555 (1.562) data 0.000 (0.006) loss 1.0332 (1.0827) acc 75.0000 (73.0804) lr 3.1417e-05 eta 0:35:24
+epoch [48/50] batch [145/500] time 1.582 (1.562) data 0.000 (0.005) loss 1.8887 (1.0807) acc 62.5000 (73.1681) lr 3.1417e-05 eta 0:35:16
+epoch [48/50] batch [150/500] time 1.559 (1.562) data 0.000 (0.005) loss 0.5601 (1.0861) acc 84.3750 (73.2083) lr 3.1417e-05 eta 0:35:08
+epoch [48/50] batch [155/500] time 1.577 (1.562) data 0.000 (0.005) loss 0.5435 (1.0868) acc 87.5000 (73.1855) lr 3.1417e-05 eta 0:35:00
+epoch [48/50] batch [160/500] time 1.581 (1.562) data 0.000 (0.005) loss 0.5952 (1.0813) acc 84.3750 (73.3008) lr 3.1417e-05 eta 0:34:53
+epoch [48/50] batch [165/500] time 1.572 (1.562) data 0.000 (0.005) loss 0.9336 (1.0806) acc 81.2500 (73.3902) lr 3.1417e-05 eta 0:34:45
+epoch [48/50] batch [170/500] time 1.561 (1.562) data 0.000 (0.005) loss 0.6602 (1.0737) acc 90.6250 (73.6029) lr 3.1417e-05 eta 0:34:37
+epoch [48/50] batch [175/500] time 1.554 (1.562) data 0.000 (0.005) loss 1.0322 (1.0748) acc 71.8750 (73.5357) lr 3.1417e-05 eta 0:34:29
+epoch [48/50] batch [180/500] time 1.572 (1.562) data 0.000 (0.004) loss 0.8149 (1.0767) acc 87.5000 (73.6111) lr 3.1417e-05 eta 0:34:22
+epoch [48/50] batch [185/500] time 1.576 (1.563) data 0.000 (0.004) loss 1.1006 (1.0769) acc 71.8750 (73.6824) lr 3.1417e-05 eta 0:34:15
+epoch [48/50] batch [190/500] time 1.568 (1.563) data 0.001 (0.004) loss 0.8501 (1.0747) acc 78.1250 (73.7829) lr 3.1417e-05 eta 0:34:08
+epoch [48/50] batch [195/500] time 1.557 (1.563) data 0.000 (0.004) loss 0.5889 (1.0700) acc 81.2500 (73.8942) lr 3.1417e-05 eta 0:34:00
+epoch [48/50] batch [200/500] time 1.560 (1.564) data 0.001 (0.004) loss 1.2676 (1.0677) acc 68.7500 (73.9375) lr 3.1417e-05 eta 0:33:52
+epoch [48/50] batch [205/500] time 1.567 (1.563) data 0.000 (0.004) loss 1.2461 (1.0686) acc 68.7500 (73.8872) lr 3.1417e-05 eta 0:33:44
+epoch [48/50] batch [210/500] time 1.545 (1.563) data 0.000 (0.004) loss 0.9170 (1.0689) acc 81.2500 (73.9583) lr 3.1417e-05 eta 0:33:36
+epoch [48/50] batch [215/500] time 1.564 (1.563) data 0.000 (0.004) loss 1.2793 (1.0710) acc 71.8750 (73.9099) lr 3.1417e-05 eta 0:33:28
+epoch [48/50] batch [220/500] time 1.543 (1.562) data 0.000 (0.004) loss 0.5332 (1.0685) acc 90.6250 (73.9489) lr 3.1417e-05 eta 0:33:19
+epoch [48/50] batch [225/500] time 1.567 (1.562) data 0.001 (0.004) loss 0.9863 (1.0646) acc 78.1250 (74.0556) lr 3.1417e-05 eta 0:33:12
+epoch [48/50] batch [230/500] time 1.548 (1.563) data 0.000 (0.004) loss 1.0850 (1.0603) acc 71.8750 (74.1168) lr 3.1417e-05 eta 0:33:04
+epoch [48/50] batch [235/500] time 1.568 (1.563) data 0.000 (0.003) loss 2.2520 (1.0630) acc 56.2500 (74.0957) lr 3.1417e-05 eta 0:32:57
+epoch [48/50] batch [240/500] time 1.549 (1.562) data 0.000 (0.003) loss 0.8628 (1.0615) acc 81.2500 (74.1667) lr 3.1417e-05 eta 0:32:48
+epoch [48/50] batch [245/500] time 1.558 (1.562) data 0.000 (0.003) loss 0.4929 (1.0636) acc 90.6250 (74.1454) lr 3.1417e-05 eta 0:32:40
+epoch [48/50] batch [250/500] time 1.578 (1.562) data 0.000 (0.003) loss 1.1689 (1.0625) acc 71.8750 (74.1250) lr 3.1417e-05 eta 0:32:32
+epoch [48/50] batch [255/500] time 1.558 (1.562) data 0.000 (0.003) loss 0.7124 (1.0607) acc 81.2500 (74.1054) lr 3.1417e-05 eta 0:32:24
+epoch [48/50] batch [260/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.2939 (1.0591) acc 68.7500 (74.0986) lr 3.1417e-05 eta 0:32:16
+epoch [48/50] batch [265/500] time 1.545 (1.562) data 0.000 (0.003) loss 1.2744 (1.0631) acc 68.7500 (74.0212) lr 3.1417e-05 eta 0:32:08
+epoch [48/50] batch [270/500] time 1.573 (1.562) data 0.001 (0.003) loss 1.1445 (1.0633) acc 71.8750 (74.0162) lr 3.1417e-05 eta 0:32:00
+epoch [48/50] batch [275/500] time 1.574 (1.562) data 0.000 (0.003) loss 1.0869 (1.0607) acc 75.0000 (74.1023) lr 3.1417e-05 eta 0:31:53
+epoch [48/50] batch [280/500] time 1.540 (1.562) data 0.000 (0.003) loss 0.7617 (1.0606) acc 75.0000 (74.0513) lr 3.1417e-05 eta 0:31:45
+epoch [48/50] batch [285/500] time 1.552 (1.562) data 0.000 (0.003) loss 0.7529 (1.0593) acc 65.6250 (74.0351) lr 3.1417e-05 eta 0:31:37
+epoch [48/50] batch [290/500] time 1.568 (1.562) data 0.001 (0.003) loss 1.4365 (1.0590) acc 65.6250 (74.0302) lr 3.1417e-05 eta 0:31:29
+epoch [48/50] batch [295/500] time 1.537 (1.562) data 0.000 (0.003) loss 1.3467 (1.0578) acc 56.2500 (73.9619) lr 3.1417e-05 eta 0:31:22
+epoch [48/50] batch [300/500] time 1.564 (1.562) data 0.000 (0.003) loss 1.6162 (1.0588) acc 68.7500 (73.9583) lr 3.1417e-05 eta 0:31:14
+epoch [48/50] batch [305/500] time 1.540 (1.562) data 0.001 (0.003) loss 0.7583 (1.0550) acc 75.0000 (73.9754) lr 3.1417e-05 eta 0:31:06
+epoch [48/50] batch [310/500] time 1.568 (1.562) data 0.000 (0.003) loss 1.0166 (1.0553) acc 65.6250 (73.9113) lr 3.1417e-05 eta 0:30:58
+epoch [48/50] batch [315/500] time 1.569 (1.562) data 0.000 (0.003) loss 0.7715 (1.0542) acc 84.3750 (73.9187) lr 3.1417e-05 eta 0:30:50
+epoch [48/50] batch [320/500] time 1.530 (1.561) data 0.000 (0.003) loss 0.7700 (1.0553) acc 71.8750 (73.8770) lr 3.1417e-05 eta 0:30:42
+epoch [48/50] batch [325/500] time 1.567 (1.562) data 0.000 (0.003) loss 1.1416 (1.0552) acc 65.6250 (73.8558) lr 3.1417e-05 eta 0:30:34
+epoch [48/50] batch [330/500] time 1.568 (1.562) data 0.000 (0.003) loss 1.7715 (1.0549) acc 62.5000 (73.9110) lr 3.1417e-05 eta 0:30:27
+epoch [48/50] batch [335/500] time 1.539 (1.561) data 0.000 (0.003) loss 0.5146 (1.0514) acc 87.5000 (73.9459) lr 3.1417e-05 eta 0:30:18
+epoch [48/50] batch [340/500] time 1.553 (1.561) data 0.000 (0.003) loss 1.7432 (1.0530) acc 62.5000 (73.9246) lr 3.1417e-05 eta 0:30:10
+epoch [48/50] batch [345/500] time 1.555 (1.561) data 0.000 (0.002) loss 1.4922 (1.0529) acc 78.1250 (73.9493) lr 3.1417e-05 eta 0:30:02
+epoch [48/50] batch [350/500] time 1.577 (1.561) data 0.000 (0.002) loss 0.6909 (1.0514) acc 81.2500 (73.9554) lr 3.1417e-05 eta 0:29:55
+epoch [48/50] batch [355/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.8335 (1.0503) acc 78.1250 (73.9701) lr 3.1417e-05 eta 0:29:47
+epoch [48/50] batch [360/500] time 1.526 (1.561) data 0.000 (0.002) loss 1.2627 (1.0506) acc 68.7500 (73.9670) lr 3.1417e-05 eta 0:29:39
+epoch [48/50] batch [365/500] time 1.563 (1.561) data 0.000 (0.002) loss 0.7983 (1.0503) acc 75.0000 (73.9812) lr 3.1417e-05 eta 0:29:31
+epoch [48/50] batch [370/500] time 1.546 (1.561) data 0.000 (0.002) loss 0.7339 (1.0501) acc 87.5000 (74.0118) lr 3.1417e-05 eta 0:29:23
+epoch [48/50] batch [375/500] time 1.563 (1.561) data 0.001 (0.002) loss 0.9204 (1.0526) acc 68.7500 (73.9833) lr 3.1417e-05 eta 0:29:15
+epoch [48/50] batch [380/500] time 1.552 (1.561) data 0.000 (0.002) loss 0.9771 (1.0509) acc 68.7500 (73.9720) lr 3.1417e-05 eta 0:29:08
+epoch [48/50] batch [385/500] time 1.570 (1.561) data 0.000 (0.002) loss 1.3916 (1.0541) acc 71.8750 (73.8555) lr 3.1417e-05 eta 0:29:00
+epoch [48/50] batch [390/500] time 1.550 (1.561) data 0.000 (0.002) loss 0.8984 (1.0524) acc 75.0000 (73.8702) lr 3.1417e-05 eta 0:28:52
+epoch [48/50] batch [395/500] time 1.554 (1.561) data 0.000 (0.002) loss 1.2578 (1.0534) acc 71.8750 (73.8528) lr 3.1417e-05 eta 0:28:44
+epoch [48/50] batch [400/500] time 1.582 (1.561) data 0.000 (0.002) loss 0.6709 (1.0508) acc 75.0000 (73.8516) lr 3.1417e-05 eta 0:28:37
+epoch [48/50] batch [405/500] time 1.571 (1.561) data 0.000 (0.002) loss 0.9565 (1.0501) acc 81.2500 (73.9352) lr 3.1417e-05 eta 0:28:29
+epoch [48/50] batch [410/500] time 1.569 (1.561) data 0.001 (0.002) loss 1.0938 (1.0478) acc 71.8750 (73.9710) lr 3.1417e-05 eta 0:28:21
+epoch [48/50] batch [415/500] time 1.650 (1.561) data 0.000 (0.002) loss 0.8398 (1.0461) acc 78.1250 (74.0136) lr 3.1417e-05 eta 0:28:14
+epoch [48/50] batch [420/500] time 1.566 (1.561) data 0.000 (0.002) loss 1.1621 (1.0452) acc 71.8750 (74.0327) lr 3.1417e-05 eta 0:28:06
+epoch [48/50] batch [425/500] time 1.572 (1.562) data 0.000 (0.002) loss 2.2266 (1.0497) acc 53.1250 (73.9118) lr 3.1417e-05 eta 0:27:58
+epoch [48/50] batch [430/500] time 1.558 (1.562) data 0.000 (0.002) loss 0.6973 (1.0490) acc 87.5000 (73.9898) lr 3.1417e-05 eta 0:27:50
+epoch [48/50] batch [435/500] time 1.564 (1.562) data 0.000 (0.002) loss 0.8799 (1.0479) acc 75.0000 (74.0086) lr 3.1417e-05 eta 0:27:43
+epoch [48/50] batch [440/500] time 1.538 (1.562) data 0.000 (0.002) loss 0.7324 (1.0473) acc 81.2500 (74.0341) lr 3.1417e-05 eta 0:27:35
+epoch [48/50] batch [445/500] time 1.557 (1.562) data 0.000 (0.002) loss 1.2256 (1.0474) acc 65.6250 (74.0449) lr 3.1417e-05 eta 0:27:27
+epoch [48/50] batch [450/500] time 1.556 (1.562) data 0.000 (0.002) loss 0.9419 (1.0470) acc 71.8750 (74.0347) lr 3.1417e-05 eta 0:27:19
+epoch [48/50] batch [455/500] time 1.556 (1.561) data 0.000 (0.002) loss 0.9688 (1.0462) acc 71.8750 (74.0247) lr 3.1417e-05 eta 0:27:11
+epoch [48/50] batch [460/500] time 1.552 (1.562) data 0.000 (0.002) loss 0.7812 (1.0454) acc 78.1250 (74.0285) lr 3.1417e-05 eta 0:27:04
+epoch [48/50] batch [465/500] time 1.586 (1.562) data 0.000 (0.002) loss 0.9917 (1.0464) acc 75.0000 (74.0457) lr 3.1417e-05 eta 0:26:56
+epoch [48/50] batch [470/500] time 1.568 (1.562) data 0.000 (0.002) loss 1.4873 (1.0485) acc 62.5000 (74.0027) lr 3.1417e-05 eta 0:26:48
+epoch [48/50] batch [475/500] time 1.568 (1.562) data 0.001 (0.002) loss 1.5146 (1.0478) acc 59.3750 (74.0066) lr 3.1417e-05 eta 0:26:40
+epoch [48/50] batch [480/500] time 1.569 (1.562) data 0.001 (0.002) loss 1.1689 (1.0453) acc 68.7500 (74.0430) lr 3.1417e-05 eta 0:26:33
+epoch [48/50] batch [485/500] time 1.545 (1.562) data 0.001 (0.002) loss 1.5010 (1.0455) acc 71.8750 (74.0464) lr 3.1417e-05 eta 0:26:25
+epoch [48/50] batch [490/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.0195 (1.0455) acc 81.2500 (74.0625) lr 3.1417e-05 eta 0:26:17
+epoch [48/50] batch [495/500] time 1.543 (1.562) data 0.000 (0.002) loss 1.1318 (1.0480) acc 71.8750 (73.9836) lr 3.1417e-05 eta 0:26:09
+epoch [48/50] batch [500/500] time 1.548 (1.562) data 0.000 (0.002) loss 1.0195 (1.0471) acc 71.8750 (73.9875) lr 1.7713e-05 eta 0:26:01
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,031
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [49/50] batch [5/500] time 1.545 (1.663) data 0.000 (0.168) loss 0.4819 (1.1761) acc 93.7500 (75.0000) lr 1.7713e-05 eta 0:27:34
+epoch [49/50] batch [10/500] time 1.542 (1.606) data 0.001 (0.085) loss 0.7598 (1.0524) acc 81.2500 (76.5625) lr 1.7713e-05 eta 0:26:29
+epoch [49/50] batch [15/500] time 1.569 (1.585) data 0.001 (0.057) loss 1.3066 (1.0935) acc 68.7500 (75.0000) lr 1.7713e-05 eta 0:26:01
+epoch [49/50] batch [20/500] time 1.560 (1.579) data 0.000 (0.043) loss 1.4961 (1.1596) acc 68.7500 (72.6562) lr 1.7713e-05 eta 0:25:47
+epoch [49/50] batch [25/500] time 1.571 (1.581) data 0.000 (0.034) loss 0.8633 (1.1758) acc 71.8750 (71.6250) lr 1.7713e-05 eta 0:25:41
+epoch [49/50] batch [30/500] time 1.545 (1.576) data 0.000 (0.028) loss 1.4980 (1.1558) acc 68.7500 (71.7708) lr 1.7713e-05 eta 0:25:28
+epoch [49/50] batch [35/500] time 1.533 (1.571) data 0.000 (0.024) loss 1.0742 (1.1338) acc 75.0000 (71.9643) lr 1.7713e-05 eta 0:25:16
+epoch [49/50] batch [40/500] time 1.547 (1.569) data 0.001 (0.022) loss 1.4629 (1.1532) acc 65.6250 (71.4844) lr 1.7713e-05 eta 0:25:05
+epoch [49/50] batch [45/500] time 1.554 (1.567) data 0.000 (0.019) loss 0.7026 (1.1334) acc 84.3750 (71.5972) lr 1.7713e-05 eta 0:24:56
+epoch [49/50] batch [50/500] time 1.546 (1.567) data 0.000 (0.017) loss 1.1035 (1.1040) acc 78.1250 (72.3750) lr 1.7713e-05 eta 0:24:48
+epoch [49/50] batch [55/500] time 1.572 (1.566) data 0.000 (0.016) loss 1.4004 (1.0991) acc 59.3750 (72.3864) lr 1.7713e-05 eta 0:24:40
+epoch [49/50] batch [60/500] time 1.577 (1.566) data 0.000 (0.014) loss 0.7686 (1.0859) acc 75.0000 (72.7604) lr 1.7713e-05 eta 0:24:32
+epoch [49/50] batch [65/500] time 1.553 (1.566) data 0.001 (0.013) loss 0.9966 (1.0773) acc 81.2500 (73.2212) lr 1.7713e-05 eta 0:24:24
+epoch [49/50] batch [70/500] time 1.557 (1.565) data 0.001 (0.012) loss 0.7637 (1.0769) acc 81.2500 (73.3482) lr 1.7713e-05 eta 0:24:15
+epoch [49/50] batch [75/500] time 1.557 (1.565) data 0.001 (0.012) loss 0.9321 (1.0704) acc 71.8750 (73.3333) lr 1.7713e-05 eta 0:24:07
+epoch [49/50] batch [80/500] time 1.553 (1.565) data 0.001 (0.011) loss 1.3545 (1.0726) acc 68.7500 (73.1641) lr 1.7713e-05 eta 0:23:59
+epoch [49/50] batch [85/500] time 1.550 (1.564) data 0.000 (0.010) loss 0.7905 (1.0729) acc 81.2500 (73.0882) lr 1.7713e-05 eta 0:23:51
+epoch [49/50] batch [90/500] time 1.562 (1.563) data 0.000 (0.010) loss 1.0254 (1.0621) acc 81.2500 (73.3681) lr 1.7713e-05 eta 0:23:42
+epoch [49/50] batch [95/500] time 1.559 (1.563) data 0.000 (0.009) loss 1.4336 (1.0650) acc 68.7500 (73.1250) lr 1.7713e-05 eta 0:23:34
+epoch [49/50] batch [100/500] time 1.568 (1.563) data 0.001 (0.009) loss 0.9600 (1.0654) acc 78.1250 (73.1250) lr 1.7713e-05 eta 0:23:26
+epoch [49/50] batch [105/500] time 1.549 (1.563) data 0.001 (0.008) loss 1.2061 (1.0607) acc 75.0000 (73.2143) lr 1.7713e-05 eta 0:23:18
+epoch [49/50] batch [110/500] time 1.556 (1.562) data 0.000 (0.008) loss 1.2559 (1.0591) acc 65.6250 (73.2670) lr 1.7713e-05 eta 0:23:10
+epoch [49/50] batch [115/500] time 1.573 (1.562) data 0.000 (0.008) loss 0.7300 (1.0565) acc 81.2500 (73.3152) lr 1.7713e-05 eta 0:23:02
+epoch [49/50] batch [120/500] time 1.635 (1.563) data 0.001 (0.007) loss 0.5713 (1.0629) acc 87.5000 (73.2292) lr 1.7713e-05 eta 0:22:55
+epoch [49/50] batch [125/500] time 1.592 (1.563) data 0.001 (0.007) loss 0.7227 (1.0572) acc 90.6250 (73.4250) lr 1.7713e-05 eta 0:22:47
+epoch [49/50] batch [130/500] time 1.559 (1.562) data 0.000 (0.007) loss 1.2949 (1.0554) acc 68.7500 (73.5096) lr 1.7713e-05 eta 0:22:39
+epoch [49/50] batch [135/500] time 1.546 (1.563) data 0.000 (0.007) loss 1.2334 (1.0562) acc 71.8750 (73.4491) lr 1.7713e-05 eta 0:22:31
+epoch [49/50] batch [140/500] time 1.551 (1.563) data 0.000 (0.006) loss 0.6538 (1.0453) acc 81.2500 (73.7500) lr 1.7713e-05 eta 0:22:24
+epoch [49/50] batch [145/500] time 1.561 (1.563) data 0.000 (0.006) loss 1.6602 (1.0453) acc 62.5000 (73.7284) lr 1.7713e-05 eta 0:22:16
+epoch [49/50] batch [150/500] time 1.537 (1.563) data 0.000 (0.006) loss 0.8169 (1.0514) acc 84.3750 (73.6042) lr 1.7713e-05 eta 0:22:08
+epoch [49/50] batch [155/500] time 1.576 (1.563) data 0.000 (0.006) loss 0.6353 (1.0425) acc 87.5000 (73.7500) lr 1.7713e-05 eta 0:22:00
+epoch [49/50] batch [160/500] time 1.553 (1.563) data 0.000 (0.006) loss 1.0293 (1.0414) acc 71.8750 (73.6719) lr 1.7713e-05 eta 0:21:52
+epoch [49/50] batch [165/500] time 1.541 (1.563) data 0.000 (0.006) loss 0.7354 (1.0448) acc 78.1250 (73.5795) lr 1.7713e-05 eta 0:21:45
+epoch [49/50] batch [170/500] time 1.571 (1.564) data 0.000 (0.005) loss 1.0654 (1.0416) acc 78.1250 (73.6949) lr 1.7713e-05 eta 0:21:37
+epoch [49/50] batch [175/500] time 1.539 (1.563) data 0.000 (0.005) loss 1.0342 (1.0396) acc 71.8750 (73.5714) lr 1.7713e-05 eta 0:21:29
+epoch [49/50] batch [180/500] time 1.570 (1.563) data 0.000 (0.005) loss 1.6963 (1.0355) acc 65.6250 (73.7153) lr 1.7713e-05 eta 0:21:21
+epoch [49/50] batch [185/500] time 1.573 (1.563) data 0.000 (0.005) loss 1.7930 (1.0437) acc 62.5000 (73.5135) lr 1.7713e-05 eta 0:21:13
+epoch [49/50] batch [190/500] time 1.551 (1.563) data 0.000 (0.005) loss 0.6748 (1.0364) acc 84.3750 (73.6184) lr 1.7713e-05 eta 0:21:05
+epoch [49/50] batch [195/500] time 1.561 (1.563) data 0.000 (0.005) loss 1.0127 (1.0451) acc 75.0000 (73.4936) lr 1.7713e-05 eta 0:20:58
+epoch [49/50] batch [200/500] time 1.559 (1.563) data 0.000 (0.005) loss 0.8911 (1.0401) acc 78.1250 (73.6094) lr 1.7713e-05 eta 0:20:50
+epoch [49/50] batch [205/500] time 1.565 (1.563) data 0.000 (0.005) loss 1.0488 (1.0399) acc 71.8750 (73.6433) lr 1.7713e-05 eta 0:20:42
+epoch [49/50] batch [210/500] time 1.565 (1.563) data 0.000 (0.004) loss 0.7842 (1.0359) acc 75.0000 (73.6756) lr 1.7713e-05 eta 0:20:34
+epoch [49/50] batch [215/500] time 1.553 (1.563) data 0.000 (0.004) loss 0.9653 (1.0372) acc 78.1250 (73.6337) lr 1.7713e-05 eta 0:20:27
+epoch [49/50] batch [220/500] time 1.565 (1.563) data 0.000 (0.004) loss 1.2412 (1.0314) acc 68.7500 (73.7358) lr 1.7713e-05 eta 0:20:19
+epoch [49/50] batch [225/500] time 1.563 (1.564) data 0.000 (0.004) loss 1.0195 (1.0265) acc 78.1250 (73.8611) lr 1.7713e-05 eta 0:20:11
+epoch [49/50] batch [230/500] time 1.579 (1.564) data 0.000 (0.004) loss 1.3242 (1.0316) acc 68.7500 (73.8043) lr 1.7713e-05 eta 0:20:04
+epoch [49/50] batch [235/500] time 1.559 (1.564) data 0.001 (0.004) loss 0.9668 (1.0307) acc 84.3750 (73.8298) lr 1.7713e-05 eta 0:19:56
+epoch [49/50] batch [240/500] time 1.557 (1.564) data 0.001 (0.004) loss 1.0186 (1.0278) acc 75.0000 (73.8802) lr 1.7713e-05 eta 0:19:48
+epoch [49/50] batch [245/500] time 1.559 (1.564) data 0.000 (0.004) loss 0.9395 (1.0281) acc 68.7500 (73.7883) lr 1.7713e-05 eta 0:19:40
+epoch [49/50] batch [250/500] time 1.574 (1.564) data 0.000 (0.004) loss 1.6133 (1.0325) acc 68.7500 (73.7625) lr 1.7713e-05 eta 0:19:32
+epoch [49/50] batch [255/500] time 1.572 (1.564) data 0.000 (0.004) loss 1.1621 (1.0358) acc 62.5000 (73.6887) lr 1.7713e-05 eta 0:19:25
+epoch [49/50] batch [260/500] time 1.594 (1.564) data 0.000 (0.004) loss 1.6807 (1.0357) acc 59.3750 (73.7139) lr 1.7713e-05 eta 0:19:17
+epoch [49/50] batch [265/500] time 1.543 (1.564) data 0.001 (0.004) loss 0.8193 (1.0360) acc 78.1250 (73.6321) lr 1.7713e-05 eta 0:19:09
+epoch [49/50] batch [270/500] time 1.555 (1.564) data 0.000 (0.004) loss 1.1035 (1.0329) acc 68.7500 (73.7153) lr 1.7713e-05 eta 0:19:01
+epoch [49/50] batch [275/500] time 1.581 (1.564) data 0.000 (0.003) loss 0.6528 (1.0275) acc 84.3750 (73.8409) lr 1.7713e-05 eta 0:18:53
+epoch [49/50] batch [280/500] time 1.539 (1.564) data 0.000 (0.003) loss 0.9067 (1.0248) acc 78.1250 (73.9174) lr 1.7713e-05 eta 0:18:46
+epoch [49/50] batch [285/500] time 1.553 (1.564) data 0.000 (0.003) loss 1.1846 (1.0289) acc 71.8750 (73.8377) lr 1.7713e-05 eta 0:18:38
+epoch [49/50] batch [290/500] time 1.547 (1.564) data 0.000 (0.003) loss 1.1826 (1.0299) acc 81.2500 (73.8685) lr 1.7713e-05 eta 0:18:30
+epoch [49/50] batch [295/500] time 1.573 (1.564) data 0.000 (0.003) loss 0.7100 (1.0306) acc 87.5000 (73.8665) lr 1.7713e-05 eta 0:18:22
+epoch [49/50] batch [300/500] time 1.541 (1.564) data 0.000 (0.003) loss 1.2402 (1.0289) acc 71.8750 (73.9167) lr 1.7713e-05 eta 0:18:14
+epoch [49/50] batch [305/500] time 1.585 (1.564) data 0.000 (0.003) loss 1.0732 (1.0287) acc 71.8750 (73.9037) lr 1.7713e-05 eta 0:18:06
+epoch [49/50] batch [310/500] time 1.565 (1.564) data 0.000 (0.003) loss 1.2119 (1.0292) acc 62.5000 (73.8306) lr 1.7713e-05 eta 0:17:59
+epoch [49/50] batch [315/500] time 1.550 (1.564) data 0.000 (0.003) loss 1.1934 (1.0308) acc 75.0000 (73.8393) lr 1.7713e-05 eta 0:17:51
+epoch [49/50] batch [320/500] time 1.546 (1.564) data 0.000 (0.003) loss 1.6826 (1.0321) acc 53.1250 (73.7988) lr 1.7713e-05 eta 0:17:43
+epoch [49/50] batch [325/500] time 1.560 (1.564) data 0.000 (0.003) loss 1.1182 (1.0301) acc 75.0000 (73.8462) lr 1.7713e-05 eta 0:17:35
+epoch [49/50] batch [330/500] time 1.565 (1.564) data 0.000 (0.003) loss 1.1377 (1.0304) acc 68.7500 (73.8447) lr 1.7713e-05 eta 0:17:27
+epoch [49/50] batch [335/500] time 1.556 (1.564) data 0.000 (0.003) loss 0.8667 (1.0291) acc 81.2500 (73.8526) lr 1.7713e-05 eta 0:17:19
+epoch [49/50] batch [340/500] time 1.553 (1.564) data 0.000 (0.003) loss 1.0137 (1.0293) acc 75.0000 (73.8327) lr 1.7713e-05 eta 0:17:12
+epoch [49/50] batch [345/500] time 1.572 (1.564) data 0.000 (0.003) loss 0.6060 (1.0271) acc 84.3750 (73.8949) lr 1.7713e-05 eta 0:17:04
+epoch [49/50] batch [350/500] time 1.555 (1.564) data 0.000 (0.003) loss 1.0400 (1.0247) acc 65.6250 (73.9375) lr 1.7713e-05 eta 0:16:56
+epoch [49/50] batch [355/500] time 1.548 (1.564) data 0.000 (0.003) loss 1.0381 (1.0249) acc 68.7500 (73.9613) lr 1.7713e-05 eta 0:16:48
+epoch [49/50] batch [360/500] time 1.569 (1.564) data 0.000 (0.003) loss 1.0986 (1.0242) acc 68.7500 (73.9583) lr 1.7713e-05 eta 0:16:40
+epoch [49/50] batch [365/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.0137 (1.0244) acc 78.1250 (73.9640) lr 1.7713e-05 eta 0:16:32
+epoch [49/50] batch [370/500] time 1.572 (1.563) data 0.000 (0.003) loss 1.0459 (1.0225) acc 68.7500 (73.9696) lr 1.7713e-05 eta 0:16:24
+epoch [49/50] batch [375/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.2012 (1.0237) acc 71.8750 (73.9583) lr 1.7713e-05 eta 0:16:17
+epoch [49/50] batch [380/500] time 1.550 (1.563) data 0.000 (0.003) loss 0.8125 (1.0250) acc 75.0000 (73.8405) lr 1.7713e-05 eta 0:16:09
+epoch [49/50] batch [385/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.6299 (1.0259) acc 50.0000 (73.7662) lr 1.7713e-05 eta 0:16:01
+epoch [49/50] batch [390/500] time 1.558 (1.563) data 0.000 (0.003) loss 0.8726 (1.0241) acc 84.3750 (73.8301) lr 1.7713e-05 eta 0:15:53
+epoch [49/50] batch [395/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.2520 (1.0242) acc 71.8750 (73.8370) lr 1.7713e-05 eta 0:15:45
+epoch [49/50] batch [400/500] time 1.571 (1.563) data 0.000 (0.003) loss 1.1699 (1.0238) acc 65.6250 (73.8281) lr 1.7713e-05 eta 0:15:38
+epoch [49/50] batch [405/500] time 1.563 (1.563) data 0.000 (0.002) loss 1.2109 (1.0240) acc 65.6250 (73.8194) lr 1.7713e-05 eta 0:15:30
+epoch [49/50] batch [410/500] time 1.567 (1.564) data 0.000 (0.002) loss 0.7573 (1.0225) acc 68.7500 (73.8262) lr 1.7713e-05 eta 0:15:22
+epoch [49/50] batch [415/500] time 1.552 (1.564) data 0.001 (0.002) loss 1.0430 (1.0231) acc 81.2500 (73.8554) lr 1.7713e-05 eta 0:15:14
+epoch [49/50] batch [420/500] time 1.543 (1.563) data 0.000 (0.002) loss 0.5078 (1.0212) acc 78.1250 (73.8542) lr 1.7713e-05 eta 0:15:06
+epoch [49/50] batch [425/500] time 1.534 (1.563) data 0.000 (0.002) loss 0.9487 (1.0230) acc 75.0000 (73.8382) lr 1.7713e-05 eta 0:14:58
+epoch [49/50] batch [430/500] time 1.547 (1.563) data 0.000 (0.002) loss 0.6167 (1.0235) acc 81.2500 (73.7863) lr 1.7713e-05 eta 0:14:51
+epoch [49/50] batch [435/500] time 1.581 (1.563) data 0.000 (0.002) loss 0.6318 (1.0236) acc 84.3750 (73.8003) lr 1.7713e-05 eta 0:14:43
+epoch [49/50] batch [440/500] time 1.575 (1.563) data 0.000 (0.002) loss 0.6172 (1.0243) acc 81.2500 (73.7713) lr 1.7713e-05 eta 0:14:35
+epoch [49/50] batch [445/500] time 1.552 (1.563) data 0.001 (0.002) loss 0.4946 (1.0261) acc 84.3750 (73.7008) lr 1.7713e-05 eta 0:14:27
+epoch [49/50] batch [450/500] time 1.672 (1.563) data 0.000 (0.002) loss 1.2393 (1.0258) acc 68.7500 (73.7292) lr 1.7713e-05 eta 0:14:19
+epoch [49/50] batch [455/500] time 1.557 (1.563) data 0.000 (0.002) loss 1.5742 (1.0279) acc 65.6250 (73.7225) lr 1.7713e-05 eta 0:14:11
+epoch [49/50] batch [460/500] time 1.567 (1.563) data 0.000 (0.002) loss 0.5708 (1.0282) acc 87.5000 (73.7092) lr 1.7713e-05 eta 0:14:04
+epoch [49/50] batch [465/500] time 1.556 (1.563) data 0.000 (0.002) loss 1.5352 (1.0293) acc 56.2500 (73.6492) lr 1.7713e-05 eta 0:13:56
+epoch [49/50] batch [470/500] time 1.586 (1.563) data 0.000 (0.002) loss 0.9819 (1.0270) acc 71.8750 (73.7035) lr 1.7713e-05 eta 0:13:48
+epoch [49/50] batch [475/500] time 1.579 (1.563) data 0.000 (0.002) loss 1.4443 (1.0268) acc 65.6250 (73.7105) lr 1.7713e-05 eta 0:13:40
+epoch [49/50] batch [480/500] time 1.569 (1.564) data 0.000 (0.002) loss 0.7148 (1.0285) acc 84.3750 (73.6914) lr 1.7713e-05 eta 0:13:33
+epoch [49/50] batch [485/500] time 1.536 (1.563) data 0.001 (0.002) loss 1.0166 (1.0291) acc 75.0000 (73.7178) lr 1.7713e-05 eta 0:13:25
+epoch [49/50] batch [490/500] time 1.543 (1.563) data 0.000 (0.002) loss 0.5801 (1.0305) acc 75.0000 (73.6926) lr 1.7713e-05 eta 0:13:17
+epoch [49/50] batch [495/500] time 1.568 (1.563) data 0.000 (0.002) loss 1.0146 (1.0299) acc 71.8750 (73.6995) lr 1.7713e-05 eta 0:13:09
+epoch [49/50] batch [500/500] time 1.568 (1.563) data 0.000 (0.002) loss 1.2500 (1.0298) acc 71.8750 (73.7125) lr 7.8853e-06 eta 0:13:01
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,031
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [50/50] batch [5/500] time 1.553 (1.675) data 0.000 (0.165) loss 1.2383 (1.0951) acc 68.7500 (72.5000) lr 7.8853e-06 eta 0:13:49
+epoch [50/50] batch [10/500] time 1.578 (1.613) data 0.000 (0.083) loss 1.2822 (1.0132) acc 62.5000 (72.5000) lr 7.8853e-06 eta 0:13:10
+epoch [50/50] batch [15/500] time 1.541 (1.590) data 0.000 (0.055) loss 0.8442 (1.0407) acc 75.0000 (71.6667) lr 7.8853e-06 eta 0:12:50
+epoch [50/50] batch [20/500] time 1.580 (1.585) data 0.000 (0.042) loss 1.6572 (1.0825) acc 62.5000 (72.1875) lr 7.8853e-06 eta 0:12:40
+epoch [50/50] batch [25/500] time 1.557 (1.581) data 0.001 (0.033) loss 0.8125 (1.0586) acc 81.2500 (73.3750) lr 7.8853e-06 eta 0:12:30
+epoch [50/50] batch [30/500] time 1.569 (1.576) data 0.000 (0.028) loss 0.8975 (1.0560) acc 65.6250 (73.4375) lr 7.8853e-06 eta 0:12:20
+epoch [50/50] batch [35/500] time 1.561 (1.575) data 0.000 (0.024) loss 1.0029 (1.0495) acc 78.1250 (73.6607) lr 7.8853e-06 eta 0:12:12
+epoch [50/50] batch [40/500] time 1.573 (1.577) data 0.000 (0.021) loss 0.7979 (1.0479) acc 81.2500 (73.8281) lr 7.8853e-06 eta 0:12:05
+epoch [50/50] batch [45/500] time 1.539 (1.575) data 0.000 (0.019) loss 1.0254 (1.0513) acc 78.1250 (73.8194) lr 7.8853e-06 eta 0:11:56
+epoch [50/50] batch [50/500] time 1.552 (1.574) data 0.000 (0.017) loss 0.5010 (1.0366) acc 87.5000 (74.4375) lr 7.8853e-06 eta 0:11:48
+epoch [50/50] batch [55/500] time 1.561 (1.571) data 0.001 (0.015) loss 1.1396 (1.0600) acc 71.8750 (74.0909) lr 7.8853e-06 eta 0:11:39
+epoch [50/50] batch [60/500] time 1.543 (1.570) data 0.000 (0.014) loss 0.6738 (1.0489) acc 81.2500 (74.2188) lr 7.8853e-06 eta 0:11:30
+epoch [50/50] batch [65/500] time 1.552 (1.568) data 0.000 (0.013) loss 0.8530 (1.0330) acc 84.3750 (74.4712) lr 7.8853e-06 eta 0:11:22
+epoch [50/50] batch [70/500] time 1.563 (1.568) data 0.000 (0.012) loss 0.6328 (1.0183) acc 87.5000 (74.7768) lr 7.8853e-06 eta 0:11:14
+epoch [50/50] batch [75/500] time 1.572 (1.567) data 0.000 (0.011) loss 0.9751 (1.0213) acc 75.0000 (74.4583) lr 7.8853e-06 eta 0:11:05
+epoch [50/50] batch [80/500] time 1.559 (1.567) data 0.001 (0.011) loss 1.5928 (1.0395) acc 65.6250 (74.0234) lr 7.8853e-06 eta 0:10:58
+epoch [50/50] batch [85/500] time 1.553 (1.567) data 0.001 (0.010) loss 0.4905 (1.0326) acc 84.3750 (74.0074) lr 7.8853e-06 eta 0:10:50
+epoch [50/50] batch [90/500] time 1.557 (1.567) data 0.000 (0.010) loss 1.0879 (1.0269) acc 65.6250 (74.2014) lr 7.8853e-06 eta 0:10:42
+epoch [50/50] batch [95/500] time 1.560 (1.567) data 0.000 (0.009) loss 1.1426 (1.0302) acc 68.7500 (73.9145) lr 7.8853e-06 eta 0:10:34
+epoch [50/50] batch [100/500] time 1.557 (1.567) data 0.000 (0.009) loss 0.9683 (1.0373) acc 78.1250 (73.9062) lr 7.8853e-06 eta 0:10:26
+epoch [50/50] batch [105/500] time 1.557 (1.567) data 0.000 (0.008) loss 0.9785 (1.0457) acc 75.0000 (73.6905) lr 7.8853e-06 eta 0:10:19
+epoch [50/50] batch [110/500] time 1.586 (1.567) data 0.000 (0.008) loss 0.6924 (1.0581) acc 78.1250 (73.2670) lr 7.8853e-06 eta 0:10:11
+epoch [50/50] batch [115/500] time 1.557 (1.567) data 0.000 (0.008) loss 1.7217 (1.0579) acc 56.2500 (73.1522) lr 7.8853e-06 eta 0:10:03
+epoch [50/50] batch [120/500] time 1.532 (1.566) data 0.000 (0.007) loss 1.3594 (1.0624) acc 68.7500 (72.9948) lr 7.8853e-06 eta 0:09:55
+epoch [50/50] batch [125/500] time 1.560 (1.566) data 0.001 (0.007) loss 1.1084 (1.0736) acc 78.1250 (72.8750) lr 7.8853e-06 eta 0:09:47
+epoch [50/50] batch [130/500] time 1.547 (1.565) data 0.000 (0.007) loss 0.6816 (1.0569) acc 78.1250 (73.2692) lr 7.8853e-06 eta 0:09:39
+epoch [50/50] batch [135/500] time 1.553 (1.565) data 0.000 (0.007) loss 0.5181 (1.0461) acc 84.3750 (73.5185) lr 7.8853e-06 eta 0:09:31
+epoch [50/50] batch [140/500] time 1.560 (1.565) data 0.000 (0.006) loss 1.2969 (1.0470) acc 75.0000 (73.4821) lr 7.8853e-06 eta 0:09:23
+epoch [50/50] batch [145/500] time 1.578 (1.564) data 0.000 (0.006) loss 0.7729 (1.0434) acc 68.7500 (73.3836) lr 7.8853e-06 eta 0:09:15
+epoch [50/50] batch [150/500] time 1.563 (1.564) data 0.001 (0.006) loss 0.7690 (1.0359) acc 71.8750 (73.3750) lr 7.8853e-06 eta 0:09:07
+epoch [50/50] batch [155/500] time 1.573 (1.564) data 0.000 (0.006) loss 1.3350 (1.0330) acc 68.7500 (73.5081) lr 7.8853e-06 eta 0:08:59
+epoch [50/50] batch [160/500] time 1.526 (1.564) data 0.000 (0.006) loss 1.1377 (1.0289) acc 71.8750 (73.6328) lr 7.8853e-06 eta 0:08:51
+epoch [50/50] batch [165/500] time 1.527 (1.564) data 0.001 (0.005) loss 1.3301 (1.0328) acc 65.6250 (73.5985) lr 7.8853e-06 eta 0:08:43
+epoch [50/50] batch [170/500] time 1.575 (1.563) data 0.000 (0.005) loss 2.2051 (1.0365) acc 62.5000 (73.5662) lr 7.8853e-06 eta 0:08:35
+epoch [50/50] batch [175/500] time 1.572 (1.563) data 0.000 (0.005) loss 0.9712 (1.0398) acc 81.2500 (73.5536) lr 7.8853e-06 eta 0:08:28
+epoch [50/50] batch [180/500] time 1.555 (1.564) data 0.000 (0.005) loss 0.6914 (1.0347) acc 87.5000 (73.6979) lr 7.8853e-06 eta 0:08:20
+epoch [50/50] batch [185/500] time 1.540 (1.564) data 0.000 (0.005) loss 1.0586 (1.0335) acc 78.1250 (73.7838) lr 7.8853e-06 eta 0:08:12
+epoch [50/50] batch [190/500] time 1.548 (1.563) data 0.000 (0.005) loss 1.7656 (1.0403) acc 62.5000 (73.6678) lr 7.8853e-06 eta 0:08:04
+epoch [50/50] batch [195/500] time 1.537 (1.563) data 0.000 (0.005) loss 0.8960 (1.0379) acc 71.8750 (73.7179) lr 7.8853e-06 eta 0:07:56
+epoch [50/50] batch [200/500] time 1.551 (1.563) data 0.000 (0.005) loss 0.9351 (1.0351) acc 78.1250 (73.8594) lr 7.8853e-06 eta 0:07:48
+epoch [50/50] batch [205/500] time 1.561 (1.562) data 0.000 (0.004) loss 1.0850 (1.0343) acc 71.8750 (73.8872) lr 7.8853e-06 eta 0:07:40
+epoch [50/50] batch [210/500] time 1.539 (1.562) data 0.000 (0.004) loss 1.3506 (1.0338) acc 65.6250 (73.8244) lr 7.8853e-06 eta 0:07:32
+epoch [50/50] batch [215/500] time 1.554 (1.562) data 0.000 (0.004) loss 0.7168 (1.0271) acc 78.1250 (73.9826) lr 7.8853e-06 eta 0:07:25
+epoch [50/50] batch [220/500] time 1.565 (1.562) data 0.000 (0.004) loss 0.9805 (1.0235) acc 75.0000 (73.9631) lr 7.8853e-06 eta 0:07:17
+epoch [50/50] batch [225/500] time 1.561 (1.562) data 0.000 (0.004) loss 0.5205 (1.0199) acc 81.2500 (74.0417) lr 7.8853e-06 eta 0:07:09
+epoch [50/50] batch [230/500] time 1.574 (1.562) data 0.000 (0.004) loss 0.5200 (1.0205) acc 84.3750 (74.0489) lr 7.8853e-06 eta 0:07:01
+epoch [50/50] batch [235/500] time 1.547 (1.562) data 0.000 (0.004) loss 0.9917 (1.0198) acc 78.1250 (74.0426) lr 7.8853e-06 eta 0:06:54
+epoch [50/50] batch [240/500] time 1.562 (1.563) data 0.000 (0.004) loss 0.7383 (1.0222) acc 81.2500 (73.9974) lr 7.8853e-06 eta 0:06:46
+epoch [50/50] batch [245/500] time 1.567 (1.563) data 0.000 (0.004) loss 0.6909 (1.0202) acc 81.2500 (74.0306) lr 7.8853e-06 eta 0:06:38
+epoch [50/50] batch [250/500] time 1.584 (1.563) data 0.001 (0.004) loss 0.8047 (1.0163) acc 81.2500 (74.0875) lr 7.8853e-06 eta 0:06:30
+epoch [50/50] batch [255/500] time 1.555 (1.563) data 0.000 (0.004) loss 1.7402 (1.0221) acc 65.6250 (73.9583) lr 7.8853e-06 eta 0:06:22
+epoch [50/50] batch [260/500] time 1.562 (1.563) data 0.000 (0.004) loss 1.6025 (1.0227) acc 65.6250 (73.8942) lr 7.8853e-06 eta 0:06:15
+epoch [50/50] batch [265/500] time 1.563 (1.563) data 0.000 (0.004) loss 0.9736 (1.0194) acc 75.0000 (73.9387) lr 7.8853e-06 eta 0:06:07
+epoch [50/50] batch [270/500] time 1.550 (1.563) data 0.000 (0.003) loss 0.6562 (1.0215) acc 87.5000 (73.9468) lr 7.8853e-06 eta 0:05:59
+epoch [50/50] batch [275/500] time 1.548 (1.563) data 0.000 (0.003) loss 0.7607 (1.0205) acc 78.1250 (73.9659) lr 7.8853e-06 eta 0:05:51
+epoch [50/50] batch [280/500] time 1.572 (1.563) data 0.000 (0.003) loss 0.9507 (1.0221) acc 81.2500 (73.9062) lr 7.8853e-06 eta 0:05:43
+epoch [50/50] batch [285/500] time 1.565 (1.563) data 0.000 (0.003) loss 1.0654 (1.0267) acc 62.5000 (73.7939) lr 7.8853e-06 eta 0:05:36
+epoch [50/50] batch [290/500] time 1.567 (1.563) data 0.001 (0.003) loss 0.5005 (1.0262) acc 90.6250 (73.8362) lr 7.8853e-06 eta 0:05:28
+epoch [50/50] batch [295/500] time 1.536 (1.563) data 0.000 (0.003) loss 1.4512 (1.0295) acc 81.2500 (73.8347) lr 7.8853e-06 eta 0:05:20
+epoch [50/50] batch [300/500] time 1.572 (1.563) data 0.000 (0.003) loss 0.9019 (1.0282) acc 71.8750 (73.8229) lr 7.8853e-06 eta 0:05:12
+epoch [50/50] batch [305/500] time 1.573 (1.563) data 0.000 (0.003) loss 1.4365 (1.0293) acc 71.8750 (73.9037) lr 7.8853e-06 eta 0:05:04
+epoch [50/50] batch [310/500] time 1.555 (1.563) data 0.000 (0.003) loss 0.9390 (1.0288) acc 71.8750 (73.9113) lr 7.8853e-06 eta 0:04:57
+epoch [50/50] batch [315/500] time 1.566 (1.564) data 0.000 (0.003) loss 1.3096 (1.0273) acc 65.6250 (73.9187) lr 7.8853e-06 eta 0:04:49
+epoch [50/50] batch [320/500] time 1.564 (1.563) data 0.000 (0.003) loss 1.5840 (1.0283) acc 71.8750 (73.9258) lr 7.8853e-06 eta 0:04:41
+epoch [50/50] batch [325/500] time 1.583 (1.564) data 0.000 (0.003) loss 1.1504 (1.0291) acc 75.0000 (73.9423) lr 7.8853e-06 eta 0:04:33
+epoch [50/50] batch [330/500] time 1.595 (1.564) data 0.000 (0.003) loss 0.6060 (1.0279) acc 81.2500 (73.9299) lr 7.8853e-06 eta 0:04:25
+epoch [50/50] batch [335/500] time 1.574 (1.564) data 0.000 (0.003) loss 1.0781 (1.0287) acc 71.8750 (73.9552) lr 7.8853e-06 eta 0:04:18
+epoch [50/50] batch [340/500] time 1.579 (1.564) data 0.000 (0.003) loss 0.3896 (1.0296) acc 81.2500 (73.9062) lr 7.8853e-06 eta 0:04:10
+epoch [50/50] batch [345/500] time 1.561 (1.564) data 0.000 (0.003) loss 1.4238 (1.0289) acc 71.8750 (73.9402) lr 7.8853e-06 eta 0:04:02
+epoch [50/50] batch [350/500] time 1.559 (1.564) data 0.000 (0.003) loss 0.8379 (1.0294) acc 81.2500 (73.9554) lr 7.8853e-06 eta 0:03:54
+epoch [50/50] batch [355/500] time 1.543 (1.564) data 0.000 (0.003) loss 1.5156 (1.0299) acc 68.7500 (74.0053) lr 7.8853e-06 eta 0:03:46
+epoch [50/50] batch [360/500] time 1.564 (1.564) data 0.000 (0.003) loss 1.1367 (1.0311) acc 65.6250 (73.9323) lr 7.8853e-06 eta 0:03:38
+epoch [50/50] batch [365/500] time 1.543 (1.564) data 0.001 (0.003) loss 0.7285 (1.0282) acc 78.1250 (73.9897) lr 7.8853e-06 eta 0:03:31
+epoch [50/50] batch [370/500] time 1.569 (1.564) data 0.000 (0.003) loss 0.9312 (1.0302) acc 75.0000 (73.9527) lr 7.8853e-06 eta 0:03:23
+epoch [50/50] batch [375/500] time 1.548 (1.564) data 0.000 (0.003) loss 0.4570 (1.0258) acc 90.6250 (74.0750) lr 7.8853e-06 eta 0:03:15
+epoch [50/50] batch [380/500] time 1.557 (1.564) data 0.000 (0.003) loss 0.5576 (1.0245) acc 84.3750 (74.1530) lr 7.8853e-06 eta 0:03:07
+epoch [50/50] batch [385/500] time 1.551 (1.564) data 0.000 (0.003) loss 0.9648 (1.0257) acc 71.8750 (74.1153) lr 7.8853e-06 eta 0:02:59
+epoch [50/50] batch [390/500] time 1.571 (1.564) data 0.000 (0.003) loss 0.9028 (1.0231) acc 75.0000 (74.1827) lr 7.8853e-06 eta 0:02:52
+epoch [50/50] batch [395/500] time 1.553 (1.564) data 0.000 (0.002) loss 1.7705 (1.0244) acc 65.6250 (74.1851) lr 7.8853e-06 eta 0:02:44
+epoch [50/50] batch [400/500] time 1.546 (1.564) data 0.000 (0.002) loss 1.2012 (1.0251) acc 62.5000 (74.0703) lr 7.8853e-06 eta 0:02:36
+epoch [50/50] batch [405/500] time 1.550 (1.563) data 0.000 (0.002) loss 1.3545 (1.0265) acc 65.6250 (74.0201) lr 7.8853e-06 eta 0:02:28
+epoch [50/50] batch [410/500] time 1.553 (1.563) data 0.000 (0.002) loss 0.6792 (1.0256) acc 84.3750 (74.0320) lr 7.8853e-06 eta 0:02:20
+epoch [50/50] batch [415/500] time 1.566 (1.563) data 0.000 (0.002) loss 1.4648 (1.0251) acc 62.5000 (74.0361) lr 7.8853e-06 eta 0:02:12
+epoch [50/50] batch [420/500] time 1.545 (1.563) data 0.000 (0.002) loss 0.9683 (1.0251) acc 78.1250 (74.0625) lr 7.8853e-06 eta 0:02:05
+epoch [50/50] batch [425/500] time 1.573 (1.563) data 0.000 (0.002) loss 1.4932 (1.0272) acc 75.0000 (74.0368) lr 7.8853e-06 eta 0:01:57
+epoch [50/50] batch [430/500] time 1.548 (1.563) data 0.000 (0.002) loss 0.8813 (1.0283) acc 78.1250 (74.0044) lr 7.8853e-06 eta 0:01:49
+epoch [50/50] batch [435/500] time 1.544 (1.563) data 0.000 (0.002) loss 0.7407 (1.0273) acc 75.0000 (74.0158) lr 7.8853e-06 eta 0:01:41
+epoch [50/50] batch [440/500] time 1.538 (1.563) data 0.000 (0.002) loss 1.3008 (1.0274) acc 62.5000 (73.9702) lr 7.8853e-06 eta 0:01:33
+epoch [50/50] batch [445/500] time 1.558 (1.563) data 0.000 (0.002) loss 0.4863 (1.0238) acc 84.3750 (74.0520) lr 7.8853e-06 eta 0:01:25
+epoch [50/50] batch [450/500] time 1.555 (1.562) data 0.000 (0.002) loss 1.1406 (1.0241) acc 65.6250 (74.0000) lr 7.8853e-06 eta 0:01:18
+epoch [50/50] batch [455/500] time 1.577 (1.563) data 0.000 (0.002) loss 0.9609 (1.0224) acc 81.2500 (74.0591) lr 7.8853e-06 eta 0:01:10
+epoch [50/50] batch [460/500] time 1.550 (1.563) data 0.000 (0.002) loss 1.4707 (1.0233) acc 71.8750 (74.0761) lr 7.8853e-06 eta 0:01:02
+epoch [50/50] batch [465/500] time 1.635 (1.563) data 0.000 (0.002) loss 0.9512 (1.0223) acc 75.0000 (74.0524) lr 7.8853e-06 eta 0:00:54
+epoch [50/50] batch [470/500] time 1.606 (1.563) data 0.000 (0.002) loss 0.4978 (1.0214) acc 78.1250 (74.0824) lr 7.8853e-06 eta 0:00:46
+epoch [50/50] batch [475/500] time 1.546 (1.563) data 0.001 (0.002) loss 0.6152 (1.0225) acc 81.2500 (74.0526) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [480/500] time 1.558 (1.563) data 0.000 (0.002) loss 1.6885 (1.0217) acc 59.3750 (74.0885) lr 7.8853e-06 eta 0:00:31
+epoch [50/50] batch [485/500] time 1.557 (1.563) data 0.001 (0.002) loss 0.7749 (1.0223) acc 75.0000 (74.0528) lr 7.8853e-06 eta 0:00:23
+epoch [50/50] batch [490/500] time 1.533 (1.562) data 0.000 (0.002) loss 1.2568 (1.0236) acc 71.8750 (74.0370) lr 7.8853e-06 eta 0:00:15
+epoch [50/50] batch [495/500] time 1.561 (1.562) data 0.001 (0.002) loss 0.9731 (1.0228) acc 78.1250 (74.0593) lr 7.8853e-06 eta 0:00:07
+epoch [50/50] batch [500/500] time 1.539 (1.562) data 0.000 (0.002) loss 1.6846 (1.0230) acc 65.6250 (74.0187) lr 1.9733e-06 eta 0:00:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,023
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.6%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the model with the best val performance
+Loading weights to prompt_learner from "output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar" (epoch = 38)
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 39,086
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+Elapsed: 16:11:34
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
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+model.pth.tar-50
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@@ -0,0 +1,1822 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.113
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/50] batch [5/500] time 1.547 (2.691) data 0.000 (0.239) loss 2.3516 (2.9645) acc 50.0000 (42.5000) lr 1.0000e-05 eta 18:41:07
+epoch [1/50] batch [10/500] time 1.566 (2.126) data 0.001 (0.120) loss 2.1309 (2.6680) acc 59.3750 (47.8125) lr 1.0000e-05 eta 14:45:35
+epoch [1/50] batch [15/500] time 1.535 (1.935) data 0.000 (0.080) loss 1.8623 (2.4137) acc 46.8750 (50.0000) lr 1.0000e-05 eta 13:25:39
+epoch [1/50] batch [20/500] time 1.561 (1.841) data 0.000 (0.060) loss 2.0859 (2.3499) acc 46.8750 (50.0000) lr 1.0000e-05 eta 12:46:36
+epoch [1/50] batch [25/500] time 1.551 (1.784) data 0.000 (0.048) loss 1.4434 (2.2323) acc 71.8750 (52.0000) lr 1.0000e-05 eta 12:22:36
+epoch [1/50] batch [30/500] time 1.542 (1.746) data 0.000 (0.040) loss 1.3477 (2.1673) acc 65.6250 (53.1250) lr 1.0000e-05 eta 12:06:38
+epoch [1/50] batch [35/500] time 1.559 (1.720) data 0.001 (0.035) loss 1.2822 (2.1095) acc 68.7500 (53.8393) lr 1.0000e-05 eta 11:55:37
+epoch [1/50] batch [40/500] time 1.556 (1.700) data 0.000 (0.030) loss 2.2305 (2.0646) acc 50.0000 (54.5312) lr 1.0000e-05 eta 11:47:23
+epoch [1/50] batch [45/500] time 1.565 (1.685) data 0.000 (0.027) loss 1.4141 (1.9933) acc 53.1250 (55.7639) lr 1.0000e-05 eta 11:40:37
+epoch [1/50] batch [50/500] time 1.575 (1.673) data 0.000 (0.024) loss 1.7217 (1.9862) acc 56.2500 (55.5625) lr 1.0000e-05 eta 11:35:36
+epoch [1/50] batch [55/500] time 1.555 (1.664) data 0.000 (0.022) loss 2.0098 (1.9550) acc 53.1250 (56.0795) lr 1.0000e-05 eta 11:31:42
+epoch [1/50] batch [60/500] time 1.570 (1.655) data 0.001 (0.020) loss 1.7812 (1.9369) acc 62.5000 (56.5104) lr 1.0000e-05 eta 11:28:01
+epoch [1/50] batch [65/500] time 1.588 (1.649) data 0.001 (0.019) loss 0.9331 (1.8873) acc 71.8750 (57.3558) lr 1.0000e-05 eta 11:25:18
+epoch [1/50] batch [70/500] time 1.556 (1.642) data 0.000 (0.017) loss 1.4219 (1.8519) acc 65.6250 (57.8571) lr 1.0000e-05 eta 11:22:18
+epoch [1/50] batch [75/500] time 1.559 (1.637) data 0.000 (0.016) loss 1.8936 (1.8255) acc 59.3750 (58.5833) lr 1.0000e-05 eta 11:19:51
+epoch [1/50] batch [80/500] time 1.573 (1.632) data 0.000 (0.015) loss 1.1973 (1.8048) acc 56.2500 (58.9453) lr 1.0000e-05 eta 11:17:41
+epoch [1/50] batch [85/500] time 1.564 (1.627) data 0.000 (0.014) loss 1.7314 (1.8046) acc 59.3750 (58.6029) lr 1.0000e-05 eta 11:15:35
+epoch [1/50] batch [90/500] time 1.569 (1.623) data 0.000 (0.014) loss 1.2559 (1.7868) acc 56.2500 (58.8194) lr 1.0000e-05 eta 11:13:53
+epoch [1/50] batch [95/500] time 1.566 (1.620) data 0.000 (0.013) loss 1.3594 (1.7709) acc 71.8750 (59.1447) lr 1.0000e-05 eta 11:12:21
+epoch [1/50] batch [100/500] time 1.540 (1.616) data 0.000 (0.012) loss 1.1426 (1.7555) acc 68.7500 (59.4375) lr 1.0000e-05 eta 11:10:44
+epoch [1/50] batch [105/500] time 1.579 (1.613) data 0.000 (0.012) loss 1.5840 (1.7365) acc 68.7500 (59.8512) lr 1.0000e-05 eta 11:09:14
+epoch [1/50] batch [110/500] time 1.562 (1.611) data 0.000 (0.011) loss 1.3252 (1.7170) acc 78.1250 (60.3693) lr 1.0000e-05 eta 11:08:11
+epoch [1/50] batch [115/500] time 1.543 (1.609) data 0.000 (0.011) loss 1.1426 (1.7074) acc 71.8750 (60.6522) lr 1.0000e-05 eta 11:07:10
+epoch [1/50] batch [120/500] time 1.550 (1.606) data 0.000 (0.010) loss 1.3359 (1.6981) acc 59.3750 (60.8333) lr 1.0000e-05 eta 11:06:07
+epoch [1/50] batch [125/500] time 1.557 (1.604) data 0.000 (0.010) loss 1.1455 (1.6868) acc 68.7500 (61.1250) lr 1.0000e-05 eta 11:05:06
+epoch [1/50] batch [130/500] time 1.558 (1.603) data 0.000 (0.010) loss 1.6211 (1.6871) acc 62.5000 (61.2019) lr 1.0000e-05 eta 11:04:16
+epoch [1/50] batch [135/500] time 1.549 (1.601) data 0.000 (0.009) loss 1.7881 (1.6815) acc 56.2500 (61.4352) lr 1.0000e-05 eta 11:03:26
+epoch [1/50] batch [140/500] time 1.572 (1.600) data 0.001 (0.009) loss 1.6777 (1.6734) acc 59.3750 (61.5179) lr 1.0000e-05 eta 11:02:48
+epoch [1/50] batch [145/500] time 1.533 (1.598) data 0.001 (0.009) loss 1.7451 (1.6702) acc 62.5000 (61.5733) lr 1.0000e-05 eta 11:01:51
+epoch [1/50] batch [150/500] time 1.544 (1.596) data 0.000 (0.008) loss 1.4453 (1.6645) acc 62.5000 (61.7708) lr 1.0000e-05 eta 11:01:11
+epoch [1/50] batch [155/500] time 1.541 (1.595) data 0.000 (0.008) loss 1.2041 (1.6575) acc 68.7500 (61.8145) lr 1.0000e-05 eta 11:00:28
+epoch [1/50] batch [160/500] time 1.564 (1.594) data 0.001 (0.008) loss 1.4932 (1.6524) acc 68.7500 (61.9531) lr 1.0000e-05 eta 10:59:57
+epoch [1/50] batch [165/500] time 1.590 (1.593) data 0.000 (0.008) loss 1.2861 (1.6442) acc 50.0000 (61.9886) lr 1.0000e-05 eta 10:59:31
+epoch [1/50] batch [170/500] time 1.567 (1.594) data 0.001 (0.007) loss 1.9189 (1.6443) acc 62.5000 (62.0037) lr 1.0000e-05 eta 10:59:34
+epoch [1/50] batch [175/500] time 1.556 (1.593) data 0.001 (0.007) loss 0.9751 (1.6303) acc 75.0000 (62.1429) lr 1.0000e-05 eta 10:58:56
+epoch [1/50] batch [180/500] time 1.550 (1.592) data 0.000 (0.007) loss 1.6406 (1.6306) acc 62.5000 (62.3438) lr 1.0000e-05 eta 10:58:24
+epoch [1/50] batch [185/500] time 1.575 (1.591) data 0.001 (0.007) loss 1.8311 (1.6264) acc 62.5000 (62.5338) lr 1.0000e-05 eta 10:57:59
+epoch [1/50] batch [190/500] time 1.597 (1.590) data 0.000 (0.007) loss 1.7725 (1.6253) acc 71.8750 (62.6480) lr 1.0000e-05 eta 10:57:29
+epoch [1/50] batch [195/500] time 1.595 (1.589) data 0.000 (0.007) loss 1.0312 (1.6205) acc 71.8750 (62.7404) lr 1.0000e-05 eta 10:57:06
+epoch [1/50] batch [200/500] time 1.569 (1.589) data 0.001 (0.006) loss 2.3223 (1.6221) acc 50.0000 (62.7344) lr 1.0000e-05 eta 10:56:40
+epoch [1/50] batch [205/500] time 1.550 (1.588) data 0.000 (0.006) loss 0.9180 (1.6103) acc 71.8750 (62.9268) lr 1.0000e-05 eta 10:56:23
+epoch [1/50] batch [210/500] time 1.575 (1.588) data 0.001 (0.006) loss 0.9785 (1.6064) acc 75.0000 (62.9613) lr 1.0000e-05 eta 10:56:02
+epoch [1/50] batch [215/500] time 1.564 (1.588) data 0.000 (0.006) loss 1.7549 (1.6034) acc 59.3750 (62.9651) lr 1.0000e-05 eta 10:55:49
+epoch [1/50] batch [220/500] time 1.569 (1.587) data 0.000 (0.006) loss 1.6797 (1.5979) acc 56.2500 (63.0682) lr 1.0000e-05 eta 10:55:34
+epoch [1/50] batch [225/500] time 1.555 (1.587) data 0.000 (0.006) loss 1.4434 (1.5980) acc 59.3750 (63.0278) lr 1.0000e-05 eta 10:55:13
+epoch [1/50] batch [230/500] time 1.563 (1.587) data 0.000 (0.006) loss 1.0625 (1.5912) acc 75.0000 (63.1793) lr 1.0000e-05 eta 10:54:58
+epoch [1/50] batch [235/500] time 1.562 (1.586) data 0.001 (0.006) loss 1.8760 (1.5891) acc 56.2500 (63.1649) lr 1.0000e-05 eta 10:54:39
+epoch [1/50] batch [240/500] time 1.556 (1.586) data 0.000 (0.005) loss 2.0391 (1.5904) acc 53.1250 (63.2031) lr 1.0000e-05 eta 10:54:17
+epoch [1/50] batch [245/500] time 1.541 (1.585) data 0.000 (0.005) loss 1.5566 (1.5902) acc 71.8750 (63.2781) lr 1.0000e-05 eta 10:53:57
+epoch [1/50] batch [250/500] time 1.549 (1.584) data 0.001 (0.005) loss 1.2197 (1.5844) acc 71.8750 (63.4625) lr 1.0000e-05 eta 10:53:31
+epoch [1/50] batch [255/500] time 1.544 (1.584) data 0.000 (0.005) loss 0.9180 (1.5802) acc 75.0000 (63.5294) lr 1.0000e-05 eta 10:53:10
+epoch [1/50] batch [260/500] time 1.558 (1.583) data 0.000 (0.005) loss 1.3281 (1.5795) acc 71.8750 (63.5457) lr 1.0000e-05 eta 10:52:54
+epoch [1/50] batch [265/500] time 1.555 (1.583) data 0.000 (0.005) loss 1.2812 (1.5703) acc 53.1250 (63.6557) lr 1.0000e-05 eta 10:52:37
+epoch [1/50] batch [270/500] time 1.558 (1.583) data 0.000 (0.005) loss 1.4941 (1.5716) acc 62.5000 (63.5880) lr 1.0000e-05 eta 10:52:21
+epoch [1/50] batch [275/500] time 1.558 (1.582) data 0.000 (0.005) loss 1.4463 (1.5643) acc 71.8750 (63.7386) lr 1.0000e-05 eta 10:52:00
+epoch [1/50] batch [280/500] time 1.543 (1.582) data 0.001 (0.005) loss 1.3633 (1.5627) acc 56.2500 (63.6830) lr 1.0000e-05 eta 10:51:43
+epoch [1/50] batch [285/500] time 1.565 (1.582) data 0.000 (0.005) loss 1.5410 (1.5625) acc 56.2500 (63.6294) lr 1.0000e-05 eta 10:51:27
+epoch [1/50] batch [290/500] time 1.565 (1.581) data 0.000 (0.005) loss 1.6826 (1.5611) acc 68.7500 (63.7177) lr 1.0000e-05 eta 10:51:11
+epoch [1/50] batch [295/500] time 1.573 (1.581) data 0.000 (0.005) loss 1.7686 (1.5635) acc 53.1250 (63.6653) lr 1.0000e-05 eta 10:50:54
+epoch [1/50] batch [300/500] time 1.560 (1.581) data 0.000 (0.004) loss 2.2227 (1.5659) acc 59.3750 (63.5938) lr 1.0000e-05 eta 10:50:43
+epoch [1/50] batch [305/500] time 1.565 (1.580) data 0.000 (0.004) loss 1.2344 (1.5631) acc 68.7500 (63.6578) lr 1.0000e-05 eta 10:50:27
+epoch [1/50] batch [310/500] time 1.671 (1.580) data 0.000 (0.004) loss 1.2109 (1.5629) acc 68.7500 (63.6391) lr 1.0000e-05 eta 10:50:20
+epoch [1/50] batch [315/500] time 1.563 (1.580) data 0.001 (0.004) loss 1.5137 (1.5651) acc 59.3750 (63.5417) lr 1.0000e-05 eta 10:50:12
+epoch [1/50] batch [320/500] time 1.566 (1.580) data 0.001 (0.004) loss 1.7041 (1.5659) acc 56.2500 (63.5156) lr 1.0000e-05 eta 10:49:58
+epoch [1/50] batch [325/500] time 1.554 (1.580) data 0.000 (0.004) loss 0.9395 (1.5644) acc 71.8750 (63.5769) lr 1.0000e-05 eta 10:49:44
+epoch [1/50] batch [330/500] time 1.543 (1.580) data 0.000 (0.004) loss 1.0830 (1.5617) acc 68.7500 (63.5985) lr 1.0000e-05 eta 10:49:26
+epoch [1/50] batch [335/500] time 1.594 (1.580) data 0.000 (0.004) loss 1.2061 (1.5593) acc 68.7500 (63.6474) lr 1.0000e-05 eta 10:49:19
+epoch [1/50] batch [340/500] time 1.580 (1.579) data 0.000 (0.004) loss 1.9912 (1.5579) acc 50.0000 (63.6305) lr 1.0000e-05 eta 10:49:10
+epoch [1/50] batch [345/500] time 1.570 (1.579) data 0.000 (0.004) loss 1.8516 (1.5545) acc 56.2500 (63.7319) lr 1.0000e-05 eta 10:48:59
+epoch [1/50] batch [350/500] time 1.597 (1.579) data 0.000 (0.004) loss 1.3428 (1.5545) acc 62.5000 (63.7589) lr 1.0000e-05 eta 10:48:49
+epoch [1/50] batch [355/500] time 1.588 (1.579) data 0.000 (0.004) loss 1.2578 (1.5474) acc 65.6250 (63.9173) lr 1.0000e-05 eta 10:48:44
+epoch [1/50] batch [360/500] time 1.564 (1.579) data 0.000 (0.004) loss 0.7729 (1.5438) acc 81.2500 (63.9931) lr 1.0000e-05 eta 10:48:32
+epoch [1/50] batch [365/500] time 1.561 (1.579) data 0.000 (0.004) loss 1.3926 (1.5444) acc 71.8750 (63.9897) lr 1.0000e-05 eta 10:48:17
+epoch [1/50] batch [370/500] time 1.536 (1.579) data 0.001 (0.004) loss 1.4385 (1.5431) acc 59.3750 (64.0034) lr 1.0000e-05 eta 10:47:58
+epoch [1/50] batch [375/500] time 1.567 (1.578) data 0.000 (0.004) loss 1.2969 (1.5439) acc 65.6250 (63.9333) lr 1.0000e-05 eta 10:47:48
+epoch [1/50] batch [380/500] time 1.552 (1.578) data 0.000 (0.004) loss 0.9595 (1.5426) acc 78.1250 (63.9309) lr 1.0000e-05 eta 10:47:35
+epoch [1/50] batch [385/500] time 1.585 (1.578) data 0.001 (0.004) loss 1.4238 (1.5422) acc 65.6250 (63.9042) lr 1.0000e-05 eta 10:47:21
+epoch [1/50] batch [390/500] time 1.575 (1.578) data 0.000 (0.003) loss 1.2520 (1.5395) acc 78.1250 (63.9744) lr 1.0000e-05 eta 10:47:09
+epoch [1/50] batch [395/500] time 1.583 (1.578) data 0.001 (0.003) loss 1.2812 (1.5387) acc 75.0000 (63.9873) lr 1.0000e-05 eta 10:47:02
+epoch [1/50] batch [400/500] time 1.559 (1.578) data 0.000 (0.003) loss 1.4199 (1.5356) acc 71.8750 (64.0547) lr 1.0000e-05 eta 10:46:48
+epoch [1/50] batch [405/500] time 1.577 (1.577) data 0.000 (0.003) loss 1.0479 (1.5369) acc 68.7500 (64.0123) lr 1.0000e-05 eta 10:46:38
+epoch [1/50] batch [410/500] time 1.579 (1.577) data 0.000 (0.003) loss 0.8203 (1.5333) acc 81.2500 (64.1159) lr 1.0000e-05 eta 10:46:27
+epoch [1/50] batch [415/500] time 1.574 (1.577) data 0.000 (0.003) loss 0.7832 (1.5305) acc 78.1250 (64.1792) lr 1.0000e-05 eta 10:46:15
+epoch [1/50] batch [420/500] time 1.576 (1.577) data 0.001 (0.003) loss 1.0283 (1.5273) acc 71.8750 (64.2113) lr 1.0000e-05 eta 10:46:04
+epoch [1/50] batch [425/500] time 1.552 (1.577) data 0.000 (0.003) loss 1.6855 (1.5247) acc 59.3750 (64.2206) lr 1.0000e-05 eta 10:45:53
+epoch [1/50] batch [430/500] time 1.564 (1.577) data 0.000 (0.003) loss 0.9365 (1.5220) acc 81.2500 (64.2951) lr 1.0000e-05 eta 10:45:40
+epoch [1/50] batch [435/500] time 1.572 (1.576) data 0.001 (0.003) loss 0.8037 (1.5216) acc 90.6250 (64.3750) lr 1.0000e-05 eta 10:45:26
+epoch [1/50] batch [440/500] time 1.572 (1.576) data 0.000 (0.003) loss 0.9634 (1.5231) acc 71.8750 (64.3608) lr 1.0000e-05 eta 10:45:17
+epoch [1/50] batch [445/500] time 1.559 (1.576) data 0.001 (0.003) loss 1.5225 (1.5218) acc 59.3750 (64.3258) lr 1.0000e-05 eta 10:45:06
+epoch [1/50] batch [450/500] time 1.605 (1.576) data 0.000 (0.003) loss 1.6738 (1.5194) acc 62.5000 (64.3681) lr 1.0000e-05 eta 10:45:01
+epoch [1/50] batch [455/500] time 1.538 (1.576) data 0.000 (0.003) loss 1.3281 (1.5197) acc 62.5000 (64.3544) lr 1.0000e-05 eta 10:44:54
+epoch [1/50] batch [460/500] time 1.571 (1.576) data 0.000 (0.003) loss 1.2383 (1.5183) acc 68.7500 (64.3410) lr 1.0000e-05 eta 10:44:43
+epoch [1/50] batch [465/500] time 1.547 (1.576) data 0.000 (0.003) loss 1.4102 (1.5167) acc 68.7500 (64.3952) lr 1.0000e-05 eta 10:44:30
+epoch [1/50] batch [470/500] time 1.554 (1.576) data 0.001 (0.003) loss 0.8804 (1.5152) acc 78.1250 (64.4082) lr 1.0000e-05 eta 10:44:19
+epoch [1/50] batch [475/500] time 1.555 (1.576) data 0.000 (0.003) loss 1.4150 (1.5133) acc 68.7500 (64.4342) lr 1.0000e-05 eta 10:44:09
+epoch [1/50] batch [480/500] time 1.561 (1.576) data 0.000 (0.003) loss 1.2441 (1.5096) acc 65.6250 (64.4596) lr 1.0000e-05 eta 10:43:58
+epoch [1/50] batch [485/500] time 1.551 (1.576) data 0.001 (0.003) loss 1.3711 (1.5074) acc 68.7500 (64.5232) lr 1.0000e-05 eta 10:43:46
+epoch [1/50] batch [490/500] time 1.574 (1.576) data 0.000 (0.003) loss 1.4854 (1.5057) acc 46.8750 (64.5281) lr 1.0000e-05 eta 10:43:36
+epoch [1/50] batch [495/500] time 1.557 (1.575) data 0.000 (0.003) loss 1.3203 (1.5032) acc 62.5000 (64.5581) lr 1.0000e-05 eta 10:43:24
+epoch [1/50] batch [500/500] time 1.542 (1.575) data 0.000 (0.003) loss 1.1758 (1.4989) acc 75.0000 (64.6562) lr 2.0000e-03 eta 10:43:16
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,022
+* accuracy: 74.0%
+* error: 26.0%
+* macro_f1: 73.2%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/500] time 1.548 (1.711) data 0.001 (0.209) loss 1.7939 (1.8871) acc 53.1250 (59.3750) lr 2.0000e-03 eta 11:38:36
+epoch [2/50] batch [10/500] time 1.556 (1.649) data 0.001 (0.105) loss 1.0879 (1.6939) acc 75.0000 (61.5625) lr 2.0000e-03 eta 11:13:10
+epoch [2/50] batch [15/500] time 1.556 (1.620) data 0.000 (0.070) loss 1.5400 (1.5844) acc 71.8750 (64.5833) lr 2.0000e-03 eta 11:01:10
+epoch [2/50] batch [20/500] time 1.558 (1.606) data 0.000 (0.053) loss 1.2637 (1.5213) acc 65.6250 (65.3125) lr 2.0000e-03 eta 10:55:17
+epoch [2/50] batch [25/500] time 1.581 (1.598) data 0.000 (0.042) loss 1.7734 (1.5128) acc 68.7500 (65.6250) lr 2.0000e-03 eta 10:52:00
+epoch [2/50] batch [30/500] time 1.570 (1.593) data 0.000 (0.035) loss 0.7622 (1.4763) acc 65.6250 (65.7292) lr 2.0000e-03 eta 10:49:46
+epoch [2/50] batch [35/500] time 1.567 (1.588) data 0.000 (0.030) loss 1.2793 (1.4962) acc 68.7500 (65.2679) lr 2.0000e-03 eta 10:47:25
+epoch [2/50] batch [40/500] time 1.568 (1.585) data 0.000 (0.026) loss 0.9712 (1.5117) acc 71.8750 (64.5312) lr 2.0000e-03 eta 10:46:08
+epoch [2/50] batch [45/500] time 1.571 (1.582) data 0.000 (0.024) loss 1.3662 (1.5049) acc 62.5000 (64.2361) lr 2.0000e-03 eta 10:44:41
+epoch [2/50] batch [50/500] time 1.557 (1.580) data 0.000 (0.021) loss 1.0000 (1.5088) acc 81.2500 (64.6250) lr 2.0000e-03 eta 10:43:49
+epoch [2/50] batch [55/500] time 1.557 (1.578) data 0.000 (0.019) loss 1.4316 (1.4831) acc 65.6250 (64.9432) lr 2.0000e-03 eta 10:42:56
+epoch [2/50] batch [60/500] time 1.552 (1.577) data 0.000 (0.018) loss 0.7456 (1.4687) acc 84.3750 (65.1042) lr 2.0000e-03 eta 10:42:11
+epoch [2/50] batch [65/500] time 1.555 (1.575) data 0.000 (0.016) loss 1.4736 (1.4392) acc 68.7500 (65.5769) lr 2.0000e-03 eta 10:41:33
+epoch [2/50] batch [70/500] time 1.563 (1.574) data 0.001 (0.015) loss 1.2520 (1.4131) acc 78.1250 (66.2946) lr 2.0000e-03 eta 10:41:00
+epoch [2/50] batch [75/500] time 1.553 (1.573) data 0.000 (0.014) loss 0.6807 (1.3990) acc 71.8750 (66.4583) lr 2.0000e-03 eta 10:40:30
+epoch [2/50] batch [80/500] time 1.571 (1.573) data 0.001 (0.013) loss 1.1221 (1.3959) acc 71.8750 (66.6797) lr 2.0000e-03 eta 10:40:16
+epoch [2/50] batch [85/500] time 1.572 (1.573) data 0.000 (0.013) loss 1.0098 (1.3837) acc 81.2500 (66.8015) lr 2.0000e-03 eta 10:39:57
+epoch [2/50] batch [90/500] time 1.558 (1.572) data 0.001 (0.012) loss 1.1465 (1.3791) acc 78.1250 (66.9792) lr 2.0000e-03 eta 10:39:33
+epoch [2/50] batch [95/500] time 1.567 (1.571) data 0.001 (0.011) loss 1.8760 (1.3786) acc 65.6250 (66.9079) lr 2.0000e-03 eta 10:39:09
+epoch [2/50] batch [100/500] time 1.557 (1.571) data 0.000 (0.011) loss 0.9229 (1.3626) acc 78.1250 (67.0312) lr 2.0000e-03 eta 10:38:44
+epoch [2/50] batch [105/500] time 1.573 (1.571) data 0.000 (0.010) loss 1.8467 (1.3641) acc 62.5000 (66.9643) lr 2.0000e-03 eta 10:38:37
+epoch [2/50] batch [110/500] time 1.570 (1.571) data 0.000 (0.010) loss 0.9014 (1.3510) acc 81.2500 (67.2443) lr 2.0000e-03 eta 10:38:47
+epoch [2/50] batch [115/500] time 1.564 (1.571) data 0.001 (0.009) loss 1.0127 (1.3445) acc 68.7500 (67.3641) lr 2.0000e-03 eta 10:38:36
+epoch [2/50] batch [120/500] time 1.569 (1.571) data 0.000 (0.009) loss 1.4131 (1.3457) acc 65.6250 (67.2656) lr 2.0000e-03 eta 10:38:24
+epoch [2/50] batch [125/500] time 1.563 (1.571) data 0.000 (0.009) loss 1.3750 (1.3428) acc 56.2500 (67.3250) lr 2.0000e-03 eta 10:38:06
+epoch [2/50] batch [130/500] time 1.557 (1.570) data 0.000 (0.008) loss 1.4502 (1.3461) acc 50.0000 (67.2837) lr 2.0000e-03 eta 10:37:46
+epoch [2/50] batch [135/500] time 1.583 (1.570) data 0.000 (0.008) loss 1.7471 (1.3460) acc 62.5000 (67.3611) lr 2.0000e-03 eta 10:37:31
+epoch [2/50] batch [140/500] time 1.551 (1.570) data 0.000 (0.008) loss 1.7100 (1.3483) acc 62.5000 (67.3884) lr 2.0000e-03 eta 10:37:13
+epoch [2/50] batch [145/500] time 1.565 (1.570) data 0.000 (0.008) loss 1.0879 (1.3424) acc 68.7500 (67.4784) lr 2.0000e-03 eta 10:37:05
+epoch [2/50] batch [150/500] time 1.565 (1.569) data 0.000 (0.007) loss 1.4873 (1.3415) acc 62.5000 (67.5208) lr 2.0000e-03 eta 10:36:52
+epoch [2/50] batch [155/500] time 1.556 (1.570) data 0.000 (0.007) loss 1.4609 (1.3347) acc 62.5000 (67.6613) lr 2.0000e-03 eta 10:36:50
+epoch [2/50] batch [160/500] time 1.588 (1.570) data 0.000 (0.007) loss 0.7773 (1.3264) acc 78.1250 (67.7734) lr 2.0000e-03 eta 10:36:51
+epoch [2/50] batch [165/500] time 1.532 (1.570) data 0.000 (0.007) loss 1.0420 (1.3270) acc 62.5000 (67.7462) lr 2.0000e-03 eta 10:36:41
+epoch [2/50] batch [170/500] time 1.585 (1.570) data 0.000 (0.007) loss 1.0264 (1.3241) acc 84.3750 (67.9044) lr 2.0000e-03 eta 10:36:32
+epoch [2/50] batch [175/500] time 1.552 (1.569) data 0.000 (0.006) loss 0.9590 (1.3183) acc 75.0000 (67.9821) lr 2.0000e-03 eta 10:36:16
+epoch [2/50] batch [180/500] time 1.566 (1.570) data 0.000 (0.006) loss 2.2129 (1.3207) acc 59.3750 (67.9340) lr 2.0000e-03 eta 10:36:10
+epoch [2/50] batch [185/500] time 1.552 (1.569) data 0.000 (0.006) loss 1.5068 (1.3188) acc 65.6250 (68.1419) lr 2.0000e-03 eta 10:35:52
+epoch [2/50] batch [190/500] time 1.561 (1.569) data 0.000 (0.006) loss 0.9888 (1.3158) acc 65.6250 (68.0592) lr 2.0000e-03 eta 10:35:37
+epoch [2/50] batch [195/500] time 1.573 (1.569) data 0.000 (0.006) loss 1.2607 (1.3266) acc 78.1250 (67.9487) lr 2.0000e-03 eta 10:35:24
+epoch [2/50] batch [200/500] time 1.582 (1.569) data 0.000 (0.006) loss 2.0859 (1.3289) acc 53.1250 (67.9219) lr 2.0000e-03 eta 10:35:15
+epoch [2/50] batch [205/500] time 1.557 (1.568) data 0.000 (0.006) loss 1.3711 (1.3270) acc 68.7500 (67.9268) lr 2.0000e-03 eta 10:34:58
+epoch [2/50] batch [210/500] time 1.563 (1.568) data 0.000 (0.005) loss 0.7485 (1.3210) acc 81.2500 (68.0804) lr 2.0000e-03 eta 10:34:52
+epoch [2/50] batch [215/500] time 1.542 (1.568) data 0.000 (0.005) loss 1.3516 (1.3200) acc 62.5000 (68.1250) lr 2.0000e-03 eta 10:34:38
+epoch [2/50] batch [220/500] time 1.562 (1.568) data 0.000 (0.005) loss 1.6396 (1.3234) acc 65.6250 (68.1250) lr 2.0000e-03 eta 10:34:23
+epoch [2/50] batch [225/500] time 1.563 (1.567) data 0.000 (0.005) loss 0.7334 (1.3216) acc 71.8750 (68.2083) lr 2.0000e-03 eta 10:34:08
+epoch [2/50] batch [230/500] time 1.555 (1.567) data 0.000 (0.005) loss 1.2139 (1.3230) acc 62.5000 (68.1386) lr 2.0000e-03 eta 10:33:52
+epoch [2/50] batch [235/500] time 1.570 (1.567) data 0.000 (0.005) loss 0.6670 (1.3188) acc 87.5000 (68.2314) lr 2.0000e-03 eta 10:33:43
+epoch [2/50] batch [240/500] time 1.550 (1.567) data 0.000 (0.005) loss 1.0303 (1.3153) acc 71.8750 (68.2552) lr 2.0000e-03 eta 10:33:29
+epoch [2/50] batch [245/500] time 1.580 (1.566) data 0.000 (0.005) loss 1.5928 (1.3170) acc 65.6250 (68.2270) lr 2.0000e-03 eta 10:33:15
+epoch [2/50] batch [250/500] time 1.644 (1.567) data 0.000 (0.005) loss 1.3203 (1.3213) acc 75.0000 (68.2500) lr 2.0000e-03 eta 10:33:15
+epoch [2/50] batch [255/500] time 1.586 (1.567) data 0.000 (0.005) loss 1.1250 (1.3208) acc 65.6250 (68.2108) lr 2.0000e-03 eta 10:33:08
+epoch [2/50] batch [260/500] time 1.566 (1.567) data 0.000 (0.004) loss 1.5342 (1.3188) acc 65.6250 (68.1971) lr 2.0000e-03 eta 10:33:03
+epoch [2/50] batch [265/500] time 1.576 (1.567) data 0.000 (0.004) loss 1.5488 (1.3206) acc 62.5000 (68.1014) lr 2.0000e-03 eta 10:33:00
+epoch [2/50] batch [270/500] time 1.560 (1.567) data 0.000 (0.004) loss 1.0879 (1.3167) acc 78.1250 (68.1481) lr 2.0000e-03 eta 10:32:47
+epoch [2/50] batch [275/500] time 1.545 (1.567) data 0.000 (0.004) loss 1.3262 (1.3158) acc 62.5000 (68.1364) lr 2.0000e-03 eta 10:32:32
+epoch [2/50] batch [280/500] time 1.562 (1.567) data 0.000 (0.004) loss 1.2900 (1.3166) acc 68.7500 (68.1473) lr 2.0000e-03 eta 10:32:25
+epoch [2/50] batch [285/500] time 1.583 (1.567) data 0.000 (0.004) loss 1.7959 (1.3190) acc 65.6250 (68.0702) lr 2.0000e-03 eta 10:32:19
+epoch [2/50] batch [290/500] time 1.565 (1.567) data 0.000 (0.004) loss 1.1172 (1.3139) acc 71.8750 (68.1681) lr 2.0000e-03 eta 10:32:10
+epoch [2/50] batch [295/500] time 1.543 (1.567) data 0.001 (0.004) loss 1.0518 (1.3119) acc 71.8750 (68.1886) lr 2.0000e-03 eta 10:32:03
+epoch [2/50] batch [300/500] time 1.564 (1.567) data 0.000 (0.004) loss 0.8979 (1.3131) acc 78.1250 (68.1979) lr 2.0000e-03 eta 10:31:59
+epoch [2/50] batch [305/500] time 1.582 (1.567) data 0.000 (0.004) loss 1.4443 (1.3099) acc 65.6250 (68.2070) lr 2.0000e-03 eta 10:31:48
+epoch [2/50] batch [310/500] time 1.551 (1.567) data 0.000 (0.004) loss 1.2939 (1.3123) acc 71.8750 (68.1552) lr 2.0000e-03 eta 10:31:33
+epoch [2/50] batch [315/500] time 1.551 (1.566) data 0.001 (0.004) loss 1.5166 (1.3121) acc 65.6250 (68.2044) lr 2.0000e-03 eta 10:31:22
+epoch [2/50] batch [320/500] time 1.553 (1.566) data 0.000 (0.004) loss 1.7461 (1.3087) acc 59.3750 (68.2715) lr 2.0000e-03 eta 10:31:11
+epoch [2/50] batch [325/500] time 1.554 (1.566) data 0.000 (0.004) loss 1.4365 (1.3084) acc 71.8750 (68.2788) lr 2.0000e-03 eta 10:31:01
+epoch [2/50] batch [330/500] time 1.569 (1.566) data 0.000 (0.004) loss 1.8252 (1.3108) acc 65.6250 (68.3144) lr 2.0000e-03 eta 10:30:49
+epoch [2/50] batch [335/500] time 1.536 (1.566) data 0.000 (0.004) loss 0.6475 (1.3070) acc 78.1250 (68.3675) lr 2.0000e-03 eta 10:30:36
+epoch [2/50] batch [340/500] time 1.547 (1.566) data 0.000 (0.003) loss 1.6201 (1.3070) acc 68.7500 (68.4007) lr 2.0000e-03 eta 10:30:25
+epoch [2/50] batch [345/500] time 1.562 (1.566) data 0.000 (0.003) loss 1.3291 (1.3085) acc 65.6250 (68.3696) lr 2.0000e-03 eta 10:30:15
+epoch [2/50] batch [350/500] time 1.563 (1.565) data 0.000 (0.003) loss 0.6616 (1.3104) acc 81.2500 (68.3482) lr 2.0000e-03 eta 10:30:04
+epoch [2/50] batch [355/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.5146 (1.3092) acc 65.6250 (68.3979) lr 2.0000e-03 eta 10:29:55
+epoch [2/50] batch [360/500] time 1.541 (1.565) data 0.000 (0.003) loss 1.0664 (1.3108) acc 62.5000 (68.3594) lr 2.0000e-03 eta 10:29:48
+epoch [2/50] batch [365/500] time 1.571 (1.565) data 0.000 (0.003) loss 1.3340 (1.3116) acc 71.8750 (68.3562) lr 2.0000e-03 eta 10:29:41
+epoch [2/50] batch [370/500] time 1.586 (1.565) data 0.000 (0.003) loss 0.8262 (1.3089) acc 71.8750 (68.3530) lr 2.0000e-03 eta 10:29:33
+epoch [2/50] batch [375/500] time 1.577 (1.565) data 0.000 (0.003) loss 1.0449 (1.3128) acc 78.1250 (68.2917) lr 2.0000e-03 eta 10:29:24
+epoch [2/50] batch [380/500] time 1.555 (1.565) data 0.000 (0.003) loss 0.6396 (1.3089) acc 75.0000 (68.3388) lr 2.0000e-03 eta 10:29:15
+epoch [2/50] batch [385/500] time 1.565 (1.565) data 0.000 (0.003) loss 1.8672 (1.3083) acc 59.3750 (68.3766) lr 2.0000e-03 eta 10:29:01
+epoch [2/50] batch [390/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.3525 (1.3082) acc 65.6250 (68.3413) lr 2.0000e-03 eta 10:28:54
+epoch [2/50] batch [395/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.6152 (1.3099) acc 56.2500 (68.3386) lr 2.0000e-03 eta 10:28:51
+epoch [2/50] batch [400/500] time 1.555 (1.565) data 0.001 (0.003) loss 1.0342 (1.3123) acc 81.2500 (68.3047) lr 2.0000e-03 eta 10:28:41
+epoch [2/50] batch [405/500] time 1.569 (1.565) data 0.000 (0.003) loss 0.9194 (1.3083) acc 75.0000 (68.3719) lr 2.0000e-03 eta 10:28:32
+epoch [2/50] batch [410/500] time 1.540 (1.565) data 0.000 (0.003) loss 1.5625 (1.3089) acc 62.5000 (68.3841) lr 2.0000e-03 eta 10:28:21
+epoch [2/50] batch [415/500] time 1.563 (1.565) data 0.000 (0.003) loss 0.5527 (1.3054) acc 90.6250 (68.5166) lr 2.0000e-03 eta 10:28:11
+epoch [2/50] batch [420/500] time 1.567 (1.565) data 0.000 (0.003) loss 1.9014 (1.3074) acc 59.3750 (68.5045) lr 2.0000e-03 eta 10:28:01
+epoch [2/50] batch [425/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.6377 (1.3074) acc 62.5000 (68.5221) lr 2.0000e-03 eta 10:27:50
+epoch [2/50] batch [430/500] time 1.539 (1.565) data 0.000 (0.003) loss 1.3330 (1.3064) acc 62.5000 (68.4811) lr 2.0000e-03 eta 10:27:43
+epoch [2/50] batch [435/500] time 1.591 (1.565) data 0.000 (0.003) loss 0.7407 (1.3072) acc 78.1250 (68.4555) lr 2.0000e-03 eta 10:27:38
+epoch [2/50] batch [440/500] time 1.583 (1.565) data 0.000 (0.003) loss 1.1201 (1.3039) acc 71.8750 (68.5085) lr 2.0000e-03 eta 10:27:34
+epoch [2/50] batch [445/500] time 1.574 (1.565) data 0.000 (0.003) loss 1.0986 (1.3051) acc 78.1250 (68.5183) lr 2.0000e-03 eta 10:27:25
+epoch [2/50] batch [450/500] time 1.552 (1.565) data 0.000 (0.003) loss 0.9971 (1.3030) acc 75.0000 (68.5556) lr 2.0000e-03 eta 10:27:19
+epoch [2/50] batch [455/500] time 1.568 (1.565) data 0.000 (0.003) loss 2.0117 (1.3023) acc 56.2500 (68.5852) lr 2.0000e-03 eta 10:27:11
+epoch [2/50] batch [460/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.5977 (1.3031) acc 62.5000 (68.5666) lr 2.0000e-03 eta 10:27:02
+epoch [2/50] batch [465/500] time 1.564 (1.565) data 0.000 (0.003) loss 0.9819 (1.3011) acc 68.7500 (68.5282) lr 2.0000e-03 eta 10:26:53
+epoch [2/50] batch [470/500] time 1.538 (1.565) data 0.000 (0.003) loss 0.7437 (1.2982) acc 78.1250 (68.5572) lr 2.0000e-03 eta 10:26:41
+epoch [2/50] batch [475/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.2246 (1.2996) acc 75.0000 (68.5592) lr 2.0000e-03 eta 10:26:30
+epoch [2/50] batch [480/500] time 1.531 (1.565) data 0.000 (0.003) loss 0.8628 (1.2967) acc 71.8750 (68.6198) lr 2.0000e-03 eta 10:26:19
+epoch [2/50] batch [485/500] time 1.559 (1.564) data 0.001 (0.003) loss 1.0752 (1.2947) acc 81.2500 (68.6534) lr 2.0000e-03 eta 10:26:09
+epoch [2/50] batch [490/500] time 1.513 (1.564) data 0.000 (0.003) loss 1.5518 (1.2930) acc 56.2500 (68.6543) lr 2.0000e-03 eta 10:25:56
+epoch [2/50] batch [495/500] time 1.555 (1.564) data 0.000 (0.003) loss 1.0498 (1.2895) acc 71.8750 (68.7311) lr 2.0000e-03 eta 10:25:45
+epoch [2/50] batch [500/500] time 1.536 (1.564) data 0.000 (0.002) loss 1.1924 (1.2871) acc 62.5000 (68.7625) lr 1.9980e-03 eta 10:25:32
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,474
+* accuracy: 76.9%
+* error: 23.1%
+* macro_f1: 76.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/500] time 1.530 (1.701) data 0.001 (0.196) loss 1.0645 (1.0272) acc 75.0000 (72.5000) lr 1.9980e-03 eta 11:20:16
+epoch [3/50] batch [10/500] time 1.526 (1.625) data 0.001 (0.098) loss 1.5176 (1.0540) acc 62.5000 (73.7500) lr 1.9980e-03 eta 10:49:48
+epoch [3/50] batch [15/500] time 1.550 (1.598) data 0.000 (0.066) loss 1.0430 (1.0806) acc 68.7500 (72.0833) lr 1.9980e-03 eta 10:38:47
+epoch [3/50] batch [20/500] time 1.556 (1.590) data 0.000 (0.049) loss 1.0537 (1.1208) acc 75.0000 (72.3438) lr 1.9980e-03 eta 10:35:23
+epoch [3/50] batch [25/500] time 1.566 (1.585) data 0.001 (0.040) loss 0.8604 (1.1368) acc 81.2500 (72.1250) lr 1.9980e-03 eta 10:33:13
+epoch [3/50] batch [30/500] time 1.577 (1.580) data 0.000 (0.033) loss 1.3623 (1.1972) acc 78.1250 (71.3542) lr 1.9980e-03 eta 10:31:17
+epoch [3/50] batch [35/500] time 1.562 (1.576) data 0.001 (0.028) loss 0.5063 (1.1668) acc 87.5000 (72.0536) lr 1.9980e-03 eta 10:29:39
+epoch [3/50] batch [40/500] time 1.592 (1.579) data 0.000 (0.025) loss 1.0732 (1.1641) acc 65.6250 (72.1875) lr 1.9980e-03 eta 10:30:27
+epoch [3/50] batch [45/500] time 1.558 (1.578) data 0.000 (0.022) loss 1.3545 (1.1634) acc 65.6250 (72.0139) lr 1.9980e-03 eta 10:29:49
+epoch [3/50] batch [50/500] time 1.553 (1.576) data 0.001 (0.020) loss 0.7183 (1.1433) acc 81.2500 (72.5000) lr 1.9980e-03 eta 10:28:58
+epoch [3/50] batch [55/500] time 1.560 (1.575) data 0.000 (0.018) loss 1.0586 (1.1695) acc 75.0000 (71.8182) lr 1.9980e-03 eta 10:28:25
+epoch [3/50] batch [60/500] time 1.552 (1.574) data 0.001 (0.017) loss 1.0137 (1.1949) acc 65.6250 (71.1458) lr 1.9980e-03 eta 10:27:54
+epoch [3/50] batch [65/500] time 1.576 (1.572) data 0.001 (0.016) loss 0.9312 (1.1818) acc 71.8750 (71.4423) lr 1.9980e-03 eta 10:27:14
+epoch [3/50] batch [70/500] time 1.570 (1.572) data 0.000 (0.014) loss 1.0010 (1.1879) acc 78.1250 (71.3839) lr 1.9980e-03 eta 10:26:46
+epoch [3/50] batch [75/500] time 1.570 (1.571) data 0.000 (0.014) loss 0.7905 (1.1924) acc 78.1250 (71.1667) lr 1.9980e-03 eta 10:26:35
+epoch [3/50] batch [80/500] time 1.574 (1.572) data 0.000 (0.013) loss 1.0312 (1.1987) acc 78.1250 (70.9766) lr 1.9980e-03 eta 10:26:32
+epoch [3/50] batch [85/500] time 1.558 (1.571) data 0.000 (0.012) loss 1.4355 (1.2010) acc 68.7500 (70.7721) lr 1.9980e-03 eta 10:26:10
+epoch [3/50] batch [90/500] time 1.573 (1.571) data 0.000 (0.011) loss 1.2314 (1.1905) acc 62.5000 (70.7986) lr 1.9980e-03 eta 10:26:03
+epoch [3/50] batch [95/500] time 1.580 (1.571) data 0.000 (0.011) loss 1.8574 (1.1919) acc 65.6250 (70.8224) lr 1.9980e-03 eta 10:25:46
+epoch [3/50] batch [100/500] time 1.552 (1.570) data 0.000 (0.010) loss 0.8765 (1.1760) acc 84.3750 (71.2812) lr 1.9980e-03 eta 10:25:28
+epoch [3/50] batch [105/500] time 1.572 (1.570) data 0.000 (0.010) loss 1.5996 (1.1816) acc 62.5000 (70.9821) lr 1.9980e-03 eta 10:25:13
+epoch [3/50] batch [110/500] time 1.540 (1.569) data 0.001 (0.009) loss 1.5449 (1.1886) acc 65.6250 (70.6818) lr 1.9980e-03 eta 10:24:50
+epoch [3/50] batch [115/500] time 1.569 (1.569) data 0.000 (0.009) loss 1.2197 (1.1928) acc 65.6250 (70.5978) lr 1.9980e-03 eta 10:24:37
+epoch [3/50] batch [120/500] time 1.572 (1.569) data 0.000 (0.009) loss 0.7881 (1.1859) acc 81.2500 (70.5469) lr 1.9980e-03 eta 10:24:20
+epoch [3/50] batch [125/500] time 1.548 (1.568) data 0.000 (0.008) loss 1.2715 (1.2017) acc 65.6250 (70.2750) lr 1.9980e-03 eta 10:24:03
+epoch [3/50] batch [130/500] time 1.557 (1.568) data 0.001 (0.008) loss 0.9473 (1.1980) acc 68.7500 (70.2885) lr 1.9980e-03 eta 10:23:53
+epoch [3/50] batch [135/500] time 1.573 (1.568) data 0.000 (0.008) loss 1.0381 (1.1989) acc 65.6250 (70.1620) lr 1.9980e-03 eta 10:23:34
+epoch [3/50] batch [140/500] time 1.553 (1.568) data 0.000 (0.007) loss 1.4629 (1.2097) acc 65.6250 (69.9777) lr 1.9980e-03 eta 10:23:31
+epoch [3/50] batch [145/500] time 1.526 (1.568) data 0.000 (0.007) loss 1.0244 (1.2150) acc 75.0000 (69.8060) lr 1.9980e-03 eta 10:23:14
+epoch [3/50] batch [150/500] time 1.531 (1.567) data 0.000 (0.007) loss 1.0342 (1.2182) acc 78.1250 (69.9167) lr 1.9980e-03 eta 10:22:53
+epoch [3/50] batch [155/500] time 1.546 (1.567) data 0.001 (0.007) loss 1.1094 (1.2276) acc 75.0000 (69.7379) lr 1.9980e-03 eta 10:22:39
+epoch [3/50] batch [160/500] time 1.609 (1.567) data 0.000 (0.007) loss 1.1230 (1.2221) acc 71.8750 (69.8047) lr 1.9980e-03 eta 10:22:44
+epoch [3/50] batch [165/500] time 1.607 (1.570) data 0.000 (0.006) loss 1.5088 (1.2231) acc 56.2500 (69.7348) lr 1.9980e-03 eta 10:23:29
+epoch [3/50] batch [170/500] time 1.552 (1.571) data 0.000 (0.006) loss 1.3027 (1.2230) acc 68.7500 (69.6140) lr 1.9980e-03 eta 10:23:54
+epoch [3/50] batch [175/500] time 1.559 (1.570) data 0.000 (0.006) loss 1.1973 (1.2240) acc 62.5000 (69.5179) lr 1.9980e-03 eta 10:23:33
+epoch [3/50] batch [180/500] time 1.558 (1.570) data 0.000 (0.006) loss 1.1006 (1.2252) acc 62.5000 (69.5139) lr 1.9980e-03 eta 10:23:14
+epoch [3/50] batch [185/500] time 1.558 (1.570) data 0.000 (0.006) loss 1.5400 (1.2219) acc 65.6250 (69.6115) lr 1.9980e-03 eta 10:23:12
+epoch [3/50] batch [190/500] time 1.553 (1.570) data 0.000 (0.006) loss 1.4570 (1.2230) acc 59.3750 (69.5230) lr 1.9980e-03 eta 10:22:53
+epoch [3/50] batch [195/500] time 1.578 (1.569) data 0.000 (0.005) loss 1.5811 (1.2252) acc 62.5000 (69.5032) lr 1.9980e-03 eta 10:22:39
+epoch [3/50] batch [200/500] time 1.555 (1.569) data 0.000 (0.005) loss 0.8022 (1.2273) acc 71.8750 (69.3906) lr 1.9980e-03 eta 10:22:28
+epoch [3/50] batch [205/500] time 1.579 (1.569) data 0.001 (0.005) loss 1.2549 (1.2286) acc 75.0000 (69.3598) lr 1.9980e-03 eta 10:22:19
+epoch [3/50] batch [210/500] time 1.574 (1.569) data 0.000 (0.005) loss 1.0605 (1.2263) acc 71.8750 (69.4494) lr 1.9980e-03 eta 10:22:09
+epoch [3/50] batch [215/500] time 1.570 (1.569) data 0.000 (0.005) loss 0.9790 (1.2264) acc 71.8750 (69.5640) lr 1.9980e-03 eta 10:21:56
+epoch [3/50] batch [220/500] time 1.575 (1.569) data 0.000 (0.005) loss 1.0342 (1.2281) acc 81.2500 (69.5739) lr 1.9980e-03 eta 10:21:41
+epoch [3/50] batch [225/500] time 1.569 (1.568) data 0.000 (0.005) loss 1.4854 (1.2287) acc 68.7500 (69.4861) lr 1.9980e-03 eta 10:21:30
+epoch [3/50] batch [230/500] time 1.599 (1.569) data 0.001 (0.005) loss 1.4150 (1.2337) acc 68.7500 (69.4022) lr 1.9980e-03 eta 10:21:30
+epoch [3/50] batch [235/500] time 1.575 (1.569) data 0.001 (0.005) loss 0.9829 (1.2346) acc 75.0000 (69.4016) lr 1.9980e-03 eta 10:21:19
+epoch [3/50] batch [240/500] time 1.552 (1.569) data 0.000 (0.005) loss 1.9053 (1.2344) acc 56.2500 (69.4792) lr 1.9980e-03 eta 10:21:07
+epoch [3/50] batch [245/500] time 1.572 (1.569) data 0.000 (0.004) loss 1.4160 (1.2380) acc 56.2500 (69.4005) lr 1.9980e-03 eta 10:21:02
+epoch [3/50] batch [250/500] time 1.543 (1.568) data 0.000 (0.004) loss 1.0020 (1.2367) acc 81.2500 (69.5000) lr 1.9980e-03 eta 10:20:50
+epoch [3/50] batch [255/500] time 1.556 (1.568) data 0.000 (0.004) loss 1.5078 (1.2353) acc 68.7500 (69.4975) lr 1.9980e-03 eta 10:20:35
+epoch [3/50] batch [260/500] time 1.565 (1.568) data 0.000 (0.004) loss 1.2783 (1.2385) acc 71.8750 (69.4231) lr 1.9980e-03 eta 10:20:27
+epoch [3/50] batch [265/500] time 1.586 (1.568) data 0.001 (0.004) loss 1.1611 (1.2440) acc 71.8750 (69.4340) lr 1.9980e-03 eta 10:20:18
+epoch [3/50] batch [270/500] time 1.571 (1.568) data 0.000 (0.004) loss 1.1523 (1.2425) acc 78.1250 (69.5139) lr 1.9980e-03 eta 10:20:07
+epoch [3/50] batch [275/500] time 1.565 (1.568) data 0.000 (0.004) loss 1.9248 (1.2448) acc 62.5000 (69.5682) lr 1.9980e-03 eta 10:19:59
+epoch [3/50] batch [280/500] time 1.565 (1.568) data 0.000 (0.004) loss 0.7241 (1.2434) acc 75.0000 (69.6094) lr 1.9980e-03 eta 10:19:54
+epoch [3/50] batch [285/500] time 1.539 (1.568) data 0.000 (0.004) loss 1.0303 (1.2407) acc 71.8750 (69.6820) lr 1.9980e-03 eta 10:19:51
+epoch [3/50] batch [290/500] time 1.556 (1.568) data 0.000 (0.004) loss 1.5234 (1.2406) acc 62.5000 (69.6444) lr 1.9980e-03 eta 10:19:42
+epoch [3/50] batch [295/500] time 1.564 (1.568) data 0.000 (0.004) loss 1.1660 (1.2391) acc 71.8750 (69.6716) lr 1.9980e-03 eta 10:19:33
+epoch [3/50] batch [300/500] time 1.568 (1.568) data 0.000 (0.004) loss 1.6709 (1.2360) acc 59.3750 (69.6979) lr 1.9980e-03 eta 10:19:22
+epoch [3/50] batch [305/500] time 1.565 (1.568) data 0.000 (0.004) loss 0.6401 (1.2350) acc 81.2500 (69.6926) lr 1.9980e-03 eta 10:19:14
+epoch [3/50] batch [310/500] time 1.550 (1.568) data 0.000 (0.004) loss 1.7422 (1.2362) acc 59.3750 (69.7077) lr 1.9980e-03 eta 10:19:04
+epoch [3/50] batch [315/500] time 1.576 (1.568) data 0.000 (0.004) loss 1.2539 (1.2363) acc 65.6250 (69.7123) lr 1.9980e-03 eta 10:18:58
+epoch [3/50] batch [320/500] time 1.565 (1.568) data 0.000 (0.003) loss 1.3418 (1.2380) acc 71.8750 (69.6875) lr 1.9980e-03 eta 10:18:43
+epoch [3/50] batch [325/500] time 1.665 (1.568) data 0.001 (0.003) loss 0.8140 (1.2384) acc 78.1250 (69.7212) lr 1.9980e-03 eta 10:18:38
+epoch [3/50] batch [330/500] time 1.565 (1.568) data 0.000 (0.003) loss 0.9102 (1.2371) acc 78.1250 (69.6875) lr 1.9980e-03 eta 10:18:27
+epoch [3/50] batch [335/500] time 1.562 (1.568) data 0.000 (0.003) loss 1.2266 (1.2398) acc 65.6250 (69.6455) lr 1.9980e-03 eta 10:18:16
+epoch [3/50] batch [340/500] time 1.552 (1.567) data 0.000 (0.003) loss 1.9355 (1.2436) acc 56.2500 (69.5956) lr 1.9980e-03 eta 10:18:06
+epoch [3/50] batch [345/500] time 1.570 (1.567) data 0.000 (0.003) loss 1.6348 (1.2445) acc 62.5000 (69.6014) lr 1.9980e-03 eta 10:17:56
+epoch [3/50] batch [350/500] time 1.585 (1.567) data 0.000 (0.003) loss 0.7671 (1.2416) acc 84.3750 (69.6696) lr 1.9980e-03 eta 10:17:48
+epoch [3/50] batch [355/500] time 1.544 (1.567) data 0.000 (0.003) loss 0.8955 (1.2361) acc 65.6250 (69.7711) lr 1.9980e-03 eta 10:17:39
+epoch [3/50] batch [360/500] time 1.576 (1.567) data 0.000 (0.003) loss 0.9863 (1.2368) acc 68.7500 (69.7569) lr 1.9980e-03 eta 10:17:32
+epoch [3/50] batch [365/500] time 1.572 (1.567) data 0.000 (0.003) loss 1.3438 (1.2349) acc 65.6250 (69.7774) lr 1.9980e-03 eta 10:17:25
+epoch [3/50] batch [370/500] time 1.574 (1.568) data 0.000 (0.003) loss 1.1377 (1.2334) acc 65.6250 (69.7720) lr 1.9980e-03 eta 10:17:21
+epoch [3/50] batch [375/500] time 1.557 (1.568) data 0.001 (0.003) loss 1.0742 (1.2304) acc 71.8750 (69.8333) lr 1.9980e-03 eta 10:17:12
+epoch [3/50] batch [380/500] time 1.555 (1.567) data 0.000 (0.003) loss 1.3584 (1.2329) acc 62.5000 (69.7697) lr 1.9980e-03 eta 10:17:03
+epoch [3/50] batch [385/500] time 1.540 (1.567) data 0.000 (0.003) loss 1.0459 (1.2316) acc 78.1250 (69.7646) lr 1.9980e-03 eta 10:16:52
+epoch [3/50] batch [390/500] time 1.551 (1.567) data 0.000 (0.003) loss 1.1270 (1.2315) acc 65.6250 (69.7276) lr 1.9980e-03 eta 10:16:46
+epoch [3/50] batch [395/500] time 1.567 (1.567) data 0.000 (0.003) loss 1.7861 (1.2344) acc 68.7500 (69.7389) lr 1.9980e-03 eta 10:16:37
+epoch [3/50] batch [400/500] time 1.543 (1.567) data 0.000 (0.003) loss 1.8174 (1.2361) acc 56.2500 (69.7109) lr 1.9980e-03 eta 10:16:24
+epoch [3/50] batch [405/500] time 1.545 (1.567) data 0.000 (0.003) loss 0.9321 (1.2343) acc 75.0000 (69.7299) lr 1.9980e-03 eta 10:16:15
+epoch [3/50] batch [410/500] time 1.565 (1.567) data 0.001 (0.003) loss 1.3125 (1.2341) acc 75.0000 (69.7332) lr 1.9980e-03 eta 10:16:06
+epoch [3/50] batch [415/500] time 1.564 (1.567) data 0.001 (0.003) loss 1.3633 (1.2309) acc 56.2500 (69.8042) lr 1.9980e-03 eta 10:15:59
+epoch [3/50] batch [420/500] time 1.565 (1.567) data 0.001 (0.003) loss 0.7520 (1.2326) acc 81.2500 (69.7470) lr 1.9980e-03 eta 10:15:50
+epoch [3/50] batch [425/500] time 1.553 (1.567) data 0.000 (0.003) loss 1.4951 (1.2344) acc 62.5000 (69.6912) lr 1.9980e-03 eta 10:15:46
+epoch [3/50] batch [430/500] time 1.537 (1.567) data 0.000 (0.003) loss 1.8086 (1.2370) acc 59.3750 (69.6366) lr 1.9980e-03 eta 10:15:35
+epoch [3/50] batch [435/500] time 1.550 (1.567) data 0.000 (0.003) loss 1.6162 (1.2400) acc 71.8750 (69.6049) lr 1.9980e-03 eta 10:15:25
+epoch [3/50] batch [440/500] time 1.565 (1.567) data 0.000 (0.003) loss 0.7725 (1.2396) acc 78.1250 (69.5739) lr 1.9980e-03 eta 10:15:14
+epoch [3/50] batch [445/500] time 1.568 (1.567) data 0.000 (0.003) loss 1.2998 (1.2393) acc 59.3750 (69.5154) lr 1.9980e-03 eta 10:15:05
+epoch [3/50] batch [450/500] time 1.544 (1.567) data 0.000 (0.003) loss 1.7070 (1.2427) acc 65.6250 (69.4583) lr 1.9980e-03 eta 10:14:54
+epoch [3/50] batch [455/500] time 1.567 (1.567) data 0.000 (0.003) loss 0.5923 (1.2406) acc 84.3750 (69.5055) lr 1.9980e-03 eta 10:14:45
+epoch [3/50] batch [460/500] time 1.561 (1.567) data 0.000 (0.003) loss 1.3623 (1.2407) acc 71.8750 (69.5041) lr 1.9980e-03 eta 10:14:38
+epoch [3/50] batch [465/500] time 1.565 (1.566) data 0.000 (0.003) loss 0.9004 (1.2406) acc 81.2500 (69.5296) lr 1.9980e-03 eta 10:14:27
+epoch [3/50] batch [470/500] time 1.527 (1.567) data 0.000 (0.002) loss 0.8750 (1.2387) acc 75.0000 (69.5545) lr 1.9980e-03 eta 10:14:20
+epoch [3/50] batch [475/500] time 1.560 (1.566) data 0.000 (0.002) loss 1.7803 (1.2396) acc 56.2500 (69.5789) lr 1.9980e-03 eta 10:14:11
+epoch [3/50] batch [480/500] time 1.550 (1.566) data 0.000 (0.002) loss 1.2930 (1.2380) acc 68.7500 (69.6224) lr 1.9980e-03 eta 10:14:01
+epoch [3/50] batch [485/500] time 1.571 (1.566) data 0.001 (0.002) loss 1.5693 (1.2375) acc 65.6250 (69.6521) lr 1.9980e-03 eta 10:13:54
+epoch [3/50] batch [490/500] time 1.569 (1.566) data 0.000 (0.002) loss 1.1201 (1.2382) acc 68.7500 (69.6429) lr 1.9980e-03 eta 10:13:44
+epoch [3/50] batch [495/500] time 1.543 (1.566) data 0.000 (0.002) loss 1.4541 (1.2395) acc 62.5000 (69.6275) lr 1.9980e-03 eta 10:13:33
+epoch [3/50] batch [500/500] time 1.536 (1.566) data 0.000 (0.002) loss 1.4707 (1.2421) acc 78.1250 (69.6188) lr 1.9921e-03 eta 10:13:22
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,594
+* accuracy: 77.2%
+* error: 22.8%
+* macro_f1: 76.5%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [4/50] batch [5/500] time 1.551 (1.688) data 0.000 (0.178) loss 1.4854 (1.4664) acc 71.8750 (63.1250) lr 1.9921e-03 eta 11:00:51
+epoch [4/50] batch [10/500] time 1.536 (1.626) data 0.000 (0.089) loss 1.9824 (1.4054) acc 62.5000 (69.0625) lr 1.9921e-03 eta 10:36:26
+epoch [4/50] batch [15/500] time 1.544 (1.605) data 0.000 (0.060) loss 1.4111 (1.3109) acc 62.5000 (71.0417) lr 1.9921e-03 eta 10:28:11
+epoch [4/50] batch [20/500] time 1.579 (1.598) data 0.000 (0.045) loss 0.9487 (1.3033) acc 75.0000 (72.1875) lr 1.9921e-03 eta 10:25:15
+epoch [4/50] batch [25/500] time 1.551 (1.590) data 0.000 (0.036) loss 1.4209 (1.2379) acc 65.6250 (72.3750) lr 1.9921e-03 eta 10:22:09
+epoch [4/50] batch [30/500] time 1.576 (1.586) data 0.000 (0.030) loss 1.2549 (1.2384) acc 78.1250 (71.9792) lr 1.9921e-03 eta 10:20:18
+epoch [4/50] batch [35/500] time 1.562 (1.582) data 0.000 (0.026) loss 0.5991 (1.2099) acc 90.6250 (72.7679) lr 1.9921e-03 eta 10:18:41
+epoch [4/50] batch [40/500] time 1.571 (1.580) data 0.000 (0.023) loss 1.5186 (1.2139) acc 68.7500 (72.1094) lr 1.9921e-03 eta 10:17:37
+epoch [4/50] batch [45/500] time 1.584 (1.577) data 0.000 (0.020) loss 1.0625 (1.2405) acc 78.1250 (71.3194) lr 1.9921e-03 eta 10:16:39
+epoch [4/50] batch [50/500] time 1.555 (1.576) data 0.000 (0.018) loss 1.1904 (1.2415) acc 71.8750 (70.7500) lr 1.9921e-03 eta 10:16:04
+epoch [4/50] batch [55/500] time 1.562 (1.574) data 0.000 (0.017) loss 2.2988 (1.2680) acc 53.1250 (70.2273) lr 1.9921e-03 eta 10:15:07
+epoch [4/50] batch [60/500] time 1.542 (1.574) data 0.000 (0.015) loss 1.0059 (1.2726) acc 68.7500 (69.9479) lr 1.9921e-03 eta 10:15:06
+epoch [4/50] batch [65/500] time 1.557 (1.573) data 0.000 (0.014) loss 1.7998 (1.2636) acc 59.3750 (70.0962) lr 1.9921e-03 eta 10:14:20
+epoch [4/50] batch [70/500] time 1.557 (1.572) data 0.001 (0.013) loss 1.2725 (1.2744) acc 71.8750 (69.9107) lr 1.9921e-03 eta 10:13:48
+epoch [4/50] batch [75/500] time 1.565 (1.571) data 0.000 (0.012) loss 1.5010 (1.2858) acc 59.3750 (69.5417) lr 1.9921e-03 eta 10:13:20
+epoch [4/50] batch [80/500] time 1.563 (1.570) data 0.000 (0.012) loss 0.4321 (1.2884) acc 87.5000 (69.5312) lr 1.9921e-03 eta 10:12:58
+epoch [4/50] batch [85/500] time 1.586 (1.570) data 0.000 (0.011) loss 1.2236 (1.2861) acc 75.0000 (69.6691) lr 1.9921e-03 eta 10:12:40
+epoch [4/50] batch [90/500] time 1.563 (1.569) data 0.000 (0.010) loss 1.2373 (1.2912) acc 75.0000 (69.7569) lr 1.9921e-03 eta 10:12:13
+epoch [4/50] batch [95/500] time 1.557 (1.569) data 0.000 (0.010) loss 1.2510 (1.3018) acc 71.8750 (69.4079) lr 1.9921e-03 eta 10:11:56
+epoch [4/50] batch [100/500] time 1.541 (1.568) data 0.000 (0.009) loss 1.3379 (1.2919) acc 68.7500 (69.6562) lr 1.9921e-03 eta 10:11:32
+epoch [4/50] batch [105/500] time 1.553 (1.568) data 0.001 (0.009) loss 1.3486 (1.2944) acc 62.5000 (69.5536) lr 1.9921e-03 eta 10:11:34
+epoch [4/50] batch [110/500] time 1.554 (1.568) data 0.000 (0.008) loss 1.0234 (1.2869) acc 75.0000 (69.8580) lr 1.9921e-03 eta 10:11:14
+epoch [4/50] batch [115/500] time 1.560 (1.567) data 0.000 (0.008) loss 0.7393 (1.2789) acc 81.2500 (70.1630) lr 1.9921e-03 eta 10:10:49
+epoch [4/50] batch [120/500] time 1.554 (1.567) data 0.000 (0.008) loss 1.6777 (1.2811) acc 59.3750 (69.8958) lr 1.9921e-03 eta 10:10:37
+epoch [4/50] batch [125/500] time 1.547 (1.567) data 0.000 (0.007) loss 1.5283 (1.2723) acc 68.7500 (70.0750) lr 1.9921e-03 eta 10:10:19
+epoch [4/50] batch [130/500] time 1.561 (1.566) data 0.000 (0.007) loss 1.3047 (1.2737) acc 59.3750 (70.0721) lr 1.9921e-03 eta 10:10:00
+epoch [4/50] batch [135/500] time 1.533 (1.566) data 0.001 (0.007) loss 0.7705 (1.2627) acc 81.2500 (70.2778) lr 1.9921e-03 eta 10:09:40
+epoch [4/50] batch [140/500] time 1.543 (1.565) data 0.000 (0.007) loss 0.9863 (1.2619) acc 71.8750 (70.2902) lr 1.9921e-03 eta 10:09:17
+epoch [4/50] batch [145/500] time 1.565 (1.564) data 0.000 (0.007) loss 1.6250 (1.2640) acc 62.5000 (70.1940) lr 1.9921e-03 eta 10:08:57
+epoch [4/50] batch [150/500] time 1.557 (1.564) data 0.000 (0.006) loss 1.6123 (1.2636) acc 62.5000 (69.9167) lr 1.9921e-03 eta 10:08:46
+epoch [4/50] batch [155/500] time 1.563 (1.564) data 0.000 (0.006) loss 1.1582 (1.2632) acc 84.3750 (70.0000) lr 1.9921e-03 eta 10:08:33
+epoch [4/50] batch [160/500] time 1.550 (1.563) data 0.001 (0.006) loss 1.4717 (1.2603) acc 68.7500 (69.8828) lr 1.9921e-03 eta 10:08:11
+epoch [4/50] batch [165/500] time 1.552 (1.563) data 0.000 (0.006) loss 0.9897 (1.2518) acc 81.2500 (70.1705) lr 1.9921e-03 eta 10:07:57
+epoch [4/50] batch [170/500] time 1.562 (1.563) data 0.000 (0.006) loss 1.1895 (1.2512) acc 68.7500 (70.2390) lr 1.9921e-03 eta 10:07:47
+epoch [4/50] batch [175/500] time 1.573 (1.563) data 0.000 (0.005) loss 1.2402 (1.2497) acc 65.6250 (70.1607) lr 1.9921e-03 eta 10:07:38
+epoch [4/50] batch [180/500] time 1.566 (1.563) data 0.000 (0.005) loss 1.3711 (1.2498) acc 59.3750 (70.1215) lr 1.9921e-03 eta 10:07:29
+epoch [4/50] batch [185/500] time 1.562 (1.563) data 0.000 (0.005) loss 0.4265 (1.2436) acc 87.5000 (70.2365) lr 1.9921e-03 eta 10:07:16
+epoch [4/50] batch [190/500] time 1.538 (1.563) data 0.000 (0.005) loss 0.8657 (1.2375) acc 68.7500 (70.3783) lr 1.9921e-03 eta 10:07:04
+epoch [4/50] batch [195/500] time 1.571 (1.563) data 0.000 (0.005) loss 1.5332 (1.2430) acc 62.5000 (70.3365) lr 1.9921e-03 eta 10:06:59
+epoch [4/50] batch [200/500] time 1.560 (1.563) data 0.001 (0.005) loss 0.8960 (1.2429) acc 81.2500 (70.3438) lr 1.9921e-03 eta 10:06:48
+epoch [4/50] batch [205/500] time 1.534 (1.563) data 0.000 (0.005) loss 1.0225 (1.2448) acc 71.8750 (70.2134) lr 1.9921e-03 eta 10:06:43
+epoch [4/50] batch [210/500] time 1.558 (1.563) data 0.000 (0.005) loss 1.3252 (1.2440) acc 71.8750 (70.1637) lr 1.9921e-03 eta 10:06:33
+epoch [4/50] batch [215/500] time 1.542 (1.562) data 0.000 (0.005) loss 1.5381 (1.2456) acc 62.5000 (70.1308) lr 1.9921e-03 eta 10:06:19
+epoch [4/50] batch [220/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.2031 (1.2503) acc 62.5000 (69.9858) lr 1.9921e-03 eta 10:06:06
+epoch [4/50] batch [225/500] time 1.580 (1.562) data 0.000 (0.004) loss 0.7485 (1.2470) acc 78.1250 (70.0417) lr 1.9921e-03 eta 10:05:55
+epoch [4/50] batch [230/500] time 1.551 (1.562) data 0.000 (0.004) loss 1.1836 (1.2465) acc 68.7500 (70.0951) lr 1.9921e-03 eta 10:05:40
+epoch [4/50] batch [235/500] time 1.529 (1.561) data 0.001 (0.004) loss 0.6914 (1.2456) acc 81.2500 (70.0798) lr 1.9921e-03 eta 10:05:26
+epoch [4/50] batch [240/500] time 1.558 (1.561) data 0.000 (0.004) loss 0.7607 (1.2449) acc 81.2500 (70.0391) lr 1.9921e-03 eta 10:05:16
+epoch [4/50] batch [245/500] time 1.685 (1.562) data 0.000 (0.004) loss 1.5811 (1.2481) acc 71.8750 (69.9617) lr 1.9921e-03 eta 10:05:20
+epoch [4/50] batch [250/500] time 1.556 (1.562) data 0.000 (0.004) loss 0.7466 (1.2466) acc 71.8750 (70.0875) lr 1.9921e-03 eta 10:05:13
+epoch [4/50] batch [255/500] time 1.572 (1.562) data 0.000 (0.004) loss 1.1992 (1.2482) acc 75.0000 (70.0368) lr 1.9921e-03 eta 10:05:01
+epoch [4/50] batch [260/500] time 1.571 (1.562) data 0.000 (0.004) loss 0.7485 (1.2445) acc 75.0000 (70.1322) lr 1.9921e-03 eta 10:04:55
+epoch [4/50] batch [265/500] time 1.570 (1.562) data 0.000 (0.004) loss 1.2227 (1.2446) acc 68.7500 (70.0708) lr 1.9921e-03 eta 10:04:51
+epoch [4/50] batch [270/500] time 1.533 (1.562) data 0.001 (0.004) loss 1.2002 (1.2475) acc 68.7500 (70.0116) lr 1.9921e-03 eta 10:04:40
+epoch [4/50] batch [275/500] time 1.571 (1.562) data 0.000 (0.004) loss 1.7070 (1.2478) acc 65.6250 (69.9886) lr 1.9921e-03 eta 10:04:36
+epoch [4/50] batch [280/500] time 1.587 (1.562) data 0.000 (0.004) loss 1.3047 (1.2524) acc 59.3750 (69.8884) lr 1.9921e-03 eta 10:04:30
+epoch [4/50] batch [285/500] time 1.568 (1.562) data 0.000 (0.004) loss 0.7754 (1.2501) acc 90.6250 (69.9452) lr 1.9921e-03 eta 10:04:26
+epoch [4/50] batch [290/500] time 1.571 (1.562) data 0.000 (0.003) loss 1.4648 (1.2549) acc 71.8750 (69.8276) lr 1.9921e-03 eta 10:04:19
+epoch [4/50] batch [295/500] time 1.542 (1.562) data 0.000 (0.003) loss 1.4092 (1.2571) acc 71.8750 (69.8093) lr 1.9921e-03 eta 10:04:10
+epoch [4/50] batch [300/500] time 1.581 (1.562) data 0.000 (0.003) loss 0.9795 (1.2527) acc 81.2500 (69.9167) lr 1.9921e-03 eta 10:04:08
+epoch [4/50] batch [305/500] time 1.578 (1.563) data 0.000 (0.003) loss 1.3301 (1.2516) acc 71.8750 (69.9488) lr 1.9921e-03 eta 10:04:03
+epoch [4/50] batch [310/500] time 1.569 (1.563) data 0.000 (0.003) loss 0.9902 (1.2508) acc 75.0000 (69.9395) lr 1.9921e-03 eta 10:04:00
+epoch [4/50] batch [315/500] time 1.580 (1.563) data 0.000 (0.003) loss 1.4834 (1.2507) acc 65.6250 (69.9008) lr 1.9921e-03 eta 10:03:53
+epoch [4/50] batch [320/500] time 1.563 (1.563) data 0.000 (0.003) loss 1.9580 (1.2538) acc 50.0000 (69.7461) lr 1.9921e-03 eta 10:03:40
+epoch [4/50] batch [325/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.6006 (1.2550) acc 68.7500 (69.7308) lr 1.9921e-03 eta 10:03:31
+epoch [4/50] batch [330/500] time 1.573 (1.563) data 0.001 (0.003) loss 1.9521 (1.2583) acc 59.3750 (69.6970) lr 1.9921e-03 eta 10:03:25
+epoch [4/50] batch [335/500] time 1.578 (1.563) data 0.000 (0.003) loss 1.0889 (1.2595) acc 65.6250 (69.5896) lr 1.9921e-03 eta 10:03:20
+epoch [4/50] batch [340/500] time 1.577 (1.563) data 0.000 (0.003) loss 1.0762 (1.2574) acc 71.8750 (69.6415) lr 1.9921e-03 eta 10:03:13
+epoch [4/50] batch [345/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.2998 (1.2561) acc 62.5000 (69.6739) lr 1.9921e-03 eta 10:03:13
+epoch [4/50] batch [350/500] time 1.544 (1.563) data 0.000 (0.003) loss 1.3037 (1.2575) acc 68.7500 (69.6607) lr 1.9921e-03 eta 10:03:04
+epoch [4/50] batch [355/500] time 1.569 (1.563) data 0.000 (0.003) loss 0.7642 (1.2559) acc 75.0000 (69.7007) lr 1.9921e-03 eta 10:02:58
+epoch [4/50] batch [360/500] time 1.578 (1.563) data 0.000 (0.003) loss 1.5850 (1.2542) acc 56.2500 (69.7049) lr 1.9921e-03 eta 10:02:54
+epoch [4/50] batch [365/500] time 1.563 (1.563) data 0.000 (0.003) loss 0.9937 (1.2577) acc 68.7500 (69.6404) lr 1.9921e-03 eta 10:02:49
+epoch [4/50] batch [370/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.3604 (1.2569) acc 78.1250 (69.6706) lr 1.9921e-03 eta 10:02:40
+epoch [4/50] batch [375/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.3193 (1.2552) acc 62.5000 (69.6417) lr 1.9921e-03 eta 10:02:32
+epoch [4/50] batch [380/500] time 1.569 (1.563) data 0.000 (0.003) loss 1.3369 (1.2575) acc 75.0000 (69.5970) lr 1.9921e-03 eta 10:02:22
+epoch [4/50] batch [385/500] time 1.533 (1.563) data 0.000 (0.003) loss 0.9590 (1.2563) acc 84.3750 (69.6429) lr 1.9921e-03 eta 10:02:09
+epoch [4/50] batch [390/500] time 1.533 (1.563) data 0.000 (0.003) loss 1.0547 (1.2590) acc 71.8750 (69.5753) lr 1.9921e-03 eta 10:02:02
+epoch [4/50] batch [395/500] time 1.534 (1.563) data 0.000 (0.003) loss 0.9268 (1.2602) acc 75.0000 (69.5570) lr 1.9921e-03 eta 10:01:51
+epoch [4/50] batch [400/500] time 1.563 (1.563) data 0.000 (0.003) loss 1.1670 (1.2616) acc 71.8750 (69.5234) lr 1.9921e-03 eta 10:01:42
+epoch [4/50] batch [405/500] time 1.543 (1.563) data 0.000 (0.003) loss 0.8950 (1.2584) acc 78.1250 (69.6065) lr 1.9921e-03 eta 10:01:31
+epoch [4/50] batch [410/500] time 1.566 (1.563) data 0.000 (0.003) loss 1.0928 (1.2593) acc 68.7500 (69.5351) lr 1.9921e-03 eta 10:01:22
+epoch [4/50] batch [415/500] time 1.566 (1.563) data 0.001 (0.003) loss 1.2178 (1.2572) acc 71.8750 (69.5256) lr 1.9921e-03 eta 10:01:15
+epoch [4/50] batch [420/500] time 1.557 (1.563) data 0.000 (0.003) loss 0.8784 (1.2548) acc 81.2500 (69.5461) lr 1.9921e-03 eta 10:01:06
+epoch [4/50] batch [425/500] time 1.562 (1.563) data 0.000 (0.002) loss 1.3984 (1.2535) acc 65.6250 (69.5588) lr 1.9921e-03 eta 10:00:59
+epoch [4/50] batch [430/500] time 1.562 (1.563) data 0.000 (0.002) loss 1.5410 (1.2536) acc 59.3750 (69.5567) lr 1.9921e-03 eta 10:00:51
+epoch [4/50] batch [435/500] time 1.554 (1.563) data 0.001 (0.002) loss 1.7158 (1.2558) acc 62.5000 (69.5115) lr 1.9921e-03 eta 10:00:39
+epoch [4/50] batch [440/500] time 1.561 (1.563) data 0.000 (0.002) loss 0.8828 (1.2529) acc 78.1250 (69.5170) lr 1.9921e-03 eta 10:00:32
+epoch [4/50] batch [445/500] time 1.566 (1.563) data 0.000 (0.002) loss 1.6270 (1.2529) acc 65.6250 (69.4874) lr 1.9921e-03 eta 10:00:24
+epoch [4/50] batch [450/500] time 1.584 (1.563) data 0.000 (0.002) loss 1.5215 (1.2522) acc 65.6250 (69.5347) lr 1.9921e-03 eta 10:00:18
+epoch [4/50] batch [455/500] time 1.553 (1.563) data 0.001 (0.002) loss 1.4844 (1.2540) acc 65.6250 (69.5536) lr 1.9921e-03 eta 10:00:08
+epoch [4/50] batch [460/500] time 1.535 (1.562) data 0.000 (0.002) loss 1.8447 (1.2557) acc 59.3750 (69.5177) lr 1.9921e-03 eta 9:59:58
+epoch [4/50] batch [465/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.7285 (1.2559) acc 59.3750 (69.4892) lr 1.9921e-03 eta 9:59:49
+epoch [4/50] batch [470/500] time 1.556 (1.562) data 0.000 (0.002) loss 1.7480 (1.2564) acc 62.5000 (69.4814) lr 1.9921e-03 eta 9:59:40
+epoch [4/50] batch [475/500] time 1.573 (1.562) data 0.000 (0.002) loss 1.0996 (1.2597) acc 71.8750 (69.4737) lr 1.9921e-03 eta 9:59:32
+epoch [4/50] batch [480/500] time 1.543 (1.562) data 0.000 (0.002) loss 0.7256 (1.2569) acc 87.5000 (69.5638) lr 1.9921e-03 eta 9:59:23
+epoch [4/50] batch [485/500] time 1.540 (1.562) data 0.001 (0.002) loss 1.5234 (1.2582) acc 53.1250 (69.4910) lr 1.9921e-03 eta 9:59:14
+epoch [4/50] batch [490/500] time 1.583 (1.562) data 0.000 (0.002) loss 1.1230 (1.2597) acc 68.7500 (69.4515) lr 1.9921e-03 eta 9:59:10
+epoch [4/50] batch [495/500] time 1.549 (1.562) data 0.000 (0.002) loss 1.0020 (1.2562) acc 65.6250 (69.5013) lr 1.9921e-03 eta 9:59:04
+epoch [4/50] batch [500/500] time 1.547 (1.562) data 0.000 (0.002) loss 1.8877 (1.2575) acc 62.5000 (69.5062) lr 1.9823e-03 eta 9:58:55
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,735
+* accuracy: 77.5%
+* error: 22.5%
+* macro_f1: 76.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [5/50] batch [5/500] time 1.551 (1.681) data 0.000 (0.176) loss 1.0010 (1.0877) acc 75.0000 (73.1250) lr 1.9823e-03 eta 10:44:04
+epoch [5/50] batch [10/500] time 1.567 (1.621) data 0.001 (0.088) loss 1.3730 (1.1820) acc 65.6250 (71.2500) lr 1.9823e-03 eta 10:21:01
+epoch [5/50] batch [15/500] time 1.590 (1.601) data 0.001 (0.059) loss 1.5391 (1.1960) acc 59.3750 (70.2083) lr 1.9823e-03 eta 10:13:24
+epoch [5/50] batch [20/500] time 1.563 (1.593) data 0.000 (0.044) loss 1.1064 (1.1754) acc 65.6250 (69.0625) lr 1.9823e-03 eta 10:09:57
+epoch [5/50] batch [25/500] time 1.556 (1.588) data 0.001 (0.036) loss 1.2754 (1.1703) acc 75.0000 (69.2500) lr 1.9823e-03 eta 10:08:11
+epoch [5/50] batch [30/500] time 1.573 (1.586) data 0.000 (0.030) loss 1.3799 (1.1621) acc 68.7500 (69.8958) lr 1.9823e-03 eta 10:07:15
+epoch [5/50] batch [35/500] time 1.561 (1.587) data 0.001 (0.026) loss 0.9971 (1.2037) acc 75.0000 (69.2857) lr 1.9823e-03 eta 10:07:34
+epoch [5/50] batch [40/500] time 1.586 (1.585) data 0.001 (0.022) loss 0.8579 (1.2088) acc 78.1250 (69.3750) lr 1.9823e-03 eta 10:06:40
+epoch [5/50] batch [45/500] time 1.556 (1.583) data 0.001 (0.020) loss 1.2354 (1.2125) acc 75.0000 (69.6528) lr 1.9823e-03 eta 10:05:31
+epoch [5/50] batch [50/500] time 1.567 (1.580) data 0.001 (0.018) loss 0.9668 (1.1911) acc 81.2500 (70.8125) lr 1.9823e-03 eta 10:04:28
+epoch [5/50] batch [55/500] time 1.553 (1.579) data 0.001 (0.016) loss 1.0176 (1.1832) acc 75.0000 (71.1932) lr 1.9823e-03 eta 10:03:38
+epoch [5/50] batch [60/500] time 1.567 (1.578) data 0.001 (0.015) loss 1.1230 (1.1713) acc 65.6250 (71.3021) lr 1.9823e-03 eta 10:03:18
+epoch [5/50] batch [65/500] time 1.559 (1.577) data 0.001 (0.014) loss 1.5273 (1.1772) acc 62.5000 (71.2019) lr 1.9823e-03 eta 10:02:46
+epoch [5/50] batch [70/500] time 1.581 (1.576) data 0.001 (0.013) loss 0.8960 (1.1949) acc 78.1250 (71.0714) lr 1.9823e-03 eta 10:02:26
+epoch [5/50] batch [75/500] time 1.553 (1.575) data 0.000 (0.012) loss 0.9331 (1.1868) acc 68.7500 (71.0833) lr 1.9823e-03 eta 10:01:56
+epoch [5/50] batch [80/500] time 1.553 (1.574) data 0.000 (0.011) loss 1.4219 (1.1849) acc 59.3750 (71.2109) lr 1.9823e-03 eta 10:01:23
+epoch [5/50] batch [85/500] time 1.550 (1.573) data 0.000 (0.011) loss 0.6548 (1.1794) acc 81.2500 (71.0662) lr 1.9823e-03 eta 10:00:54
+epoch [5/50] batch [90/500] time 1.588 (1.573) data 0.000 (0.010) loss 1.1406 (1.1973) acc 71.8750 (70.7986) lr 1.9823e-03 eta 10:00:36
+epoch [5/50] batch [95/500] time 1.541 (1.572) data 0.000 (0.010) loss 0.9512 (1.1940) acc 68.7500 (70.7566) lr 1.9823e-03 eta 10:00:13
+epoch [5/50] batch [100/500] time 1.567 (1.572) data 0.000 (0.009) loss 1.3926 (1.2064) acc 68.7500 (70.5938) lr 1.9823e-03 eta 10:00:00
+epoch [5/50] batch [105/500] time 1.582 (1.572) data 0.000 (0.009) loss 1.7236 (1.2110) acc 65.6250 (70.4464) lr 1.9823e-03 eta 9:59:51
+epoch [5/50] batch [110/500] time 1.556 (1.572) data 0.000 (0.008) loss 1.5322 (1.2205) acc 65.6250 (70.3125) lr 1.9823e-03 eta 9:59:38
+epoch [5/50] batch [115/500] time 1.569 (1.571) data 0.000 (0.008) loss 1.1963 (1.2167) acc 65.6250 (70.3261) lr 1.9823e-03 eta 9:59:12
+epoch [5/50] batch [120/500] time 1.557 (1.571) data 0.000 (0.008) loss 0.8960 (1.2204) acc 81.2500 (70.1823) lr 1.9823e-03 eta 9:58:57
+epoch [5/50] batch [125/500] time 1.565 (1.570) data 0.000 (0.008) loss 1.7852 (1.2206) acc 53.1250 (70.1250) lr 1.9823e-03 eta 9:58:44
+epoch [5/50] batch [130/500] time 1.578 (1.570) data 0.000 (0.007) loss 1.0840 (1.2239) acc 71.8750 (70.1442) lr 1.9823e-03 eta 9:58:32
+epoch [5/50] batch [135/500] time 1.568 (1.571) data 0.000 (0.007) loss 1.3672 (1.2217) acc 71.8750 (70.2083) lr 1.9823e-03 eta 9:58:41
+epoch [5/50] batch [140/500] time 1.542 (1.571) data 0.000 (0.007) loss 1.3037 (1.2144) acc 59.3750 (70.2902) lr 1.9823e-03 eta 9:58:27
+epoch [5/50] batch [145/500] time 1.587 (1.570) data 0.000 (0.007) loss 1.0342 (1.2074) acc 78.1250 (70.3879) lr 1.9823e-03 eta 9:58:13
+epoch [5/50] batch [150/500] time 1.568 (1.570) data 0.000 (0.006) loss 1.0010 (1.2076) acc 71.8750 (70.3125) lr 1.9823e-03 eta 9:57:58
+epoch [5/50] batch [155/500] time 1.563 (1.570) data 0.000 (0.006) loss 1.2734 (1.2099) acc 62.5000 (70.1815) lr 1.9823e-03 eta 9:57:54
+epoch [5/50] batch [160/500] time 1.616 (1.570) data 0.000 (0.006) loss 0.8662 (1.2055) acc 84.3750 (70.3125) lr 1.9823e-03 eta 9:57:49
+epoch [5/50] batch [165/500] time 1.555 (1.571) data 0.000 (0.006) loss 0.9346 (1.2044) acc 71.8750 (70.2083) lr 1.9823e-03 eta 9:57:42
+epoch [5/50] batch [170/500] time 1.582 (1.571) data 0.000 (0.006) loss 1.0439 (1.2024) acc 81.2500 (70.2757) lr 1.9823e-03 eta 9:57:36
+epoch [5/50] batch [175/500] time 1.545 (1.570) data 0.000 (0.005) loss 1.3516 (1.2051) acc 62.5000 (70.1607) lr 1.9823e-03 eta 9:57:22
+epoch [5/50] batch [180/500] time 1.553 (1.571) data 0.000 (0.005) loss 0.8096 (1.1951) acc 78.1250 (70.3993) lr 1.9823e-03 eta 9:57:26
+epoch [5/50] batch [185/500] time 1.546 (1.570) data 0.000 (0.005) loss 1.0596 (1.2006) acc 68.7500 (70.2534) lr 1.9823e-03 eta 9:57:08
+epoch [5/50] batch [190/500] time 1.557 (1.570) data 0.000 (0.005) loss 0.6582 (1.1927) acc 78.1250 (70.3783) lr 1.9823e-03 eta 9:56:51
+epoch [5/50] batch [195/500] time 1.566 (1.570) data 0.000 (0.005) loss 1.2197 (1.1925) acc 71.8750 (70.4006) lr 1.9823e-03 eta 9:56:43
+epoch [5/50] batch [200/500] time 1.579 (1.570) data 0.000 (0.005) loss 1.1699 (1.1967) acc 71.8750 (70.4062) lr 1.9823e-03 eta 9:56:29
+epoch [5/50] batch [205/500] time 1.571 (1.570) data 0.000 (0.005) loss 1.2510 (1.1994) acc 71.8750 (70.3354) lr 1.9823e-03 eta 9:56:20
+epoch [5/50] batch [210/500] time 1.571 (1.570) data 0.000 (0.005) loss 1.2100 (1.1984) acc 78.1250 (70.4613) lr 1.9823e-03 eta 9:56:09
+epoch [5/50] batch [215/500] time 1.558 (1.569) data 0.000 (0.005) loss 1.3760 (1.2022) acc 62.5000 (70.3488) lr 1.9823e-03 eta 9:55:54
+epoch [5/50] batch [220/500] time 1.578 (1.569) data 0.000 (0.004) loss 1.0244 (1.1984) acc 71.8750 (70.3551) lr 1.9823e-03 eta 9:55:45
+epoch [5/50] batch [225/500] time 1.561 (1.569) data 0.000 (0.004) loss 0.9741 (1.2029) acc 68.7500 (70.1389) lr 1.9823e-03 eta 9:55:34
+epoch [5/50] batch [230/500] time 1.575 (1.569) data 0.000 (0.004) loss 0.7915 (1.1984) acc 81.2500 (70.2038) lr 1.9823e-03 eta 9:55:28
+epoch [5/50] batch [235/500] time 1.552 (1.569) data 0.000 (0.004) loss 1.1729 (1.2004) acc 71.8750 (70.1330) lr 1.9823e-03 eta 9:55:21
+epoch [5/50] batch [240/500] time 1.562 (1.569) data 0.000 (0.004) loss 1.9922 (1.2053) acc 59.3750 (70.0521) lr 1.9823e-03 eta 9:55:06
+epoch [5/50] batch [245/500] time 1.555 (1.569) data 0.000 (0.004) loss 0.7271 (1.2032) acc 75.0000 (70.1148) lr 1.9823e-03 eta 9:54:54
+epoch [5/50] batch [250/500] time 1.555 (1.568) data 0.000 (0.004) loss 1.1826 (1.2047) acc 78.1250 (70.1000) lr 1.9823e-03 eta 9:54:39
+epoch [5/50] batch [255/500] time 1.530 (1.568) data 0.000 (0.004) loss 0.8115 (1.2035) acc 78.1250 (70.1348) lr 1.9823e-03 eta 9:54:23
+epoch [5/50] batch [260/500] time 1.552 (1.568) data 0.000 (0.004) loss 1.4453 (1.2031) acc 56.2500 (70.0721) lr 1.9823e-03 eta 9:54:10
+epoch [5/50] batch [265/500] time 1.572 (1.568) data 0.000 (0.004) loss 1.3975 (1.2001) acc 59.3750 (70.1061) lr 1.9823e-03 eta 9:54:04
+epoch [5/50] batch [270/500] time 1.567 (1.568) data 0.001 (0.004) loss 1.2773 (1.2050) acc 71.8750 (70.1157) lr 1.9823e-03 eta 9:53:57
+epoch [5/50] batch [275/500] time 1.558 (1.568) data 0.001 (0.004) loss 1.0312 (1.2068) acc 75.0000 (70.1932) lr 1.9823e-03 eta 9:53:46
+epoch [5/50] batch [280/500] time 1.558 (1.568) data 0.000 (0.004) loss 1.3037 (1.2089) acc 68.7500 (70.0893) lr 1.9823e-03 eta 9:53:48
+epoch [5/50] batch [285/500] time 1.546 (1.568) data 0.000 (0.004) loss 1.6865 (1.2087) acc 62.5000 (70.1425) lr 1.9823e-03 eta 9:53:39
+epoch [5/50] batch [290/500] time 1.567 (1.568) data 0.000 (0.003) loss 0.9023 (1.2038) acc 75.0000 (70.2478) lr 1.9823e-03 eta 9:53:30
+epoch [5/50] batch [295/500] time 1.571 (1.568) data 0.000 (0.003) loss 1.4609 (1.2048) acc 62.5000 (70.2225) lr 1.9823e-03 eta 9:53:19
+epoch [5/50] batch [300/500] time 1.558 (1.568) data 0.000 (0.003) loss 1.5303 (1.2054) acc 62.5000 (70.2188) lr 1.9823e-03 eta 9:53:09
+epoch [5/50] batch [305/500] time 1.549 (1.568) data 0.000 (0.003) loss 1.1875 (1.2048) acc 65.6250 (70.2254) lr 1.9823e-03 eta 9:52:57
+epoch [5/50] batch [310/500] time 1.608 (1.568) data 0.000 (0.003) loss 1.2314 (1.2056) acc 71.8750 (70.2117) lr 1.9823e-03 eta 9:52:55
+epoch [5/50] batch [315/500] time 1.561 (1.568) data 0.000 (0.003) loss 0.7339 (1.2074) acc 78.1250 (70.1984) lr 1.9823e-03 eta 9:52:47
+epoch [5/50] batch [320/500] time 1.641 (1.568) data 0.000 (0.003) loss 1.1846 (1.2042) acc 53.1250 (70.2441) lr 1.9823e-03 eta 9:52:44
+epoch [5/50] batch [325/500] time 1.584 (1.568) data 0.000 (0.003) loss 1.8145 (1.2046) acc 62.5000 (70.2788) lr 1.9823e-03 eta 9:52:35
+epoch [5/50] batch [330/500] time 1.564 (1.568) data 0.000 (0.003) loss 0.9209 (1.2039) acc 71.8750 (70.2367) lr 1.9823e-03 eta 9:52:26
+epoch [5/50] batch [335/500] time 1.557 (1.568) data 0.000 (0.003) loss 1.5352 (1.2077) acc 65.6250 (70.2239) lr 1.9823e-03 eta 9:52:17
+epoch [5/50] batch [340/500] time 1.542 (1.568) data 0.000 (0.003) loss 1.5225 (1.2088) acc 59.3750 (70.2114) lr 1.9823e-03 eta 9:52:05
+epoch [5/50] batch [345/500] time 1.563 (1.568) data 0.001 (0.003) loss 1.3096 (1.2111) acc 62.5000 (70.1449) lr 1.9823e-03 eta 9:51:52
+epoch [5/50] batch [350/500] time 1.583 (1.568) data 0.000 (0.003) loss 1.1318 (1.2105) acc 75.0000 (70.1875) lr 1.9823e-03 eta 9:51:44
+epoch [5/50] batch [355/500] time 1.561 (1.568) data 0.000 (0.003) loss 1.3594 (1.2107) acc 68.7500 (70.2113) lr 1.9823e-03 eta 9:51:36
+epoch [5/50] batch [360/500] time 1.547 (1.567) data 0.000 (0.003) loss 1.2178 (1.2097) acc 68.7500 (70.1736) lr 1.9823e-03 eta 9:51:20
+epoch [5/50] batch [365/500] time 1.577 (1.567) data 0.000 (0.003) loss 1.7188 (1.2124) acc 50.0000 (70.0942) lr 1.9823e-03 eta 9:51:11
+epoch [5/50] batch [370/500] time 1.546 (1.567) data 0.000 (0.003) loss 1.3115 (1.2143) acc 71.8750 (70.0253) lr 1.9823e-03 eta 9:50:57
+epoch [5/50] batch [375/500] time 1.555 (1.567) data 0.000 (0.003) loss 1.2236 (1.2164) acc 78.1250 (70.0333) lr 1.9823e-03 eta 9:50:48
+epoch [5/50] batch [380/500] time 1.570 (1.567) data 0.000 (0.003) loss 1.1631 (1.2152) acc 75.0000 (70.0493) lr 1.9823e-03 eta 9:50:36
+epoch [5/50] batch [385/500] time 1.554 (1.567) data 0.000 (0.003) loss 1.4688 (1.2162) acc 68.7500 (70.0000) lr 1.9823e-03 eta 9:50:28
+epoch [5/50] batch [390/500] time 1.567 (1.567) data 0.000 (0.003) loss 1.1084 (1.2133) acc 68.7500 (70.0160) lr 1.9823e-03 eta 9:50:20
+epoch [5/50] batch [395/500] time 1.574 (1.567) data 0.000 (0.003) loss 1.2852 (1.2104) acc 75.0000 (70.0712) lr 1.9823e-03 eta 9:50:12
+epoch [5/50] batch [400/500] time 1.569 (1.566) data 0.001 (0.003) loss 1.4170 (1.2170) acc 71.8750 (69.9609) lr 1.9823e-03 eta 9:50:00
+epoch [5/50] batch [405/500] time 1.547 (1.566) data 0.001 (0.003) loss 1.4238 (1.2173) acc 65.6250 (69.9383) lr 1.9823e-03 eta 9:49:52
+epoch [5/50] batch [410/500] time 1.563 (1.566) data 0.001 (0.003) loss 0.8584 (1.2185) acc 81.2500 (69.9009) lr 1.9823e-03 eta 9:49:44
+epoch [5/50] batch [415/500] time 1.538 (1.566) data 0.000 (0.003) loss 1.1221 (1.2180) acc 68.7500 (69.8870) lr 1.9823e-03 eta 9:49:33
+epoch [5/50] batch [420/500] time 1.545 (1.566) data 0.000 (0.003) loss 0.9976 (1.2175) acc 68.7500 (69.8884) lr 1.9823e-03 eta 9:49:27
+epoch [5/50] batch [425/500] time 1.556 (1.566) data 0.000 (0.003) loss 0.9551 (1.2185) acc 78.1250 (69.8603) lr 1.9823e-03 eta 9:49:19
+epoch [5/50] batch [430/500] time 1.562 (1.566) data 0.000 (0.002) loss 1.4551 (1.2194) acc 56.2500 (69.8474) lr 1.9823e-03 eta 9:49:06
+epoch [5/50] batch [435/500] time 1.572 (1.566) data 0.000 (0.002) loss 0.9526 (1.2174) acc 71.8750 (69.8707) lr 1.9823e-03 eta 9:48:59
+epoch [5/50] batch [440/500] time 1.552 (1.566) data 0.000 (0.002) loss 1.3916 (1.2171) acc 62.5000 (69.8722) lr 1.9823e-03 eta 9:48:50
+epoch [5/50] batch [445/500] time 1.566 (1.566) data 0.001 (0.002) loss 0.6177 (1.2179) acc 87.5000 (69.9228) lr 1.9823e-03 eta 9:48:43
+epoch [5/50] batch [450/500] time 1.560 (1.566) data 0.000 (0.002) loss 0.8750 (1.2154) acc 68.7500 (69.9583) lr 1.9823e-03 eta 9:48:35
+epoch [5/50] batch [455/500] time 1.574 (1.566) data 0.000 (0.002) loss 1.2910 (1.2153) acc 68.7500 (69.9725) lr 1.9823e-03 eta 9:48:27
+epoch [5/50] batch [460/500] time 1.564 (1.566) data 0.000 (0.002) loss 1.9346 (1.2191) acc 53.1250 (69.8505) lr 1.9823e-03 eta 9:48:20
+epoch [5/50] batch [465/500] time 1.558 (1.566) data 0.000 (0.002) loss 1.4932 (1.2189) acc 71.8750 (69.8253) lr 1.9823e-03 eta 9:48:16
+epoch [5/50] batch [470/500] time 1.560 (1.566) data 0.000 (0.002) loss 1.4326 (1.2173) acc 65.6250 (69.8537) lr 1.9823e-03 eta 9:48:07
+epoch [5/50] batch [475/500] time 1.562 (1.566) data 0.000 (0.002) loss 1.2256 (1.2175) acc 75.0000 (69.8684) lr 1.9823e-03 eta 9:48:00
+epoch [5/50] batch [480/500] time 1.561 (1.566) data 0.001 (0.002) loss 1.1875 (1.2172) acc 75.0000 (69.8763) lr 1.9823e-03 eta 9:47:51
+epoch [5/50] batch [485/500] time 1.556 (1.566) data 0.001 (0.002) loss 1.1514 (1.2186) acc 68.7500 (69.8582) lr 1.9823e-03 eta 9:47:43
+epoch [5/50] batch [490/500] time 1.551 (1.566) data 0.001 (0.002) loss 1.5000 (1.2175) acc 62.5000 (69.8852) lr 1.9823e-03 eta 9:47:36
+epoch [5/50] batch [495/500] time 1.559 (1.566) data 0.000 (0.002) loss 1.1895 (1.2171) acc 78.1250 (69.9242) lr 1.9823e-03 eta 9:47:27
+epoch [5/50] batch [500/500] time 1.564 (1.566) data 0.000 (0.002) loss 1.0947 (1.2176) acc 75.0000 (69.9688) lr 1.9686e-03 eta 9:47:16
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,697
+* accuracy: 77.4%
+* error: 22.6%
+* macro_f1: 76.7%
+epoch [6/50] batch [5/500] time 1.557 (1.688) data 0.002 (0.176) loss 1.8281 (1.2459) acc 59.3750 (70.0000) lr 1.9686e-03 eta 10:32:58
+epoch [6/50] batch [10/500] time 1.567 (1.626) data 0.000 (0.088) loss 1.2871 (1.1916) acc 75.0000 (73.4375) lr 1.9686e-03 eta 10:09:21
+epoch [6/50] batch [15/500] time 1.570 (1.607) data 0.001 (0.059) loss 1.2324 (1.1583) acc 78.1250 (72.5000) lr 1.9686e-03 eta 10:02:09
+epoch [6/50] batch [20/500] time 1.545 (1.595) data 0.001 (0.044) loss 1.5693 (1.1527) acc 68.7500 (72.3438) lr 1.9686e-03 eta 9:57:45
+epoch [6/50] batch [25/500] time 1.577 (1.596) data 0.000 (0.036) loss 0.4331 (1.1209) acc 84.3750 (73.5000) lr 1.9686e-03 eta 9:57:47
+epoch [6/50] batch [30/500] time 1.553 (1.589) data 0.000 (0.030) loss 1.2061 (1.1113) acc 68.7500 (73.2292) lr 1.9686e-03 eta 9:54:56
+epoch [6/50] batch [35/500] time 1.563 (1.586) data 0.000 (0.026) loss 1.1143 (1.1018) acc 68.7500 (73.5714) lr 1.9686e-03 eta 9:53:52
+epoch [6/50] batch [40/500] time 1.549 (1.585) data 0.000 (0.022) loss 1.0439 (1.1211) acc 75.0000 (72.9688) lr 1.9686e-03 eta 9:53:09
+epoch [6/50] batch [45/500] time 1.552 (1.582) data 0.001 (0.020) loss 1.5391 (1.1262) acc 65.6250 (72.4306) lr 1.9686e-03 eta 9:51:57
+epoch [6/50] batch [50/500] time 1.576 (1.579) data 0.000 (0.018) loss 1.0947 (1.1372) acc 75.0000 (72.2500) lr 1.9686e-03 eta 9:50:59
+epoch [6/50] batch [55/500] time 1.563 (1.577) data 0.001 (0.016) loss 1.3379 (1.1523) acc 68.7500 (72.1591) lr 1.9686e-03 eta 9:49:56
+epoch [6/50] batch [60/500] time 1.582 (1.576) data 0.000 (0.015) loss 1.0762 (1.1540) acc 71.8750 (71.6146) lr 1.9686e-03 eta 9:49:28
+epoch [6/50] batch [65/500] time 1.544 (1.575) data 0.000 (0.014) loss 1.0361 (1.1594) acc 75.0000 (71.6346) lr 1.9686e-03 eta 9:48:47
+epoch [6/50] batch [70/500] time 1.553 (1.574) data 0.000 (0.013) loss 1.3203 (1.1534) acc 75.0000 (72.0089) lr 1.9686e-03 eta 9:48:27
+epoch [6/50] batch [75/500] time 1.566 (1.574) data 0.000 (0.012) loss 1.0879 (1.1565) acc 71.8750 (71.7917) lr 1.9686e-03 eta 9:48:20
+epoch [6/50] batch [80/500] time 1.580 (1.573) data 0.000 (0.011) loss 0.9722 (1.1620) acc 65.6250 (71.6016) lr 1.9686e-03 eta 9:47:54
+epoch [6/50] batch [85/500] time 1.542 (1.574) data 0.000 (0.011) loss 0.9673 (1.1599) acc 71.8750 (71.6544) lr 1.9686e-03 eta 9:48:03
+epoch [6/50] batch [90/500] time 1.571 (1.573) data 0.000 (0.010) loss 1.4521 (1.1624) acc 62.5000 (71.7014) lr 1.9686e-03 eta 9:47:41
+epoch [6/50] batch [95/500] time 1.542 (1.573) data 0.000 (0.010) loss 0.8809 (1.1584) acc 75.0000 (71.7105) lr 1.9686e-03 eta 9:47:12
+epoch [6/50] batch [100/500] time 1.568 (1.572) data 0.001 (0.009) loss 0.7290 (1.1685) acc 75.0000 (71.4688) lr 1.9686e-03 eta 9:47:01
+epoch [6/50] batch [105/500] time 1.565 (1.572) data 0.000 (0.009) loss 1.2158 (1.1656) acc 68.7500 (71.3988) lr 1.9686e-03 eta 9:46:43
+epoch [6/50] batch [110/500] time 1.537 (1.571) data 0.000 (0.008) loss 1.6680 (1.1606) acc 71.8750 (71.5909) lr 1.9686e-03 eta 9:46:24
+epoch [6/50] batch [115/500] time 1.543 (1.571) data 0.000 (0.008) loss 1.7314 (1.1581) acc 59.3750 (71.6304) lr 1.9686e-03 eta 9:45:56
+epoch [6/50] batch [120/500] time 1.553 (1.570) data 0.000 (0.008) loss 1.0605 (1.1586) acc 62.5000 (71.4323) lr 1.9686e-03 eta 9:45:38
+epoch [6/50] batch [125/500] time 1.663 (1.571) data 0.000 (0.007) loss 0.9990 (1.1596) acc 71.8750 (71.5500) lr 1.9686e-03 eta 9:45:51
+epoch [6/50] batch [130/500] time 1.540 (1.570) data 0.000 (0.007) loss 1.4756 (1.1589) acc 65.6250 (71.4183) lr 1.9686e-03 eta 9:45:31
+epoch [6/50] batch [135/500] time 1.555 (1.570) data 0.000 (0.007) loss 1.0273 (1.1620) acc 71.8750 (71.3194) lr 1.9686e-03 eta 9:45:06
+epoch [6/50] batch [140/500] time 1.571 (1.569) data 0.000 (0.007) loss 1.1377 (1.1572) acc 68.7500 (71.4286) lr 1.9686e-03 eta 9:44:49
+epoch [6/50] batch [145/500] time 1.558 (1.569) data 0.000 (0.007) loss 1.1777 (1.1577) acc 56.2500 (71.3578) lr 1.9686e-03 eta 9:44:39
+epoch [6/50] batch [150/500] time 1.563 (1.568) data 0.000 (0.006) loss 1.3125 (1.1705) acc 75.0000 (71.2083) lr 1.9686e-03 eta 9:44:15
+epoch [6/50] batch [155/500] time 1.547 (1.568) data 0.000 (0.006) loss 0.9741 (1.1727) acc 75.0000 (71.2298) lr 1.9686e-03 eta 9:43:56
+epoch [6/50] batch [160/500] time 1.559 (1.568) data 0.000 (0.006) loss 1.2080 (1.1769) acc 71.8750 (71.1719) lr 1.9686e-03 eta 9:43:44
+epoch [6/50] batch [165/500] time 1.557 (1.568) data 0.001 (0.006) loss 1.8232 (1.1788) acc 59.3750 (71.0417) lr 1.9686e-03 eta 9:43:30
+epoch [6/50] batch [170/500] time 1.565 (1.567) data 0.000 (0.006) loss 1.2539 (1.1847) acc 78.1250 (71.0110) lr 1.9686e-03 eta 9:43:18
+epoch [6/50] batch [175/500] time 1.591 (1.567) data 0.000 (0.005) loss 1.2324 (1.1863) acc 65.6250 (71.0179) lr 1.9686e-03 eta 9:43:04
+epoch [6/50] batch [180/500] time 1.568 (1.567) data 0.000 (0.005) loss 1.6201 (1.1966) acc 59.3750 (70.8854) lr 1.9686e-03 eta 9:42:57
+epoch [6/50] batch [185/500] time 1.558 (1.567) data 0.000 (0.005) loss 1.1748 (1.2042) acc 65.6250 (70.6757) lr 1.9686e-03 eta 9:42:46
+epoch [6/50] batch [190/500] time 1.569 (1.567) data 0.000 (0.005) loss 0.6348 (1.2003) acc 84.3750 (70.8059) lr 1.9686e-03 eta 9:42:38
+epoch [6/50] batch [195/500] time 1.559 (1.567) data 0.000 (0.005) loss 1.3623 (1.2018) acc 62.5000 (70.6571) lr 1.9686e-03 eta 9:42:31
+epoch [6/50] batch [200/500] time 1.553 (1.567) data 0.001 (0.005) loss 0.8955 (1.1979) acc 71.8750 (70.5781) lr 1.9686e-03 eta 9:42:27
+epoch [6/50] batch [205/500] time 1.555 (1.567) data 0.000 (0.005) loss 1.1729 (1.2006) acc 68.7500 (70.4726) lr 1.9686e-03 eta 9:42:14
+epoch [6/50] batch [210/500] time 1.580 (1.567) data 0.000 (0.005) loss 0.7197 (1.1999) acc 87.5000 (70.5506) lr 1.9686e-03 eta 9:42:08
+epoch [6/50] batch [215/500] time 1.589 (1.567) data 0.000 (0.005) loss 0.8008 (1.1992) acc 75.0000 (70.5087) lr 1.9686e-03 eta 9:42:04
+epoch [6/50] batch [220/500] time 1.575 (1.567) data 0.000 (0.004) loss 1.2529 (1.2004) acc 68.7500 (70.4830) lr 1.9686e-03 eta 9:41:48
+epoch [6/50] batch [225/500] time 1.550 (1.567) data 0.001 (0.004) loss 1.2041 (1.1989) acc 68.7500 (70.5139) lr 1.9686e-03 eta 9:41:50
+epoch [6/50] batch [230/500] time 1.576 (1.567) data 0.001 (0.004) loss 0.5713 (1.1947) acc 78.1250 (70.5707) lr 1.9686e-03 eta 9:41:44
+epoch [6/50] batch [235/500] time 1.558 (1.567) data 0.000 (0.004) loss 1.2441 (1.1908) acc 62.5000 (70.5984) lr 1.9686e-03 eta 9:41:39
+epoch [6/50] batch [240/500] time 1.560 (1.567) data 0.000 (0.004) loss 1.2539 (1.1898) acc 56.2500 (70.6250) lr 1.9686e-03 eta 9:41:30
+epoch [6/50] batch [245/500] time 1.555 (1.567) data 0.000 (0.004) loss 0.6279 (1.1888) acc 84.3750 (70.7015) lr 1.9686e-03 eta 9:41:22
+epoch [6/50] batch [250/500] time 1.557 (1.567) data 0.000 (0.004) loss 1.3262 (1.1876) acc 68.7500 (70.7250) lr 1.9686e-03 eta 9:41:16
+epoch [6/50] batch [255/500] time 1.552 (1.567) data 0.000 (0.004) loss 1.0420 (1.1879) acc 81.2500 (70.7353) lr 1.9686e-03 eta 9:41:04
+epoch [6/50] batch [260/500] time 1.566 (1.567) data 0.000 (0.004) loss 1.2842 (1.1905) acc 62.5000 (70.6611) lr 1.9686e-03 eta 9:40:58
+epoch [6/50] batch [265/500] time 1.559 (1.567) data 0.000 (0.004) loss 1.0518 (1.1943) acc 65.6250 (70.5425) lr 1.9686e-03 eta 9:40:49
+epoch [6/50] batch [270/500] time 1.529 (1.568) data 0.000 (0.004) loss 1.8232 (1.1968) acc 71.8750 (70.5556) lr 1.9686e-03 eta 9:40:47
+epoch [6/50] batch [275/500] time 1.572 (1.567) data 0.000 (0.004) loss 1.4824 (1.1992) acc 56.2500 (70.5000) lr 1.9686e-03 eta 9:40:37
+epoch [6/50] batch [280/500] time 1.560 (1.567) data 0.000 (0.004) loss 1.2783 (1.2011) acc 71.8750 (70.5134) lr 1.9686e-03 eta 9:40:29
+epoch [6/50] batch [285/500] time 1.563 (1.567) data 0.000 (0.004) loss 0.5815 (1.1957) acc 78.1250 (70.6140) lr 1.9686e-03 eta 9:40:17
+epoch [6/50] batch [290/500] time 1.591 (1.567) data 0.000 (0.003) loss 1.6221 (1.1990) acc 53.1250 (70.5603) lr 1.9686e-03 eta 9:40:11
+epoch [6/50] batch [295/500] time 1.550 (1.567) data 0.001 (0.003) loss 0.7642 (1.2013) acc 78.1250 (70.4767) lr 1.9686e-03 eta 9:39:57
+epoch [6/50] batch [300/500] time 1.555 (1.567) data 0.000 (0.003) loss 1.5137 (1.1984) acc 68.7500 (70.5833) lr 1.9686e-03 eta 9:39:44
+epoch [6/50] batch [305/500] time 1.553 (1.567) data 0.000 (0.003) loss 0.8574 (1.1957) acc 78.1250 (70.6352) lr 1.9686e-03 eta 9:39:32
+epoch [6/50] batch [310/500] time 1.535 (1.567) data 0.000 (0.003) loss 0.9521 (1.1930) acc 78.1250 (70.6956) lr 1.9686e-03 eta 9:39:22
+epoch [6/50] batch [315/500] time 1.559 (1.567) data 0.001 (0.003) loss 1.2051 (1.1937) acc 75.0000 (70.6944) lr 1.9686e-03 eta 9:39:13
+epoch [6/50] batch [320/500] time 1.557 (1.566) data 0.000 (0.003) loss 1.5342 (1.1952) acc 65.6250 (70.6348) lr 1.9686e-03 eta 9:39:02
+epoch [6/50] batch [325/500] time 1.583 (1.566) data 0.000 (0.003) loss 0.9585 (1.1975) acc 68.7500 (70.5769) lr 1.9686e-03 eta 9:38:56
+epoch [6/50] batch [330/500] time 1.550 (1.566) data 0.001 (0.003) loss 1.1240 (1.1975) acc 62.5000 (70.5682) lr 1.9686e-03 eta 9:38:45
+epoch [6/50] batch [335/500] time 1.574 (1.566) data 0.000 (0.003) loss 0.9370 (1.1963) acc 75.0000 (70.5690) lr 1.9686e-03 eta 9:38:34
+epoch [6/50] batch [340/500] time 1.554 (1.566) data 0.001 (0.003) loss 1.4531 (1.1969) acc 75.0000 (70.5699) lr 1.9686e-03 eta 9:38:22
+epoch [6/50] batch [345/500] time 1.559 (1.566) data 0.000 (0.003) loss 0.9253 (1.1963) acc 78.1250 (70.6341) lr 1.9686e-03 eta 9:38:10
+epoch [6/50] batch [350/500] time 1.562 (1.566) data 0.000 (0.003) loss 0.8413 (1.1975) acc 65.6250 (70.6429) lr 1.9686e-03 eta 9:37:58
+epoch [6/50] batch [355/500] time 1.577 (1.566) data 0.000 (0.003) loss 1.1992 (1.1934) acc 59.3750 (70.6690) lr 1.9686e-03 eta 9:37:48
+epoch [6/50] batch [360/500] time 1.563 (1.566) data 0.000 (0.003) loss 1.5605 (1.1980) acc 71.8750 (70.7031) lr 1.9686e-03 eta 9:37:40
+epoch [6/50] batch [365/500] time 1.545 (1.565) data 0.000 (0.003) loss 0.9805 (1.1989) acc 78.1250 (70.7192) lr 1.9686e-03 eta 9:37:29
+epoch [6/50] batch [370/500] time 1.539 (1.566) data 0.000 (0.003) loss 1.3242 (1.1992) acc 65.6250 (70.7601) lr 1.9686e-03 eta 9:37:25
+epoch [6/50] batch [375/500] time 1.558 (1.565) data 0.000 (0.003) loss 1.9551 (1.2024) acc 56.2500 (70.6750) lr 1.9686e-03 eta 9:37:14
+epoch [6/50] batch [380/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.0566 (1.2016) acc 71.8750 (70.6743) lr 1.9686e-03 eta 9:37:02
+epoch [6/50] batch [385/500] time 1.595 (1.565) data 0.000 (0.003) loss 1.3223 (1.2020) acc 71.8750 (70.6899) lr 1.9686e-03 eta 9:36:56
+epoch [6/50] batch [390/500] time 1.584 (1.565) data 0.000 (0.003) loss 2.0117 (1.2051) acc 65.6250 (70.6731) lr 1.9686e-03 eta 9:36:50
+epoch [6/50] batch [395/500] time 1.560 (1.565) data 0.000 (0.003) loss 1.4551 (1.2061) acc 68.7500 (70.6566) lr 1.9686e-03 eta 9:36:42
+epoch [6/50] batch [400/500] time 1.561 (1.565) data 0.000 (0.003) loss 0.8291 (1.2050) acc 78.1250 (70.5859) lr 1.9686e-03 eta 9:36:31
+epoch [6/50] batch [405/500] time 1.573 (1.565) data 0.000 (0.003) loss 1.8779 (1.2063) acc 46.8750 (70.5015) lr 1.9686e-03 eta 9:36:25
+epoch [6/50] batch [410/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.4502 (1.2055) acc 62.5000 (70.4878) lr 1.9686e-03 eta 9:36:16
+epoch [6/50] batch [415/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.1035 (1.2072) acc 68.7500 (70.4066) lr 1.9686e-03 eta 9:36:12
+epoch [6/50] batch [420/500] time 1.552 (1.565) data 0.000 (0.002) loss 1.1240 (1.2051) acc 71.8750 (70.4613) lr 1.9686e-03 eta 9:36:03
+epoch [6/50] batch [425/500] time 1.579 (1.565) data 0.000 (0.002) loss 0.6382 (1.2062) acc 90.6250 (70.4559) lr 1.9686e-03 eta 9:35:56
+epoch [6/50] batch [430/500] time 1.578 (1.565) data 0.000 (0.002) loss 1.3662 (1.2063) acc 65.6250 (70.4433) lr 1.9686e-03 eta 9:35:48
+epoch [6/50] batch [435/500] time 1.550 (1.565) data 0.000 (0.002) loss 0.8423 (1.2028) acc 84.3750 (70.5532) lr 1.9686e-03 eta 9:35:42
+epoch [6/50] batch [440/500] time 1.536 (1.565) data 0.000 (0.002) loss 0.4758 (1.2024) acc 93.7500 (70.5611) lr 1.9686e-03 eta 9:35:31
+epoch [6/50] batch [445/500] time 1.559 (1.565) data 0.000 (0.002) loss 0.8774 (1.2031) acc 75.0000 (70.5267) lr 1.9686e-03 eta 9:35:23
+epoch [6/50] batch [450/500] time 1.537 (1.565) data 0.000 (0.002) loss 0.7856 (1.2005) acc 78.1250 (70.5833) lr 1.9686e-03 eta 9:35:10
+epoch [6/50] batch [455/500] time 1.560 (1.565) data 0.000 (0.002) loss 0.9824 (1.1960) acc 75.0000 (70.6868) lr 1.9686e-03 eta 9:35:02
+epoch [6/50] batch [460/500] time 1.559 (1.565) data 0.000 (0.002) loss 1.1875 (1.1993) acc 71.8750 (70.6793) lr 1.9686e-03 eta 9:34:53
+epoch [6/50] batch [465/500] time 1.560 (1.565) data 0.000 (0.002) loss 1.4033 (1.2004) acc 71.8750 (70.6922) lr 1.9686e-03 eta 9:34:41
+epoch [6/50] batch [470/500] time 1.599 (1.565) data 0.000 (0.002) loss 0.6172 (1.1989) acc 87.5000 (70.7048) lr 1.9686e-03 eta 9:34:36
+epoch [6/50] batch [475/500] time 1.558 (1.565) data 0.000 (0.002) loss 1.3066 (1.1990) acc 78.1250 (70.7303) lr 1.9686e-03 eta 9:34:26
+epoch [6/50] batch [480/500] time 1.538 (1.565) data 0.000 (0.002) loss 0.6997 (1.1967) acc 87.5000 (70.7943) lr 1.9686e-03 eta 9:34:14
+epoch [6/50] batch [485/500] time 1.556 (1.565) data 0.001 (0.002) loss 1.1553 (1.1961) acc 62.5000 (70.7990) lr 1.9686e-03 eta 9:34:04
+epoch [6/50] batch [490/500] time 1.551 (1.564) data 0.000 (0.002) loss 1.7998 (1.1961) acc 68.7500 (70.8099) lr 1.9686e-03 eta 9:33:54
+epoch [6/50] batch [495/500] time 1.551 (1.564) data 0.000 (0.002) loss 1.6709 (1.1961) acc 56.2500 (70.7955) lr 1.9686e-03 eta 9:33:44
+epoch [6/50] batch [500/500] time 1.570 (1.564) data 0.000 (0.002) loss 1.6064 (1.1961) acc 59.3750 (70.7625) lr 1.9511e-03 eta 9:33:36
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,799
+* accuracy: 77.6%
+* error: 22.4%
+* macro_f1: 76.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [7/50] batch [5/500] time 1.558 (1.737) data 0.001 (0.231) loss 2.4043 (1.4098) acc 43.7500 (65.6250) lr 1.9511e-03 eta 10:36:55
+epoch [7/50] batch [10/500] time 1.556 (1.644) data 0.000 (0.116) loss 1.1553 (1.2192) acc 75.0000 (70.3125) lr 1.9511e-03 eta 10:02:26
+epoch [7/50] batch [15/500] time 1.568 (1.614) data 0.000 (0.077) loss 1.0078 (1.1534) acc 68.7500 (70.2083) lr 1.9511e-03 eta 9:51:18
+epoch [7/50] batch [20/500] time 1.551 (1.608) data 0.000 (0.058) loss 1.0938 (1.1602) acc 62.5000 (70.0000) lr 1.9511e-03 eta 9:49:09
+epoch [7/50] batch [25/500] time 1.560 (1.599) data 0.001 (0.047) loss 1.3926 (1.1676) acc 68.7500 (70.7500) lr 1.9511e-03 eta 9:45:31
+epoch [7/50] batch [30/500] time 1.570 (1.592) data 0.000 (0.039) loss 0.9141 (1.1679) acc 68.7500 (70.4167) lr 1.9511e-03 eta 9:42:58
+epoch [7/50] batch [35/500] time 1.575 (1.589) data 0.000 (0.033) loss 1.8281 (1.1529) acc 56.2500 (71.2500) lr 1.9511e-03 eta 9:41:38
+epoch [7/50] batch [40/500] time 1.559 (1.587) data 0.000 (0.029) loss 1.1143 (1.1699) acc 71.8750 (71.4062) lr 1.9511e-03 eta 9:40:44
+epoch [7/50] batch [45/500] time 1.559 (1.585) data 0.000 (0.026) loss 0.3442 (1.1581) acc 90.6250 (71.7361) lr 1.9511e-03 eta 9:39:58
+epoch [7/50] batch [50/500] time 1.591 (1.583) data 0.000 (0.024) loss 0.5449 (1.1335) acc 87.5000 (72.3125) lr 1.9511e-03 eta 9:39:00
+epoch [7/50] batch [55/500] time 1.579 (1.582) data 0.001 (0.021) loss 0.8608 (1.1116) acc 87.5000 (72.8409) lr 1.9511e-03 eta 9:38:32
+epoch [7/50] batch [60/500] time 1.549 (1.580) data 0.001 (0.020) loss 0.9121 (1.1193) acc 81.2500 (72.7604) lr 1.9511e-03 eta 9:37:40
+epoch [7/50] batch [65/500] time 1.558 (1.579) data 0.000 (0.018) loss 0.9839 (1.1143) acc 75.0000 (72.7885) lr 1.9511e-03 eta 9:37:13
+epoch [7/50] batch [70/500] time 1.569 (1.578) data 0.000 (0.017) loss 1.3096 (1.1421) acc 71.8750 (72.3214) lr 1.9511e-03 eta 9:36:44
+epoch [7/50] batch [75/500] time 1.565 (1.577) data 0.000 (0.016) loss 0.9912 (1.1328) acc 68.7500 (72.3750) lr 1.9511e-03 eta 9:36:17
+epoch [7/50] batch [80/500] time 1.574 (1.576) data 0.000 (0.015) loss 0.9380 (1.1307) acc 78.1250 (72.1484) lr 1.9511e-03 eta 9:35:50
+epoch [7/50] batch [85/500] time 1.546 (1.575) data 0.000 (0.014) loss 1.3643 (1.1200) acc 65.6250 (72.2059) lr 1.9511e-03 eta 9:35:16
+epoch [7/50] batch [90/500] time 1.550 (1.574) data 0.000 (0.013) loss 1.3750 (1.1256) acc 75.0000 (72.0833) lr 1.9511e-03 eta 9:34:46
+epoch [7/50] batch [95/500] time 1.573 (1.573) data 0.001 (0.013) loss 0.7822 (1.1168) acc 84.3750 (72.2368) lr 1.9511e-03 eta 9:34:15
+epoch [7/50] batch [100/500] time 1.548 (1.572) data 0.000 (0.012) loss 1.3125 (1.1222) acc 62.5000 (72.2500) lr 1.9511e-03 eta 9:33:54
+epoch [7/50] batch [105/500] time 1.540 (1.572) data 0.000 (0.011) loss 0.8945 (1.1329) acc 84.3750 (72.4107) lr 1.9511e-03 eta 9:33:38
+epoch [7/50] batch [110/500] time 1.548 (1.571) data 0.000 (0.011) loss 0.9712 (1.1281) acc 71.8750 (72.4432) lr 1.9511e-03 eta 9:33:18
+epoch [7/50] batch [115/500] time 1.578 (1.571) data 0.000 (0.010) loss 1.0850 (1.1322) acc 71.8750 (72.3913) lr 1.9511e-03 eta 9:32:59
+epoch [7/50] batch [120/500] time 1.577 (1.572) data 0.001 (0.010) loss 0.9897 (1.1331) acc 81.2500 (72.3698) lr 1.9511e-03 eta 9:33:13
+epoch [7/50] batch [125/500] time 1.584 (1.572) data 0.000 (0.010) loss 1.4434 (1.1382) acc 71.8750 (72.0500) lr 1.9511e-03 eta 9:33:05
+epoch [7/50] batch [130/500] time 1.567 (1.571) data 0.000 (0.009) loss 1.4004 (1.1393) acc 68.7500 (71.9471) lr 1.9511e-03 eta 9:32:46
+epoch [7/50] batch [135/500] time 1.540 (1.571) data 0.000 (0.009) loss 0.8687 (1.1376) acc 78.1250 (72.0833) lr 1.9511e-03 eta 9:32:32
+epoch [7/50] batch [140/500] time 1.583 (1.571) data 0.000 (0.009) loss 1.0713 (1.1407) acc 62.5000 (71.8750) lr 1.9511e-03 eta 9:32:18
+epoch [7/50] batch [145/500] time 1.554 (1.570) data 0.000 (0.008) loss 1.5703 (1.1357) acc 65.6250 (71.9181) lr 1.9511e-03 eta 9:31:53
+epoch [7/50] batch [150/500] time 1.549 (1.570) data 0.000 (0.008) loss 1.0771 (1.1373) acc 71.8750 (71.8542) lr 1.9511e-03 eta 9:31:33
+epoch [7/50] batch [155/500] time 1.561 (1.569) data 0.000 (0.008) loss 1.3379 (1.1387) acc 62.5000 (71.7137) lr 1.9511e-03 eta 9:31:13
+epoch [7/50] batch [160/500] time 1.640 (1.569) data 0.001 (0.008) loss 1.0508 (1.1360) acc 75.0000 (71.7773) lr 1.9511e-03 eta 9:31:11
+epoch [7/50] batch [165/500] time 1.555 (1.569) data 0.000 (0.007) loss 2.4277 (1.1428) acc 56.2500 (71.7235) lr 1.9511e-03 eta 9:31:01
+epoch [7/50] batch [170/500] time 1.551 (1.568) data 0.000 (0.007) loss 1.0889 (1.1431) acc 62.5000 (71.5809) lr 1.9511e-03 eta 9:30:39
+epoch [7/50] batch [175/500] time 1.545 (1.568) data 0.000 (0.007) loss 1.5029 (1.1512) acc 68.7500 (71.4107) lr 1.9511e-03 eta 9:30:28
+epoch [7/50] batch [180/500] time 1.547 (1.568) data 0.000 (0.007) loss 0.5542 (1.1507) acc 78.1250 (71.4236) lr 1.9511e-03 eta 9:30:16
+epoch [7/50] batch [185/500] time 1.563 (1.568) data 0.000 (0.007) loss 0.9175 (1.1510) acc 78.1250 (71.4020) lr 1.9511e-03 eta 9:29:57
+epoch [7/50] batch [190/500] time 1.559 (1.568) data 0.000 (0.007) loss 0.7002 (1.1579) acc 87.5000 (71.3651) lr 1.9511e-03 eta 9:29:48
+epoch [7/50] batch [195/500] time 1.577 (1.567) data 0.000 (0.006) loss 1.9375 (1.1621) acc 56.2500 (71.2981) lr 1.9511e-03 eta 9:29:37
+epoch [7/50] batch [200/500] time 1.554 (1.567) data 0.001 (0.006) loss 0.6860 (1.1589) acc 81.2500 (71.3125) lr 1.9511e-03 eta 9:29:24
+epoch [7/50] batch [205/500] time 1.553 (1.567) data 0.000 (0.006) loss 1.0732 (1.1592) acc 68.7500 (71.2805) lr 1.9511e-03 eta 9:29:14
+epoch [7/50] batch [210/500] time 1.567 (1.567) data 0.001 (0.006) loss 1.5439 (1.1645) acc 65.6250 (71.1458) lr 1.9511e-03 eta 9:29:05
+epoch [7/50] batch [215/500] time 1.557 (1.567) data 0.000 (0.006) loss 1.0215 (1.1634) acc 68.7500 (71.1773) lr 1.9511e-03 eta 9:28:52
+epoch [7/50] batch [220/500] time 1.539 (1.566) data 0.000 (0.006) loss 1.2803 (1.1654) acc 78.1250 (71.1648) lr 1.9511e-03 eta 9:28:35
+epoch [7/50] batch [225/500] time 1.557 (1.566) data 0.000 (0.006) loss 1.9346 (1.1641) acc 59.3750 (71.1806) lr 1.9511e-03 eta 9:28:28
+epoch [7/50] batch [230/500] time 1.560 (1.566) data 0.000 (0.005) loss 1.0586 (1.1603) acc 75.0000 (71.1821) lr 1.9511e-03 eta 9:28:22
+epoch [7/50] batch [235/500] time 1.539 (1.566) data 0.000 (0.005) loss 1.3076 (1.1615) acc 78.1250 (71.2367) lr 1.9511e-03 eta 9:28:07
+epoch [7/50] batch [240/500] time 1.569 (1.566) data 0.000 (0.005) loss 1.4639 (1.1639) acc 71.8750 (71.2760) lr 1.9511e-03 eta 9:27:58
+epoch [7/50] batch [245/500] time 1.568 (1.566) data 0.000 (0.005) loss 1.3467 (1.1685) acc 62.5000 (71.1607) lr 1.9511e-03 eta 9:27:53
+epoch [7/50] batch [250/500] time 1.552 (1.566) data 0.000 (0.005) loss 1.1982 (1.1674) acc 78.1250 (71.2375) lr 1.9511e-03 eta 9:27:41
+epoch [7/50] batch [255/500] time 1.549 (1.566) data 0.000 (0.005) loss 1.9785 (1.1704) acc 56.2500 (71.2255) lr 1.9511e-03 eta 9:27:25
+epoch [7/50] batch [260/500] time 1.572 (1.566) data 0.000 (0.005) loss 0.9160 (1.1686) acc 78.1250 (71.2260) lr 1.9511e-03 eta 9:27:24
+epoch [7/50] batch [265/500] time 1.565 (1.566) data 0.000 (0.005) loss 1.0078 (1.1707) acc 75.0000 (71.1910) lr 1.9511e-03 eta 9:27:14
+epoch [7/50] batch [270/500] time 1.560 (1.566) data 0.000 (0.005) loss 1.0859 (1.1718) acc 62.5000 (71.1343) lr 1.9511e-03 eta 9:27:03
+epoch [7/50] batch [275/500] time 1.553 (1.566) data 0.000 (0.005) loss 0.6470 (1.1655) acc 84.3750 (71.2841) lr 1.9511e-03 eta 9:26:55
+epoch [7/50] batch [280/500] time 1.532 (1.566) data 0.000 (0.005) loss 0.8511 (1.1662) acc 75.0000 (71.3281) lr 1.9511e-03 eta 9:26:43
+epoch [7/50] batch [285/500] time 1.564 (1.565) data 0.000 (0.004) loss 1.1738 (1.1653) acc 59.3750 (71.3158) lr 1.9511e-03 eta 9:26:31
+epoch [7/50] batch [290/500] time 1.568 (1.565) data 0.000 (0.004) loss 1.2051 (1.1670) acc 65.6250 (71.3039) lr 1.9511e-03 eta 9:26:22
+epoch [7/50] batch [295/500] time 1.556 (1.565) data 0.001 (0.004) loss 0.8135 (1.1635) acc 75.0000 (71.3665) lr 1.9511e-03 eta 9:26:14
+epoch [7/50] batch [300/500] time 1.552 (1.565) data 0.000 (0.004) loss 1.3037 (1.1643) acc 68.7500 (71.3542) lr 1.9511e-03 eta 9:26:02
+epoch [7/50] batch [305/500] time 1.559 (1.565) data 0.000 (0.004) loss 1.2734 (1.1626) acc 75.0000 (71.3525) lr 1.9511e-03 eta 9:25:59
+epoch [7/50] batch [310/500] time 1.571 (1.566) data 0.000 (0.004) loss 1.4062 (1.1605) acc 68.7500 (71.3810) lr 1.9511e-03 eta 9:25:55
+epoch [7/50] batch [315/500] time 1.543 (1.565) data 0.001 (0.004) loss 1.3789 (1.1580) acc 78.1250 (71.5079) lr 1.9511e-03 eta 9:25:46
+epoch [7/50] batch [320/500] time 1.600 (1.566) data 0.000 (0.004) loss 1.4951 (1.1602) acc 56.2500 (71.4746) lr 1.9511e-03 eta 9:25:42
+epoch [7/50] batch [325/500] time 1.586 (1.566) data 0.000 (0.004) loss 2.3008 (1.1672) acc 53.1250 (71.4231) lr 1.9511e-03 eta 9:25:38
+epoch [7/50] batch [330/500] time 1.568 (1.566) data 0.000 (0.004) loss 0.9717 (1.1681) acc 81.2500 (71.4299) lr 1.9511e-03 eta 9:25:27
+epoch [7/50] batch [335/500] time 1.577 (1.566) data 0.000 (0.004) loss 1.5625 (1.1699) acc 65.6250 (71.3993) lr 1.9511e-03 eta 9:25:20
+epoch [7/50] batch [340/500] time 1.568 (1.566) data 0.000 (0.004) loss 1.5840 (1.1699) acc 53.1250 (71.3511) lr 1.9511e-03 eta 9:25:10
+epoch [7/50] batch [345/500] time 1.555 (1.566) data 0.000 (0.004) loss 0.9321 (1.1716) acc 75.0000 (71.3134) lr 1.9511e-03 eta 9:25:03
+epoch [7/50] batch [350/500] time 1.580 (1.566) data 0.000 (0.004) loss 1.6113 (1.1704) acc 59.3750 (71.3393) lr 1.9511e-03 eta 9:24:58
+epoch [7/50] batch [355/500] time 1.565 (1.566) data 0.000 (0.004) loss 2.0781 (1.1736) acc 59.3750 (71.3116) lr 1.9511e-03 eta 9:24:48
+epoch [7/50] batch [360/500] time 1.553 (1.566) data 0.000 (0.004) loss 0.9224 (1.1727) acc 75.0000 (71.3542) lr 1.9511e-03 eta 9:24:38
+epoch [7/50] batch [365/500] time 1.563 (1.565) data 0.000 (0.004) loss 0.7915 (1.1750) acc 87.5000 (71.3442) lr 1.9511e-03 eta 9:24:28
+epoch [7/50] batch [370/500] time 1.554 (1.565) data 0.000 (0.004) loss 1.0762 (1.1785) acc 68.7500 (71.2416) lr 1.9511e-03 eta 9:24:18
+epoch [7/50] batch [375/500] time 1.553 (1.565) data 0.000 (0.003) loss 1.1240 (1.1781) acc 71.8750 (71.2167) lr 1.9511e-03 eta 9:24:05
+epoch [7/50] batch [380/500] time 1.550 (1.565) data 0.000 (0.003) loss 0.9219 (1.1760) acc 78.1250 (71.2664) lr 1.9511e-03 eta 9:23:56
+epoch [7/50] batch [385/500] time 1.558 (1.565) data 0.000 (0.003) loss 1.1943 (1.1757) acc 81.2500 (71.2987) lr 1.9511e-03 eta 9:23:49
+epoch [7/50] batch [390/500] time 1.545 (1.565) data 0.000 (0.003) loss 1.3691 (1.1749) acc 68.7500 (71.3462) lr 1.9511e-03 eta 9:23:41
+epoch [7/50] batch [395/500] time 1.557 (1.565) data 0.000 (0.003) loss 0.7363 (1.1738) acc 71.8750 (71.2975) lr 1.9511e-03 eta 9:23:36
+epoch [7/50] batch [400/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.4453 (1.1718) acc 68.7500 (71.3125) lr 1.9511e-03 eta 9:23:29
+epoch [7/50] batch [405/500] time 1.577 (1.565) data 0.000 (0.003) loss 1.2471 (1.1709) acc 71.8750 (71.3040) lr 1.9511e-03 eta 9:23:26
+epoch [7/50] batch [410/500] time 1.559 (1.565) data 0.001 (0.003) loss 1.1338 (1.1712) acc 65.6250 (71.2805) lr 1.9511e-03 eta 9:23:15
+epoch [7/50] batch [415/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.0537 (1.1723) acc 81.2500 (71.2877) lr 1.9511e-03 eta 9:23:05
+epoch [7/50] batch [420/500] time 1.559 (1.565) data 0.000 (0.003) loss 1.0732 (1.1726) acc 71.8750 (71.2946) lr 1.9511e-03 eta 9:22:56
+epoch [7/50] batch [425/500] time 1.559 (1.565) data 0.000 (0.003) loss 0.7041 (1.1686) acc 81.2500 (71.3456) lr 1.9511e-03 eta 9:22:48
+epoch [7/50] batch [430/500] time 1.555 (1.565) data 0.000 (0.003) loss 1.2383 (1.1696) acc 65.6250 (71.3227) lr 1.9511e-03 eta 9:22:42
+epoch [7/50] batch [435/500] time 1.554 (1.565) data 0.000 (0.003) loss 0.9473 (1.1721) acc 75.0000 (71.2859) lr 1.9511e-03 eta 9:22:36
+epoch [7/50] batch [440/500] time 1.567 (1.565) data 0.000 (0.003) loss 1.0352 (1.1711) acc 71.8750 (71.2997) lr 1.9511e-03 eta 9:22:28
+epoch [7/50] batch [445/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.2549 (1.1720) acc 62.5000 (71.2430) lr 1.9511e-03 eta 9:22:17
+epoch [7/50] batch [450/500] time 1.539 (1.565) data 0.000 (0.003) loss 1.6553 (1.1732) acc 53.1250 (71.1806) lr 1.9511e-03 eta 9:22:12
+epoch [7/50] batch [455/500] time 1.545 (1.565) data 0.000 (0.003) loss 1.1357 (1.1742) acc 71.8750 (71.1470) lr 1.9511e-03 eta 9:22:02
+epoch [7/50] batch [460/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.3252 (1.1762) acc 68.7500 (71.1345) lr 1.9511e-03 eta 9:21:51
+epoch [7/50] batch [465/500] time 1.554 (1.565) data 0.000 (0.003) loss 1.1543 (1.1769) acc 68.7500 (71.1156) lr 1.9511e-03 eta 9:21:43
+epoch [7/50] batch [470/500] time 1.562 (1.565) data 0.000 (0.003) loss 0.9370 (1.1789) acc 71.8750 (71.0505) lr 1.9511e-03 eta 9:21:34
+epoch [7/50] batch [475/500] time 1.553 (1.565) data 0.001 (0.003) loss 0.4363 (1.1786) acc 87.5000 (71.1118) lr 1.9511e-03 eta 9:21:23
+epoch [7/50] batch [480/500] time 1.567 (1.565) data 0.000 (0.003) loss 1.1260 (1.1761) acc 71.8750 (71.1654) lr 1.9511e-03 eta 9:21:16
+epoch [7/50] batch [485/500] time 1.553 (1.565) data 0.001 (0.003) loss 1.3135 (1.1774) acc 56.2500 (71.1018) lr 1.9511e-03 eta 9:21:07
+epoch [7/50] batch [490/500] time 1.515 (1.565) data 0.000 (0.003) loss 0.5913 (1.1759) acc 81.2500 (71.1352) lr 1.9511e-03 eta 9:20:55
+epoch [7/50] batch [495/500] time 1.568 (1.564) data 0.000 (0.003) loss 1.6543 (1.1759) acc 50.0000 (71.1237) lr 1.9511e-03 eta 9:20:42
+epoch [7/50] batch [500/500] time 1.544 (1.564) data 0.000 (0.003) loss 0.3960 (1.1761) acc 87.5000 (71.1375) lr 1.9298e-03 eta 9:20:35
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,828
+* accuracy: 77.7%
+* error: 22.3%
+* macro_f1: 77.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [8/50] batch [5/500] time 1.563 (1.702) data 0.000 (0.189) loss 0.7524 (1.0770) acc 81.2500 (71.8750) lr 1.9298e-03 eta 10:09:35
+epoch [8/50] batch [10/500] time 1.553 (1.630) data 0.000 (0.095) loss 0.9478 (1.0654) acc 81.2500 (74.0625) lr 1.9298e-03 eta 9:43:42
+epoch [8/50] batch [15/500] time 1.547 (1.609) data 0.000 (0.063) loss 1.4238 (1.0653) acc 59.3750 (72.5000) lr 1.9298e-03 eta 9:36:04
+epoch [8/50] batch [20/500] time 1.545 (1.595) data 0.000 (0.048) loss 1.0713 (1.1403) acc 71.8750 (70.4688) lr 1.9298e-03 eta 9:31:00
+epoch [8/50] batch [25/500] time 1.546 (1.587) data 0.000 (0.038) loss 1.1670 (1.1177) acc 71.8750 (71.5000) lr 1.9298e-03 eta 9:28:08
+epoch [8/50] batch [30/500] time 1.570 (1.583) data 0.000 (0.032) loss 1.4697 (1.1174) acc 65.6250 (71.6667) lr 1.9298e-03 eta 9:26:21
+epoch [8/50] batch [35/500] time 1.556 (1.579) data 0.001 (0.027) loss 0.9971 (1.1403) acc 71.8750 (71.1607) lr 1.9298e-03 eta 9:24:54
+epoch [8/50] batch [40/500] time 1.558 (1.580) data 0.000 (0.024) loss 0.7466 (1.1037) acc 81.2500 (72.1875) lr 1.9298e-03 eta 9:25:06
+epoch [8/50] batch [45/500] time 1.563 (1.578) data 0.000 (0.021) loss 1.1885 (1.1326) acc 75.0000 (71.8056) lr 1.9298e-03 eta 9:24:22
+epoch [8/50] batch [50/500] time 1.575 (1.577) data 0.000 (0.019) loss 1.0410 (1.1266) acc 75.0000 (72.0625) lr 1.9298e-03 eta 9:23:47
+epoch [8/50] batch [55/500] time 1.551 (1.576) data 0.000 (0.018) loss 1.0908 (1.1355) acc 65.6250 (71.4773) lr 1.9298e-03 eta 9:23:08
+epoch [8/50] batch [60/500] time 1.545 (1.574) data 0.000 (0.016) loss 1.3848 (1.1690) acc 71.8750 (70.9375) lr 1.9298e-03 eta 9:22:18
+epoch [8/50] batch [65/500] time 1.575 (1.573) data 0.000 (0.015) loss 1.9619 (1.2071) acc 53.1250 (70.1923) lr 1.9298e-03 eta 9:21:48
+epoch [8/50] batch [70/500] time 1.566 (1.571) data 0.000 (0.014) loss 1.3809 (1.2192) acc 75.0000 (70.1339) lr 1.9298e-03 eta 9:21:04
+epoch [8/50] batch [75/500] time 1.573 (1.570) data 0.001 (0.013) loss 0.5806 (1.2171) acc 78.1250 (70.1250) lr 1.9298e-03 eta 9:20:40
+epoch [8/50] batch [80/500] time 1.651 (1.571) data 0.000 (0.012) loss 1.3105 (1.2201) acc 68.7500 (70.1562) lr 1.9298e-03 eta 9:20:45
+epoch [8/50] batch [85/500] time 1.582 (1.570) data 0.000 (0.011) loss 0.9370 (1.2327) acc 75.0000 (69.8897) lr 1.9298e-03 eta 9:20:29
+epoch [8/50] batch [90/500] time 1.554 (1.570) data 0.000 (0.011) loss 1.1250 (1.2395) acc 71.8750 (69.5139) lr 1.9298e-03 eta 9:20:17
+epoch [8/50] batch [95/500] time 1.554 (1.570) data 0.001 (0.010) loss 0.6030 (1.2273) acc 81.2500 (69.9013) lr 1.9298e-03 eta 9:19:57
+epoch [8/50] batch [100/500] time 1.554 (1.569) data 0.000 (0.010) loss 0.7993 (1.2177) acc 78.1250 (70.1875) lr 1.9298e-03 eta 9:19:36
+epoch [8/50] batch [105/500] time 1.565 (1.568) data 0.000 (0.009) loss 1.0996 (1.2257) acc 71.8750 (69.9405) lr 1.9298e-03 eta 9:19:13
+epoch [8/50] batch [110/500] time 1.570 (1.568) data 0.000 (0.009) loss 1.2949 (1.2342) acc 68.7500 (69.8011) lr 1.9298e-03 eta 9:18:57
+epoch [8/50] batch [115/500] time 1.573 (1.567) data 0.000 (0.009) loss 0.7393 (1.2226) acc 68.7500 (69.9728) lr 1.9298e-03 eta 9:18:39
+epoch [8/50] batch [120/500] time 1.563 (1.567) data 0.000 (0.008) loss 1.7617 (1.2252) acc 62.5000 (70.0260) lr 1.9298e-03 eta 9:18:22
+epoch [8/50] batch [125/500] time 1.540 (1.567) data 0.000 (0.008) loss 0.5918 (1.2212) acc 78.1250 (70.1250) lr 1.9298e-03 eta 9:18:08
+epoch [8/50] batch [130/500] time 1.545 (1.566) data 0.000 (0.008) loss 2.0039 (1.2276) acc 56.2500 (70.0481) lr 1.9298e-03 eta 9:17:52
+epoch [8/50] batch [135/500] time 1.564 (1.566) data 0.000 (0.007) loss 1.7773 (1.2302) acc 62.5000 (70.0463) lr 1.9298e-03 eta 9:17:42
+epoch [8/50] batch [140/500] time 1.571 (1.566) data 0.000 (0.007) loss 1.9014 (1.2322) acc 62.5000 (70.0000) lr 1.9298e-03 eta 9:17:28
+epoch [8/50] batch [145/500] time 1.555 (1.566) data 0.000 (0.007) loss 1.4521 (1.2334) acc 68.7500 (70.1078) lr 1.9298e-03 eta 9:17:22
+epoch [8/50] batch [150/500] time 1.560 (1.566) data 0.001 (0.007) loss 0.9214 (1.2291) acc 68.7500 (70.1458) lr 1.9298e-03 eta 9:17:14
+epoch [8/50] batch [155/500] time 1.559 (1.566) data 0.001 (0.006) loss 1.0049 (1.2255) acc 71.8750 (70.3427) lr 1.9298e-03 eta 9:17:02
+epoch [8/50] batch [160/500] time 1.580 (1.566) data 0.001 (0.006) loss 1.1504 (1.2222) acc 68.7500 (70.3906) lr 1.9298e-03 eta 9:16:52
+epoch [8/50] batch [165/500] time 1.559 (1.566) data 0.000 (0.006) loss 0.7178 (1.2128) acc 81.2500 (70.4924) lr 1.9298e-03 eta 9:16:40
+epoch [8/50] batch [170/500] time 1.554 (1.565) data 0.000 (0.006) loss 0.9839 (1.2043) acc 75.0000 (70.7537) lr 1.9298e-03 eta 9:16:25
+epoch [8/50] batch [175/500] time 1.551 (1.565) data 0.000 (0.006) loss 1.2637 (1.2036) acc 65.6250 (70.8214) lr 1.9298e-03 eta 9:16:10
+epoch [8/50] batch [180/500] time 1.575 (1.565) data 0.001 (0.006) loss 0.9614 (1.2064) acc 78.1250 (70.8854) lr 1.9298e-03 eta 9:16:14
+epoch [8/50] batch [185/500] time 1.579 (1.565) data 0.000 (0.006) loss 1.1865 (1.2023) acc 75.0000 (71.0135) lr 1.9298e-03 eta 9:16:05
+epoch [8/50] batch [190/500] time 1.576 (1.566) data 0.000 (0.005) loss 0.7559 (1.2024) acc 71.8750 (70.9539) lr 1.9298e-03 eta 9:16:02
+epoch [8/50] batch [195/500] time 1.573 (1.566) data 0.000 (0.005) loss 1.4541 (1.1992) acc 71.8750 (70.9615) lr 1.9298e-03 eta 9:15:55
+epoch [8/50] batch [200/500] time 1.558 (1.565) data 0.000 (0.005) loss 0.5215 (1.1949) acc 90.6250 (71.0469) lr 1.9298e-03 eta 9:15:42
+epoch [8/50] batch [205/500] time 1.559 (1.565) data 0.000 (0.005) loss 0.6499 (1.1881) acc 84.3750 (71.2043) lr 1.9298e-03 eta 9:15:28
+epoch [8/50] batch [210/500] time 1.539 (1.565) data 0.000 (0.005) loss 1.1289 (1.1844) acc 75.0000 (71.2054) lr 1.9298e-03 eta 9:15:13
+epoch [8/50] batch [215/500] time 1.566 (1.565) data 0.000 (0.005) loss 0.7539 (1.1842) acc 78.1250 (71.2064) lr 1.9298e-03 eta 9:15:02
+epoch [8/50] batch [220/500] time 1.582 (1.565) data 0.000 (0.005) loss 1.6895 (1.1882) acc 59.3750 (71.1932) lr 1.9298e-03 eta 9:14:53
+epoch [8/50] batch [225/500] time 1.545 (1.565) data 0.000 (0.005) loss 0.7246 (1.1883) acc 84.3750 (71.1667) lr 1.9298e-03 eta 9:14:54
+epoch [8/50] batch [230/500] time 1.601 (1.565) data 0.000 (0.005) loss 1.0938 (1.1833) acc 75.0000 (71.2636) lr 1.9298e-03 eta 9:14:44
+epoch [8/50] batch [235/500] time 1.563 (1.565) data 0.000 (0.004) loss 1.6494 (1.1836) acc 59.3750 (71.1968) lr 1.9298e-03 eta 9:14:37
+epoch [8/50] batch [240/500] time 1.565 (1.565) data 0.001 (0.004) loss 0.6821 (1.1798) acc 71.8750 (71.2500) lr 1.9298e-03 eta 9:14:32
+epoch [8/50] batch [245/500] time 1.544 (1.565) data 0.001 (0.004) loss 0.8320 (1.1770) acc 84.3750 (71.3138) lr 1.9298e-03 eta 9:14:21
+epoch [8/50] batch [250/500] time 1.540 (1.565) data 0.000 (0.004) loss 0.7310 (1.1778) acc 78.1250 (71.3000) lr 1.9298e-03 eta 9:14:10
+epoch [8/50] batch [255/500] time 1.562 (1.564) data 0.000 (0.004) loss 1.2051 (1.1801) acc 65.6250 (71.2377) lr 1.9298e-03 eta 9:13:57
+epoch [8/50] batch [260/500] time 1.533 (1.564) data 0.000 (0.004) loss 0.9683 (1.1792) acc 75.0000 (71.3101) lr 1.9298e-03 eta 9:13:44
+epoch [8/50] batch [265/500] time 1.551 (1.564) data 0.001 (0.004) loss 1.8086 (1.1847) acc 62.5000 (71.2264) lr 1.9298e-03 eta 9:13:30
+epoch [8/50] batch [270/500] time 1.537 (1.564) data 0.000 (0.004) loss 1.4033 (1.1837) acc 59.3750 (71.1921) lr 1.9298e-03 eta 9:13:23
+epoch [8/50] batch [275/500] time 1.569 (1.564) data 0.000 (0.004) loss 1.7490 (1.1836) acc 62.5000 (71.0795) lr 1.9298e-03 eta 9:13:14
+epoch [8/50] batch [280/500] time 1.568 (1.564) data 0.000 (0.004) loss 0.6963 (1.1831) acc 81.2500 (71.0826) lr 1.9298e-03 eta 9:13:05
+epoch [8/50] batch [285/500] time 1.575 (1.564) data 0.000 (0.004) loss 1.2100 (1.1833) acc 75.0000 (71.0965) lr 1.9298e-03 eta 9:12:55
+epoch [8/50] batch [290/500] time 1.589 (1.564) data 0.000 (0.004) loss 1.7949 (1.1897) acc 56.2500 (71.0237) lr 1.9298e-03 eta 9:12:49
+epoch [8/50] batch [295/500] time 1.572 (1.564) data 0.000 (0.004) loss 0.6899 (1.1905) acc 87.5000 (71.0699) lr 1.9298e-03 eta 9:12:38
+epoch [8/50] batch [300/500] time 1.586 (1.564) data 0.000 (0.004) loss 1.5723 (1.1939) acc 68.7500 (71.0417) lr 1.9298e-03 eta 9:12:33
+epoch [8/50] batch [305/500] time 1.563 (1.564) data 0.000 (0.003) loss 1.4189 (1.1928) acc 68.7500 (71.0348) lr 1.9298e-03 eta 9:12:25
+epoch [8/50] batch [310/500] time 1.568 (1.564) data 0.000 (0.003) loss 1.8105 (1.2001) acc 59.3750 (70.9476) lr 1.9298e-03 eta 9:12:17
+epoch [8/50] batch [315/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.1689 (1.1980) acc 75.0000 (70.9821) lr 1.9298e-03 eta 9:12:15
+epoch [8/50] batch [320/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.4561 (1.1967) acc 65.6250 (70.9766) lr 1.9298e-03 eta 9:12:10
+epoch [8/50] batch [325/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.2559 (1.1962) acc 71.8750 (71.0096) lr 1.9298e-03 eta 9:12:10
+epoch [8/50] batch [330/500] time 1.571 (1.565) data 0.000 (0.003) loss 0.7998 (1.1989) acc 78.1250 (71.0038) lr 1.9298e-03 eta 9:12:01
+epoch [8/50] batch [335/500] time 1.548 (1.564) data 0.000 (0.003) loss 1.3633 (1.1959) acc 65.6250 (71.0541) lr 1.9298e-03 eta 9:11:50
+epoch [8/50] batch [340/500] time 1.566 (1.564) data 0.000 (0.003) loss 1.8486 (1.1997) acc 62.5000 (71.0110) lr 1.9298e-03 eta 9:11:40
+epoch [8/50] batch [345/500] time 1.576 (1.564) data 0.000 (0.003) loss 1.0576 (1.1980) acc 59.3750 (70.9692) lr 1.9298e-03 eta 9:11:32
+epoch [8/50] batch [350/500] time 1.557 (1.564) data 0.000 (0.003) loss 1.0439 (1.1965) acc 71.8750 (70.9732) lr 1.9298e-03 eta 9:11:24
+epoch [8/50] batch [355/500] time 1.586 (1.564) data 0.000 (0.003) loss 1.0645 (1.1935) acc 78.1250 (71.0211) lr 1.9298e-03 eta 9:11:19
+epoch [8/50] batch [360/500] time 1.567 (1.564) data 0.000 (0.003) loss 1.0586 (1.1972) acc 65.6250 (70.9549) lr 1.9298e-03 eta 9:11:11
+epoch [8/50] batch [365/500] time 1.571 (1.564) data 0.000 (0.003) loss 1.0049 (1.1974) acc 71.8750 (70.9675) lr 1.9298e-03 eta 9:11:04
+epoch [8/50] batch [370/500] time 1.556 (1.565) data 0.000 (0.003) loss 1.3047 (1.1991) acc 65.6250 (70.9037) lr 1.9298e-03 eta 9:11:03
+epoch [8/50] batch [375/500] time 1.554 (1.565) data 0.000 (0.003) loss 0.8311 (1.1999) acc 78.1250 (70.8917) lr 1.9298e-03 eta 9:10:54
+epoch [8/50] batch [380/500] time 1.580 (1.565) data 0.000 (0.003) loss 0.7817 (1.1969) acc 75.0000 (70.9128) lr 1.9298e-03 eta 9:10:49
+epoch [8/50] batch [385/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.2969 (1.1981) acc 68.7500 (70.8685) lr 1.9298e-03 eta 9:10:41
+epoch [8/50] batch [390/500] time 1.560 (1.565) data 0.000 (0.003) loss 1.2148 (1.2012) acc 81.2500 (70.8974) lr 1.9298e-03 eta 9:10:34
+epoch [8/50] batch [395/500] time 1.549 (1.565) data 0.001 (0.003) loss 0.7646 (1.2016) acc 78.1250 (70.9177) lr 1.9298e-03 eta 9:10:26
+epoch [8/50] batch [400/500] time 1.565 (1.565) data 0.000 (0.003) loss 1.4414 (1.2009) acc 65.6250 (70.9297) lr 1.9298e-03 eta 9:10:17
+epoch [8/50] batch [405/500] time 1.571 (1.565) data 0.000 (0.003) loss 1.1855 (1.2006) acc 71.8750 (70.8951) lr 1.9298e-03 eta 9:10:09
+epoch [8/50] batch [410/500] time 1.533 (1.565) data 0.000 (0.003) loss 1.3271 (1.1999) acc 81.2500 (70.9451) lr 1.9298e-03 eta 9:09:59
+epoch [8/50] batch [415/500] time 1.568 (1.565) data 0.000 (0.003) loss 0.8320 (1.1972) acc 81.2500 (70.9864) lr 1.9298e-03 eta 9:09:51
+epoch [8/50] batch [420/500] time 1.569 (1.565) data 0.000 (0.003) loss 0.7397 (1.1941) acc 75.0000 (71.0491) lr 1.9298e-03 eta 9:09:43
+epoch [8/50] batch [425/500] time 1.570 (1.565) data 0.001 (0.003) loss 0.8159 (1.1948) acc 75.0000 (71.0074) lr 1.9298e-03 eta 9:09:34
+epoch [8/50] batch [430/500] time 1.556 (1.565) data 0.001 (0.003) loss 0.7563 (1.1928) acc 81.2500 (71.0102) lr 1.9298e-03 eta 9:09:26
+epoch [8/50] batch [435/500] time 1.566 (1.565) data 0.000 (0.003) loss 1.5391 (1.1948) acc 62.5000 (70.9267) lr 1.9298e-03 eta 9:09:17
+epoch [8/50] batch [440/500] time 1.548 (1.565) data 0.000 (0.003) loss 1.1885 (1.1935) acc 71.8750 (70.9517) lr 1.9298e-03 eta 9:09:09
+epoch [8/50] batch [445/500] time 1.569 (1.565) data 0.000 (0.003) loss 1.1377 (1.1954) acc 75.0000 (70.9270) lr 1.9298e-03 eta 9:09:02
+epoch [8/50] batch [450/500] time 1.540 (1.565) data 0.000 (0.002) loss 1.0869 (1.1951) acc 68.7500 (70.8611) lr 1.9298e-03 eta 9:08:53
+epoch [8/50] batch [455/500] time 1.545 (1.564) data 0.000 (0.002) loss 0.9941 (1.1937) acc 78.1250 (70.8860) lr 1.9298e-03 eta 9:08:42
+epoch [8/50] batch [460/500] time 1.552 (1.564) data 0.000 (0.002) loss 0.8564 (1.1916) acc 90.6250 (70.9375) lr 1.9298e-03 eta 9:08:32
+epoch [8/50] batch [465/500] time 1.674 (1.564) data 0.000 (0.002) loss 0.8735 (1.1909) acc 78.1250 (70.9879) lr 1.9298e-03 eta 9:08:28
+epoch [8/50] batch [470/500] time 1.578 (1.564) data 0.000 (0.002) loss 1.2373 (1.1917) acc 68.7500 (70.9508) lr 1.9298e-03 eta 9:08:21
+epoch [8/50] batch [475/500] time 1.555 (1.565) data 0.000 (0.002) loss 1.1953 (1.1905) acc 65.6250 (70.9539) lr 1.9298e-03 eta 9:08:14
+epoch [8/50] batch [480/500] time 1.557 (1.565) data 0.000 (0.002) loss 0.9517 (1.1887) acc 68.7500 (70.9701) lr 1.9298e-03 eta 9:08:07
+epoch [8/50] batch [485/500] time 1.553 (1.564) data 0.001 (0.002) loss 0.9443 (1.1867) acc 68.7500 (71.0180) lr 1.9298e-03 eta 9:07:57
+epoch [8/50] batch [490/500] time 1.559 (1.564) data 0.000 (0.002) loss 1.0664 (1.1869) acc 71.8750 (70.9885) lr 1.9298e-03 eta 9:07:48
+epoch [8/50] batch [495/500] time 1.528 (1.564) data 0.000 (0.002) loss 1.3633 (1.1889) acc 68.7500 (70.9533) lr 1.9298e-03 eta 9:07:38
+epoch [8/50] batch [500/500] time 1.554 (1.564) data 0.000 (0.002) loss 1.0586 (1.1891) acc 78.1250 (71.0000) lr 1.9048e-03 eta 9:07:25
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,811
+* accuracy: 77.6%
+* error: 22.4%
+* macro_f1: 77.0%
+epoch [9/50] batch [5/500] time 1.551 (1.708) data 0.000 (0.202) loss 0.7822 (1.0520) acc 81.2500 (75.0000) lr 1.9048e-03 eta 9:57:30
+epoch [9/50] batch [10/500] time 1.567 (1.639) data 0.000 (0.101) loss 1.8652 (1.0984) acc 59.3750 (74.3750) lr 1.9048e-03 eta 9:33:27
+epoch [9/50] batch [15/500] time 1.576 (1.611) data 0.000 (0.067) loss 1.6367 (1.1623) acc 65.6250 (72.7083) lr 1.9048e-03 eta 9:23:37
+epoch [9/50] batch [20/500] time 1.556 (1.605) data 0.001 (0.051) loss 0.5752 (1.1429) acc 84.3750 (73.5938) lr 1.9048e-03 eta 9:21:14
+epoch [9/50] batch [25/500] time 1.570 (1.600) data 0.001 (0.041) loss 0.7690 (1.1541) acc 78.1250 (73.0000) lr 1.9048e-03 eta 9:19:18
+epoch [9/50] batch [30/500] time 1.548 (1.593) data 0.000 (0.034) loss 0.6641 (1.1245) acc 75.0000 (73.2292) lr 1.9048e-03 eta 9:16:39
+epoch [9/50] batch [35/500] time 1.573 (1.589) data 0.000 (0.029) loss 0.9414 (1.1154) acc 75.0000 (73.3036) lr 1.9048e-03 eta 9:15:21
+epoch [9/50] batch [40/500] time 1.555 (1.586) data 0.001 (0.026) loss 0.9082 (1.1416) acc 65.6250 (72.5000) lr 1.9048e-03 eta 9:14:05
+epoch [9/50] batch [45/500] time 1.538 (1.583) data 0.000 (0.023) loss 1.3398 (1.1239) acc 65.6250 (72.5694) lr 1.9048e-03 eta 9:12:55
+epoch [9/50] batch [50/500] time 1.545 (1.582) data 0.000 (0.021) loss 0.7910 (1.1142) acc 71.8750 (72.6250) lr 1.9048e-03 eta 9:12:20
+epoch [9/50] batch [55/500] time 1.546 (1.579) data 0.000 (0.019) loss 1.6738 (1.1506) acc 50.0000 (71.5341) lr 1.9048e-03 eta 9:11:04
+epoch [9/50] batch [60/500] time 1.555 (1.578) data 0.001 (0.017) loss 0.9268 (1.1317) acc 81.2500 (71.6146) lr 1.9048e-03 eta 9:10:34
+epoch [9/50] batch [65/500] time 1.587 (1.577) data 0.000 (0.016) loss 0.7383 (1.1220) acc 78.1250 (71.7308) lr 1.9048e-03 eta 9:10:10
+epoch [9/50] batch [70/500] time 1.559 (1.575) data 0.000 (0.015) loss 0.9043 (1.1256) acc 75.0000 (71.5625) lr 1.9048e-03 eta 9:09:30
+epoch [9/50] batch [75/500] time 1.564 (1.574) data 0.000 (0.014) loss 0.9248 (1.1316) acc 75.0000 (71.5000) lr 1.9048e-03 eta 9:08:53
+epoch [9/50] batch [80/500] time 1.556 (1.573) data 0.000 (0.013) loss 1.4492 (1.1352) acc 68.7500 (71.6016) lr 1.9048e-03 eta 9:08:24
+epoch [9/50] batch [85/500] time 1.560 (1.572) data 0.001 (0.012) loss 1.1650 (1.1352) acc 75.0000 (71.5809) lr 1.9048e-03 eta 9:08:00
+epoch [9/50] batch [90/500] time 1.553 (1.571) data 0.000 (0.012) loss 0.7622 (1.1262) acc 75.0000 (71.7361) lr 1.9048e-03 eta 9:07:31
+epoch [9/50] batch [95/500] time 1.579 (1.571) data 0.000 (0.011) loss 0.7930 (1.1277) acc 78.1250 (71.8421) lr 1.9048e-03 eta 9:07:17
+epoch [9/50] batch [100/500] time 1.541 (1.570) data 0.001 (0.010) loss 1.7178 (1.1345) acc 59.3750 (71.6250) lr 1.9048e-03 eta 9:06:53
+epoch [9/50] batch [105/500] time 1.533 (1.569) data 0.001 (0.010) loss 0.6895 (1.1327) acc 84.3750 (71.6667) lr 1.9048e-03 eta 9:06:24
+epoch [9/50] batch [110/500] time 1.547 (1.568) data 0.000 (0.010) loss 1.2783 (1.1291) acc 71.8750 (71.7045) lr 1.9048e-03 eta 9:05:57
+epoch [9/50] batch [115/500] time 1.642 (1.568) data 0.001 (0.009) loss 0.8467 (1.1356) acc 75.0000 (71.6848) lr 1.9048e-03 eta 9:05:54
+epoch [9/50] batch [120/500] time 1.569 (1.568) data 0.000 (0.009) loss 1.1768 (1.1419) acc 78.1250 (71.5365) lr 1.9048e-03 eta 9:05:44
+epoch [9/50] batch [125/500] time 1.541 (1.567) data 0.000 (0.008) loss 0.9048 (1.1438) acc 81.2500 (71.5000) lr 1.9048e-03 eta 9:05:16
+epoch [9/50] batch [130/500] time 1.562 (1.567) data 0.000 (0.008) loss 1.1484 (1.1465) acc 78.1250 (71.5865) lr 1.9048e-03 eta 9:04:59
+epoch [9/50] batch [135/500] time 1.567 (1.567) data 0.000 (0.008) loss 0.7612 (1.1449) acc 78.1250 (71.7130) lr 1.9048e-03 eta 9:04:57
+epoch [9/50] batch [140/500] time 1.577 (1.567) data 0.000 (0.008) loss 1.0049 (1.1482) acc 78.1250 (71.8080) lr 1.9048e-03 eta 9:04:52
+epoch [9/50] batch [145/500] time 1.549 (1.567) data 0.000 (0.007) loss 1.2188 (1.1426) acc 78.1250 (72.0259) lr 1.9048e-03 eta 9:04:39
+epoch [9/50] batch [150/500] time 1.559 (1.567) data 0.000 (0.007) loss 1.3838 (1.1418) acc 68.7500 (72.1250) lr 1.9048e-03 eta 9:04:23
+epoch [9/50] batch [155/500] time 1.555 (1.566) data 0.000 (0.007) loss 1.8936 (1.1437) acc 62.5000 (72.0565) lr 1.9048e-03 eta 9:04:09
+epoch [9/50] batch [160/500] time 1.561 (1.567) data 0.001 (0.007) loss 1.1025 (1.1467) acc 78.1250 (72.0898) lr 1.9048e-03 eta 9:04:09
+epoch [9/50] batch [165/500] time 1.543 (1.566) data 0.000 (0.007) loss 0.8389 (1.1407) acc 78.1250 (72.0076) lr 1.9048e-03 eta 9:03:57
+epoch [9/50] batch [170/500] time 1.557 (1.566) data 0.000 (0.006) loss 1.1455 (1.1398) acc 62.5000 (71.9485) lr 1.9048e-03 eta 9:03:46
+epoch [9/50] batch [175/500] time 1.570 (1.566) data 0.001 (0.006) loss 1.1631 (1.1447) acc 75.0000 (71.8750) lr 1.9048e-03 eta 9:03:37
+epoch [9/50] batch [180/500] time 1.581 (1.567) data 0.000 (0.006) loss 1.4160 (1.1421) acc 75.0000 (71.9618) lr 1.9048e-03 eta 9:03:35
+epoch [9/50] batch [185/500] time 1.549 (1.566) data 0.000 (0.006) loss 1.5918 (1.1504) acc 65.6250 (71.7905) lr 1.9048e-03 eta 9:03:18
+epoch [9/50] batch [190/500] time 1.577 (1.566) data 0.000 (0.006) loss 0.9946 (1.1470) acc 71.8750 (71.9243) lr 1.9048e-03 eta 9:03:13
+epoch [9/50] batch [195/500] time 1.579 (1.566) data 0.000 (0.006) loss 0.9565 (1.1486) acc 71.8750 (71.8910) lr 1.9048e-03 eta 9:03:02
+epoch [9/50] batch [200/500] time 1.540 (1.566) data 0.000 (0.005) loss 1.3125 (1.1494) acc 65.6250 (71.9688) lr 1.9048e-03 eta 9:02:53
+epoch [9/50] batch [205/500] time 1.547 (1.566) data 0.000 (0.005) loss 1.6035 (1.1503) acc 62.5000 (71.9665) lr 1.9048e-03 eta 9:02:37
+epoch [9/50] batch [210/500] time 1.565 (1.565) data 0.000 (0.005) loss 1.1182 (1.1496) acc 75.0000 (71.9494) lr 1.9048e-03 eta 9:02:24
+epoch [9/50] batch [215/500] time 1.545 (1.565) data 0.000 (0.005) loss 1.2568 (1.1514) acc 62.5000 (71.9767) lr 1.9048e-03 eta 9:02:11
+epoch [9/50] batch [220/500] time 1.552 (1.565) data 0.000 (0.005) loss 1.1377 (1.1503) acc 75.0000 (72.0028) lr 1.9048e-03 eta 9:01:56
+epoch [9/50] batch [225/500] time 1.567 (1.565) data 0.000 (0.005) loss 1.3193 (1.1580) acc 71.8750 (71.7500) lr 1.9048e-03 eta 9:01:43
+epoch [9/50] batch [230/500] time 1.544 (1.564) data 0.000 (0.005) loss 1.0244 (1.1550) acc 71.8750 (71.7120) lr 1.9048e-03 eta 9:01:29
+epoch [9/50] batch [235/500] time 1.556 (1.564) data 0.000 (0.005) loss 0.9292 (1.1532) acc 81.2500 (71.7819) lr 1.9048e-03 eta 9:01:20
+epoch [9/50] batch [240/500] time 1.563 (1.564) data 0.000 (0.005) loss 1.2900 (1.1539) acc 75.0000 (71.8099) lr 1.9048e-03 eta 9:01:11
+epoch [9/50] batch [245/500] time 1.571 (1.564) data 0.000 (0.005) loss 1.1680 (1.1526) acc 68.7500 (71.7092) lr 1.9048e-03 eta 9:01:00
+epoch [9/50] batch [250/500] time 1.553 (1.564) data 0.001 (0.004) loss 1.1094 (1.1586) acc 78.1250 (71.6125) lr 1.9048e-03 eta 9:00:45
+epoch [9/50] batch [255/500] time 1.547 (1.563) data 0.000 (0.004) loss 0.7847 (1.1537) acc 87.5000 (71.7647) lr 1.9048e-03 eta 9:00:34
+epoch [9/50] batch [260/500] time 1.568 (1.564) data 0.000 (0.004) loss 1.0264 (1.1525) acc 84.3750 (71.7548) lr 1.9048e-03 eta 9:00:36
+epoch [9/50] batch [265/500] time 1.557 (1.564) data 0.000 (0.004) loss 1.0986 (1.1495) acc 68.7500 (71.8042) lr 1.9048e-03 eta 9:00:28
+epoch [9/50] batch [270/500] time 1.552 (1.564) data 0.000 (0.004) loss 1.0176 (1.1426) acc 81.2500 (71.9329) lr 1.9048e-03 eta 9:00:17
+epoch [9/50] batch [275/500] time 1.551 (1.563) data 0.000 (0.004) loss 1.8242 (1.1486) acc 65.6250 (71.7273) lr 1.9048e-03 eta 9:00:00
+epoch [9/50] batch [280/500] time 1.557 (1.563) data 0.000 (0.004) loss 1.3086 (1.1532) acc 71.8750 (71.6071) lr 1.9048e-03 eta 8:59:45
+epoch [9/50] batch [285/500] time 1.559 (1.563) data 0.000 (0.004) loss 0.7144 (1.1545) acc 78.1250 (71.5789) lr 1.9048e-03 eta 8:59:31
+epoch [9/50] batch [290/500] time 1.550 (1.563) data 0.000 (0.004) loss 1.2188 (1.1573) acc 68.7500 (71.5517) lr 1.9048e-03 eta 8:59:19
+epoch [9/50] batch [295/500] time 1.573 (1.563) data 0.001 (0.004) loss 1.1094 (1.1594) acc 71.8750 (71.5784) lr 1.9048e-03 eta 8:59:13
+epoch [9/50] batch [300/500] time 1.552 (1.563) data 0.000 (0.004) loss 1.5059 (1.1607) acc 71.8750 (71.5833) lr 1.9048e-03 eta 8:59:05
+epoch [9/50] batch [305/500] time 1.584 (1.563) data 0.001 (0.004) loss 1.3730 (1.1599) acc 71.8750 (71.6189) lr 1.9048e-03 eta 8:59:06
+epoch [9/50] batch [310/500] time 1.573 (1.563) data 0.000 (0.004) loss 1.7822 (1.1617) acc 59.3750 (71.5222) lr 1.9048e-03 eta 8:59:01
+epoch [9/50] batch [315/500] time 1.569 (1.563) data 0.000 (0.004) loss 1.0391 (1.1632) acc 75.0000 (71.5179) lr 1.9048e-03 eta 8:58:51
+epoch [9/50] batch [320/500] time 1.550 (1.563) data 0.000 (0.004) loss 1.0322 (1.1622) acc 81.2500 (71.5234) lr 1.9048e-03 eta 8:58:39
+epoch [9/50] batch [325/500] time 1.546 (1.563) data 0.000 (0.004) loss 1.5439 (1.1592) acc 68.7500 (71.6154) lr 1.9048e-03 eta 8:58:27
+epoch [9/50] batch [330/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.9053 (1.1556) acc 81.2500 (71.6667) lr 1.9048e-03 eta 8:58:23
+epoch [9/50] batch [335/500] time 1.553 (1.563) data 0.000 (0.003) loss 1.3018 (1.1581) acc 62.5000 (71.5858) lr 1.9048e-03 eta 8:58:21
+epoch [9/50] batch [340/500] time 1.580 (1.563) data 0.000 (0.003) loss 0.8647 (1.1570) acc 78.1250 (71.6085) lr 1.9048e-03 eta 8:58:15
+epoch [9/50] batch [345/500] time 1.562 (1.563) data 0.000 (0.003) loss 1.2354 (1.1557) acc 65.6250 (71.6304) lr 1.9048e-03 eta 8:58:06
+epoch [9/50] batch [350/500] time 1.549 (1.563) data 0.000 (0.003) loss 0.8540 (1.1568) acc 78.1250 (71.6339) lr 1.9048e-03 eta 8:57:55
+epoch [9/50] batch [355/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.4648 (1.1586) acc 62.5000 (71.5581) lr 1.9048e-03 eta 8:57:43
+epoch [9/50] batch [360/500] time 1.539 (1.563) data 0.000 (0.003) loss 0.7832 (1.1597) acc 75.0000 (71.5538) lr 1.9048e-03 eta 8:57:32
+epoch [9/50] batch [365/500] time 1.560 (1.562) data 0.000 (0.003) loss 1.1279 (1.1598) acc 75.0000 (71.5753) lr 1.9048e-03 eta 8:57:20
+epoch [9/50] batch [370/500] time 1.576 (1.563) data 0.000 (0.003) loss 1.0566 (1.1573) acc 75.0000 (71.6301) lr 1.9048e-03 eta 8:57:15
+epoch [9/50] batch [375/500] time 1.561 (1.562) data 0.000 (0.003) loss 1.4053 (1.1580) acc 62.5000 (71.6083) lr 1.9048e-03 eta 8:57:05
+epoch [9/50] batch [380/500] time 1.552 (1.562) data 0.001 (0.003) loss 1.2500 (1.1547) acc 71.8750 (71.6941) lr 1.9048e-03 eta 8:56:56
+epoch [9/50] batch [385/500] time 1.541 (1.562) data 0.000 (0.003) loss 0.8213 (1.1558) acc 81.2500 (71.6477) lr 1.9048e-03 eta 8:56:47
+epoch [9/50] batch [390/500] time 1.559 (1.562) data 0.000 (0.003) loss 1.6611 (1.1590) acc 71.8750 (71.6346) lr 1.9048e-03 eta 8:56:37
+epoch [9/50] batch [395/500] time 1.557 (1.562) data 0.000 (0.003) loss 1.3398 (1.1594) acc 65.6250 (71.6060) lr 1.9048e-03 eta 8:56:25
+epoch [9/50] batch [400/500] time 1.546 (1.562) data 0.000 (0.003) loss 0.9731 (1.1585) acc 75.0000 (71.6094) lr 1.9048e-03 eta 8:56:16
+epoch [9/50] batch [405/500] time 1.575 (1.562) data 0.000 (0.003) loss 1.4766 (1.1598) acc 65.6250 (71.5818) lr 1.9048e-03 eta 8:56:16
+epoch [9/50] batch [410/500] time 1.585 (1.562) data 0.000 (0.003) loss 1.2041 (1.1595) acc 71.8750 (71.5244) lr 1.9048e-03 eta 8:56:08
+epoch [9/50] batch [415/500] time 1.574 (1.562) data 0.000 (0.003) loss 0.9561 (1.1593) acc 78.1250 (71.5286) lr 1.9048e-03 eta 8:56:00
+epoch [9/50] batch [420/500] time 1.562 (1.562) data 0.000 (0.003) loss 1.1680 (1.1618) acc 71.8750 (71.4658) lr 1.9048e-03 eta 8:55:54
+epoch [9/50] batch [425/500] time 1.569 (1.563) data 0.000 (0.003) loss 1.5596 (1.1597) acc 71.8750 (71.5221) lr 1.9048e-03 eta 8:55:49
+epoch [9/50] batch [430/500] time 1.541 (1.563) data 0.000 (0.003) loss 1.1152 (1.1608) acc 65.6250 (71.4971) lr 1.9048e-03 eta 8:55:40
+epoch [9/50] batch [435/500] time 1.555 (1.562) data 0.000 (0.003) loss 0.9956 (1.1657) acc 81.2500 (71.3649) lr 1.9048e-03 eta 8:55:31
+epoch [9/50] batch [440/500] time 1.569 (1.562) data 0.000 (0.003) loss 0.7002 (1.1642) acc 75.0000 (71.3849) lr 1.9048e-03 eta 8:55:20
+epoch [9/50] batch [445/500] time 1.686 (1.562) data 0.000 (0.003) loss 0.9902 (1.1648) acc 71.8750 (71.3764) lr 1.9048e-03 eta 8:55:16
+epoch [9/50] batch [450/500] time 1.578 (1.563) data 0.000 (0.003) loss 1.3906 (1.1668) acc 65.6250 (71.3056) lr 1.9048e-03 eta 8:55:09
+epoch [9/50] batch [455/500] time 1.542 (1.562) data 0.000 (0.003) loss 1.0557 (1.1651) acc 78.1250 (71.3255) lr 1.9048e-03 eta 8:54:58
+epoch [9/50] batch [460/500] time 1.558 (1.562) data 0.000 (0.003) loss 1.8730 (1.1658) acc 59.3750 (71.2840) lr 1.9048e-03 eta 8:54:48
+epoch [9/50] batch [465/500] time 1.569 (1.562) data 0.000 (0.003) loss 0.7700 (1.1655) acc 84.3750 (71.2970) lr 1.9048e-03 eta 8:54:40
+epoch [9/50] batch [470/500] time 1.562 (1.562) data 0.000 (0.003) loss 1.4893 (1.1674) acc 68.7500 (71.2633) lr 1.9048e-03 eta 8:54:33
+epoch [9/50] batch [475/500] time 1.555 (1.562) data 0.000 (0.003) loss 1.2871 (1.1712) acc 65.6250 (71.1645) lr 1.9048e-03 eta 8:54:24
+epoch [9/50] batch [480/500] time 1.543 (1.562) data 0.000 (0.003) loss 1.5801 (1.1717) acc 59.3750 (71.1328) lr 1.9048e-03 eta 8:54:13
+epoch [9/50] batch [485/500] time 1.576 (1.562) data 0.001 (0.002) loss 1.4551 (1.1719) acc 65.6250 (71.1405) lr 1.9048e-03 eta 8:54:05
+epoch [9/50] batch [490/500] time 1.565 (1.562) data 0.000 (0.002) loss 1.1699 (1.1725) acc 62.5000 (71.1288) lr 1.9048e-03 eta 8:53:55
+epoch [9/50] batch [495/500] time 1.561 (1.562) data 0.000 (0.002) loss 1.0703 (1.1743) acc 71.8750 (71.0795) lr 1.9048e-03 eta 8:53:43
+epoch [9/50] batch [500/500] time 1.571 (1.562) data 0.000 (0.002) loss 1.0186 (1.1746) acc 78.1250 (71.0625) lr 1.8763e-03 eta 8:53:34
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,779
+* accuracy: 77.6%
+* error: 22.4%
+* macro_f1: 77.0%
+epoch [10/50] batch [5/500] time 1.551 (1.769) data 0.001 (0.232) loss 1.0039 (0.9812) acc 71.8750 (73.1250) lr 1.8763e-03 eta 10:04:21
+epoch [10/50] batch [10/500] time 1.558 (1.672) data 0.001 (0.117) loss 1.2129 (1.0738) acc 68.7500 (70.9375) lr 1.8763e-03 eta 9:30:53
+epoch [10/50] batch [15/500] time 1.540 (1.633) data 0.000 (0.078) loss 1.5156 (1.1214) acc 56.2500 (69.5833) lr 1.8763e-03 eta 9:17:32
+epoch [10/50] batch [20/500] time 1.577 (1.614) data 0.001 (0.059) loss 0.7798 (1.1111) acc 81.2500 (70.6250) lr 1.8763e-03 eta 9:10:45
+epoch [10/50] batch [25/500] time 1.556 (1.604) data 0.000 (0.047) loss 0.9282 (1.1374) acc 75.0000 (70.3750) lr 1.8763e-03 eta 9:07:18
+epoch [10/50] batch [30/500] time 1.560 (1.596) data 0.000 (0.039) loss 0.9141 (1.1261) acc 71.8750 (70.8333) lr 1.8763e-03 eta 9:04:35
+epoch [10/50] batch [35/500] time 1.556 (1.590) data 0.000 (0.034) loss 1.1270 (1.1166) acc 71.8750 (70.8036) lr 1.8763e-03 eta 9:02:11
+epoch [10/50] batch [40/500] time 1.567 (1.587) data 0.000 (0.030) loss 1.2900 (1.1407) acc 68.7500 (70.4688) lr 1.8763e-03 eta 9:01:02
+epoch [10/50] batch [45/500] time 1.554 (1.587) data 0.000 (0.026) loss 1.0674 (1.1339) acc 78.1250 (71.0417) lr 1.8763e-03 eta 9:01:01
+epoch [10/50] batch [50/500] time 1.571 (1.584) data 0.000 (0.024) loss 0.9277 (1.1429) acc 78.1250 (70.9375) lr 1.8763e-03 eta 9:00:02
+epoch [10/50] batch [55/500] time 1.554 (1.582) data 0.000 (0.022) loss 0.9624 (1.1496) acc 68.7500 (70.6818) lr 1.8763e-03 eta 8:59:10
+epoch [10/50] batch [60/500] time 1.587 (1.581) data 0.000 (0.020) loss 1.6348 (1.1575) acc 59.3750 (70.6250) lr 1.8763e-03 eta 8:58:41
+epoch [10/50] batch [65/500] time 1.564 (1.579) data 0.000 (0.018) loss 0.8862 (1.1464) acc 78.1250 (70.7692) lr 1.8763e-03 eta 8:57:50
+epoch [10/50] batch [70/500] time 1.568 (1.578) data 0.000 (0.017) loss 0.9116 (1.1407) acc 75.0000 (70.8036) lr 1.8763e-03 eta 8:57:13
+epoch [10/50] batch [75/500] time 1.536 (1.576) data 0.000 (0.016) loss 1.7314 (1.1503) acc 65.6250 (70.5833) lr 1.8763e-03 eta 8:56:35
+epoch [10/50] batch [80/500] time 1.573 (1.575) data 0.001 (0.015) loss 1.2764 (1.1559) acc 75.0000 (70.5859) lr 1.8763e-03 eta 8:56:07
+epoch [10/50] batch [85/500] time 1.576 (1.574) data 0.000 (0.014) loss 0.9458 (1.1453) acc 65.6250 (70.7353) lr 1.8763e-03 eta 8:55:33
+epoch [10/50] batch [90/500] time 1.558 (1.573) data 0.000 (0.013) loss 0.7070 (1.1415) acc 81.2500 (70.9722) lr 1.8763e-03 eta 8:55:03
+epoch [10/50] batch [95/500] time 1.545 (1.572) data 0.000 (0.013) loss 0.9941 (1.1448) acc 68.7500 (70.6579) lr 1.8763e-03 eta 8:54:46
+epoch [10/50] batch [100/500] time 1.545 (1.572) data 0.000 (0.012) loss 1.1865 (1.1470) acc 68.7500 (70.6562) lr 1.8763e-03 eta 8:54:21
+epoch [10/50] batch [105/500] time 1.578 (1.571) data 0.001 (0.012) loss 1.2324 (1.1569) acc 71.8750 (70.4762) lr 1.8763e-03 eta 8:54:04
+epoch [10/50] batch [110/500] time 1.551 (1.571) data 0.000 (0.011) loss 0.6099 (1.1539) acc 87.5000 (70.4545) lr 1.8763e-03 eta 8:53:48
+epoch [10/50] batch [115/500] time 1.565 (1.570) data 0.000 (0.011) loss 1.0195 (1.1510) acc 81.2500 (70.7065) lr 1.8763e-03 eta 8:53:25
+epoch [10/50] batch [120/500] time 1.551 (1.569) data 0.000 (0.010) loss 0.7988 (1.1558) acc 78.1250 (70.5990) lr 1.8763e-03 eta 8:52:59
+epoch [10/50] batch [125/500] time 1.586 (1.569) data 0.001 (0.010) loss 2.0039 (1.1703) acc 53.1250 (70.4250) lr 1.8763e-03 eta 8:52:49
+epoch [10/50] batch [130/500] time 1.545 (1.569) data 0.001 (0.009) loss 1.5967 (1.1684) acc 59.3750 (70.3125) lr 1.8763e-03 eta 8:52:30
+epoch [10/50] batch [135/500] time 1.566 (1.568) data 0.000 (0.009) loss 1.4365 (1.1700) acc 59.3750 (70.3472) lr 1.8763e-03 eta 8:52:16
+epoch [10/50] batch [140/500] time 1.540 (1.568) data 0.001 (0.009) loss 1.3047 (1.1659) acc 71.8750 (70.5580) lr 1.8763e-03 eta 8:52:08
+epoch [10/50] batch [145/500] time 1.566 (1.569) data 0.001 (0.008) loss 1.2373 (1.1687) acc 68.7500 (70.5603) lr 1.8763e-03 eta 8:52:13
+epoch [10/50] batch [150/500] time 1.557 (1.568) data 0.001 (0.008) loss 0.8237 (1.1665) acc 78.1250 (70.5625) lr 1.8763e-03 eta 8:51:56
+epoch [10/50] batch [155/500] time 1.562 (1.568) data 0.000 (0.008) loss 0.9688 (1.1647) acc 68.7500 (70.6452) lr 1.8763e-03 eta 8:51:46
+epoch [10/50] batch [160/500] time 1.561 (1.568) data 0.000 (0.008) loss 0.5903 (1.1568) acc 78.1250 (70.7812) lr 1.8763e-03 eta 8:51:31
+epoch [10/50] batch [165/500] time 1.563 (1.568) data 0.000 (0.007) loss 1.0039 (1.1524) acc 78.1250 (70.7955) lr 1.8763e-03 eta 8:51:20
+epoch [10/50] batch [170/500] time 1.547 (1.567) data 0.000 (0.007) loss 1.7822 (1.1549) acc 53.1250 (70.6985) lr 1.8763e-03 eta 8:51:07
+epoch [10/50] batch [175/500] time 1.532 (1.567) data 0.000 (0.007) loss 1.3857 (1.1604) acc 65.6250 (70.6071) lr 1.8763e-03 eta 8:50:50
+epoch [10/50] batch [180/500] time 1.548 (1.567) data 0.001 (0.007) loss 1.4980 (1.1591) acc 59.3750 (70.5208) lr 1.8763e-03 eta 8:50:38
+epoch [10/50] batch [185/500] time 1.550 (1.567) data 0.000 (0.007) loss 1.1445 (1.1549) acc 65.6250 (70.6081) lr 1.8763e-03 eta 8:50:25
+epoch [10/50] batch [190/500] time 1.541 (1.567) data 0.000 (0.007) loss 0.7451 (1.1547) acc 81.2500 (70.6086) lr 1.8763e-03 eta 8:50:22
+epoch [10/50] batch [195/500] time 1.555 (1.567) data 0.000 (0.006) loss 0.7422 (1.1498) acc 87.5000 (70.8013) lr 1.8763e-03 eta 8:50:10
+epoch [10/50] batch [200/500] time 1.567 (1.567) data 0.000 (0.006) loss 1.4385 (1.1557) acc 75.0000 (70.7344) lr 1.8763e-03 eta 8:50:01
+epoch [10/50] batch [205/500] time 1.564 (1.567) data 0.000 (0.006) loss 1.4756 (1.1559) acc 65.6250 (70.6707) lr 1.8763e-03 eta 8:49:56
+epoch [10/50] batch [210/500] time 1.539 (1.567) data 0.000 (0.006) loss 0.9521 (1.1569) acc 65.6250 (70.6250) lr 1.8763e-03 eta 8:49:44
+epoch [10/50] batch [215/500] time 1.548 (1.566) data 0.000 (0.006) loss 1.4004 (1.1563) acc 68.7500 (70.7267) lr 1.8763e-03 eta 8:49:29
+epoch [10/50] batch [220/500] time 1.553 (1.566) data 0.000 (0.006) loss 1.7031 (1.1541) acc 62.5000 (70.7955) lr 1.8763e-03 eta 8:49:11
+epoch [10/50] batch [225/500] time 1.567 (1.566) data 0.000 (0.006) loss 1.3291 (1.1542) acc 71.8750 (70.7778) lr 1.8763e-03 eta 8:49:02
+epoch [10/50] batch [230/500] time 1.555 (1.565) data 0.000 (0.005) loss 1.1758 (1.1561) acc 71.8750 (70.8016) lr 1.8763e-03 eta 8:48:50
+epoch [10/50] batch [235/500] time 1.558 (1.565) data 0.000 (0.005) loss 1.5420 (1.1580) acc 62.5000 (70.7979) lr 1.8763e-03 eta 8:48:40
+epoch [10/50] batch [240/500] time 1.553 (1.565) data 0.000 (0.005) loss 1.2900 (1.1563) acc 75.0000 (70.8594) lr 1.8763e-03 eta 8:48:28
+epoch [10/50] batch [245/500] time 1.562 (1.565) data 0.000 (0.005) loss 1.2617 (1.1607) acc 71.8750 (70.7781) lr 1.8763e-03 eta 8:48:18
+epoch [10/50] batch [250/500] time 1.586 (1.565) data 0.000 (0.005) loss 1.3076 (1.1588) acc 68.7500 (70.7875) lr 1.8763e-03 eta 8:48:11
+epoch [10/50] batch [255/500] time 1.558 (1.565) data 0.000 (0.005) loss 1.2402 (1.1590) acc 78.1250 (70.8333) lr 1.8763e-03 eta 8:48:02
+epoch [10/50] batch [260/500] time 1.562 (1.565) data 0.000 (0.005) loss 1.4160 (1.1601) acc 68.7500 (70.8413) lr 1.8763e-03 eta 8:47:58
+epoch [10/50] batch [265/500] time 1.545 (1.565) data 0.001 (0.005) loss 1.8174 (1.1618) acc 56.2500 (70.8373) lr 1.8763e-03 eta 8:47:50
+epoch [10/50] batch [270/500] time 1.573 (1.565) data 0.000 (0.005) loss 1.2178 (1.1669) acc 65.6250 (70.7407) lr 1.8763e-03 eta 8:47:43
+epoch [10/50] batch [275/500] time 1.562 (1.565) data 0.001 (0.005) loss 1.4863 (1.1727) acc 68.7500 (70.6818) lr 1.8763e-03 eta 8:47:35
+epoch [10/50] batch [280/500] time 1.545 (1.565) data 0.000 (0.005) loss 1.5234 (1.1773) acc 62.5000 (70.5580) lr 1.8763e-03 eta 8:47:23
+epoch [10/50] batch [285/500] time 1.675 (1.565) data 0.000 (0.005) loss 1.1846 (1.1766) acc 71.8750 (70.5921) lr 1.8763e-03 eta 8:47:23
+epoch [10/50] batch [290/500] time 1.568 (1.565) data 0.000 (0.004) loss 1.3945 (1.1796) acc 71.8750 (70.6034) lr 1.8763e-03 eta 8:47:13
+epoch [10/50] batch [295/500] time 1.573 (1.565) data 0.000 (0.004) loss 1.7666 (1.1827) acc 68.7500 (70.5614) lr 1.8763e-03 eta 8:47:06
+epoch [10/50] batch [300/500] time 1.563 (1.565) data 0.000 (0.004) loss 1.7207 (1.1844) acc 56.2500 (70.5625) lr 1.8763e-03 eta 8:46:55
+epoch [10/50] batch [305/500] time 1.549 (1.565) data 0.001 (0.004) loss 1.0107 (1.1858) acc 78.1250 (70.5328) lr 1.8763e-03 eta 8:46:46
+epoch [10/50] batch [310/500] time 1.532 (1.565) data 0.000 (0.004) loss 1.2197 (1.1863) acc 68.7500 (70.5645) lr 1.8763e-03 eta 8:46:35
+epoch [10/50] batch [315/500] time 1.537 (1.565) data 0.000 (0.004) loss 1.3369 (1.1854) acc 71.8750 (70.5952) lr 1.8763e-03 eta 8:46:24
+epoch [10/50] batch [320/500] time 1.567 (1.565) data 0.000 (0.004) loss 1.4736 (1.1877) acc 59.3750 (70.5469) lr 1.8763e-03 eta 8:46:13
+epoch [10/50] batch [325/500] time 1.571 (1.565) data 0.000 (0.004) loss 1.1416 (1.1917) acc 65.6250 (70.4423) lr 1.8763e-03 eta 8:46:05
+epoch [10/50] batch [330/500] time 1.544 (1.565) data 0.001 (0.004) loss 0.9180 (1.1903) acc 78.1250 (70.5019) lr 1.8763e-03 eta 8:46:01
+epoch [10/50] batch [335/500] time 1.549 (1.565) data 0.000 (0.004) loss 1.5908 (1.1892) acc 65.6250 (70.5410) lr 1.8763e-03 eta 8:45:49
+epoch [10/50] batch [340/500] time 1.554 (1.564) data 0.000 (0.004) loss 0.9663 (1.1892) acc 71.8750 (70.5055) lr 1.8763e-03 eta 8:45:37
+epoch [10/50] batch [345/500] time 1.536 (1.564) data 0.001 (0.004) loss 0.5547 (1.1848) acc 87.5000 (70.6159) lr 1.8763e-03 eta 8:45:26
+epoch [10/50] batch [350/500] time 1.533 (1.564) data 0.000 (0.004) loss 0.8555 (1.1867) acc 81.2500 (70.6161) lr 1.8763e-03 eta 8:45:13
+epoch [10/50] batch [355/500] time 1.563 (1.564) data 0.000 (0.004) loss 1.6279 (1.1881) acc 62.5000 (70.6250) lr 1.8763e-03 eta 8:45:05
+epoch [10/50] batch [360/500] time 1.570 (1.564) data 0.000 (0.004) loss 1.4971 (1.1864) acc 62.5000 (70.6510) lr 1.8763e-03 eta 8:44:57
+epoch [10/50] batch [365/500] time 1.552 (1.564) data 0.000 (0.004) loss 1.2715 (1.1855) acc 78.1250 (70.6507) lr 1.8763e-03 eta 8:44:50
+epoch [10/50] batch [370/500] time 1.560 (1.564) data 0.000 (0.004) loss 0.7446 (1.1842) acc 84.3750 (70.7348) lr 1.8763e-03 eta 8:44:42
+epoch [10/50] batch [375/500] time 1.535 (1.564) data 0.000 (0.004) loss 1.2520 (1.1812) acc 75.0000 (70.8167) lr 1.8763e-03 eta 8:44:31
+epoch [10/50] batch [380/500] time 1.558 (1.564) data 0.000 (0.003) loss 1.5176 (1.1811) acc 65.6250 (70.8224) lr 1.8763e-03 eta 8:44:19
+epoch [10/50] batch [385/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.5205 (1.1777) acc 65.6250 (70.8523) lr 1.8763e-03 eta 8:44:09
+epoch [10/50] batch [390/500] time 1.555 (1.563) data 0.001 (0.003) loss 1.6895 (1.1775) acc 65.6250 (70.9215) lr 1.8763e-03 eta 8:43:59
+epoch [10/50] batch [395/500] time 1.571 (1.563) data 0.001 (0.003) loss 1.3252 (1.1811) acc 71.8750 (70.8861) lr 1.8763e-03 eta 8:43:50
+epoch [10/50] batch [400/500] time 1.548 (1.563) data 0.000 (0.003) loss 1.0547 (1.1802) acc 68.7500 (70.8594) lr 1.8763e-03 eta 8:43:42
+epoch [10/50] batch [405/500] time 1.549 (1.563) data 0.000 (0.003) loss 1.0869 (1.1824) acc 71.8750 (70.8719) lr 1.8763e-03 eta 8:43:34
+epoch [10/50] batch [410/500] time 1.562 (1.563) data 0.000 (0.003) loss 0.7329 (1.1810) acc 84.3750 (70.9146) lr 1.8763e-03 eta 8:43:25
+epoch [10/50] batch [415/500] time 1.581 (1.563) data 0.000 (0.003) loss 1.1523 (1.1786) acc 75.0000 (70.9714) lr 1.8763e-03 eta 8:43:20
+epoch [10/50] batch [420/500] time 1.545 (1.563) data 0.000 (0.003) loss 1.1123 (1.1778) acc 75.0000 (71.0268) lr 1.8763e-03 eta 8:43:11
+epoch [10/50] batch [425/500] time 1.552 (1.563) data 0.000 (0.003) loss 0.6802 (1.1792) acc 81.2500 (71.0294) lr 1.8763e-03 eta 8:43:01
+epoch [10/50] batch [430/500] time 1.583 (1.564) data 0.000 (0.003) loss 1.3740 (1.1803) acc 62.5000 (70.9956) lr 1.8763e-03 eta 8:43:01
+epoch [10/50] batch [435/500] time 1.570 (1.564) data 0.001 (0.003) loss 1.6846 (1.1826) acc 59.3750 (70.9411) lr 1.8763e-03 eta 8:42:52
+epoch [10/50] batch [440/500] time 1.553 (1.563) data 0.000 (0.003) loss 1.0508 (1.1796) acc 78.1250 (70.9588) lr 1.8763e-03 eta 8:42:42
+epoch [10/50] batch [445/500] time 1.553 (1.563) data 0.001 (0.003) loss 1.8975 (1.1800) acc 53.1250 (70.9480) lr 1.8763e-03 eta 8:42:34
+epoch [10/50] batch [450/500] time 1.547 (1.563) data 0.000 (0.003) loss 1.1689 (1.1783) acc 68.7500 (70.9722) lr 1.8763e-03 eta 8:42:24
+epoch [10/50] batch [455/500] time 1.571 (1.563) data 0.000 (0.003) loss 0.7993 (1.1781) acc 78.1250 (70.9547) lr 1.8763e-03 eta 8:42:16
+epoch [10/50] batch [460/500] time 1.572 (1.563) data 0.000 (0.003) loss 1.4561 (1.1784) acc 65.6250 (71.0054) lr 1.8763e-03 eta 8:42:07
+epoch [10/50] batch [465/500] time 1.555 (1.563) data 0.000 (0.003) loss 1.2178 (1.1773) acc 62.5000 (71.0282) lr 1.8763e-03 eta 8:42:00
+epoch [10/50] batch [470/500] time 1.559 (1.563) data 0.001 (0.003) loss 1.2539 (1.1777) acc 75.0000 (71.0239) lr 1.8763e-03 eta 8:41:55
+epoch [10/50] batch [475/500] time 1.574 (1.564) data 0.000 (0.003) loss 0.7480 (1.1753) acc 78.1250 (71.0658) lr 1.8763e-03 eta 8:41:53
+epoch [10/50] batch [480/500] time 1.532 (1.564) data 0.000 (0.003) loss 1.5908 (1.1773) acc 56.2500 (71.0156) lr 1.8763e-03 eta 8:41:43
+epoch [10/50] batch [485/500] time 1.548 (1.563) data 0.001 (0.003) loss 1.4111 (1.1774) acc 71.8750 (71.0180) lr 1.8763e-03 eta 8:41:31
+epoch [10/50] batch [490/500] time 1.560 (1.563) data 0.000 (0.003) loss 1.1367 (1.1763) acc 75.0000 (71.0842) lr 1.8763e-03 eta 8:41:22
+epoch [10/50] batch [495/500] time 1.577 (1.563) data 0.000 (0.003) loss 0.7261 (1.1751) acc 84.3750 (71.1111) lr 1.8763e-03 eta 8:41:15
+epoch [10/50] batch [500/500] time 1.586 (1.563) data 0.000 (0.003) loss 1.0049 (1.1736) acc 75.0000 (71.1625) lr 1.8443e-03 eta 8:41:07
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,919
+* accuracy: 77.8%
+* error: 22.2%
+* macro_f1: 77.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [11/50] batch [5/500] time 1.552 (1.700) data 0.001 (0.203) loss 1.6348 (1.1372) acc 65.6250 (73.1250) lr 1.8443e-03 eta 9:26:26
+epoch [11/50] batch [10/500] time 1.554 (1.629) data 0.002 (0.102) loss 1.9365 (1.3328) acc 59.3750 (70.6250) lr 1.8443e-03 eta 9:02:48
+epoch [11/50] batch [15/500] time 1.578 (1.606) data 0.000 (0.068) loss 1.1143 (1.3206) acc 65.6250 (69.7917) lr 1.8443e-03 eta 8:54:54
+epoch [11/50] batch [20/500] time 1.564 (1.595) data 0.000 (0.051) loss 0.9004 (1.2604) acc 68.7500 (70.4688) lr 1.8443e-03 eta 8:51:13
+epoch [11/50] batch [25/500] time 1.553 (1.589) data 0.000 (0.041) loss 0.8188 (1.2182) acc 81.2500 (71.5000) lr 1.8443e-03 eta 8:49:06
+epoch [11/50] batch [30/500] time 1.577 (1.587) data 0.000 (0.034) loss 1.1445 (1.2121) acc 62.5000 (71.4583) lr 1.8443e-03 eta 8:48:07
+epoch [11/50] batch [35/500] time 1.582 (1.582) data 0.001 (0.029) loss 1.6211 (1.2106) acc 62.5000 (71.8750) lr 1.8443e-03 eta 8:46:31
+epoch [11/50] batch [40/500] time 1.565 (1.581) data 0.001 (0.026) loss 0.9165 (1.2310) acc 75.0000 (71.3281) lr 1.8443e-03 eta 8:45:54
+epoch [11/50] batch [45/500] time 1.560 (1.579) data 0.001 (0.023) loss 1.7549 (1.2278) acc 46.8750 (70.7639) lr 1.8443e-03 eta 8:44:59
+epoch [11/50] batch [50/500] time 1.570 (1.578) data 0.000 (0.021) loss 0.8984 (1.2158) acc 71.8750 (70.8750) lr 1.8443e-03 eta 8:44:32
+epoch [11/50] batch [55/500] time 1.572 (1.576) data 0.000 (0.019) loss 1.0977 (1.2091) acc 68.7500 (70.7955) lr 1.8443e-03 eta 8:43:45
+epoch [11/50] batch [60/500] time 1.550 (1.575) data 0.001 (0.017) loss 1.2129 (1.1896) acc 68.7500 (70.8854) lr 1.8443e-03 eta 8:43:17
+epoch [11/50] batch [65/500] time 1.542 (1.574) data 0.000 (0.016) loss 1.7783 (1.2130) acc 68.7500 (70.6250) lr 1.8443e-03 eta 8:43:06
+epoch [11/50] batch [70/500] time 1.590 (1.573) data 0.000 (0.015) loss 0.5117 (1.1888) acc 87.5000 (71.0714) lr 1.8443e-03 eta 8:42:24
+epoch [11/50] batch [75/500] time 1.559 (1.572) data 0.000 (0.014) loss 0.6987 (1.1746) acc 81.2500 (71.3750) lr 1.8443e-03 eta 8:42:04
+epoch [11/50] batch [80/500] time 1.552 (1.571) data 0.000 (0.013) loss 1.3652 (1.1732) acc 71.8750 (71.5234) lr 1.8443e-03 eta 8:41:25
+epoch [11/50] batch [85/500] time 1.569 (1.570) data 0.001 (0.012) loss 1.1133 (1.1785) acc 65.6250 (71.4706) lr 1.8443e-03 eta 8:41:08
+epoch [11/50] batch [90/500] time 1.554 (1.569) data 0.000 (0.012) loss 1.8604 (1.1933) acc 59.3750 (71.2153) lr 1.8443e-03 eta 8:40:40
+epoch [11/50] batch [95/500] time 1.556 (1.569) data 0.000 (0.011) loss 1.6904 (1.1812) acc 59.3750 (71.4474) lr 1.8443e-03 eta 8:40:30
+epoch [11/50] batch [100/500] time 1.529 (1.568) data 0.000 (0.011) loss 1.0928 (1.1765) acc 75.0000 (71.3750) lr 1.8443e-03 eta 8:40:02
+epoch [11/50] batch [105/500] time 1.571 (1.567) data 0.000 (0.010) loss 1.5029 (1.1778) acc 56.2500 (71.3095) lr 1.8443e-03 eta 8:39:43
+epoch [11/50] batch [110/500] time 1.548 (1.568) data 0.000 (0.010) loss 1.6885 (1.1803) acc 71.8750 (71.4489) lr 1.8443e-03 eta 8:39:51
+epoch [11/50] batch [115/500] time 1.552 (1.568) data 0.000 (0.009) loss 0.8628 (1.1806) acc 78.1250 (71.3043) lr 1.8443e-03 eta 8:39:31
+epoch [11/50] batch [120/500] time 1.548 (1.567) data 0.001 (0.009) loss 1.0205 (1.1833) acc 68.7500 (71.2240) lr 1.8443e-03 eta 8:39:18
+epoch [11/50] batch [125/500] time 1.539 (1.567) data 0.000 (0.009) loss 1.2764 (1.1962) acc 62.5000 (71.0250) lr 1.8443e-03 eta 8:38:57
+epoch [11/50] batch [130/500] time 1.552 (1.566) data 0.000 (0.008) loss 1.1514 (1.1906) acc 75.0000 (71.0337) lr 1.8443e-03 eta 8:38:38
+epoch [11/50] batch [135/500] time 1.565 (1.566) data 0.000 (0.008) loss 1.3672 (1.1966) acc 62.5000 (70.8796) lr 1.8443e-03 eta 8:38:26
+epoch [11/50] batch [140/500] time 1.552 (1.566) data 0.001 (0.008) loss 1.8848 (1.1988) acc 56.2500 (70.8705) lr 1.8443e-03 eta 8:38:14
+epoch [11/50] batch [145/500] time 1.560 (1.565) data 0.001 (0.007) loss 1.2080 (1.1981) acc 62.5000 (70.7974) lr 1.8443e-03 eta 8:38:02
+epoch [11/50] batch [150/500] time 1.576 (1.566) data 0.000 (0.007) loss 0.7163 (1.1864) acc 81.2500 (71.0208) lr 1.8443e-03 eta 8:37:58
+epoch [11/50] batch [155/500] time 1.567 (1.566) data 0.000 (0.007) loss 1.1836 (1.1868) acc 71.8750 (70.8871) lr 1.8443e-03 eta 8:37:55
+epoch [11/50] batch [160/500] time 1.538 (1.566) data 0.001 (0.007) loss 1.0059 (1.1851) acc 78.1250 (70.9570) lr 1.8443e-03 eta 8:37:39
+epoch [11/50] batch [165/500] time 1.567 (1.566) data 0.000 (0.007) loss 1.0654 (1.1871) acc 71.8750 (70.9470) lr 1.8443e-03 eta 8:37:33
+epoch [11/50] batch [170/500] time 1.557 (1.565) data 0.001 (0.006) loss 1.5830 (1.1887) acc 53.1250 (70.8088) lr 1.8443e-03 eta 8:37:21
+epoch [11/50] batch [175/500] time 1.575 (1.565) data 0.000 (0.006) loss 1.0791 (1.1858) acc 68.7500 (70.9286) lr 1.8443e-03 eta 8:37:11
+epoch [11/50] batch [180/500] time 1.541 (1.565) data 0.000 (0.006) loss 1.3545 (1.1906) acc 56.2500 (70.7986) lr 1.8443e-03 eta 8:37:02
+epoch [11/50] batch [185/500] time 1.545 (1.565) data 0.000 (0.006) loss 0.5903 (1.1894) acc 78.1250 (70.8277) lr 1.8443e-03 eta 8:36:47
+epoch [11/50] batch [190/500] time 1.556 (1.565) data 0.000 (0.006) loss 1.2607 (1.1866) acc 75.0000 (70.9539) lr 1.8443e-03 eta 8:36:39
+epoch [11/50] batch [195/500] time 1.577 (1.565) data 0.000 (0.006) loss 1.2188 (1.1865) acc 75.0000 (70.9776) lr 1.8443e-03 eta 8:36:32
+epoch [11/50] batch [200/500] time 1.579 (1.565) data 0.001 (0.006) loss 1.4561 (1.1857) acc 62.5000 (70.9844) lr 1.8443e-03 eta 8:36:24
+epoch [11/50] batch [205/500] time 1.669 (1.566) data 0.000 (0.005) loss 1.0645 (1.1847) acc 78.1250 (70.9604) lr 1.8443e-03 eta 8:36:29
+epoch [11/50] batch [210/500] time 1.564 (1.566) data 0.001 (0.005) loss 1.2363 (1.1822) acc 75.0000 (71.0268) lr 1.8443e-03 eta 8:36:28
+epoch [11/50] batch [215/500] time 1.575 (1.566) data 0.000 (0.005) loss 1.6797 (1.1928) acc 59.3750 (70.9012) lr 1.8443e-03 eta 8:36:22
+epoch [11/50] batch [220/500] time 1.556 (1.566) data 0.000 (0.005) loss 1.1338 (1.1922) acc 71.8750 (70.8807) lr 1.8443e-03 eta 8:36:15
+epoch [11/50] batch [225/500] time 1.572 (1.566) data 0.000 (0.005) loss 1.1582 (1.1890) acc 65.6250 (70.9583) lr 1.8443e-03 eta 8:36:05
+epoch [11/50] batch [230/500] time 1.559 (1.566) data 0.000 (0.005) loss 0.7822 (1.1803) acc 84.3750 (71.1821) lr 1.8443e-03 eta 8:36:02
+epoch [11/50] batch [235/500] time 1.599 (1.566) data 0.000 (0.005) loss 1.3291 (1.1764) acc 62.5000 (71.2101) lr 1.8443e-03 eta 8:35:55
+epoch [11/50] batch [240/500] time 1.574 (1.566) data 0.000 (0.005) loss 1.2490 (1.1717) acc 71.8750 (71.2500) lr 1.8443e-03 eta 8:35:53
+epoch [11/50] batch [245/500] time 1.553 (1.566) data 0.000 (0.005) loss 1.5010 (1.1759) acc 59.3750 (71.1990) lr 1.8443e-03 eta 8:35:41
+epoch [11/50] batch [250/500] time 1.580 (1.567) data 0.000 (0.005) loss 1.1445 (1.1834) acc 68.7500 (71.0125) lr 1.8443e-03 eta 8:35:45
+epoch [11/50] batch [255/500] time 1.588 (1.567) data 0.000 (0.004) loss 1.0176 (1.1821) acc 62.5000 (70.9926) lr 1.8443e-03 eta 8:35:39
+epoch [11/50] batch [260/500] time 1.555 (1.567) data 0.000 (0.004) loss 1.0117 (1.1784) acc 78.1250 (71.0938) lr 1.8443e-03 eta 8:35:27
+epoch [11/50] batch [265/500] time 1.545 (1.566) data 0.000 (0.004) loss 0.7905 (1.1748) acc 78.1250 (71.2028) lr 1.8443e-03 eta 8:35:13
+epoch [11/50] batch [270/500] time 1.542 (1.566) data 0.000 (0.004) loss 1.0195 (1.1767) acc 71.8750 (71.1574) lr 1.8443e-03 eta 8:35:03
+epoch [11/50] batch [275/500] time 1.596 (1.566) data 0.001 (0.004) loss 0.7827 (1.1756) acc 81.2500 (71.1932) lr 1.8443e-03 eta 8:34:57
+epoch [11/50] batch [280/500] time 1.555 (1.567) data 0.000 (0.004) loss 1.6123 (1.1738) acc 59.3750 (71.1384) lr 1.8443e-03 eta 8:34:52
+epoch [11/50] batch [285/500] time 1.576 (1.567) data 0.000 (0.004) loss 1.7490 (1.1770) acc 62.5000 (71.0636) lr 1.8443e-03 eta 8:34:45
+epoch [11/50] batch [290/500] time 1.591 (1.567) data 0.001 (0.004) loss 1.8047 (1.1846) acc 43.7500 (70.8190) lr 1.8443e-03 eta 8:34:39
+epoch [11/50] batch [295/500] time 1.558 (1.566) data 0.001 (0.004) loss 0.8125 (1.1837) acc 78.1250 (70.8686) lr 1.8443e-03 eta 8:34:27
+epoch [11/50] batch [300/500] time 1.560 (1.567) data 0.000 (0.004) loss 1.3574 (1.1838) acc 84.3750 (70.8750) lr 1.8443e-03 eta 8:34:21
+epoch [11/50] batch [305/500] time 1.559 (1.567) data 0.000 (0.004) loss 0.9663 (1.1809) acc 71.8750 (70.9016) lr 1.8443e-03 eta 8:34:13
+epoch [11/50] batch [310/500] time 1.557 (1.567) data 0.000 (0.004) loss 1.4219 (1.1802) acc 71.8750 (70.9375) lr 1.8443e-03 eta 8:34:04
+epoch [11/50] batch [315/500] time 1.580 (1.567) data 0.001 (0.004) loss 0.7939 (1.1786) acc 68.7500 (70.9325) lr 1.8443e-03 eta 8:33:57
+epoch [11/50] batch [320/500] time 1.564 (1.567) data 0.000 (0.004) loss 0.7617 (1.1747) acc 71.8750 (70.9082) lr 1.8443e-03 eta 8:33:51
+epoch [11/50] batch [325/500] time 1.552 (1.567) data 0.001 (0.004) loss 1.1982 (1.1766) acc 78.1250 (70.8365) lr 1.8443e-03 eta 8:33:42
+epoch [11/50] batch [330/500] time 1.557 (1.566) data 0.000 (0.004) loss 1.3574 (1.1737) acc 65.6250 (70.8996) lr 1.8443e-03 eta 8:33:32
+epoch [11/50] batch [335/500] time 1.567 (1.566) data 0.000 (0.003) loss 1.7627 (1.1750) acc 59.3750 (70.9235) lr 1.8443e-03 eta 8:33:22
+epoch [11/50] batch [340/500] time 1.563 (1.566) data 0.000 (0.003) loss 1.4482 (1.1782) acc 59.3750 (70.9007) lr 1.8443e-03 eta 8:33:09
+epoch [11/50] batch [345/500] time 1.569 (1.566) data 0.000 (0.003) loss 1.4023 (1.1802) acc 68.7500 (70.8424) lr 1.8443e-03 eta 8:33:00
+epoch [11/50] batch [350/500] time 1.554 (1.566) data 0.000 (0.003) loss 0.9805 (1.1792) acc 84.3750 (70.8929) lr 1.8443e-03 eta 8:32:56
+epoch [11/50] batch [355/500] time 1.584 (1.566) data 0.000 (0.003) loss 0.8525 (1.1759) acc 75.0000 (70.9507) lr 1.8443e-03 eta 8:32:49
+epoch [11/50] batch [360/500] time 1.559 (1.566) data 0.000 (0.003) loss 1.2041 (1.1757) acc 75.0000 (70.9722) lr 1.8443e-03 eta 8:32:38
+epoch [11/50] batch [365/500] time 1.544 (1.566) data 0.000 (0.003) loss 0.6016 (1.1771) acc 81.2500 (70.9418) lr 1.8443e-03 eta 8:32:28
+epoch [11/50] batch [370/500] time 1.536 (1.566) data 0.001 (0.003) loss 0.8530 (1.1770) acc 87.5000 (70.9291) lr 1.8443e-03 eta 8:32:17
+epoch [11/50] batch [375/500] time 1.581 (1.566) data 0.000 (0.003) loss 0.7690 (1.1773) acc 75.0000 (70.9000) lr 1.8443e-03 eta 8:32:09
+epoch [11/50] batch [380/500] time 1.543 (1.566) data 0.000 (0.003) loss 1.6562 (1.1792) acc 71.8750 (70.9293) lr 1.8443e-03 eta 8:31:58
+epoch [11/50] batch [385/500] time 1.538 (1.566) data 0.000 (0.003) loss 1.1982 (1.1777) acc 78.1250 (70.9253) lr 1.8443e-03 eta 8:31:48
+epoch [11/50] batch [390/500] time 1.539 (1.565) data 0.000 (0.003) loss 1.0576 (1.1788) acc 78.1250 (70.9295) lr 1.8443e-03 eta 8:31:35
+epoch [11/50] batch [395/500] time 1.551 (1.565) data 0.000 (0.003) loss 1.2197 (1.1810) acc 62.5000 (70.8623) lr 1.8443e-03 eta 8:31:29
+epoch [11/50] batch [400/500] time 1.557 (1.565) data 0.000 (0.003) loss 1.7646 (1.1824) acc 56.2500 (70.8438) lr 1.8443e-03 eta 8:31:18
+epoch [11/50] batch [405/500] time 1.565 (1.565) data 0.000 (0.003) loss 1.5879 (1.1835) acc 62.5000 (70.8410) lr 1.8443e-03 eta 8:31:07
+epoch [11/50] batch [410/500] time 1.561 (1.565) data 0.000 (0.003) loss 1.2461 (1.1821) acc 68.7500 (70.8384) lr 1.8443e-03 eta 8:30:58
+epoch [11/50] batch [415/500] time 1.564 (1.565) data 0.000 (0.003) loss 0.9663 (1.1816) acc 78.1250 (70.8434) lr 1.8443e-03 eta 8:30:48
+epoch [11/50] batch [420/500] time 1.550 (1.565) data 0.000 (0.003) loss 0.7061 (1.1819) acc 87.5000 (70.8408) lr 1.8443e-03 eta 8:30:35
+epoch [11/50] batch [425/500] time 1.552 (1.564) data 0.000 (0.003) loss 1.3281 (1.1841) acc 75.0000 (70.8382) lr 1.8443e-03 eta 8:30:25
+epoch [11/50] batch [430/500] time 1.558 (1.564) data 0.000 (0.003) loss 0.5757 (1.1829) acc 87.5000 (70.8648) lr 1.8443e-03 eta 8:30:17
+epoch [11/50] batch [435/500] time 1.548 (1.564) data 0.000 (0.003) loss 0.9360 (1.1838) acc 68.7500 (70.8405) lr 1.8443e-03 eta 8:30:09
+epoch [11/50] batch [440/500] time 1.543 (1.564) data 0.000 (0.003) loss 1.2754 (1.1834) acc 68.7500 (70.8523) lr 1.8443e-03 eta 8:29:58
+epoch [11/50] batch [445/500] time 1.584 (1.564) data 0.000 (0.003) loss 1.3096 (1.1835) acc 68.7500 (70.8427) lr 1.8443e-03 eta 8:29:48
+epoch [11/50] batch [450/500] time 1.540 (1.564) data 0.000 (0.003) loss 1.1982 (1.1852) acc 71.8750 (70.8194) lr 1.8443e-03 eta 8:29:37
+epoch [11/50] batch [455/500] time 1.550 (1.564) data 0.001 (0.003) loss 1.3457 (1.1833) acc 68.7500 (70.8448) lr 1.8443e-03 eta 8:29:27
+epoch [11/50] batch [460/500] time 1.566 (1.564) data 0.000 (0.003) loss 1.7275 (1.1811) acc 59.3750 (70.9103) lr 1.8443e-03 eta 8:29:17
+epoch [11/50] batch [465/500] time 1.583 (1.564) data 0.000 (0.003) loss 1.7842 (1.1807) acc 62.5000 (70.9140) lr 1.8443e-03 eta 8:29:06
+epoch [11/50] batch [470/500] time 1.572 (1.564) data 0.000 (0.003) loss 1.5596 (1.1797) acc 68.7500 (70.9574) lr 1.8443e-03 eta 8:28:58
+epoch [11/50] batch [475/500] time 1.559 (1.564) data 0.000 (0.003) loss 0.7241 (1.1784) acc 81.2500 (71.0395) lr 1.8443e-03 eta 8:28:48
+epoch [11/50] batch [480/500] time 1.551 (1.564) data 0.001 (0.003) loss 0.8628 (1.1774) acc 75.0000 (71.0417) lr 1.8443e-03 eta 8:28:40
+epoch [11/50] batch [485/500] time 1.528 (1.563) data 0.001 (0.003) loss 1.5547 (1.1784) acc 65.6250 (71.0116) lr 1.8443e-03 eta 8:28:28
+epoch [11/50] batch [490/500] time 1.557 (1.563) data 0.000 (0.003) loss 1.4346 (1.1784) acc 59.3750 (70.9821) lr 1.8443e-03 eta 8:28:17
+epoch [11/50] batch [495/500] time 1.540 (1.563) data 0.000 (0.002) loss 0.9971 (1.1787) acc 75.0000 (71.0101) lr 1.8443e-03 eta 8:28:09
+epoch [11/50] batch [500/500] time 1.553 (1.563) data 0.000 (0.002) loss 0.8047 (1.1765) acc 78.1250 (71.0563) lr 1.8090e-03 eta 8:28:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,934
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [12/50] batch [5/500] time 1.546 (1.694) data 0.000 (0.189) loss 0.8926 (1.1154) acc 78.1250 (70.0000) lr 1.8090e-03 eta 9:10:31
+epoch [12/50] batch [10/500] time 1.569 (1.626) data 0.001 (0.095) loss 1.2637 (1.2128) acc 71.8750 (69.0625) lr 1.8090e-03 eta 8:48:12
+epoch [12/50] batch [15/500] time 1.555 (1.605) data 0.000 (0.063) loss 1.5762 (1.2310) acc 62.5000 (68.5417) lr 1.8090e-03 eta 8:41:09
+epoch [12/50] batch [20/500] time 1.553 (1.596) data 0.000 (0.048) loss 1.3389 (1.2262) acc 78.1250 (69.3750) lr 1.8090e-03 eta 8:38:02
+epoch [12/50] batch [25/500] time 1.563 (1.588) data 0.001 (0.038) loss 0.8535 (1.1769) acc 78.1250 (70.5000) lr 1.8090e-03 eta 8:35:28
+epoch [12/50] batch [30/500] time 1.567 (1.584) data 0.000 (0.032) loss 0.9189 (1.1612) acc 68.7500 (70.3125) lr 1.8090e-03 eta 8:34:04
+epoch [12/50] batch [35/500] time 1.537 (1.581) data 0.000 (0.027) loss 1.5654 (1.1825) acc 62.5000 (70.3571) lr 1.8090e-03 eta 8:32:51
+epoch [12/50] batch [40/500] time 1.579 (1.583) data 0.001 (0.024) loss 1.4766 (1.1952) acc 62.5000 (69.9219) lr 1.8090e-03 eta 8:33:19
+epoch [12/50] batch [45/500] time 1.567 (1.580) data 0.000 (0.021) loss 1.4297 (1.1991) acc 56.2500 (69.8611) lr 1.8090e-03 eta 8:32:24
+epoch [12/50] batch [50/500] time 1.557 (1.578) data 0.000 (0.019) loss 1.2666 (1.2147) acc 62.5000 (69.5000) lr 1.8090e-03 eta 8:31:38
+epoch [12/50] batch [55/500] time 1.559 (1.576) data 0.000 (0.018) loss 1.2061 (1.2171) acc 78.1250 (69.6023) lr 1.8090e-03 eta 8:30:53
+epoch [12/50] batch [60/500] time 1.563 (1.575) data 0.000 (0.016) loss 1.1270 (1.2117) acc 75.0000 (69.8958) lr 1.8090e-03 eta 8:30:09
+epoch [12/50] batch [65/500] time 1.545 (1.574) data 0.000 (0.015) loss 1.2852 (1.2209) acc 68.7500 (70.0481) lr 1.8090e-03 eta 8:29:51
+epoch [12/50] batch [70/500] time 1.553 (1.573) data 0.001 (0.014) loss 0.7285 (1.2169) acc 81.2500 (70.0446) lr 1.8090e-03 eta 8:29:31
+epoch [12/50] batch [75/500] time 1.562 (1.572) data 0.000 (0.013) loss 1.0830 (1.2053) acc 75.0000 (70.4167) lr 1.8090e-03 eta 8:29:03
+epoch [12/50] batch [80/500] time 1.597 (1.573) data 0.000 (0.012) loss 1.0762 (1.2024) acc 75.0000 (70.3125) lr 1.8090e-03 eta 8:29:14
+epoch [12/50] batch [85/500] time 1.559 (1.573) data 0.000 (0.012) loss 0.6001 (1.2048) acc 87.5000 (70.3309) lr 1.8090e-03 eta 8:29:07
+epoch [12/50] batch [90/500] time 1.576 (1.572) data 0.000 (0.011) loss 1.1084 (1.2092) acc 75.0000 (70.3819) lr 1.8090e-03 eta 8:28:40
+epoch [12/50] batch [95/500] time 1.549 (1.572) data 0.000 (0.010) loss 1.1221 (1.2007) acc 71.8750 (70.1645) lr 1.8090e-03 eta 8:28:16
+epoch [12/50] batch [100/500] time 1.574 (1.571) data 0.000 (0.010) loss 0.9258 (1.2018) acc 75.0000 (70.0625) lr 1.8090e-03 eta 8:28:00
+epoch [12/50] batch [105/500] time 1.554 (1.570) data 0.001 (0.009) loss 0.8188 (1.2078) acc 84.3750 (70.0893) lr 1.8090e-03 eta 8:27:35
+epoch [12/50] batch [110/500] time 1.547 (1.570) data 0.000 (0.009) loss 1.0850 (1.2049) acc 75.0000 (70.1705) lr 1.8090e-03 eta 8:27:13
+epoch [12/50] batch [115/500] time 1.566 (1.569) data 0.000 (0.009) loss 1.2549 (1.2039) acc 75.0000 (70.2717) lr 1.8090e-03 eta 8:27:02
+epoch [12/50] batch [120/500] time 1.573 (1.569) data 0.000 (0.008) loss 1.7363 (1.1987) acc 50.0000 (70.4688) lr 1.8090e-03 eta 8:26:48
+epoch [12/50] batch [125/500] time 1.558 (1.569) data 0.000 (0.008) loss 1.2305 (1.1959) acc 65.6250 (70.5500) lr 1.8090e-03 eta 8:26:32
+epoch [12/50] batch [130/500] time 1.572 (1.568) data 0.000 (0.008) loss 1.0977 (1.2039) acc 62.5000 (70.4327) lr 1.8090e-03 eta 8:26:14
+epoch [12/50] batch [135/500] time 1.555 (1.568) data 0.000 (0.007) loss 0.9087 (1.2027) acc 75.0000 (70.3009) lr 1.8090e-03 eta 8:26:02
+epoch [12/50] batch [140/500] time 1.578 (1.569) data 0.000 (0.007) loss 1.3174 (1.2011) acc 65.6250 (70.3348) lr 1.8090e-03 eta 8:26:13
+epoch [12/50] batch [145/500] time 1.537 (1.569) data 0.000 (0.007) loss 1.5566 (1.1958) acc 65.6250 (70.3017) lr 1.8090e-03 eta 8:26:02
+epoch [12/50] batch [150/500] time 1.555 (1.569) data 0.000 (0.007) loss 1.2139 (1.1942) acc 71.8750 (70.3125) lr 1.8090e-03 eta 8:25:53
+epoch [12/50] batch [155/500] time 1.575 (1.569) data 0.000 (0.007) loss 0.8223 (1.1941) acc 75.0000 (70.3226) lr 1.8090e-03 eta 8:25:45
+epoch [12/50] batch [160/500] time 1.547 (1.569) data 0.000 (0.006) loss 1.1387 (1.1911) acc 75.0000 (70.4297) lr 1.8090e-03 eta 8:25:36
+epoch [12/50] batch [165/500] time 1.557 (1.568) data 0.000 (0.006) loss 0.9839 (1.1908) acc 71.8750 (70.3977) lr 1.8090e-03 eta 8:25:24
+epoch [12/50] batch [170/500] time 1.547 (1.568) data 0.001 (0.006) loss 1.5781 (1.1919) acc 59.3750 (70.3676) lr 1.8090e-03 eta 8:25:16
+epoch [12/50] batch [175/500] time 1.582 (1.568) data 0.000 (0.006) loss 1.0400 (1.1901) acc 75.0000 (70.3571) lr 1.8090e-03 eta 8:25:08
+epoch [12/50] batch [180/500] time 1.553 (1.568) data 0.000 (0.006) loss 1.3564 (1.1916) acc 59.3750 (70.2604) lr 1.8090e-03 eta 8:24:56
+epoch [12/50] batch [185/500] time 1.564 (1.568) data 0.000 (0.006) loss 0.9814 (1.1912) acc 78.1250 (70.2534) lr 1.8090e-03 eta 8:24:53
+epoch [12/50] batch [190/500] time 1.562 (1.568) data 0.000 (0.005) loss 1.4033 (1.1890) acc 68.7500 (70.3618) lr 1.8090e-03 eta 8:24:39
+epoch [12/50] batch [195/500] time 1.562 (1.568) data 0.000 (0.005) loss 0.7153 (1.1881) acc 78.1250 (70.3526) lr 1.8090e-03 eta 8:24:30
+epoch [12/50] batch [200/500] time 1.565 (1.568) data 0.000 (0.005) loss 1.5850 (1.1791) acc 59.3750 (70.5156) lr 1.8090e-03 eta 8:24:16
+epoch [12/50] batch [205/500] time 1.555 (1.568) data 0.000 (0.005) loss 1.4150 (1.1761) acc 78.1250 (70.5640) lr 1.8090e-03 eta 8:24:05
+epoch [12/50] batch [210/500] time 1.552 (1.567) data 0.000 (0.005) loss 0.5503 (1.1714) acc 87.5000 (70.6548) lr 1.8090e-03 eta 8:23:54
+epoch [12/50] batch [215/500] time 1.555 (1.567) data 0.000 (0.005) loss 0.7905 (1.1666) acc 81.2500 (70.7267) lr 1.8090e-03 eta 8:23:42
+epoch [12/50] batch [220/500] time 1.572 (1.567) data 0.000 (0.005) loss 0.7305 (1.1752) acc 78.1250 (70.6534) lr 1.8090e-03 eta 8:23:36
+epoch [12/50] batch [225/500] time 1.582 (1.567) data 0.000 (0.005) loss 1.4805 (1.1731) acc 62.5000 (70.7083) lr 1.8090e-03 eta 8:23:28
+epoch [12/50] batch [230/500] time 1.573 (1.567) data 0.000 (0.005) loss 0.7515 (1.1713) acc 71.8750 (70.7473) lr 1.8090e-03 eta 8:23:19
+epoch [12/50] batch [235/500] time 1.600 (1.567) data 0.000 (0.004) loss 1.2842 (1.1707) acc 62.5000 (70.7713) lr 1.8090e-03 eta 8:23:12
+epoch [12/50] batch [240/500] time 1.544 (1.567) data 0.001 (0.004) loss 0.9966 (1.1666) acc 81.2500 (70.8724) lr 1.8090e-03 eta 8:23:02
+epoch [12/50] batch [245/500] time 1.555 (1.567) data 0.000 (0.004) loss 0.7661 (1.1645) acc 78.1250 (70.9439) lr 1.8090e-03 eta 8:22:51
+epoch [12/50] batch [250/500] time 1.569 (1.567) data 0.000 (0.004) loss 1.8760 (1.1677) acc 56.2500 (70.8750) lr 1.8090e-03 eta 8:22:43
+epoch [12/50] batch [255/500] time 1.577 (1.567) data 0.000 (0.004) loss 0.7339 (1.1677) acc 81.2500 (70.9314) lr 1.8090e-03 eta 8:22:39
+epoch [12/50] batch [260/500] time 1.537 (1.567) data 0.000 (0.004) loss 1.4473 (1.1667) acc 68.7500 (70.9255) lr 1.8090e-03 eta 8:22:29
+epoch [12/50] batch [265/500] time 1.549 (1.567) data 0.001 (0.004) loss 1.6934 (1.1687) acc 56.2500 (70.8255) lr 1.8090e-03 eta 8:22:17
+epoch [12/50] batch [270/500] time 1.576 (1.567) data 0.000 (0.004) loss 1.2783 (1.1669) acc 68.7500 (70.8681) lr 1.8090e-03 eta 8:22:09
+epoch [12/50] batch [275/500] time 1.569 (1.567) data 0.001 (0.004) loss 1.6289 (1.1704) acc 62.5000 (70.7955) lr 1.8090e-03 eta 8:22:00
+epoch [12/50] batch [280/500] time 1.658 (1.567) data 0.000 (0.004) loss 1.3418 (1.1770) acc 71.8750 (70.7031) lr 1.8090e-03 eta 8:22:00
+epoch [12/50] batch [285/500] time 1.547 (1.567) data 0.000 (0.004) loss 2.0605 (1.1802) acc 53.1250 (70.5811) lr 1.8090e-03 eta 8:21:51
+epoch [12/50] batch [290/500] time 1.551 (1.567) data 0.000 (0.004) loss 0.6538 (1.1825) acc 87.5000 (70.5172) lr 1.8090e-03 eta 8:21:42
+epoch [12/50] batch [295/500] time 1.573 (1.567) data 0.000 (0.004) loss 1.3711 (1.1838) acc 71.8750 (70.5191) lr 1.8090e-03 eta 8:21:34
+epoch [12/50] batch [300/500] time 1.555 (1.567) data 0.000 (0.004) loss 1.2754 (1.1871) acc 71.8750 (70.4792) lr 1.8090e-03 eta 8:21:25
+epoch [12/50] batch [305/500] time 1.558 (1.567) data 0.000 (0.004) loss 1.1562 (1.1868) acc 75.0000 (70.5020) lr 1.8090e-03 eta 8:21:13
+epoch [12/50] batch [310/500] time 1.565 (1.567) data 0.000 (0.003) loss 1.3174 (1.1853) acc 71.8750 (70.5141) lr 1.8090e-03 eta 8:21:02
+epoch [12/50] batch [315/500] time 1.556 (1.566) data 0.001 (0.003) loss 1.6133 (1.1886) acc 62.5000 (70.4365) lr 1.8090e-03 eta 8:20:48
+epoch [12/50] batch [320/500] time 1.559 (1.566) data 0.000 (0.003) loss 1.4473 (1.1905) acc 65.6250 (70.3613) lr 1.8090e-03 eta 8:20:37
+epoch [12/50] batch [325/500] time 1.562 (1.566) data 0.001 (0.003) loss 1.4521 (1.1937) acc 59.3750 (70.2981) lr 1.8090e-03 eta 8:20:32
+epoch [12/50] batch [330/500] time 1.545 (1.566) data 0.000 (0.003) loss 1.0820 (1.1933) acc 71.8750 (70.2936) lr 1.8090e-03 eta 8:20:21
+epoch [12/50] batch [335/500] time 1.547 (1.566) data 0.000 (0.003) loss 0.6738 (1.1920) acc 78.1250 (70.2892) lr 1.8090e-03 eta 8:20:09
+epoch [12/50] batch [340/500] time 1.541 (1.565) data 0.000 (0.003) loss 1.4531 (1.1929) acc 68.7500 (70.3033) lr 1.8090e-03 eta 8:19:54
+epoch [12/50] batch [345/500] time 1.566 (1.565) data 0.000 (0.003) loss 0.6870 (1.1876) acc 81.2500 (70.3714) lr 1.8090e-03 eta 8:19:46
+epoch [12/50] batch [350/500] time 1.535 (1.565) data 0.001 (0.003) loss 0.9863 (1.1842) acc 65.6250 (70.4196) lr 1.8090e-03 eta 8:19:35
+epoch [12/50] batch [355/500] time 1.564 (1.565) data 0.000 (0.003) loss 1.3555 (1.1835) acc 62.5000 (70.4049) lr 1.8090e-03 eta 8:19:25
+epoch [12/50] batch [360/500] time 1.552 (1.565) data 0.000 (0.003) loss 1.4238 (1.1857) acc 68.7500 (70.3906) lr 1.8090e-03 eta 8:19:16
+epoch [12/50] batch [365/500] time 1.577 (1.565) data 0.000 (0.003) loss 1.1621 (1.1821) acc 75.0000 (70.4452) lr 1.8090e-03 eta 8:19:08
+epoch [12/50] batch [370/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.1455 (1.1824) acc 59.3750 (70.4392) lr 1.8090e-03 eta 8:18:58
+epoch [12/50] batch [375/500] time 1.543 (1.565) data 0.000 (0.003) loss 1.2979 (1.1823) acc 65.6250 (70.4417) lr 1.8090e-03 eta 8:18:47
+epoch [12/50] batch [380/500] time 1.558 (1.565) data 0.000 (0.003) loss 1.1689 (1.1809) acc 71.8750 (70.4934) lr 1.8090e-03 eta 8:18:36
+epoch [12/50] batch [385/500] time 1.553 (1.564) data 0.000 (0.003) loss 1.4023 (1.1786) acc 62.5000 (70.5357) lr 1.8090e-03 eta 8:18:25
+epoch [12/50] batch [390/500] time 1.544 (1.564) data 0.000 (0.003) loss 0.8701 (1.1778) acc 81.2500 (70.5288) lr 1.8090e-03 eta 8:18:17
+epoch [12/50] batch [395/500] time 1.576 (1.565) data 0.000 (0.003) loss 1.2139 (1.1758) acc 65.6250 (70.5301) lr 1.8090e-03 eta 8:18:12
+epoch [12/50] batch [400/500] time 1.585 (1.565) data 0.001 (0.003) loss 1.1094 (1.1796) acc 71.8750 (70.5000) lr 1.8090e-03 eta 8:18:06
+epoch [12/50] batch [405/500] time 1.551 (1.565) data 0.000 (0.003) loss 1.7725 (1.1790) acc 68.7500 (70.5478) lr 1.8090e-03 eta 8:17:55
+epoch [12/50] batch [410/500] time 1.545 (1.565) data 0.000 (0.003) loss 0.6118 (1.1805) acc 84.3750 (70.5412) lr 1.8090e-03 eta 8:17:46
+epoch [12/50] batch [415/500] time 1.584 (1.564) data 0.000 (0.003) loss 1.2158 (1.1813) acc 75.0000 (70.5045) lr 1.8090e-03 eta 8:17:38
+epoch [12/50] batch [420/500] time 1.562 (1.564) data 0.000 (0.003) loss 0.5122 (1.1801) acc 84.3750 (70.4985) lr 1.8090e-03 eta 8:17:29
+epoch [12/50] batch [425/500] time 1.542 (1.565) data 0.001 (0.003) loss 0.7734 (1.1783) acc 81.2500 (70.5735) lr 1.8090e-03 eta 8:17:25
+epoch [12/50] batch [430/500] time 1.542 (1.565) data 0.000 (0.003) loss 0.6489 (1.1748) acc 71.8750 (70.6105) lr 1.8090e-03 eta 8:17:17
+epoch [12/50] batch [435/500] time 1.553 (1.565) data 0.000 (0.003) loss 0.7476 (1.1742) acc 78.1250 (70.5963) lr 1.8090e-03 eta 8:17:09
+epoch [12/50] batch [440/500] time 1.579 (1.565) data 0.000 (0.003) loss 0.5874 (1.1753) acc 81.2500 (70.5824) lr 1.8090e-03 eta 8:17:00
+epoch [12/50] batch [445/500] time 1.567 (1.565) data 0.000 (0.003) loss 1.1055 (1.1737) acc 78.1250 (70.6180) lr 1.8090e-03 eta 8:16:54
+epoch [12/50] batch [450/500] time 1.554 (1.565) data 0.000 (0.003) loss 1.2178 (1.1742) acc 71.8750 (70.6111) lr 1.8090e-03 eta 8:16:45
+epoch [12/50] batch [455/500] time 1.563 (1.565) data 0.000 (0.002) loss 0.7354 (1.1741) acc 84.3750 (70.6250) lr 1.8090e-03 eta 8:16:37
+epoch [12/50] batch [460/500] time 1.577 (1.565) data 0.000 (0.002) loss 1.6885 (1.1756) acc 62.5000 (70.5842) lr 1.8090e-03 eta 8:16:29
+epoch [12/50] batch [465/500] time 1.578 (1.565) data 0.000 (0.002) loss 1.1895 (1.1760) acc 62.5000 (70.5712) lr 1.8090e-03 eta 8:16:21
+epoch [12/50] batch [470/500] time 1.571 (1.565) data 0.000 (0.002) loss 1.9795 (1.1775) acc 59.3750 (70.5585) lr 1.8090e-03 eta 8:16:16
+epoch [12/50] batch [475/500] time 1.547 (1.565) data 0.000 (0.002) loss 1.7754 (1.1770) acc 56.2500 (70.5789) lr 1.8090e-03 eta 8:16:07
+epoch [12/50] batch [480/500] time 1.560 (1.565) data 0.000 (0.002) loss 0.8237 (1.1773) acc 75.0000 (70.5469) lr 1.8090e-03 eta 8:15:59
+epoch [12/50] batch [485/500] time 1.580 (1.565) data 0.001 (0.002) loss 1.0703 (1.1771) acc 78.1250 (70.5412) lr 1.8090e-03 eta 8:15:52
+epoch [12/50] batch [490/500] time 1.547 (1.565) data 0.000 (0.002) loss 1.0371 (1.1770) acc 78.1250 (70.5357) lr 1.8090e-03 eta 8:15:44
+epoch [12/50] batch [495/500] time 1.573 (1.565) data 0.000 (0.002) loss 1.1650 (1.1791) acc 75.0000 (70.5177) lr 1.8090e-03 eta 8:15:37
+epoch [12/50] batch [500/500] time 1.538 (1.565) data 0.000 (0.002) loss 1.0879 (1.1775) acc 78.1250 (70.5250) lr 1.7705e-03 eta 8:15:26
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,939
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [13/50] batch [5/500] time 1.568 (1.692) data 0.000 (0.187) loss 1.4023 (1.2686) acc 59.3750 (67.5000) lr 1.7705e-03 eta 8:55:34
+epoch [13/50] batch [10/500] time 1.552 (1.629) data 0.000 (0.094) loss 0.8232 (1.1961) acc 75.0000 (69.6875) lr 1.7705e-03 eta 8:35:25
+epoch [13/50] batch [15/500] time 1.550 (1.608) data 0.001 (0.063) loss 1.1309 (1.1639) acc 59.3750 (68.9583) lr 1.7705e-03 eta 8:28:49
+epoch [13/50] batch [20/500] time 1.586 (1.596) data 0.000 (0.047) loss 1.6973 (1.1297) acc 65.6250 (70.7812) lr 1.7705e-03 eta 8:24:56
+epoch [13/50] batch [25/500] time 1.701 (1.594) data 0.000 (0.038) loss 1.0449 (1.1209) acc 75.0000 (71.0000) lr 1.7705e-03 eta 8:24:15
+epoch [13/50] batch [30/500] time 1.556 (1.588) data 0.001 (0.032) loss 1.5361 (1.2021) acc 71.8750 (70.4167) lr 1.7705e-03 eta 8:22:06
+epoch [13/50] batch [35/500] time 1.552 (1.584) data 0.000 (0.027) loss 1.1465 (1.1791) acc 71.8750 (70.8036) lr 1.7705e-03 eta 8:20:44
+epoch [13/50] batch [40/500] time 1.578 (1.582) data 0.000 (0.024) loss 1.1162 (1.1576) acc 65.6250 (71.2500) lr 1.7705e-03 eta 8:19:47
+epoch [13/50] batch [45/500] time 1.562 (1.579) data 0.000 (0.021) loss 1.0107 (1.1777) acc 68.7500 (70.8333) lr 1.7705e-03 eta 8:18:40
+epoch [13/50] batch [50/500] time 1.561 (1.577) data 0.000 (0.019) loss 1.8555 (1.2033) acc 53.1250 (70.2500) lr 1.7705e-03 eta 8:17:56
+epoch [13/50] batch [55/500] time 1.578 (1.575) data 0.000 (0.017) loss 1.4756 (1.1866) acc 65.6250 (70.5682) lr 1.7705e-03 eta 8:17:27
+epoch [13/50] batch [60/500] time 1.579 (1.576) data 0.000 (0.016) loss 1.2119 (1.1614) acc 68.7500 (71.0938) lr 1.7705e-03 eta 8:17:21
+epoch [13/50] batch [65/500] time 1.571 (1.574) data 0.000 (0.015) loss 1.5049 (1.1661) acc 68.7500 (71.2500) lr 1.7705e-03 eta 8:16:39
+epoch [13/50] batch [70/500] time 1.553 (1.573) data 0.000 (0.014) loss 1.3320 (1.1660) acc 62.5000 (71.4286) lr 1.7705e-03 eta 8:16:18
+epoch [13/50] batch [75/500] time 1.555 (1.572) data 0.000 (0.013) loss 1.8154 (1.1879) acc 53.1250 (70.8333) lr 1.7705e-03 eta 8:15:54
+epoch [13/50] batch [80/500] time 1.595 (1.573) data 0.001 (0.012) loss 0.9209 (1.1928) acc 81.2500 (70.9375) lr 1.7705e-03 eta 8:16:06
+epoch [13/50] batch [85/500] time 1.691 (1.574) data 0.001 (0.011) loss 1.3037 (1.1867) acc 75.0000 (71.2500) lr 1.7705e-03 eta 8:16:20
+epoch [13/50] batch [90/500] time 1.555 (1.574) data 0.000 (0.011) loss 0.5176 (1.1733) acc 84.3750 (71.4931) lr 1.7705e-03 eta 8:16:02
+epoch [13/50] batch [95/500] time 1.577 (1.573) data 0.000 (0.010) loss 0.4529 (1.1680) acc 90.6250 (71.7434) lr 1.7705e-03 eta 8:15:44
+epoch [13/50] batch [100/500] time 1.588 (1.573) data 0.000 (0.010) loss 0.5903 (1.1570) acc 78.1250 (71.5625) lr 1.7705e-03 eta 8:15:33
+epoch [13/50] batch [105/500] time 1.590 (1.573) data 0.000 (0.009) loss 0.9419 (1.1532) acc 75.0000 (71.8155) lr 1.7705e-03 eta 8:15:28
+epoch [13/50] batch [110/500] time 1.567 (1.573) data 0.001 (0.009) loss 0.6147 (1.1540) acc 81.2500 (71.8182) lr 1.7705e-03 eta 8:15:05
+epoch [13/50] batch [115/500] time 1.574 (1.572) data 0.000 (0.009) loss 1.5176 (1.1554) acc 68.7500 (71.8207) lr 1.7705e-03 eta 8:14:53
+epoch [13/50] batch [120/500] time 1.561 (1.572) data 0.000 (0.008) loss 1.3301 (1.1462) acc 65.6250 (71.8750) lr 1.7705e-03 eta 8:14:39
+epoch [13/50] batch [125/500] time 1.561 (1.572) data 0.000 (0.008) loss 1.0723 (1.1410) acc 71.8750 (72.0250) lr 1.7705e-03 eta 8:14:31
+epoch [13/50] batch [130/500] time 1.580 (1.573) data 0.000 (0.008) loss 1.4229 (1.1417) acc 62.5000 (71.9471) lr 1.7705e-03 eta 8:14:35
+epoch [13/50] batch [135/500] time 1.564 (1.573) data 0.001 (0.007) loss 1.2168 (1.1393) acc 65.6250 (71.9444) lr 1.7705e-03 eta 8:14:25
+epoch [13/50] batch [140/500] time 1.583 (1.572) data 0.001 (0.007) loss 0.8091 (1.1311) acc 78.1250 (72.0759) lr 1.7705e-03 eta 8:14:12
+epoch [13/50] batch [145/500] time 1.553 (1.572) data 0.000 (0.007) loss 1.1699 (1.1277) acc 62.5000 (72.0043) lr 1.7705e-03 eta 8:14:04
+epoch [13/50] batch [150/500] time 1.562 (1.573) data 0.001 (0.007) loss 0.6748 (1.1260) acc 75.0000 (72.0417) lr 1.7705e-03 eta 8:14:02
+epoch [13/50] batch [155/500] time 1.577 (1.573) data 0.000 (0.006) loss 1.3350 (1.1251) acc 68.7500 (72.1976) lr 1.7705e-03 eta 8:13:56
+epoch [13/50] batch [160/500] time 1.582 (1.573) data 0.000 (0.006) loss 0.8755 (1.1190) acc 75.0000 (72.4023) lr 1.7705e-03 eta 8:13:48
+epoch [13/50] batch [165/500] time 1.578 (1.573) data 0.001 (0.006) loss 1.2627 (1.1192) acc 68.7500 (72.3106) lr 1.7705e-03 eta 8:13:40
+epoch [13/50] batch [170/500] time 1.560 (1.572) data 0.001 (0.006) loss 1.3291 (1.1260) acc 59.3750 (72.0588) lr 1.7705e-03 eta 8:13:27
+epoch [13/50] batch [175/500] time 1.561 (1.572) data 0.000 (0.006) loss 1.5518 (1.1285) acc 65.6250 (72.1071) lr 1.7705e-03 eta 8:13:10
+epoch [13/50] batch [180/500] time 1.568 (1.572) data 0.000 (0.006) loss 0.8076 (1.1311) acc 81.2500 (72.0660) lr 1.7705e-03 eta 8:12:59
+epoch [13/50] batch [185/500] time 1.560 (1.571) data 0.000 (0.005) loss 0.9351 (1.1271) acc 78.1250 (72.0946) lr 1.7705e-03 eta 8:12:42
+epoch [13/50] batch [190/500] time 1.565 (1.571) data 0.000 (0.005) loss 0.7373 (1.1244) acc 87.5000 (72.0559) lr 1.7705e-03 eta 8:12:27
+epoch [13/50] batch [195/500] time 1.566 (1.570) data 0.000 (0.005) loss 1.0586 (1.1259) acc 71.8750 (72.0513) lr 1.7705e-03 eta 8:12:11
+epoch [13/50] batch [200/500] time 1.568 (1.570) data 0.000 (0.005) loss 1.5088 (1.1201) acc 65.6250 (72.2188) lr 1.7705e-03 eta 8:12:02
+epoch [13/50] batch [205/500] time 1.555 (1.570) data 0.000 (0.005) loss 1.0010 (1.1171) acc 78.1250 (72.3323) lr 1.7705e-03 eta 8:11:48
+epoch [13/50] batch [210/500] time 1.569 (1.570) data 0.000 (0.005) loss 0.8477 (1.1195) acc 71.8750 (72.2470) lr 1.7705e-03 eta 8:11:37
+epoch [13/50] batch [215/500] time 1.577 (1.570) data 0.000 (0.005) loss 1.5264 (1.1201) acc 65.6250 (72.2965) lr 1.7705e-03 eta 8:11:23
+epoch [13/50] batch [220/500] time 1.559 (1.569) data 0.000 (0.005) loss 1.0977 (1.1203) acc 84.3750 (72.2443) lr 1.7705e-03 eta 8:11:11
+epoch [13/50] batch [225/500] time 1.564 (1.569) data 0.001 (0.005) loss 1.1953 (1.1209) acc 75.0000 (72.2778) lr 1.7705e-03 eta 8:11:02
+epoch [13/50] batch [230/500] time 1.572 (1.570) data 0.000 (0.005) loss 0.9565 (1.1234) acc 71.8750 (72.1875) lr 1.7705e-03 eta 8:11:02
+epoch [13/50] batch [235/500] time 1.567 (1.569) data 0.000 (0.004) loss 0.9839 (1.1254) acc 78.1250 (72.1809) lr 1.7705e-03 eta 8:10:51
+epoch [13/50] batch [240/500] time 1.567 (1.569) data 0.000 (0.004) loss 1.9648 (1.1306) acc 71.8750 (72.2135) lr 1.7705e-03 eta 8:10:41
+epoch [13/50] batch [245/500] time 1.561 (1.569) data 0.000 (0.004) loss 0.9800 (1.1281) acc 71.8750 (72.2321) lr 1.7705e-03 eta 8:10:34
+epoch [13/50] batch [250/500] time 1.559 (1.569) data 0.000 (0.004) loss 0.9834 (1.1265) acc 75.0000 (72.2250) lr 1.7705e-03 eta 8:10:22
+epoch [13/50] batch [255/500] time 1.554 (1.569) data 0.000 (0.004) loss 0.9395 (1.1275) acc 75.0000 (72.2059) lr 1.7705e-03 eta 8:10:11
+epoch [13/50] batch [260/500] time 1.546 (1.569) data 0.000 (0.004) loss 0.9473 (1.1275) acc 78.1250 (72.1995) lr 1.7705e-03 eta 8:09:58
+epoch [13/50] batch [265/500] time 1.556 (1.568) data 0.000 (0.004) loss 0.9873 (1.1286) acc 81.2500 (72.2642) lr 1.7705e-03 eta 8:09:44
+epoch [13/50] batch [270/500] time 1.570 (1.568) data 0.000 (0.004) loss 1.0049 (1.1263) acc 84.3750 (72.3264) lr 1.7705e-03 eta 8:09:34
+epoch [13/50] batch [275/500] time 1.574 (1.569) data 0.000 (0.004) loss 1.8086 (1.1285) acc 56.2500 (72.2386) lr 1.7705e-03 eta 8:09:31
+epoch [13/50] batch [280/500] time 1.545 (1.568) data 0.000 (0.004) loss 1.5117 (1.1308) acc 65.6250 (72.1540) lr 1.7705e-03 eta 8:09:18
+epoch [13/50] batch [285/500] time 1.537 (1.568) data 0.000 (0.004) loss 1.8682 (1.1321) acc 65.6250 (72.2259) lr 1.7705e-03 eta 8:09:05
+epoch [13/50] batch [290/500] time 1.529 (1.568) data 0.000 (0.004) loss 0.5522 (1.1331) acc 87.5000 (72.2091) lr 1.7705e-03 eta 8:08:50
+epoch [13/50] batch [295/500] time 1.569 (1.568) data 0.000 (0.004) loss 0.7471 (1.1324) acc 81.2500 (72.2352) lr 1.7705e-03 eta 8:08:42
+epoch [13/50] batch [300/500] time 1.550 (1.567) data 0.001 (0.004) loss 0.5283 (1.1274) acc 84.3750 (72.2708) lr 1.7705e-03 eta 8:08:30
+epoch [13/50] batch [305/500] time 1.594 (1.567) data 0.000 (0.004) loss 0.5962 (1.1242) acc 81.2500 (72.2848) lr 1.7705e-03 eta 8:08:22
+epoch [13/50] batch [310/500] time 1.574 (1.567) data 0.000 (0.003) loss 1.0801 (1.1238) acc 78.1250 (72.3690) lr 1.7705e-03 eta 8:08:16
+epoch [13/50] batch [315/500] time 1.541 (1.567) data 0.001 (0.003) loss 0.9150 (1.1225) acc 65.6250 (72.3214) lr 1.7705e-03 eta 8:08:03
+epoch [13/50] batch [320/500] time 1.572 (1.567) data 0.000 (0.003) loss 0.7803 (1.1245) acc 87.5000 (72.3145) lr 1.7705e-03 eta 8:07:53
+epoch [13/50] batch [325/500] time 1.562 (1.567) data 0.000 (0.003) loss 1.4629 (1.1248) acc 65.6250 (72.2885) lr 1.7705e-03 eta 8:07:43
+epoch [13/50] batch [330/500] time 1.546 (1.567) data 0.000 (0.003) loss 1.2988 (1.1229) acc 59.3750 (72.2538) lr 1.7705e-03 eta 8:07:31
+epoch [13/50] batch [335/500] time 1.546 (1.567) data 0.001 (0.003) loss 1.5420 (1.1256) acc 62.5000 (72.1642) lr 1.7705e-03 eta 8:07:22
+epoch [13/50] batch [340/500] time 1.571 (1.567) data 0.001 (0.003) loss 1.1426 (1.1232) acc 62.5000 (72.1691) lr 1.7705e-03 eta 8:07:13
+epoch [13/50] batch [345/500] time 1.565 (1.566) data 0.000 (0.003) loss 1.1016 (1.1244) acc 75.0000 (72.1286) lr 1.7705e-03 eta 8:07:02
+epoch [13/50] batch [350/500] time 1.566 (1.566) data 0.000 (0.003) loss 1.5518 (1.1274) acc 68.7500 (72.0714) lr 1.7705e-03 eta 8:06:52
+epoch [13/50] batch [355/500] time 1.558 (1.566) data 0.000 (0.003) loss 1.2783 (1.1256) acc 65.6250 (72.1127) lr 1.7705e-03 eta 8:06:45
+epoch [13/50] batch [360/500] time 1.555 (1.566) data 0.000 (0.003) loss 1.4121 (1.1252) acc 65.6250 (72.1267) lr 1.7705e-03 eta 8:06:36
+epoch [13/50] batch [365/500] time 1.556 (1.566) data 0.000 (0.003) loss 0.7231 (1.1231) acc 87.5000 (72.1918) lr 1.7705e-03 eta 8:06:26
+epoch [13/50] batch [370/500] time 1.523 (1.566) data 0.000 (0.003) loss 1.3047 (1.1244) acc 75.0000 (72.1875) lr 1.7705e-03 eta 8:06:13
+epoch [13/50] batch [375/500] time 1.549 (1.566) data 0.000 (0.003) loss 1.2764 (1.1259) acc 62.5000 (72.1750) lr 1.7705e-03 eta 8:06:07
+epoch [13/50] batch [380/500] time 1.539 (1.566) data 0.000 (0.003) loss 0.9976 (1.1266) acc 78.1250 (72.1957) lr 1.7705e-03 eta 8:05:58
+epoch [13/50] batch [385/500] time 1.544 (1.566) data 0.000 (0.003) loss 2.0781 (1.1280) acc 59.3750 (72.1591) lr 1.7705e-03 eta 8:05:46
+epoch [13/50] batch [390/500] time 1.554 (1.566) data 0.000 (0.003) loss 1.3223 (1.1292) acc 62.5000 (72.0913) lr 1.7705e-03 eta 8:05:35
+epoch [13/50] batch [395/500] time 1.560 (1.565) data 0.000 (0.003) loss 0.9292 (1.1271) acc 68.7500 (72.1203) lr 1.7705e-03 eta 8:05:24
+epoch [13/50] batch [400/500] time 1.560 (1.565) data 0.000 (0.003) loss 0.6021 (1.1250) acc 87.5000 (72.1797) lr 1.7705e-03 eta 8:05:15
+epoch [13/50] batch [405/500] time 1.576 (1.565) data 0.000 (0.003) loss 1.6250 (1.1287) acc 59.3750 (72.0833) lr 1.7705e-03 eta 8:05:08
+epoch [13/50] batch [410/500] time 1.556 (1.565) data 0.000 (0.003) loss 1.3545 (1.1300) acc 65.6250 (72.0884) lr 1.7705e-03 eta 8:05:00
+epoch [13/50] batch [415/500] time 1.647 (1.566) data 0.000 (0.003) loss 1.0127 (1.1300) acc 78.1250 (72.1084) lr 1.7705e-03 eta 8:04:55
+epoch [13/50] batch [420/500] time 1.571 (1.566) data 0.000 (0.003) loss 1.1689 (1.1304) acc 68.7500 (72.0908) lr 1.7705e-03 eta 8:04:48
+epoch [13/50] batch [425/500] time 1.558 (1.565) data 0.000 (0.003) loss 0.9985 (1.1306) acc 84.3750 (72.0956) lr 1.7705e-03 eta 8:04:37
+epoch [13/50] batch [430/500] time 1.562 (1.565) data 0.000 (0.003) loss 1.7705 (1.1331) acc 68.7500 (72.1076) lr 1.7705e-03 eta 8:04:28
+epoch [13/50] batch [435/500] time 1.558 (1.565) data 0.000 (0.003) loss 0.7461 (1.1353) acc 78.1250 (72.0402) lr 1.7705e-03 eta 8:04:21
+epoch [13/50] batch [440/500] time 1.568 (1.566) data 0.000 (0.003) loss 1.3799 (1.1353) acc 68.7500 (72.0241) lr 1.7705e-03 eta 8:04:16
+epoch [13/50] batch [445/500] time 1.558 (1.565) data 0.001 (0.003) loss 0.9341 (1.1374) acc 71.8750 (71.9733) lr 1.7705e-03 eta 8:04:06
+epoch [13/50] batch [450/500] time 1.558 (1.565) data 0.000 (0.002) loss 1.2920 (1.1366) acc 68.7500 (71.9722) lr 1.7705e-03 eta 8:03:58
+epoch [13/50] batch [455/500] time 1.569 (1.565) data 0.000 (0.002) loss 1.2930 (1.1373) acc 62.5000 (71.9231) lr 1.7705e-03 eta 8:03:50
+epoch [13/50] batch [460/500] time 1.562 (1.565) data 0.000 (0.002) loss 1.1250 (1.1389) acc 71.8750 (71.9293) lr 1.7705e-03 eta 8:03:42
+epoch [13/50] batch [465/500] time 1.579 (1.565) data 0.000 (0.002) loss 1.1777 (1.1400) acc 71.8750 (71.9288) lr 1.7705e-03 eta 8:03:35
+epoch [13/50] batch [470/500] time 1.558 (1.565) data 0.000 (0.002) loss 1.5703 (1.1433) acc 68.7500 (71.8949) lr 1.7705e-03 eta 8:03:26
+epoch [13/50] batch [475/500] time 1.566 (1.565) data 0.000 (0.002) loss 0.8569 (1.1456) acc 75.0000 (71.8289) lr 1.7705e-03 eta 8:03:16
+epoch [13/50] batch [480/500] time 1.577 (1.565) data 0.000 (0.002) loss 1.1826 (1.1474) acc 71.8750 (71.7708) lr 1.7705e-03 eta 8:03:07
+epoch [13/50] batch [485/500] time 1.573 (1.565) data 0.001 (0.002) loss 1.4863 (1.1479) acc 62.5000 (71.7719) lr 1.7705e-03 eta 8:02:58
+epoch [13/50] batch [490/500] time 1.552 (1.565) data 0.000 (0.002) loss 1.3623 (1.1499) acc 65.6250 (71.7538) lr 1.7705e-03 eta 8:02:49
+epoch [13/50] batch [495/500] time 1.588 (1.565) data 0.000 (0.002) loss 1.4883 (1.1489) acc 62.5000 (71.7740) lr 1.7705e-03 eta 8:02:41
+epoch [13/50] batch [500/500] time 1.571 (1.565) data 0.000 (0.002) loss 1.1387 (1.1500) acc 65.6250 (71.7625) lr 1.7290e-03 eta 8:02:32
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,882
+* accuracy: 77.8%
+* error: 22.2%
+* macro_f1: 77.2%
+epoch [14/50] batch [5/500] time 1.558 (1.685) data 0.000 (0.182) loss 1.6699 (1.1060) acc 68.7500 (75.6250) lr 1.7290e-03 eta 8:39:20
+epoch [14/50] batch [10/500] time 1.549 (1.626) data 0.001 (0.091) loss 1.6719 (1.1472) acc 59.3750 (71.8750) lr 1.7290e-03 eta 8:21:07
+epoch [14/50] batch [15/500] time 1.561 (1.600) data 0.001 (0.061) loss 1.1865 (1.1371) acc 71.8750 (72.7083) lr 1.7290e-03 eta 8:12:55
+epoch [14/50] batch [20/500] time 1.552 (1.591) data 0.000 (0.046) loss 0.9512 (1.1165) acc 78.1250 (73.1250) lr 1.7290e-03 eta 8:10:09
+epoch [14/50] batch [25/500] time 1.566 (1.591) data 0.001 (0.037) loss 1.4453 (1.1604) acc 65.6250 (72.3750) lr 1.7290e-03 eta 8:10:02
+epoch [14/50] batch [30/500] time 1.556 (1.586) data 0.000 (0.031) loss 0.6357 (1.1209) acc 84.3750 (73.0208) lr 1.7290e-03 eta 8:08:18
+epoch [14/50] batch [35/500] time 1.547 (1.582) data 0.001 (0.026) loss 0.7749 (1.1366) acc 71.8750 (72.1429) lr 1.7290e-03 eta 8:06:57
+epoch [14/50] batch [40/500] time 1.577 (1.580) data 0.000 (0.023) loss 0.6245 (1.1208) acc 81.2500 (71.7969) lr 1.7290e-03 eta 8:06:06
+epoch [14/50] batch [45/500] time 1.556 (1.578) data 0.000 (0.021) loss 1.3994 (1.1218) acc 62.5000 (71.3889) lr 1.7290e-03 eta 8:05:24
+epoch [14/50] batch [50/500] time 1.561 (1.576) data 0.000 (0.019) loss 0.8740 (1.1170) acc 75.0000 (71.1250) lr 1.7290e-03 eta 8:04:35
+epoch [14/50] batch [55/500] time 1.562 (1.575) data 0.000 (0.017) loss 1.0195 (1.1128) acc 78.1250 (71.3636) lr 1.7290e-03 eta 8:04:18
+epoch [14/50] batch [60/500] time 1.567 (1.575) data 0.002 (0.016) loss 0.8916 (1.1099) acc 75.0000 (71.2500) lr 1.7290e-03 eta 8:04:01
+epoch [14/50] batch [65/500] time 1.573 (1.574) data 0.001 (0.014) loss 0.6753 (1.0995) acc 87.5000 (71.5865) lr 1.7290e-03 eta 8:03:35
+epoch [14/50] batch [70/500] time 1.580 (1.574) data 0.000 (0.013) loss 0.8735 (1.0890) acc 71.8750 (71.6518) lr 1.7290e-03 eta 8:03:27
+epoch [14/50] batch [75/500] time 1.556 (1.573) data 0.000 (0.013) loss 1.5195 (1.0758) acc 56.2500 (71.9583) lr 1.7290e-03 eta 8:03:03
+epoch [14/50] batch [80/500] time 1.560 (1.572) data 0.001 (0.012) loss 0.8354 (1.0669) acc 71.8750 (72.1875) lr 1.7290e-03 eta 8:02:37
+epoch [14/50] batch [85/500] time 1.546 (1.572) data 0.000 (0.011) loss 0.8843 (1.0622) acc 68.7500 (71.9485) lr 1.7290e-03 eta 8:02:19
+epoch [14/50] batch [90/500] time 1.572 (1.571) data 0.001 (0.011) loss 0.9424 (1.0546) acc 75.0000 (72.2917) lr 1.7290e-03 eta 8:02:00
+epoch [14/50] batch [95/500] time 1.555 (1.571) data 0.001 (0.010) loss 1.4521 (1.0513) acc 56.2500 (72.5000) lr 1.7290e-03 eta 8:01:50
+epoch [14/50] batch [100/500] time 1.563 (1.570) data 0.000 (0.010) loss 0.7183 (1.0506) acc 78.1250 (72.5625) lr 1.7290e-03 eta 8:01:32
+epoch [14/50] batch [105/500] time 1.547 (1.570) data 0.000 (0.009) loss 0.7397 (1.0436) acc 71.8750 (72.7679) lr 1.7290e-03 eta 8:01:21
+epoch [14/50] batch [110/500] time 1.571 (1.570) data 0.000 (0.009) loss 1.0020 (1.0365) acc 81.2500 (72.9830) lr 1.7290e-03 eta 8:01:12
+epoch [14/50] batch [115/500] time 1.564 (1.570) data 0.000 (0.008) loss 0.9219 (1.0401) acc 71.8750 (72.8804) lr 1.7290e-03 eta 8:00:57
+epoch [14/50] batch [120/500] time 1.666 (1.571) data 0.000 (0.008) loss 0.5801 (1.0370) acc 81.2500 (72.9427) lr 1.7290e-03 eta 8:01:07
+epoch [14/50] batch [125/500] time 1.576 (1.570) data 0.000 (0.008) loss 1.3545 (1.0403) acc 65.6250 (72.8750) lr 1.7290e-03 eta 8:00:53
+epoch [14/50] batch [130/500] time 1.562 (1.570) data 0.001 (0.007) loss 0.8560 (1.0361) acc 78.1250 (72.9808) lr 1.7290e-03 eta 8:00:36
+epoch [14/50] batch [135/500] time 1.566 (1.569) data 0.000 (0.007) loss 1.3047 (1.0431) acc 59.3750 (72.6620) lr 1.7290e-03 eta 8:00:23
+epoch [14/50] batch [140/500] time 1.551 (1.569) data 0.001 (0.007) loss 0.8604 (1.0465) acc 71.8750 (72.5446) lr 1.7290e-03 eta 8:00:04
+epoch [14/50] batch [145/500] time 1.582 (1.569) data 0.000 (0.007) loss 1.1221 (1.0496) acc 75.0000 (72.5431) lr 1.7290e-03 eta 7:59:54
+epoch [14/50] batch [150/500] time 1.576 (1.569) data 0.000 (0.007) loss 0.8403 (1.0588) acc 81.2500 (72.4792) lr 1.7290e-03 eta 7:59:44
+epoch [14/50] batch [155/500] time 1.568 (1.568) data 0.000 (0.006) loss 1.2393 (1.0669) acc 75.0000 (72.4395) lr 1.7290e-03 eta 7:59:32
+epoch [14/50] batch [160/500] time 1.573 (1.569) data 0.000 (0.006) loss 1.3604 (1.0757) acc 68.7500 (72.3047) lr 1.7290e-03 eta 7:59:28
+epoch [14/50] batch [165/500] time 1.555 (1.569) data 0.001 (0.006) loss 1.1025 (1.0767) acc 78.1250 (72.2538) lr 1.7290e-03 eta 7:59:29
+epoch [14/50] batch [170/500] time 1.565 (1.569) data 0.000 (0.006) loss 0.7393 (1.0732) acc 81.2500 (72.3713) lr 1.7290e-03 eta 7:59:15
+epoch [14/50] batch [175/500] time 1.552 (1.569) data 0.000 (0.006) loss 0.8130 (1.0724) acc 81.2500 (72.3393) lr 1.7290e-03 eta 7:59:08
+epoch [14/50] batch [180/500] time 1.555 (1.569) data 0.000 (0.006) loss 0.6216 (1.0717) acc 81.2500 (72.4132) lr 1.7290e-03 eta 7:58:55
+epoch [14/50] batch [185/500] time 1.572 (1.569) data 0.000 (0.005) loss 1.4141 (1.0723) acc 71.8750 (72.4493) lr 1.7290e-03 eta 7:58:47
+epoch [14/50] batch [190/500] time 1.546 (1.568) data 0.000 (0.005) loss 0.9702 (1.0734) acc 75.0000 (72.4671) lr 1.7290e-03 eta 7:58:33
+epoch [14/50] batch [195/500] time 1.537 (1.567) data 0.000 (0.005) loss 1.5400 (1.0808) acc 65.6250 (72.3077) lr 1.7290e-03 eta 7:58:12
+epoch [14/50] batch [200/500] time 1.569 (1.567) data 0.000 (0.005) loss 1.1094 (1.0913) acc 78.1250 (72.1875) lr 1.7290e-03 eta 7:58:01
+epoch [14/50] batch [205/500] time 1.543 (1.567) data 0.000 (0.005) loss 1.0117 (1.0900) acc 75.0000 (72.3018) lr 1.7290e-03 eta 7:57:43
+epoch [14/50] batch [210/500] time 1.556 (1.567) data 0.001 (0.005) loss 1.4043 (1.0909) acc 65.6250 (72.2768) lr 1.7290e-03 eta 7:57:32
+epoch [14/50] batch [215/500] time 1.555 (1.566) data 0.000 (0.005) loss 1.1328 (1.0943) acc 62.5000 (72.0494) lr 1.7290e-03 eta 7:57:20
+epoch [14/50] batch [220/500] time 1.548 (1.566) data 0.000 (0.005) loss 0.5225 (1.0927) acc 87.5000 (72.0881) lr 1.7290e-03 eta 7:57:06
+epoch [14/50] batch [225/500] time 1.547 (1.566) data 0.000 (0.004) loss 0.9956 (1.0937) acc 75.0000 (71.9861) lr 1.7290e-03 eta 7:56:58
+epoch [14/50] batch [230/500] time 1.561 (1.566) data 0.000 (0.004) loss 1.1094 (1.0941) acc 68.7500 (71.9429) lr 1.7290e-03 eta 7:56:46
+epoch [14/50] batch [235/500] time 1.549 (1.566) data 0.000 (0.004) loss 0.9902 (1.0977) acc 71.8750 (71.8351) lr 1.7290e-03 eta 7:56:37
+epoch [14/50] batch [240/500] time 1.557 (1.566) data 0.000 (0.004) loss 1.1924 (1.0996) acc 71.8750 (71.7839) lr 1.7290e-03 eta 7:56:31
+epoch [14/50] batch [245/500] time 1.554 (1.566) data 0.000 (0.004) loss 1.4561 (1.1069) acc 62.5000 (71.6327) lr 1.7290e-03 eta 7:56:21
+epoch [14/50] batch [250/500] time 1.562 (1.566) data 0.000 (0.004) loss 1.4111 (1.1066) acc 65.6250 (71.6125) lr 1.7290e-03 eta 7:56:11
+epoch [14/50] batch [255/500] time 1.553 (1.566) data 0.000 (0.004) loss 1.5078 (1.1100) acc 65.6250 (71.5564) lr 1.7290e-03 eta 7:56:02
+epoch [14/50] batch [260/500] time 1.587 (1.566) data 0.000 (0.004) loss 1.4082 (1.1104) acc 71.8750 (71.5865) lr 1.7290e-03 eta 7:55:55
+epoch [14/50] batch [265/500] time 1.557 (1.566) data 0.000 (0.004) loss 0.8193 (1.1070) acc 75.0000 (71.6509) lr 1.7290e-03 eta 7:55:54
+epoch [14/50] batch [270/500] time 1.566 (1.566) data 0.000 (0.004) loss 0.8232 (1.1043) acc 75.0000 (71.6898) lr 1.7290e-03 eta 7:55:50
+epoch [14/50] batch [275/500] time 1.566 (1.566) data 0.000 (0.004) loss 0.9526 (1.1048) acc 65.6250 (71.6591) lr 1.7290e-03 eta 7:55:39
+epoch [14/50] batch [280/500] time 1.564 (1.566) data 0.000 (0.004) loss 1.1846 (1.1045) acc 75.0000 (71.6406) lr 1.7290e-03 eta 7:55:33
+epoch [14/50] batch [285/500] time 1.559 (1.566) data 0.000 (0.004) loss 1.0244 (1.1045) acc 71.8750 (71.6886) lr 1.7290e-03 eta 7:55:27
+epoch [14/50] batch [290/500] time 1.580 (1.566) data 0.000 (0.004) loss 1.8477 (1.1068) acc 62.5000 (71.6595) lr 1.7290e-03 eta 7:55:20
+epoch [14/50] batch [295/500] time 1.585 (1.566) data 0.000 (0.004) loss 0.9258 (1.1015) acc 75.0000 (71.7797) lr 1.7290e-03 eta 7:55:16
+epoch [14/50] batch [300/500] time 1.547 (1.566) data 0.000 (0.003) loss 1.4131 (1.1039) acc 59.3750 (71.7188) lr 1.7290e-03 eta 7:55:07
+epoch [14/50] batch [305/500] time 1.558 (1.566) data 0.000 (0.003) loss 1.0605 (1.1087) acc 71.8750 (71.6701) lr 1.7290e-03 eta 7:55:02
+epoch [14/50] batch [310/500] time 1.574 (1.567) data 0.001 (0.003) loss 0.6431 (1.1064) acc 81.2500 (71.7641) lr 1.7290e-03 eta 7:55:01
+epoch [14/50] batch [315/500] time 1.591 (1.567) data 0.001 (0.003) loss 1.3613 (1.1075) acc 71.8750 (71.7758) lr 1.7290e-03 eta 7:54:56
+epoch [14/50] batch [320/500] time 1.567 (1.567) data 0.001 (0.003) loss 1.9014 (1.1126) acc 46.8750 (71.6699) lr 1.7290e-03 eta 7:54:49
+epoch [14/50] batch [325/500] time 1.567 (1.567) data 0.000 (0.003) loss 1.1914 (1.1149) acc 78.1250 (71.6923) lr 1.7290e-03 eta 7:54:41
+epoch [14/50] batch [330/500] time 1.538 (1.567) data 0.001 (0.003) loss 0.6123 (1.1158) acc 78.1250 (71.6288) lr 1.7290e-03 eta 7:54:31
+epoch [14/50] batch [335/500] time 1.551 (1.567) data 0.000 (0.003) loss 0.9512 (1.1173) acc 78.1250 (71.6045) lr 1.7290e-03 eta 7:54:21
+epoch [14/50] batch [340/500] time 1.554 (1.567) data 0.000 (0.003) loss 1.4336 (1.1180) acc 68.7500 (71.6544) lr 1.7290e-03 eta 7:54:11
+epoch [14/50] batch [345/500] time 1.585 (1.567) data 0.000 (0.003) loss 1.2080 (1.1183) acc 59.3750 (71.6123) lr 1.7290e-03 eta 7:54:04
+epoch [14/50] batch [350/500] time 1.567 (1.567) data 0.000 (0.003) loss 1.3291 (1.1218) acc 59.3750 (71.5268) lr 1.7290e-03 eta 7:53:56
+epoch [14/50] batch [355/500] time 1.554 (1.567) data 0.000 (0.003) loss 0.8003 (1.1192) acc 71.8750 (71.5757) lr 1.7290e-03 eta 7:53:48
+epoch [14/50] batch [360/500] time 1.585 (1.567) data 0.000 (0.003) loss 1.6455 (1.1184) acc 53.1250 (71.5712) lr 1.7290e-03 eta 7:53:41
+epoch [14/50] batch [365/500] time 1.571 (1.567) data 0.000 (0.003) loss 0.9199 (1.1169) acc 68.7500 (71.5668) lr 1.7290e-03 eta 7:53:33
+epoch [14/50] batch [370/500] time 1.579 (1.567) data 0.000 (0.003) loss 1.4580 (1.1217) acc 75.0000 (71.4949) lr 1.7290e-03 eta 7:53:26
+epoch [14/50] batch [375/500] time 1.573 (1.567) data 0.000 (0.003) loss 0.8262 (1.1192) acc 78.1250 (71.5583) lr 1.7290e-03 eta 7:53:19
+epoch [14/50] batch [380/500] time 1.555 (1.567) data 0.000 (0.003) loss 1.1621 (1.1189) acc 78.1250 (71.5461) lr 1.7290e-03 eta 7:53:11
+epoch [14/50] batch [385/500] time 1.566 (1.567) data 0.000 (0.003) loss 0.9863 (1.1157) acc 78.1250 (71.6153) lr 1.7290e-03 eta 7:52:59
+epoch [14/50] batch [390/500] time 1.577 (1.567) data 0.000 (0.003) loss 1.4541 (1.1214) acc 62.5000 (71.5304) lr 1.7290e-03 eta 7:52:51
+epoch [14/50] batch [395/500] time 1.562 (1.567) data 0.000 (0.003) loss 1.0439 (1.1226) acc 78.1250 (71.4953) lr 1.7290e-03 eta 7:52:42
+epoch [14/50] batch [400/500] time 1.546 (1.566) data 0.000 (0.003) loss 1.2949 (1.1248) acc 71.8750 (71.4688) lr 1.7290e-03 eta 7:52:33
+epoch [14/50] batch [405/500] time 1.561 (1.566) data 0.001 (0.003) loss 1.3506 (1.1241) acc 68.7500 (71.4892) lr 1.7290e-03 eta 7:52:25
+epoch [14/50] batch [410/500] time 1.573 (1.567) data 0.001 (0.003) loss 1.2119 (1.1233) acc 68.7500 (71.4634) lr 1.7290e-03 eta 7:52:20
+epoch [14/50] batch [415/500] time 1.587 (1.567) data 0.000 (0.003) loss 1.3340 (1.1250) acc 71.8750 (71.4533) lr 1.7290e-03 eta 7:52:12
+epoch [14/50] batch [420/500] time 1.580 (1.567) data 0.000 (0.003) loss 0.6797 (1.1209) acc 84.3750 (71.5476) lr 1.7290e-03 eta 7:52:04
+epoch [14/50] batch [425/500] time 1.579 (1.567) data 0.000 (0.003) loss 1.3740 (1.1213) acc 59.3750 (71.5588) lr 1.7290e-03 eta 7:51:58
+epoch [14/50] batch [430/500] time 1.556 (1.567) data 0.000 (0.003) loss 1.3076 (1.1222) acc 68.7500 (71.5625) lr 1.7290e-03 eta 7:51:50
+epoch [14/50] batch [435/500] time 1.551 (1.567) data 0.000 (0.003) loss 0.5659 (1.1217) acc 87.5000 (71.5805) lr 1.7290e-03 eta 7:51:41
+epoch [14/50] batch [440/500] time 1.550 (1.567) data 0.000 (0.002) loss 1.4209 (1.1247) acc 68.7500 (71.5412) lr 1.7290e-03 eta 7:51:32
+epoch [14/50] batch [445/500] time 1.561 (1.567) data 0.000 (0.002) loss 1.3652 (1.1255) acc 62.5000 (71.5028) lr 1.7290e-03 eta 7:51:25
+epoch [14/50] batch [450/500] time 1.685 (1.567) data 0.001 (0.002) loss 1.5645 (1.1264) acc 65.6250 (71.4792) lr 1.7290e-03 eta 7:51:21
+epoch [14/50] batch [455/500] time 1.553 (1.567) data 0.000 (0.002) loss 1.5098 (1.1280) acc 75.0000 (71.4698) lr 1.7290e-03 eta 7:51:08
+epoch [14/50] batch [460/500] time 1.577 (1.567) data 0.000 (0.002) loss 1.1562 (1.1282) acc 78.1250 (71.4674) lr 1.7290e-03 eta 7:51:01
+epoch [14/50] batch [465/500] time 1.558 (1.567) data 0.000 (0.002) loss 2.1562 (1.1322) acc 50.0000 (71.4113) lr 1.7290e-03 eta 7:50:52
+epoch [14/50] batch [470/500] time 1.547 (1.567) data 0.000 (0.002) loss 1.4219 (1.1335) acc 62.5000 (71.3630) lr 1.7290e-03 eta 7:50:45
+epoch [14/50] batch [475/500] time 1.575 (1.567) data 0.000 (0.002) loss 1.1953 (1.1350) acc 68.7500 (71.3092) lr 1.7290e-03 eta 7:50:39
+epoch [14/50] batch [480/500] time 1.545 (1.567) data 0.001 (0.002) loss 1.0039 (1.1373) acc 71.8750 (71.2695) lr 1.7290e-03 eta 7:50:31
+epoch [14/50] batch [485/500] time 1.579 (1.567) data 0.001 (0.002) loss 0.8340 (1.1384) acc 84.3750 (71.3080) lr 1.7290e-03 eta 7:50:25
+epoch [14/50] batch [490/500] time 1.549 (1.567) data 0.000 (0.002) loss 1.3145 (1.1374) acc 68.7500 (71.3074) lr 1.7290e-03 eta 7:50:16
+epoch [14/50] batch [495/500] time 1.553 (1.567) data 0.000 (0.002) loss 1.3906 (1.1374) acc 75.0000 (71.3321) lr 1.7290e-03 eta 7:50:08
+epoch [14/50] batch [500/500] time 1.572 (1.567) data 0.000 (0.002) loss 1.2373 (1.1379) acc 65.6250 (71.3000) lr 1.6845e-03 eta 7:50:01
+Evaluate on the *val* set
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
new file mode 100644
index 00000000..22cb2ffb
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model-best.pth.tar
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
new file mode 100644
index 00000000..674afaca
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1699535713.ckb-gpu-v.mitre.org.78974.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1699535713.ckb-gpu-v.mitre.org.78974.0
new file mode 100644
index 00000000..7af1c463
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1699535713.ckb-gpu-v.mitre.org.78974.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..9afb9ed4
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,999 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 525.125.06
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 43 bits physical, 48 bits virtual
+CPU(s): 256
+On-line CPU(s) list: 0-255
+Thread(s) per core: 2
+Core(s) per socket: 64
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: AuthenticAMD
+CPU family: 23
+Model: 49
+Model name: AMD EPYC 7H12 64-Core Processor
+Stepping: 0
+Frequency boost: enabled
+CPU MHz: 1430.454
+CPU max MHz: 2600.0000
+CPU min MHz: 1500.0000
+BogoMIPS: 5200.20
+Virtualization: AMD-V
+L1d cache: 4 MiB
+L1i cache: 4 MiB
+L2 cache: 64 MiB
+L3 cache: 512 MiB
+NUMA node0 CPU(s): 0-63,128-191
+NUMA node1 CPU(s): 64-127,192-255
+Vulnerability Gather data sampling: Not affected
+Vulnerability Itlb multihit: Not affected
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Mmio stale data: Not affected
+Vulnerability Retbleed: Vulnerable
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Not affected
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca sme sev sev_es
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/31] time 0.848 (1.791) data 0.000 (0.242) loss 3.2520 (3.2883) acc 34.3750 (34.3750) lr 1.0000e-05 eta 0:46:07
+epoch [1/50] batch [10/31] time 0.899 (1.338) data 0.000 (0.121) loss 3.3301 (3.0773) acc 34.3750 (36.2500) lr 1.0000e-05 eta 0:34:20
+epoch [1/50] batch [15/31] time 0.869 (1.184) data 0.000 (0.081) loss 2.5020 (2.8862) acc 46.8750 (40.0000) lr 1.0000e-05 eta 0:30:17
+epoch [1/50] batch [20/31] time 0.871 (1.106) data 0.000 (0.061) loss 3.4648 (2.8348) acc 34.3750 (42.0312) lr 1.0000e-05 eta 0:28:11
+epoch [1/50] batch [25/31] time 0.895 (1.063) data 0.000 (0.049) loss 2.0664 (2.6439) acc 65.6250 (45.2500) lr 1.0000e-05 eta 0:27:01
+epoch [1/50] batch [30/31] time 0.931 (1.036) data 0.000 (0.041) loss 2.5938 (2.5394) acc 43.7500 (46.7708) lr 1.0000e-05 eta 0:26:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 32,755
+* accuracy: 65.5%
+* error: 34.5%
+* macro_f1: 63.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/31] time 0.885 (0.972) data 0.000 (0.146) loss 0.9092 (1.4906) acc 75.0000 (59.3750) lr 2.0000e-03 eta 0:24:31
+epoch [2/50] batch [10/31] time 0.866 (0.927) data 0.000 (0.073) loss 1.9473 (1.6865) acc 56.2500 (57.8125) lr 2.0000e-03 eta 0:23:18
+epoch [2/50] batch [15/31] time 0.885 (0.911) data 0.000 (0.049) loss 1.6846 (1.6270) acc 59.3750 (61.4583) lr 2.0000e-03 eta 0:22:49
+epoch [2/50] batch [20/31] time 0.875 (0.904) data 0.000 (0.037) loss 1.9307 (1.5381) acc 46.8750 (62.8125) lr 2.0000e-03 eta 0:22:34
+epoch [2/50] batch [25/31] time 0.899 (0.901) data 0.000 (0.029) loss 1.3008 (1.5084) acc 62.5000 (63.2500) lr 2.0000e-03 eta 0:22:25
+epoch [2/50] batch [30/31] time 0.886 (0.903) data 0.000 (0.024) loss 1.5566 (1.4461) acc 65.6250 (64.3750) lr 2.0000e-03 eta 0:22:25
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,297
+* accuracy: 74.6%
+* error: 25.4%
+* macro_f1: 73.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/31] time 0.896 (0.991) data 0.000 (0.167) loss 0.8872 (1.3119) acc 75.0000 (69.3750) lr 1.9980e-03 eta 0:24:29
+epoch [3/50] batch [10/31] time 0.882 (0.938) data 0.000 (0.084) loss 0.8140 (1.2232) acc 71.8750 (70.0000) lr 1.9980e-03 eta 0:23:06
+epoch [3/50] batch [15/31] time 0.885 (0.921) data 0.000 (0.056) loss 0.9478 (1.2718) acc 81.2500 (70.2083) lr 1.9980e-03 eta 0:22:36
+epoch [3/50] batch [20/31] time 0.880 (0.910) data 0.000 (0.042) loss 1.2627 (1.2977) acc 71.8750 (69.5312) lr 1.9980e-03 eta 0:22:16
+epoch [3/50] batch [25/31] time 0.883 (0.907) data 0.000 (0.034) loss 1.1738 (1.3329) acc 68.7500 (68.0000) lr 1.9980e-03 eta 0:22:07
+epoch [3/50] batch [30/31] time 0.926 (0.910) data 0.000 (0.028) loss 1.1367 (1.2945) acc 75.0000 (68.3333) lr 1.9980e-03 eta 0:22:06
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,754
+* accuracy: 75.5%
+* error: 24.5%
+* macro_f1: 74.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [4/50] batch [5/31] time 0.877 (0.994) data 0.000 (0.167) loss 1.6201 (1.1210) acc 62.5000 (71.8750) lr 1.9921e-03 eta 0:24:02
+epoch [4/50] batch [10/31] time 0.890 (0.934) data 0.000 (0.083) loss 1.0127 (1.1780) acc 68.7500 (70.3125) lr 1.9921e-03 eta 0:22:32
+epoch [4/50] batch [15/31] time 0.862 (0.916) data 0.000 (0.056) loss 1.3008 (1.2424) acc 71.8750 (69.5833) lr 1.9921e-03 eta 0:22:00
+epoch [4/50] batch [20/31] time 0.893 (0.908) data 0.000 (0.042) loss 2.1914 (1.2863) acc 53.1250 (69.3750) lr 1.9921e-03 eta 0:21:44
+epoch [4/50] batch [25/31] time 0.871 (0.903) data 0.000 (0.034) loss 0.7344 (1.2553) acc 78.1250 (69.7500) lr 1.9921e-03 eta 0:21:32
+epoch [4/50] batch [30/31] time 0.897 (0.905) data 0.000 (0.028) loss 1.7480 (1.2998) acc 59.3750 (68.4375) lr 1.9921e-03 eta 0:21:31
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,892
+* accuracy: 75.8%
+* error: 24.2%
+* macro_f1: 75.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [5/50] batch [5/31] time 0.913 (0.977) data 0.000 (0.148) loss 1.3018 (1.4043) acc 75.0000 (65.6250) lr 1.9823e-03 eta 0:23:08
+epoch [5/50] batch [10/31] time 0.901 (0.931) data 0.000 (0.074) loss 0.8940 (1.2365) acc 68.7500 (69.0625) lr 1.9823e-03 eta 0:21:58
+epoch [5/50] batch [15/31] time 0.883 (0.913) data 0.000 (0.050) loss 0.9497 (1.2506) acc 68.7500 (69.3750) lr 1.9823e-03 eta 0:21:27
+epoch [5/50] batch [20/31] time 0.898 (0.908) data 0.000 (0.037) loss 1.3467 (1.2136) acc 71.8750 (70.4688) lr 1.9823e-03 eta 0:21:16
+epoch [5/50] batch [25/31] time 0.891 (0.903) data 0.000 (0.030) loss 1.3174 (1.2354) acc 68.7500 (69.5000) lr 1.9823e-03 eta 0:21:04
+epoch [5/50] batch [30/31] time 0.896 (0.900) data 0.000 (0.025) loss 1.9980 (1.2541) acc 59.3750 (69.4792) lr 1.9823e-03 eta 0:20:56
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,790
+* accuracy: 75.6%
+* error: 24.4%
+* macro_f1: 74.8%
+epoch [6/50] batch [5/31] time 0.864 (1.052) data 0.000 (0.238) loss 1.2295 (1.1853) acc 65.6250 (72.5000) lr 1.9686e-03 eta 0:24:21
+epoch [6/50] batch [10/31] time 0.901 (0.966) data 0.000 (0.119) loss 1.2041 (1.2354) acc 75.0000 (72.1875) lr 1.9686e-03 eta 0:22:17
+epoch [6/50] batch [15/31] time 0.899 (0.935) data 0.000 (0.080) loss 1.1943 (1.2196) acc 68.7500 (71.4583) lr 1.9686e-03 eta 0:21:30
+epoch [6/50] batch [20/31] time 0.900 (0.924) data 0.000 (0.060) loss 1.2080 (1.2304) acc 59.3750 (70.4688) lr 1.9686e-03 eta 0:21:10
+epoch [6/50] batch [25/31] time 0.867 (0.921) data 0.000 (0.048) loss 1.1660 (1.2649) acc 68.7500 (69.0000) lr 1.9686e-03 eta 0:21:02
+epoch [6/50] batch [30/31] time 0.875 (0.915) data 0.000 (0.040) loss 1.3740 (1.2754) acc 62.5000 (68.6458) lr 1.9686e-03 eta 0:20:49
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,897
+* accuracy: 75.8%
+* error: 24.2%
+* macro_f1: 75.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [7/50] batch [5/31] time 0.901 (1.000) data 0.000 (0.178) loss 0.9160 (1.1259) acc 78.1250 (70.6250) lr 1.9511e-03 eta 0:22:38
+epoch [7/50] batch [10/31] time 0.887 (0.940) data 0.000 (0.089) loss 1.5439 (1.1247) acc 62.5000 (71.8750) lr 1.9511e-03 eta 0:21:12
+epoch [7/50] batch [15/31] time 0.865 (0.917) data 0.000 (0.059) loss 1.6309 (1.1862) acc 65.6250 (71.2500) lr 1.9511e-03 eta 0:20:36
+epoch [7/50] batch [20/31] time 0.872 (0.907) data 0.000 (0.045) loss 1.1133 (1.1856) acc 75.0000 (71.2500) lr 1.9511e-03 eta 0:20:18
+epoch [7/50] batch [25/31] time 0.891 (0.900) data 0.000 (0.036) loss 1.1533 (1.1518) acc 75.0000 (72.2500) lr 1.9511e-03 eta 0:20:05
+epoch [7/50] batch [30/31] time 0.899 (0.899) data 0.000 (0.030) loss 1.0312 (1.1234) acc 75.0000 (71.5625) lr 1.9511e-03 eta 0:19:58
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,737
+* accuracy: 75.5%
+* error: 24.5%
+* macro_f1: 74.7%
+epoch [8/50] batch [5/31] time 0.901 (0.991) data 0.000 (0.171) loss 0.8101 (1.0462) acc 75.0000 (70.0000) lr 1.9298e-03 eta 0:21:55
+epoch [8/50] batch [10/31] time 0.863 (0.934) data 0.000 (0.086) loss 1.9209 (1.1958) acc 59.3750 (67.8125) lr 1.9298e-03 eta 0:20:35
+epoch [8/50] batch [15/31] time 0.903 (0.927) data 0.000 (0.057) loss 1.1016 (1.1840) acc 71.8750 (69.5833) lr 1.9298e-03 eta 0:20:22
+epoch [8/50] batch [20/31] time 0.874 (0.916) data 0.000 (0.043) loss 1.0625 (1.1754) acc 75.0000 (70.9375) lr 1.9298e-03 eta 0:20:02
+epoch [8/50] batch [25/31] time 0.900 (0.913) data 0.000 (0.035) loss 0.8574 (1.2035) acc 81.2500 (70.7500) lr 1.9298e-03 eta 0:19:53
+epoch [8/50] batch [30/31] time 0.872 (0.908) data 0.000 (0.029) loss 1.0049 (1.1626) acc 78.1250 (72.0833) lr 1.9298e-03 eta 0:19:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,818
+* accuracy: 75.6%
+* error: 24.4%
+* macro_f1: 74.9%
+epoch [9/50] batch [5/31] time 0.899 (0.992) data 0.000 (0.172) loss 1.2334 (1.2978) acc 68.7500 (68.7500) lr 1.9048e-03 eta 0:21:26
+epoch [9/50] batch [10/31] time 0.887 (0.934) data 0.000 (0.086) loss 1.2051 (1.1667) acc 65.6250 (70.3125) lr 1.9048e-03 eta 0:20:06
+epoch [9/50] batch [15/31] time 0.878 (0.928) data 0.000 (0.058) loss 0.9771 (1.1819) acc 71.8750 (70.2083) lr 1.9048e-03 eta 0:19:54
+epoch [9/50] batch [20/31] time 0.890 (0.917) data 0.000 (0.043) loss 1.0518 (1.1873) acc 68.7500 (69.6875) lr 1.9048e-03 eta 0:19:35
+epoch [9/50] batch [25/31] time 0.896 (0.915) data 0.000 (0.035) loss 2.2754 (1.2481) acc 59.3750 (69.5000) lr 1.9048e-03 eta 0:19:28
+epoch [9/50] batch [30/31] time 0.896 (0.911) data 0.000 (0.029) loss 0.8281 (1.2225) acc 81.2500 (69.8958) lr 1.9048e-03 eta 0:19:18
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,954
+* accuracy: 75.9%
+* error: 24.1%
+* macro_f1: 75.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
+epoch [10/50] batch [5/31] time 0.890 (0.996) data 0.000 (0.162) loss 1.6455 (1.1373) acc 65.6250 (75.0000) lr 1.8763e-03 eta 0:21:00
+epoch [10/50] batch [10/31] time 0.907 (0.938) data 0.000 (0.081) loss 1.0127 (1.0890) acc 71.8750 (71.5625) lr 1.8763e-03 eta 0:19:43
+epoch [10/50] batch [15/31] time 0.884 (0.919) data 0.000 (0.054) loss 0.8979 (1.0939) acc 81.2500 (71.6667) lr 1.8763e-03 eta 0:19:14
+epoch [10/50] batch [20/31] time 0.903 (0.917) data 0.000 (0.041) loss 1.3516 (1.1165) acc 65.6250 (70.9375) lr 1.8763e-03 eta 0:19:07
+epoch [10/50] batch [25/31] time 0.901 (0.910) data 0.000 (0.033) loss 1.0420 (1.1205) acc 71.8750 (70.7500) lr 1.8763e-03 eta 0:18:53
+epoch [10/50] batch [30/31] time 0.881 (0.905) data 0.000 (0.027) loss 1.1924 (1.1153) acc 78.1250 (71.2500) lr 1.8763e-03 eta 0:18:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,602
+* accuracy: 75.2%
+* error: 24.8%
+* macro_f1: 74.4%
+epoch [11/50] batch [5/31] time 1.061 (1.024) data 0.001 (0.165) loss 1.1670 (0.9580) acc 65.6250 (73.7500) lr 1.8443e-03 eta 0:21:04
+epoch [11/50] batch [10/31] time 0.862 (0.947) data 0.000 (0.083) loss 1.1631 (1.0304) acc 71.8750 (72.5000) lr 1.8443e-03 eta 0:19:24
+epoch [11/50] batch [15/31] time 0.876 (0.925) data 0.000 (0.055) loss 1.4395 (1.0790) acc 68.7500 (72.0833) lr 1.8443e-03 eta 0:18:53
+epoch [11/50] batch [20/31] time 0.878 (0.915) data 0.000 (0.042) loss 1.1328 (1.0555) acc 65.6250 (72.9688) lr 1.8443e-03 eta 0:18:36
+epoch [11/50] batch [25/31] time 0.849 (0.909) data 0.001 (0.033) loss 0.9546 (1.0706) acc 75.0000 (72.6250) lr 1.8443e-03 eta 0:18:24
+epoch [11/50] batch [30/31] time 0.867 (0.903) data 0.000 (0.028) loss 0.7925 (1.0717) acc 78.1250 (72.9167) lr 1.8443e-03 eta 0:18:12
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,461
+* accuracy: 74.9%
+* error: 25.1%
+* macro_f1: 74.1%
+epoch [12/50] batch [5/31] time 0.886 (0.980) data 0.000 (0.159) loss 1.3281 (1.0457) acc 71.8750 (73.1250) lr 1.8090e-03 eta 0:19:40
+epoch [12/50] batch [10/31] time 0.887 (0.928) data 0.000 (0.079) loss 1.0742 (1.2715) acc 71.8750 (67.5000) lr 1.8090e-03 eta 0:18:32
+epoch [12/50] batch [15/31] time 0.885 (0.914) data 0.000 (0.053) loss 1.4307 (1.2462) acc 68.7500 (69.1667) lr 1.8090e-03 eta 0:18:10
+epoch [12/50] batch [20/31] time 0.870 (0.903) data 0.000 (0.040) loss 0.8076 (1.1340) acc 75.0000 (71.8750) lr 1.8090e-03 eta 0:17:53
+epoch [12/50] batch [25/31] time 0.865 (0.902) data 0.000 (0.032) loss 0.6460 (1.0852) acc 84.3750 (73.2500) lr 1.8090e-03 eta 0:17:48
+epoch [12/50] batch [30/31] time 0.903 (0.900) data 0.000 (0.027) loss 1.6943 (1.0802) acc 65.6250 (73.2292) lr 1.8090e-03 eta 0:17:40
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,538
+* accuracy: 75.1%
+* error: 24.9%
+* macro_f1: 74.3%
+epoch [13/50] batch [5/31] time 0.919 (0.991) data 0.001 (0.156) loss 1.1338 (1.1184) acc 68.7500 (71.2500) lr 1.7705e-03 eta 0:19:21
+epoch [13/50] batch [10/31] time 0.889 (0.936) data 0.000 (0.078) loss 0.6470 (1.0664) acc 81.2500 (71.2500) lr 1.7705e-03 eta 0:18:13
+epoch [13/50] batch [15/31] time 0.889 (0.923) data 0.000 (0.052) loss 1.1631 (1.0973) acc 71.8750 (71.6667) lr 1.7705e-03 eta 0:17:53
+epoch [13/50] batch [20/31] time 0.893 (0.913) data 0.000 (0.039) loss 0.8389 (1.0665) acc 78.1250 (72.5000) lr 1.7705e-03 eta 0:17:36
+epoch [13/50] batch [25/31] time 0.883 (0.911) data 0.000 (0.031) loss 0.9072 (1.0519) acc 78.1250 (72.8750) lr 1.7705e-03 eta 0:17:30
+epoch [13/50] batch [30/31] time 0.885 (0.905) data 0.000 (0.026) loss 1.1982 (1.0227) acc 71.8750 (73.4375) lr 1.7705e-03 eta 0:17:18
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,587
+* accuracy: 75.2%
+* error: 24.8%
+* macro_f1: 74.3%
+epoch [14/50] batch [5/31] time 0.861 (0.977) data 0.000 (0.163) loss 0.7578 (1.1274) acc 84.3750 (70.6250) lr 1.7290e-03 eta 0:18:35
+epoch [14/50] batch [10/31] time 0.874 (0.931) data 0.000 (0.082) loss 1.5488 (1.0801) acc 75.0000 (75.0000) lr 1.7290e-03 eta 0:17:38
+epoch [14/50] batch [15/31] time 0.887 (0.918) data 0.000 (0.055) loss 0.5249 (1.1114) acc 78.1250 (73.9583) lr 1.7290e-03 eta 0:17:18
+epoch [14/50] batch [20/31] time 0.882 (0.914) data 0.000 (0.041) loss 1.3955 (1.0959) acc 71.8750 (74.8438) lr 1.7290e-03 eta 0:17:09
+epoch [14/50] batch [25/31] time 0.883 (0.908) data 0.000 (0.033) loss 0.8618 (1.0610) acc 71.8750 (74.7500) lr 1.7290e-03 eta 0:16:59
+epoch [14/50] batch [30/31] time 0.912 (0.905) data 0.000 (0.028) loss 0.7759 (1.0758) acc 84.3750 (74.4792) lr 1.7290e-03 eta 0:16:50
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,475
+* accuracy: 75.0%
+* error: 25.0%
+* macro_f1: 74.1%
+epoch [15/50] batch [5/31] time 0.915 (1.000) data 0.000 (0.164) loss 0.7251 (0.8655) acc 81.2500 (77.5000) lr 1.6845e-03 eta 0:18:30
+epoch [15/50] batch [10/31] time 0.893 (0.945) data 0.001 (0.082) loss 0.9185 (0.9269) acc 68.7500 (75.0000) lr 1.6845e-03 eta 0:17:25
+epoch [15/50] batch [15/31] time 0.875 (0.922) data 0.000 (0.055) loss 0.9561 (0.9604) acc 75.0000 (74.5833) lr 1.6845e-03 eta 0:16:55
+epoch [15/50] batch [20/31] time 0.890 (0.912) data 0.000 (0.041) loss 0.9443 (0.9170) acc 75.0000 (76.2500) lr 1.6845e-03 eta 0:16:39
+epoch [15/50] batch [25/31] time 0.886 (0.913) data 0.000 (0.033) loss 1.3262 (0.9984) acc 62.5000 (74.2500) lr 1.6845e-03 eta 0:16:35
+epoch [15/50] batch [30/31] time 0.869 (0.906) data 0.000 (0.028) loss 1.2676 (0.9855) acc 62.5000 (74.4792) lr 1.6845e-03 eta 0:16:24
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,534
+* accuracy: 75.1%
+* error: 24.9%
+* macro_f1: 74.2%
+epoch [16/50] batch [5/31] time 0.991 (1.001) data 0.000 (0.161) loss 0.5278 (1.1270) acc 81.2500 (71.2500) lr 1.6374e-03 eta 0:18:01
+epoch [16/50] batch [10/31] time 0.863 (0.939) data 0.000 (0.081) loss 1.4980 (1.1190) acc 68.7500 (72.1875) lr 1.6374e-03 eta 0:16:49
+epoch [16/50] batch [15/31] time 0.864 (0.916) data 0.001 (0.054) loss 1.8232 (1.1531) acc 62.5000 (71.2500) lr 1.6374e-03 eta 0:16:20
+epoch [16/50] batch [20/31] time 0.871 (0.906) data 0.000 (0.040) loss 1.6152 (1.1071) acc 65.6250 (72.1875) lr 1.6374e-03 eta 0:16:04
+epoch [16/50] batch [25/31] time 0.874 (0.903) data 0.000 (0.032) loss 0.5996 (1.1028) acc 93.7500 (73.5000) lr 1.6374e-03 eta 0:15:57
+epoch [16/50] batch [30/31] time 0.882 (0.901) data 0.000 (0.027) loss 1.6787 (1.1273) acc 53.1250 (73.1250) lr 1.6374e-03 eta 0:15:50
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,346
+* accuracy: 74.7%
+* error: 25.3%
+* macro_f1: 73.8%
+epoch [17/50] batch [5/31] time 0.895 (0.979) data 0.000 (0.158) loss 0.8472 (0.7923) acc 81.2500 (80.6250) lr 1.5878e-03 eta 0:17:07
+epoch [17/50] batch [10/31] time 0.898 (0.928) data 0.001 (0.079) loss 1.0752 (0.8329) acc 75.0000 (78.7500) lr 1.5878e-03 eta 0:16:08
+epoch [17/50] batch [15/31] time 0.870 (0.908) data 0.000 (0.053) loss 0.5806 (0.9451) acc 84.3750 (75.8333) lr 1.5878e-03 eta 0:15:43
+epoch [17/50] batch [20/31] time 0.863 (0.898) data 0.000 (0.040) loss 1.5801 (1.0469) acc 65.6250 (74.2188) lr 1.5878e-03 eta 0:15:28
+epoch [17/50] batch [25/31] time 0.882 (0.896) data 0.000 (0.032) loss 1.4326 (1.0514) acc 62.5000 (73.5000) lr 1.5878e-03 eta 0:15:21
+epoch [17/50] batch [30/31] time 0.906 (0.895) data 0.000 (0.027) loss 1.1055 (1.0325) acc 75.0000 (74.4792) lr 1.5878e-03 eta 0:15:16
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,314
+* accuracy: 74.6%
+* error: 25.4%
+* macro_f1: 73.7%
+epoch [18/50] batch [5/31] time 0.900 (0.974) data 0.000 (0.153) loss 0.9189 (0.9608) acc 75.0000 (77.5000) lr 1.5358e-03 eta 0:16:31
+epoch [18/50] batch [10/31] time 0.887 (0.928) data 0.000 (0.077) loss 0.8589 (0.9020) acc 75.0000 (76.2500) lr 1.5358e-03 eta 0:15:39
+epoch [18/50] batch [15/31] time 0.871 (0.916) data 0.000 (0.051) loss 0.7500 (1.0002) acc 84.3750 (76.0417) lr 1.5358e-03 eta 0:15:23
+epoch [18/50] batch [20/31] time 0.864 (0.910) data 0.000 (0.039) loss 0.7793 (0.9611) acc 84.3750 (76.5625) lr 1.5358e-03 eta 0:15:12
+epoch [18/50] batch [25/31] time 0.878 (0.904) data 0.000 (0.031) loss 0.7368 (0.9603) acc 84.3750 (76.3750) lr 1.5358e-03 eta 0:15:01
+epoch [18/50] batch [30/31] time 0.905 (0.906) data 0.000 (0.026) loss 0.4136 (0.9436) acc 90.6250 (76.7708) lr 1.5358e-03 eta 0:15:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,238
+* accuracy: 74.5%
+* error: 25.5%
+* macro_f1: 73.6%
+epoch [19/50] batch [5/31] time 0.874 (0.984) data 0.000 (0.165) loss 0.4106 (0.6513) acc 84.3750 (81.8750) lr 1.4818e-03 eta 0:16:10
+epoch [19/50] batch [10/31] time 0.855 (0.931) data 0.000 (0.083) loss 0.6143 (0.8867) acc 87.5000 (79.0625) lr 1.4818e-03 eta 0:15:14
+epoch [19/50] batch [15/31] time 0.915 (0.917) data 0.000 (0.055) loss 0.8555 (0.8581) acc 81.2500 (79.3750) lr 1.4818e-03 eta 0:14:55
+epoch [19/50] batch [20/31] time 0.902 (0.907) data 0.000 (0.042) loss 0.9316 (0.8530) acc 78.1250 (79.6875) lr 1.4818e-03 eta 0:14:41
+epoch [19/50] batch [25/31] time 0.903 (0.901) data 0.000 (0.033) loss 1.5664 (0.8866) acc 71.8750 (79.1250) lr 1.4818e-03 eta 0:14:31
+epoch [19/50] batch [30/31] time 0.907 (0.904) data 0.000 (0.028) loss 1.3672 (0.9513) acc 71.8750 (77.6042) lr 1.4818e-03 eta 0:14:29
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,016
+* accuracy: 74.0%
+* error: 26.0%
+* macro_f1: 73.1%
+epoch [20/50] batch [5/31] time 0.878 (0.993) data 0.000 (0.169) loss 1.0312 (0.7780) acc 78.1250 (83.1250) lr 1.4258e-03 eta 0:15:49
+epoch [20/50] batch [10/31] time 0.888 (0.944) data 0.000 (0.085) loss 0.7783 (0.8637) acc 81.2500 (79.0625) lr 1.4258e-03 eta 0:14:57
+epoch [20/50] batch [15/31] time 0.870 (0.922) data 0.000 (0.057) loss 1.1758 (0.9426) acc 71.8750 (77.9167) lr 1.4258e-03 eta 0:14:31
+epoch [20/50] batch [20/31] time 0.895 (0.911) data 0.000 (0.042) loss 1.1299 (0.9577) acc 78.1250 (76.7188) lr 1.4258e-03 eta 0:14:17
+epoch [20/50] batch [25/31] time 0.896 (0.910) data 0.000 (0.034) loss 1.1758 (0.9182) acc 78.1250 (76.5000) lr 1.4258e-03 eta 0:14:11
+epoch [20/50] batch [30/31] time 0.895 (0.905) data 0.000 (0.028) loss 1.8887 (0.9336) acc 65.6250 (76.9792) lr 1.4258e-03 eta 0:14:02
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,267
+* accuracy: 74.5%
+* error: 25.5%
+* macro_f1: 73.7%
+epoch [21/50] batch [5/31] time 0.857 (0.975) data 0.000 (0.161) loss 0.9673 (0.7929) acc 78.1250 (81.2500) lr 1.3681e-03 eta 0:15:02
+epoch [21/50] batch [10/31] time 0.892 (0.924) data 0.000 (0.081) loss 0.9800 (0.9207) acc 81.2500 (78.1250) lr 1.3681e-03 eta 0:14:09
+epoch [21/50] batch [15/31] time 0.859 (0.906) data 0.000 (0.054) loss 0.5186 (0.9623) acc 81.2500 (75.8333) lr 1.3681e-03 eta 0:13:49
+epoch [21/50] batch [20/31] time 0.903 (0.899) data 0.000 (0.040) loss 1.1680 (0.9311) acc 75.0000 (76.5625) lr 1.3681e-03 eta 0:13:38
+epoch [21/50] batch [25/31] time 0.876 (0.895) data 0.000 (0.032) loss 0.6650 (0.9474) acc 81.2500 (76.5000) lr 1.3681e-03 eta 0:13:30
+epoch [21/50] batch [30/31] time 0.901 (0.893) data 0.000 (0.027) loss 0.9966 (0.9209) acc 81.2500 (76.8750) lr 1.3681e-03 eta 0:13:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,230
+* accuracy: 74.5%
+* error: 25.5%
+* macro_f1: 73.6%
+epoch [22/50] batch [5/31] time 0.876 (0.973) data 0.000 (0.139) loss 1.0537 (1.1194) acc 78.1250 (73.7500) lr 1.3090e-03 eta 0:14:29
+epoch [22/50] batch [10/31] time 0.904 (0.929) data 0.000 (0.070) loss 0.5181 (0.9966) acc 87.5000 (76.2500) lr 1.3090e-03 eta 0:13:45
+epoch [22/50] batch [15/31] time 0.871 (0.910) data 0.000 (0.047) loss 0.9980 (0.9783) acc 71.8750 (76.2500) lr 1.3090e-03 eta 0:13:23
+epoch [22/50] batch [20/31] time 0.868 (0.905) data 0.000 (0.035) loss 0.8818 (0.9543) acc 75.0000 (76.0938) lr 1.3090e-03 eta 0:13:15
+epoch [22/50] batch [25/31] time 0.925 (0.901) data 0.000 (0.028) loss 0.5767 (0.9456) acc 75.0000 (75.8750) lr 1.3090e-03 eta 0:13:07
+epoch [22/50] batch [30/31] time 0.876 (0.902) data 0.000 (0.023) loss 0.6934 (0.9264) acc 84.3750 (76.3542) lr 1.3090e-03 eta 0:13:04
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,829
+* accuracy: 73.7%
+* error: 26.3%
+* macro_f1: 72.7%
+epoch [23/50] batch [5/31] time 0.884 (0.959) data 0.000 (0.148) loss 0.7070 (0.7402) acc 81.2500 (80.6250) lr 1.2487e-03 eta 0:13:47
+epoch [23/50] batch [10/31] time 0.893 (0.921) data 0.000 (0.074) loss 1.0254 (0.7157) acc 68.7500 (80.6250) lr 1.2487e-03 eta 0:13:10
+epoch [23/50] batch [15/31] time 0.885 (0.911) data 0.000 (0.049) loss 0.6724 (0.7559) acc 84.3750 (80.0000) lr 1.2487e-03 eta 0:12:57
+epoch [23/50] batch [20/31] time 0.854 (0.905) data 0.000 (0.037) loss 1.5605 (0.8691) acc 71.8750 (78.7500) lr 1.2487e-03 eta 0:12:47
+epoch [23/50] batch [25/31] time 0.899 (0.899) data 0.000 (0.030) loss 0.5332 (0.8755) acc 90.6250 (79.3750) lr 1.2487e-03 eta 0:12:38
+epoch [23/50] batch [30/31] time 0.890 (0.896) data 0.000 (0.025) loss 0.8896 (0.8682) acc 78.1250 (79.2708) lr 1.2487e-03 eta 0:12:31
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,940
+* accuracy: 73.9%
+* error: 26.1%
+* macro_f1: 73.0%
+epoch [24/50] batch [5/31] time 0.887 (0.964) data 0.000 (0.142) loss 1.0850 (0.9168) acc 71.8750 (76.2500) lr 1.1874e-03 eta 0:13:22
+epoch [24/50] batch [10/31] time 0.890 (0.921) data 0.000 (0.071) loss 1.2119 (0.9048) acc 78.1250 (78.1250) lr 1.1874e-03 eta 0:12:41
+epoch [24/50] batch [15/31] time 0.899 (0.923) data 0.000 (0.047) loss 0.9634 (0.9310) acc 75.0000 (78.5417) lr 1.1874e-03 eta 0:12:38
+epoch [24/50] batch [20/31] time 0.892 (0.911) data 0.000 (0.036) loss 1.0273 (0.9138) acc 68.7500 (78.4375) lr 1.1874e-03 eta 0:12:23
+epoch [24/50] batch [25/31] time 0.862 (0.902) data 0.000 (0.029) loss 0.6558 (0.8948) acc 84.3750 (79.2500) lr 1.1874e-03 eta 0:12:12
+epoch [24/50] batch [30/31] time 0.861 (0.897) data 0.000 (0.024) loss 1.0312 (0.8881) acc 71.8750 (79.2708) lr 1.1874e-03 eta 0:12:03
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,574
+* accuracy: 73.1%
+* error: 26.9%
+* macro_f1: 72.3%
+epoch [25/50] batch [5/31] time 0.899 (0.947) data 0.000 (0.127) loss 0.9048 (0.7045) acc 75.0000 (83.7500) lr 1.1253e-03 eta 0:12:38
+epoch [25/50] batch [10/31] time 0.899 (0.917) data 0.000 (0.063) loss 0.9634 (0.7408) acc 87.5000 (85.3125) lr 1.1253e-03 eta 0:12:09
+epoch [25/50] batch [15/31] time 0.890 (0.909) data 0.000 (0.042) loss 0.3577 (0.7461) acc 93.7500 (83.5417) lr 1.1253e-03 eta 0:11:58
+epoch [25/50] batch [20/31] time 0.914 (0.908) data 0.000 (0.032) loss 1.5703 (0.8438) acc 65.6250 (80.7812) lr 1.1253e-03 eta 0:11:53
+epoch [25/50] batch [25/31] time 0.880 (0.903) data 0.000 (0.026) loss 0.6733 (0.8183) acc 81.2500 (80.8750) lr 1.1253e-03 eta 0:11:45
+epoch [25/50] batch [30/31] time 0.888 (0.899) data 0.000 (0.021) loss 0.7671 (0.8108) acc 84.3750 (80.7292) lr 1.1253e-03 eta 0:11:37
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,642
+* accuracy: 73.3%
+* error: 26.7%
+* macro_f1: 72.4%
+epoch [26/50] batch [5/31] time 0.852 (0.968) data 0.000 (0.153) loss 0.7314 (0.7089) acc 84.3750 (82.5000) lr 1.0628e-03 eta 0:12:25
+epoch [26/50] batch [10/31] time 0.890 (0.923) data 0.000 (0.077) loss 1.0029 (0.7572) acc 81.2500 (81.5625) lr 1.0628e-03 eta 0:11:46
+epoch [26/50] batch [15/31] time 0.913 (0.912) data 0.000 (0.051) loss 1.1104 (0.8136) acc 71.8750 (80.8333) lr 1.0628e-03 eta 0:11:32
+epoch [26/50] batch [20/31] time 0.878 (0.905) data 0.000 (0.038) loss 1.1436 (0.8682) acc 71.8750 (79.5312) lr 1.0628e-03 eta 0:11:23
+epoch [26/50] batch [25/31] time 0.890 (0.901) data 0.000 (0.031) loss 0.5210 (0.8599) acc 90.6250 (79.8750) lr 1.0628e-03 eta 0:11:15
+epoch [26/50] batch [30/31] time 0.891 (0.900) data 0.000 (0.026) loss 0.9912 (0.8883) acc 87.5000 (79.3750) lr 1.0628e-03 eta 0:11:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,661
+* accuracy: 73.3%
+* error: 26.7%
+* macro_f1: 72.4%
+epoch [27/50] batch [5/31] time 0.866 (0.964) data 0.000 (0.148) loss 0.8613 (0.8125) acc 87.5000 (80.6250) lr 1.0000e-03 eta 0:11:52
+epoch [27/50] batch [10/31] time 0.892 (0.927) data 0.000 (0.074) loss 1.7266 (1.0006) acc 71.8750 (78.1250) lr 1.0000e-03 eta 0:11:20
+epoch [27/50] batch [15/31] time 0.894 (0.912) data 0.000 (0.050) loss 0.8267 (0.9043) acc 78.1250 (79.1667) lr 1.0000e-03 eta 0:11:04
+epoch [27/50] batch [20/31] time 0.869 (0.905) data 0.000 (0.037) loss 0.6045 (0.8684) acc 84.3750 (78.9062) lr 1.0000e-03 eta 0:10:55
+epoch [27/50] batch [25/31] time 0.887 (0.902) data 0.000 (0.030) loss 0.7900 (0.8614) acc 78.1250 (79.2500) lr 1.0000e-03 eta 0:10:48
+epoch [27/50] batch [30/31] time 0.878 (0.901) data 0.000 (0.025) loss 1.1357 (0.8957) acc 75.0000 (78.5417) lr 1.0000e-03 eta 0:10:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,591
+* accuracy: 73.2%
+* error: 26.8%
+* macro_f1: 72.2%
+epoch [28/50] batch [5/31] time 0.853 (0.937) data 0.000 (0.126) loss 0.4980 (0.7694) acc 90.6250 (83.7500) lr 9.3721e-04 eta 0:11:03
+epoch [28/50] batch [10/31] time 0.897 (0.911) data 0.000 (0.063) loss 0.4756 (0.6876) acc 87.5000 (84.3750) lr 9.3721e-04 eta 0:10:40
+epoch [28/50] batch [15/31] time 0.874 (0.903) data 0.000 (0.042) loss 0.5723 (0.7086) acc 87.5000 (84.3750) lr 9.3721e-04 eta 0:10:30
+epoch [28/50] batch [20/31] time 0.995 (0.908) data 0.000 (0.032) loss 0.7671 (0.7512) acc 81.2500 (83.4375) lr 9.3721e-04 eta 0:10:29
+epoch [28/50] batch [25/31] time 0.882 (0.903) data 0.000 (0.025) loss 0.6748 (0.7650) acc 81.2500 (83.1250) lr 9.3721e-04 eta 0:10:21
+epoch [28/50] batch [30/31] time 0.884 (0.902) data 0.000 (0.021) loss 1.1475 (0.7948) acc 75.0000 (81.7708) lr 9.3721e-04 eta 0:10:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,734
+* accuracy: 73.5%
+* error: 26.5%
+* macro_f1: 72.5%
+epoch [29/50] batch [5/31] time 0.874 (0.943) data 0.000 (0.124) loss 0.9292 (0.6433) acc 84.3750 (85.0000) lr 8.7467e-04 eta 0:10:38
+epoch [29/50] batch [10/31] time 0.874 (0.909) data 0.000 (0.062) loss 1.0020 (0.6859) acc 78.1250 (82.8125) lr 8.7467e-04 eta 0:10:10
+epoch [29/50] batch [15/31] time 0.864 (0.899) data 0.000 (0.042) loss 0.6758 (0.6638) acc 87.5000 (83.7500) lr 8.7467e-04 eta 0:09:59
+epoch [29/50] batch [20/31] time 1.019 (0.900) data 0.000 (0.031) loss 0.5283 (0.6947) acc 87.5000 (82.5000) lr 8.7467e-04 eta 0:09:55
+epoch [29/50] batch [25/31] time 0.859 (0.897) data 0.000 (0.025) loss 1.1602 (0.7583) acc 78.1250 (81.6250) lr 8.7467e-04 eta 0:09:49
+epoch [29/50] batch [30/31] time 0.888 (0.896) data 0.000 (0.021) loss 1.0537 (0.8103) acc 71.8750 (79.7917) lr 8.7467e-04 eta 0:09:44
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,891
+* accuracy: 73.8%
+* error: 26.2%
+* macro_f1: 72.9%
+epoch [30/50] batch [5/31] time 0.872 (0.933) data 0.000 (0.125) loss 0.8848 (0.7889) acc 78.1250 (80.0000) lr 8.1262e-04 eta 0:10:02
+epoch [30/50] batch [10/31] time 0.883 (0.913) data 0.000 (0.062) loss 0.4763 (0.7708) acc 84.3750 (81.2500) lr 8.1262e-04 eta 0:09:45
+epoch [30/50] batch [15/31] time 0.874 (0.906) data 0.000 (0.042) loss 0.6533 (0.7874) acc 84.3750 (81.2500) lr 8.1262e-04 eta 0:09:36
+epoch [30/50] batch [20/31] time 0.888 (0.900) data 0.000 (0.031) loss 0.7061 (0.8078) acc 81.2500 (80.3125) lr 8.1262e-04 eta 0:09:27
+epoch [30/50] batch [25/31] time 0.897 (0.901) data 0.000 (0.025) loss 0.6118 (0.8065) acc 78.1250 (79.8750) lr 8.1262e-04 eta 0:09:23
+epoch [30/50] batch [30/31] time 0.894 (0.898) data 0.000 (0.021) loss 1.1455 (0.7940) acc 71.8750 (80.5208) lr 8.1262e-04 eta 0:09:17
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,480
+* accuracy: 73.0%
+* error: 27.0%
+* macro_f1: 72.0%
+epoch [31/50] batch [5/31] time 0.903 (0.951) data 0.000 (0.129) loss 0.8081 (0.8092) acc 81.2500 (80.0000) lr 7.5131e-04 eta 0:09:44
+epoch [31/50] batch [10/31] time 0.838 (0.912) data 0.000 (0.065) loss 0.6001 (0.6951) acc 81.2500 (82.5000) lr 7.5131e-04 eta 0:09:16
+epoch [31/50] batch [15/31] time 0.851 (0.896) data 0.000 (0.043) loss 0.5635 (0.7180) acc 90.6250 (82.5000) lr 7.5131e-04 eta 0:09:02
+epoch [31/50] batch [20/31] time 0.916 (0.892) data 0.000 (0.033) loss 1.0459 (0.8187) acc 71.8750 (80.3125) lr 7.5131e-04 eta 0:08:55
+epoch [31/50] batch [25/31] time 0.877 (0.889) data 0.000 (0.026) loss 1.0596 (0.8350) acc 81.2500 (80.1250) lr 7.5131e-04 eta 0:08:49
+epoch [31/50] batch [30/31] time 0.897 (0.889) data 0.000 (0.022) loss 1.1309 (0.8236) acc 71.8750 (80.1042) lr 7.5131e-04 eta 0:08:44
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,526
+* accuracy: 73.1%
+* error: 26.9%
+* macro_f1: 72.1%
+epoch [32/50] batch [5/31] time 0.865 (0.941) data 0.000 (0.125) loss 0.9263 (0.8586) acc 81.2500 (81.2500) lr 6.9098e-04 eta 0:09:09
+epoch [32/50] batch [10/31] time 0.866 (0.905) data 0.000 (0.063) loss 0.6821 (0.7995) acc 87.5000 (80.6250) lr 6.9098e-04 eta 0:08:43
+epoch [32/50] batch [15/31] time 0.881 (0.898) data 0.000 (0.042) loss 0.8745 (0.7245) acc 78.1250 (81.4583) lr 6.9098e-04 eta 0:08:35
+epoch [32/50] batch [20/31] time 0.889 (0.893) data 0.000 (0.031) loss 1.3037 (0.7803) acc 78.1250 (80.9375) lr 6.9098e-04 eta 0:08:28
+epoch [32/50] batch [25/31] time 0.879 (0.891) data 0.000 (0.025) loss 1.2012 (0.7804) acc 78.1250 (80.8750) lr 6.9098e-04 eta 0:08:22
+epoch [32/50] batch [30/31] time 0.896 (0.891) data 0.000 (0.021) loss 0.3354 (0.7622) acc 90.6250 (81.5625) lr 6.9098e-04 eta 0:08:18
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,195
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.4%
+epoch [33/50] batch [5/31] time 0.875 (0.951) data 0.000 (0.133) loss 0.7554 (0.8025) acc 90.6250 (82.5000) lr 6.3188e-04 eta 0:08:45
+epoch [33/50] batch [10/31] time 0.876 (0.917) data 0.000 (0.067) loss 1.0215 (0.7839) acc 78.1250 (82.1875) lr 6.3188e-04 eta 0:08:22
+epoch [33/50] batch [15/31] time 0.897 (0.908) data 0.000 (0.045) loss 0.4202 (0.7574) acc 93.7500 (82.5000) lr 6.3188e-04 eta 0:08:13
+epoch [33/50] batch [20/31] time 0.891 (0.902) data 0.000 (0.033) loss 0.7334 (0.7677) acc 81.2500 (82.5000) lr 6.3188e-04 eta 0:08:05
+epoch [33/50] batch [25/31] time 0.883 (0.900) data 0.000 (0.027) loss 0.6753 (0.7981) acc 78.1250 (81.8750) lr 6.3188e-04 eta 0:07:59
+epoch [33/50] batch [30/31] time 0.922 (0.899) data 0.000 (0.022) loss 0.3496 (0.7874) acc 93.7500 (82.1875) lr 6.3188e-04 eta 0:07:54
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,442
+* accuracy: 72.9%
+* error: 27.1%
+* macro_f1: 71.9%
+epoch [34/50] batch [5/31] time 0.909 (0.952) data 0.000 (0.126) loss 0.8091 (0.7973) acc 78.1250 (81.2500) lr 5.7422e-04 eta 0:08:16
+epoch [34/50] batch [10/31] time 0.865 (0.914) data 0.001 (0.063) loss 0.3713 (0.7604) acc 93.7500 (84.0625) lr 5.7422e-04 eta 0:07:52
+epoch [34/50] batch [15/31] time 0.883 (0.903) data 0.000 (0.042) loss 0.5874 (0.7395) acc 90.6250 (85.0000) lr 5.7422e-04 eta 0:07:42
+epoch [34/50] batch [20/31] time 0.873 (0.895) data 0.000 (0.032) loss 0.9878 (0.7862) acc 68.7500 (81.8750) lr 5.7422e-04 eta 0:07:33
+epoch [34/50] batch [25/31] time 0.867 (0.892) data 0.000 (0.025) loss 0.9385 (0.8404) acc 78.1250 (80.7500) lr 5.7422e-04 eta 0:07:27
+epoch [34/50] batch [30/31] time 0.896 (0.891) data 0.000 (0.021) loss 0.3950 (0.8458) acc 90.6250 (80.9375) lr 5.7422e-04 eta 0:07:22
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,081
+* accuracy: 72.2%
+* error: 27.8%
+* macro_f1: 71.1%
+epoch [35/50] batch [5/31] time 0.884 (0.945) data 0.000 (0.125) loss 0.8594 (0.7630) acc 71.8750 (79.3750) lr 5.1825e-04 eta 0:07:44
+epoch [35/50] batch [10/31] time 0.879 (0.915) data 0.000 (0.063) loss 1.2666 (0.8583) acc 75.0000 (79.0625) lr 5.1825e-04 eta 0:07:24
+epoch [35/50] batch [15/31] time 0.876 (0.906) data 0.000 (0.042) loss 0.8184 (0.8342) acc 68.7500 (79.1667) lr 5.1825e-04 eta 0:07:15
+epoch [35/50] batch [20/31] time 0.868 (0.902) data 0.000 (0.031) loss 0.8721 (0.8096) acc 75.0000 (79.6875) lr 5.1825e-04 eta 0:07:09
+epoch [35/50] batch [25/31] time 0.879 (0.895) data 0.000 (0.025) loss 0.7129 (0.8309) acc 81.2500 (79.7500) lr 5.1825e-04 eta 0:07:01
+epoch [35/50] batch [30/31] time 0.890 (0.897) data 0.000 (0.021) loss 0.6582 (0.7817) acc 78.1250 (80.8333) lr 5.1825e-04 eta 0:06:57
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,549
+* accuracy: 73.1%
+* error: 26.9%
+* macro_f1: 72.2%
+epoch [36/50] batch [5/31] time 0.899 (0.953) data 0.000 (0.126) loss 0.9160 (0.8711) acc 81.2500 (81.2500) lr 4.6417e-04 eta 0:07:18
+epoch [36/50] batch [10/31] time 0.887 (0.917) data 0.000 (0.063) loss 0.4429 (0.7086) acc 87.5000 (83.7500) lr 4.6417e-04 eta 0:06:57
+epoch [36/50] batch [15/31] time 0.899 (0.909) data 0.001 (0.042) loss 0.9619 (0.7028) acc 81.2500 (84.1667) lr 4.6417e-04 eta 0:06:48
+epoch [36/50] batch [20/31] time 0.867 (0.901) data 0.000 (0.032) loss 0.7266 (0.7189) acc 90.6250 (84.5312) lr 4.6417e-04 eta 0:06:40
+epoch [36/50] batch [25/31] time 0.869 (0.896) data 0.000 (0.025) loss 0.6006 (0.7236) acc 84.3750 (84.5000) lr 4.6417e-04 eta 0:06:34
+epoch [36/50] batch [30/31] time 0.870 (0.895) data 0.000 (0.021) loss 0.9199 (0.7367) acc 87.5000 (84.1667) lr 4.6417e-04 eta 0:06:29
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,246
+* accuracy: 72.5%
+* error: 27.5%
+* macro_f1: 71.6%
+epoch [37/50] batch [5/31] time 0.890 (0.945) data 0.000 (0.128) loss 1.0371 (0.7881) acc 75.0000 (85.0000) lr 4.1221e-04 eta 0:06:45
+epoch [37/50] batch [10/31] time 0.884 (0.912) data 0.000 (0.064) loss 0.7739 (0.8287) acc 90.6250 (83.4375) lr 4.1221e-04 eta 0:06:26
+epoch [37/50] batch [15/31] time 0.886 (0.900) data 0.000 (0.043) loss 0.4636 (0.7892) acc 84.3750 (82.0833) lr 4.1221e-04 eta 0:06:17
+epoch [37/50] batch [20/31] time 0.878 (0.899) data 0.000 (0.032) loss 0.4536 (0.7667) acc 87.5000 (82.6562) lr 4.1221e-04 eta 0:06:12
+epoch [37/50] batch [25/31] time 0.886 (0.896) data 0.000 (0.026) loss 1.1055 (0.7564) acc 71.8750 (82.2500) lr 4.1221e-04 eta 0:06:06
+epoch [37/50] batch [30/31] time 0.888 (0.893) data 0.000 (0.021) loss 0.7217 (0.8151) acc 84.3750 (80.9375) lr 4.1221e-04 eta 0:06:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,454
+* accuracy: 72.9%
+* error: 27.1%
+* macro_f1: 71.9%
+epoch [38/50] batch [5/31] time 0.879 (0.934) data 0.000 (0.124) loss 0.6040 (0.7356) acc 84.3750 (80.0000) lr 3.6258e-04 eta 0:06:11
+epoch [38/50] batch [10/31] time 0.872 (0.902) data 0.000 (0.062) loss 1.0137 (0.7932) acc 81.2500 (80.9375) lr 3.6258e-04 eta 0:05:54
+epoch [38/50] batch [15/31] time 0.881 (0.892) data 0.000 (0.041) loss 0.9155 (0.7434) acc 75.0000 (82.0833) lr 3.6258e-04 eta 0:05:46
+epoch [38/50] batch [20/31] time 0.887 (0.891) data 0.000 (0.031) loss 0.6274 (0.7214) acc 78.1250 (82.0312) lr 3.6258e-04 eta 0:05:41
+epoch [38/50] batch [25/31] time 0.884 (0.892) data 0.000 (0.025) loss 0.5835 (0.7103) acc 87.5000 (82.1250) lr 3.6258e-04 eta 0:05:37
+epoch [38/50] batch [30/31] time 0.897 (0.890) data 0.000 (0.021) loss 0.5225 (0.7035) acc 81.2500 (82.2917) lr 3.6258e-04 eta 0:05:31
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,287
+* accuracy: 72.6%
+* error: 27.4%
+* macro_f1: 71.6%
+epoch [39/50] batch [5/31] time 0.895 (0.956) data 0.000 (0.130) loss 1.1689 (0.9846) acc 75.0000 (80.0000) lr 3.1545e-04 eta 0:05:50
+epoch [39/50] batch [10/31] time 0.862 (0.920) data 0.000 (0.065) loss 0.4626 (0.8553) acc 84.3750 (80.6250) lr 3.1545e-04 eta 0:05:33
+epoch [39/50] batch [15/31] time 0.876 (0.906) data 0.000 (0.044) loss 0.7241 (0.8632) acc 81.2500 (80.0000) lr 3.1545e-04 eta 0:05:23
+epoch [39/50] batch [20/31] time 0.872 (0.900) data 0.000 (0.033) loss 0.7754 (0.8133) acc 78.1250 (81.0938) lr 3.1545e-04 eta 0:05:16
+epoch [39/50] batch [25/31] time 0.861 (0.894) data 0.000 (0.026) loss 0.5146 (0.7546) acc 84.3750 (82.2500) lr 3.1545e-04 eta 0:05:10
+epoch [39/50] batch [30/31] time 0.881 (0.890) data 0.000 (0.022) loss 0.6841 (0.7965) acc 84.3750 (82.0833) lr 3.1545e-04 eta 0:05:04
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,233
+* accuracy: 72.5%
+* error: 27.5%
+* macro_f1: 71.5%
+epoch [40/50] batch [5/31] time 0.884 (0.961) data 0.000 (0.137) loss 0.4541 (0.9224) acc 90.6250 (82.5000) lr 2.7103e-04 eta 0:05:22
+epoch [40/50] batch [10/31] time 0.824 (0.914) data 0.000 (0.069) loss 1.0537 (0.8170) acc 71.8750 (82.5000) lr 2.7103e-04 eta 0:05:02
+epoch [40/50] batch [15/31] time 0.887 (0.915) data 0.000 (0.046) loss 1.0127 (0.8457) acc 87.5000 (81.6667) lr 2.7103e-04 eta 0:04:58
+epoch [40/50] batch [20/31] time 0.853 (0.907) data 0.000 (0.034) loss 0.5474 (0.8309) acc 81.2500 (81.8750) lr 2.7103e-04 eta 0:04:51
+epoch [40/50] batch [25/31] time 0.892 (0.901) data 0.000 (0.028) loss 0.9248 (0.8136) acc 75.0000 (81.8750) lr 2.7103e-04 eta 0:04:44
+epoch [40/50] batch [30/31] time 0.902 (0.898) data 0.000 (0.023) loss 0.5820 (0.8336) acc 81.2500 (81.5625) lr 2.7103e-04 eta 0:04:39
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,138
+* accuracy: 72.3%
+* error: 27.7%
+* macro_f1: 71.3%
+epoch [41/50] batch [5/31] time 0.895 (0.957) data 0.000 (0.138) loss 0.4866 (0.7594) acc 75.0000 (80.0000) lr 2.2949e-04 eta 0:04:51
+epoch [41/50] batch [10/31] time 0.911 (0.921) data 0.000 (0.069) loss 0.7866 (0.7962) acc 78.1250 (79.3750) lr 2.2949e-04 eta 0:04:36
+epoch [41/50] batch [15/31] time 1.021 (0.918) data 0.000 (0.046) loss 0.4351 (0.7328) acc 87.5000 (81.2500) lr 2.2949e-04 eta 0:04:30
+epoch [41/50] batch [20/31] time 0.868 (0.907) data 0.000 (0.035) loss 0.4490 (0.7153) acc 84.3750 (81.7188) lr 2.2949e-04 eta 0:04:23
+epoch [41/50] batch [25/31] time 0.863 (0.899) data 0.000 (0.028) loss 1.2910 (0.7311) acc 75.0000 (82.0000) lr 2.2949e-04 eta 0:04:16
+epoch [41/50] batch [30/31] time 0.884 (0.896) data 0.000 (0.023) loss 0.5405 (0.7297) acc 78.1250 (81.7708) lr 2.2949e-04 eta 0:04:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,407
+* accuracy: 72.8%
+* error: 27.2%
+* macro_f1: 71.9%
+epoch [42/50] batch [5/31] time 0.889 (0.956) data 0.000 (0.134) loss 1.4990 (0.6764) acc 78.1250 (85.6250) lr 1.9098e-04 eta 0:04:21
+epoch [42/50] batch [10/31] time 0.892 (0.918) data 0.000 (0.067) loss 1.0273 (0.7518) acc 71.8750 (83.1250) lr 1.9098e-04 eta 0:04:06
+epoch [42/50] batch [15/31] time 0.855 (0.901) data 0.000 (0.045) loss 0.7070 (0.7337) acc 84.3750 (83.9583) lr 1.9098e-04 eta 0:03:57
+epoch [42/50] batch [20/31] time 0.903 (0.906) data 0.000 (0.034) loss 0.6357 (0.7522) acc 81.2500 (83.4375) lr 1.9098e-04 eta 0:03:54
+epoch [42/50] batch [25/31] time 0.900 (0.903) data 0.000 (0.027) loss 0.8120 (0.7550) acc 84.3750 (83.0000) lr 1.9098e-04 eta 0:03:49
+epoch [42/50] batch [30/31] time 0.863 (0.897) data 0.000 (0.023) loss 0.9238 (0.7382) acc 75.0000 (83.0208) lr 1.9098e-04 eta 0:03:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,267
+* accuracy: 72.5%
+* error: 27.5%
+* macro_f1: 71.6%
+epoch [43/50] batch [5/31] time 0.877 (0.956) data 0.000 (0.135) loss 0.5815 (0.6160) acc 78.1250 (85.6250) lr 1.5567e-04 eta 0:03:52
+epoch [43/50] batch [10/31] time 0.891 (0.916) data 0.000 (0.068) loss 0.4097 (0.7043) acc 93.7500 (84.6875) lr 1.5567e-04 eta 0:03:38
+epoch [43/50] batch [15/31] time 0.871 (0.902) data 0.000 (0.045) loss 0.7144 (0.7178) acc 78.1250 (84.7917) lr 1.5567e-04 eta 0:03:30
+epoch [43/50] batch [20/31] time 0.909 (0.898) data 0.000 (0.034) loss 1.0322 (0.7757) acc 71.8750 (83.1250) lr 1.5567e-04 eta 0:03:24
+epoch [43/50] batch [25/31] time 0.873 (0.895) data 0.000 (0.027) loss 0.8765 (0.7872) acc 81.2500 (82.2500) lr 1.5567e-04 eta 0:03:19
+epoch [43/50] batch [30/31] time 0.899 (0.896) data 0.000 (0.023) loss 0.6226 (0.7641) acc 84.3750 (82.7083) lr 1.5567e-04 eta 0:03:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,346
+* accuracy: 72.7%
+* error: 27.3%
+* macro_f1: 71.7%
+epoch [44/50] batch [5/31] time 0.850 (0.937) data 0.000 (0.132) loss 0.9893 (0.7952) acc 78.1250 (84.3750) lr 1.2369e-04 eta 0:03:18
+epoch [44/50] batch [10/31] time 0.884 (0.911) data 0.000 (0.066) loss 0.7588 (0.7323) acc 84.3750 (84.6875) lr 1.2369e-04 eta 0:03:08
+epoch [44/50] batch [15/31] time 0.860 (0.902) data 0.000 (0.044) loss 0.4165 (0.6868) acc 90.6250 (84.7917) lr 1.2369e-04 eta 0:03:02
+epoch [44/50] batch [20/31] time 0.883 (0.894) data 0.000 (0.033) loss 0.5518 (0.6486) acc 87.5000 (85.9375) lr 1.2369e-04 eta 0:02:56
+epoch [44/50] batch [25/31] time 0.887 (0.898) data 0.000 (0.027) loss 0.7373 (0.6835) acc 81.2500 (84.8750) lr 1.2369e-04 eta 0:02:52
+epoch [44/50] batch [30/31] time 0.863 (0.894) data 0.000 (0.022) loss 0.6978 (0.7043) acc 84.3750 (84.4792) lr 1.2369e-04 eta 0:02:47
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,243
+* accuracy: 72.5%
+* error: 27.5%
+* macro_f1: 71.5%
+epoch [45/50] batch [5/31] time 0.890 (0.953) data 0.000 (0.129) loss 0.4839 (0.6955) acc 84.3750 (83.1250) lr 9.5173e-05 eta 0:02:52
+epoch [45/50] batch [10/31] time 0.897 (0.917) data 0.000 (0.065) loss 0.5420 (0.6375) acc 87.5000 (84.6875) lr 9.5173e-05 eta 0:02:41
+epoch [45/50] batch [15/31] time 0.896 (0.912) data 0.000 (0.043) loss 1.1738 (0.6360) acc 81.2500 (85.6250) lr 9.5173e-05 eta 0:02:35
+epoch [45/50] batch [20/31] time 0.891 (0.906) data 0.000 (0.032) loss 1.3369 (0.7331) acc 71.8750 (84.0625) lr 9.5173e-05 eta 0:02:30
+epoch [45/50] batch [25/31] time 0.874 (0.906) data 0.000 (0.026) loss 0.7939 (0.7171) acc 84.3750 (84.3750) lr 9.5173e-05 eta 0:02:25
+epoch [45/50] batch [30/31] time 0.897 (0.902) data 0.000 (0.022) loss 0.7041 (0.7388) acc 75.0000 (83.8542) lr 9.5173e-05 eta 0:02:20
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,191
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.4%
+epoch [46/50] batch [5/31] time 0.886 (0.942) data 0.000 (0.125) loss 0.7671 (0.7390) acc 78.1250 (82.5000) lr 7.0224e-05 eta 0:02:21
+epoch [46/50] batch [10/31] time 0.845 (0.908) data 0.000 (0.062) loss 0.7671 (0.6531) acc 87.5000 (85.3125) lr 7.0224e-05 eta 0:02:11
+epoch [46/50] batch [15/31] time 0.904 (0.904) data 0.000 (0.042) loss 0.4719 (0.6451) acc 87.5000 (85.4167) lr 7.0224e-05 eta 0:02:06
+epoch [46/50] batch [20/31] time 0.915 (0.903) data 0.000 (0.031) loss 0.6128 (0.7030) acc 84.3750 (84.3750) lr 7.0224e-05 eta 0:02:01
+epoch [46/50] batch [25/31] time 0.888 (0.904) data 0.000 (0.025) loss 0.6978 (0.6804) acc 81.2500 (85.1250) lr 7.0224e-05 eta 0:01:57
+epoch [46/50] batch [30/31] time 0.904 (0.901) data 0.000 (0.021) loss 0.7441 (0.7001) acc 81.2500 (84.4792) lr 7.0224e-05 eta 0:01:52
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,173
+* accuracy: 72.3%
+* error: 27.7%
+* macro_f1: 71.4%
+epoch [47/50] batch [5/31] time 0.890 (0.946) data 0.000 (0.120) loss 0.9536 (0.7832) acc 71.8750 (81.2500) lr 4.8943e-05 eta 0:01:52
+epoch [47/50] batch [10/31] time 0.871 (0.915) data 0.000 (0.060) loss 0.7979 (0.6747) acc 75.0000 (83.1250) lr 4.8943e-05 eta 0:01:44
+epoch [47/50] batch [15/31] time 0.893 (0.903) data 0.000 (0.040) loss 1.1982 (0.6806) acc 84.3750 (84.3750) lr 4.8943e-05 eta 0:01:38
+epoch [47/50] batch [20/31] time 0.865 (0.894) data 0.000 (0.030) loss 0.4045 (0.6634) acc 81.2500 (84.2188) lr 4.8943e-05 eta 0:01:32
+epoch [47/50] batch [25/31] time 0.915 (0.892) data 0.000 (0.024) loss 0.4897 (0.6641) acc 93.7500 (84.2500) lr 4.8943e-05 eta 0:01:28
+epoch [47/50] batch [30/31] time 0.852 (0.888) data 0.000 (0.020) loss 0.5640 (0.6782) acc 93.7500 (84.4792) lr 4.8943e-05 eta 0:01:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,200
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.5%
+epoch [48/50] batch [5/31] time 0.871 (0.949) data 0.001 (0.133) loss 0.6396 (0.7861) acc 93.7500 (86.2500) lr 3.1417e-05 eta 0:01:23
+epoch [48/50] batch [10/31] time 0.887 (0.922) data 0.000 (0.067) loss 1.0273 (0.7704) acc 75.0000 (85.0000) lr 3.1417e-05 eta 0:01:16
+epoch [48/50] batch [15/31] time 0.939 (0.915) data 0.000 (0.045) loss 0.4082 (0.7391) acc 96.8750 (85.0000) lr 3.1417e-05 eta 0:01:11
+epoch [48/50] batch [20/31] time 0.892 (0.905) data 0.000 (0.033) loss 0.4800 (0.6923) acc 90.6250 (85.0000) lr 3.1417e-05 eta 0:01:06
+epoch [48/50] batch [25/31] time 0.899 (0.901) data 0.000 (0.027) loss 0.4441 (0.6702) acc 90.6250 (85.3750) lr 3.1417e-05 eta 0:01:01
+epoch [48/50] batch [30/31] time 0.889 (0.902) data 0.000 (0.022) loss 0.9199 (0.7315) acc 75.0000 (83.5417) lr 3.1417e-05 eta 0:00:56
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,195
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.4%
+epoch [49/50] batch [5/31] time 0.851 (0.930) data 0.000 (0.123) loss 0.8584 (0.6711) acc 78.1250 (76.8750) lr 1.7713e-05 eta 0:00:53
+epoch [49/50] batch [10/31] time 0.888 (0.921) data 0.000 (0.062) loss 0.3171 (0.6027) acc 87.5000 (80.9375) lr 1.7713e-05 eta 0:00:47
+epoch [49/50] batch [15/31] time 0.892 (0.908) data 0.000 (0.041) loss 0.9678 (0.6439) acc 84.3750 (81.6667) lr 1.7713e-05 eta 0:00:42
+epoch [49/50] batch [20/31] time 0.864 (0.903) data 0.000 (0.031) loss 0.7812 (0.6873) acc 84.3750 (82.3438) lr 1.7713e-05 eta 0:00:37
+epoch [49/50] batch [25/31] time 0.930 (0.902) data 0.000 (0.025) loss 0.4753 (0.6559) acc 93.7500 (83.0000) lr 1.7713e-05 eta 0:00:33
+epoch [49/50] batch [30/31] time 0.886 (0.899) data 0.000 (0.021) loss 0.5386 (0.6223) acc 84.3750 (83.6458) lr 1.7713e-05 eta 0:00:28
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,191
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.4%
+epoch [50/50] batch [5/31] time 0.860 (0.936) data 0.000 (0.126) loss 0.7983 (0.7974) acc 78.1250 (81.2500) lr 7.8853e-06 eta 0:00:24
+epoch [50/50] batch [10/31] time 0.884 (0.905) data 0.000 (0.063) loss 0.4873 (0.6901) acc 93.7500 (84.0625) lr 7.8853e-06 eta 0:00:19
+epoch [50/50] batch [15/31] time 0.878 (0.896) data 0.000 (0.042) loss 0.4387 (0.6778) acc 87.5000 (85.2083) lr 7.8853e-06 eta 0:00:14
+epoch [50/50] batch [20/31] time 0.875 (0.894) data 0.000 (0.032) loss 0.4661 (0.7057) acc 87.5000 (84.8438) lr 7.8853e-06 eta 0:00:09
+epoch [50/50] batch [25/31] time 0.892 (0.893) data 0.000 (0.025) loss 1.0576 (0.7488) acc 75.0000 (84.0000) lr 7.8853e-06 eta 0:00:05
+epoch [50/50] batch [30/31] time 0.884 (0.892) data 0.000 (0.021) loss 0.8081 (0.7657) acc 78.1250 (83.5417) lr 7.8853e-06 eta 0:00:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,194
+* accuracy: 72.4%
+* error: 27.6%
+* macro_f1: 71.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the model with the best val performance
+Loading weights to prompt_learner from "output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar" (epoch = 9)
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 37,953
+* accuracy: 75.9%
+* error: 24.1%
+* macro_f1: 75.1%
+Elapsed: 2:51:12
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model-best.pth.tar
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699016832.ckb-gpu-a.2018357.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699016832.ckb-gpu-a.2018357.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..4565c873
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,366 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 525.125.06
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 43 bits physical, 48 bits virtual
+CPU(s): 256
+On-line CPU(s) list: 0-255
+Thread(s) per core: 2
+Core(s) per socket: 64
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: AuthenticAMD
+CPU family: 23
+Model: 49
+Model name: AMD EPYC 7H12 64-Core Processor
+Stepping: 0
+Frequency boost: enabled
+CPU MHz: 2889.976
+CPU max MHz: 2600.0000
+CPU min MHz: 1500.0000
+BogoMIPS: 5200.20
+Virtualization: AMD-V
+L1d cache: 4 MiB
+L1i cache: 4 MiB
+L2 cache: 64 MiB
+L3 cache: 512 MiB
+NUMA node0 CPU(s): 0-63,128-191
+NUMA node1 CPU(s): 64-127,192-255
+Vulnerability Gather data sampling: Not affected
+Vulnerability Itlb multihit: Not affected
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Mmio stale data: Not affected
+Vulnerability Retbleed: Vulnerable
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Not affected
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca sme sev sev_es
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/31] time 0.884 (1.769) data 0.000 (0.226) loss 2.6309 (3.1812) acc 50.0000 (38.1250) lr 1.0000e-05 eta 0:45:32
+epoch [1/50] batch [10/31] time 0.879 (1.321) data 0.000 (0.113) loss 2.1055 (2.9594) acc 53.1250 (41.8750) lr 1.0000e-05 eta 0:33:54
+epoch [1/50] batch [15/31] time 0.888 (1.179) data 0.000 (0.076) loss 2.1406 (2.7664) acc 68.7500 (45.2083) lr 1.0000e-05 eta 0:30:09
+epoch [1/50] batch [20/31] time 0.884 (1.103) data 0.000 (0.057) loss 1.6699 (2.6191) acc 50.0000 (46.4062) lr 1.0000e-05 eta 0:28:07
+epoch [1/50] batch [25/31] time 0.862 (1.059) data 0.000 (0.045) loss 2.6172 (2.5647) acc 50.0000 (47.8750) lr 1.0000e-05 eta 0:26:54
+epoch [1/50] batch [30/31] time 0.900 (1.030) data 0.000 (0.038) loss 1.8232 (2.4437) acc 59.3750 (49.8958) lr 1.0000e-05 eta 0:26:04
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 31,890
+* accuracy: 63.8%
+* error: 36.2%
+* macro_f1: 60.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/31] time 0.877 (0.943) data 0.000 (0.123) loss 1.2910 (1.8162) acc 78.1250 (63.7500) lr 2.0000e-03 eta 0:23:47
+epoch [2/50] batch [10/31] time 0.874 (0.905) data 0.000 (0.062) loss 1.1309 (1.6651) acc 68.7500 (64.3750) lr 2.0000e-03 eta 0:22:45
+epoch [2/50] batch [15/31] time 0.861 (0.896) data 0.000 (0.041) loss 1.0498 (1.5736) acc 81.2500 (64.7917) lr 2.0000e-03 eta 0:22:27
+epoch [2/50] batch [20/31] time 0.873 (0.893) data 0.000 (0.031) loss 1.8838 (1.5561) acc 62.5000 (65.9375) lr 2.0000e-03 eta 0:22:18
+epoch [2/50] batch [25/31] time 0.889 (0.892) data 0.000 (0.025) loss 1.1074 (1.5011) acc 71.8750 (66.5000) lr 2.0000e-03 eta 0:22:12
+epoch [2/50] batch [30/31] time 0.914 (0.895) data 0.000 (0.021) loss 1.1963 (1.4579) acc 68.7500 (66.6667) lr 2.0000e-03 eta 0:22:12
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 36,962
+* accuracy: 73.9%
+* error: 26.1%
+* macro_f1: 73.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/31] time 0.868 (0.934) data 0.000 (0.115) loss 1.2383 (1.4051) acc 62.5000 (61.8750) lr 1.9980e-03 eta 0:23:04
+epoch [3/50] batch [10/31] time 0.890 (0.905) data 0.000 (0.058) loss 1.6523 (1.3682) acc 62.5000 (65.0000) lr 1.9980e-03 eta 0:22:17
+epoch [3/50] batch [15/31] time 0.888 (0.897) data 0.000 (0.039) loss 1.5488 (1.2870) acc 65.6250 (67.0833) lr 1.9980e-03 eta 0:22:01
+epoch [3/50] batch [20/31] time 0.895 (0.893) data 0.000 (0.029) loss 1.2393 (1.2433) acc 56.2500 (67.1875) lr 1.9980e-03 eta 0:21:50
+epoch [3/50] batch [25/31] time 0.867 (0.892) data 0.000 (0.023) loss 1.1943 (1.3245) acc 62.5000 (65.6250) lr 1.9980e-03 eta 0:21:45
+epoch [3/50] batch [30/31] time 0.873 (0.889) data 0.000 (0.019) loss 1.1924 (1.3167) acc 62.5000 (66.3542) lr 1.9980e-03 eta 0:21:36
+Evaluate on the *val* set
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
new file mode 100644
index 00000000..22cb2ffb
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model-best.pth.tar
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar
new file mode 100644
index 00000000..c25ec070
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model-best.pth.tar differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1699027126.ckb-gpu-a.2173477.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1699027126.ckb-gpu-a.2173477.0
new file mode 100644
index 00000000..b4768715
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1699027126.ckb-gpu-a.2173477.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..f070b51d
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,338 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '32']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 32
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.167
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Creating a 32-shot dataset
+Saving preprocessed few-shot data to /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_32-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 32,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/1000] time 1.550 (2.862) data 0.000 (0.361) loss 2.7012 (3.1289) acc 34.3750 (37.5000) lr 1.0000e-05 eta 1 day, 15:45:01
+epoch [1/50] batch [10/1000] time 1.559 (2.210) data 0.000 (0.181) loss 2.9297 (3.1416) acc 37.5000 (37.1875) lr 1.0000e-05 eta 1 day, 6:40:59
+epoch [1/50] batch [15/1000] time 1.556 (1.992) data 0.000 (0.121) loss 2.2539 (2.8736) acc 56.2500 (41.0417) lr 1.0000e-05 eta 1 day, 3:39:47
+epoch [1/50] batch [20/1000] time 1.570 (1.885) data 0.000 (0.091) loss 2.3027 (2.6997) acc 50.0000 (44.3750) lr 1.0000e-05 eta 1 day, 2:10:15
+epoch [1/50] batch [25/1000] time 1.654 (1.828) data 0.001 (0.073) loss 1.8682 (2.5346) acc 62.5000 (46.8750) lr 1.0000e-05 eta 1 day, 1:22:15
+epoch [1/50] batch [30/1000] time 1.588 (1.796) data 0.000 (0.061) loss 2.0176 (2.4302) acc 46.8750 (48.3333) lr 1.0000e-05 eta 1 day, 0:55:49
+epoch [1/50] batch [35/1000] time 1.666 (1.769) data 0.000 (0.052) loss 2.1113 (2.3558) acc 43.7500 (49.1964) lr 1.0000e-05 eta 1 day, 0:33:33
+epoch [1/50] batch [40/1000] time 1.564 (1.743) data 0.000 (0.046) loss 1.4189 (2.2630) acc 62.5000 (51.0156) lr 1.0000e-05 eta 1 day, 0:11:17
+epoch [1/50] batch [45/1000] time 1.541 (1.722) data 0.000 (0.041) loss 1.5010 (2.2084) acc 62.5000 (51.9444) lr 1.0000e-05 eta 23:53:53
+epoch [1/50] batch [50/1000] time 1.558 (1.706) data 0.001 (0.037) loss 1.0537 (2.1655) acc 75.0000 (53.1875) lr 1.0000e-05 eta 23:40:00
+epoch [1/50] batch [55/1000] time 1.571 (1.692) data 0.000 (0.033) loss 1.7764 (2.1291) acc 59.3750 (53.8068) lr 1.0000e-05 eta 23:28:49
+epoch [1/50] batch [60/1000] time 1.581 (1.682) data 0.001 (0.031) loss 2.2754 (2.0985) acc 59.3750 (54.5312) lr 1.0000e-05 eta 23:20:05
+epoch [1/50] batch [65/1000] time 1.554 (1.673) data 0.001 (0.028) loss 2.0254 (2.0795) acc 53.1250 (55.2404) lr 1.0000e-05 eta 23:11:59
+epoch [1/50] batch [70/1000] time 1.675 (1.672) data 0.001 (0.026) loss 2.1621 (2.0580) acc 50.0000 (55.4464) lr 1.0000e-05 eta 23:11:12
+epoch [1/50] batch [75/1000] time 1.640 (1.667) data 0.001 (0.025) loss 1.5146 (2.0385) acc 62.5000 (55.7500) lr 1.0000e-05 eta 23:07:20
+epoch [1/50] batch [80/1000] time 1.629 (1.664) data 0.001 (0.023) loss 1.2988 (2.0008) acc 68.7500 (56.4062) lr 1.0000e-05 eta 23:04:22
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699551792.ckb-gpu-v.mitre.org.246734.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699551792.ckb-gpu-v.mitre.org.246734.0
new file mode 100644
index 00000000..ce7a7825
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1699551792.ckb-gpu-v.mitre.org.246734.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..80df5131
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,497 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '32']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 32
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.458
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Creating a 32-shot dataset
+Saving preprocessed few-shot data to /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_32-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 32,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/1000] time 1.552 (2.862) data 0.001 (0.331) loss 3.2129 (3.3297) acc 31.2500 (33.1250) lr 1.0000e-05 eta 1 day, 15:44:43
+epoch [1/50] batch [10/1000] time 1.577 (2.216) data 0.000 (0.166) loss 2.3770 (2.8386) acc 46.8750 (41.2500) lr 1.0000e-05 eta 1 day, 6:45:59
+epoch [1/50] batch [15/1000] time 1.538 (1.995) data 0.000 (0.111) loss 2.6895 (2.7644) acc 37.5000 (42.2917) lr 1.0000e-05 eta 1 day, 3:41:57
+epoch [1/50] batch [20/1000] time 1.542 (1.884) data 0.001 (0.083) loss 1.8750 (2.6117) acc 62.5000 (45.1562) lr 1.0000e-05 eta 1 day, 2:09:33
+epoch [1/50] batch [25/1000] time 1.543 (1.818) data 0.000 (0.067) loss 2.3906 (2.4859) acc 37.5000 (46.6250) lr 1.0000e-05 eta 1 day, 1:14:08
+epoch [1/50] batch [30/1000] time 1.568 (1.775) data 0.001 (0.056) loss 1.8633 (2.4151) acc 59.3750 (48.0208) lr 1.0000e-05 eta 1 day, 0:37:55
+epoch [1/50] batch [35/1000] time 1.540 (1.743) data 0.000 (0.048) loss 1.5547 (2.3304) acc 65.6250 (49.4643) lr 1.0000e-05 eta 1 day, 0:11:31
+epoch [1/50] batch [40/1000] time 1.581 (1.721) data 0.000 (0.042) loss 2.7773 (2.3015) acc 43.7500 (50.3906) lr 1.0000e-05 eta 23:53:04
+epoch [1/50] batch [45/1000] time 1.539 (1.704) data 0.000 (0.037) loss 2.4551 (2.2919) acc 50.0000 (50.9722) lr 1.0000e-05 eta 23:38:21
+epoch [1/50] batch [50/1000] time 1.550 (1.688) data 0.000 (0.033) loss 2.3320 (2.2593) acc 50.0000 (51.4375) lr 1.0000e-05 eta 23:25:35
+epoch [1/50] batch [55/1000] time 1.539 (1.676) data 0.001 (0.030) loss 1.6865 (2.2085) acc 50.0000 (52.2727) lr 1.0000e-05 eta 23:15:05
+epoch [1/50] batch [60/1000] time 1.548 (1.665) data 0.001 (0.028) loss 1.9121 (2.1947) acc 59.3750 (52.6042) lr 1.0000e-05 eta 23:06:14
+epoch [1/50] batch [65/1000] time 1.578 (1.658) data 0.001 (0.026) loss 1.8125 (2.1728) acc 59.3750 (52.9327) lr 1.0000e-05 eta 23:00:02
+epoch [1/50] batch [70/1000] time 1.579 (1.652) data 0.000 (0.024) loss 1.4854 (2.1328) acc 62.5000 (53.4821) lr 1.0000e-05 eta 22:54:34
+epoch [1/50] batch [75/1000] time 1.586 (1.646) data 0.001 (0.023) loss 1.6553 (2.1092) acc 56.2500 (53.7083) lr 1.0000e-05 eta 22:49:52
+epoch [1/50] batch [80/1000] time 1.546 (1.640) data 0.000 (0.021) loss 1.9111 (2.0955) acc 56.2500 (54.0234) lr 1.0000e-05 eta 22:44:49
+epoch [1/50] batch [85/1000] time 1.554 (1.635) data 0.001 (0.020) loss 1.5283 (2.0585) acc 68.7500 (54.6324) lr 1.0000e-05 eta 22:40:25
+epoch [1/50] batch [90/1000] time 1.561 (1.631) data 0.000 (0.019) loss 1.3135 (2.0386) acc 62.5000 (55.0694) lr 1.0000e-05 eta 22:36:51
+epoch [1/50] batch [95/1000] time 1.569 (1.627) data 0.001 (0.018) loss 1.4170 (2.0144) acc 68.7500 (55.7237) lr 1.0000e-05 eta 22:33:31
+epoch [1/50] batch [100/1000] time 1.577 (1.624) data 0.000 (0.017) loss 2.1562 (2.0048) acc 56.2500 (56.0938) lr 1.0000e-05 eta 22:30:28
+epoch [1/50] batch [105/1000] time 1.602 (1.622) data 0.000 (0.016) loss 1.7061 (1.9873) acc 53.1250 (56.3690) lr 1.0000e-05 eta 22:28:47
+epoch [1/50] batch [110/1000] time 1.578 (1.620) data 0.000 (0.015) loss 2.2207 (1.9801) acc 56.2500 (56.5341) lr 1.0000e-05 eta 22:26:37
+epoch [1/50] batch [115/1000] time 1.555 (1.617) data 0.000 (0.015) loss 1.6680 (1.9705) acc 62.5000 (56.6848) lr 1.0000e-05 eta 22:24:26
+epoch [1/50] batch [120/1000] time 1.547 (1.615) data 0.000 (0.014) loss 2.1875 (1.9721) acc 59.3750 (56.7188) lr 1.0000e-05 eta 22:22:11
+epoch [1/50] batch [125/1000] time 1.550 (1.615) data 0.000 (0.014) loss 1.1270 (1.9525) acc 71.8750 (57.1750) lr 1.0000e-05 eta 22:22:23
+epoch [1/50] batch [130/1000] time 1.555 (1.613) data 0.000 (0.013) loss 1.7090 (1.9327) acc 59.3750 (57.5962) lr 1.0000e-05 eta 22:20:35
+epoch [1/50] batch [135/1000] time 1.563 (1.611) data 0.001 (0.013) loss 2.1484 (1.9290) acc 50.0000 (57.5926) lr 1.0000e-05 eta 22:18:41
+epoch [1/50] batch [140/1000] time 1.561 (1.609) data 0.000 (0.012) loss 1.3672 (1.9224) acc 65.6250 (57.7902) lr 1.0000e-05 eta 22:17:07
+epoch [1/50] batch [145/1000] time 1.542 (1.607) data 0.001 (0.012) loss 1.0801 (1.9010) acc 71.8750 (58.1681) lr 1.0000e-05 eta 22:15:38
+epoch [1/50] batch [150/1000] time 1.580 (1.606) data 0.000 (0.011) loss 1.8027 (1.8871) acc 50.0000 (58.3333) lr 1.0000e-05 eta 22:14:19
+epoch [1/50] batch [155/1000] time 1.542 (1.605) data 0.000 (0.011) loss 2.1543 (1.8814) acc 56.2500 (58.5081) lr 1.0000e-05 eta 22:12:57
+epoch [1/50] batch [160/1000] time 1.561 (1.603) data 0.000 (0.011) loss 1.5566 (1.8838) acc 68.7500 (58.5352) lr 1.0000e-05 eta 22:11:45
+epoch [1/50] batch [165/1000] time 1.564 (1.602) data 0.000 (0.010) loss 2.5508 (1.8829) acc 56.2500 (58.6364) lr 1.0000e-05 eta 22:10:26
+epoch [1/50] batch [170/1000] time 1.550 (1.600) data 0.000 (0.010) loss 1.3594 (1.8768) acc 65.6250 (58.7132) lr 1.0000e-05 eta 22:09:05
+epoch [1/50] batch [175/1000] time 1.553 (1.599) data 0.001 (0.010) loss 1.7432 (1.8719) acc 59.3750 (58.8750) lr 1.0000e-05 eta 22:08:02
+epoch [1/50] batch [180/1000] time 1.541 (1.598) data 0.000 (0.010) loss 1.3662 (1.8629) acc 59.3750 (58.9931) lr 1.0000e-05 eta 22:06:44
+epoch [1/50] batch [185/1000] time 1.550 (1.597) data 0.000 (0.009) loss 1.6924 (1.8545) acc 56.2500 (59.0541) lr 1.0000e-05 eta 22:05:52
+epoch [1/50] batch [190/1000] time 1.601 (1.596) data 0.001 (0.009) loss 1.4004 (1.8490) acc 75.0000 (59.1941) lr 1.0000e-05 eta 22:05:03
+epoch [1/50] batch [195/1000] time 1.532 (1.595) data 0.000 (0.009) loss 1.7549 (1.8426) acc 56.2500 (59.3109) lr 1.0000e-05 eta 22:04:00
+epoch [1/50] batch [200/1000] time 1.541 (1.594) data 0.001 (0.009) loss 1.6318 (1.8367) acc 59.3750 (59.3281) lr 1.0000e-05 eta 22:03:09
+epoch [1/50] batch [205/1000] time 1.558 (1.593) data 0.000 (0.009) loss 1.2705 (1.8298) acc 68.7500 (59.4512) lr 1.0000e-05 eta 22:02:10
+epoch [1/50] batch [210/1000] time 1.571 (1.592) data 0.000 (0.008) loss 1.4170 (1.8193) acc 68.7500 (59.6726) lr 1.0000e-05 eta 22:01:26
+epoch [1/50] batch [215/1000] time 1.565 (1.592) data 0.000 (0.008) loss 1.5020 (1.8125) acc 65.6250 (59.8256) lr 1.0000e-05 eta 22:00:50
+epoch [1/50] batch [220/1000] time 1.552 (1.591) data 0.001 (0.008) loss 1.7783 (1.8046) acc 50.0000 (59.9148) lr 1.0000e-05 eta 21:59:52
+epoch [1/50] batch [225/1000] time 1.554 (1.590) data 0.000 (0.008) loss 1.2324 (1.7945) acc 65.6250 (60.0972) lr 1.0000e-05 eta 21:59:23
+epoch [1/50] batch [230/1000] time 1.547 (1.590) data 0.000 (0.008) loss 2.1328 (1.7924) acc 68.7500 (60.2717) lr 1.0000e-05 eta 21:59:13
+epoch [1/50] batch [235/1000] time 1.563 (1.590) data 0.001 (0.007) loss 1.2344 (1.7844) acc 68.7500 (60.3191) lr 1.0000e-05 eta 21:58:25
+epoch [1/50] batch [240/1000] time 1.554 (1.589) data 0.000 (0.007) loss 1.3184 (1.7810) acc 65.6250 (60.2995) lr 1.0000e-05 eta 21:57:51
+epoch [1/50] batch [245/1000] time 1.554 (1.588) data 0.000 (0.007) loss 1.6240 (1.7714) acc 65.6250 (60.4592) lr 1.0000e-05 eta 21:57:10
+epoch [1/50] batch [250/1000] time 1.592 (1.588) data 0.000 (0.007) loss 1.5693 (1.7690) acc 56.2500 (60.5000) lr 1.0000e-05 eta 21:56:42
+epoch [1/50] batch [255/1000] time 1.552 (1.588) data 0.000 (0.007) loss 1.1299 (1.7669) acc 75.0000 (60.5637) lr 1.0000e-05 eta 21:56:16
+epoch [1/50] batch [260/1000] time 1.564 (1.587) data 0.000 (0.007) loss 1.7207 (1.7641) acc 59.3750 (60.5769) lr 1.0000e-05 eta 21:55:44
+epoch [1/50] batch [265/1000] time 1.565 (1.587) data 0.000 (0.007) loss 1.6064 (1.7601) acc 62.5000 (60.7075) lr 1.0000e-05 eta 21:55:14
+epoch [1/50] batch [270/1000] time 1.546 (1.586) data 0.000 (0.007) loss 1.7627 (1.7612) acc 71.8750 (60.7523) lr 1.0000e-05 eta 21:54:37
+epoch [1/50] batch [275/1000] time 1.538 (1.586) data 0.000 (0.006) loss 1.8115 (1.7601) acc 65.6250 (60.7955) lr 1.0000e-05 eta 21:54:21
+epoch [1/50] batch [280/1000] time 1.562 (1.585) data 0.000 (0.006) loss 1.1152 (1.7534) acc 59.3750 (60.8705) lr 1.0000e-05 eta 21:53:46
+epoch [1/50] batch [285/1000] time 1.570 (1.585) data 0.000 (0.006) loss 1.2490 (1.7483) acc 71.8750 (60.8991) lr 1.0000e-05 eta 21:53:14
+epoch [1/50] batch [290/1000] time 1.535 (1.584) data 0.000 (0.006) loss 1.1660 (1.7450) acc 71.8750 (60.9698) lr 1.0000e-05 eta 21:52:37
+epoch [1/50] batch [295/1000] time 1.565 (1.584) data 0.000 (0.006) loss 2.1406 (1.7394) acc 59.3750 (61.1017) lr 1.0000e-05 eta 21:52:03
+epoch [1/50] batch [300/1000] time 1.546 (1.583) data 0.001 (0.006) loss 1.2344 (1.7353) acc 65.6250 (61.1979) lr 1.0000e-05 eta 21:51:33
+epoch [1/50] batch [305/1000] time 1.558 (1.583) data 0.001 (0.006) loss 1.6748 (1.7296) acc 56.2500 (61.3320) lr 1.0000e-05 eta 21:51:05
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+epoch [1/50] batch [870/1000] time 1.594 (1.571) data 0.000 (0.002) loss 1.2627 (1.5292) acc 68.7500 (64.3966) lr 1.0000e-05 eta 21:26:31
+epoch [1/50] batch [875/1000] time 1.598 (1.571) data 0.001 (0.002) loss 1.3223 (1.5280) acc 71.8750 (64.4179) lr 1.0000e-05 eta 21:26:28
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1699551960.ckb-gpu-v.mitre.org.249380.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1699551960.ckb-gpu-v.mitre.org.249380.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/log.txt
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--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/log.txt
@@ -0,0 +1,10707 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_bestval_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '32']
+output_dir: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 32
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: best_val
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: Could not collect
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-PCIE-32GB
+GPU 1: Tesla V100-PCIE-32GB
+
+Nvidia driver version: 470.223.02
+cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 48
+On-line CPU(s) list: 0-47
+Thread(s) per core: 2
+Core(s) per socket: 12
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Silver 4116 CPU @ 2.10GHz
+Stepping: 4
+CPU MHz: 800.084
+CPU max MHz: 3000.0000
+CPU min MHz: 800.0000
+BogoMIPS: 4200.00
+Virtualization: VT-x
+L1d cache: 768 KiB
+L1i cache: 768 KiB
+L2 cache: 24 MiB
+L3 cache: 33 MiB
+NUMA node0 CPU(s): 0-11,24-35
+NUMA node1 CPU(s): 12-23,36-47
+Vulnerability Gather data sampling: Mitigation; Microcode
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
+Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Meltdown: Mitigation; PTI
+Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
+Vulnerability Retbleed: Mitigation; IBRS
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Creating a 32-shot dataset
+Saving preprocessed few-shot data to /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_32-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 32,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/50] batch [5/1000] time 1.551 (2.802) data 0.000 (0.306) loss 2.5234 (2.9820) acc 46.8750 (41.2500) lr 1.0000e-05 eta 1 day, 14:54:32
+epoch [1/50] batch [10/1000] time 1.697 (2.217) data 0.000 (0.153) loss 1.9727 (2.6852) acc 62.5000 (46.5625) lr 1.0000e-05 eta 1 day, 6:46:59
+epoch [1/50] batch [15/1000] time 1.594 (2.010) data 0.000 (0.103) loss 2.2012 (2.5629) acc 46.8750 (47.0833) lr 1.0000e-05 eta 1 day, 3:54:20
+epoch [1/50] batch [20/1000] time 1.654 (1.907) data 0.001 (0.077) loss 1.2891 (2.4267) acc 78.1250 (49.2188) lr 1.0000e-05 eta 1 day, 2:28:56
+epoch [1/50] batch [25/1000] time 1.554 (1.844) data 0.001 (0.062) loss 2.0820 (2.2772) acc 53.1250 (51.6250) lr 1.0000e-05 eta 1 day, 1:36:07
+epoch [1/50] batch [30/1000] time 1.533 (1.797) data 0.000 (0.052) loss 1.6045 (2.2089) acc 65.6250 (52.9167) lr 1.0000e-05 eta 1 day, 0:56:30
+epoch [1/50] batch [35/1000] time 1.566 (1.764) data 0.001 (0.044) loss 1.2236 (2.1169) acc 68.7500 (54.5536) lr 1.0000e-05 eta 1 day, 0:28:43
+epoch [1/50] batch [40/1000] time 1.574 (1.738) data 0.000 (0.039) loss 2.5605 (2.0809) acc 50.0000 (55.5469) lr 1.0000e-05 eta 1 day, 0:06:48
+epoch [1/50] batch [45/1000] time 1.569 (1.718) data 0.000 (0.035) loss 1.2598 (2.0213) acc 71.8750 (56.3889) lr 1.0000e-05 eta 23:50:27
+epoch [1/50] batch [50/1000] time 1.579 (1.703) data 0.000 (0.031) loss 1.6699 (1.9927) acc 62.5000 (56.8125) lr 1.0000e-05 eta 23:37:22
+epoch [1/50] batch [55/1000] time 1.575 (1.690) data 0.001 (0.028) loss 2.2207 (1.9767) acc 59.3750 (57.3295) lr 1.0000e-05 eta 23:27:05
+epoch [1/50] batch [60/1000] time 1.556 (1.680) data 0.001 (0.026) loss 1.3506 (1.9538) acc 71.8750 (57.8125) lr 1.0000e-05 eta 23:18:40
+epoch [1/50] batch [65/1000] time 1.565 (1.671) data 0.001 (0.024) loss 1.4521 (1.9284) acc 71.8750 (58.1731) lr 1.0000e-05 eta 23:10:33
+epoch [1/50] batch [70/1000] time 1.581 (1.663) data 0.000 (0.022) loss 1.4893 (1.8995) acc 68.7500 (58.7500) lr 1.0000e-05 eta 23:03:37
+epoch [1/50] batch [75/1000] time 1.576 (1.656) data 0.000 (0.021) loss 1.1084 (1.8495) acc 62.5000 (59.5000) lr 1.0000e-05 eta 22:57:51
+epoch [1/50] batch [80/1000] time 1.584 (1.651) data 0.001 (0.020) loss 2.1992 (1.8455) acc 53.1250 (59.1797) lr 1.0000e-05 eta 22:53:33
+epoch [1/50] batch [85/1000] time 1.575 (1.646) data 0.000 (0.019) loss 1.3047 (1.8133) acc 68.7500 (59.7426) lr 1.0000e-05 eta 22:49:23
+epoch [1/50] batch [90/1000] time 1.590 (1.641) data 0.000 (0.018) loss 2.2207 (1.8245) acc 56.2500 (59.6528) lr 1.0000e-05 eta 22:45:20
+epoch [1/50] batch [95/1000] time 1.562 (1.638) data 0.000 (0.017) loss 1.2715 (1.8125) acc 81.2500 (59.8684) lr 1.0000e-05 eta 22:42:14
+epoch [1/50] batch [100/1000] time 1.568 (1.634) data 0.001 (0.016) loss 1.7676 (1.7934) acc 65.6250 (60.2500) lr 1.0000e-05 eta 22:38:59
+epoch [1/50] batch [105/1000] time 1.549 (1.631) data 0.000 (0.015) loss 1.5566 (1.7797) acc 62.5000 (60.6845) lr 1.0000e-05 eta 22:36:36
+epoch [1/50] batch [110/1000] time 1.571 (1.628) data 0.001 (0.014) loss 1.8145 (1.7798) acc 56.2500 (60.4830) lr 1.0000e-05 eta 22:33:58
+epoch [1/50] batch [115/1000] time 1.568 (1.625) data 0.002 (0.014) loss 1.4355 (1.7596) acc 56.2500 (60.7337) lr 1.0000e-05 eta 22:31:23
+epoch [1/50] batch [120/1000] time 1.555 (1.623) data 0.000 (0.013) loss 1.1152 (1.7482) acc 81.2500 (60.9635) lr 1.0000e-05 eta 22:29:19
+epoch [1/50] batch [125/1000] time 1.554 (1.623) data 0.001 (0.013) loss 1.8975 (1.7416) acc 59.3750 (61.0250) lr 1.0000e-05 eta 22:29:09
+epoch [1/50] batch [130/1000] time 1.566 (1.621) data 0.001 (0.012) loss 0.8594 (1.7171) acc 78.1250 (61.4663) lr 1.0000e-05 eta 22:26:59
+epoch [1/50] batch [135/1000] time 1.569 (1.619) data 0.000 (0.012) loss 1.8232 (1.7211) acc 68.7500 (61.3657) lr 1.0000e-05 eta 22:25:08
+epoch [1/50] batch [140/1000] time 1.569 (1.617) data 0.001 (0.012) loss 1.5400 (1.7107) acc 68.7500 (61.6518) lr 1.0000e-05 eta 22:23:30
+epoch [1/50] batch [145/1000] time 1.546 (1.614) data 0.002 (0.011) loss 1.4785 (1.6953) acc 59.3750 (61.7241) lr 1.0000e-05 eta 22:21:26
+epoch [1/50] batch [150/1000] time 1.537 (1.612) data 0.000 (0.011) loss 1.3145 (1.6830) acc 68.7500 (61.9167) lr 1.0000e-05 eta 22:19:39
+epoch [1/50] batch [155/1000] time 1.558 (1.611) data 0.000 (0.010) loss 1.5166 (1.6687) acc 62.5000 (62.0565) lr 1.0000e-05 eta 22:18:32
+epoch [1/50] batch [160/1000] time 1.562 (1.610) data 0.001 (0.010) loss 1.1992 (1.6632) acc 68.7500 (62.0703) lr 1.0000e-05 eta 22:17:05
+epoch [1/50] batch [165/1000] time 1.565 (1.608) data 0.001 (0.010) loss 1.0918 (1.6549) acc 65.6250 (62.2538) lr 1.0000e-05 eta 22:15:50
+epoch [1/50] batch [170/1000] time 1.567 (1.607) data 0.000 (0.010) loss 0.8086 (1.6541) acc 87.5000 (62.2243) lr 1.0000e-05 eta 22:14:37
+epoch [1/50] batch [175/1000] time 1.585 (1.606) data 0.000 (0.009) loss 1.2354 (1.6569) acc 65.6250 (62.0714) lr 1.0000e-05 eta 22:13:28
+epoch [1/50] batch [180/1000] time 1.565 (1.605) data 0.001 (0.009) loss 1.0898 (1.6505) acc 71.8750 (62.1181) lr 1.0000e-05 eta 22:12:23
+epoch [1/50] batch [185/1000] time 1.535 (1.604) data 0.001 (0.009) loss 0.8770 (1.6433) acc 75.0000 (62.2973) lr 1.0000e-05 eta 22:11:24
+epoch [1/50] batch [190/1000] time 1.573 (1.603) data 0.000 (0.009) loss 1.2021 (1.6438) acc 59.3750 (62.1875) lr 1.0000e-05 eta 22:10:34
+epoch [1/50] batch [195/1000] time 1.560 (1.602) data 0.000 (0.008) loss 1.9932 (1.6383) acc 56.2500 (62.3558) lr 1.0000e-05 eta 22:09:26
+epoch [1/50] batch [200/1000] time 1.553 (1.601) data 0.001 (0.008) loss 1.0859 (1.6334) acc 75.0000 (62.5000) lr 1.0000e-05 eta 22:08:36
+epoch [1/50] batch [205/1000] time 1.543 (1.600) data 0.001 (0.008) loss 1.2969 (1.6313) acc 68.7500 (62.5610) lr 1.0000e-05 eta 22:07:36
+epoch [1/50] batch [210/1000] time 1.561 (1.599) data 0.001 (0.008) loss 1.4824 (1.6289) acc 59.3750 (62.5000) lr 1.0000e-05 eta 22:06:43
+epoch [1/50] batch [215/1000] time 1.566 (1.598) data 0.000 (0.008) loss 1.1113 (1.6224) acc 81.2500 (62.7180) lr 1.0000e-05 eta 22:05:48
+epoch [1/50] batch [220/1000] time 1.584 (1.597) data 0.001 (0.008) loss 1.8818 (1.6194) acc 65.6250 (62.8693) lr 1.0000e-05 eta 22:05:05
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+epoch [1/50] batch [790/1000] time 1.570 (1.573) data 0.000 (0.002) loss 1.7656 (1.4727) acc 65.6250 (65.0791) lr 1.0000e-05 eta 21:29:53
+epoch [1/50] batch [795/1000] time 1.557 (1.573) data 0.000 (0.002) loss 1.0527 (1.4725) acc 81.2500 (65.0943) lr 1.0000e-05 eta 21:29:46
+epoch [1/50] batch [800/1000] time 1.561 (1.573) data 0.000 (0.002) loss 1.2998 (1.4726) acc 65.6250 (65.0547) lr 1.0000e-05 eta 21:29:33
+epoch [1/50] batch [805/1000] time 1.559 (1.573) data 0.000 (0.002) loss 1.4141 (1.4728) acc 65.6250 (65.0349) lr 1.0000e-05 eta 21:29:23
+epoch [1/50] batch [810/1000] time 1.556 (1.572) data 0.000 (0.002) loss 1.8848 (1.4729) acc 56.2500 (65.0231) lr 1.0000e-05 eta 21:29:10
+epoch [1/50] batch [815/1000] time 1.550 (1.572) data 0.000 (0.002) loss 2.1309 (1.4737) acc 59.3750 (65.0307) lr 1.0000e-05 eta 21:28:57
+epoch [1/50] batch [820/1000] time 1.551 (1.572) data 0.000 (0.002) loss 1.7998 (1.4732) acc 59.3750 (65.0457) lr 1.0000e-05 eta 21:28:48
+epoch [1/50] batch [825/1000] time 1.575 (1.572) data 0.000 (0.002) loss 1.6855 (1.4714) acc 68.7500 (65.0909) lr 1.0000e-05 eta 21:28:37
+epoch [1/50] batch [830/1000] time 1.556 (1.572) data 0.000 (0.002) loss 1.5244 (1.4701) acc 56.2500 (65.0866) lr 1.0000e-05 eta 21:28:26
+epoch [1/50] batch [835/1000] time 1.579 (1.572) data 0.000 (0.002) loss 1.5947 (1.4679) acc 68.7500 (65.1534) lr 1.0000e-05 eta 21:28:25
+epoch [1/50] batch [840/1000] time 1.548 (1.572) data 0.000 (0.002) loss 1.3008 (1.4663) acc 71.8750 (65.1860) lr 1.0000e-05 eta 21:28:13
+epoch [1/50] batch [845/1000] time 1.557 (1.572) data 0.000 (0.002) loss 2.3809 (1.4681) acc 46.8750 (65.1627) lr 1.0000e-05 eta 21:28:01
+epoch [1/50] batch [850/1000] time 1.560 (1.572) data 0.001 (0.002) loss 1.3242 (1.4669) acc 68.7500 (65.1801) lr 1.0000e-05 eta 21:27:49
+epoch [1/50] batch [855/1000] time 1.568 (1.572) data 0.001 (0.002) loss 1.1865 (1.4651) acc 71.8750 (65.2230) lr 1.0000e-05 eta 21:27:37
+epoch [1/50] batch [860/1000] time 1.552 (1.572) data 0.000 (0.002) loss 0.8887 (1.4652) acc 68.7500 (65.1817) lr 1.0000e-05 eta 21:27:23
+epoch [1/50] batch [865/1000] time 1.588 (1.572) data 0.001 (0.002) loss 1.3867 (1.4652) acc 59.3750 (65.2059) lr 1.0000e-05 eta 21:27:17
+epoch [1/50] batch [870/1000] time 1.552 (1.572) data 0.000 (0.002) loss 1.5283 (1.4657) acc 56.2500 (65.1868) lr 1.0000e-05 eta 21:27:07
+epoch [1/50] batch [875/1000] time 1.567 (1.572) data 0.001 (0.002) loss 1.3828 (1.4633) acc 68.7500 (65.2321) lr 1.0000e-05 eta 21:27:00
+epoch [1/50] batch [880/1000] time 1.570 (1.572) data 0.000 (0.002) loss 1.9609 (1.4632) acc 59.3750 (65.2450) lr 1.0000e-05 eta 21:26:56
+epoch [1/50] batch [885/1000] time 1.562 (1.572) data 0.000 (0.002) loss 1.3057 (1.4635) acc 68.7500 (65.2401) lr 1.0000e-05 eta 21:26:45
+epoch [1/50] batch [890/1000] time 1.554 (1.572) data 0.000 (0.002) loss 0.8892 (1.4619) acc 78.1250 (65.2949) lr 1.0000e-05 eta 21:26:35
+epoch [1/50] batch [895/1000] time 1.584 (1.572) data 0.001 (0.002) loss 1.2314 (1.4613) acc 71.8750 (65.3038) lr 1.0000e-05 eta 21:26:27
+epoch [1/50] batch [900/1000] time 1.542 (1.572) data 0.001 (0.002) loss 1.4355 (1.4617) acc 65.6250 (65.3125) lr 1.0000e-05 eta 21:26:16
+epoch [1/50] batch [905/1000] time 1.568 (1.572) data 0.000 (0.002) loss 1.5869 (1.4608) acc 56.2500 (65.3315) lr 1.0000e-05 eta 21:26:03
+epoch [1/50] batch [910/1000] time 1.551 (1.572) data 0.000 (0.002) loss 1.2041 (1.4600) acc 65.6250 (65.3434) lr 1.0000e-05 eta 21:25:51
+epoch [1/50] batch [915/1000] time 1.555 (1.572) data 0.000 (0.002) loss 1.3105 (1.4607) acc 75.0000 (65.3484) lr 1.0000e-05 eta 21:25:40
+epoch [1/50] batch [920/1000] time 1.562 (1.572) data 0.001 (0.002) loss 1.2646 (1.4591) acc 65.6250 (65.3668) lr 1.0000e-05 eta 21:25:39
+epoch [1/50] batch [925/1000] time 1.552 (1.572) data 0.000 (0.002) loss 0.9512 (1.4590) acc 68.7500 (65.3682) lr 1.0000e-05 eta 21:25:27
+epoch [1/50] batch [930/1000] time 1.552 (1.572) data 0.000 (0.002) loss 0.9897 (1.4598) acc 65.6250 (65.3495) lr 1.0000e-05 eta 21:25:15
+epoch [1/50] batch [935/1000] time 1.564 (1.571) data 0.000 (0.002) loss 2.1953 (1.4623) acc 53.1250 (65.2941) lr 1.0000e-05 eta 21:25:03
+epoch [1/50] batch [940/1000] time 1.566 (1.571) data 0.000 (0.002) loss 1.3955 (1.4601) acc 68.7500 (65.3324) lr 1.0000e-05 eta 21:24:52
+epoch [1/50] batch [945/1000] time 1.553 (1.571) data 0.001 (0.002) loss 1.3311 (1.4622) acc 71.8750 (65.3009) lr 1.0000e-05 eta 21:24:39
+epoch [1/50] batch [950/1000] time 1.537 (1.571) data 0.000 (0.002) loss 1.5361 (1.4607) acc 68.7500 (65.3487) lr 1.0000e-05 eta 21:24:25
+epoch [1/50] batch [955/1000] time 1.560 (1.571) data 0.000 (0.002) loss 1.9131 (1.4605) acc 56.2500 (65.3632) lr 1.0000e-05 eta 21:24:14
+epoch [1/50] batch [960/1000] time 1.568 (1.571) data 0.000 (0.002) loss 1.1133 (1.4583) acc 75.0000 (65.4069) lr 1.0000e-05 eta 21:24:02
+epoch [1/50] batch [965/1000] time 1.551 (1.571) data 0.000 (0.002) loss 1.0977 (1.4570) acc 68.7500 (65.4404) lr 1.0000e-05 eta 21:23:46
+epoch [1/50] batch [970/1000] time 1.542 (1.571) data 0.000 (0.002) loss 1.4453 (1.4572) acc 68.7500 (65.4510) lr 1.0000e-05 eta 21:23:37
+epoch [1/50] batch [975/1000] time 1.567 (1.571) data 0.001 (0.002) loss 1.6934 (1.4581) acc 62.5000 (65.4359) lr 1.0000e-05 eta 21:23:27
+epoch [1/50] batch [980/1000] time 1.606 (1.571) data 0.001 (0.002) loss 1.2520 (1.4571) acc 81.2500 (65.4719) lr 1.0000e-05 eta 21:23:18
+epoch [1/50] batch [985/1000] time 1.560 (1.571) data 0.001 (0.002) loss 0.9692 (1.4557) acc 75.0000 (65.4791) lr 1.0000e-05 eta 21:23:14
+epoch [1/50] batch [990/1000] time 1.557 (1.571) data 0.000 (0.002) loss 1.6660 (1.4568) acc 59.3750 (65.4545) lr 1.0000e-05 eta 21:23:04
+epoch [1/50] batch [995/1000] time 1.567 (1.571) data 0.000 (0.002) loss 1.8740 (1.4564) acc 56.2500 (65.4617) lr 1.0000e-05 eta 21:22:54
+epoch [1/50] batch [1000/1000] time 1.560 (1.571) data 0.000 (0.002) loss 0.9551 (1.4552) acc 71.8750 (65.4688) lr 2.0000e-03 eta 21:22:45
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 37,535
+* accuracy: 75.1%
+* error: 24.9%
+* macro_f1: 74.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [2/50] batch [5/1000] time 1.532 (1.724) data 0.000 (0.170) loss 1.1953 (1.4148) acc 65.6250 (66.2500) lr 2.0000e-03 eta 23:27:35
+epoch [2/50] batch [10/1000] time 1.562 (1.636) data 0.001 (0.085) loss 1.6836 (1.4440) acc 56.2500 (66.5625) lr 2.0000e-03 eta 22:16:03
+epoch [2/50] batch [15/1000] time 1.536 (1.609) data 0.000 (0.057) loss 2.1309 (1.4900) acc 62.5000 (66.6667) lr 2.0000e-03 eta 21:53:39
+epoch [2/50] batch [20/1000] time 1.555 (1.600) data 0.001 (0.043) loss 0.9731 (1.4653) acc 71.8750 (66.4062) lr 2.0000e-03 eta 21:45:52
+epoch [2/50] batch [25/1000] time 1.587 (1.594) data 0.000 (0.034) loss 0.9907 (1.4076) acc 65.6250 (66.8750) lr 2.0000e-03 eta 21:41:17
+epoch [2/50] batch [30/1000] time 1.553 (1.590) data 0.001 (0.029) loss 1.0518 (1.3659) acc 68.7500 (67.7083) lr 2.0000e-03 eta 21:37:20
+epoch [2/50] batch [35/1000] time 1.556 (1.585) data 0.001 (0.025) loss 0.7041 (1.3660) acc 81.2500 (67.5000) lr 2.0000e-03 eta 21:33:25
+epoch [2/50] batch [40/1000] time 1.557 (1.581) data 0.000 (0.022) loss 1.4395 (1.3722) acc 65.6250 (67.1875) lr 2.0000e-03 eta 21:29:53
+epoch [2/50] batch [45/1000] time 1.562 (1.578) data 0.001 (0.019) loss 1.9824 (1.3866) acc 56.2500 (66.8056) lr 2.0000e-03 eta 21:27:39
+epoch [2/50] batch [50/1000] time 1.584 (1.580) data 0.000 (0.018) loss 0.9009 (1.3722) acc 81.2500 (66.9375) lr 2.0000e-03 eta 21:29:16
+epoch [2/50] batch [55/1000] time 1.551 (1.577) data 0.000 (0.016) loss 1.3848 (1.3897) acc 62.5000 (66.7614) lr 2.0000e-03 eta 21:26:44
+epoch [2/50] batch [60/1000] time 1.564 (1.577) data 0.001 (0.015) loss 2.0254 (1.4114) acc 56.2500 (66.5625) lr 2.0000e-03 eta 21:26:20
+epoch [2/50] batch [65/1000] time 1.551 (1.577) data 0.001 (0.014) loss 1.0742 (1.4002) acc 68.7500 (66.6827) lr 2.0000e-03 eta 21:25:47
+epoch [2/50] batch [70/1000] time 1.541 (1.576) data 0.000 (0.013) loss 1.4502 (1.3822) acc 62.5000 (66.9196) lr 2.0000e-03 eta 21:25:03
+epoch [2/50] batch [75/1000] time 1.580 (1.574) data 0.001 (0.012) loss 0.9331 (1.3586) acc 81.2500 (67.5417) lr 2.0000e-03 eta 21:23:47
+epoch [2/50] batch [80/1000] time 1.561 (1.573) data 0.000 (0.011) loss 0.8877 (1.3394) acc 75.0000 (67.8516) lr 2.0000e-03 eta 21:22:52
+epoch [2/50] batch [85/1000] time 1.553 (1.573) data 0.000 (0.011) loss 1.8271 (1.3421) acc 68.7500 (67.7574) lr 2.0000e-03 eta 21:22:07
+epoch [2/50] batch [90/1000] time 1.567 (1.572) data 0.000 (0.010) loss 1.3115 (1.3297) acc 65.6250 (67.9167) lr 2.0000e-03 eta 21:21:41
+epoch [2/50] batch [95/1000] time 1.557 (1.572) data 0.000 (0.009) loss 1.3271 (1.3263) acc 71.8750 (68.0921) lr 2.0000e-03 eta 21:20:58
+epoch [2/50] batch [100/1000] time 1.550 (1.571) data 0.001 (0.009) loss 1.4473 (1.3294) acc 59.3750 (67.9688) lr 2.0000e-03 eta 21:20:03
+epoch [2/50] batch [105/1000] time 1.550 (1.571) data 0.001 (0.009) loss 1.3545 (1.3259) acc 68.7500 (68.0357) lr 2.0000e-03 eta 21:19:53
+epoch [2/50] batch [110/1000] time 1.557 (1.570) data 0.001 (0.008) loss 1.0781 (1.3224) acc 75.0000 (68.1250) lr 2.0000e-03 eta 21:19:27
+epoch [2/50] batch [115/1000] time 1.564 (1.570) data 0.000 (0.008) loss 1.4492 (1.3230) acc 68.7500 (68.1522) lr 2.0000e-03 eta 21:18:50
+epoch [2/50] batch [120/1000] time 1.541 (1.569) data 0.000 (0.008) loss 1.4473 (1.3174) acc 62.5000 (68.2812) lr 2.0000e-03 eta 21:18:15
+epoch [2/50] batch [125/1000] time 1.536 (1.569) data 0.001 (0.007) loss 1.6924 (1.3073) acc 65.6250 (68.6000) lr 2.0000e-03 eta 21:17:51
+epoch [2/50] batch [130/1000] time 1.572 (1.569) data 0.000 (0.007) loss 1.6182 (1.3030) acc 71.8750 (68.7019) lr 2.0000e-03 eta 21:17:41
+epoch [2/50] batch [135/1000] time 1.552 (1.569) data 0.001 (0.007) loss 1.4189 (1.3067) acc 65.6250 (68.5417) lr 2.0000e-03 eta 21:17:26
+epoch [2/50] batch [140/1000] time 1.558 (1.568) data 0.000 (0.007) loss 0.8872 (1.3032) acc 68.7500 (68.5268) lr 2.0000e-03 eta 21:17:06
+epoch [2/50] batch [145/1000] time 1.587 (1.568) data 0.000 (0.006) loss 1.4385 (1.3008) acc 56.2500 (68.3836) lr 2.0000e-03 eta 21:17:01
+epoch [2/50] batch [150/1000] time 1.552 (1.568) data 0.000 (0.006) loss 2.3809 (1.3067) acc 59.3750 (68.2917) lr 2.0000e-03 eta 21:16:43
+epoch [2/50] batch [155/1000] time 1.547 (1.569) data 0.001 (0.006) loss 1.3965 (1.3086) acc 62.5000 (68.2863) lr 2.0000e-03 eta 21:17:14
+epoch [2/50] batch [160/1000] time 1.574 (1.569) data 0.000 (0.006) loss 1.2803 (1.3059) acc 75.0000 (68.3398) lr 2.0000e-03 eta 21:17:03
+epoch [2/50] batch [165/1000] time 1.555 (1.569) data 0.000 (0.006) loss 1.3633 (1.3070) acc 65.6250 (68.2576) lr 2.0000e-03 eta 21:16:52
+epoch [2/50] batch [170/1000] time 1.584 (1.569) data 0.000 (0.005) loss 1.0693 (1.3085) acc 65.6250 (68.1434) lr 2.0000e-03 eta 21:16:34
+epoch [2/50] batch [175/1000] time 1.548 (1.568) data 0.000 (0.005) loss 1.3457 (1.3081) acc 59.3750 (68.1250) lr 2.0000e-03 eta 21:16:01
+epoch [2/50] batch [180/1000] time 1.550 (1.568) data 0.000 (0.005) loss 1.5205 (1.3052) acc 65.6250 (68.1424) lr 2.0000e-03 eta 21:15:44
+epoch [2/50] batch [185/1000] time 1.558 (1.568) data 0.000 (0.005) loss 0.9556 (1.3034) acc 75.0000 (68.2939) lr 2.0000e-03 eta 21:15:25
+epoch [2/50] batch [190/1000] time 1.561 (1.568) data 0.000 (0.005) loss 1.4160 (1.3059) acc 65.6250 (68.1743) lr 2.0000e-03 eta 21:15:15
+epoch [2/50] batch [195/1000] time 1.535 (1.567) data 0.001 (0.005) loss 1.1045 (1.3039) acc 71.8750 (68.2051) lr 2.0000e-03 eta 21:14:46
+epoch [2/50] batch [200/1000] time 1.536 (1.568) data 0.001 (0.005) loss 0.8379 (1.2964) acc 75.0000 (68.3438) lr 2.0000e-03 eta 21:14:59
+epoch [2/50] batch [205/1000] time 1.572 (1.568) data 0.000 (0.005) loss 1.5098 (1.3018) acc 56.2500 (68.2012) lr 2.0000e-03 eta 21:14:50
+epoch [2/50] batch [210/1000] time 1.553 (1.568) data 0.000 (0.005) loss 0.6797 (1.2986) acc 75.0000 (68.2589) lr 2.0000e-03 eta 21:14:43
+epoch [2/50] batch [215/1000] time 1.555 (1.567) data 0.000 (0.004) loss 1.0830 (1.2987) acc 71.8750 (68.3430) lr 2.0000e-03 eta 21:14:17
+epoch [2/50] batch [220/1000] time 1.582 (1.567) data 0.000 (0.004) loss 1.4297 (1.2959) acc 59.3750 (68.3949) lr 2.0000e-03 eta 21:14:06
+epoch [2/50] batch [225/1000] time 1.529 (1.567) data 0.000 (0.004) loss 1.1836 (1.2889) acc 68.7500 (68.5417) lr 2.0000e-03 eta 21:13:44
+epoch [2/50] batch [230/1000] time 1.562 (1.567) data 0.000 (0.004) loss 1.6787 (1.2957) acc 62.5000 (68.3967) lr 2.0000e-03 eta 21:13:28
+epoch [2/50] batch [235/1000] time 1.569 (1.567) data 0.000 (0.004) loss 1.6807 (1.3023) acc 65.6250 (68.3644) lr 2.0000e-03 eta 21:13:20
+epoch [2/50] batch [240/1000] time 1.731 (1.567) data 0.000 (0.004) loss 1.5215 (1.3064) acc 56.2500 (68.2292) lr 2.0000e-03 eta 21:13:40
+epoch [2/50] batch [245/1000] time 1.556 (1.567) data 0.000 (0.004) loss 0.7769 (1.3038) acc 84.3750 (68.3163) lr 2.0000e-03 eta 21:13:27
+epoch [2/50] batch [250/1000] time 1.556 (1.567) data 0.000 (0.004) loss 1.1250 (1.3033) acc 81.2500 (68.2875) lr 2.0000e-03 eta 21:13:11
+epoch [2/50] batch [255/1000] time 1.565 (1.567) data 0.000 (0.004) loss 1.0771 (1.2987) acc 59.3750 (68.3578) lr 2.0000e-03 eta 21:13:08
+epoch [2/50] batch [260/1000] time 1.581 (1.567) data 0.001 (0.004) loss 0.5649 (1.2947) acc 81.2500 (68.4976) lr 2.0000e-03 eta 21:13:03
+epoch [2/50] batch [265/1000] time 1.555 (1.567) data 0.001 (0.004) loss 1.3984 (1.2940) acc 71.8750 (68.5613) lr 2.0000e-03 eta 21:12:49
+epoch [2/50] batch [270/1000] time 1.576 (1.567) data 0.001 (0.004) loss 1.1426 (1.2954) acc 68.7500 (68.5185) lr 2.0000e-03 eta 21:12:39
+epoch [2/50] batch [275/1000] time 1.542 (1.567) data 0.000 (0.004) loss 1.5625 (1.2972) acc 65.6250 (68.5227) lr 2.0000e-03 eta 21:12:22
+epoch [2/50] batch [280/1000] time 1.574 (1.567) data 0.000 (0.004) loss 1.2295 (1.2966) acc 71.8750 (68.5379) lr 2.0000e-03 eta 21:12:07
+epoch [2/50] batch [285/1000] time 1.554 (1.567) data 0.000 (0.003) loss 2.0664 (1.2966) acc 53.1250 (68.5746) lr 2.0000e-03 eta 21:11:54
+epoch [2/50] batch [290/1000] time 1.545 (1.566) data 0.000 (0.003) loss 1.1191 (1.2978) acc 68.7500 (68.4914) lr 2.0000e-03 eta 21:11:41
+epoch [2/50] batch [295/1000] time 1.570 (1.566) data 0.001 (0.003) loss 1.7344 (1.2992) acc 59.3750 (68.5064) lr 2.0000e-03 eta 21:11:27
+epoch [2/50] batch [300/1000] time 1.532 (1.566) data 0.001 (0.003) loss 1.5850 (1.2989) acc 71.8750 (68.5208) lr 2.0000e-03 eta 21:11:06
+epoch [2/50] batch [305/1000] time 1.550 (1.566) data 0.000 (0.003) loss 1.0107 (1.2953) acc 68.7500 (68.5246) lr 2.0000e-03 eta 21:11:17
+epoch [2/50] batch [310/1000] time 1.570 (1.566) data 0.000 (0.003) loss 1.3271 (1.2910) acc 68.7500 (68.5887) lr 2.0000e-03 eta 21:11:09
+epoch [2/50] batch [315/1000] time 1.553 (1.566) data 0.000 (0.003) loss 1.2666 (1.2900) acc 75.0000 (68.5714) lr 2.0000e-03 eta 21:10:56
+epoch [2/50] batch [320/1000] time 1.559 (1.566) data 0.000 (0.003) loss 1.6084 (1.2899) acc 62.5000 (68.5449) lr 2.0000e-03 eta 21:10:41
+epoch [2/50] batch [325/1000] time 1.552 (1.566) data 0.000 (0.003) loss 0.8291 (1.2890) acc 78.1250 (68.5192) lr 2.0000e-03 eta 21:10:21
+epoch [2/50] batch [330/1000] time 1.561 (1.566) data 0.001 (0.003) loss 1.0625 (1.2860) acc 71.8750 (68.5795) lr 2.0000e-03 eta 21:10:09
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+epoch [2/50] batch [905/1000] time 1.576 (1.564) data 0.000 (0.001) loss 1.2441 (1.2664) acc 68.7500 (68.9157) lr 2.0000e-03 eta 20:53:43
+epoch [2/50] batch [910/1000] time 1.566 (1.564) data 0.000 (0.001) loss 1.3047 (1.2652) acc 71.8750 (68.9595) lr 2.0000e-03 eta 20:53:40
+epoch [2/50] batch [915/1000] time 1.576 (1.564) data 0.000 (0.001) loss 1.3701 (1.2659) acc 68.7500 (68.9515) lr 2.0000e-03 eta 20:53:32
+epoch [2/50] batch [920/1000] time 1.522 (1.564) data 0.001 (0.001) loss 1.5547 (1.2660) acc 71.8750 (68.9538) lr 2.0000e-03 eta 20:53:20
+epoch [2/50] batch [925/1000] time 1.542 (1.564) data 0.000 (0.001) loss 1.3975 (1.2656) acc 59.3750 (68.9459) lr 2.0000e-03 eta 20:53:11
+epoch [2/50] batch [930/1000] time 1.561 (1.564) data 0.001 (0.001) loss 1.1045 (1.2654) acc 75.0000 (68.9247) lr 2.0000e-03 eta 20:53:04
+epoch [2/50] batch [935/1000] time 1.561 (1.564) data 0.000 (0.001) loss 1.2715 (1.2654) acc 65.6250 (68.9271) lr 2.0000e-03 eta 20:52:56
+epoch [2/50] batch [940/1000] time 1.560 (1.564) data 0.000 (0.001) loss 1.1768 (1.2655) acc 75.0000 (68.9162) lr 2.0000e-03 eta 20:52:47
+epoch [2/50] batch [945/1000] time 1.548 (1.564) data 0.000 (0.001) loss 1.5332 (1.2656) acc 71.8750 (68.9153) lr 2.0000e-03 eta 20:52:39
+epoch [2/50] batch [950/1000] time 1.569 (1.564) data 0.000 (0.001) loss 0.9360 (1.2653) acc 75.0000 (68.9211) lr 2.0000e-03 eta 20:52:34
+epoch [2/50] batch [955/1000] time 1.549 (1.564) data 0.000 (0.001) loss 0.8174 (1.2647) acc 78.1250 (68.9103) lr 2.0000e-03 eta 20:52:30
+epoch [2/50] batch [960/1000] time 1.593 (1.564) data 0.001 (0.001) loss 1.2842 (1.2643) acc 75.0000 (68.9225) lr 2.0000e-03 eta 20:52:23
+epoch [2/50] batch [965/1000] time 1.560 (1.564) data 0.000 (0.001) loss 1.5293 (1.2643) acc 71.8750 (68.9184) lr 2.0000e-03 eta 20:52:13
+epoch [2/50] batch [970/1000] time 1.567 (1.564) data 0.000 (0.001) loss 1.3418 (1.2649) acc 68.7500 (68.9143) lr 2.0000e-03 eta 20:52:03
+epoch [2/50] batch [975/1000] time 1.556 (1.564) data 0.000 (0.001) loss 0.8062 (1.2638) acc 84.3750 (68.9712) lr 2.0000e-03 eta 20:51:52
+epoch [2/50] batch [980/1000] time 1.552 (1.564) data 0.000 (0.001) loss 1.3604 (1.2644) acc 68.7500 (68.9541) lr 2.0000e-03 eta 20:51:43
+epoch [2/50] batch [985/1000] time 1.549 (1.564) data 0.001 (0.001) loss 1.2314 (1.2641) acc 71.8750 (68.9721) lr 2.0000e-03 eta 20:51:31
+epoch [2/50] batch [990/1000] time 1.567 (1.564) data 0.000 (0.001) loss 1.2051 (1.2642) acc 62.5000 (68.9615) lr 2.0000e-03 eta 20:51:21
+epoch [2/50] batch [995/1000] time 1.554 (1.564) data 0.001 (0.001) loss 1.1875 (1.2639) acc 84.3750 (68.9761) lr 2.0000e-03 eta 20:51:13
+epoch [2/50] batch [1000/1000] time 1.558 (1.564) data 0.000 (0.001) loss 1.2979 (1.2654) acc 78.1250 (68.9656) lr 1.9980e-03 eta 20:51:04
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,575
+* accuracy: 77.2%
+* error: 22.8%
+* macro_f1: 76.5%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [3/50] batch [5/1000] time 1.527 (1.738) data 0.000 (0.188) loss 2.0801 (1.4611) acc 56.2500 (66.8750) lr 1.9980e-03 eta 23:10:29
+epoch [3/50] batch [10/1000] time 1.568 (1.651) data 0.000 (0.094) loss 0.9062 (1.3557) acc 75.0000 (67.1875) lr 1.9980e-03 eta 22:00:31
+epoch [3/50] batch [15/1000] time 1.585 (1.625) data 0.000 (0.063) loss 1.7275 (1.3215) acc 53.1250 (68.7500) lr 1.9980e-03 eta 21:39:25
+epoch [3/50] batch [20/1000] time 1.585 (1.609) data 0.001 (0.047) loss 1.7432 (1.3365) acc 59.3750 (69.2188) lr 1.9980e-03 eta 21:26:58
+epoch [3/50] batch [25/1000] time 1.543 (1.599) data 0.000 (0.038) loss 1.0498 (1.3341) acc 81.2500 (69.6250) lr 1.9980e-03 eta 21:18:30
+epoch [3/50] batch [30/1000] time 1.567 (1.592) data 0.001 (0.032) loss 0.7441 (1.2851) acc 78.1250 (70.0000) lr 1.9980e-03 eta 21:12:26
+epoch [3/50] batch [35/1000] time 1.573 (1.586) data 0.000 (0.027) loss 1.0879 (1.2679) acc 68.7500 (69.9107) lr 1.9980e-03 eta 21:08:05
+epoch [3/50] batch [40/1000] time 1.556 (1.585) data 0.000 (0.024) loss 0.8931 (1.2291) acc 71.8750 (70.1562) lr 1.9980e-03 eta 21:06:38
+epoch [3/50] batch [45/1000] time 1.535 (1.581) data 0.000 (0.021) loss 0.9663 (1.2088) acc 78.1250 (70.7639) lr 1.9980e-03 eta 21:03:25
+epoch [3/50] batch [50/1000] time 1.564 (1.578) data 0.000 (0.019) loss 0.9712 (1.1975) acc 78.1250 (70.6250) lr 1.9980e-03 eta 21:01:05
+epoch [3/50] batch [55/1000] time 1.552 (1.576) data 0.000 (0.018) loss 1.2061 (1.1926) acc 75.0000 (70.6818) lr 1.9980e-03 eta 20:59:16
+epoch [3/50] batch [60/1000] time 1.583 (1.575) data 0.001 (0.016) loss 1.2471 (1.2048) acc 59.3750 (70.1562) lr 1.9980e-03 eta 20:58:13
+epoch [3/50] batch [65/1000] time 1.572 (1.576) data 0.001 (0.015) loss 1.1865 (1.1946) acc 68.7500 (70.2885) lr 1.9980e-03 eta 20:58:54
+epoch [3/50] batch [70/1000] time 1.550 (1.574) data 0.000 (0.014) loss 1.6357 (1.1975) acc 56.2500 (70.1786) lr 1.9980e-03 eta 20:57:33
+epoch [3/50] batch [75/1000] time 1.575 (1.573) data 0.001 (0.013) loss 1.0840 (1.1822) acc 71.8750 (70.2500) lr 1.9980e-03 eta 20:56:34
+epoch [3/50] batch [80/1000] time 1.566 (1.573) data 0.000 (0.012) loss 1.4004 (1.1767) acc 62.5000 (70.4688) lr 1.9980e-03 eta 20:56:02
+epoch [3/50] batch [85/1000] time 1.558 (1.572) data 0.000 (0.012) loss 0.9800 (1.1810) acc 87.5000 (70.6250) lr 1.9980e-03 eta 20:55:06
+epoch [3/50] batch [90/1000] time 1.540 (1.571) data 0.000 (0.011) loss 2.1387 (1.1919) acc 62.5000 (70.6250) lr 1.9980e-03 eta 20:54:08
+epoch [3/50] batch [95/1000] time 1.547 (1.570) data 0.000 (0.010) loss 1.5986 (1.1956) acc 56.2500 (70.5263) lr 1.9980e-03 eta 20:53:20
+epoch [3/50] batch [100/1000] time 1.551 (1.569) data 0.000 (0.010) loss 1.3389 (1.1964) acc 78.1250 (70.8438) lr 1.9980e-03 eta 20:52:36
+epoch [3/50] batch [105/1000] time 1.563 (1.568) data 0.000 (0.009) loss 1.4404 (1.1889) acc 56.2500 (71.0417) lr 1.9980e-03 eta 20:51:57
+epoch [3/50] batch [110/1000] time 1.566 (1.569) data 0.000 (0.009) loss 1.5049 (1.1785) acc 68.7500 (71.3352) lr 1.9980e-03 eta 20:52:22
+epoch [3/50] batch [115/1000] time 1.563 (1.569) data 0.000 (0.009) loss 0.8179 (1.1801) acc 75.0000 (71.3043) lr 1.9980e-03 eta 20:51:49
+epoch [3/50] batch [120/1000] time 1.582 (1.568) data 0.000 (0.008) loss 1.2100 (1.1816) acc 62.5000 (71.0156) lr 1.9980e-03 eta 20:51:19
+epoch [3/50] batch [125/1000] time 1.559 (1.568) data 0.001 (0.008) loss 1.2080 (1.1803) acc 62.5000 (71.0250) lr 1.9980e-03 eta 20:50:49
+epoch [3/50] batch [130/1000] time 1.549 (1.567) data 0.000 (0.008) loss 1.2686 (1.1775) acc 71.8750 (71.0577) lr 1.9980e-03 eta 20:50:02
+epoch [3/50] batch [135/1000] time 1.561 (1.567) data 0.000 (0.007) loss 1.3770 (1.1807) acc 59.3750 (70.9954) lr 1.9980e-03 eta 20:49:40
+epoch [3/50] batch [140/1000] time 1.576 (1.566) data 0.000 (0.007) loss 0.9995 (1.1840) acc 68.7500 (70.9821) lr 1.9980e-03 eta 20:49:11
+epoch [3/50] batch [145/1000] time 1.545 (1.566) data 0.000 (0.007) loss 1.1289 (1.1900) acc 75.0000 (70.9052) lr 1.9980e-03 eta 20:48:41
+epoch [3/50] batch [150/1000] time 1.571 (1.565) data 0.000 (0.007) loss 1.3965 (1.1916) acc 62.5000 (70.7917) lr 1.9980e-03 eta 20:48:12
+epoch [3/50] batch [155/1000] time 1.532 (1.565) data 0.000 (0.006) loss 1.5488 (1.1900) acc 62.5000 (70.7056) lr 1.9980e-03 eta 20:47:35
+epoch [3/50] batch [160/1000] time 1.549 (1.564) data 0.000 (0.006) loss 1.4160 (1.1978) acc 68.7500 (70.6641) lr 1.9980e-03 eta 20:47:01
+epoch [3/50] batch [165/1000] time 1.581 (1.564) data 0.000 (0.006) loss 0.9897 (1.2016) acc 75.0000 (70.7008) lr 1.9980e-03 eta 20:46:50
+epoch [3/50] batch [170/1000] time 1.545 (1.563) data 0.000 (0.006) loss 0.9482 (1.2086) acc 78.1250 (70.6250) lr 1.9980e-03 eta 20:46:09
+epoch [3/50] batch [175/1000] time 1.541 (1.563) data 0.000 (0.006) loss 1.0332 (1.2121) acc 62.5000 (70.4821) lr 1.9980e-03 eta 20:45:30
+epoch [3/50] batch [180/1000] time 1.548 (1.562) data 0.000 (0.006) loss 1.2402 (1.2109) acc 75.0000 (70.5382) lr 1.9980e-03 eta 20:45:09
+epoch [3/50] batch [185/1000] time 1.562 (1.562) data 0.001 (0.005) loss 1.3623 (1.2062) acc 71.8750 (70.6926) lr 1.9980e-03 eta 20:45:03
+epoch [3/50] batch [190/1000] time 1.560 (1.562) data 0.000 (0.005) loss 1.9668 (1.2101) acc 59.3750 (70.5921) lr 1.9980e-03 eta 20:44:59
+epoch [3/50] batch [195/1000] time 1.557 (1.562) data 0.001 (0.005) loss 0.9326 (1.2093) acc 75.0000 (70.6250) lr 1.9980e-03 eta 20:44:40
+epoch [3/50] batch [200/1000] time 1.550 (1.562) data 0.000 (0.005) loss 1.6650 (1.2143) acc 62.5000 (70.4688) lr 1.9980e-03 eta 20:44:28
+epoch [3/50] batch [205/1000] time 1.558 (1.562) data 0.000 (0.005) loss 0.9419 (1.2156) acc 75.0000 (70.5030) lr 1.9980e-03 eta 20:44:21
+epoch [3/50] batch [210/1000] time 1.545 (1.562) data 0.000 (0.005) loss 1.1230 (1.2151) acc 68.7500 (70.5060) lr 1.9980e-03 eta 20:44:06
+epoch [3/50] batch [215/1000] time 1.548 (1.562) data 0.000 (0.005) loss 0.8740 (1.2119) acc 75.0000 (70.5669) lr 1.9980e-03 eta 20:44:12
+epoch [3/50] batch [220/1000] time 1.541 (1.562) data 0.000 (0.005) loss 1.0889 (1.2136) acc 81.2500 (70.5398) lr 1.9980e-03 eta 20:43:55
+epoch [3/50] batch [225/1000] time 1.553 (1.562) data 0.000 (0.005) loss 1.3203 (1.2175) acc 75.0000 (70.5278) lr 1.9980e-03 eta 20:43:33
+epoch [3/50] batch [230/1000] time 1.562 (1.562) data 0.000 (0.005) loss 0.8594 (1.2172) acc 75.0000 (70.5707) lr 1.9980e-03 eta 20:43:26
+epoch [3/50] batch [235/1000] time 1.578 (1.562) data 0.000 (0.004) loss 1.1738 (1.2161) acc 75.0000 (70.6649) lr 1.9980e-03 eta 20:43:27
+epoch [3/50] batch [240/1000] time 1.539 (1.562) data 0.000 (0.004) loss 1.6885 (1.2179) acc 53.1250 (70.6120) lr 1.9980e-03 eta 20:43:20
+epoch [3/50] batch [245/1000] time 1.543 (1.562) data 0.000 (0.004) loss 1.8232 (1.2200) acc 56.2500 (70.5357) lr 1.9980e-03 eta 20:43:09
+epoch [3/50] batch [250/1000] time 1.552 (1.562) data 0.000 (0.004) loss 1.1475 (1.2191) acc 71.8750 (70.5250) lr 1.9980e-03 eta 20:42:59
+epoch [3/50] batch [255/1000] time 1.539 (1.562) data 0.000 (0.004) loss 1.3174 (1.2138) acc 65.6250 (70.6127) lr 1.9980e-03 eta 20:42:56
+epoch [3/50] batch [260/1000] time 1.552 (1.562) data 0.000 (0.004) loss 0.9419 (1.2094) acc 68.7500 (70.6490) lr 1.9980e-03 eta 20:43:06
+epoch [3/50] batch [265/1000] time 1.557 (1.562) data 0.000 (0.004) loss 0.7778 (1.2116) acc 78.1250 (70.6840) lr 1.9980e-03 eta 20:42:45
+epoch [3/50] batch [270/1000] time 1.534 (1.562) data 0.000 (0.004) loss 0.9941 (1.2109) acc 65.6250 (70.6829) lr 1.9980e-03 eta 20:42:20
+epoch [3/50] batch [275/1000] time 1.600 (1.562) data 0.000 (0.004) loss 0.7314 (1.2099) acc 75.0000 (70.6477) lr 1.9980e-03 eta 20:42:17
+epoch [3/50] batch [280/1000] time 1.567 (1.562) data 0.000 (0.004) loss 1.0693 (1.2073) acc 75.0000 (70.6808) lr 1.9980e-03 eta 20:42:08
+epoch [3/50] batch [285/1000] time 1.561 (1.562) data 0.000 (0.004) loss 1.3516 (1.2097) acc 65.6250 (70.5482) lr 1.9980e-03 eta 20:41:58
+epoch [3/50] batch [290/1000] time 1.564 (1.562) data 0.001 (0.004) loss 0.9893 (1.2115) acc 81.2500 (70.5496) lr 1.9980e-03 eta 20:41:50
+epoch [3/50] batch [295/1000] time 1.555 (1.562) data 0.000 (0.004) loss 1.5771 (1.2132) acc 59.3750 (70.4979) lr 1.9980e-03 eta 20:41:39
+epoch [3/50] batch [300/1000] time 1.722 (1.562) data 0.001 (0.004) loss 1.8408 (1.2164) acc 56.2500 (70.4167) lr 1.9980e-03 eta 20:41:59
+epoch [3/50] batch [305/1000] time 1.579 (1.562) data 0.000 (0.003) loss 1.0654 (1.2189) acc 75.0000 (70.3176) lr 1.9980e-03 eta 20:41:54
+epoch [3/50] batch [310/1000] time 1.553 (1.562) data 0.000 (0.003) loss 1.4365 (1.2228) acc 62.5000 (70.2520) lr 1.9980e-03 eta 20:41:47
+epoch [3/50] batch [315/1000] time 1.589 (1.562) data 0.000 (0.003) loss 1.1143 (1.2251) acc 81.2500 (70.1984) lr 1.9980e-03 eta 20:41:47
+epoch [3/50] batch [320/1000] time 1.566 (1.563) data 0.000 (0.003) loss 1.4023 (1.2255) acc 65.6250 (70.2051) lr 1.9980e-03 eta 20:41:41
+epoch [3/50] batch [325/1000] time 1.568 (1.562) data 0.000 (0.003) loss 1.5156 (1.2291) acc 50.0000 (70.1346) lr 1.9980e-03 eta 20:41:27
+epoch [3/50] batch [330/1000] time 1.545 (1.562) data 0.000 (0.003) loss 1.3203 (1.2298) acc 62.5000 (70.1610) lr 1.9980e-03 eta 20:41:19
+epoch [3/50] batch [335/1000] time 1.568 (1.562) data 0.000 (0.003) loss 1.5645 (1.2338) acc 71.8750 (70.1119) lr 1.9980e-03 eta 20:41:13
+epoch [3/50] batch [340/1000] time 1.576 (1.563) data 0.000 (0.003) loss 0.9443 (1.2341) acc 75.0000 (70.0827) lr 1.9980e-03 eta 20:41:09
+epoch [3/50] batch [345/1000] time 1.569 (1.562) data 0.000 (0.003) loss 0.4917 (1.2336) acc 87.5000 (70.0996) lr 1.9980e-03 eta 20:41:00
+epoch [3/50] batch [350/1000] time 1.576 (1.563) data 0.001 (0.003) loss 1.6602 (1.2356) acc 65.6250 (70.0625) lr 1.9980e-03 eta 20:40:57
+epoch [3/50] batch [355/1000] time 1.584 (1.563) data 0.001 (0.003) loss 0.8398 (1.2354) acc 84.3750 (70.0704) lr 1.9980e-03 eta 20:40:48
+epoch [3/50] batch [360/1000] time 1.561 (1.563) data 0.000 (0.003) loss 0.7280 (1.2304) acc 78.1250 (70.1476) lr 1.9980e-03 eta 20:40:41
+epoch [3/50] batch [365/1000] time 1.557 (1.563) data 0.000 (0.003) loss 0.9565 (1.2289) acc 68.7500 (70.1627) lr 1.9980e-03 eta 20:40:50
+epoch [3/50] batch [370/1000] time 1.534 (1.563) data 0.000 (0.003) loss 1.2607 (1.2270) acc 71.8750 (70.2111) lr 1.9980e-03 eta 20:40:33
+epoch [3/50] batch [375/1000] time 1.549 (1.563) data 0.000 (0.003) loss 1.1582 (1.2266) acc 75.0000 (70.1833) lr 1.9980e-03 eta 20:40:24
+epoch [3/50] batch [380/1000] time 1.543 (1.563) data 0.001 (0.003) loss 1.5020 (1.2257) acc 62.5000 (70.1645) lr 1.9980e-03 eta 20:40:11
+epoch [3/50] batch [385/1000] time 1.535 (1.562) data 0.000 (0.003) loss 1.1377 (1.2272) acc 75.0000 (70.1218) lr 1.9980e-03 eta 20:39:54
+epoch [3/50] batch [390/1000] time 1.564 (1.562) data 0.000 (0.003) loss 1.6748 (1.2268) acc 71.8750 (70.1122) lr 1.9980e-03 eta 20:39:42
+epoch [3/50] batch [395/1000] time 1.551 (1.562) data 0.001 (0.003) loss 1.9619 (1.2288) acc 50.0000 (70.0396) lr 1.9980e-03 eta 20:39:36
+epoch [3/50] batch [400/1000] time 1.564 (1.562) data 0.000 (0.003) loss 2.0645 (1.2295) acc 65.6250 (70.0469) lr 1.9980e-03 eta 20:39:25
+epoch [3/50] batch [405/1000] time 1.551 (1.562) data 0.000 (0.003) loss 0.8145 (1.2292) acc 81.2500 (70.0540) lr 1.9980e-03 eta 20:39:15
+epoch [3/50] batch [410/1000] time 1.560 (1.563) data 0.000 (0.003) loss 1.0459 (1.2269) acc 78.1250 (70.1296) lr 1.9980e-03 eta 20:39:29
+epoch [3/50] batch [415/1000] time 1.564 (1.563) data 0.000 (0.003) loss 1.4795 (1.2276) acc 68.7500 (70.1054) lr 1.9980e-03 eta 20:39:23
+epoch [3/50] batch [420/1000] time 1.553 (1.563) data 0.000 (0.003) loss 0.9253 (1.2287) acc 75.0000 (70.1116) lr 1.9980e-03 eta 20:39:19
+epoch [3/50] batch [425/1000] time 1.564 (1.563) data 0.000 (0.003) loss 1.6338 (1.2281) acc 68.7500 (70.0956) lr 1.9980e-03 eta 20:39:07
+epoch [3/50] batch [430/1000] time 1.548 (1.563) data 0.001 (0.003) loss 0.8496 (1.2281) acc 84.3750 (70.0945) lr 1.9980e-03 eta 20:39:00
+epoch [3/50] batch [435/1000] time 1.584 (1.563) data 0.000 (0.003) loss 1.1279 (1.2268) acc 65.6250 (70.1149) lr 1.9980e-03 eta 20:38:53
+epoch [3/50] batch [440/1000] time 1.556 (1.563) data 0.000 (0.003) loss 0.9297 (1.2293) acc 75.0000 (70.1065) lr 1.9980e-03 eta 20:38:39
+epoch [3/50] batch [445/1000] time 1.570 (1.563) data 0.000 (0.003) loss 1.9004 (1.2313) acc 56.2500 (70.0843) lr 1.9980e-03 eta 20:38:28
+epoch [3/50] batch [450/1000] time 1.558 (1.563) data 0.000 (0.002) loss 1.1670 (1.2302) acc 78.1250 (70.1250) lr 1.9980e-03 eta 20:38:19
+epoch [3/50] batch [455/1000] time 1.577 (1.563) data 0.000 (0.002) loss 1.2031 (1.2312) acc 68.7500 (70.0549) lr 1.9980e-03 eta 20:38:28
+epoch [3/50] batch [460/1000] time 1.605 (1.563) data 0.001 (0.002) loss 1.0186 (1.2324) acc 71.8750 (69.9932) lr 1.9980e-03 eta 20:38:30
+epoch [3/50] batch [465/1000] time 1.538 (1.563) data 0.000 (0.002) loss 1.0762 (1.2332) acc 71.8750 (69.9395) lr 1.9980e-03 eta 20:38:24
+epoch [3/50] batch [470/1000] time 1.567 (1.563) data 0.000 (0.002) loss 1.3936 (1.2320) acc 62.5000 (69.8803) lr 1.9980e-03 eta 20:38:15
+epoch [3/50] batch [475/1000] time 1.564 (1.563) data 0.000 (0.002) loss 1.3262 (1.2312) acc 71.8750 (69.9079) lr 1.9980e-03 eta 20:38:07
+epoch [3/50] batch [480/1000] time 1.553 (1.563) data 0.000 (0.002) loss 0.8618 (1.2286) acc 75.0000 (69.9349) lr 1.9980e-03 eta 20:37:55
+epoch [3/50] batch [485/1000] time 1.562 (1.563) data 0.000 (0.002) loss 0.5293 (1.2290) acc 78.1250 (69.9162) lr 1.9980e-03 eta 20:37:42
+epoch [3/50] batch [490/1000] time 1.553 (1.563) data 0.000 (0.002) loss 1.2637 (1.2277) acc 68.7500 (69.9362) lr 1.9980e-03 eta 20:37:26
+epoch [3/50] batch [495/1000] time 1.549 (1.563) data 0.000 (0.002) loss 1.3877 (1.2290) acc 68.7500 (69.9369) lr 1.9980e-03 eta 20:37:18
+epoch [3/50] batch [500/1000] time 1.578 (1.563) data 0.001 (0.002) loss 0.9663 (1.2319) acc 75.0000 (69.8937) lr 1.9980e-03 eta 20:37:05
+epoch [3/50] batch [505/1000] time 1.539 (1.562) data 0.000 (0.002) loss 0.8657 (1.2312) acc 78.1250 (69.9443) lr 1.9980e-03 eta 20:36:49
+epoch [3/50] batch [510/1000] time 1.540 (1.562) data 0.000 (0.002) loss 1.1895 (1.2306) acc 68.7500 (69.9449) lr 1.9980e-03 eta 20:36:39
+epoch [3/50] batch [515/1000] time 1.574 (1.563) data 0.000 (0.002) loss 0.9258 (1.2290) acc 68.7500 (69.9515) lr 1.9980e-03 eta 20:36:45
+epoch [3/50] batch [520/1000] time 1.556 (1.563) data 0.000 (0.002) loss 0.8320 (1.2284) acc 75.0000 (69.9279) lr 1.9980e-03 eta 20:36:36
+epoch [3/50] batch [525/1000] time 1.551 (1.563) data 0.000 (0.002) loss 1.4912 (1.2276) acc 68.7500 (69.9286) lr 1.9980e-03 eta 20:36:21
+epoch [3/50] batch [530/1000] time 1.568 (1.563) data 0.000 (0.002) loss 0.8857 (1.2264) acc 65.6250 (69.9175) lr 1.9980e-03 eta 20:36:15
+epoch [3/50] batch [535/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.0723 (1.2259) acc 68.7500 (69.9007) lr 1.9980e-03 eta 20:36:12
+epoch [3/50] batch [540/1000] time 1.559 (1.563) data 0.000 (0.002) loss 1.1211 (1.2257) acc 81.2500 (69.9306) lr 1.9980e-03 eta 20:36:05
+epoch [3/50] batch [545/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.3652 (1.2263) acc 59.3750 (69.9140) lr 1.9980e-03 eta 20:35:55
+epoch [3/50] batch [550/1000] time 1.540 (1.563) data 0.001 (0.002) loss 1.5713 (1.2282) acc 59.3750 (69.8864) lr 1.9980e-03 eta 20:35:40
+epoch [3/50] batch [555/1000] time 1.545 (1.562) data 0.000 (0.002) loss 1.5557 (1.2270) acc 62.5000 (69.8930) lr 1.9980e-03 eta 20:35:29
+epoch [3/50] batch [560/1000] time 1.582 (1.563) data 0.000 (0.002) loss 1.3662 (1.2257) acc 56.2500 (69.8717) lr 1.9980e-03 eta 20:35:29
+epoch [3/50] batch [565/1000] time 1.529 (1.562) data 0.000 (0.002) loss 1.1113 (1.2237) acc 65.6250 (69.9060) lr 1.9980e-03 eta 20:35:10
+epoch [3/50] batch [570/1000] time 1.554 (1.562) data 0.000 (0.002) loss 1.0664 (1.2231) acc 68.7500 (69.8958) lr 1.9980e-03 eta 20:34:59
+epoch [3/50] batch [575/1000] time 1.532 (1.562) data 0.000 (0.002) loss 1.6367 (1.2240) acc 59.3750 (69.8424) lr 1.9980e-03 eta 20:34:50
+epoch [3/50] batch [580/1000] time 1.562 (1.562) data 0.001 (0.002) loss 1.0801 (1.2234) acc 71.8750 (69.8491) lr 1.9980e-03 eta 20:34:38
+epoch [3/50] batch [585/1000] time 1.549 (1.562) data 0.001 (0.002) loss 1.2168 (1.2237) acc 65.6250 (69.8024) lr 1.9980e-03 eta 20:34:33
+epoch [3/50] batch [590/1000] time 1.547 (1.562) data 0.000 (0.002) loss 1.3730 (1.2231) acc 53.1250 (69.8252) lr 1.9980e-03 eta 20:34:22
+epoch [3/50] batch [595/1000] time 1.568 (1.562) data 0.000 (0.002) loss 1.8623 (1.2236) acc 62.5000 (69.7899) lr 1.9980e-03 eta 20:34:11
+epoch [3/50] batch [600/1000] time 1.555 (1.562) data 0.001 (0.002) loss 0.9302 (1.2246) acc 75.0000 (69.7969) lr 1.9980e-03 eta 20:34:02
+epoch [3/50] batch [605/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.2988 (1.2243) acc 65.6250 (69.8037) lr 1.9980e-03 eta 20:34:07
+epoch [3/50] batch [610/1000] time 1.551 (1.562) data 0.001 (0.002) loss 0.8320 (1.2241) acc 78.1250 (69.8258) lr 1.9980e-03 eta 20:33:54
+epoch [3/50] batch [615/1000] time 1.549 (1.562) data 0.001 (0.002) loss 1.6465 (1.2260) acc 62.5000 (69.8120) lr 1.9980e-03 eta 20:33:48
+epoch [3/50] batch [620/1000] time 1.594 (1.562) data 0.000 (0.002) loss 1.3955 (1.2261) acc 56.2500 (69.7681) lr 1.9980e-03 eta 20:33:43
+epoch [3/50] batch [625/1000] time 1.562 (1.562) data 0.001 (0.002) loss 1.3975 (1.2266) acc 65.6250 (69.7400) lr 1.9980e-03 eta 20:33:38
+epoch [3/50] batch [630/1000] time 1.553 (1.562) data 0.000 (0.002) loss 1.0586 (1.2271) acc 71.8750 (69.7272) lr 1.9980e-03 eta 20:33:30
+epoch [3/50] batch [635/1000] time 1.552 (1.562) data 0.000 (0.002) loss 1.3789 (1.2267) acc 62.5000 (69.7195) lr 1.9980e-03 eta 20:33:18
+epoch [3/50] batch [640/1000] time 1.568 (1.562) data 0.000 (0.002) loss 0.8892 (1.2277) acc 78.1250 (69.6680) lr 1.9980e-03 eta 20:33:05
+epoch [3/50] batch [645/1000] time 1.567 (1.562) data 0.000 (0.002) loss 1.1670 (1.2273) acc 68.7500 (69.6512) lr 1.9980e-03 eta 20:32:57
+epoch [3/50] batch [650/1000] time 1.553 (1.562) data 0.001 (0.002) loss 1.1670 (1.2267) acc 62.5000 (69.6587) lr 1.9980e-03 eta 20:32:49
+epoch [3/50] batch [655/1000] time 1.565 (1.562) data 0.000 (0.002) loss 0.7646 (1.2279) acc 71.8750 (69.6422) lr 1.9980e-03 eta 20:32:45
+epoch [3/50] batch [660/1000] time 1.566 (1.562) data 0.000 (0.002) loss 1.0430 (1.2274) acc 81.2500 (69.6496) lr 1.9980e-03 eta 20:32:35
+epoch [3/50] batch [665/1000] time 1.712 (1.562) data 0.000 (0.002) loss 1.8486 (1.2289) acc 50.0000 (69.6147) lr 1.9980e-03 eta 20:32:37
+epoch [3/50] batch [670/1000] time 1.539 (1.562) data 0.000 (0.002) loss 1.3262 (1.2298) acc 71.8750 (69.6035) lr 1.9980e-03 eta 20:32:27
+epoch [3/50] batch [675/1000] time 1.555 (1.562) data 0.000 (0.002) loss 1.2451 (1.2287) acc 71.8750 (69.6296) lr 1.9980e-03 eta 20:32:18
+epoch [3/50] batch [680/1000] time 1.580 (1.562) data 0.000 (0.002) loss 1.5439 (1.2282) acc 65.6250 (69.6461) lr 1.9980e-03 eta 20:32:07
+epoch [3/50] batch [685/1000] time 1.549 (1.562) data 0.000 (0.002) loss 1.6982 (1.2285) acc 50.0000 (69.6533) lr 1.9980e-03 eta 20:31:58
+epoch [3/50] batch [690/1000] time 1.545 (1.562) data 0.000 (0.002) loss 1.4932 (1.2268) acc 56.2500 (69.6784) lr 1.9980e-03 eta 20:31:48
+epoch [3/50] batch [695/1000] time 1.541 (1.562) data 0.000 (0.002) loss 1.1064 (1.2250) acc 75.0000 (69.7347) lr 1.9980e-03 eta 20:31:36
+epoch [3/50] batch [700/1000] time 1.562 (1.562) data 0.000 (0.002) loss 1.3115 (1.2251) acc 75.0000 (69.7455) lr 1.9980e-03 eta 20:31:26
+epoch [3/50] batch [705/1000] time 1.557 (1.562) data 0.001 (0.002) loss 1.1670 (1.2254) acc 75.0000 (69.7429) lr 1.9980e-03 eta 20:31:13
+epoch [3/50] batch [710/1000] time 1.738 (1.562) data 0.000 (0.002) loss 1.7256 (1.2280) acc 68.7500 (69.6875) lr 1.9980e-03 eta 20:31:16
+epoch [3/50] batch [715/1000] time 1.557 (1.562) data 0.001 (0.002) loss 0.7476 (1.2273) acc 81.2500 (69.7247) lr 1.9980e-03 eta 20:31:07
+epoch [3/50] batch [720/1000] time 1.544 (1.562) data 0.001 (0.002) loss 1.6035 (1.2280) acc 62.5000 (69.7049) lr 1.9980e-03 eta 20:30:58
+epoch [3/50] batch [725/1000] time 1.568 (1.562) data 0.000 (0.002) loss 1.1426 (1.2291) acc 71.8750 (69.7069) lr 1.9980e-03 eta 20:30:50
+epoch [3/50] batch [730/1000] time 1.569 (1.562) data 0.000 (0.002) loss 1.5127 (1.2310) acc 65.6250 (69.6875) lr 1.9980e-03 eta 20:30:44
+epoch [3/50] batch [735/1000] time 1.572 (1.562) data 0.001 (0.002) loss 0.7930 (1.2299) acc 81.2500 (69.7194) lr 1.9980e-03 eta 20:30:40
+epoch [3/50] batch [740/1000] time 1.585 (1.562) data 0.000 (0.002) loss 1.5439 (1.2291) acc 53.1250 (69.6959) lr 1.9980e-03 eta 20:30:35
+epoch [3/50] batch [745/1000] time 1.541 (1.562) data 0.000 (0.002) loss 1.1406 (1.2281) acc 71.8750 (69.7273) lr 1.9980e-03 eta 20:30:26
+epoch [3/50] batch [750/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.3232 (1.2281) acc 65.6250 (69.7375) lr 1.9980e-03 eta 20:30:21
+epoch [3/50] batch [755/1000] time 1.564 (1.563) data 0.000 (0.002) loss 1.1455 (1.2255) acc 65.6250 (69.7848) lr 1.9980e-03 eta 20:30:21
+epoch [3/50] batch [760/1000] time 1.556 (1.562) data 0.001 (0.002) loss 0.4861 (1.2245) acc 84.3750 (69.8191) lr 1.9980e-03 eta 20:30:12
+epoch [3/50] batch [765/1000] time 1.547 (1.562) data 0.000 (0.002) loss 1.7646 (1.2242) acc 59.3750 (69.8243) lr 1.9980e-03 eta 20:30:01
+epoch [3/50] batch [770/1000] time 1.575 (1.562) data 0.001 (0.002) loss 1.0605 (1.2247) acc 68.7500 (69.7971) lr 1.9980e-03 eta 20:29:52
+epoch [3/50] batch [775/1000] time 1.534 (1.562) data 0.000 (0.002) loss 1.5537 (1.2254) acc 59.3750 (69.7581) lr 1.9980e-03 eta 20:29:41
+epoch [3/50] batch [780/1000] time 1.524 (1.562) data 0.000 (0.002) loss 1.3105 (1.2265) acc 65.6250 (69.7436) lr 1.9980e-03 eta 20:29:28
+epoch [3/50] batch [785/1000] time 1.573 (1.562) data 0.000 (0.002) loss 0.9727 (1.2271) acc 71.8750 (69.7412) lr 1.9980e-03 eta 20:29:16
+epoch [3/50] batch [790/1000] time 1.571 (1.562) data 0.001 (0.002) loss 2.1387 (1.2265) acc 53.1250 (69.7429) lr 1.9980e-03 eta 20:29:07
+epoch [3/50] batch [795/1000] time 1.531 (1.562) data 0.001 (0.002) loss 0.8057 (1.2245) acc 78.1250 (69.7720) lr 1.9980e-03 eta 20:28:58
+epoch [3/50] batch [800/1000] time 1.556 (1.562) data 0.001 (0.002) loss 1.3457 (1.2233) acc 62.5000 (69.7773) lr 1.9980e-03 eta 20:28:49
+epoch [3/50] batch [805/1000] time 1.550 (1.562) data 0.000 (0.002) loss 2.1641 (1.2246) acc 56.2500 (69.7671) lr 1.9980e-03 eta 20:28:39
+epoch [3/50] batch [810/1000] time 1.560 (1.562) data 0.000 (0.002) loss 1.3311 (1.2255) acc 65.6250 (69.7338) lr 1.9980e-03 eta 20:28:31
+epoch [3/50] batch [815/1000] time 1.590 (1.562) data 0.001 (0.002) loss 1.0654 (1.2262) acc 65.6250 (69.7201) lr 1.9980e-03 eta 20:28:24
+epoch [3/50] batch [820/1000] time 1.549 (1.562) data 0.000 (0.002) loss 1.0029 (1.2264) acc 78.1250 (69.6875) lr 1.9980e-03 eta 20:28:27
+epoch [3/50] batch [825/1000] time 1.574 (1.562) data 0.000 (0.002) loss 1.3809 (1.2255) acc 71.8750 (69.6932) lr 1.9980e-03 eta 20:28:19
+epoch [3/50] batch [830/1000] time 1.552 (1.562) data 0.000 (0.002) loss 1.2441 (1.2260) acc 56.2500 (69.6687) lr 1.9980e-03 eta 20:28:11
+epoch [3/50] batch [835/1000] time 1.566 (1.562) data 0.000 (0.002) loss 1.2314 (1.2272) acc 65.6250 (69.6257) lr 1.9980e-03 eta 20:28:04
+epoch [3/50] batch [840/1000] time 1.558 (1.562) data 0.001 (0.002) loss 0.9380 (1.2274) acc 75.0000 (69.6280) lr 1.9980e-03 eta 20:27:53
+epoch [3/50] batch [845/1000] time 1.557 (1.562) data 0.000 (0.002) loss 1.1201 (1.2302) acc 65.6250 (69.6006) lr 1.9980e-03 eta 20:27:43
+epoch [3/50] batch [850/1000] time 1.563 (1.562) data 0.000 (0.002) loss 1.2256 (1.2306) acc 71.8750 (69.6029) lr 1.9980e-03 eta 20:27:34
+epoch [3/50] batch [855/1000] time 1.564 (1.562) data 0.001 (0.002) loss 0.7588 (1.2297) acc 78.1250 (69.6235) lr 1.9980e-03 eta 20:27:26
+epoch [3/50] batch [860/1000] time 1.531 (1.562) data 0.000 (0.002) loss 1.3418 (1.2300) acc 71.8750 (69.6148) lr 1.9980e-03 eta 20:27:17
+epoch [3/50] batch [865/1000] time 1.586 (1.562) data 0.000 (0.002) loss 1.0068 (1.2302) acc 71.8750 (69.5990) lr 1.9980e-03 eta 20:27:19
+epoch [3/50] batch [870/1000] time 1.583 (1.562) data 0.001 (0.002) loss 1.3174 (1.2304) acc 68.7500 (69.6049) lr 1.9980e-03 eta 20:27:15
+epoch [3/50] batch [875/1000] time 1.578 (1.562) data 0.000 (0.002) loss 1.2217 (1.2293) acc 75.0000 (69.6286) lr 1.9980e-03 eta 20:27:10
+epoch [3/50] batch [880/1000] time 1.560 (1.562) data 0.000 (0.002) loss 1.0596 (1.2289) acc 75.0000 (69.6662) lr 1.9980e-03 eta 20:27:04
+epoch [3/50] batch [885/1000] time 1.560 (1.563) data 0.000 (0.001) loss 0.7783 (1.2277) acc 81.2500 (69.6857) lr 1.9980e-03 eta 20:26:57
+epoch [3/50] batch [890/1000] time 1.562 (1.563) data 0.000 (0.001) loss 0.8130 (1.2260) acc 71.8750 (69.7015) lr 1.9980e-03 eta 20:26:49
+epoch [3/50] batch [895/1000] time 1.549 (1.563) data 0.001 (0.001) loss 1.4287 (1.2270) acc 75.0000 (69.7067) lr 1.9980e-03 eta 20:26:42
+epoch [3/50] batch [900/1000] time 1.555 (1.563) data 0.000 (0.001) loss 1.6562 (1.2264) acc 59.3750 (69.7292) lr 1.9980e-03 eta 20:26:33
+epoch [3/50] batch [905/1000] time 1.547 (1.563) data 0.001 (0.001) loss 2.0762 (1.2272) acc 62.5000 (69.7134) lr 1.9980e-03 eta 20:26:31
+epoch [3/50] batch [910/1000] time 1.553 (1.563) data 0.001 (0.001) loss 1.4189 (1.2289) acc 68.7500 (69.6978) lr 1.9980e-03 eta 20:26:22
+epoch [3/50] batch [915/1000] time 1.567 (1.563) data 0.001 (0.001) loss 0.8867 (1.2288) acc 78.1250 (69.6995) lr 1.9980e-03 eta 20:26:11
+epoch [3/50] batch [920/1000] time 1.602 (1.563) data 0.000 (0.001) loss 0.5156 (1.2276) acc 90.6250 (69.7317) lr 1.9980e-03 eta 20:26:02
+epoch [3/50] batch [925/1000] time 1.572 (1.562) data 0.001 (0.001) loss 1.5391 (1.2282) acc 59.3750 (69.7162) lr 1.9980e-03 eta 20:25:52
+epoch [3/50] batch [930/1000] time 1.556 (1.562) data 0.000 (0.001) loss 1.6650 (1.2274) acc 68.7500 (69.7413) lr 1.9980e-03 eta 20:25:44
+epoch [3/50] batch [935/1000] time 1.547 (1.562) data 0.001 (0.001) loss 1.5879 (1.2283) acc 59.3750 (69.7360) lr 1.9980e-03 eta 20:25:36
+epoch [3/50] batch [940/1000] time 1.542 (1.562) data 0.001 (0.001) loss 0.6431 (1.2275) acc 90.6250 (69.7507) lr 1.9980e-03 eta 20:25:29
+epoch [3/50] batch [945/1000] time 1.547 (1.562) data 0.000 (0.001) loss 1.6084 (1.2272) acc 53.1250 (69.7421) lr 1.9980e-03 eta 20:25:17
+epoch [3/50] batch [950/1000] time 1.565 (1.562) data 0.000 (0.001) loss 1.1270 (1.2275) acc 65.6250 (69.7237) lr 1.9980e-03 eta 20:25:08
+epoch [3/50] batch [955/1000] time 1.567 (1.562) data 0.001 (0.001) loss 1.0547 (1.2276) acc 68.7500 (69.7153) lr 1.9980e-03 eta 20:25:01
+epoch [3/50] batch [960/1000] time 1.578 (1.562) data 0.001 (0.001) loss 0.8994 (1.2273) acc 71.8750 (69.6973) lr 1.9980e-03 eta 20:24:53
+epoch [3/50] batch [965/1000] time 1.558 (1.562) data 0.000 (0.001) loss 0.6440 (1.2273) acc 81.2500 (69.7085) lr 1.9980e-03 eta 20:24:43
+epoch [3/50] batch [970/1000] time 1.560 (1.562) data 0.000 (0.001) loss 1.3125 (1.2278) acc 56.2500 (69.6843) lr 1.9980e-03 eta 20:24:41
+epoch [3/50] batch [975/1000] time 1.560 (1.562) data 0.000 (0.001) loss 0.8984 (1.2276) acc 75.0000 (69.6795) lr 1.9980e-03 eta 20:24:33
+epoch [3/50] batch [980/1000] time 1.549 (1.562) data 0.000 (0.001) loss 1.0098 (1.2275) acc 65.6250 (69.6588) lr 1.9980e-03 eta 20:24:24
+epoch [3/50] batch [985/1000] time 1.554 (1.562) data 0.001 (0.001) loss 1.4463 (1.2287) acc 62.5000 (69.6288) lr 1.9980e-03 eta 20:24:15
+epoch [3/50] batch [990/1000] time 1.567 (1.562) data 0.000 (0.001) loss 0.8389 (1.2266) acc 81.2500 (69.6843) lr 1.9980e-03 eta 20:24:06
+epoch [3/50] batch [995/1000] time 1.555 (1.562) data 0.000 (0.001) loss 1.5352 (1.2269) acc 71.8750 (69.6796) lr 1.9980e-03 eta 20:23:56
+epoch [3/50] batch [1000/1000] time 1.556 (1.562) data 0.000 (0.001) loss 0.6152 (1.2268) acc 84.3750 (69.6813) lr 1.9921e-03 eta 20:23:46
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,792
+* accuracy: 77.6%
+* error: 22.4%
+* macro_f1: 77.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [4/50] batch [5/1000] time 1.560 (1.673) data 0.000 (0.170) loss 1.1875 (1.2334) acc 81.2500 (70.0000) lr 1.9921e-03 eta 21:50:31
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+epoch [4/50] batch [560/1000] time 1.571 (1.564) data 0.000 (0.002) loss 1.0566 (1.2173) acc 68.7500 (69.6373) lr 1.9921e-03 eta 20:10:11
+epoch [4/50] batch [565/1000] time 1.561 (1.564) data 0.001 (0.002) loss 0.9302 (1.2168) acc 78.1250 (69.6294) lr 1.9921e-03 eta 20:10:03
+epoch [4/50] batch [570/1000] time 1.541 (1.564) data 0.001 (0.002) loss 1.0410 (1.2152) acc 78.1250 (69.6711) lr 1.9921e-03 eta 20:09:54
+epoch [4/50] batch [575/1000] time 1.577 (1.563) data 0.001 (0.002) loss 1.2979 (1.2159) acc 75.0000 (69.6848) lr 1.9921e-03 eta 20:09:43
+epoch [4/50] batch [580/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.4932 (1.2167) acc 59.3750 (69.6228) lr 1.9921e-03 eta 20:09:34
+epoch [4/50] batch [585/1000] time 1.555 (1.563) data 0.000 (0.002) loss 1.2178 (1.2174) acc 75.0000 (69.6581) lr 1.9921e-03 eta 20:09:23
+epoch [4/50] batch [590/1000] time 1.554 (1.563) data 0.000 (0.002) loss 0.8691 (1.2180) acc 78.1250 (69.6928) lr 1.9921e-03 eta 20:09:10
+epoch [4/50] batch [595/1000] time 1.555 (1.563) data 0.001 (0.002) loss 1.3467 (1.2211) acc 71.8750 (69.6271) lr 1.9921e-03 eta 20:08:55
+epoch [4/50] batch [600/1000] time 1.563 (1.563) data 0.000 (0.002) loss 1.1270 (1.2208) acc 71.8750 (69.6354) lr 1.9921e-03 eta 20:08:56
+epoch [4/50] batch [605/1000] time 1.560 (1.563) data 0.000 (0.002) loss 1.0625 (1.2196) acc 59.3750 (69.6488) lr 1.9921e-03 eta 20:08:46
+epoch [4/50] batch [610/1000] time 1.569 (1.563) data 0.001 (0.002) loss 1.6348 (1.2203) acc 71.8750 (69.6107) lr 1.9921e-03 eta 20:08:39
+epoch [4/50] batch [615/1000] time 1.581 (1.563) data 0.000 (0.002) loss 0.8550 (1.2193) acc 78.1250 (69.6189) lr 1.9921e-03 eta 20:08:34
+epoch [4/50] batch [620/1000] time 1.554 (1.563) data 0.000 (0.002) loss 1.1631 (1.2167) acc 71.8750 (69.6724) lr 1.9921e-03 eta 20:08:26
+epoch [4/50] batch [625/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.1816 (1.2176) acc 65.6250 (69.6700) lr 1.9921e-03 eta 20:08:13
+epoch [4/50] batch [630/1000] time 1.558 (1.563) data 0.001 (0.002) loss 1.6123 (1.2203) acc 68.7500 (69.6478) lr 1.9921e-03 eta 20:08:03
+epoch [4/50] batch [635/1000] time 1.553 (1.563) data 0.001 (0.002) loss 0.8906 (1.2218) acc 68.7500 (69.5915) lr 1.9921e-03 eta 20:07:52
+epoch [4/50] batch [640/1000] time 1.534 (1.563) data 0.000 (0.002) loss 1.1758 (1.2222) acc 71.8750 (69.5703) lr 1.9921e-03 eta 20:07:37
+epoch [4/50] batch [645/1000] time 1.539 (1.563) data 0.001 (0.002) loss 1.5820 (1.2227) acc 59.3750 (69.5397) lr 1.9921e-03 eta 20:07:38
+epoch [4/50] batch [650/1000] time 1.563 (1.563) data 0.001 (0.002) loss 1.4580 (1.2235) acc 65.6250 (69.5288) lr 1.9921e-03 eta 20:07:25
+epoch [4/50] batch [655/1000] time 1.548 (1.563) data 0.001 (0.002) loss 1.1602 (1.2222) acc 71.8750 (69.5802) lr 1.9921e-03 eta 20:07:13
+epoch [4/50] batch [660/1000] time 1.572 (1.563) data 0.001 (0.002) loss 1.0518 (1.2213) acc 84.3750 (69.6117) lr 1.9921e-03 eta 20:07:03
+epoch [4/50] batch [665/1000] time 1.549 (1.563) data 0.000 (0.002) loss 2.0742 (1.2228) acc 50.0000 (69.6053) lr 1.9921e-03 eta 20:06:51
+epoch [4/50] batch [670/1000] time 1.574 (1.563) data 0.000 (0.002) loss 0.9512 (1.2210) acc 75.0000 (69.6362) lr 1.9921e-03 eta 20:06:40
+epoch [4/50] batch [675/1000] time 1.574 (1.563) data 0.000 (0.002) loss 2.0176 (1.2218) acc 59.3750 (69.6343) lr 1.9921e-03 eta 20:06:32
+epoch [4/50] batch [680/1000] time 1.568 (1.563) data 0.000 (0.002) loss 0.9976 (1.2210) acc 68.7500 (69.6140) lr 1.9921e-03 eta 20:06:26
+epoch [4/50] batch [685/1000] time 1.555 (1.563) data 0.000 (0.002) loss 1.2578 (1.2213) acc 75.0000 (69.6168) lr 1.9921e-03 eta 20:06:19
+epoch [4/50] batch [690/1000] time 1.568 (1.563) data 0.001 (0.002) loss 1.0742 (1.2202) acc 75.0000 (69.6422) lr 1.9921e-03 eta 20:06:07
+epoch [4/50] batch [695/1000] time 1.551 (1.563) data 0.000 (0.002) loss 1.5469 (1.2194) acc 62.5000 (69.6673) lr 1.9921e-03 eta 20:05:59
+epoch [4/50] batch [700/1000] time 1.562 (1.563) data 0.000 (0.002) loss 1.0498 (1.2176) acc 81.2500 (69.7054) lr 1.9921e-03 eta 20:05:50
+epoch [4/50] batch [705/1000] time 1.702 (1.563) data 0.001 (0.002) loss 0.7285 (1.2157) acc 93.7500 (69.7518) lr 1.9921e-03 eta 20:05:49
+epoch [4/50] batch [710/1000] time 1.526 (1.563) data 0.000 (0.002) loss 1.2363 (1.2150) acc 78.1250 (69.7843) lr 1.9921e-03 eta 20:05:36
+epoch [4/50] batch [715/1000] time 1.580 (1.563) data 0.000 (0.002) loss 1.3164 (1.2140) acc 68.7500 (69.8033) lr 1.9921e-03 eta 20:05:29
+epoch [4/50] batch [720/1000] time 1.568 (1.563) data 0.000 (0.002) loss 0.8442 (1.2130) acc 75.0000 (69.8003) lr 1.9921e-03 eta 20:05:22
+epoch [4/50] batch [725/1000] time 1.560 (1.563) data 0.001 (0.002) loss 1.4277 (1.2133) acc 68.7500 (69.8017) lr 1.9921e-03 eta 20:05:14
+epoch [4/50] batch [730/1000] time 1.550 (1.563) data 0.000 (0.002) loss 0.7676 (1.2131) acc 78.1250 (69.8288) lr 1.9921e-03 eta 20:05:04
+epoch [4/50] batch [735/1000] time 1.544 (1.563) data 0.001 (0.002) loss 0.9214 (1.2137) acc 81.2500 (69.8299) lr 1.9921e-03 eta 20:04:51
+epoch [4/50] batch [740/1000] time 1.551 (1.563) data 0.001 (0.002) loss 1.6211 (1.2123) acc 65.6250 (69.8564) lr 1.9921e-03 eta 20:04:45
+epoch [4/50] batch [745/1000] time 1.541 (1.562) data 0.000 (0.002) loss 1.4023 (1.2133) acc 62.5000 (69.8532) lr 1.9921e-03 eta 20:04:31
+epoch [4/50] batch [750/1000] time 1.709 (1.563) data 0.001 (0.002) loss 2.4238 (1.2161) acc 62.5000 (69.8292) lr 1.9921e-03 eta 20:04:31
+epoch [4/50] batch [755/1000] time 1.554 (1.563) data 0.000 (0.002) loss 1.0195 (1.2162) acc 71.8750 (69.8593) lr 1.9921e-03 eta 20:04:19
+epoch [4/50] batch [760/1000] time 1.562 (1.563) data 0.000 (0.002) loss 0.9961 (1.2145) acc 68.7500 (69.9137) lr 1.9921e-03 eta 20:04:13
+epoch [4/50] batch [765/1000] time 1.553 (1.563) data 0.001 (0.002) loss 0.8110 (1.2133) acc 75.0000 (69.9306) lr 1.9921e-03 eta 20:04:04
+epoch [4/50] batch [770/1000] time 1.539 (1.563) data 0.000 (0.002) loss 0.5562 (1.2126) acc 84.3750 (69.9513) lr 1.9921e-03 eta 20:03:58
+epoch [4/50] batch [775/1000] time 1.552 (1.563) data 0.001 (0.002) loss 1.2412 (1.2139) acc 65.6250 (69.9476) lr 1.9921e-03 eta 20:03:47
+epoch [4/50] batch [780/1000] time 1.595 (1.563) data 0.000 (0.002) loss 0.7144 (1.2126) acc 81.2500 (69.9639) lr 1.9921e-03 eta 20:03:43
+epoch [4/50] batch [785/1000] time 1.582 (1.563) data 0.001 (0.002) loss 0.7021 (1.2115) acc 81.2500 (69.9841) lr 1.9921e-03 eta 20:03:36
+epoch [4/50] batch [790/1000] time 1.557 (1.563) data 0.001 (0.002) loss 1.6152 (1.2119) acc 65.6250 (70.0040) lr 1.9921e-03 eta 20:03:28
+epoch [4/50] batch [795/1000] time 1.548 (1.563) data 0.000 (0.002) loss 1.0791 (1.2125) acc 59.3750 (70.0000) lr 1.9921e-03 eta 20:03:24
+epoch [4/50] batch [800/1000] time 1.542 (1.563) data 0.000 (0.002) loss 1.5557 (1.2138) acc 65.6250 (69.9805) lr 1.9921e-03 eta 20:03:12
+epoch [4/50] batch [805/1000] time 1.564 (1.563) data 0.000 (0.002) loss 0.9116 (1.2133) acc 81.2500 (69.9806) lr 1.9921e-03 eta 20:03:00
+epoch [4/50] batch [810/1000] time 1.538 (1.562) data 0.000 (0.002) loss 0.7856 (1.2129) acc 78.1250 (70.0039) lr 1.9921e-03 eta 20:02:47
+epoch [4/50] batch [815/1000] time 1.527 (1.562) data 0.000 (0.002) loss 2.0430 (1.2151) acc 53.1250 (69.9578) lr 1.9921e-03 eta 20:02:36
+epoch [4/50] batch [820/1000] time 1.583 (1.562) data 0.000 (0.002) loss 1.7979 (1.2160) acc 59.3750 (69.9314) lr 1.9921e-03 eta 20:02:26
+epoch [4/50] batch [825/1000] time 1.562 (1.562) data 0.001 (0.002) loss 1.0576 (1.2156) acc 68.7500 (69.9242) lr 1.9921e-03 eta 20:02:17
+epoch [4/50] batch [830/1000] time 1.532 (1.562) data 0.001 (0.002) loss 1.4316 (1.2163) acc 68.7500 (69.8983) lr 1.9921e-03 eta 20:02:08
+epoch [4/50] batch [835/1000] time 1.571 (1.562) data 0.000 (0.001) loss 1.2852 (1.2162) acc 71.8750 (69.9027) lr 1.9921e-03 eta 20:02:02
+epoch [4/50] batch [840/1000] time 1.566 (1.562) data 0.000 (0.001) loss 1.5332 (1.2165) acc 62.5000 (69.8958) lr 1.9921e-03 eta 20:01:53
+epoch [4/50] batch [845/1000] time 1.583 (1.562) data 0.000 (0.001) loss 1.1846 (1.2171) acc 62.5000 (69.8706) lr 1.9921e-03 eta 20:01:45
+epoch [4/50] batch [850/1000] time 1.550 (1.562) data 0.001 (0.001) loss 1.4746 (1.2170) acc 62.5000 (69.8640) lr 1.9921e-03 eta 20:01:35
+epoch [4/50] batch [855/1000] time 1.569 (1.562) data 0.000 (0.001) loss 0.9414 (1.2165) acc 78.1250 (69.8830) lr 1.9921e-03 eta 20:01:28
+epoch [4/50] batch [860/1000] time 1.567 (1.562) data 0.001 (0.001) loss 1.5918 (1.2166) acc 62.5000 (69.8765) lr 1.9921e-03 eta 20:01:29
+epoch [4/50] batch [865/1000] time 1.586 (1.562) data 0.000 (0.001) loss 0.9258 (1.2159) acc 81.2500 (69.9277) lr 1.9921e-03 eta 20:01:20
+epoch [4/50] batch [870/1000] time 1.551 (1.562) data 0.001 (0.001) loss 0.8770 (1.2145) acc 68.7500 (69.9425) lr 1.9921e-03 eta 20:01:08
+epoch [4/50] batch [875/1000] time 1.591 (1.562) data 0.000 (0.001) loss 1.2061 (1.2139) acc 71.8750 (69.9679) lr 1.9921e-03 eta 20:01:00
+epoch [4/50] batch [880/1000] time 1.591 (1.562) data 0.000 (0.001) loss 0.6748 (1.2132) acc 81.2500 (69.9751) lr 1.9921e-03 eta 20:00:53
+epoch [4/50] batch [885/1000] time 1.569 (1.562) data 0.001 (0.001) loss 1.7041 (1.2145) acc 62.5000 (69.9541) lr 1.9921e-03 eta 20:00:48
+epoch [4/50] batch [890/1000] time 1.544 (1.562) data 0.000 (0.001) loss 1.5244 (1.2154) acc 62.5000 (69.9438) lr 1.9921e-03 eta 20:00:37
+epoch [4/50] batch [895/1000] time 1.563 (1.562) data 0.000 (0.001) loss 1.1562 (1.2162) acc 65.6250 (69.9127) lr 1.9921e-03 eta 20:00:30
+epoch [4/50] batch [900/1000] time 1.575 (1.562) data 0.001 (0.001) loss 1.2158 (1.2167) acc 68.7500 (69.9132) lr 1.9921e-03 eta 20:00:22
+epoch [4/50] batch [905/1000] time 1.560 (1.562) data 0.000 (0.001) loss 1.3545 (1.2164) acc 71.8750 (69.9378) lr 1.9921e-03 eta 20:00:22
+epoch [4/50] batch [910/1000] time 1.591 (1.562) data 0.000 (0.001) loss 1.5303 (1.2175) acc 59.3750 (69.8970) lr 1.9921e-03 eta 20:00:14
+epoch [4/50] batch [915/1000] time 1.603 (1.562) data 0.000 (0.001) loss 0.7158 (1.2174) acc 78.1250 (69.9010) lr 1.9921e-03 eta 20:00:07
+epoch [4/50] batch [920/1000] time 1.548 (1.562) data 0.000 (0.001) loss 1.4971 (1.2168) acc 53.1250 (69.9219) lr 1.9921e-03 eta 19:59:59
+epoch [4/50] batch [925/1000] time 1.559 (1.563) data 0.000 (0.001) loss 1.5615 (1.2161) acc 56.2500 (69.9020) lr 1.9921e-03 eta 19:59:54
+epoch [4/50] batch [930/1000] time 1.569 (1.563) data 0.000 (0.001) loss 0.7949 (1.2159) acc 75.0000 (69.8992) lr 1.9921e-03 eta 19:59:47
+epoch [4/50] batch [935/1000] time 1.571 (1.563) data 0.000 (0.001) loss 1.5186 (1.2166) acc 62.5000 (69.9131) lr 1.9921e-03 eta 19:59:41
+epoch [4/50] batch [940/1000] time 1.572 (1.563) data 0.000 (0.001) loss 1.5430 (1.2164) acc 75.0000 (69.9368) lr 1.9921e-03 eta 19:59:33
+epoch [4/50] batch [945/1000] time 1.588 (1.563) data 0.000 (0.001) loss 1.3838 (1.2161) acc 62.5000 (69.9405) lr 1.9921e-03 eta 19:59:35
+epoch [4/50] batch [950/1000] time 1.595 (1.563) data 0.000 (0.001) loss 1.3711 (1.2165) acc 65.6250 (69.9408) lr 1.9921e-03 eta 19:59:31
+epoch [4/50] batch [955/1000] time 1.545 (1.563) data 0.000 (0.001) loss 1.1582 (1.2168) acc 65.6250 (69.9215) lr 1.9921e-03 eta 19:59:23
+epoch [4/50] batch [960/1000] time 1.572 (1.563) data 0.001 (0.001) loss 1.0674 (1.2158) acc 71.8750 (69.9382) lr 1.9921e-03 eta 19:59:14
+epoch [4/50] batch [965/1000] time 1.564 (1.563) data 0.001 (0.001) loss 0.9771 (1.2161) acc 78.1250 (69.9417) lr 1.9921e-03 eta 19:59:04
+epoch [4/50] batch [970/1000] time 1.564 (1.563) data 0.000 (0.001) loss 1.3711 (1.2154) acc 62.5000 (69.9646) lr 1.9921e-03 eta 19:58:54
+epoch [4/50] batch [975/1000] time 1.556 (1.563) data 0.000 (0.001) loss 1.2363 (1.2167) acc 75.0000 (69.9615) lr 1.9921e-03 eta 19:58:45
+epoch [4/50] batch [980/1000] time 1.547 (1.563) data 0.000 (0.001) loss 0.6323 (1.2160) acc 78.1250 (69.9809) lr 1.9921e-03 eta 19:58:35
+epoch [4/50] batch [985/1000] time 1.549 (1.563) data 0.001 (0.001) loss 0.9854 (1.2161) acc 81.2500 (69.9810) lr 1.9921e-03 eta 19:58:25
+epoch [4/50] batch [990/1000] time 1.547 (1.563) data 0.000 (0.001) loss 1.1338 (1.2164) acc 65.6250 (69.9716) lr 1.9921e-03 eta 19:58:13
+epoch [4/50] batch [995/1000] time 1.553 (1.563) data 0.001 (0.001) loss 0.9897 (1.2158) acc 71.8750 (69.9937) lr 1.9921e-03 eta 19:58:04
+epoch [4/50] batch [1000/1000] time 1.589 (1.563) data 0.000 (0.001) loss 1.2998 (1.2156) acc 68.7500 (69.9906) lr 1.9823e-03 eta 19:57:59
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,946
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [5/50] batch [5/1000] time 1.557 (1.699) data 0.000 (0.194) loss 0.6040 (0.9898) acc 81.2500 (70.6250) lr 1.9823e-03 eta 21:42:03
+epoch [5/50] batch [10/1000] time 1.554 (1.624) data 0.000 (0.097) loss 1.4121 (1.2194) acc 53.1250 (66.5625) lr 1.9823e-03 eta 20:45:07
+epoch [5/50] batch [15/1000] time 1.570 (1.605) data 0.001 (0.065) loss 1.2227 (1.1979) acc 71.8750 (67.2917) lr 1.9823e-03 eta 20:29:53
+epoch [5/50] batch [20/1000] time 1.565 (1.595) data 0.001 (0.049) loss 1.2559 (1.1629) acc 71.8750 (68.5938) lr 1.9823e-03 eta 20:22:16
+epoch [5/50] batch [25/1000] time 1.572 (1.600) data 0.000 (0.039) loss 1.0498 (1.1458) acc 75.0000 (69.1250) lr 1.9823e-03 eta 20:25:37
+epoch [5/50] batch [30/1000] time 1.563 (1.593) data 0.001 (0.033) loss 1.5205 (1.1559) acc 56.2500 (68.9583) lr 1.9823e-03 eta 20:20:41
+epoch [5/50] batch [35/1000] time 1.548 (1.588) data 0.001 (0.028) loss 1.0312 (1.1335) acc 75.0000 (70.0000) lr 1.9823e-03 eta 20:16:52
+epoch [5/50] batch [40/1000] time 1.549 (1.585) data 0.001 (0.025) loss 1.1719 (1.1381) acc 75.0000 (70.6250) lr 1.9823e-03 eta 20:13:48
+epoch [5/50] batch [45/1000] time 1.545 (1.581) data 0.001 (0.022) loss 1.3418 (1.1384) acc 68.7500 (71.0417) lr 1.9823e-03 eta 20:10:54
+epoch [5/50] batch [50/1000] time 1.532 (1.579) data 0.000 (0.020) loss 1.4023 (1.1307) acc 56.2500 (71.3750) lr 1.9823e-03 eta 20:09:11
+epoch [5/50] batch [55/1000] time 1.562 (1.578) data 0.001 (0.018) loss 1.1113 (1.1151) acc 81.2500 (71.9886) lr 1.9823e-03 eta 20:08:01
+epoch [5/50] batch [60/1000] time 1.558 (1.577) data 0.002 (0.017) loss 0.9883 (1.1247) acc 68.7500 (71.4062) lr 1.9823e-03 eta 20:07:04
+epoch [5/50] batch [65/1000] time 1.559 (1.575) data 0.001 (0.016) loss 1.3604 (1.1240) acc 65.6250 (71.2019) lr 1.9823e-03 eta 20:05:41
+epoch [5/50] batch [70/1000] time 1.545 (1.574) data 0.000 (0.015) loss 0.7715 (1.1191) acc 68.7500 (71.3393) lr 1.9823e-03 eta 20:04:32
+epoch [5/50] batch [75/1000] time 1.570 (1.573) data 0.001 (0.014) loss 1.5752 (1.1377) acc 59.3750 (71.0833) lr 1.9823e-03 eta 20:03:55
+epoch [5/50] batch [80/1000] time 1.543 (1.572) data 0.000 (0.013) loss 1.2354 (1.1532) acc 71.8750 (70.7422) lr 1.9823e-03 eta 20:02:58
+epoch [5/50] batch [85/1000] time 1.555 (1.571) data 0.001 (0.012) loss 1.3164 (1.1635) acc 68.7500 (70.5515) lr 1.9823e-03 eta 20:02:29
+epoch [5/50] batch [90/1000] time 1.549 (1.571) data 0.001 (0.011) loss 0.9980 (1.1645) acc 71.8750 (70.4167) lr 1.9823e-03 eta 20:01:57
+epoch [5/50] batch [95/1000] time 1.561 (1.570) data 0.001 (0.011) loss 1.4609 (1.1764) acc 62.5000 (70.2961) lr 1.9823e-03 eta 20:01:19
+epoch [5/50] batch [100/1000] time 1.563 (1.569) data 0.001 (0.010) loss 0.9033 (1.1730) acc 81.2500 (70.6250) lr 1.9823e-03 eta 20:00:37
+epoch [5/50] batch [105/1000] time 1.534 (1.569) data 0.000 (0.010) loss 0.7417 (1.1635) acc 81.2500 (70.9524) lr 1.9823e-03 eta 20:00:06
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+epoch [5/50] batch [680/1000] time 1.573 (1.563) data 0.001 (0.002) loss 0.6504 (1.2074) acc 68.7500 (70.1149) lr 1.9823e-03 eta 19:40:44
+epoch [5/50] batch [685/1000] time 1.563 (1.563) data 0.000 (0.002) loss 1.0020 (1.2068) acc 71.8750 (70.0776) lr 1.9823e-03 eta 19:40:36
+epoch [5/50] batch [690/1000] time 1.560 (1.563) data 0.000 (0.002) loss 0.8271 (1.2060) acc 78.1250 (70.0906) lr 1.9823e-03 eta 19:40:23
+epoch [5/50] batch [695/1000] time 1.577 (1.563) data 0.000 (0.002) loss 1.2510 (1.2064) acc 59.3750 (70.0719) lr 1.9823e-03 eta 19:40:15
+epoch [5/50] batch [700/1000] time 1.569 (1.563) data 0.001 (0.002) loss 0.8853 (1.2043) acc 71.8750 (70.0982) lr 1.9823e-03 eta 19:40:10
+epoch [5/50] batch [705/1000] time 1.558 (1.563) data 0.000 (0.002) loss 1.7197 (1.2046) acc 59.3750 (70.0798) lr 1.9823e-03 eta 19:40:05
+epoch [5/50] batch [710/1000] time 1.562 (1.563) data 0.001 (0.002) loss 0.9326 (1.2026) acc 71.8750 (70.0836) lr 1.9823e-03 eta 19:39:59
+epoch [5/50] batch [715/1000] time 1.565 (1.563) data 0.000 (0.002) loss 1.5518 (1.2042) acc 59.3750 (70.0437) lr 1.9823e-03 eta 19:39:51
+epoch [5/50] batch [720/1000] time 1.579 (1.563) data 0.000 (0.002) loss 0.9624 (1.2031) acc 75.0000 (70.0651) lr 1.9823e-03 eta 19:39:43
+epoch [5/50] batch [725/1000] time 1.544 (1.563) data 0.000 (0.002) loss 1.2939 (1.2035) acc 71.8750 (70.0431) lr 1.9823e-03 eta 19:39:35
+epoch [5/50] batch [730/1000] time 1.573 (1.563) data 0.000 (0.002) loss 0.7285 (1.2021) acc 81.2500 (70.0771) lr 1.9823e-03 eta 19:39:35
+epoch [5/50] batch [735/1000] time 1.570 (1.563) data 0.001 (0.002) loss 1.7725 (1.2021) acc 68.7500 (70.1063) lr 1.9823e-03 eta 19:39:28
+epoch [5/50] batch [740/1000] time 1.569 (1.563) data 0.001 (0.002) loss 1.4121 (1.2033) acc 59.3750 (70.0845) lr 1.9823e-03 eta 19:39:18
+epoch [5/50] batch [745/1000] time 1.542 (1.563) data 0.001 (0.002) loss 0.8574 (1.2039) acc 71.8750 (70.0713) lr 1.9823e-03 eta 19:39:06
+epoch [5/50] batch [750/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.0479 (1.2042) acc 75.0000 (70.0875) lr 1.9823e-03 eta 19:38:58
+epoch [5/50] batch [755/1000] time 1.524 (1.563) data 0.000 (0.002) loss 0.9585 (1.2050) acc 71.8750 (70.0745) lr 1.9823e-03 eta 19:38:49
+epoch [5/50] batch [760/1000] time 1.552 (1.563) data 0.001 (0.002) loss 1.1045 (1.2062) acc 75.0000 (70.0822) lr 1.9823e-03 eta 19:38:39
+epoch [5/50] batch [765/1000] time 1.579 (1.563) data 0.000 (0.002) loss 1.8232 (1.2053) acc 46.8750 (70.0735) lr 1.9823e-03 eta 19:38:34
+epoch [5/50] batch [770/1000] time 1.564 (1.563) data 0.000 (0.002) loss 2.0801 (1.2059) acc 56.2500 (70.0609) lr 1.9823e-03 eta 19:38:25
+epoch [5/50] batch [775/1000] time 1.547 (1.563) data 0.000 (0.002) loss 1.7686 (1.2061) acc 59.3750 (70.0565) lr 1.9823e-03 eta 19:38:22
+epoch [5/50] batch [780/1000] time 1.581 (1.563) data 0.001 (0.002) loss 1.0352 (1.2061) acc 75.0000 (70.0521) lr 1.9823e-03 eta 19:38:15
+epoch [5/50] batch [785/1000] time 1.571 (1.563) data 0.000 (0.002) loss 1.1240 (1.2069) acc 68.7500 (70.0398) lr 1.9823e-03 eta 19:38:07
+epoch [5/50] batch [790/1000] time 1.541 (1.563) data 0.000 (0.002) loss 0.6694 (1.2049) acc 81.2500 (70.0633) lr 1.9823e-03 eta 19:37:59
+epoch [5/50] batch [795/1000] time 1.551 (1.563) data 0.000 (0.002) loss 1.7080 (1.2057) acc 59.3750 (70.0393) lr 1.9823e-03 eta 19:37:47
+epoch [5/50] batch [800/1000] time 1.556 (1.563) data 0.001 (0.002) loss 0.7964 (1.2042) acc 78.1250 (70.0469) lr 1.9823e-03 eta 19:37:36
+epoch [5/50] batch [805/1000] time 1.560 (1.563) data 0.000 (0.002) loss 1.6211 (1.2062) acc 56.2500 (69.9884) lr 1.9823e-03 eta 19:37:27
+epoch [5/50] batch [810/1000] time 1.555 (1.563) data 0.000 (0.002) loss 1.1865 (1.2048) acc 71.8750 (70.0309) lr 1.9823e-03 eta 19:37:16
+epoch [5/50] batch [815/1000] time 1.567 (1.563) data 0.000 (0.002) loss 1.0156 (1.2043) acc 68.7500 (70.0307) lr 1.9823e-03 eta 19:37:04
+epoch [5/50] batch [820/1000] time 1.549 (1.563) data 0.000 (0.002) loss 1.1924 (1.2043) acc 75.0000 (70.0229) lr 1.9823e-03 eta 19:37:05
+epoch [5/50] batch [825/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.3760 (1.2045) acc 62.5000 (70.0038) lr 1.9823e-03 eta 19:36:55
+epoch [5/50] batch [830/1000] time 1.563 (1.563) data 0.000 (0.002) loss 1.4424 (1.2040) acc 56.2500 (70.0000) lr 1.9823e-03 eta 19:36:50
+epoch [5/50] batch [835/1000] time 1.570 (1.563) data 0.001 (0.002) loss 1.1836 (1.2040) acc 75.0000 (69.9738) lr 1.9823e-03 eta 19:36:40
+epoch [5/50] batch [840/1000] time 1.549 (1.563) data 0.000 (0.002) loss 1.4434 (1.2062) acc 62.5000 (69.9256) lr 1.9823e-03 eta 19:36:33
+epoch [5/50] batch [845/1000] time 1.538 (1.563) data 0.000 (0.002) loss 0.8804 (1.2067) acc 78.1250 (69.9001) lr 1.9823e-03 eta 19:36:24
+epoch [5/50] batch [850/1000] time 1.557 (1.563) data 0.001 (0.002) loss 1.7734 (1.2068) acc 56.2500 (69.8934) lr 1.9823e-03 eta 19:36:17
+epoch [5/50] batch [855/1000] time 1.577 (1.563) data 0.000 (0.002) loss 1.3350 (1.2067) acc 71.8750 (69.9050) lr 1.9823e-03 eta 19:36:10
+epoch [5/50] batch [860/1000] time 1.566 (1.563) data 0.000 (0.002) loss 1.0420 (1.2046) acc 71.8750 (69.9491) lr 1.9823e-03 eta 19:36:03
+epoch [5/50] batch [865/1000] time 1.544 (1.563) data 0.000 (0.002) loss 0.8911 (1.2027) acc 68.7500 (69.9711) lr 1.9823e-03 eta 19:35:54
+epoch [5/50] batch [870/1000] time 1.540 (1.563) data 0.001 (0.002) loss 1.1289 (1.2026) acc 81.2500 (69.9964) lr 1.9823e-03 eta 19:35:45
+epoch [5/50] batch [875/1000] time 1.557 (1.563) data 0.001 (0.002) loss 1.5596 (1.2034) acc 62.5000 (69.9714) lr 1.9823e-03 eta 19:35:35
+epoch [5/50] batch [880/1000] time 1.730 (1.563) data 0.000 (0.002) loss 1.0879 (1.2030) acc 68.7500 (69.9787) lr 1.9823e-03 eta 19:35:34
+epoch [5/50] batch [885/1000] time 1.572 (1.563) data 0.000 (0.002) loss 0.7153 (1.2024) acc 81.2500 (69.9894) lr 1.9823e-03 eta 19:35:24
+epoch [5/50] batch [890/1000] time 1.551 (1.563) data 0.001 (0.002) loss 1.1299 (1.2037) acc 75.0000 (69.9754) lr 1.9823e-03 eta 19:35:14
+epoch [5/50] batch [895/1000] time 1.559 (1.563) data 0.000 (0.002) loss 1.4854 (1.2036) acc 62.5000 (69.9756) lr 1.9823e-03 eta 19:35:04
+epoch [5/50] batch [900/1000] time 1.555 (1.563) data 0.000 (0.002) loss 0.6562 (1.2020) acc 84.3750 (69.9792) lr 1.9823e-03 eta 19:34:57
+epoch [5/50] batch [905/1000] time 1.562 (1.563) data 0.000 (0.002) loss 0.9214 (1.2014) acc 65.6250 (69.9793) lr 1.9823e-03 eta 19:34:46
+epoch [5/50] batch [910/1000] time 1.556 (1.563) data 0.000 (0.002) loss 0.8794 (1.2014) acc 84.3750 (69.9966) lr 1.9823e-03 eta 19:34:37
+epoch [5/50] batch [915/1000] time 1.543 (1.563) data 0.000 (0.002) loss 1.1113 (1.2013) acc 59.3750 (69.9898) lr 1.9823e-03 eta 19:34:28
+epoch [5/50] batch [920/1000] time 1.534 (1.563) data 0.000 (0.002) loss 1.5742 (1.2019) acc 56.2500 (69.9660) lr 1.9823e-03 eta 19:34:17
+epoch [5/50] batch [925/1000] time 1.720 (1.563) data 0.000 (0.002) loss 1.4385 (1.2020) acc 62.5000 (69.9493) lr 1.9823e-03 eta 19:34:14
+epoch [5/50] batch [930/1000] time 1.554 (1.563) data 0.000 (0.002) loss 1.2168 (1.2014) acc 71.8750 (69.9664) lr 1.9823e-03 eta 19:34:06
+epoch [5/50] batch [935/1000] time 1.559 (1.563) data 0.001 (0.002) loss 1.2031 (1.2020) acc 62.5000 (69.9499) lr 1.9823e-03 eta 19:34:00
+epoch [5/50] batch [940/1000] time 1.569 (1.563) data 0.000 (0.002) loss 1.6406 (1.2023) acc 59.3750 (69.9202) lr 1.9823e-03 eta 19:33:51
+epoch [5/50] batch [945/1000] time 1.576 (1.563) data 0.000 (0.002) loss 1.5986 (1.2034) acc 62.5000 (69.9107) lr 1.9823e-03 eta 19:33:42
+epoch [5/50] batch [950/1000] time 1.565 (1.563) data 0.000 (0.002) loss 0.8931 (1.2038) acc 78.1250 (69.8980) lr 1.9823e-03 eta 19:33:30
+epoch [5/50] batch [955/1000] time 1.544 (1.563) data 0.000 (0.001) loss 1.4629 (1.2032) acc 71.8750 (69.9215) lr 1.9823e-03 eta 19:33:22
+epoch [5/50] batch [960/1000] time 1.564 (1.563) data 0.000 (0.001) loss 1.1201 (1.2029) acc 71.8750 (69.9251) lr 1.9823e-03 eta 19:33:14
+epoch [5/50] batch [965/1000] time 1.560 (1.563) data 0.000 (0.001) loss 1.6035 (1.2040) acc 65.6250 (69.9126) lr 1.9823e-03 eta 19:33:08
+epoch [5/50] batch [970/1000] time 1.557 (1.563) data 0.000 (0.001) loss 1.2646 (1.2047) acc 71.8750 (69.9195) lr 1.9823e-03 eta 19:33:07
+epoch [5/50] batch [975/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.2842 (1.2044) acc 65.6250 (69.9167) lr 1.9823e-03 eta 19:32:58
+epoch [5/50] batch [980/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.2070 (1.2042) acc 68.7500 (69.9107) lr 1.9823e-03 eta 19:32:51
+epoch [5/50] batch [985/1000] time 1.558 (1.563) data 0.001 (0.001) loss 1.4707 (1.2036) acc 71.8750 (69.9365) lr 1.9823e-03 eta 19:32:43
+epoch [5/50] batch [990/1000] time 1.547 (1.563) data 0.000 (0.001) loss 1.5449 (1.2027) acc 68.7500 (69.9716) lr 1.9823e-03 eta 19:32:33
+epoch [5/50] batch [995/1000] time 1.570 (1.563) data 0.000 (0.001) loss 1.3369 (1.2030) acc 53.1250 (69.9623) lr 1.9823e-03 eta 19:32:23
+epoch [5/50] batch [1000/1000] time 1.548 (1.563) data 0.000 (0.001) loss 1.2070 (1.2019) acc 62.5000 (69.9562) lr 1.9686e-03 eta 19:32:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,005
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [6/50] batch [5/1000] time 1.547 (1.685) data 0.000 (0.186) loss 1.0752 (0.8844) acc 65.6250 (75.6250) lr 1.9686e-03 eta 21:03:53
+epoch [6/50] batch [10/1000] time 1.585 (1.629) data 0.001 (0.093) loss 1.0830 (1.0910) acc 75.0000 (73.7500) lr 1.9686e-03 eta 20:21:12
+epoch [6/50] batch [15/1000] time 1.551 (1.608) data 0.000 (0.062) loss 1.1768 (1.0743) acc 75.0000 (73.9583) lr 1.9686e-03 eta 20:05:44
+epoch [6/50] batch [20/1000] time 1.553 (1.598) data 0.001 (0.047) loss 1.2480 (1.1402) acc 59.3750 (73.2812) lr 1.9686e-03 eta 19:57:50
+epoch [6/50] batch [25/1000] time 1.560 (1.590) data 0.000 (0.038) loss 0.6343 (1.1473) acc 84.3750 (73.6250) lr 1.9686e-03 eta 19:52:11
+epoch [6/50] batch [30/1000] time 1.567 (1.586) data 0.001 (0.031) loss 0.7534 (1.0942) acc 81.2500 (74.3750) lr 1.9686e-03 eta 19:48:34
+epoch [6/50] batch [35/1000] time 1.551 (1.582) data 0.000 (0.027) loss 1.2500 (1.0941) acc 71.8750 (74.3750) lr 1.9686e-03 eta 19:45:18
+epoch [6/50] batch [40/1000] time 1.574 (1.586) data 0.001 (0.024) loss 1.2002 (1.1071) acc 68.7500 (73.7500) lr 1.9686e-03 eta 19:48:28
+epoch [6/50] batch [45/1000] time 1.573 (1.584) data 0.000 (0.021) loss 1.5791 (1.1430) acc 56.2500 (72.9861) lr 1.9686e-03 eta 19:46:28
+epoch [6/50] batch [50/1000] time 1.572 (1.582) data 0.001 (0.019) loss 1.6260 (1.1559) acc 62.5000 (72.6250) lr 1.9686e-03 eta 19:45:12
+epoch [6/50] batch [55/1000] time 1.564 (1.581) data 0.000 (0.017) loss 0.9302 (1.1767) acc 78.1250 (72.0455) lr 1.9686e-03 eta 19:44:13
+epoch [6/50] batch [60/1000] time 1.572 (1.580) data 0.001 (0.016) loss 1.3330 (1.1907) acc 65.6250 (71.6667) lr 1.9686e-03 eta 19:43:28
+epoch [6/50] batch [65/1000] time 1.558 (1.579) data 0.000 (0.015) loss 1.1982 (1.2033) acc 71.8750 (71.3462) lr 1.9686e-03 eta 19:42:40
+epoch [6/50] batch [70/1000] time 1.567 (1.578) data 0.000 (0.014) loss 1.1104 (1.1951) acc 78.1250 (71.6518) lr 1.9686e-03 eta 19:42:01
+epoch [6/50] batch [75/1000] time 1.555 (1.578) data 0.001 (0.013) loss 0.9390 (1.2039) acc 75.0000 (71.4167) lr 1.9686e-03 eta 19:41:27
+epoch [6/50] batch [80/1000] time 1.570 (1.577) data 0.000 (0.012) loss 1.4697 (1.2128) acc 68.7500 (71.1328) lr 1.9686e-03 eta 19:40:37
+epoch [6/50] batch [85/1000] time 1.540 (1.578) data 0.000 (0.011) loss 1.2402 (1.2235) acc 71.8750 (70.9559) lr 1.9686e-03 eta 19:40:59
+epoch [6/50] batch [90/1000] time 1.560 (1.577) data 0.000 (0.011) loss 0.9917 (1.2042) acc 68.7500 (71.2500) lr 1.9686e-03 eta 19:40:34
+epoch [6/50] batch [95/1000] time 1.546 (1.576) data 0.001 (0.010) loss 0.9634 (1.2064) acc 81.2500 (71.0855) lr 1.9686e-03 eta 19:39:26
+epoch [6/50] batch [100/1000] time 1.542 (1.575) data 0.000 (0.010) loss 1.1299 (1.2007) acc 68.7500 (71.0000) lr 1.9686e-03 eta 19:38:40
+epoch [6/50] batch [105/1000] time 1.560 (1.574) data 0.000 (0.009) loss 1.6484 (1.2186) acc 68.7500 (70.8929) lr 1.9686e-03 eta 19:37:48
+epoch [6/50] batch [110/1000] time 1.584 (1.574) data 0.000 (0.009) loss 1.2705 (1.2340) acc 59.3750 (70.4830) lr 1.9686e-03 eta 19:37:39
+epoch [6/50] batch [115/1000] time 1.556 (1.574) data 0.000 (0.009) loss 1.5518 (1.2377) acc 62.5000 (70.3261) lr 1.9686e-03 eta 19:37:09
+epoch [6/50] batch [120/1000] time 1.536 (1.573) data 0.001 (0.008) loss 1.0371 (1.2431) acc 75.0000 (70.1042) lr 1.9686e-03 eta 19:36:18
+epoch [6/50] batch [125/1000] time 1.544 (1.572) data 0.001 (0.008) loss 1.1602 (1.2390) acc 71.8750 (70.2750) lr 1.9686e-03 eta 19:35:49
+epoch [6/50] batch [130/1000] time 1.555 (1.572) data 0.000 (0.008) loss 0.6162 (1.2282) acc 87.5000 (70.4808) lr 1.9686e-03 eta 19:35:24
+epoch [6/50] batch [135/1000] time 1.586 (1.572) data 0.000 (0.007) loss 1.1016 (1.2137) acc 68.7500 (70.7870) lr 1.9686e-03 eta 19:35:20
+epoch [6/50] batch [140/1000] time 1.550 (1.572) data 0.000 (0.007) loss 1.2852 (1.2099) acc 56.2500 (70.5804) lr 1.9686e-03 eta 19:35:09
+epoch [6/50] batch [145/1000] time 1.725 (1.573) data 0.000 (0.007) loss 1.2393 (1.2138) acc 62.5000 (70.3448) lr 1.9686e-03 eta 19:35:44
+epoch [6/50] batch [150/1000] time 1.573 (1.573) data 0.001 (0.007) loss 0.8320 (1.2065) acc 75.0000 (70.3958) lr 1.9686e-03 eta 19:35:39
+epoch [6/50] batch [155/1000] time 1.561 (1.573) data 0.000 (0.007) loss 1.0195 (1.2086) acc 68.7500 (70.2218) lr 1.9686e-03 eta 19:35:21
+epoch [6/50] batch [160/1000] time 1.573 (1.572) data 0.000 (0.006) loss 0.9268 (1.2036) acc 71.8750 (70.3711) lr 1.9686e-03 eta 19:34:55
+epoch [6/50] batch [165/1000] time 1.571 (1.572) data 0.000 (0.006) loss 1.4482 (1.1977) acc 68.7500 (70.5303) lr 1.9686e-03 eta 19:34:45
+epoch [6/50] batch [170/1000] time 1.567 (1.572) data 0.001 (0.006) loss 1.0742 (1.1957) acc 75.0000 (70.5331) lr 1.9686e-03 eta 19:34:30
+epoch [6/50] batch [175/1000] time 1.584 (1.572) data 0.001 (0.006) loss 0.9712 (1.1895) acc 75.0000 (70.6607) lr 1.9686e-03 eta 19:34:32
+epoch [6/50] batch [180/1000] time 1.545 (1.572) data 0.000 (0.006) loss 1.2852 (1.1951) acc 68.7500 (70.6424) lr 1.9686e-03 eta 19:34:11
+epoch [6/50] batch [185/1000] time 1.550 (1.572) data 0.000 (0.006) loss 1.0225 (1.1919) acc 84.3750 (70.8108) lr 1.9686e-03 eta 19:33:50
+epoch [6/50] batch [190/1000] time 1.720 (1.572) data 0.001 (0.005) loss 1.8262 (1.1906) acc 62.5000 (70.9046) lr 1.9686e-03 eta 19:34:19
+epoch [6/50] batch [195/1000] time 1.568 (1.572) data 0.001 (0.005) loss 1.1357 (1.1871) acc 75.0000 (71.0577) lr 1.9686e-03 eta 19:33:54
+epoch [6/50] batch [200/1000] time 1.571 (1.572) data 0.000 (0.005) loss 0.8867 (1.1812) acc 78.1250 (71.2031) lr 1.9686e-03 eta 19:33:43
+epoch [6/50] batch [205/1000] time 1.553 (1.572) data 0.001 (0.005) loss 1.4434 (1.1872) acc 62.5000 (71.0671) lr 1.9686e-03 eta 19:33:24
+epoch [6/50] batch [210/1000] time 1.576 (1.572) data 0.000 (0.005) loss 0.5552 (1.1838) acc 84.3750 (71.1458) lr 1.9686e-03 eta 19:33:18
+epoch [6/50] batch [215/1000] time 1.553 (1.571) data 0.000 (0.005) loss 0.8525 (1.1814) acc 75.0000 (71.1628) lr 1.9686e-03 eta 19:32:56
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+epoch [6/50] batch [785/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.5732 (1.2077) acc 68.7500 (70.3025) lr 1.9686e-03 eta 19:13:25
+epoch [6/50] batch [790/1000] time 1.572 (1.565) data 0.000 (0.002) loss 1.3262 (1.2072) acc 71.8750 (70.3204) lr 1.9686e-03 eta 19:13:19
+epoch [6/50] batch [795/1000] time 1.541 (1.565) data 0.000 (0.002) loss 1.3496 (1.2066) acc 62.5000 (70.3341) lr 1.9686e-03 eta 19:13:19
+epoch [6/50] batch [800/1000] time 1.564 (1.565) data 0.000 (0.002) loss 0.7148 (1.2062) acc 81.2500 (70.3398) lr 1.9686e-03 eta 19:13:10
+epoch [6/50] batch [805/1000] time 1.565 (1.565) data 0.001 (0.002) loss 1.2568 (1.2056) acc 65.6250 (70.3455) lr 1.9686e-03 eta 19:12:58
+epoch [6/50] batch [810/1000] time 1.566 (1.565) data 0.001 (0.002) loss 1.3760 (1.2060) acc 62.5000 (70.3511) lr 1.9686e-03 eta 19:12:47
+epoch [6/50] batch [815/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.7607 (1.2061) acc 71.8750 (70.3528) lr 1.9686e-03 eta 19:12:38
+epoch [6/50] batch [820/1000] time 1.558 (1.565) data 0.000 (0.002) loss 1.3535 (1.2063) acc 62.5000 (70.3620) lr 1.9686e-03 eta 19:12:31
+epoch [6/50] batch [825/1000] time 1.556 (1.565) data 0.000 (0.002) loss 0.9780 (1.2052) acc 81.2500 (70.3750) lr 1.9686e-03 eta 19:12:23
+epoch [6/50] batch [830/1000] time 1.582 (1.565) data 0.001 (0.002) loss 0.7896 (1.2045) acc 75.0000 (70.3765) lr 1.9686e-03 eta 19:12:14
+epoch [6/50] batch [835/1000] time 1.586 (1.565) data 0.000 (0.002) loss 0.9839 (1.2042) acc 78.1250 (70.3705) lr 1.9686e-03 eta 19:12:07
+epoch [6/50] batch [840/1000] time 1.590 (1.565) data 0.000 (0.002) loss 1.7197 (1.2053) acc 62.5000 (70.3571) lr 1.9686e-03 eta 19:12:05
+epoch [6/50] batch [845/1000] time 1.530 (1.565) data 0.000 (0.002) loss 1.1279 (1.2060) acc 75.0000 (70.3439) lr 1.9686e-03 eta 19:11:59
+epoch [6/50] batch [850/1000] time 1.555 (1.565) data 0.001 (0.002) loss 1.2021 (1.2041) acc 71.8750 (70.3750) lr 1.9686e-03 eta 19:11:49
+epoch [6/50] batch [855/1000] time 1.559 (1.565) data 0.001 (0.002) loss 1.2900 (1.2045) acc 68.7500 (70.3618) lr 1.9686e-03 eta 19:11:42
+epoch [6/50] batch [860/1000] time 1.586 (1.565) data 0.000 (0.002) loss 1.8564 (1.2057) acc 59.3750 (70.3634) lr 1.9686e-03 eta 19:11:36
+epoch [6/50] batch [865/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.9492 (1.2048) acc 62.5000 (70.3577) lr 1.9686e-03 eta 19:11:29
+epoch [6/50] batch [870/1000] time 1.588 (1.565) data 0.001 (0.002) loss 1.7393 (1.2054) acc 62.5000 (70.3556) lr 1.9686e-03 eta 19:11:20
+epoch [6/50] batch [875/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.1934 (1.2052) acc 68.7500 (70.3571) lr 1.9686e-03 eta 19:11:11
+epoch [6/50] batch [880/1000] time 1.545 (1.565) data 0.001 (0.002) loss 1.6992 (1.2056) acc 65.6250 (70.3587) lr 1.9686e-03 eta 19:10:58
+epoch [6/50] batch [885/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.7852 (1.2054) acc 62.5000 (70.3672) lr 1.9686e-03 eta 19:10:51
+epoch [6/50] batch [890/1000] time 1.580 (1.565) data 0.000 (0.002) loss 1.1611 (1.2055) acc 75.0000 (70.3617) lr 1.9686e-03 eta 19:10:42
+epoch [6/50] batch [895/1000] time 1.542 (1.565) data 0.000 (0.002) loss 1.9619 (1.2046) acc 53.1250 (70.3666) lr 1.9686e-03 eta 19:10:33
+epoch [6/50] batch [900/1000] time 1.729 (1.565) data 0.000 (0.002) loss 1.4316 (1.2038) acc 65.6250 (70.3750) lr 1.9686e-03 eta 19:10:33
+epoch [6/50] batch [905/1000] time 1.538 (1.565) data 0.001 (0.002) loss 1.1826 (1.2031) acc 65.6250 (70.3936) lr 1.9686e-03 eta 19:10:26
+epoch [6/50] batch [910/1000] time 1.566 (1.565) data 0.000 (0.002) loss 0.9668 (1.2036) acc 81.2500 (70.3571) lr 1.9686e-03 eta 19:10:16
+epoch [6/50] batch [915/1000] time 1.534 (1.565) data 0.001 (0.002) loss 0.4675 (1.2023) acc 87.5000 (70.3996) lr 1.9686e-03 eta 19:10:07
+epoch [6/50] batch [920/1000] time 1.585 (1.565) data 0.000 (0.001) loss 0.9097 (1.2004) acc 75.0000 (70.4212) lr 1.9686e-03 eta 19:10:00
+epoch [6/50] batch [925/1000] time 1.547 (1.565) data 0.001 (0.001) loss 1.0273 (1.1999) acc 65.6250 (70.4088) lr 1.9686e-03 eta 19:09:51
+epoch [6/50] batch [930/1000] time 1.540 (1.565) data 0.001 (0.001) loss 0.8335 (1.1988) acc 78.1250 (70.4167) lr 1.9686e-03 eta 19:09:42
+epoch [6/50] batch [935/1000] time 1.561 (1.565) data 0.000 (0.001) loss 1.0889 (1.1982) acc 71.8750 (70.4278) lr 1.9686e-03 eta 19:09:31
+epoch [6/50] batch [940/1000] time 1.559 (1.565) data 0.000 (0.001) loss 0.8789 (1.1979) acc 78.1250 (70.4289) lr 1.9686e-03 eta 19:09:20
+epoch [6/50] batch [945/1000] time 1.690 (1.565) data 0.001 (0.001) loss 0.9614 (1.1977) acc 62.5000 (70.4200) lr 1.9686e-03 eta 19:09:18
+epoch [6/50] batch [950/1000] time 1.550 (1.565) data 0.001 (0.001) loss 1.4756 (1.1983) acc 65.6250 (70.4046) lr 1.9686e-03 eta 19:09:08
+epoch [6/50] batch [955/1000] time 1.555 (1.565) data 0.000 (0.001) loss 1.0898 (1.1972) acc 78.1250 (70.4058) lr 1.9686e-03 eta 19:08:57
+epoch [6/50] batch [960/1000] time 1.545 (1.565) data 0.000 (0.001) loss 0.9009 (1.1961) acc 68.7500 (70.4004) lr 1.9686e-03 eta 19:08:46
+epoch [6/50] batch [965/1000] time 1.565 (1.565) data 0.001 (0.001) loss 1.1748 (1.1955) acc 68.7500 (70.4242) lr 1.9686e-03 eta 19:08:37
+epoch [6/50] batch [970/1000] time 1.566 (1.565) data 0.000 (0.001) loss 1.5146 (1.1958) acc 65.6250 (70.4253) lr 1.9686e-03 eta 19:08:29
+epoch [6/50] batch [975/1000] time 1.571 (1.565) data 0.000 (0.001) loss 1.2480 (1.1952) acc 68.7500 (70.4391) lr 1.9686e-03 eta 19:08:20
+epoch [6/50] batch [980/1000] time 1.562 (1.565) data 0.001 (0.001) loss 1.2617 (1.1954) acc 75.0000 (70.4528) lr 1.9686e-03 eta 19:08:10
+epoch [6/50] batch [985/1000] time 1.590 (1.565) data 0.001 (0.001) loss 0.7124 (1.1949) acc 81.2500 (70.4537) lr 1.9686e-03 eta 19:08:03
+epoch [6/50] batch [990/1000] time 1.561 (1.565) data 0.000 (0.001) loss 0.9800 (1.1950) acc 78.1250 (70.4640) lr 1.9686e-03 eta 19:07:59
+epoch [6/50] batch [995/1000] time 1.558 (1.565) data 0.000 (0.001) loss 1.5010 (1.1950) acc 50.0000 (70.4554) lr 1.9686e-03 eta 19:07:49
+epoch [6/50] batch [1000/1000] time 1.534 (1.565) data 0.000 (0.001) loss 1.0176 (1.1949) acc 68.7500 (70.4594) lr 1.9511e-03 eta 19:07:40
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,084
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [7/50] batch [5/1000] time 1.548 (1.697) data 0.000 (0.190) loss 0.9692 (1.0441) acc 75.0000 (73.1250) lr 1.9511e-03 eta 20:44:40
+epoch [7/50] batch [10/1000] time 1.555 (1.623) data 0.000 (0.095) loss 0.8799 (0.9870) acc 75.0000 (74.0625) lr 1.9511e-03 eta 19:50:13
+epoch [7/50] batch [15/1000] time 1.561 (1.603) data 0.000 (0.063) loss 0.6147 (0.9664) acc 78.1250 (74.7917) lr 1.9511e-03 eta 19:35:27
+epoch [7/50] batch [20/1000] time 1.588 (1.597) data 0.001 (0.048) loss 0.9727 (0.9669) acc 75.0000 (74.5312) lr 1.9511e-03 eta 19:30:17
+epoch [7/50] batch [25/1000] time 1.552 (1.593) data 0.000 (0.038) loss 1.1377 (1.0412) acc 78.1250 (73.3750) lr 1.9511e-03 eta 19:27:15
+epoch [7/50] batch [30/1000] time 1.554 (1.588) data 0.000 (0.032) loss 1.6064 (1.0652) acc 59.3750 (73.4375) lr 1.9511e-03 eta 19:24:05
+epoch [7/50] batch [35/1000] time 1.531 (1.584) data 0.000 (0.027) loss 2.1777 (1.0993) acc 46.8750 (72.6786) lr 1.9511e-03 eta 19:20:36
+epoch [7/50] batch [40/1000] time 1.550 (1.580) data 0.000 (0.024) loss 0.4983 (1.0875) acc 84.3750 (73.5156) lr 1.9511e-03 eta 19:17:32
+epoch [7/50] batch [45/1000] time 1.540 (1.577) data 0.000 (0.022) loss 1.5605 (1.1227) acc 65.6250 (72.7083) lr 1.9511e-03 eta 19:15:00
+epoch [7/50] batch [50/1000] time 1.556 (1.574) data 0.000 (0.019) loss 1.0459 (1.1099) acc 68.7500 (72.6875) lr 1.9511e-03 eta 19:12:49
+epoch [7/50] batch [55/1000] time 1.549 (1.572) data 0.001 (0.018) loss 1.1670 (1.1114) acc 53.1250 (71.9318) lr 1.9511e-03 eta 19:11:31
+epoch [7/50] batch [60/1000] time 1.574 (1.575) data 0.000 (0.016) loss 0.8618 (1.1012) acc 71.8750 (71.8750) lr 1.9511e-03 eta 19:13:37
+epoch [7/50] batch [65/1000] time 1.541 (1.573) data 0.001 (0.015) loss 1.0957 (1.0955) acc 68.7500 (71.8269) lr 1.9511e-03 eta 19:12:10
+epoch [7/50] batch [70/1000] time 1.566 (1.572) data 0.000 (0.014) loss 1.4160 (1.1249) acc 62.5000 (71.2946) lr 1.9511e-03 eta 19:11:07
+epoch [7/50] batch [75/1000] time 1.555 (1.571) data 0.000 (0.013) loss 1.4004 (1.1199) acc 62.5000 (71.4167) lr 1.9511e-03 eta 19:09:56
+epoch [7/50] batch [80/1000] time 1.539 (1.570) data 0.001 (0.012) loss 1.2861 (1.1305) acc 65.6250 (71.3672) lr 1.9511e-03 eta 19:08:58
+epoch [7/50] batch [85/1000] time 1.581 (1.569) data 0.000 (0.012) loss 1.3203 (1.1437) acc 68.7500 (71.2132) lr 1.9511e-03 eta 19:08:33
+epoch [7/50] batch [90/1000] time 1.548 (1.568) data 0.000 (0.011) loss 1.1973 (1.1566) acc 75.0000 (70.9722) lr 1.9511e-03 eta 19:07:46
+epoch [7/50] batch [95/1000] time 1.563 (1.568) data 0.000 (0.010) loss 1.6113 (1.1702) acc 71.8750 (70.8882) lr 1.9511e-03 eta 19:07:25
+epoch [7/50] batch [100/1000] time 1.569 (1.568) data 0.000 (0.010) loss 1.0596 (1.1754) acc 75.0000 (70.9062) lr 1.9511e-03 eta 19:07:22
+epoch [7/50] batch [105/1000] time 1.557 (1.568) data 0.000 (0.010) loss 1.3809 (1.1795) acc 65.6250 (70.7738) lr 1.9511e-03 eta 19:07:14
+epoch [7/50] batch [110/1000] time 1.570 (1.569) data 0.000 (0.009) loss 1.0908 (1.1735) acc 62.5000 (70.8807) lr 1.9511e-03 eta 19:07:57
+epoch [7/50] batch [115/1000] time 1.562 (1.569) data 0.001 (0.009) loss 1.8652 (1.1810) acc 62.5000 (70.8152) lr 1.9511e-03 eta 19:07:22
+epoch [7/50] batch [120/1000] time 1.546 (1.568) data 0.000 (0.008) loss 1.1045 (1.1805) acc 65.6250 (70.5990) lr 1.9511e-03 eta 19:06:57
+epoch [7/50] batch [125/1000] time 1.561 (1.568) data 0.000 (0.008) loss 0.9482 (1.1757) acc 71.8750 (70.7000) lr 1.9511e-03 eta 19:06:45
+epoch [7/50] batch [130/1000] time 1.591 (1.568) data 0.001 (0.008) loss 1.3984 (1.1857) acc 68.7500 (70.5769) lr 1.9511e-03 eta 19:06:42
+epoch [7/50] batch [135/1000] time 1.575 (1.568) data 0.000 (0.008) loss 1.1777 (1.1868) acc 71.8750 (70.6944) lr 1.9511e-03 eta 19:06:14
+epoch [7/50] batch [140/1000] time 1.536 (1.567) data 0.000 (0.007) loss 1.5371 (1.1987) acc 68.7500 (70.6696) lr 1.9511e-03 eta 19:05:46
+epoch [7/50] batch [145/1000] time 1.556 (1.567) data 0.000 (0.007) loss 1.3828 (1.1953) acc 59.3750 (70.6681) lr 1.9511e-03 eta 19:05:26
+epoch [7/50] batch [150/1000] time 1.548 (1.567) data 0.001 (0.007) loss 1.2998 (1.1998) acc 68.7500 (70.4583) lr 1.9511e-03 eta 19:05:00
+epoch [7/50] batch [155/1000] time 1.535 (1.566) data 0.001 (0.007) loss 1.5752 (1.2090) acc 62.5000 (70.2218) lr 1.9511e-03 eta 19:04:35
+epoch [7/50] batch [160/1000] time 1.558 (1.566) data 0.001 (0.006) loss 1.0840 (1.2112) acc 78.1250 (70.2148) lr 1.9511e-03 eta 19:04:05
+epoch [7/50] batch [165/1000] time 1.557 (1.566) data 0.000 (0.006) loss 0.8696 (1.2086) acc 75.0000 (70.1515) lr 1.9511e-03 eta 19:04:00
+epoch [7/50] batch [170/1000] time 1.577 (1.566) data 0.000 (0.006) loss 1.3418 (1.2088) acc 65.6250 (70.0000) lr 1.9511e-03 eta 19:03:47
+epoch [7/50] batch [175/1000] time 1.551 (1.566) data 0.000 (0.006) loss 0.9565 (1.2091) acc 78.1250 (70.0893) lr 1.9511e-03 eta 19:03:32
+epoch [7/50] batch [180/1000] time 1.559 (1.566) data 0.000 (0.006) loss 1.3320 (1.2054) acc 65.6250 (70.1562) lr 1.9511e-03 eta 19:03:31
+epoch [7/50] batch [185/1000] time 1.561 (1.566) data 0.000 (0.006) loss 1.2412 (1.2071) acc 65.6250 (70.0845) lr 1.9511e-03 eta 19:03:25
+epoch [7/50] batch [190/1000] time 1.548 (1.565) data 0.000 (0.005) loss 0.9854 (1.2037) acc 75.0000 (70.0987) lr 1.9511e-03 eta 19:03:04
+epoch [7/50] batch [195/1000] time 1.526 (1.565) data 0.001 (0.005) loss 1.7598 (1.2051) acc 56.2500 (70.0801) lr 1.9511e-03 eta 19:02:41
+epoch [7/50] batch [200/1000] time 1.579 (1.565) data 0.000 (0.005) loss 1.3926 (1.2025) acc 59.3750 (70.0938) lr 1.9511e-03 eta 19:02:26
+epoch [7/50] batch [205/1000] time 1.564 (1.565) data 0.000 (0.005) loss 1.3115 (1.1976) acc 68.7500 (70.1524) lr 1.9511e-03 eta 19:02:14
+epoch [7/50] batch [210/1000] time 1.545 (1.565) data 0.000 (0.005) loss 1.3398 (1.1981) acc 68.7500 (70.1637) lr 1.9511e-03 eta 19:02:32
+epoch [7/50] batch [215/1000] time 1.604 (1.565) data 0.000 (0.005) loss 1.2432 (1.1934) acc 62.5000 (70.1453) lr 1.9511e-03 eta 19:02:24
+epoch [7/50] batch [220/1000] time 1.548 (1.565) data 0.000 (0.005) loss 1.5049 (1.1915) acc 71.8750 (70.2131) lr 1.9511e-03 eta 19:02:10
+epoch [7/50] batch [225/1000] time 1.562 (1.565) data 0.000 (0.005) loss 1.3271 (1.1970) acc 65.6250 (70.1667) lr 1.9511e-03 eta 19:02:04
+epoch [7/50] batch [230/1000] time 1.572 (1.565) data 0.000 (0.005) loss 1.0566 (1.1953) acc 75.0000 (70.2446) lr 1.9511e-03 eta 19:01:45
+epoch [7/50] batch [235/1000] time 1.561 (1.565) data 0.001 (0.004) loss 1.5127 (1.1917) acc 65.6250 (70.2793) lr 1.9511e-03 eta 19:01:30
+epoch [7/50] batch [240/1000] time 1.541 (1.565) data 0.001 (0.004) loss 1.1270 (1.1968) acc 65.6250 (70.1823) lr 1.9511e-03 eta 19:01:16
+epoch [7/50] batch [245/1000] time 1.556 (1.565) data 0.000 (0.004) loss 1.0107 (1.1979) acc 71.8750 (70.2168) lr 1.9511e-03 eta 19:01:05
+epoch [7/50] batch [250/1000] time 1.547 (1.565) data 0.000 (0.004) loss 1.2754 (1.1943) acc 59.3750 (70.2375) lr 1.9511e-03 eta 19:01:03
+epoch [7/50] batch [255/1000] time 1.574 (1.565) data 0.000 (0.004) loss 1.8369 (1.1989) acc 68.7500 (70.2328) lr 1.9511e-03 eta 19:01:22
+epoch [7/50] batch [260/1000] time 1.564 (1.566) data 0.001 (0.004) loss 0.9570 (1.1962) acc 71.8750 (70.2885) lr 1.9511e-03 eta 19:01:15
+epoch [7/50] batch [265/1000] time 1.561 (1.565) data 0.000 (0.004) loss 1.2012 (1.1956) acc 65.6250 (70.2476) lr 1.9511e-03 eta 19:00:57
+epoch [7/50] batch [270/1000] time 1.564 (1.565) data 0.000 (0.004) loss 1.6768 (1.1990) acc 65.6250 (70.3009) lr 1.9511e-03 eta 19:00:50
+epoch [7/50] batch [275/1000] time 1.553 (1.565) data 0.000 (0.004) loss 1.3027 (1.1989) acc 75.0000 (70.2955) lr 1.9511e-03 eta 19:00:50
+epoch [7/50] batch [280/1000] time 1.578 (1.565) data 0.001 (0.004) loss 1.0801 (1.1972) acc 71.8750 (70.2679) lr 1.9511e-03 eta 19:00:40
+epoch [7/50] batch [285/1000] time 1.577 (1.565) data 0.000 (0.004) loss 1.4365 (1.1958) acc 68.7500 (70.3509) lr 1.9511e-03 eta 19:00:34
+epoch [7/50] batch [290/1000] time 1.553 (1.565) data 0.000 (0.004) loss 1.5020 (1.1947) acc 68.7500 (70.4095) lr 1.9511e-03 eta 19:00:19
+epoch [7/50] batch [295/1000] time 1.551 (1.565) data 0.000 (0.004) loss 0.8403 (1.1937) acc 75.0000 (70.4131) lr 1.9511e-03 eta 19:00:03
+epoch [7/50] batch [300/1000] time 1.574 (1.566) data 0.000 (0.004) loss 1.4580 (1.1898) acc 65.6250 (70.5312) lr 1.9511e-03 eta 19:00:17
+epoch [7/50] batch [305/1000] time 1.572 (1.566) data 0.000 (0.004) loss 1.6553 (1.1938) acc 62.5000 (70.4406) lr 1.9511e-03 eta 19:00:16
+epoch [7/50] batch [310/1000] time 1.548 (1.566) data 0.000 (0.004) loss 0.5869 (1.1898) acc 90.6250 (70.4940) lr 1.9511e-03 eta 19:00:01
+epoch [7/50] batch [315/1000] time 1.551 (1.565) data 0.000 (0.003) loss 0.6787 (1.1863) acc 81.2500 (70.5456) lr 1.9511e-03 eta 18:59:41
+epoch [7/50] batch [320/1000] time 1.580 (1.565) data 0.000 (0.003) loss 1.3018 (1.1889) acc 71.8750 (70.5371) lr 1.9511e-03 eta 18:59:26
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+epoch [7/50] batch [885/1000] time 1.552 (1.564) data 0.000 (0.002) loss 1.8398 (1.1872) acc 62.5000 (70.6815) lr 1.9511e-03 eta 18:43:48
+epoch [7/50] batch [890/1000] time 1.557 (1.564) data 0.001 (0.002) loss 1.1758 (1.1890) acc 71.8750 (70.6390) lr 1.9511e-03 eta 18:43:37
+epoch [7/50] batch [895/1000] time 1.556 (1.564) data 0.001 (0.002) loss 0.9194 (1.1888) acc 78.1250 (70.6320) lr 1.9511e-03 eta 18:43:26
+epoch [7/50] batch [900/1000] time 1.560 (1.564) data 0.000 (0.002) loss 0.8374 (1.1879) acc 78.1250 (70.6285) lr 1.9511e-03 eta 18:43:15
+epoch [7/50] batch [905/1000] time 1.543 (1.564) data 0.000 (0.002) loss 0.6328 (1.1862) acc 87.5000 (70.6733) lr 1.9511e-03 eta 18:43:14
+epoch [7/50] batch [910/1000] time 1.561 (1.564) data 0.000 (0.002) loss 1.2803 (1.1851) acc 65.6250 (70.6696) lr 1.9511e-03 eta 18:43:06
+epoch [7/50] batch [915/1000] time 1.580 (1.564) data 0.001 (0.002) loss 0.9878 (1.1859) acc 68.7500 (70.6626) lr 1.9511e-03 eta 18:42:58
+epoch [7/50] batch [920/1000] time 1.560 (1.564) data 0.000 (0.001) loss 1.2061 (1.1853) acc 65.6250 (70.6658) lr 1.9511e-03 eta 18:42:49
+epoch [7/50] batch [925/1000] time 1.541 (1.564) data 0.000 (0.001) loss 0.9170 (1.1844) acc 78.1250 (70.6892) lr 1.9511e-03 eta 18:42:36
+epoch [7/50] batch [930/1000] time 1.554 (1.564) data 0.000 (0.001) loss 0.6084 (1.1840) acc 87.5000 (70.6754) lr 1.9511e-03 eta 18:42:28
+epoch [7/50] batch [935/1000] time 1.568 (1.564) data 0.000 (0.001) loss 1.3369 (1.1850) acc 71.8750 (70.6651) lr 1.9511e-03 eta 18:42:23
+epoch [7/50] batch [940/1000] time 1.559 (1.564) data 0.000 (0.001) loss 1.0391 (1.1857) acc 62.5000 (70.6616) lr 1.9511e-03 eta 18:42:12
+epoch [7/50] batch [945/1000] time 1.569 (1.564) data 0.000 (0.001) loss 1.5977 (1.1871) acc 78.1250 (70.6548) lr 1.9511e-03 eta 18:42:06
+epoch [7/50] batch [950/1000] time 1.584 (1.564) data 0.000 (0.001) loss 1.3701 (1.1882) acc 65.6250 (70.6316) lr 1.9511e-03 eta 18:42:00
+epoch [7/50] batch [955/1000] time 1.584 (1.564) data 0.001 (0.001) loss 1.1631 (1.1881) acc 65.6250 (70.6512) lr 1.9511e-03 eta 18:41:56
+epoch [7/50] batch [960/1000] time 1.559 (1.564) data 0.001 (0.001) loss 0.8843 (1.1875) acc 75.0000 (70.6413) lr 1.9511e-03 eta 18:41:47
+epoch [7/50] batch [965/1000] time 1.568 (1.564) data 0.000 (0.001) loss 0.9565 (1.1875) acc 78.1250 (70.6412) lr 1.9511e-03 eta 18:41:45
+epoch [7/50] batch [970/1000] time 1.550 (1.564) data 0.001 (0.001) loss 1.6143 (1.1880) acc 65.6250 (70.6347) lr 1.9511e-03 eta 18:41:36
+epoch [7/50] batch [975/1000] time 1.552 (1.564) data 0.000 (0.001) loss 1.1514 (1.1883) acc 65.6250 (70.6314) lr 1.9511e-03 eta 18:41:25
+epoch [7/50] batch [980/1000] time 1.560 (1.564) data 0.001 (0.001) loss 1.5156 (1.1886) acc 62.5000 (70.6250) lr 1.9511e-03 eta 18:41:17
+epoch [7/50] batch [985/1000] time 1.542 (1.564) data 0.001 (0.001) loss 0.7861 (1.1882) acc 78.1250 (70.6345) lr 1.9511e-03 eta 18:41:06
+epoch [7/50] batch [990/1000] time 1.559 (1.564) data 0.000 (0.001) loss 1.1270 (1.1880) acc 59.3750 (70.6250) lr 1.9511e-03 eta 18:40:57
+epoch [7/50] batch [995/1000] time 1.546 (1.564) data 0.000 (0.001) loss 1.5557 (1.1890) acc 59.3750 (70.6093) lr 1.9511e-03 eta 18:40:47
+epoch [7/50] batch [1000/1000] time 1.560 (1.564) data 0.000 (0.001) loss 1.0996 (1.1891) acc 75.0000 (70.5938) lr 1.9298e-03 eta 18:40:38
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,042
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [8/50] batch [5/1000] time 1.544 (1.687) data 0.000 (0.188) loss 1.3320 (1.1812) acc 65.6250 (67.5000) lr 1.9298e-03 eta 20:09:06
+epoch [8/50] batch [10/1000] time 1.550 (1.623) data 0.000 (0.094) loss 0.7056 (1.0885) acc 81.2500 (71.2500) lr 1.9298e-03 eta 19:22:55
+epoch [8/50] batch [15/1000] time 1.537 (1.599) data 0.000 (0.063) loss 1.3496 (1.1596) acc 65.6250 (70.6250) lr 1.9298e-03 eta 19:05:44
+epoch [8/50] batch [20/1000] time 1.563 (1.590) data 0.001 (0.047) loss 1.1045 (1.1479) acc 62.5000 (70.0000) lr 1.9298e-03 eta 18:58:55
+epoch [8/50] batch [25/1000] time 1.557 (1.584) data 0.000 (0.038) loss 1.5352 (1.1564) acc 62.5000 (69.8750) lr 1.9298e-03 eta 18:54:34
+epoch [8/50] batch [30/1000] time 1.550 (1.581) data 0.000 (0.032) loss 1.2344 (1.1777) acc 75.0000 (70.2083) lr 1.9298e-03 eta 18:52:04
+epoch [8/50] batch [35/1000] time 1.548 (1.585) data 0.000 (0.027) loss 1.0156 (1.1605) acc 68.7500 (70.5357) lr 1.9298e-03 eta 18:55:13
+epoch [8/50] batch [40/1000] time 1.602 (1.583) data 0.000 (0.024) loss 1.3320 (1.1792) acc 68.7500 (70.3906) lr 1.9298e-03 eta 18:53:37
+epoch [8/50] batch [45/1000] time 1.553 (1.581) data 0.000 (0.021) loss 1.3877 (1.1714) acc 62.5000 (70.6250) lr 1.9298e-03 eta 18:51:52
+epoch [8/50] batch [50/1000] time 1.565 (1.580) data 0.001 (0.019) loss 1.1885 (1.1823) acc 75.0000 (70.3750) lr 1.9298e-03 eta 18:51:09
+epoch [8/50] batch [55/1000] time 1.525 (1.578) data 0.000 (0.017) loss 1.1738 (1.1891) acc 75.0000 (70.2841) lr 1.9298e-03 eta 18:49:19
+epoch [8/50] batch [60/1000] time 1.569 (1.576) data 0.000 (0.016) loss 0.6143 (1.1886) acc 84.3750 (70.4167) lr 1.9298e-03 eta 18:47:41
+epoch [8/50] batch [65/1000] time 1.575 (1.575) data 0.001 (0.015) loss 0.7515 (1.2060) acc 71.8750 (70.1442) lr 1.9298e-03 eta 18:47:22
+epoch [8/50] batch [70/1000] time 1.544 (1.574) data 0.000 (0.014) loss 0.9399 (1.1957) acc 81.2500 (70.1786) lr 1.9298e-03 eta 18:46:13
+epoch [8/50] batch [75/1000] time 1.725 (1.575) data 0.001 (0.013) loss 0.9863 (1.1894) acc 75.0000 (70.2083) lr 1.9298e-03 eta 18:47:06
+epoch [8/50] batch [80/1000] time 1.559 (1.575) data 0.001 (0.012) loss 0.6470 (1.1803) acc 87.5000 (70.3516) lr 1.9298e-03 eta 18:46:32
+epoch [8/50] batch [85/1000] time 1.575 (1.574) data 0.000 (0.012) loss 1.3125 (1.1889) acc 71.8750 (70.2206) lr 1.9298e-03 eta 18:45:57
+epoch [8/50] batch [90/1000] time 1.562 (1.574) data 0.000 (0.011) loss 1.7363 (1.1957) acc 68.7500 (70.2083) lr 1.9298e-03 eta 18:45:48
+epoch [8/50] batch [95/1000] time 1.568 (1.574) data 0.000 (0.010) loss 1.0127 (1.1930) acc 75.0000 (70.2303) lr 1.9298e-03 eta 18:45:24
+epoch [8/50] batch [100/1000] time 1.573 (1.574) data 0.000 (0.010) loss 1.5576 (1.2130) acc 56.2500 (69.9062) lr 1.9298e-03 eta 18:45:05
+epoch [8/50] batch [105/1000] time 1.548 (1.572) data 0.001 (0.009) loss 1.3672 (1.2107) acc 68.7500 (70.0000) lr 1.9298e-03 eta 18:44:07
+epoch [8/50] batch [110/1000] time 1.538 (1.571) data 0.001 (0.009) loss 1.1992 (1.2132) acc 65.6250 (69.8295) lr 1.9298e-03 eta 18:43:09
+epoch [8/50] batch [115/1000] time 1.571 (1.571) data 0.000 (0.009) loss 0.9858 (1.2111) acc 65.6250 (69.8913) lr 1.9298e-03 eta 18:42:38
+epoch [8/50] batch [120/1000] time 1.529 (1.570) data 0.000 (0.008) loss 1.4385 (1.2044) acc 71.8750 (70.0260) lr 1.9298e-03 eta 18:41:50
+epoch [8/50] batch [125/1000] time 1.551 (1.569) data 0.000 (0.008) loss 0.9673 (1.1979) acc 78.1250 (70.1250) lr 1.9298e-03 eta 18:41:09
+epoch [8/50] batch [130/1000] time 1.557 (1.569) data 0.000 (0.008) loss 1.6006 (1.2054) acc 62.5000 (69.9760) lr 1.9298e-03 eta 18:40:45
+epoch [8/50] batch [135/1000] time 1.534 (1.568) data 0.000 (0.007) loss 1.2275 (1.2070) acc 65.6250 (69.8611) lr 1.9298e-03 eta 18:40:02
+epoch [8/50] batch [140/1000] time 1.556 (1.568) data 0.000 (0.007) loss 1.6064 (1.2089) acc 62.5000 (69.7545) lr 1.9298e-03 eta 18:40:12
+epoch [8/50] batch [145/1000] time 1.552 (1.568) data 0.000 (0.007) loss 1.1514 (1.2109) acc 62.5000 (69.6121) lr 1.9298e-03 eta 18:39:43
+epoch [8/50] batch [150/1000] time 1.567 (1.568) data 0.001 (0.007) loss 1.2051 (1.2137) acc 62.5000 (69.6042) lr 1.9298e-03 eta 18:39:28
+epoch [8/50] batch [155/1000] time 1.533 (1.567) data 0.000 (0.007) loss 0.9434 (1.2140) acc 78.1250 (69.6976) lr 1.9298e-03 eta 18:39:11
+epoch [8/50] batch [160/1000] time 1.544 (1.567) data 0.000 (0.006) loss 1.7197 (1.2211) acc 59.3750 (69.6094) lr 1.9298e-03 eta 18:38:35
+epoch [8/50] batch [165/1000] time 1.554 (1.566) data 0.000 (0.006) loss 1.0303 (1.2163) acc 84.3750 (69.6591) lr 1.9298e-03 eta 18:38:09
+epoch [8/50] batch [170/1000] time 1.551 (1.566) data 0.000 (0.006) loss 1.3594 (1.2162) acc 59.3750 (69.4853) lr 1.9298e-03 eta 18:37:55
+epoch [8/50] batch [175/1000] time 1.544 (1.566) data 0.000 (0.006) loss 1.3428 (1.2152) acc 71.8750 (69.5536) lr 1.9298e-03 eta 18:37:34
+epoch [8/50] batch [180/1000] time 1.571 (1.566) data 0.000 (0.006) loss 1.8105 (1.2206) acc 59.3750 (69.5660) lr 1.9298e-03 eta 18:37:26
+epoch [8/50] batch [185/1000] time 1.544 (1.567) data 0.000 (0.006) loss 0.6704 (1.2117) acc 81.2500 (69.6959) lr 1.9298e-03 eta 18:37:49
+epoch [8/50] batch [190/1000] time 1.555 (1.566) data 0.001 (0.005) loss 1.2490 (1.2116) acc 59.3750 (69.6217) lr 1.9298e-03 eta 18:37:30
+epoch [8/50] batch [195/1000] time 1.557 (1.566) data 0.000 (0.005) loss 0.9282 (1.2059) acc 71.8750 (69.7276) lr 1.9298e-03 eta 18:37:06
+epoch [8/50] batch [200/1000] time 1.556 (1.566) data 0.000 (0.005) loss 0.9761 (1.2060) acc 71.8750 (69.6406) lr 1.9298e-03 eta 18:36:47
+epoch [8/50] batch [205/1000] time 1.555 (1.565) data 0.001 (0.005) loss 0.7979 (1.2000) acc 75.0000 (69.6951) lr 1.9298e-03 eta 18:36:28
+epoch [8/50] batch [210/1000] time 1.526 (1.565) data 0.000 (0.005) loss 1.5371 (1.2027) acc 71.8750 (69.7024) lr 1.9298e-03 eta 18:35:56
+epoch [8/50] batch [215/1000] time 1.561 (1.565) data 0.000 (0.005) loss 0.6274 (1.2019) acc 81.2500 (69.6802) lr 1.9298e-03 eta 18:35:52
+epoch [8/50] batch [220/1000] time 1.527 (1.565) data 0.001 (0.005) loss 1.5830 (1.2088) acc 75.0000 (69.5739) lr 1.9298e-03 eta 18:35:46
+epoch [8/50] batch [225/1000] time 1.547 (1.565) data 0.001 (0.005) loss 0.9619 (1.2059) acc 68.7500 (69.5417) lr 1.9298e-03 eta 18:35:27
+epoch [8/50] batch [230/1000] time 1.537 (1.565) data 0.000 (0.005) loss 0.8447 (1.2052) acc 78.1250 (69.5788) lr 1.9298e-03 eta 18:35:32
+epoch [8/50] batch [235/1000] time 1.556 (1.565) data 0.001 (0.004) loss 0.9009 (1.2013) acc 71.8750 (69.6277) lr 1.9298e-03 eta 18:35:22
+epoch [8/50] batch [240/1000] time 1.548 (1.565) data 0.000 (0.004) loss 0.3743 (1.1943) acc 90.6250 (69.8177) lr 1.9298e-03 eta 18:35:11
+epoch [8/50] batch [245/1000] time 1.546 (1.565) data 0.001 (0.004) loss 1.2139 (1.2019) acc 71.8750 (69.7066) lr 1.9298e-03 eta 18:34:51
+epoch [8/50] batch [250/1000] time 1.561 (1.565) data 0.001 (0.004) loss 0.7314 (1.2024) acc 84.3750 (69.7250) lr 1.9298e-03 eta 18:34:44
+epoch [8/50] batch [255/1000] time 1.576 (1.564) data 0.000 (0.004) loss 1.0078 (1.2024) acc 71.8750 (69.7304) lr 1.9298e-03 eta 18:34:31
+epoch [8/50] batch [260/1000] time 1.536 (1.564) data 0.000 (0.004) loss 1.0625 (1.1982) acc 75.0000 (69.8197) lr 1.9298e-03 eta 18:34:12
+epoch [8/50] batch [265/1000] time 1.547 (1.564) data 0.000 (0.004) loss 1.7139 (1.1982) acc 56.2500 (69.7524) lr 1.9298e-03 eta 18:34:01
+epoch [8/50] batch [270/1000] time 1.554 (1.564) data 0.001 (0.004) loss 1.1543 (1.1962) acc 81.2500 (69.8148) lr 1.9298e-03 eta 18:33:48
+epoch [8/50] batch [275/1000] time 1.536 (1.564) data 0.001 (0.004) loss 0.8159 (1.1899) acc 84.3750 (69.9659) lr 1.9298e-03 eta 18:33:33
+epoch [8/50] batch [280/1000] time 1.538 (1.564) data 0.001 (0.004) loss 0.7812 (1.1846) acc 78.1250 (70.0223) lr 1.9298e-03 eta 18:33:25
+epoch [8/50] batch [285/1000] time 1.545 (1.564) data 0.001 (0.004) loss 1.8027 (1.1815) acc 71.8750 (70.1096) lr 1.9298e-03 eta 18:33:11
+epoch [8/50] batch [290/1000] time 1.558 (1.564) data 0.000 (0.004) loss 0.8892 (1.1831) acc 75.0000 (70.0754) lr 1.9298e-03 eta 18:33:18
+epoch [8/50] batch [295/1000] time 1.568 (1.564) data 0.000 (0.004) loss 1.4746 (1.1857) acc 65.6250 (70.0424) lr 1.9298e-03 eta 18:32:59
+epoch [8/50] batch [300/1000] time 1.554 (1.564) data 0.000 (0.004) loss 0.9312 (1.1820) acc 71.8750 (70.0729) lr 1.9298e-03 eta 18:32:51
+epoch [8/50] batch [305/1000] time 1.536 (1.563) data 0.001 (0.004) loss 1.4482 (1.1799) acc 65.6250 (70.1742) lr 1.9298e-03 eta 18:32:30
+epoch [8/50] batch [310/1000] time 1.561 (1.563) data 0.000 (0.003) loss 1.6562 (1.1853) acc 53.1250 (70.1008) lr 1.9298e-03 eta 18:32:16
+epoch [8/50] batch [315/1000] time 1.549 (1.563) data 0.000 (0.003) loss 1.3584 (1.1841) acc 75.0000 (70.0992) lr 1.9298e-03 eta 18:32:01
+epoch [8/50] batch [320/1000] time 1.526 (1.563) data 0.001 (0.003) loss 1.2568 (1.1848) acc 59.3750 (70.0781) lr 1.9298e-03 eta 18:31:44
+epoch [8/50] batch [325/1000] time 1.550 (1.563) data 0.000 (0.003) loss 1.1357 (1.1858) acc 75.0000 (70.0769) lr 1.9298e-03 eta 18:31:27
+epoch [8/50] batch [330/1000] time 1.558 (1.563) data 0.001 (0.003) loss 1.4736 (1.1852) acc 59.3750 (70.1042) lr 1.9298e-03 eta 18:31:23
+epoch [8/50] batch [335/1000] time 1.585 (1.563) data 0.000 (0.003) loss 1.1191 (1.1844) acc 71.8750 (70.1026) lr 1.9298e-03 eta 18:31:33
+epoch [8/50] batch [340/1000] time 1.554 (1.563) data 0.001 (0.003) loss 0.7993 (1.1840) acc 78.1250 (70.1011) lr 1.9298e-03 eta 18:31:18
+epoch [8/50] batch [345/1000] time 1.539 (1.563) data 0.000 (0.003) loss 0.9453 (1.1858) acc 81.2500 (70.0906) lr 1.9298e-03 eta 18:31:07
+epoch [8/50] batch [350/1000] time 1.584 (1.563) data 0.000 (0.003) loss 1.1406 (1.1850) acc 65.6250 (70.1339) lr 1.9298e-03 eta 18:31:03
+epoch [8/50] batch [355/1000] time 1.563 (1.563) data 0.001 (0.003) loss 0.9067 (1.1832) acc 75.0000 (70.1232) lr 1.9298e-03 eta 18:30:55
+epoch [8/50] batch [360/1000] time 1.564 (1.563) data 0.001 (0.003) loss 1.6201 (1.1821) acc 68.7500 (70.1649) lr 1.9298e-03 eta 18:30:49
+epoch [8/50] batch [365/1000] time 1.541 (1.563) data 0.001 (0.003) loss 2.0195 (1.1841) acc 62.5000 (70.1541) lr 1.9298e-03 eta 18:30:32
+epoch [8/50] batch [370/1000] time 1.580 (1.563) data 0.000 (0.003) loss 0.9097 (1.1812) acc 71.8750 (70.2027) lr 1.9298e-03 eta 18:30:20
+epoch [8/50] batch [375/1000] time 1.562 (1.563) data 0.001 (0.003) loss 1.2568 (1.1799) acc 71.8750 (70.2250) lr 1.9298e-03 eta 18:30:10
+epoch [8/50] batch [380/1000] time 1.539 (1.563) data 0.000 (0.003) loss 1.3701 (1.1807) acc 65.6250 (70.2549) lr 1.9298e-03 eta 18:30:13
+epoch [8/50] batch [385/1000] time 1.567 (1.563) data 0.001 (0.003) loss 1.4170 (1.1806) acc 65.6250 (70.2354) lr 1.9298e-03 eta 18:30:03
+epoch [8/50] batch [390/1000] time 1.576 (1.563) data 0.001 (0.003) loss 1.3945 (1.1805) acc 68.7500 (70.1923) lr 1.9298e-03 eta 18:29:51
+epoch [8/50] batch [395/1000] time 1.576 (1.563) data 0.001 (0.003) loss 1.0020 (1.1780) acc 68.7500 (70.2611) lr 1.9298e-03 eta 18:29:45
+epoch [8/50] batch [400/1000] time 1.591 (1.563) data 0.001 (0.003) loss 1.1895 (1.1787) acc 65.6250 (70.2578) lr 1.9298e-03 eta 18:29:38
+epoch [8/50] batch [405/1000] time 1.560 (1.563) data 0.001 (0.003) loss 1.4971 (1.1801) acc 68.7500 (70.2469) lr 1.9298e-03 eta 18:29:29
+epoch [8/50] batch [410/1000] time 1.552 (1.563) data 0.001 (0.003) loss 1.1230 (1.1788) acc 75.0000 (70.3201) lr 1.9298e-03 eta 18:29:15
+epoch [8/50] batch [415/1000] time 1.595 (1.563) data 0.001 (0.003) loss 1.0293 (1.1768) acc 71.8750 (70.3840) lr 1.9298e-03 eta 18:29:11
+epoch [8/50] batch [420/1000] time 1.564 (1.563) data 0.000 (0.003) loss 1.0889 (1.1778) acc 71.8750 (70.3795) lr 1.9298e-03 eta 18:29:09
+epoch [8/50] batch [425/1000] time 1.554 (1.563) data 0.001 (0.003) loss 0.9087 (1.1789) acc 78.1250 (70.3750) lr 1.9298e-03 eta 18:29:02
+epoch [8/50] batch [430/1000] time 1.569 (1.563) data 0.000 (0.003) loss 0.7441 (1.1776) acc 84.3750 (70.4433) lr 1.9298e-03 eta 18:28:57
+epoch [8/50] batch [435/1000] time 1.580 (1.563) data 0.000 (0.003) loss 1.2490 (1.1799) acc 56.2500 (70.4023) lr 1.9298e-03 eta 18:28:55
+epoch [8/50] batch [440/1000] time 1.707 (1.563) data 0.000 (0.003) loss 1.4414 (1.1808) acc 62.5000 (70.3977) lr 1.9298e-03 eta 18:28:59
+epoch [8/50] batch [445/1000] time 1.542 (1.563) data 0.001 (0.003) loss 0.9717 (1.1805) acc 75.0000 (70.3722) lr 1.9298e-03 eta 18:28:50
+epoch [8/50] batch [450/1000] time 1.587 (1.563) data 0.001 (0.003) loss 0.9312 (1.1843) acc 75.0000 (70.2917) lr 1.9298e-03 eta 18:28:42
+epoch [8/50] batch [455/1000] time 1.555 (1.563) data 0.000 (0.003) loss 0.9277 (1.1816) acc 78.1250 (70.3365) lr 1.9298e-03 eta 18:28:30
+epoch [8/50] batch [460/1000] time 1.549 (1.563) data 0.000 (0.003) loss 1.2441 (1.1785) acc 78.1250 (70.4416) lr 1.9298e-03 eta 18:28:18
+epoch [8/50] batch [465/1000] time 1.550 (1.563) data 0.000 (0.002) loss 1.0420 (1.1772) acc 68.7500 (70.4772) lr 1.9298e-03 eta 18:28:06
+epoch [8/50] batch [470/1000] time 1.584 (1.563) data 0.000 (0.002) loss 0.9277 (1.1758) acc 68.7500 (70.4787) lr 1.9298e-03 eta 18:27:57
+epoch [8/50] batch [475/1000] time 1.540 (1.563) data 0.000 (0.002) loss 0.8525 (1.1753) acc 71.8750 (70.4737) lr 1.9298e-03 eta 18:27:42
+epoch [8/50] batch [480/1000] time 1.547 (1.563) data 0.001 (0.002) loss 1.4775 (1.1778) acc 65.6250 (70.4232) lr 1.9298e-03 eta 18:27:28
+epoch [8/50] batch [485/1000] time 1.690 (1.563) data 0.000 (0.002) loss 1.5840 (1.1786) acc 65.6250 (70.4253) lr 1.9298e-03 eta 18:27:27
+epoch [8/50] batch [490/1000] time 1.575 (1.563) data 0.000 (0.002) loss 1.5527 (1.1806) acc 71.8750 (70.4082) lr 1.9298e-03 eta 18:27:22
+epoch [8/50] batch [495/1000] time 1.545 (1.563) data 0.000 (0.002) loss 1.2549 (1.1810) acc 75.0000 (70.4356) lr 1.9298e-03 eta 18:27:16
+epoch [8/50] batch [500/1000] time 1.547 (1.563) data 0.001 (0.002) loss 0.8652 (1.1816) acc 71.8750 (70.4250) lr 1.9298e-03 eta 18:27:04
+epoch [8/50] batch [505/1000] time 1.546 (1.563) data 0.001 (0.002) loss 0.7427 (1.1791) acc 84.3750 (70.4641) lr 1.9298e-03 eta 18:26:50
+epoch [8/50] batch [510/1000] time 1.549 (1.563) data 0.001 (0.002) loss 1.4814 (1.1808) acc 71.8750 (70.4412) lr 1.9298e-03 eta 18:26:37
+epoch [8/50] batch [515/1000] time 1.561 (1.563) data 0.001 (0.002) loss 0.9985 (1.1784) acc 62.5000 (70.4915) lr 1.9298e-03 eta 18:26:25
+epoch [8/50] batch [520/1000] time 1.576 (1.562) data 0.000 (0.002) loss 0.6968 (1.1771) acc 71.8750 (70.5108) lr 1.9298e-03 eta 18:26:14
+epoch [8/50] batch [525/1000] time 1.570 (1.562) data 0.000 (0.002) loss 1.2090 (1.1796) acc 62.5000 (70.4702) lr 1.9298e-03 eta 18:26:06
+epoch [8/50] batch [530/1000] time 1.595 (1.563) data 0.001 (0.002) loss 0.5049 (1.1777) acc 87.5000 (70.5307) lr 1.9298e-03 eta 18:26:12
+epoch [8/50] batch [535/1000] time 1.548 (1.563) data 0.001 (0.002) loss 1.1074 (1.1785) acc 62.5000 (70.4790) lr 1.9298e-03 eta 18:26:04
+epoch [8/50] batch [540/1000] time 1.540 (1.563) data 0.001 (0.002) loss 1.0586 (1.1801) acc 78.1250 (70.4572) lr 1.9298e-03 eta 18:25:51
+epoch [8/50] batch [545/1000] time 1.543 (1.563) data 0.000 (0.002) loss 0.4468 (1.1778) acc 81.2500 (70.4989) lr 1.9298e-03 eta 18:25:40
+epoch [8/50] batch [550/1000] time 1.544 (1.563) data 0.000 (0.002) loss 1.0215 (1.1778) acc 75.0000 (70.4773) lr 1.9298e-03 eta 18:25:30
+epoch [8/50] batch [555/1000] time 1.557 (1.563) data 0.001 (0.002) loss 1.9268 (1.1795) acc 50.0000 (70.4279) lr 1.9298e-03 eta 18:25:21
+epoch [8/50] batch [560/1000] time 1.576 (1.563) data 0.000 (0.002) loss 1.0059 (1.1794) acc 81.2500 (70.4520) lr 1.9298e-03 eta 18:25:13
+epoch [8/50] batch [565/1000] time 1.594 (1.563) data 0.000 (0.002) loss 1.3047 (1.1799) acc 75.0000 (70.4369) lr 1.9298e-03 eta 18:25:12
+epoch [8/50] batch [570/1000] time 1.555 (1.563) data 0.000 (0.002) loss 0.9097 (1.1795) acc 75.0000 (70.4496) lr 1.9298e-03 eta 18:25:05
+epoch [8/50] batch [575/1000] time 1.550 (1.563) data 0.001 (0.002) loss 0.9487 (1.1794) acc 75.0000 (70.4511) lr 1.9298e-03 eta 18:24:55
+epoch [8/50] batch [580/1000] time 1.553 (1.563) data 0.000 (0.002) loss 0.8848 (1.1778) acc 81.2500 (70.4903) lr 1.9298e-03 eta 18:24:43
+epoch [8/50] batch [585/1000] time 1.544 (1.562) data 0.001 (0.002) loss 1.2666 (1.1788) acc 68.7500 (70.5021) lr 1.9298e-03 eta 18:24:30
+epoch [8/50] batch [590/1000] time 1.554 (1.562) data 0.000 (0.002) loss 1.1426 (1.1786) acc 75.0000 (70.4926) lr 1.9298e-03 eta 18:24:22
+epoch [8/50] batch [595/1000] time 1.564 (1.563) data 0.001 (0.002) loss 0.8823 (1.1797) acc 78.1250 (70.4727) lr 1.9298e-03 eta 18:24:24
+epoch [8/50] batch [600/1000] time 1.548 (1.563) data 0.000 (0.002) loss 0.9199 (1.1796) acc 71.8750 (70.4740) lr 1.9298e-03 eta 18:24:11
+epoch [8/50] batch [605/1000] time 1.561 (1.563) data 0.001 (0.002) loss 0.7759 (1.1774) acc 78.1250 (70.5165) lr 1.9298e-03 eta 18:24:03
+epoch [8/50] batch [610/1000] time 1.549 (1.562) data 0.000 (0.002) loss 0.9604 (1.1769) acc 71.8750 (70.4918) lr 1.9298e-03 eta 18:23:54
+epoch [8/50] batch [615/1000] time 1.535 (1.562) data 0.001 (0.002) loss 0.4668 (1.1758) acc 90.6250 (70.5386) lr 1.9298e-03 eta 18:23:42
+epoch [8/50] batch [620/1000] time 1.562 (1.562) data 0.000 (0.002) loss 1.5889 (1.1760) acc 59.3750 (70.5444) lr 1.9298e-03 eta 18:23:30
+epoch [8/50] batch [625/1000] time 1.581 (1.562) data 0.000 (0.002) loss 1.1611 (1.1769) acc 75.0000 (70.5400) lr 1.9298e-03 eta 18:23:19
+epoch [8/50] batch [630/1000] time 1.571 (1.562) data 0.000 (0.002) loss 1.4180 (1.1765) acc 68.7500 (70.5556) lr 1.9298e-03 eta 18:23:15
+epoch [8/50] batch [635/1000] time 1.561 (1.562) data 0.000 (0.002) loss 0.9658 (1.1774) acc 78.1250 (70.5561) lr 1.9298e-03 eta 18:23:07
+epoch [8/50] batch [640/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.1387 (1.1776) acc 65.6250 (70.5566) lr 1.9298e-03 eta 18:23:09
+epoch [8/50] batch [645/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.1348 (1.1764) acc 71.8750 (70.5620) lr 1.9298e-03 eta 18:23:01
+epoch [8/50] batch [650/1000] time 1.550 (1.562) data 0.001 (0.002) loss 0.8032 (1.1758) acc 81.2500 (70.5817) lr 1.9298e-03 eta 18:22:50
+epoch [8/50] batch [655/1000] time 1.555 (1.562) data 0.000 (0.002) loss 0.9932 (1.1745) acc 75.0000 (70.5964) lr 1.9298e-03 eta 18:22:38
+epoch [8/50] batch [660/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.3652 (1.1754) acc 62.5000 (70.5634) lr 1.9298e-03 eta 18:22:25
+epoch [8/50] batch [665/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.5000 (1.1758) acc 65.6250 (70.5921) lr 1.9298e-03 eta 18:22:13
+epoch [8/50] batch [670/1000] time 1.531 (1.562) data 0.000 (0.002) loss 0.8672 (1.1751) acc 75.0000 (70.6017) lr 1.9298e-03 eta 18:22:02
+epoch [8/50] batch [675/1000] time 1.580 (1.562) data 0.001 (0.002) loss 0.5918 (1.1731) acc 75.0000 (70.6389) lr 1.9298e-03 eta 18:21:55
+epoch [8/50] batch [680/1000] time 1.537 (1.562) data 0.000 (0.002) loss 1.3682 (1.1740) acc 62.5000 (70.6158) lr 1.9298e-03 eta 18:21:53
+epoch [8/50] batch [685/1000] time 1.556 (1.562) data 0.000 (0.002) loss 1.2139 (1.1744) acc 68.7500 (70.6204) lr 1.9298e-03 eta 18:21:43
+epoch [8/50] batch [690/1000] time 1.530 (1.562) data 0.001 (0.002) loss 1.0342 (1.1743) acc 78.1250 (70.6295) lr 1.9298e-03 eta 18:21:36
+epoch [8/50] batch [695/1000] time 1.574 (1.562) data 0.000 (0.002) loss 0.6772 (1.1740) acc 81.2500 (70.6250) lr 1.9298e-03 eta 18:21:29
+epoch [8/50] batch [700/1000] time 1.557 (1.562) data 0.000 (0.002) loss 0.7168 (1.1736) acc 71.8750 (70.6161) lr 1.9298e-03 eta 18:21:21
+epoch [8/50] batch [705/1000] time 1.536 (1.562) data 0.001 (0.002) loss 1.2861 (1.1734) acc 68.7500 (70.6117) lr 1.9298e-03 eta 18:21:11
+epoch [8/50] batch [710/1000] time 1.541 (1.562) data 0.000 (0.002) loss 0.9927 (1.1738) acc 71.8750 (70.5854) lr 1.9298e-03 eta 18:21:00
+epoch [8/50] batch [715/1000] time 1.559 (1.562) data 0.000 (0.002) loss 0.6807 (1.1727) acc 84.3750 (70.6425) lr 1.9298e-03 eta 18:20:52
+epoch [8/50] batch [720/1000] time 1.567 (1.562) data 0.000 (0.002) loss 1.7051 (1.1734) acc 59.3750 (70.6293) lr 1.9298e-03 eta 18:20:46
+epoch [8/50] batch [725/1000] time 1.557 (1.562) data 0.000 (0.002) loss 0.8389 (1.1744) acc 65.6250 (70.6164) lr 1.9298e-03 eta 18:20:37
+epoch [8/50] batch [730/1000] time 1.546 (1.562) data 0.000 (0.002) loss 1.5732 (1.1759) acc 68.7500 (70.5822) lr 1.9298e-03 eta 18:20:29
+epoch [8/50] batch [735/1000] time 1.589 (1.562) data 0.001 (0.002) loss 1.0723 (1.1764) acc 62.5000 (70.5400) lr 1.9298e-03 eta 18:20:23
+epoch [8/50] batch [740/1000] time 1.560 (1.562) data 0.001 (0.002) loss 0.9526 (1.1787) acc 75.0000 (70.4983) lr 1.9298e-03 eta 18:20:15
+epoch [8/50] batch [745/1000] time 1.561 (1.562) data 0.000 (0.002) loss 1.4121 (1.1789) acc 75.0000 (70.5159) lr 1.9298e-03 eta 18:20:16
+epoch [8/50] batch [750/1000] time 1.562 (1.562) data 0.000 (0.002) loss 0.9731 (1.1783) acc 68.7500 (70.5000) lr 1.9298e-03 eta 18:20:08
+epoch [8/50] batch [755/1000] time 1.566 (1.562) data 0.001 (0.002) loss 1.1123 (1.1798) acc 62.5000 (70.4594) lr 1.9298e-03 eta 18:20:00
+epoch [8/50] batch [760/1000] time 1.526 (1.562) data 0.000 (0.002) loss 1.0078 (1.1781) acc 65.6250 (70.4893) lr 1.9298e-03 eta 18:19:49
+epoch [8/50] batch [765/1000] time 1.554 (1.562) data 0.000 (0.002) loss 1.1484 (1.1782) acc 62.5000 (70.4616) lr 1.9298e-03 eta 18:19:40
+epoch [8/50] batch [770/1000] time 1.587 (1.562) data 0.000 (0.002) loss 0.9385 (1.1777) acc 75.0000 (70.4667) lr 1.9298e-03 eta 18:19:37
+epoch [8/50] batch [775/1000] time 1.587 (1.562) data 0.000 (0.002) loss 1.7920 (1.1772) acc 59.3750 (70.4839) lr 1.9298e-03 eta 18:19:35
+epoch [8/50] batch [780/1000] time 1.556 (1.563) data 0.001 (0.002) loss 1.1279 (1.1759) acc 59.3750 (70.4888) lr 1.9298e-03 eta 18:19:29
+epoch [8/50] batch [785/1000] time 1.541 (1.562) data 0.000 (0.002) loss 0.7905 (1.1754) acc 84.3750 (70.5135) lr 1.9298e-03 eta 18:19:17
+epoch [8/50] batch [790/1000] time 1.573 (1.563) data 0.000 (0.002) loss 1.4082 (1.1754) acc 65.6250 (70.5380) lr 1.9298e-03 eta 18:19:16
+epoch [8/50] batch [795/1000] time 1.551 (1.562) data 0.000 (0.002) loss 1.9385 (1.1765) acc 62.5000 (70.5346) lr 1.9298e-03 eta 18:19:04
+epoch [8/50] batch [800/1000] time 1.589 (1.563) data 0.000 (0.002) loss 1.2793 (1.1773) acc 68.7500 (70.5195) lr 1.9298e-03 eta 18:18:57
+epoch [8/50] batch [805/1000] time 1.545 (1.562) data 0.000 (0.002) loss 1.1104 (1.1773) acc 62.5000 (70.4930) lr 1.9298e-03 eta 18:18:47
+epoch [8/50] batch [810/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.2500 (1.1773) acc 68.7500 (70.4938) lr 1.9298e-03 eta 18:18:38
+epoch [8/50] batch [815/1000] time 1.542 (1.562) data 0.000 (0.002) loss 0.8198 (1.1761) acc 84.3750 (70.5215) lr 1.9298e-03 eta 18:18:30
+epoch [8/50] batch [820/1000] time 1.590 (1.562) data 0.000 (0.002) loss 1.2422 (1.1760) acc 71.8750 (70.5221) lr 1.9298e-03 eta 18:18:25
+epoch [8/50] batch [825/1000] time 1.534 (1.562) data 0.001 (0.002) loss 1.0010 (1.1749) acc 75.0000 (70.5492) lr 1.9298e-03 eta 18:18:16
+epoch [8/50] batch [830/1000] time 1.702 (1.563) data 0.000 (0.002) loss 0.5137 (1.1735) acc 87.5000 (70.5798) lr 1.9298e-03 eta 18:18:13
+epoch [8/50] batch [835/1000] time 1.561 (1.563) data 0.000 (0.002) loss 1.5693 (1.1757) acc 62.5000 (70.5539) lr 1.9298e-03 eta 18:18:07
+epoch [8/50] batch [840/1000] time 1.560 (1.563) data 0.000 (0.002) loss 1.0469 (1.1753) acc 71.8750 (70.5766) lr 1.9298e-03 eta 18:17:58
+epoch [8/50] batch [845/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.5332 (1.1766) acc 62.5000 (70.5362) lr 1.9298e-03 eta 18:17:50
+epoch [8/50] batch [850/1000] time 1.565 (1.563) data 0.000 (0.002) loss 1.4209 (1.1760) acc 65.6250 (70.5625) lr 1.9298e-03 eta 18:17:42
+epoch [8/50] batch [855/1000] time 1.554 (1.563) data 0.000 (0.002) loss 0.8384 (1.1758) acc 87.5000 (70.5629) lr 1.9298e-03 eta 18:17:33
+epoch [8/50] batch [860/1000] time 1.534 (1.562) data 0.001 (0.002) loss 1.4697 (1.1762) acc 59.3750 (70.5378) lr 1.9298e-03 eta 18:17:23
+epoch [8/50] batch [865/1000] time 1.542 (1.562) data 0.000 (0.002) loss 0.5859 (1.1754) acc 71.8750 (70.5419) lr 1.9298e-03 eta 18:17:11
+epoch [8/50] batch [870/1000] time 1.565 (1.562) data 0.000 (0.002) loss 1.1973 (1.1750) acc 62.5000 (70.5352) lr 1.9298e-03 eta 18:17:01
+epoch [8/50] batch [875/1000] time 1.574 (1.562) data 0.000 (0.002) loss 1.2910 (1.1748) acc 62.5000 (70.5179) lr 1.9298e-03 eta 18:16:54
+epoch [8/50] batch [880/1000] time 1.536 (1.562) data 0.000 (0.002) loss 1.5078 (1.1755) acc 59.3750 (70.5043) lr 1.9298e-03 eta 18:16:46
+epoch [8/50] batch [885/1000] time 1.556 (1.562) data 0.000 (0.002) loss 1.2256 (1.1760) acc 68.7500 (70.4873) lr 1.9298e-03 eta 18:16:36
+epoch [8/50] batch [890/1000] time 1.550 (1.562) data 0.000 (0.002) loss 1.5068 (1.1756) acc 59.3750 (70.5056) lr 1.9298e-03 eta 18:16:28
+epoch [8/50] batch [895/1000] time 1.560 (1.563) data 0.001 (0.002) loss 1.4619 (1.1756) acc 75.0000 (70.5237) lr 1.9298e-03 eta 18:16:29
+epoch [8/50] batch [900/1000] time 1.542 (1.562) data 0.001 (0.002) loss 0.9448 (1.1761) acc 81.2500 (70.5347) lr 1.9298e-03 eta 18:16:18
+epoch [8/50] batch [905/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.4932 (1.1763) acc 62.5000 (70.5456) lr 1.9298e-03 eta 18:16:13
+epoch [8/50] batch [910/1000] time 1.551 (1.563) data 0.001 (0.001) loss 1.4150 (1.1762) acc 62.5000 (70.5632) lr 1.9298e-03 eta 18:16:06
+epoch [8/50] batch [915/1000] time 1.560 (1.563) data 0.000 (0.001) loss 1.4883 (1.1765) acc 68.7500 (70.5430) lr 1.9298e-03 eta 18:15:59
+epoch [8/50] batch [920/1000] time 1.540 (1.563) data 0.000 (0.001) loss 1.2500 (1.1765) acc 59.3750 (70.5265) lr 1.9298e-03 eta 18:15:50
+epoch [8/50] batch [925/1000] time 1.567 (1.562) data 0.000 (0.001) loss 1.0693 (1.1763) acc 71.8750 (70.5236) lr 1.9298e-03 eta 18:15:40
+epoch [8/50] batch [930/1000] time 1.562 (1.562) data 0.001 (0.001) loss 1.1328 (1.1776) acc 78.1250 (70.5309) lr 1.9298e-03 eta 18:15:32
+epoch [8/50] batch [935/1000] time 1.555 (1.562) data 0.001 (0.001) loss 2.1523 (1.1778) acc 53.1250 (70.5348) lr 1.9298e-03 eta 18:15:25
+epoch [8/50] batch [940/1000] time 1.578 (1.563) data 0.000 (0.001) loss 0.7563 (1.1769) acc 81.2500 (70.5452) lr 1.9298e-03 eta 18:15:25
+epoch [8/50] batch [945/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.1963 (1.1773) acc 75.0000 (70.5522) lr 1.9298e-03 eta 18:15:18
+epoch [8/50] batch [950/1000] time 1.573 (1.563) data 0.000 (0.001) loss 1.3721 (1.1781) acc 59.3750 (70.5428) lr 1.9298e-03 eta 18:15:08
+epoch [8/50] batch [955/1000] time 1.539 (1.563) data 0.000 (0.001) loss 1.4453 (1.1797) acc 71.8750 (70.5007) lr 1.9298e-03 eta 18:15:00
+epoch [8/50] batch [960/1000] time 1.559 (1.563) data 0.000 (0.001) loss 0.7344 (1.1786) acc 75.0000 (70.5176) lr 1.9298e-03 eta 18:14:52
+epoch [8/50] batch [965/1000] time 1.543 (1.563) data 0.000 (0.001) loss 1.4082 (1.1796) acc 62.5000 (70.4987) lr 1.9298e-03 eta 18:14:41
+epoch [8/50] batch [970/1000] time 1.570 (1.562) data 0.000 (0.001) loss 1.4395 (1.1805) acc 78.1250 (70.4800) lr 1.9298e-03 eta 18:14:31
+epoch [8/50] batch [975/1000] time 1.571 (1.562) data 0.000 (0.001) loss 1.4600 (1.1807) acc 62.5000 (70.4712) lr 1.9298e-03 eta 18:14:22
+epoch [8/50] batch [980/1000] time 1.559 (1.562) data 0.000 (0.001) loss 1.3486 (1.1810) acc 71.8750 (70.4783) lr 1.9298e-03 eta 18:14:15
+epoch [8/50] batch [985/1000] time 1.587 (1.563) data 0.001 (0.001) loss 0.9312 (1.1815) acc 71.8750 (70.4854) lr 1.9298e-03 eta 18:14:16
+epoch [8/50] batch [990/1000] time 1.555 (1.563) data 0.000 (0.001) loss 1.5342 (1.1837) acc 53.1250 (70.4388) lr 1.9298e-03 eta 18:14:08
+epoch [8/50] batch [995/1000] time 1.574 (1.563) data 0.000 (0.001) loss 1.0576 (1.1838) acc 65.6250 (70.4366) lr 1.9298e-03 eta 18:13:56
+epoch [8/50] batch [1000/1000] time 1.568 (1.563) data 0.000 (0.001) loss 1.0010 (1.1849) acc 81.2500 (70.4281) lr 1.9048e-03 eta 18:13:48
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,054
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
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+epoch [9/50] batch [560/1000] time 1.556 (1.561) data 0.000 (0.002) loss 1.5459 (1.1777) acc 68.7500 (70.8147) lr 1.9048e-03 eta 17:57:52
+epoch [9/50] batch [565/1000] time 1.546 (1.561) data 0.001 (0.002) loss 0.6201 (1.1788) acc 78.1250 (70.7799) lr 1.9048e-03 eta 17:57:43
+epoch [9/50] batch [570/1000] time 1.558 (1.561) data 0.000 (0.002) loss 1.2637 (1.1807) acc 78.1250 (70.7675) lr 1.9048e-03 eta 17:57:32
+epoch [9/50] batch [575/1000] time 1.569 (1.561) data 0.000 (0.002) loss 0.7979 (1.1795) acc 75.0000 (70.8043) lr 1.9048e-03 eta 17:57:24
+epoch [9/50] batch [580/1000] time 1.548 (1.560) data 0.000 (0.002) loss 0.8438 (1.1793) acc 78.1250 (70.8136) lr 1.9048e-03 eta 17:57:13
+epoch [9/50] batch [585/1000] time 1.571 (1.560) data 0.001 (0.002) loss 1.3770 (1.1785) acc 75.0000 (70.8280) lr 1.9048e-03 eta 17:57:07
+epoch [9/50] batch [590/1000] time 1.556 (1.560) data 0.000 (0.002) loss 1.0361 (1.1793) acc 71.8750 (70.8157) lr 1.9048e-03 eta 17:56:57
+epoch [9/50] batch [595/1000] time 1.577 (1.561) data 0.001 (0.002) loss 0.9746 (1.1787) acc 81.2500 (70.8298) lr 1.9048e-03 eta 17:57:05
+epoch [9/50] batch [600/1000] time 1.526 (1.561) data 0.001 (0.002) loss 0.7832 (1.1789) acc 68.7500 (70.8073) lr 1.9048e-03 eta 17:56:53
+epoch [9/50] batch [605/1000] time 1.565 (1.561) data 0.000 (0.002) loss 1.1250 (1.1772) acc 65.6250 (70.8006) lr 1.9048e-03 eta 17:56:44
+epoch [9/50] batch [610/1000] time 1.536 (1.561) data 0.000 (0.002) loss 1.2715 (1.1775) acc 68.7500 (70.7889) lr 1.9048e-03 eta 17:56:32
+epoch [9/50] batch [615/1000] time 1.543 (1.561) data 0.000 (0.002) loss 1.0527 (1.1770) acc 75.0000 (70.7978) lr 1.9048e-03 eta 17:56:22
+epoch [9/50] batch [620/1000] time 1.538 (1.560) data 0.000 (0.002) loss 1.6670 (1.1775) acc 68.7500 (70.8014) lr 1.9048e-03 eta 17:56:10
+epoch [9/50] batch [625/1000] time 1.564 (1.560) data 0.000 (0.002) loss 1.5254 (1.1795) acc 71.8750 (70.7900) lr 1.9048e-03 eta 17:56:02
+epoch [9/50] batch [630/1000] time 1.546 (1.560) data 0.000 (0.002) loss 0.7051 (1.1786) acc 78.1250 (70.8234) lr 1.9048e-03 eta 17:55:52
+epoch [9/50] batch [635/1000] time 1.548 (1.560) data 0.000 (0.002) loss 0.9492 (1.1776) acc 78.1250 (70.8563) lr 1.9048e-03 eta 17:55:41
+epoch [9/50] batch [640/1000] time 1.558 (1.560) data 0.000 (0.002) loss 1.2637 (1.1779) acc 68.7500 (70.8350) lr 1.9048e-03 eta 17:55:31
+epoch [9/50] batch [645/1000] time 1.582 (1.560) data 0.001 (0.002) loss 0.8877 (1.1748) acc 68.7500 (70.8866) lr 1.9048e-03 eta 17:55:27
+epoch [9/50] batch [650/1000] time 1.526 (1.560) data 0.000 (0.002) loss 1.0977 (1.1740) acc 65.6250 (70.8846) lr 1.9048e-03 eta 17:55:17
+epoch [9/50] batch [655/1000] time 1.728 (1.560) data 0.001 (0.002) loss 0.7861 (1.1750) acc 78.1250 (70.8922) lr 1.9048e-03 eta 17:55:18
+epoch [9/50] batch [660/1000] time 1.546 (1.560) data 0.000 (0.002) loss 1.0439 (1.1767) acc 75.0000 (70.8570) lr 1.9048e-03 eta 17:55:08
+epoch [9/50] batch [665/1000] time 1.572 (1.560) data 0.000 (0.002) loss 1.0625 (1.1777) acc 68.7500 (70.8412) lr 1.9048e-03 eta 17:54:58
+epoch [9/50] batch [670/1000] time 1.522 (1.560) data 0.000 (0.002) loss 0.9180 (1.1764) acc 71.8750 (70.8722) lr 1.9048e-03 eta 17:54:41
+epoch [9/50] batch [675/1000] time 1.549 (1.560) data 0.000 (0.002) loss 1.4033 (1.1768) acc 65.6250 (70.8611) lr 1.9048e-03 eta 17:54:31
+epoch [9/50] batch [680/1000] time 1.580 (1.560) data 0.001 (0.002) loss 1.4258 (1.1758) acc 68.7500 (70.8869) lr 1.9048e-03 eta 17:54:25
+epoch [9/50] batch [685/1000] time 1.552 (1.560) data 0.001 (0.002) loss 1.7881 (1.1782) acc 46.8750 (70.8212) lr 1.9048e-03 eta 17:54:17
+epoch [9/50] batch [690/1000] time 1.567 (1.560) data 0.000 (0.002) loss 1.1562 (1.1773) acc 68.7500 (70.8107) lr 1.9048e-03 eta 17:54:11
+epoch [9/50] batch [695/1000] time 1.567 (1.560) data 0.001 (0.002) loss 0.5806 (1.1754) acc 87.5000 (70.8498) lr 1.9048e-03 eta 17:54:02
+epoch [9/50] batch [700/1000] time 1.701 (1.560) data 0.001 (0.002) loss 1.3047 (1.1750) acc 62.5000 (70.8482) lr 1.9048e-03 eta 17:54:01
+epoch [9/50] batch [705/1000] time 1.579 (1.560) data 0.001 (0.002) loss 1.2666 (1.1765) acc 75.0000 (70.8200) lr 1.9048e-03 eta 17:53:54
+epoch [9/50] batch [710/1000] time 1.555 (1.560) data 0.001 (0.002) loss 1.6191 (1.1774) acc 65.6250 (70.8143) lr 1.9048e-03 eta 17:53:44
+epoch [9/50] batch [715/1000] time 1.551 (1.560) data 0.000 (0.002) loss 0.8208 (1.1785) acc 78.1250 (70.8042) lr 1.9048e-03 eta 17:53:36
+epoch [9/50] batch [720/1000] time 1.551 (1.560) data 0.001 (0.002) loss 1.2295 (1.1785) acc 59.3750 (70.7899) lr 1.9048e-03 eta 17:53:28
+epoch [9/50] batch [725/1000] time 1.579 (1.560) data 0.001 (0.002) loss 1.0381 (1.1778) acc 71.8750 (70.7845) lr 1.9048e-03 eta 17:53:18
+epoch [9/50] batch [730/1000] time 1.551 (1.560) data 0.000 (0.002) loss 1.4170 (1.1790) acc 62.5000 (70.7449) lr 1.9048e-03 eta 17:53:10
+epoch [9/50] batch [735/1000] time 1.580 (1.560) data 0.001 (0.002) loss 1.2939 (1.1780) acc 68.7500 (70.7483) lr 1.9048e-03 eta 17:53:04
+epoch [9/50] batch [740/1000] time 1.566 (1.560) data 0.000 (0.002) loss 0.7725 (1.1783) acc 78.1250 (70.7475) lr 1.9048e-03 eta 17:52:56
+epoch [9/50] batch [745/1000] time 1.556 (1.560) data 0.001 (0.002) loss 0.7837 (1.1779) acc 78.1250 (70.7508) lr 1.9048e-03 eta 17:52:57
+epoch [9/50] batch [750/1000] time 1.569 (1.561) data 0.000 (0.002) loss 1.6455 (1.1787) acc 59.3750 (70.7583) lr 1.9048e-03 eta 17:52:50
+epoch [9/50] batch [755/1000] time 1.577 (1.560) data 0.001 (0.002) loss 1.0693 (1.1778) acc 75.0000 (70.7947) lr 1.9048e-03 eta 17:52:42
+epoch [9/50] batch [760/1000] time 1.544 (1.560) data 0.000 (0.002) loss 0.6201 (1.1769) acc 87.5000 (70.8183) lr 1.9048e-03 eta 17:52:34
+epoch [9/50] batch [765/1000] time 1.537 (1.560) data 0.000 (0.002) loss 1.1309 (1.1778) acc 81.2500 (70.8007) lr 1.9048e-03 eta 17:52:25
+epoch [9/50] batch [770/1000] time 1.584 (1.561) data 0.001 (0.002) loss 1.2021 (1.1785) acc 68.7500 (70.7630) lr 1.9048e-03 eta 17:52:20
+epoch [9/50] batch [775/1000] time 1.566 (1.561) data 0.000 (0.002) loss 0.9458 (1.1782) acc 71.8750 (70.7742) lr 1.9048e-03 eta 17:52:12
+epoch [9/50] batch [780/1000] time 1.535 (1.560) data 0.000 (0.002) loss 0.7437 (1.1781) acc 68.7500 (70.7452) lr 1.9048e-03 eta 17:52:01
+epoch [9/50] batch [785/1000] time 1.565 (1.560) data 0.000 (0.002) loss 0.8906 (1.1782) acc 78.1250 (70.7643) lr 1.9048e-03 eta 17:51:53
+epoch [9/50] batch [790/1000] time 1.557 (1.560) data 0.000 (0.002) loss 1.7656 (1.1794) acc 68.7500 (70.7318) lr 1.9048e-03 eta 17:51:45
+epoch [9/50] batch [795/1000] time 1.572 (1.560) data 0.001 (0.002) loss 1.0078 (1.1785) acc 78.1250 (70.7429) lr 1.9048e-03 eta 17:51:38
+epoch [9/50] batch [800/1000] time 1.576 (1.560) data 0.000 (0.002) loss 0.8833 (1.1767) acc 78.1250 (70.7852) lr 1.9048e-03 eta 17:51:29
+epoch [9/50] batch [805/1000] time 1.558 (1.560) data 0.000 (0.002) loss 0.9009 (1.1757) acc 75.0000 (70.7764) lr 1.9048e-03 eta 17:51:24
+epoch [9/50] batch [810/1000] time 1.568 (1.561) data 0.001 (0.002) loss 1.1865 (1.1762) acc 65.6250 (70.7446) lr 1.9048e-03 eta 17:51:23
+epoch [9/50] batch [815/1000] time 1.561 (1.561) data 0.000 (0.002) loss 1.7734 (1.1777) acc 65.6250 (70.7285) lr 1.9048e-03 eta 17:51:17
+epoch [9/50] batch [820/1000] time 1.566 (1.561) data 0.001 (0.002) loss 1.9961 (1.1814) acc 62.5000 (70.6593) lr 1.9048e-03 eta 17:51:11
+epoch [9/50] batch [825/1000] time 1.537 (1.561) data 0.000 (0.002) loss 1.2480 (1.1819) acc 68.7500 (70.6439) lr 1.9048e-03 eta 17:51:01
+epoch [9/50] batch [830/1000] time 1.551 (1.561) data 0.000 (0.002) loss 0.9204 (1.1821) acc 71.8750 (70.6438) lr 1.9048e-03 eta 17:50:50
+epoch [9/50] batch [835/1000] time 1.563 (1.561) data 0.000 (0.002) loss 0.9478 (1.1818) acc 65.6250 (70.6325) lr 1.9048e-03 eta 17:50:40
+epoch [9/50] batch [840/1000] time 1.585 (1.561) data 0.000 (0.002) loss 1.1670 (1.1818) acc 65.6250 (70.6399) lr 1.9048e-03 eta 17:50:32
+epoch [9/50] batch [845/1000] time 1.537 (1.561) data 0.001 (0.002) loss 0.8477 (1.1822) acc 71.8750 (70.6361) lr 1.9048e-03 eta 17:50:24
+epoch [9/50] batch [850/1000] time 1.569 (1.561) data 0.000 (0.002) loss 0.9565 (1.1829) acc 68.7500 (70.5993) lr 1.9048e-03 eta 17:50:15
+epoch [9/50] batch [855/1000] time 1.549 (1.561) data 0.001 (0.002) loss 0.6621 (1.1813) acc 78.1250 (70.6177) lr 1.9048e-03 eta 17:50:14
+epoch [9/50] batch [860/1000] time 1.559 (1.561) data 0.000 (0.002) loss 1.2334 (1.1813) acc 68.7500 (70.6068) lr 1.9048e-03 eta 17:50:07
+epoch [9/50] batch [865/1000] time 1.551 (1.561) data 0.000 (0.002) loss 0.8867 (1.1805) acc 75.0000 (70.6214) lr 1.9048e-03 eta 17:49:57
+epoch [9/50] batch [870/1000] time 1.550 (1.561) data 0.001 (0.002) loss 1.2314 (1.1793) acc 65.6250 (70.6430) lr 1.9048e-03 eta 17:49:48
+epoch [9/50] batch [875/1000] time 1.549 (1.561) data 0.001 (0.002) loss 0.9541 (1.1775) acc 65.6250 (70.6750) lr 1.9048e-03 eta 17:49:39
+epoch [9/50] batch [880/1000] time 1.565 (1.561) data 0.000 (0.001) loss 1.2402 (1.1764) acc 78.1250 (70.7173) lr 1.9048e-03 eta 17:49:31
+epoch [9/50] batch [885/1000] time 1.532 (1.561) data 0.001 (0.001) loss 1.3945 (1.1757) acc 78.1250 (70.7486) lr 1.9048e-03 eta 17:49:20
+epoch [9/50] batch [890/1000] time 1.552 (1.560) data 0.000 (0.001) loss 1.0352 (1.1762) acc 62.5000 (70.7233) lr 1.9048e-03 eta 17:49:09
+epoch [9/50] batch [895/1000] time 1.553 (1.561) data 0.000 (0.001) loss 1.3096 (1.1757) acc 71.8750 (70.7332) lr 1.9048e-03 eta 17:49:05
+epoch [9/50] batch [900/1000] time 1.560 (1.561) data 0.000 (0.001) loss 1.3643 (1.1764) acc 68.7500 (70.7153) lr 1.9048e-03 eta 17:48:58
+epoch [9/50] batch [905/1000] time 1.572 (1.561) data 0.000 (0.001) loss 0.7100 (1.1758) acc 84.3750 (70.7148) lr 1.9048e-03 eta 17:48:51
+epoch [9/50] batch [910/1000] time 1.572 (1.561) data 0.000 (0.001) loss 1.1357 (1.1758) acc 81.2500 (70.7040) lr 1.9048e-03 eta 17:48:45
+epoch [9/50] batch [915/1000] time 1.562 (1.561) data 0.000 (0.001) loss 0.9766 (1.1754) acc 68.7500 (70.6933) lr 1.9048e-03 eta 17:48:36
+epoch [9/50] batch [920/1000] time 1.559 (1.561) data 0.000 (0.001) loss 0.5757 (1.1745) acc 81.2500 (70.7099) lr 1.9048e-03 eta 17:48:27
+epoch [9/50] batch [925/1000] time 1.577 (1.561) data 0.000 (0.001) loss 1.0811 (1.1739) acc 81.2500 (70.7095) lr 1.9048e-03 eta 17:48:22
+epoch [9/50] batch [930/1000] time 1.541 (1.561) data 0.001 (0.001) loss 1.1211 (1.1722) acc 71.8750 (70.7527) lr 1.9048e-03 eta 17:48:12
+epoch [9/50] batch [935/1000] time 1.555 (1.561) data 0.000 (0.001) loss 1.0127 (1.1720) acc 71.8750 (70.7420) lr 1.9048e-03 eta 17:48:06
+epoch [9/50] batch [940/1000] time 1.550 (1.561) data 0.000 (0.001) loss 1.6709 (1.1729) acc 68.7500 (70.7447) lr 1.9048e-03 eta 17:47:55
+epoch [9/50] batch [945/1000] time 1.584 (1.561) data 0.001 (0.001) loss 0.8096 (1.1725) acc 84.3750 (70.7771) lr 1.9048e-03 eta 17:47:47
+epoch [9/50] batch [950/1000] time 1.558 (1.561) data 0.000 (0.001) loss 1.2021 (1.1738) acc 71.8750 (70.7599) lr 1.9048e-03 eta 17:47:39
+epoch [9/50] batch [955/1000] time 1.535 (1.560) data 0.000 (0.001) loss 0.9800 (1.1737) acc 81.2500 (70.7428) lr 1.9048e-03 eta 17:47:29
+epoch [9/50] batch [960/1000] time 1.555 (1.561) data 0.000 (0.001) loss 0.8271 (1.1733) acc 75.0000 (70.7487) lr 1.9048e-03 eta 17:47:28
+epoch [9/50] batch [965/1000] time 1.583 (1.561) data 0.000 (0.001) loss 0.9419 (1.1736) acc 71.8750 (70.7481) lr 1.9048e-03 eta 17:47:23
+epoch [9/50] batch [970/1000] time 1.565 (1.561) data 0.001 (0.001) loss 1.0342 (1.1720) acc 65.6250 (70.7700) lr 1.9048e-03 eta 17:47:16
+epoch [9/50] batch [975/1000] time 1.560 (1.561) data 0.000 (0.001) loss 0.9097 (1.1731) acc 84.3750 (70.7628) lr 1.9048e-03 eta 17:47:08
+epoch [9/50] batch [980/1000] time 1.544 (1.561) data 0.001 (0.001) loss 1.5059 (1.1719) acc 65.6250 (70.7812) lr 1.9048e-03 eta 17:47:01
+epoch [9/50] batch [985/1000] time 1.554 (1.561) data 0.001 (0.001) loss 1.2607 (1.1725) acc 65.6250 (70.7582) lr 1.9048e-03 eta 17:46:53
+epoch [9/50] batch [990/1000] time 1.583 (1.561) data 0.000 (0.001) loss 1.9268 (1.1740) acc 65.6250 (70.7449) lr 1.9048e-03 eta 17:46:49
+epoch [9/50] batch [995/1000] time 1.583 (1.561) data 0.000 (0.001) loss 1.5508 (1.1736) acc 62.5000 (70.7632) lr 1.9048e-03 eta 17:46:42
+epoch [9/50] batch [1000/1000] time 1.559 (1.561) data 0.000 (0.001) loss 1.0879 (1.1740) acc 62.5000 (70.7469) lr 1.8763e-03 eta 17:46:36
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 38,963
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+epoch [10/50] batch [5/1000] time 1.532 (1.655) data 0.000 (0.167) loss 0.7480 (0.9463) acc 75.0000 (71.8750) lr 1.8763e-03 eta 18:51:05
+epoch [10/50] batch [10/1000] time 1.571 (1.606) data 0.000 (0.084) loss 0.7959 (1.0112) acc 75.0000 (71.8750) lr 1.8763e-03 eta 18:17:30
+epoch [10/50] batch [15/1000] time 1.542 (1.589) data 0.000 (0.056) loss 1.1914 (1.0839) acc 75.0000 (71.4583) lr 1.8763e-03 eta 18:05:37
+epoch [10/50] batch [20/1000] time 1.557 (1.582) data 0.000 (0.042) loss 1.1250 (1.1332) acc 78.1250 (72.0312) lr 1.8763e-03 eta 18:00:15
+epoch [10/50] batch [25/1000] time 1.559 (1.580) data 0.000 (0.034) loss 1.3271 (1.1643) acc 68.7500 (71.8750) lr 1.8763e-03 eta 17:58:50
+epoch [10/50] batch [30/1000] time 1.562 (1.577) data 0.000 (0.028) loss 1.0176 (1.1418) acc 65.6250 (71.2500) lr 1.8763e-03 eta 17:56:35
+epoch [10/50] batch [35/1000] time 1.586 (1.581) data 0.000 (0.024) loss 2.0742 (1.1635) acc 59.3750 (71.4286) lr 1.8763e-03 eta 17:59:22
+epoch [10/50] batch [40/1000] time 1.576 (1.579) data 0.000 (0.021) loss 1.1816 (1.1547) acc 78.1250 (72.0312) lr 1.8763e-03 eta 17:57:50
+epoch [10/50] batch [45/1000] time 1.587 (1.578) data 0.001 (0.019) loss 0.5630 (1.1352) acc 87.5000 (72.3611) lr 1.8763e-03 eta 17:56:55
+epoch [10/50] batch [50/1000] time 1.564 (1.576) data 0.001 (0.017) loss 1.3984 (1.1259) acc 65.6250 (72.5625) lr 1.8763e-03 eta 17:55:27
+epoch [10/50] batch [55/1000] time 1.585 (1.577) data 0.001 (0.016) loss 0.7456 (1.1396) acc 75.0000 (72.3864) lr 1.8763e-03 eta 17:55:53
+epoch [10/50] batch [60/1000] time 1.563 (1.575) data 0.001 (0.014) loss 0.6021 (1.1080) acc 81.2500 (72.8646) lr 1.8763e-03 eta 17:54:47
+epoch [10/50] batch [65/1000] time 1.570 (1.574) data 0.000 (0.013) loss 1.6787 (1.1366) acc 59.3750 (72.4519) lr 1.8763e-03 eta 17:54:00
+epoch [10/50] batch [70/1000] time 1.573 (1.574) data 0.001 (0.012) loss 1.1475 (1.1521) acc 71.8750 (72.1429) lr 1.8763e-03 eta 17:53:31
+epoch [10/50] batch [75/1000] time 1.561 (1.573) data 0.000 (0.012) loss 1.2373 (1.1595) acc 71.8750 (72.2500) lr 1.8763e-03 eta 17:52:43
+epoch [10/50] batch [80/1000] time 1.561 (1.572) data 0.001 (0.011) loss 0.8140 (1.1497) acc 78.1250 (72.4609) lr 1.8763e-03 eta 17:52:03
+epoch [10/50] batch [85/1000] time 1.565 (1.571) data 0.000 (0.010) loss 1.5781 (1.1626) acc 62.5000 (72.2426) lr 1.8763e-03 eta 17:51:15
+epoch [10/50] batch [90/1000] time 1.528 (1.570) data 0.000 (0.010) loss 1.1123 (1.1557) acc 65.6250 (72.1875) lr 1.8763e-03 eta 17:50:26
+epoch [10/50] batch [95/1000] time 1.545 (1.571) data 0.000 (0.009) loss 0.9429 (1.1544) acc 65.6250 (72.0066) lr 1.8763e-03 eta 17:51:20
+epoch [10/50] batch [100/1000] time 1.558 (1.571) data 0.000 (0.009) loss 1.0176 (1.1459) acc 78.1250 (71.9062) lr 1.8763e-03 eta 17:51:01
+epoch [10/50] batch [105/1000] time 1.571 (1.571) data 0.001 (0.008) loss 1.2412 (1.1441) acc 59.3750 (71.8155) lr 1.8763e-03 eta 17:50:45
+epoch [10/50] batch [110/1000] time 1.563 (1.571) data 0.000 (0.008) loss 1.0439 (1.1278) acc 71.8750 (71.8182) lr 1.8763e-03 eta 17:50:47
+epoch [10/50] batch [115/1000] time 1.569 (1.571) data 0.000 (0.008) loss 0.8394 (1.1285) acc 78.1250 (71.9022) lr 1.8763e-03 eta 17:50:33
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+epoch [10/50] batch [675/1000] time 1.543 (1.565) data 0.000 (0.002) loss 1.0703 (1.1333) acc 75.0000 (71.4398) lr 1.8763e-03 eta 17:31:41
+epoch [10/50] batch [680/1000] time 1.546 (1.565) data 0.000 (0.002) loss 1.3135 (1.1357) acc 59.3750 (71.3787) lr 1.8763e-03 eta 17:31:31
+epoch [10/50] batch [685/1000] time 1.567 (1.565) data 0.000 (0.002) loss 0.5522 (1.1335) acc 87.5000 (71.4325) lr 1.8763e-03 eta 17:31:20
+epoch [10/50] batch [690/1000] time 1.550 (1.565) data 0.000 (0.002) loss 1.2686 (1.1339) acc 68.7500 (71.4402) lr 1.8763e-03 eta 17:31:10
+epoch [10/50] batch [695/1000] time 1.717 (1.565) data 0.000 (0.002) loss 0.9414 (1.1343) acc 75.0000 (71.4119) lr 1.8763e-03 eta 17:31:08
+epoch [10/50] batch [700/1000] time 1.533 (1.565) data 0.000 (0.002) loss 0.7168 (1.1337) acc 78.1250 (71.4330) lr 1.8763e-03 eta 17:30:58
+epoch [10/50] batch [705/1000] time 1.558 (1.565) data 0.001 (0.002) loss 1.0586 (1.1341) acc 78.1250 (71.4450) lr 1.8763e-03 eta 17:30:47
+epoch [10/50] batch [710/1000] time 1.572 (1.565) data 0.000 (0.002) loss 1.8486 (1.1354) acc 50.0000 (71.3908) lr 1.8763e-03 eta 17:30:41
+epoch [10/50] batch [715/1000] time 1.555 (1.565) data 0.000 (0.002) loss 1.4160 (1.1350) acc 68.7500 (71.4030) lr 1.8763e-03 eta 17:30:33
+epoch [10/50] batch [720/1000] time 1.555 (1.565) data 0.001 (0.002) loss 1.3066 (1.1356) acc 65.6250 (71.4106) lr 1.8763e-03 eta 17:30:25
+epoch [10/50] batch [725/1000] time 1.590 (1.565) data 0.000 (0.002) loss 0.7129 (1.1357) acc 81.2500 (71.3750) lr 1.8763e-03 eta 17:30:16
+epoch [10/50] batch [730/1000] time 1.553 (1.565) data 0.001 (0.002) loss 0.7788 (1.1342) acc 81.2500 (71.3870) lr 1.8763e-03 eta 17:30:03
+epoch [10/50] batch [735/1000] time 1.541 (1.565) data 0.001 (0.002) loss 1.2607 (1.1354) acc 78.1250 (71.3818) lr 1.8763e-03 eta 17:29:55
+epoch [10/50] batch [740/1000] time 1.706 (1.565) data 0.001 (0.002) loss 1.1426 (1.1346) acc 62.5000 (71.3767) lr 1.8763e-03 eta 17:29:53
+epoch [10/50] batch [745/1000] time 1.551 (1.565) data 0.000 (0.002) loss 1.7676 (1.1377) acc 56.2500 (71.3297) lr 1.8763e-03 eta 17:29:43
+epoch [10/50] batch [750/1000] time 1.552 (1.565) data 0.000 (0.002) loss 0.9023 (1.1366) acc 78.1250 (71.3250) lr 1.8763e-03 eta 17:29:34
+epoch [10/50] batch [755/1000] time 1.564 (1.565) data 0.000 (0.002) loss 0.6499 (1.1368) acc 78.1250 (71.3204) lr 1.8763e-03 eta 17:29:27
+epoch [10/50] batch [760/1000] time 1.538 (1.564) data 0.000 (0.002) loss 1.0547 (1.1357) acc 68.7500 (71.3322) lr 1.8763e-03 eta 17:29:15
+epoch [10/50] batch [765/1000] time 1.553 (1.564) data 0.000 (0.002) loss 0.8594 (1.1360) acc 71.8750 (71.3154) lr 1.8763e-03 eta 17:29:01
+epoch [10/50] batch [770/1000] time 1.549 (1.564) data 0.000 (0.002) loss 2.0996 (1.1364) acc 62.5000 (71.3352) lr 1.8763e-03 eta 17:28:52
+epoch [10/50] batch [775/1000] time 1.563 (1.564) data 0.001 (0.002) loss 1.3477 (1.1358) acc 68.7500 (71.3185) lr 1.8763e-03 eta 17:28:43
+epoch [10/50] batch [780/1000] time 1.578 (1.564) data 0.001 (0.002) loss 0.8755 (1.1352) acc 71.8750 (71.3502) lr 1.8763e-03 eta 17:28:34
+epoch [10/50] batch [785/1000] time 1.559 (1.564) data 0.001 (0.002) loss 0.8989 (1.1335) acc 78.1250 (71.4172) lr 1.8763e-03 eta 17:28:32
+epoch [10/50] batch [790/1000] time 1.582 (1.564) data 0.000 (0.002) loss 1.2363 (1.1333) acc 59.3750 (71.4003) lr 1.8763e-03 eta 17:28:26
+epoch [10/50] batch [795/1000] time 1.569 (1.564) data 0.000 (0.002) loss 0.9873 (1.1343) acc 71.8750 (71.3640) lr 1.8763e-03 eta 17:28:14
+epoch [10/50] batch [800/1000] time 1.562 (1.564) data 0.000 (0.002) loss 0.9243 (1.1343) acc 75.0000 (71.3555) lr 1.8763e-03 eta 17:28:08
+epoch [10/50] batch [805/1000] time 1.573 (1.564) data 0.000 (0.002) loss 1.6982 (1.1368) acc 62.5000 (71.2927) lr 1.8763e-03 eta 17:28:00
+epoch [10/50] batch [810/1000] time 1.559 (1.564) data 0.000 (0.001) loss 0.6353 (1.1378) acc 84.3750 (71.2770) lr 1.8763e-03 eta 17:27:49
+epoch [10/50] batch [815/1000] time 1.608 (1.564) data 0.000 (0.001) loss 1.0312 (1.1383) acc 71.8750 (71.2615) lr 1.8763e-03 eta 17:27:46
+epoch [10/50] batch [820/1000] time 1.561 (1.564) data 0.001 (0.001) loss 1.0049 (1.1378) acc 84.3750 (71.2767) lr 1.8763e-03 eta 17:27:38
+epoch [10/50] batch [825/1000] time 1.585 (1.564) data 0.001 (0.001) loss 1.8838 (1.1386) acc 62.5000 (71.2652) lr 1.8763e-03 eta 17:27:32
+epoch [10/50] batch [830/1000] time 1.577 (1.565) data 0.000 (0.001) loss 1.1084 (1.1403) acc 75.0000 (71.2199) lr 1.8763e-03 eta 17:27:26
+epoch [10/50] batch [835/1000] time 1.601 (1.565) data 0.000 (0.001) loss 1.6934 (1.1407) acc 56.2500 (71.1976) lr 1.8763e-03 eta 17:27:20
+epoch [10/50] batch [840/1000] time 1.565 (1.565) data 0.000 (0.001) loss 1.6660 (1.1395) acc 62.5000 (71.2202) lr 1.8763e-03 eta 17:27:13
+epoch [10/50] batch [845/1000] time 1.568 (1.565) data 0.000 (0.001) loss 0.9414 (1.1401) acc 68.7500 (71.1908) lr 1.8763e-03 eta 17:27:03
+epoch [10/50] batch [850/1000] time 1.557 (1.565) data 0.000 (0.001) loss 0.9507 (1.1403) acc 71.8750 (71.1912) lr 1.8763e-03 eta 17:27:02
+epoch [10/50] batch [855/1000] time 1.566 (1.565) data 0.000 (0.001) loss 1.3789 (1.1401) acc 65.6250 (71.1879) lr 1.8763e-03 eta 17:26:57
+epoch [10/50] batch [860/1000] time 1.566 (1.565) data 0.001 (0.001) loss 1.1572 (1.1405) acc 59.3750 (71.1737) lr 1.8763e-03 eta 17:26:48
+epoch [10/50] batch [865/1000] time 1.554 (1.565) data 0.000 (0.001) loss 0.3577 (1.1394) acc 90.6250 (71.1886) lr 1.8763e-03 eta 17:26:38
+epoch [10/50] batch [870/1000] time 1.548 (1.565) data 0.001 (0.001) loss 0.9727 (1.1398) acc 78.1250 (71.1961) lr 1.8763e-03 eta 17:26:29
+epoch [10/50] batch [875/1000] time 1.556 (1.565) data 0.000 (0.001) loss 2.2559 (1.1413) acc 40.6250 (71.1643) lr 1.8763e-03 eta 17:26:19
+epoch [10/50] batch [880/1000] time 1.554 (1.565) data 0.000 (0.001) loss 1.4658 (1.1428) acc 65.6250 (71.1328) lr 1.8763e-03 eta 17:26:13
+epoch [10/50] batch [885/1000] time 1.557 (1.565) data 0.000 (0.001) loss 0.7871 (1.1425) acc 75.0000 (71.1370) lr 1.8763e-03 eta 17:26:03
+epoch [10/50] batch [890/1000] time 1.563 (1.565) data 0.000 (0.001) loss 1.1328 (1.1417) acc 75.0000 (71.1692) lr 1.8763e-03 eta 17:25:54
+epoch [10/50] batch [895/1000] time 1.547 (1.565) data 0.001 (0.001) loss 1.6729 (1.1433) acc 65.6250 (71.1522) lr 1.8763e-03 eta 17:25:55
+epoch [10/50] batch [900/1000] time 1.582 (1.565) data 0.001 (0.001) loss 1.4717 (1.1431) acc 71.8750 (71.1528) lr 1.8763e-03 eta 17:25:47
+epoch [10/50] batch [905/1000] time 1.567 (1.565) data 0.001 (0.001) loss 1.4824 (1.1430) acc 71.8750 (71.1602) lr 1.8763e-03 eta 17:25:39
+epoch [10/50] batch [910/1000] time 1.540 (1.565) data 0.000 (0.001) loss 0.9043 (1.1430) acc 71.8750 (71.1538) lr 1.8763e-03 eta 17:25:30
+epoch [10/50] batch [915/1000] time 1.556 (1.565) data 0.000 (0.001) loss 1.5977 (1.1435) acc 53.1250 (71.1270) lr 1.8763e-03 eta 17:25:21
+epoch [10/50] batch [920/1000] time 1.555 (1.565) data 0.000 (0.001) loss 1.0293 (1.1430) acc 68.7500 (71.1345) lr 1.8763e-03 eta 17:25:14
+epoch [10/50] batch [925/1000] time 1.529 (1.565) data 0.000 (0.001) loss 1.1426 (1.1435) acc 62.5000 (71.1149) lr 1.8763e-03 eta 17:25:06
+epoch [10/50] batch [930/1000] time 1.564 (1.565) data 0.000 (0.001) loss 1.5918 (1.1438) acc 59.3750 (71.0988) lr 1.8763e-03 eta 17:24:59
+epoch [10/50] batch [935/1000] time 1.556 (1.565) data 0.000 (0.001) loss 0.7583 (1.1431) acc 75.0000 (71.1130) lr 1.8763e-03 eta 17:24:56
+epoch [10/50] batch [940/1000] time 1.557 (1.565) data 0.000 (0.001) loss 1.5166 (1.1435) acc 59.3750 (71.1004) lr 1.8763e-03 eta 17:24:48
+epoch [10/50] batch [945/1000] time 1.524 (1.565) data 0.000 (0.001) loss 0.8218 (1.1440) acc 75.0000 (71.1045) lr 1.8763e-03 eta 17:24:37
+epoch [10/50] batch [950/1000] time 1.537 (1.565) data 0.000 (0.001) loss 1.9297 (1.1451) acc 59.3750 (71.0954) lr 1.8763e-03 eta 17:24:23
+epoch [10/50] batch [955/1000] time 1.546 (1.565) data 0.001 (0.001) loss 1.0068 (1.1459) acc 75.0000 (71.0798) lr 1.8763e-03 eta 17:24:13
+epoch [10/50] batch [960/1000] time 1.574 (1.565) data 0.001 (0.001) loss 1.7451 (1.1482) acc 56.2500 (71.0286) lr 1.8763e-03 eta 17:24:05
+epoch [10/50] batch [965/1000] time 1.539 (1.565) data 0.000 (0.001) loss 0.9658 (1.1478) acc 75.0000 (71.0233) lr 1.8763e-03 eta 17:23:56
+epoch [10/50] batch [970/1000] time 1.537 (1.564) data 0.000 (0.001) loss 1.1377 (1.1482) acc 65.6250 (71.0213) lr 1.8763e-03 eta 17:23:44
+epoch [10/50] batch [975/1000] time 1.561 (1.564) data 0.001 (0.001) loss 1.9053 (1.1495) acc 59.3750 (70.9968) lr 1.8763e-03 eta 17:23:35
+epoch [10/50] batch [980/1000] time 1.542 (1.564) data 0.001 (0.001) loss 1.1963 (1.1492) acc 78.1250 (70.9981) lr 1.8763e-03 eta 17:23:27
+epoch [10/50] batch [985/1000] time 1.550 (1.564) data 0.001 (0.001) loss 1.2676 (1.1499) acc 65.6250 (70.9772) lr 1.8763e-03 eta 17:23:19
+epoch [10/50] batch [990/1000] time 1.552 (1.564) data 0.000 (0.001) loss 0.8218 (1.1497) acc 81.2500 (71.0006) lr 1.8763e-03 eta 17:23:11
+epoch [10/50] batch [995/1000] time 1.551 (1.564) data 0.000 (0.001) loss 1.1152 (1.1493) acc 71.8750 (71.0082) lr 1.8763e-03 eta 17:23:01
+epoch [10/50] batch [1000/1000] time 1.557 (1.564) data 0.000 (0.001) loss 1.5801 (1.1496) acc 65.6250 (71.0031) lr 1.8443e-03 eta 17:22:58
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,136
+* accuracy: 78.3%
+* error: 21.7%
+* macro_f1: 77.7%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [11/50] batch [5/1000] time 1.544 (1.690) data 0.000 (0.190) loss 1.4355 (1.0134) acc 65.6250 (73.7500) lr 1.8443e-03 eta 18:46:14
+epoch [11/50] batch [10/1000] time 1.565 (1.626) data 0.001 (0.095) loss 0.9097 (0.9962) acc 78.1250 (75.3125) lr 1.8443e-03 eta 18:03:45
+epoch [11/50] batch [15/1000] time 1.559 (1.603) data 0.000 (0.064) loss 0.5117 (1.0487) acc 84.3750 (73.9583) lr 1.8443e-03 eta 17:48:34
+epoch [11/50] batch [20/1000] time 1.566 (1.605) data 0.000 (0.048) loss 1.3994 (1.1441) acc 68.7500 (71.5625) lr 1.8443e-03 eta 17:49:28
+epoch [11/50] batch [25/1000] time 1.570 (1.599) data 0.000 (0.038) loss 0.8037 (1.1495) acc 84.3750 (72.3750) lr 1.8443e-03 eta 17:45:33
+epoch [11/50] batch [30/1000] time 1.561 (1.592) data 0.001 (0.032) loss 1.1074 (1.1664) acc 78.1250 (72.6042) lr 1.8443e-03 eta 17:40:35
+epoch [11/50] batch [35/1000] time 1.550 (1.586) data 0.000 (0.028) loss 1.1318 (1.1429) acc 59.3750 (72.1429) lr 1.8443e-03 eta 17:36:23
+epoch [11/50] batch [40/1000] time 1.552 (1.582) data 0.000 (0.024) loss 1.0195 (1.1484) acc 75.0000 (71.9531) lr 1.8443e-03 eta 17:33:25
+epoch [11/50] batch [45/1000] time 1.581 (1.579) data 0.000 (0.022) loss 1.3232 (1.1437) acc 65.6250 (72.2917) lr 1.8443e-03 eta 17:31:38
+epoch [11/50] batch [50/1000] time 1.557 (1.577) data 0.001 (0.020) loss 1.2979 (1.1619) acc 71.8750 (71.9375) lr 1.8443e-03 eta 17:29:54
+epoch [11/50] batch [55/1000] time 1.565 (1.575) data 0.000 (0.018) loss 0.7002 (1.1377) acc 87.5000 (72.3864) lr 1.8443e-03 eta 17:28:21
+epoch [11/50] batch [60/1000] time 1.535 (1.575) data 0.000 (0.016) loss 1.2012 (1.1523) acc 75.0000 (71.9792) lr 1.8443e-03 eta 17:28:06
+epoch [11/50] batch [65/1000] time 1.557 (1.573) data 0.000 (0.015) loss 1.2617 (1.1775) acc 68.7500 (71.1058) lr 1.8443e-03 eta 17:26:42
+epoch [11/50] batch [70/1000] time 1.541 (1.571) data 0.000 (0.014) loss 0.9927 (1.1765) acc 78.1250 (71.1161) lr 1.8443e-03 eta 17:25:28
+epoch [11/50] batch [75/1000] time 1.541 (1.570) data 0.001 (0.013) loss 0.8696 (1.1645) acc 75.0000 (71.3333) lr 1.8443e-03 eta 17:24:34
+epoch [11/50] batch [80/1000] time 1.545 (1.568) data 0.001 (0.012) loss 1.5352 (1.1644) acc 68.7500 (71.4062) lr 1.8443e-03 eta 17:23:26
+epoch [11/50] batch [85/1000] time 1.545 (1.568) data 0.000 (0.012) loss 1.8213 (1.1773) acc 50.0000 (71.0294) lr 1.8443e-03 eta 17:22:59
+epoch [11/50] batch [90/1000] time 1.555 (1.567) data 0.000 (0.011) loss 0.8535 (1.1702) acc 78.1250 (71.1458) lr 1.8443e-03 eta 17:22:37
+epoch [11/50] batch [95/1000] time 1.535 (1.567) data 0.000 (0.011) loss 1.3701 (1.1779) acc 59.3750 (70.8553) lr 1.8443e-03 eta 17:22:18
+epoch [11/50] batch [100/1000] time 1.585 (1.568) data 0.001 (0.010) loss 1.3271 (1.1796) acc 65.6250 (70.9062) lr 1.8443e-03 eta 17:22:24
+epoch [11/50] batch [105/1000] time 1.554 (1.568) data 0.000 (0.010) loss 1.6250 (1.1791) acc 71.8750 (71.0119) lr 1.8443e-03 eta 17:22:17
+epoch [11/50] batch [110/1000] time 1.566 (1.567) data 0.001 (0.009) loss 1.0205 (1.1819) acc 78.1250 (70.9943) lr 1.8443e-03 eta 17:21:52
+epoch [11/50] batch [115/1000] time 1.558 (1.567) data 0.001 (0.009) loss 0.8188 (1.1714) acc 81.2500 (71.1685) lr 1.8443e-03 eta 17:21:40
+epoch [11/50] batch [120/1000] time 1.592 (1.569) data 0.000 (0.008) loss 1.0869 (1.1717) acc 65.6250 (71.1979) lr 1.8443e-03 eta 17:22:36
+epoch [11/50] batch [125/1000] time 1.575 (1.568) data 0.000 (0.008) loss 0.9170 (1.1627) acc 78.1250 (71.3500) lr 1.8443e-03 eta 17:22:21
+epoch [11/50] batch [130/1000] time 1.599 (1.568) data 0.000 (0.008) loss 1.1641 (1.1625) acc 62.5000 (71.3462) lr 1.8443e-03 eta 17:22:08
+epoch [11/50] batch [135/1000] time 1.548 (1.568) data 0.000 (0.008) loss 1.4199 (1.1580) acc 68.7500 (71.2500) lr 1.8443e-03 eta 17:21:38
+epoch [11/50] batch [140/1000] time 1.555 (1.568) data 0.000 (0.007) loss 1.2471 (1.1574) acc 59.3750 (71.1384) lr 1.8443e-03 eta 17:21:25
+epoch [11/50] batch [145/1000] time 1.577 (1.567) data 0.000 (0.007) loss 1.2246 (1.1538) acc 81.2500 (71.2931) lr 1.8443e-03 eta 17:21:07
+epoch [11/50] batch [150/1000] time 1.577 (1.567) data 0.000 (0.007) loss 1.2402 (1.1522) acc 68.7500 (71.3750) lr 1.8443e-03 eta 17:20:56
+epoch [11/50] batch [155/1000] time 1.554 (1.567) data 0.001 (0.007) loss 1.2461 (1.1526) acc 68.7500 (71.4315) lr 1.8443e-03 eta 17:20:38
+epoch [11/50] batch [160/1000] time 1.568 (1.567) data 0.001 (0.006) loss 0.7764 (1.1499) acc 84.3750 (71.5234) lr 1.8443e-03 eta 17:20:37
+epoch [11/50] batch [165/1000] time 1.556 (1.568) data 0.000 (0.006) loss 1.2676 (1.1512) acc 65.6250 (71.3826) lr 1.8443e-03 eta 17:20:56
+epoch [11/50] batch [170/1000] time 1.572 (1.567) data 0.001 (0.006) loss 1.2734 (1.1506) acc 65.6250 (71.3051) lr 1.8443e-03 eta 17:20:29
+epoch [11/50] batch [175/1000] time 1.546 (1.567) data 0.000 (0.006) loss 0.9238 (1.1495) acc 78.1250 (71.3214) lr 1.8443e-03 eta 17:20:07
+epoch [11/50] batch [180/1000] time 1.561 (1.567) data 0.000 (0.006) loss 1.2764 (1.1451) acc 59.3750 (71.3194) lr 1.8443e-03 eta 17:19:46
+epoch [11/50] batch [185/1000] time 1.556 (1.567) data 0.000 (0.006) loss 1.2305 (1.1421) acc 75.0000 (71.3851) lr 1.8443e-03 eta 17:19:31
+epoch [11/50] batch [190/1000] time 1.543 (1.566) data 0.000 (0.005) loss 0.5811 (1.1426) acc 90.6250 (71.4145) lr 1.8443e-03 eta 17:19:14
+epoch [11/50] batch [195/1000] time 1.581 (1.566) data 0.000 (0.005) loss 1.4873 (1.1441) acc 62.5000 (71.3462) lr 1.8443e-03 eta 17:19:02
+epoch [11/50] batch [200/1000] time 1.545 (1.567) data 0.000 (0.005) loss 1.3291 (1.1488) acc 71.8750 (71.2656) lr 1.8443e-03 eta 17:19:10
+epoch [11/50] batch [205/1000] time 1.566 (1.566) data 0.000 (0.005) loss 0.8906 (1.1442) acc 62.5000 (71.2805) lr 1.8443e-03 eta 17:18:58
+epoch [11/50] batch [210/1000] time 1.562 (1.567) data 0.000 (0.005) loss 1.5654 (1.1452) acc 56.2500 (71.2202) lr 1.8443e-03 eta 17:19:06
+epoch [11/50] batch [215/1000] time 1.558 (1.567) data 0.000 (0.005) loss 1.0195 (1.1356) acc 68.7500 (71.3372) lr 1.8443e-03 eta 17:18:46
+epoch [11/50] batch [220/1000] time 1.555 (1.567) data 0.000 (0.005) loss 2.0059 (1.1324) acc 62.5000 (71.4347) lr 1.8443e-03 eta 17:18:36
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+epoch [11/50] batch [775/1000] time 1.565 (1.562) data 0.000 (0.002) loss 1.9014 (1.1552) acc 71.8750 (71.1532) lr 1.8443e-03 eta 17:01:26
+epoch [11/50] batch [780/1000] time 1.538 (1.562) data 0.001 (0.002) loss 0.7598 (1.1561) acc 87.5000 (71.1498) lr 1.8443e-03 eta 17:01:16
+epoch [11/50] batch [785/1000] time 1.552 (1.562) data 0.001 (0.002) loss 0.8276 (1.1552) acc 75.0000 (71.1863) lr 1.8443e-03 eta 17:01:08
+epoch [11/50] batch [790/1000] time 1.550 (1.562) data 0.000 (0.002) loss 1.4932 (1.1557) acc 62.5000 (71.1748) lr 1.8443e-03 eta 17:01:01
+epoch [11/50] batch [795/1000] time 1.566 (1.562) data 0.000 (0.002) loss 1.3867 (1.1566) acc 65.6250 (71.1478) lr 1.8443e-03 eta 17:00:54
+epoch [11/50] batch [800/1000] time 1.579 (1.562) data 0.001 (0.002) loss 1.4814 (1.1578) acc 65.6250 (71.1094) lr 1.8443e-03 eta 17:00:47
+epoch [11/50] batch [805/1000] time 1.557 (1.562) data 0.001 (0.002) loss 1.2588 (1.1578) acc 59.3750 (71.0986) lr 1.8443e-03 eta 17:00:39
+epoch [11/50] batch [810/1000] time 1.565 (1.563) data 0.001 (0.002) loss 1.1846 (1.1584) acc 75.0000 (71.0880) lr 1.8443e-03 eta 17:00:34
+epoch [11/50] batch [815/1000] time 1.621 (1.563) data 0.000 (0.002) loss 0.9966 (1.1565) acc 75.0000 (71.1350) lr 1.8443e-03 eta 17:00:38
+epoch [11/50] batch [820/1000] time 1.557 (1.563) data 0.000 (0.002) loss 1.0781 (1.1569) acc 78.1250 (71.1395) lr 1.8443e-03 eta 17:00:29
+epoch [11/50] batch [825/1000] time 1.552 (1.563) data 0.001 (0.002) loss 0.9180 (1.1575) acc 71.8750 (71.1061) lr 1.8443e-03 eta 17:00:22
+epoch [11/50] batch [830/1000] time 1.556 (1.563) data 0.000 (0.002) loss 0.7998 (1.1579) acc 81.2500 (71.0919) lr 1.8443e-03 eta 17:00:15
+epoch [11/50] batch [835/1000] time 1.543 (1.563) data 0.000 (0.002) loss 1.0713 (1.1579) acc 75.0000 (71.0928) lr 1.8443e-03 eta 17:00:05
+epoch [11/50] batch [840/1000] time 1.558 (1.563) data 0.001 (0.002) loss 0.8892 (1.1580) acc 78.1250 (71.0789) lr 1.8443e-03 eta 16:59:55
+epoch [11/50] batch [845/1000] time 1.536 (1.563) data 0.001 (0.002) loss 0.9272 (1.1575) acc 75.0000 (71.0799) lr 1.8443e-03 eta 16:59:44
+epoch [11/50] batch [850/1000] time 1.585 (1.563) data 0.001 (0.002) loss 1.3086 (1.1569) acc 71.8750 (71.0919) lr 1.8443e-03 eta 16:59:36
+epoch [11/50] batch [855/1000] time 1.565 (1.563) data 0.001 (0.002) loss 1.7686 (1.1572) acc 68.7500 (71.1001) lr 1.8443e-03 eta 16:59:30
+epoch [11/50] batch [860/1000] time 1.559 (1.563) data 0.000 (0.002) loss 1.0176 (1.1573) acc 71.8750 (71.0901) lr 1.8443e-03 eta 16:59:22
+epoch [11/50] batch [865/1000] time 1.585 (1.563) data 0.000 (0.002) loss 0.7393 (1.1565) acc 81.2500 (71.1091) lr 1.8443e-03 eta 16:59:15
+epoch [11/50] batch [870/1000] time 1.573 (1.563) data 0.001 (0.002) loss 0.7197 (1.1563) acc 78.1250 (71.1315) lr 1.8443e-03 eta 16:59:10
+epoch [11/50] batch [875/1000] time 1.564 (1.563) data 0.001 (0.002) loss 1.7656 (1.1581) acc 62.5000 (71.1179) lr 1.8443e-03 eta 16:59:10
+epoch [11/50] batch [880/1000] time 1.580 (1.563) data 0.001 (0.002) loss 0.8604 (1.1579) acc 81.2500 (71.1399) lr 1.8443e-03 eta 16:59:04
+epoch [11/50] batch [885/1000] time 1.575 (1.563) data 0.000 (0.002) loss 0.9902 (1.1573) acc 81.2500 (71.1476) lr 1.8443e-03 eta 16:58:55
+epoch [11/50] batch [890/1000] time 1.555 (1.563) data 0.000 (0.002) loss 1.5859 (1.1578) acc 65.6250 (71.1376) lr 1.8443e-03 eta 16:58:47
+epoch [11/50] batch [895/1000] time 1.577 (1.563) data 0.000 (0.002) loss 1.2119 (1.1582) acc 65.6250 (71.1453) lr 1.8443e-03 eta 16:58:39
+epoch [11/50] batch [900/1000] time 1.563 (1.563) data 0.000 (0.001) loss 1.3984 (1.1587) acc 62.5000 (71.1076) lr 1.8443e-03 eta 16:58:30
+epoch [11/50] batch [905/1000] time 1.554 (1.563) data 0.000 (0.001) loss 1.0488 (1.1573) acc 71.8750 (71.1291) lr 1.8443e-03 eta 16:58:23
+epoch [11/50] batch [910/1000] time 1.552 (1.563) data 0.000 (0.001) loss 0.9355 (1.1567) acc 71.8750 (71.1435) lr 1.8443e-03 eta 16:58:14
+epoch [11/50] batch [915/1000] time 1.575 (1.563) data 0.000 (0.001) loss 0.7134 (1.1553) acc 84.3750 (71.1783) lr 1.8443e-03 eta 16:58:07
+epoch [11/50] batch [920/1000] time 1.536 (1.563) data 0.001 (0.001) loss 1.1416 (1.1571) acc 75.0000 (71.1583) lr 1.8443e-03 eta 16:58:03
+epoch [11/50] batch [925/1000] time 1.570 (1.563) data 0.000 (0.001) loss 1.0771 (1.1567) acc 75.0000 (71.1622) lr 1.8443e-03 eta 16:57:56
+epoch [11/50] batch [930/1000] time 1.554 (1.563) data 0.001 (0.001) loss 1.1104 (1.1554) acc 62.5000 (71.1660) lr 1.8443e-03 eta 16:57:48
+epoch [11/50] batch [935/1000] time 1.556 (1.563) data 0.000 (0.001) loss 1.3545 (1.1551) acc 68.7500 (71.1932) lr 1.8443e-03 eta 16:57:41
+epoch [11/50] batch [940/1000] time 1.563 (1.563) data 0.000 (0.001) loss 1.4580 (1.1540) acc 59.3750 (71.2134) lr 1.8443e-03 eta 16:57:32
+epoch [11/50] batch [945/1000] time 1.543 (1.563) data 0.000 (0.001) loss 1.2607 (1.1543) acc 81.2500 (71.2169) lr 1.8443e-03 eta 16:57:25
+epoch [11/50] batch [950/1000] time 1.562 (1.563) data 0.000 (0.001) loss 0.5688 (1.1542) acc 81.2500 (71.2204) lr 1.8443e-03 eta 16:57:17
+epoch [11/50] batch [955/1000] time 1.570 (1.563) data 0.000 (0.001) loss 1.1064 (1.1543) acc 68.7500 (71.2336) lr 1.8443e-03 eta 16:57:08
+epoch [11/50] batch [960/1000] time 1.564 (1.563) data 0.000 (0.001) loss 1.1943 (1.1544) acc 71.8750 (71.2305) lr 1.8443e-03 eta 16:56:57
+epoch [11/50] batch [965/1000] time 1.575 (1.563) data 0.000 (0.001) loss 0.9111 (1.1535) acc 78.1250 (71.2662) lr 1.8443e-03 eta 16:56:58
+epoch [11/50] batch [970/1000] time 1.570 (1.563) data 0.000 (0.001) loss 1.4365 (1.1542) acc 65.6250 (71.2371) lr 1.8443e-03 eta 16:56:50
+epoch [11/50] batch [975/1000] time 1.555 (1.563) data 0.000 (0.001) loss 0.8789 (1.1553) acc 75.0000 (71.2276) lr 1.8443e-03 eta 16:56:38
+epoch [11/50] batch [980/1000] time 1.554 (1.563) data 0.000 (0.001) loss 1.4453 (1.1548) acc 62.5000 (71.2245) lr 1.8443e-03 eta 16:56:28
+epoch [11/50] batch [985/1000] time 1.552 (1.563) data 0.001 (0.001) loss 1.2314 (1.1554) acc 81.2500 (71.2341) lr 1.8443e-03 eta 16:56:18
+epoch [11/50] batch [990/1000] time 1.546 (1.563) data 0.000 (0.001) loss 1.4434 (1.1560) acc 62.5000 (71.2247) lr 1.8443e-03 eta 16:56:07
+epoch [11/50] batch [995/1000] time 1.569 (1.563) data 0.000 (0.001) loss 1.3125 (1.1558) acc 65.6250 (71.2217) lr 1.8443e-03 eta 16:55:57
+epoch [11/50] batch [1000/1000] time 1.565 (1.563) data 0.000 (0.001) loss 1.6523 (1.1561) acc 65.6250 (71.2281) lr 1.8090e-03 eta 16:55:49
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,078
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+epoch [12/50] batch [5/1000] time 1.542 (1.665) data 0.001 (0.170) loss 1.5059 (1.3250) acc 71.8750 (70.0000) lr 1.8090e-03 eta 18:02:23
+epoch [12/50] batch [10/1000] time 1.552 (1.609) data 0.001 (0.085) loss 1.1797 (1.1437) acc 75.0000 (74.3750) lr 1.8090e-03 eta 17:25:23
+epoch [12/50] batch [15/1000] time 1.539 (1.593) data 0.000 (0.057) loss 1.5498 (1.1576) acc 56.2500 (72.2917) lr 1.8090e-03 eta 17:14:48
+epoch [12/50] batch [20/1000] time 1.575 (1.586) data 0.000 (0.043) loss 0.6538 (1.0891) acc 81.2500 (73.1250) lr 1.8090e-03 eta 17:10:08
+epoch [12/50] batch [25/1000] time 1.578 (1.584) data 0.000 (0.034) loss 1.4482 (1.0965) acc 62.5000 (72.6250) lr 1.8090e-03 eta 17:08:46
+epoch [12/50] batch [30/1000] time 1.530 (1.578) data 0.001 (0.029) loss 0.9619 (1.0919) acc 78.1250 (72.7083) lr 1.8090e-03 eta 17:05:07
+epoch [12/50] batch [35/1000] time 1.563 (1.584) data 0.000 (0.025) loss 0.7812 (1.1016) acc 78.1250 (72.7679) lr 1.8090e-03 eta 17:08:36
+epoch [12/50] batch [40/1000] time 1.589 (1.583) data 0.001 (0.022) loss 1.7988 (1.1424) acc 59.3750 (72.3438) lr 1.8090e-03 eta 17:07:38
+epoch [12/50] batch [45/1000] time 1.564 (1.581) data 0.000 (0.019) loss 1.5303 (1.1528) acc 43.7500 (71.5278) lr 1.8090e-03 eta 17:06:18
+epoch [12/50] batch [50/1000] time 1.534 (1.578) data 0.000 (0.017) loss 0.9399 (1.1544) acc 71.8750 (71.3125) lr 1.8090e-03 eta 17:04:26
+epoch [12/50] batch [55/1000] time 1.571 (1.576) data 0.000 (0.016) loss 0.9194 (1.1574) acc 75.0000 (71.4773) lr 1.8090e-03 eta 17:03:12
+epoch [12/50] batch [60/1000] time 1.581 (1.575) data 0.000 (0.015) loss 1.0166 (1.1518) acc 78.1250 (71.8750) lr 1.8090e-03 eta 17:02:27
+epoch [12/50] batch [65/1000] time 1.558 (1.575) data 0.000 (0.013) loss 1.5205 (1.1652) acc 59.3750 (71.6827) lr 1.8090e-03 eta 17:02:03
+epoch [12/50] batch [70/1000] time 1.587 (1.575) data 0.000 (0.013) loss 1.3105 (1.1688) acc 71.8750 (71.8304) lr 1.8090e-03 eta 17:01:36
+epoch [12/50] batch [75/1000] time 1.569 (1.574) data 0.001 (0.012) loss 1.5137 (1.1668) acc 71.8750 (71.9167) lr 1.8090e-03 eta 17:01:02
+epoch [12/50] batch [80/1000] time 1.587 (1.575) data 0.000 (0.011) loss 0.5469 (1.1545) acc 90.6250 (72.1484) lr 1.8090e-03 eta 17:01:52
+epoch [12/50] batch [85/1000] time 1.540 (1.575) data 0.000 (0.010) loss 0.8516 (1.1487) acc 78.1250 (72.2426) lr 1.8090e-03 eta 17:01:26
+epoch [12/50] batch [90/1000] time 1.551 (1.574) data 0.000 (0.010) loss 1.2402 (1.1471) acc 62.5000 (72.0486) lr 1.8090e-03 eta 17:00:56
+epoch [12/50] batch [95/1000] time 1.545 (1.573) data 0.000 (0.009) loss 0.9653 (1.1459) acc 62.5000 (71.7763) lr 1.8090e-03 eta 17:00:12
+epoch [12/50] batch [100/1000] time 1.555 (1.573) data 0.001 (0.009) loss 1.4932 (1.1508) acc 59.3750 (71.5000) lr 1.8090e-03 eta 16:59:47
+epoch [12/50] batch [105/1000] time 1.560 (1.572) data 0.000 (0.009) loss 1.2432 (1.1564) acc 62.5000 (71.1310) lr 1.8090e-03 eta 16:59:13
+epoch [12/50] batch [110/1000] time 1.557 (1.572) data 0.000 (0.008) loss 1.1885 (1.1512) acc 71.8750 (71.2784) lr 1.8090e-03 eta 16:58:37
+epoch [12/50] batch [115/1000] time 1.574 (1.571) data 0.000 (0.008) loss 1.4746 (1.1525) acc 75.0000 (71.4402) lr 1.8090e-03 eta 16:58:21
+epoch [12/50] batch [120/1000] time 1.567 (1.571) data 0.001 (0.008) loss 1.0635 (1.1481) acc 71.8750 (71.5365) lr 1.8090e-03 eta 16:58:02
+epoch [12/50] batch [125/1000] time 1.539 (1.570) data 0.000 (0.007) loss 0.7842 (1.1488) acc 78.1250 (71.5500) lr 1.8090e-03 eta 16:57:23
+epoch [12/50] batch [130/1000] time 1.543 (1.569) data 0.000 (0.007) loss 0.9438 (1.1402) acc 71.8750 (71.6346) lr 1.8090e-03 eta 16:56:43
+epoch [12/50] batch [135/1000] time 1.585 (1.569) data 0.000 (0.007) loss 1.0654 (1.1395) acc 68.7500 (71.6435) lr 1.8090e-03 eta 16:56:26
+epoch [12/50] batch [140/1000] time 1.576 (1.570) data 0.000 (0.007) loss 1.1523 (1.1367) acc 65.6250 (71.6964) lr 1.8090e-03 eta 16:56:54
+epoch [12/50] batch [145/1000] time 1.561 (1.570) data 0.001 (0.006) loss 1.0107 (1.1339) acc 71.8750 (71.6595) lr 1.8090e-03 eta 16:56:40
+epoch [12/50] batch [150/1000] time 1.590 (1.571) data 0.000 (0.006) loss 1.5938 (1.1371) acc 62.5000 (71.6667) lr 1.8090e-03 eta 16:56:56
+epoch [12/50] batch [155/1000] time 1.552 (1.570) data 0.001 (0.006) loss 0.8013 (1.1362) acc 75.0000 (71.6935) lr 1.8090e-03 eta 16:56:22
+epoch [12/50] batch [160/1000] time 1.548 (1.569) data 0.000 (0.006) loss 0.8369 (1.1364) acc 84.3750 (71.6406) lr 1.8090e-03 eta 16:55:51
+epoch [12/50] batch [165/1000] time 1.543 (1.569) data 0.000 (0.006) loss 0.9067 (1.1392) acc 84.3750 (71.5720) lr 1.8090e-03 eta 16:55:33
+epoch [12/50] batch [170/1000] time 1.571 (1.569) data 0.001 (0.005) loss 1.2910 (1.1443) acc 65.6250 (71.3971) lr 1.8090e-03 eta 16:55:32
+epoch [12/50] batch [175/1000] time 1.569 (1.569) data 0.001 (0.005) loss 1.2520 (1.1425) acc 62.5000 (71.3929) lr 1.8090e-03 eta 16:55:12
+epoch [12/50] batch [180/1000] time 1.573 (1.568) data 0.000 (0.005) loss 0.6978 (1.1481) acc 75.0000 (71.3368) lr 1.8090e-03 eta 16:54:44
+epoch [12/50] batch [185/1000] time 1.558 (1.569) data 0.001 (0.005) loss 1.0234 (1.1439) acc 78.1250 (71.3514) lr 1.8090e-03 eta 16:54:55
+epoch [12/50] batch [190/1000] time 1.537 (1.568) data 0.001 (0.005) loss 1.1543 (1.1436) acc 62.5000 (71.3487) lr 1.8090e-03 eta 16:54:25
+epoch [12/50] batch [195/1000] time 1.572 (1.568) data 0.000 (0.005) loss 1.1250 (1.1459) acc 78.1250 (71.3462) lr 1.8090e-03 eta 16:54:01
+epoch [12/50] batch [200/1000] time 1.542 (1.568) data 0.000 (0.005) loss 1.0137 (1.1495) acc 75.0000 (71.1719) lr 1.8090e-03 eta 16:53:41
+epoch [12/50] batch [205/1000] time 1.550 (1.567) data 0.000 (0.005) loss 0.2617 (1.1472) acc 96.8750 (71.2195) lr 1.8090e-03 eta 16:53:17
+epoch [12/50] batch [210/1000] time 1.548 (1.567) data 0.000 (0.005) loss 1.1504 (1.1515) acc 62.5000 (71.1161) lr 1.8090e-03 eta 16:52:59
+epoch [12/50] batch [215/1000] time 1.551 (1.567) data 0.000 (0.004) loss 1.2520 (1.1526) acc 78.1250 (71.1773) lr 1.8090e-03 eta 16:52:37
+epoch [12/50] batch [220/1000] time 1.552 (1.566) data 0.001 (0.004) loss 1.5098 (1.1566) acc 68.7500 (71.1222) lr 1.8090e-03 eta 16:52:21
+epoch [12/50] batch [225/1000] time 1.550 (1.566) data 0.001 (0.004) loss 0.9849 (1.1508) acc 68.7500 (71.1528) lr 1.8090e-03 eta 16:52:04
+epoch [12/50] batch [230/1000] time 1.542 (1.566) data 0.000 (0.004) loss 1.1504 (1.1480) acc 65.6250 (71.1957) lr 1.8090e-03 eta 16:52:11
+epoch [12/50] batch [235/1000] time 1.591 (1.566) data 0.000 (0.004) loss 1.3936 (1.1475) acc 68.7500 (71.1702) lr 1.8090e-03 eta 16:52:03
+epoch [12/50] batch [240/1000] time 1.552 (1.566) data 0.000 (0.004) loss 1.1904 (1.1480) acc 75.0000 (71.1719) lr 1.8090e-03 eta 16:51:49
+epoch [12/50] batch [245/1000] time 1.550 (1.566) data 0.000 (0.004) loss 1.4531 (1.1521) acc 68.7500 (71.0714) lr 1.8090e-03 eta 16:51:39
+epoch [12/50] batch [250/1000] time 1.551 (1.566) data 0.000 (0.004) loss 0.9590 (1.1484) acc 71.8750 (71.1625) lr 1.8090e-03 eta 16:51:28
+epoch [12/50] batch [255/1000] time 1.539 (1.566) data 0.000 (0.004) loss 0.6812 (1.1487) acc 81.2500 (71.1029) lr 1.8090e-03 eta 16:51:13
+epoch [12/50] batch [260/1000] time 1.544 (1.566) data 0.000 (0.004) loss 1.1113 (1.1490) acc 62.5000 (70.9976) lr 1.8090e-03 eta 16:50:55
+epoch [12/50] batch [265/1000] time 1.540 (1.566) data 0.001 (0.004) loss 1.3311 (1.1486) acc 68.7500 (71.0142) lr 1.8090e-03 eta 16:50:40
+epoch [12/50] batch [270/1000] time 1.554 (1.565) data 0.000 (0.004) loss 1.3174 (1.1508) acc 62.5000 (70.9838) lr 1.8090e-03 eta 16:50:19
+epoch [12/50] batch [275/1000] time 1.579 (1.565) data 0.000 (0.004) loss 1.3594 (1.1517) acc 59.3750 (70.8977) lr 1.8090e-03 eta 16:50:12
+epoch [12/50] batch [280/1000] time 1.560 (1.565) data 0.000 (0.003) loss 1.7607 (1.1511) acc 59.3750 (70.9040) lr 1.8090e-03 eta 16:50:04
+epoch [12/50] batch [285/1000] time 1.554 (1.565) data 0.001 (0.003) loss 1.5332 (1.1499) acc 65.6250 (70.9649) lr 1.8090e-03 eta 16:49:56
+epoch [12/50] batch [290/1000] time 1.688 (1.566) data 0.000 (0.003) loss 1.0186 (1.1548) acc 81.2500 (70.8190) lr 1.8090e-03 eta 16:50:01
+epoch [12/50] batch [295/1000] time 1.578 (1.566) data 0.000 (0.003) loss 1.0752 (1.1569) acc 75.0000 (70.8263) lr 1.8090e-03 eta 16:49:59
+epoch [12/50] batch [300/1000] time 1.549 (1.566) data 0.001 (0.003) loss 1.3525 (1.1580) acc 65.6250 (70.8438) lr 1.8090e-03 eta 16:49:49
+epoch [12/50] batch [305/1000] time 1.565 (1.565) data 0.001 (0.003) loss 1.4463 (1.1588) acc 65.6250 (70.8504) lr 1.8090e-03 eta 16:49:31
+epoch [12/50] batch [310/1000] time 1.577 (1.565) data 0.000 (0.003) loss 0.7983 (1.1559) acc 71.8750 (70.8468) lr 1.8090e-03 eta 16:49:26
+epoch [12/50] batch [315/1000] time 1.544 (1.565) data 0.000 (0.003) loss 1.7256 (1.1548) acc 56.2500 (70.8036) lr 1.8090e-03 eta 16:49:15
+epoch [12/50] batch [320/1000] time 1.549 (1.565) data 0.000 (0.003) loss 1.1191 (1.1585) acc 71.8750 (70.7227) lr 1.8090e-03 eta 16:49:10
+epoch [12/50] batch [325/1000] time 1.540 (1.565) data 0.001 (0.003) loss 1.4932 (1.1576) acc 53.1250 (70.7308) lr 1.8090e-03 eta 16:48:48
+epoch [12/50] batch [330/1000] time 1.545 (1.565) data 0.001 (0.003) loss 1.1113 (1.1611) acc 71.8750 (70.6818) lr 1.8090e-03 eta 16:48:33
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+epoch [12/50] batch [885/1000] time 1.544 (1.563) data 0.000 (0.001) loss 0.7427 (1.1519) acc 71.8750 (71.0523) lr 1.8090e-03 eta 16:32:57
+epoch [12/50] batch [890/1000] time 1.534 (1.563) data 0.000 (0.001) loss 1.3320 (1.1521) acc 75.0000 (71.0674) lr 1.8090e-03 eta 16:32:48
+epoch [12/50] batch [895/1000] time 1.549 (1.563) data 0.000 (0.001) loss 0.8062 (1.1514) acc 81.2500 (71.0824) lr 1.8090e-03 eta 16:32:43
+epoch [12/50] batch [900/1000] time 1.571 (1.563) data 0.000 (0.001) loss 0.6118 (1.1504) acc 84.3750 (71.1076) lr 1.8090e-03 eta 16:32:35
+epoch [12/50] batch [905/1000] time 1.587 (1.563) data 0.000 (0.001) loss 2.1855 (1.1514) acc 53.1250 (71.0946) lr 1.8090e-03 eta 16:32:27
+epoch [12/50] batch [910/1000] time 1.563 (1.563) data 0.001 (0.001) loss 1.1270 (1.1499) acc 65.6250 (71.1023) lr 1.8090e-03 eta 16:32:15
+epoch [12/50] batch [915/1000] time 1.574 (1.563) data 0.000 (0.001) loss 1.0342 (1.1496) acc 78.1250 (71.1031) lr 1.8090e-03 eta 16:32:07
+epoch [12/50] batch [920/1000] time 1.572 (1.563) data 0.001 (0.001) loss 1.4932 (1.1498) acc 68.7500 (71.1107) lr 1.8090e-03 eta 16:31:58
+epoch [12/50] batch [925/1000] time 1.559 (1.563) data 0.000 (0.001) loss 2.1035 (1.1513) acc 43.7500 (71.0878) lr 1.8090e-03 eta 16:31:49
+epoch [12/50] batch [930/1000] time 1.552 (1.563) data 0.001 (0.001) loss 1.9180 (1.1523) acc 62.5000 (71.0753) lr 1.8090e-03 eta 16:31:41
+epoch [12/50] batch [935/1000] time 1.569 (1.563) data 0.000 (0.001) loss 0.8896 (1.1517) acc 75.0000 (71.0896) lr 1.8090e-03 eta 16:31:31
+epoch [12/50] batch [940/1000] time 1.571 (1.563) data 0.000 (0.001) loss 1.0938 (1.1527) acc 71.8750 (71.0705) lr 1.8090e-03 eta 16:31:29
+epoch [12/50] batch [945/1000] time 1.539 (1.563) data 0.000 (0.001) loss 0.6963 (1.1531) acc 78.1250 (71.0681) lr 1.8090e-03 eta 16:31:19
+epoch [12/50] batch [950/1000] time 1.556 (1.563) data 0.000 (0.001) loss 1.0820 (1.1527) acc 68.7500 (71.0888) lr 1.8090e-03 eta 16:31:11
+epoch [12/50] batch [955/1000] time 1.532 (1.563) data 0.000 (0.001) loss 0.9351 (1.1526) acc 81.2500 (71.1060) lr 1.8090e-03 eta 16:31:02
+epoch [12/50] batch [960/1000] time 1.571 (1.563) data 0.000 (0.001) loss 1.0498 (1.1527) acc 68.7500 (71.0905) lr 1.8090e-03 eta 16:30:53
+epoch [12/50] batch [965/1000] time 1.545 (1.563) data 0.000 (0.001) loss 1.2188 (1.1522) acc 65.6250 (71.0978) lr 1.8090e-03 eta 16:30:44
+epoch [12/50] batch [970/1000] time 1.575 (1.563) data 0.000 (0.001) loss 0.6641 (1.1519) acc 84.3750 (71.0986) lr 1.8090e-03 eta 16:30:34
+epoch [12/50] batch [975/1000] time 1.545 (1.563) data 0.000 (0.001) loss 0.7173 (1.1508) acc 81.2500 (71.1314) lr 1.8090e-03 eta 16:30:26
+epoch [12/50] batch [980/1000] time 1.563 (1.563) data 0.000 (0.001) loss 0.9443 (1.1503) acc 68.7500 (71.1320) lr 1.8090e-03 eta 16:30:17
+epoch [12/50] batch [985/1000] time 1.554 (1.563) data 0.001 (0.001) loss 0.9248 (1.1499) acc 78.1250 (71.1326) lr 1.8090e-03 eta 16:30:10
+epoch [12/50] batch [990/1000] time 1.552 (1.563) data 0.000 (0.001) loss 0.8105 (1.1497) acc 71.8750 (71.1364) lr 1.8090e-03 eta 16:30:00
+epoch [12/50] batch [995/1000] time 1.575 (1.563) data 0.000 (0.001) loss 1.7764 (1.1502) acc 65.6250 (71.1212) lr 1.8090e-03 eta 16:29:53
+epoch [12/50] batch [1000/1000] time 1.546 (1.563) data 0.000 (0.001) loss 1.9102 (1.1511) acc 62.5000 (71.1125) lr 1.7705e-03 eta 16:29:42
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,060
+* accuracy: 78.1%
+* error: 21.9%
+* macro_f1: 77.6%
+epoch [13/50] batch [5/1000] time 1.556 (1.672) data 0.000 (0.179) loss 1.1758 (1.2706) acc 59.3750 (66.8750) lr 1.7705e-03 eta 17:38:46
+epoch [13/50] batch [10/1000] time 1.545 (1.616) data 0.001 (0.090) loss 1.1934 (1.1813) acc 81.2500 (70.9375) lr 1.7705e-03 eta 17:03:24
+epoch [13/50] batch [15/1000] time 1.560 (1.596) data 0.000 (0.060) loss 1.3535 (1.2141) acc 75.0000 (71.4583) lr 1.7705e-03 eta 16:50:13
+epoch [13/50] batch [20/1000] time 1.544 (1.582) data 0.000 (0.045) loss 0.6431 (1.1523) acc 71.8750 (71.8750) lr 1.7705e-03 eta 16:41:37
+epoch [13/50] batch [25/1000] time 1.573 (1.579) data 0.001 (0.036) loss 0.9165 (1.1435) acc 75.0000 (72.6250) lr 1.7705e-03 eta 16:39:35
+epoch [13/50] batch [30/1000] time 1.563 (1.576) data 0.000 (0.030) loss 1.3311 (1.1584) acc 68.7500 (72.0833) lr 1.7705e-03 eta 16:37:13
+epoch [13/50] batch [35/1000] time 1.599 (1.582) data 0.001 (0.026) loss 1.0977 (1.1639) acc 78.1250 (72.0536) lr 1.7705e-03 eta 16:40:57
+epoch [13/50] batch [40/1000] time 1.559 (1.579) data 0.000 (0.023) loss 1.8428 (1.1987) acc 46.8750 (71.0938) lr 1.7705e-03 eta 16:39:00
+epoch [13/50] batch [45/1000] time 1.533 (1.577) data 0.000 (0.020) loss 1.4238 (1.1940) acc 62.5000 (70.6250) lr 1.7705e-03 eta 16:37:45
+epoch [13/50] batch [50/1000] time 1.542 (1.575) data 0.000 (0.018) loss 0.8569 (1.1821) acc 75.0000 (71.0000) lr 1.7705e-03 eta 16:36:00
+epoch [13/50] batch [55/1000] time 1.547 (1.572) data 0.001 (0.017) loss 1.6562 (1.1714) acc 71.8750 (71.2500) lr 1.7705e-03 eta 16:34:06
+epoch [13/50] batch [60/1000] time 1.563 (1.571) data 0.000 (0.015) loss 1.9170 (1.1790) acc 62.5000 (71.1979) lr 1.7705e-03 eta 16:33:24
+epoch [13/50] batch [65/1000] time 1.573 (1.571) data 0.000 (0.014) loss 1.0596 (1.1747) acc 75.0000 (71.2981) lr 1.7705e-03 eta 16:32:58
+epoch [13/50] batch [70/1000] time 1.552 (1.569) data 0.001 (0.013) loss 0.9966 (1.1773) acc 78.1250 (71.1607) lr 1.7705e-03 eta 16:31:51
+epoch [13/50] batch [75/1000] time 1.550 (1.568) data 0.000 (0.012) loss 1.8125 (1.1852) acc 71.8750 (71.3333) lr 1.7705e-03 eta 16:31:20
+epoch [13/50] batch [80/1000] time 1.543 (1.569) data 0.000 (0.012) loss 1.4775 (1.1924) acc 62.5000 (71.0156) lr 1.7705e-03 eta 16:31:29
+epoch [13/50] batch [85/1000] time 1.536 (1.567) data 0.000 (0.011) loss 1.1699 (1.1927) acc 68.7500 (70.9926) lr 1.7705e-03 eta 16:30:21
+epoch [13/50] batch [90/1000] time 1.584 (1.567) data 0.000 (0.010) loss 0.8716 (1.1928) acc 78.1250 (71.2847) lr 1.7705e-03 eta 16:29:52
+epoch [13/50] batch [95/1000] time 1.565 (1.566) data 0.000 (0.010) loss 0.9990 (1.1843) acc 81.2500 (71.3487) lr 1.7705e-03 eta 16:29:29
+epoch [13/50] batch [100/1000] time 1.565 (1.566) data 0.001 (0.009) loss 0.5591 (1.1821) acc 87.5000 (71.4375) lr 1.7705e-03 eta 16:29:01
+epoch [13/50] batch [105/1000] time 1.540 (1.565) data 0.000 (0.009) loss 0.7686 (1.1821) acc 81.2500 (71.3393) lr 1.7705e-03 eta 16:28:36
+epoch [13/50] batch [110/1000] time 1.532 (1.565) data 0.001 (0.009) loss 1.5615 (1.1844) acc 65.6250 (71.3068) lr 1.7705e-03 eta 16:28:09
+epoch [13/50] batch [115/1000] time 1.558 (1.564) data 0.000 (0.008) loss 1.2188 (1.1842) acc 65.6250 (71.3587) lr 1.7705e-03 eta 16:27:41
+epoch [13/50] batch [120/1000] time 1.734 (1.565) data 0.000 (0.008) loss 0.8877 (1.1723) acc 81.2500 (71.5885) lr 1.7705e-03 eta 16:28:12
+epoch [13/50] batch [125/1000] time 1.563 (1.565) data 0.000 (0.008) loss 0.4519 (1.1713) acc 84.3750 (71.5750) lr 1.7705e-03 eta 16:27:44
+epoch [13/50] batch [130/1000] time 1.547 (1.564) data 0.000 (0.007) loss 1.3594 (1.1743) acc 68.7500 (71.2981) lr 1.7705e-03 eta 16:27:27
+epoch [13/50] batch [135/1000] time 1.560 (1.565) data 0.001 (0.007) loss 1.0449 (1.1642) acc 71.8750 (71.5509) lr 1.7705e-03 eta 16:27:20
+epoch [13/50] batch [140/1000] time 1.540 (1.564) data 0.000 (0.007) loss 1.0869 (1.1723) acc 71.8750 (71.5179) lr 1.7705e-03 eta 16:26:55
+epoch [13/50] batch [145/1000] time 1.566 (1.564) data 0.000 (0.007) loss 0.5913 (1.1602) acc 78.1250 (71.6595) lr 1.7705e-03 eta 16:26:43
+epoch [13/50] batch [150/1000] time 1.546 (1.564) data 0.000 (0.006) loss 0.6382 (1.1607) acc 84.3750 (71.6875) lr 1.7705e-03 eta 16:26:28
+epoch [13/50] batch [155/1000] time 1.541 (1.564) data 0.000 (0.006) loss 1.2764 (1.1614) acc 65.6250 (71.6331) lr 1.7705e-03 eta 16:26:22
+epoch [13/50] batch [160/1000] time 1.576 (1.564) data 0.000 (0.006) loss 0.7559 (1.1662) acc 78.1250 (71.4453) lr 1.7705e-03 eta 16:26:18
+epoch [13/50] batch [165/1000] time 1.535 (1.564) data 0.000 (0.006) loss 0.6499 (1.1609) acc 81.2500 (71.5341) lr 1.7705e-03 eta 16:25:55
+epoch [13/50] batch [170/1000] time 1.546 (1.563) data 0.000 (0.006) loss 1.0791 (1.1634) acc 65.6250 (71.4338) lr 1.7705e-03 eta 16:25:44
+epoch [13/50] batch [175/1000] time 1.564 (1.563) data 0.001 (0.006) loss 0.8242 (1.1627) acc 75.0000 (71.4821) lr 1.7705e-03 eta 16:25:24
+epoch [13/50] batch [180/1000] time 1.554 (1.563) data 0.000 (0.005) loss 1.3691 (1.1611) acc 62.5000 (71.4931) lr 1.7705e-03 eta 16:25:11
+epoch [13/50] batch [185/1000] time 1.564 (1.564) data 0.000 (0.005) loss 1.7041 (1.1614) acc 68.7500 (71.5372) lr 1.7705e-03 eta 16:25:54
+epoch [13/50] batch [190/1000] time 1.552 (1.564) data 0.001 (0.005) loss 1.0547 (1.1569) acc 75.0000 (71.7105) lr 1.7705e-03 eta 16:25:38
+epoch [13/50] batch [195/1000] time 1.534 (1.564) data 0.000 (0.005) loss 0.8120 (1.1589) acc 68.7500 (71.6827) lr 1.7705e-03 eta 16:25:21
+epoch [13/50] batch [200/1000] time 1.589 (1.564) data 0.000 (0.005) loss 1.1943 (1.1648) acc 68.7500 (71.4375) lr 1.7705e-03 eta 16:25:06
+epoch [13/50] batch [205/1000] time 1.567 (1.564) data 0.000 (0.005) loss 0.5151 (1.1582) acc 96.8750 (71.5396) lr 1.7705e-03 eta 16:24:59
+epoch [13/50] batch [210/1000] time 1.547 (1.563) data 0.000 (0.005) loss 0.8154 (1.1546) acc 81.2500 (71.5625) lr 1.7705e-03 eta 16:24:37
+epoch [13/50] batch [215/1000] time 1.532 (1.563) data 0.000 (0.005) loss 0.8916 (1.1476) acc 78.1250 (71.5988) lr 1.7705e-03 eta 16:24:17
+epoch [13/50] batch [220/1000] time 1.546 (1.563) data 0.000 (0.005) loss 0.8618 (1.1430) acc 81.2500 (71.7188) lr 1.7705e-03 eta 16:23:52
+epoch [13/50] batch [225/1000] time 1.566 (1.562) data 0.000 (0.004) loss 1.2021 (1.1509) acc 71.8750 (71.5833) lr 1.7705e-03 eta 16:23:35
+epoch [13/50] batch [230/1000] time 1.552 (1.563) data 0.000 (0.004) loss 2.0586 (1.1546) acc 68.7500 (71.5217) lr 1.7705e-03 eta 16:23:51
+epoch [13/50] batch [235/1000] time 1.574 (1.563) data 0.000 (0.004) loss 1.3604 (1.1532) acc 62.5000 (71.4229) lr 1.7705e-03 eta 16:23:44
+epoch [13/50] batch [240/1000] time 1.563 (1.563) data 0.000 (0.004) loss 1.2852 (1.1515) acc 65.6250 (71.4583) lr 1.7705e-03 eta 16:23:40
+epoch [13/50] batch [245/1000] time 1.550 (1.563) data 0.001 (0.004) loss 1.6777 (1.1514) acc 59.3750 (71.5561) lr 1.7705e-03 eta 16:23:21
+epoch [13/50] batch [250/1000] time 1.560 (1.563) data 0.000 (0.004) loss 1.0469 (1.1540) acc 65.6250 (71.4625) lr 1.7705e-03 eta 16:23:06
+epoch [13/50] batch [255/1000] time 1.576 (1.563) data 0.000 (0.004) loss 1.1758 (1.1542) acc 75.0000 (71.4338) lr 1.7705e-03 eta 16:23:05
+epoch [13/50] batch [260/1000] time 1.538 (1.562) data 0.000 (0.004) loss 0.9990 (1.1549) acc 81.2500 (71.4543) lr 1.7705e-03 eta 16:22:47
+epoch [13/50] batch [265/1000] time 1.550 (1.562) data 0.000 (0.004) loss 1.2344 (1.1610) acc 71.8750 (71.3561) lr 1.7705e-03 eta 16:22:39
+epoch [13/50] batch [270/1000] time 1.565 (1.563) data 0.001 (0.004) loss 1.3604 (1.1621) acc 68.7500 (71.2384) lr 1.7705e-03 eta 16:22:35
+epoch [13/50] batch [275/1000] time 1.552 (1.563) data 0.000 (0.004) loss 1.4629 (1.1676) acc 65.6250 (71.0682) lr 1.7705e-03 eta 16:22:48
+epoch [13/50] batch [280/1000] time 1.545 (1.563) data 0.001 (0.004) loss 1.0625 (1.1654) acc 75.0000 (71.0714) lr 1.7705e-03 eta 16:22:32
+epoch [13/50] batch [285/1000] time 1.565 (1.563) data 0.000 (0.004) loss 0.7271 (1.1612) acc 84.3750 (71.1294) lr 1.7705e-03 eta 16:22:16
+epoch [13/50] batch [290/1000] time 1.550 (1.563) data 0.000 (0.003) loss 1.4463 (1.1637) acc 56.2500 (71.0991) lr 1.7705e-03 eta 16:22:07
+epoch [13/50] batch [295/1000] time 1.552 (1.563) data 0.000 (0.003) loss 0.9507 (1.1615) acc 78.1250 (71.1547) lr 1.7705e-03 eta 16:21:56
+epoch [13/50] batch [300/1000] time 1.563 (1.563) data 0.000 (0.003) loss 0.9165 (1.1622) acc 75.0000 (71.1458) lr 1.7705e-03 eta 16:21:48
+epoch [13/50] batch [305/1000] time 1.568 (1.563) data 0.000 (0.003) loss 1.1836 (1.1644) acc 75.0000 (71.1373) lr 1.7705e-03 eta 16:21:41
+epoch [13/50] batch [310/1000] time 1.552 (1.563) data 0.000 (0.003) loss 1.5459 (1.1661) acc 65.6250 (71.0585) lr 1.7705e-03 eta 16:21:33
+epoch [13/50] batch [315/1000] time 1.553 (1.562) data 0.001 (0.003) loss 1.0850 (1.1610) acc 75.0000 (71.1607) lr 1.7705e-03 eta 16:21:19
+epoch [13/50] batch [320/1000] time 1.564 (1.562) data 0.000 (0.003) loss 0.6333 (1.1600) acc 75.0000 (71.2109) lr 1.7705e-03 eta 16:21:07
+epoch [13/50] batch [325/1000] time 1.580 (1.562) data 0.000 (0.003) loss 1.3203 (1.1615) acc 68.7500 (71.1731) lr 1.7705e-03 eta 16:21:05
+epoch [13/50] batch [330/1000] time 1.565 (1.562) data 0.000 (0.003) loss 1.0879 (1.1588) acc 71.8750 (71.2121) lr 1.7705e-03 eta 16:20:50
+epoch [13/50] batch [335/1000] time 1.565 (1.563) data 0.000 (0.003) loss 0.3965 (1.1552) acc 87.5000 (71.2966) lr 1.7705e-03 eta 16:20:52
+epoch [13/50] batch [340/1000] time 1.572 (1.562) data 0.001 (0.003) loss 1.4189 (1.1544) acc 68.7500 (71.2868) lr 1.7705e-03 eta 16:20:40
+epoch [13/50] batch [345/1000] time 1.567 (1.562) data 0.000 (0.003) loss 0.8232 (1.1554) acc 71.8750 (71.2319) lr 1.7705e-03 eta 16:20:33
+epoch [13/50] batch [350/1000] time 1.568 (1.562) data 0.000 (0.003) loss 1.0840 (1.1593) acc 71.8750 (71.1339) lr 1.7705e-03 eta 16:20:27
+epoch [13/50] batch [355/1000] time 1.552 (1.562) data 0.000 (0.003) loss 1.5029 (1.1605) acc 75.0000 (71.1092) lr 1.7705e-03 eta 16:20:15
+epoch [13/50] batch [360/1000] time 1.562 (1.562) data 0.000 (0.003) loss 1.2529 (1.1606) acc 62.5000 (71.1024) lr 1.7705e-03 eta 16:20:06
+epoch [13/50] batch [365/1000] time 1.550 (1.562) data 0.001 (0.003) loss 0.9658 (1.1584) acc 78.1250 (71.1473) lr 1.7705e-03 eta 16:19:57
+epoch [13/50] batch [370/1000] time 1.575 (1.562) data 0.001 (0.003) loss 1.0801 (1.1565) acc 84.3750 (71.1655) lr 1.7705e-03 eta 16:19:39
+epoch [13/50] batch [375/1000] time 1.536 (1.562) data 0.000 (0.003) loss 0.8145 (1.1553) acc 78.1250 (71.2000) lr 1.7705e-03 eta 16:19:20
+epoch [13/50] batch [380/1000] time 1.571 (1.562) data 0.001 (0.003) loss 1.4434 (1.1560) acc 68.7500 (71.2007) lr 1.7705e-03 eta 16:19:26
+epoch [13/50] batch [385/1000] time 1.532 (1.562) data 0.000 (0.003) loss 0.7988 (1.1559) acc 81.2500 (71.2338) lr 1.7705e-03 eta 16:19:11
+epoch [13/50] batch [390/1000] time 1.558 (1.562) data 0.000 (0.003) loss 0.7983 (1.1552) acc 75.0000 (71.1859) lr 1.7705e-03 eta 16:18:55
+epoch [13/50] batch [395/1000] time 1.545 (1.562) data 0.000 (0.003) loss 1.0264 (1.1538) acc 78.1250 (71.2342) lr 1.7705e-03 eta 16:18:45
+epoch [13/50] batch [400/1000] time 1.554 (1.562) data 0.000 (0.003) loss 1.2529 (1.1585) acc 65.6250 (71.1484) lr 1.7705e-03 eta 16:18:36
+epoch [13/50] batch [405/1000] time 1.560 (1.562) data 0.000 (0.003) loss 0.8525 (1.1577) acc 81.2500 (71.2114) lr 1.7705e-03 eta 16:18:32
+epoch [13/50] batch [410/1000] time 1.547 (1.562) data 0.000 (0.003) loss 0.8477 (1.1572) acc 68.7500 (71.1738) lr 1.7705e-03 eta 16:18:23
+epoch [13/50] batch [415/1000] time 1.565 (1.562) data 0.000 (0.003) loss 1.0547 (1.1583) acc 71.8750 (71.1747) lr 1.7705e-03 eta 16:18:14
+epoch [13/50] batch [420/1000] time 1.554 (1.562) data 0.000 (0.003) loss 0.9370 (1.1572) acc 75.0000 (71.2128) lr 1.7705e-03 eta 16:18:08
+epoch [13/50] batch [425/1000] time 1.566 (1.562) data 0.000 (0.003) loss 1.3330 (1.1574) acc 62.5000 (71.1912) lr 1.7705e-03 eta 16:18:15
+epoch [13/50] batch [430/1000] time 1.528 (1.562) data 0.000 (0.002) loss 0.9497 (1.1582) acc 81.2500 (71.1773) lr 1.7705e-03 eta 16:18:06
+epoch [13/50] batch [435/1000] time 1.547 (1.562) data 0.000 (0.002) loss 0.8120 (1.1573) acc 78.1250 (71.2213) lr 1.7705e-03 eta 16:18:01
+epoch [13/50] batch [440/1000] time 1.576 (1.562) data 0.001 (0.002) loss 1.4785 (1.1570) acc 71.8750 (71.2287) lr 1.7705e-03 eta 16:17:52
+epoch [13/50] batch [445/1000] time 1.563 (1.562) data 0.000 (0.002) loss 0.7153 (1.1549) acc 75.0000 (71.2851) lr 1.7705e-03 eta 16:17:40
+epoch [13/50] batch [450/1000] time 1.588 (1.562) data 0.000 (0.002) loss 1.3633 (1.1563) acc 78.1250 (71.2708) lr 1.7705e-03 eta 16:17:34
+epoch [13/50] batch [455/1000] time 1.561 (1.562) data 0.000 (0.002) loss 1.0576 (1.1576) acc 71.8750 (71.2706) lr 1.7705e-03 eta 16:17:27
+epoch [13/50] batch [460/1000] time 1.583 (1.562) data 0.000 (0.002) loss 1.2529 (1.1576) acc 71.8750 (71.3383) lr 1.7705e-03 eta 16:17:20
+epoch [13/50] batch [465/1000] time 1.549 (1.562) data 0.001 (0.002) loss 1.4395 (1.1589) acc 68.7500 (71.3306) lr 1.7705e-03 eta 16:17:12
+epoch [13/50] batch [470/1000] time 1.580 (1.562) data 0.000 (0.002) loss 0.8301 (1.1607) acc 71.8750 (71.2766) lr 1.7705e-03 eta 16:17:05
+epoch [13/50] batch [475/1000] time 1.573 (1.562) data 0.000 (0.002) loss 0.6846 (1.1616) acc 84.3750 (71.2500) lr 1.7705e-03 eta 16:17:02
+epoch [13/50] batch [480/1000] time 1.564 (1.562) data 0.000 (0.002) loss 1.1680 (1.1604) acc 62.5000 (71.2500) lr 1.7705e-03 eta 16:16:57
+epoch [13/50] batch [485/1000] time 1.731 (1.563) data 0.001 (0.002) loss 0.9165 (1.1603) acc 71.8750 (71.2242) lr 1.7705e-03 eta 16:17:04
+epoch [13/50] batch [490/1000] time 1.566 (1.563) data 0.000 (0.002) loss 0.9868 (1.1592) acc 71.8750 (71.2117) lr 1.7705e-03 eta 16:16:57
+epoch [13/50] batch [495/1000] time 1.560 (1.563) data 0.001 (0.002) loss 1.0576 (1.1619) acc 65.6250 (71.1364) lr 1.7705e-03 eta 16:16:45
+epoch [13/50] batch [500/1000] time 1.572 (1.563) data 0.000 (0.002) loss 1.2900 (1.1630) acc 75.0000 (71.1063) lr 1.7705e-03 eta 16:16:41
+epoch [13/50] batch [505/1000] time 1.590 (1.563) data 0.000 (0.002) loss 0.8945 (1.1603) acc 75.0000 (71.1634) lr 1.7705e-03 eta 16:16:33
+epoch [13/50] batch [510/1000] time 1.541 (1.563) data 0.000 (0.002) loss 1.1953 (1.1598) acc 68.7500 (71.1949) lr 1.7705e-03 eta 16:16:24
+epoch [13/50] batch [515/1000] time 1.533 (1.563) data 0.000 (0.002) loss 0.9019 (1.1592) acc 75.0000 (71.2136) lr 1.7705e-03 eta 16:16:13
+epoch [13/50] batch [520/1000] time 1.549 (1.563) data 0.001 (0.002) loss 1.1279 (1.1580) acc 71.8750 (71.2500) lr 1.7705e-03 eta 16:16:06
+epoch [13/50] batch [525/1000] time 1.556 (1.563) data 0.000 (0.002) loss 0.8892 (1.1556) acc 75.0000 (71.2976) lr 1.7705e-03 eta 16:15:55
+epoch [13/50] batch [530/1000] time 1.726 (1.563) data 0.001 (0.002) loss 0.4480 (1.1529) acc 78.1250 (71.3384) lr 1.7705e-03 eta 16:16:00
+epoch [13/50] batch [535/1000] time 1.553 (1.563) data 0.001 (0.002) loss 1.5293 (1.1524) acc 59.3750 (71.3376) lr 1.7705e-03 eta 16:15:50
+epoch [13/50] batch [540/1000] time 1.554 (1.563) data 0.000 (0.002) loss 1.0322 (1.1518) acc 68.7500 (71.3252) lr 1.7705e-03 eta 16:15:41
+epoch [13/50] batch [545/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.1572 (1.1516) acc 62.5000 (71.3245) lr 1.7705e-03 eta 16:15:26
+epoch [13/50] batch [550/1000] time 1.542 (1.562) data 0.000 (0.002) loss 1.3359 (1.1545) acc 62.5000 (71.2330) lr 1.7705e-03 eta 16:15:13
+epoch [13/50] batch [555/1000] time 1.600 (1.562) data 0.000 (0.002) loss 1.1914 (1.1555) acc 68.7500 (71.2331) lr 1.7705e-03 eta 16:15:03
+epoch [13/50] batch [560/1000] time 1.537 (1.562) data 0.001 (0.002) loss 0.9785 (1.1551) acc 68.7500 (71.2333) lr 1.7705e-03 eta 16:14:49
+epoch [13/50] batch [565/1000] time 1.549 (1.562) data 0.000 (0.002) loss 0.6260 (1.1542) acc 84.3750 (71.2389) lr 1.7705e-03 eta 16:14:39
+epoch [13/50] batch [570/1000] time 1.554 (1.562) data 0.000 (0.002) loss 0.7896 (1.1531) acc 81.2500 (71.2445) lr 1.7705e-03 eta 16:14:26
+epoch [13/50] batch [575/1000] time 1.548 (1.562) data 0.000 (0.002) loss 0.7373 (1.1532) acc 75.0000 (71.2391) lr 1.7705e-03 eta 16:14:23
+epoch [13/50] batch [580/1000] time 1.554 (1.562) data 0.000 (0.002) loss 1.6318 (1.1518) acc 75.0000 (71.2877) lr 1.7705e-03 eta 16:14:10
+epoch [13/50] batch [585/1000] time 1.561 (1.562) data 0.001 (0.002) loss 1.1445 (1.1502) acc 62.5000 (71.2821) lr 1.7705e-03 eta 16:13:58
+epoch [13/50] batch [590/1000] time 1.558 (1.562) data 0.000 (0.002) loss 0.9761 (1.1501) acc 75.0000 (71.2765) lr 1.7705e-03 eta 16:13:46
+epoch [13/50] batch [595/1000] time 1.557 (1.562) data 0.000 (0.002) loss 1.2998 (1.1498) acc 75.0000 (71.3288) lr 1.7705e-03 eta 16:13:36
+epoch [13/50] batch [600/1000] time 1.548 (1.562) data 0.000 (0.002) loss 1.4590 (1.1496) acc 62.5000 (71.3385) lr 1.7705e-03 eta 16:13:25
+epoch [13/50] batch [605/1000] time 1.572 (1.562) data 0.000 (0.002) loss 0.9556 (1.1493) acc 71.8750 (71.3533) lr 1.7705e-03 eta 16:13:20
+epoch [13/50] batch [610/1000] time 1.545 (1.562) data 0.000 (0.002) loss 0.5884 (1.1493) acc 81.2500 (71.3627) lr 1.7705e-03 eta 16:13:07
+epoch [13/50] batch [615/1000] time 1.570 (1.562) data 0.000 (0.002) loss 1.2227 (1.1488) acc 71.8750 (71.3821) lr 1.7705e-03 eta 16:13:00
+epoch [13/50] batch [620/1000] time 1.547 (1.562) data 0.000 (0.002) loss 1.3438 (1.1512) acc 59.3750 (71.3306) lr 1.7705e-03 eta 16:12:50
+epoch [13/50] batch [625/1000] time 1.582 (1.562) data 0.000 (0.002) loss 1.4609 (1.1511) acc 71.8750 (71.3350) lr 1.7705e-03 eta 16:12:44
+epoch [13/50] batch [630/1000] time 1.546 (1.562) data 0.000 (0.002) loss 1.4883 (1.1522) acc 65.6250 (71.3046) lr 1.7705e-03 eta 16:12:34
+epoch [13/50] batch [635/1000] time 1.566 (1.561) data 0.000 (0.002) loss 1.4209 (1.1512) acc 68.7500 (71.2943) lr 1.7705e-03 eta 16:12:25
+epoch [13/50] batch [640/1000] time 1.519 (1.562) data 0.001 (0.002) loss 1.1729 (1.1491) acc 68.7500 (71.3232) lr 1.7705e-03 eta 16:12:22
+epoch [13/50] batch [645/1000] time 1.559 (1.562) data 0.000 (0.002) loss 1.2158 (1.1492) acc 75.0000 (71.3275) lr 1.7705e-03 eta 16:12:12
+epoch [13/50] batch [650/1000] time 1.573 (1.562) data 0.000 (0.002) loss 1.1953 (1.1491) acc 65.6250 (71.3462) lr 1.7705e-03 eta 16:12:05
+epoch [13/50] batch [655/1000] time 1.583 (1.562) data 0.000 (0.002) loss 0.6401 (1.1479) acc 81.2500 (71.3836) lr 1.7705e-03 eta 16:11:58
+epoch [13/50] batch [660/1000] time 1.562 (1.562) data 0.000 (0.002) loss 1.0430 (1.1475) acc 75.0000 (71.3968) lr 1.7705e-03 eta 16:11:51
+epoch [13/50] batch [665/1000] time 1.538 (1.562) data 0.000 (0.002) loss 1.3408 (1.1487) acc 59.3750 (71.3440) lr 1.7705e-03 eta 16:11:39
+epoch [13/50] batch [670/1000] time 1.538 (1.561) data 0.000 (0.002) loss 0.9927 (1.1493) acc 71.8750 (71.3200) lr 1.7705e-03 eta 16:11:29
+epoch [13/50] batch [675/1000] time 1.564 (1.561) data 0.000 (0.002) loss 1.7266 (1.1487) acc 56.2500 (71.3287) lr 1.7705e-03 eta 16:11:19
+epoch [13/50] batch [680/1000] time 1.555 (1.561) data 0.001 (0.002) loss 1.0801 (1.1476) acc 81.2500 (71.3879) lr 1.7705e-03 eta 16:11:10
+epoch [13/50] batch [685/1000] time 1.566 (1.562) data 0.000 (0.002) loss 0.9614 (1.1482) acc 75.0000 (71.3641) lr 1.7705e-03 eta 16:11:12
+epoch [13/50] batch [690/1000] time 1.534 (1.562) data 0.001 (0.002) loss 0.9668 (1.1478) acc 71.8750 (71.3587) lr 1.7705e-03 eta 16:11:03
+epoch [13/50] batch [695/1000] time 1.584 (1.562) data 0.000 (0.002) loss 0.9473 (1.1480) acc 75.0000 (71.3804) lr 1.7705e-03 eta 16:10:55
+epoch [13/50] batch [700/1000] time 1.545 (1.561) data 0.001 (0.002) loss 1.4941 (1.1501) acc 65.6250 (71.3348) lr 1.7705e-03 eta 16:10:43
+epoch [13/50] batch [705/1000] time 1.552 (1.561) data 0.000 (0.002) loss 0.9727 (1.1494) acc 68.7500 (71.3652) lr 1.7705e-03 eta 16:10:33
+epoch [13/50] batch [710/1000] time 1.561 (1.562) data 0.000 (0.002) loss 1.0391 (1.1498) acc 84.3750 (71.3468) lr 1.7705e-03 eta 16:10:28
+epoch [13/50] batch [715/1000] time 1.539 (1.561) data 0.000 (0.002) loss 1.0859 (1.1500) acc 78.1250 (71.3855) lr 1.7705e-03 eta 16:10:17
+epoch [13/50] batch [720/1000] time 1.547 (1.561) data 0.000 (0.002) loss 1.4131 (1.1500) acc 62.5000 (71.3759) lr 1.7705e-03 eta 16:10:06
+epoch [13/50] batch [725/1000] time 1.530 (1.562) data 0.000 (0.002) loss 1.4404 (1.1500) acc 65.6250 (71.3664) lr 1.7705e-03 eta 16:10:06
+epoch [13/50] batch [730/1000] time 1.538 (1.561) data 0.000 (0.002) loss 0.6240 (1.1484) acc 84.3750 (71.4127) lr 1.7705e-03 eta 16:09:56
+epoch [13/50] batch [735/1000] time 1.547 (1.561) data 0.001 (0.002) loss 1.4941 (1.1492) acc 75.0000 (71.4371) lr 1.7705e-03 eta 16:09:44
+epoch [13/50] batch [740/1000] time 1.544 (1.561) data 0.000 (0.002) loss 1.5469 (1.1496) acc 68.7500 (71.4231) lr 1.7705e-03 eta 16:09:36
+epoch [13/50] batch [745/1000] time 1.540 (1.561) data 0.001 (0.002) loss 0.9854 (1.1486) acc 75.0000 (71.4136) lr 1.7705e-03 eta 16:09:28
+epoch [13/50] batch [750/1000] time 1.558 (1.561) data 0.000 (0.002) loss 0.9473 (1.1480) acc 81.2500 (71.4250) lr 1.7705e-03 eta 16:09:20
+epoch [13/50] batch [755/1000] time 1.564 (1.561) data 0.001 (0.002) loss 1.2441 (1.1482) acc 68.7500 (71.4280) lr 1.7705e-03 eta 16:09:14
+epoch [13/50] batch [760/1000] time 1.534 (1.561) data 0.001 (0.002) loss 0.9126 (1.1476) acc 78.1250 (71.4433) lr 1.7705e-03 eta 16:09:08
+epoch [13/50] batch [765/1000] time 1.614 (1.562) data 0.000 (0.002) loss 1.2803 (1.1461) acc 68.7500 (71.4747) lr 1.7705e-03 eta 16:09:03
+epoch [13/50] batch [770/1000] time 1.557 (1.562) data 0.000 (0.002) loss 1.7100 (1.1460) acc 56.2500 (71.4570) lr 1.7705e-03 eta 16:08:56
+epoch [13/50] batch [775/1000] time 1.572 (1.562) data 0.000 (0.002) loss 0.7065 (1.1446) acc 78.1250 (71.4879) lr 1.7705e-03 eta 16:08:47
+epoch [13/50] batch [780/1000] time 1.558 (1.561) data 0.000 (0.002) loss 0.7822 (1.1446) acc 87.5000 (71.5064) lr 1.7705e-03 eta 16:08:37
+epoch [13/50] batch [785/1000] time 1.575 (1.562) data 0.000 (0.002) loss 0.7422 (1.1446) acc 75.0000 (71.4769) lr 1.7705e-03 eta 16:08:32
+epoch [13/50] batch [790/1000] time 1.581 (1.562) data 0.000 (0.002) loss 0.8086 (1.1445) acc 75.0000 (71.4834) lr 1.7705e-03 eta 16:08:32
+epoch [13/50] batch [795/1000] time 1.579 (1.562) data 0.000 (0.002) loss 0.9785 (1.1439) acc 68.7500 (71.4937) lr 1.7705e-03 eta 16:08:24
+epoch [13/50] batch [800/1000] time 1.575 (1.562) data 0.000 (0.002) loss 1.3262 (1.1441) acc 71.8750 (71.4805) lr 1.7705e-03 eta 16:08:18
+epoch [13/50] batch [805/1000] time 1.557 (1.562) data 0.000 (0.002) loss 1.1240 (1.1446) acc 71.8750 (71.4713) lr 1.7705e-03 eta 16:08:09
+epoch [13/50] batch [810/1000] time 1.580 (1.562) data 0.000 (0.002) loss 1.2295 (1.1450) acc 65.6250 (71.4699) lr 1.7705e-03 eta 16:08:02
+epoch [13/50] batch [815/1000] time 1.545 (1.562) data 0.000 (0.002) loss 1.1777 (1.1459) acc 68.7500 (71.4647) lr 1.7705e-03 eta 16:07:55
+epoch [13/50] batch [820/1000] time 1.547 (1.562) data 0.000 (0.002) loss 0.6709 (1.1474) acc 84.3750 (71.4177) lr 1.7705e-03 eta 16:07:46
+epoch [13/50] batch [825/1000] time 1.550 (1.562) data 0.001 (0.002) loss 0.9688 (1.1474) acc 81.2500 (71.4356) lr 1.7705e-03 eta 16:07:42
+epoch [13/50] batch [830/1000] time 1.559 (1.562) data 0.000 (0.001) loss 1.0547 (1.1475) acc 78.1250 (71.4194) lr 1.7705e-03 eta 16:07:35
+epoch [13/50] batch [835/1000] time 1.574 (1.562) data 0.000 (0.001) loss 1.3965 (1.1491) acc 68.7500 (71.3698) lr 1.7705e-03 eta 16:07:35
+epoch [13/50] batch [840/1000] time 1.552 (1.562) data 0.001 (0.001) loss 0.9365 (1.1487) acc 71.8750 (71.3876) lr 1.7705e-03 eta 16:07:24
+epoch [13/50] batch [845/1000] time 1.539 (1.562) data 0.000 (0.001) loss 1.4873 (1.1484) acc 62.5000 (71.3720) lr 1.7705e-03 eta 16:07:14
+epoch [13/50] batch [850/1000] time 1.558 (1.562) data 0.000 (0.001) loss 1.4482 (1.1477) acc 62.5000 (71.3713) lr 1.7705e-03 eta 16:07:05
+epoch [13/50] batch [855/1000] time 1.565 (1.562) data 0.001 (0.001) loss 1.4531 (1.1486) acc 62.5000 (71.3852) lr 1.7705e-03 eta 16:06:56
+epoch [13/50] batch [860/1000] time 1.539 (1.562) data 0.000 (0.001) loss 0.9126 (1.1481) acc 71.8750 (71.4062) lr 1.7705e-03 eta 16:06:47
+epoch [13/50] batch [865/1000] time 1.569 (1.562) data 0.000 (0.001) loss 1.2686 (1.1482) acc 75.0000 (71.4234) lr 1.7705e-03 eta 16:06:39
+epoch [13/50] batch [870/1000] time 1.542 (1.562) data 0.000 (0.001) loss 0.9014 (1.1479) acc 71.8750 (71.4296) lr 1.7705e-03 eta 16:06:30
+epoch [13/50] batch [875/1000] time 1.717 (1.562) data 0.001 (0.001) loss 1.4736 (1.1478) acc 65.6250 (71.4214) lr 1.7705e-03 eta 16:06:29
+epoch [13/50] batch [880/1000] time 1.544 (1.562) data 0.000 (0.001) loss 0.7451 (1.1491) acc 84.3750 (71.4240) lr 1.7705e-03 eta 16:06:20
+epoch [13/50] batch [885/1000] time 1.558 (1.562) data 0.000 (0.001) loss 0.9585 (1.1489) acc 84.3750 (71.4301) lr 1.7705e-03 eta 16:06:11
+epoch [13/50] batch [890/1000] time 1.545 (1.562) data 0.000 (0.001) loss 0.5708 (1.1483) acc 81.2500 (71.4221) lr 1.7705e-03 eta 16:06:01
+epoch [13/50] batch [895/1000] time 1.529 (1.562) data 0.001 (0.001) loss 0.6533 (1.1470) acc 81.2500 (71.4420) lr 1.7705e-03 eta 16:05:51
+epoch [13/50] batch [900/1000] time 1.576 (1.562) data 0.000 (0.001) loss 1.0654 (1.1471) acc 68.7500 (71.4236) lr 1.7705e-03 eta 16:05:43
+epoch [13/50] batch [905/1000] time 1.559 (1.562) data 0.001 (0.001) loss 1.2686 (1.1477) acc 71.8750 (71.4054) lr 1.7705e-03 eta 16:05:34
+epoch [13/50] batch [910/1000] time 1.572 (1.562) data 0.001 (0.001) loss 1.2529 (1.1479) acc 62.5000 (71.3874) lr 1.7705e-03 eta 16:05:26
+epoch [13/50] batch [915/1000] time 1.570 (1.562) data 0.000 (0.001) loss 1.4404 (1.1483) acc 68.7500 (71.3900) lr 1.7705e-03 eta 16:05:19
+epoch [13/50] batch [920/1000] time 1.556 (1.562) data 0.000 (0.001) loss 1.1582 (1.1479) acc 68.7500 (71.4164) lr 1.7705e-03 eta 16:05:12
+epoch [13/50] batch [925/1000] time 1.537 (1.562) data 0.000 (0.001) loss 1.2842 (1.1495) acc 68.7500 (71.4020) lr 1.7705e-03 eta 16:05:04
+epoch [13/50] batch [930/1000] time 1.570 (1.562) data 0.000 (0.001) loss 0.8091 (1.1489) acc 75.0000 (71.3978) lr 1.7705e-03 eta 16:04:56
+epoch [13/50] batch [935/1000] time 1.551 (1.562) data 0.000 (0.001) loss 1.0781 (1.1484) acc 81.2500 (71.4372) lr 1.7705e-03 eta 16:04:48
+epoch [13/50] batch [940/1000] time 1.563 (1.562) data 0.001 (0.001) loss 1.7656 (1.1480) acc 59.3750 (71.4328) lr 1.7705e-03 eta 16:04:46
+epoch [13/50] batch [945/1000] time 1.551 (1.562) data 0.000 (0.001) loss 1.0908 (1.1471) acc 65.6250 (71.4253) lr 1.7705e-03 eta 16:04:35
+epoch [13/50] batch [950/1000] time 1.575 (1.562) data 0.000 (0.001) loss 0.9189 (1.1454) acc 81.2500 (71.4704) lr 1.7705e-03 eta 16:04:27
+epoch [13/50] batch [955/1000] time 1.568 (1.562) data 0.001 (0.001) loss 1.5576 (1.1449) acc 59.3750 (71.4594) lr 1.7705e-03 eta 16:04:21
+epoch [13/50] batch [960/1000] time 1.569 (1.562) data 0.001 (0.001) loss 1.1338 (1.1447) acc 71.8750 (71.4681) lr 1.7705e-03 eta 16:04:14
+epoch [13/50] batch [965/1000] time 1.553 (1.562) data 0.000 (0.001) loss 0.7856 (1.1447) acc 84.3750 (71.4832) lr 1.7705e-03 eta 16:04:03
+epoch [13/50] batch [970/1000] time 1.549 (1.562) data 0.000 (0.001) loss 1.4326 (1.1439) acc 62.5000 (71.5013) lr 1.7705e-03 eta 16:03:53
+epoch [13/50] batch [975/1000] time 1.581 (1.562) data 0.000 (0.001) loss 1.4990 (1.1446) acc 68.7500 (71.4904) lr 1.7705e-03 eta 16:03:44
+epoch [13/50] batch [980/1000] time 1.560 (1.562) data 0.000 (0.001) loss 0.5796 (1.1462) acc 81.2500 (71.4445) lr 1.7705e-03 eta 16:03:34
+epoch [13/50] batch [985/1000] time 1.563 (1.562) data 0.001 (0.001) loss 1.3262 (1.1474) acc 68.7500 (71.4055) lr 1.7705e-03 eta 16:03:31
+epoch [13/50] batch [990/1000] time 1.545 (1.562) data 0.000 (0.001) loss 0.5967 (1.1469) acc 81.2500 (71.4236) lr 1.7705e-03 eta 16:03:21
+epoch [13/50] batch [995/1000] time 1.572 (1.562) data 0.000 (0.001) loss 0.7983 (1.1473) acc 87.5000 (71.4227) lr 1.7705e-03 eta 16:03:15
+epoch [13/50] batch [1000/1000] time 1.536 (1.562) data 0.000 (0.001) loss 1.0947 (1.1472) acc 68.7500 (71.4219) lr 1.7290e-03 eta 16:03:05
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,145
+* accuracy: 78.3%
+* error: 21.7%
+* macro_f1: 77.8%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [14/50] batch [5/1000] time 1.556 (1.661) data 0.000 (0.159) loss 1.1748 (0.9594) acc 78.1250 (78.1250) lr 1.7290e-03 eta 17:04:09
+epoch [14/50] batch [10/1000] time 1.561 (1.611) data 0.001 (0.080) loss 1.0645 (0.9932) acc 75.0000 (75.6250) lr 1.7290e-03 eta 16:32:57
+epoch [14/50] batch [15/1000] time 1.551 (1.609) data 0.000 (0.053) loss 0.9272 (0.9783) acc 75.0000 (75.0000) lr 1.7290e-03 eta 16:31:36
+epoch [14/50] batch [20/1000] time 1.562 (1.598) data 0.000 (0.040) loss 0.9580 (1.0396) acc 81.2500 (74.3750) lr 1.7290e-03 eta 16:24:42
+epoch [14/50] batch [25/1000] time 1.551 (1.592) data 0.000 (0.032) loss 0.5840 (1.0379) acc 87.5000 (74.1250) lr 1.7290e-03 eta 16:21:13
+epoch [14/50] batch [30/1000] time 1.570 (1.587) data 0.001 (0.027) loss 1.3408 (1.0725) acc 68.7500 (73.9583) lr 1.7290e-03 eta 16:18:06
+epoch [14/50] batch [35/1000] time 1.570 (1.585) data 0.001 (0.023) loss 1.4922 (1.1223) acc 68.7500 (73.5714) lr 1.7290e-03 eta 16:16:28
+epoch [14/50] batch [40/1000] time 1.538 (1.581) data 0.000 (0.020) loss 1.4395 (1.1249) acc 75.0000 (73.3594) lr 1.7290e-03 eta 16:13:53
+epoch [14/50] batch [45/1000] time 1.543 (1.577) data 0.000 (0.018) loss 1.1719 (1.1592) acc 65.6250 (72.9167) lr 1.7290e-03 eta 16:11:36
+epoch [14/50] batch [50/1000] time 1.558 (1.576) data 0.000 (0.016) loss 0.9277 (1.1364) acc 75.0000 (73.3750) lr 1.7290e-03 eta 16:10:16
+epoch [14/50] batch [55/1000] time 1.543 (1.573) data 0.000 (0.015) loss 1.2178 (1.1224) acc 65.6250 (73.5227) lr 1.7290e-03 eta 16:08:47
+epoch [14/50] batch [60/1000] time 1.544 (1.574) data 0.000 (0.014) loss 0.9751 (1.1217) acc 81.2500 (73.1771) lr 1.7290e-03 eta 16:09:01
+epoch [14/50] batch [65/1000] time 1.546 (1.573) data 0.000 (0.013) loss 1.6377 (1.1422) acc 68.7500 (72.7885) lr 1.7290e-03 eta 16:08:14
+epoch [14/50] batch [70/1000] time 1.559 (1.571) data 0.000 (0.012) loss 1.5059 (1.1453) acc 75.0000 (72.5893) lr 1.7290e-03 eta 16:06:43
+epoch [14/50] batch [75/1000] time 1.541 (1.570) data 0.000 (0.011) loss 1.1035 (1.1325) acc 78.1250 (72.8750) lr 1.7290e-03 eta 16:05:55
+epoch [14/50] batch [80/1000] time 1.561 (1.569) data 0.001 (0.010) loss 1.1162 (1.1239) acc 71.8750 (73.0469) lr 1.7290e-03 eta 16:05:23
+epoch [14/50] batch [85/1000] time 1.541 (1.568) data 0.001 (0.010) loss 1.1289 (1.1272) acc 71.8750 (72.6471) lr 1.7290e-03 eta 16:04:33
+epoch [14/50] batch [90/1000] time 1.565 (1.567) data 0.000 (0.009) loss 2.3984 (1.1445) acc 56.2500 (72.4653) lr 1.7290e-03 eta 16:04:09
+epoch [14/50] batch [95/1000] time 1.581 (1.568) data 0.000 (0.009) loss 1.2744 (1.1406) acc 68.7500 (72.5658) lr 1.7290e-03 eta 16:04:11
+epoch [14/50] batch [100/1000] time 1.563 (1.567) data 0.000 (0.008) loss 2.0273 (1.1457) acc 62.5000 (72.7500) lr 1.7290e-03 eta 16:03:53
+epoch [14/50] batch [105/1000] time 1.554 (1.567) data 0.000 (0.008) loss 1.1582 (1.1354) acc 68.7500 (72.7976) lr 1.7290e-03 eta 16:03:16
+epoch [14/50] batch [110/1000] time 1.555 (1.566) data 0.000 (0.008) loss 0.8882 (1.1367) acc 71.8750 (72.6705) lr 1.7290e-03 eta 16:02:49
+epoch [14/50] batch [115/1000] time 1.575 (1.566) data 0.000 (0.007) loss 1.3164 (1.1355) acc 62.5000 (72.4457) lr 1.7290e-03 eta 16:02:28
+epoch [14/50] batch [120/1000] time 1.714 (1.567) data 0.000 (0.007) loss 1.4941 (1.1413) acc 71.8750 (72.3698) lr 1.7290e-03 eta 16:02:56
+epoch [14/50] batch [125/1000] time 1.566 (1.566) data 0.000 (0.007) loss 0.7822 (1.1260) acc 62.5000 (72.4750) lr 1.7290e-03 eta 16:02:42
+epoch [14/50] batch [130/1000] time 1.562 (1.566) data 0.000 (0.007) loss 0.6797 (1.1169) acc 71.8750 (72.5240) lr 1.7290e-03 eta 16:02:26
+epoch [14/50] batch [135/1000] time 1.566 (1.566) data 0.000 (0.006) loss 1.5654 (1.1172) acc 50.0000 (72.5231) lr 1.7290e-03 eta 16:02:24
+epoch [14/50] batch [140/1000] time 1.585 (1.567) data 0.001 (0.006) loss 0.8794 (1.1152) acc 68.7500 (72.5223) lr 1.7290e-03 eta 16:02:22
+epoch [14/50] batch [145/1000] time 1.548 (1.566) data 0.000 (0.006) loss 1.6240 (1.1316) acc 68.7500 (72.2198) lr 1.7290e-03 eta 16:01:53
+epoch [14/50] batch [150/1000] time 1.559 (1.566) data 0.001 (0.006) loss 0.9067 (1.1295) acc 81.2500 (72.3333) lr 1.7290e-03 eta 16:01:35
+epoch [14/50] batch [155/1000] time 1.556 (1.565) data 0.000 (0.006) loss 1.4297 (1.1330) acc 65.6250 (72.2581) lr 1.7290e-03 eta 16:01:15
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+epoch [14/50] batch [200/1000] time 1.540 (1.565) data 0.000 (0.004) loss 1.3271 (1.1278) acc 68.7500 (72.2344) lr 1.7290e-03 eta 16:00:06
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+epoch [14/50] batch [210/1000] time 1.569 (1.566) data 0.000 (0.004) loss 1.0303 (1.1288) acc 75.0000 (72.1875) lr 1.7290e-03 eta 16:00:24
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+epoch [14/50] batch [220/1000] time 1.569 (1.566) data 0.000 (0.004) loss 1.6934 (1.1311) acc 59.3750 (72.0170) lr 1.7290e-03 eta 15:59:59
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+epoch [14/50] batch [235/1000] time 1.565 (1.566) data 0.000 (0.004) loss 1.9258 (1.1342) acc 53.1250 (71.9415) lr 1.7290e-03 eta 15:59:27
+epoch [14/50] batch [240/1000] time 1.593 (1.566) data 0.000 (0.004) loss 0.9863 (1.1364) acc 78.1250 (71.9531) lr 1.7290e-03 eta 15:59:25
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+epoch [14/50] batch [255/1000] time 1.523 (1.565) data 0.000 (0.004) loss 1.2021 (1.1455) acc 62.5000 (71.6176) lr 1.7290e-03 eta 15:58:43
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+epoch [14/50] batch [300/1000] time 1.584 (1.565) data 0.000 (0.003) loss 0.8491 (1.1415) acc 75.0000 (71.5208) lr 1.7290e-03 eta 15:57:23
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+epoch [14/50] batch [555/1000] time 1.574 (1.564) data 0.000 (0.002) loss 0.7959 (1.1444) acc 81.2500 (71.7905) lr 1.7290e-03 eta 15:50:02
+epoch [14/50] batch [560/1000] time 1.582 (1.564) data 0.000 (0.002) loss 1.3066 (1.1444) acc 78.1250 (71.7690) lr 1.7290e-03 eta 15:49:54
+epoch [14/50] batch [565/1000] time 1.595 (1.564) data 0.000 (0.002) loss 1.2891 (1.1442) acc 71.8750 (71.7699) lr 1.7290e-03 eta 15:49:44
+epoch [14/50] batch [570/1000] time 1.535 (1.564) data 0.000 (0.002) loss 1.0645 (1.1442) acc 71.8750 (71.7489) lr 1.7290e-03 eta 15:49:30
+epoch [14/50] batch [575/1000] time 1.560 (1.564) data 0.000 (0.002) loss 1.2393 (1.1426) acc 75.0000 (71.7935) lr 1.7290e-03 eta 15:49:29
+epoch [14/50] batch [580/1000] time 1.552 (1.564) data 0.001 (0.002) loss 1.3770 (1.1426) acc 62.5000 (71.8050) lr 1.7290e-03 eta 15:49:15
+epoch [14/50] batch [585/1000] time 1.556 (1.564) data 0.000 (0.002) loss 0.6895 (1.1423) acc 84.3750 (71.8536) lr 1.7290e-03 eta 15:49:02
+epoch [14/50] batch [590/1000] time 1.542 (1.564) data 0.000 (0.002) loss 0.4690 (1.1408) acc 90.6250 (71.9015) lr 1.7290e-03 eta 15:48:49
+epoch [14/50] batch [595/1000] time 1.536 (1.563) data 0.000 (0.002) loss 1.1650 (1.1423) acc 75.0000 (71.8540) lr 1.7290e-03 eta 15:48:37
+epoch [14/50] batch [600/1000] time 1.553 (1.563) data 0.001 (0.002) loss 0.9844 (1.1410) acc 71.8750 (71.8802) lr 1.7290e-03 eta 15:48:26
+epoch [14/50] batch [605/1000] time 1.548 (1.563) data 0.001 (0.002) loss 1.1631 (1.1396) acc 71.8750 (71.9318) lr 1.7290e-03 eta 15:48:16
+epoch [14/50] batch [610/1000] time 1.540 (1.563) data 0.000 (0.002) loss 1.0322 (1.1387) acc 68.7500 (71.9416) lr 1.7290e-03 eta 15:48:04
+epoch [14/50] batch [615/1000] time 1.536 (1.563) data 0.000 (0.002) loss 1.1553 (1.1376) acc 71.8750 (71.9461) lr 1.7290e-03 eta 15:47:53
+epoch [14/50] batch [620/1000] time 1.569 (1.563) data 0.000 (0.002) loss 1.1865 (1.1398) acc 81.2500 (71.9002) lr 1.7290e-03 eta 15:47:53
+epoch [14/50] batch [625/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.8418 (1.1406) acc 53.1250 (71.9100) lr 1.7290e-03 eta 15:47:41
+epoch [14/50] batch [630/1000] time 1.581 (1.563) data 0.000 (0.002) loss 1.3291 (1.1404) acc 71.8750 (71.8948) lr 1.7290e-03 eta 15:47:32
+epoch [14/50] batch [635/1000] time 1.548 (1.563) data 0.000 (0.002) loss 1.4053 (1.1404) acc 68.7500 (71.8996) lr 1.7290e-03 eta 15:47:24
+epoch [14/50] batch [640/1000] time 1.564 (1.563) data 0.000 (0.002) loss 1.1143 (1.1387) acc 81.2500 (71.9629) lr 1.7290e-03 eta 15:47:16
+epoch [14/50] batch [645/1000] time 1.532 (1.563) data 0.001 (0.002) loss 1.0713 (1.1408) acc 78.1250 (71.9186) lr 1.7290e-03 eta 15:47:05
+epoch [14/50] batch [650/1000] time 1.579 (1.563) data 0.000 (0.002) loss 0.9507 (1.1402) acc 75.0000 (71.9375) lr 1.7290e-03 eta 15:46:56
+epoch [14/50] batch [655/1000] time 1.552 (1.563) data 0.000 (0.002) loss 1.7920 (1.1412) acc 65.6250 (71.9323) lr 1.7290e-03 eta 15:46:46
+epoch [14/50] batch [660/1000] time 1.551 (1.563) data 0.000 (0.002) loss 1.0771 (1.1422) acc 78.1250 (71.9129) lr 1.7290e-03 eta 15:46:35
+epoch [14/50] batch [665/1000] time 1.555 (1.563) data 0.000 (0.002) loss 1.0381 (1.1416) acc 71.8750 (71.9079) lr 1.7290e-03 eta 15:46:36
+epoch [14/50] batch [670/1000] time 1.578 (1.563) data 0.000 (0.002) loss 1.1689 (1.1412) acc 75.0000 (71.8937) lr 1.7290e-03 eta 15:46:27
+epoch [14/50] batch [675/1000] time 1.545 (1.563) data 0.000 (0.002) loss 1.4629 (1.1413) acc 68.7500 (71.8981) lr 1.7290e-03 eta 15:46:20
+epoch [14/50] batch [680/1000] time 1.564 (1.563) data 0.000 (0.002) loss 0.6240 (1.1392) acc 81.2500 (71.9118) lr 1.7290e-03 eta 15:46:10
+epoch [14/50] batch [685/1000] time 1.578 (1.563) data 0.000 (0.002) loss 1.0117 (1.1384) acc 68.7500 (71.9206) lr 1.7290e-03 eta 15:46:05
+epoch [14/50] batch [690/1000] time 1.522 (1.563) data 0.000 (0.002) loss 1.4453 (1.1374) acc 65.6250 (71.9384) lr 1.7290e-03 eta 15:45:55
+epoch [14/50] batch [695/1000] time 1.551 (1.563) data 0.000 (0.002) loss 0.8730 (1.1382) acc 78.1250 (71.9110) lr 1.7290e-03 eta 15:45:44
+epoch [14/50] batch [700/1000] time 1.557 (1.563) data 0.000 (0.002) loss 0.9204 (1.1379) acc 71.8750 (71.8973) lr 1.7290e-03 eta 15:45:35
+epoch [14/50] batch [705/1000] time 1.567 (1.563) data 0.000 (0.002) loss 1.0283 (1.1394) acc 71.8750 (71.8573) lr 1.7290e-03 eta 15:45:27
+epoch [14/50] batch [710/1000] time 1.565 (1.563) data 0.000 (0.002) loss 1.2402 (1.1397) acc 65.6250 (71.8574) lr 1.7290e-03 eta 15:45:17
+epoch [14/50] batch [715/1000] time 1.545 (1.563) data 0.000 (0.002) loss 0.6230 (1.1395) acc 81.2500 (71.8881) lr 1.7290e-03 eta 15:45:10
+epoch [14/50] batch [720/1000] time 1.565 (1.563) data 0.001 (0.002) loss 1.1621 (1.1399) acc 68.7500 (71.8707) lr 1.7290e-03 eta 15:45:03
+epoch [14/50] batch [725/1000] time 1.546 (1.563) data 0.000 (0.002) loss 0.5811 (1.1392) acc 90.6250 (71.8750) lr 1.7290e-03 eta 15:45:00
+epoch [14/50] batch [730/1000] time 1.572 (1.563) data 0.000 (0.002) loss 1.1328 (1.1409) acc 71.8750 (71.8365) lr 1.7290e-03 eta 15:44:52
+epoch [14/50] batch [735/1000] time 1.585 (1.563) data 0.001 (0.002) loss 1.3242 (1.1415) acc 62.5000 (71.8240) lr 1.7290e-03 eta 15:44:42
+epoch [14/50] batch [740/1000] time 1.553 (1.563) data 0.001 (0.001) loss 0.9175 (1.1401) acc 71.8750 (71.8412) lr 1.7290e-03 eta 15:44:32
+epoch [14/50] batch [745/1000] time 1.576 (1.563) data 0.000 (0.001) loss 1.4053 (1.1429) acc 65.6250 (71.8037) lr 1.7290e-03 eta 15:44:25
+epoch [14/50] batch [750/1000] time 1.564 (1.563) data 0.000 (0.001) loss 1.2764 (1.1428) acc 65.6250 (71.7917) lr 1.7290e-03 eta 15:44:17
+epoch [14/50] batch [755/1000] time 1.557 (1.563) data 0.000 (0.001) loss 1.0811 (1.1448) acc 71.8750 (71.7591) lr 1.7290e-03 eta 15:44:11
+epoch [14/50] batch [760/1000] time 1.539 (1.563) data 0.001 (0.001) loss 1.1641 (1.1440) acc 56.2500 (71.7434) lr 1.7290e-03 eta 15:44:04
+epoch [14/50] batch [765/1000] time 1.575 (1.563) data 0.000 (0.001) loss 1.8057 (1.1449) acc 46.8750 (71.7239) lr 1.7290e-03 eta 15:43:57
+epoch [14/50] batch [770/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.0479 (1.1447) acc 68.7500 (71.7248) lr 1.7290e-03 eta 15:43:54
+epoch [14/50] batch [775/1000] time 1.588 (1.563) data 0.001 (0.001) loss 1.1104 (1.1448) acc 65.6250 (71.7016) lr 1.7290e-03 eta 15:43:47
+epoch [14/50] batch [780/1000] time 1.580 (1.563) data 0.001 (0.001) loss 0.9741 (1.1448) acc 81.2500 (71.6987) lr 1.7290e-03 eta 15:43:41
+epoch [14/50] batch [785/1000] time 1.561 (1.563) data 0.001 (0.001) loss 1.3701 (1.1453) acc 68.7500 (71.6839) lr 1.7290e-03 eta 15:43:36
+epoch [14/50] batch [790/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.0957 (1.1444) acc 68.7500 (71.6772) lr 1.7290e-03 eta 15:43:27
+epoch [14/50] batch [795/1000] time 1.554 (1.563) data 0.000 (0.001) loss 1.1592 (1.1443) acc 59.3750 (71.6352) lr 1.7290e-03 eta 15:43:18
+epoch [14/50] batch [800/1000] time 1.571 (1.563) data 0.000 (0.001) loss 1.4443 (1.1451) acc 78.1250 (71.6523) lr 1.7290e-03 eta 15:43:10
+epoch [14/50] batch [805/1000] time 1.549 (1.563) data 0.001 (0.001) loss 1.3301 (1.1447) acc 75.0000 (71.6498) lr 1.7290e-03 eta 15:43:03
+epoch [14/50] batch [810/1000] time 1.556 (1.563) data 0.001 (0.001) loss 0.6519 (1.1438) acc 87.5000 (71.6821) lr 1.7290e-03 eta 15:42:55
+epoch [14/50] batch [815/1000] time 1.554 (1.563) data 0.001 (0.001) loss 0.8813 (1.1429) acc 81.2500 (71.7216) lr 1.7290e-03 eta 15:42:50
+epoch [14/50] batch [820/1000] time 1.545 (1.563) data 0.001 (0.001) loss 0.9717 (1.1428) acc 68.7500 (71.7149) lr 1.7290e-03 eta 15:42:40
+epoch [14/50] batch [825/1000] time 1.541 (1.563) data 0.000 (0.001) loss 1.0449 (1.1421) acc 75.0000 (71.7311) lr 1.7290e-03 eta 15:42:31
+epoch [14/50] batch [830/1000] time 1.543 (1.563) data 0.001 (0.001) loss 0.9990 (1.1417) acc 81.2500 (71.7508) lr 1.7290e-03 eta 15:42:21
+epoch [14/50] batch [835/1000] time 1.538 (1.563) data 0.000 (0.001) loss 1.0508 (1.1411) acc 68.7500 (71.7665) lr 1.7290e-03 eta 15:42:08
+epoch [14/50] batch [840/1000] time 1.544 (1.563) data 0.000 (0.001) loss 0.4375 (1.1396) acc 84.3750 (71.8006) lr 1.7290e-03 eta 15:41:59
+epoch [14/50] batch [845/1000] time 1.551 (1.563) data 0.000 (0.001) loss 0.9014 (1.1405) acc 75.0000 (71.7751) lr 1.7290e-03 eta 15:41:48
+epoch [14/50] batch [850/1000] time 1.549 (1.563) data 0.000 (0.001) loss 0.8818 (1.1404) acc 68.7500 (71.7647) lr 1.7290e-03 eta 15:41:39
+epoch [14/50] batch [855/1000] time 1.536 (1.563) data 0.000 (0.001) loss 0.7964 (1.1391) acc 75.0000 (71.7763) lr 1.7290e-03 eta 15:41:30
+epoch [14/50] batch [860/1000] time 1.554 (1.563) data 0.000 (0.001) loss 1.6299 (1.1393) acc 59.3750 (71.7551) lr 1.7290e-03 eta 15:41:21
+epoch [14/50] batch [865/1000] time 1.541 (1.563) data 0.000 (0.001) loss 1.2295 (1.1397) acc 68.7500 (71.7594) lr 1.7290e-03 eta 15:41:11
+epoch [14/50] batch [870/1000] time 1.560 (1.563) data 0.000 (0.001) loss 1.0693 (1.1411) acc 68.7500 (71.7241) lr 1.7290e-03 eta 15:41:00
+epoch [14/50] batch [875/1000] time 1.735 (1.563) data 0.000 (0.001) loss 1.1387 (1.1407) acc 71.8750 (71.7286) lr 1.7290e-03 eta 15:40:57
+epoch [14/50] batch [880/1000] time 1.557 (1.563) data 0.000 (0.001) loss 0.8989 (1.1406) acc 81.2500 (71.7401) lr 1.7290e-03 eta 15:40:51
+epoch [14/50] batch [885/1000] time 1.565 (1.563) data 0.000 (0.001) loss 1.3076 (1.1397) acc 75.0000 (71.7549) lr 1.7290e-03 eta 15:40:42
+epoch [14/50] batch [890/1000] time 1.579 (1.563) data 0.000 (0.001) loss 0.8677 (1.1389) acc 81.2500 (71.7662) lr 1.7290e-03 eta 15:40:36
+epoch [14/50] batch [895/1000] time 1.582 (1.563) data 0.000 (0.001) loss 0.5293 (1.1384) acc 84.3750 (71.7633) lr 1.7290e-03 eta 15:40:28
+epoch [14/50] batch [900/1000] time 1.568 (1.563) data 0.000 (0.001) loss 1.3750 (1.1386) acc 59.3750 (71.7708) lr 1.7290e-03 eta 15:40:20
+epoch [14/50] batch [905/1000] time 1.551 (1.563) data 0.000 (0.001) loss 1.0430 (1.1383) acc 68.7500 (71.7472) lr 1.7290e-03 eta 15:40:10
+epoch [14/50] batch [910/1000] time 1.548 (1.563) data 0.000 (0.001) loss 1.3164 (1.1388) acc 68.7500 (71.7411) lr 1.7290e-03 eta 15:40:01
+epoch [14/50] batch [915/1000] time 1.556 (1.563) data 0.001 (0.001) loss 1.3936 (1.1388) acc 65.6250 (71.7418) lr 1.7290e-03 eta 15:39:51
+epoch [14/50] batch [920/1000] time 1.691 (1.563) data 0.000 (0.001) loss 1.6904 (1.1386) acc 65.6250 (71.7561) lr 1.7290e-03 eta 15:39:48
+epoch [14/50] batch [925/1000] time 1.561 (1.563) data 0.000 (0.001) loss 0.5264 (1.1389) acc 90.6250 (71.7635) lr 1.7290e-03 eta 15:39:40
+epoch [14/50] batch [930/1000] time 1.551 (1.563) data 0.000 (0.001) loss 1.0869 (1.1403) acc 75.0000 (71.7339) lr 1.7290e-03 eta 15:39:33
+epoch [14/50] batch [935/1000] time 1.578 (1.563) data 0.000 (0.001) loss 1.2080 (1.1404) acc 75.0000 (71.7447) lr 1.7290e-03 eta 15:39:26
+epoch [14/50] batch [940/1000] time 1.555 (1.563) data 0.001 (0.001) loss 0.9189 (1.1400) acc 75.0000 (71.7653) lr 1.7290e-03 eta 15:39:16
+epoch [14/50] batch [945/1000] time 1.536 (1.563) data 0.000 (0.001) loss 0.6846 (1.1390) acc 84.3750 (71.7824) lr 1.7290e-03 eta 15:39:07
+epoch [14/50] batch [950/1000] time 1.550 (1.563) data 0.000 (0.001) loss 1.1201 (1.1385) acc 65.6250 (71.7566) lr 1.7290e-03 eta 15:38:59
+epoch [14/50] batch [955/1000] time 1.545 (1.563) data 0.001 (0.001) loss 1.7637 (1.1384) acc 56.2500 (71.7637) lr 1.7290e-03 eta 15:38:52
+epoch [14/50] batch [960/1000] time 1.550 (1.563) data 0.000 (0.001) loss 1.5898 (1.1388) acc 56.2500 (71.7350) lr 1.7290e-03 eta 15:38:44
+epoch [14/50] batch [965/1000] time 1.532 (1.563) data 0.001 (0.001) loss 1.3682 (1.1394) acc 68.7500 (71.7260) lr 1.7290e-03 eta 15:38:39
+epoch [14/50] batch [970/1000] time 1.570 (1.563) data 0.000 (0.001) loss 0.8672 (1.1390) acc 71.8750 (71.7204) lr 1.7290e-03 eta 15:38:32
+epoch [14/50] batch [975/1000] time 1.557 (1.563) data 0.000 (0.001) loss 1.1924 (1.1401) acc 68.7500 (71.7147) lr 1.7290e-03 eta 15:38:25
+epoch [14/50] batch [980/1000] time 1.564 (1.563) data 0.000 (0.001) loss 0.9536 (1.1400) acc 78.1250 (71.7124) lr 1.7290e-03 eta 15:38:16
+epoch [14/50] batch [985/1000] time 1.541 (1.563) data 0.001 (0.001) loss 1.1104 (1.1396) acc 71.8750 (71.7195) lr 1.7290e-03 eta 15:38:06
+epoch [14/50] batch [990/1000] time 1.543 (1.563) data 0.000 (0.001) loss 0.9302 (1.1396) acc 81.2500 (71.7298) lr 1.7290e-03 eta 15:37:56
+epoch [14/50] batch [995/1000] time 1.557 (1.563) data 0.000 (0.001) loss 1.3604 (1.1404) acc 68.7500 (71.7242) lr 1.7290e-03 eta 15:37:47
+epoch [14/50] batch [1000/1000] time 1.564 (1.563) data 0.000 (0.001) loss 0.8628 (1.1403) acc 78.1250 (71.7031) lr 1.6845e-03 eta 15:37:40
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,110
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+epoch [15/50] batch [5/1000] time 1.579 (1.682) data 0.001 (0.180) loss 1.2568 (0.8736) acc 65.6250 (78.1250) lr 1.6845e-03 eta 16:48:54
+epoch [15/50] batch [10/1000] time 1.545 (1.617) data 0.000 (0.090) loss 1.2402 (1.0376) acc 71.8750 (73.7500) lr 1.6845e-03 eta 16:10:07
+epoch [15/50] batch [15/1000] time 1.570 (1.600) data 0.001 (0.060) loss 0.6738 (1.0433) acc 87.5000 (72.7083) lr 1.6845e-03 eta 15:59:18
+epoch [15/50] batch [20/1000] time 1.583 (1.593) data 0.000 (0.045) loss 0.8760 (1.0423) acc 75.0000 (73.1250) lr 1.6845e-03 eta 15:55:05
+epoch [15/50] batch [25/1000] time 1.553 (1.587) data 0.001 (0.036) loss 2.1758 (1.1422) acc 46.8750 (71.6250) lr 1.6845e-03 eta 15:51:32
+epoch [15/50] batch [30/1000] time 1.555 (1.582) data 0.001 (0.030) loss 0.7339 (1.1549) acc 84.3750 (71.8750) lr 1.6845e-03 eta 15:48:30
+epoch [15/50] batch [35/1000] time 1.559 (1.579) data 0.000 (0.026) loss 0.7065 (1.1348) acc 75.0000 (71.9643) lr 1.6845e-03 eta 15:46:46
+epoch [15/50] batch [40/1000] time 1.563 (1.582) data 0.001 (0.023) loss 0.7065 (1.1374) acc 81.2500 (71.7188) lr 1.6845e-03 eta 15:47:58
+epoch [15/50] batch [45/1000] time 1.569 (1.579) data 0.001 (0.020) loss 1.5098 (1.1419) acc 65.6250 (71.5972) lr 1.6845e-03 eta 15:46:10
+epoch [15/50] batch [50/1000] time 1.549 (1.576) data 0.001 (0.018) loss 1.1348 (1.1214) acc 71.8750 (72.0000) lr 1.6845e-03 eta 15:44:05
+epoch [15/50] batch [55/1000] time 1.570 (1.574) data 0.000 (0.017) loss 1.0000 (1.1376) acc 71.8750 (71.9318) lr 1.6845e-03 eta 15:43:11
+epoch [15/50] batch [60/1000] time 1.578 (1.575) data 0.001 (0.015) loss 0.9004 (1.1361) acc 84.3750 (72.5000) lr 1.6845e-03 eta 15:43:14
+epoch [15/50] batch [65/1000] time 1.554 (1.573) data 0.000 (0.014) loss 1.0391 (1.1322) acc 78.1250 (72.5481) lr 1.6845e-03 eta 15:42:10
+epoch [15/50] batch [70/1000] time 1.574 (1.572) data 0.000 (0.013) loss 1.1445 (1.1295) acc 71.8750 (72.5446) lr 1.6845e-03 eta 15:41:20
+epoch [15/50] batch [75/1000] time 1.572 (1.572) data 0.000 (0.012) loss 1.1689 (1.1404) acc 68.7500 (72.4167) lr 1.6845e-03 eta 15:41:00
+epoch [15/50] batch [80/1000] time 1.560 (1.571) data 0.000 (0.012) loss 0.8672 (1.1392) acc 71.8750 (72.3438) lr 1.6845e-03 eta 15:40:21
+epoch [15/50] batch [85/1000] time 1.586 (1.572) data 0.000 (0.011) loss 1.0273 (1.1389) acc 71.8750 (72.3162) lr 1.6845e-03 eta 15:41:06
+epoch [15/50] batch [90/1000] time 1.554 (1.572) data 0.000 (0.010) loss 1.3662 (1.1335) acc 75.0000 (72.4653) lr 1.6845e-03 eta 15:40:38
+epoch [15/50] batch [95/1000] time 1.529 (1.570) data 0.000 (0.010) loss 1.1270 (1.1361) acc 59.3750 (72.2039) lr 1.6845e-03 eta 15:39:39
+epoch [15/50] batch [100/1000] time 1.554 (1.570) data 0.000 (0.009) loss 0.9604 (1.1356) acc 81.2500 (72.4375) lr 1.6845e-03 eta 15:39:13
+epoch [15/50] batch [105/1000] time 1.571 (1.569) data 0.000 (0.009) loss 1.1484 (1.1468) acc 78.1250 (72.3512) lr 1.6845e-03 eta 15:38:43
+epoch [15/50] batch [110/1000] time 1.562 (1.569) data 0.000 (0.009) loss 0.7856 (1.1334) acc 81.2500 (72.5568) lr 1.6845e-03 eta 15:38:40
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+epoch [15/50] batch [670/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.6870 (1.1408) acc 93.7500 (71.3153) lr 1.6845e-03 eta 15:21:24
+epoch [15/50] batch [675/1000] time 1.554 (1.565) data 0.000 (0.002) loss 1.1523 (1.1405) acc 65.6250 (71.3009) lr 1.6845e-03 eta 15:21:15
+epoch [15/50] batch [680/1000] time 1.558 (1.565) data 0.000 (0.002) loss 0.5371 (1.1413) acc 84.3750 (71.2730) lr 1.6845e-03 eta 15:21:05
+epoch [15/50] batch [685/1000] time 1.563 (1.565) data 0.000 (0.002) loss 1.2617 (1.1420) acc 68.7500 (71.2591) lr 1.6845e-03 eta 15:20:59
+epoch [15/50] batch [690/1000] time 1.547 (1.565) data 0.000 (0.002) loss 1.4521 (1.1425) acc 68.7500 (71.2455) lr 1.6845e-03 eta 15:20:53
+epoch [15/50] batch [695/1000] time 1.549 (1.565) data 0.000 (0.002) loss 1.2158 (1.1412) acc 65.6250 (71.2545) lr 1.6845e-03 eta 15:20:44
+epoch [15/50] batch [700/1000] time 1.571 (1.565) data 0.000 (0.002) loss 0.6079 (1.1391) acc 81.2500 (71.3080) lr 1.6845e-03 eta 15:20:34
+epoch [15/50] batch [705/1000] time 1.559 (1.565) data 0.000 (0.002) loss 0.8882 (1.1397) acc 75.0000 (71.3165) lr 1.6845e-03 eta 15:20:27
+epoch [15/50] batch [710/1000] time 1.567 (1.565) data 0.001 (0.002) loss 0.6431 (1.1387) acc 81.2500 (71.3424) lr 1.6845e-03 eta 15:20:19
+epoch [15/50] batch [715/1000] time 1.558 (1.565) data 0.000 (0.002) loss 1.3809 (1.1394) acc 56.2500 (71.3112) lr 1.6845e-03 eta 15:20:11
+epoch [15/50] batch [720/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0205 (1.1386) acc 78.1250 (71.3368) lr 1.6845e-03 eta 15:20:01
+epoch [15/50] batch [725/1000] time 1.554 (1.565) data 0.001 (0.002) loss 1.0986 (1.1381) acc 75.0000 (71.3491) lr 1.6845e-03 eta 15:19:51
+epoch [15/50] batch [730/1000] time 1.545 (1.565) data 0.000 (0.002) loss 1.3066 (1.1402) acc 59.3750 (71.3057) lr 1.6845e-03 eta 15:19:50
+epoch [15/50] batch [735/1000] time 1.572 (1.565) data 0.001 (0.002) loss 1.0605 (1.1404) acc 71.8750 (71.3138) lr 1.6845e-03 eta 15:19:42
+epoch [15/50] batch [740/1000] time 1.572 (1.565) data 0.000 (0.002) loss 1.9502 (1.1407) acc 59.3750 (71.3218) lr 1.6845e-03 eta 15:19:35
+epoch [15/50] batch [745/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.0732 (1.1406) acc 68.7500 (71.3255) lr 1.6845e-03 eta 15:19:29
+epoch [15/50] batch [750/1000] time 1.552 (1.565) data 0.000 (0.002) loss 0.9507 (1.1392) acc 71.8750 (71.3708) lr 1.6845e-03 eta 15:19:20
+epoch [15/50] batch [755/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.4229 (1.1402) acc 62.5000 (71.3659) lr 1.6845e-03 eta 15:19:10
+epoch [15/50] batch [760/1000] time 1.567 (1.565) data 0.001 (0.002) loss 1.7217 (1.1402) acc 68.7500 (71.3734) lr 1.6845e-03 eta 15:19:00
+epoch [15/50] batch [765/1000] time 1.572 (1.565) data 0.000 (0.002) loss 0.9292 (1.1394) acc 78.1250 (71.4093) lr 1.6845e-03 eta 15:18:51
+epoch [15/50] batch [770/1000] time 1.554 (1.565) data 0.000 (0.002) loss 0.9150 (1.1394) acc 71.8750 (71.3961) lr 1.6845e-03 eta 15:18:42
+epoch [15/50] batch [775/1000] time 1.560 (1.565) data 0.000 (0.002) loss 0.9658 (1.1386) acc 78.1250 (71.4153) lr 1.6845e-03 eta 15:18:32
+epoch [15/50] batch [780/1000] time 1.542 (1.565) data 0.000 (0.002) loss 0.8296 (1.1372) acc 81.2500 (71.4423) lr 1.6845e-03 eta 15:18:23
+epoch [15/50] batch [785/1000] time 1.545 (1.564) data 0.000 (0.002) loss 1.2559 (1.1381) acc 71.8750 (71.4490) lr 1.6845e-03 eta 15:18:11
+epoch [15/50] batch [790/1000] time 1.567 (1.564) data 0.001 (0.002) loss 1.6797 (1.1381) acc 65.6250 (71.4557) lr 1.6845e-03 eta 15:18:02
+epoch [15/50] batch [795/1000] time 1.584 (1.565) data 0.000 (0.002) loss 0.6582 (1.1372) acc 81.2500 (71.4426) lr 1.6845e-03 eta 15:18:02
+epoch [15/50] batch [800/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0986 (1.1372) acc 71.8750 (71.4609) lr 1.6845e-03 eta 15:17:54
+epoch [15/50] batch [805/1000] time 1.545 (1.565) data 0.000 (0.002) loss 0.8340 (1.1356) acc 81.2500 (71.5179) lr 1.6845e-03 eta 15:17:45
+epoch [15/50] batch [810/1000] time 1.559 (1.565) data 0.000 (0.002) loss 1.2363 (1.1348) acc 75.0000 (71.5586) lr 1.6845e-03 eta 15:17:37
+epoch [15/50] batch [815/1000] time 1.548 (1.564) data 0.000 (0.002) loss 1.1094 (1.1347) acc 75.0000 (71.5567) lr 1.6845e-03 eta 15:17:26
+epoch [15/50] batch [820/1000] time 1.586 (1.565) data 0.000 (0.002) loss 1.4727 (1.1362) acc 68.7500 (71.5396) lr 1.6845e-03 eta 15:17:20
+epoch [15/50] batch [825/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.7290 (1.1349) acc 81.2500 (71.5758) lr 1.6845e-03 eta 15:17:13
+epoch [15/50] batch [830/1000] time 1.569 (1.565) data 0.000 (0.002) loss 0.9888 (1.1349) acc 78.1250 (71.5663) lr 1.6845e-03 eta 15:17:06
+epoch [15/50] batch [835/1000] time 1.571 (1.565) data 0.000 (0.002) loss 0.8950 (1.1349) acc 78.1250 (71.5606) lr 1.6845e-03 eta 15:16:57
+epoch [15/50] batch [840/1000] time 1.548 (1.565) data 0.001 (0.002) loss 1.5615 (1.1358) acc 65.6250 (71.5327) lr 1.6845e-03 eta 15:16:55
+epoch [15/50] batch [845/1000] time 1.584 (1.565) data 0.000 (0.002) loss 1.2861 (1.1374) acc 65.6250 (71.4904) lr 1.6845e-03 eta 15:16:46
+epoch [15/50] batch [850/1000] time 1.537 (1.565) data 0.000 (0.002) loss 1.1846 (1.1367) acc 75.0000 (71.5147) lr 1.6845e-03 eta 15:16:37
+epoch [15/50] batch [855/1000] time 1.553 (1.565) data 0.000 (0.001) loss 1.4229 (1.1376) acc 59.3750 (71.5022) lr 1.6845e-03 eta 15:16:27
+epoch [15/50] batch [860/1000] time 1.562 (1.565) data 0.000 (0.001) loss 0.9321 (1.1377) acc 75.0000 (71.5116) lr 1.6845e-03 eta 15:16:19
+epoch [15/50] batch [865/1000] time 1.567 (1.565) data 0.000 (0.001) loss 1.0234 (1.1370) acc 75.0000 (71.5282) lr 1.6845e-03 eta 15:16:13
+epoch [15/50] batch [870/1000] time 1.566 (1.565) data 0.000 (0.001) loss 0.9253 (1.1366) acc 81.2500 (71.5445) lr 1.6845e-03 eta 15:16:05
+epoch [15/50] batch [875/1000] time 1.538 (1.565) data 0.000 (0.001) loss 0.9326 (1.1364) acc 78.1250 (71.5571) lr 1.6845e-03 eta 15:15:54
+epoch [15/50] batch [880/1000] time 1.700 (1.565) data 0.000 (0.001) loss 0.9385 (1.1362) acc 78.1250 (71.5447) lr 1.6845e-03 eta 15:15:51
+epoch [15/50] batch [885/1000] time 1.571 (1.565) data 0.000 (0.001) loss 1.5811 (1.1372) acc 53.1250 (71.5290) lr 1.6845e-03 eta 15:15:44
+epoch [15/50] batch [890/1000] time 1.585 (1.565) data 0.000 (0.001) loss 1.3350 (1.1380) acc 68.7500 (71.5028) lr 1.6845e-03 eta 15:15:38
+epoch [15/50] batch [895/1000] time 1.579 (1.565) data 0.000 (0.001) loss 1.2520 (1.1380) acc 65.6250 (71.4944) lr 1.6845e-03 eta 15:15:32
+epoch [15/50] batch [900/1000] time 1.582 (1.565) data 0.001 (0.001) loss 1.3906 (1.1377) acc 59.3750 (71.4722) lr 1.6845e-03 eta 15:15:26
+epoch [15/50] batch [905/1000] time 1.559 (1.565) data 0.000 (0.001) loss 1.7988 (1.1388) acc 65.6250 (71.4399) lr 1.6845e-03 eta 15:15:16
+epoch [15/50] batch [910/1000] time 1.553 (1.565) data 0.000 (0.001) loss 0.8955 (1.1390) acc 75.0000 (71.4148) lr 1.6845e-03 eta 15:15:06
+epoch [15/50] batch [915/1000] time 1.555 (1.565) data 0.000 (0.001) loss 1.1357 (1.1387) acc 75.0000 (71.4276) lr 1.6845e-03 eta 15:14:54
+epoch [15/50] batch [920/1000] time 1.567 (1.565) data 0.000 (0.001) loss 1.3018 (1.1378) acc 71.8750 (71.4368) lr 1.6845e-03 eta 15:14:46
+epoch [15/50] batch [925/1000] time 1.594 (1.565) data 0.001 (0.001) loss 1.1689 (1.1380) acc 71.8750 (71.4324) lr 1.6845e-03 eta 15:14:39
+epoch [15/50] batch [930/1000] time 1.537 (1.565) data 0.001 (0.001) loss 1.8662 (1.1403) acc 65.6250 (71.4147) lr 1.6845e-03 eta 15:14:29
+epoch [15/50] batch [935/1000] time 1.563 (1.565) data 0.000 (0.001) loss 1.1943 (1.1393) acc 78.1250 (71.4439) lr 1.6845e-03 eta 15:14:21
+epoch [15/50] batch [940/1000] time 1.575 (1.565) data 0.000 (0.001) loss 1.3604 (1.1390) acc 62.5000 (71.4461) lr 1.6845e-03 eta 15:14:11
+epoch [15/50] batch [945/1000] time 1.537 (1.565) data 0.000 (0.001) loss 1.2910 (1.1398) acc 68.7500 (71.4220) lr 1.6845e-03 eta 15:14:08
+epoch [15/50] batch [950/1000] time 1.542 (1.565) data 0.000 (0.001) loss 1.1182 (1.1409) acc 78.1250 (71.4211) lr 1.6845e-03 eta 15:13:59
+epoch [15/50] batch [955/1000] time 1.575 (1.565) data 0.000 (0.001) loss 1.3877 (1.1399) acc 75.0000 (71.4692) lr 1.6845e-03 eta 15:13:50
+epoch [15/50] batch [960/1000] time 1.569 (1.565) data 0.000 (0.001) loss 1.5527 (1.1402) acc 62.5000 (71.4616) lr 1.6845e-03 eta 15:13:42
+epoch [15/50] batch [965/1000] time 1.550 (1.565) data 0.000 (0.001) loss 0.7915 (1.1387) acc 81.2500 (71.5026) lr 1.6845e-03 eta 15:13:33
+epoch [15/50] batch [970/1000] time 1.548 (1.564) data 0.001 (0.001) loss 1.0410 (1.1388) acc 65.6250 (71.4884) lr 1.6845e-03 eta 15:13:23
+epoch [15/50] batch [975/1000] time 1.565 (1.564) data 0.000 (0.001) loss 1.4219 (1.1400) acc 75.0000 (71.4615) lr 1.6845e-03 eta 15:13:14
+epoch [15/50] batch [980/1000] time 1.562 (1.564) data 0.000 (0.001) loss 0.7485 (1.1392) acc 75.0000 (71.4509) lr 1.6845e-03 eta 15:13:06
+epoch [15/50] batch [985/1000] time 1.558 (1.564) data 0.001 (0.001) loss 1.0664 (1.1390) acc 65.6250 (71.4435) lr 1.6845e-03 eta 15:12:57
+epoch [15/50] batch [990/1000] time 1.569 (1.565) data 0.000 (0.001) loss 0.7446 (1.1392) acc 78.1250 (71.4583) lr 1.6845e-03 eta 15:12:55
+epoch [15/50] batch [995/1000] time 1.560 (1.565) data 0.000 (0.001) loss 1.2998 (1.1400) acc 75.0000 (71.4447) lr 1.6845e-03 eta 15:12:46
+epoch [15/50] batch [1000/1000] time 1.557 (1.565) data 0.000 (0.001) loss 1.0596 (1.1406) acc 78.1250 (71.4344) lr 1.6374e-03 eta 15:12:39
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,175
+* accuracy: 78.3%
+* error: 21.7%
+* macro_f1: 77.8%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [16/50] batch [5/1000] time 1.545 (1.701) data 0.000 (0.199) loss 1.0889 (0.9160) acc 71.8750 (73.1250) lr 1.6374e-03 eta 16:32:00
+epoch [16/50] batch [10/1000] time 1.562 (1.631) data 0.001 (0.100) loss 0.9609 (1.0070) acc 68.7500 (71.2500) lr 1.6374e-03 eta 15:50:54
+epoch [16/50] batch [15/1000] time 1.582 (1.608) data 0.001 (0.067) loss 0.6626 (1.0417) acc 84.3750 (71.8750) lr 1.6374e-03 eta 15:37:18
+epoch [16/50] batch [20/1000] time 1.586 (1.597) data 0.000 (0.050) loss 0.8140 (1.0689) acc 75.0000 (70.9375) lr 1.6374e-03 eta 15:30:48
+epoch [16/50] batch [25/1000] time 1.575 (1.601) data 0.000 (0.040) loss 1.1895 (1.0754) acc 71.8750 (70.8750) lr 1.6374e-03 eta 15:33:25
+epoch [16/50] batch [30/1000] time 1.555 (1.595) data 0.000 (0.034) loss 1.2500 (1.0725) acc 68.7500 (71.6667) lr 1.6374e-03 eta 15:29:24
+epoch [16/50] batch [35/1000] time 1.564 (1.590) data 0.001 (0.029) loss 1.3213 (1.1039) acc 62.5000 (71.1607) lr 1.6374e-03 eta 15:26:24
+epoch [16/50] batch [40/1000] time 1.565 (1.586) data 0.000 (0.025) loss 0.9673 (1.1188) acc 81.2500 (71.1719) lr 1.6374e-03 eta 15:24:04
+epoch [16/50] batch [45/1000] time 1.572 (1.584) data 0.001 (0.023) loss 1.0264 (1.0977) acc 75.0000 (71.6667) lr 1.6374e-03 eta 15:22:44
+epoch [16/50] batch [50/1000] time 1.570 (1.581) data 0.000 (0.020) loss 0.9644 (1.1100) acc 75.0000 (71.3750) lr 1.6374e-03 eta 15:20:45
+epoch [16/50] batch [55/1000] time 1.556 (1.579) data 0.000 (0.019) loss 1.3105 (1.1152) acc 68.7500 (71.5341) lr 1.6374e-03 eta 15:19:46
+epoch [16/50] batch [60/1000] time 1.546 (1.578) data 0.001 (0.017) loss 0.9746 (1.1044) acc 68.7500 (71.5625) lr 1.6374e-03 eta 15:19:07
+epoch [16/50] batch [65/1000] time 1.558 (1.577) data 0.000 (0.016) loss 1.6670 (1.1235) acc 56.2500 (71.0577) lr 1.6374e-03 eta 15:18:26
+epoch [16/50] batch [70/1000] time 1.570 (1.576) data 0.000 (0.015) loss 1.5420 (1.1223) acc 59.3750 (71.4732) lr 1.6374e-03 eta 15:17:33
+epoch [16/50] batch [75/1000] time 1.566 (1.576) data 0.000 (0.014) loss 1.0674 (1.1334) acc 81.2500 (71.4583) lr 1.6374e-03 eta 15:17:07
+epoch [16/50] batch [80/1000] time 1.592 (1.575) data 0.001 (0.013) loss 1.4092 (1.1351) acc 68.7500 (71.4844) lr 1.6374e-03 eta 15:16:34
+epoch [16/50] batch [85/1000] time 1.713 (1.576) data 0.001 (0.012) loss 1.8389 (1.1444) acc 59.3750 (71.4706) lr 1.6374e-03 eta 15:17:07
+epoch [16/50] batch [90/1000] time 1.562 (1.575) data 0.001 (0.012) loss 1.0605 (1.1382) acc 81.2500 (71.7361) lr 1.6374e-03 eta 15:16:22
+epoch [16/50] batch [95/1000] time 1.574 (1.574) data 0.000 (0.011) loss 1.1367 (1.1321) acc 75.0000 (71.9737) lr 1.6374e-03 eta 15:15:43
+epoch [16/50] batch [100/1000] time 1.548 (1.573) data 0.000 (0.010) loss 1.0859 (1.1218) acc 75.0000 (72.1562) lr 1.6374e-03 eta 15:14:59
+epoch [16/50] batch [105/1000] time 1.564 (1.572) data 0.001 (0.010) loss 0.5112 (1.1177) acc 81.2500 (72.2321) lr 1.6374e-03 eta 15:14:16
+epoch [16/50] batch [110/1000] time 1.553 (1.572) data 0.001 (0.010) loss 1.1963 (1.1101) acc 68.7500 (72.1875) lr 1.6374e-03 eta 15:13:59
+epoch [16/50] batch [115/1000] time 1.566 (1.571) data 0.000 (0.009) loss 0.9521 (1.1215) acc 62.5000 (71.9837) lr 1.6374e-03 eta 15:13:28
+epoch [16/50] batch [120/1000] time 1.565 (1.571) data 0.000 (0.009) loss 1.4346 (1.1230) acc 68.7500 (72.0052) lr 1.6374e-03 eta 15:13:01
+epoch [16/50] batch [125/1000] time 1.541 (1.570) data 0.001 (0.008) loss 1.0684 (1.1212) acc 78.1250 (72.0000) lr 1.6374e-03 eta 15:12:26
+epoch [16/50] batch [130/1000] time 1.727 (1.571) data 0.000 (0.008) loss 0.7422 (1.1168) acc 81.2500 (72.1875) lr 1.6374e-03 eta 15:12:55
+epoch [16/50] batch [135/1000] time 1.558 (1.571) data 0.000 (0.008) loss 1.5439 (1.1214) acc 62.5000 (71.9444) lr 1.6374e-03 eta 15:12:40
+epoch [16/50] batch [140/1000] time 1.559 (1.571) data 0.000 (0.008) loss 1.1982 (1.1298) acc 75.0000 (71.8304) lr 1.6374e-03 eta 15:12:30
+epoch [16/50] batch [145/1000] time 1.537 (1.570) data 0.000 (0.007) loss 1.7178 (1.1244) acc 59.3750 (71.9612) lr 1.6374e-03 eta 15:12:02
+epoch [16/50] batch [150/1000] time 1.553 (1.570) data 0.000 (0.007) loss 1.0645 (1.1261) acc 68.7500 (72.0000) lr 1.6374e-03 eta 15:11:50
+epoch [16/50] batch [155/1000] time 1.576 (1.570) data 0.000 (0.007) loss 1.2549 (1.1290) acc 71.8750 (71.9758) lr 1.6374e-03 eta 15:11:36
+epoch [16/50] batch [160/1000] time 1.565 (1.569) data 0.000 (0.007) loss 0.9790 (1.1314) acc 68.7500 (71.7383) lr 1.6374e-03 eta 15:11:13
+epoch [16/50] batch [165/1000] time 1.547 (1.569) data 0.000 (0.006) loss 1.1396 (1.1308) acc 71.8750 (71.7803) lr 1.6374e-03 eta 15:10:51
+epoch [16/50] batch [170/1000] time 1.572 (1.568) data 0.001 (0.006) loss 1.2168 (1.1279) acc 68.7500 (71.8015) lr 1.6374e-03 eta 15:10:28
+epoch [16/50] batch [175/1000] time 1.570 (1.570) data 0.000 (0.006) loss 0.7368 (1.1300) acc 87.5000 (71.8929) lr 1.6374e-03 eta 15:11:03
+epoch [16/50] batch [180/1000] time 1.555 (1.569) data 0.000 (0.006) loss 0.8926 (1.1345) acc 71.8750 (71.8924) lr 1.6374e-03 eta 15:10:46
+epoch [16/50] batch [185/1000] time 1.567 (1.569) data 0.000 (0.006) loss 1.0234 (1.1313) acc 71.8750 (71.9764) lr 1.6374e-03 eta 15:10:31
+epoch [16/50] batch [190/1000] time 1.560 (1.569) data 0.000 (0.006) loss 1.5967 (1.1333) acc 71.8750 (71.9079) lr 1.6374e-03 eta 15:10:17
+epoch [16/50] batch [195/1000] time 1.567 (1.569) data 0.001 (0.006) loss 1.0801 (1.1345) acc 78.1250 (71.7949) lr 1.6374e-03 eta 15:10:12
+epoch [16/50] batch [200/1000] time 1.564 (1.569) data 0.001 (0.005) loss 1.1592 (1.1356) acc 71.8750 (71.7031) lr 1.6374e-03 eta 15:09:54
+epoch [16/50] batch [205/1000] time 1.573 (1.569) data 0.000 (0.005) loss 1.3438 (1.1319) acc 62.5000 (71.6463) lr 1.6374e-03 eta 15:09:44
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+epoch [16/50] batch [770/1000] time 1.567 (1.564) data 0.001 (0.002) loss 1.2520 (1.1398) acc 65.6250 (71.2581) lr 1.6374e-03 eta 14:52:19
+epoch [16/50] batch [775/1000] time 1.561 (1.564) data 0.001 (0.002) loss 1.1953 (1.1413) acc 71.8750 (71.2097) lr 1.6374e-03 eta 14:52:11
+epoch [16/50] batch [780/1000] time 1.549 (1.564) data 0.001 (0.002) loss 1.4238 (1.1416) acc 62.5000 (71.1899) lr 1.6374e-03 eta 14:52:06
+epoch [16/50] batch [785/1000] time 1.558 (1.564) data 0.000 (0.002) loss 1.4531 (1.1406) acc 71.8750 (71.2221) lr 1.6374e-03 eta 14:51:55
+epoch [16/50] batch [790/1000] time 1.551 (1.564) data 0.000 (0.002) loss 1.2832 (1.1398) acc 68.7500 (71.2342) lr 1.6374e-03 eta 14:51:47
+epoch [16/50] batch [795/1000] time 1.580 (1.564) data 0.000 (0.002) loss 0.6450 (1.1382) acc 81.2500 (71.2775) lr 1.6374e-03 eta 14:51:38
+epoch [16/50] batch [800/1000] time 1.567 (1.564) data 0.001 (0.002) loss 1.6709 (1.1379) acc 62.5000 (71.3008) lr 1.6374e-03 eta 14:51:31
+epoch [16/50] batch [805/1000] time 1.561 (1.564) data 0.001 (0.002) loss 1.2549 (1.1382) acc 62.5000 (71.2655) lr 1.6374e-03 eta 14:51:24
+epoch [16/50] batch [810/1000] time 1.545 (1.564) data 0.000 (0.002) loss 1.6025 (1.1399) acc 65.6250 (71.2346) lr 1.6374e-03 eta 14:51:16
+epoch [16/50] batch [815/1000] time 1.549 (1.564) data 0.000 (0.002) loss 1.1152 (1.1405) acc 71.8750 (71.2385) lr 1.6374e-03 eta 14:51:05
+epoch [16/50] batch [820/1000] time 1.531 (1.564) data 0.001 (0.002) loss 0.7783 (1.1394) acc 71.8750 (71.2538) lr 1.6374e-03 eta 14:50:53
+epoch [16/50] batch [825/1000] time 1.542 (1.564) data 0.001 (0.002) loss 1.1045 (1.1386) acc 75.0000 (71.2652) lr 1.6374e-03 eta 14:50:44
+epoch [16/50] batch [830/1000] time 1.579 (1.564) data 0.000 (0.002) loss 0.9854 (1.1379) acc 71.8750 (71.2613) lr 1.6374e-03 eta 14:50:35
+epoch [16/50] batch [835/1000] time 1.532 (1.564) data 0.000 (0.002) loss 1.2227 (1.1401) acc 75.0000 (71.2275) lr 1.6374e-03 eta 14:50:24
+epoch [16/50] batch [840/1000] time 1.726 (1.564) data 0.000 (0.002) loss 1.3496 (1.1394) acc 75.0000 (71.2537) lr 1.6374e-03 eta 14:50:22
+epoch [16/50] batch [845/1000] time 1.575 (1.564) data 0.000 (0.002) loss 1.5762 (1.1395) acc 68.7500 (71.2574) lr 1.6374e-03 eta 14:50:14
+epoch [16/50] batch [850/1000] time 1.562 (1.564) data 0.000 (0.002) loss 1.1445 (1.1395) acc 65.6250 (71.2574) lr 1.6374e-03 eta 14:50:07
+epoch [16/50] batch [855/1000] time 1.565 (1.564) data 0.000 (0.002) loss 1.4688 (1.1398) acc 62.5000 (71.2573) lr 1.6374e-03 eta 14:49:59
+epoch [16/50] batch [860/1000] time 1.558 (1.564) data 0.000 (0.002) loss 1.2490 (1.1406) acc 71.8750 (71.2391) lr 1.6374e-03 eta 14:49:50
+epoch [16/50] batch [865/1000] time 1.564 (1.564) data 0.000 (0.002) loss 1.2451 (1.1414) acc 62.5000 (71.2103) lr 1.6374e-03 eta 14:49:43
+epoch [16/50] batch [870/1000] time 1.554 (1.564) data 0.001 (0.002) loss 0.9604 (1.1409) acc 71.8750 (71.2284) lr 1.6374e-03 eta 14:49:33
+epoch [16/50] batch [875/1000] time 1.541 (1.564) data 0.001 (0.002) loss 0.9517 (1.1402) acc 78.1250 (71.2393) lr 1.6374e-03 eta 14:49:24
+epoch [16/50] batch [880/1000] time 1.541 (1.564) data 0.001 (0.002) loss 1.1045 (1.1404) acc 75.0000 (71.2322) lr 1.6374e-03 eta 14:49:16
+epoch [16/50] batch [885/1000] time 1.718 (1.564) data 0.000 (0.002) loss 0.8022 (1.1392) acc 78.1250 (71.2500) lr 1.6374e-03 eta 14:49:16
+epoch [16/50] batch [890/1000] time 1.547 (1.564) data 0.000 (0.002) loss 1.3584 (1.1404) acc 71.8750 (71.2395) lr 1.6374e-03 eta 14:49:07
+epoch [16/50] batch [895/1000] time 1.558 (1.564) data 0.000 (0.002) loss 0.9268 (1.1398) acc 75.0000 (71.2535) lr 1.6374e-03 eta 14:48:59
+epoch [16/50] batch [900/1000] time 1.558 (1.564) data 0.000 (0.002) loss 1.4268 (1.1399) acc 65.6250 (71.2604) lr 1.6374e-03 eta 14:48:50
+epoch [16/50] batch [905/1000] time 1.533 (1.564) data 0.000 (0.002) loss 1.3418 (1.1399) acc 68.7500 (71.2638) lr 1.6374e-03 eta 14:48:38
+epoch [16/50] batch [910/1000] time 1.554 (1.564) data 0.000 (0.002) loss 1.2754 (1.1407) acc 68.7500 (71.2466) lr 1.6374e-03 eta 14:48:29
+epoch [16/50] batch [915/1000] time 1.572 (1.564) data 0.000 (0.002) loss 1.8115 (1.1413) acc 65.6250 (71.2295) lr 1.6374e-03 eta 14:48:19
+epoch [16/50] batch [920/1000] time 1.566 (1.564) data 0.000 (0.002) loss 0.9678 (1.1421) acc 84.3750 (71.2296) lr 1.6374e-03 eta 14:48:11
+epoch [16/50] batch [925/1000] time 1.583 (1.564) data 0.000 (0.002) loss 0.7817 (1.1424) acc 84.3750 (71.2162) lr 1.6374e-03 eta 14:48:02
+epoch [16/50] batch [930/1000] time 1.557 (1.564) data 0.000 (0.002) loss 1.6650 (1.1427) acc 62.5000 (71.2097) lr 1.6374e-03 eta 14:48:00
+epoch [16/50] batch [935/1000] time 1.575 (1.564) data 0.001 (0.002) loss 1.1104 (1.1424) acc 78.1250 (71.2099) lr 1.6374e-03 eta 14:47:53
+epoch [16/50] batch [940/1000] time 1.553 (1.564) data 0.001 (0.002) loss 1.4893 (1.1424) acc 71.8750 (71.2201) lr 1.6374e-03 eta 14:47:44
+epoch [16/50] batch [945/1000] time 1.564 (1.564) data 0.001 (0.002) loss 1.1211 (1.1430) acc 71.8750 (71.2169) lr 1.6374e-03 eta 14:47:36
+epoch [16/50] batch [950/1000] time 1.569 (1.564) data 0.000 (0.002) loss 0.6655 (1.1421) acc 84.3750 (71.2664) lr 1.6374e-03 eta 14:47:30
+epoch [16/50] batch [955/1000] time 1.572 (1.564) data 0.000 (0.002) loss 1.3398 (1.1415) acc 68.7500 (71.2696) lr 1.6374e-03 eta 14:47:23
+epoch [16/50] batch [960/1000] time 1.573 (1.564) data 0.000 (0.002) loss 1.1270 (1.1417) acc 78.1250 (71.2728) lr 1.6374e-03 eta 14:47:16
+epoch [16/50] batch [965/1000] time 1.550 (1.564) data 0.001 (0.002) loss 0.8608 (1.1410) acc 81.2500 (71.3018) lr 1.6374e-03 eta 14:47:07
+epoch [16/50] batch [970/1000] time 1.556 (1.564) data 0.001 (0.001) loss 1.5146 (1.1425) acc 65.6250 (71.2951) lr 1.6374e-03 eta 14:46:59
+epoch [16/50] batch [975/1000] time 1.559 (1.564) data 0.001 (0.001) loss 1.2725 (1.1418) acc 62.5000 (71.3045) lr 1.6374e-03 eta 14:46:51
+epoch [16/50] batch [980/1000] time 1.564 (1.564) data 0.001 (0.001) loss 1.1982 (1.1415) acc 71.8750 (71.3170) lr 1.6374e-03 eta 14:46:42
+epoch [16/50] batch [985/1000] time 1.560 (1.564) data 0.001 (0.001) loss 0.8921 (1.1417) acc 75.0000 (71.3166) lr 1.6374e-03 eta 14:46:34
+epoch [16/50] batch [990/1000] time 1.542 (1.564) data 0.000 (0.001) loss 1.4121 (1.1425) acc 75.0000 (71.3005) lr 1.6374e-03 eta 14:46:24
+epoch [16/50] batch [995/1000] time 1.553 (1.564) data 0.000 (0.001) loss 1.4795 (1.1430) acc 68.7500 (71.2908) lr 1.6374e-03 eta 14:46:20
+epoch [16/50] batch [1000/1000] time 1.563 (1.564) data 0.000 (0.001) loss 1.0127 (1.1438) acc 75.0000 (71.2719) lr 1.5878e-03 eta 14:46:09
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,123
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.8%
+epoch [17/50] batch [5/1000] time 1.574 (1.689) data 0.000 (0.177) loss 0.9268 (1.0384) acc 75.0000 (72.5000) lr 1.5878e-03 eta 15:56:53
+epoch [17/50] batch [10/1000] time 1.552 (1.622) data 0.000 (0.089) loss 1.2764 (1.0252) acc 75.0000 (73.4375) lr 1.5878e-03 eta 15:18:39
+epoch [17/50] batch [15/1000] time 1.585 (1.624) data 0.000 (0.059) loss 0.9077 (0.9819) acc 75.0000 (73.9583) lr 1.5878e-03 eta 15:19:39
+epoch [17/50] batch [20/1000] time 1.568 (1.608) data 0.000 (0.045) loss 1.1504 (1.0757) acc 81.2500 (73.1250) lr 1.5878e-03 eta 15:10:25
+epoch [17/50] batch [25/1000] time 1.565 (1.598) data 0.000 (0.036) loss 1.0566 (1.0434) acc 78.1250 (74.2500) lr 1.5878e-03 eta 15:05:09
+epoch [17/50] batch [30/1000] time 1.549 (1.592) data 0.000 (0.030) loss 0.4631 (1.0458) acc 84.3750 (74.2708) lr 1.5878e-03 eta 15:01:30
+epoch [17/50] batch [35/1000] time 1.564 (1.589) data 0.000 (0.026) loss 1.5283 (1.0642) acc 59.3750 (73.0357) lr 1.5878e-03 eta 14:59:21
+epoch [17/50] batch [40/1000] time 1.575 (1.586) data 0.000 (0.022) loss 1.1328 (1.0771) acc 68.7500 (72.5000) lr 1.5878e-03 eta 14:57:44
+epoch [17/50] batch [45/1000] time 1.542 (1.582) data 0.000 (0.020) loss 1.2031 (1.0704) acc 68.7500 (72.4306) lr 1.5878e-03 eta 14:55:10
+epoch [17/50] batch [50/1000] time 1.582 (1.581) data 0.000 (0.018) loss 1.6738 (1.0704) acc 68.7500 (72.2500) lr 1.5878e-03 eta 14:54:29
+epoch [17/50] batch [55/1000] time 1.544 (1.579) data 0.000 (0.016) loss 1.3701 (1.0907) acc 62.5000 (71.7614) lr 1.5878e-03 eta 14:53:14
+epoch [17/50] batch [60/1000] time 1.569 (1.578) data 0.000 (0.015) loss 0.6768 (1.0852) acc 78.1250 (71.7188) lr 1.5878e-03 eta 14:52:33
+epoch [17/50] batch [65/1000] time 1.564 (1.576) data 0.000 (0.014) loss 0.4988 (1.0874) acc 87.5000 (71.8750) lr 1.5878e-03 eta 14:51:11
+epoch [17/50] batch [70/1000] time 1.556 (1.574) data 0.000 (0.013) loss 0.8862 (1.0764) acc 81.2500 (72.5446) lr 1.5878e-03 eta 14:50:18
+epoch [17/50] batch [75/1000] time 1.544 (1.574) data 0.000 (0.012) loss 1.4561 (1.0719) acc 56.2500 (72.5000) lr 1.5878e-03 eta 14:49:52
+epoch [17/50] batch [80/1000] time 1.556 (1.573) data 0.000 (0.011) loss 0.4932 (1.0730) acc 84.3750 (72.5391) lr 1.5878e-03 eta 14:49:18
+epoch [17/50] batch [85/1000] time 1.556 (1.572) data 0.001 (0.011) loss 1.3438 (1.0693) acc 65.6250 (72.5368) lr 1.5878e-03 eta 14:48:37
+epoch [17/50] batch [90/1000] time 1.558 (1.571) data 0.000 (0.010) loss 1.0498 (1.0674) acc 75.0000 (72.5000) lr 1.5878e-03 eta 14:48:04
+epoch [17/50] batch [95/1000] time 1.568 (1.571) data 0.000 (0.010) loss 0.9526 (1.0663) acc 81.2500 (72.5329) lr 1.5878e-03 eta 14:47:34
+epoch [17/50] batch [100/1000] time 1.565 (1.571) data 0.000 (0.009) loss 1.2256 (1.0820) acc 75.0000 (72.1875) lr 1.5878e-03 eta 14:47:23
+epoch [17/50] batch [105/1000] time 1.589 (1.571) data 0.000 (0.009) loss 1.5127 (1.0883) acc 65.6250 (72.1726) lr 1.5878e-03 eta 14:47:12
+epoch [17/50] batch [110/1000] time 1.536 (1.570) data 0.000 (0.008) loss 0.8320 (1.0782) acc 78.1250 (72.3580) lr 1.5878e-03 eta 14:46:35
+epoch [17/50] batch [115/1000] time 1.540 (1.569) data 0.000 (0.008) loss 1.4443 (1.0775) acc 62.5000 (72.3913) lr 1.5878e-03 eta 14:46:05
+epoch [17/50] batch [120/1000] time 1.542 (1.569) data 0.000 (0.008) loss 1.2246 (1.0753) acc 71.8750 (72.4740) lr 1.5878e-03 eta 14:46:13
+epoch [17/50] batch [125/1000] time 1.597 (1.570) data 0.001 (0.007) loss 1.0352 (1.0732) acc 65.6250 (72.4750) lr 1.5878e-03 eta 14:46:09
+epoch [17/50] batch [130/1000] time 1.574 (1.570) data 0.000 (0.007) loss 0.9062 (1.0761) acc 65.6250 (72.4760) lr 1.5878e-03 eta 14:46:00
+epoch [17/50] batch [135/1000] time 1.573 (1.569) data 0.000 (0.007) loss 0.7695 (1.0699) acc 75.0000 (72.5694) lr 1.5878e-03 eta 14:45:42
+epoch [17/50] batch [140/1000] time 1.551 (1.569) data 0.000 (0.007) loss 0.4324 (1.0660) acc 87.5000 (72.7679) lr 1.5878e-03 eta 14:45:33
+epoch [17/50] batch [145/1000] time 1.535 (1.569) data 0.000 (0.006) loss 1.1309 (1.0652) acc 78.1250 (72.8664) lr 1.5878e-03 eta 14:45:09
+epoch [17/50] batch [150/1000] time 1.534 (1.568) data 0.000 (0.006) loss 0.9736 (1.0672) acc 75.0000 (72.8333) lr 1.5878e-03 eta 14:44:44
+epoch [17/50] batch [155/1000] time 1.572 (1.568) data 0.001 (0.006) loss 1.0059 (1.0654) acc 68.7500 (72.7823) lr 1.5878e-03 eta 14:44:24
+epoch [17/50] batch [160/1000] time 1.574 (1.568) data 0.000 (0.006) loss 0.7524 (1.0610) acc 81.2500 (72.8906) lr 1.5878e-03 eta 14:44:11
+epoch [17/50] batch [165/1000] time 1.557 (1.568) data 0.000 (0.006) loss 1.6416 (1.0663) acc 68.7500 (72.7273) lr 1.5878e-03 eta 14:44:23
+epoch [17/50] batch [170/1000] time 1.535 (1.568) data 0.000 (0.006) loss 0.9360 (1.0775) acc 68.7500 (72.5184) lr 1.5878e-03 eta 14:44:13
+epoch [17/50] batch [175/1000] time 1.560 (1.568) data 0.000 (0.005) loss 0.7954 (1.0705) acc 81.2500 (72.6250) lr 1.5878e-03 eta 14:43:43
+epoch [17/50] batch [180/1000] time 1.589 (1.567) data 0.000 (0.005) loss 0.7168 (1.0691) acc 81.2500 (72.6562) lr 1.5878e-03 eta 14:43:27
+epoch [17/50] batch [185/1000] time 1.551 (1.567) data 0.000 (0.005) loss 0.7109 (1.0663) acc 84.3750 (72.7703) lr 1.5878e-03 eta 14:43:11
+epoch [17/50] batch [190/1000] time 1.588 (1.568) data 0.000 (0.005) loss 1.0967 (1.0723) acc 71.8750 (72.6480) lr 1.5878e-03 eta 14:43:18
+epoch [17/50] batch [195/1000] time 1.557 (1.567) data 0.000 (0.005) loss 1.0156 (1.0739) acc 68.7500 (72.5801) lr 1.5878e-03 eta 14:43:01
+epoch [17/50] batch [200/1000] time 1.554 (1.567) data 0.000 (0.005) loss 1.1416 (1.0781) acc 68.7500 (72.5000) lr 1.5878e-03 eta 14:42:46
+epoch [17/50] batch [205/1000] time 1.698 (1.568) data 0.000 (0.005) loss 1.3838 (1.0823) acc 56.2500 (72.3476) lr 1.5878e-03 eta 14:43:07
+epoch [17/50] batch [210/1000] time 1.605 (1.568) data 0.000 (0.005) loss 1.3633 (1.0857) acc 68.7500 (72.2470) lr 1.5878e-03 eta 14:43:11
+epoch [17/50] batch [215/1000] time 1.528 (1.568) data 0.000 (0.004) loss 1.7168 (1.0885) acc 62.5000 (72.2093) lr 1.5878e-03 eta 14:42:56
+epoch [17/50] batch [220/1000] time 1.563 (1.568) data 0.000 (0.004) loss 1.0439 (1.0906) acc 59.3750 (72.1449) lr 1.5878e-03 eta 14:42:41
+epoch [17/50] batch [225/1000] time 1.542 (1.567) data 0.001 (0.004) loss 1.5713 (1.0952) acc 62.5000 (72.0278) lr 1.5878e-03 eta 14:42:14
+epoch [17/50] batch [230/1000] time 1.552 (1.567) data 0.000 (0.004) loss 0.8750 (1.0920) acc 75.0000 (72.0516) lr 1.5878e-03 eta 14:41:54
+epoch [17/50] batch [235/1000] time 1.546 (1.567) data 0.000 (0.004) loss 1.4580 (1.0957) acc 56.2500 (71.9681) lr 1.5878e-03 eta 14:41:42
+epoch [17/50] batch [240/1000] time 1.569 (1.567) data 0.000 (0.004) loss 0.5601 (1.0978) acc 87.5000 (71.9661) lr 1.5878e-03 eta 14:41:28
+epoch [17/50] batch [245/1000] time 1.549 (1.567) data 0.000 (0.004) loss 0.6577 (1.0947) acc 78.1250 (71.9643) lr 1.5878e-03 eta 14:41:18
+epoch [17/50] batch [250/1000] time 1.571 (1.566) data 0.000 (0.004) loss 1.1562 (1.0954) acc 68.7500 (71.9375) lr 1.5878e-03 eta 14:41:00
+epoch [17/50] batch [255/1000] time 1.543 (1.566) data 0.000 (0.004) loss 1.0361 (1.0965) acc 78.1250 (71.9363) lr 1.5878e-03 eta 14:40:42
+epoch [17/50] batch [260/1000] time 1.548 (1.566) data 0.001 (0.004) loss 0.9541 (1.0937) acc 71.8750 (71.9231) lr 1.5878e-03 eta 14:40:28
+epoch [17/50] batch [265/1000] time 1.561 (1.566) data 0.001 (0.004) loss 0.6807 (1.0962) acc 84.3750 (71.8986) lr 1.5878e-03 eta 14:40:20
+epoch [17/50] batch [270/1000] time 1.555 (1.566) data 0.000 (0.004) loss 0.8193 (1.0942) acc 75.0000 (71.8403) lr 1.5878e-03 eta 14:40:25
+epoch [17/50] batch [275/1000] time 1.553 (1.566) data 0.000 (0.004) loss 0.7627 (1.0947) acc 81.2500 (71.8636) lr 1.5878e-03 eta 14:40:18
+epoch [17/50] batch [280/1000] time 1.538 (1.566) data 0.001 (0.004) loss 1.5020 (1.0961) acc 68.7500 (71.8750) lr 1.5878e-03 eta 14:40:01
+epoch [17/50] batch [285/1000] time 1.557 (1.566) data 0.000 (0.003) loss 0.4050 (1.0925) acc 90.6250 (71.9627) lr 1.5878e-03 eta 14:39:48
+epoch [17/50] batch [290/1000] time 1.565 (1.566) data 0.000 (0.003) loss 1.4492 (1.0966) acc 56.2500 (71.8211) lr 1.5878e-03 eta 14:39:38
+epoch [17/50] batch [295/1000] time 1.550 (1.565) data 0.000 (0.003) loss 1.1162 (1.0977) acc 75.0000 (71.8114) lr 1.5878e-03 eta 14:39:24
+epoch [17/50] batch [300/1000] time 1.558 (1.566) data 0.000 (0.003) loss 0.7056 (1.0948) acc 87.5000 (71.8542) lr 1.5878e-03 eta 14:39:19
+epoch [17/50] batch [305/1000] time 1.567 (1.566) data 0.001 (0.003) loss 1.3330 (1.1002) acc 68.7500 (71.7520) lr 1.5878e-03 eta 14:39:12
+epoch [17/50] batch [310/1000] time 1.549 (1.565) data 0.000 (0.003) loss 0.4089 (1.0979) acc 90.6250 (71.8649) lr 1.5878e-03 eta 14:38:56
+epoch [17/50] batch [315/1000] time 1.594 (1.566) data 0.000 (0.003) loss 1.1914 (1.0948) acc 78.1250 (71.9345) lr 1.5878e-03 eta 14:39:04
+epoch [17/50] batch [320/1000] time 1.572 (1.566) data 0.001 (0.003) loss 1.0244 (1.0906) acc 68.7500 (72.0117) lr 1.5878e-03 eta 14:38:58
+epoch [17/50] batch [325/1000] time 1.557 (1.566) data 0.001 (0.003) loss 1.1484 (1.0893) acc 78.1250 (72.0481) lr 1.5878e-03 eta 14:38:46
+epoch [17/50] batch [330/1000] time 1.558 (1.566) data 0.000 (0.003) loss 1.2773 (1.0913) acc 71.8750 (71.9697) lr 1.5878e-03 eta 14:38:32
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+epoch [17/50] batch [880/1000] time 1.578 (1.564) data 0.000 (0.001) loss 1.1396 (1.1280) acc 71.8750 (71.4134) lr 1.5878e-03 eta 14:23:18
+epoch [17/50] batch [885/1000] time 1.533 (1.564) data 0.000 (0.001) loss 1.0645 (1.1266) acc 68.7500 (71.4301) lr 1.5878e-03 eta 14:23:06
+epoch [17/50] batch [890/1000] time 1.531 (1.564) data 0.001 (0.001) loss 1.0137 (1.1275) acc 78.1250 (71.4185) lr 1.5878e-03 eta 14:22:56
+epoch [17/50] batch [895/1000] time 1.553 (1.564) data 0.001 (0.001) loss 1.1338 (1.1277) acc 75.0000 (71.4106) lr 1.5878e-03 eta 14:22:46
+epoch [17/50] batch [900/1000] time 1.579 (1.564) data 0.000 (0.001) loss 1.7354 (1.1285) acc 62.5000 (71.4028) lr 1.5878e-03 eta 14:22:38
+epoch [17/50] batch [905/1000] time 1.562 (1.564) data 0.000 (0.001) loss 1.4150 (1.1301) acc 75.0000 (71.3950) lr 1.5878e-03 eta 14:22:29
+epoch [17/50] batch [910/1000] time 1.575 (1.564) data 0.000 (0.001) loss 1.5830 (1.1294) acc 53.1250 (71.3977) lr 1.5878e-03 eta 14:22:21
+epoch [17/50] batch [915/1000] time 1.569 (1.564) data 0.000 (0.001) loss 1.1074 (1.1305) acc 78.1250 (71.3832) lr 1.5878e-03 eta 14:22:13
+epoch [17/50] batch [920/1000] time 1.576 (1.564) data 0.000 (0.001) loss 1.3057 (1.1315) acc 68.7500 (71.3723) lr 1.5878e-03 eta 14:22:12
+epoch [17/50] batch [925/1000] time 1.566 (1.564) data 0.001 (0.001) loss 0.9629 (1.1302) acc 75.0000 (71.3818) lr 1.5878e-03 eta 14:22:06
+epoch [17/50] batch [930/1000] time 1.563 (1.564) data 0.000 (0.001) loss 0.8711 (1.1305) acc 68.7500 (71.3743) lr 1.5878e-03 eta 14:21:58
+epoch [17/50] batch [935/1000] time 1.541 (1.564) data 0.000 (0.001) loss 1.7490 (1.1310) acc 56.2500 (71.3670) lr 1.5878e-03 eta 14:21:48
+epoch [17/50] batch [940/1000] time 1.573 (1.564) data 0.000 (0.001) loss 0.8315 (1.1311) acc 84.3750 (71.3763) lr 1.5878e-03 eta 14:21:39
+epoch [17/50] batch [945/1000] time 1.554 (1.564) data 0.000 (0.001) loss 1.2666 (1.1313) acc 78.1250 (71.3790) lr 1.5878e-03 eta 14:21:30
+epoch [17/50] batch [950/1000] time 1.548 (1.564) data 0.000 (0.001) loss 1.3398 (1.1311) acc 68.7500 (71.3684) lr 1.5878e-03 eta 14:21:20
+epoch [17/50] batch [955/1000] time 1.537 (1.564) data 0.000 (0.001) loss 1.2188 (1.1307) acc 68.7500 (71.3907) lr 1.5878e-03 eta 14:21:10
+epoch [17/50] batch [960/1000] time 1.711 (1.564) data 0.000 (0.001) loss 0.9336 (1.1294) acc 75.0000 (71.3997) lr 1.5878e-03 eta 14:21:03
+epoch [17/50] batch [965/1000] time 1.556 (1.564) data 0.000 (0.001) loss 0.8032 (1.1288) acc 78.1250 (71.4249) lr 1.5878e-03 eta 14:20:53
+epoch [17/50] batch [970/1000] time 1.547 (1.564) data 0.000 (0.001) loss 0.8711 (1.1281) acc 84.3750 (71.4369) lr 1.5878e-03 eta 14:20:43
+epoch [17/50] batch [975/1000] time 1.573 (1.564) data 0.000 (0.001) loss 0.9751 (1.1278) acc 75.0000 (71.4487) lr 1.5878e-03 eta 14:20:36
+epoch [17/50] batch [980/1000] time 1.564 (1.564) data 0.001 (0.001) loss 0.8408 (1.1281) acc 78.1250 (71.4541) lr 1.5878e-03 eta 14:20:29
+epoch [17/50] batch [985/1000] time 1.542 (1.564) data 0.001 (0.001) loss 0.9102 (1.1283) acc 81.2500 (71.4562) lr 1.5878e-03 eta 14:20:20
+epoch [17/50] batch [990/1000] time 1.528 (1.563) data 0.000 (0.001) loss 0.8745 (1.1282) acc 75.0000 (71.4520) lr 1.5878e-03 eta 14:20:09
+epoch [17/50] batch [995/1000] time 1.575 (1.563) data 0.000 (0.001) loss 1.2266 (1.1277) acc 65.6250 (71.4604) lr 1.5878e-03 eta 14:19:58
+epoch [17/50] batch [1000/1000] time 1.566 (1.563) data 0.000 (0.001) loss 1.1641 (1.1282) acc 75.0000 (71.4531) lr 1.5358e-03 eta 14:19:48
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,179
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [18/50] batch [5/1000] time 1.562 (1.673) data 0.000 (0.176) loss 0.9316 (1.1216) acc 78.1250 (71.8750) lr 1.5358e-03 eta 15:19:47
+epoch [18/50] batch [10/1000] time 1.562 (1.616) data 0.000 (0.089) loss 0.9238 (1.1423) acc 71.8750 (70.3125) lr 1.5358e-03 eta 14:48:20
+epoch [18/50] batch [15/1000] time 1.581 (1.597) data 0.000 (0.059) loss 0.9238 (1.1020) acc 78.1250 (72.0833) lr 1.5358e-03 eta 14:38:03
+epoch [18/50] batch [20/1000] time 1.567 (1.588) data 0.000 (0.044) loss 1.7285 (1.1349) acc 59.3750 (72.1875) lr 1.5358e-03 eta 14:33:04
+epoch [18/50] batch [25/1000] time 1.596 (1.587) data 0.000 (0.036) loss 1.8154 (1.1662) acc 62.5000 (71.1250) lr 1.5358e-03 eta 14:32:06
+epoch [18/50] batch [30/1000] time 1.566 (1.584) data 0.001 (0.030) loss 1.0127 (1.1749) acc 71.8750 (71.1458) lr 1.5358e-03 eta 14:30:11
+epoch [18/50] batch [35/1000] time 1.572 (1.591) data 0.000 (0.026) loss 0.5137 (1.1602) acc 90.6250 (71.9643) lr 1.5358e-03 eta 14:34:02
+epoch [18/50] batch [40/1000] time 1.566 (1.588) data 0.001 (0.023) loss 0.6484 (1.1288) acc 87.5000 (72.6562) lr 1.5358e-03 eta 14:32:34
+epoch [18/50] batch [45/1000] time 1.563 (1.586) data 0.001 (0.020) loss 1.4619 (1.1500) acc 56.2500 (71.7361) lr 1.5358e-03 eta 14:31:13
+epoch [18/50] batch [50/1000] time 1.559 (1.584) data 0.001 (0.018) loss 1.2324 (1.1447) acc 75.0000 (71.8125) lr 1.5358e-03 eta 14:29:47
+epoch [18/50] batch [55/1000] time 1.566 (1.582) data 0.000 (0.017) loss 1.0928 (1.1379) acc 75.0000 (71.6477) lr 1.5358e-03 eta 14:28:36
+epoch [18/50] batch [60/1000] time 1.591 (1.581) data 0.001 (0.015) loss 0.9907 (1.1262) acc 71.8750 (71.9792) lr 1.5358e-03 eta 14:27:51
+epoch [18/50] batch [65/1000] time 1.554 (1.579) data 0.001 (0.014) loss 1.0352 (1.1164) acc 71.8750 (72.2115) lr 1.5358e-03 eta 14:26:56
+epoch [18/50] batch [70/1000] time 1.571 (1.578) data 0.000 (0.013) loss 1.4814 (1.1190) acc 62.5000 (72.1429) lr 1.5358e-03 eta 14:26:03
+epoch [18/50] batch [75/1000] time 1.564 (1.577) data 0.000 (0.012) loss 1.4551 (1.1404) acc 65.6250 (71.6667) lr 1.5358e-03 eta 14:25:11
+epoch [18/50] batch [80/1000] time 1.559 (1.576) data 0.001 (0.012) loss 1.2236 (1.1642) acc 68.7500 (70.9766) lr 1.5358e-03 eta 14:24:35
+epoch [18/50] batch [85/1000] time 1.563 (1.575) data 0.001 (0.011) loss 0.7358 (1.1542) acc 81.2500 (71.1397) lr 1.5358e-03 eta 14:24:00
+epoch [18/50] batch [90/1000] time 1.574 (1.574) data 0.000 (0.010) loss 0.9077 (1.1524) acc 75.0000 (71.1458) lr 1.5358e-03 eta 14:23:12
+epoch [18/50] batch [95/1000] time 1.563 (1.573) data 0.001 (0.010) loss 0.9302 (1.1570) acc 71.8750 (70.7895) lr 1.5358e-03 eta 14:22:55
+epoch [18/50] batch [100/1000] time 1.554 (1.573) data 0.000 (0.009) loss 1.0371 (1.1530) acc 71.8750 (70.6875) lr 1.5358e-03 eta 14:22:22
+epoch [18/50] batch [105/1000] time 1.566 (1.572) data 0.000 (0.009) loss 1.1416 (1.1616) acc 78.1250 (70.7440) lr 1.5358e-03 eta 14:22:03
+epoch [18/50] batch [110/1000] time 1.569 (1.572) data 0.000 (0.009) loss 1.0469 (1.1573) acc 78.1250 (70.9943) lr 1.5358e-03 eta 14:21:36
+epoch [18/50] batch [115/1000] time 1.542 (1.571) data 0.000 (0.008) loss 0.7451 (1.1603) acc 71.8750 (70.8152) lr 1.5358e-03 eta 14:21:08
+epoch [18/50] batch [120/1000] time 1.549 (1.570) data 0.000 (0.008) loss 0.6084 (1.1488) acc 87.5000 (71.1719) lr 1.5358e-03 eta 14:20:35
+epoch [18/50] batch [125/1000] time 1.557 (1.570) data 0.001 (0.008) loss 0.9165 (1.1449) acc 78.1250 (71.1500) lr 1.5358e-03 eta 14:20:09
+epoch [18/50] batch [130/1000] time 1.573 (1.570) data 0.000 (0.007) loss 0.8799 (1.1416) acc 81.2500 (71.2981) lr 1.5358e-03 eta 14:19:50
+epoch [18/50] batch [135/1000] time 1.539 (1.569) data 0.000 (0.007) loss 1.9521 (1.1369) acc 56.2500 (71.4352) lr 1.5358e-03 eta 14:19:18
+epoch [18/50] batch [140/1000] time 1.583 (1.570) data 0.000 (0.007) loss 1.6191 (1.1304) acc 62.5000 (71.4732) lr 1.5358e-03 eta 14:19:46
+epoch [18/50] batch [145/1000] time 1.554 (1.570) data 0.000 (0.007) loss 1.1113 (1.1335) acc 71.8750 (71.5733) lr 1.5358e-03 eta 14:19:34
+epoch [18/50] batch [150/1000] time 1.565 (1.570) data 0.000 (0.006) loss 0.8965 (1.1324) acc 78.1250 (71.5625) lr 1.5358e-03 eta 14:19:20
+epoch [18/50] batch [155/1000] time 1.562 (1.569) data 0.000 (0.006) loss 1.1914 (1.1304) acc 78.1250 (71.6734) lr 1.5358e-03 eta 14:19:05
+epoch [18/50] batch [160/1000] time 1.598 (1.569) data 0.000 (0.006) loss 1.5332 (1.1343) acc 53.1250 (71.5430) lr 1.5358e-03 eta 14:18:58
+epoch [18/50] batch [165/1000] time 1.556 (1.569) data 0.000 (0.006) loss 1.8594 (1.1311) acc 50.0000 (71.6667) lr 1.5358e-03 eta 14:18:38
+epoch [18/50] batch [170/1000] time 1.557 (1.569) data 0.000 (0.006) loss 1.5703 (1.1332) acc 56.2500 (71.6544) lr 1.5358e-03 eta 14:18:17
+epoch [18/50] batch [175/1000] time 1.565 (1.568) data 0.000 (0.006) loss 1.3193 (1.1371) acc 75.0000 (71.5893) lr 1.5358e-03 eta 14:18:04
+epoch [18/50] batch [180/1000] time 1.549 (1.568) data 0.000 (0.005) loss 0.6943 (1.1351) acc 81.2500 (71.5451) lr 1.5358e-03 eta 14:17:51
+epoch [18/50] batch [185/1000] time 1.573 (1.569) data 0.000 (0.005) loss 1.1689 (1.1330) acc 71.8750 (71.6047) lr 1.5358e-03 eta 14:18:11
+epoch [18/50] batch [190/1000] time 1.564 (1.569) data 0.000 (0.005) loss 1.3350 (1.1349) acc 65.6250 (71.5461) lr 1.5358e-03 eta 14:17:51
+epoch [18/50] batch [195/1000] time 1.542 (1.569) data 0.000 (0.005) loss 1.2363 (1.1340) acc 65.6250 (71.5545) lr 1.5358e-03 eta 14:17:36
+epoch [18/50] batch [200/1000] time 1.552 (1.568) data 0.000 (0.005) loss 2.1035 (1.1436) acc 53.1250 (71.3281) lr 1.5358e-03 eta 14:17:19
+epoch [18/50] batch [205/1000] time 1.556 (1.568) data 0.000 (0.005) loss 0.6553 (1.1420) acc 75.0000 (71.2500) lr 1.5358e-03 eta 14:16:58
+epoch [18/50] batch [210/1000] time 1.567 (1.568) data 0.000 (0.005) loss 1.2217 (1.1387) acc 65.6250 (71.2946) lr 1.5358e-03 eta 14:16:46
+epoch [18/50] batch [215/1000] time 1.567 (1.568) data 0.000 (0.005) loss 1.5059 (1.1437) acc 65.6250 (71.2500) lr 1.5358e-03 eta 14:16:35
+epoch [18/50] batch [220/1000] time 1.552 (1.567) data 0.000 (0.004) loss 0.8682 (1.1492) acc 71.8750 (71.1364) lr 1.5358e-03 eta 14:16:14
+epoch [18/50] batch [225/1000] time 1.708 (1.567) data 0.000 (0.004) loss 1.4326 (1.1472) acc 65.6250 (71.0972) lr 1.5358e-03 eta 14:16:14
+epoch [18/50] batch [230/1000] time 1.553 (1.567) data 0.000 (0.004) loss 1.5254 (1.1501) acc 65.6250 (70.9918) lr 1.5358e-03 eta 14:16:02
+epoch [18/50] batch [235/1000] time 1.568 (1.567) data 0.000 (0.004) loss 1.5879 (1.1497) acc 62.5000 (71.0239) lr 1.5358e-03 eta 14:15:45
+epoch [18/50] batch [240/1000] time 1.529 (1.567) data 0.000 (0.004) loss 1.3115 (1.1503) acc 71.8750 (70.9766) lr 1.5358e-03 eta 14:15:26
+epoch [18/50] batch [245/1000] time 1.558 (1.567) data 0.000 (0.004) loss 0.7251 (1.1468) acc 84.3750 (70.9949) lr 1.5358e-03 eta 14:15:13
+epoch [18/50] batch [250/1000] time 1.557 (1.566) data 0.000 (0.004) loss 1.4375 (1.1489) acc 59.3750 (71.0250) lr 1.5358e-03 eta 14:14:53
+epoch [18/50] batch [255/1000] time 1.577 (1.566) data 0.001 (0.004) loss 1.1113 (1.1455) acc 68.7500 (71.1152) lr 1.5358e-03 eta 14:14:38
+epoch [18/50] batch [260/1000] time 1.562 (1.566) data 0.000 (0.004) loss 1.7988 (1.1471) acc 65.6250 (71.1058) lr 1.5358e-03 eta 14:14:27
+epoch [18/50] batch [265/1000] time 1.564 (1.566) data 0.000 (0.004) loss 1.6006 (1.1491) acc 59.3750 (71.0731) lr 1.5358e-03 eta 14:14:15
+epoch [18/50] batch [270/1000] time 1.544 (1.566) data 0.000 (0.004) loss 0.9224 (1.1444) acc 71.8750 (71.1343) lr 1.5358e-03 eta 14:14:03
+epoch [18/50] batch [275/1000] time 1.565 (1.566) data 0.000 (0.004) loss 1.7031 (1.1469) acc 65.6250 (71.1023) lr 1.5358e-03 eta 14:13:52
+epoch [18/50] batch [280/1000] time 1.535 (1.565) data 0.000 (0.004) loss 1.2861 (1.1475) acc 68.7500 (71.0603) lr 1.5358e-03 eta 14:13:39
+epoch [18/50] batch [285/1000] time 1.556 (1.565) data 0.001 (0.004) loss 0.7964 (1.1455) acc 81.2500 (71.0965) lr 1.5358e-03 eta 14:13:24
+epoch [18/50] batch [290/1000] time 1.535 (1.565) data 0.000 (0.003) loss 0.7754 (1.1454) acc 81.2500 (71.1315) lr 1.5358e-03 eta 14:13:22
+epoch [18/50] batch [295/1000] time 1.554 (1.565) data 0.000 (0.003) loss 1.2070 (1.1434) acc 68.7500 (71.1970) lr 1.5358e-03 eta 14:13:08
+epoch [18/50] batch [300/1000] time 1.574 (1.565) data 0.000 (0.003) loss 1.0664 (1.1456) acc 68.7500 (71.1562) lr 1.5358e-03 eta 14:13:04
+epoch [18/50] batch [305/1000] time 1.606 (1.565) data 0.000 (0.003) loss 1.4570 (1.1474) acc 71.8750 (71.1168) lr 1.5358e-03 eta 14:12:58
+epoch [18/50] batch [310/1000] time 1.571 (1.565) data 0.001 (0.003) loss 1.0020 (1.1444) acc 75.0000 (71.1593) lr 1.5358e-03 eta 14:12:47
+epoch [18/50] batch [315/1000] time 1.562 (1.565) data 0.000 (0.003) loss 0.7905 (1.1420) acc 84.3750 (71.2897) lr 1.5358e-03 eta 14:12:35
+epoch [18/50] batch [320/1000] time 1.575 (1.565) data 0.000 (0.003) loss 1.3730 (1.1408) acc 56.2500 (71.2598) lr 1.5358e-03 eta 14:12:28
+epoch [18/50] batch [325/1000] time 1.547 (1.565) data 0.000 (0.003) loss 1.3584 (1.1412) acc 62.5000 (71.2788) lr 1.5358e-03 eta 14:12:16
+epoch [18/50] batch [330/1000] time 1.564 (1.565) data 0.001 (0.003) loss 1.5234 (1.1426) acc 62.5000 (71.2879) lr 1.5358e-03 eta 14:12:07
+epoch [18/50] batch [335/1000] time 1.549 (1.565) data 0.000 (0.003) loss 1.0430 (1.1413) acc 65.6250 (71.2407) lr 1.5358e-03 eta 14:12:11
+epoch [18/50] batch [340/1000] time 1.571 (1.565) data 0.000 (0.003) loss 0.8828 (1.1459) acc 75.0000 (71.1121) lr 1.5358e-03 eta 14:11:58
+epoch [18/50] batch [345/1000] time 1.576 (1.565) data 0.001 (0.003) loss 1.4434 (1.1467) acc 59.3750 (71.1232) lr 1.5358e-03 eta 14:11:57
+epoch [18/50] batch [350/1000] time 1.583 (1.565) data 0.000 (0.003) loss 1.1748 (1.1504) acc 65.6250 (71.0536) lr 1.5358e-03 eta 14:11:52
+epoch [18/50] batch [355/1000] time 1.564 (1.565) data 0.000 (0.003) loss 1.6777 (1.1490) acc 71.8750 (71.1092) lr 1.5358e-03 eta 14:11:45
+epoch [18/50] batch [360/1000] time 1.541 (1.565) data 0.000 (0.003) loss 1.3008 (1.1529) acc 65.6250 (70.9809) lr 1.5358e-03 eta 14:11:29
+epoch [18/50] batch [365/1000] time 1.531 (1.565) data 0.000 (0.003) loss 0.9922 (1.1504) acc 78.1250 (71.0103) lr 1.5358e-03 eta 14:11:15
+epoch [18/50] batch [370/1000] time 1.571 (1.565) data 0.001 (0.003) loss 1.1699 (1.1509) acc 75.0000 (71.0473) lr 1.5358e-03 eta 14:11:07
+epoch [18/50] batch [375/1000] time 1.546 (1.565) data 0.000 (0.003) loss 1.4385 (1.1517) acc 68.7500 (71.0250) lr 1.5358e-03 eta 14:10:59
+epoch [18/50] batch [380/1000] time 1.575 (1.566) data 0.000 (0.003) loss 1.0332 (1.1487) acc 81.2500 (71.1431) lr 1.5358e-03 eta 14:11:06
+epoch [18/50] batch [385/1000] time 1.562 (1.565) data 0.000 (0.003) loss 0.9375 (1.1473) acc 78.1250 (71.2338) lr 1.5358e-03 eta 14:10:57
+epoch [18/50] batch [390/1000] time 1.551 (1.565) data 0.000 (0.003) loss 0.9434 (1.1444) acc 68.7500 (71.2821) lr 1.5358e-03 eta 14:10:43
+epoch [18/50] batch [395/1000] time 1.533 (1.565) data 0.000 (0.003) loss 0.9351 (1.1412) acc 71.8750 (71.3687) lr 1.5358e-03 eta 14:10:33
+epoch [18/50] batch [400/1000] time 1.574 (1.565) data 0.000 (0.003) loss 1.7295 (1.1427) acc 68.7500 (71.3594) lr 1.5358e-03 eta 14:10:21
+epoch [18/50] batch [405/1000] time 1.579 (1.565) data 0.001 (0.003) loss 1.5303 (1.1435) acc 65.6250 (71.3812) lr 1.5358e-03 eta 14:10:14
+epoch [18/50] batch [410/1000] time 1.573 (1.565) data 0.001 (0.003) loss 0.8184 (1.1427) acc 78.1250 (71.3796) lr 1.5358e-03 eta 14:10:07
+epoch [18/50] batch [415/1000] time 1.539 (1.565) data 0.000 (0.003) loss 1.1465 (1.1428) acc 71.8750 (71.3630) lr 1.5358e-03 eta 14:10:00
+epoch [18/50] batch [420/1000] time 1.554 (1.565) data 0.001 (0.003) loss 0.8003 (1.1424) acc 78.1250 (71.3765) lr 1.5358e-03 eta 14:09:47
+epoch [18/50] batch [425/1000] time 1.566 (1.565) data 0.001 (0.003) loss 1.5791 (1.1406) acc 62.5000 (71.3897) lr 1.5358e-03 eta 14:09:36
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+epoch [18/50] batch [990/1000] time 1.568 (1.563) data 0.000 (0.001) loss 0.5596 (1.1367) acc 84.3750 (71.6667) lr 1.5358e-03 eta 13:53:54
+epoch [18/50] batch [995/1000] time 1.557 (1.563) data 0.000 (0.001) loss 0.8232 (1.1380) acc 71.8750 (71.6112) lr 1.5358e-03 eta 13:53:44
+epoch [18/50] batch [1000/1000] time 1.582 (1.563) data 0.000 (0.001) loss 0.6934 (1.1371) acc 78.1250 (71.6219) lr 1.4818e-03 eta 13:53:36
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,195
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [19/50] batch [5/1000] time 1.531 (1.674) data 0.000 (0.176) loss 0.8545 (0.8663) acc 81.2500 (79.3750) lr 1.4818e-03 eta 14:52:45
+epoch [19/50] batch [10/1000] time 1.554 (1.610) data 0.000 (0.088) loss 1.1094 (1.0231) acc 68.7500 (76.5625) lr 1.4818e-03 eta 14:18:28
+epoch [19/50] batch [15/1000] time 1.795 (1.606) data 0.000 (0.059) loss 1.2852 (1.0726) acc 65.6250 (74.7917) lr 1.4818e-03 eta 14:16:02
+epoch [19/50] batch [20/1000] time 1.550 (1.594) data 0.001 (0.044) loss 0.6938 (1.0673) acc 78.1250 (73.2812) lr 1.4818e-03 eta 14:09:20
+epoch [19/50] batch [25/1000] time 1.556 (1.585) data 0.000 (0.036) loss 0.7271 (1.0367) acc 84.3750 (74.1250) lr 1.4818e-03 eta 14:04:38
+epoch [19/50] batch [30/1000] time 1.560 (1.581) data 0.000 (0.030) loss 1.2178 (1.0568) acc 65.6250 (73.2292) lr 1.4818e-03 eta 14:02:14
+epoch [19/50] batch [35/1000] time 1.569 (1.578) data 0.000 (0.026) loss 1.1875 (1.0818) acc 75.0000 (73.0357) lr 1.4818e-03 eta 14:00:46
+epoch [19/50] batch [40/1000] time 1.572 (1.577) data 0.001 (0.022) loss 0.7246 (1.0596) acc 84.3750 (73.7500) lr 1.4818e-03 eta 13:59:47
+epoch [19/50] batch [45/1000] time 1.556 (1.576) data 0.000 (0.020) loss 1.5117 (1.0695) acc 68.7500 (73.4028) lr 1.4818e-03 eta 13:59:05
+epoch [19/50] batch [50/1000] time 1.544 (1.574) data 0.000 (0.018) loss 0.9365 (1.0740) acc 81.2500 (73.1875) lr 1.4818e-03 eta 13:58:13
+epoch [19/50] batch [55/1000] time 1.574 (1.574) data 0.000 (0.016) loss 1.4053 (1.0766) acc 68.7500 (72.7841) lr 1.4818e-03 eta 13:57:46
+epoch [19/50] batch [60/1000] time 1.581 (1.573) data 0.000 (0.015) loss 0.9067 (1.0861) acc 78.1250 (72.7604) lr 1.4818e-03 eta 13:57:13
+epoch [19/50] batch [65/1000] time 1.567 (1.575) data 0.001 (0.014) loss 1.2051 (1.0829) acc 65.6250 (72.5962) lr 1.4818e-03 eta 13:58:18
+epoch [19/50] batch [70/1000] time 1.558 (1.573) data 0.000 (0.013) loss 0.7686 (1.0741) acc 81.2500 (72.8571) lr 1.4818e-03 eta 13:57:18
+epoch [19/50] batch [75/1000] time 1.539 (1.572) data 0.000 (0.012) loss 1.0010 (1.0678) acc 65.6250 (72.8750) lr 1.4818e-03 eta 13:56:41
+epoch [19/50] batch [80/1000] time 1.547 (1.572) data 0.000 (0.011) loss 1.1357 (1.0750) acc 71.8750 (72.7344) lr 1.4818e-03 eta 13:56:08
+epoch [19/50] batch [85/1000] time 1.535 (1.570) data 0.000 (0.011) loss 1.1572 (1.0828) acc 59.3750 (72.6471) lr 1.4818e-03 eta 13:55:10
+epoch [19/50] batch [90/1000] time 1.557 (1.569) data 0.001 (0.010) loss 0.9863 (1.0949) acc 78.1250 (72.6042) lr 1.4818e-03 eta 13:54:41
+epoch [19/50] batch [95/1000] time 1.549 (1.569) data 0.001 (0.010) loss 0.7773 (1.1054) acc 75.0000 (72.3355) lr 1.4818e-03 eta 13:54:03
+epoch [19/50] batch [100/1000] time 1.537 (1.568) data 0.000 (0.009) loss 1.1631 (1.1229) acc 78.1250 (71.8750) lr 1.4818e-03 eta 13:53:27
+epoch [19/50] batch [105/1000] time 1.544 (1.567) data 0.001 (0.009) loss 0.9448 (1.1340) acc 84.3750 (71.6964) lr 1.4818e-03 eta 13:53:11
+epoch [19/50] batch [110/1000] time 1.549 (1.567) data 0.000 (0.008) loss 1.7021 (1.1407) acc 59.3750 (71.4489) lr 1.4818e-03 eta 13:52:50
+epoch [19/50] batch [115/1000] time 1.545 (1.567) data 0.000 (0.008) loss 0.8486 (1.1343) acc 81.2500 (71.4674) lr 1.4818e-03 eta 13:52:36
+epoch [19/50] batch [120/1000] time 1.547 (1.566) data 0.001 (0.008) loss 0.7212 (1.1292) acc 78.1250 (71.4583) lr 1.4818e-03 eta 13:52:13
+epoch [19/50] batch [125/1000] time 1.549 (1.566) data 0.001 (0.007) loss 1.4209 (1.1262) acc 68.7500 (71.4500) lr 1.4818e-03 eta 13:51:55
+epoch [19/50] batch [130/1000] time 1.538 (1.566) data 0.000 (0.007) loss 1.3271 (1.1289) acc 68.7500 (71.3942) lr 1.4818e-03 eta 13:51:32
+epoch [19/50] batch [135/1000] time 1.567 (1.565) data 0.000 (0.007) loss 1.4434 (1.1311) acc 71.8750 (71.3426) lr 1.4818e-03 eta 13:51:13
+epoch [19/50] batch [140/1000] time 1.559 (1.565) data 0.000 (0.007) loss 1.4570 (1.1387) acc 62.5000 (71.1830) lr 1.4818e-03 eta 13:50:57
+epoch [19/50] batch [145/1000] time 1.556 (1.564) data 0.000 (0.007) loss 1.5068 (1.1413) acc 65.6250 (71.1207) lr 1.4818e-03 eta 13:50:36
+epoch [19/50] batch [150/1000] time 1.559 (1.564) data 0.001 (0.006) loss 0.7812 (1.1386) acc 81.2500 (71.2708) lr 1.4818e-03 eta 13:50:16
+epoch [19/50] batch [155/1000] time 1.568 (1.564) data 0.001 (0.006) loss 1.2979 (1.1384) acc 65.6250 (71.3105) lr 1.4818e-03 eta 13:50:09
+epoch [19/50] batch [160/1000] time 1.547 (1.564) data 0.000 (0.006) loss 0.4556 (1.1437) acc 87.5000 (71.2305) lr 1.4818e-03 eta 13:49:50
+epoch [19/50] batch [165/1000] time 1.719 (1.565) data 0.000 (0.006) loss 1.4834 (1.1381) acc 59.3750 (71.2879) lr 1.4818e-03 eta 13:50:08
+epoch [19/50] batch [170/1000] time 1.558 (1.564) data 0.000 (0.006) loss 0.6973 (1.1371) acc 84.3750 (71.3603) lr 1.4818e-03 eta 13:49:46
+epoch [19/50] batch [175/1000] time 1.550 (1.564) data 0.000 (0.005) loss 0.8555 (1.1318) acc 68.7500 (71.4643) lr 1.4818e-03 eta 13:49:26
+epoch [19/50] batch [180/1000] time 1.561 (1.564) data 0.000 (0.005) loss 0.9775 (1.1283) acc 65.6250 (71.4410) lr 1.4818e-03 eta 13:49:11
+epoch [19/50] batch [185/1000] time 1.561 (1.563) data 0.000 (0.005) loss 0.9688 (1.1356) acc 75.0000 (71.3007) lr 1.4818e-03 eta 13:48:58
+epoch [19/50] batch [190/1000] time 1.561 (1.563) data 0.000 (0.005) loss 0.7451 (1.1300) acc 81.2500 (71.4309) lr 1.4818e-03 eta 13:48:40
+epoch [19/50] batch [195/1000] time 1.540 (1.563) data 0.000 (0.005) loss 1.0439 (1.1318) acc 71.8750 (71.3141) lr 1.4818e-03 eta 13:48:26
+epoch [19/50] batch [200/1000] time 1.559 (1.563) data 0.000 (0.005) loss 1.1045 (1.1257) acc 84.3750 (71.5469) lr 1.4818e-03 eta 13:48:15
+epoch [19/50] batch [205/1000] time 1.528 (1.562) data 0.000 (0.005) loss 0.5293 (1.1207) acc 84.3750 (71.6311) lr 1.4818e-03 eta 13:47:55
+epoch [19/50] batch [210/1000] time 1.671 (1.563) data 0.000 (0.005) loss 1.4258 (1.1245) acc 59.3750 (71.5476) lr 1.4818e-03 eta 13:47:59
+epoch [19/50] batch [215/1000] time 1.584 (1.563) data 0.000 (0.005) loss 0.7603 (1.1205) acc 78.1250 (71.5552) lr 1.4818e-03 eta 13:48:02
+epoch [19/50] batch [220/1000] time 1.547 (1.563) data 0.000 (0.004) loss 0.7910 (1.1181) acc 78.1250 (71.6477) lr 1.4818e-03 eta 13:47:48
+epoch [19/50] batch [225/1000] time 1.557 (1.563) data 0.000 (0.004) loss 0.7563 (1.1193) acc 78.1250 (71.5833) lr 1.4818e-03 eta 13:47:39
+epoch [19/50] batch [230/1000] time 1.578 (1.563) data 0.000 (0.004) loss 0.8203 (1.1193) acc 81.2500 (71.6440) lr 1.4818e-03 eta 13:47:28
+epoch [19/50] batch [235/1000] time 1.567 (1.563) data 0.000 (0.004) loss 1.5107 (1.1198) acc 62.5000 (71.6622) lr 1.4818e-03 eta 13:47:26
+epoch [19/50] batch [240/1000] time 1.562 (1.563) data 0.000 (0.004) loss 1.2783 (1.1186) acc 59.3750 (71.6276) lr 1.4818e-03 eta 13:47:21
+epoch [19/50] batch [245/1000] time 1.576 (1.563) data 0.000 (0.004) loss 1.9414 (1.1225) acc 59.3750 (71.5561) lr 1.4818e-03 eta 13:47:20
+epoch [19/50] batch [250/1000] time 1.539 (1.563) data 0.000 (0.004) loss 0.9868 (1.1201) acc 75.0000 (71.5625) lr 1.4818e-03 eta 13:47:14
+epoch [19/50] batch [255/1000] time 1.585 (1.564) data 0.000 (0.004) loss 0.9204 (1.1225) acc 78.1250 (71.4828) lr 1.4818e-03 eta 13:47:27
+epoch [19/50] batch [260/1000] time 1.547 (1.564) data 0.001 (0.004) loss 1.5684 (1.1262) acc 62.5000 (71.5144) lr 1.4818e-03 eta 13:47:21
+epoch [19/50] batch [265/1000] time 1.583 (1.564) data 0.001 (0.004) loss 1.1562 (1.1315) acc 71.8750 (71.4387) lr 1.4818e-03 eta 13:47:18
+epoch [19/50] batch [270/1000] time 1.555 (1.564) data 0.001 (0.004) loss 1.0156 (1.1295) acc 78.1250 (71.4583) lr 1.4818e-03 eta 13:47:09
+epoch [19/50] batch [275/1000] time 1.567 (1.564) data 0.001 (0.004) loss 1.4609 (1.1303) acc 71.8750 (71.5114) lr 1.4818e-03 eta 13:46:56
+epoch [19/50] batch [280/1000] time 1.552 (1.564) data 0.000 (0.004) loss 0.8916 (1.1302) acc 78.1250 (71.5513) lr 1.4818e-03 eta 13:46:39
+epoch [19/50] batch [285/1000] time 1.556 (1.563) data 0.000 (0.004) loss 0.6782 (1.1286) acc 81.2500 (71.6009) lr 1.4818e-03 eta 13:46:22
+epoch [19/50] batch [290/1000] time 1.562 (1.563) data 0.000 (0.003) loss 1.7598 (1.1284) acc 59.3750 (71.5948) lr 1.4818e-03 eta 13:46:11
+epoch [19/50] batch [295/1000] time 1.541 (1.563) data 0.000 (0.003) loss 1.8223 (1.1301) acc 56.2500 (71.5890) lr 1.4818e-03 eta 13:45:59
+epoch [19/50] batch [300/1000] time 1.534 (1.563) data 0.000 (0.003) loss 1.1006 (1.1329) acc 75.0000 (71.5417) lr 1.4818e-03 eta 13:45:45
+epoch [19/50] batch [305/1000] time 1.547 (1.563) data 0.000 (0.003) loss 1.2861 (1.1324) acc 68.7500 (71.5881) lr 1.4818e-03 eta 13:45:39
+epoch [19/50] batch [310/1000] time 1.575 (1.563) data 0.000 (0.003) loss 1.1543 (1.1336) acc 68.7500 (71.5927) lr 1.4818e-03 eta 13:45:35
+epoch [19/50] batch [315/1000] time 1.564 (1.563) data 0.000 (0.003) loss 1.3877 (1.1376) acc 71.8750 (71.5774) lr 1.4818e-03 eta 13:45:29
+epoch [19/50] batch [320/1000] time 1.575 (1.564) data 0.000 (0.003) loss 0.9858 (1.1350) acc 68.7500 (71.6016) lr 1.4818e-03 eta 13:45:42
+epoch [19/50] batch [325/1000] time 1.561 (1.564) data 0.000 (0.003) loss 1.0000 (1.1320) acc 71.8750 (71.7596) lr 1.4818e-03 eta 13:45:32
+epoch [19/50] batch [330/1000] time 1.586 (1.564) data 0.001 (0.003) loss 1.0488 (1.1301) acc 78.1250 (71.8182) lr 1.4818e-03 eta 13:45:23
+epoch [19/50] batch [335/1000] time 1.569 (1.564) data 0.000 (0.003) loss 1.1621 (1.1325) acc 68.7500 (71.7537) lr 1.4818e-03 eta 13:45:21
+epoch [19/50] batch [340/1000] time 1.577 (1.564) data 0.000 (0.003) loss 0.6216 (1.1267) acc 84.3750 (71.9210) lr 1.4818e-03 eta 13:45:23
+epoch [19/50] batch [345/1000] time 1.577 (1.564) data 0.000 (0.003) loss 1.3770 (1.1302) acc 62.5000 (71.8569) lr 1.4818e-03 eta 13:45:24
+epoch [19/50] batch [350/1000] time 1.582 (1.565) data 0.000 (0.003) loss 1.1367 (1.1311) acc 68.7500 (71.8036) lr 1.4818e-03 eta 13:45:17
+epoch [19/50] batch [355/1000] time 1.587 (1.565) data 0.000 (0.003) loss 1.1602 (1.1336) acc 68.7500 (71.6549) lr 1.4818e-03 eta 13:45:12
+epoch [19/50] batch [360/1000] time 1.561 (1.565) data 0.000 (0.003) loss 0.6953 (1.1326) acc 78.1250 (71.6840) lr 1.4818e-03 eta 13:45:02
+epoch [19/50] batch [365/1000] time 1.543 (1.565) data 0.000 (0.003) loss 1.4229 (1.1334) acc 68.7500 (71.6866) lr 1.4818e-03 eta 13:45:06
+epoch [19/50] batch [370/1000] time 1.560 (1.565) data 0.000 (0.003) loss 0.7944 (1.1317) acc 78.1250 (71.7483) lr 1.4818e-03 eta 13:44:52
+epoch [19/50] batch [375/1000] time 1.551 (1.565) data 0.000 (0.003) loss 1.2061 (1.1338) acc 68.7500 (71.6917) lr 1.4818e-03 eta 13:44:45
+epoch [19/50] batch [380/1000] time 1.556 (1.565) data 0.000 (0.003) loss 1.1582 (1.1322) acc 56.2500 (71.6776) lr 1.4818e-03 eta 13:44:31
+epoch [19/50] batch [385/1000] time 1.554 (1.565) data 0.000 (0.003) loss 1.4258 (1.1351) acc 62.5000 (71.6234) lr 1.4818e-03 eta 13:44:26
+epoch [19/50] batch [390/1000] time 1.546 (1.565) data 0.000 (0.003) loss 1.5947 (1.1364) acc 68.7500 (71.6186) lr 1.4818e-03 eta 13:44:17
+epoch [19/50] batch [395/1000] time 1.563 (1.565) data 0.000 (0.003) loss 0.9727 (1.1360) acc 68.7500 (71.5981) lr 1.4818e-03 eta 13:44:09
+epoch [19/50] batch [400/1000] time 1.557 (1.564) data 0.000 (0.003) loss 1.6279 (1.1388) acc 62.5000 (71.5625) lr 1.4818e-03 eta 13:43:57
+epoch [19/50] batch [405/1000] time 1.553 (1.565) data 0.000 (0.003) loss 1.0654 (1.1384) acc 68.7500 (71.5432) lr 1.4818e-03 eta 13:43:56
+epoch [19/50] batch [410/1000] time 1.568 (1.565) data 0.000 (0.003) loss 1.7422 (1.1395) acc 62.5000 (71.5473) lr 1.4818e-03 eta 13:43:45
+epoch [19/50] batch [415/1000] time 1.537 (1.564) data 0.000 (0.003) loss 1.2881 (1.1407) acc 81.2500 (71.5587) lr 1.4818e-03 eta 13:43:31
+epoch [19/50] batch [420/1000] time 1.572 (1.564) data 0.000 (0.003) loss 1.1133 (1.1389) acc 65.6250 (71.5699) lr 1.4818e-03 eta 13:43:20
+epoch [19/50] batch [425/1000] time 1.554 (1.564) data 0.001 (0.002) loss 1.6084 (1.1412) acc 56.2500 (71.4853) lr 1.4818e-03 eta 13:43:13
+epoch [19/50] batch [430/1000] time 1.548 (1.564) data 0.001 (0.002) loss 1.2207 (1.1406) acc 71.8750 (71.5407) lr 1.4818e-03 eta 13:43:01
+epoch [19/50] batch [435/1000] time 1.593 (1.564) data 0.000 (0.002) loss 1.2461 (1.1397) acc 75.0000 (71.5661) lr 1.4818e-03 eta 13:42:50
+epoch [19/50] batch [440/1000] time 1.573 (1.564) data 0.000 (0.002) loss 1.5781 (1.1422) acc 68.7500 (71.5128) lr 1.4818e-03 eta 13:42:48
+epoch [19/50] batch [445/1000] time 1.563 (1.564) data 0.000 (0.002) loss 1.5068 (1.1432) acc 62.5000 (71.4817) lr 1.4818e-03 eta 13:42:43
+epoch [19/50] batch [450/1000] time 1.594 (1.565) data 0.000 (0.002) loss 0.4280 (1.1431) acc 87.5000 (71.4722) lr 1.4818e-03 eta 13:42:40
+epoch [19/50] batch [455/1000] time 1.580 (1.565) data 0.000 (0.002) loss 0.8423 (1.1416) acc 75.0000 (71.4698) lr 1.4818e-03 eta 13:42:38
+epoch [19/50] batch [460/1000] time 1.571 (1.565) data 0.000 (0.002) loss 1.2559 (1.1412) acc 71.8750 (71.4810) lr 1.4818e-03 eta 13:42:32
+epoch [19/50] batch [465/1000] time 1.562 (1.565) data 0.001 (0.002) loss 1.8262 (1.1414) acc 46.8750 (71.4180) lr 1.4818e-03 eta 13:42:23
+epoch [19/50] batch [470/1000] time 1.560 (1.565) data 0.001 (0.002) loss 1.0273 (1.1425) acc 75.0000 (71.3963) lr 1.4818e-03 eta 13:42:22
+epoch [19/50] batch [475/1000] time 1.575 (1.565) data 0.000 (0.002) loss 1.0215 (1.1438) acc 84.3750 (71.4408) lr 1.4818e-03 eta 13:42:12
+epoch [19/50] batch [480/1000] time 1.546 (1.565) data 0.001 (0.002) loss 1.3848 (1.1460) acc 62.5000 (71.3997) lr 1.4818e-03 eta 13:41:58
+epoch [19/50] batch [485/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.0684 (1.1473) acc 81.2500 (71.3853) lr 1.4818e-03 eta 13:41:49
+epoch [19/50] batch [490/1000] time 1.549 (1.565) data 0.000 (0.002) loss 1.2812 (1.1481) acc 62.5000 (71.3329) lr 1.4818e-03 eta 13:41:40
+epoch [19/50] batch [495/1000] time 1.555 (1.565) data 0.000 (0.002) loss 1.8389 (1.1505) acc 56.2500 (71.2816) lr 1.4818e-03 eta 13:41:31
+epoch [19/50] batch [500/1000] time 1.570 (1.565) data 0.001 (0.002) loss 0.9922 (1.1505) acc 68.7500 (71.2562) lr 1.4818e-03 eta 13:41:24
+epoch [19/50] batch [505/1000] time 1.576 (1.565) data 0.000 (0.002) loss 0.9307 (1.1489) acc 78.1250 (71.3490) lr 1.4818e-03 eta 13:41:18
+epoch [19/50] batch [510/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.0898 (1.1468) acc 65.6250 (71.3848) lr 1.4818e-03 eta 13:41:12
+epoch [19/50] batch [515/1000] time 1.588 (1.565) data 0.000 (0.002) loss 0.8315 (1.1465) acc 81.2500 (71.3896) lr 1.4818e-03 eta 13:41:15
+epoch [19/50] batch [520/1000] time 1.562 (1.565) data 0.001 (0.002) loss 1.1758 (1.1473) acc 71.8750 (71.3762) lr 1.4818e-03 eta 13:41:08
+epoch [19/50] batch [525/1000] time 1.544 (1.565) data 0.001 (0.002) loss 1.3662 (1.1473) acc 62.5000 (71.3929) lr 1.4818e-03 eta 13:40:57
+epoch [19/50] batch [530/1000] time 1.570 (1.565) data 0.000 (0.002) loss 0.9336 (1.1479) acc 78.1250 (71.3856) lr 1.4818e-03 eta 13:40:47
+epoch [19/50] batch [535/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.1963 (1.1471) acc 68.7500 (71.4194) lr 1.4818e-03 eta 13:40:35
+epoch [19/50] batch [540/1000] time 1.547 (1.565) data 0.000 (0.002) loss 1.5869 (1.1498) acc 65.6250 (71.3484) lr 1.4818e-03 eta 13:40:25
+epoch [19/50] batch [545/1000] time 1.568 (1.565) data 0.001 (0.002) loss 1.0781 (1.1510) acc 65.6250 (71.3303) lr 1.4818e-03 eta 13:40:16
+epoch [19/50] batch [550/1000] time 1.559 (1.565) data 0.001 (0.002) loss 1.6035 (1.1533) acc 62.5000 (71.3011) lr 1.4818e-03 eta 13:40:06
+epoch [19/50] batch [555/1000] time 1.702 (1.565) data 0.000 (0.002) loss 0.9136 (1.1537) acc 81.2500 (71.3288) lr 1.4818e-03 eta 13:40:05
+epoch [19/50] batch [560/1000] time 1.555 (1.565) data 0.000 (0.002) loss 1.3486 (1.1535) acc 68.7500 (71.3114) lr 1.4818e-03 eta 13:39:54
+epoch [19/50] batch [565/1000] time 1.545 (1.565) data 0.001 (0.002) loss 0.5469 (1.1524) acc 87.5000 (71.3662) lr 1.4818e-03 eta 13:39:41
+epoch [19/50] batch [570/1000] time 1.563 (1.564) data 0.000 (0.002) loss 0.9644 (1.1542) acc 78.1250 (71.3158) lr 1.4818e-03 eta 13:39:31
+epoch [19/50] batch [575/1000] time 1.539 (1.564) data 0.001 (0.002) loss 0.7603 (1.1521) acc 75.0000 (71.3261) lr 1.4818e-03 eta 13:39:22
+epoch [19/50] batch [580/1000] time 1.551 (1.564) data 0.000 (0.002) loss 1.0234 (1.1540) acc 68.7500 (71.2769) lr 1.4818e-03 eta 13:39:14
+epoch [19/50] batch [585/1000] time 1.562 (1.564) data 0.000 (0.002) loss 1.5752 (1.1561) acc 59.3750 (71.2286) lr 1.4818e-03 eta 13:39:06
+epoch [19/50] batch [590/1000] time 1.570 (1.564) data 0.001 (0.002) loss 1.2061 (1.1570) acc 71.8750 (71.1970) lr 1.4818e-03 eta 13:38:55
+epoch [19/50] batch [595/1000] time 1.562 (1.564) data 0.001 (0.002) loss 0.8218 (1.1570) acc 84.3750 (71.1975) lr 1.4818e-03 eta 13:38:46
+epoch [19/50] batch [600/1000] time 1.558 (1.564) data 0.000 (0.002) loss 0.6016 (1.1561) acc 78.1250 (71.2344) lr 1.4818e-03 eta 13:38:37
+epoch [19/50] batch [605/1000] time 1.580 (1.564) data 0.000 (0.002) loss 2.1465 (1.1573) acc 56.2500 (71.2138) lr 1.4818e-03 eta 13:38:33
+epoch [19/50] batch [610/1000] time 1.564 (1.564) data 0.000 (0.002) loss 1.2354 (1.1580) acc 65.6250 (71.2039) lr 1.4818e-03 eta 13:38:27
+epoch [19/50] batch [615/1000] time 1.539 (1.564) data 0.001 (0.002) loss 1.0068 (1.1589) acc 62.5000 (71.1636) lr 1.4818e-03 eta 13:38:15
+epoch [19/50] batch [620/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.9541 (1.1579) acc 71.8750 (71.2097) lr 1.4818e-03 eta 13:38:14
+epoch [19/50] batch [625/1000] time 1.550 (1.565) data 0.001 (0.002) loss 1.1758 (1.1565) acc 65.6250 (71.2100) lr 1.4818e-03 eta 13:38:06
+epoch [19/50] batch [630/1000] time 1.546 (1.564) data 0.000 (0.002) loss 1.2422 (1.1553) acc 78.1250 (71.2500) lr 1.4818e-03 eta 13:37:53
+epoch [19/50] batch [635/1000] time 1.547 (1.564) data 0.000 (0.002) loss 1.5898 (1.1538) acc 59.3750 (71.2697) lr 1.4818e-03 eta 13:37:43
+epoch [19/50] batch [640/1000] time 1.603 (1.564) data 0.001 (0.002) loss 0.8540 (1.1538) acc 78.1250 (71.2598) lr 1.4818e-03 eta 13:37:36
+epoch [19/50] batch [645/1000] time 1.564 (1.564) data 0.000 (0.002) loss 1.1787 (1.1534) acc 75.0000 (71.2694) lr 1.4818e-03 eta 13:37:30
+epoch [19/50] batch [650/1000] time 1.559 (1.564) data 0.000 (0.002) loss 0.9927 (1.1538) acc 75.0000 (71.2740) lr 1.4818e-03 eta 13:37:21
+epoch [19/50] batch [655/1000] time 1.547 (1.564) data 0.000 (0.002) loss 1.2627 (1.1545) acc 75.0000 (71.2548) lr 1.4818e-03 eta 13:37:09
+epoch [19/50] batch [660/1000] time 1.561 (1.564) data 0.000 (0.002) loss 1.6094 (1.1551) acc 65.6250 (71.2595) lr 1.4818e-03 eta 13:36:59
+epoch [19/50] batch [665/1000] time 1.575 (1.564) data 0.000 (0.002) loss 1.0547 (1.1555) acc 68.7500 (71.2124) lr 1.4818e-03 eta 13:36:58
+epoch [19/50] batch [670/1000] time 1.555 (1.564) data 0.000 (0.002) loss 1.4893 (1.1566) acc 71.8750 (71.1894) lr 1.4818e-03 eta 13:36:51
+epoch [19/50] batch [675/1000] time 1.559 (1.564) data 0.000 (0.002) loss 1.8682 (1.1582) acc 59.3750 (71.1667) lr 1.4818e-03 eta 13:36:44
+epoch [19/50] batch [680/1000] time 1.557 (1.564) data 0.000 (0.002) loss 1.5264 (1.1575) acc 65.6250 (71.1949) lr 1.4818e-03 eta 13:36:37
+epoch [19/50] batch [685/1000] time 1.565 (1.564) data 0.000 (0.002) loss 1.1045 (1.1579) acc 78.1250 (71.1816) lr 1.4818e-03 eta 13:36:28
+epoch [19/50] batch [690/1000] time 1.546 (1.564) data 0.001 (0.002) loss 0.8628 (1.1589) acc 84.3750 (71.1685) lr 1.4818e-03 eta 13:36:19
+epoch [19/50] batch [695/1000] time 1.555 (1.564) data 0.000 (0.002) loss 0.9111 (1.1569) acc 78.1250 (71.2050) lr 1.4818e-03 eta 13:36:11
+epoch [19/50] batch [700/1000] time 1.537 (1.564) data 0.000 (0.002) loss 0.9497 (1.1562) acc 75.0000 (71.2009) lr 1.4818e-03 eta 13:36:01
+epoch [19/50] batch [705/1000] time 1.556 (1.564) data 0.001 (0.002) loss 1.2314 (1.1549) acc 71.8750 (71.2145) lr 1.4818e-03 eta 13:35:50
+epoch [19/50] batch [710/1000] time 1.559 (1.564) data 0.001 (0.002) loss 0.7158 (1.1542) acc 68.7500 (71.2060) lr 1.4818e-03 eta 13:35:49
+epoch [19/50] batch [715/1000] time 1.537 (1.564) data 0.000 (0.002) loss 1.4609 (1.1537) acc 65.6250 (71.2281) lr 1.4818e-03 eta 13:35:37
+epoch [19/50] batch [720/1000] time 1.557 (1.564) data 0.001 (0.002) loss 1.3096 (1.1525) acc 65.6250 (71.2500) lr 1.4818e-03 eta 13:35:27
+epoch [19/50] batch [725/1000] time 1.560 (1.564) data 0.000 (0.002) loss 0.7061 (1.1508) acc 75.0000 (71.2629) lr 1.4818e-03 eta 13:35:18
+epoch [19/50] batch [730/1000] time 1.557 (1.564) data 0.001 (0.002) loss 0.6411 (1.1494) acc 84.3750 (71.2971) lr 1.4818e-03 eta 13:35:10
+epoch [19/50] batch [735/1000] time 1.577 (1.564) data 0.001 (0.002) loss 1.2930 (1.1508) acc 68.7500 (71.2670) lr 1.4818e-03 eta 13:35:05
+epoch [19/50] batch [740/1000] time 1.570 (1.564) data 0.000 (0.002) loss 1.1650 (1.1502) acc 78.1250 (71.2922) lr 1.4818e-03 eta 13:34:58
+epoch [19/50] batch [745/1000] time 1.582 (1.564) data 0.000 (0.002) loss 1.4580 (1.1527) acc 62.5000 (71.2542) lr 1.4818e-03 eta 13:34:50
+epoch [19/50] batch [750/1000] time 1.571 (1.564) data 0.000 (0.002) loss 1.0488 (1.1531) acc 75.0000 (71.2500) lr 1.4818e-03 eta 13:34:43
+epoch [19/50] batch [755/1000] time 1.564 (1.564) data 0.000 (0.002) loss 0.8096 (1.1516) acc 75.0000 (71.2666) lr 1.4818e-03 eta 13:34:36
+epoch [19/50] batch [760/1000] time 1.561 (1.564) data 0.000 (0.002) loss 1.2227 (1.1514) acc 78.1250 (71.3035) lr 1.4818e-03 eta 13:34:28
+epoch [19/50] batch [765/1000] time 1.556 (1.564) data 0.000 (0.002) loss 1.1221 (1.1505) acc 78.1250 (71.3276) lr 1.4818e-03 eta 13:34:19
+epoch [19/50] batch [770/1000] time 1.549 (1.564) data 0.000 (0.002) loss 1.2148 (1.1508) acc 68.7500 (71.3312) lr 1.4818e-03 eta 13:34:17
+epoch [19/50] batch [775/1000] time 1.551 (1.564) data 0.000 (0.002) loss 1.3730 (1.1518) acc 62.5000 (71.2984) lr 1.4818e-03 eta 13:34:07
+epoch [19/50] batch [780/1000] time 1.557 (1.564) data 0.000 (0.002) loss 1.0420 (1.1509) acc 68.7500 (71.3141) lr 1.4818e-03 eta 13:33:58
+epoch [19/50] batch [785/1000] time 1.543 (1.564) data 0.001 (0.002) loss 1.4121 (1.1512) acc 65.6250 (71.3177) lr 1.4818e-03 eta 13:33:50
+epoch [19/50] batch [790/1000] time 1.543 (1.564) data 0.000 (0.002) loss 0.7432 (1.1510) acc 84.3750 (71.3410) lr 1.4818e-03 eta 13:33:41
+epoch [19/50] batch [795/1000] time 1.556 (1.564) data 0.000 (0.002) loss 1.2275 (1.1499) acc 75.0000 (71.3679) lr 1.4818e-03 eta 13:33:31
+epoch [19/50] batch [800/1000] time 1.559 (1.564) data 0.000 (0.002) loss 0.4622 (1.1478) acc 84.3750 (71.4023) lr 1.4818e-03 eta 13:33:21
+epoch [19/50] batch [805/1000] time 1.548 (1.564) data 0.000 (0.002) loss 1.4775 (1.1485) acc 56.2500 (71.3781) lr 1.4818e-03 eta 13:33:11
+epoch [19/50] batch [810/1000] time 1.592 (1.564) data 0.000 (0.002) loss 2.0625 (1.1491) acc 56.2500 (71.3927) lr 1.4818e-03 eta 13:33:04
+epoch [19/50] batch [815/1000] time 1.550 (1.564) data 0.000 (0.002) loss 1.0391 (1.1487) acc 71.8750 (71.3880) lr 1.4818e-03 eta 13:33:01
+epoch [19/50] batch [820/1000] time 1.557 (1.564) data 0.000 (0.002) loss 1.4824 (1.1482) acc 71.8750 (71.3948) lr 1.4818e-03 eta 13:32:52
+epoch [19/50] batch [825/1000] time 1.542 (1.564) data 0.000 (0.001) loss 0.9521 (1.1467) acc 75.0000 (71.4091) lr 1.4818e-03 eta 13:32:42
+epoch [19/50] batch [830/1000] time 1.542 (1.564) data 0.001 (0.001) loss 1.9248 (1.1472) acc 53.1250 (71.3855) lr 1.4818e-03 eta 13:32:33
+epoch [19/50] batch [835/1000] time 1.573 (1.564) data 0.000 (0.001) loss 1.6309 (1.1477) acc 56.2500 (71.3922) lr 1.4818e-03 eta 13:32:23
+epoch [19/50] batch [840/1000] time 1.547 (1.564) data 0.000 (0.001) loss 1.1309 (1.1467) acc 68.7500 (71.4174) lr 1.4818e-03 eta 13:32:13
+epoch [19/50] batch [845/1000] time 1.572 (1.564) data 0.000 (0.001) loss 1.3877 (1.1475) acc 68.7500 (71.4238) lr 1.4818e-03 eta 13:32:04
+epoch [19/50] batch [850/1000] time 1.544 (1.564) data 0.000 (0.001) loss 0.9424 (1.1466) acc 78.1250 (71.4412) lr 1.4818e-03 eta 13:31:55
+epoch [19/50] batch [855/1000] time 1.541 (1.564) data 0.000 (0.001) loss 1.4580 (1.1476) acc 68.7500 (71.4145) lr 1.4818e-03 eta 13:31:47
+epoch [19/50] batch [860/1000] time 1.553 (1.564) data 0.001 (0.001) loss 0.9722 (1.1470) acc 68.7500 (71.4172) lr 1.4818e-03 eta 13:31:45
+epoch [19/50] batch [865/1000] time 1.569 (1.564) data 0.000 (0.001) loss 1.1309 (1.1474) acc 71.8750 (71.4126) lr 1.4818e-03 eta 13:31:34
+epoch [19/50] batch [870/1000] time 1.569 (1.564) data 0.001 (0.001) loss 1.5078 (1.1477) acc 81.2500 (71.4224) lr 1.4818e-03 eta 13:31:26
+epoch [19/50] batch [875/1000] time 1.541 (1.564) data 0.000 (0.001) loss 0.6401 (1.1466) acc 84.3750 (71.4500) lr 1.4818e-03 eta 13:31:17
+epoch [19/50] batch [880/1000] time 1.571 (1.564) data 0.000 (0.001) loss 1.0557 (1.1447) acc 75.0000 (71.4808) lr 1.4818e-03 eta 13:31:09
+epoch [19/50] batch [885/1000] time 1.558 (1.564) data 0.001 (0.001) loss 0.5972 (1.1426) acc 84.3750 (71.5078) lr 1.4818e-03 eta 13:31:00
+epoch [19/50] batch [890/1000] time 1.567 (1.564) data 0.000 (0.001) loss 1.2178 (1.1420) acc 71.8750 (71.5204) lr 1.4818e-03 eta 13:30:50
+epoch [19/50] batch [895/1000] time 1.578 (1.564) data 0.000 (0.001) loss 1.1094 (1.1428) acc 75.0000 (71.5154) lr 1.4818e-03 eta 13:30:42
+epoch [19/50] batch [900/1000] time 1.553 (1.564) data 0.001 (0.001) loss 1.1758 (1.1436) acc 68.7500 (71.4896) lr 1.4818e-03 eta 13:30:33
+epoch [19/50] batch [905/1000] time 1.564 (1.564) data 0.001 (0.001) loss 1.8291 (1.1444) acc 59.3750 (71.4779) lr 1.4818e-03 eta 13:30:23
+epoch [19/50] batch [910/1000] time 1.562 (1.564) data 0.000 (0.001) loss 0.8101 (1.1438) acc 81.2500 (71.4973) lr 1.4818e-03 eta 13:30:14
+epoch [19/50] batch [915/1000] time 1.556 (1.564) data 0.000 (0.001) loss 0.8306 (1.1420) acc 75.0000 (71.5198) lr 1.4818e-03 eta 13:30:08
+epoch [19/50] batch [920/1000] time 1.723 (1.564) data 0.000 (0.001) loss 0.9258 (1.1408) acc 78.1250 (71.5319) lr 1.4818e-03 eta 13:30:03
+epoch [19/50] batch [925/1000] time 1.557 (1.564) data 0.001 (0.001) loss 1.5225 (1.1414) acc 75.0000 (71.5338) lr 1.4818e-03 eta 13:29:56
+epoch [19/50] batch [930/1000] time 1.566 (1.564) data 0.000 (0.001) loss 1.1357 (1.1405) acc 75.0000 (71.5491) lr 1.4818e-03 eta 13:29:48
+epoch [19/50] batch [935/1000] time 1.573 (1.564) data 0.000 (0.001) loss 1.2910 (1.1415) acc 68.7500 (71.5341) lr 1.4818e-03 eta 13:29:43
+epoch [19/50] batch [940/1000] time 1.567 (1.564) data 0.000 (0.001) loss 1.3662 (1.1418) acc 59.3750 (71.5226) lr 1.4818e-03 eta 13:29:35
+epoch [19/50] batch [945/1000] time 1.557 (1.564) data 0.000 (0.001) loss 0.6045 (1.1406) acc 84.3750 (71.5443) lr 1.4818e-03 eta 13:29:26
+epoch [19/50] batch [950/1000] time 1.578 (1.564) data 0.000 (0.001) loss 1.4854 (1.1401) acc 71.8750 (71.5493) lr 1.4818e-03 eta 13:29:19
+epoch [19/50] batch [955/1000] time 1.554 (1.564) data 0.000 (0.001) loss 0.9868 (1.1409) acc 75.0000 (71.5314) lr 1.4818e-03 eta 13:29:11
+epoch [19/50] batch [960/1000] time 1.566 (1.564) data 0.001 (0.001) loss 1.3936 (1.1421) acc 68.7500 (71.5072) lr 1.4818e-03 eta 13:29:02
+epoch [19/50] batch [965/1000] time 1.725 (1.564) data 0.001 (0.001) loss 0.9302 (1.1428) acc 71.8750 (71.4929) lr 1.4818e-03 eta 13:29:00
+epoch [19/50] batch [970/1000] time 1.569 (1.564) data 0.000 (0.001) loss 1.1279 (1.1430) acc 68.7500 (71.4820) lr 1.4818e-03 eta 13:28:51
+epoch [19/50] batch [975/1000] time 1.561 (1.564) data 0.000 (0.001) loss 1.1865 (1.1426) acc 68.7500 (71.4744) lr 1.4818e-03 eta 13:28:43
+epoch [19/50] batch [980/1000] time 1.545 (1.564) data 0.000 (0.001) loss 1.3125 (1.1430) acc 68.7500 (71.4541) lr 1.4818e-03 eta 13:28:35
+epoch [19/50] batch [985/1000] time 1.554 (1.564) data 0.001 (0.001) loss 1.8486 (1.1429) acc 62.5000 (71.4530) lr 1.4818e-03 eta 13:28:26
+epoch [19/50] batch [990/1000] time 1.534 (1.564) data 0.000 (0.001) loss 1.1592 (1.1425) acc 75.0000 (71.4741) lr 1.4818e-03 eta 13:28:17
+epoch [19/50] batch [995/1000] time 1.560 (1.564) data 0.000 (0.001) loss 1.0000 (1.1414) acc 78.1250 (71.5107) lr 1.4818e-03 eta 13:28:08
+epoch [19/50] batch [1000/1000] time 1.566 (1.564) data 0.000 (0.001) loss 0.8564 (1.1408) acc 84.3750 (71.5344) lr 1.4258e-03 eta 13:27:59
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,203
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [20/50] batch [5/1000] time 1.553 (1.692) data 0.000 (0.190) loss 1.2295 (1.0469) acc 65.6250 (75.6250) lr 1.4258e-03 eta 14:34:07
+epoch [20/50] batch [10/1000] time 1.580 (1.629) data 0.001 (0.095) loss 0.7710 (1.0288) acc 81.2500 (75.3125) lr 1.4258e-03 eta 14:01:19
+epoch [20/50] batch [15/1000] time 1.544 (1.604) data 0.000 (0.064) loss 1.4014 (1.1186) acc 59.3750 (73.1250) lr 1.4258e-03 eta 13:48:19
+epoch [20/50] batch [20/1000] time 1.590 (1.594) data 0.001 (0.048) loss 1.0664 (1.0892) acc 65.6250 (72.8125) lr 1.4258e-03 eta 13:43:17
+epoch [20/50] batch [25/1000] time 1.558 (1.586) data 0.001 (0.039) loss 0.8945 (1.0655) acc 78.1250 (73.0000) lr 1.4258e-03 eta 13:38:47
+epoch [20/50] batch [30/1000] time 1.552 (1.580) data 0.000 (0.032) loss 1.0898 (1.0760) acc 71.8750 (72.3958) lr 1.4258e-03 eta 13:35:43
+epoch [20/50] batch [35/1000] time 1.563 (1.577) data 0.000 (0.028) loss 1.1826 (1.0812) acc 78.1250 (72.8571) lr 1.4258e-03 eta 13:33:57
+epoch [20/50] batch [40/1000] time 1.545 (1.574) data 0.001 (0.024) loss 1.2607 (1.0624) acc 75.0000 (73.2031) lr 1.4258e-03 eta 13:31:57
+epoch [20/50] batch [45/1000] time 1.560 (1.578) data 0.000 (0.022) loss 1.5566 (1.0645) acc 59.3750 (72.8472) lr 1.4258e-03 eta 13:33:59
+epoch [20/50] batch [50/1000] time 1.568 (1.575) data 0.000 (0.020) loss 0.6997 (1.0830) acc 84.3750 (72.7500) lr 1.4258e-03 eta 13:32:27
+epoch [20/50] batch [55/1000] time 1.560 (1.574) data 0.001 (0.018) loss 1.1436 (1.0682) acc 75.0000 (72.9545) lr 1.4258e-03 eta 13:31:51
+epoch [20/50] batch [60/1000] time 1.549 (1.573) data 0.001 (0.016) loss 0.4839 (1.0774) acc 81.2500 (72.6042) lr 1.4258e-03 eta 13:31:00
+epoch [20/50] batch [65/1000] time 1.584 (1.572) data 0.001 (0.015) loss 1.3896 (1.0900) acc 62.5000 (71.9231) lr 1.4258e-03 eta 13:30:29
+epoch [20/50] batch [70/1000] time 1.561 (1.571) data 0.000 (0.014) loss 1.4717 (1.0883) acc 62.5000 (71.9643) lr 1.4258e-03 eta 13:29:59
+epoch [20/50] batch [75/1000] time 1.537 (1.570) data 0.001 (0.013) loss 0.8286 (1.0839) acc 75.0000 (72.0417) lr 1.4258e-03 eta 13:29:07
+epoch [20/50] batch [80/1000] time 1.562 (1.569) data 0.000 (0.012) loss 1.5967 (1.0940) acc 71.8750 (71.8750) lr 1.4258e-03 eta 13:28:34
+epoch [20/50] batch [85/1000] time 1.553 (1.568) data 0.000 (0.012) loss 1.0049 (1.0944) acc 75.0000 (71.9485) lr 1.4258e-03 eta 13:27:54
+epoch [20/50] batch [90/1000] time 1.590 (1.568) data 0.000 (0.011) loss 1.1455 (1.1006) acc 81.2500 (72.1181) lr 1.4258e-03 eta 13:27:34
+epoch [20/50] batch [95/1000] time 1.539 (1.566) data 0.000 (0.010) loss 0.8203 (1.0961) acc 78.1250 (72.3026) lr 1.4258e-03 eta 13:26:50
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+epoch [20/50] batch [655/1000] time 1.556 (1.563) data 0.001 (0.002) loss 0.8145 (1.1367) acc 71.8750 (71.5792) lr 1.4258e-03 eta 13:10:15
+epoch [20/50] batch [660/1000] time 1.575 (1.563) data 0.001 (0.002) loss 0.8467 (1.1362) acc 87.5000 (71.6335) lr 1.4258e-03 eta 13:10:08
+epoch [20/50] batch [665/1000] time 1.569 (1.562) data 0.000 (0.002) loss 0.9766 (1.1363) acc 75.0000 (71.6588) lr 1.4258e-03 eta 13:09:57
+epoch [20/50] batch [670/1000] time 1.556 (1.563) data 0.000 (0.002) loss 1.3955 (1.1378) acc 65.6250 (71.6465) lr 1.4258e-03 eta 13:09:51
+epoch [20/50] batch [675/1000] time 1.556 (1.562) data 0.000 (0.002) loss 1.3848 (1.1396) acc 65.6250 (71.6111) lr 1.4258e-03 eta 13:09:42
+epoch [20/50] batch [680/1000] time 1.543 (1.562) data 0.001 (0.002) loss 1.1543 (1.1399) acc 65.6250 (71.5671) lr 1.4258e-03 eta 13:09:30
+epoch [20/50] batch [685/1000] time 1.541 (1.562) data 0.000 (0.002) loss 1.4023 (1.1398) acc 65.6250 (71.5465) lr 1.4258e-03 eta 13:09:19
+epoch [20/50] batch [690/1000] time 1.545 (1.562) data 0.000 (0.002) loss 0.8525 (1.1382) acc 78.1250 (71.5489) lr 1.4258e-03 eta 13:09:07
+epoch [20/50] batch [695/1000] time 1.546 (1.562) data 0.000 (0.002) loss 1.4307 (1.1401) acc 65.6250 (71.5108) lr 1.4258e-03 eta 13:08:57
+epoch [20/50] batch [700/1000] time 1.534 (1.562) data 0.000 (0.002) loss 0.7451 (1.1396) acc 71.8750 (71.5268) lr 1.4258e-03 eta 13:08:47
+epoch [20/50] batch [705/1000] time 1.555 (1.562) data 0.000 (0.002) loss 1.4756 (1.1405) acc 65.6250 (71.5115) lr 1.4258e-03 eta 13:08:36
+epoch [20/50] batch [710/1000] time 1.715 (1.562) data 0.000 (0.002) loss 0.8960 (1.1387) acc 75.0000 (71.5493) lr 1.4258e-03 eta 13:08:35
+epoch [20/50] batch [715/1000] time 1.568 (1.562) data 0.001 (0.002) loss 0.6890 (1.1373) acc 78.1250 (71.5734) lr 1.4258e-03 eta 13:08:28
+epoch [20/50] batch [720/1000] time 1.562 (1.562) data 0.000 (0.002) loss 0.8730 (1.1371) acc 65.6250 (71.5712) lr 1.4258e-03 eta 13:08:20
+epoch [20/50] batch [725/1000] time 1.579 (1.562) data 0.000 (0.002) loss 1.5400 (1.1371) acc 59.3750 (71.5560) lr 1.4258e-03 eta 13:08:11
+epoch [20/50] batch [730/1000] time 1.555 (1.562) data 0.001 (0.002) loss 0.8682 (1.1354) acc 71.8750 (71.5753) lr 1.4258e-03 eta 13:08:01
+epoch [20/50] batch [735/1000] time 1.569 (1.562) data 0.001 (0.002) loss 1.4082 (1.1361) acc 59.3750 (71.5476) lr 1.4258e-03 eta 13:07:53
+epoch [20/50] batch [740/1000] time 1.555 (1.562) data 0.000 (0.002) loss 1.0586 (1.1367) acc 71.8750 (71.5372) lr 1.4258e-03 eta 13:07:44
+epoch [20/50] batch [745/1000] time 1.541 (1.562) data 0.000 (0.002) loss 1.0566 (1.1363) acc 81.2500 (71.5562) lr 1.4258e-03 eta 13:07:33
+epoch [20/50] batch [750/1000] time 1.579 (1.562) data 0.001 (0.002) loss 1.6660 (1.1359) acc 56.2500 (71.5375) lr 1.4258e-03 eta 13:07:25
+epoch [20/50] batch [755/1000] time 1.727 (1.562) data 0.001 (0.002) loss 1.3066 (1.1375) acc 75.0000 (71.5356) lr 1.4258e-03 eta 13:07:24
+epoch [20/50] batch [760/1000] time 1.545 (1.562) data 0.000 (0.002) loss 1.5107 (1.1387) acc 71.8750 (71.5378) lr 1.4258e-03 eta 13:07:13
+epoch [20/50] batch [765/1000] time 1.540 (1.562) data 0.000 (0.002) loss 1.0439 (1.1402) acc 75.0000 (71.5033) lr 1.4258e-03 eta 13:07:03
+epoch [20/50] batch [770/1000] time 1.539 (1.562) data 0.000 (0.002) loss 0.9111 (1.1409) acc 68.7500 (71.4692) lr 1.4258e-03 eta 13:06:52
+epoch [20/50] batch [775/1000] time 1.576 (1.562) data 0.000 (0.002) loss 1.3418 (1.1410) acc 71.8750 (71.4556) lr 1.4258e-03 eta 13:06:44
+epoch [20/50] batch [780/1000] time 1.567 (1.562) data 0.000 (0.002) loss 1.1230 (1.1394) acc 75.0000 (71.4944) lr 1.4258e-03 eta 13:06:36
+epoch [20/50] batch [785/1000] time 1.546 (1.562) data 0.001 (0.002) loss 0.9263 (1.1391) acc 75.0000 (71.5048) lr 1.4258e-03 eta 13:06:27
+epoch [20/50] batch [790/1000] time 1.543 (1.562) data 0.001 (0.002) loss 0.9355 (1.1387) acc 75.0000 (71.5229) lr 1.4258e-03 eta 13:06:19
+epoch [20/50] batch [795/1000] time 1.563 (1.562) data 0.000 (0.002) loss 0.8257 (1.1377) acc 68.7500 (71.5134) lr 1.4258e-03 eta 13:06:11
+epoch [20/50] batch [800/1000] time 1.585 (1.562) data 0.000 (0.002) loss 0.9453 (1.1391) acc 71.8750 (71.4805) lr 1.4258e-03 eta 13:06:11
+epoch [20/50] batch [805/1000] time 1.564 (1.562) data 0.001 (0.002) loss 1.3408 (1.1388) acc 65.6250 (71.4790) lr 1.4258e-03 eta 13:06:03
+epoch [20/50] batch [810/1000] time 1.560 (1.562) data 0.000 (0.002) loss 0.7700 (1.1386) acc 81.2500 (71.4622) lr 1.4258e-03 eta 13:05:55
+epoch [20/50] batch [815/1000] time 1.540 (1.562) data 0.000 (0.002) loss 1.0176 (1.1380) acc 71.8750 (71.4686) lr 1.4258e-03 eta 13:05:42
+epoch [20/50] batch [820/1000] time 1.560 (1.562) data 0.000 (0.002) loss 0.8262 (1.1369) acc 81.2500 (71.4977) lr 1.4258e-03 eta 13:05:32
+epoch [20/50] batch [825/1000] time 1.563 (1.562) data 0.000 (0.002) loss 0.9302 (1.1368) acc 75.0000 (71.5000) lr 1.4258e-03 eta 13:05:22
+epoch [20/50] batch [830/1000] time 1.562 (1.562) data 0.000 (0.002) loss 1.3584 (1.1389) acc 75.0000 (71.4721) lr 1.4258e-03 eta 13:05:14
+epoch [20/50] batch [835/1000] time 1.534 (1.562) data 0.000 (0.002) loss 1.4404 (1.1395) acc 75.0000 (71.4783) lr 1.4258e-03 eta 13:05:03
+epoch [20/50] batch [840/1000] time 1.558 (1.561) data 0.000 (0.002) loss 1.1855 (1.1410) acc 75.0000 (71.4583) lr 1.4258e-03 eta 13:04:54
+epoch [20/50] batch [845/1000] time 1.546 (1.561) data 0.000 (0.002) loss 1.0098 (1.1402) acc 75.0000 (71.4645) lr 1.4258e-03 eta 13:04:46
+epoch [20/50] batch [850/1000] time 1.544 (1.561) data 0.000 (0.002) loss 1.1465 (1.1404) acc 68.7500 (71.4559) lr 1.4258e-03 eta 13:04:35
+epoch [20/50] batch [855/1000] time 1.541 (1.561) data 0.000 (0.002) loss 1.4365 (1.1410) acc 68.7500 (71.4437) lr 1.4258e-03 eta 13:04:24
+epoch [20/50] batch [860/1000] time 1.532 (1.561) data 0.000 (0.002) loss 0.6636 (1.1398) acc 81.2500 (71.4717) lr 1.4258e-03 eta 13:04:12
+epoch [20/50] batch [865/1000] time 1.579 (1.561) data 0.000 (0.002) loss 1.2910 (1.1389) acc 68.7500 (71.5282) lr 1.4258e-03 eta 13:04:11
+epoch [20/50] batch [870/1000] time 1.525 (1.561) data 0.000 (0.002) loss 1.1475 (1.1392) acc 68.7500 (71.5050) lr 1.4258e-03 eta 13:03:59
+epoch [20/50] batch [875/1000] time 1.552 (1.561) data 0.000 (0.002) loss 1.3896 (1.1397) acc 71.8750 (71.5000) lr 1.4258e-03 eta 13:03:50
+epoch [20/50] batch [880/1000] time 1.553 (1.561) data 0.000 (0.002) loss 1.4219 (1.1388) acc 65.6250 (71.4879) lr 1.4258e-03 eta 13:03:40
+epoch [20/50] batch [885/1000] time 1.572 (1.561) data 0.000 (0.002) loss 0.6704 (1.1384) acc 87.5000 (71.5148) lr 1.4258e-03 eta 13:03:32
+epoch [20/50] batch [890/1000] time 1.544 (1.561) data 0.000 (0.002) loss 1.4775 (1.1382) acc 62.5000 (71.5169) lr 1.4258e-03 eta 13:03:24
+epoch [20/50] batch [895/1000] time 1.563 (1.561) data 0.000 (0.001) loss 1.0459 (1.1376) acc 78.1250 (71.5538) lr 1.4258e-03 eta 13:03:16
+epoch [20/50] batch [900/1000] time 1.556 (1.561) data 0.000 (0.001) loss 1.0479 (1.1373) acc 81.2500 (71.5660) lr 1.4258e-03 eta 13:03:08
+epoch [20/50] batch [905/1000] time 1.537 (1.561) data 0.000 (0.001) loss 1.4160 (1.1370) acc 65.6250 (71.5746) lr 1.4258e-03 eta 13:03:00
+epoch [20/50] batch [910/1000] time 1.579 (1.561) data 0.000 (0.001) loss 0.9771 (1.1358) acc 68.7500 (71.5865) lr 1.4258e-03 eta 13:02:56
+epoch [20/50] batch [915/1000] time 1.547 (1.561) data 0.000 (0.001) loss 0.7305 (1.1346) acc 81.2500 (71.6052) lr 1.4258e-03 eta 13:02:48
+epoch [20/50] batch [920/1000] time 1.584 (1.561) data 0.001 (0.001) loss 0.9131 (1.1350) acc 75.0000 (71.5931) lr 1.4258e-03 eta 13:02:40
+epoch [20/50] batch [925/1000] time 1.536 (1.561) data 0.000 (0.001) loss 1.2812 (1.1356) acc 75.0000 (71.5980) lr 1.4258e-03 eta 13:02:33
+epoch [20/50] batch [930/1000] time 1.568 (1.561) data 0.000 (0.001) loss 0.9043 (1.1348) acc 81.2500 (71.6230) lr 1.4258e-03 eta 13:02:26
+epoch [20/50] batch [935/1000] time 1.555 (1.561) data 0.000 (0.001) loss 0.6309 (1.1344) acc 87.5000 (71.6444) lr 1.4258e-03 eta 13:02:18
+epoch [20/50] batch [940/1000] time 1.559 (1.561) data 0.000 (0.001) loss 1.6133 (1.1342) acc 62.5000 (71.6423) lr 1.4258e-03 eta 13:02:09
+epoch [20/50] batch [945/1000] time 1.552 (1.561) data 0.000 (0.001) loss 0.9653 (1.1335) acc 65.6250 (71.6303) lr 1.4258e-03 eta 13:02:01
+epoch [20/50] batch [950/1000] time 1.573 (1.561) data 0.000 (0.001) loss 1.4307 (1.1348) acc 75.0000 (71.6151) lr 1.4258e-03 eta 13:01:57
+epoch [20/50] batch [955/1000] time 1.560 (1.561) data 0.000 (0.001) loss 1.0459 (1.1343) acc 78.1250 (71.6263) lr 1.4258e-03 eta 13:01:50
+epoch [20/50] batch [960/1000] time 1.544 (1.561) data 0.000 (0.001) loss 0.9629 (1.1334) acc 71.8750 (71.6406) lr 1.4258e-03 eta 13:01:42
+epoch [20/50] batch [965/1000] time 1.528 (1.561) data 0.000 (0.001) loss 0.7666 (1.1325) acc 84.3750 (71.6645) lr 1.4258e-03 eta 13:01:32
+epoch [20/50] batch [970/1000] time 1.567 (1.561) data 0.000 (0.001) loss 1.9326 (1.1345) acc 62.5000 (71.6302) lr 1.4258e-03 eta 13:01:25
+epoch [20/50] batch [975/1000] time 1.543 (1.561) data 0.000 (0.001) loss 0.8652 (1.1342) acc 78.1250 (71.6410) lr 1.4258e-03 eta 13:01:15
+epoch [20/50] batch [980/1000] time 1.556 (1.561) data 0.000 (0.001) loss 1.7754 (1.1341) acc 53.1250 (71.6358) lr 1.4258e-03 eta 13:01:07
+epoch [20/50] batch [985/1000] time 1.532 (1.561) data 0.001 (0.001) loss 0.9780 (1.1356) acc 65.6250 (71.5926) lr 1.4258e-03 eta 13:00:58
+epoch [20/50] batch [990/1000] time 1.568 (1.561) data 0.000 (0.001) loss 1.6377 (1.1367) acc 71.8750 (71.5720) lr 1.4258e-03 eta 13:00:50
+epoch [20/50] batch [995/1000] time 1.553 (1.561) data 0.000 (0.001) loss 0.9790 (1.1369) acc 78.1250 (71.5735) lr 1.4258e-03 eta 13:00:40
+epoch [20/50] batch [1000/1000] time 1.541 (1.561) data 0.000 (0.001) loss 1.4287 (1.1370) acc 50.0000 (71.5750) lr 1.3681e-03 eta 13:00:29
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,193
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+epoch [21/50] batch [5/1000] time 1.524 (1.669) data 0.001 (0.177) loss 1.1973 (1.4265) acc 68.7500 (66.8750) lr 1.3681e-03 eta 13:54:14
+epoch [21/50] batch [10/1000] time 1.559 (1.613) data 0.000 (0.089) loss 0.9585 (1.2646) acc 81.2500 (70.3125) lr 1.3681e-03 eta 13:26:02
+epoch [21/50] batch [15/1000] time 1.564 (1.598) data 0.000 (0.059) loss 1.1035 (1.2122) acc 68.7500 (71.6667) lr 1.3681e-03 eta 13:18:29
+epoch [21/50] batch [20/1000] time 1.538 (1.588) data 0.001 (0.045) loss 1.5898 (1.2149) acc 71.8750 (71.8750) lr 1.3681e-03 eta 13:13:34
+epoch [21/50] batch [25/1000] time 1.561 (1.584) data 0.000 (0.036) loss 1.3555 (1.1823) acc 65.6250 (72.3750) lr 1.3681e-03 eta 13:11:26
+epoch [21/50] batch [30/1000] time 1.544 (1.588) data 0.000 (0.030) loss 1.3613 (1.1701) acc 65.6250 (72.6042) lr 1.3681e-03 eta 13:13:13
+epoch [21/50] batch [35/1000] time 1.548 (1.583) data 0.001 (0.026) loss 1.2598 (1.1607) acc 68.7500 (72.5000) lr 1.3681e-03 eta 13:10:48
+epoch [21/50] batch [40/1000] time 1.554 (1.580) data 0.000 (0.023) loss 1.1260 (1.1738) acc 71.8750 (72.3438) lr 1.3681e-03 eta 13:09:05
+epoch [21/50] batch [45/1000] time 1.562 (1.578) data 0.000 (0.020) loss 1.5146 (1.1585) acc 68.7500 (72.7778) lr 1.3681e-03 eta 13:07:58
+epoch [21/50] batch [50/1000] time 1.563 (1.577) data 0.000 (0.018) loss 0.9648 (1.1618) acc 71.8750 (72.7500) lr 1.3681e-03 eta 13:07:20
+epoch [21/50] batch [55/1000] time 1.543 (1.576) data 0.000 (0.017) loss 0.9199 (1.1332) acc 68.7500 (72.8977) lr 1.3681e-03 eta 13:06:22
+epoch [21/50] batch [60/1000] time 1.565 (1.574) data 0.001 (0.015) loss 1.5703 (1.1590) acc 68.7500 (72.1354) lr 1.3681e-03 eta 13:05:33
+epoch [21/50] batch [65/1000] time 1.555 (1.573) data 0.000 (0.014) loss 0.9155 (1.1531) acc 81.2500 (72.3077) lr 1.3681e-03 eta 13:04:32
+epoch [21/50] batch [70/1000] time 1.569 (1.572) data 0.000 (0.013) loss 1.2725 (1.1564) acc 59.3750 (72.0536) lr 1.3681e-03 eta 13:04:04
+epoch [21/50] batch [75/1000] time 1.541 (1.572) data 0.000 (0.012) loss 1.0352 (1.1579) acc 75.0000 (72.0833) lr 1.3681e-03 eta 13:04:16
+epoch [21/50] batch [80/1000] time 1.578 (1.572) data 0.000 (0.011) loss 0.4375 (1.1464) acc 84.3750 (72.1875) lr 1.3681e-03 eta 13:04:05
+epoch [21/50] batch [85/1000] time 1.553 (1.571) data 0.000 (0.011) loss 1.4482 (1.1409) acc 75.0000 (72.2794) lr 1.3681e-03 eta 13:03:21
+epoch [21/50] batch [90/1000] time 1.572 (1.571) data 0.000 (0.010) loss 0.5415 (1.1237) acc 84.3750 (72.5347) lr 1.3681e-03 eta 13:02:54
+epoch [21/50] batch [95/1000] time 1.557 (1.570) data 0.000 (0.010) loss 0.8594 (1.1231) acc 71.8750 (72.3026) lr 1.3681e-03 eta 13:02:27
+epoch [21/50] batch [100/1000] time 1.544 (1.569) data 0.000 (0.009) loss 1.4463 (1.1315) acc 62.5000 (72.0938) lr 1.3681e-03 eta 13:01:55
+epoch [21/50] batch [105/1000] time 1.574 (1.569) data 0.000 (0.009) loss 0.8462 (1.1305) acc 75.0000 (72.1131) lr 1.3681e-03 eta 13:01:44
+epoch [21/50] batch [110/1000] time 1.543 (1.568) data 0.000 (0.008) loss 1.0605 (1.1237) acc 75.0000 (72.0739) lr 1.3681e-03 eta 13:01:15
+epoch [21/50] batch [115/1000] time 1.732 (1.569) data 0.001 (0.008) loss 1.0186 (1.1175) acc 71.8750 (72.2283) lr 1.3681e-03 eta 13:01:38
+epoch [21/50] batch [120/1000] time 1.533 (1.569) data 0.001 (0.008) loss 1.4199 (1.1240) acc 62.5000 (72.0052) lr 1.3681e-03 eta 13:01:19
+epoch [21/50] batch [125/1000] time 1.573 (1.569) data 0.000 (0.008) loss 0.6968 (1.1289) acc 78.1250 (71.9500) lr 1.3681e-03 eta 13:01:09
+epoch [21/50] batch [130/1000] time 1.556 (1.569) data 0.001 (0.007) loss 1.3145 (1.1407) acc 71.8750 (71.6346) lr 1.3681e-03 eta 13:00:58
+epoch [21/50] batch [135/1000] time 1.563 (1.568) data 0.000 (0.007) loss 1.6621 (1.1402) acc 62.5000 (71.5741) lr 1.3681e-03 eta 13:00:29
+epoch [21/50] batch [140/1000] time 1.552 (1.567) data 0.000 (0.007) loss 0.7681 (1.1383) acc 81.2500 (71.6964) lr 1.3681e-03 eta 13:00:01
+epoch [21/50] batch [145/1000] time 1.572 (1.567) data 0.000 (0.007) loss 1.4277 (1.1403) acc 71.8750 (71.6810) lr 1.3681e-03 eta 12:59:49
+epoch [21/50] batch [150/1000] time 1.581 (1.568) data 0.000 (0.006) loss 1.1943 (1.1458) acc 75.0000 (71.6458) lr 1.3681e-03 eta 12:59:55
+epoch [21/50] batch [155/1000] time 1.593 (1.568) data 0.000 (0.006) loss 1.1660 (1.1469) acc 71.8750 (71.5726) lr 1.3681e-03 eta 12:59:52
+epoch [21/50] batch [160/1000] time 1.564 (1.568) data 0.000 (0.006) loss 1.3066 (1.1454) acc 65.6250 (71.4648) lr 1.3681e-03 eta 12:59:43
+epoch [21/50] batch [165/1000] time 1.547 (1.567) data 0.000 (0.006) loss 1.1230 (1.1373) acc 78.1250 (71.6288) lr 1.3681e-03 eta 12:59:19
+epoch [21/50] batch [170/1000] time 1.537 (1.567) data 0.000 (0.006) loss 1.0283 (1.1326) acc 78.1250 (71.7096) lr 1.3681e-03 eta 12:59:01
+epoch [21/50] batch [175/1000] time 1.573 (1.567) data 0.000 (0.005) loss 1.3340 (1.1293) acc 65.6250 (71.7143) lr 1.3681e-03 eta 12:58:47
+epoch [21/50] batch [180/1000] time 1.553 (1.567) data 0.000 (0.005) loss 1.9355 (1.1274) acc 56.2500 (71.7361) lr 1.3681e-03 eta 12:58:44
+epoch [21/50] batch [185/1000] time 1.559 (1.567) data 0.000 (0.005) loss 0.7881 (1.1329) acc 84.3750 (71.7230) lr 1.3681e-03 eta 12:58:29
+epoch [21/50] batch [190/1000] time 1.556 (1.566) data 0.000 (0.005) loss 0.8604 (1.1268) acc 75.0000 (71.7434) lr 1.3681e-03 eta 12:58:15
+epoch [21/50] batch [195/1000] time 1.561 (1.566) data 0.001 (0.005) loss 0.8506 (1.1240) acc 75.0000 (71.7468) lr 1.3681e-03 eta 12:58:05
+epoch [21/50] batch [200/1000] time 1.554 (1.566) data 0.000 (0.005) loss 0.9219 (1.1221) acc 81.2500 (71.8125) lr 1.3681e-03 eta 12:57:50
+epoch [21/50] batch [205/1000] time 1.565 (1.566) data 0.000 (0.005) loss 0.9595 (1.1176) acc 75.0000 (71.9360) lr 1.3681e-03 eta 12:57:30
+epoch [21/50] batch [210/1000] time 1.575 (1.566) data 0.001 (0.005) loss 0.8589 (1.1159) acc 81.2500 (71.9494) lr 1.3681e-03 eta 12:57:20
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+epoch [21/50] batch [760/1000] time 1.574 (1.566) data 0.000 (0.002) loss 0.8911 (1.1321) acc 71.8750 (71.9449) lr 1.3681e-03 eta 12:42:55
+epoch [21/50] batch [765/1000] time 1.551 (1.565) data 0.000 (0.002) loss 1.1924 (1.1324) acc 59.3750 (71.9158) lr 1.3681e-03 eta 12:42:46
+epoch [21/50] batch [770/1000] time 1.542 (1.565) data 0.000 (0.002) loss 0.9414 (1.1323) acc 78.1250 (71.9115) lr 1.3681e-03 eta 12:42:36
+epoch [21/50] batch [775/1000] time 1.565 (1.565) data 0.000 (0.002) loss 0.7300 (1.1316) acc 81.2500 (71.9476) lr 1.3681e-03 eta 12:42:26
+epoch [21/50] batch [780/1000] time 1.527 (1.565) data 0.001 (0.002) loss 1.1982 (1.1300) acc 56.2500 (71.9631) lr 1.3681e-03 eta 12:42:15
+epoch [21/50] batch [785/1000] time 1.559 (1.565) data 0.000 (0.002) loss 0.7788 (1.1299) acc 78.1250 (71.9546) lr 1.3681e-03 eta 12:42:09
+epoch [21/50] batch [790/1000] time 1.576 (1.565) data 0.000 (0.002) loss 0.6514 (1.1306) acc 71.8750 (71.9422) lr 1.3681e-03 eta 12:42:00
+epoch [21/50] batch [795/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0049 (1.1322) acc 78.1250 (71.9379) lr 1.3681e-03 eta 12:41:49
+epoch [21/50] batch [800/1000] time 1.575 (1.565) data 0.000 (0.002) loss 1.3311 (1.1315) acc 68.7500 (71.9375) lr 1.3681e-03 eta 12:41:41
+epoch [21/50] batch [805/1000] time 1.561 (1.565) data 0.000 (0.002) loss 1.0078 (1.1307) acc 78.1250 (71.9565) lr 1.3681e-03 eta 12:41:34
+epoch [21/50] batch [810/1000] time 1.556 (1.565) data 0.001 (0.002) loss 1.1709 (1.1304) acc 75.0000 (71.9483) lr 1.3681e-03 eta 12:41:26
+epoch [21/50] batch [815/1000] time 1.582 (1.565) data 0.000 (0.002) loss 0.9160 (1.1285) acc 71.8750 (71.9670) lr 1.3681e-03 eta 12:41:19
+epoch [21/50] batch [820/1000] time 1.546 (1.565) data 0.000 (0.002) loss 0.8564 (1.1272) acc 78.1250 (72.0008) lr 1.3681e-03 eta 12:41:09
+epoch [21/50] batch [825/1000] time 1.575 (1.565) data 0.001 (0.001) loss 1.3789 (1.1290) acc 68.7500 (72.0038) lr 1.3681e-03 eta 12:41:02
+epoch [21/50] batch [830/1000] time 1.572 (1.565) data 0.000 (0.001) loss 0.7515 (1.1286) acc 81.2500 (71.9992) lr 1.3681e-03 eta 12:41:00
+epoch [21/50] batch [835/1000] time 1.547 (1.565) data 0.000 (0.001) loss 0.6489 (1.1277) acc 75.0000 (72.0060) lr 1.3681e-03 eta 12:40:52
+epoch [21/50] batch [840/1000] time 1.545 (1.565) data 0.000 (0.001) loss 1.4863 (1.1273) acc 53.1250 (71.9903) lr 1.3681e-03 eta 12:40:40
+epoch [21/50] batch [845/1000] time 1.569 (1.565) data 0.000 (0.001) loss 1.2021 (1.1271) acc 68.7500 (71.9822) lr 1.3681e-03 eta 12:40:31
+epoch [21/50] batch [850/1000] time 1.574 (1.565) data 0.000 (0.001) loss 1.4990 (1.1279) acc 65.6250 (71.9596) lr 1.3681e-03 eta 12:40:24
+epoch [21/50] batch [855/1000] time 1.548 (1.565) data 0.000 (0.001) loss 0.6953 (1.1267) acc 84.3750 (71.9956) lr 1.3681e-03 eta 12:40:16
+epoch [21/50] batch [860/1000] time 1.569 (1.565) data 0.000 (0.001) loss 1.2930 (1.1287) acc 68.7500 (71.9695) lr 1.3681e-03 eta 12:40:08
+epoch [21/50] batch [865/1000] time 1.570 (1.565) data 0.000 (0.001) loss 0.9727 (1.1280) acc 68.7500 (71.9473) lr 1.3681e-03 eta 12:40:01
+epoch [21/50] batch [870/1000] time 1.725 (1.565) data 0.001 (0.001) loss 1.1875 (1.1274) acc 68.7500 (71.9540) lr 1.3681e-03 eta 12:40:00
+epoch [21/50] batch [875/1000] time 1.545 (1.565) data 0.000 (0.001) loss 0.8369 (1.1264) acc 84.3750 (71.9821) lr 1.3681e-03 eta 12:39:50
+epoch [21/50] batch [880/1000] time 1.550 (1.565) data 0.000 (0.001) loss 1.2676 (1.1265) acc 71.8750 (71.9815) lr 1.3681e-03 eta 12:39:39
+epoch [21/50] batch [885/1000] time 1.574 (1.565) data 0.000 (0.001) loss 1.0576 (1.1254) acc 71.8750 (71.9915) lr 1.3681e-03 eta 12:39:31
+epoch [21/50] batch [890/1000] time 1.579 (1.565) data 0.000 (0.001) loss 0.8496 (1.1258) acc 75.0000 (71.9628) lr 1.3681e-03 eta 12:39:22
+epoch [21/50] batch [895/1000] time 1.547 (1.565) data 0.000 (0.001) loss 0.6733 (1.1239) acc 78.1250 (71.9797) lr 1.3681e-03 eta 12:39:13
+epoch [21/50] batch [900/1000] time 1.554 (1.565) data 0.000 (0.001) loss 0.8472 (1.1226) acc 71.8750 (72.0069) lr 1.3681e-03 eta 12:39:06
+epoch [21/50] batch [905/1000] time 1.582 (1.565) data 0.000 (0.001) loss 0.7134 (1.1218) acc 78.1250 (72.0304) lr 1.3681e-03 eta 12:39:00
+epoch [21/50] batch [910/1000] time 1.566 (1.565) data 0.000 (0.001) loss 0.6323 (1.1205) acc 84.3750 (72.0673) lr 1.3681e-03 eta 12:38:51
+epoch [21/50] batch [915/1000] time 1.584 (1.565) data 0.001 (0.001) loss 0.7202 (1.1209) acc 81.2500 (72.0594) lr 1.3681e-03 eta 12:38:43
+epoch [21/50] batch [920/1000] time 1.553 (1.565) data 0.000 (0.001) loss 1.1846 (1.1206) acc 71.8750 (72.0584) lr 1.3681e-03 eta 12:38:34
+epoch [21/50] batch [925/1000] time 1.573 (1.565) data 0.001 (0.001) loss 0.8462 (1.1199) acc 78.1250 (72.0743) lr 1.3681e-03 eta 12:38:26
+epoch [21/50] batch [930/1000] time 1.587 (1.565) data 0.000 (0.001) loss 0.9053 (1.1194) acc 71.8750 (72.0867) lr 1.3681e-03 eta 12:38:18
+epoch [21/50] batch [935/1000] time 1.547 (1.565) data 0.000 (0.001) loss 0.4727 (1.1189) acc 81.2500 (72.0956) lr 1.3681e-03 eta 12:38:12
+epoch [21/50] batch [940/1000] time 1.534 (1.565) data 0.001 (0.001) loss 1.4727 (1.1199) acc 62.5000 (72.0844) lr 1.3681e-03 eta 12:38:02
+epoch [21/50] batch [945/1000] time 1.546 (1.565) data 0.001 (0.001) loss 0.9053 (1.1211) acc 78.1250 (72.0734) lr 1.3681e-03 eta 12:37:52
+epoch [21/50] batch [950/1000] time 1.560 (1.565) data 0.000 (0.001) loss 1.2754 (1.1213) acc 59.3750 (72.0625) lr 1.3681e-03 eta 12:37:44
+epoch [21/50] batch [955/1000] time 1.550 (1.565) data 0.000 (0.001) loss 1.2246 (1.1221) acc 65.6250 (72.0386) lr 1.3681e-03 eta 12:37:35
+epoch [21/50] batch [960/1000] time 1.575 (1.565) data 0.001 (0.001) loss 1.1406 (1.1226) acc 84.3750 (72.0410) lr 1.3681e-03 eta 12:37:25
+epoch [21/50] batch [965/1000] time 1.553 (1.565) data 0.001 (0.001) loss 1.2109 (1.1219) acc 65.6250 (72.0369) lr 1.3681e-03 eta 12:37:16
+epoch [21/50] batch [970/1000] time 1.561 (1.565) data 0.000 (0.001) loss 1.3096 (1.1225) acc 75.0000 (72.0425) lr 1.3681e-03 eta 12:37:06
+epoch [21/50] batch [975/1000] time 1.549 (1.565) data 0.001 (0.001) loss 1.2490 (1.1218) acc 75.0000 (72.0513) lr 1.3681e-03 eta 12:36:55
+epoch [21/50] batch [980/1000] time 1.564 (1.565) data 0.000 (0.001) loss 1.2041 (1.1226) acc 71.8750 (72.0568) lr 1.3681e-03 eta 12:36:50
+epoch [21/50] batch [985/1000] time 1.559 (1.565) data 0.001 (0.001) loss 0.6050 (1.1216) acc 84.3750 (72.0876) lr 1.3681e-03 eta 12:36:41
+epoch [21/50] batch [990/1000] time 1.562 (1.565) data 0.000 (0.001) loss 1.1621 (1.1210) acc 62.5000 (72.0802) lr 1.3681e-03 eta 12:36:33
+epoch [21/50] batch [995/1000] time 1.544 (1.565) data 0.000 (0.001) loss 0.8076 (1.1209) acc 68.7500 (72.0729) lr 1.3681e-03 eta 12:36:24
+epoch [21/50] batch [1000/1000] time 1.569 (1.565) data 0.000 (0.001) loss 0.8716 (1.1211) acc 78.1250 (72.0750) lr 1.3090e-03 eta 12:36:15
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,192
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+epoch [22/50] batch [5/1000] time 1.560 (1.682) data 0.001 (0.176) loss 0.5220 (0.8425) acc 84.3750 (78.7500) lr 1.3090e-03 eta 13:32:43
+epoch [22/50] batch [10/1000] time 1.560 (1.618) data 0.000 (0.088) loss 0.6963 (0.9248) acc 75.0000 (76.8750) lr 1.3090e-03 eta 13:01:47
+epoch [22/50] batch [15/1000] time 1.549 (1.597) data 0.001 (0.059) loss 1.1230 (0.9644) acc 71.8750 (75.8333) lr 1.3090e-03 eta 12:51:40
+epoch [22/50] batch [20/1000] time 1.580 (1.604) data 0.001 (0.044) loss 1.6143 (1.0203) acc 56.2500 (74.0625) lr 1.3090e-03 eta 12:54:42
+epoch [22/50] batch [25/1000] time 1.565 (1.596) data 0.001 (0.036) loss 0.8335 (0.9738) acc 65.6250 (74.3750) lr 1.3090e-03 eta 12:50:42
+epoch [22/50] batch [30/1000] time 1.579 (1.591) data 0.001 (0.030) loss 0.7495 (0.9841) acc 78.1250 (74.0625) lr 1.3090e-03 eta 12:47:57
+epoch [22/50] batch [35/1000] time 1.546 (1.585) data 0.000 (0.026) loss 1.0879 (0.9650) acc 68.7500 (74.4643) lr 1.3090e-03 eta 12:45:02
+epoch [22/50] batch [40/1000] time 1.538 (1.580) data 0.000 (0.022) loss 0.9360 (0.9801) acc 81.2500 (74.4531) lr 1.3090e-03 eta 12:42:32
+epoch [22/50] batch [45/1000] time 1.557 (1.578) data 0.000 (0.020) loss 0.9565 (1.0074) acc 75.0000 (73.6111) lr 1.3090e-03 eta 12:41:19
+epoch [22/50] batch [50/1000] time 1.541 (1.575) data 0.001 (0.018) loss 0.7866 (0.9980) acc 71.8750 (74.1250) lr 1.3090e-03 eta 12:40:05
+epoch [22/50] batch [55/1000] time 1.568 (1.574) data 0.001 (0.016) loss 0.7520 (1.0096) acc 78.1250 (74.0909) lr 1.3090e-03 eta 12:39:05
+epoch [22/50] batch [60/1000] time 1.569 (1.572) data 0.001 (0.015) loss 0.9492 (1.0013) acc 71.8750 (74.3229) lr 1.3090e-03 eta 12:38:21
+epoch [22/50] batch [65/1000] time 1.559 (1.572) data 0.000 (0.014) loss 1.1592 (1.0153) acc 62.5000 (73.8942) lr 1.3090e-03 eta 12:38:01
+epoch [22/50] batch [70/1000] time 1.551 (1.571) data 0.001 (0.013) loss 0.4736 (1.0158) acc 87.5000 (74.0179) lr 1.3090e-03 eta 12:37:21
+epoch [22/50] batch [75/1000] time 1.539 (1.570) data 0.001 (0.012) loss 0.9941 (1.0124) acc 75.0000 (73.7917) lr 1.3090e-03 eta 12:36:47
+epoch [22/50] batch [80/1000] time 1.563 (1.572) data 0.001 (0.011) loss 0.4521 (1.0036) acc 87.5000 (74.1016) lr 1.3090e-03 eta 12:37:36
+epoch [22/50] batch [85/1000] time 1.548 (1.570) data 0.001 (0.011) loss 0.8330 (1.0025) acc 81.2500 (74.1176) lr 1.3090e-03 eta 12:36:48
+epoch [22/50] batch [90/1000] time 1.552 (1.570) data 0.001 (0.010) loss 1.1543 (1.0076) acc 68.7500 (73.9236) lr 1.3090e-03 eta 12:36:28
+epoch [22/50] batch [95/1000] time 1.559 (1.570) data 0.000 (0.010) loss 1.1738 (1.0106) acc 71.8750 (73.8487) lr 1.3090e-03 eta 12:36:15
+epoch [22/50] batch [100/1000] time 1.561 (1.569) data 0.000 (0.009) loss 0.9839 (1.0233) acc 71.8750 (73.7188) lr 1.3090e-03 eta 12:35:50
+epoch [22/50] batch [105/1000] time 1.536 (1.569) data 0.000 (0.009) loss 1.2520 (1.0354) acc 68.7500 (73.6905) lr 1.3090e-03 eta 12:35:38
+epoch [22/50] batch [110/1000] time 1.556 (1.569) data 0.001 (0.008) loss 0.7822 (1.0365) acc 78.1250 (73.7784) lr 1.3090e-03 eta 12:35:14
+epoch [22/50] batch [115/1000] time 1.560 (1.568) data 0.001 (0.008) loss 1.5029 (1.0388) acc 62.5000 (73.9130) lr 1.3090e-03 eta 12:34:52
+epoch [22/50] batch [120/1000] time 1.569 (1.568) data 0.001 (0.008) loss 1.1426 (1.0479) acc 75.0000 (73.6719) lr 1.3090e-03 eta 12:34:43
+epoch [22/50] batch [125/1000] time 1.572 (1.569) data 0.000 (0.008) loss 1.0156 (1.0538) acc 68.7500 (73.6250) lr 1.3090e-03 eta 12:35:19
+epoch [22/50] batch [130/1000] time 1.540 (1.569) data 0.000 (0.007) loss 0.4160 (1.0503) acc 90.6250 (73.7019) lr 1.3090e-03 eta 12:34:51
+epoch [22/50] batch [135/1000] time 1.573 (1.569) data 0.000 (0.007) loss 1.0059 (1.0560) acc 81.2500 (73.7269) lr 1.3090e-03 eta 12:34:39
+epoch [22/50] batch [140/1000] time 1.549 (1.569) data 0.001 (0.007) loss 0.8145 (1.0504) acc 84.3750 (73.8839) lr 1.3090e-03 eta 12:34:27
+epoch [22/50] batch [145/1000] time 1.540 (1.568) data 0.001 (0.007) loss 0.5015 (1.0500) acc 90.6250 (73.9009) lr 1.3090e-03 eta 12:34:11
+epoch [22/50] batch [150/1000] time 1.561 (1.568) data 0.000 (0.006) loss 1.2793 (1.0531) acc 59.3750 (73.7708) lr 1.3090e-03 eta 12:33:45
+epoch [22/50] batch [155/1000] time 1.554 (1.567) data 0.000 (0.006) loss 1.2197 (1.0542) acc 68.7500 (73.6694) lr 1.3090e-03 eta 12:33:23
+epoch [22/50] batch [160/1000] time 1.578 (1.567) data 0.001 (0.006) loss 0.9453 (1.0595) acc 68.7500 (73.4766) lr 1.3090e-03 eta 12:33:04
+epoch [22/50] batch [165/1000] time 1.564 (1.567) data 0.001 (0.006) loss 1.1670 (1.0696) acc 75.0000 (73.3523) lr 1.3090e-03 eta 12:32:55
+epoch [22/50] batch [170/1000] time 1.554 (1.567) data 0.000 (0.006) loss 0.9194 (1.0679) acc 78.1250 (73.3272) lr 1.3090e-03 eta 12:33:02
+epoch [22/50] batch [175/1000] time 1.547 (1.567) data 0.000 (0.006) loss 1.4502 (1.0689) acc 62.5000 (73.1786) lr 1.3090e-03 eta 12:32:42
+epoch [22/50] batch [180/1000] time 1.555 (1.566) data 0.000 (0.005) loss 0.6333 (1.0736) acc 90.6250 (73.1076) lr 1.3090e-03 eta 12:32:21
+epoch [22/50] batch [185/1000] time 1.560 (1.566) data 0.000 (0.005) loss 0.9727 (1.0706) acc 78.1250 (73.2264) lr 1.3090e-03 eta 12:32:07
+epoch [22/50] batch [190/1000] time 1.558 (1.566) data 0.000 (0.005) loss 1.3994 (1.0730) acc 71.8750 (73.2237) lr 1.3090e-03 eta 12:31:58
+epoch [22/50] batch [195/1000] time 1.582 (1.566) data 0.000 (0.005) loss 1.3652 (1.0738) acc 62.5000 (73.1571) lr 1.3090e-03 eta 12:31:51
+epoch [22/50] batch [200/1000] time 1.530 (1.566) data 0.000 (0.005) loss 0.9927 (1.0785) acc 75.0000 (73.0938) lr 1.3090e-03 eta 12:31:33
+epoch [22/50] batch [205/1000] time 1.563 (1.566) data 0.000 (0.005) loss 1.2363 (1.0826) acc 78.1250 (73.0945) lr 1.3090e-03 eta 12:31:25
+epoch [22/50] batch [210/1000] time 1.542 (1.566) data 0.000 (0.005) loss 1.4336 (1.0827) acc 65.6250 (73.1101) lr 1.3090e-03 eta 12:31:13
+epoch [22/50] batch [215/1000] time 1.556 (1.565) data 0.000 (0.005) loss 1.1133 (1.0794) acc 75.0000 (73.2122) lr 1.3090e-03 eta 12:31:00
+epoch [22/50] batch [220/1000] time 1.529 (1.565) data 0.001 (0.004) loss 1.7432 (1.0821) acc 62.5000 (73.1392) lr 1.3090e-03 eta 12:30:43
+epoch [22/50] batch [225/1000] time 1.549 (1.565) data 0.000 (0.004) loss 1.5684 (1.0830) acc 62.5000 (73.1806) lr 1.3090e-03 eta 12:30:37
+epoch [22/50] batch [230/1000] time 1.740 (1.566) data 0.001 (0.004) loss 0.9912 (1.0794) acc 71.8750 (73.3016) lr 1.3090e-03 eta 12:30:50
+epoch [22/50] batch [235/1000] time 1.554 (1.566) data 0.000 (0.004) loss 0.9399 (1.0837) acc 78.1250 (73.1649) lr 1.3090e-03 eta 12:30:38
+epoch [22/50] batch [240/1000] time 1.558 (1.566) data 0.000 (0.004) loss 1.2285 (1.0847) acc 62.5000 (73.0990) lr 1.3090e-03 eta 12:30:25
+epoch [22/50] batch [245/1000] time 1.537 (1.565) data 0.000 (0.004) loss 1.7109 (1.0882) acc 62.5000 (73.0740) lr 1.3090e-03 eta 12:30:05
+epoch [22/50] batch [250/1000] time 1.552 (1.565) data 0.000 (0.004) loss 1.3652 (1.0917) acc 68.7500 (73.0250) lr 1.3090e-03 eta 12:29:51
+epoch [22/50] batch [255/1000] time 1.556 (1.565) data 0.001 (0.004) loss 1.2354 (1.0941) acc 68.7500 (72.9289) lr 1.3090e-03 eta 12:29:35
+epoch [22/50] batch [260/1000] time 1.558 (1.564) data 0.000 (0.004) loss 1.0527 (1.0943) acc 68.7500 (72.8966) lr 1.3090e-03 eta 12:29:23
+epoch [22/50] batch [265/1000] time 1.551 (1.564) data 0.000 (0.004) loss 2.2930 (1.1001) acc 40.6250 (72.7358) lr 1.3090e-03 eta 12:29:07
+epoch [22/50] batch [270/1000] time 1.567 (1.564) data 0.000 (0.004) loss 1.3516 (1.1020) acc 62.5000 (72.6736) lr 1.3090e-03 eta 12:28:57
+epoch [22/50] batch [275/1000] time 1.691 (1.564) data 0.000 (0.004) loss 1.0107 (1.1000) acc 81.2500 (72.6932) lr 1.3090e-03 eta 12:28:54
+epoch [22/50] batch [280/1000] time 1.525 (1.564) data 0.000 (0.004) loss 1.2793 (1.1032) acc 65.6250 (72.6228) lr 1.3090e-03 eta 12:28:40
+epoch [22/50] batch [285/1000] time 1.573 (1.564) data 0.001 (0.004) loss 1.1904 (1.1032) acc 75.0000 (72.5877) lr 1.3090e-03 eta 12:28:29
+epoch [22/50] batch [290/1000] time 1.582 (1.564) data 0.001 (0.003) loss 1.4141 (1.1052) acc 56.2500 (72.5323) lr 1.3090e-03 eta 12:28:23
+epoch [22/50] batch [295/1000] time 1.559 (1.564) data 0.000 (0.003) loss 1.1553 (1.1082) acc 62.5000 (72.5318) lr 1.3090e-03 eta 12:28:14
+epoch [22/50] batch [300/1000] time 1.539 (1.564) data 0.001 (0.003) loss 0.7690 (1.1056) acc 78.1250 (72.6250) lr 1.3090e-03 eta 12:28:02
+epoch [22/50] batch [305/1000] time 1.561 (1.564) data 0.001 (0.003) loss 1.3857 (1.1024) acc 68.7500 (72.6947) lr 1.3090e-03 eta 12:27:52
+epoch [22/50] batch [310/1000] time 1.550 (1.564) data 0.001 (0.003) loss 1.4424 (1.1014) acc 65.6250 (72.7016) lr 1.3090e-03 eta 12:27:37
+epoch [22/50] batch [315/1000] time 1.568 (1.564) data 0.000 (0.003) loss 1.2900 (1.1012) acc 56.2500 (72.7183) lr 1.3090e-03 eta 12:27:32
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+epoch [22/50] batch [870/1000] time 1.557 (1.561) data 0.000 (0.001) loss 1.6006 (1.1192) acc 65.6250 (72.0223) lr 1.3090e-03 eta 12:11:56
+epoch [22/50] batch [875/1000] time 1.541 (1.561) data 0.000 (0.001) loss 0.6519 (1.1175) acc 81.2500 (72.0643) lr 1.3090e-03 eta 12:11:46
+epoch [22/50] batch [880/1000] time 1.583 (1.561) data 0.000 (0.001) loss 1.0479 (1.1173) acc 71.8750 (72.0810) lr 1.3090e-03 eta 12:11:46
+epoch [22/50] batch [885/1000] time 1.583 (1.561) data 0.000 (0.001) loss 1.1836 (1.1175) acc 71.8750 (72.0763) lr 1.3090e-03 eta 12:11:39
+epoch [22/50] batch [890/1000] time 1.547 (1.561) data 0.000 (0.001) loss 1.5928 (1.1175) acc 68.7500 (72.0892) lr 1.3090e-03 eta 12:11:28
+epoch [22/50] batch [895/1000] time 1.548 (1.561) data 0.000 (0.001) loss 1.8086 (1.1188) acc 56.2500 (72.0426) lr 1.3090e-03 eta 12:11:19
+epoch [22/50] batch [900/1000] time 1.538 (1.561) data 0.001 (0.001) loss 1.7715 (1.1197) acc 59.3750 (72.0417) lr 1.3090e-03 eta 12:11:11
+epoch [22/50] batch [905/1000] time 1.550 (1.561) data 0.000 (0.001) loss 0.7778 (1.1209) acc 81.2500 (72.0373) lr 1.3090e-03 eta 12:11:02
+epoch [22/50] batch [910/1000] time 1.526 (1.561) data 0.000 (0.001) loss 0.9390 (1.1217) acc 78.1250 (72.0364) lr 1.3090e-03 eta 12:10:51
+epoch [22/50] batch [915/1000] time 1.571 (1.561) data 0.000 (0.001) loss 0.7173 (1.1225) acc 84.3750 (72.0287) lr 1.3090e-03 eta 12:10:45
+epoch [22/50] batch [920/1000] time 1.556 (1.561) data 0.000 (0.001) loss 1.4424 (1.1225) acc 75.0000 (72.0211) lr 1.3090e-03 eta 12:10:38
+epoch [22/50] batch [925/1000] time 1.558 (1.561) data 0.000 (0.001) loss 1.1523 (1.1219) acc 75.0000 (72.0304) lr 1.3090e-03 eta 12:10:34
+epoch [22/50] batch [930/1000] time 1.576 (1.561) data 0.001 (0.001) loss 1.1299 (1.1221) acc 75.0000 (72.0296) lr 1.3090e-03 eta 12:10:25
+epoch [22/50] batch [935/1000] time 1.546 (1.561) data 0.001 (0.001) loss 1.0029 (1.1214) acc 78.1250 (72.0321) lr 1.3090e-03 eta 12:10:16
+epoch [22/50] batch [940/1000] time 1.548 (1.561) data 0.001 (0.001) loss 1.5234 (1.1215) acc 59.3750 (72.0246) lr 1.3090e-03 eta 12:10:06
+epoch [22/50] batch [945/1000] time 1.564 (1.561) data 0.000 (0.001) loss 0.6396 (1.1211) acc 87.5000 (72.0337) lr 1.3090e-03 eta 12:09:57
+epoch [22/50] batch [950/1000] time 1.550 (1.561) data 0.001 (0.001) loss 1.0635 (1.1216) acc 71.8750 (72.0197) lr 1.3090e-03 eta 12:09:49
+epoch [22/50] batch [955/1000] time 1.547 (1.561) data 0.001 (0.001) loss 0.7891 (1.1211) acc 90.6250 (72.0419) lr 1.3090e-03 eta 12:09:41
+epoch [22/50] batch [960/1000] time 1.546 (1.561) data 0.000 (0.001) loss 1.1221 (1.1217) acc 68.7500 (72.0345) lr 1.3090e-03 eta 12:09:33
+epoch [22/50] batch [965/1000] time 1.536 (1.561) data 0.001 (0.001) loss 0.6318 (1.1206) acc 90.6250 (72.0628) lr 1.3090e-03 eta 12:09:24
+epoch [22/50] batch [970/1000] time 1.559 (1.561) data 0.000 (0.001) loss 0.9053 (1.1190) acc 75.0000 (72.0973) lr 1.3090e-03 eta 12:09:16
+epoch [22/50] batch [975/1000] time 1.559 (1.561) data 0.000 (0.001) loss 0.9165 (1.1193) acc 65.6250 (72.0897) lr 1.3090e-03 eta 12:09:07
+epoch [22/50] batch [980/1000] time 1.555 (1.561) data 0.000 (0.001) loss 1.4883 (1.1207) acc 59.3750 (72.0568) lr 1.3090e-03 eta 12:08:58
+epoch [22/50] batch [985/1000] time 1.704 (1.561) data 0.001 (0.001) loss 1.2910 (1.1203) acc 65.6250 (72.0495) lr 1.3090e-03 eta 12:08:53
+epoch [22/50] batch [990/1000] time 1.546 (1.561) data 0.000 (0.001) loss 1.3252 (1.1207) acc 59.3750 (72.0328) lr 1.3090e-03 eta 12:08:44
+epoch [22/50] batch [995/1000] time 1.559 (1.561) data 0.000 (0.001) loss 1.1338 (1.1212) acc 71.8750 (72.0195) lr 1.3090e-03 eta 12:08:35
+epoch [22/50] batch [1000/1000] time 1.552 (1.561) data 0.000 (0.001) loss 1.5049 (1.1211) acc 62.5000 (72.0219) lr 1.2487e-03 eta 12:08:26
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,222
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 78.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [23/50] batch [5/1000] time 1.546 (1.692) data 0.000 (0.194) loss 1.4141 (1.2255) acc 68.7500 (71.8750) lr 1.2487e-03 eta 13:09:14
+epoch [23/50] batch [10/1000] time 1.561 (1.646) data 0.000 (0.097) loss 0.8633 (1.1249) acc 71.8750 (71.8750) lr 1.2487e-03 eta 12:47:42
+epoch [23/50] batch [15/1000] time 1.545 (1.617) data 0.000 (0.065) loss 0.8970 (1.0840) acc 75.0000 (72.7083) lr 1.2487e-03 eta 12:34:02
+epoch [23/50] batch [20/1000] time 1.577 (1.604) data 0.000 (0.049) loss 0.9395 (1.0400) acc 75.0000 (73.7500) lr 1.2487e-03 eta 12:27:48
+epoch [23/50] batch [25/1000] time 1.561 (1.595) data 0.000 (0.039) loss 1.6387 (1.0865) acc 62.5000 (73.2500) lr 1.2487e-03 eta 12:23:39
+epoch [23/50] batch [30/1000] time 1.557 (1.589) data 0.000 (0.033) loss 1.2188 (1.0666) acc 71.8750 (73.5417) lr 1.2487e-03 eta 12:20:31
+epoch [23/50] batch [35/1000] time 1.552 (1.583) data 0.001 (0.028) loss 1.3203 (1.0529) acc 65.6250 (74.1964) lr 1.2487e-03 eta 12:17:40
+epoch [23/50] batch [40/1000] time 1.545 (1.579) data 0.001 (0.025) loss 0.9458 (1.0740) acc 75.0000 (73.9062) lr 1.2487e-03 eta 12:15:52
+epoch [23/50] batch [45/1000] time 1.569 (1.577) data 0.000 (0.022) loss 1.2666 (1.0773) acc 65.6250 (73.5417) lr 1.2487e-03 eta 12:14:50
+epoch [23/50] batch [50/1000] time 1.587 (1.577) data 0.000 (0.020) loss 0.9897 (1.0648) acc 71.8750 (73.5625) lr 1.2487e-03 eta 12:14:42
+epoch [23/50] batch [55/1000] time 1.574 (1.579) data 0.001 (0.018) loss 1.4053 (1.0602) acc 56.2500 (73.5795) lr 1.2487e-03 eta 12:15:28
+epoch [23/50] batch [60/1000] time 1.580 (1.578) data 0.001 (0.017) loss 0.9648 (1.0643) acc 71.8750 (73.0729) lr 1.2487e-03 eta 12:15:01
+epoch [23/50] batch [65/1000] time 1.572 (1.577) data 0.000 (0.015) loss 0.9541 (1.0589) acc 84.3750 (73.0769) lr 1.2487e-03 eta 12:14:14
+epoch [23/50] batch [70/1000] time 1.552 (1.576) data 0.000 (0.014) loss 0.9106 (1.0497) acc 75.0000 (73.3929) lr 1.2487e-03 eta 12:13:26
+epoch [23/50] batch [75/1000] time 1.570 (1.575) data 0.000 (0.013) loss 0.6836 (1.0565) acc 81.2500 (73.1667) lr 1.2487e-03 eta 12:13:05
+epoch [23/50] batch [80/1000] time 1.544 (1.574) data 0.000 (0.013) loss 0.9561 (1.0455) acc 68.7500 (73.0859) lr 1.2487e-03 eta 12:12:19
+epoch [23/50] batch [85/1000] time 1.535 (1.573) data 0.001 (0.012) loss 1.2959 (1.0441) acc 71.8750 (73.1618) lr 1.2487e-03 eta 12:11:52
+epoch [23/50] batch [90/1000] time 1.568 (1.573) data 0.001 (0.011) loss 1.1045 (1.0539) acc 65.6250 (73.0556) lr 1.2487e-03 eta 12:11:28
+epoch [23/50] batch [95/1000] time 1.566 (1.572) data 0.000 (0.011) loss 1.3779 (1.0604) acc 65.6250 (72.6974) lr 1.2487e-03 eta 12:11:10
+epoch [23/50] batch [100/1000] time 1.565 (1.572) data 0.000 (0.010) loss 1.0713 (1.0783) acc 75.0000 (72.4375) lr 1.2487e-03 eta 12:10:53
+epoch [23/50] batch [105/1000] time 1.545 (1.571) data 0.000 (0.010) loss 1.2168 (1.0765) acc 71.8750 (72.5595) lr 1.2487e-03 eta 12:10:30
+epoch [23/50] batch [110/1000] time 1.548 (1.571) data 0.001 (0.009) loss 0.7041 (1.0707) acc 78.1250 (72.6705) lr 1.2487e-03 eta 12:10:01
+epoch [23/50] batch [115/1000] time 1.723 (1.572) data 0.001 (0.009) loss 1.0693 (1.0630) acc 71.8750 (72.6902) lr 1.2487e-03 eta 12:10:40
+epoch [23/50] batch [120/1000] time 1.557 (1.572) data 0.001 (0.009) loss 0.6104 (1.0534) acc 81.2500 (72.9167) lr 1.2487e-03 eta 12:10:23
+epoch [23/50] batch [125/1000] time 1.560 (1.571) data 0.001 (0.008) loss 1.2988 (1.0598) acc 68.7500 (72.8000) lr 1.2487e-03 eta 12:09:56
+epoch [23/50] batch [130/1000] time 1.547 (1.571) data 0.000 (0.008) loss 1.3838 (1.0553) acc 75.0000 (72.8846) lr 1.2487e-03 eta 12:09:35
+epoch [23/50] batch [135/1000] time 1.535 (1.570) data 0.000 (0.008) loss 1.0889 (1.0522) acc 75.0000 (73.0787) lr 1.2487e-03 eta 12:09:02
+epoch [23/50] batch [140/1000] time 1.549 (1.569) data 0.000 (0.007) loss 0.8691 (1.0529) acc 71.8750 (72.9911) lr 1.2487e-03 eta 12:08:44
+epoch [23/50] batch [145/1000] time 1.566 (1.569) data 0.000 (0.007) loss 0.9561 (1.0602) acc 81.2500 (72.8664) lr 1.2487e-03 eta 12:08:27
+epoch [23/50] batch [150/1000] time 1.562 (1.569) data 0.000 (0.007) loss 0.7212 (1.0609) acc 75.0000 (72.8542) lr 1.2487e-03 eta 12:08:12
+epoch [23/50] batch [155/1000] time 1.562 (1.569) data 0.000 (0.007) loss 1.1641 (1.0640) acc 62.5000 (72.8226) lr 1.2487e-03 eta 12:08:00
+epoch [23/50] batch [160/1000] time 1.722 (1.569) data 0.000 (0.007) loss 0.9272 (1.0682) acc 75.0000 (72.8125) lr 1.2487e-03 eta 12:08:12
+epoch [23/50] batch [165/1000] time 1.579 (1.569) data 0.000 (0.006) loss 0.7148 (1.0629) acc 78.1250 (72.8409) lr 1.2487e-03 eta 12:07:58
+epoch [23/50] batch [170/1000] time 1.538 (1.568) data 0.000 (0.006) loss 1.5566 (1.0734) acc 62.5000 (72.5551) lr 1.2487e-03 eta 12:07:30
+epoch [23/50] batch [175/1000] time 1.542 (1.568) data 0.001 (0.006) loss 0.8726 (1.0742) acc 81.2500 (72.5893) lr 1.2487e-03 eta 12:07:19
+epoch [23/50] batch [180/1000] time 1.550 (1.568) data 0.000 (0.006) loss 0.9404 (1.0727) acc 75.0000 (72.6910) lr 1.2487e-03 eta 12:07:08
+epoch [23/50] batch [185/1000] time 1.560 (1.568) data 0.000 (0.006) loss 0.8579 (1.0710) acc 81.2500 (72.8041) lr 1.2487e-03 eta 12:06:59
+epoch [23/50] batch [190/1000] time 1.552 (1.568) data 0.001 (0.006) loss 0.9575 (1.0669) acc 68.7500 (72.8125) lr 1.2487e-03 eta 12:06:33
+epoch [23/50] batch [195/1000] time 1.526 (1.567) data 0.001 (0.005) loss 0.9707 (1.0757) acc 75.0000 (72.5801) lr 1.2487e-03 eta 12:06:07
+epoch [23/50] batch [200/1000] time 1.548 (1.567) data 0.000 (0.005) loss 1.2236 (1.0770) acc 62.5000 (72.5469) lr 1.2487e-03 eta 12:05:48
+epoch [23/50] batch [205/1000] time 1.547 (1.567) data 0.000 (0.005) loss 0.8442 (1.0827) acc 75.0000 (72.5152) lr 1.2487e-03 eta 12:05:50
+epoch [23/50] batch [210/1000] time 1.551 (1.567) data 0.000 (0.005) loss 1.3604 (1.0873) acc 65.6250 (72.3661) lr 1.2487e-03 eta 12:05:36
+epoch [23/50] batch [215/1000] time 1.559 (1.567) data 0.001 (0.005) loss 1.3398 (1.0885) acc 75.0000 (72.2674) lr 1.2487e-03 eta 12:05:28
+epoch [23/50] batch [220/1000] time 1.558 (1.566) data 0.001 (0.005) loss 0.8394 (1.0880) acc 81.2500 (72.3011) lr 1.2487e-03 eta 12:05:17
+epoch [23/50] batch [225/1000] time 1.549 (1.566) data 0.000 (0.005) loss 0.9297 (1.0871) acc 68.7500 (72.3056) lr 1.2487e-03 eta 12:05:06
+epoch [23/50] batch [230/1000] time 1.558 (1.566) data 0.000 (0.005) loss 0.9844 (1.0848) acc 71.8750 (72.2147) lr 1.2487e-03 eta 12:05:01
+epoch [23/50] batch [235/1000] time 1.572 (1.566) data 0.000 (0.005) loss 1.2168 (1.0848) acc 65.6250 (72.1809) lr 1.2487e-03 eta 12:04:51
+epoch [23/50] batch [240/1000] time 1.550 (1.566) data 0.001 (0.004) loss 0.7920 (1.0840) acc 78.1250 (72.2526) lr 1.2487e-03 eta 12:04:40
+epoch [23/50] batch [245/1000] time 1.544 (1.566) data 0.001 (0.004) loss 1.6348 (1.0884) acc 62.5000 (72.1811) lr 1.2487e-03 eta 12:04:28
+epoch [23/50] batch [250/1000] time 1.564 (1.566) data 0.001 (0.004) loss 0.8804 (1.0876) acc 75.0000 (72.1875) lr 1.2487e-03 eta 12:04:15
+epoch [23/50] batch [255/1000] time 1.570 (1.566) data 0.000 (0.004) loss 0.6245 (1.0877) acc 81.2500 (72.1936) lr 1.2487e-03 eta 12:04:07
+epoch [23/50] batch [260/1000] time 1.566 (1.566) data 0.000 (0.004) loss 0.9043 (1.0880) acc 78.1250 (72.2596) lr 1.2487e-03 eta 12:03:52
+epoch [23/50] batch [265/1000] time 1.558 (1.565) data 0.001 (0.004) loss 0.8447 (1.0858) acc 78.1250 (72.3585) lr 1.2487e-03 eta 12:03:39
+epoch [23/50] batch [270/1000] time 1.537 (1.566) data 0.000 (0.004) loss 1.0791 (1.0864) acc 68.7500 (72.3495) lr 1.2487e-03 eta 12:03:43
+epoch [23/50] batch [275/1000] time 1.579 (1.566) data 0.000 (0.004) loss 0.7900 (1.0853) acc 87.5000 (72.4205) lr 1.2487e-03 eta 12:03:33
+epoch [23/50] batch [280/1000] time 1.556 (1.566) data 0.001 (0.004) loss 0.7222 (1.0834) acc 87.5000 (72.4665) lr 1.2487e-03 eta 12:03:21
+epoch [23/50] batch [285/1000] time 1.571 (1.566) data 0.000 (0.004) loss 0.8101 (1.0820) acc 84.3750 (72.5548) lr 1.2487e-03 eta 12:03:15
+epoch [23/50] batch [290/1000] time 1.572 (1.566) data 0.001 (0.004) loss 0.9316 (1.0844) acc 68.7500 (72.5323) lr 1.2487e-03 eta 12:03:02
+epoch [23/50] batch [295/1000] time 1.560 (1.566) data 0.000 (0.004) loss 1.0869 (1.0861) acc 68.7500 (72.5318) lr 1.2487e-03 eta 12:02:58
+epoch [23/50] batch [300/1000] time 1.592 (1.566) data 0.000 (0.004) loss 1.0312 (1.0851) acc 78.1250 (72.5729) lr 1.2487e-03 eta 12:02:52
+epoch [23/50] batch [305/1000] time 1.560 (1.566) data 0.000 (0.004) loss 1.6348 (1.0857) acc 68.7500 (72.6127) lr 1.2487e-03 eta 12:02:43
+epoch [23/50] batch [310/1000] time 1.538 (1.566) data 0.001 (0.004) loss 2.0273 (1.0887) acc 59.3750 (72.5706) lr 1.2487e-03 eta 12:02:30
+epoch [23/50] batch [315/1000] time 1.553 (1.566) data 0.000 (0.004) loss 0.9004 (1.0909) acc 78.1250 (72.5000) lr 1.2487e-03 eta 12:02:34
+epoch [23/50] batch [320/1000] time 1.556 (1.566) data 0.000 (0.003) loss 0.8154 (1.0865) acc 78.1250 (72.6270) lr 1.2487e-03 eta 12:02:21
+epoch [23/50] batch [325/1000] time 1.556 (1.566) data 0.000 (0.003) loss 1.0693 (1.0871) acc 71.8750 (72.5769) lr 1.2487e-03 eta 12:02:16
+epoch [23/50] batch [330/1000] time 1.557 (1.566) data 0.001 (0.003) loss 1.0039 (1.0853) acc 68.7500 (72.5473) lr 1.2487e-03 eta 12:02:04
+epoch [23/50] batch [335/1000] time 1.532 (1.565) data 0.000 (0.003) loss 1.2617 (1.0893) acc 71.8750 (72.5187) lr 1.2487e-03 eta 12:01:49
+epoch [23/50] batch [340/1000] time 1.567 (1.565) data 0.000 (0.003) loss 0.7534 (1.0903) acc 78.1250 (72.5184) lr 1.2487e-03 eta 12:01:37
+epoch [23/50] batch [345/1000] time 1.593 (1.565) data 0.000 (0.003) loss 1.1074 (1.0892) acc 75.0000 (72.5181) lr 1.2487e-03 eta 12:01:28
+epoch [23/50] batch [350/1000] time 1.549 (1.565) data 0.001 (0.003) loss 0.8569 (1.0909) acc 78.1250 (72.5089) lr 1.2487e-03 eta 12:01:16
+epoch [23/50] batch [355/1000] time 1.573 (1.566) data 0.000 (0.003) loss 1.6816 (1.0948) acc 53.1250 (72.4120) lr 1.2487e-03 eta 12:01:21
+epoch [23/50] batch [360/1000] time 1.563 (1.566) data 0.000 (0.003) loss 1.0693 (1.0987) acc 59.3750 (72.2656) lr 1.2487e-03 eta 12:01:15
+epoch [23/50] batch [365/1000] time 1.575 (1.566) data 0.000 (0.003) loss 0.9141 (1.0971) acc 78.1250 (72.2860) lr 1.2487e-03 eta 12:01:05
+epoch [23/50] batch [370/1000] time 1.553 (1.565) data 0.001 (0.003) loss 1.3604 (1.0972) acc 71.8750 (72.2804) lr 1.2487e-03 eta 12:00:53
+epoch [23/50] batch [375/1000] time 1.569 (1.565) data 0.000 (0.003) loss 0.7646 (1.0964) acc 71.8750 (72.2750) lr 1.2487e-03 eta 12:00:43
+epoch [23/50] batch [380/1000] time 1.575 (1.565) data 0.000 (0.003) loss 1.9180 (1.1010) acc 62.5000 (72.1875) lr 1.2487e-03 eta 12:00:34
+epoch [23/50] batch [385/1000] time 1.570 (1.565) data 0.000 (0.003) loss 0.3682 (1.0990) acc 93.7500 (72.2240) lr 1.2487e-03 eta 12:00:21
+epoch [23/50] batch [390/1000] time 1.560 (1.565) data 0.000 (0.003) loss 1.3164 (1.0980) acc 65.6250 (72.1955) lr 1.2487e-03 eta 12:00:09
+epoch [23/50] batch [395/1000] time 1.550 (1.565) data 0.000 (0.003) loss 0.8643 (1.0965) acc 78.1250 (72.2389) lr 1.2487e-03 eta 12:00:00
+epoch [23/50] batch [400/1000] time 1.566 (1.565) data 0.000 (0.003) loss 1.3242 (1.0967) acc 75.0000 (72.2891) lr 1.2487e-03 eta 11:59:54
+epoch [23/50] batch [405/1000] time 1.535 (1.565) data 0.000 (0.003) loss 1.1309 (1.0980) acc 62.5000 (72.2685) lr 1.2487e-03 eta 11:59:43
+epoch [23/50] batch [410/1000] time 1.549 (1.565) data 0.001 (0.003) loss 0.7681 (1.0969) acc 75.0000 (72.2866) lr 1.2487e-03 eta 11:59:32
+epoch [23/50] batch [415/1000] time 1.559 (1.565) data 0.000 (0.003) loss 0.4709 (1.0927) acc 84.3750 (72.4096) lr 1.2487e-03 eta 11:59:22
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+epoch [23/50] batch [980/1000] time 1.565 (1.563) data 0.000 (0.001) loss 1.0879 (1.1082) acc 75.0000 (72.0217) lr 1.2487e-03 eta 11:43:46
+epoch [23/50] batch [985/1000] time 1.539 (1.563) data 0.001 (0.001) loss 1.3506 (1.1083) acc 65.6250 (72.0209) lr 1.2487e-03 eta 11:43:37
+epoch [23/50] batch [990/1000] time 1.539 (1.563) data 0.000 (0.001) loss 1.3154 (1.1096) acc 65.6250 (71.9855) lr 1.2487e-03 eta 11:43:29
+epoch [23/50] batch [995/1000] time 1.553 (1.563) data 0.000 (0.001) loss 1.4551 (1.1103) acc 71.8750 (71.9755) lr 1.2487e-03 eta 11:43:19
+epoch [23/50] batch [1000/1000] time 1.547 (1.563) data 0.000 (0.001) loss 0.9277 (1.1114) acc 75.0000 (71.9750) lr 1.1874e-03 eta 11:43:10
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,204
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 77.9%
+epoch [24/50] batch [5/1000] time 1.565 (1.691) data 0.000 (0.191) loss 1.9678 (1.4041) acc 56.2500 (69.3750) lr 1.1874e-03 eta 12:41:00
+epoch [24/50] batch [10/1000] time 1.562 (1.625) data 0.001 (0.096) loss 1.3672 (1.3235) acc 71.8750 (68.4375) lr 1.1874e-03 eta 12:11:05
+epoch [24/50] batch [15/1000] time 1.586 (1.605) data 0.001 (0.064) loss 2.1836 (1.2462) acc 50.0000 (69.7917) lr 1.1874e-03 eta 12:01:41
+epoch [24/50] batch [20/1000] time 1.567 (1.594) data 0.000 (0.048) loss 1.6699 (1.2052) acc 62.5000 (70.4688) lr 1.1874e-03 eta 11:56:40
+epoch [24/50] batch [25/1000] time 1.567 (1.590) data 0.000 (0.039) loss 1.0752 (1.1995) acc 78.1250 (70.6250) lr 1.1874e-03 eta 11:54:41
+epoch [24/50] batch [30/1000] time 1.560 (1.586) data 0.001 (0.032) loss 1.2402 (1.1995) acc 81.2500 (70.7292) lr 1.1874e-03 eta 11:52:51
+epoch [24/50] batch [35/1000] time 1.573 (1.591) data 0.001 (0.028) loss 1.4395 (1.2204) acc 59.3750 (70.4464) lr 1.1874e-03 eta 11:55:04
+epoch [24/50] batch [40/1000] time 1.546 (1.587) data 0.001 (0.024) loss 0.7432 (1.1795) acc 75.0000 (71.3281) lr 1.1874e-03 eta 11:53:08
+epoch [24/50] batch [45/1000] time 1.576 (1.585) data 0.000 (0.022) loss 0.9038 (1.1623) acc 65.6250 (71.3889) lr 1.1874e-03 eta 11:51:58
+epoch [24/50] batch [50/1000] time 1.545 (1.582) data 0.001 (0.020) loss 1.2451 (1.1609) acc 75.0000 (71.5000) lr 1.1874e-03 eta 11:50:43
+epoch [24/50] batch [55/1000] time 1.576 (1.581) data 0.001 (0.018) loss 0.8315 (1.1439) acc 68.7500 (71.3636) lr 1.1874e-03 eta 11:49:57
+epoch [24/50] batch [60/1000] time 1.535 (1.578) data 0.001 (0.016) loss 1.0586 (1.1469) acc 75.0000 (71.5104) lr 1.1874e-03 eta 11:48:31
+epoch [24/50] batch [65/1000] time 1.564 (1.577) data 0.000 (0.015) loss 0.9746 (1.1397) acc 75.0000 (71.6346) lr 1.1874e-03 eta 11:47:54
+epoch [24/50] batch [70/1000] time 1.584 (1.576) data 0.000 (0.014) loss 1.4219 (1.1679) acc 56.2500 (71.0714) lr 1.1874e-03 eta 11:47:26
+epoch [24/50] batch [75/1000] time 1.547 (1.575) data 0.000 (0.013) loss 0.8345 (1.1475) acc 78.1250 (71.6250) lr 1.1874e-03 eta 11:46:50
+epoch [24/50] batch [80/1000] time 1.588 (1.575) data 0.000 (0.012) loss 1.2324 (1.1527) acc 59.3750 (71.4844) lr 1.1874e-03 eta 11:46:26
+epoch [24/50] batch [85/1000] time 1.564 (1.574) data 0.001 (0.012) loss 0.7119 (1.1323) acc 84.3750 (71.9853) lr 1.1874e-03 eta 11:45:52
+epoch [24/50] batch [90/1000] time 1.560 (1.573) data 0.000 (0.011) loss 1.2725 (1.1351) acc 75.0000 (72.0833) lr 1.1874e-03 eta 11:45:34
+epoch [24/50] batch [95/1000] time 1.573 (1.573) data 0.001 (0.011) loss 1.1475 (1.1420) acc 71.8750 (71.9079) lr 1.1874e-03 eta 11:45:21
+epoch [24/50] batch [100/1000] time 1.586 (1.572) data 0.000 (0.010) loss 0.9829 (1.1343) acc 68.7500 (72.0000) lr 1.1874e-03 eta 11:44:50
+epoch [24/50] batch [105/1000] time 1.576 (1.572) data 0.000 (0.010) loss 1.4189 (1.1214) acc 59.3750 (72.1131) lr 1.1874e-03 eta 11:44:40
+epoch [24/50] batch [110/1000] time 1.574 (1.572) data 0.000 (0.009) loss 0.9297 (1.1118) acc 75.0000 (72.4148) lr 1.1874e-03 eta 11:44:18
+epoch [24/50] batch [115/1000] time 1.571 (1.571) data 0.000 (0.009) loss 1.2998 (1.1175) acc 71.8750 (72.2283) lr 1.1874e-03 eta 11:43:56
+epoch [24/50] batch [120/1000] time 1.589 (1.571) data 0.000 (0.008) loss 1.7520 (1.1179) acc 65.6250 (72.1615) lr 1.1874e-03 eta 11:43:57
+epoch [24/50] batch [125/1000] time 1.582 (1.572) data 0.000 (0.008) loss 1.4053 (1.1108) acc 62.5000 (72.0500) lr 1.1874e-03 eta 11:43:59
+epoch [24/50] batch [130/1000] time 1.578 (1.572) data 0.000 (0.008) loss 0.9565 (1.1116) acc 75.0000 (72.0433) lr 1.1874e-03 eta 11:43:49
+epoch [24/50] batch [135/1000] time 1.713 (1.572) data 0.000 (0.008) loss 0.7397 (1.1167) acc 78.1250 (71.9676) lr 1.1874e-03 eta 11:44:01
+epoch [24/50] batch [140/1000] time 1.583 (1.572) data 0.001 (0.007) loss 1.5615 (1.1181) acc 71.8750 (72.0312) lr 1.1874e-03 eta 11:43:50
+epoch [24/50] batch [145/1000] time 1.554 (1.572) data 0.001 (0.007) loss 0.7461 (1.1091) acc 78.1250 (72.2198) lr 1.1874e-03 eta 11:43:30
+epoch [24/50] batch [150/1000] time 1.556 (1.571) data 0.001 (0.007) loss 2.0996 (1.1154) acc 56.2500 (72.1250) lr 1.1874e-03 eta 11:43:11
+epoch [24/50] batch [155/1000] time 1.568 (1.571) data 0.000 (0.007) loss 0.6719 (1.1129) acc 87.5000 (72.3185) lr 1.1874e-03 eta 11:42:54
+epoch [24/50] batch [160/1000] time 1.568 (1.571) data 0.001 (0.006) loss 0.7349 (1.1123) acc 84.3750 (72.3047) lr 1.1874e-03 eta 11:42:38
+epoch [24/50] batch [165/1000] time 1.577 (1.571) data 0.000 (0.006) loss 1.2998 (1.1118) acc 65.6250 (72.2348) lr 1.1874e-03 eta 11:42:35
+epoch [24/50] batch [170/1000] time 1.575 (1.571) data 0.001 (0.006) loss 0.6968 (1.1123) acc 78.1250 (72.1507) lr 1.1874e-03 eta 11:42:30
+epoch [24/50] batch [175/1000] time 1.575 (1.571) data 0.000 (0.006) loss 0.9102 (1.1113) acc 84.3750 (72.1071) lr 1.1874e-03 eta 11:42:18
+epoch [24/50] batch [180/1000] time 1.747 (1.572) data 0.000 (0.006) loss 0.9346 (1.1089) acc 71.8750 (72.1181) lr 1.1874e-03 eta 11:42:38
+epoch [24/50] batch [185/1000] time 1.574 (1.572) data 0.001 (0.006) loss 1.2246 (1.1085) acc 75.0000 (72.1115) lr 1.1874e-03 eta 11:42:32
+epoch [24/50] batch [190/1000] time 1.553 (1.572) data 0.001 (0.006) loss 1.2832 (1.1073) acc 71.8750 (72.1711) lr 1.1874e-03 eta 11:42:15
+epoch [24/50] batch [195/1000] time 1.570 (1.571) data 0.001 (0.005) loss 0.7583 (1.1013) acc 84.3750 (72.2756) lr 1.1874e-03 eta 11:42:01
+epoch [24/50] batch [200/1000] time 1.581 (1.571) data 0.000 (0.005) loss 1.1494 (1.0994) acc 71.8750 (72.4062) lr 1.1874e-03 eta 11:41:45
+epoch [24/50] batch [205/1000] time 1.566 (1.571) data 0.001 (0.005) loss 0.7832 (1.1031) acc 81.2500 (72.3323) lr 1.1874e-03 eta 11:41:32
+epoch [24/50] batch [210/1000] time 1.574 (1.571) data 0.001 (0.005) loss 1.3184 (1.1101) acc 75.0000 (72.2619) lr 1.1874e-03 eta 11:41:14
+epoch [24/50] batch [215/1000] time 1.559 (1.570) data 0.000 (0.005) loss 1.1582 (1.1077) acc 75.0000 (72.2674) lr 1.1874e-03 eta 11:40:57
+epoch [24/50] batch [220/1000] time 1.579 (1.570) data 0.000 (0.005) loss 1.0176 (1.1068) acc 78.1250 (72.3011) lr 1.1874e-03 eta 11:40:47
+epoch [24/50] batch [225/1000] time 1.556 (1.571) data 0.000 (0.005) loss 1.3721 (1.1064) acc 71.8750 (72.2361) lr 1.1874e-03 eta 11:40:53
+epoch [24/50] batch [230/1000] time 1.546 (1.570) data 0.000 (0.005) loss 1.4492 (1.1055) acc 59.3750 (72.2690) lr 1.1874e-03 eta 11:40:41
+epoch [24/50] batch [235/1000] time 1.549 (1.570) data 0.000 (0.005) loss 0.8945 (1.1006) acc 78.1250 (72.3670) lr 1.1874e-03 eta 11:40:30
+epoch [24/50] batch [240/1000] time 1.566 (1.570) data 0.000 (0.004) loss 0.6074 (1.0960) acc 84.3750 (72.4219) lr 1.1874e-03 eta 11:40:16
+epoch [24/50] batch [245/1000] time 1.581 (1.570) data 0.001 (0.004) loss 1.6787 (1.1039) acc 62.5000 (72.1811) lr 1.1874e-03 eta 11:40:03
+epoch [24/50] batch [250/1000] time 1.589 (1.570) data 0.000 (0.004) loss 1.0566 (1.1029) acc 68.7500 (72.2375) lr 1.1874e-03 eta 11:40:01
+epoch [24/50] batch [255/1000] time 1.571 (1.570) data 0.001 (0.004) loss 1.1865 (1.1094) acc 65.6250 (72.1814) lr 1.1874e-03 eta 11:39:50
+epoch [24/50] batch [260/1000] time 1.581 (1.570) data 0.001 (0.004) loss 0.8271 (1.1067) acc 78.1250 (72.2476) lr 1.1874e-03 eta 11:39:39
+epoch [24/50] batch [265/1000] time 1.549 (1.570) data 0.000 (0.004) loss 1.1016 (1.1055) acc 75.0000 (72.2759) lr 1.1874e-03 eta 11:39:33
+epoch [24/50] batch [270/1000] time 1.538 (1.570) data 0.001 (0.004) loss 1.4756 (1.1101) acc 71.8750 (72.1412) lr 1.1874e-03 eta 11:39:15
+epoch [24/50] batch [275/1000] time 1.569 (1.570) data 0.001 (0.004) loss 0.5283 (1.1070) acc 87.5000 (72.1932) lr 1.1874e-03 eta 11:39:05
+epoch [24/50] batch [280/1000] time 1.568 (1.569) data 0.001 (0.004) loss 1.3125 (1.1077) acc 71.8750 (72.1763) lr 1.1874e-03 eta 11:38:54
+epoch [24/50] batch [285/1000] time 1.581 (1.569) data 0.000 (0.004) loss 1.2646 (1.1106) acc 71.8750 (72.0943) lr 1.1874e-03 eta 11:38:45
+epoch [24/50] batch [290/1000] time 1.563 (1.570) data 0.000 (0.004) loss 1.3301 (1.1099) acc 65.6250 (72.1336) lr 1.1874e-03 eta 11:38:53
+epoch [24/50] batch [295/1000] time 1.540 (1.570) data 0.000 (0.004) loss 1.3203 (1.1128) acc 62.5000 (72.0551) lr 1.1874e-03 eta 11:38:42
+epoch [24/50] batch [300/1000] time 1.576 (1.570) data 0.001 (0.004) loss 1.1074 (1.1146) acc 68.7500 (72.0521) lr 1.1874e-03 eta 11:38:36
+epoch [24/50] batch [305/1000] time 1.571 (1.570) data 0.000 (0.004) loss 1.3721 (1.1151) acc 68.7500 (72.0082) lr 1.1874e-03 eta 11:38:27
+epoch [24/50] batch [310/1000] time 1.578 (1.570) data 0.001 (0.004) loss 1.3330 (1.1174) acc 62.5000 (71.9052) lr 1.1874e-03 eta 11:38:18
+epoch [24/50] batch [315/1000] time 1.549 (1.570) data 0.001 (0.004) loss 1.4092 (1.1214) acc 59.3750 (71.9048) lr 1.1874e-03 eta 11:38:05
+epoch [24/50] batch [320/1000] time 1.571 (1.569) data 0.001 (0.003) loss 0.7114 (1.1223) acc 78.1250 (71.8750) lr 1.1874e-03 eta 11:37:51
+epoch [24/50] batch [325/1000] time 1.570 (1.569) data 0.000 (0.003) loss 1.0449 (1.1237) acc 71.8750 (71.8173) lr 1.1874e-03 eta 11:37:41
+epoch [24/50] batch [330/1000] time 1.571 (1.569) data 0.000 (0.003) loss 1.5537 (1.1246) acc 62.5000 (71.8087) lr 1.1874e-03 eta 11:37:33
+epoch [24/50] batch [335/1000] time 1.572 (1.570) data 0.000 (0.003) loss 1.2715 (1.1257) acc 71.8750 (71.7910) lr 1.1874e-03 eta 11:37:36
+epoch [24/50] batch [340/1000] time 1.558 (1.570) data 0.001 (0.003) loss 0.9546 (1.1251) acc 68.7500 (71.7371) lr 1.1874e-03 eta 11:37:24
+epoch [24/50] batch [345/1000] time 1.561 (1.569) data 0.000 (0.003) loss 0.9341 (1.1240) acc 81.2500 (71.8297) lr 1.1874e-03 eta 11:37:14
+epoch [24/50] batch [350/1000] time 1.543 (1.569) data 0.000 (0.003) loss 0.9390 (1.1261) acc 71.8750 (71.7500) lr 1.1874e-03 eta 11:37:04
+epoch [24/50] batch [355/1000] time 1.556 (1.569) data 0.000 (0.003) loss 0.9146 (1.1236) acc 68.7500 (71.7430) lr 1.1874e-03 eta 11:36:51
+epoch [24/50] batch [360/1000] time 1.544 (1.569) data 0.001 (0.003) loss 1.2021 (1.1215) acc 62.5000 (71.7969) lr 1.1874e-03 eta 11:36:38
+epoch [24/50] batch [365/1000] time 1.553 (1.569) data 0.001 (0.003) loss 0.8057 (1.1207) acc 71.8750 (71.8151) lr 1.1874e-03 eta 11:36:28
+epoch [24/50] batch [370/1000] time 1.570 (1.569) data 0.001 (0.003) loss 1.3984 (1.1223) acc 62.5000 (71.7399) lr 1.1874e-03 eta 11:36:17
+epoch [24/50] batch [375/1000] time 1.560 (1.569) data 0.001 (0.003) loss 1.0664 (1.1254) acc 78.1250 (71.7167) lr 1.1874e-03 eta 11:36:23
+epoch [24/50] batch [380/1000] time 1.561 (1.569) data 0.001 (0.003) loss 0.5659 (1.1244) acc 84.3750 (71.7516) lr 1.1874e-03 eta 11:36:10
+epoch [24/50] batch [385/1000] time 1.571 (1.569) data 0.001 (0.003) loss 1.1846 (1.1232) acc 68.7500 (71.7695) lr 1.1874e-03 eta 11:36:02
+epoch [24/50] batch [390/1000] time 1.542 (1.569) data 0.000 (0.003) loss 0.8706 (1.1208) acc 71.8750 (71.8750) lr 1.1874e-03 eta 11:35:49
+epoch [24/50] batch [395/1000] time 1.564 (1.569) data 0.000 (0.003) loss 1.1963 (1.1217) acc 71.8750 (71.8908) lr 1.1874e-03 eta 11:35:39
+epoch [24/50] batch [400/1000] time 1.567 (1.569) data 0.001 (0.003) loss 1.5244 (1.1233) acc 65.6250 (71.8438) lr 1.1874e-03 eta 11:35:28
+epoch [24/50] batch [405/1000] time 1.560 (1.569) data 0.000 (0.003) loss 1.4941 (1.1268) acc 65.6250 (71.7824) lr 1.1874e-03 eta 11:35:21
+epoch [24/50] batch [410/1000] time 1.571 (1.569) data 0.001 (0.003) loss 1.3604 (1.1252) acc 65.6250 (71.8140) lr 1.1874e-03 eta 11:35:13
+epoch [24/50] batch [415/1000] time 1.560 (1.569) data 0.001 (0.003) loss 1.4844 (1.1248) acc 53.1250 (71.8148) lr 1.1874e-03 eta 11:35:00
+epoch [24/50] batch [420/1000] time 1.556 (1.569) data 0.001 (0.003) loss 1.3340 (1.1234) acc 59.3750 (71.8527) lr 1.1874e-03 eta 11:34:52
+epoch [24/50] batch [425/1000] time 1.554 (1.568) data 0.001 (0.003) loss 1.1309 (1.1229) acc 68.7500 (71.8309) lr 1.1874e-03 eta 11:34:40
+epoch [24/50] batch [430/1000] time 1.580 (1.568) data 0.001 (0.003) loss 0.8599 (1.1235) acc 75.0000 (71.7878) lr 1.1874e-03 eta 11:34:32
+epoch [24/50] batch [435/1000] time 1.580 (1.568) data 0.001 (0.003) loss 1.4189 (1.1251) acc 71.8750 (71.7744) lr 1.1874e-03 eta 11:34:26
+epoch [24/50] batch [440/1000] time 1.540 (1.569) data 0.001 (0.003) loss 1.3193 (1.1290) acc 65.6250 (71.6974) lr 1.1874e-03 eta 11:34:24
+epoch [24/50] batch [445/1000] time 1.566 (1.569) data 0.001 (0.003) loss 1.5986 (1.1287) acc 75.0000 (71.7205) lr 1.1874e-03 eta 11:34:16
+epoch [24/50] batch [450/1000] time 1.547 (1.569) data 0.000 (0.003) loss 1.3633 (1.1292) acc 65.6250 (71.7569) lr 1.1874e-03 eta 11:34:04
+epoch [24/50] batch [455/1000] time 1.550 (1.569) data 0.001 (0.003) loss 1.6504 (1.1293) acc 62.5000 (71.7788) lr 1.1874e-03 eta 11:33:55
+epoch [24/50] batch [460/1000] time 1.543 (1.568) data 0.000 (0.003) loss 0.9082 (1.1265) acc 78.1250 (71.8546) lr 1.1874e-03 eta 11:33:45
+epoch [24/50] batch [465/1000] time 1.547 (1.568) data 0.001 (0.003) loss 1.0479 (1.1265) acc 71.8750 (71.8481) lr 1.1874e-03 eta 11:33:33
+epoch [24/50] batch [470/1000] time 1.575 (1.568) data 0.001 (0.003) loss 1.0801 (1.1270) acc 71.8750 (71.8551) lr 1.1874e-03 eta 11:33:24
+epoch [24/50] batch [475/1000] time 1.564 (1.568) data 0.001 (0.003) loss 1.1289 (1.1308) acc 68.7500 (71.7763) lr 1.1874e-03 eta 11:33:13
+epoch [24/50] batch [480/1000] time 1.565 (1.568) data 0.000 (0.003) loss 1.4707 (1.1315) acc 71.8750 (71.7773) lr 1.1874e-03 eta 11:33:04
+epoch [24/50] batch [485/1000] time 1.575 (1.568) data 0.001 (0.002) loss 1.0654 (1.1316) acc 71.8750 (71.7848) lr 1.1874e-03 eta 11:33:04
+epoch [24/50] batch [490/1000] time 1.563 (1.568) data 0.000 (0.002) loss 1.9443 (1.1326) acc 53.1250 (71.7283) lr 1.1874e-03 eta 11:32:58
+epoch [24/50] batch [495/1000] time 1.598 (1.568) data 0.000 (0.002) loss 0.8569 (1.1308) acc 78.1250 (71.7929) lr 1.1874e-03 eta 11:32:50
+epoch [24/50] batch [500/1000] time 1.575 (1.568) data 0.001 (0.002) loss 1.0605 (1.1304) acc 71.8750 (71.7687) lr 1.1874e-03 eta 11:32:40
+epoch [24/50] batch [505/1000] time 1.568 (1.568) data 0.000 (0.002) loss 1.1016 (1.1313) acc 71.8750 (71.7450) lr 1.1874e-03 eta 11:32:31
+epoch [24/50] batch [510/1000] time 1.551 (1.568) data 0.000 (0.002) loss 0.7544 (1.1297) acc 81.2500 (71.7708) lr 1.1874e-03 eta 11:32:25
+epoch [24/50] batch [515/1000] time 1.579 (1.568) data 0.001 (0.002) loss 1.3848 (1.1316) acc 65.6250 (71.7112) lr 1.1874e-03 eta 11:32:17
+epoch [24/50] batch [520/1000] time 1.556 (1.568) data 0.001 (0.002) loss 0.7593 (1.1309) acc 78.1250 (71.7368) lr 1.1874e-03 eta 11:32:10
+epoch [24/50] batch [525/1000] time 1.752 (1.569) data 0.000 (0.002) loss 1.6641 (1.1324) acc 68.7500 (71.7143) lr 1.1874e-03 eta 11:32:11
+epoch [24/50] batch [530/1000] time 1.524 (1.569) data 0.001 (0.002) loss 1.9482 (1.1321) acc 59.3750 (71.7217) lr 1.1874e-03 eta 11:31:59
+epoch [24/50] batch [535/1000] time 1.528 (1.568) data 0.001 (0.002) loss 1.0723 (1.1325) acc 65.6250 (71.7290) lr 1.1874e-03 eta 11:31:47
+epoch [24/50] batch [540/1000] time 1.542 (1.568) data 0.000 (0.002) loss 1.3301 (1.1340) acc 75.0000 (71.7188) lr 1.1874e-03 eta 11:31:39
+epoch [24/50] batch [545/1000] time 1.536 (1.568) data 0.001 (0.002) loss 0.9639 (1.1360) acc 68.7500 (71.6743) lr 1.1874e-03 eta 11:31:27
+epoch [24/50] batch [550/1000] time 1.553 (1.568) data 0.001 (0.002) loss 1.2344 (1.1379) acc 65.6250 (71.6420) lr 1.1874e-03 eta 11:31:14
+epoch [24/50] batch [555/1000] time 1.567 (1.568) data 0.000 (0.002) loss 0.9771 (1.1360) acc 71.8750 (71.6948) lr 1.1874e-03 eta 11:31:04
+epoch [24/50] batch [560/1000] time 1.540 (1.568) data 0.000 (0.002) loss 1.4326 (1.1368) acc 75.0000 (71.6908) lr 1.1874e-03 eta 11:30:50
+epoch [24/50] batch [565/1000] time 1.546 (1.568) data 0.000 (0.002) loss 1.0576 (1.1346) acc 71.8750 (71.7644) lr 1.1874e-03 eta 11:30:42
+epoch [24/50] batch [570/1000] time 1.555 (1.568) data 0.001 (0.002) loss 1.7041 (1.1342) acc 68.7500 (71.7818) lr 1.1874e-03 eta 11:30:33
+epoch [24/50] batch [575/1000] time 1.583 (1.568) data 0.001 (0.002) loss 1.0244 (1.1348) acc 71.8750 (71.7554) lr 1.1874e-03 eta 11:30:26
+epoch [24/50] batch [580/1000] time 1.588 (1.568) data 0.000 (0.002) loss 1.1729 (1.1335) acc 71.8750 (71.8103) lr 1.1874e-03 eta 11:30:15
+epoch [24/50] batch [585/1000] time 1.566 (1.568) data 0.001 (0.002) loss 0.6904 (1.1303) acc 81.2500 (71.9124) lr 1.1874e-03 eta 11:30:08
+epoch [24/50] batch [590/1000] time 1.555 (1.568) data 0.001 (0.002) loss 0.7417 (1.1286) acc 81.2500 (71.9386) lr 1.1874e-03 eta 11:30:06
+epoch [24/50] batch [595/1000] time 1.547 (1.568) data 0.001 (0.002) loss 0.9912 (1.1302) acc 62.5000 (71.8750) lr 1.1874e-03 eta 11:29:57
+epoch [24/50] batch [600/1000] time 1.581 (1.568) data 0.001 (0.002) loss 0.7686 (1.1291) acc 75.0000 (71.8802) lr 1.1874e-03 eta 11:29:50
+epoch [24/50] batch [605/1000] time 1.591 (1.568) data 0.001 (0.002) loss 1.1387 (1.1300) acc 75.0000 (71.8802) lr 1.1874e-03 eta 11:29:44
+epoch [24/50] batch [610/1000] time 1.597 (1.568) data 0.001 (0.002) loss 1.1953 (1.1301) acc 65.6250 (71.8852) lr 1.1874e-03 eta 11:29:37
+epoch [24/50] batch [615/1000] time 1.568 (1.568) data 0.001 (0.002) loss 0.5317 (1.1290) acc 90.6250 (71.9055) lr 1.1874e-03 eta 11:29:27
+epoch [24/50] batch [620/1000] time 1.561 (1.568) data 0.000 (0.002) loss 0.8179 (1.1291) acc 84.3750 (71.9304) lr 1.1874e-03 eta 11:29:18
+epoch [24/50] batch [625/1000] time 1.541 (1.568) data 0.000 (0.002) loss 1.4141 (1.1306) acc 65.6250 (71.9100) lr 1.1874e-03 eta 11:29:09
+epoch [24/50] batch [630/1000] time 1.573 (1.568) data 0.000 (0.002) loss 0.9937 (1.1300) acc 71.8750 (71.8948) lr 1.1874e-03 eta 11:29:02
+epoch [24/50] batch [635/1000] time 1.572 (1.568) data 0.001 (0.002) loss 1.1484 (1.1288) acc 71.8750 (71.8996) lr 1.1874e-03 eta 11:29:01
+epoch [24/50] batch [640/1000] time 1.553 (1.568) data 0.000 (0.002) loss 1.0547 (1.1283) acc 68.7500 (71.9189) lr 1.1874e-03 eta 11:28:52
+epoch [24/50] batch [645/1000] time 1.581 (1.568) data 0.001 (0.002) loss 0.9067 (1.1284) acc 78.1250 (71.9428) lr 1.1874e-03 eta 11:28:43
+epoch [24/50] batch [650/1000] time 1.553 (1.568) data 0.000 (0.002) loss 1.1514 (1.1277) acc 78.1250 (71.9471) lr 1.1874e-03 eta 11:28:33
+epoch [24/50] batch [655/1000] time 1.542 (1.568) data 0.000 (0.002) loss 1.4297 (1.1267) acc 65.6250 (71.9895) lr 1.1874e-03 eta 11:28:24
+epoch [24/50] batch [660/1000] time 1.579 (1.568) data 0.001 (0.002) loss 1.1348 (1.1266) acc 81.2500 (71.9981) lr 1.1874e-03 eta 11:28:19
+epoch [24/50] batch [665/1000] time 1.581 (1.568) data 0.001 (0.002) loss 0.7129 (1.1269) acc 81.2500 (71.9972) lr 1.1874e-03 eta 11:28:13
+epoch [24/50] batch [670/1000] time 1.558 (1.568) data 0.000 (0.002) loss 1.0020 (1.1255) acc 75.0000 (72.0243) lr 1.1874e-03 eta 11:28:05
+epoch [24/50] batch [675/1000] time 1.601 (1.568) data 0.001 (0.002) loss 0.8179 (1.1237) acc 78.1250 (72.0694) lr 1.1874e-03 eta 11:27:56
+epoch [24/50] batch [680/1000] time 1.549 (1.568) data 0.000 (0.002) loss 1.3105 (1.1247) acc 65.6250 (72.0129) lr 1.1874e-03 eta 11:27:52
+epoch [24/50] batch [685/1000] time 1.572 (1.568) data 0.000 (0.002) loss 1.0420 (1.1262) acc 75.0000 (71.9982) lr 1.1874e-03 eta 11:27:41
+epoch [24/50] batch [690/1000] time 1.564 (1.568) data 0.000 (0.002) loss 0.9028 (1.1264) acc 81.2500 (72.0245) lr 1.1874e-03 eta 11:27:32
+epoch [24/50] batch [695/1000] time 1.557 (1.568) data 0.000 (0.002) loss 0.7627 (1.1243) acc 87.5000 (72.0773) lr 1.1874e-03 eta 11:27:21
+epoch [24/50] batch [700/1000] time 1.562 (1.568) data 0.000 (0.002) loss 1.4326 (1.1256) acc 71.8750 (72.0893) lr 1.1874e-03 eta 11:27:12
+epoch [24/50] batch [705/1000] time 1.576 (1.568) data 0.000 (0.002) loss 1.1846 (1.1265) acc 68.7500 (72.0523) lr 1.1874e-03 eta 11:27:07
+epoch [24/50] batch [710/1000] time 1.565 (1.568) data 0.000 (0.002) loss 1.1074 (1.1259) acc 62.5000 (72.0335) lr 1.1874e-03 eta 11:26:57
+epoch [24/50] batch [715/1000] time 1.546 (1.568) data 0.000 (0.002) loss 0.7539 (1.1254) acc 75.0000 (72.0411) lr 1.1874e-03 eta 11:26:48
+epoch [24/50] batch [720/1000] time 1.548 (1.568) data 0.000 (0.002) loss 1.0830 (1.1244) acc 75.0000 (72.0747) lr 1.1874e-03 eta 11:26:38
+epoch [24/50] batch [725/1000] time 1.564 (1.568) data 0.001 (0.002) loss 0.9189 (1.1245) acc 75.0000 (72.0776) lr 1.1874e-03 eta 11:26:31
+epoch [24/50] batch [730/1000] time 1.605 (1.568) data 0.001 (0.002) loss 0.9473 (1.1235) acc 71.8750 (72.0762) lr 1.1874e-03 eta 11:26:22
+epoch [24/50] batch [735/1000] time 1.546 (1.568) data 0.000 (0.002) loss 1.4160 (1.1242) acc 68.7500 (72.0663) lr 1.1874e-03 eta 11:26:14
+epoch [24/50] batch [740/1000] time 1.556 (1.568) data 0.000 (0.002) loss 1.9355 (1.1254) acc 62.5000 (72.0481) lr 1.1874e-03 eta 11:26:12
+epoch [24/50] batch [745/1000] time 1.539 (1.568) data 0.001 (0.002) loss 0.8525 (1.1249) acc 78.1250 (72.0512) lr 1.1874e-03 eta 11:26:01
+epoch [24/50] batch [750/1000] time 1.549 (1.568) data 0.000 (0.002) loss 0.5991 (1.1231) acc 81.2500 (72.0708) lr 1.1874e-03 eta 11:25:53
+epoch [24/50] batch [755/1000] time 1.596 (1.568) data 0.001 (0.002) loss 0.9185 (1.1232) acc 78.1250 (72.0861) lr 1.1874e-03 eta 11:25:45
+epoch [24/50] batch [760/1000] time 1.544 (1.568) data 0.000 (0.002) loss 0.8091 (1.1222) acc 78.1250 (72.1135) lr 1.1874e-03 eta 11:25:38
+epoch [24/50] batch [765/1000] time 1.567 (1.568) data 0.000 (0.002) loss 1.2637 (1.1216) acc 68.7500 (72.1324) lr 1.1874e-03 eta 11:25:30
+epoch [24/50] batch [770/1000] time 1.546 (1.568) data 0.000 (0.002) loss 1.8730 (1.1243) acc 59.3750 (72.0576) lr 1.1874e-03 eta 11:25:19
+epoch [24/50] batch [775/1000] time 1.560 (1.568) data 0.000 (0.002) loss 1.1338 (1.1256) acc 68.7500 (72.0161) lr 1.1874e-03 eta 11:25:10
+epoch [24/50] batch [780/1000] time 1.570 (1.568) data 0.001 (0.002) loss 0.8770 (1.1257) acc 78.1250 (72.0312) lr 1.1874e-03 eta 11:25:02
+epoch [24/50] batch [785/1000] time 1.557 (1.568) data 0.001 (0.002) loss 1.8594 (1.1270) acc 56.2500 (71.9904) lr 1.1874e-03 eta 11:24:58
+epoch [24/50] batch [790/1000] time 1.541 (1.568) data 0.000 (0.002) loss 0.6914 (1.1268) acc 71.8750 (71.9699) lr 1.1874e-03 eta 11:24:46
+epoch [24/50] batch [795/1000] time 1.563 (1.568) data 0.001 (0.002) loss 2.0254 (1.1284) acc 56.2500 (71.9300) lr 1.1874e-03 eta 11:24:38
+epoch [24/50] batch [800/1000] time 1.607 (1.568) data 0.000 (0.002) loss 1.1533 (1.1267) acc 75.0000 (71.9766) lr 1.1874e-03 eta 11:24:29
+epoch [24/50] batch [805/1000] time 1.569 (1.568) data 0.000 (0.002) loss 0.7163 (1.1264) acc 84.3750 (71.9643) lr 1.1874e-03 eta 11:24:23
+epoch [24/50] batch [810/1000] time 1.562 (1.568) data 0.000 (0.002) loss 0.9097 (1.1268) acc 65.6250 (71.9406) lr 1.1874e-03 eta 11:24:13
+epoch [24/50] batch [815/1000] time 1.544 (1.567) data 0.000 (0.002) loss 1.1660 (1.1276) acc 59.3750 (71.9018) lr 1.1874e-03 eta 11:24:03
+epoch [24/50] batch [820/1000] time 1.563 (1.567) data 0.000 (0.002) loss 1.0137 (1.1284) acc 68.7500 (71.8636) lr 1.1874e-03 eta 11:23:55
+epoch [24/50] batch [825/1000] time 1.575 (1.567) data 0.000 (0.002) loss 1.2119 (1.1296) acc 75.0000 (71.8636) lr 1.1874e-03 eta 11:23:46
+epoch [24/50] batch [830/1000] time 1.598 (1.568) data 0.000 (0.002) loss 1.0049 (1.1298) acc 84.3750 (71.8788) lr 1.1874e-03 eta 11:23:45
+epoch [24/50] batch [835/1000] time 1.565 (1.568) data 0.000 (0.002) loss 1.4697 (1.1300) acc 65.6250 (71.8787) lr 1.1874e-03 eta 11:23:38
+epoch [24/50] batch [840/1000] time 1.549 (1.568) data 0.000 (0.002) loss 1.7686 (1.1309) acc 62.5000 (71.8676) lr 1.1874e-03 eta 11:23:28
+epoch [24/50] batch [845/1000] time 1.543 (1.568) data 0.001 (0.002) loss 1.1396 (1.1312) acc 78.1250 (71.8676) lr 1.1874e-03 eta 11:23:19
+epoch [24/50] batch [850/1000] time 1.586 (1.568) data 0.001 (0.002) loss 0.7275 (1.1298) acc 75.0000 (71.8971) lr 1.1874e-03 eta 11:23:12
+epoch [24/50] batch [855/1000] time 1.571 (1.568) data 0.000 (0.002) loss 1.3730 (1.1291) acc 59.3750 (71.9006) lr 1.1874e-03 eta 11:23:03
+epoch [24/50] batch [860/1000] time 1.540 (1.568) data 0.000 (0.002) loss 1.1533 (1.1296) acc 65.6250 (71.8750) lr 1.1874e-03 eta 11:22:57
+epoch [24/50] batch [865/1000] time 1.567 (1.568) data 0.000 (0.002) loss 1.0117 (1.1295) acc 75.0000 (71.8497) lr 1.1874e-03 eta 11:22:48
+epoch [24/50] batch [870/1000] time 1.561 (1.567) data 0.001 (0.002) loss 1.2031 (1.1288) acc 71.8750 (71.8570) lr 1.1874e-03 eta 11:22:38
+epoch [24/50] batch [875/1000] time 1.546 (1.567) data 0.000 (0.002) loss 0.8853 (1.1292) acc 75.0000 (71.8643) lr 1.1874e-03 eta 11:22:29
+epoch [24/50] batch [880/1000] time 1.575 (1.567) data 0.000 (0.002) loss 1.0547 (1.1287) acc 65.6250 (71.8643) lr 1.1874e-03 eta 11:22:21
+epoch [24/50] batch [885/1000] time 1.559 (1.567) data 0.000 (0.002) loss 1.4248 (1.1287) acc 65.6250 (71.8538) lr 1.1874e-03 eta 11:22:11
+epoch [24/50] batch [890/1000] time 1.689 (1.567) data 0.000 (0.002) loss 1.6035 (1.1289) acc 62.5000 (71.8610) lr 1.1874e-03 eta 11:22:06
+epoch [24/50] batch [895/1000] time 1.542 (1.567) data 0.000 (0.002) loss 1.4600 (1.1291) acc 65.6250 (71.8575) lr 1.1874e-03 eta 11:21:56
+epoch [24/50] batch [900/1000] time 1.547 (1.567) data 0.000 (0.002) loss 0.7217 (1.1293) acc 81.2500 (71.8472) lr 1.1874e-03 eta 11:21:47
+epoch [24/50] batch [905/1000] time 1.573 (1.567) data 0.000 (0.002) loss 0.8975 (1.1308) acc 68.7500 (71.8094) lr 1.1874e-03 eta 11:21:38
+epoch [24/50] batch [910/1000] time 1.551 (1.567) data 0.001 (0.002) loss 1.0078 (1.1300) acc 68.7500 (71.8235) lr 1.1874e-03 eta 11:21:30
+epoch [24/50] batch [915/1000] time 1.553 (1.567) data 0.000 (0.002) loss 1.2744 (1.1302) acc 84.3750 (71.8340) lr 1.1874e-03 eta 11:21:21
+epoch [24/50] batch [920/1000] time 1.531 (1.567) data 0.000 (0.002) loss 0.9204 (1.1300) acc 75.0000 (71.8342) lr 1.1874e-03 eta 11:21:12
+epoch [24/50] batch [925/1000] time 1.557 (1.567) data 0.000 (0.002) loss 0.9951 (1.1303) acc 71.8750 (71.8243) lr 1.1874e-03 eta 11:21:03
+epoch [24/50] batch [930/1000] time 1.574 (1.567) data 0.001 (0.002) loss 1.2227 (1.1307) acc 78.1250 (71.8448) lr 1.1874e-03 eta 11:20:55
+epoch [24/50] batch [935/1000] time 1.746 (1.567) data 0.001 (0.002) loss 0.9688 (1.1294) acc 78.1250 (71.8817) lr 1.1874e-03 eta 11:20:53
+epoch [24/50] batch [940/1000] time 1.587 (1.567) data 0.000 (0.002) loss 1.0645 (1.1297) acc 71.8750 (71.8650) lr 1.1874e-03 eta 11:20:45
+epoch [24/50] batch [945/1000] time 1.551 (1.567) data 0.000 (0.002) loss 1.5020 (1.1315) acc 71.8750 (71.8519) lr 1.1874e-03 eta 11:20:36
+epoch [24/50] batch [950/1000] time 1.540 (1.567) data 0.000 (0.002) loss 0.7998 (1.1308) acc 71.8750 (71.8651) lr 1.1874e-03 eta 11:20:26
+epoch [24/50] batch [955/1000] time 1.551 (1.567) data 0.001 (0.002) loss 1.7441 (1.1308) acc 62.5000 (71.8685) lr 1.1874e-03 eta 11:20:17
+epoch [24/50] batch [960/1000] time 1.529 (1.567) data 0.000 (0.002) loss 1.7549 (1.1321) acc 59.3750 (71.8229) lr 1.1874e-03 eta 11:20:07
+epoch [24/50] batch [965/1000] time 1.547 (1.567) data 0.001 (0.001) loss 1.3115 (1.1311) acc 71.8750 (71.8491) lr 1.1874e-03 eta 11:19:58
+epoch [24/50] batch [970/1000] time 1.565 (1.567) data 0.001 (0.001) loss 1.2217 (1.1308) acc 68.7500 (71.8492) lr 1.1874e-03 eta 11:19:47
+epoch [24/50] batch [975/1000] time 1.560 (1.567) data 0.001 (0.001) loss 1.6113 (1.1321) acc 71.8750 (71.8397) lr 1.1874e-03 eta 11:19:37
+epoch [24/50] batch [980/1000] time 1.594 (1.567) data 0.000 (0.001) loss 1.0459 (1.1309) acc 71.8750 (71.8559) lr 1.1874e-03 eta 11:19:34
+epoch [24/50] batch [985/1000] time 1.575 (1.567) data 0.001 (0.001) loss 0.7095 (1.1305) acc 87.5000 (71.8560) lr 1.1874e-03 eta 11:19:26
+epoch [24/50] batch [990/1000] time 1.583 (1.567) data 0.000 (0.001) loss 0.8892 (1.1313) acc 65.6250 (71.8403) lr 1.1874e-03 eta 11:19:17
+epoch [24/50] batch [995/1000] time 1.552 (1.567) data 0.000 (0.001) loss 0.9990 (1.1308) acc 71.8750 (71.8373) lr 1.1874e-03 eta 11:19:08
+epoch [24/50] batch [1000/1000] time 1.565 (1.567) data 0.000 (0.001) loss 1.5068 (1.1316) acc 62.5000 (71.8031) lr 1.1253e-03 eta 11:18:59
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,224
+* accuracy: 78.4%
+* error: 21.6%
+* macro_f1: 78.0%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [25/50] batch [5/1000] time 1.569 (1.707) data 0.000 (0.206) loss 1.7559 (1.3606) acc 62.5000 (68.1250) lr 1.1253e-03 eta 12:19:33
+epoch [25/50] batch [10/1000] time 1.572 (1.634) data 0.000 (0.103) loss 0.7637 (1.1656) acc 78.1250 (71.5625) lr 1.1253e-03 eta 11:47:42
+epoch [25/50] batch [15/1000] time 1.570 (1.611) data 0.000 (0.069) loss 0.9561 (1.1291) acc 71.8750 (71.2500) lr 1.1253e-03 eta 11:37:52
+epoch [25/50] batch [20/1000] time 1.565 (1.601) data 0.000 (0.052) loss 0.7603 (1.0638) acc 75.0000 (72.3438) lr 1.1253e-03 eta 11:33:23
+epoch [25/50] batch [25/1000] time 1.551 (1.594) data 0.001 (0.041) loss 1.1572 (1.1465) acc 71.8750 (71.8750) lr 1.1253e-03 eta 11:29:53
+epoch [25/50] batch [30/1000] time 1.545 (1.588) data 0.001 (0.035) loss 1.3828 (1.1410) acc 68.7500 (71.4583) lr 1.1253e-03 eta 11:27:32
+epoch [25/50] batch [35/1000] time 1.578 (1.588) data 0.001 (0.030) loss 1.4229 (1.1475) acc 62.5000 (71.3393) lr 1.1253e-03 eta 11:27:06
+epoch [25/50] batch [40/1000] time 1.530 (1.585) data 0.000 (0.026) loss 0.7407 (1.1552) acc 78.1250 (71.6406) lr 1.1253e-03 eta 11:25:44
+epoch [25/50] batch [45/1000] time 1.566 (1.582) data 0.001 (0.023) loss 1.0107 (1.1380) acc 68.7500 (71.6667) lr 1.1253e-03 eta 11:24:17
+epoch [25/50] batch [50/1000] time 1.536 (1.579) data 0.001 (0.021) loss 0.6953 (1.1262) acc 87.5000 (72.3125) lr 1.1253e-03 eta 11:23:02
+epoch [25/50] batch [55/1000] time 1.575 (1.577) data 0.001 (0.019) loss 1.3086 (1.1345) acc 71.8750 (71.9886) lr 1.1253e-03 eta 11:21:54
+epoch [25/50] batch [60/1000] time 1.560 (1.579) data 0.000 (0.018) loss 0.8262 (1.1243) acc 78.1250 (72.0312) lr 1.1253e-03 eta 11:22:33
+epoch [25/50] batch [65/1000] time 1.565 (1.578) data 0.000 (0.016) loss 0.8926 (1.1280) acc 75.0000 (71.8269) lr 1.1253e-03 eta 11:22:03
+epoch [25/50] batch [70/1000] time 1.567 (1.577) data 0.001 (0.015) loss 1.0996 (1.1234) acc 65.6250 (71.8304) lr 1.1253e-03 eta 11:21:28
+epoch [25/50] batch [75/1000] time 1.559 (1.576) data 0.001 (0.014) loss 1.0459 (1.1093) acc 75.0000 (72.0000) lr 1.1253e-03 eta 11:21:00
+epoch [25/50] batch [80/1000] time 1.570 (1.575) data 0.000 (0.013) loss 1.0410 (1.0897) acc 71.8750 (72.4609) lr 1.1253e-03 eta 11:20:36
+epoch [25/50] batch [85/1000] time 1.567 (1.574) data 0.000 (0.013) loss 1.1826 (1.0860) acc 68.7500 (72.4632) lr 1.1253e-03 eta 11:19:59
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+epoch [25/50] batch [645/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.7471 (1.1109) acc 50.0000 (71.8120) lr 1.1253e-03 eta 11:01:47
+epoch [25/50] batch [650/1000] time 1.567 (1.566) data 0.000 (0.002) loss 1.1016 (1.1104) acc 71.8750 (71.8221) lr 1.1253e-03 eta 11:01:40
+epoch [25/50] batch [655/1000] time 1.574 (1.566) data 0.000 (0.002) loss 0.8550 (1.1105) acc 78.1250 (71.8034) lr 1.1253e-03 eta 11:01:32
+epoch [25/50] batch [660/1000] time 1.745 (1.566) data 0.000 (0.002) loss 1.8135 (1.1134) acc 62.5000 (71.7472) lr 1.1253e-03 eta 11:01:31
+epoch [25/50] batch [665/1000] time 1.592 (1.566) data 0.000 (0.002) loss 1.1553 (1.1129) acc 78.1250 (71.7763) lr 1.1253e-03 eta 11:01:24
+epoch [25/50] batch [670/1000] time 1.565 (1.566) data 0.000 (0.002) loss 1.1738 (1.1116) acc 75.0000 (71.8190) lr 1.1253e-03 eta 11:01:16
+epoch [25/50] batch [675/1000] time 1.551 (1.566) data 0.001 (0.002) loss 0.7803 (1.1124) acc 81.2500 (71.8333) lr 1.1253e-03 eta 11:01:07
+epoch [25/50] batch [680/1000] time 1.569 (1.566) data 0.001 (0.002) loss 0.7715 (1.1129) acc 81.2500 (71.8612) lr 1.1253e-03 eta 11:00:59
+epoch [25/50] batch [685/1000] time 1.576 (1.566) data 0.000 (0.002) loss 1.6113 (1.1135) acc 59.3750 (71.8522) lr 1.1253e-03 eta 11:00:50
+epoch [25/50] batch [690/1000] time 1.543 (1.566) data 0.000 (0.002) loss 1.1973 (1.1131) acc 68.7500 (71.8614) lr 1.1253e-03 eta 11:00:40
+epoch [25/50] batch [695/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.3096 (1.1135) acc 68.7500 (71.8660) lr 1.1253e-03 eta 11:00:31
+epoch [25/50] batch [700/1000] time 1.556 (1.566) data 0.000 (0.002) loss 0.8433 (1.1139) acc 75.0000 (71.8750) lr 1.1253e-03 eta 11:00:23
+epoch [25/50] batch [705/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.9155 (1.1137) acc 78.1250 (71.8750) lr 1.1253e-03 eta 11:00:13
+epoch [25/50] batch [710/1000] time 1.569 (1.566) data 0.001 (0.002) loss 1.0742 (1.1135) acc 81.2500 (71.8794) lr 1.1253e-03 eta 11:00:09
+epoch [25/50] batch [715/1000] time 1.552 (1.566) data 0.000 (0.002) loss 0.8125 (1.1145) acc 68.7500 (71.8444) lr 1.1253e-03 eta 10:59:59
+epoch [25/50] batch [720/1000] time 1.564 (1.566) data 0.001 (0.002) loss 1.4961 (1.1144) acc 71.8750 (71.8403) lr 1.1253e-03 eta 10:59:52
+epoch [25/50] batch [725/1000] time 1.560 (1.566) data 0.000 (0.002) loss 0.9385 (1.1144) acc 75.0000 (71.8664) lr 1.1253e-03 eta 10:59:45
+epoch [25/50] batch [730/1000] time 1.590 (1.566) data 0.000 (0.002) loss 0.7954 (1.1145) acc 75.0000 (71.8793) lr 1.1253e-03 eta 10:59:39
+epoch [25/50] batch [735/1000] time 1.544 (1.566) data 0.000 (0.002) loss 0.9829 (1.1143) acc 71.8750 (71.9090) lr 1.1253e-03 eta 10:59:28
+epoch [25/50] batch [740/1000] time 1.550 (1.566) data 0.000 (0.002) loss 1.3467 (1.1151) acc 71.8750 (71.9003) lr 1.1253e-03 eta 10:59:19
+epoch [25/50] batch [745/1000] time 1.564 (1.566) data 0.000 (0.002) loss 1.0361 (1.1152) acc 71.8750 (71.9002) lr 1.1253e-03 eta 10:59:10
+epoch [25/50] batch [750/1000] time 1.555 (1.566) data 0.001 (0.002) loss 0.9009 (1.1152) acc 71.8750 (71.8750) lr 1.1253e-03 eta 10:59:01
+epoch [25/50] batch [755/1000] time 1.549 (1.566) data 0.000 (0.002) loss 0.8442 (1.1141) acc 71.8750 (71.9164) lr 1.1253e-03 eta 10:58:52
+epoch [25/50] batch [760/1000] time 1.579 (1.566) data 0.000 (0.002) loss 1.0029 (1.1144) acc 71.8750 (71.9079) lr 1.1253e-03 eta 10:58:42
+epoch [25/50] batch [765/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.7280 (1.1140) acc 84.3750 (71.9118) lr 1.1253e-03 eta 10:58:40
+epoch [25/50] batch [770/1000] time 1.573 (1.566) data 0.000 (0.002) loss 1.4062 (1.1155) acc 62.5000 (71.8750) lr 1.1253e-03 eta 10:58:30
+epoch [25/50] batch [775/1000] time 1.540 (1.566) data 0.000 (0.002) loss 1.4102 (1.1157) acc 59.3750 (71.8629) lr 1.1253e-03 eta 10:58:19
+epoch [25/50] batch [780/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.8477 (1.1152) acc 71.8750 (71.8950) lr 1.1253e-03 eta 10:58:11
+epoch [25/50] batch [785/1000] time 1.580 (1.566) data 0.000 (0.002) loss 2.1035 (1.1163) acc 43.7500 (71.8710) lr 1.1253e-03 eta 10:58:03
+epoch [25/50] batch [790/1000] time 1.585 (1.566) data 0.000 (0.002) loss 0.8159 (1.1165) acc 81.2500 (71.8790) lr 1.1253e-03 eta 10:57:57
+epoch [25/50] batch [795/1000] time 1.551 (1.566) data 0.000 (0.002) loss 1.4814 (1.1169) acc 68.7500 (71.8514) lr 1.1253e-03 eta 10:57:50
+epoch [25/50] batch [800/1000] time 1.542 (1.566) data 0.000 (0.002) loss 1.0635 (1.1171) acc 71.8750 (71.8633) lr 1.1253e-03 eta 10:57:40
+epoch [25/50] batch [805/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.5459 (1.1165) acc 71.8750 (71.8983) lr 1.1253e-03 eta 10:57:31
+epoch [25/50] batch [810/1000] time 1.559 (1.566) data 0.000 (0.002) loss 0.7080 (1.1158) acc 81.2500 (71.9097) lr 1.1253e-03 eta 10:57:27
+epoch [25/50] batch [815/1000] time 1.554 (1.566) data 0.000 (0.002) loss 1.2324 (1.1169) acc 68.7500 (71.8942) lr 1.1253e-03 eta 10:57:17
+epoch [25/50] batch [820/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.7012 (1.1167) acc 65.6250 (71.8979) lr 1.1253e-03 eta 10:57:10
+epoch [25/50] batch [825/1000] time 1.542 (1.566) data 0.000 (0.002) loss 1.4189 (1.1176) acc 68.7500 (71.8523) lr 1.1253e-03 eta 10:57:01
+epoch [25/50] batch [830/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.8960 (1.1175) acc 78.1250 (71.8562) lr 1.1253e-03 eta 10:56:50
+epoch [25/50] batch [835/1000] time 1.549 (1.566) data 0.001 (0.002) loss 1.3877 (1.1186) acc 68.7500 (71.8563) lr 1.1253e-03 eta 10:56:41
+epoch [25/50] batch [840/1000] time 1.581 (1.566) data 0.000 (0.002) loss 1.0625 (1.1199) acc 75.0000 (71.8266) lr 1.1253e-03 eta 10:56:33
+epoch [25/50] batch [845/1000] time 1.579 (1.566) data 0.000 (0.002) loss 1.3057 (1.1209) acc 75.0000 (71.8232) lr 1.1253e-03 eta 10:56:27
+epoch [25/50] batch [850/1000] time 1.552 (1.566) data 0.000 (0.002) loss 1.4238 (1.1222) acc 65.6250 (71.8088) lr 1.1253e-03 eta 10:56:20
+epoch [25/50] batch [855/1000] time 1.543 (1.566) data 0.000 (0.002) loss 1.0674 (1.1223) acc 71.8750 (71.8202) lr 1.1253e-03 eta 10:56:15
+epoch [25/50] batch [860/1000] time 1.583 (1.566) data 0.001 (0.002) loss 1.8223 (1.1234) acc 68.7500 (71.8096) lr 1.1253e-03 eta 10:56:07
+epoch [25/50] batch [865/1000] time 1.575 (1.566) data 0.000 (0.002) loss 1.6221 (1.1233) acc 59.3750 (71.8280) lr 1.1253e-03 eta 10:55:58
+epoch [25/50] batch [870/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.6406 (1.1235) acc 68.7500 (71.8463) lr 1.1253e-03 eta 10:55:49
+epoch [25/50] batch [875/1000] time 1.546 (1.566) data 0.001 (0.002) loss 1.0010 (1.1244) acc 68.7500 (71.8250) lr 1.1253e-03 eta 10:55:39
+epoch [25/50] batch [880/1000] time 1.572 (1.566) data 0.000 (0.002) loss 1.0332 (1.1233) acc 71.8750 (71.8572) lr 1.1253e-03 eta 10:55:31
+epoch [25/50] batch [885/1000] time 1.527 (1.566) data 0.001 (0.002) loss 1.8408 (1.1235) acc 65.6250 (71.8432) lr 1.1253e-03 eta 10:55:22
+epoch [25/50] batch [890/1000] time 1.541 (1.566) data 0.000 (0.002) loss 0.9341 (1.1232) acc 71.8750 (71.8469) lr 1.1253e-03 eta 10:55:12
+epoch [25/50] batch [895/1000] time 1.574 (1.566) data 0.001 (0.002) loss 0.9976 (1.1229) acc 75.0000 (71.8610) lr 1.1253e-03 eta 10:55:02
+epoch [25/50] batch [900/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.2617 (1.1238) acc 68.7500 (71.8368) lr 1.1253e-03 eta 10:54:52
+epoch [25/50] batch [905/1000] time 1.534 (1.565) data 0.000 (0.002) loss 1.3779 (1.1236) acc 71.8750 (71.8577) lr 1.1253e-03 eta 10:54:43
+epoch [25/50] batch [910/1000] time 1.550 (1.565) data 0.000 (0.002) loss 0.9155 (1.1241) acc 75.0000 (71.8338) lr 1.1253e-03 eta 10:54:34
+epoch [25/50] batch [915/1000] time 1.563 (1.565) data 0.001 (0.002) loss 0.8745 (1.1248) acc 81.2500 (71.8340) lr 1.1253e-03 eta 10:54:29
+epoch [25/50] batch [920/1000] time 1.547 (1.565) data 0.001 (0.002) loss 1.1787 (1.1249) acc 75.0000 (71.8410) lr 1.1253e-03 eta 10:54:20
+epoch [25/50] batch [925/1000] time 1.585 (1.565) data 0.000 (0.002) loss 1.2373 (1.1255) acc 71.8750 (71.8311) lr 1.1253e-03 eta 10:54:11
+epoch [25/50] batch [930/1000] time 1.578 (1.565) data 0.000 (0.002) loss 0.9219 (1.1248) acc 75.0000 (71.8313) lr 1.1253e-03 eta 10:54:04
+epoch [25/50] batch [935/1000] time 1.548 (1.565) data 0.000 (0.002) loss 0.8779 (1.1253) acc 71.8750 (71.8249) lr 1.1253e-03 eta 10:53:54
+epoch [25/50] batch [940/1000] time 1.550 (1.565) data 0.001 (0.002) loss 1.1650 (1.1257) acc 65.6250 (71.8318) lr 1.1253e-03 eta 10:53:47
+epoch [25/50] batch [945/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0537 (1.1262) acc 71.8750 (71.8122) lr 1.1253e-03 eta 10:53:39
+epoch [25/50] batch [950/1000] time 1.567 (1.565) data 0.001 (0.002) loss 1.5234 (1.1266) acc 56.2500 (71.7862) lr 1.1253e-03 eta 10:53:32
+epoch [25/50] batch [955/1000] time 1.564 (1.565) data 0.000 (0.002) loss 1.4375 (1.1283) acc 62.5000 (71.7605) lr 1.1253e-03 eta 10:53:25
+epoch [25/50] batch [960/1000] time 1.570 (1.566) data 0.001 (0.001) loss 0.9058 (1.1292) acc 75.0000 (71.7513) lr 1.1253e-03 eta 10:53:21
+epoch [25/50] batch [965/1000] time 1.556 (1.566) data 0.000 (0.001) loss 0.4104 (1.1278) acc 90.6250 (71.7843) lr 1.1253e-03 eta 10:53:12
+epoch [25/50] batch [970/1000] time 1.531 (1.566) data 0.000 (0.001) loss 1.1377 (1.1278) acc 78.1250 (71.7977) lr 1.1253e-03 eta 10:53:04
+epoch [25/50] batch [975/1000] time 1.547 (1.565) data 0.000 (0.001) loss 1.5674 (1.1289) acc 59.3750 (71.7692) lr 1.1253e-03 eta 10:52:55
+epoch [25/50] batch [980/1000] time 1.539 (1.565) data 0.000 (0.001) loss 0.9043 (1.1282) acc 84.3750 (71.7857) lr 1.1253e-03 eta 10:52:47
+epoch [25/50] batch [985/1000] time 1.542 (1.565) data 0.001 (0.001) loss 1.0820 (1.1277) acc 65.6250 (71.7798) lr 1.1253e-03 eta 10:52:38
+epoch [25/50] batch [990/1000] time 1.556 (1.565) data 0.000 (0.001) loss 1.4160 (1.1286) acc 65.6250 (71.7803) lr 1.1253e-03 eta 10:52:29
+epoch [25/50] batch [995/1000] time 1.547 (1.565) data 0.000 (0.001) loss 1.2754 (1.1293) acc 75.0000 (71.7776) lr 1.1253e-03 eta 10:52:19
+epoch [25/50] batch [1000/1000] time 1.563 (1.565) data 0.000 (0.001) loss 0.7871 (1.1293) acc 81.2500 (71.7812) lr 1.0628e-03 eta 10:52:11
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,275
+* accuracy: 78.5%
+* error: 21.5%
+* macro_f1: 78.1%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [26/50] batch [5/1000] time 1.551 (1.692) data 0.001 (0.192) loss 1.3682 (1.0519) acc 65.6250 (74.3750) lr 1.0628e-03 eta 11:45:02
+epoch [26/50] batch [10/1000] time 1.554 (1.626) data 0.000 (0.096) loss 1.3301 (1.0194) acc 65.6250 (74.0625) lr 1.0628e-03 eta 11:17:09
+epoch [26/50] batch [15/1000] time 1.555 (1.603) data 0.000 (0.064) loss 1.7139 (1.1103) acc 71.8750 (72.5000) lr 1.0628e-03 eta 11:07:40
+epoch [26/50] batch [20/1000] time 1.572 (1.594) data 0.001 (0.048) loss 1.5928 (1.1180) acc 65.6250 (71.7188) lr 1.0628e-03 eta 11:03:40
+epoch [26/50] batch [25/1000] time 1.570 (1.590) data 0.001 (0.039) loss 1.0664 (1.0737) acc 71.8750 (72.6250) lr 1.0628e-03 eta 11:01:55
+epoch [26/50] batch [30/1000] time 1.564 (1.585) data 0.001 (0.032) loss 1.3916 (1.0968) acc 62.5000 (72.6042) lr 1.0628e-03 eta 10:59:44
+epoch [26/50] batch [35/1000] time 1.552 (1.580) data 0.000 (0.028) loss 0.9932 (1.1236) acc 71.8750 (71.7857) lr 1.0628e-03 eta 10:57:32
+epoch [26/50] batch [40/1000] time 1.560 (1.578) data 0.000 (0.024) loss 0.5664 (1.0972) acc 84.3750 (72.2656) lr 1.0628e-03 eta 10:56:36
+epoch [26/50] batch [45/1000] time 1.554 (1.576) data 0.000 (0.022) loss 1.4258 (1.1092) acc 62.5000 (72.2222) lr 1.0628e-03 eta 10:55:27
+epoch [26/50] batch [50/1000] time 1.548 (1.574) data 0.001 (0.020) loss 0.9355 (1.1058) acc 68.7500 (72.2500) lr 1.0628e-03 eta 10:54:29
+epoch [26/50] batch [55/1000] time 1.563 (1.572) data 0.000 (0.018) loss 1.8955 (1.1585) acc 53.1250 (70.9091) lr 1.0628e-03 eta 10:53:36
+epoch [26/50] batch [60/1000] time 1.572 (1.572) data 0.001 (0.016) loss 1.1006 (1.1431) acc 65.6250 (71.1458) lr 1.0628e-03 eta 10:53:26
+epoch [26/50] batch [65/1000] time 1.597 (1.571) data 0.000 (0.015) loss 0.4026 (1.1238) acc 90.6250 (71.4904) lr 1.0628e-03 eta 10:53:04
+epoch [26/50] batch [70/1000] time 1.543 (1.571) data 0.000 (0.014) loss 1.1729 (1.1324) acc 75.0000 (71.5625) lr 1.0628e-03 eta 10:52:54
+epoch [26/50] batch [75/1000] time 1.541 (1.570) data 0.000 (0.013) loss 0.9858 (1.1306) acc 68.7500 (71.4583) lr 1.0628e-03 eta 10:52:21
+epoch [26/50] batch [80/1000] time 1.570 (1.570) data 0.000 (0.012) loss 0.6997 (1.1258) acc 75.0000 (71.4844) lr 1.0628e-03 eta 10:52:00
+epoch [26/50] batch [85/1000] time 1.575 (1.569) data 0.001 (0.012) loss 1.1670 (1.1234) acc 68.7500 (71.4338) lr 1.0628e-03 eta 10:51:41
+epoch [26/50] batch [90/1000] time 1.560 (1.569) data 0.000 (0.011) loss 0.8765 (1.1139) acc 78.1250 (71.6667) lr 1.0628e-03 eta 10:51:12
+epoch [26/50] batch [95/1000] time 1.560 (1.568) data 0.001 (0.011) loss 2.1328 (1.1179) acc 59.3750 (71.7105) lr 1.0628e-03 eta 10:50:55
+epoch [26/50] batch [100/1000] time 1.747 (1.570) data 0.001 (0.010) loss 0.8081 (1.1134) acc 68.7500 (71.7812) lr 1.0628e-03 eta 10:51:30
+epoch [26/50] batch [105/1000] time 1.557 (1.570) data 0.001 (0.010) loss 1.6816 (1.1259) acc 75.0000 (71.7857) lr 1.0628e-03 eta 10:51:18
+epoch [26/50] batch [110/1000] time 1.553 (1.569) data 0.001 (0.009) loss 1.3730 (1.1206) acc 75.0000 (72.1307) lr 1.0628e-03 eta 10:50:57
+epoch [26/50] batch [115/1000] time 1.544 (1.569) data 0.001 (0.009) loss 1.2627 (1.1208) acc 81.2500 (72.2554) lr 1.0628e-03 eta 10:50:38
+epoch [26/50] batch [120/1000] time 1.560 (1.569) data 0.000 (0.008) loss 0.8311 (1.1160) acc 68.7500 (72.2656) lr 1.0628e-03 eta 10:50:28
+epoch [26/50] batch [125/1000] time 1.560 (1.568) data 0.000 (0.008) loss 1.2246 (1.1151) acc 65.6250 (72.3250) lr 1.0628e-03 eta 10:50:12
+epoch [26/50] batch [130/1000] time 1.590 (1.568) data 0.000 (0.008) loss 1.5879 (1.1116) acc 62.5000 (72.5000) lr 1.0628e-03 eta 10:50:07
+epoch [26/50] batch [135/1000] time 1.558 (1.568) data 0.001 (0.008) loss 1.5947 (1.1148) acc 56.2500 (72.3843) lr 1.0628e-03 eta 10:49:59
+epoch [26/50] batch [140/1000] time 1.553 (1.568) data 0.000 (0.007) loss 1.1680 (1.1167) acc 84.3750 (72.5223) lr 1.0628e-03 eta 10:49:40
+epoch [26/50] batch [145/1000] time 1.757 (1.570) data 0.000 (0.007) loss 0.6831 (1.1109) acc 78.1250 (72.5431) lr 1.0628e-03 eta 10:50:12
+epoch [26/50] batch [150/1000] time 1.577 (1.570) data 0.001 (0.007) loss 1.1377 (1.1089) acc 62.5000 (72.5208) lr 1.0628e-03 eta 10:50:04
+epoch [26/50] batch [155/1000] time 1.561 (1.569) data 0.001 (0.007) loss 1.4863 (1.1154) acc 62.5000 (72.2984) lr 1.0628e-03 eta 10:49:51
+epoch [26/50] batch [160/1000] time 1.567 (1.569) data 0.001 (0.006) loss 1.4961 (1.1168) acc 65.6250 (72.3438) lr 1.0628e-03 eta 10:49:35
+epoch [26/50] batch [165/1000] time 1.544 (1.569) data 0.001 (0.006) loss 1.1533 (1.1116) acc 59.3750 (72.3674) lr 1.0628e-03 eta 10:49:21
+epoch [26/50] batch [170/1000] time 1.568 (1.569) data 0.001 (0.006) loss 1.4951 (1.1159) acc 56.2500 (72.2243) lr 1.0628e-03 eta 10:49:12
+epoch [26/50] batch [175/1000] time 1.577 (1.569) data 0.000 (0.006) loss 0.9614 (1.1170) acc 78.1250 (72.1964) lr 1.0628e-03 eta 10:49:03
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+epoch [26/50] batch [735/1000] time 1.533 (1.566) data 0.001 (0.002) loss 1.2295 (1.0999) acc 75.0000 (72.1726) lr 1.0628e-03 eta 10:33:15
+epoch [26/50] batch [740/1000] time 1.582 (1.566) data 0.000 (0.002) loss 1.4775 (1.1000) acc 56.2500 (72.1917) lr 1.0628e-03 eta 10:33:07
+epoch [26/50] batch [745/1000] time 1.561 (1.566) data 0.000 (0.002) loss 1.1533 (1.0999) acc 68.7500 (72.1980) lr 1.0628e-03 eta 10:32:58
+epoch [26/50] batch [750/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.8799 (1.0986) acc 71.8750 (72.2292) lr 1.0628e-03 eta 10:32:55
+epoch [26/50] batch [755/1000] time 1.582 (1.566) data 0.000 (0.002) loss 0.8584 (1.0994) acc 81.2500 (72.2020) lr 1.0628e-03 eta 10:32:46
+epoch [26/50] batch [760/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.8921 (1.0986) acc 75.0000 (72.2122) lr 1.0628e-03 eta 10:32:37
+epoch [26/50] batch [765/1000] time 1.581 (1.566) data 0.000 (0.002) loss 1.0781 (1.0988) acc 71.8750 (72.2059) lr 1.0628e-03 eta 10:32:29
+epoch [26/50] batch [770/1000] time 1.573 (1.566) data 0.001 (0.002) loss 0.8804 (1.0980) acc 84.3750 (72.2240) lr 1.0628e-03 eta 10:32:21
+epoch [26/50] batch [775/1000] time 1.572 (1.566) data 0.000 (0.002) loss 1.6328 (1.0977) acc 68.7500 (72.2460) lr 1.0628e-03 eta 10:32:12
+epoch [26/50] batch [780/1000] time 1.574 (1.566) data 0.001 (0.002) loss 0.8877 (1.0986) acc 84.3750 (72.2436) lr 1.0628e-03 eta 10:32:04
+epoch [26/50] batch [785/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.9717 (1.0996) acc 78.1250 (72.2333) lr 1.0628e-03 eta 10:31:55
+epoch [26/50] batch [790/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.2275 (1.0997) acc 65.6250 (72.2389) lr 1.0628e-03 eta 10:31:46
+epoch [26/50] batch [795/1000] time 1.549 (1.566) data 0.000 (0.002) loss 1.2783 (1.1000) acc 75.0000 (72.2524) lr 1.0628e-03 eta 10:31:42
+epoch [26/50] batch [800/1000] time 1.527 (1.566) data 0.000 (0.002) loss 1.0068 (1.0992) acc 65.6250 (72.2422) lr 1.0628e-03 eta 10:31:31
+epoch [26/50] batch [805/1000] time 1.539 (1.566) data 0.000 (0.002) loss 1.2227 (1.0986) acc 75.0000 (72.2593) lr 1.0628e-03 eta 10:31:23
+epoch [26/50] batch [810/1000] time 1.561 (1.566) data 0.001 (0.002) loss 0.7729 (1.0980) acc 75.0000 (72.2762) lr 1.0628e-03 eta 10:31:16
+epoch [26/50] batch [815/1000] time 1.580 (1.566) data 0.001 (0.002) loss 1.3594 (1.0984) acc 68.7500 (72.2929) lr 1.0628e-03 eta 10:31:08
+epoch [26/50] batch [820/1000] time 1.567 (1.566) data 0.001 (0.002) loss 1.6143 (1.1010) acc 71.8750 (72.2523) lr 1.0628e-03 eta 10:31:02
+epoch [26/50] batch [825/1000] time 1.562 (1.566) data 0.000 (0.002) loss 1.3457 (1.1029) acc 65.6250 (72.2235) lr 1.0628e-03 eta 10:30:54
+epoch [26/50] batch [830/1000] time 1.576 (1.566) data 0.000 (0.002) loss 0.9492 (1.1033) acc 78.1250 (72.2252) lr 1.0628e-03 eta 10:30:47
+epoch [26/50] batch [835/1000] time 1.580 (1.566) data 0.000 (0.002) loss 1.2559 (1.1031) acc 62.5000 (72.2305) lr 1.0628e-03 eta 10:30:38
+epoch [26/50] batch [840/1000] time 1.539 (1.566) data 0.001 (0.002) loss 0.8486 (1.1016) acc 78.1250 (72.2545) lr 1.0628e-03 eta 10:30:29
+epoch [26/50] batch [845/1000] time 1.584 (1.566) data 0.001 (0.002) loss 1.3838 (1.1024) acc 71.8750 (72.2300) lr 1.0628e-03 eta 10:30:22
+epoch [26/50] batch [850/1000] time 1.570 (1.566) data 0.000 (0.002) loss 0.6431 (1.1024) acc 78.1250 (72.2279) lr 1.0628e-03 eta 10:30:14
+epoch [26/50] batch [855/1000] time 1.724 (1.566) data 0.001 (0.002) loss 1.9326 (1.1033) acc 62.5000 (72.2113) lr 1.0628e-03 eta 10:30:12
+epoch [26/50] batch [860/1000] time 1.575 (1.566) data 0.001 (0.002) loss 1.4082 (1.1042) acc 62.5000 (72.1948) lr 1.0628e-03 eta 10:30:04
+epoch [26/50] batch [865/1000] time 1.578 (1.566) data 0.001 (0.002) loss 0.8188 (1.1045) acc 81.2500 (72.1749) lr 1.0628e-03 eta 10:29:56
+epoch [26/50] batch [870/1000] time 1.569 (1.566) data 0.000 (0.002) loss 0.7778 (1.1034) acc 87.5000 (72.2126) lr 1.0628e-03 eta 10:29:48
+epoch [26/50] batch [875/1000] time 1.567 (1.566) data 0.001 (0.002) loss 1.7812 (1.1030) acc 59.3750 (72.2071) lr 1.0628e-03 eta 10:29:40
+epoch [26/50] batch [880/1000] time 1.573 (1.566) data 0.000 (0.002) loss 1.2314 (1.1033) acc 62.5000 (72.1839) lr 1.0628e-03 eta 10:29:32
+epoch [26/50] batch [885/1000] time 1.551 (1.566) data 0.001 (0.002) loss 1.2861 (1.1024) acc 65.6250 (72.2140) lr 1.0628e-03 eta 10:29:25
+epoch [26/50] batch [890/1000] time 1.531 (1.566) data 0.001 (0.002) loss 0.8887 (1.1014) acc 78.1250 (72.2261) lr 1.0628e-03 eta 10:29:16
+epoch [26/50] batch [895/1000] time 1.535 (1.566) data 0.001 (0.002) loss 0.6162 (1.1014) acc 81.2500 (72.2207) lr 1.0628e-03 eta 10:29:07
+epoch [26/50] batch [900/1000] time 1.714 (1.566) data 0.000 (0.002) loss 1.7168 (1.1025) acc 59.3750 (72.1979) lr 1.0628e-03 eta 10:29:02
+epoch [26/50] batch [905/1000] time 1.582 (1.566) data 0.001 (0.002) loss 0.8716 (1.1019) acc 75.0000 (72.2134) lr 1.0628e-03 eta 10:28:53
+epoch [26/50] batch [910/1000] time 1.529 (1.566) data 0.001 (0.002) loss 1.3926 (1.1019) acc 62.5000 (72.2012) lr 1.0628e-03 eta 10:28:44
+epoch [26/50] batch [915/1000] time 1.594 (1.566) data 0.001 (0.002) loss 1.0342 (1.1018) acc 81.2500 (72.2029) lr 1.0628e-03 eta 10:28:35
+epoch [26/50] batch [920/1000] time 1.550 (1.566) data 0.001 (0.002) loss 1.2256 (1.1021) acc 68.7500 (72.2147) lr 1.0628e-03 eta 10:28:27
+epoch [26/50] batch [925/1000] time 1.585 (1.566) data 0.000 (0.002) loss 1.6914 (1.1039) acc 59.3750 (72.1824) lr 1.0628e-03 eta 10:28:20
+epoch [26/50] batch [930/1000] time 1.561 (1.566) data 0.000 (0.002) loss 0.5356 (1.1033) acc 87.5000 (72.2144) lr 1.0628e-03 eta 10:28:11
+epoch [26/50] batch [935/1000] time 1.559 (1.566) data 0.000 (0.002) loss 1.3340 (1.1038) acc 65.6250 (72.1959) lr 1.0628e-03 eta 10:28:04
+epoch [26/50] batch [940/1000] time 1.570 (1.566) data 0.001 (0.002) loss 1.3945 (1.1043) acc 62.5000 (72.1975) lr 1.0628e-03 eta 10:27:55
+epoch [26/50] batch [945/1000] time 1.564 (1.566) data 0.001 (0.002) loss 0.8296 (1.1047) acc 75.0000 (72.1825) lr 1.0628e-03 eta 10:27:51
+epoch [26/50] batch [950/1000] time 1.587 (1.566) data 0.001 (0.002) loss 0.9976 (1.1044) acc 75.0000 (72.2007) lr 1.0628e-03 eta 10:27:43
+epoch [26/50] batch [955/1000] time 1.566 (1.566) data 0.000 (0.001) loss 1.3975 (1.1042) acc 62.5000 (72.1957) lr 1.0628e-03 eta 10:27:34
+epoch [26/50] batch [960/1000] time 1.557 (1.566) data 0.001 (0.001) loss 0.8735 (1.1050) acc 78.1250 (72.1810) lr 1.0628e-03 eta 10:27:25
+epoch [26/50] batch [965/1000] time 1.565 (1.566) data 0.000 (0.001) loss 1.4609 (1.1057) acc 65.6250 (72.1794) lr 1.0628e-03 eta 10:27:18
+epoch [26/50] batch [970/1000] time 1.600 (1.566) data 0.001 (0.001) loss 1.2490 (1.1060) acc 71.8750 (72.1617) lr 1.0628e-03 eta 10:27:10
+epoch [26/50] batch [975/1000] time 1.554 (1.566) data 0.001 (0.001) loss 1.2188 (1.1075) acc 71.8750 (72.1282) lr 1.0628e-03 eta 10:27:03
+epoch [26/50] batch [980/1000] time 1.578 (1.566) data 0.001 (0.001) loss 0.5898 (1.1073) acc 87.5000 (72.1429) lr 1.0628e-03 eta 10:26:56
+epoch [26/50] batch [985/1000] time 1.563 (1.566) data 0.001 (0.001) loss 1.1152 (1.1092) acc 68.7500 (72.0876) lr 1.0628e-03 eta 10:26:47
+epoch [26/50] batch [990/1000] time 1.543 (1.566) data 0.000 (0.001) loss 1.2051 (1.1102) acc 75.0000 (72.0676) lr 1.0628e-03 eta 10:26:39
+epoch [26/50] batch [995/1000] time 1.557 (1.566) data 0.000 (0.001) loss 1.0303 (1.1095) acc 78.1250 (72.0760) lr 1.0628e-03 eta 10:26:31
+epoch [26/50] batch [1000/1000] time 1.551 (1.566) data 0.000 (0.001) loss 0.9736 (1.1094) acc 78.1250 (72.0781) lr 1.0000e-03 eta 10:26:22
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,227
+* accuracy: 78.5%
+* error: 21.5%
+* macro_f1: 78.0%
+epoch [27/50] batch [5/1000] time 1.532 (1.691) data 0.000 (0.185) loss 0.9019 (1.0143) acc 78.1250 (75.0000) lr 1.0000e-03 eta 11:16:23
+epoch [27/50] batch [10/1000] time 1.566 (1.624) data 0.000 (0.093) loss 1.1934 (1.0619) acc 75.0000 (73.1250) lr 1.0000e-03 eta 10:49:14
+epoch [27/50] batch [15/1000] time 1.557 (1.601) data 0.000 (0.062) loss 0.5830 (0.9937) acc 87.5000 (73.7500) lr 1.0000e-03 eta 10:39:56
+epoch [27/50] batch [20/1000] time 1.545 (1.589) data 0.001 (0.047) loss 0.9517 (1.0061) acc 78.1250 (74.0625) lr 1.0000e-03 eta 10:35:15
+epoch [27/50] batch [25/1000] time 1.584 (1.597) data 0.000 (0.037) loss 1.4443 (1.0250) acc 65.6250 (74.1250) lr 1.0000e-03 eta 10:38:18
+epoch [27/50] batch [30/1000] time 1.573 (1.595) data 0.001 (0.031) loss 0.9224 (1.0546) acc 81.2500 (73.9583) lr 1.0000e-03 eta 10:37:05
+epoch [27/50] batch [35/1000] time 1.542 (1.590) data 0.001 (0.027) loss 1.1748 (1.0801) acc 75.0000 (73.3929) lr 1.0000e-03 eta 10:35:07
+epoch [27/50] batch [40/1000] time 1.577 (1.588) data 0.001 (0.024) loss 1.1543 (1.0924) acc 65.6250 (73.0469) lr 1.0000e-03 eta 10:34:08
+epoch [27/50] batch [45/1000] time 1.530 (1.584) data 0.000 (0.021) loss 1.0283 (1.0659) acc 84.3750 (73.6806) lr 1.0000e-03 eta 10:32:34
+epoch [27/50] batch [50/1000] time 1.574 (1.583) data 0.000 (0.019) loss 1.2520 (1.0796) acc 62.5000 (73.1875) lr 1.0000e-03 eta 10:31:59
+epoch [27/50] batch [55/1000] time 1.538 (1.581) data 0.000 (0.017) loss 1.0537 (1.0932) acc 75.0000 (72.7273) lr 1.0000e-03 eta 10:31:01
+epoch [27/50] batch [60/1000] time 1.575 (1.580) data 0.001 (0.016) loss 0.8774 (1.1098) acc 75.0000 (72.7604) lr 1.0000e-03 eta 10:30:15
+epoch [27/50] batch [65/1000] time 1.576 (1.578) data 0.000 (0.015) loss 0.7422 (1.1157) acc 81.2500 (72.9327) lr 1.0000e-03 eta 10:29:25
+epoch [27/50] batch [70/1000] time 1.576 (1.580) data 0.000 (0.014) loss 0.8579 (1.1090) acc 68.7500 (73.0357) lr 1.0000e-03 eta 10:30:07
+epoch [27/50] batch [75/1000] time 1.552 (1.579) data 0.001 (0.013) loss 1.1855 (1.1044) acc 75.0000 (73.0833) lr 1.0000e-03 eta 10:29:27
+epoch [27/50] batch [80/1000] time 1.557 (1.577) data 0.001 (0.012) loss 0.9331 (1.1011) acc 75.0000 (73.2031) lr 1.0000e-03 eta 10:28:35
+epoch [27/50] batch [85/1000] time 1.588 (1.576) data 0.000 (0.011) loss 0.6748 (1.1010) acc 84.3750 (73.3824) lr 1.0000e-03 eta 10:28:16
+epoch [27/50] batch [90/1000] time 1.563 (1.576) data 0.001 (0.011) loss 1.3994 (1.1106) acc 71.8750 (73.1597) lr 1.0000e-03 eta 10:27:54
+epoch [27/50] batch [95/1000] time 1.561 (1.575) data 0.000 (0.010) loss 0.9487 (1.1081) acc 71.8750 (73.1579) lr 1.0000e-03 eta 10:27:27
+epoch [27/50] batch [100/1000] time 1.553 (1.574) data 0.001 (0.010) loss 1.6924 (1.1042) acc 65.6250 (73.2812) lr 1.0000e-03 eta 10:27:01
+epoch [27/50] batch [105/1000] time 1.551 (1.573) data 0.000 (0.009) loss 1.0938 (1.1165) acc 65.6250 (73.0060) lr 1.0000e-03 eta 10:26:37
+epoch [27/50] batch [110/1000] time 1.569 (1.573) data 0.001 (0.009) loss 0.7861 (1.1203) acc 81.2500 (72.9261) lr 1.0000e-03 eta 10:26:26
+epoch [27/50] batch [115/1000] time 1.574 (1.573) data 0.000 (0.009) loss 0.9062 (1.1167) acc 71.8750 (72.8533) lr 1.0000e-03 eta 10:26:15
+epoch [27/50] batch [120/1000] time 1.574 (1.574) data 0.000 (0.008) loss 0.9355 (1.1265) acc 78.1250 (72.6823) lr 1.0000e-03 eta 10:26:24
+epoch [27/50] batch [125/1000] time 1.585 (1.574) data 0.001 (0.008) loss 1.1279 (1.1247) acc 78.1250 (72.8250) lr 1.0000e-03 eta 10:26:20
+epoch [27/50] batch [130/1000] time 1.546 (1.573) data 0.001 (0.008) loss 1.3340 (1.1278) acc 59.3750 (72.6923) lr 1.0000e-03 eta 10:25:53
+epoch [27/50] batch [135/1000] time 1.533 (1.573) data 0.000 (0.007) loss 1.5410 (1.1273) acc 65.6250 (72.5231) lr 1.0000e-03 eta 10:25:32
+epoch [27/50] batch [140/1000] time 1.555 (1.572) data 0.000 (0.007) loss 1.1064 (1.1232) acc 71.8750 (72.6116) lr 1.0000e-03 eta 10:25:11
+epoch [27/50] batch [145/1000] time 1.558 (1.572) data 0.001 (0.007) loss 1.2617 (1.1244) acc 71.8750 (72.5862) lr 1.0000e-03 eta 10:25:00
+epoch [27/50] batch [150/1000] time 1.566 (1.572) data 0.001 (0.007) loss 1.0098 (1.1250) acc 65.6250 (72.5208) lr 1.0000e-03 eta 10:24:46
+epoch [27/50] batch [155/1000] time 1.562 (1.571) data 0.000 (0.006) loss 0.9727 (1.1262) acc 75.0000 (72.4194) lr 1.0000e-03 eta 10:24:29
+epoch [27/50] batch [160/1000] time 1.552 (1.571) data 0.001 (0.006) loss 1.6035 (1.1216) acc 59.3750 (72.4414) lr 1.0000e-03 eta 10:24:12
+epoch [27/50] batch [165/1000] time 1.570 (1.571) data 0.001 (0.006) loss 1.5186 (1.1252) acc 71.8750 (72.3674) lr 1.0000e-03 eta 10:24:10
+epoch [27/50] batch [170/1000] time 1.560 (1.571) data 0.000 (0.006) loss 1.8506 (1.1259) acc 56.2500 (72.3529) lr 1.0000e-03 eta 10:23:58
+epoch [27/50] batch [175/1000] time 1.581 (1.572) data 0.000 (0.006) loss 1.4502 (1.1240) acc 68.7500 (72.3571) lr 1.0000e-03 eta 10:24:15
+epoch [27/50] batch [180/1000] time 1.582 (1.572) data 0.000 (0.006) loss 0.5562 (1.1197) acc 84.3750 (72.3785) lr 1.0000e-03 eta 10:24:00
+epoch [27/50] batch [185/1000] time 1.568 (1.571) data 0.000 (0.005) loss 1.3555 (1.1250) acc 68.7500 (72.3311) lr 1.0000e-03 eta 10:23:41
+epoch [27/50] batch [190/1000] time 1.560 (1.571) data 0.000 (0.005) loss 1.4873 (1.1284) acc 65.6250 (72.2862) lr 1.0000e-03 eta 10:23:25
+epoch [27/50] batch [195/1000] time 1.554 (1.571) data 0.001 (0.005) loss 0.5220 (1.1295) acc 81.2500 (72.2756) lr 1.0000e-03 eta 10:23:16
+epoch [27/50] batch [200/1000] time 1.579 (1.571) data 0.001 (0.005) loss 0.8784 (1.1286) acc 71.8750 (72.1406) lr 1.0000e-03 eta 10:23:05
+epoch [27/50] batch [205/1000] time 1.541 (1.571) data 0.000 (0.005) loss 0.7007 (1.1245) acc 81.2500 (72.2713) lr 1.0000e-03 eta 10:22:51
+epoch [27/50] batch [210/1000] time 1.547 (1.570) data 0.001 (0.005) loss 0.9180 (1.1236) acc 81.2500 (72.3512) lr 1.0000e-03 eta 10:22:35
+epoch [27/50] batch [215/1000] time 1.570 (1.570) data 0.000 (0.005) loss 0.6514 (1.1208) acc 78.1250 (72.3837) lr 1.0000e-03 eta 10:22:15
+epoch [27/50] batch [220/1000] time 1.565 (1.570) data 0.000 (0.005) loss 1.2637 (1.1277) acc 78.1250 (72.2301) lr 1.0000e-03 eta 10:22:23
+epoch [27/50] batch [225/1000] time 1.542 (1.570) data 0.000 (0.005) loss 1.1377 (1.1304) acc 68.7500 (72.2222) lr 1.0000e-03 eta 10:22:09
+epoch [27/50] batch [230/1000] time 1.556 (1.570) data 0.000 (0.005) loss 1.8037 (1.1316) acc 53.1250 (72.1332) lr 1.0000e-03 eta 10:21:53
+epoch [27/50] batch [235/1000] time 1.559 (1.570) data 0.000 (0.004) loss 0.7754 (1.1276) acc 81.2500 (72.1277) lr 1.0000e-03 eta 10:21:40
+epoch [27/50] batch [240/1000] time 1.587 (1.570) data 0.001 (0.004) loss 1.3467 (1.1288) acc 65.6250 (72.0964) lr 1.0000e-03 eta 10:21:33
+epoch [27/50] batch [245/1000] time 1.557 (1.569) data 0.001 (0.004) loss 1.8008 (1.1306) acc 65.6250 (72.0918) lr 1.0000e-03 eta 10:21:23
+epoch [27/50] batch [250/1000] time 1.560 (1.570) data 0.001 (0.004) loss 0.7803 (1.1257) acc 71.8750 (72.1500) lr 1.0000e-03 eta 10:21:16
+epoch [27/50] batch [255/1000] time 1.574 (1.570) data 0.000 (0.004) loss 1.1816 (1.1192) acc 68.7500 (72.2549) lr 1.0000e-03 eta 10:21:07
+epoch [27/50] batch [260/1000] time 1.701 (1.570) data 0.000 (0.004) loss 1.0049 (1.1125) acc 75.0000 (72.4279) lr 1.0000e-03 eta 10:21:13
+epoch [27/50] batch [265/1000] time 1.552 (1.570) data 0.000 (0.004) loss 1.4150 (1.1139) acc 71.8750 (72.3939) lr 1.0000e-03 eta 10:21:02
+epoch [27/50] batch [270/1000] time 1.567 (1.570) data 0.000 (0.004) loss 1.5664 (1.1107) acc 65.6250 (72.5231) lr 1.0000e-03 eta 10:20:49
+epoch [27/50] batch [275/1000] time 1.554 (1.569) data 0.000 (0.004) loss 0.5684 (1.1062) acc 78.1250 (72.6136) lr 1.0000e-03 eta 10:20:34
+epoch [27/50] batch [280/1000] time 1.574 (1.570) data 0.000 (0.004) loss 1.1953 (1.1060) acc 71.8750 (72.5781) lr 1.0000e-03 eta 10:20:32
+epoch [27/50] batch [285/1000] time 1.566 (1.570) data 0.001 (0.004) loss 0.9849 (1.1067) acc 75.0000 (72.5987) lr 1.0000e-03 eta 10:20:22
+epoch [27/50] batch [290/1000] time 1.569 (1.569) data 0.000 (0.004) loss 0.7217 (1.1036) acc 84.3750 (72.6401) lr 1.0000e-03 eta 10:20:11
+epoch [27/50] batch [295/1000] time 1.558 (1.569) data 0.000 (0.004) loss 0.8862 (1.0998) acc 81.2500 (72.7436) lr 1.0000e-03 eta 10:19:55
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+epoch [27/50] batch [855/1000] time 1.543 (1.566) data 0.000 (0.002) loss 1.0889 (1.1099) acc 71.8750 (72.2332) lr 1.0000e-03 eta 10:03:55
+epoch [27/50] batch [860/1000] time 1.583 (1.566) data 0.000 (0.002) loss 1.1543 (1.1101) acc 71.8750 (72.2384) lr 1.0000e-03 eta 10:03:47
+epoch [27/50] batch [865/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.3848 (1.1110) acc 56.2500 (72.2146) lr 1.0000e-03 eta 10:03:44
+epoch [27/50] batch [870/1000] time 1.539 (1.566) data 0.000 (0.002) loss 1.2041 (1.1117) acc 68.7500 (72.1947) lr 1.0000e-03 eta 10:03:36
+epoch [27/50] batch [875/1000] time 1.579 (1.566) data 0.000 (0.002) loss 0.6948 (1.1113) acc 81.2500 (72.1821) lr 1.0000e-03 eta 10:03:26
+epoch [27/50] batch [880/1000] time 1.556 (1.566) data 0.001 (0.002) loss 0.5776 (1.1112) acc 84.3750 (72.2053) lr 1.0000e-03 eta 10:03:18
+epoch [27/50] batch [885/1000] time 1.575 (1.566) data 0.001 (0.002) loss 1.1738 (1.1112) acc 75.0000 (72.2140) lr 1.0000e-03 eta 10:03:10
+epoch [27/50] batch [890/1000] time 1.571 (1.566) data 0.001 (0.002) loss 1.7324 (1.1119) acc 62.5000 (72.1945) lr 1.0000e-03 eta 10:03:02
+epoch [27/50] batch [895/1000] time 1.555 (1.566) data 0.000 (0.002) loss 1.1279 (1.1114) acc 68.7500 (72.1892) lr 1.0000e-03 eta 10:02:54
+epoch [27/50] batch [900/1000] time 1.565 (1.566) data 0.001 (0.002) loss 0.9790 (1.1118) acc 75.0000 (72.1667) lr 1.0000e-03 eta 10:02:45
+epoch [27/50] batch [905/1000] time 1.576 (1.566) data 0.000 (0.002) loss 0.8921 (1.1114) acc 65.6250 (72.1581) lr 1.0000e-03 eta 10:02:37
+epoch [27/50] batch [910/1000] time 1.558 (1.565) data 0.000 (0.002) loss 1.4580 (1.1118) acc 68.7500 (72.1600) lr 1.0000e-03 eta 10:02:27
+epoch [27/50] batch [915/1000] time 1.553 (1.565) data 0.000 (0.001) loss 0.7437 (1.1121) acc 84.3750 (72.1721) lr 1.0000e-03 eta 10:02:17
+epoch [27/50] batch [920/1000] time 1.547 (1.565) data 0.000 (0.001) loss 0.8496 (1.1115) acc 75.0000 (72.1637) lr 1.0000e-03 eta 10:02:07
+epoch [27/50] batch [925/1000] time 1.552 (1.565) data 0.001 (0.001) loss 0.6416 (1.1118) acc 87.5000 (72.1723) lr 1.0000e-03 eta 10:01:58
+epoch [27/50] batch [930/1000] time 1.548 (1.565) data 0.000 (0.001) loss 1.1719 (1.1122) acc 71.8750 (72.1472) lr 1.0000e-03 eta 10:01:53
+epoch [27/50] batch [935/1000] time 1.560 (1.565) data 0.000 (0.001) loss 1.2900 (1.1118) acc 65.6250 (72.1557) lr 1.0000e-03 eta 10:01:43
+epoch [27/50] batch [940/1000] time 1.567 (1.565) data 0.000 (0.001) loss 1.9531 (1.1122) acc 56.2500 (72.1310) lr 1.0000e-03 eta 10:01:35
+epoch [27/50] batch [945/1000] time 1.572 (1.565) data 0.001 (0.001) loss 0.8911 (1.1123) acc 78.1250 (72.1462) lr 1.0000e-03 eta 10:01:28
+epoch [27/50] batch [950/1000] time 1.539 (1.565) data 0.000 (0.001) loss 0.8223 (1.1115) acc 78.1250 (72.1645) lr 1.0000e-03 eta 10:01:19
+epoch [27/50] batch [955/1000] time 1.549 (1.565) data 0.000 (0.001) loss 0.9087 (1.1103) acc 75.0000 (72.1728) lr 1.0000e-03 eta 10:01:10
+epoch [27/50] batch [960/1000] time 1.550 (1.565) data 0.000 (0.001) loss 0.9263 (1.1096) acc 71.8750 (72.1842) lr 1.0000e-03 eta 10:01:00
+epoch [27/50] batch [965/1000] time 1.561 (1.565) data 0.000 (0.001) loss 1.1699 (1.1093) acc 65.6250 (72.1859) lr 1.0000e-03 eta 10:00:51
+epoch [27/50] batch [970/1000] time 1.570 (1.565) data 0.001 (0.001) loss 1.0762 (1.1099) acc 65.6250 (72.1843) lr 1.0000e-03 eta 10:00:44
+epoch [27/50] batch [975/1000] time 1.566 (1.565) data 0.000 (0.001) loss 1.4365 (1.1101) acc 62.5000 (72.1763) lr 1.0000e-03 eta 10:00:38
+epoch [27/50] batch [980/1000] time 1.544 (1.565) data 0.000 (0.001) loss 1.0293 (1.1105) acc 75.0000 (72.1939) lr 1.0000e-03 eta 10:00:29
+epoch [27/50] batch [985/1000] time 1.560 (1.565) data 0.001 (0.001) loss 0.6167 (1.1102) acc 78.1250 (72.1954) lr 1.0000e-03 eta 10:00:20
+epoch [27/50] batch [990/1000] time 1.534 (1.565) data 0.000 (0.001) loss 1.1523 (1.1111) acc 62.5000 (72.1812) lr 1.0000e-03 eta 10:00:11
+epoch [27/50] batch [995/1000] time 1.579 (1.565) data 0.000 (0.001) loss 1.7959 (1.1124) acc 59.3750 (72.1577) lr 1.0000e-03 eta 10:00:03
+epoch [27/50] batch [1000/1000] time 1.548 (1.565) data 0.000 (0.001) loss 1.1621 (1.1112) acc 71.8750 (72.1750) lr 9.3721e-04 eta 9:59:55
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,274
+* accuracy: 78.5%
+* error: 21.5%
+* macro_f1: 78.1%
+epoch [28/50] batch [5/1000] time 1.535 (1.693) data 0.001 (0.192) loss 1.0273 (0.9180) acc 75.0000 (77.5000) lr 9.3721e-04 eta 10:48:39
+epoch [28/50] batch [10/1000] time 1.555 (1.623) data 0.000 (0.096) loss 1.3701 (1.0493) acc 68.7500 (74.3750) lr 9.3721e-04 eta 10:21:42
+epoch [28/50] batch [15/1000] time 1.544 (1.600) data 0.001 (0.064) loss 1.1572 (1.0903) acc 62.5000 (71.4583) lr 9.3721e-04 eta 10:13:00
+epoch [28/50] batch [20/1000] time 1.531 (1.587) data 0.000 (0.048) loss 0.6626 (1.0807) acc 87.5000 (71.5625) lr 9.3721e-04 eta 10:07:51
+epoch [28/50] batch [25/1000] time 1.578 (1.583) data 0.001 (0.039) loss 0.7300 (1.0912) acc 81.2500 (71.2500) lr 9.3721e-04 eta 10:06:10
+epoch [28/50] batch [30/1000] time 1.551 (1.579) data 0.000 (0.032) loss 1.0410 (1.0970) acc 68.7500 (71.1458) lr 9.3721e-04 eta 10:04:31
+epoch [28/50] batch [35/1000] time 1.583 (1.578) data 0.000 (0.028) loss 0.9844 (1.1314) acc 75.0000 (70.6250) lr 9.3721e-04 eta 10:03:59
+epoch [28/50] batch [40/1000] time 1.576 (1.577) data 0.000 (0.024) loss 1.2969 (1.1552) acc 65.6250 (69.9219) lr 9.3721e-04 eta 10:03:27
+epoch [28/50] batch [45/1000] time 1.543 (1.575) data 0.000 (0.022) loss 0.8599 (1.1384) acc 78.1250 (70.9722) lr 9.3721e-04 eta 10:02:30
+epoch [28/50] batch [50/1000] time 1.574 (1.574) data 0.000 (0.020) loss 1.3623 (1.1296) acc 62.5000 (71.1250) lr 9.3721e-04 eta 10:02:07
+epoch [28/50] batch [55/1000] time 1.565 (1.573) data 0.000 (0.018) loss 1.1230 (1.1526) acc 71.8750 (70.5114) lr 9.3721e-04 eta 10:01:24
+epoch [28/50] batch [60/1000] time 1.547 (1.572) data 0.001 (0.016) loss 1.5273 (1.1450) acc 65.6250 (70.8333) lr 9.3721e-04 eta 10:00:51
+epoch [28/50] batch [65/1000] time 1.569 (1.571) data 0.001 (0.015) loss 0.8652 (1.1402) acc 75.0000 (70.8654) lr 9.3721e-04 eta 10:00:27
+epoch [28/50] batch [70/1000] time 1.571 (1.570) data 0.000 (0.014) loss 1.1797 (1.1404) acc 71.8750 (70.6696) lr 9.3721e-04 eta 10:00:03
+epoch [28/50] batch [75/1000] time 1.563 (1.573) data 0.000 (0.013) loss 1.6445 (1.1480) acc 62.5000 (70.6667) lr 9.3721e-04 eta 10:00:51
+epoch [28/50] batch [80/1000] time 1.572 (1.572) data 0.001 (0.012) loss 1.5527 (1.1391) acc 65.6250 (71.0156) lr 9.3721e-04 eta 10:00:25
+epoch [28/50] batch [85/1000] time 1.543 (1.570) data 0.000 (0.012) loss 0.8198 (1.1304) acc 71.8750 (71.1397) lr 9.3721e-04 eta 9:59:36
+epoch [28/50] batch [90/1000] time 1.559 (1.569) data 0.000 (0.011) loss 1.1729 (1.1333) acc 65.6250 (70.9028) lr 9.3721e-04 eta 9:59:14
+epoch [28/50] batch [95/1000] time 1.588 (1.570) data 0.000 (0.011) loss 1.8926 (1.1360) acc 56.2500 (70.8882) lr 9.3721e-04 eta 9:59:13
+epoch [28/50] batch [100/1000] time 1.587 (1.570) data 0.001 (0.010) loss 1.0674 (1.1385) acc 75.0000 (70.7188) lr 9.3721e-04 eta 9:59:11
+epoch [28/50] batch [105/1000] time 1.588 (1.570) data 0.000 (0.010) loss 1.2188 (1.1431) acc 75.0000 (70.8036) lr 9.3721e-04 eta 9:58:55
+epoch [28/50] batch [110/1000] time 1.552 (1.569) data 0.000 (0.009) loss 0.8599 (1.1377) acc 71.8750 (71.0227) lr 9.3721e-04 eta 9:58:40
+epoch [28/50] batch [115/1000] time 1.556 (1.569) data 0.000 (0.009) loss 1.1934 (1.1335) acc 68.7500 (71.0326) lr 9.3721e-04 eta 9:58:30
+epoch [28/50] batch [120/1000] time 1.567 (1.569) data 0.001 (0.008) loss 0.9800 (1.1360) acc 75.0000 (71.2240) lr 9.3721e-04 eta 9:58:22
+epoch [28/50] batch [125/1000] time 1.569 (1.571) data 0.001 (0.008) loss 0.8735 (1.1362) acc 75.0000 (71.2000) lr 9.3721e-04 eta 9:58:54
+epoch [28/50] batch [130/1000] time 1.568 (1.571) data 0.000 (0.008) loss 1.1641 (1.1325) acc 75.0000 (71.2500) lr 9.3721e-04 eta 9:58:43
+epoch [28/50] batch [135/1000] time 1.543 (1.570) data 0.000 (0.008) loss 1.1191 (1.1292) acc 75.0000 (71.3426) lr 9.3721e-04 eta 9:58:28
+epoch [28/50] batch [140/1000] time 1.553 (1.570) data 0.001 (0.007) loss 0.9531 (1.1234) acc 71.8750 (71.3839) lr 9.3721e-04 eta 9:58:15
+epoch [28/50] batch [145/1000] time 1.554 (1.570) data 0.000 (0.007) loss 1.0439 (1.1247) acc 68.7500 (71.2500) lr 9.3721e-04 eta 9:58:02
+epoch [28/50] batch [150/1000] time 1.592 (1.570) data 0.000 (0.007) loss 1.1699 (1.1192) acc 75.0000 (71.3750) lr 9.3721e-04 eta 9:57:54
+epoch [28/50] batch [155/1000] time 1.573 (1.570) data 0.000 (0.007) loss 1.3682 (1.1116) acc 68.7500 (71.5726) lr 9.3721e-04 eta 9:57:42
+epoch [28/50] batch [160/1000] time 1.534 (1.569) data 0.001 (0.006) loss 0.7153 (1.1186) acc 78.1250 (71.2891) lr 9.3721e-04 eta 9:57:18
+epoch [28/50] batch [165/1000] time 1.572 (1.569) data 0.000 (0.006) loss 0.8369 (1.1121) acc 78.1250 (71.3826) lr 9.3721e-04 eta 9:56:57
+epoch [28/50] batch [170/1000] time 1.559 (1.568) data 0.000 (0.006) loss 1.3486 (1.1154) acc 65.6250 (71.2868) lr 9.3721e-04 eta 9:56:41
+epoch [28/50] batch [175/1000] time 1.566 (1.568) data 0.000 (0.006) loss 1.3359 (1.1130) acc 71.8750 (71.3393) lr 9.3721e-04 eta 9:56:22
+epoch [28/50] batch [180/1000] time 1.540 (1.567) data 0.000 (0.006) loss 0.5430 (1.1016) acc 87.5000 (71.5972) lr 9.3721e-04 eta 9:56:08
+epoch [28/50] batch [185/1000] time 1.574 (1.567) data 0.001 (0.006) loss 1.0039 (1.1009) acc 81.2500 (71.7230) lr 9.3721e-04 eta 9:55:54
+epoch [28/50] batch [190/1000] time 1.582 (1.567) data 0.001 (0.006) loss 0.8872 (1.1057) acc 78.1250 (71.6447) lr 9.3721e-04 eta 9:55:45
+epoch [28/50] batch [195/1000] time 1.556 (1.567) data 0.001 (0.005) loss 0.8750 (1.1043) acc 78.1250 (71.7308) lr 9.3721e-04 eta 9:55:39
+epoch [28/50] batch [200/1000] time 1.558 (1.567) data 0.000 (0.005) loss 1.2490 (1.1056) acc 71.8750 (71.7344) lr 9.3721e-04 eta 9:55:33
+epoch [28/50] batch [205/1000] time 1.572 (1.567) data 0.000 (0.005) loss 1.0176 (1.1050) acc 84.3750 (71.8445) lr 9.3721e-04 eta 9:55:26
+epoch [28/50] batch [210/1000] time 1.536 (1.567) data 0.000 (0.005) loss 1.2227 (1.1014) acc 56.2500 (71.8155) lr 9.3721e-04 eta 9:55:13
+epoch [28/50] batch [215/1000] time 1.572 (1.567) data 0.000 (0.005) loss 1.2256 (1.1027) acc 68.7500 (71.8605) lr 9.3721e-04 eta 9:54:58
+epoch [28/50] batch [220/1000] time 1.554 (1.567) data 0.001 (0.005) loss 0.5098 (1.0996) acc 90.6250 (71.9744) lr 9.3721e-04 eta 9:54:49
+epoch [28/50] batch [225/1000] time 1.729 (1.567) data 0.000 (0.005) loss 1.1045 (1.0960) acc 68.7500 (72.0417) lr 9.3721e-04 eta 9:54:53
+epoch [28/50] batch [230/1000] time 1.573 (1.567) data 0.000 (0.005) loss 1.1279 (1.0967) acc 75.0000 (72.0245) lr 9.3721e-04 eta 9:54:45
+epoch [28/50] batch [235/1000] time 1.555 (1.567) data 0.001 (0.005) loss 0.7822 (1.0964) acc 71.8750 (71.8750) lr 9.3721e-04 eta 9:54:36
+epoch [28/50] batch [240/1000] time 1.561 (1.567) data 0.000 (0.004) loss 1.9727 (1.1004) acc 46.8750 (71.7969) lr 9.3721e-04 eta 9:54:27
+epoch [28/50] batch [245/1000] time 1.582 (1.567) data 0.001 (0.004) loss 1.5137 (1.1018) acc 65.6250 (71.7985) lr 9.3721e-04 eta 9:54:20
+epoch [28/50] batch [250/1000] time 1.566 (1.567) data 0.000 (0.004) loss 1.4492 (1.1053) acc 75.0000 (71.8500) lr 9.3721e-04 eta 9:54:11
+epoch [28/50] batch [255/1000] time 1.571 (1.567) data 0.000 (0.004) loss 0.7251 (1.1071) acc 81.2500 (71.8137) lr 9.3721e-04 eta 9:53:59
+epoch [28/50] batch [260/1000] time 1.575 (1.567) data 0.000 (0.004) loss 1.1914 (1.1076) acc 68.7500 (71.8510) lr 9.3721e-04 eta 9:53:52
+epoch [28/50] batch [265/1000] time 1.574 (1.567) data 0.000 (0.004) loss 0.9644 (1.1041) acc 68.7500 (71.9340) lr 9.3721e-04 eta 9:53:41
+epoch [28/50] batch [270/1000] time 1.546 (1.567) data 0.000 (0.004) loss 0.8521 (1.1056) acc 75.0000 (71.9329) lr 9.3721e-04 eta 9:53:26
+epoch [28/50] batch [275/1000] time 1.587 (1.567) data 0.000 (0.004) loss 1.2256 (1.1037) acc 65.6250 (71.9659) lr 9.3721e-04 eta 9:53:29
+epoch [28/50] batch [280/1000] time 1.529 (1.567) data 0.000 (0.004) loss 1.1973 (1.1012) acc 75.0000 (72.0871) lr 9.3721e-04 eta 9:53:18
+epoch [28/50] batch [285/1000] time 1.567 (1.567) data 0.000 (0.004) loss 1.2617 (1.1044) acc 62.5000 (71.9956) lr 9.3721e-04 eta 9:53:03
+epoch [28/50] batch [290/1000] time 1.566 (1.566) data 0.001 (0.004) loss 0.5991 (1.1008) acc 87.5000 (72.0582) lr 9.3721e-04 eta 9:52:50
+epoch [28/50] batch [295/1000] time 1.547 (1.566) data 0.000 (0.004) loss 1.0244 (1.1022) acc 71.8750 (72.0339) lr 9.3721e-04 eta 9:52:39
+epoch [28/50] batch [300/1000] time 1.544 (1.566) data 0.001 (0.004) loss 1.6777 (1.1053) acc 59.3750 (71.9375) lr 9.3721e-04 eta 9:52:22
+epoch [28/50] batch [305/1000] time 1.554 (1.566) data 0.000 (0.004) loss 1.7695 (1.1091) acc 68.7500 (71.9262) lr 9.3721e-04 eta 9:52:11
+epoch [28/50] batch [310/1000] time 1.540 (1.565) data 0.000 (0.004) loss 1.0596 (1.1085) acc 78.1250 (71.9556) lr 9.3721e-04 eta 9:51:58
+epoch [28/50] batch [315/1000] time 1.587 (1.565) data 0.000 (0.003) loss 0.9531 (1.1102) acc 71.8750 (71.9147) lr 9.3721e-04 eta 9:51:52
+epoch [28/50] batch [320/1000] time 1.566 (1.565) data 0.000 (0.003) loss 1.3730 (1.1079) acc 62.5000 (71.9531) lr 9.3721e-04 eta 9:51:38
+epoch [28/50] batch [325/1000] time 1.575 (1.565) data 0.000 (0.003) loss 0.8989 (1.1048) acc 78.1250 (72.0192) lr 9.3721e-04 eta 9:51:35
+epoch [28/50] batch [330/1000] time 1.566 (1.565) data 0.001 (0.003) loss 1.3652 (1.1028) acc 59.3750 (72.0644) lr 9.3721e-04 eta 9:51:26
+epoch [28/50] batch [335/1000] time 1.574 (1.565) data 0.000 (0.003) loss 0.6582 (1.1020) acc 84.3750 (72.1269) lr 9.3721e-04 eta 9:51:16
+epoch [28/50] batch [340/1000] time 1.550 (1.565) data 0.001 (0.003) loss 0.8740 (1.1025) acc 75.0000 (72.1415) lr 9.3721e-04 eta 9:51:08
+epoch [28/50] batch [345/1000] time 1.559 (1.565) data 0.000 (0.003) loss 1.3008 (1.1002) acc 59.3750 (72.1920) lr 9.3721e-04 eta 9:50:59
+epoch [28/50] batch [350/1000] time 1.569 (1.565) data 0.000 (0.003) loss 1.3076 (1.0992) acc 62.5000 (72.2054) lr 9.3721e-04 eta 9:50:53
+epoch [28/50] batch [355/1000] time 1.577 (1.565) data 0.000 (0.003) loss 1.2627 (1.0979) acc 65.6250 (72.2535) lr 9.3721e-04 eta 9:50:43
+epoch [28/50] batch [360/1000] time 1.582 (1.565) data 0.000 (0.003) loss 0.9883 (1.1009) acc 75.0000 (72.1962) lr 9.3721e-04 eta 9:50:33
+epoch [28/50] batch [365/1000] time 1.556 (1.565) data 0.001 (0.003) loss 0.6899 (1.1018) acc 75.0000 (72.1490) lr 9.3721e-04 eta 9:50:20
+epoch [28/50] batch [370/1000] time 1.562 (1.565) data 0.000 (0.003) loss 0.9058 (1.1001) acc 81.2500 (72.1791) lr 9.3721e-04 eta 9:50:10
+epoch [28/50] batch [375/1000] time 1.565 (1.565) data 0.000 (0.003) loss 0.7266 (1.1005) acc 81.2500 (72.1750) lr 9.3721e-04 eta 9:50:01
+epoch [28/50] batch [380/1000] time 1.543 (1.565) data 0.000 (0.003) loss 1.4053 (1.1043) acc 62.5000 (72.0970) lr 9.3721e-04 eta 9:49:56
+epoch [28/50] batch [385/1000] time 1.541 (1.565) data 0.000 (0.003) loss 1.2617 (1.1060) acc 68.7500 (72.0373) lr 9.3721e-04 eta 9:49:46
+epoch [28/50] batch [390/1000] time 1.568 (1.565) data 0.000 (0.003) loss 0.9150 (1.1071) acc 75.0000 (72.0192) lr 9.3721e-04 eta 9:49:36
+epoch [28/50] batch [395/1000] time 1.537 (1.564) data 0.000 (0.003) loss 1.0264 (1.1064) acc 75.0000 (72.0253) lr 9.3721e-04 eta 9:49:23
+epoch [28/50] batch [400/1000] time 1.556 (1.564) data 0.000 (0.003) loss 1.1611 (1.1059) acc 71.8750 (72.0547) lr 9.3721e-04 eta 9:49:15
+epoch [28/50] batch [405/1000] time 1.575 (1.564) data 0.000 (0.003) loss 1.2705 (1.1061) acc 71.8750 (72.0988) lr 9.3721e-04 eta 9:49:04
+epoch [28/50] batch [410/1000] time 1.558 (1.564) data 0.000 (0.003) loss 0.6685 (1.1028) acc 84.3750 (72.1799) lr 9.3721e-04 eta 9:48:55
+epoch [28/50] batch [415/1000] time 1.579 (1.564) data 0.000 (0.003) loss 0.7900 (1.1011) acc 75.0000 (72.2063) lr 9.3721e-04 eta 9:48:49
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+epoch [28/50] batch [970/1000] time 1.539 (1.564) data 0.000 (0.001) loss 0.8916 (1.1047) acc 75.0000 (72.2938) lr 9.3721e-04 eta 9:34:21
+epoch [28/50] batch [975/1000] time 1.541 (1.564) data 0.000 (0.001) loss 1.2295 (1.1050) acc 71.8750 (72.3013) lr 9.3721e-04 eta 9:34:12
+epoch [28/50] batch [980/1000] time 1.540 (1.564) data 0.000 (0.001) loss 1.1357 (1.1052) acc 78.1250 (72.2927) lr 9.3721e-04 eta 9:34:08
+epoch [28/50] batch [985/1000] time 1.558 (1.564) data 0.001 (0.001) loss 0.8657 (1.1042) acc 78.1250 (72.3001) lr 9.3721e-04 eta 9:34:00
+epoch [28/50] batch [990/1000] time 1.542 (1.564) data 0.000 (0.001) loss 1.2803 (1.1048) acc 68.7500 (72.3138) lr 9.3721e-04 eta 9:33:52
+epoch [28/50] batch [995/1000] time 1.576 (1.564) data 0.000 (0.001) loss 0.8628 (1.1052) acc 68.7500 (72.2802) lr 9.3721e-04 eta 9:33:44
+epoch [28/50] batch [1000/1000] time 1.553 (1.564) data 0.000 (0.001) loss 0.8125 (1.1054) acc 71.8750 (72.2594) lr 8.7467e-04 eta 9:33:36
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,313
+* accuracy: 78.6%
+* error: 21.4%
+* macro_f1: 78.2%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [29/50] batch [5/1000] time 1.533 (1.768) data 0.000 (0.270) loss 1.0645 (1.0448) acc 71.8750 (75.6250) lr 8.7467e-04 eta 10:47:58
+epoch [29/50] batch [10/1000] time 1.547 (1.690) data 0.000 (0.135) loss 1.3799 (1.0825) acc 65.6250 (74.3750) lr 8.7467e-04 eta 10:19:24
+epoch [29/50] batch [15/1000] time 1.567 (1.646) data 0.001 (0.090) loss 0.8784 (1.0154) acc 62.5000 (74.7917) lr 8.7467e-04 eta 10:03:03
+epoch [29/50] batch [20/1000] time 1.587 (1.626) data 0.000 (0.068) loss 0.9658 (1.0087) acc 71.8750 (74.6875) lr 8.7467e-04 eta 9:55:29
+epoch [29/50] batch [25/1000] time 1.540 (1.612) data 0.000 (0.054) loss 0.9175 (1.0119) acc 84.3750 (74.8750) lr 8.7467e-04 eta 9:50:13
+epoch [29/50] batch [30/1000] time 1.569 (1.602) data 0.000 (0.045) loss 1.5244 (1.0269) acc 68.7500 (74.8958) lr 8.7467e-04 eta 9:46:46
+epoch [29/50] batch [35/1000] time 1.567 (1.598) data 0.000 (0.039) loss 0.9624 (1.0342) acc 75.0000 (74.8214) lr 8.7467e-04 eta 9:45:08
+epoch [29/50] batch [40/1000] time 1.575 (1.594) data 0.000 (0.034) loss 0.4290 (1.0263) acc 87.5000 (75.0000) lr 8.7467e-04 eta 9:43:29
+epoch [29/50] batch [45/1000] time 1.579 (1.592) data 0.000 (0.030) loss 1.0527 (1.0479) acc 78.1250 (74.3056) lr 8.7467e-04 eta 9:42:25
+epoch [29/50] batch [50/1000] time 1.554 (1.589) data 0.001 (0.027) loss 1.5000 (1.0557) acc 56.2500 (73.8125) lr 8.7467e-04 eta 9:41:24
+epoch [29/50] batch [55/1000] time 1.535 (1.586) data 0.001 (0.025) loss 1.1328 (1.0772) acc 75.0000 (73.6932) lr 8.7467e-04 eta 9:40:04
+epoch [29/50] batch [60/1000] time 1.554 (1.583) data 0.000 (0.023) loss 1.2646 (1.0875) acc 71.8750 (73.4896) lr 8.7467e-04 eta 9:39:01
+epoch [29/50] batch [65/1000] time 1.565 (1.581) data 0.000 (0.021) loss 1.0439 (1.1025) acc 75.0000 (73.0769) lr 8.7467e-04 eta 9:38:09
+epoch [29/50] batch [70/1000] time 1.587 (1.580) data 0.010 (0.020) loss 1.4043 (1.0871) acc 65.6250 (73.2143) lr 8.7467e-04 eta 9:37:33
+epoch [29/50] batch [75/1000] time 1.555 (1.579) data 0.000 (0.019) loss 1.0840 (1.0944) acc 71.8750 (73.0000) lr 8.7467e-04 eta 9:36:56
+epoch [29/50] batch [80/1000] time 1.553 (1.578) data 0.000 (0.017) loss 1.3662 (1.0977) acc 65.6250 (72.8906) lr 8.7467e-04 eta 9:36:25
+epoch [29/50] batch [85/1000] time 1.551 (1.577) data 0.001 (0.016) loss 1.6748 (1.1106) acc 65.6250 (72.3529) lr 8.7467e-04 eta 9:35:54
+epoch [29/50] batch [90/1000] time 1.555 (1.576) data 0.000 (0.016) loss 1.3076 (1.1185) acc 59.3750 (72.0486) lr 8.7467e-04 eta 9:35:35
+epoch [29/50] batch [95/1000] time 1.567 (1.576) data 0.000 (0.015) loss 1.0840 (1.1226) acc 68.7500 (72.0724) lr 8.7467e-04 eta 9:35:14
+epoch [29/50] batch [100/1000] time 1.581 (1.575) data 0.000 (0.014) loss 1.5898 (1.1230) acc 62.5000 (72.0312) lr 8.7467e-04 eta 9:35:02
+epoch [29/50] batch [105/1000] time 1.583 (1.575) data 0.001 (0.013) loss 1.1631 (1.1212) acc 71.8750 (72.0833) lr 8.7467e-04 eta 9:34:52
+epoch [29/50] batch [110/1000] time 1.561 (1.576) data 0.001 (0.013) loss 1.6230 (1.1232) acc 59.3750 (72.0170) lr 8.7467e-04 eta 9:35:02
+epoch [29/50] batch [115/1000] time 1.580 (1.576) data 0.001 (0.012) loss 0.7456 (1.1168) acc 81.2500 (72.1196) lr 8.7467e-04 eta 9:34:41
+epoch [29/50] batch [120/1000] time 1.549 (1.575) data 0.000 (0.012) loss 1.1357 (1.1166) acc 68.7500 (71.9792) lr 8.7467e-04 eta 9:34:29
+epoch [29/50] batch [125/1000] time 1.578 (1.575) data 0.001 (0.011) loss 0.5894 (1.1092) acc 84.3750 (72.2250) lr 8.7467e-04 eta 9:34:11
+epoch [29/50] batch [130/1000] time 1.578 (1.575) data 0.000 (0.011) loss 0.9209 (1.1009) acc 71.8750 (72.3798) lr 8.7467e-04 eta 9:33:56
+epoch [29/50] batch [135/1000] time 1.550 (1.574) data 0.001 (0.011) loss 1.2627 (1.1058) acc 71.8750 (72.2222) lr 8.7467e-04 eta 9:33:32
+epoch [29/50] batch [140/1000] time 1.568 (1.574) data 0.000 (0.010) loss 0.9971 (1.1098) acc 71.8750 (72.1652) lr 8.7467e-04 eta 9:33:33
+epoch [29/50] batch [145/1000] time 1.572 (1.574) data 0.000 (0.010) loss 0.9443 (1.1070) acc 78.1250 (72.2845) lr 8.7467e-04 eta 9:33:26
+epoch [29/50] batch [150/1000] time 1.558 (1.574) data 0.001 (0.010) loss 1.6143 (1.1149) acc 68.7500 (72.1042) lr 8.7467e-04 eta 9:33:16
+epoch [29/50] batch [155/1000] time 1.558 (1.575) data 0.000 (0.009) loss 0.9922 (1.1116) acc 68.7500 (72.1774) lr 8.7467e-04 eta 9:33:26
+epoch [29/50] batch [160/1000] time 1.561 (1.575) data 0.000 (0.009) loss 0.7168 (1.1045) acc 78.1250 (72.3242) lr 8.7467e-04 eta 9:33:15
+epoch [29/50] batch [165/1000] time 1.585 (1.575) data 0.001 (0.009) loss 0.9736 (1.1083) acc 81.2500 (72.2348) lr 8.7467e-04 eta 9:33:12
+epoch [29/50] batch [170/1000] time 1.564 (1.575) data 0.001 (0.008) loss 1.1748 (1.1101) acc 75.0000 (72.2243) lr 8.7467e-04 eta 9:32:58
+epoch [29/50] batch [175/1000] time 1.564 (1.574) data 0.000 (0.008) loss 0.9316 (1.1127) acc 81.2500 (72.2679) lr 8.7467e-04 eta 9:32:41
+epoch [29/50] batch [180/1000] time 1.557 (1.574) data 0.001 (0.008) loss 0.8364 (1.1143) acc 68.7500 (72.2917) lr 8.7467e-04 eta 9:32:25
+epoch [29/50] batch [185/1000] time 1.553 (1.574) data 0.000 (0.008) loss 1.4238 (1.1141) acc 56.2500 (72.2804) lr 8.7467e-04 eta 9:32:10
+epoch [29/50] batch [190/1000] time 1.552 (1.573) data 0.000 (0.008) loss 0.9375 (1.1152) acc 65.6250 (72.1711) lr 8.7467e-04 eta 9:31:50
+epoch [29/50] batch [195/1000] time 1.574 (1.573) data 0.000 (0.007) loss 1.2832 (1.1153) acc 62.5000 (72.1635) lr 8.7467e-04 eta 9:31:35
+epoch [29/50] batch [200/1000] time 1.563 (1.573) data 0.000 (0.007) loss 0.7549 (1.1146) acc 81.2500 (72.2188) lr 8.7467e-04 eta 9:31:36
+epoch [29/50] batch [205/1000] time 1.560 (1.573) data 0.001 (0.007) loss 0.9033 (1.1179) acc 75.0000 (72.1037) lr 8.7467e-04 eta 9:31:28
+epoch [29/50] batch [210/1000] time 1.559 (1.573) data 0.000 (0.007) loss 1.5068 (1.1153) acc 68.7500 (72.2024) lr 8.7467e-04 eta 9:31:10
+epoch [29/50] batch [215/1000] time 1.561 (1.573) data 0.001 (0.007) loss 1.5654 (1.1196) acc 65.6250 (72.1512) lr 8.7467e-04 eta 9:30:56
+epoch [29/50] batch [220/1000] time 1.570 (1.572) data 0.000 (0.007) loss 1.5537 (1.1220) acc 59.3750 (72.0597) lr 8.7467e-04 eta 9:30:44
+epoch [29/50] batch [225/1000] time 1.567 (1.572) data 0.000 (0.006) loss 0.8452 (1.1150) acc 78.1250 (72.2500) lr 8.7467e-04 eta 9:30:34
+epoch [29/50] batch [230/1000] time 1.572 (1.572) data 0.000 (0.006) loss 1.3066 (1.1116) acc 75.0000 (72.2690) lr 8.7467e-04 eta 9:30:22
+epoch [29/50] batch [235/1000] time 1.572 (1.572) data 0.000 (0.006) loss 1.3105 (1.1148) acc 68.7500 (72.2074) lr 8.7467e-04 eta 9:30:11
+epoch [29/50] batch [240/1000] time 1.555 (1.572) data 0.000 (0.006) loss 0.8652 (1.1090) acc 71.8750 (72.3438) lr 8.7467e-04 eta 9:30:01
+epoch [29/50] batch [245/1000] time 1.547 (1.571) data 0.000 (0.006) loss 0.8428 (1.1112) acc 81.2500 (72.2577) lr 8.7467e-04 eta 9:29:44
+epoch [29/50] batch [250/1000] time 1.600 (1.571) data 0.000 (0.006) loss 1.5156 (1.1112) acc 65.6250 (72.2500) lr 8.7467e-04 eta 9:29:29
+epoch [29/50] batch [255/1000] time 1.571 (1.571) data 0.000 (0.006) loss 2.2031 (1.1190) acc 46.8750 (72.0466) lr 8.7467e-04 eta 9:29:14
+epoch [29/50] batch [260/1000] time 1.737 (1.571) data 0.000 (0.006) loss 1.2285 (1.1196) acc 71.8750 (72.0433) lr 8.7467e-04 eta 9:29:18
+epoch [29/50] batch [265/1000] time 1.572 (1.571) data 0.000 (0.006) loss 0.8750 (1.1175) acc 75.0000 (72.0991) lr 8.7467e-04 eta 9:29:09
+epoch [29/50] batch [270/1000] time 1.564 (1.571) data 0.001 (0.005) loss 0.8140 (1.1152) acc 81.2500 (72.1528) lr 8.7467e-04 eta 9:28:57
+epoch [29/50] batch [275/1000] time 1.590 (1.571) data 0.000 (0.005) loss 0.9829 (1.1148) acc 78.1250 (72.1477) lr 8.7467e-04 eta 9:28:51
+epoch [29/50] batch [280/1000] time 1.564 (1.571) data 0.001 (0.005) loss 1.3809 (1.1169) acc 65.6250 (72.1205) lr 8.7467e-04 eta 9:28:39
+epoch [29/50] batch [285/1000] time 1.549 (1.571) data 0.001 (0.005) loss 0.5708 (1.1126) acc 81.2500 (72.1491) lr 8.7467e-04 eta 9:28:28
+epoch [29/50] batch [290/1000] time 1.581 (1.571) data 0.000 (0.005) loss 1.1250 (1.1145) acc 68.7500 (72.1444) lr 8.7467e-04 eta 9:28:18
+epoch [29/50] batch [295/1000] time 1.567 (1.570) data 0.001 (0.005) loss 1.0205 (1.1148) acc 68.7500 (72.1398) lr 8.7467e-04 eta 9:28:02
+epoch [29/50] batch [300/1000] time 1.549 (1.570) data 0.000 (0.005) loss 1.0889 (1.1174) acc 68.7500 (72.0729) lr 8.7467e-04 eta 9:27:48
+epoch [29/50] batch [305/1000] time 1.711 (1.570) data 0.001 (0.005) loss 1.2363 (1.1142) acc 68.7500 (72.1619) lr 8.7467e-04 eta 9:27:49
+epoch [29/50] batch [310/1000] time 1.542 (1.570) data 0.001 (0.005) loss 1.4404 (1.1128) acc 62.5000 (72.1472) lr 8.7467e-04 eta 9:27:32
+epoch [29/50] batch [315/1000] time 1.587 (1.570) data 0.001 (0.005) loss 1.3320 (1.1142) acc 68.7500 (72.0933) lr 8.7467e-04 eta 9:27:21
+epoch [29/50] batch [320/1000] time 1.573 (1.570) data 0.000 (0.005) loss 0.8174 (1.1143) acc 75.0000 (72.0898) lr 8.7467e-04 eta 9:27:13
+epoch [29/50] batch [325/1000] time 1.569 (1.570) data 0.000 (0.005) loss 1.1826 (1.1136) acc 78.1250 (72.1058) lr 8.7467e-04 eta 9:27:04
+epoch [29/50] batch [330/1000] time 1.542 (1.570) data 0.000 (0.005) loss 0.5469 (1.1119) acc 81.2500 (72.1117) lr 8.7467e-04 eta 9:26:56
+epoch [29/50] batch [335/1000] time 1.581 (1.570) data 0.001 (0.005) loss 0.9902 (1.1107) acc 65.6250 (72.0989) lr 8.7467e-04 eta 9:26:46
+epoch [29/50] batch [340/1000] time 1.570 (1.570) data 0.000 (0.004) loss 1.4951 (1.1118) acc 59.3750 (72.0037) lr 8.7467e-04 eta 9:26:35
+epoch [29/50] batch [345/1000] time 1.562 (1.569) data 0.000 (0.004) loss 1.2373 (1.1145) acc 59.3750 (71.9293) lr 8.7467e-04 eta 9:26:23
+epoch [29/50] batch [350/1000] time 1.549 (1.570) data 0.000 (0.004) loss 1.0811 (1.1127) acc 71.8750 (71.9554) lr 8.7467e-04 eta 9:26:21
+epoch [29/50] batch [355/1000] time 1.583 (1.569) data 0.000 (0.004) loss 0.8716 (1.1122) acc 78.1250 (72.0070) lr 8.7467e-04 eta 9:26:10
+epoch [29/50] batch [360/1000] time 1.576 (1.570) data 0.001 (0.004) loss 0.6875 (1.1057) acc 81.2500 (72.1354) lr 8.7467e-04 eta 9:26:07
+epoch [29/50] batch [365/1000] time 1.565 (1.570) data 0.001 (0.004) loss 1.0039 (1.1050) acc 75.0000 (72.1404) lr 8.7467e-04 eta 9:25:56
+epoch [29/50] batch [370/1000] time 1.576 (1.569) data 0.000 (0.004) loss 1.0840 (1.1046) acc 65.6250 (72.1030) lr 8.7467e-04 eta 9:25:44
+epoch [29/50] batch [375/1000] time 1.553 (1.569) data 0.000 (0.004) loss 1.3184 (1.1010) acc 78.1250 (72.2000) lr 8.7467e-04 eta 9:25:35
+epoch [29/50] batch [380/1000] time 1.563 (1.569) data 0.000 (0.004) loss 1.5723 (1.1051) acc 65.6250 (72.1546) lr 8.7467e-04 eta 9:25:26
+epoch [29/50] batch [385/1000] time 1.546 (1.569) data 0.000 (0.004) loss 1.1953 (1.1004) acc 78.1250 (72.2565) lr 8.7467e-04 eta 9:25:18
+epoch [29/50] batch [390/1000] time 1.583 (1.569) data 0.000 (0.004) loss 0.9727 (1.0999) acc 78.1250 (72.2997) lr 8.7467e-04 eta 9:25:08
+epoch [29/50] batch [395/1000] time 1.551 (1.569) data 0.001 (0.004) loss 1.3154 (1.0994) acc 65.6250 (72.2706) lr 8.7467e-04 eta 9:25:02
+epoch [29/50] batch [400/1000] time 1.560 (1.569) data 0.000 (0.004) loss 0.9438 (1.0993) acc 81.2500 (72.2891) lr 8.7467e-04 eta 9:24:48
+epoch [29/50] batch [405/1000] time 1.563 (1.569) data 0.001 (0.004) loss 1.5361 (1.1005) acc 71.8750 (72.2531) lr 8.7467e-04 eta 9:24:39
+epoch [29/50] batch [410/1000] time 1.597 (1.569) data 0.001 (0.004) loss 1.3975 (1.1028) acc 68.7500 (72.1646) lr 8.7467e-04 eta 9:24:32
+epoch [29/50] batch [415/1000] time 1.554 (1.569) data 0.001 (0.004) loss 1.3477 (1.1054) acc 68.7500 (72.1235) lr 8.7467e-04 eta 9:24:33
+epoch [29/50] batch [420/1000] time 1.567 (1.569) data 0.001 (0.004) loss 1.1367 (1.1067) acc 75.0000 (72.1280) lr 8.7467e-04 eta 9:24:26
+epoch [29/50] batch [425/1000] time 1.568 (1.569) data 0.001 (0.004) loss 1.0361 (1.1056) acc 65.6250 (72.1250) lr 8.7467e-04 eta 9:24:17
+epoch [29/50] batch [430/1000] time 1.566 (1.569) data 0.000 (0.004) loss 0.8062 (1.1066) acc 75.0000 (72.1512) lr 8.7467e-04 eta 9:24:09
+epoch [29/50] batch [435/1000] time 1.577 (1.569) data 0.000 (0.004) loss 1.3555 (1.1070) acc 62.5000 (72.1336) lr 8.7467e-04 eta 9:24:01
+epoch [29/50] batch [440/1000] time 1.557 (1.569) data 0.001 (0.004) loss 0.8574 (1.1061) acc 71.8750 (72.1307) lr 8.7467e-04 eta 9:23:49
+epoch [29/50] batch [445/1000] time 1.573 (1.569) data 0.001 (0.004) loss 1.5059 (1.1043) acc 53.1250 (72.0997) lr 8.7467e-04 eta 9:23:42
+epoch [29/50] batch [450/1000] time 1.555 (1.569) data 0.000 (0.003) loss 1.3350 (1.1047) acc 68.7500 (72.1181) lr 8.7467e-04 eta 9:23:32
+epoch [29/50] batch [455/1000] time 1.574 (1.569) data 0.001 (0.003) loss 1.8096 (1.1067) acc 62.5000 (72.0879) lr 8.7467e-04 eta 9:23:21
+epoch [29/50] batch [460/1000] time 1.562 (1.569) data 0.000 (0.003) loss 1.0654 (1.1068) acc 75.0000 (72.1060) lr 8.7467e-04 eta 9:23:18
+epoch [29/50] batch [465/1000] time 1.547 (1.569) data 0.000 (0.003) loss 0.9609 (1.1084) acc 84.3750 (72.1035) lr 8.7467e-04 eta 9:23:05
+epoch [29/50] batch [470/1000] time 1.572 (1.569) data 0.000 (0.003) loss 0.7646 (1.1060) acc 87.5000 (72.1543) lr 8.7467e-04 eta 9:22:55
+epoch [29/50] batch [475/1000] time 1.570 (1.569) data 0.001 (0.003) loss 0.8789 (1.1068) acc 71.8750 (72.1579) lr 8.7467e-04 eta 9:22:45
+epoch [29/50] batch [480/1000] time 1.552 (1.569) data 0.001 (0.003) loss 0.5337 (1.1060) acc 81.2500 (72.1680) lr 8.7467e-04 eta 9:22:34
+epoch [29/50] batch [485/1000] time 1.553 (1.568) data 0.000 (0.003) loss 1.0479 (1.1055) acc 75.0000 (72.1392) lr 8.7467e-04 eta 9:22:24
+epoch [29/50] batch [490/1000] time 1.539 (1.568) data 0.000 (0.003) loss 0.9375 (1.1070) acc 75.0000 (72.1365) lr 8.7467e-04 eta 9:22:14
+epoch [29/50] batch [495/1000] time 1.559 (1.568) data 0.000 (0.003) loss 1.2207 (1.1059) acc 71.8750 (72.1528) lr 8.7467e-04 eta 9:22:05
+epoch [29/50] batch [500/1000] time 1.557 (1.568) data 0.000 (0.003) loss 1.7578 (1.1074) acc 59.3750 (72.1250) lr 8.7467e-04 eta 9:22:02
+epoch [29/50] batch [505/1000] time 1.550 (1.568) data 0.000 (0.003) loss 0.8223 (1.1070) acc 75.0000 (72.1349) lr 8.7467e-04 eta 9:21:51
+epoch [29/50] batch [510/1000] time 1.555 (1.568) data 0.000 (0.003) loss 0.8286 (1.1065) acc 78.1250 (72.1507) lr 8.7467e-04 eta 9:21:40
+epoch [29/50] batch [515/1000] time 1.549 (1.568) data 0.000 (0.003) loss 1.1895 (1.1050) acc 68.7500 (72.1299) lr 8.7467e-04 eta 9:21:33
+epoch [29/50] batch [520/1000] time 1.556 (1.568) data 0.000 (0.003) loss 0.7051 (1.1015) acc 75.0000 (72.1875) lr 8.7467e-04 eta 9:21:23
+epoch [29/50] batch [525/1000] time 1.566 (1.568) data 0.000 (0.003) loss 1.2725 (1.0996) acc 65.6250 (72.2440) lr 8.7467e-04 eta 9:21:14
+epoch [29/50] batch [530/1000] time 1.574 (1.568) data 0.000 (0.003) loss 1.2451 (1.0997) acc 65.6250 (72.2347) lr 8.7467e-04 eta 9:21:02
+epoch [29/50] batch [535/1000] time 1.557 (1.568) data 0.001 (0.003) loss 1.0967 (1.1012) acc 71.8750 (72.2138) lr 8.7467e-04 eta 9:20:54
+epoch [29/50] batch [540/1000] time 1.589 (1.568) data 0.000 (0.003) loss 0.6431 (1.1006) acc 84.3750 (72.2338) lr 8.7467e-04 eta 9:20:46
+epoch [29/50] batch [545/1000] time 1.560 (1.568) data 0.000 (0.003) loss 1.0566 (1.0992) acc 68.7500 (72.2362) lr 8.7467e-04 eta 9:20:36
+epoch [29/50] batch [550/1000] time 1.573 (1.568) data 0.000 (0.003) loss 0.8213 (1.0990) acc 75.0000 (72.2784) lr 8.7467e-04 eta 9:20:28
+epoch [29/50] batch [555/1000] time 1.547 (1.568) data 0.000 (0.003) loss 0.7783 (1.1000) acc 81.2500 (72.2635) lr 8.7467e-04 eta 9:20:18
+epoch [29/50] batch [560/1000] time 1.535 (1.568) data 0.000 (0.003) loss 0.6636 (1.1008) acc 84.3750 (72.2321) lr 8.7467e-04 eta 9:20:08
+epoch [29/50] batch [565/1000] time 1.551 (1.568) data 0.000 (0.003) loss 1.0957 (1.0994) acc 75.0000 (72.2677) lr 8.7467e-04 eta 9:20:07
+epoch [29/50] batch [570/1000] time 1.559 (1.568) data 0.000 (0.003) loss 0.8501 (1.0999) acc 81.2500 (72.2917) lr 8.7467e-04 eta 9:19:59
+epoch [29/50] batch [575/1000] time 1.574 (1.568) data 0.001 (0.003) loss 0.9443 (1.1020) acc 75.0000 (72.2663) lr 8.7467e-04 eta 9:19:51
+epoch [29/50] batch [580/1000] time 1.563 (1.568) data 0.000 (0.003) loss 1.0605 (1.1021) acc 75.0000 (72.2414) lr 8.7467e-04 eta 9:19:44
+epoch [29/50] batch [585/1000] time 1.556 (1.568) data 0.000 (0.003) loss 1.3965 (1.1021) acc 50.0000 (72.1955) lr 8.7467e-04 eta 9:19:35
+epoch [29/50] batch [590/1000] time 1.568 (1.568) data 0.000 (0.003) loss 1.1924 (1.1034) acc 81.2500 (72.2034) lr 8.7467e-04 eta 9:19:26
+epoch [29/50] batch [595/1000] time 1.581 (1.568) data 0.000 (0.003) loss 1.1299 (1.1033) acc 81.2500 (72.2059) lr 8.7467e-04 eta 9:19:17
+epoch [29/50] batch [600/1000] time 1.542 (1.568) data 0.001 (0.003) loss 1.4277 (1.1021) acc 65.6250 (72.2344) lr 8.7467e-04 eta 9:19:07
+epoch [29/50] batch [605/1000] time 1.570 (1.568) data 0.000 (0.003) loss 0.6812 (1.1001) acc 75.0000 (72.2624) lr 8.7467e-04 eta 9:18:58
+epoch [29/50] batch [610/1000] time 1.554 (1.568) data 0.001 (0.003) loss 1.5166 (1.0997) acc 65.6250 (72.2951) lr 8.7467e-04 eta 9:18:55
+epoch [29/50] batch [615/1000] time 1.560 (1.568) data 0.000 (0.003) loss 1.5557 (1.0997) acc 62.5000 (72.2815) lr 8.7467e-04 eta 9:18:45
+epoch [29/50] batch [620/1000] time 1.581 (1.568) data 0.000 (0.003) loss 0.8198 (1.0996) acc 81.2500 (72.3034) lr 8.7467e-04 eta 9:18:38
+epoch [29/50] batch [625/1000] time 1.553 (1.568) data 0.000 (0.003) loss 1.3955 (1.1007) acc 68.7500 (72.2550) lr 8.7467e-04 eta 9:18:28
+epoch [29/50] batch [630/1000] time 1.575 (1.568) data 0.000 (0.003) loss 1.1348 (1.0992) acc 78.1250 (72.2917) lr 8.7467e-04 eta 9:18:21
+epoch [29/50] batch [635/1000] time 1.570 (1.568) data 0.000 (0.003) loss 1.0576 (1.0986) acc 71.8750 (72.3081) lr 8.7467e-04 eta 9:18:12
+epoch [29/50] batch [640/1000] time 1.534 (1.567) data 0.000 (0.003) loss 0.6665 (1.0974) acc 84.3750 (72.3389) lr 8.7467e-04 eta 9:17:59
+epoch [29/50] batch [645/1000] time 1.601 (1.567) data 0.000 (0.003) loss 1.5020 (1.0994) acc 68.7500 (72.3401) lr 8.7467e-04 eta 9:17:50
+epoch [29/50] batch [650/1000] time 1.725 (1.567) data 0.000 (0.003) loss 1.3496 (1.0995) acc 62.5000 (72.3317) lr 8.7467e-04 eta 9:17:45
+epoch [29/50] batch [655/1000] time 1.573 (1.567) data 0.000 (0.003) loss 1.2012 (1.0994) acc 68.7500 (72.2901) lr 8.7467e-04 eta 9:17:35
+epoch [29/50] batch [660/1000] time 1.561 (1.567) data 0.000 (0.003) loss 0.9268 (1.1010) acc 75.0000 (72.2680) lr 8.7467e-04 eta 9:17:27
+epoch [29/50] batch [665/1000] time 1.547 (1.567) data 0.000 (0.003) loss 0.6016 (1.1016) acc 84.3750 (72.2650) lr 8.7467e-04 eta 9:17:17
+epoch [29/50] batch [670/1000] time 1.541 (1.567) data 0.000 (0.002) loss 1.0664 (1.1001) acc 68.7500 (72.3041) lr 8.7467e-04 eta 9:17:08
+epoch [29/50] batch [675/1000] time 1.535 (1.567) data 0.000 (0.002) loss 1.2402 (1.0991) acc 71.8750 (72.3148) lr 8.7467e-04 eta 9:16:59
+epoch [29/50] batch [680/1000] time 1.547 (1.567) data 0.000 (0.002) loss 1.2461 (1.0997) acc 59.3750 (72.2932) lr 8.7467e-04 eta 9:16:50
+epoch [29/50] batch [685/1000] time 1.558 (1.567) data 0.000 (0.002) loss 1.2256 (1.1000) acc 68.7500 (72.2947) lr 8.7467e-04 eta 9:16:40
+epoch [29/50] batch [690/1000] time 1.559 (1.567) data 0.000 (0.002) loss 1.2383 (1.1015) acc 56.2500 (72.2645) lr 8.7467e-04 eta 9:16:31
+epoch [29/50] batch [695/1000] time 1.559 (1.567) data 0.000 (0.002) loss 1.0283 (1.1000) acc 75.0000 (72.2887) lr 8.7467e-04 eta 9:16:23
+epoch [29/50] batch [700/1000] time 1.579 (1.567) data 0.000 (0.002) loss 0.4111 (1.0991) acc 93.7500 (72.3259) lr 8.7467e-04 eta 9:16:15
+epoch [29/50] batch [705/1000] time 1.579 (1.567) data 0.001 (0.002) loss 1.4512 (1.1018) acc 59.3750 (72.2917) lr 8.7467e-04 eta 9:16:07
+epoch [29/50] batch [710/1000] time 1.582 (1.567) data 0.000 (0.002) loss 1.9346 (1.1037) acc 62.5000 (72.2711) lr 8.7467e-04 eta 9:15:58
+epoch [29/50] batch [715/1000] time 1.556 (1.567) data 0.000 (0.002) loss 0.8877 (1.1036) acc 71.8750 (72.2684) lr 8.7467e-04 eta 9:15:55
+epoch [29/50] batch [720/1000] time 1.546 (1.567) data 0.000 (0.002) loss 0.9917 (1.1037) acc 81.2500 (72.2483) lr 8.7467e-04 eta 9:15:46
+epoch [29/50] batch [725/1000] time 1.552 (1.567) data 0.000 (0.002) loss 0.8501 (1.1039) acc 84.3750 (72.2586) lr 8.7467e-04 eta 9:15:37
+epoch [29/50] batch [730/1000] time 1.580 (1.567) data 0.001 (0.002) loss 1.8271 (1.1031) acc 56.2500 (72.2817) lr 8.7467e-04 eta 9:15:30
+epoch [29/50] batch [735/1000] time 1.561 (1.567) data 0.001 (0.002) loss 1.8721 (1.1036) acc 65.6250 (72.2662) lr 8.7467e-04 eta 9:15:22
+epoch [29/50] batch [740/1000] time 1.547 (1.567) data 0.000 (0.002) loss 1.4131 (1.1049) acc 62.5000 (72.2593) lr 8.7467e-04 eta 9:15:14
+epoch [29/50] batch [745/1000] time 1.561 (1.567) data 0.001 (0.002) loss 0.7393 (1.1043) acc 75.0000 (72.2693) lr 8.7467e-04 eta 9:15:06
+epoch [29/50] batch [750/1000] time 1.568 (1.567) data 0.000 (0.002) loss 0.7051 (1.1049) acc 87.5000 (72.2542) lr 8.7467e-04 eta 9:14:57
+epoch [29/50] batch [755/1000] time 1.581 (1.567) data 0.000 (0.002) loss 0.7627 (1.1048) acc 81.2500 (72.2806) lr 8.7467e-04 eta 9:14:48
+epoch [29/50] batch [760/1000] time 1.548 (1.567) data 0.001 (0.002) loss 1.2471 (1.1048) acc 78.1250 (72.3026) lr 8.7467e-04 eta 9:14:43
+epoch [29/50] batch [765/1000] time 1.532 (1.567) data 0.000 (0.002) loss 0.8550 (1.1039) acc 75.0000 (72.2998) lr 8.7467e-04 eta 9:14:32
+epoch [29/50] batch [770/1000] time 1.555 (1.567) data 0.000 (0.002) loss 0.9922 (1.1045) acc 78.1250 (72.2930) lr 8.7467e-04 eta 9:14:22
+epoch [29/50] batch [775/1000] time 1.546 (1.567) data 0.000 (0.002) loss 1.3916 (1.1046) acc 62.5000 (72.3024) lr 8.7467e-04 eta 9:14:11
+epoch [29/50] batch [780/1000] time 1.549 (1.566) data 0.000 (0.002) loss 1.0098 (1.1051) acc 71.8750 (72.2917) lr 8.7467e-04 eta 9:14:00
+epoch [29/50] batch [785/1000] time 1.537 (1.566) data 0.000 (0.002) loss 1.1934 (1.1057) acc 71.8750 (72.2651) lr 8.7467e-04 eta 9:13:53
+epoch [29/50] batch [790/1000] time 1.559 (1.567) data 0.001 (0.002) loss 1.1006 (1.1053) acc 71.8750 (72.2785) lr 8.7467e-04 eta 9:13:45
+epoch [29/50] batch [795/1000] time 1.536 (1.566) data 0.001 (0.002) loss 1.0381 (1.1058) acc 71.8750 (72.2799) lr 8.7467e-04 eta 9:13:36
+epoch [29/50] batch [800/1000] time 1.543 (1.566) data 0.001 (0.002) loss 0.3418 (1.1054) acc 93.7500 (72.2930) lr 8.7467e-04 eta 9:13:26
+epoch [29/50] batch [805/1000] time 1.567 (1.566) data 0.000 (0.002) loss 0.8218 (1.1057) acc 84.3750 (72.2865) lr 8.7467e-04 eta 9:13:21
+epoch [29/50] batch [810/1000] time 1.594 (1.566) data 0.001 (0.002) loss 1.2734 (1.1043) acc 65.6250 (72.3225) lr 8.7467e-04 eta 9:13:12
+epoch [29/50] batch [815/1000] time 1.564 (1.566) data 0.001 (0.002) loss 1.2754 (1.1046) acc 62.5000 (72.3236) lr 8.7467e-04 eta 9:13:04
+epoch [29/50] batch [820/1000] time 1.547 (1.566) data 0.000 (0.002) loss 1.0225 (1.1036) acc 78.1250 (72.3514) lr 8.7467e-04 eta 9:12:55
+epoch [29/50] batch [825/1000] time 1.544 (1.566) data 0.000 (0.002) loss 1.1035 (1.1028) acc 75.0000 (72.3598) lr 8.7467e-04 eta 9:12:46
+epoch [29/50] batch [830/1000] time 1.550 (1.566) data 0.000 (0.002) loss 1.7666 (1.1024) acc 59.3750 (72.3758) lr 8.7467e-04 eta 9:12:37
+epoch [29/50] batch [835/1000] time 1.559 (1.566) data 0.000 (0.002) loss 1.1504 (1.1037) acc 71.8750 (72.3503) lr 8.7467e-04 eta 9:12:29
+epoch [29/50] batch [840/1000] time 1.548 (1.566) data 0.000 (0.002) loss 1.1895 (1.1041) acc 65.6250 (72.3251) lr 8.7467e-04 eta 9:12:21
+epoch [29/50] batch [845/1000] time 1.564 (1.566) data 0.001 (0.002) loss 0.7378 (1.1044) acc 81.2500 (72.3225) lr 8.7467e-04 eta 9:12:13
+epoch [29/50] batch [850/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.3350 (1.1055) acc 59.3750 (72.3088) lr 8.7467e-04 eta 9:12:04
+epoch [29/50] batch [855/1000] time 1.567 (1.566) data 0.001 (0.002) loss 1.5576 (1.1054) acc 59.3750 (72.3063) lr 8.7467e-04 eta 9:11:55
+epoch [29/50] batch [860/1000] time 1.575 (1.566) data 0.001 (0.002) loss 1.2900 (1.1059) acc 71.8750 (72.3183) lr 8.7467e-04 eta 9:11:48
+epoch [29/50] batch [865/1000] time 1.564 (1.566) data 0.000 (0.002) loss 1.1631 (1.1046) acc 71.8750 (72.3483) lr 8.7467e-04 eta 9:11:44
+epoch [29/50] batch [870/1000] time 1.569 (1.566) data 0.000 (0.002) loss 0.9087 (1.1053) acc 71.8750 (72.3384) lr 8.7467e-04 eta 9:11:36
+epoch [29/50] batch [875/1000] time 1.592 (1.566) data 0.001 (0.002) loss 0.9321 (1.1048) acc 68.7500 (72.3429) lr 8.7467e-04 eta 9:11:28
+epoch [29/50] batch [880/1000] time 1.548 (1.566) data 0.000 (0.002) loss 2.0898 (1.1050) acc 59.3750 (72.3260) lr 8.7467e-04 eta 9:11:20
+epoch [29/50] batch [885/1000] time 1.539 (1.566) data 0.001 (0.002) loss 1.7520 (1.1071) acc 68.7500 (72.2669) lr 8.7467e-04 eta 9:11:10
+epoch [29/50] batch [890/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.9233 (1.1061) acc 75.0000 (72.2963) lr 8.7467e-04 eta 9:11:01
+epoch [29/50] batch [895/1000] time 1.553 (1.566) data 0.001 (0.002) loss 1.0508 (1.1066) acc 68.7500 (72.2800) lr 8.7467e-04 eta 9:10:51
+epoch [29/50] batch [900/1000] time 1.559 (1.566) data 0.001 (0.002) loss 1.1777 (1.1060) acc 68.7500 (72.2812) lr 8.7467e-04 eta 9:10:43
+epoch [29/50] batch [905/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.0684 (1.1055) acc 75.0000 (72.2894) lr 8.7467e-04 eta 9:10:36
+epoch [29/50] batch [910/1000] time 1.567 (1.566) data 0.000 (0.002) loss 1.1357 (1.1051) acc 68.7500 (72.2905) lr 8.7467e-04 eta 9:10:32
+epoch [29/50] batch [915/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.7056 (1.1047) acc 84.3750 (72.3122) lr 8.7467e-04 eta 9:10:23
+epoch [29/50] batch [920/1000] time 1.567 (1.566) data 0.000 (0.002) loss 1.1045 (1.1048) acc 68.7500 (72.3030) lr 8.7467e-04 eta 9:10:15
+epoch [29/50] batch [925/1000] time 1.581 (1.566) data 0.000 (0.002) loss 1.0107 (1.1050) acc 68.7500 (72.3074) lr 8.7467e-04 eta 9:10:07
+epoch [29/50] batch [930/1000] time 1.586 (1.566) data 0.001 (0.002) loss 1.1572 (1.1051) acc 62.5000 (72.3051) lr 8.7467e-04 eta 9:10:00
+epoch [29/50] batch [935/1000] time 1.547 (1.566) data 0.001 (0.002) loss 0.7563 (1.1044) acc 75.0000 (72.3061) lr 8.7467e-04 eta 9:09:52
+epoch [29/50] batch [940/1000] time 1.573 (1.566) data 0.000 (0.002) loss 1.2598 (1.1047) acc 75.0000 (72.3105) lr 8.7467e-04 eta 9:09:43
+epoch [29/50] batch [945/1000] time 1.565 (1.566) data 0.000 (0.002) loss 0.9980 (1.1047) acc 81.2500 (72.3247) lr 8.7467e-04 eta 9:09:34
+epoch [29/50] batch [950/1000] time 1.540 (1.566) data 0.001 (0.002) loss 0.9814 (1.1048) acc 81.2500 (72.3355) lr 8.7467e-04 eta 9:09:25
+epoch [29/50] batch [955/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.1719 (1.1043) acc 71.8750 (72.3527) lr 8.7467e-04 eta 9:09:20
+epoch [29/50] batch [960/1000] time 1.590 (1.566) data 0.000 (0.002) loss 1.0244 (1.1045) acc 71.8750 (72.3372) lr 8.7467e-04 eta 9:09:12
+epoch [29/50] batch [965/1000] time 1.600 (1.566) data 0.000 (0.002) loss 1.0479 (1.1036) acc 71.8750 (72.3413) lr 8.7467e-04 eta 9:09:06
+epoch [29/50] batch [970/1000] time 1.572 (1.566) data 0.000 (0.002) loss 1.3936 (1.1040) acc 62.5000 (72.3164) lr 8.7467e-04 eta 9:08:58
+epoch [29/50] batch [975/1000] time 1.569 (1.566) data 0.001 (0.002) loss 1.2168 (1.1048) acc 59.3750 (72.3045) lr 8.7467e-04 eta 9:08:50
+epoch [29/50] batch [980/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.3643 (1.1047) acc 65.6250 (72.3087) lr 8.7467e-04 eta 9:08:41
+epoch [29/50] batch [985/1000] time 1.551 (1.566) data 0.001 (0.002) loss 1.1865 (1.1055) acc 65.6250 (72.2970) lr 8.7467e-04 eta 9:08:33
+epoch [29/50] batch [990/1000] time 1.586 (1.566) data 0.000 (0.002) loss 0.9321 (1.1051) acc 75.0000 (72.3106) lr 8.7467e-04 eta 9:08:25
+epoch [29/50] batch [995/1000] time 1.577 (1.566) data 0.000 (0.002) loss 1.3535 (1.1047) acc 59.3750 (72.3178) lr 8.7467e-04 eta 9:08:17
+epoch [29/50] batch [1000/1000] time 1.561 (1.566) data 0.000 (0.002) loss 1.0703 (1.1057) acc 78.1250 (72.3125) lr 8.1262e-04 eta 9:08:08
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,333
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.2%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [30/50] batch [5/1000] time 1.567 (1.703) data 0.000 (0.195) loss 1.0635 (0.8290) acc 87.5000 (81.2500) lr 8.1262e-04 eta 9:55:46
+epoch [30/50] batch [10/1000] time 1.588 (1.636) data 0.001 (0.098) loss 1.6396 (0.9426) acc 65.6250 (78.1250) lr 8.1262e-04 eta 9:32:25
+epoch [30/50] batch [15/1000] time 1.561 (1.612) data 0.000 (0.065) loss 1.5205 (0.9632) acc 71.8750 (77.7083) lr 8.1262e-04 eta 9:23:50
+epoch [30/50] batch [20/1000] time 1.555 (1.601) data 0.000 (0.049) loss 1.0508 (0.9953) acc 65.6250 (76.2500) lr 8.1262e-04 eta 9:19:56
+epoch [30/50] batch [25/1000] time 1.561 (1.594) data 0.000 (0.039) loss 0.8916 (1.0317) acc 75.0000 (74.3750) lr 8.1262e-04 eta 9:17:23
+epoch [30/50] batch [30/1000] time 1.561 (1.598) data 0.000 (0.033) loss 1.1885 (1.0150) acc 71.8750 (74.0625) lr 8.1262e-04 eta 9:18:34
+epoch [30/50] batch [35/1000] time 1.559 (1.593) data 0.000 (0.028) loss 1.3701 (1.0452) acc 59.3750 (73.3929) lr 8.1262e-04 eta 9:16:46
+epoch [30/50] batch [40/1000] time 1.568 (1.590) data 0.000 (0.025) loss 0.8379 (1.0400) acc 75.0000 (73.4375) lr 8.1262e-04 eta 9:15:18
+epoch [30/50] batch [45/1000] time 1.580 (1.587) data 0.000 (0.022) loss 0.8438 (1.0473) acc 71.8750 (73.1250) lr 8.1262e-04 eta 9:14:13
+epoch [30/50] batch [50/1000] time 1.566 (1.586) data 0.000 (0.020) loss 1.7412 (1.0532) acc 65.6250 (72.8750) lr 8.1262e-04 eta 9:13:37
+epoch [30/50] batch [55/1000] time 1.558 (1.584) data 0.001 (0.018) loss 1.5039 (1.0591) acc 62.5000 (72.7841) lr 8.1262e-04 eta 9:12:53
+epoch [30/50] batch [60/1000] time 1.546 (1.581) data 0.000 (0.017) loss 0.8330 (1.0489) acc 87.5000 (72.9688) lr 8.1262e-04 eta 9:11:52
+epoch [30/50] batch [65/1000] time 1.562 (1.581) data 0.001 (0.015) loss 1.3281 (1.0647) acc 75.0000 (72.7885) lr 8.1262e-04 eta 9:11:30
+epoch [30/50] batch [70/1000] time 1.716 (1.582) data 0.000 (0.014) loss 0.9141 (1.0650) acc 78.1250 (72.6786) lr 8.1262e-04 eta 9:11:41
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+epoch [30/50] batch [620/1000] time 1.543 (1.565) data 0.000 (0.002) loss 1.3672 (1.1131) acc 75.0000 (72.1623) lr 8.1262e-04 eta 8:51:44
+epoch [30/50] batch [625/1000] time 1.577 (1.566) data 0.000 (0.002) loss 0.8647 (1.1129) acc 75.0000 (72.1550) lr 8.1262e-04 eta 8:51:37
+epoch [30/50] batch [630/1000] time 1.577 (1.566) data 0.000 (0.002) loss 0.9492 (1.1123) acc 65.6250 (72.1726) lr 8.1262e-04 eta 8:51:30
+epoch [30/50] batch [635/1000] time 1.547 (1.566) data 0.001 (0.002) loss 0.6270 (1.1126) acc 84.3750 (72.1506) lr 8.1262e-04 eta 8:51:27
+epoch [30/50] batch [640/1000] time 1.574 (1.566) data 0.001 (0.002) loss 1.0781 (1.1128) acc 71.8750 (72.1582) lr 8.1262e-04 eta 8:51:19
+epoch [30/50] batch [645/1000] time 1.566 (1.566) data 0.001 (0.002) loss 1.7412 (1.1143) acc 53.1250 (72.1172) lr 8.1262e-04 eta 8:51:12
+epoch [30/50] batch [650/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.4756 (1.1145) acc 75.0000 (72.1346) lr 8.1262e-04 eta 8:51:03
+epoch [30/50] batch [655/1000] time 1.560 (1.566) data 0.001 (0.002) loss 1.3057 (1.1153) acc 68.7500 (72.1279) lr 8.1262e-04 eta 8:50:52
+epoch [30/50] batch [660/1000] time 1.541 (1.566) data 0.000 (0.002) loss 1.0996 (1.1158) acc 71.8750 (72.1023) lr 8.1262e-04 eta 8:50:44
+epoch [30/50] batch [665/1000] time 1.548 (1.566) data 0.000 (0.002) loss 1.1963 (1.1145) acc 78.1250 (72.1523) lr 8.1262e-04 eta 8:50:35
+epoch [30/50] batch [670/1000] time 1.557 (1.566) data 0.001 (0.002) loss 1.8652 (1.1150) acc 50.0000 (72.1595) lr 8.1262e-04 eta 8:50:27
+epoch [30/50] batch [675/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.5039 (1.1149) acc 65.6250 (72.1806) lr 8.1262e-04 eta 8:50:23
+epoch [30/50] batch [680/1000] time 1.562 (1.566) data 0.001 (0.002) loss 0.7593 (1.1140) acc 81.2500 (72.1783) lr 8.1262e-04 eta 8:50:15
+epoch [30/50] batch [685/1000] time 1.579 (1.566) data 0.001 (0.002) loss 1.1338 (1.1154) acc 75.0000 (72.1715) lr 8.1262e-04 eta 8:50:07
+epoch [30/50] batch [690/1000] time 1.597 (1.566) data 0.000 (0.002) loss 1.0195 (1.1186) acc 71.8750 (72.1467) lr 8.1262e-04 eta 8:49:59
+epoch [30/50] batch [695/1000] time 1.566 (1.566) data 0.001 (0.002) loss 0.4495 (1.1175) acc 84.3750 (72.1583) lr 8.1262e-04 eta 8:49:52
+epoch [30/50] batch [700/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.3721 (1.1171) acc 65.6250 (72.1473) lr 8.1262e-04 eta 8:49:44
+epoch [30/50] batch [705/1000] time 1.569 (1.566) data 0.000 (0.002) loss 0.8828 (1.1159) acc 78.1250 (72.1720) lr 8.1262e-04 eta 8:49:35
+epoch [30/50] batch [710/1000] time 1.536 (1.566) data 0.000 (0.002) loss 1.6338 (1.1163) acc 62.5000 (72.1743) lr 8.1262e-04 eta 8:49:25
+epoch [30/50] batch [715/1000] time 1.584 (1.566) data 0.001 (0.002) loss 0.9858 (1.1157) acc 65.6250 (72.1897) lr 8.1262e-04 eta 8:49:17
+epoch [30/50] batch [720/1000] time 1.555 (1.565) data 0.000 (0.002) loss 0.9932 (1.1144) acc 71.8750 (72.1918) lr 8.1262e-04 eta 8:49:07
+epoch [30/50] batch [725/1000] time 1.533 (1.565) data 0.001 (0.002) loss 1.3838 (1.1161) acc 59.3750 (72.1509) lr 8.1262e-04 eta 8:48:58
+epoch [30/50] batch [730/1000] time 1.578 (1.565) data 0.000 (0.002) loss 0.9736 (1.1163) acc 68.7500 (72.1447) lr 8.1262e-04 eta 8:48:51
+epoch [30/50] batch [735/1000] time 1.544 (1.565) data 0.001 (0.002) loss 1.1787 (1.1163) acc 68.7500 (72.1173) lr 8.1262e-04 eta 8:48:41
+epoch [30/50] batch [740/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.2844 (1.1154) acc 93.7500 (72.1622) lr 8.1262e-04 eta 8:48:36
+epoch [30/50] batch [745/1000] time 1.549 (1.565) data 0.000 (0.002) loss 0.7666 (1.1159) acc 87.5000 (72.1477) lr 8.1262e-04 eta 8:48:27
+epoch [30/50] batch [750/1000] time 1.558 (1.565) data 0.000 (0.002) loss 0.7563 (1.1146) acc 84.3750 (72.1833) lr 8.1262e-04 eta 8:48:18
+epoch [30/50] batch [755/1000] time 1.553 (1.565) data 0.001 (0.002) loss 1.0322 (1.1139) acc 78.1250 (72.1978) lr 8.1262e-04 eta 8:48:10
+epoch [30/50] batch [760/1000] time 1.582 (1.565) data 0.001 (0.002) loss 0.5430 (1.1147) acc 81.2500 (72.1505) lr 8.1262e-04 eta 8:48:02
+epoch [30/50] batch [765/1000] time 1.564 (1.565) data 0.000 (0.002) loss 1.3994 (1.1153) acc 75.0000 (72.1528) lr 8.1262e-04 eta 8:47:53
+epoch [30/50] batch [770/1000] time 1.580 (1.565) data 0.000 (0.002) loss 0.8794 (1.1149) acc 78.1250 (72.1510) lr 8.1262e-04 eta 8:47:45
+epoch [30/50] batch [775/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0303 (1.1153) acc 75.0000 (72.1129) lr 8.1262e-04 eta 8:47:36
+epoch [30/50] batch [780/1000] time 1.563 (1.565) data 0.001 (0.002) loss 1.1123 (1.1163) acc 68.7500 (72.0913) lr 8.1262e-04 eta 8:47:28
+epoch [30/50] batch [785/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.4893 (1.1170) acc 62.5000 (72.0661) lr 8.1262e-04 eta 8:47:25
+epoch [30/50] batch [790/1000] time 1.545 (1.565) data 0.000 (0.002) loss 0.9150 (1.1171) acc 71.8750 (72.0728) lr 8.1262e-04 eta 8:47:16
+epoch [30/50] batch [795/1000] time 1.587 (1.565) data 0.000 (0.002) loss 1.2139 (1.1175) acc 62.5000 (72.0676) lr 8.1262e-04 eta 8:47:08
+epoch [30/50] batch [800/1000] time 1.577 (1.565) data 0.001 (0.002) loss 1.9209 (1.1188) acc 62.5000 (72.0391) lr 8.1262e-04 eta 8:47:00
+epoch [30/50] batch [805/1000] time 1.576 (1.565) data 0.000 (0.002) loss 1.0176 (1.1197) acc 71.8750 (72.0264) lr 8.1262e-04 eta 8:46:51
+epoch [30/50] batch [810/1000] time 1.558 (1.565) data 0.000 (0.002) loss 1.5830 (1.1197) acc 59.3750 (72.0409) lr 8.1262e-04 eta 8:46:41
+epoch [30/50] batch [815/1000] time 1.564 (1.565) data 0.000 (0.002) loss 1.3193 (1.1199) acc 68.7500 (72.0130) lr 8.1262e-04 eta 8:46:33
+epoch [30/50] batch [820/1000] time 1.557 (1.565) data 0.000 (0.002) loss 1.3047 (1.1214) acc 56.2500 (71.9741) lr 8.1262e-04 eta 8:46:24
+epoch [30/50] batch [825/1000] time 1.704 (1.565) data 0.000 (0.002) loss 0.8486 (1.1209) acc 75.0000 (71.9583) lr 8.1262e-04 eta 8:46:19
+epoch [30/50] batch [830/1000] time 1.556 (1.565) data 0.000 (0.002) loss 0.8315 (1.1218) acc 75.0000 (71.9465) lr 8.1262e-04 eta 8:46:10
+epoch [30/50] batch [835/1000] time 1.540 (1.565) data 0.000 (0.002) loss 1.5127 (1.1223) acc 68.7500 (71.9461) lr 8.1262e-04 eta 8:46:01
+epoch [30/50] batch [840/1000] time 1.558 (1.565) data 0.001 (0.002) loss 1.2793 (1.1224) acc 65.6250 (71.9271) lr 8.1262e-04 eta 8:45:52
+epoch [30/50] batch [845/1000] time 1.544 (1.565) data 0.000 (0.002) loss 0.7964 (1.1209) acc 84.3750 (71.9712) lr 8.1262e-04 eta 8:45:44
+epoch [30/50] batch [850/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.6353 (1.1195) acc 78.1250 (71.9926) lr 8.1262e-04 eta 8:45:36
+epoch [30/50] batch [855/1000] time 1.571 (1.565) data 0.000 (0.002) loss 1.0469 (1.1186) acc 65.6250 (71.9956) lr 8.1262e-04 eta 8:45:28
+epoch [30/50] batch [860/1000] time 1.555 (1.565) data 0.000 (0.002) loss 0.9404 (1.1183) acc 68.7500 (71.9804) lr 8.1262e-04 eta 8:45:21
+epoch [30/50] batch [865/1000] time 1.557 (1.565) data 0.000 (0.002) loss 1.0742 (1.1185) acc 75.0000 (71.9870) lr 8.1262e-04 eta 8:45:12
+epoch [30/50] batch [870/1000] time 1.556 (1.565) data 0.001 (0.002) loss 0.8896 (1.1186) acc 71.8750 (71.9648) lr 8.1262e-04 eta 8:45:03
+epoch [30/50] batch [875/1000] time 1.578 (1.565) data 0.000 (0.002) loss 0.8115 (1.1187) acc 75.0000 (71.9750) lr 8.1262e-04 eta 8:44:55
+epoch [30/50] batch [880/1000] time 1.567 (1.565) data 0.001 (0.002) loss 0.9609 (1.1181) acc 75.0000 (71.9638) lr 8.1262e-04 eta 8:44:46
+epoch [30/50] batch [885/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.2041 (1.1183) acc 75.0000 (71.9527) lr 8.1262e-04 eta 8:44:37
+epoch [30/50] batch [890/1000] time 1.592 (1.565) data 0.000 (0.002) loss 0.8584 (1.1174) acc 81.2500 (71.9698) lr 8.1262e-04 eta 8:44:33
+epoch [30/50] batch [895/1000] time 1.561 (1.565) data 0.001 (0.002) loss 0.7744 (1.1173) acc 81.2500 (71.9763) lr 8.1262e-04 eta 8:44:23
+epoch [30/50] batch [900/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.0459 (1.1169) acc 71.8750 (71.9826) lr 8.1262e-04 eta 8:44:15
+epoch [30/50] batch [905/1000] time 1.564 (1.565) data 0.001 (0.002) loss 1.4268 (1.1178) acc 56.2500 (71.9613) lr 8.1262e-04 eta 8:44:07
+epoch [30/50] batch [910/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.8569 (1.1178) acc 68.7500 (71.9334) lr 8.1262e-04 eta 8:43:57
+epoch [30/50] batch [915/1000] time 1.550 (1.565) data 0.000 (0.002) loss 1.3008 (1.1167) acc 68.7500 (71.9604) lr 8.1262e-04 eta 8:43:49
+epoch [30/50] batch [920/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.8354 (1.1165) acc 81.2500 (71.9735) lr 8.1262e-04 eta 8:43:41
+epoch [30/50] batch [925/1000] time 1.575 (1.565) data 0.001 (0.002) loss 1.3418 (1.1175) acc 65.6250 (71.9392) lr 8.1262e-04 eta 8:43:34
+epoch [30/50] batch [930/1000] time 1.534 (1.565) data 0.000 (0.002) loss 1.9355 (1.1188) acc 53.1250 (71.9153) lr 8.1262e-04 eta 8:43:25
+epoch [30/50] batch [935/1000] time 1.559 (1.565) data 0.001 (0.002) loss 0.9653 (1.1177) acc 75.0000 (71.9352) lr 8.1262e-04 eta 8:43:18
+epoch [30/50] batch [940/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.2109 (1.1176) acc 71.8750 (71.9415) lr 8.1262e-04 eta 8:43:10
+epoch [30/50] batch [945/1000] time 1.606 (1.565) data 0.000 (0.002) loss 1.0693 (1.1176) acc 75.0000 (71.9577) lr 8.1262e-04 eta 8:43:03
+epoch [30/50] batch [950/1000] time 1.561 (1.565) data 0.001 (0.002) loss 0.5894 (1.1163) acc 84.3750 (72.0000) lr 8.1262e-04 eta 8:42:54
+epoch [30/50] batch [955/1000] time 1.551 (1.565) data 0.000 (0.002) loss 1.0488 (1.1162) acc 65.6250 (72.0059) lr 8.1262e-04 eta 8:42:47
+epoch [30/50] batch [960/1000] time 1.569 (1.565) data 0.000 (0.001) loss 1.1104 (1.1169) acc 78.1250 (72.0085) lr 8.1262e-04 eta 8:42:38
+epoch [30/50] batch [965/1000] time 1.551 (1.565) data 0.000 (0.001) loss 1.1895 (1.1184) acc 75.0000 (72.0013) lr 8.1262e-04 eta 8:42:28
+epoch [30/50] batch [970/1000] time 1.556 (1.565) data 0.000 (0.001) loss 0.8682 (1.1194) acc 78.1250 (71.9684) lr 8.1262e-04 eta 8:42:19
+epoch [30/50] batch [975/1000] time 1.558 (1.565) data 0.001 (0.001) loss 0.9922 (1.1196) acc 78.1250 (71.9776) lr 8.1262e-04 eta 8:42:10
+epoch [30/50] batch [980/1000] time 1.564 (1.565) data 0.001 (0.001) loss 1.1387 (1.1204) acc 71.8750 (71.9579) lr 8.1262e-04 eta 8:42:05
+epoch [30/50] batch [985/1000] time 1.559 (1.565) data 0.001 (0.001) loss 1.3350 (1.1209) acc 65.6250 (71.9543) lr 8.1262e-04 eta 8:41:57
+epoch [30/50] batch [990/1000] time 1.580 (1.565) data 0.000 (0.001) loss 1.0059 (1.1214) acc 65.6250 (71.9571) lr 8.1262e-04 eta 8:41:49
+epoch [30/50] batch [995/1000] time 1.574 (1.565) data 0.000 (0.001) loss 0.7163 (1.1204) acc 90.6250 (71.9912) lr 8.1262e-04 eta 8:41:41
+epoch [30/50] batch [1000/1000] time 1.551 (1.565) data 0.000 (0.001) loss 0.8726 (1.1196) acc 87.5000 (72.0187) lr 7.5131e-04 eta 8:41:33
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,282
+* accuracy: 78.6%
+* error: 21.4%
+* macro_f1: 78.1%
+epoch [31/50] batch [5/1000] time 1.563 (1.698) data 0.000 (0.194) loss 0.5386 (1.1358) acc 90.6250 (73.1250) lr 7.5131e-04 eta 9:25:52
+epoch [31/50] batch [10/1000] time 1.553 (1.627) data 0.000 (0.097) loss 0.6226 (0.9636) acc 81.2500 (75.6250) lr 7.5131e-04 eta 9:02:04
+epoch [31/50] batch [15/1000] time 1.547 (1.602) data 0.000 (0.065) loss 0.9932 (0.9703) acc 75.0000 (75.0000) lr 7.5131e-04 eta 8:53:38
+epoch [31/50] batch [20/1000] time 1.563 (1.589) data 0.001 (0.049) loss 0.6162 (0.9857) acc 78.1250 (74.3750) lr 7.5131e-04 eta 8:49:17
+epoch [31/50] batch [25/1000] time 1.551 (1.585) data 0.001 (0.039) loss 1.4766 (1.0104) acc 59.3750 (73.3750) lr 7.5131e-04 eta 8:47:48
+epoch [31/50] batch [30/1000] time 1.581 (1.584) data 0.000 (0.033) loss 0.7700 (0.9952) acc 78.1250 (73.7500) lr 7.5131e-04 eta 8:47:10
+epoch [31/50] batch [35/1000] time 1.551 (1.580) data 0.001 (0.028) loss 0.9370 (1.0059) acc 71.8750 (73.5714) lr 7.5131e-04 eta 8:45:46
+epoch [31/50] batch [40/1000] time 1.568 (1.578) data 0.000 (0.025) loss 1.1406 (1.0592) acc 68.7500 (72.4219) lr 7.5131e-04 eta 8:44:47
+epoch [31/50] batch [45/1000] time 1.773 (1.580) data 0.000 (0.022) loss 0.9536 (1.0536) acc 71.8750 (72.7083) lr 7.5131e-04 eta 8:45:25
+epoch [31/50] batch [50/1000] time 1.545 (1.579) data 0.001 (0.020) loss 1.0732 (1.0451) acc 75.0000 (73.2500) lr 7.5131e-04 eta 8:44:53
+epoch [31/50] batch [55/1000] time 1.544 (1.576) data 0.001 (0.018) loss 0.8560 (1.0406) acc 84.3750 (73.6364) lr 7.5131e-04 eta 8:43:48
+epoch [31/50] batch [60/1000] time 1.558 (1.575) data 0.001 (0.017) loss 1.8955 (1.0586) acc 59.3750 (73.5417) lr 7.5131e-04 eta 8:43:15
+epoch [31/50] batch [65/1000] time 1.580 (1.573) data 0.001 (0.015) loss 1.1162 (1.0739) acc 71.8750 (72.9808) lr 7.5131e-04 eta 8:42:31
+epoch [31/50] batch [70/1000] time 1.570 (1.573) data 0.001 (0.014) loss 0.8130 (1.0826) acc 84.3750 (73.1250) lr 7.5131e-04 eta 8:42:20
+epoch [31/50] batch [75/1000] time 1.577 (1.572) data 0.001 (0.013) loss 0.8564 (1.0784) acc 75.0000 (73.2083) lr 7.5131e-04 eta 8:42:02
+epoch [31/50] batch [80/1000] time 1.586 (1.571) data 0.000 (0.013) loss 0.9751 (1.0712) acc 75.0000 (73.4766) lr 7.5131e-04 eta 8:41:44
+epoch [31/50] batch [85/1000] time 1.555 (1.571) data 0.001 (0.012) loss 1.3555 (1.0701) acc 75.0000 (73.7132) lr 7.5131e-04 eta 8:41:27
+epoch [31/50] batch [90/1000] time 1.546 (1.572) data 0.000 (0.011) loss 1.0312 (1.0766) acc 65.6250 (73.4028) lr 7.5131e-04 eta 8:41:38
+epoch [31/50] batch [95/1000] time 1.567 (1.571) data 0.001 (0.011) loss 0.6104 (1.0788) acc 81.2500 (73.3553) lr 7.5131e-04 eta 8:41:20
+epoch [31/50] batch [100/1000] time 1.562 (1.571) data 0.001 (0.010) loss 0.9531 (1.0763) acc 75.0000 (73.3750) lr 7.5131e-04 eta 8:40:55
+epoch [31/50] batch [105/1000] time 1.564 (1.570) data 0.000 (0.010) loss 0.8506 (1.0672) acc 87.5000 (73.6607) lr 7.5131e-04 eta 8:40:39
+epoch [31/50] batch [110/1000] time 1.540 (1.569) data 0.000 (0.009) loss 1.2188 (1.0702) acc 75.0000 (73.6080) lr 7.5131e-04 eta 8:40:15
+epoch [31/50] batch [115/1000] time 1.558 (1.569) data 0.001 (0.009) loss 0.6797 (1.0732) acc 87.5000 (73.4511) lr 7.5131e-04 eta 8:40:02
+epoch [31/50] batch [120/1000] time 1.546 (1.569) data 0.000 (0.009) loss 0.9834 (1.0788) acc 78.1250 (73.3854) lr 7.5131e-04 eta 8:39:50
+epoch [31/50] batch [125/1000] time 1.569 (1.569) data 0.001 (0.008) loss 0.7490 (1.0761) acc 71.8750 (73.4750) lr 7.5131e-04 eta 8:39:43
+epoch [31/50] batch [130/1000] time 1.549 (1.568) data 0.001 (0.008) loss 1.4004 (1.0733) acc 71.8750 (73.5817) lr 7.5131e-04 eta 8:39:18
+epoch [31/50] batch [135/1000] time 1.570 (1.568) data 0.001 (0.008) loss 0.9106 (1.0750) acc 71.8750 (73.5185) lr 7.5131e-04 eta 8:39:07
+epoch [31/50] batch [140/1000] time 1.572 (1.567) data 0.001 (0.007) loss 1.0264 (1.0800) acc 75.0000 (73.3929) lr 7.5131e-04 eta 8:38:49
+epoch [31/50] batch [145/1000] time 1.559 (1.567) data 0.000 (0.007) loss 0.7432 (1.0763) acc 78.1250 (73.3836) lr 7.5131e-04 eta 8:38:38
+epoch [31/50] batch [150/1000] time 1.571 (1.569) data 0.001 (0.007) loss 1.6279 (1.0807) acc 62.5000 (73.3958) lr 7.5131e-04 eta 8:38:56
+epoch [31/50] batch [155/1000] time 1.556 (1.568) data 0.001 (0.007) loss 1.0439 (1.0820) acc 81.2500 (73.2661) lr 7.5131e-04 eta 8:38:44
+epoch [31/50] batch [160/1000] time 1.576 (1.568) data 0.001 (0.007) loss 1.9590 (1.0887) acc 56.2500 (73.1055) lr 7.5131e-04 eta 8:38:35
+epoch [31/50] batch [165/1000] time 1.567 (1.568) data 0.001 (0.006) loss 1.0635 (1.0993) acc 71.8750 (72.9167) lr 7.5131e-04 eta 8:38:22
+epoch [31/50] batch [170/1000] time 1.549 (1.568) data 0.001 (0.006) loss 1.0908 (1.0987) acc 71.8750 (72.9412) lr 7.5131e-04 eta 8:38:04
+epoch [31/50] batch [175/1000] time 1.582 (1.567) data 0.001 (0.006) loss 1.0166 (1.0963) acc 65.6250 (72.8571) lr 7.5131e-04 eta 8:37:47
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+epoch [31/50] batch [730/1000] time 1.542 (1.565) data 0.001 (0.002) loss 0.8188 (1.1040) acc 81.2500 (72.1704) lr 7.5131e-04 eta 8:22:37
+epoch [31/50] batch [735/1000] time 1.583 (1.565) data 0.001 (0.002) loss 1.1943 (1.1028) acc 78.1250 (72.1896) lr 7.5131e-04 eta 8:22:30
+epoch [31/50] batch [740/1000] time 1.563 (1.565) data 0.000 (0.002) loss 1.1406 (1.1027) acc 68.7500 (72.1833) lr 7.5131e-04 eta 8:22:21
+epoch [31/50] batch [745/1000] time 1.557 (1.565) data 0.001 (0.002) loss 1.4854 (1.1035) acc 65.6250 (72.2106) lr 7.5131e-04 eta 8:22:14
+epoch [31/50] batch [750/1000] time 1.566 (1.565) data 0.000 (0.002) loss 0.8975 (1.1048) acc 71.8750 (72.1792) lr 7.5131e-04 eta 8:22:05
+epoch [31/50] batch [755/1000] time 1.575 (1.565) data 0.001 (0.002) loss 1.3643 (1.1080) acc 68.7500 (72.1233) lr 7.5131e-04 eta 8:22:03
+epoch [31/50] batch [760/1000] time 1.566 (1.565) data 0.001 (0.002) loss 1.0977 (1.1081) acc 71.8750 (72.1176) lr 7.5131e-04 eta 8:21:54
+epoch [31/50] batch [765/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.1094 (1.1074) acc 75.0000 (72.1324) lr 7.5131e-04 eta 8:21:48
+epoch [31/50] batch [770/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.0264 (1.1080) acc 75.0000 (72.1144) lr 7.5131e-04 eta 8:21:38
+epoch [31/50] batch [775/1000] time 1.543 (1.565) data 0.000 (0.002) loss 1.0820 (1.1090) acc 71.8750 (72.1129) lr 7.5131e-04 eta 8:21:30
+epoch [31/50] batch [780/1000] time 1.560 (1.565) data 0.001 (0.002) loss 1.4629 (1.1093) acc 62.5000 (72.0994) lr 7.5131e-04 eta 8:21:23
+epoch [31/50] batch [785/1000] time 1.552 (1.565) data 0.001 (0.002) loss 0.9053 (1.1086) acc 78.1250 (72.1019) lr 7.5131e-04 eta 8:21:14
+epoch [31/50] batch [790/1000] time 1.570 (1.565) data 0.001 (0.002) loss 1.1846 (1.1075) acc 68.7500 (72.1203) lr 7.5131e-04 eta 8:21:05
+epoch [31/50] batch [795/1000] time 1.557 (1.565) data 0.001 (0.002) loss 0.6880 (1.1068) acc 84.3750 (72.1423) lr 7.5131e-04 eta 8:20:56
+epoch [31/50] batch [800/1000] time 1.553 (1.565) data 0.000 (0.002) loss 1.1230 (1.1064) acc 71.8750 (72.1523) lr 7.5131e-04 eta 8:20:52
+epoch [31/50] batch [805/1000] time 1.582 (1.565) data 0.000 (0.002) loss 1.2051 (1.1090) acc 75.0000 (72.1390) lr 7.5131e-04 eta 8:20:45
+epoch [31/50] batch [810/1000] time 1.548 (1.565) data 0.001 (0.002) loss 1.2393 (1.1079) acc 75.0000 (72.1644) lr 7.5131e-04 eta 8:20:36
+epoch [31/50] batch [815/1000] time 1.556 (1.565) data 0.000 (0.002) loss 1.0938 (1.1077) acc 78.1250 (72.1894) lr 7.5131e-04 eta 8:20:28
+epoch [31/50] batch [820/1000] time 1.586 (1.565) data 0.000 (0.002) loss 1.4033 (1.1088) acc 71.8750 (72.1494) lr 7.5131e-04 eta 8:20:21
+epoch [31/50] batch [825/1000] time 1.596 (1.565) data 0.000 (0.002) loss 0.9204 (1.1075) acc 71.8750 (72.1667) lr 7.5131e-04 eta 8:20:14
+epoch [31/50] batch [830/1000] time 1.577 (1.565) data 0.000 (0.002) loss 0.9263 (1.1075) acc 75.0000 (72.1762) lr 7.5131e-04 eta 8:20:08
+epoch [31/50] batch [835/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.8379 (1.1067) acc 71.8750 (72.1707) lr 7.5131e-04 eta 8:19:59
+epoch [31/50] batch [840/1000] time 1.543 (1.565) data 0.001 (0.002) loss 0.9126 (1.1054) acc 68.7500 (72.1912) lr 7.5131e-04 eta 8:19:50
+epoch [31/50] batch [845/1000] time 1.596 (1.566) data 0.001 (0.002) loss 1.1279 (1.1043) acc 75.0000 (72.2189) lr 7.5131e-04 eta 8:19:47
+epoch [31/50] batch [850/1000] time 1.555 (1.566) data 0.000 (0.002) loss 1.0928 (1.1053) acc 65.6250 (72.1801) lr 7.5131e-04 eta 8:19:40
+epoch [31/50] batch [855/1000] time 1.537 (1.566) data 0.000 (0.002) loss 1.2295 (1.1056) acc 75.0000 (72.1820) lr 7.5131e-04 eta 8:19:31
+epoch [31/50] batch [860/1000] time 1.577 (1.566) data 0.001 (0.002) loss 1.1348 (1.1066) acc 78.1250 (72.1657) lr 7.5131e-04 eta 8:19:24
+epoch [31/50] batch [865/1000] time 1.548 (1.566) data 0.001 (0.002) loss 1.3574 (1.1064) acc 75.0000 (72.1532) lr 7.5131e-04 eta 8:19:15
+epoch [31/50] batch [870/1000] time 1.575 (1.566) data 0.001 (0.002) loss 1.3428 (1.1060) acc 68.7500 (72.1552) lr 7.5131e-04 eta 8:19:08
+epoch [31/50] batch [875/1000] time 1.556 (1.566) data 0.001 (0.002) loss 0.6338 (1.1047) acc 84.3750 (72.1786) lr 7.5131e-04 eta 8:19:00
+epoch [31/50] batch [880/1000] time 1.559 (1.565) data 0.000 (0.002) loss 0.9019 (1.1040) acc 75.0000 (72.2017) lr 7.5131e-04 eta 8:18:51
+epoch [31/50] batch [885/1000] time 1.581 (1.565) data 0.000 (0.002) loss 0.7563 (1.1042) acc 75.0000 (72.2034) lr 7.5131e-04 eta 8:18:43
+epoch [31/50] batch [890/1000] time 1.575 (1.565) data 0.000 (0.002) loss 0.8057 (1.1035) acc 78.1250 (72.2331) lr 7.5131e-04 eta 8:18:35
+epoch [31/50] batch [895/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.2764 (1.1039) acc 62.5000 (72.2207) lr 7.5131e-04 eta 8:18:27
+epoch [31/50] batch [900/1000] time 1.576 (1.565) data 0.000 (0.002) loss 1.0410 (1.1018) acc 78.1250 (72.2743) lr 7.5131e-04 eta 8:18:18
+epoch [31/50] batch [905/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.0361 (1.1032) acc 62.5000 (72.2445) lr 7.5131e-04 eta 8:18:13
+epoch [31/50] batch [910/1000] time 1.576 (1.566) data 0.001 (0.002) loss 0.7798 (1.1026) acc 78.1250 (72.2699) lr 7.5131e-04 eta 8:18:05
+epoch [31/50] batch [915/1000] time 1.560 (1.566) data 0.001 (0.002) loss 0.8506 (1.1024) acc 75.0000 (72.2814) lr 7.5131e-04 eta 8:17:58
+epoch [31/50] batch [920/1000] time 1.551 (1.566) data 0.001 (0.002) loss 0.8394 (1.1024) acc 71.8750 (72.2588) lr 7.5131e-04 eta 8:17:50
+epoch [31/50] batch [925/1000] time 1.525 (1.565) data 0.001 (0.002) loss 1.3096 (1.1021) acc 68.7500 (72.2703) lr 7.5131e-04 eta 8:17:41
+epoch [31/50] batch [930/1000] time 1.590 (1.565) data 0.000 (0.002) loss 1.9307 (1.1022) acc 56.2500 (72.2648) lr 7.5131e-04 eta 8:17:33
+epoch [31/50] batch [935/1000] time 1.557 (1.565) data 0.001 (0.002) loss 0.9658 (1.1013) acc 71.8750 (72.2527) lr 7.5131e-04 eta 8:17:25
+epoch [31/50] batch [940/1000] time 1.539 (1.565) data 0.001 (0.002) loss 1.1055 (1.1004) acc 71.8750 (72.2739) lr 7.5131e-04 eta 8:17:16
+epoch [31/50] batch [945/1000] time 1.565 (1.565) data 0.001 (0.002) loss 2.2617 (1.1005) acc 50.0000 (72.2718) lr 7.5131e-04 eta 8:17:08
+epoch [31/50] batch [950/1000] time 1.571 (1.566) data 0.000 (0.002) loss 0.7852 (1.0991) acc 75.0000 (72.2928) lr 7.5131e-04 eta 8:17:02
+epoch [31/50] batch [955/1000] time 1.556 (1.565) data 0.001 (0.002) loss 1.4932 (1.1006) acc 59.3750 (72.2677) lr 7.5131e-04 eta 8:16:54
+epoch [31/50] batch [960/1000] time 1.566 (1.565) data 0.001 (0.002) loss 0.8921 (1.0996) acc 68.7500 (72.2721) lr 7.5131e-04 eta 8:16:46
+epoch [31/50] batch [965/1000] time 1.563 (1.565) data 0.000 (0.002) loss 1.0146 (1.0990) acc 84.3750 (72.2960) lr 7.5131e-04 eta 8:16:38
+epoch [31/50] batch [970/1000] time 1.590 (1.566) data 0.000 (0.002) loss 1.1006 (1.1002) acc 71.8750 (72.3003) lr 7.5131e-04 eta 8:16:31
+epoch [31/50] batch [975/1000] time 1.582 (1.566) data 0.000 (0.001) loss 1.0947 (1.1002) acc 59.3750 (72.3013) lr 7.5131e-04 eta 8:16:23
+epoch [31/50] batch [980/1000] time 1.556 (1.566) data 0.000 (0.001) loss 1.2998 (1.0996) acc 62.5000 (72.3055) lr 7.5131e-04 eta 8:16:16
+epoch [31/50] batch [985/1000] time 1.543 (1.565) data 0.001 (0.001) loss 0.7173 (1.0989) acc 84.3750 (72.3319) lr 7.5131e-04 eta 8:16:07
+epoch [31/50] batch [990/1000] time 1.564 (1.565) data 0.000 (0.001) loss 1.3994 (1.0996) acc 62.5000 (72.3232) lr 7.5131e-04 eta 8:15:59
+epoch [31/50] batch [995/1000] time 1.571 (1.566) data 0.000 (0.001) loss 1.6270 (1.0996) acc 62.5000 (72.3178) lr 7.5131e-04 eta 8:15:55
+epoch [31/50] batch [1000/1000] time 1.559 (1.566) data 0.000 (0.001) loss 0.8525 (1.0995) acc 84.3750 (72.3219) lr 6.9098e-04 eta 8:15:47
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,309
+* accuracy: 78.6%
+* error: 21.4%
+* macro_f1: 78.2%
+epoch [32/50] batch [5/1000] time 1.562 (1.698) data 0.001 (0.201) loss 1.4502 (1.2303) acc 71.8750 (68.1250) lr 6.9098e-04 eta 8:57:32
+epoch [32/50] batch [10/1000] time 1.539 (1.626) data 0.001 (0.101) loss 1.1777 (1.2021) acc 71.8750 (69.6875) lr 6.9098e-04 eta 8:34:38
+epoch [32/50] batch [15/1000] time 1.537 (1.619) data 0.001 (0.067) loss 0.5049 (1.1011) acc 81.2500 (73.1250) lr 6.9098e-04 eta 8:32:13
+epoch [32/50] batch [20/1000] time 1.555 (1.602) data 0.000 (0.051) loss 0.8457 (1.1016) acc 81.2500 (73.9062) lr 6.9098e-04 eta 8:26:41
+epoch [32/50] batch [25/1000] time 1.582 (1.593) data 0.001 (0.041) loss 1.9092 (1.1453) acc 59.3750 (72.8750) lr 6.9098e-04 eta 8:23:42
+epoch [32/50] batch [30/1000] time 1.561 (1.587) data 0.001 (0.034) loss 1.8975 (1.1535) acc 65.6250 (72.9167) lr 6.9098e-04 eta 8:21:44
+epoch [32/50] batch [35/1000] time 1.551 (1.583) data 0.001 (0.029) loss 1.2832 (1.1276) acc 68.7500 (73.5714) lr 6.9098e-04 eta 8:20:16
+epoch [32/50] batch [40/1000] time 1.565 (1.580) data 0.000 (0.026) loss 1.4111 (1.1488) acc 65.6250 (72.7344) lr 6.9098e-04 eta 8:19:14
+epoch [32/50] batch [45/1000] time 1.573 (1.579) data 0.001 (0.023) loss 0.9517 (1.1446) acc 71.8750 (72.8472) lr 6.9098e-04 eta 8:18:51
+epoch [32/50] batch [50/1000] time 1.571 (1.578) data 0.001 (0.021) loss 1.1309 (1.1273) acc 71.8750 (73.3750) lr 6.9098e-04 eta 8:18:25
+epoch [32/50] batch [55/1000] time 1.569 (1.577) data 0.001 (0.019) loss 0.6431 (1.0961) acc 84.3750 (73.9773) lr 6.9098e-04 eta 8:17:57
+epoch [32/50] batch [60/1000] time 1.546 (1.576) data 0.001 (0.017) loss 1.4102 (1.0933) acc 53.1250 (73.7500) lr 6.9098e-04 eta 8:17:22
+epoch [32/50] batch [65/1000] time 1.585 (1.576) data 0.001 (0.016) loss 0.8696 (1.0920) acc 71.8750 (73.7500) lr 6.9098e-04 eta 8:17:15
+epoch [32/50] batch [70/1000] time 1.575 (1.576) data 0.001 (0.015) loss 1.0312 (1.0942) acc 75.0000 (73.8393) lr 6.9098e-04 eta 8:17:08
+epoch [32/50] batch [75/1000] time 1.556 (1.579) data 0.000 (0.014) loss 2.3301 (1.1170) acc 53.1250 (73.4583) lr 6.9098e-04 eta 8:18:01
+epoch [32/50] batch [80/1000] time 1.548 (1.578) data 0.001 (0.013) loss 1.4512 (1.1210) acc 71.8750 (73.3984) lr 6.9098e-04 eta 8:17:43
+epoch [32/50] batch [85/1000] time 1.569 (1.578) data 0.001 (0.012) loss 0.9502 (1.1137) acc 75.0000 (73.4559) lr 6.9098e-04 eta 8:17:33
+epoch [32/50] batch [90/1000] time 1.554 (1.577) data 0.000 (0.012) loss 1.5586 (1.1183) acc 62.5000 (73.1944) lr 6.9098e-04 eta 8:17:08
+epoch [32/50] batch [95/1000] time 1.543 (1.577) data 0.000 (0.011) loss 1.4229 (1.1129) acc 65.6250 (73.2237) lr 6.9098e-04 eta 8:16:44
+epoch [32/50] batch [100/1000] time 1.582 (1.576) data 0.000 (0.011) loss 1.0566 (1.1048) acc 81.2500 (73.2188) lr 6.9098e-04 eta 8:16:17
+epoch [32/50] batch [105/1000] time 1.547 (1.575) data 0.000 (0.010) loss 0.8413 (1.0957) acc 81.2500 (73.5417) lr 6.9098e-04 eta 8:15:50
+epoch [32/50] batch [110/1000] time 1.554 (1.574) data 0.000 (0.010) loss 1.1904 (1.0889) acc 78.1250 (73.6648) lr 6.9098e-04 eta 8:15:32
+epoch [32/50] batch [115/1000] time 1.584 (1.574) data 0.001 (0.009) loss 1.4570 (1.1070) acc 62.5000 (73.2609) lr 6.9098e-04 eta 8:15:22
+epoch [32/50] batch [120/1000] time 1.594 (1.575) data 0.000 (0.009) loss 0.6538 (1.1057) acc 81.2500 (73.3594) lr 6.9098e-04 eta 8:15:41
+epoch [32/50] batch [125/1000] time 1.563 (1.575) data 0.000 (0.009) loss 0.6499 (1.1102) acc 90.6250 (73.3500) lr 6.9098e-04 eta 8:15:22
+epoch [32/50] batch [130/1000] time 1.581 (1.574) data 0.001 (0.008) loss 0.7017 (1.1117) acc 87.5000 (73.2212) lr 6.9098e-04 eta 8:15:09
+epoch [32/50] batch [135/1000] time 1.542 (1.574) data 0.000 (0.008) loss 1.2812 (1.1068) acc 81.2500 (73.3565) lr 6.9098e-04 eta 8:14:55
+epoch [32/50] batch [140/1000] time 1.580 (1.574) data 0.001 (0.008) loss 0.6050 (1.1039) acc 84.3750 (73.4821) lr 6.9098e-04 eta 8:14:43
+epoch [32/50] batch [145/1000] time 1.559 (1.573) data 0.001 (0.007) loss 0.9092 (1.1006) acc 75.0000 (73.6422) lr 6.9098e-04 eta 8:14:27
+epoch [32/50] batch [150/1000] time 1.557 (1.573) data 0.000 (0.007) loss 0.4524 (1.0944) acc 90.6250 (73.7292) lr 6.9098e-04 eta 8:14:15
+epoch [32/50] batch [155/1000] time 1.543 (1.572) data 0.000 (0.007) loss 0.9492 (1.0905) acc 71.8750 (73.7097) lr 6.9098e-04 eta 8:13:52
+epoch [32/50] batch [160/1000] time 1.578 (1.572) data 0.001 (0.007) loss 0.8179 (1.0883) acc 78.1250 (73.5938) lr 6.9098e-04 eta 8:13:42
+epoch [32/50] batch [165/1000] time 1.549 (1.573) data 0.000 (0.007) loss 1.1855 (1.0929) acc 78.1250 (73.4659) lr 6.9098e-04 eta 8:13:49
+epoch [32/50] batch [170/1000] time 1.557 (1.573) data 0.000 (0.006) loss 0.5620 (1.0927) acc 84.3750 (73.5294) lr 6.9098e-04 eta 8:13:34
+epoch [32/50] batch [175/1000] time 1.569 (1.572) data 0.000 (0.006) loss 1.2842 (1.0919) acc 65.6250 (73.5893) lr 6.9098e-04 eta 8:13:22
+epoch [32/50] batch [180/1000] time 1.559 (1.572) data 0.001 (0.006) loss 1.2480 (1.0907) acc 59.3750 (73.5243) lr 6.9098e-04 eta 8:13:08
+epoch [32/50] batch [185/1000] time 1.555 (1.572) data 0.000 (0.006) loss 1.7314 (1.0946) acc 59.3750 (73.4628) lr 6.9098e-04 eta 8:13:01
+epoch [32/50] batch [190/1000] time 1.561 (1.572) data 0.001 (0.006) loss 1.0850 (1.0950) acc 65.6250 (73.4046) lr 6.9098e-04 eta 8:12:53
+epoch [32/50] batch [195/1000] time 1.552 (1.572) data 0.001 (0.006) loss 1.0361 (1.1041) acc 65.6250 (73.1090) lr 6.9098e-04 eta 8:12:41
+epoch [32/50] batch [200/1000] time 1.560 (1.572) data 0.000 (0.006) loss 0.9814 (1.0986) acc 71.8750 (73.2031) lr 6.9098e-04 eta 8:12:27
+epoch [32/50] batch [205/1000] time 1.554 (1.571) data 0.001 (0.005) loss 1.0732 (1.1028) acc 78.1250 (73.0793) lr 6.9098e-04 eta 8:12:11
+epoch [32/50] batch [210/1000] time 1.543 (1.571) data 0.000 (0.005) loss 1.1621 (1.0991) acc 71.8750 (73.1696) lr 6.9098e-04 eta 8:12:02
+epoch [32/50] batch [215/1000] time 1.562 (1.571) data 0.000 (0.005) loss 0.9448 (1.0954) acc 71.8750 (73.2267) lr 6.9098e-04 eta 8:11:54
+epoch [32/50] batch [220/1000] time 1.565 (1.571) data 0.000 (0.005) loss 1.6162 (1.0951) acc 59.3750 (73.1250) lr 6.9098e-04 eta 8:11:44
+epoch [32/50] batch [225/1000] time 1.714 (1.571) data 0.000 (0.005) loss 0.6206 (1.0921) acc 87.5000 (73.2083) lr 6.9098e-04 eta 8:11:43
+epoch [32/50] batch [230/1000] time 1.555 (1.571) data 0.000 (0.005) loss 1.0293 (1.0954) acc 75.0000 (73.1386) lr 6.9098e-04 eta 8:11:30
+epoch [32/50] batch [235/1000] time 1.579 (1.571) data 0.000 (0.005) loss 1.0996 (1.0943) acc 78.1250 (73.1782) lr 6.9098e-04 eta 8:11:21
+epoch [32/50] batch [240/1000] time 1.558 (1.571) data 0.000 (0.005) loss 0.8271 (1.0938) acc 78.1250 (73.2161) lr 6.9098e-04 eta 8:11:12
+epoch [32/50] batch [245/1000] time 1.551 (1.571) data 0.000 (0.005) loss 1.5352 (1.0946) acc 59.3750 (73.2908) lr 6.9098e-04 eta 8:11:00
+epoch [32/50] batch [250/1000] time 1.564 (1.571) data 0.000 (0.004) loss 1.6797 (1.0989) acc 62.5000 (73.2250) lr 6.9098e-04 eta 8:10:47
+epoch [32/50] batch [255/1000] time 1.567 (1.570) data 0.000 (0.004) loss 1.1025 (1.0977) acc 65.6250 (73.2598) lr 6.9098e-04 eta 8:10:38
+epoch [32/50] batch [260/1000] time 1.557 (1.570) data 0.001 (0.004) loss 0.8994 (1.0968) acc 75.0000 (73.2212) lr 6.9098e-04 eta 8:10:27
+epoch [32/50] batch [265/1000] time 1.561 (1.570) data 0.001 (0.004) loss 1.3252 (1.1002) acc 71.8750 (73.1722) lr 6.9098e-04 eta 8:10:15
+epoch [32/50] batch [270/1000] time 1.732 (1.571) data 0.001 (0.004) loss 0.8101 (1.1046) acc 81.2500 (73.1713) lr 6.9098e-04 eta 8:10:15
+epoch [32/50] batch [275/1000] time 1.572 (1.570) data 0.000 (0.004) loss 0.9829 (1.1005) acc 81.2500 (73.2841) lr 6.9098e-04 eta 8:10:01
+epoch [32/50] batch [280/1000] time 1.571 (1.570) data 0.000 (0.004) loss 1.3896 (1.1029) acc 65.6250 (73.2366) lr 6.9098e-04 eta 8:09:52
+epoch [32/50] batch [285/1000] time 1.564 (1.570) data 0.001 (0.004) loss 0.8965 (1.1050) acc 78.1250 (73.2127) lr 6.9098e-04 eta 8:09:44
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+epoch [32/50] batch [835/1000] time 1.558 (1.567) data 0.001 (0.002) loss 1.2480 (1.0977) acc 78.1250 (72.6909) lr 6.9098e-04 eta 7:54:17
+epoch [32/50] batch [840/1000] time 1.533 (1.567) data 0.000 (0.002) loss 1.8789 (1.0978) acc 62.5000 (72.6786) lr 6.9098e-04 eta 7:54:09
+epoch [32/50] batch [845/1000] time 1.537 (1.566) data 0.001 (0.002) loss 0.9614 (1.1001) acc 75.0000 (72.6479) lr 6.9098e-04 eta 7:53:59
+epoch [32/50] batch [850/1000] time 1.568 (1.566) data 0.001 (0.002) loss 1.1162 (1.1002) acc 75.0000 (72.6471) lr 6.9098e-04 eta 7:53:51
+epoch [32/50] batch [855/1000] time 1.569 (1.566) data 0.000 (0.002) loss 0.5801 (1.1004) acc 84.3750 (72.6608) lr 6.9098e-04 eta 7:53:43
+epoch [32/50] batch [860/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.9844 (1.1013) acc 68.7500 (72.6562) lr 6.9098e-04 eta 7:53:35
+epoch [32/50] batch [865/1000] time 1.568 (1.567) data 0.000 (0.002) loss 1.1699 (1.1028) acc 75.0000 (72.6264) lr 6.9098e-04 eta 7:53:28
+epoch [32/50] batch [870/1000] time 1.598 (1.567) data 0.000 (0.002) loss 1.0576 (1.1026) acc 75.0000 (72.6293) lr 6.9098e-04 eta 7:53:20
+epoch [32/50] batch [875/1000] time 1.537 (1.567) data 0.001 (0.002) loss 0.9014 (1.1017) acc 81.2500 (72.6536) lr 6.9098e-04 eta 7:53:15
+epoch [32/50] batch [880/1000] time 1.576 (1.567) data 0.001 (0.002) loss 1.8018 (1.1026) acc 59.3750 (72.6491) lr 6.9098e-04 eta 7:53:07
+epoch [32/50] batch [885/1000] time 1.594 (1.567) data 0.001 (0.002) loss 1.5352 (1.1033) acc 59.3750 (72.6130) lr 6.9098e-04 eta 7:52:59
+epoch [32/50] batch [890/1000] time 1.579 (1.567) data 0.000 (0.002) loss 1.2988 (1.1033) acc 75.0000 (72.6334) lr 6.9098e-04 eta 7:52:51
+epoch [32/50] batch [895/1000] time 1.572 (1.567) data 0.000 (0.002) loss 0.7427 (1.1044) acc 93.7500 (72.6222) lr 6.9098e-04 eta 7:52:41
+epoch [32/50] batch [900/1000] time 1.557 (1.566) data 0.000 (0.002) loss 0.5693 (1.1041) acc 81.2500 (72.6215) lr 6.9098e-04 eta 7:52:31
+epoch [32/50] batch [905/1000] time 1.575 (1.566) data 0.001 (0.002) loss 0.9331 (1.1050) acc 81.2500 (72.6105) lr 6.9098e-04 eta 7:52:23
+epoch [32/50] batch [910/1000] time 1.535 (1.566) data 0.001 (0.002) loss 1.6807 (1.1060) acc 65.6250 (72.5962) lr 6.9098e-04 eta 7:52:14
+epoch [32/50] batch [915/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.7856 (1.1063) acc 75.0000 (72.5990) lr 6.9098e-04 eta 7:52:06
+epoch [32/50] batch [920/1000] time 1.543 (1.566) data 0.001 (0.002) loss 1.4414 (1.1071) acc 53.1250 (72.5713) lr 6.9098e-04 eta 7:52:00
+epoch [32/50] batch [925/1000] time 1.542 (1.566) data 0.000 (0.002) loss 1.3311 (1.1075) acc 71.8750 (72.5642) lr 6.9098e-04 eta 7:51:52
+epoch [32/50] batch [930/1000] time 1.577 (1.566) data 0.000 (0.002) loss 0.8848 (1.1069) acc 78.1250 (72.5538) lr 6.9098e-04 eta 7:51:44
+epoch [32/50] batch [935/1000] time 1.569 (1.566) data 0.001 (0.002) loss 0.6548 (1.1056) acc 81.2500 (72.5635) lr 6.9098e-04 eta 7:51:36
+epoch [32/50] batch [940/1000] time 1.564 (1.566) data 0.001 (0.002) loss 0.6006 (1.1056) acc 75.0000 (72.5465) lr 6.9098e-04 eta 7:51:28
+epoch [32/50] batch [945/1000] time 1.571 (1.566) data 0.000 (0.002) loss 0.8740 (1.1074) acc 84.3750 (72.5198) lr 6.9098e-04 eta 7:51:19
+epoch [32/50] batch [950/1000] time 1.572 (1.566) data 0.001 (0.002) loss 1.0430 (1.1064) acc 78.1250 (72.5658) lr 6.9098e-04 eta 7:51:11
+epoch [32/50] batch [955/1000] time 1.542 (1.566) data 0.000 (0.002) loss 1.1025 (1.1056) acc 65.6250 (72.5622) lr 6.9098e-04 eta 7:51:02
+epoch [32/50] batch [960/1000] time 1.546 (1.566) data 0.000 (0.002) loss 1.0527 (1.1050) acc 68.7500 (72.5423) lr 6.9098e-04 eta 7:50:53
+epoch [32/50] batch [965/1000] time 1.581 (1.566) data 0.002 (0.002) loss 1.9971 (1.1055) acc 59.3750 (72.5389) lr 6.9098e-04 eta 7:50:46
+epoch [32/50] batch [970/1000] time 1.554 (1.566) data 0.000 (0.002) loss 1.0732 (1.1051) acc 71.8750 (72.5580) lr 6.9098e-04 eta 7:50:38
+epoch [32/50] batch [975/1000] time 1.587 (1.566) data 0.001 (0.002) loss 0.6440 (1.1053) acc 75.0000 (72.5481) lr 6.9098e-04 eta 7:50:29
+epoch [32/50] batch [980/1000] time 1.731 (1.566) data 0.001 (0.002) loss 0.8618 (1.1056) acc 75.0000 (72.5351) lr 6.9098e-04 eta 7:50:24
+epoch [32/50] batch [985/1000] time 1.581 (1.566) data 0.001 (0.002) loss 0.9595 (1.1058) acc 75.0000 (72.5286) lr 6.9098e-04 eta 7:50:15
+epoch [32/50] batch [990/1000] time 1.573 (1.566) data 0.000 (0.002) loss 1.1729 (1.1061) acc 71.8750 (72.5095) lr 6.9098e-04 eta 7:50:07
+epoch [32/50] batch [995/1000] time 1.567 (1.566) data 0.000 (0.002) loss 1.0576 (1.1049) acc 78.1250 (72.5345) lr 6.9098e-04 eta 7:49:58
+epoch [32/50] batch [1000/1000] time 1.572 (1.566) data 0.000 (0.001) loss 1.2246 (1.1064) acc 75.0000 (72.5156) lr 6.3188e-04 eta 7:49:50
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,349
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.2%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [33/50] batch [5/1000] time 1.549 (1.752) data 0.001 (0.250) loss 0.9609 (1.1116) acc 81.2500 (72.5000) lr 6.3188e-04 eta 8:45:28
+epoch [33/50] batch [10/1000] time 1.542 (1.653) data 0.001 (0.125) loss 0.6885 (1.0459) acc 78.1250 (74.3750) lr 6.3188e-04 eta 8:15:37
+epoch [33/50] batch [15/1000] time 1.555 (1.620) data 0.000 (0.084) loss 1.1924 (1.0623) acc 68.7500 (73.7500) lr 6.3188e-04 eta 8:05:43
+epoch [33/50] batch [20/1000] time 1.578 (1.609) data 0.001 (0.063) loss 1.0547 (1.0429) acc 81.2500 (74.6875) lr 6.3188e-04 eta 8:02:07
+epoch [33/50] batch [25/1000] time 1.576 (1.600) data 0.001 (0.051) loss 1.2119 (1.0816) acc 71.8750 (73.0000) lr 6.3188e-04 eta 7:59:18
+epoch [33/50] batch [30/1000] time 1.557 (1.594) data 0.001 (0.042) loss 1.1611 (1.0755) acc 78.1250 (73.4375) lr 6.3188e-04 eta 7:57:26
+epoch [33/50] batch [35/1000] time 1.589 (1.591) data 0.001 (0.036) loss 1.0215 (1.0954) acc 84.3750 (73.0357) lr 6.3188e-04 eta 7:56:30
+epoch [33/50] batch [40/1000] time 1.557 (1.588) data 0.001 (0.032) loss 0.7417 (1.0669) acc 75.0000 (73.4375) lr 6.3188e-04 eta 7:55:25
+epoch [33/50] batch [45/1000] time 1.560 (1.586) data 0.001 (0.028) loss 0.9683 (1.0608) acc 78.1250 (73.8194) lr 6.3188e-04 eta 7:54:42
+epoch [33/50] batch [50/1000] time 1.557 (1.589) data 0.000 (0.026) loss 1.1270 (1.0522) acc 59.3750 (73.8125) lr 6.3188e-04 eta 7:55:26
+epoch [33/50] batch [55/1000] time 1.576 (1.587) data 0.001 (0.023) loss 0.6714 (1.0403) acc 84.3750 (73.5795) lr 6.3188e-04 eta 7:54:39
+epoch [33/50] batch [60/1000] time 1.570 (1.585) data 0.001 (0.021) loss 1.1299 (1.0385) acc 65.6250 (73.3333) lr 6.3188e-04 eta 7:53:55
+epoch [33/50] batch [65/1000] time 1.549 (1.584) data 0.000 (0.020) loss 1.1436 (1.0337) acc 68.7500 (73.4135) lr 6.3188e-04 eta 7:53:26
+epoch [33/50] batch [70/1000] time 1.574 (1.582) data 0.000 (0.018) loss 1.1914 (1.0274) acc 71.8750 (73.7946) lr 6.3188e-04 eta 7:52:48
+epoch [33/50] batch [75/1000] time 1.577 (1.581) data 0.001 (0.017) loss 1.7178 (1.0307) acc 71.8750 (73.9167) lr 6.3188e-04 eta 7:52:26
+epoch [33/50] batch [80/1000] time 1.568 (1.580) data 0.001 (0.016) loss 1.2217 (1.0390) acc 68.7500 (73.7109) lr 6.3188e-04 eta 7:51:55
+epoch [33/50] batch [85/1000] time 1.536 (1.579) data 0.001 (0.015) loss 0.5107 (1.0377) acc 81.2500 (73.5662) lr 6.3188e-04 eta 7:51:27
+epoch [33/50] batch [90/1000] time 1.536 (1.580) data 0.001 (0.014) loss 1.6006 (1.0358) acc 59.3750 (73.4722) lr 6.3188e-04 eta 7:51:31
+epoch [33/50] batch [95/1000] time 1.566 (1.579) data 0.000 (0.014) loss 0.8223 (1.0358) acc 75.0000 (73.2895) lr 6.3188e-04 eta 7:51:07
+epoch [33/50] batch [100/1000] time 1.561 (1.578) data 0.000 (0.013) loss 0.7476 (1.0353) acc 81.2500 (73.4375) lr 6.3188e-04 eta 7:50:47
+epoch [33/50] batch [105/1000] time 1.565 (1.577) data 0.000 (0.012) loss 0.5229 (1.0306) acc 84.3750 (73.5417) lr 6.3188e-04 eta 7:50:25
+epoch [33/50] batch [110/1000] time 1.567 (1.577) data 0.001 (0.012) loss 0.8940 (1.0227) acc 78.1250 (73.9205) lr 6.3188e-04 eta 7:50:08
+epoch [33/50] batch [115/1000] time 1.534 (1.576) data 0.001 (0.011) loss 1.2109 (1.0279) acc 75.0000 (73.9130) lr 6.3188e-04 eta 7:49:51
+epoch [33/50] batch [120/1000] time 1.580 (1.576) data 0.001 (0.011) loss 1.3945 (1.0445) acc 75.0000 (73.6198) lr 6.3188e-04 eta 7:49:35
+epoch [33/50] batch [125/1000] time 1.558 (1.575) data 0.000 (0.011) loss 1.2480 (1.0447) acc 75.0000 (73.7000) lr 6.3188e-04 eta 7:49:15
+epoch [33/50] batch [130/1000] time 1.558 (1.575) data 0.000 (0.010) loss 1.2803 (1.0447) acc 65.6250 (73.7019) lr 6.3188e-04 eta 7:49:08
+epoch [33/50] batch [135/1000] time 1.555 (1.575) data 0.000 (0.010) loss 0.8345 (1.0444) acc 75.0000 (73.7269) lr 6.3188e-04 eta 7:48:48
+epoch [33/50] batch [140/1000] time 1.564 (1.574) data 0.001 (0.009) loss 0.9692 (1.0422) acc 78.1250 (73.6830) lr 6.3188e-04 eta 7:48:36
+epoch [33/50] batch [145/1000] time 1.564 (1.574) data 0.001 (0.009) loss 0.8467 (1.0483) acc 78.1250 (73.6422) lr 6.3188e-04 eta 7:48:20
+epoch [33/50] batch [150/1000] time 1.699 (1.574) data 0.000 (0.009) loss 0.6616 (1.0579) acc 78.1250 (73.4375) lr 6.3188e-04 eta 7:48:20
+epoch [33/50] batch [155/1000] time 1.572 (1.574) data 0.001 (0.009) loss 1.1855 (1.0621) acc 65.6250 (73.4073) lr 6.3188e-04 eta 7:48:02
+epoch [33/50] batch [160/1000] time 1.542 (1.573) data 0.000 (0.008) loss 0.8164 (1.0570) acc 87.5000 (73.6133) lr 6.3188e-04 eta 7:47:45
+epoch [33/50] batch [165/1000] time 1.581 (1.573) data 0.001 (0.008) loss 0.6978 (1.0602) acc 81.2500 (73.5795) lr 6.3188e-04 eta 7:47:29
+epoch [33/50] batch [170/1000] time 1.562 (1.572) data 0.000 (0.008) loss 0.9448 (1.0663) acc 68.7500 (73.4007) lr 6.3188e-04 eta 7:47:14
+epoch [33/50] batch [175/1000] time 1.588 (1.572) data 0.001 (0.008) loss 0.8276 (1.0633) acc 71.8750 (73.4107) lr 6.3188e-04 eta 7:47:06
+epoch [33/50] batch [180/1000] time 1.557 (1.572) data 0.000 (0.007) loss 1.3643 (1.0608) acc 65.6250 (73.3854) lr 6.3188e-04 eta 7:46:49
+epoch [33/50] batch [185/1000] time 1.556 (1.572) data 0.000 (0.007) loss 1.0020 (1.0597) acc 81.2500 (73.4291) lr 6.3188e-04 eta 7:46:38
+epoch [33/50] batch [190/1000] time 1.548 (1.571) data 0.000 (0.007) loss 1.3135 (1.0647) acc 71.8750 (73.3388) lr 6.3188e-04 eta 7:46:19
+epoch [33/50] batch [195/1000] time 1.750 (1.572) data 0.001 (0.007) loss 1.5518 (1.0638) acc 56.2500 (73.3494) lr 6.3188e-04 eta 7:46:24
+epoch [33/50] batch [200/1000] time 1.566 (1.571) data 0.001 (0.007) loss 1.0742 (1.0642) acc 59.3750 (73.3281) lr 6.3188e-04 eta 7:46:11
+epoch [33/50] batch [205/1000] time 1.550 (1.572) data 0.000 (0.007) loss 1.1826 (1.0667) acc 62.5000 (73.2165) lr 6.3188e-04 eta 7:46:05
+epoch [33/50] batch [210/1000] time 1.568 (1.571) data 0.000 (0.006) loss 1.0762 (1.0651) acc 65.6250 (73.1845) lr 6.3188e-04 eta 7:45:52
+epoch [33/50] batch [215/1000] time 1.565 (1.571) data 0.000 (0.006) loss 1.0859 (1.0681) acc 71.8750 (73.0959) lr 6.3188e-04 eta 7:45:42
+epoch [33/50] batch [220/1000] time 1.585 (1.571) data 0.000 (0.006) loss 0.6113 (1.0693) acc 84.3750 (73.0824) lr 6.3188e-04 eta 7:45:34
+epoch [33/50] batch [225/1000] time 1.571 (1.571) data 0.001 (0.006) loss 1.2881 (1.0705) acc 75.0000 (73.0278) lr 6.3188e-04 eta 7:45:22
+epoch [33/50] batch [230/1000] time 1.585 (1.571) data 0.000 (0.006) loss 1.8340 (1.0713) acc 46.8750 (72.9348) lr 6.3188e-04 eta 7:45:12
+epoch [33/50] batch [235/1000] time 1.570 (1.570) data 0.000 (0.006) loss 1.2529 (1.0747) acc 68.7500 (72.8856) lr 6.3188e-04 eta 7:44:58
+epoch [33/50] batch [240/1000] time 1.561 (1.571) data 0.000 (0.006) loss 1.7891 (1.0761) acc 50.0000 (72.7995) lr 6.3188e-04 eta 7:44:59
+epoch [33/50] batch [245/1000] time 1.552 (1.571) data 0.001 (0.006) loss 0.6533 (1.0734) acc 81.2500 (72.8061) lr 6.3188e-04 eta 7:44:47
+epoch [33/50] batch [250/1000] time 1.561 (1.570) data 0.000 (0.006) loss 0.6050 (1.0663) acc 84.3750 (72.9875) lr 6.3188e-04 eta 7:44:35
+epoch [33/50] batch [255/1000] time 1.566 (1.570) data 0.001 (0.005) loss 1.2812 (1.0664) acc 65.6250 (72.9412) lr 6.3188e-04 eta 7:44:26
+epoch [33/50] batch [260/1000] time 1.593 (1.571) data 0.000 (0.005) loss 0.5967 (1.0683) acc 84.3750 (72.8966) lr 6.3188e-04 eta 7:44:21
+epoch [33/50] batch [265/1000] time 1.561 (1.570) data 0.000 (0.005) loss 0.8740 (1.0627) acc 84.3750 (73.0778) lr 6.3188e-04 eta 7:44:12
+epoch [33/50] batch [270/1000] time 1.568 (1.570) data 0.000 (0.005) loss 1.2314 (1.0645) acc 65.6250 (73.0787) lr 6.3188e-04 eta 7:44:01
+epoch [33/50] batch [275/1000] time 1.574 (1.570) data 0.001 (0.005) loss 1.4941 (1.0672) acc 68.7500 (73.0227) lr 6.3188e-04 eta 7:43:52
+epoch [33/50] batch [280/1000] time 1.546 (1.570) data 0.001 (0.005) loss 0.9937 (1.0662) acc 68.7500 (73.0134) lr 6.3188e-04 eta 7:43:41
+epoch [33/50] batch [285/1000] time 1.571 (1.570) data 0.000 (0.005) loss 1.3740 (1.0664) acc 62.5000 (72.9386) lr 6.3188e-04 eta 7:43:31
+epoch [33/50] batch [290/1000] time 1.572 (1.570) data 0.001 (0.005) loss 1.4023 (1.0690) acc 65.6250 (72.8233) lr 6.3188e-04 eta 7:43:24
+epoch [33/50] batch [295/1000] time 1.558 (1.570) data 0.001 (0.005) loss 1.3418 (1.0692) acc 65.6250 (72.7966) lr 6.3188e-04 eta 7:43:15
+epoch [33/50] batch [300/1000] time 1.575 (1.570) data 0.001 (0.005) loss 0.8662 (1.0680) acc 75.0000 (72.8854) lr 6.3188e-04 eta 7:43:07
+epoch [33/50] batch [305/1000] time 1.553 (1.570) data 0.000 (0.005) loss 1.7168 (1.0706) acc 56.2500 (72.8586) lr 6.3188e-04 eta 7:43:09
+epoch [33/50] batch [310/1000] time 1.555 (1.570) data 0.000 (0.005) loss 0.9272 (1.0731) acc 84.3750 (72.8730) lr 6.3188e-04 eta 7:42:59
+epoch [33/50] batch [315/1000] time 1.559 (1.570) data 0.000 (0.004) loss 1.0791 (1.0720) acc 78.1250 (72.9067) lr 6.3188e-04 eta 7:42:46
+epoch [33/50] batch [320/1000] time 1.554 (1.570) data 0.001 (0.004) loss 0.7764 (1.0710) acc 78.1250 (72.9785) lr 6.3188e-04 eta 7:42:36
+epoch [33/50] batch [325/1000] time 1.585 (1.570) data 0.000 (0.004) loss 1.3594 (1.0705) acc 68.7500 (72.9904) lr 6.3188e-04 eta 7:42:29
+epoch [33/50] batch [330/1000] time 1.560 (1.570) data 0.000 (0.004) loss 0.4893 (1.0706) acc 84.3750 (73.0587) lr 6.3188e-04 eta 7:42:18
+epoch [33/50] batch [335/1000] time 1.549 (1.570) data 0.000 (0.004) loss 1.5010 (1.0727) acc 68.7500 (73.0317) lr 6.3188e-04 eta 7:42:06
+epoch [33/50] batch [340/1000] time 1.583 (1.569) data 0.000 (0.004) loss 0.6221 (1.0735) acc 84.3750 (72.9871) lr 6.3188e-04 eta 7:41:55
+epoch [33/50] batch [345/1000] time 1.571 (1.569) data 0.000 (0.004) loss 0.6987 (1.0735) acc 71.8750 (72.9710) lr 6.3188e-04 eta 7:41:44
+epoch [33/50] batch [350/1000] time 1.562 (1.570) data 0.001 (0.004) loss 1.2041 (1.0716) acc 68.7500 (73.0179) lr 6.3188e-04 eta 7:41:44
+epoch [33/50] batch [355/1000] time 1.556 (1.570) data 0.000 (0.004) loss 1.0166 (1.0714) acc 71.8750 (73.0458) lr 6.3188e-04 eta 7:41:35
+epoch [33/50] batch [360/1000] time 1.566 (1.570) data 0.000 (0.004) loss 0.8120 (1.0704) acc 78.1250 (73.0295) lr 6.3188e-04 eta 7:41:26
+epoch [33/50] batch [365/1000] time 1.562 (1.570) data 0.000 (0.004) loss 1.4766 (1.0727) acc 68.7500 (73.0137) lr 6.3188e-04 eta 7:41:18
+epoch [33/50] batch [370/1000] time 1.559 (1.569) data 0.001 (0.004) loss 1.0254 (1.0744) acc 75.0000 (72.9899) lr 6.3188e-04 eta 7:41:07
+epoch [33/50] batch [375/1000] time 1.544 (1.569) data 0.001 (0.004) loss 1.2061 (1.0771) acc 75.0000 (72.9917) lr 6.3188e-04 eta 7:40:55
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+epoch [33/50] batch [940/1000] time 1.572 (1.568) data 0.001 (0.002) loss 1.4424 (1.1004) acc 71.8750 (72.5432) lr 6.3188e-04 eta 7:25:43
+epoch [33/50] batch [945/1000] time 1.581 (1.568) data 0.001 (0.002) loss 0.8711 (1.1017) acc 71.8750 (72.5099) lr 6.3188e-04 eta 7:25:35
+epoch [33/50] batch [950/1000] time 1.722 (1.568) data 0.000 (0.002) loss 1.2979 (1.1020) acc 68.7500 (72.5263) lr 6.3188e-04 eta 7:25:29
+epoch [33/50] batch [955/1000] time 1.571 (1.568) data 0.000 (0.002) loss 0.6348 (1.1009) acc 78.1250 (72.5491) lr 6.3188e-04 eta 7:25:21
+epoch [33/50] batch [960/1000] time 1.571 (1.568) data 0.000 (0.002) loss 0.6548 (1.1011) acc 78.1250 (72.5488) lr 6.3188e-04 eta 7:25:13
+epoch [33/50] batch [965/1000] time 1.569 (1.568) data 0.000 (0.002) loss 0.9800 (1.1012) acc 75.0000 (72.5421) lr 6.3188e-04 eta 7:25:05
+epoch [33/50] batch [970/1000] time 1.568 (1.568) data 0.001 (0.002) loss 1.2148 (1.1013) acc 62.5000 (72.5290) lr 6.3188e-04 eta 7:24:57
+epoch [33/50] batch [975/1000] time 1.563 (1.568) data 0.000 (0.002) loss 0.9844 (1.1022) acc 75.0000 (72.5192) lr 6.3188e-04 eta 7:24:49
+epoch [33/50] batch [980/1000] time 1.558 (1.568) data 0.000 (0.002) loss 0.6499 (1.1011) acc 78.1250 (72.5383) lr 6.3188e-04 eta 7:24:40
+epoch [33/50] batch [985/1000] time 1.571 (1.568) data 0.001 (0.002) loss 2.1777 (1.1024) acc 53.1250 (72.5159) lr 6.3188e-04 eta 7:24:34
+epoch [33/50] batch [990/1000] time 1.545 (1.568) data 0.000 (0.002) loss 1.4658 (1.1042) acc 62.5000 (72.4716) lr 6.3188e-04 eta 7:24:25
+epoch [33/50] batch [995/1000] time 1.563 (1.568) data 0.000 (0.002) loss 1.4990 (1.1053) acc 62.5000 (72.4749) lr 6.3188e-04 eta 7:24:17
+epoch [33/50] batch [1000/1000] time 1.564 (1.568) data 0.000 (0.002) loss 0.9502 (1.1054) acc 71.8750 (72.4750) lr 5.7422e-04 eta 7:24:08
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,364
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [34/50] batch [5/1000] time 1.551 (1.691) data 0.000 (0.184) loss 0.8276 (1.0518) acc 71.8750 (72.5000) lr 5.7422e-04 eta 7:58:59
+epoch [34/50] batch [10/1000] time 1.559 (1.628) data 0.000 (0.092) loss 1.1660 (1.0277) acc 68.7500 (72.8125) lr 5.7422e-04 eta 7:40:56
+epoch [34/50] batch [15/1000] time 1.552 (1.610) data 0.000 (0.062) loss 0.8013 (1.0152) acc 75.0000 (73.1250) lr 5.7422e-04 eta 7:35:41
+epoch [34/50] batch [20/1000] time 1.547 (1.594) data 0.001 (0.046) loss 1.6523 (0.9846) acc 62.5000 (73.9062) lr 5.7422e-04 eta 7:31:00
+epoch [34/50] batch [25/1000] time 1.536 (1.589) data 0.001 (0.037) loss 0.7646 (1.0036) acc 84.3750 (74.0000) lr 5.7422e-04 eta 7:29:37
+epoch [34/50] batch [30/1000] time 1.579 (1.585) data 0.001 (0.031) loss 1.6094 (1.0511) acc 68.7500 (73.1250) lr 5.7422e-04 eta 7:28:19
+epoch [34/50] batch [35/1000] time 1.542 (1.582) data 0.000 (0.027) loss 0.6377 (1.0882) acc 84.3750 (72.5000) lr 5.7422e-04 eta 7:27:17
+epoch [34/50] batch [40/1000] time 1.547 (1.579) data 0.000 (0.023) loss 1.0566 (1.0711) acc 75.0000 (73.2031) lr 5.7422e-04 eta 7:26:24
+epoch [34/50] batch [45/1000] time 1.573 (1.577) data 0.001 (0.021) loss 1.0918 (1.0567) acc 84.3750 (74.0278) lr 5.7422e-04 eta 7:25:41
+epoch [34/50] batch [50/1000] time 1.570 (1.576) data 0.001 (0.019) loss 0.6592 (1.0722) acc 81.2500 (73.6875) lr 5.7422e-04 eta 7:25:19
+epoch [34/50] batch [55/1000] time 1.591 (1.575) data 0.000 (0.017) loss 0.7539 (1.0553) acc 84.3750 (73.8068) lr 5.7422e-04 eta 7:24:56
+epoch [34/50] batch [60/1000] time 1.845 (1.580) data 0.000 (0.016) loss 1.2021 (1.0605) acc 68.7500 (73.9583) lr 5.7422e-04 eta 7:26:02
+epoch [34/50] batch [65/1000] time 1.544 (1.579) data 0.000 (0.015) loss 0.8374 (1.0558) acc 78.1250 (73.7981) lr 5.7422e-04 eta 7:25:42
+epoch [34/50] batch [70/1000] time 1.576 (1.579) data 0.000 (0.014) loss 0.8413 (1.0571) acc 71.8750 (73.6161) lr 5.7422e-04 eta 7:25:24
+epoch [34/50] batch [75/1000] time 1.557 (1.578) data 0.000 (0.013) loss 0.9395 (1.0541) acc 84.3750 (73.6250) lr 5.7422e-04 eta 7:24:59
+epoch [34/50] batch [80/1000] time 1.579 (1.577) data 0.000 (0.012) loss 0.5928 (1.0645) acc 87.5000 (73.3984) lr 5.7422e-04 eta 7:24:44
+epoch [34/50] batch [85/1000] time 1.575 (1.577) data 0.001 (0.011) loss 1.2529 (1.0723) acc 81.2500 (73.2353) lr 5.7422e-04 eta 7:24:32
+epoch [34/50] batch [90/1000] time 1.574 (1.576) data 0.000 (0.011) loss 1.0127 (1.0695) acc 87.5000 (73.4028) lr 5.7422e-04 eta 7:24:05
+epoch [34/50] batch [95/1000] time 1.534 (1.574) data 0.000 (0.010) loss 0.6294 (1.0586) acc 87.5000 (73.7171) lr 5.7422e-04 eta 7:23:33
+epoch [34/50] batch [100/1000] time 1.549 (1.573) data 0.000 (0.010) loss 1.2852 (1.0560) acc 78.1250 (73.7188) lr 5.7422e-04 eta 7:23:06
+epoch [34/50] batch [105/1000] time 1.561 (1.573) data 0.001 (0.009) loss 0.7690 (1.0528) acc 87.5000 (73.7202) lr 5.7422e-04 eta 7:22:50
+epoch [34/50] batch [110/1000] time 1.568 (1.574) data 0.001 (0.009) loss 0.8218 (1.0588) acc 75.0000 (73.6648) lr 5.7422e-04 eta 7:23:06
+epoch [34/50] batch [115/1000] time 1.546 (1.574) data 0.000 (0.008) loss 0.6973 (1.0618) acc 78.1250 (73.4511) lr 5.7422e-04 eta 7:22:59
+epoch [34/50] batch [120/1000] time 1.573 (1.573) data 0.000 (0.008) loss 1.0332 (1.0595) acc 75.0000 (73.3333) lr 5.7422e-04 eta 7:22:35
+epoch [34/50] batch [125/1000] time 1.549 (1.573) data 0.001 (0.008) loss 0.8389 (1.0546) acc 84.3750 (73.5250) lr 5.7422e-04 eta 7:22:18
+epoch [34/50] batch [130/1000] time 1.577 (1.572) data 0.000 (0.008) loss 1.2881 (1.0602) acc 71.8750 (73.4615) lr 5.7422e-04 eta 7:22:04
+epoch [34/50] batch [135/1000] time 1.572 (1.572) data 0.000 (0.007) loss 0.7881 (1.0643) acc 84.3750 (73.3333) lr 5.7422e-04 eta 7:21:55
+epoch [34/50] batch [140/1000] time 1.569 (1.572) data 0.000 (0.007) loss 1.1670 (1.0751) acc 71.8750 (73.2589) lr 5.7422e-04 eta 7:21:51
+epoch [34/50] batch [145/1000] time 1.567 (1.572) data 0.000 (0.007) loss 0.9199 (1.0740) acc 75.0000 (73.2974) lr 5.7422e-04 eta 7:21:42
+epoch [34/50] batch [150/1000] time 1.552 (1.572) data 0.001 (0.007) loss 0.8247 (1.0746) acc 81.2500 (73.3542) lr 5.7422e-04 eta 7:21:31
+epoch [34/50] batch [155/1000] time 1.582 (1.572) data 0.001 (0.006) loss 0.9399 (1.0745) acc 81.2500 (73.3266) lr 5.7422e-04 eta 7:21:20
+epoch [34/50] batch [160/1000] time 1.561 (1.572) data 0.000 (0.006) loss 0.7798 (1.0735) acc 75.0000 (73.3789) lr 5.7422e-04 eta 7:21:10
+epoch [34/50] batch [165/1000] time 1.545 (1.571) data 0.001 (0.006) loss 0.8569 (1.0701) acc 81.2500 (73.5417) lr 5.7422e-04 eta 7:20:54
+epoch [34/50] batch [170/1000] time 1.558 (1.571) data 0.000 (0.006) loss 1.0020 (1.0681) acc 78.1250 (73.6029) lr 5.7422e-04 eta 7:20:38
+epoch [34/50] batch [175/1000] time 1.560 (1.571) data 0.001 (0.006) loss 0.9429 (1.0710) acc 71.8750 (73.4643) lr 5.7422e-04 eta 7:20:27
+epoch [34/50] batch [180/1000] time 1.551 (1.570) data 0.001 (0.006) loss 1.5479 (1.0710) acc 59.3750 (73.4896) lr 5.7422e-04 eta 7:20:12
+epoch [34/50] batch [185/1000] time 1.544 (1.570) data 0.001 (0.005) loss 1.3096 (1.0682) acc 68.7500 (73.4966) lr 5.7422e-04 eta 7:19:55
+epoch [34/50] batch [190/1000] time 1.598 (1.570) data 0.000 (0.005) loss 0.7295 (1.0684) acc 84.3750 (73.5362) lr 5.7422e-04 eta 7:19:48
+epoch [34/50] batch [195/1000] time 1.570 (1.570) data 0.000 (0.005) loss 1.1982 (1.0651) acc 71.8750 (73.6058) lr 5.7422e-04 eta 7:19:41
+epoch [34/50] batch [200/1000] time 1.545 (1.570) data 0.000 (0.005) loss 1.0889 (1.0720) acc 68.7500 (73.4219) lr 5.7422e-04 eta 7:19:32
+epoch [34/50] batch [205/1000] time 1.589 (1.570) data 0.000 (0.005) loss 1.1279 (1.0700) acc 71.8750 (73.4756) lr 5.7422e-04 eta 7:19:28
+epoch [34/50] batch [210/1000] time 1.575 (1.570) data 0.001 (0.005) loss 1.1191 (1.0716) acc 68.7500 (73.4226) lr 5.7422e-04 eta 7:19:19
+epoch [34/50] batch [215/1000] time 1.555 (1.570) data 0.001 (0.005) loss 0.6860 (1.0718) acc 90.6250 (73.4448) lr 5.7422e-04 eta 7:19:20
+epoch [34/50] batch [220/1000] time 1.548 (1.570) data 0.000 (0.005) loss 0.8335 (1.0749) acc 78.1250 (73.3807) lr 5.7422e-04 eta 7:19:08
+epoch [34/50] batch [225/1000] time 1.576 (1.570) data 0.000 (0.005) loss 1.1465 (1.0741) acc 59.3750 (73.3194) lr 5.7422e-04 eta 7:18:58
+epoch [34/50] batch [230/1000] time 1.559 (1.570) data 0.000 (0.004) loss 0.8730 (1.0729) acc 78.1250 (73.3288) lr 5.7422e-04 eta 7:18:46
+epoch [34/50] batch [235/1000] time 1.539 (1.570) data 0.000 (0.004) loss 1.9150 (1.0763) acc 59.3750 (73.3245) lr 5.7422e-04 eta 7:18:35
+epoch [34/50] batch [240/1000] time 1.540 (1.569) data 0.000 (0.004) loss 0.8403 (1.0741) acc 71.8750 (73.3594) lr 5.7422e-04 eta 7:18:22
+epoch [34/50] batch [245/1000] time 1.553 (1.569) data 0.000 (0.004) loss 0.3257 (1.0686) acc 90.6250 (73.4949) lr 5.7422e-04 eta 7:18:11
+epoch [34/50] batch [250/1000] time 1.553 (1.569) data 0.001 (0.004) loss 1.2539 (1.0714) acc 75.0000 (73.4625) lr 5.7422e-04 eta 7:18:00
+epoch [34/50] batch [255/1000] time 1.566 (1.569) data 0.000 (0.004) loss 0.8813 (1.0702) acc 68.7500 (73.3333) lr 5.7422e-04 eta 7:17:50
+epoch [34/50] batch [260/1000] time 1.583 (1.569) data 0.000 (0.004) loss 1.3340 (1.0680) acc 71.8750 (73.3894) lr 5.7422e-04 eta 7:17:53
+epoch [34/50] batch [265/1000] time 1.576 (1.570) data 0.000 (0.004) loss 1.1553 (1.0653) acc 75.0000 (73.4552) lr 5.7422e-04 eta 7:17:46
+epoch [34/50] batch [270/1000] time 1.547 (1.569) data 0.001 (0.004) loss 0.8105 (1.0637) acc 84.3750 (73.5069) lr 5.7422e-04 eta 7:17:32
+epoch [34/50] batch [275/1000] time 1.555 (1.569) data 0.000 (0.004) loss 0.9131 (1.0608) acc 75.0000 (73.5341) lr 5.7422e-04 eta 7:17:20
+epoch [34/50] batch [280/1000] time 1.561 (1.569) data 0.001 (0.004) loss 0.5840 (1.0622) acc 81.2500 (73.4598) lr 5.7422e-04 eta 7:17:07
+epoch [34/50] batch [285/1000] time 1.561 (1.569) data 0.000 (0.004) loss 0.6519 (1.0619) acc 81.2500 (73.4539) lr 5.7422e-04 eta 7:17:00
+epoch [34/50] batch [290/1000] time 1.587 (1.569) data 0.001 (0.004) loss 0.7183 (1.0618) acc 71.8750 (73.4375) lr 5.7422e-04 eta 7:16:52
+epoch [34/50] batch [295/1000] time 1.559 (1.569) data 0.000 (0.004) loss 0.8491 (1.0639) acc 81.2500 (73.4110) lr 5.7422e-04 eta 7:16:43
+epoch [34/50] batch [300/1000] time 1.535 (1.568) data 0.000 (0.004) loss 0.8174 (1.0637) acc 84.3750 (73.4375) lr 5.7422e-04 eta 7:16:33
+epoch [34/50] batch [305/1000] time 1.559 (1.568) data 0.000 (0.003) loss 0.8242 (1.0642) acc 71.8750 (73.3914) lr 5.7422e-04 eta 7:16:20
+epoch [34/50] batch [310/1000] time 1.547 (1.568) data 0.000 (0.003) loss 1.1895 (1.0661) acc 75.0000 (73.3569) lr 5.7422e-04 eta 7:16:09
+epoch [34/50] batch [315/1000] time 1.578 (1.568) data 0.000 (0.003) loss 0.9839 (1.0653) acc 65.6250 (73.2937) lr 5.7422e-04 eta 7:16:01
+epoch [34/50] batch [320/1000] time 1.572 (1.568) data 0.000 (0.003) loss 1.1670 (1.0648) acc 65.6250 (73.3105) lr 5.7422e-04 eta 7:15:52
+epoch [34/50] batch [325/1000] time 1.554 (1.568) data 0.000 (0.003) loss 1.5371 (1.0677) acc 68.7500 (73.3173) lr 5.7422e-04 eta 7:15:41
+epoch [34/50] batch [330/1000] time 1.562 (1.568) data 0.000 (0.003) loss 0.8599 (1.0693) acc 71.8750 (73.2386) lr 5.7422e-04 eta 7:15:30
+epoch [34/50] batch [335/1000] time 1.567 (1.567) data 0.000 (0.003) loss 1.6562 (1.0713) acc 65.6250 (73.1996) lr 5.7422e-04 eta 7:15:22
+epoch [34/50] batch [340/1000] time 1.555 (1.567) data 0.001 (0.003) loss 1.1338 (1.0743) acc 71.8750 (73.1710) lr 5.7422e-04 eta 7:15:14
+epoch [34/50] batch [345/1000] time 1.564 (1.567) data 0.001 (0.003) loss 1.0596 (1.0726) acc 81.2500 (73.2428) lr 5.7422e-04 eta 7:15:03
+epoch [34/50] batch [350/1000] time 1.541 (1.567) data 0.000 (0.003) loss 1.1201 (1.0744) acc 75.0000 (73.2054) lr 5.7422e-04 eta 7:14:53
+epoch [34/50] batch [355/1000] time 1.566 (1.567) data 0.000 (0.003) loss 1.9268 (1.0765) acc 62.5000 (73.1690) lr 5.7422e-04 eta 7:14:43
+epoch [34/50] batch [360/1000] time 1.560 (1.567) data 0.000 (0.003) loss 1.1797 (1.0787) acc 78.1250 (73.1771) lr 5.7422e-04 eta 7:14:34
+epoch [34/50] batch [365/1000] time 1.566 (1.568) data 0.000 (0.003) loss 1.6504 (1.0806) acc 62.5000 (73.0993) lr 5.7422e-04 eta 7:14:36
+epoch [34/50] batch [370/1000] time 1.562 (1.568) data 0.000 (0.003) loss 1.0781 (1.0825) acc 81.2500 (73.0574) lr 5.7422e-04 eta 7:14:29
+epoch [34/50] batch [375/1000] time 1.568 (1.568) data 0.001 (0.003) loss 1.1348 (1.0842) acc 78.1250 (73.0750) lr 5.7422e-04 eta 7:14:21
+epoch [34/50] batch [380/1000] time 1.586 (1.568) data 0.001 (0.003) loss 1.4902 (1.0832) acc 68.7500 (73.0757) lr 5.7422e-04 eta 7:14:17
+epoch [34/50] batch [385/1000] time 1.576 (1.568) data 0.000 (0.003) loss 0.6665 (1.0840) acc 87.5000 (73.0763) lr 5.7422e-04 eta 7:14:07
+epoch [34/50] batch [390/1000] time 1.570 (1.568) data 0.001 (0.003) loss 1.1885 (1.0858) acc 68.7500 (73.0449) lr 5.7422e-04 eta 7:14:00
+epoch [34/50] batch [395/1000] time 1.551 (1.568) data 0.000 (0.003) loss 0.9946 (1.0862) acc 75.0000 (73.0301) lr 5.7422e-04 eta 7:13:52
+epoch [34/50] batch [400/1000] time 1.568 (1.568) data 0.000 (0.003) loss 0.4734 (1.0839) acc 84.3750 (73.0625) lr 5.7422e-04 eta 7:13:43
+epoch [34/50] batch [405/1000] time 1.590 (1.568) data 0.001 (0.003) loss 1.1309 (1.0848) acc 71.8750 (73.0324) lr 5.7422e-04 eta 7:13:38
+epoch [34/50] batch [410/1000] time 1.530 (1.568) data 0.000 (0.003) loss 0.9526 (1.0869) acc 62.5000 (72.9726) lr 5.7422e-04 eta 7:13:34
+epoch [34/50] batch [415/1000] time 1.584 (1.568) data 0.000 (0.003) loss 1.1211 (1.0873) acc 68.7500 (72.9443) lr 5.7422e-04 eta 7:13:25
+epoch [34/50] batch [420/1000] time 1.563 (1.568) data 0.000 (0.003) loss 1.0010 (1.0864) acc 75.0000 (72.9315) lr 5.7422e-04 eta 7:13:17
+epoch [34/50] batch [425/1000] time 1.566 (1.568) data 0.000 (0.003) loss 0.6768 (1.0858) acc 78.1250 (72.9191) lr 5.7422e-04 eta 7:13:07
+epoch [34/50] batch [430/1000] time 1.569 (1.568) data 0.000 (0.003) loss 1.1162 (1.0833) acc 62.5000 (72.9651) lr 5.7422e-04 eta 7:12:59
+epoch [34/50] batch [435/1000] time 1.573 (1.568) data 0.000 (0.003) loss 1.6367 (1.0830) acc 65.6250 (72.9526) lr 5.7422e-04 eta 7:12:47
+epoch [34/50] batch [440/1000] time 1.562 (1.567) data 0.000 (0.003) loss 1.0586 (1.0830) acc 62.5000 (72.9616) lr 5.7422e-04 eta 7:12:36
+epoch [34/50] batch [445/1000] time 1.572 (1.567) data 0.000 (0.003) loss 1.2568 (1.0846) acc 68.7500 (72.9213) lr 5.7422e-04 eta 7:12:29
+epoch [34/50] batch [450/1000] time 1.578 (1.568) data 0.000 (0.002) loss 0.9473 (1.0856) acc 71.8750 (72.8819) lr 5.7422e-04 eta 7:12:26
+epoch [34/50] batch [455/1000] time 1.558 (1.568) data 0.001 (0.002) loss 0.9644 (1.0845) acc 78.1250 (72.9258) lr 5.7422e-04 eta 7:12:16
+epoch [34/50] batch [460/1000] time 1.551 (1.567) data 0.000 (0.002) loss 0.9814 (1.0844) acc 75.0000 (72.9076) lr 5.7422e-04 eta 7:12:06
+epoch [34/50] batch [465/1000] time 1.573 (1.567) data 0.001 (0.002) loss 0.9194 (1.0855) acc 75.0000 (72.8495) lr 5.7422e-04 eta 7:11:56
+epoch [34/50] batch [470/1000] time 1.555 (1.567) data 0.000 (0.002) loss 0.3586 (1.0843) acc 90.6250 (72.8790) lr 5.7422e-04 eta 7:11:46
+epoch [34/50] batch [475/1000] time 1.575 (1.567) data 0.000 (0.002) loss 0.9487 (1.0847) acc 81.2500 (72.8684) lr 5.7422e-04 eta 7:11:39
+epoch [34/50] batch [480/1000] time 1.547 (1.567) data 0.000 (0.002) loss 1.1768 (1.0853) acc 78.1250 (72.8581) lr 5.7422e-04 eta 7:11:30
+epoch [34/50] batch [485/1000] time 1.585 (1.567) data 0.000 (0.002) loss 0.9575 (1.0852) acc 78.1250 (72.8544) lr 5.7422e-04 eta 7:11:21
+epoch [34/50] batch [490/1000] time 1.579 (1.567) data 0.000 (0.002) loss 1.3643 (1.0853) acc 71.8750 (72.8444) lr 5.7422e-04 eta 7:11:12
+epoch [34/50] batch [495/1000] time 1.579 (1.567) data 0.000 (0.002) loss 1.3887 (1.0881) acc 62.5000 (72.7967) lr 5.7422e-04 eta 7:11:03
+epoch [34/50] batch [500/1000] time 1.558 (1.567) data 0.001 (0.002) loss 0.9009 (1.0860) acc 78.1250 (72.8312) lr 5.7422e-04 eta 7:10:56
+epoch [34/50] batch [505/1000] time 1.574 (1.567) data 0.000 (0.002) loss 1.2666 (1.0890) acc 68.7500 (72.7661) lr 5.7422e-04 eta 7:10:46
+epoch [34/50] batch [510/1000] time 1.565 (1.567) data 0.000 (0.002) loss 0.7031 (1.0883) acc 78.1250 (72.7635) lr 5.7422e-04 eta 7:10:37
+epoch [34/50] batch [515/1000] time 1.562 (1.567) data 0.000 (0.002) loss 1.2930 (1.0886) acc 68.7500 (72.7427) lr 5.7422e-04 eta 7:10:34
+epoch [34/50] batch [520/1000] time 1.561 (1.567) data 0.000 (0.002) loss 0.9111 (1.0910) acc 75.0000 (72.7284) lr 5.7422e-04 eta 7:10:27
+epoch [34/50] batch [525/1000] time 1.569 (1.567) data 0.000 (0.002) loss 0.7979 (1.0909) acc 84.3750 (72.7321) lr 5.7422e-04 eta 7:10:18
+epoch [34/50] batch [530/1000] time 1.559 (1.567) data 0.001 (0.002) loss 1.0967 (1.0906) acc 71.8750 (72.7476) lr 5.7422e-04 eta 7:10:10
+epoch [34/50] batch [535/1000] time 1.557 (1.567) data 0.001 (0.002) loss 0.8130 (1.0906) acc 81.2500 (72.7512) lr 5.7422e-04 eta 7:10:02
+epoch [34/50] batch [540/1000] time 1.574 (1.567) data 0.001 (0.002) loss 1.1055 (1.0896) acc 68.7500 (72.7836) lr 5.7422e-04 eta 7:09:55
+epoch [34/50] batch [545/1000] time 1.559 (1.567) data 0.000 (0.002) loss 0.9556 (1.0883) acc 81.2500 (72.8612) lr 5.7422e-04 eta 7:09:45
+epoch [34/50] batch [550/1000] time 1.581 (1.567) data 0.000 (0.002) loss 1.0371 (1.0910) acc 68.7500 (72.8636) lr 5.7422e-04 eta 7:09:36
+epoch [34/50] batch [555/1000] time 1.553 (1.567) data 0.001 (0.002) loss 1.1885 (1.0916) acc 71.8750 (72.8660) lr 5.7422e-04 eta 7:09:28
+epoch [34/50] batch [560/1000] time 1.607 (1.567) data 0.001 (0.002) loss 1.3467 (1.0922) acc 62.5000 (72.8516) lr 5.7422e-04 eta 7:09:26
+epoch [34/50] batch [565/1000] time 1.551 (1.567) data 0.000 (0.002) loss 0.9707 (1.0935) acc 81.2500 (72.8595) lr 5.7422e-04 eta 7:09:18
+epoch [34/50] batch [570/1000] time 1.552 (1.567) data 0.001 (0.002) loss 1.2920 (1.0929) acc 75.0000 (72.8673) lr 5.7422e-04 eta 7:09:10
+epoch [34/50] batch [575/1000] time 1.559 (1.567) data 0.000 (0.002) loss 0.7739 (1.0908) acc 87.5000 (72.9185) lr 5.7422e-04 eta 7:09:01
+epoch [34/50] batch [580/1000] time 1.546 (1.567) data 0.000 (0.002) loss 1.1260 (1.0914) acc 65.6250 (72.8933) lr 5.7422e-04 eta 7:08:51
+epoch [34/50] batch [585/1000] time 1.555 (1.567) data 0.000 (0.002) loss 0.9858 (1.0903) acc 78.1250 (72.8900) lr 5.7422e-04 eta 7:08:44
+epoch [34/50] batch [590/1000] time 1.548 (1.567) data 0.001 (0.002) loss 1.0439 (1.0896) acc 78.1250 (72.9078) lr 5.7422e-04 eta 7:08:34
+epoch [34/50] batch [595/1000] time 1.562 (1.567) data 0.001 (0.002) loss 1.2812 (1.0897) acc 68.7500 (72.9202) lr 5.7422e-04 eta 7:08:28
+epoch [34/50] batch [600/1000] time 1.752 (1.567) data 0.001 (0.002) loss 1.1113 (1.0897) acc 75.0000 (72.9062) lr 5.7422e-04 eta 7:08:25
+epoch [34/50] batch [605/1000] time 1.556 (1.567) data 0.001 (0.002) loss 0.9897 (1.0895) acc 65.6250 (72.8874) lr 5.7422e-04 eta 7:08:17
+epoch [34/50] batch [610/1000] time 1.586 (1.567) data 0.000 (0.002) loss 1.2109 (1.0876) acc 68.7500 (72.9201) lr 5.7422e-04 eta 7:08:09
+epoch [34/50] batch [615/1000] time 1.553 (1.567) data 0.000 (0.002) loss 0.9189 (1.0880) acc 78.1250 (72.9217) lr 5.7422e-04 eta 7:08:01
+epoch [34/50] batch [620/1000] time 1.553 (1.567) data 0.000 (0.002) loss 0.4580 (1.0860) acc 84.3750 (72.9637) lr 5.7422e-04 eta 7:07:51
+epoch [34/50] batch [625/1000] time 1.558 (1.567) data 0.004 (0.002) loss 0.7944 (1.0857) acc 75.0000 (72.9650) lr 5.7422e-04 eta 7:07:42
+epoch [34/50] batch [630/1000] time 1.554 (1.567) data 0.001 (0.002) loss 0.7666 (1.0851) acc 81.2500 (72.9911) lr 5.7422e-04 eta 7:07:33
+epoch [34/50] batch [635/1000] time 1.569 (1.567) data 0.001 (0.002) loss 0.9746 (1.0856) acc 68.7500 (72.9380) lr 5.7422e-04 eta 7:07:25
+epoch [34/50] batch [640/1000] time 1.598 (1.567) data 0.000 (0.002) loss 0.9854 (1.0878) acc 71.8750 (72.8955) lr 5.7422e-04 eta 7:07:17
+epoch [34/50] batch [645/1000] time 1.551 (1.567) data 0.000 (0.002) loss 1.1846 (1.0895) acc 68.7500 (72.8391) lr 5.7422e-04 eta 7:07:08
+epoch [34/50] batch [650/1000] time 1.566 (1.567) data 0.000 (0.002) loss 0.9927 (1.0894) acc 78.1250 (72.8558) lr 5.7422e-04 eta 7:07:00
+epoch [34/50] batch [655/1000] time 1.566 (1.567) data 0.001 (0.002) loss 1.3281 (1.0900) acc 71.8750 (72.8483) lr 5.7422e-04 eta 7:06:51
+epoch [34/50] batch [660/1000] time 1.569 (1.567) data 0.001 (0.002) loss 0.7905 (1.0912) acc 81.2500 (72.8551) lr 5.7422e-04 eta 7:06:43
+epoch [34/50] batch [665/1000] time 1.551 (1.567) data 0.000 (0.002) loss 1.3115 (1.0914) acc 62.5000 (72.8195) lr 5.7422e-04 eta 7:06:38
+epoch [34/50] batch [670/1000] time 1.574 (1.567) data 0.001 (0.002) loss 1.5186 (1.0907) acc 59.3750 (72.8265) lr 5.7422e-04 eta 7:06:30
+epoch [34/50] batch [675/1000] time 1.545 (1.567) data 0.001 (0.002) loss 1.3672 (1.0912) acc 68.7500 (72.8472) lr 5.7422e-04 eta 7:06:23
+epoch [34/50] batch [680/1000] time 1.575 (1.567) data 0.000 (0.002) loss 0.4180 (1.0905) acc 87.5000 (72.8860) lr 5.7422e-04 eta 7:06:14
+epoch [34/50] batch [685/1000] time 1.556 (1.567) data 0.000 (0.002) loss 0.8398 (1.0907) acc 71.8750 (72.8786) lr 5.7422e-04 eta 7:06:06
+epoch [34/50] batch [690/1000] time 1.565 (1.567) data 0.000 (0.002) loss 1.6982 (1.0924) acc 59.3750 (72.8397) lr 5.7422e-04 eta 7:05:58
+epoch [34/50] batch [695/1000] time 1.555 (1.567) data 0.000 (0.002) loss 0.9204 (1.0921) acc 78.1250 (72.8642) lr 5.7422e-04 eta 7:05:49
+epoch [34/50] batch [700/1000] time 1.567 (1.567) data 0.001 (0.002) loss 0.8613 (1.0912) acc 78.1250 (72.9018) lr 5.7422e-04 eta 7:05:41
+epoch [34/50] batch [705/1000] time 1.577 (1.567) data 0.001 (0.002) loss 0.9927 (1.0905) acc 75.0000 (72.9078) lr 5.7422e-04 eta 7:05:33
+epoch [34/50] batch [710/1000] time 1.557 (1.567) data 0.001 (0.002) loss 1.2168 (1.0898) acc 68.7500 (72.9313) lr 5.7422e-04 eta 7:05:29
+epoch [34/50] batch [715/1000] time 1.532 (1.567) data 0.000 (0.002) loss 0.9502 (1.0887) acc 78.1250 (72.9371) lr 5.7422e-04 eta 7:05:20
+epoch [34/50] batch [720/1000] time 1.558 (1.567) data 0.001 (0.002) loss 1.1797 (1.0891) acc 68.7500 (72.9253) lr 5.7422e-04 eta 7:05:12
+epoch [34/50] batch [725/1000] time 1.588 (1.567) data 0.001 (0.002) loss 1.3545 (1.0897) acc 59.3750 (72.9095) lr 5.7422e-04 eta 7:05:04
+epoch [34/50] batch [730/1000] time 1.570 (1.567) data 0.001 (0.002) loss 0.7817 (1.0899) acc 84.3750 (72.9195) lr 5.7422e-04 eta 7:04:56
+epoch [34/50] batch [735/1000] time 1.557 (1.567) data 0.001 (0.002) loss 0.6841 (1.0901) acc 75.0000 (72.9124) lr 5.7422e-04 eta 7:04:46
+epoch [34/50] batch [740/1000] time 1.574 (1.567) data 0.001 (0.002) loss 1.1484 (1.0904) acc 78.1250 (72.9096) lr 5.7422e-04 eta 7:04:38
+epoch [34/50] batch [745/1000] time 1.574 (1.567) data 0.001 (0.002) loss 1.5332 (1.0909) acc 59.3750 (72.8985) lr 5.7422e-04 eta 7:04:30
+epoch [34/50] batch [750/1000] time 1.578 (1.567) data 0.001 (0.002) loss 0.7256 (1.0904) acc 87.5000 (72.9042) lr 5.7422e-04 eta 7:04:23
+epoch [34/50] batch [755/1000] time 1.579 (1.567) data 0.001 (0.002) loss 1.4805 (1.0923) acc 59.3750 (72.8435) lr 5.7422e-04 eta 7:04:18
+epoch [34/50] batch [760/1000] time 1.588 (1.567) data 0.000 (0.002) loss 0.7744 (1.0924) acc 75.0000 (72.8372) lr 5.7422e-04 eta 7:04:11
+epoch [34/50] batch [765/1000] time 1.558 (1.567) data 0.001 (0.002) loss 1.2109 (1.0920) acc 65.6250 (72.8472) lr 5.7422e-04 eta 7:04:03
+epoch [34/50] batch [770/1000] time 1.548 (1.567) data 0.000 (0.002) loss 0.8267 (1.0916) acc 68.7500 (72.8369) lr 5.7422e-04 eta 7:03:53
+epoch [34/50] batch [775/1000] time 1.586 (1.567) data 0.001 (0.002) loss 0.7598 (1.0909) acc 71.8750 (72.8226) lr 5.7422e-04 eta 7:03:47
+epoch [34/50] batch [780/1000] time 1.576 (1.567) data 0.001 (0.002) loss 0.8091 (1.0901) acc 75.0000 (72.8446) lr 5.7422e-04 eta 7:03:38
+epoch [34/50] batch [785/1000] time 1.562 (1.567) data 0.000 (0.002) loss 1.6631 (1.0916) acc 62.5000 (72.8105) lr 5.7422e-04 eta 7:03:30
+epoch [34/50] batch [790/1000] time 1.571 (1.567) data 0.000 (0.002) loss 0.9219 (1.0910) acc 75.0000 (72.8125) lr 5.7422e-04 eta 7:03:22
+epoch [34/50] batch [795/1000] time 1.559 (1.567) data 0.001 (0.002) loss 0.8247 (1.0918) acc 78.1250 (72.8184) lr 5.7422e-04 eta 7:03:14
+epoch [34/50] batch [800/1000] time 1.555 (1.567) data 0.000 (0.002) loss 1.1621 (1.0926) acc 68.7500 (72.8125) lr 5.7422e-04 eta 7:03:04
+epoch [34/50] batch [805/1000] time 1.556 (1.567) data 0.001 (0.002) loss 0.7324 (1.0921) acc 81.2500 (72.8222) lr 5.7422e-04 eta 7:02:56
+epoch [34/50] batch [810/1000] time 1.548 (1.567) data 0.000 (0.002) loss 1.2949 (1.0926) acc 65.6250 (72.8125) lr 5.7422e-04 eta 7:02:47
+epoch [34/50] batch [815/1000] time 1.577 (1.567) data 0.001 (0.002) loss 1.5791 (1.0922) acc 71.8750 (72.8106) lr 5.7422e-04 eta 7:02:42
+epoch [34/50] batch [820/1000] time 1.575 (1.567) data 0.001 (0.002) loss 1.4189 (1.0934) acc 59.3750 (72.7973) lr 5.7422e-04 eta 7:02:35
+epoch [34/50] batch [825/1000] time 1.554 (1.567) data 0.001 (0.002) loss 0.6675 (1.0918) acc 81.2500 (72.8220) lr 5.7422e-04 eta 7:02:27
+epoch [34/50] batch [830/1000] time 1.552 (1.567) data 0.000 (0.002) loss 1.7129 (1.0931) acc 53.1250 (72.7899) lr 5.7422e-04 eta 7:02:18
+epoch [34/50] batch [835/1000] time 1.553 (1.567) data 0.001 (0.002) loss 0.5210 (1.0941) acc 84.3750 (72.7769) lr 5.7422e-04 eta 7:02:11
+epoch [34/50] batch [840/1000] time 1.564 (1.567) data 0.001 (0.002) loss 1.4658 (1.0949) acc 56.2500 (72.7344) lr 5.7422e-04 eta 7:02:02
+epoch [34/50] batch [845/1000] time 1.573 (1.567) data 0.000 (0.002) loss 1.6484 (1.0947) acc 71.8750 (72.7478) lr 5.7422e-04 eta 7:01:53
+epoch [34/50] batch [850/1000] time 1.551 (1.567) data 0.001 (0.002) loss 1.9170 (1.0955) acc 50.0000 (72.7279) lr 5.7422e-04 eta 7:01:43
+epoch [34/50] batch [855/1000] time 1.557 (1.567) data 0.000 (0.002) loss 0.9448 (1.0942) acc 81.2500 (72.7595) lr 5.7422e-04 eta 7:01:35
+epoch [34/50] batch [860/1000] time 1.550 (1.567) data 0.001 (0.002) loss 1.4375 (1.0965) acc 68.7500 (72.7071) lr 5.7422e-04 eta 7:01:28
+epoch [34/50] batch [865/1000] time 1.569 (1.567) data 0.001 (0.002) loss 1.2852 (1.0960) acc 65.6250 (72.7132) lr 5.7422e-04 eta 7:01:21
+epoch [34/50] batch [870/1000] time 1.583 (1.567) data 0.000 (0.002) loss 0.7896 (1.0965) acc 84.3750 (72.7047) lr 5.7422e-04 eta 7:01:13
+epoch [34/50] batch [875/1000] time 1.551 (1.567) data 0.000 (0.002) loss 1.0762 (1.0969) acc 65.6250 (72.6821) lr 5.7422e-04 eta 7:01:05
+epoch [34/50] batch [880/1000] time 1.573 (1.567) data 0.001 (0.002) loss 1.4600 (1.0983) acc 62.5000 (72.6314) lr 5.7422e-04 eta 7:00:57
+epoch [34/50] batch [885/1000] time 1.544 (1.567) data 0.001 (0.002) loss 1.7930 (1.0989) acc 56.2500 (72.6201) lr 5.7422e-04 eta 7:00:48
+epoch [34/50] batch [890/1000] time 1.547 (1.567) data 0.000 (0.002) loss 1.5146 (1.0996) acc 78.1250 (72.6088) lr 5.7422e-04 eta 7:00:39
+epoch [34/50] batch [895/1000] time 1.575 (1.567) data 0.000 (0.001) loss 1.0967 (1.0991) acc 68.7500 (72.6292) lr 5.7422e-04 eta 7:00:31
+epoch [34/50] batch [900/1000] time 1.532 (1.567) data 0.000 (0.001) loss 1.4814 (1.0988) acc 62.5000 (72.6181) lr 5.7422e-04 eta 7:00:22
+epoch [34/50] batch [905/1000] time 1.568 (1.567) data 0.000 (0.001) loss 0.9893 (1.0986) acc 68.7500 (72.6070) lr 5.7422e-04 eta 7:00:16
+epoch [34/50] batch [910/1000] time 1.558 (1.567) data 0.001 (0.001) loss 1.2695 (1.0983) acc 71.8750 (72.6168) lr 5.7422e-04 eta 7:00:08
+epoch [34/50] batch [915/1000] time 1.555 (1.567) data 0.000 (0.001) loss 0.9497 (1.0971) acc 71.8750 (72.6264) lr 5.7422e-04 eta 6:59:59
+epoch [34/50] batch [920/1000] time 1.559 (1.567) data 0.000 (0.001) loss 0.7554 (1.0968) acc 71.8750 (72.6155) lr 5.7422e-04 eta 6:59:51
+epoch [34/50] batch [925/1000] time 1.559 (1.567) data 0.001 (0.001) loss 1.5039 (1.0974) acc 65.6250 (72.6047) lr 5.7422e-04 eta 6:59:43
+epoch [34/50] batch [930/1000] time 1.564 (1.567) data 0.001 (0.001) loss 1.2764 (1.0973) acc 65.6250 (72.5874) lr 5.7422e-04 eta 6:59:35
+epoch [34/50] batch [935/1000] time 1.554 (1.567) data 0.001 (0.001) loss 1.1133 (1.0990) acc 75.0000 (72.5802) lr 5.7422e-04 eta 6:59:27
+epoch [34/50] batch [940/1000] time 1.558 (1.567) data 0.000 (0.001) loss 1.0479 (1.0990) acc 71.8750 (72.5598) lr 5.7422e-04 eta 6:59:19
+epoch [34/50] batch [945/1000] time 1.588 (1.567) data 0.001 (0.001) loss 1.2031 (1.0982) acc 56.2500 (72.5661) lr 5.7422e-04 eta 6:59:12
+epoch [34/50] batch [950/1000] time 1.560 (1.567) data 0.001 (0.001) loss 0.4988 (1.0980) acc 78.1250 (72.5658) lr 5.7422e-04 eta 6:59:04
+epoch [34/50] batch [955/1000] time 1.572 (1.567) data 0.000 (0.001) loss 1.2637 (1.0984) acc 68.7500 (72.5589) lr 5.7422e-04 eta 6:58:57
+epoch [34/50] batch [960/1000] time 1.558 (1.567) data 0.000 (0.001) loss 1.1963 (1.0987) acc 71.8750 (72.5716) lr 5.7422e-04 eta 6:58:48
+epoch [34/50] batch [965/1000] time 1.732 (1.567) data 0.000 (0.001) loss 0.8550 (1.0980) acc 87.5000 (72.6036) lr 5.7422e-04 eta 6:58:43
+epoch [34/50] batch [970/1000] time 1.561 (1.567) data 0.001 (0.001) loss 1.2119 (1.0979) acc 65.6250 (72.5902) lr 5.7422e-04 eta 6:58:34
+epoch [34/50] batch [975/1000] time 1.556 (1.567) data 0.000 (0.001) loss 0.8281 (1.0975) acc 75.0000 (72.6058) lr 5.7422e-04 eta 6:58:26
+epoch [34/50] batch [980/1000] time 1.549 (1.567) data 0.001 (0.001) loss 1.0225 (1.0972) acc 78.1250 (72.6084) lr 5.7422e-04 eta 6:58:17
+epoch [34/50] batch [985/1000] time 1.548 (1.567) data 0.001 (0.001) loss 0.9824 (1.0964) acc 81.2500 (72.6396) lr 5.7422e-04 eta 6:58:09
+epoch [34/50] batch [990/1000] time 1.557 (1.567) data 0.001 (0.001) loss 0.8320 (1.0976) acc 75.0000 (72.6168) lr 5.7422e-04 eta 6:58:00
+epoch [34/50] batch [995/1000] time 1.581 (1.567) data 0.000 (0.001) loss 1.0020 (1.0975) acc 68.7500 (72.6005) lr 5.7422e-04 eta 6:57:52
+epoch [34/50] batch [1000/1000] time 1.554 (1.566) data 0.000 (0.001) loss 0.7886 (1.0973) acc 78.1250 (72.5938) lr 5.1825e-04 eta 6:57:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,326
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.2%
+epoch [35/50] batch [5/1000] time 1.568 (1.703) data 0.000 (0.191) loss 1.0830 (1.0053) acc 71.8750 (72.5000) lr 5.1825e-04 eta 7:33:57
+epoch [35/50] batch [10/1000] time 1.550 (1.624) data 0.000 (0.096) loss 0.8267 (0.9676) acc 75.0000 (73.7500) lr 5.1825e-04 eta 7:12:41
+epoch [35/50] batch [15/1000] time 1.581 (1.605) data 0.000 (0.064) loss 0.6294 (1.0411) acc 81.2500 (71.8750) lr 5.1825e-04 eta 7:07:28
+epoch [35/50] batch [20/1000] time 1.551 (1.593) data 0.000 (0.048) loss 0.9419 (1.0371) acc 81.2500 (72.1875) lr 5.1825e-04 eta 7:04:14
+epoch [35/50] batch [25/1000] time 1.566 (1.585) data 0.000 (0.038) loss 1.2119 (1.0445) acc 68.7500 (72.7500) lr 5.1825e-04 eta 7:02:03
+epoch [35/50] batch [30/1000] time 1.561 (1.582) data 0.000 (0.032) loss 0.9766 (1.0386) acc 65.6250 (72.1875) lr 5.1825e-04 eta 7:01:01
+epoch [35/50] batch [35/1000] time 1.557 (1.580) data 0.000 (0.028) loss 0.8862 (1.0389) acc 78.1250 (72.6786) lr 5.1825e-04 eta 7:00:20
+epoch [35/50] batch [40/1000] time 1.576 (1.578) data 0.000 (0.024) loss 1.1982 (1.0492) acc 71.8750 (72.9688) lr 5.1825e-04 eta 6:59:41
+epoch [35/50] batch [45/1000] time 1.571 (1.577) data 0.000 (0.022) loss 1.0947 (1.0635) acc 75.0000 (72.7083) lr 5.1825e-04 eta 6:59:16
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+epoch [35/50] batch [595/1000] time 1.545 (1.565) data 0.000 (0.002) loss 1.1299 (1.0813) acc 68.7500 (72.8676) lr 5.1825e-04 eta 6:41:43
+epoch [35/50] batch [600/1000] time 1.554 (1.565) data 0.000 (0.002) loss 1.3086 (1.0831) acc 62.5000 (72.7969) lr 5.1825e-04 eta 6:41:38
+epoch [35/50] batch [605/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.1318 (1.0841) acc 75.0000 (72.7841) lr 5.1825e-04 eta 6:41:31
+epoch [35/50] batch [610/1000] time 1.579 (1.565) data 0.000 (0.002) loss 0.6699 (1.0840) acc 84.3750 (72.8023) lr 5.1825e-04 eta 6:41:24
+epoch [35/50] batch [615/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.6943 (1.0847) acc 65.6250 (72.7591) lr 5.1825e-04 eta 6:41:16
+epoch [35/50] batch [620/1000] time 1.552 (1.565) data 0.001 (0.002) loss 1.2900 (1.0866) acc 78.1250 (72.7470) lr 5.1825e-04 eta 6:41:07
+epoch [35/50] batch [625/1000] time 1.558 (1.565) data 0.000 (0.002) loss 0.7896 (1.0847) acc 78.1250 (72.7550) lr 5.1825e-04 eta 6:40:59
+epoch [35/50] batch [630/1000] time 1.564 (1.565) data 0.001 (0.002) loss 0.8438 (1.0848) acc 81.2500 (72.7183) lr 5.1825e-04 eta 6:40:51
+epoch [35/50] batch [635/1000] time 1.533 (1.565) data 0.001 (0.002) loss 1.4375 (1.0841) acc 62.5000 (72.7018) lr 5.1825e-04 eta 6:40:42
+epoch [35/50] batch [640/1000] time 1.725 (1.565) data 0.000 (0.002) loss 1.5234 (1.0856) acc 59.3750 (72.6416) lr 5.1825e-04 eta 6:40:39
+epoch [35/50] batch [645/1000] time 1.610 (1.565) data 0.000 (0.002) loss 1.2783 (1.0857) acc 62.5000 (72.6453) lr 5.1825e-04 eta 6:40:34
+epoch [35/50] batch [650/1000] time 1.560 (1.565) data 0.001 (0.002) loss 1.5039 (1.0862) acc 68.7500 (72.6442) lr 5.1825e-04 eta 6:40:26
+epoch [35/50] batch [655/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.4663 (1.0837) acc 81.2500 (72.6765) lr 5.1825e-04 eta 6:40:17
+epoch [35/50] batch [660/1000] time 1.548 (1.565) data 0.000 (0.002) loss 0.9351 (1.0823) acc 81.2500 (72.7131) lr 5.1825e-04 eta 6:40:10
+epoch [35/50] batch [665/1000] time 1.552 (1.565) data 0.000 (0.002) loss 1.5078 (1.0828) acc 68.7500 (72.7162) lr 5.1825e-04 eta 6:40:01
+epoch [35/50] batch [670/1000] time 1.584 (1.565) data 0.001 (0.002) loss 1.1768 (1.0836) acc 81.2500 (72.7146) lr 5.1825e-04 eta 6:39:55
+epoch [35/50] batch [675/1000] time 1.563 (1.565) data 0.000 (0.002) loss 1.9873 (1.0844) acc 59.3750 (72.6898) lr 5.1825e-04 eta 6:39:47
+epoch [35/50] batch [680/1000] time 1.581 (1.565) data 0.001 (0.002) loss 1.3311 (1.0847) acc 71.8750 (72.6746) lr 5.1825e-04 eta 6:39:40
+epoch [35/50] batch [685/1000] time 1.551 (1.565) data 0.000 (0.002) loss 1.4668 (1.0850) acc 56.2500 (72.6505) lr 5.1825e-04 eta 6:39:31
+epoch [35/50] batch [690/1000] time 1.546 (1.565) data 0.000 (0.002) loss 1.2939 (1.0862) acc 68.7500 (72.6087) lr 5.1825e-04 eta 6:39:22
+epoch [35/50] batch [695/1000] time 1.550 (1.565) data 0.001 (0.002) loss 0.9658 (1.0871) acc 59.3750 (72.5405) lr 5.1825e-04 eta 6:39:13
+epoch [35/50] batch [700/1000] time 1.558 (1.565) data 0.001 (0.002) loss 0.8965 (1.0865) acc 75.0000 (72.5580) lr 5.1825e-04 eta 6:39:04
+epoch [35/50] batch [705/1000] time 1.566 (1.565) data 0.001 (0.002) loss 0.6348 (1.0849) acc 78.1250 (72.5754) lr 5.1825e-04 eta 6:39:00
+epoch [35/50] batch [710/1000] time 1.566 (1.565) data 0.001 (0.002) loss 1.6807 (1.0857) acc 62.5000 (72.5792) lr 5.1825e-04 eta 6:38:51
+epoch [35/50] batch [715/1000] time 1.588 (1.565) data 0.001 (0.002) loss 1.1260 (1.0856) acc 68.7500 (72.6049) lr 5.1825e-04 eta 6:38:44
+epoch [35/50] batch [720/1000] time 1.578 (1.565) data 0.000 (0.002) loss 0.7915 (1.0844) acc 87.5000 (72.6389) lr 5.1825e-04 eta 6:38:36
+epoch [35/50] batch [725/1000] time 1.567 (1.565) data 0.000 (0.002) loss 0.7905 (1.0834) acc 81.2500 (72.6595) lr 5.1825e-04 eta 6:38:28
+epoch [35/50] batch [730/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.3574 (1.0853) acc 75.0000 (72.6370) lr 5.1825e-04 eta 6:38:20
+epoch [35/50] batch [735/1000] time 1.546 (1.565) data 0.000 (0.002) loss 0.6880 (1.0850) acc 84.3750 (72.6446) lr 5.1825e-04 eta 6:38:11
+epoch [35/50] batch [740/1000] time 1.559 (1.565) data 0.000 (0.002) loss 1.5664 (1.0857) acc 56.2500 (72.6351) lr 5.1825e-04 eta 6:38:03
+epoch [35/50] batch [745/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.1973 (1.0870) acc 75.0000 (72.6174) lr 5.1825e-04 eta 6:37:56
+epoch [35/50] batch [750/1000] time 1.554 (1.565) data 0.000 (0.002) loss 1.4707 (1.0867) acc 71.8750 (72.6167) lr 5.1825e-04 eta 6:37:51
+epoch [35/50] batch [755/1000] time 1.594 (1.565) data 0.001 (0.002) loss 1.4365 (1.0855) acc 68.7500 (72.6325) lr 5.1825e-04 eta 6:37:44
+epoch [35/50] batch [760/1000] time 1.563 (1.565) data 0.001 (0.002) loss 0.6484 (1.0858) acc 81.2500 (72.6234) lr 5.1825e-04 eta 6:37:37
+epoch [35/50] batch [765/1000] time 1.558 (1.565) data 0.001 (0.002) loss 0.9219 (1.0868) acc 75.0000 (72.5817) lr 5.1825e-04 eta 6:37:28
+epoch [35/50] batch [770/1000] time 1.549 (1.565) data 0.001 (0.002) loss 0.6333 (1.0856) acc 84.3750 (72.6096) lr 5.1825e-04 eta 6:37:20
+epoch [35/50] batch [775/1000] time 1.554 (1.565) data 0.001 (0.002) loss 1.3594 (1.0873) acc 65.6250 (72.5685) lr 5.1825e-04 eta 6:37:11
+epoch [35/50] batch [780/1000] time 1.534 (1.565) data 0.000 (0.002) loss 0.9355 (1.0873) acc 84.3750 (72.5721) lr 5.1825e-04 eta 6:37:01
+epoch [35/50] batch [785/1000] time 1.576 (1.565) data 0.001 (0.002) loss 1.2344 (1.0879) acc 46.8750 (72.5159) lr 5.1825e-04 eta 6:36:54
+epoch [35/50] batch [790/1000] time 1.557 (1.565) data 0.001 (0.002) loss 0.8213 (1.0871) acc 78.1250 (72.5396) lr 5.1825e-04 eta 6:36:46
+epoch [35/50] batch [795/1000] time 1.571 (1.565) data 0.000 (0.002) loss 1.3564 (1.0878) acc 65.6250 (72.5079) lr 5.1825e-04 eta 6:36:42
+epoch [35/50] batch [800/1000] time 1.571 (1.565) data 0.000 (0.002) loss 1.3154 (1.0877) acc 65.6250 (72.5117) lr 5.1825e-04 eta 6:36:33
+epoch [35/50] batch [805/1000] time 1.579 (1.565) data 0.001 (0.002) loss 1.5156 (1.0871) acc 65.6250 (72.5388) lr 5.1825e-04 eta 6:36:25
+epoch [35/50] batch [810/1000] time 1.563 (1.565) data 0.001 (0.002) loss 1.4121 (1.0881) acc 62.5000 (72.5270) lr 5.1825e-04 eta 6:36:17
+epoch [35/50] batch [815/1000] time 1.573 (1.565) data 0.000 (0.002) loss 1.1924 (1.0875) acc 65.6250 (72.5575) lr 5.1825e-04 eta 6:36:10
+epoch [35/50] batch [820/1000] time 1.562 (1.565) data 0.000 (0.002) loss 0.5698 (1.0881) acc 81.2500 (72.5343) lr 5.1825e-04 eta 6:36:02
+epoch [35/50] batch [825/1000] time 1.569 (1.565) data 0.001 (0.002) loss 1.1230 (1.0881) acc 75.0000 (72.5303) lr 5.1825e-04 eta 6:35:55
+epoch [35/50] batch [830/1000] time 1.554 (1.565) data 0.000 (0.002) loss 0.9639 (1.0875) acc 75.0000 (72.5339) lr 5.1825e-04 eta 6:35:47
+epoch [35/50] batch [835/1000] time 1.561 (1.566) data 0.000 (0.002) loss 1.0273 (1.0877) acc 84.3750 (72.5449) lr 5.1825e-04 eta 6:35:41
+epoch [35/50] batch [840/1000] time 1.549 (1.565) data 0.000 (0.002) loss 1.6514 (1.0894) acc 59.3750 (72.4926) lr 5.1825e-04 eta 6:35:32
+epoch [35/50] batch [845/1000] time 1.543 (1.565) data 0.001 (0.002) loss 1.4336 (1.0904) acc 71.8750 (72.5148) lr 5.1825e-04 eta 6:35:23
+epoch [35/50] batch [850/1000] time 1.537 (1.565) data 0.000 (0.002) loss 1.3730 (1.0896) acc 65.6250 (72.5110) lr 5.1825e-04 eta 6:35:14
+epoch [35/50] batch [855/1000] time 1.579 (1.566) data 0.000 (0.002) loss 1.2539 (1.0900) acc 75.0000 (72.5000) lr 5.1825e-04 eta 6:35:09
+epoch [35/50] batch [860/1000] time 1.561 (1.565) data 0.000 (0.002) loss 1.0479 (1.0916) acc 71.8750 (72.4709) lr 5.1825e-04 eta 6:35:00
+epoch [35/50] batch [865/1000] time 1.574 (1.565) data 0.000 (0.002) loss 0.7808 (1.0906) acc 84.3750 (72.5036) lr 5.1825e-04 eta 6:34:52
+epoch [35/50] batch [870/1000] time 1.575 (1.565) data 0.001 (0.002) loss 1.4131 (1.0913) acc 78.1250 (72.4964) lr 5.1825e-04 eta 6:34:45
+epoch [35/50] batch [875/1000] time 1.571 (1.565) data 0.001 (0.002) loss 0.9956 (1.0905) acc 75.0000 (72.5143) lr 5.1825e-04 eta 6:34:37
+epoch [35/50] batch [880/1000] time 1.555 (1.565) data 0.000 (0.002) loss 1.3799 (1.0917) acc 62.5000 (72.4858) lr 5.1825e-04 eta 6:34:29
+epoch [35/50] batch [885/1000] time 1.565 (1.565) data 0.001 (0.002) loss 1.8213 (1.0915) acc 65.6250 (72.4894) lr 5.1825e-04 eta 6:34:21
+epoch [35/50] batch [890/1000] time 1.557 (1.565) data 0.001 (0.002) loss 1.2471 (1.0916) acc 65.6250 (72.4719) lr 5.1825e-04 eta 6:34:12
+epoch [35/50] batch [895/1000] time 1.558 (1.565) data 0.000 (0.002) loss 1.2822 (1.0910) acc 56.2500 (72.4511) lr 5.1825e-04 eta 6:34:03
+epoch [35/50] batch [900/1000] time 1.562 (1.565) data 0.000 (0.002) loss 0.5894 (1.0913) acc 78.1250 (72.4549) lr 5.1825e-04 eta 6:33:57
+epoch [35/50] batch [905/1000] time 1.559 (1.565) data 0.000 (0.002) loss 1.2275 (1.0904) acc 65.6250 (72.4793) lr 5.1825e-04 eta 6:33:49
+epoch [35/50] batch [910/1000] time 1.565 (1.565) data 0.000 (0.002) loss 0.8994 (1.0906) acc 81.2500 (72.4863) lr 5.1825e-04 eta 6:33:41
+epoch [35/50] batch [915/1000] time 1.569 (1.565) data 0.000 (0.002) loss 0.5884 (1.0897) acc 87.5000 (72.5171) lr 5.1825e-04 eta 6:33:33
+epoch [35/50] batch [920/1000] time 1.557 (1.565) data 0.001 (0.001) loss 1.1387 (1.0905) acc 78.1250 (72.5102) lr 5.1825e-04 eta 6:33:25
+epoch [35/50] batch [925/1000] time 1.557 (1.565) data 0.000 (0.001) loss 0.9819 (1.0900) acc 75.0000 (72.5304) lr 5.1825e-04 eta 6:33:18
+epoch [35/50] batch [930/1000] time 1.580 (1.565) data 0.001 (0.001) loss 1.0586 (1.0906) acc 65.6250 (72.4966) lr 5.1825e-04 eta 6:33:11
+epoch [35/50] batch [935/1000] time 1.551 (1.565) data 0.001 (0.001) loss 0.9038 (1.0902) acc 78.1250 (72.5000) lr 5.1825e-04 eta 6:33:03
+epoch [35/50] batch [940/1000] time 1.549 (1.565) data 0.000 (0.001) loss 1.0420 (1.0900) acc 65.6250 (72.4801) lr 5.1825e-04 eta 6:32:54
+epoch [35/50] batch [945/1000] time 1.568 (1.566) data 0.000 (0.001) loss 0.6348 (1.0898) acc 87.5000 (72.4868) lr 5.1825e-04 eta 6:32:49
+epoch [35/50] batch [950/1000] time 1.596 (1.566) data 0.000 (0.001) loss 0.5327 (1.0897) acc 84.3750 (72.4901) lr 5.1825e-04 eta 6:32:42
+epoch [35/50] batch [955/1000] time 1.556 (1.566) data 0.001 (0.001) loss 0.3501 (1.0889) acc 84.3750 (72.5131) lr 5.1825e-04 eta 6:32:33
+epoch [35/50] batch [960/1000] time 1.542 (1.566) data 0.001 (0.001) loss 0.8789 (1.0883) acc 81.2500 (72.5260) lr 5.1825e-04 eta 6:32:25
+epoch [35/50] batch [965/1000] time 1.543 (1.565) data 0.001 (0.001) loss 0.8701 (1.0887) acc 68.7500 (72.5227) lr 5.1825e-04 eta 6:32:16
+epoch [35/50] batch [970/1000] time 1.562 (1.566) data 0.000 (0.001) loss 1.1758 (1.0883) acc 65.6250 (72.5387) lr 5.1825e-04 eta 6:32:09
+epoch [35/50] batch [975/1000] time 1.548 (1.565) data 0.001 (0.001) loss 1.7402 (1.0885) acc 75.0000 (72.5673) lr 5.1825e-04 eta 6:32:01
+epoch [35/50] batch [980/1000] time 1.560 (1.565) data 0.000 (0.001) loss 1.0156 (1.0885) acc 68.7500 (72.5670) lr 5.1825e-04 eta 6:31:52
+epoch [35/50] batch [985/1000] time 1.558 (1.565) data 0.001 (0.001) loss 1.2832 (1.0888) acc 71.8750 (72.5698) lr 5.1825e-04 eta 6:31:43
+epoch [35/50] batch [990/1000] time 1.571 (1.565) data 0.000 (0.001) loss 1.3906 (1.0902) acc 71.8750 (72.5663) lr 5.1825e-04 eta 6:31:35
+epoch [35/50] batch [995/1000] time 1.564 (1.565) data 0.000 (0.001) loss 1.0645 (1.0912) acc 71.8750 (72.5408) lr 5.1825e-04 eta 6:31:27
+epoch [35/50] batch [1000/1000] time 1.567 (1.565) data 0.000 (0.001) loss 0.9634 (1.0904) acc 78.1250 (72.5469) lr 4.6417e-04 eta 6:31:19
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,306
+* accuracy: 78.6%
+* error: 21.4%
+* macro_f1: 78.1%
+epoch [36/50] batch [5/1000] time 1.575 (1.709) data 0.001 (0.193) loss 0.8984 (1.0677) acc 71.8750 (70.6250) lr 4.6417e-04 eta 7:07:10
+epoch [36/50] batch [10/1000] time 1.571 (1.638) data 0.000 (0.097) loss 1.0605 (0.9944) acc 68.7500 (73.7500) lr 4.6417e-04 eta 6:49:20
+epoch [36/50] batch [15/1000] time 1.552 (1.612) data 0.000 (0.065) loss 1.3447 (1.0453) acc 65.6250 (74.3750) lr 4.6417e-04 eta 6:42:37
+epoch [36/50] batch [20/1000] time 1.847 (1.614) data 0.001 (0.049) loss 0.8823 (1.0066) acc 81.2500 (75.0000) lr 4.6417e-04 eta 6:42:52
+epoch [36/50] batch [25/1000] time 1.540 (1.603) data 0.001 (0.039) loss 1.0420 (0.9769) acc 71.8750 (74.8750) lr 4.6417e-04 eta 6:40:00
+epoch [36/50] batch [30/1000] time 1.558 (1.595) data 0.001 (0.033) loss 1.0615 (0.9996) acc 71.8750 (74.1667) lr 4.6417e-04 eta 6:38:00
+epoch [36/50] batch [35/1000] time 1.590 (1.593) data 0.001 (0.028) loss 0.9419 (1.0246) acc 78.1250 (74.1071) lr 4.6417e-04 eta 6:37:20
+epoch [36/50] batch [40/1000] time 1.548 (1.587) data 0.001 (0.025) loss 0.9243 (1.0332) acc 71.8750 (73.8281) lr 4.6417e-04 eta 6:35:47
+epoch [36/50] batch [45/1000] time 1.580 (1.585) data 0.000 (0.022) loss 1.0898 (1.0479) acc 81.2500 (73.7500) lr 4.6417e-04 eta 6:35:01
+epoch [36/50] batch [50/1000] time 1.531 (1.583) data 0.001 (0.020) loss 1.1914 (1.0648) acc 71.8750 (73.2500) lr 4.6417e-04 eta 6:34:24
+epoch [36/50] batch [55/1000] time 1.539 (1.580) data 0.001 (0.018) loss 0.8950 (1.0583) acc 75.0000 (73.4091) lr 4.6417e-04 eta 6:33:40
+epoch [36/50] batch [60/1000] time 1.565 (1.578) data 0.001 (0.017) loss 1.3662 (1.0540) acc 68.7500 (73.1771) lr 4.6417e-04 eta 6:32:57
+epoch [36/50] batch [65/1000] time 1.583 (1.577) data 0.001 (0.015) loss 0.6782 (1.0657) acc 68.7500 (72.4519) lr 4.6417e-04 eta 6:32:32
+epoch [36/50] batch [70/1000] time 1.537 (1.576) data 0.000 (0.014) loss 0.8740 (1.0615) acc 71.8750 (72.5000) lr 4.6417e-04 eta 6:32:13
+epoch [36/50] batch [75/1000] time 1.536 (1.574) data 0.001 (0.013) loss 1.0293 (1.0625) acc 78.1250 (72.2083) lr 4.6417e-04 eta 6:31:35
+epoch [36/50] batch [80/1000] time 1.565 (1.573) data 0.001 (0.013) loss 0.9326 (1.0508) acc 81.2500 (72.6172) lr 4.6417e-04 eta 6:31:13
+epoch [36/50] batch [85/1000] time 1.578 (1.573) data 0.000 (0.012) loss 0.8970 (1.0612) acc 68.7500 (72.1324) lr 4.6417e-04 eta 6:31:01
+epoch [36/50] batch [90/1000] time 1.576 (1.573) data 0.000 (0.011) loss 1.2676 (1.0676) acc 65.6250 (72.1181) lr 4.6417e-04 eta 6:30:50
+epoch [36/50] batch [95/1000] time 1.559 (1.572) data 0.001 (0.011) loss 0.6958 (1.0680) acc 75.0000 (72.0395) lr 4.6417e-04 eta 6:30:30
+epoch [36/50] batch [100/1000] time 1.543 (1.572) data 0.001 (0.010) loss 0.8062 (1.0669) acc 75.0000 (72.0000) lr 4.6417e-04 eta 6:30:18
+epoch [36/50] batch [105/1000] time 1.535 (1.570) data 0.001 (0.010) loss 1.2158 (1.0745) acc 62.5000 (71.9048) lr 4.6417e-04 eta 6:29:48
+epoch [36/50] batch [110/1000] time 1.556 (1.570) data 0.001 (0.009) loss 0.8433 (1.0646) acc 75.0000 (72.1591) lr 4.6417e-04 eta 6:29:34
+epoch [36/50] batch [115/1000] time 1.585 (1.570) data 0.001 (0.009) loss 0.7856 (1.0645) acc 78.1250 (72.1467) lr 4.6417e-04 eta 6:29:23
+epoch [36/50] batch [120/1000] time 1.548 (1.569) data 0.000 (0.009) loss 1.4580 (1.0733) acc 71.8750 (72.1354) lr 4.6417e-04 eta 6:29:06
+epoch [36/50] batch [125/1000] time 1.565 (1.570) data 0.000 (0.008) loss 1.1523 (1.0647) acc 78.1250 (72.3750) lr 4.6417e-04 eta 6:29:13
+epoch [36/50] batch [130/1000] time 1.545 (1.570) data 0.001 (0.008) loss 1.0293 (1.0676) acc 78.1250 (72.4279) lr 4.6417e-04 eta 6:29:02
+epoch [36/50] batch [135/1000] time 1.583 (1.570) data 0.000 (0.008) loss 1.6006 (1.0739) acc 65.6250 (72.2685) lr 4.6417e-04 eta 6:28:52
+epoch [36/50] batch [140/1000] time 1.576 (1.570) data 0.000 (0.007) loss 0.9360 (1.0722) acc 71.8750 (72.2545) lr 4.6417e-04 eta 6:28:46
+epoch [36/50] batch [145/1000] time 1.574 (1.569) data 0.000 (0.007) loss 1.2031 (1.0769) acc 65.6250 (72.1552) lr 4.6417e-04 eta 6:28:34
+epoch [36/50] batch [150/1000] time 1.559 (1.569) data 0.001 (0.007) loss 0.8960 (1.0755) acc 71.8750 (72.2292) lr 4.6417e-04 eta 6:28:19
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+epoch [36/50] batch [700/1000] time 1.567 (1.566) data 0.001 (0.002) loss 0.5278 (1.0922) acc 87.5000 (72.2768) lr 4.6417e-04 eta 6:13:07
+epoch [36/50] batch [705/1000] time 1.549 (1.566) data 0.000 (0.002) loss 0.7891 (1.0928) acc 75.0000 (72.2518) lr 4.6417e-04 eta 6:12:59
+epoch [36/50] batch [710/1000] time 1.559 (1.566) data 0.001 (0.002) loss 1.1895 (1.0931) acc 65.6250 (72.2403) lr 4.6417e-04 eta 6:12:52
+epoch [36/50] batch [715/1000] time 1.572 (1.566) data 0.000 (0.002) loss 1.0928 (1.0933) acc 68.7500 (72.2509) lr 4.6417e-04 eta 6:12:44
+epoch [36/50] batch [720/1000] time 1.578 (1.566) data 0.000 (0.002) loss 1.1963 (1.0927) acc 71.8750 (72.2483) lr 4.6417e-04 eta 6:12:37
+epoch [36/50] batch [725/1000] time 1.558 (1.566) data 0.001 (0.002) loss 0.8623 (1.0902) acc 78.1250 (72.2888) lr 4.6417e-04 eta 6:12:29
+epoch [36/50] batch [730/1000] time 1.547 (1.566) data 0.001 (0.002) loss 1.1465 (1.0917) acc 68.7500 (72.2603) lr 4.6417e-04 eta 6:12:23
+epoch [36/50] batch [735/1000] time 1.557 (1.566) data 0.000 (0.002) loss 0.8945 (1.0906) acc 81.2500 (72.2789) lr 4.6417e-04 eta 6:12:14
+epoch [36/50] batch [740/1000] time 1.544 (1.566) data 0.000 (0.002) loss 0.6860 (1.0908) acc 87.5000 (72.2804) lr 4.6417e-04 eta 6:12:05
+epoch [36/50] batch [745/1000] time 1.574 (1.566) data 0.001 (0.002) loss 0.9165 (1.0907) acc 81.2500 (72.2819) lr 4.6417e-04 eta 6:11:57
+epoch [36/50] batch [750/1000] time 1.550 (1.566) data 0.000 (0.002) loss 1.2998 (1.0923) acc 68.7500 (72.2750) lr 4.6417e-04 eta 6:11:48
+epoch [36/50] batch [755/1000] time 1.549 (1.565) data 0.000 (0.002) loss 1.0693 (1.0929) acc 78.1250 (72.2848) lr 4.6417e-04 eta 6:11:40
+epoch [36/50] batch [760/1000] time 1.573 (1.565) data 0.000 (0.002) loss 1.6797 (1.0931) acc 59.3750 (72.2862) lr 4.6417e-04 eta 6:11:31
+epoch [36/50] batch [765/1000] time 1.580 (1.565) data 0.001 (0.002) loss 1.1826 (1.0924) acc 65.6250 (72.2876) lr 4.6417e-04 eta 6:11:23
+epoch [36/50] batch [770/1000] time 1.571 (1.565) data 0.001 (0.002) loss 1.0176 (1.0921) acc 78.1250 (72.2890) lr 4.6417e-04 eta 6:11:16
+epoch [36/50] batch [775/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.8921 (1.0928) acc 84.3750 (72.2581) lr 4.6417e-04 eta 6:11:11
+epoch [36/50] batch [780/1000] time 1.565 (1.566) data 0.000 (0.002) loss 1.3262 (1.0942) acc 68.7500 (72.2276) lr 4.6417e-04 eta 6:11:03
+epoch [36/50] batch [785/1000] time 1.541 (1.566) data 0.000 (0.002) loss 1.2979 (1.0942) acc 71.8750 (72.2174) lr 4.6417e-04 eta 6:10:54
+epoch [36/50] batch [790/1000] time 1.552 (1.566) data 0.000 (0.002) loss 1.0361 (1.0934) acc 75.0000 (72.2152) lr 4.6417e-04 eta 6:10:46
+epoch [36/50] batch [795/1000] time 1.584 (1.566) data 0.000 (0.002) loss 1.3594 (1.0931) acc 75.0000 (72.2288) lr 4.6417e-04 eta 6:10:38
+epoch [36/50] batch [800/1000] time 1.555 (1.565) data 0.001 (0.002) loss 1.2783 (1.0937) acc 81.2500 (72.2305) lr 4.6417e-04 eta 6:10:29
+epoch [36/50] batch [805/1000] time 1.568 (1.565) data 0.000 (0.002) loss 0.7749 (1.0946) acc 78.1250 (72.2205) lr 4.6417e-04 eta 6:10:21
+epoch [36/50] batch [810/1000] time 1.543 (1.565) data 0.000 (0.002) loss 0.5337 (1.0939) acc 81.2500 (72.2184) lr 4.6417e-04 eta 6:10:14
+epoch [36/50] batch [815/1000] time 1.742 (1.566) data 0.001 (0.002) loss 1.2822 (1.0939) acc 68.7500 (72.2508) lr 4.6417e-04 eta 6:10:09
+epoch [36/50] batch [820/1000] time 1.569 (1.566) data 0.000 (0.002) loss 1.4658 (1.0943) acc 62.5000 (72.2409) lr 4.6417e-04 eta 6:10:02
+epoch [36/50] batch [825/1000] time 1.602 (1.566) data 0.001 (0.002) loss 1.6855 (1.0967) acc 53.1250 (72.1780) lr 4.6417e-04 eta 6:09:55
+epoch [36/50] batch [830/1000] time 1.563 (1.566) data 0.001 (0.002) loss 1.0752 (1.0970) acc 81.2500 (72.1724) lr 4.6417e-04 eta 6:09:48
+epoch [36/50] batch [835/1000] time 1.575 (1.566) data 0.000 (0.002) loss 0.7744 (1.0967) acc 81.2500 (72.1856) lr 4.6417e-04 eta 6:09:41
+epoch [36/50] batch [840/1000] time 1.577 (1.566) data 0.000 (0.002) loss 0.6504 (1.0968) acc 81.2500 (72.1875) lr 4.6417e-04 eta 6:09:34
+epoch [36/50] batch [845/1000] time 1.566 (1.566) data 0.001 (0.002) loss 1.1953 (1.0965) acc 65.6250 (72.1930) lr 4.6417e-04 eta 6:09:26
+epoch [36/50] batch [850/1000] time 1.570 (1.566) data 0.000 (0.002) loss 0.7012 (1.0950) acc 81.2500 (72.2096) lr 4.6417e-04 eta 6:09:18
+epoch [36/50] batch [855/1000] time 1.562 (1.566) data 0.001 (0.002) loss 0.6821 (1.0932) acc 78.1250 (72.2405) lr 4.6417e-04 eta 6:09:10
+epoch [36/50] batch [860/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.2578 (1.0936) acc 62.5000 (72.2529) lr 4.6417e-04 eta 6:09:02
+epoch [36/50] batch [865/1000] time 1.543 (1.566) data 0.000 (0.002) loss 1.5869 (1.0949) acc 65.6250 (72.2146) lr 4.6417e-04 eta 6:08:54
+epoch [36/50] batch [870/1000] time 1.557 (1.566) data 0.001 (0.002) loss 0.8540 (1.0943) acc 81.2500 (72.2306) lr 4.6417e-04 eta 6:08:45
+epoch [36/50] batch [875/1000] time 1.554 (1.566) data 0.000 (0.002) loss 0.7461 (1.0943) acc 81.2500 (72.2429) lr 4.6417e-04 eta 6:08:36
+epoch [36/50] batch [880/1000] time 1.557 (1.566) data 0.001 (0.002) loss 0.7363 (1.0939) acc 84.3750 (72.2585) lr 4.6417e-04 eta 6:08:30
+epoch [36/50] batch [885/1000] time 1.561 (1.566) data 0.001 (0.002) loss 0.9229 (1.0924) acc 75.0000 (72.2952) lr 4.6417e-04 eta 6:08:22
+epoch [36/50] batch [890/1000] time 1.562 (1.566) data 0.001 (0.002) loss 1.2100 (1.0933) acc 62.5000 (72.2753) lr 4.6417e-04 eta 6:08:14
+epoch [36/50] batch [895/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.3311 (1.0930) acc 65.6250 (72.2730) lr 4.6417e-04 eta 6:08:05
+epoch [36/50] batch [900/1000] time 1.575 (1.566) data 0.001 (0.002) loss 0.8340 (1.0935) acc 75.0000 (72.2674) lr 4.6417e-04 eta 6:07:57
+epoch [36/50] batch [905/1000] time 1.540 (1.566) data 0.001 (0.002) loss 1.3262 (1.0932) acc 75.0000 (72.2894) lr 4.6417e-04 eta 6:07:48
+epoch [36/50] batch [910/1000] time 1.556 (1.566) data 0.000 (0.002) loss 0.7974 (1.0929) acc 84.3750 (72.3043) lr 4.6417e-04 eta 6:07:40
+epoch [36/50] batch [915/1000] time 1.550 (1.566) data 0.001 (0.002) loss 0.8521 (1.0919) acc 75.0000 (72.3087) lr 4.6417e-04 eta 6:07:33
+epoch [36/50] batch [920/1000] time 1.548 (1.566) data 0.000 (0.002) loss 1.1094 (1.0917) acc 68.7500 (72.3268) lr 4.6417e-04 eta 6:07:24
+epoch [36/50] batch [925/1000] time 1.566 (1.566) data 0.001 (0.002) loss 0.8916 (1.0905) acc 75.0000 (72.3547) lr 4.6417e-04 eta 6:07:20
+epoch [36/50] batch [930/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.2690 (1.0897) acc 87.5000 (72.3555) lr 4.6417e-04 eta 6:07:12
+epoch [36/50] batch [935/1000] time 1.559 (1.566) data 0.000 (0.002) loss 1.1807 (1.0886) acc 62.5000 (72.3797) lr 4.6417e-04 eta 6:07:03
+epoch [36/50] batch [940/1000] time 1.553 (1.566) data 0.000 (0.002) loss 1.0068 (1.0882) acc 78.1250 (72.4069) lr 4.6417e-04 eta 6:06:54
+epoch [36/50] batch [945/1000] time 1.570 (1.566) data 0.000 (0.002) loss 0.5010 (1.0879) acc 87.5000 (72.4339) lr 4.6417e-04 eta 6:06:46
+epoch [36/50] batch [950/1000] time 1.570 (1.566) data 0.000 (0.002) loss 0.7671 (1.0882) acc 78.1250 (72.4309) lr 4.6417e-04 eta 6:06:39
+epoch [36/50] batch [955/1000] time 1.574 (1.566) data 0.000 (0.002) loss 1.5029 (1.0894) acc 65.6250 (72.4149) lr 4.6417e-04 eta 6:06:30
+epoch [36/50] batch [960/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.2861 (1.0899) acc 62.5000 (72.3991) lr 4.6417e-04 eta 6:06:22
+epoch [36/50] batch [965/1000] time 1.547 (1.566) data 0.000 (0.002) loss 1.0967 (1.0888) acc 56.2500 (72.4223) lr 4.6417e-04 eta 6:06:13
+epoch [36/50] batch [970/1000] time 1.544 (1.566) data 0.000 (0.001) loss 1.2705 (1.0892) acc 71.8750 (72.4356) lr 4.6417e-04 eta 6:06:07
+epoch [36/50] batch [975/1000] time 1.566 (1.566) data 0.000 (0.001) loss 1.4395 (1.0901) acc 65.6250 (72.4231) lr 4.6417e-04 eta 6:05:59
+epoch [36/50] batch [980/1000] time 1.572 (1.566) data 0.000 (0.001) loss 0.8301 (1.0902) acc 81.2500 (72.4203) lr 4.6417e-04 eta 6:05:52
+epoch [36/50] batch [985/1000] time 1.555 (1.566) data 0.001 (0.001) loss 1.3193 (1.0893) acc 65.6250 (72.4429) lr 4.6417e-04 eta 6:05:44
+epoch [36/50] batch [990/1000] time 1.558 (1.566) data 0.000 (0.001) loss 1.3535 (1.0901) acc 71.8750 (72.4369) lr 4.6417e-04 eta 6:05:36
+epoch [36/50] batch [995/1000] time 1.600 (1.566) data 0.000 (0.001) loss 0.8325 (1.0898) acc 75.0000 (72.4466) lr 4.6417e-04 eta 6:05:28
+epoch [36/50] batch [1000/1000] time 1.560 (1.566) data 0.000 (0.001) loss 1.2178 (1.0893) acc 75.0000 (72.4656) lr 4.1221e-04 eta 6:05:21
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,330
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.2%
+epoch [37/50] batch [5/1000] time 1.552 (1.706) data 0.000 (0.193) loss 0.9331 (1.0564) acc 75.0000 (75.6250) lr 4.1221e-04 eta 6:37:53
+epoch [37/50] batch [10/1000] time 1.575 (1.630) data 0.001 (0.097) loss 1.5088 (1.2935) acc 68.7500 (69.6875) lr 4.1221e-04 eta 6:20:09
+epoch [37/50] batch [15/1000] time 1.567 (1.608) data 0.001 (0.065) loss 1.1748 (1.2136) acc 65.6250 (69.7917) lr 4.1221e-04 eta 6:14:51
+epoch [37/50] batch [20/1000] time 1.575 (1.601) data 0.001 (0.049) loss 1.0410 (1.1495) acc 75.0000 (71.5625) lr 4.1221e-04 eta 6:13:02
+epoch [37/50] batch [25/1000] time 1.572 (1.594) data 0.001 (0.039) loss 1.3066 (1.1751) acc 75.0000 (71.0000) lr 4.1221e-04 eta 6:11:19
+epoch [37/50] batch [30/1000] time 1.546 (1.589) data 0.001 (0.033) loss 1.3848 (1.1861) acc 75.0000 (70.5208) lr 4.1221e-04 eta 6:10:00
+epoch [37/50] batch [35/1000] time 1.555 (1.585) data 0.001 (0.028) loss 1.3896 (1.1833) acc 65.6250 (70.8036) lr 4.1221e-04 eta 6:08:48
+epoch [37/50] batch [40/1000] time 1.583 (1.591) data 0.000 (0.025) loss 0.8428 (1.1503) acc 75.0000 (71.6406) lr 4.1221e-04 eta 6:10:12
+epoch [37/50] batch [45/1000] time 1.556 (1.589) data 0.000 (0.022) loss 0.7144 (1.1260) acc 81.2500 (71.9444) lr 4.1221e-04 eta 6:09:38
+epoch [37/50] batch [50/1000] time 1.547 (1.585) data 0.001 (0.020) loss 0.9062 (1.1542) acc 75.0000 (71.1250) lr 4.1221e-04 eta 6:08:29
+epoch [37/50] batch [55/1000] time 1.550 (1.581) data 0.000 (0.018) loss 0.7251 (1.1441) acc 78.1250 (71.3068) lr 4.1221e-04 eta 6:07:30
+epoch [37/50] batch [60/1000] time 1.573 (1.579) data 0.001 (0.017) loss 1.1592 (1.1716) acc 71.8750 (71.0938) lr 4.1221e-04 eta 6:06:48
+epoch [37/50] batch [65/1000] time 1.561 (1.578) data 0.000 (0.015) loss 1.1182 (1.1521) acc 65.6250 (71.4423) lr 4.1221e-04 eta 6:06:33
+epoch [37/50] batch [70/1000] time 1.554 (1.577) data 0.000 (0.014) loss 1.0029 (1.1521) acc 71.8750 (71.4732) lr 4.1221e-04 eta 6:06:04
+epoch [37/50] batch [75/1000] time 1.564 (1.576) data 0.000 (0.013) loss 0.8218 (1.1505) acc 81.2500 (71.2917) lr 4.1221e-04 eta 6:05:45
+epoch [37/50] batch [80/1000] time 1.754 (1.577) data 0.000 (0.013) loss 0.7129 (1.1355) acc 81.2500 (71.5625) lr 4.1221e-04 eta 6:05:57
+epoch [37/50] batch [85/1000] time 1.554 (1.576) data 0.000 (0.012) loss 1.4336 (1.1378) acc 68.7500 (71.3971) lr 4.1221e-04 eta 6:05:28
+epoch [37/50] batch [90/1000] time 1.552 (1.576) data 0.000 (0.011) loss 1.1709 (1.1353) acc 75.0000 (71.5278) lr 4.1221e-04 eta 6:05:16
+epoch [37/50] batch [95/1000] time 1.575 (1.575) data 0.000 (0.011) loss 0.7578 (1.1324) acc 78.1250 (71.6118) lr 4.1221e-04 eta 6:04:55
+epoch [37/50] batch [100/1000] time 1.598 (1.575) data 0.000 (0.010) loss 1.1279 (1.1315) acc 68.7500 (71.4375) lr 4.1221e-04 eta 6:04:48
+epoch [37/50] batch [105/1000] time 1.567 (1.574) data 0.000 (0.010) loss 1.4414 (1.1343) acc 71.8750 (71.4583) lr 4.1221e-04 eta 6:04:34
+epoch [37/50] batch [110/1000] time 1.545 (1.573) data 0.000 (0.009) loss 1.3369 (1.1321) acc 68.7500 (71.5341) lr 4.1221e-04 eta 6:04:12
+epoch [37/50] batch [115/1000] time 1.558 (1.573) data 0.000 (0.009) loss 1.0869 (1.1312) acc 68.7500 (71.4946) lr 4.1221e-04 eta 6:04:04
+epoch [37/50] batch [120/1000] time 1.586 (1.573) data 0.001 (0.008) loss 1.2480 (1.1258) acc 65.6250 (71.4323) lr 4.1221e-04 eta 6:03:53
+epoch [37/50] batch [125/1000] time 1.580 (1.573) data 0.000 (0.008) loss 1.3633 (1.1252) acc 71.8750 (71.6000) lr 4.1221e-04 eta 6:03:41
+epoch [37/50] batch [130/1000] time 1.571 (1.572) data 0.000 (0.008) loss 0.4436 (1.1092) acc 84.3750 (71.8510) lr 4.1221e-04 eta 6:03:30
+epoch [37/50] batch [135/1000] time 1.567 (1.572) data 0.000 (0.008) loss 0.7593 (1.1080) acc 81.2500 (71.7593) lr 4.1221e-04 eta 6:03:21
+epoch [37/50] batch [140/1000] time 1.560 (1.572) data 0.000 (0.007) loss 0.6797 (1.1094) acc 78.1250 (71.7188) lr 4.1221e-04 eta 6:03:06
+epoch [37/50] batch [145/1000] time 1.546 (1.572) data 0.000 (0.007) loss 0.4895 (1.0941) acc 81.2500 (71.8966) lr 4.1221e-04 eta 6:03:03
+epoch [37/50] batch [150/1000] time 1.581 (1.572) data 0.001 (0.007) loss 0.9614 (1.0995) acc 71.8750 (71.8333) lr 4.1221e-04 eta 6:02:57
+epoch [37/50] batch [155/1000] time 1.557 (1.572) data 0.001 (0.007) loss 0.8687 (1.0977) acc 75.0000 (71.9758) lr 4.1221e-04 eta 6:02:47
+epoch [37/50] batch [160/1000] time 1.560 (1.572) data 0.000 (0.006) loss 1.2266 (1.1016) acc 65.6250 (71.9141) lr 4.1221e-04 eta 6:02:31
+epoch [37/50] batch [165/1000] time 1.566 (1.571) data 0.001 (0.006) loss 1.4287 (1.1110) acc 65.6250 (71.7045) lr 4.1221e-04 eta 6:02:20
+epoch [37/50] batch [170/1000] time 1.566 (1.571) data 0.000 (0.006) loss 1.7100 (1.1118) acc 65.6250 (71.6544) lr 4.1221e-04 eta 6:02:08
+epoch [37/50] batch [175/1000] time 1.578 (1.571) data 0.001 (0.006) loss 0.7314 (1.1097) acc 84.3750 (71.7500) lr 4.1221e-04 eta 6:01:56
+epoch [37/50] batch [180/1000] time 1.583 (1.570) data 0.001 (0.006) loss 0.8662 (1.1048) acc 68.7500 (71.8403) lr 4.1221e-04 eta 6:01:39
+epoch [37/50] batch [185/1000] time 1.578 (1.570) data 0.000 (0.006) loss 1.2285 (1.1062) acc 75.0000 (71.8919) lr 4.1221e-04 eta 6:01:27
+epoch [37/50] batch [190/1000] time 1.567 (1.570) data 0.001 (0.006) loss 1.2490 (1.1065) acc 78.1250 (71.9737) lr 4.1221e-04 eta 6:01:25
+epoch [37/50] batch [195/1000] time 1.594 (1.570) data 0.001 (0.005) loss 1.1816 (1.1078) acc 65.6250 (71.9391) lr 4.1221e-04 eta 6:01:15
+epoch [37/50] batch [200/1000] time 1.541 (1.569) data 0.001 (0.005) loss 1.4150 (1.1089) acc 65.6250 (71.9531) lr 4.1221e-04 eta 6:00:58
+epoch [37/50] batch [205/1000] time 1.557 (1.569) data 0.000 (0.005) loss 1.0059 (1.1109) acc 78.1250 (71.8598) lr 4.1221e-04 eta 6:00:47
+epoch [37/50] batch [210/1000] time 1.548 (1.569) data 0.001 (0.005) loss 0.5815 (1.1095) acc 90.6250 (71.8899) lr 4.1221e-04 eta 6:00:37
+epoch [37/50] batch [215/1000] time 1.572 (1.569) data 0.000 (0.005) loss 0.9819 (1.1077) acc 75.0000 (71.9767) lr 4.1221e-04 eta 6:00:29
+epoch [37/50] batch [220/1000] time 1.572 (1.569) data 0.001 (0.005) loss 1.3076 (1.1103) acc 75.0000 (71.9744) lr 4.1221e-04 eta 6:00:20
+epoch [37/50] batch [225/1000] time 1.545 (1.569) data 0.000 (0.005) loss 0.9209 (1.1017) acc 68.7500 (72.0972) lr 4.1221e-04 eta 6:00:09
+epoch [37/50] batch [230/1000] time 1.548 (1.568) data 0.000 (0.005) loss 0.8721 (1.0958) acc 71.8750 (72.2283) lr 4.1221e-04 eta 5:59:57
+epoch [37/50] batch [235/1000] time 1.541 (1.569) data 0.000 (0.005) loss 0.6382 (1.0916) acc 78.1250 (72.2739) lr 4.1221e-04 eta 5:59:51
+epoch [37/50] batch [240/1000] time 1.565 (1.568) data 0.000 (0.004) loss 1.2979 (1.0918) acc 65.6250 (72.2786) lr 4.1221e-04 eta 5:59:37
+epoch [37/50] batch [245/1000] time 1.524 (1.568) data 0.000 (0.004) loss 0.7725 (1.0940) acc 75.0000 (72.2321) lr 4.1221e-04 eta 5:59:24
+epoch [37/50] batch [250/1000] time 1.537 (1.568) data 0.000 (0.004) loss 1.1494 (1.0944) acc 81.2500 (72.2625) lr 4.1221e-04 eta 5:59:13
+epoch [37/50] batch [255/1000] time 1.553 (1.567) data 0.001 (0.004) loss 0.9097 (1.0933) acc 81.2500 (72.3284) lr 4.1221e-04 eta 5:59:02
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+epoch [37/50] batch [810/1000] time 1.527 (1.567) data 0.000 (0.002) loss 1.4912 (1.1040) acc 65.6250 (72.3418) lr 4.1221e-04 eta 5:44:27
+epoch [37/50] batch [815/1000] time 1.546 (1.567) data 0.000 (0.002) loss 1.1143 (1.1025) acc 78.1250 (72.3965) lr 4.1221e-04 eta 5:44:19
+epoch [37/50] batch [820/1000] time 1.581 (1.567) data 0.000 (0.002) loss 0.9282 (1.1015) acc 78.1250 (72.4009) lr 4.1221e-04 eta 5:44:11
+epoch [37/50] batch [825/1000] time 1.595 (1.567) data 0.001 (0.002) loss 1.3750 (1.1008) acc 62.5000 (72.4129) lr 4.1221e-04 eta 5:44:03
+epoch [37/50] batch [830/1000] time 1.599 (1.567) data 0.001 (0.002) loss 1.0225 (1.1006) acc 71.8750 (72.4247) lr 4.1221e-04 eta 5:43:56
+epoch [37/50] batch [835/1000] time 1.719 (1.567) data 0.000 (0.002) loss 1.1914 (1.0997) acc 71.8750 (72.4364) lr 4.1221e-04 eta 5:43:49
+epoch [37/50] batch [840/1000] time 1.535 (1.567) data 0.001 (0.002) loss 0.7744 (1.0979) acc 84.3750 (72.4888) lr 4.1221e-04 eta 5:43:41
+epoch [37/50] batch [845/1000] time 1.602 (1.567) data 0.000 (0.002) loss 0.8242 (1.0979) acc 65.6250 (72.4889) lr 4.1221e-04 eta 5:43:34
+epoch [37/50] batch [850/1000] time 1.550 (1.567) data 0.000 (0.002) loss 0.9810 (1.0980) acc 68.7500 (72.4669) lr 4.1221e-04 eta 5:43:25
+epoch [37/50] batch [855/1000] time 1.550 (1.567) data 0.000 (0.002) loss 1.1758 (1.0995) acc 75.0000 (72.4342) lr 4.1221e-04 eta 5:43:16
+epoch [37/50] batch [860/1000] time 1.544 (1.567) data 0.000 (0.002) loss 1.0391 (1.0992) acc 71.8750 (72.4419) lr 4.1221e-04 eta 5:43:08
+epoch [37/50] batch [865/1000] time 1.559 (1.567) data 0.000 (0.002) loss 0.9531 (1.0998) acc 84.3750 (72.4386) lr 4.1221e-04 eta 5:42:59
+epoch [37/50] batch [870/1000] time 1.569 (1.567) data 0.000 (0.002) loss 0.7417 (1.0991) acc 78.1250 (72.4282) lr 4.1221e-04 eta 5:42:51
+epoch [37/50] batch [875/1000] time 1.576 (1.567) data 0.000 (0.002) loss 1.0225 (1.0996) acc 62.5000 (72.3786) lr 4.1221e-04 eta 5:42:45
+epoch [37/50] batch [880/1000] time 1.555 (1.567) data 0.001 (0.002) loss 1.5371 (1.1003) acc 59.3750 (72.3757) lr 4.1221e-04 eta 5:42:37
+epoch [37/50] batch [885/1000] time 1.549 (1.567) data 0.001 (0.002) loss 0.5952 (1.0998) acc 81.2500 (72.3976) lr 4.1221e-04 eta 5:42:29
+epoch [37/50] batch [890/1000] time 1.575 (1.567) data 0.000 (0.002) loss 0.6436 (1.0994) acc 81.2500 (72.4017) lr 4.1221e-04 eta 5:42:21
+epoch [37/50] batch [895/1000] time 1.555 (1.567) data 0.000 (0.002) loss 1.7598 (1.0999) acc 62.5000 (72.4022) lr 4.1221e-04 eta 5:42:12
+epoch [37/50] batch [900/1000] time 1.559 (1.567) data 0.000 (0.002) loss 1.2256 (1.1002) acc 62.5000 (72.3854) lr 4.1221e-04 eta 5:42:07
+epoch [37/50] batch [905/1000] time 1.569 (1.567) data 0.001 (0.002) loss 1.4697 (1.1009) acc 65.6250 (72.3895) lr 4.1221e-04 eta 5:41:59
+epoch [37/50] batch [910/1000] time 1.593 (1.567) data 0.001 (0.002) loss 1.4873 (1.1026) acc 62.5000 (72.3283) lr 4.1221e-04 eta 5:41:52
+epoch [37/50] batch [915/1000] time 1.555 (1.567) data 0.001 (0.002) loss 1.0664 (1.1029) acc 75.0000 (72.3156) lr 4.1221e-04 eta 5:41:44
+epoch [37/50] batch [920/1000] time 1.564 (1.567) data 0.001 (0.002) loss 0.7505 (1.1021) acc 90.6250 (72.3471) lr 4.1221e-04 eta 5:41:35
+epoch [37/50] batch [925/1000] time 1.556 (1.567) data 0.001 (0.002) loss 1.7861 (1.1025) acc 59.3750 (72.3446) lr 4.1221e-04 eta 5:41:26
+epoch [37/50] batch [930/1000] time 1.545 (1.567) data 0.000 (0.001) loss 1.0596 (1.1017) acc 65.6250 (72.3589) lr 4.1221e-04 eta 5:41:18
+epoch [37/50] batch [935/1000] time 1.576 (1.567) data 0.001 (0.001) loss 1.0049 (1.1012) acc 78.1250 (72.3830) lr 4.1221e-04 eta 5:41:10
+epoch [37/50] batch [940/1000] time 1.589 (1.567) data 0.001 (0.001) loss 1.0371 (1.1010) acc 71.8750 (72.3903) lr 4.1221e-04 eta 5:41:03
+epoch [37/50] batch [945/1000] time 1.557 (1.567) data 0.000 (0.001) loss 0.7881 (1.1010) acc 71.8750 (72.3942) lr 4.1221e-04 eta 5:40:56
+epoch [37/50] batch [950/1000] time 1.561 (1.567) data 0.000 (0.001) loss 1.0850 (1.1004) acc 71.8750 (72.3882) lr 4.1221e-04 eta 5:40:48
+epoch [37/50] batch [955/1000] time 1.546 (1.567) data 0.000 (0.001) loss 0.9121 (1.1008) acc 71.8750 (72.3822) lr 4.1221e-04 eta 5:40:39
+epoch [37/50] batch [960/1000] time 1.557 (1.567) data 0.000 (0.001) loss 1.2764 (1.1011) acc 71.8750 (72.3665) lr 4.1221e-04 eta 5:40:31
+epoch [37/50] batch [965/1000] time 1.587 (1.567) data 0.000 (0.001) loss 0.8140 (1.0997) acc 81.2500 (72.3964) lr 4.1221e-04 eta 5:40:22
+epoch [37/50] batch [970/1000] time 1.543 (1.567) data 0.001 (0.001) loss 0.6514 (1.0985) acc 84.3750 (72.4227) lr 4.1221e-04 eta 5:40:14
+epoch [37/50] batch [975/1000] time 1.549 (1.567) data 0.000 (0.001) loss 1.1348 (1.0995) acc 78.1250 (72.4071) lr 4.1221e-04 eta 5:40:05
+epoch [37/50] batch [980/1000] time 1.557 (1.567) data 0.001 (0.001) loss 1.1709 (1.0996) acc 65.6250 (72.3948) lr 4.1221e-04 eta 5:39:56
+epoch [37/50] batch [985/1000] time 1.558 (1.567) data 0.001 (0.001) loss 0.5767 (1.0991) acc 81.2500 (72.4048) lr 4.1221e-04 eta 5:39:48
+epoch [37/50] batch [990/1000] time 1.541 (1.567) data 0.000 (0.001) loss 1.3369 (1.0989) acc 75.0000 (72.4085) lr 4.1221e-04 eta 5:39:41
+epoch [37/50] batch [995/1000] time 1.546 (1.567) data 0.000 (0.001) loss 1.1182 (1.0992) acc 68.7500 (72.3869) lr 4.1221e-04 eta 5:39:32
+epoch [37/50] batch [1000/1000] time 1.553 (1.566) data 0.000 (0.001) loss 1.8027 (1.0997) acc 50.0000 (72.3656) lr 3.6258e-04 eta 5:39:23
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,360
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.3%
+epoch [38/50] batch [5/1000] time 1.533 (1.947) data 0.000 (0.464) loss 0.9707 (1.0643) acc 71.8750 (72.5000) lr 3.6258e-04 eta 7:01:36
+epoch [38/50] batch [10/1000] time 1.575 (1.756) data 0.000 (0.232) loss 0.9370 (1.0711) acc 81.2500 (74.6875) lr 3.6258e-04 eta 6:20:04
+epoch [38/50] batch [15/1000] time 1.572 (1.692) data 0.001 (0.155) loss 1.3896 (1.1267) acc 71.8750 (72.9167) lr 3.6258e-04 eta 6:06:17
+epoch [38/50] batch [20/1000] time 1.565 (1.661) data 0.001 (0.116) loss 0.9624 (1.1211) acc 68.7500 (72.0312) lr 3.6258e-04 eta 5:59:18
+epoch [38/50] batch [25/1000] time 1.563 (1.643) data 0.001 (0.093) loss 1.2539 (1.1345) acc 75.0000 (71.5000) lr 3.6258e-04 eta 5:55:19
+epoch [38/50] batch [30/1000] time 1.532 (1.628) data 0.001 (0.078) loss 0.9023 (1.1398) acc 84.3750 (71.9792) lr 3.6258e-04 eta 5:51:53
+epoch [38/50] batch [35/1000] time 1.582 (1.619) data 0.000 (0.067) loss 1.2490 (1.1122) acc 62.5000 (72.5893) lr 3.6258e-04 eta 5:49:49
+epoch [38/50] batch [40/1000] time 1.580 (1.613) data 0.000 (0.058) loss 1.3486 (1.1160) acc 65.6250 (72.5781) lr 3.6258e-04 eta 5:48:30
+epoch [38/50] batch [45/1000] time 1.576 (1.609) data 0.001 (0.052) loss 1.8848 (1.1384) acc 59.3750 (71.9444) lr 3.6258e-04 eta 5:47:20
+epoch [38/50] batch [50/1000] time 1.556 (1.603) data 0.000 (0.047) loss 0.8608 (1.1488) acc 71.8750 (71.8750) lr 3.6258e-04 eta 5:46:03
+epoch [38/50] batch [55/1000] time 1.566 (1.600) data 0.000 (0.043) loss 1.4883 (1.1384) acc 68.7500 (72.1591) lr 3.6258e-04 eta 5:45:06
+epoch [38/50] batch [60/1000] time 1.541 (1.599) data 0.001 (0.039) loss 1.1670 (1.1532) acc 81.2500 (72.0833) lr 3.6258e-04 eta 5:44:54
+epoch [38/50] batch [65/1000] time 1.582 (1.596) data 0.000 (0.036) loss 0.8804 (1.1374) acc 78.1250 (72.3077) lr 3.6258e-04 eta 5:44:07
+epoch [38/50] batch [70/1000] time 1.566 (1.594) data 0.000 (0.034) loss 1.3633 (1.1366) acc 71.8750 (72.6339) lr 3.6258e-04 eta 5:43:28
+epoch [38/50] batch [75/1000] time 1.558 (1.592) data 0.000 (0.031) loss 1.2061 (1.1370) acc 75.0000 (72.6667) lr 3.6258e-04 eta 5:43:00
+epoch [38/50] batch [80/1000] time 1.571 (1.591) data 0.001 (0.029) loss 1.4277 (1.1361) acc 65.6250 (72.5000) lr 3.6258e-04 eta 5:42:30
+epoch [38/50] batch [85/1000] time 1.573 (1.589) data 0.000 (0.028) loss 1.0527 (1.1393) acc 78.1250 (72.5735) lr 3.6258e-04 eta 5:42:07
+epoch [38/50] batch [90/1000] time 1.572 (1.588) data 0.000 (0.026) loss 1.4131 (1.1347) acc 65.6250 (72.5000) lr 3.6258e-04 eta 5:41:44
+epoch [38/50] batch [95/1000] time 1.563 (1.586) data 0.001 (0.025) loss 1.1924 (1.1433) acc 65.6250 (72.1053) lr 3.6258e-04 eta 5:41:10
+epoch [38/50] batch [100/1000] time 1.553 (1.585) data 0.001 (0.024) loss 1.0537 (1.1402) acc 71.8750 (72.3438) lr 3.6258e-04 eta 5:40:48
+epoch [38/50] batch [105/1000] time 1.560 (1.585) data 0.000 (0.023) loss 0.9438 (1.1297) acc 81.2500 (72.5298) lr 3.6258e-04 eta 5:40:42
+epoch [38/50] batch [110/1000] time 1.560 (1.584) data 0.000 (0.022) loss 0.9712 (1.1273) acc 78.1250 (72.6705) lr 3.6258e-04 eta 5:40:17
+epoch [38/50] batch [115/1000] time 1.584 (1.583) data 0.000 (0.021) loss 0.8081 (1.1266) acc 68.7500 (72.3370) lr 3.6258e-04 eta 5:40:02
+epoch [38/50] batch [120/1000] time 1.539 (1.582) data 0.000 (0.020) loss 2.3066 (1.1363) acc 46.8750 (72.1094) lr 3.6258e-04 eta 5:39:36
+epoch [38/50] batch [125/1000] time 1.561 (1.582) data 0.001 (0.019) loss 0.8643 (1.1271) acc 78.1250 (72.3750) lr 3.6258e-04 eta 5:39:22
+epoch [38/50] batch [130/1000] time 1.563 (1.581) data 0.000 (0.018) loss 1.1885 (1.1189) acc 62.5000 (72.5000) lr 3.6258e-04 eta 5:39:06
+epoch [38/50] batch [135/1000] time 1.533 (1.580) data 0.000 (0.018) loss 1.2666 (1.1197) acc 62.5000 (72.4074) lr 3.6258e-04 eta 5:38:44
+epoch [38/50] batch [140/1000] time 1.560 (1.579) data 0.001 (0.017) loss 1.3037 (1.1248) acc 78.1250 (72.4777) lr 3.6258e-04 eta 5:38:29
+epoch [38/50] batch [145/1000] time 1.568 (1.580) data 0.001 (0.016) loss 0.6406 (1.1249) acc 75.0000 (72.4353) lr 3.6258e-04 eta 5:38:30
+epoch [38/50] batch [150/1000] time 1.549 (1.580) data 0.000 (0.016) loss 0.6938 (1.1186) acc 78.1250 (72.5208) lr 3.6258e-04 eta 5:38:18
+epoch [38/50] batch [155/1000] time 1.574 (1.579) data 0.000 (0.015) loss 0.9141 (1.1174) acc 81.2500 (72.6008) lr 3.6258e-04 eta 5:38:06
+epoch [38/50] batch [160/1000] time 1.557 (1.579) data 0.001 (0.015) loss 1.0010 (1.1160) acc 75.0000 (72.5391) lr 3.6258e-04 eta 5:37:49
+epoch [38/50] batch [165/1000] time 1.542 (1.578) data 0.001 (0.015) loss 0.8384 (1.1099) acc 81.2500 (72.4621) lr 3.6258e-04 eta 5:37:33
+epoch [38/50] batch [170/1000] time 1.553 (1.577) data 0.000 (0.014) loss 0.9053 (1.1070) acc 71.8750 (72.5184) lr 3.6258e-04 eta 5:37:19
+epoch [38/50] batch [175/1000] time 1.576 (1.577) data 0.000 (0.014) loss 0.9121 (1.1041) acc 84.3750 (72.6250) lr 3.6258e-04 eta 5:37:09
+epoch [38/50] batch [180/1000] time 1.556 (1.577) data 0.001 (0.013) loss 1.2979 (1.1017) acc 68.7500 (72.6562) lr 3.6258e-04 eta 5:36:56
+epoch [38/50] batch [185/1000] time 1.540 (1.576) data 0.001 (0.013) loss 0.8403 (1.0960) acc 84.3750 (72.8209) lr 3.6258e-04 eta 5:36:42
+epoch [38/50] batch [190/1000] time 1.558 (1.576) data 0.001 (0.013) loss 0.7915 (1.0948) acc 87.5000 (72.8783) lr 3.6258e-04 eta 5:36:30
+epoch [38/50] batch [195/1000] time 1.557 (1.576) data 0.000 (0.012) loss 0.7334 (1.0912) acc 75.0000 (72.9647) lr 3.6258e-04 eta 5:36:20
+epoch [38/50] batch [200/1000] time 1.561 (1.576) data 0.001 (0.012) loss 1.0059 (1.0927) acc 68.7500 (72.9531) lr 3.6258e-04 eta 5:36:06
+epoch [38/50] batch [205/1000] time 1.549 (1.575) data 0.000 (0.012) loss 1.2891 (1.0950) acc 71.8750 (72.9573) lr 3.6258e-04 eta 5:35:54
+epoch [38/50] batch [210/1000] time 1.553 (1.576) data 0.000 (0.012) loss 0.8828 (1.0891) acc 71.8750 (73.0655) lr 3.6258e-04 eta 5:35:53
+epoch [38/50] batch [215/1000] time 1.553 (1.575) data 0.001 (0.011) loss 1.3428 (1.0883) acc 75.0000 (73.0959) lr 3.6258e-04 eta 5:35:40
+epoch [38/50] batch [220/1000] time 1.547 (1.575) data 0.001 (0.011) loss 0.9258 (1.0839) acc 78.1250 (73.2244) lr 3.6258e-04 eta 5:35:24
+epoch [38/50] batch [225/1000] time 1.522 (1.574) data 0.000 (0.011) loss 1.2861 (1.0854) acc 71.8750 (73.2222) lr 3.6258e-04 eta 5:35:08
+epoch [38/50] batch [230/1000] time 1.552 (1.574) data 0.001 (0.011) loss 0.7969 (1.0804) acc 84.3750 (73.4375) lr 3.6258e-04 eta 5:34:56
+epoch [38/50] batch [235/1000] time 1.572 (1.573) data 0.000 (0.010) loss 1.0156 (1.0811) acc 81.2500 (73.3910) lr 3.6258e-04 eta 5:34:44
+epoch [38/50] batch [240/1000] time 1.558 (1.573) data 0.000 (0.010) loss 0.5474 (1.0811) acc 87.5000 (73.3724) lr 3.6258e-04 eta 5:34:35
+epoch [38/50] batch [245/1000] time 1.582 (1.573) data 0.001 (0.010) loss 0.7798 (1.0784) acc 81.2500 (73.4311) lr 3.6258e-04 eta 5:34:28
+epoch [38/50] batch [250/1000] time 1.575 (1.573) data 0.000 (0.010) loss 0.9473 (1.0767) acc 71.8750 (73.3125) lr 3.6258e-04 eta 5:34:18
+epoch [38/50] batch [255/1000] time 1.558 (1.574) data 0.000 (0.010) loss 0.7036 (1.0748) acc 78.1250 (73.3701) lr 3.6258e-04 eta 5:34:14
+epoch [38/50] batch [260/1000] time 1.553 (1.573) data 0.000 (0.009) loss 0.8032 (1.0734) acc 71.8750 (73.4255) lr 3.6258e-04 eta 5:34:04
+epoch [38/50] batch [265/1000] time 1.560 (1.573) data 0.001 (0.009) loss 0.9053 (1.0770) acc 81.2500 (73.2665) lr 3.6258e-04 eta 5:33:51
+epoch [38/50] batch [270/1000] time 1.524 (1.573) data 0.001 (0.009) loss 0.8105 (1.0753) acc 84.3750 (73.2986) lr 3.6258e-04 eta 5:33:38
+epoch [38/50] batch [275/1000] time 1.556 (1.572) data 0.000 (0.009) loss 1.2383 (1.0748) acc 71.8750 (73.2727) lr 3.6258e-04 eta 5:33:28
+epoch [38/50] batch [280/1000] time 1.597 (1.572) data 0.001 (0.009) loss 0.9795 (1.0743) acc 75.0000 (73.3036) lr 3.6258e-04 eta 5:33:17
+epoch [38/50] batch [285/1000] time 1.591 (1.572) data 0.000 (0.009) loss 1.1855 (1.0763) acc 71.8750 (73.2785) lr 3.6258e-04 eta 5:33:07
+epoch [38/50] batch [290/1000] time 1.546 (1.572) data 0.000 (0.008) loss 1.4482 (1.0790) acc 68.7500 (73.1897) lr 3.6258e-04 eta 5:32:57
+epoch [38/50] batch [295/1000] time 1.731 (1.572) data 0.000 (0.008) loss 0.9365 (1.0762) acc 75.0000 (73.1992) lr 3.6258e-04 eta 5:32:55
+epoch [38/50] batch [300/1000] time 1.553 (1.572) data 0.001 (0.008) loss 1.1494 (1.0753) acc 65.6250 (73.1458) lr 3.6258e-04 eta 5:32:43
+epoch [38/50] batch [305/1000] time 1.581 (1.572) data 0.000 (0.008) loss 0.6006 (1.0759) acc 81.2500 (73.1148) lr 3.6258e-04 eta 5:32:32
+epoch [38/50] batch [310/1000] time 1.563 (1.572) data 0.000 (0.008) loss 1.0869 (1.0780) acc 78.1250 (73.0444) lr 3.6258e-04 eta 5:32:23
+epoch [38/50] batch [315/1000] time 1.544 (1.571) data 0.001 (0.008) loss 0.8105 (1.0765) acc 81.2500 (73.0456) lr 3.6258e-04 eta 5:32:11
+epoch [38/50] batch [320/1000] time 1.571 (1.571) data 0.001 (0.008) loss 0.8774 (1.0754) acc 75.0000 (73.0371) lr 3.6258e-04 eta 5:32:00
+epoch [38/50] batch [325/1000] time 1.570 (1.571) data 0.000 (0.008) loss 0.8633 (1.0733) acc 75.0000 (73.0385) lr 3.6258e-04 eta 5:31:53
+epoch [38/50] batch [330/1000] time 1.548 (1.571) data 0.001 (0.007) loss 0.8369 (1.0730) acc 81.2500 (73.0019) lr 3.6258e-04 eta 5:31:44
+epoch [38/50] batch [335/1000] time 1.558 (1.571) data 0.000 (0.007) loss 1.4902 (1.0723) acc 71.8750 (72.9757) lr 3.6258e-04 eta 5:31:35
+epoch [38/50] batch [340/1000] time 1.583 (1.571) data 0.001 (0.007) loss 0.5566 (1.0703) acc 87.5000 (73.0331) lr 3.6258e-04 eta 5:31:28
+epoch [38/50] batch [345/1000] time 1.573 (1.571) data 0.001 (0.007) loss 0.9502 (1.0689) acc 78.1250 (73.0344) lr 3.6258e-04 eta 5:31:20
+epoch [38/50] batch [350/1000] time 1.573 (1.571) data 0.001 (0.007) loss 1.0732 (1.0714) acc 75.0000 (72.9554) lr 3.6258e-04 eta 5:31:11
+epoch [38/50] batch [355/1000] time 1.547 (1.571) data 0.001 (0.007) loss 1.0273 (1.0690) acc 75.0000 (73.0018) lr 3.6258e-04 eta 5:31:01
+epoch [38/50] batch [360/1000] time 1.569 (1.571) data 0.001 (0.007) loss 0.7900 (1.0664) acc 84.3750 (73.0556) lr 3.6258e-04 eta 5:30:57
+epoch [38/50] batch [365/1000] time 1.537 (1.571) data 0.000 (0.007) loss 0.9121 (1.0653) acc 71.8750 (73.0565) lr 3.6258e-04 eta 5:30:46
+epoch [38/50] batch [370/1000] time 1.562 (1.571) data 0.000 (0.007) loss 1.0850 (1.0686) acc 68.7500 (72.9730) lr 3.6258e-04 eta 5:30:39
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+epoch [38/50] batch [920/1000] time 1.562 (1.569) data 0.000 (0.003) loss 1.3203 (1.0747) acc 78.1250 (72.8533) lr 3.6258e-04 eta 5:15:51
+epoch [38/50] batch [925/1000] time 1.563 (1.569) data 0.000 (0.003) loss 1.0713 (1.0746) acc 78.1250 (72.8649) lr 3.6258e-04 eta 5:15:43
+epoch [38/50] batch [930/1000] time 1.578 (1.569) data 0.001 (0.003) loss 1.0811 (1.0735) acc 62.5000 (72.8696) lr 3.6258e-04 eta 5:15:35
+epoch [38/50] batch [935/1000] time 1.581 (1.569) data 0.000 (0.003) loss 1.0264 (1.0735) acc 71.8750 (72.8676) lr 3.6258e-04 eta 5:15:27
+epoch [38/50] batch [940/1000] time 1.572 (1.569) data 0.000 (0.003) loss 0.7637 (1.0725) acc 81.2500 (72.9122) lr 3.6258e-04 eta 5:15:18
+epoch [38/50] batch [945/1000] time 1.538 (1.569) data 0.001 (0.003) loss 1.3271 (1.0718) acc 65.6250 (72.9167) lr 3.6258e-04 eta 5:15:10
+epoch [38/50] batch [950/1000] time 1.552 (1.569) data 0.000 (0.003) loss 0.8716 (1.0708) acc 81.2500 (72.9572) lr 3.6258e-04 eta 5:15:01
+epoch [38/50] batch [955/1000] time 1.556 (1.569) data 0.001 (0.003) loss 1.2227 (1.0708) acc 71.8750 (72.9450) lr 3.6258e-04 eta 5:14:52
+epoch [38/50] batch [960/1000] time 1.580 (1.569) data 0.001 (0.003) loss 1.0586 (1.0704) acc 75.0000 (72.9525) lr 3.6258e-04 eta 5:14:45
+epoch [38/50] batch [965/1000] time 1.579 (1.569) data 0.001 (0.003) loss 1.4512 (1.0711) acc 65.6250 (72.9598) lr 3.6258e-04 eta 5:14:39
+epoch [38/50] batch [970/1000] time 1.574 (1.569) data 0.000 (0.003) loss 1.3457 (1.0717) acc 68.7500 (72.9543) lr 3.6258e-04 eta 5:14:31
+epoch [38/50] batch [975/1000] time 1.574 (1.569) data 0.001 (0.003) loss 1.5352 (1.0724) acc 59.3750 (72.9327) lr 3.6258e-04 eta 5:14:23
+epoch [38/50] batch [980/1000] time 1.564 (1.569) data 0.000 (0.003) loss 1.3984 (1.0725) acc 68.7500 (72.9273) lr 3.6258e-04 eta 5:14:15
+epoch [38/50] batch [985/1000] time 1.562 (1.569) data 0.001 (0.003) loss 1.3965 (1.0726) acc 65.6250 (72.9093) lr 3.6258e-04 eta 5:14:07
+epoch [38/50] batch [990/1000] time 1.563 (1.569) data 0.000 (0.003) loss 0.7676 (1.0724) acc 78.1250 (72.9230) lr 3.6258e-04 eta 5:13:59
+epoch [38/50] batch [995/1000] time 1.570 (1.569) data 0.000 (0.003) loss 1.3223 (1.0721) acc 62.5000 (72.9177) lr 3.6258e-04 eta 5:13:50
+epoch [38/50] batch [1000/1000] time 1.569 (1.569) data 0.000 (0.003) loss 0.6133 (1.0732) acc 84.3750 (72.8969) lr 3.1545e-04 eta 5:13:42
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,352
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.3%
+epoch [39/50] batch [5/1000] time 1.565 (1.710) data 0.001 (0.208) loss 1.5947 (1.4638) acc 62.5000 (67.5000) lr 3.1545e-04 eta 5:41:49
+epoch [39/50] batch [10/1000] time 1.557 (1.643) data 0.001 (0.105) loss 1.3545 (1.2501) acc 56.2500 (68.1250) lr 3.1545e-04 eta 5:28:19
+epoch [39/50] batch [15/1000] time 1.544 (1.616) data 0.000 (0.070) loss 1.2178 (1.1426) acc 68.7500 (70.4167) lr 3.1545e-04 eta 5:22:46
+epoch [39/50] batch [20/1000] time 1.553 (1.604) data 0.001 (0.053) loss 1.1406 (1.1414) acc 71.8750 (70.9375) lr 3.1545e-04 eta 5:20:16
+epoch [39/50] batch [25/1000] time 1.552 (1.597) data 0.001 (0.042) loss 1.7314 (1.1653) acc 59.3750 (70.2500) lr 3.1545e-04 eta 5:18:43
+epoch [39/50] batch [30/1000] time 1.530 (1.589) data 0.001 (0.035) loss 0.9331 (1.1761) acc 78.1250 (70.5208) lr 3.1545e-04 eta 5:17:02
+epoch [39/50] batch [35/1000] time 1.557 (1.591) data 0.000 (0.030) loss 1.3779 (1.1273) acc 65.6250 (71.8750) lr 3.1545e-04 eta 5:17:20
+epoch [39/50] batch [40/1000] time 1.548 (1.588) data 0.001 (0.027) loss 1.5400 (1.1357) acc 71.8750 (71.9531) lr 3.1545e-04 eta 5:16:26
+epoch [39/50] batch [45/1000] time 1.549 (1.583) data 0.001 (0.024) loss 1.3896 (1.1347) acc 65.6250 (71.4583) lr 3.1545e-04 eta 5:15:29
+epoch [39/50] batch [50/1000] time 1.560 (1.581) data 0.000 (0.021) loss 0.5864 (1.1151) acc 84.3750 (71.6250) lr 3.1545e-04 eta 5:14:49
+epoch [39/50] batch [55/1000] time 1.544 (1.578) data 0.000 (0.019) loss 1.3340 (1.1100) acc 68.7500 (72.0455) lr 3.1545e-04 eta 5:14:10
+epoch [39/50] batch [60/1000] time 1.561 (1.576) data 0.001 (0.018) loss 1.7949 (1.1108) acc 62.5000 (72.0833) lr 3.1545e-04 eta 5:13:37
+epoch [39/50] batch [65/1000] time 1.559 (1.575) data 0.000 (0.017) loss 1.4170 (1.1201) acc 65.6250 (71.7788) lr 3.1545e-04 eta 5:13:16
+epoch [39/50] batch [70/1000] time 1.546 (1.573) data 0.000 (0.015) loss 1.5010 (1.1310) acc 68.7500 (71.5179) lr 3.1545e-04 eta 5:12:47
+epoch [39/50] batch [75/1000] time 1.577 (1.575) data 0.001 (0.014) loss 1.6113 (1.1456) acc 59.3750 (71.0000) lr 3.1545e-04 eta 5:13:00
+epoch [39/50] batch [80/1000] time 1.582 (1.574) data 0.001 (0.014) loss 1.4727 (1.1431) acc 75.0000 (70.8594) lr 3.1545e-04 eta 5:12:43
+epoch [39/50] batch [85/1000] time 1.546 (1.573) data 0.000 (0.013) loss 1.1426 (1.1394) acc 65.6250 (70.9559) lr 3.1545e-04 eta 5:12:26
+epoch [39/50] batch [90/1000] time 1.553 (1.573) data 0.001 (0.012) loss 2.1973 (1.1442) acc 56.2500 (70.9028) lr 3.1545e-04 eta 5:12:14
+epoch [39/50] batch [95/1000] time 1.584 (1.572) data 0.001 (0.011) loss 0.9995 (1.1481) acc 75.0000 (70.9539) lr 3.1545e-04 eta 5:11:58
+epoch [39/50] batch [100/1000] time 1.563 (1.572) data 0.000 (0.011) loss 1.2910 (1.1470) acc 59.3750 (70.7812) lr 3.1545e-04 eta 5:11:42
+epoch [39/50] batch [105/1000] time 1.583 (1.572) data 0.000 (0.010) loss 1.1797 (1.1475) acc 68.7500 (70.7440) lr 3.1545e-04 eta 5:11:33
+epoch [39/50] batch [110/1000] time 1.587 (1.572) data 0.001 (0.010) loss 1.8252 (1.1485) acc 56.2500 (70.7670) lr 3.1545e-04 eta 5:11:30
+epoch [39/50] batch [115/1000] time 1.561 (1.572) data 0.001 (0.010) loss 1.1602 (1.1403) acc 65.6250 (70.8696) lr 3.1545e-04 eta 5:11:21
+epoch [39/50] batch [120/1000] time 1.554 (1.572) data 0.000 (0.009) loss 0.8579 (1.1450) acc 75.0000 (70.7812) lr 3.1545e-04 eta 5:11:11
+epoch [39/50] batch [125/1000] time 1.550 (1.571) data 0.000 (0.009) loss 0.9956 (1.1473) acc 71.8750 (70.5500) lr 3.1545e-04 eta 5:10:57
+epoch [39/50] batch [130/1000] time 1.556 (1.571) data 0.000 (0.009) loss 1.3037 (1.1451) acc 68.7500 (70.6010) lr 3.1545e-04 eta 5:10:45
+epoch [39/50] batch [135/1000] time 1.566 (1.571) data 0.000 (0.008) loss 0.9678 (1.1436) acc 68.7500 (70.7407) lr 3.1545e-04 eta 5:10:35
+epoch [39/50] batch [140/1000] time 1.564 (1.571) data 0.000 (0.008) loss 1.2129 (1.1509) acc 62.5000 (70.7143) lr 3.1545e-04 eta 5:10:36
+epoch [39/50] batch [145/1000] time 1.576 (1.571) data 0.001 (0.008) loss 0.4233 (1.1364) acc 90.6250 (70.9483) lr 3.1545e-04 eta 5:10:29
+epoch [39/50] batch [150/1000] time 1.560 (1.571) data 0.001 (0.007) loss 0.9150 (1.1326) acc 65.6250 (70.8958) lr 3.1545e-04 eta 5:10:16
+epoch [39/50] batch [155/1000] time 1.562 (1.571) data 0.000 (0.007) loss 0.7041 (1.1300) acc 75.0000 (70.9476) lr 3.1545e-04 eta 5:10:07
+epoch [39/50] batch [160/1000] time 1.565 (1.571) data 0.000 (0.007) loss 0.8867 (1.1305) acc 68.7500 (70.9375) lr 3.1545e-04 eta 5:09:55
+epoch [39/50] batch [165/1000] time 1.568 (1.570) data 0.000 (0.007) loss 0.8027 (1.1312) acc 81.2500 (71.0038) lr 3.1545e-04 eta 5:09:46
+epoch [39/50] batch [170/1000] time 1.558 (1.570) data 0.000 (0.007) loss 1.0000 (1.1318) acc 71.8750 (71.0110) lr 3.1545e-04 eta 5:09:34
+epoch [39/50] batch [175/1000] time 1.566 (1.570) data 0.000 (0.006) loss 0.8726 (1.1262) acc 84.3750 (71.1964) lr 3.1545e-04 eta 5:09:25
+epoch [39/50] batch [180/1000] time 1.573 (1.570) data 0.000 (0.006) loss 0.5645 (1.1258) acc 78.1250 (71.2326) lr 3.1545e-04 eta 5:09:17
+epoch [39/50] batch [185/1000] time 1.554 (1.570) data 0.000 (0.006) loss 1.1826 (1.1234) acc 71.8750 (71.3682) lr 3.1545e-04 eta 5:09:11
+epoch [39/50] batch [190/1000] time 1.544 (1.570) data 0.000 (0.006) loss 1.2822 (1.1206) acc 75.0000 (71.4967) lr 3.1545e-04 eta 5:08:57
+epoch [39/50] batch [195/1000] time 1.566 (1.569) data 0.001 (0.006) loss 1.3193 (1.1222) acc 65.6250 (71.5064) lr 3.1545e-04 eta 5:08:44
+epoch [39/50] batch [200/1000] time 1.563 (1.569) data 0.000 (0.006) loss 1.5557 (1.1248) acc 65.6250 (71.4531) lr 3.1545e-04 eta 5:08:35
+epoch [39/50] batch [205/1000] time 1.574 (1.569) data 0.001 (0.006) loss 1.2627 (1.1296) acc 71.8750 (71.3872) lr 3.1545e-04 eta 5:08:27
+epoch [39/50] batch [210/1000] time 1.552 (1.569) data 0.001 (0.005) loss 0.9390 (1.1288) acc 78.1250 (71.5179) lr 3.1545e-04 eta 5:08:15
+epoch [39/50] batch [215/1000] time 1.542 (1.568) data 0.001 (0.005) loss 1.0537 (1.1295) acc 65.6250 (71.5116) lr 3.1545e-04 eta 5:08:04
+epoch [39/50] batch [220/1000] time 1.545 (1.568) data 0.000 (0.005) loss 0.9624 (1.1270) acc 78.1250 (71.6193) lr 3.1545e-04 eta 5:07:51
+epoch [39/50] batch [225/1000] time 1.551 (1.569) data 0.001 (0.005) loss 1.1475 (1.1247) acc 75.0000 (71.6806) lr 3.1545e-04 eta 5:07:50
+epoch [39/50] batch [230/1000] time 1.563 (1.568) data 0.001 (0.005) loss 1.1533 (1.1184) acc 65.6250 (71.8071) lr 3.1545e-04 eta 5:07:37
+epoch [39/50] batch [235/1000] time 1.570 (1.568) data 0.000 (0.005) loss 0.9165 (1.1219) acc 75.0000 (71.7686) lr 3.1545e-04 eta 5:07:26
+epoch [39/50] batch [240/1000] time 1.567 (1.568) data 0.001 (0.005) loss 1.1533 (1.1184) acc 71.8750 (71.7839) lr 3.1545e-04 eta 5:07:20
+epoch [39/50] batch [245/1000] time 1.535 (1.567) data 0.001 (0.005) loss 0.7568 (1.1174) acc 84.3750 (71.9005) lr 3.1545e-04 eta 5:07:05
+epoch [39/50] batch [250/1000] time 1.563 (1.567) data 0.001 (0.005) loss 1.2217 (1.1204) acc 68.7500 (71.7625) lr 3.1545e-04 eta 5:06:56
+epoch [39/50] batch [255/1000] time 1.562 (1.567) data 0.000 (0.005) loss 0.7212 (1.1169) acc 81.2500 (71.8750) lr 3.1545e-04 eta 5:06:48
+epoch [39/50] batch [260/1000] time 1.544 (1.567) data 0.000 (0.004) loss 1.2979 (1.1216) acc 81.2500 (71.8630) lr 3.1545e-04 eta 5:06:37
+epoch [39/50] batch [265/1000] time 1.552 (1.567) data 0.000 (0.004) loss 1.8154 (1.1234) acc 59.3750 (71.7807) lr 3.1545e-04 eta 5:06:27
+epoch [39/50] batch [270/1000] time 1.567 (1.567) data 0.000 (0.004) loss 1.1807 (1.1266) acc 65.6250 (71.7593) lr 3.1545e-04 eta 5:06:18
+epoch [39/50] batch [275/1000] time 1.572 (1.567) data 0.001 (0.004) loss 0.8550 (1.1217) acc 75.0000 (71.8864) lr 3.1545e-04 eta 5:06:11
+epoch [39/50] batch [280/1000] time 1.561 (1.567) data 0.001 (0.004) loss 0.8252 (1.1176) acc 84.3750 (71.9866) lr 3.1545e-04 eta 5:06:02
+epoch [39/50] batch [285/1000] time 1.552 (1.567) data 0.000 (0.004) loss 1.4561 (1.1203) acc 65.6250 (71.9737) lr 3.1545e-04 eta 5:05:55
+epoch [39/50] batch [290/1000] time 1.568 (1.567) data 0.001 (0.004) loss 1.2002 (1.1181) acc 71.8750 (71.9935) lr 3.1545e-04 eta 5:05:54
+epoch [39/50] batch [295/1000] time 1.566 (1.567) data 0.001 (0.004) loss 1.1279 (1.1212) acc 65.6250 (71.9068) lr 3.1545e-04 eta 5:05:43
+epoch [39/50] batch [300/1000] time 1.557 (1.567) data 0.000 (0.004) loss 0.8071 (1.1173) acc 78.1250 (71.9896) lr 3.1545e-04 eta 5:05:35
+epoch [39/50] batch [305/1000] time 1.535 (1.567) data 0.000 (0.004) loss 0.8569 (1.1180) acc 78.1250 (72.0082) lr 3.1545e-04 eta 5:05:24
+epoch [39/50] batch [310/1000] time 1.551 (1.567) data 0.000 (0.004) loss 1.6250 (1.1206) acc 68.7500 (71.9960) lr 3.1545e-04 eta 5:05:14
+epoch [39/50] batch [315/1000] time 1.573 (1.567) data 0.000 (0.004) loss 1.0898 (1.1235) acc 78.1250 (71.9246) lr 3.1545e-04 eta 5:05:05
+epoch [39/50] batch [320/1000] time 1.563 (1.567) data 0.000 (0.004) loss 0.8745 (1.1227) acc 78.1250 (71.9238) lr 3.1545e-04 eta 5:04:57
+epoch [39/50] batch [325/1000] time 1.571 (1.567) data 0.000 (0.004) loss 1.0244 (1.1240) acc 71.8750 (71.9231) lr 3.1545e-04 eta 5:04:49
+epoch [39/50] batch [330/1000] time 1.552 (1.566) data 0.001 (0.004) loss 0.8330 (1.1225) acc 71.8750 (71.9223) lr 3.1545e-04 eta 5:04:40
+epoch [39/50] batch [335/1000] time 1.560 (1.567) data 0.001 (0.004) loss 0.9106 (1.1221) acc 71.8750 (71.9030) lr 3.1545e-04 eta 5:04:36
+epoch [39/50] batch [340/1000] time 1.565 (1.567) data 0.001 (0.004) loss 0.9028 (1.1186) acc 75.0000 (72.0037) lr 3.1545e-04 eta 5:04:27
+epoch [39/50] batch [345/1000] time 1.567 (1.567) data 0.000 (0.004) loss 1.2178 (1.1199) acc 78.1250 (72.0471) lr 3.1545e-04 eta 5:04:19
+epoch [39/50] batch [350/1000] time 1.569 (1.566) data 0.000 (0.003) loss 0.6323 (1.1168) acc 84.3750 (72.0357) lr 3.1545e-04 eta 5:04:09
+epoch [39/50] batch [355/1000] time 1.576 (1.566) data 0.001 (0.003) loss 0.8140 (1.1156) acc 78.1250 (72.0863) lr 3.1545e-04 eta 5:04:01
+epoch [39/50] batch [360/1000] time 1.564 (1.566) data 0.000 (0.003) loss 0.9419 (1.1142) acc 81.2500 (72.1007) lr 3.1545e-04 eta 5:03:52
+epoch [39/50] batch [365/1000] time 1.557 (1.566) data 0.000 (0.003) loss 1.0967 (1.1155) acc 71.8750 (72.1318) lr 3.1545e-04 eta 5:03:44
+epoch [39/50] batch [370/1000] time 1.554 (1.566) data 0.000 (0.003) loss 1.1445 (1.1167) acc 75.0000 (72.1199) lr 3.1545e-04 eta 5:03:35
+epoch [39/50] batch [375/1000] time 1.722 (1.567) data 0.000 (0.003) loss 0.9355 (1.1148) acc 75.0000 (72.1500) lr 3.1545e-04 eta 5:03:32
+epoch [39/50] batch [380/1000] time 1.558 (1.567) data 0.001 (0.003) loss 1.6367 (1.1153) acc 59.3750 (72.1217) lr 3.1545e-04 eta 5:03:24
+epoch [39/50] batch [385/1000] time 1.560 (1.567) data 0.001 (0.003) loss 1.2021 (1.1152) acc 68.7500 (72.1185) lr 3.1545e-04 eta 5:03:15
+epoch [39/50] batch [390/1000] time 1.559 (1.566) data 0.001 (0.003) loss 1.2266 (1.1152) acc 65.6250 (72.1154) lr 3.1545e-04 eta 5:03:06
+epoch [39/50] batch [395/1000] time 1.567 (1.566) data 0.000 (0.003) loss 1.2021 (1.1151) acc 71.8750 (72.1044) lr 3.1545e-04 eta 5:02:58
+epoch [39/50] batch [400/1000] time 1.535 (1.566) data 0.000 (0.003) loss 1.3682 (1.1177) acc 75.0000 (72.0703) lr 3.1545e-04 eta 5:02:47
+epoch [39/50] batch [405/1000] time 1.539 (1.566) data 0.000 (0.003) loss 0.9438 (1.1162) acc 78.1250 (72.0602) lr 3.1545e-04 eta 5:02:37
+epoch [39/50] batch [410/1000] time 1.571 (1.566) data 0.000 (0.003) loss 1.0664 (1.1174) acc 71.8750 (72.0732) lr 3.1545e-04 eta 5:02:29
+epoch [39/50] batch [415/1000] time 1.572 (1.566) data 0.001 (0.003) loss 1.8320 (1.1185) acc 62.5000 (72.0557) lr 3.1545e-04 eta 5:02:20
+epoch [39/50] batch [420/1000] time 1.587 (1.566) data 0.001 (0.003) loss 0.9297 (1.1193) acc 78.1250 (72.0312) lr 3.1545e-04 eta 5:02:13
+epoch [39/50] batch [425/1000] time 1.555 (1.566) data 0.001 (0.003) loss 1.5938 (1.1213) acc 68.7500 (72.0000) lr 3.1545e-04 eta 5:02:04
+epoch [39/50] batch [430/1000] time 1.584 (1.566) data 0.000 (0.003) loss 0.9478 (1.1215) acc 68.7500 (71.9767) lr 3.1545e-04 eta 5:01:57
+epoch [39/50] batch [435/1000] time 1.597 (1.566) data 0.001 (0.003) loss 1.0654 (1.1214) acc 65.6250 (71.9899) lr 3.1545e-04 eta 5:01:51
+epoch [39/50] batch [440/1000] time 1.593 (1.567) data 0.000 (0.003) loss 1.4258 (1.1217) acc 62.5000 (71.9957) lr 3.1545e-04 eta 5:01:49
+epoch [39/50] batch [445/1000] time 1.567 (1.567) data 0.001 (0.003) loss 1.0791 (1.1204) acc 71.8750 (72.0506) lr 3.1545e-04 eta 5:01:40
+epoch [39/50] batch [450/1000] time 1.563 (1.566) data 0.000 (0.003) loss 1.1992 (1.1202) acc 71.8750 (72.0694) lr 3.1545e-04 eta 5:01:32
+epoch [39/50] batch [455/1000] time 1.553 (1.566) data 0.000 (0.003) loss 1.1963 (1.1213) acc 71.8750 (72.0536) lr 3.1545e-04 eta 5:01:22
+epoch [39/50] batch [460/1000] time 1.567 (1.566) data 0.000 (0.003) loss 1.3535 (1.1220) acc 65.6250 (72.0245) lr 3.1545e-04 eta 5:01:14
+epoch [39/50] batch [465/1000] time 1.560 (1.566) data 0.001 (0.003) loss 0.9419 (1.1190) acc 84.3750 (72.0833) lr 3.1545e-04 eta 5:01:06
+epoch [39/50] batch [470/1000] time 1.559 (1.566) data 0.000 (0.003) loss 1.0830 (1.1174) acc 71.8750 (72.1144) lr 3.1545e-04 eta 5:00:57
+epoch [39/50] batch [475/1000] time 1.581 (1.566) data 0.001 (0.003) loss 1.5137 (1.1158) acc 56.2500 (72.1118) lr 3.1545e-04 eta 5:00:50
+epoch [39/50] batch [480/1000] time 1.592 (1.566) data 0.001 (0.003) loss 0.6221 (1.1153) acc 78.1250 (72.1549) lr 3.1545e-04 eta 5:00:43
+epoch [39/50] batch [485/1000] time 1.571 (1.567) data 0.000 (0.003) loss 0.7778 (1.1146) acc 78.1250 (72.1972) lr 3.1545e-04 eta 5:00:38
+epoch [39/50] batch [490/1000] time 1.538 (1.566) data 0.000 (0.003) loss 0.5991 (1.1128) acc 81.2500 (72.2194) lr 3.1545e-04 eta 5:00:29
+epoch [39/50] batch [495/1000] time 1.571 (1.566) data 0.000 (0.003) loss 1.4219 (1.1121) acc 59.3750 (72.2159) lr 3.1545e-04 eta 5:00:22
+epoch [39/50] batch [500/1000] time 1.555 (1.566) data 0.001 (0.003) loss 1.1270 (1.1121) acc 75.0000 (72.2562) lr 3.1545e-04 eta 5:00:13
+epoch [39/50] batch [505/1000] time 1.559 (1.566) data 0.000 (0.003) loss 1.1191 (1.1115) acc 75.0000 (72.2463) lr 3.1545e-04 eta 5:00:04
+epoch [39/50] batch [510/1000] time 1.563 (1.566) data 0.001 (0.003) loss 1.2002 (1.1137) acc 65.6250 (72.1875) lr 3.1545e-04 eta 4:59:56
+epoch [39/50] batch [515/1000] time 1.543 (1.566) data 0.000 (0.003) loss 1.2412 (1.1147) acc 71.8750 (72.1723) lr 3.1545e-04 eta 4:59:47
+epoch [39/50] batch [520/1000] time 1.548 (1.566) data 0.001 (0.002) loss 1.0283 (1.1141) acc 78.1250 (72.2115) lr 3.1545e-04 eta 4:59:38
+epoch [39/50] batch [525/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.5273 (1.1155) acc 75.0000 (72.1786) lr 3.1545e-04 eta 4:59:28
+epoch [39/50] batch [530/1000] time 1.552 (1.566) data 0.000 (0.002) loss 1.1748 (1.1157) acc 59.3750 (72.1285) lr 3.1545e-04 eta 4:59:24
+epoch [39/50] batch [535/1000] time 1.528 (1.566) data 0.000 (0.002) loss 1.8379 (1.1176) acc 59.3750 (72.1086) lr 3.1545e-04 eta 4:59:15
+epoch [39/50] batch [540/1000] time 1.541 (1.566) data 0.000 (0.002) loss 0.7100 (1.1171) acc 81.2500 (72.0949) lr 3.1545e-04 eta 4:59:06
+epoch [39/50] batch [545/1000] time 1.553 (1.566) data 0.000 (0.002) loss 1.8906 (1.1187) acc 68.7500 (72.0470) lr 3.1545e-04 eta 4:58:57
+epoch [39/50] batch [550/1000] time 1.585 (1.566) data 0.001 (0.002) loss 1.2793 (1.1203) acc 75.0000 (72.0000) lr 3.1545e-04 eta 4:58:49
+epoch [39/50] batch [555/1000] time 1.554 (1.566) data 0.000 (0.002) loss 1.0391 (1.1205) acc 75.0000 (71.9989) lr 3.1545e-04 eta 4:58:41
+epoch [39/50] batch [560/1000] time 1.524 (1.566) data 0.000 (0.002) loss 0.8950 (1.1200) acc 68.7500 (71.9587) lr 3.1545e-04 eta 4:58:32
+epoch [39/50] batch [565/1000] time 1.547 (1.566) data 0.001 (0.002) loss 1.0430 (1.1194) acc 68.7500 (71.9580) lr 3.1545e-04 eta 4:58:22
+epoch [39/50] batch [570/1000] time 1.546 (1.565) data 0.000 (0.002) loss 1.1133 (1.1172) acc 71.8750 (71.9956) lr 3.1545e-04 eta 4:58:13
+epoch [39/50] batch [575/1000] time 1.571 (1.566) data 0.001 (0.002) loss 0.4985 (1.1166) acc 81.2500 (72.0109) lr 3.1545e-04 eta 4:58:05
+epoch [39/50] batch [580/1000] time 1.573 (1.565) data 0.000 (0.002) loss 1.6953 (1.1172) acc 62.5000 (72.0097) lr 3.1545e-04 eta 4:57:57
+epoch [39/50] batch [585/1000] time 1.535 (1.565) data 0.001 (0.002) loss 1.1992 (1.1159) acc 68.7500 (72.0406) lr 3.1545e-04 eta 4:57:47
+epoch [39/50] batch [590/1000] time 1.558 (1.566) data 0.001 (0.002) loss 0.7510 (1.1163) acc 75.0000 (72.0286) lr 3.1545e-04 eta 4:57:42
+epoch [39/50] batch [595/1000] time 1.575 (1.565) data 0.000 (0.002) loss 1.1084 (1.1173) acc 71.8750 (71.9958) lr 3.1545e-04 eta 4:57:34
+epoch [39/50] batch [600/1000] time 1.555 (1.565) data 0.001 (0.002) loss 0.9873 (1.1167) acc 71.8750 (71.9844) lr 3.1545e-04 eta 4:57:25
+epoch [39/50] batch [605/1000] time 1.562 (1.565) data 0.001 (0.002) loss 0.4536 (1.1140) acc 87.5000 (72.0455) lr 3.1545e-04 eta 4:57:17
+epoch [39/50] batch [610/1000] time 1.542 (1.565) data 0.000 (0.002) loss 0.8501 (1.1131) acc 71.8750 (72.0543) lr 3.1545e-04 eta 4:57:08
+epoch [39/50] batch [615/1000] time 1.553 (1.565) data 0.001 (0.002) loss 1.2783 (1.1121) acc 68.7500 (72.0681) lr 3.1545e-04 eta 4:57:00
+epoch [39/50] batch [620/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.0430 (1.1138) acc 71.8750 (72.0363) lr 3.1545e-04 eta 4:56:51
+epoch [39/50] batch [625/1000] time 1.589 (1.565) data 0.001 (0.002) loss 1.0547 (1.1165) acc 68.7500 (72.0200) lr 3.1545e-04 eta 4:56:42
+epoch [39/50] batch [630/1000] time 1.556 (1.565) data 0.001 (0.002) loss 0.5435 (1.1146) acc 87.5000 (72.0188) lr 3.1545e-04 eta 4:56:33
+epoch [39/50] batch [635/1000] time 1.550 (1.565) data 0.001 (0.002) loss 0.5728 (1.1129) acc 84.3750 (72.0571) lr 3.1545e-04 eta 4:56:27
+epoch [39/50] batch [640/1000] time 1.558 (1.565) data 0.001 (0.002) loss 1.1650 (1.1132) acc 71.8750 (72.0605) lr 3.1545e-04 eta 4:56:19
+epoch [39/50] batch [645/1000] time 1.550 (1.565) data 0.000 (0.002) loss 0.8994 (1.1137) acc 78.1250 (72.0591) lr 3.1545e-04 eta 4:56:09
+epoch [39/50] batch [650/1000] time 1.559 (1.565) data 0.000 (0.002) loss 0.8008 (1.1111) acc 75.0000 (72.1250) lr 3.1545e-04 eta 4:56:01
+epoch [39/50] batch [655/1000] time 1.549 (1.565) data 0.001 (0.002) loss 1.5254 (1.1127) acc 68.7500 (72.0897) lr 3.1545e-04 eta 4:55:52
+epoch [39/50] batch [660/1000] time 1.565 (1.565) data 0.001 (0.002) loss 0.8462 (1.1135) acc 78.1250 (72.0644) lr 3.1545e-04 eta 4:55:45
+epoch [39/50] batch [665/1000] time 1.577 (1.565) data 0.000 (0.002) loss 1.2402 (1.1145) acc 68.7500 (72.0536) lr 3.1545e-04 eta 4:55:36
+epoch [39/50] batch [670/1000] time 1.547 (1.565) data 0.000 (0.002) loss 1.1611 (1.1147) acc 78.1250 (72.0382) lr 3.1545e-04 eta 4:55:28
+epoch [39/50] batch [675/1000] time 1.565 (1.565) data 0.001 (0.002) loss 0.6196 (1.1135) acc 84.3750 (72.0787) lr 3.1545e-04 eta 4:55:21
+epoch [39/50] batch [680/1000] time 1.557 (1.565) data 0.000 (0.002) loss 1.0381 (1.1121) acc 65.6250 (72.1002) lr 3.1545e-04 eta 4:55:15
+epoch [39/50] batch [685/1000] time 1.563 (1.565) data 0.000 (0.002) loss 0.6909 (1.1110) acc 87.5000 (72.1214) lr 3.1545e-04 eta 4:55:07
+epoch [39/50] batch [690/1000] time 1.548 (1.565) data 0.000 (0.002) loss 1.1553 (1.1115) acc 62.5000 (72.0924) lr 3.1545e-04 eta 4:54:59
+epoch [39/50] batch [695/1000] time 1.580 (1.565) data 0.001 (0.002) loss 1.3350 (1.1113) acc 68.7500 (72.1133) lr 3.1545e-04 eta 4:54:52
+epoch [39/50] batch [700/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.9429 (1.1114) acc 75.0000 (72.1205) lr 3.1545e-04 eta 4:54:43
+epoch [39/50] batch [705/1000] time 1.576 (1.565) data 0.000 (0.002) loss 1.0283 (1.1098) acc 71.8750 (72.1720) lr 3.1545e-04 eta 4:54:36
+epoch [39/50] batch [710/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.1758 (1.1114) acc 68.7500 (72.1347) lr 3.1545e-04 eta 4:54:28
+epoch [39/50] batch [715/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.5708 (1.1106) acc 84.3750 (72.1329) lr 3.1545e-04 eta 4:54:19
+epoch [39/50] batch [720/1000] time 1.541 (1.565) data 0.001 (0.002) loss 1.1494 (1.1101) acc 59.3750 (72.1441) lr 3.1545e-04 eta 4:54:11
+epoch [39/50] batch [725/1000] time 1.546 (1.565) data 0.001 (0.002) loss 1.1367 (1.1097) acc 75.0000 (72.1466) lr 3.1545e-04 eta 4:54:02
+epoch [39/50] batch [730/1000] time 1.565 (1.565) data 0.000 (0.002) loss 0.9971 (1.1106) acc 81.2500 (72.1361) lr 3.1545e-04 eta 4:53:53
+epoch [39/50] batch [735/1000] time 1.551 (1.565) data 0.001 (0.002) loss 0.9668 (1.1101) acc 78.1250 (72.1301) lr 3.1545e-04 eta 4:53:45
+epoch [39/50] batch [740/1000] time 1.738 (1.565) data 0.000 (0.002) loss 1.2109 (1.1113) acc 68.7500 (72.0988) lr 3.1545e-04 eta 4:53:40
+epoch [39/50] batch [745/1000] time 1.559 (1.565) data 0.000 (0.002) loss 0.7759 (1.1121) acc 75.0000 (72.0889) lr 3.1545e-04 eta 4:53:31
+epoch [39/50] batch [750/1000] time 1.569 (1.565) data 0.000 (0.002) loss 0.8662 (1.1102) acc 71.8750 (72.1042) lr 3.1545e-04 eta 4:53:23
+epoch [39/50] batch [755/1000] time 1.548 (1.565) data 0.001 (0.002) loss 0.9492 (1.1100) acc 75.0000 (72.1192) lr 3.1545e-04 eta 4:53:15
+epoch [39/50] batch [760/1000] time 1.555 (1.565) data 0.001 (0.002) loss 0.9287 (1.1113) acc 75.0000 (72.0847) lr 3.1545e-04 eta 4:53:07
+epoch [39/50] batch [765/1000] time 1.541 (1.565) data 0.000 (0.002) loss 1.6895 (1.1113) acc 56.2500 (72.0792) lr 3.1545e-04 eta 4:52:58
+epoch [39/50] batch [770/1000] time 1.553 (1.565) data 0.000 (0.002) loss 1.0811 (1.1103) acc 71.8750 (72.0657) lr 3.1545e-04 eta 4:52:49
+epoch [39/50] batch [775/1000] time 1.574 (1.565) data 0.000 (0.002) loss 1.4902 (1.1112) acc 65.6250 (72.0444) lr 3.1545e-04 eta 4:52:41
+epoch [39/50] batch [780/1000] time 1.580 (1.565) data 0.000 (0.002) loss 1.4014 (1.1116) acc 75.0000 (72.0553) lr 3.1545e-04 eta 4:52:34
+epoch [39/50] batch [785/1000] time 1.712 (1.565) data 0.000 (0.002) loss 1.3896 (1.1124) acc 56.2500 (72.0462) lr 3.1545e-04 eta 4:52:28
+epoch [39/50] batch [790/1000] time 1.551 (1.565) data 0.001 (0.002) loss 0.9414 (1.1116) acc 75.0000 (72.0570) lr 3.1545e-04 eta 4:52:20
+epoch [39/50] batch [795/1000] time 1.555 (1.565) data 0.000 (0.002) loss 0.8921 (1.1113) acc 71.8750 (72.0676) lr 3.1545e-04 eta 4:52:11
+epoch [39/50] batch [800/1000] time 1.568 (1.565) data 0.000 (0.002) loss 1.5088 (1.1118) acc 71.8750 (72.0625) lr 3.1545e-04 eta 4:52:03
+epoch [39/50] batch [805/1000] time 1.536 (1.565) data 0.000 (0.002) loss 1.5107 (1.1115) acc 59.3750 (72.0613) lr 3.1545e-04 eta 4:51:54
+epoch [39/50] batch [810/1000] time 1.529 (1.564) data 0.001 (0.002) loss 1.4775 (1.1122) acc 68.7500 (72.0370) lr 3.1545e-04 eta 4:51:46
+epoch [39/50] batch [815/1000] time 1.560 (1.564) data 0.000 (0.002) loss 0.7803 (1.1117) acc 75.0000 (72.0475) lr 3.1545e-04 eta 4:51:37
+epoch [39/50] batch [820/1000] time 1.545 (1.564) data 0.000 (0.002) loss 0.8545 (1.1115) acc 78.1250 (72.0389) lr 3.1545e-04 eta 4:51:30
+epoch [39/50] batch [825/1000] time 1.569 (1.564) data 0.001 (0.002) loss 0.8013 (1.1117) acc 84.3750 (72.0379) lr 3.1545e-04 eta 4:51:22
+epoch [39/50] batch [830/1000] time 1.593 (1.565) data 0.001 (0.002) loss 1.4922 (1.1122) acc 56.2500 (71.9917) lr 3.1545e-04 eta 4:51:17
+epoch [39/50] batch [835/1000] time 1.569 (1.565) data 0.000 (0.002) loss 1.6084 (1.1128) acc 53.1250 (71.9798) lr 3.1545e-04 eta 4:51:10
+epoch [39/50] batch [840/1000] time 1.552 (1.565) data 0.001 (0.002) loss 1.1123 (1.1131) acc 65.6250 (71.9680) lr 3.1545e-04 eta 4:51:02
+epoch [39/50] batch [845/1000] time 1.581 (1.565) data 0.000 (0.002) loss 1.0771 (1.1129) acc 71.8750 (71.9859) lr 3.1545e-04 eta 4:50:54
+epoch [39/50] batch [850/1000] time 1.572 (1.565) data 0.000 (0.002) loss 0.7959 (1.1128) acc 78.1250 (71.9816) lr 3.1545e-04 eta 4:50:46
+epoch [39/50] batch [855/1000] time 1.576 (1.565) data 0.000 (0.002) loss 1.4512 (1.1125) acc 62.5000 (71.9810) lr 3.1545e-04 eta 4:50:39
+epoch [39/50] batch [860/1000] time 1.600 (1.565) data 0.000 (0.002) loss 0.8076 (1.1127) acc 81.2500 (71.9695) lr 3.1545e-04 eta 4:50:31
+epoch [39/50] batch [865/1000] time 1.555 (1.565) data 0.001 (0.002) loss 0.8696 (1.1134) acc 75.0000 (71.9617) lr 3.1545e-04 eta 4:50:24
+epoch [39/50] batch [870/1000] time 1.549 (1.565) data 0.001 (0.002) loss 1.3408 (1.1136) acc 68.7500 (71.9792) lr 3.1545e-04 eta 4:50:15
+epoch [39/50] batch [875/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.2236 (1.1131) acc 68.7500 (71.9786) lr 3.1545e-04 eta 4:50:07
+epoch [39/50] batch [880/1000] time 1.545 (1.565) data 0.000 (0.002) loss 1.4170 (1.1135) acc 71.8750 (71.9851) lr 3.1545e-04 eta 4:49:59
+epoch [39/50] batch [885/1000] time 1.551 (1.565) data 0.000 (0.002) loss 1.4355 (1.1163) acc 68.7500 (71.9597) lr 3.1545e-04 eta 4:49:51
+epoch [39/50] batch [890/1000] time 1.577 (1.565) data 0.001 (0.002) loss 0.7178 (1.1161) acc 87.5000 (71.9803) lr 3.1545e-04 eta 4:49:43
+epoch [39/50] batch [895/1000] time 1.550 (1.565) data 0.001 (0.002) loss 1.0186 (1.1154) acc 65.6250 (71.9797) lr 3.1545e-04 eta 4:49:37
+epoch [39/50] batch [900/1000] time 1.548 (1.565) data 0.001 (0.002) loss 0.9517 (1.1151) acc 78.1250 (71.9826) lr 3.1545e-04 eta 4:49:29
+epoch [39/50] batch [905/1000] time 1.570 (1.565) data 0.000 (0.002) loss 0.7388 (1.1146) acc 81.2500 (71.9993) lr 3.1545e-04 eta 4:49:21
+epoch [39/50] batch [910/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.4307 (1.1160) acc 65.6250 (71.9574) lr 3.1545e-04 eta 4:49:13
+epoch [39/50] batch [915/1000] time 1.514 (1.565) data 0.000 (0.002) loss 1.0410 (1.1159) acc 75.0000 (71.9706) lr 3.1545e-04 eta 4:49:04
+epoch [39/50] batch [920/1000] time 1.567 (1.565) data 0.001 (0.002) loss 1.8750 (1.1162) acc 56.2500 (71.9837) lr 3.1545e-04 eta 4:48:56
+epoch [39/50] batch [925/1000] time 1.551 (1.565) data 0.001 (0.002) loss 1.0938 (1.1152) acc 75.0000 (72.0034) lr 3.1545e-04 eta 4:48:49
+epoch [39/50] batch [930/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.5566 (1.1149) acc 71.8750 (72.0094) lr 3.1545e-04 eta 4:48:41
+epoch [39/50] batch [935/1000] time 1.580 (1.565) data 0.001 (0.002) loss 1.1777 (1.1141) acc 78.1250 (72.0455) lr 3.1545e-04 eta 4:48:33
+epoch [39/50] batch [940/1000] time 1.576 (1.565) data 0.000 (0.002) loss 1.0312 (1.1136) acc 71.8750 (72.0578) lr 3.1545e-04 eta 4:48:27
+epoch [39/50] batch [945/1000] time 1.553 (1.565) data 0.001 (0.002) loss 1.0830 (1.1126) acc 71.8750 (72.0833) lr 3.1545e-04 eta 4:48:19
+epoch [39/50] batch [950/1000] time 1.552 (1.565) data 0.001 (0.002) loss 0.8477 (1.1114) acc 78.1250 (72.1020) lr 3.1545e-04 eta 4:48:10
+epoch [39/50] batch [955/1000] time 1.573 (1.565) data 0.000 (0.002) loss 0.7373 (1.1099) acc 78.1250 (72.1204) lr 3.1545e-04 eta 4:48:02
+epoch [39/50] batch [960/1000] time 1.551 (1.565) data 0.001 (0.002) loss 1.1123 (1.1100) acc 71.8750 (72.1159) lr 3.1545e-04 eta 4:47:54
+epoch [39/50] batch [965/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.8916 (1.1091) acc 75.0000 (72.1341) lr 3.1545e-04 eta 4:47:46
+epoch [39/50] batch [970/1000] time 1.557 (1.565) data 0.001 (0.002) loss 0.6372 (1.1086) acc 84.3750 (72.1424) lr 3.1545e-04 eta 4:47:37
+epoch [39/50] batch [975/1000] time 1.587 (1.565) data 0.001 (0.002) loss 0.6182 (1.1079) acc 75.0000 (72.1474) lr 3.1545e-04 eta 4:47:30
+epoch [39/50] batch [980/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.8281 (1.1083) acc 81.2500 (72.1237) lr 3.1545e-04 eta 4:47:24
+epoch [39/50] batch [985/1000] time 1.554 (1.565) data 0.001 (0.002) loss 1.4814 (1.1095) acc 65.6250 (72.0971) lr 3.1545e-04 eta 4:47:17
+epoch [39/50] batch [990/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.1318 (1.1089) acc 75.0000 (72.1244) lr 3.1545e-04 eta 4:47:09
+epoch [39/50] batch [995/1000] time 1.568 (1.565) data 0.000 (0.002) loss 0.4731 (1.1064) acc 78.1250 (72.1734) lr 3.1545e-04 eta 4:47:01
+epoch [39/50] batch [1000/1000] time 1.588 (1.565) data 0.000 (0.002) loss 1.5508 (1.1069) acc 62.5000 (72.1719) lr 2.7103e-04 eta 4:46:53
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,398
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [40/50] batch [5/1000] time 1.582 (1.717) data 0.000 (0.206) loss 1.4727 (1.1109) acc 59.3750 (75.6250) lr 2.7103e-04 eta 5:14:39
+epoch [40/50] batch [10/1000] time 1.572 (1.636) data 0.001 (0.104) loss 0.5205 (1.1531) acc 87.5000 (74.3750) lr 2.7103e-04 eta 4:59:41
+epoch [40/50] batch [15/1000] time 1.561 (1.614) data 0.001 (0.069) loss 1.5771 (1.1555) acc 62.5000 (73.9583) lr 2.7103e-04 eta 4:55:24
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+epoch [40/50] batch [25/1000] time 1.558 (1.593) data 0.000 (0.042) loss 1.6230 (1.0959) acc 53.1250 (73.0000) lr 2.7103e-04 eta 4:51:26
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+epoch [40/50] batch [585/1000] time 1.558 (1.566) data 0.000 (0.002) loss 0.9351 (1.0811) acc 78.1250 (72.7778) lr 2.7103e-04 eta 4:31:50
+epoch [40/50] batch [590/1000] time 1.735 (1.566) data 0.000 (0.002) loss 0.7778 (1.0821) acc 71.8750 (72.7701) lr 2.7103e-04 eta 4:31:45
+epoch [40/50] batch [595/1000] time 1.541 (1.566) data 0.001 (0.002) loss 1.0654 (1.0820) acc 75.0000 (72.7941) lr 2.7103e-04 eta 4:31:35
+epoch [40/50] batch [600/1000] time 1.572 (1.566) data 0.001 (0.002) loss 0.4854 (1.0810) acc 90.6250 (72.8281) lr 2.7103e-04 eta 4:31:28
+epoch [40/50] batch [605/1000] time 1.534 (1.566) data 0.001 (0.002) loss 1.2744 (1.0820) acc 65.6250 (72.7893) lr 2.7103e-04 eta 4:31:19
+epoch [40/50] batch [610/1000] time 1.589 (1.566) data 0.001 (0.002) loss 1.3047 (1.0838) acc 56.2500 (72.7254) lr 2.7103e-04 eta 4:31:12
+epoch [40/50] batch [615/1000] time 1.571 (1.566) data 0.000 (0.002) loss 1.2998 (1.0828) acc 65.6250 (72.7388) lr 2.7103e-04 eta 4:31:04
+epoch [40/50] batch [620/1000] time 1.565 (1.566) data 0.000 (0.002) loss 0.8623 (1.0820) acc 78.1250 (72.7520) lr 2.7103e-04 eta 4:30:56
+epoch [40/50] batch [625/1000] time 1.538 (1.566) data 0.001 (0.002) loss 1.2607 (1.0826) acc 68.7500 (72.7300) lr 2.7103e-04 eta 4:30:47
+epoch [40/50] batch [630/1000] time 1.576 (1.566) data 0.000 (0.002) loss 0.8076 (1.0814) acc 75.0000 (72.7530) lr 2.7103e-04 eta 4:30:39
+epoch [40/50] batch [635/1000] time 1.545 (1.566) data 0.000 (0.002) loss 1.2188 (1.0815) acc 75.0000 (72.7904) lr 2.7103e-04 eta 4:30:30
+epoch [40/50] batch [640/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.7808 (1.0808) acc 81.2500 (72.8174) lr 2.7103e-04 eta 4:30:23
+epoch [40/50] batch [645/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.1406 (1.0812) acc 68.7500 (72.7859) lr 2.7103e-04 eta 4:30:15
+epoch [40/50] batch [650/1000] time 1.554 (1.566) data 0.000 (0.002) loss 0.7866 (1.0823) acc 84.3750 (72.7356) lr 2.7103e-04 eta 4:30:06
+epoch [40/50] batch [655/1000] time 1.572 (1.566) data 0.001 (0.002) loss 1.3564 (1.0809) acc 71.8750 (72.7767) lr 2.7103e-04 eta 4:30:00
+epoch [40/50] batch [660/1000] time 1.576 (1.566) data 0.001 (0.002) loss 1.3525 (1.0811) acc 65.6250 (72.7652) lr 2.7103e-04 eta 4:29:51
+epoch [40/50] batch [665/1000] time 1.569 (1.566) data 0.000 (0.002) loss 1.2227 (1.0801) acc 75.0000 (72.7820) lr 2.7103e-04 eta 4:29:42
+epoch [40/50] batch [670/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.7808 (1.0784) acc 78.1250 (72.8125) lr 2.7103e-04 eta 4:29:35
+epoch [40/50] batch [675/1000] time 1.535 (1.566) data 0.000 (0.002) loss 1.2314 (1.0801) acc 71.8750 (72.7963) lr 2.7103e-04 eta 4:29:26
+epoch [40/50] batch [680/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.0566 (1.0803) acc 75.0000 (72.7895) lr 2.7103e-04 eta 4:29:19
+epoch [40/50] batch [685/1000] time 1.584 (1.566) data 0.001 (0.002) loss 0.9707 (1.0809) acc 71.8750 (72.7600) lr 2.7103e-04 eta 4:29:11
+epoch [40/50] batch [690/1000] time 1.564 (1.566) data 0.000 (0.002) loss 2.0996 (1.0811) acc 53.1250 (72.7763) lr 2.7103e-04 eta 4:29:04
+epoch [40/50] batch [695/1000] time 1.578 (1.566) data 0.001 (0.002) loss 0.4526 (1.0803) acc 84.3750 (72.7968) lr 2.7103e-04 eta 4:28:57
+epoch [40/50] batch [700/1000] time 1.570 (1.566) data 0.001 (0.002) loss 1.1797 (1.0795) acc 75.0000 (72.8259) lr 2.7103e-04 eta 4:28:51
+epoch [40/50] batch [705/1000] time 1.569 (1.566) data 0.001 (0.002) loss 0.9370 (1.0798) acc 68.7500 (72.8413) lr 2.7103e-04 eta 4:28:44
+epoch [40/50] batch [710/1000] time 1.564 (1.566) data 0.000 (0.002) loss 1.6494 (1.0809) acc 56.2500 (72.8345) lr 2.7103e-04 eta 4:28:36
+epoch [40/50] batch [715/1000] time 1.541 (1.566) data 0.000 (0.002) loss 0.9731 (1.0799) acc 71.8750 (72.8497) lr 2.7103e-04 eta 4:28:28
+epoch [40/50] batch [720/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.7803 (1.0799) acc 75.0000 (72.8472) lr 2.7103e-04 eta 4:28:20
+epoch [40/50] batch [725/1000] time 1.570 (1.566) data 0.000 (0.002) loss 1.3555 (1.0801) acc 65.6250 (72.8362) lr 2.7103e-04 eta 4:28:12
+epoch [40/50] batch [730/1000] time 1.568 (1.566) data 0.001 (0.002) loss 1.1582 (1.0797) acc 78.1250 (72.8510) lr 2.7103e-04 eta 4:28:03
+epoch [40/50] batch [735/1000] time 1.581 (1.566) data 0.000 (0.002) loss 0.7422 (1.0787) acc 75.0000 (72.8656) lr 2.7103e-04 eta 4:27:56
+epoch [40/50] batch [740/1000] time 1.561 (1.566) data 0.000 (0.002) loss 1.1768 (1.0799) acc 78.1250 (72.8378) lr 2.7103e-04 eta 4:27:47
+epoch [40/50] batch [745/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.2314 (1.0806) acc 62.5000 (72.8020) lr 2.7103e-04 eta 4:27:42
+epoch [40/50] batch [750/1000] time 1.546 (1.566) data 0.000 (0.002) loss 1.3516 (1.0825) acc 65.6250 (72.7667) lr 2.7103e-04 eta 4:27:33
+epoch [40/50] batch [755/1000] time 1.556 (1.566) data 0.000 (0.002) loss 0.9023 (1.0821) acc 71.8750 (72.7608) lr 2.7103e-04 eta 4:27:25
+epoch [40/50] batch [760/1000] time 1.568 (1.566) data 0.001 (0.002) loss 1.8838 (1.0825) acc 56.2500 (72.7508) lr 2.7103e-04 eta 4:27:18
+epoch [40/50] batch [765/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.1885 (1.0818) acc 62.5000 (72.7614) lr 2.7103e-04 eta 4:27:10
+epoch [40/50] batch [770/1000] time 1.558 (1.566) data 0.000 (0.002) loss 0.5884 (1.0818) acc 84.3750 (72.7881) lr 2.7103e-04 eta 4:27:02
+epoch [40/50] batch [775/1000] time 1.551 (1.566) data 0.000 (0.002) loss 1.0283 (1.0811) acc 71.8750 (72.7944) lr 2.7103e-04 eta 4:26:54
+epoch [40/50] batch [780/1000] time 1.552 (1.566) data 0.001 (0.002) loss 1.0596 (1.0801) acc 71.8750 (72.7965) lr 2.7103e-04 eta 4:26:45
+epoch [40/50] batch [785/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.5420 (1.0811) acc 68.7500 (72.7906) lr 2.7103e-04 eta 4:26:37
+epoch [40/50] batch [790/1000] time 1.575 (1.566) data 0.000 (0.002) loss 1.2178 (1.0812) acc 81.2500 (72.7769) lr 2.7103e-04 eta 4:26:30
+epoch [40/50] batch [795/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.8164 (1.0814) acc 78.1250 (72.7909) lr 2.7103e-04 eta 4:26:21
+epoch [40/50] batch [800/1000] time 1.552 (1.566) data 0.001 (0.002) loss 1.3564 (1.0819) acc 71.8750 (72.7891) lr 2.7103e-04 eta 4:26:13
+epoch [40/50] batch [805/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.1465 (1.0818) acc 78.1250 (72.7834) lr 2.7103e-04 eta 4:26:07
+epoch [40/50] batch [810/1000] time 1.562 (1.566) data 0.001 (0.002) loss 1.1543 (1.0817) acc 71.8750 (72.7894) lr 2.7103e-04 eta 4:25:59
+epoch [40/50] batch [815/1000] time 1.582 (1.566) data 0.000 (0.002) loss 0.8120 (1.0805) acc 75.0000 (72.8106) lr 2.7103e-04 eta 4:25:52
+epoch [40/50] batch [820/1000] time 1.545 (1.566) data 0.001 (0.002) loss 0.9785 (1.0804) acc 71.8750 (72.8087) lr 2.7103e-04 eta 4:25:43
+epoch [40/50] batch [825/1000] time 1.540 (1.566) data 0.000 (0.002) loss 0.9009 (1.0795) acc 87.5000 (72.8258) lr 2.7103e-04 eta 4:25:35
+epoch [40/50] batch [830/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.1357 (1.0805) acc 78.1250 (72.8200) lr 2.7103e-04 eta 4:25:27
+epoch [40/50] batch [835/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.8262 (1.0797) acc 78.1250 (72.8256) lr 2.7103e-04 eta 4:25:18
+epoch [40/50] batch [840/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.5518 (1.0796) acc 90.6250 (72.8385) lr 2.7103e-04 eta 4:25:09
+epoch [40/50] batch [845/1000] time 1.562 (1.566) data 0.000 (0.002) loss 1.0303 (1.0785) acc 65.6250 (72.8624) lr 2.7103e-04 eta 4:25:01
+epoch [40/50] batch [850/1000] time 1.555 (1.566) data 0.000 (0.002) loss 1.6338 (1.0786) acc 71.8750 (72.8676) lr 2.7103e-04 eta 4:24:55
+epoch [40/50] batch [855/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.8203 (1.0777) acc 78.1250 (72.9094) lr 2.7103e-04 eta 4:24:47
+epoch [40/50] batch [860/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.7349 (1.0776) acc 81.2500 (72.9324) lr 2.7103e-04 eta 4:24:38
+epoch [40/50] batch [865/1000] time 1.579 (1.566) data 0.000 (0.002) loss 0.6997 (1.0774) acc 84.3750 (72.9408) lr 2.7103e-04 eta 4:24:30
+epoch [40/50] batch [870/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.0859 (1.0771) acc 71.8750 (72.9418) lr 2.7103e-04 eta 4:24:22
+epoch [40/50] batch [875/1000] time 1.563 (1.566) data 0.001 (0.002) loss 1.0635 (1.0767) acc 71.8750 (72.9607) lr 2.7103e-04 eta 4:24:15
+epoch [40/50] batch [880/1000] time 1.551 (1.566) data 0.001 (0.002) loss 1.6094 (1.0764) acc 75.0000 (72.9865) lr 2.7103e-04 eta 4:24:07
+epoch [40/50] batch [885/1000] time 1.584 (1.566) data 0.000 (0.002) loss 1.7051 (1.0772) acc 62.5000 (72.9555) lr 2.7103e-04 eta 4:23:59
+epoch [40/50] batch [890/1000] time 1.535 (1.566) data 0.000 (0.002) loss 0.8809 (1.0783) acc 71.8750 (72.9494) lr 2.7103e-04 eta 4:23:50
+epoch [40/50] batch [895/1000] time 1.586 (1.566) data 0.001 (0.002) loss 0.9795 (1.0787) acc 81.2500 (72.9504) lr 2.7103e-04 eta 4:23:45
+epoch [40/50] batch [900/1000] time 1.574 (1.566) data 0.000 (0.002) loss 0.8999 (1.0786) acc 87.5000 (72.9653) lr 2.7103e-04 eta 4:23:37
+epoch [40/50] batch [905/1000] time 1.550 (1.566) data 0.001 (0.002) loss 1.3115 (1.0792) acc 68.7500 (72.9420) lr 2.7103e-04 eta 4:23:29
+epoch [40/50] batch [910/1000] time 1.538 (1.566) data 0.000 (0.002) loss 1.0439 (1.0799) acc 62.5000 (72.9293) lr 2.7103e-04 eta 4:23:20
+epoch [40/50] batch [915/1000] time 1.538 (1.566) data 0.001 (0.002) loss 0.7812 (1.0801) acc 87.5000 (72.9337) lr 2.7103e-04 eta 4:23:12
+epoch [40/50] batch [920/1000] time 1.572 (1.566) data 0.000 (0.002) loss 0.5566 (1.0797) acc 78.1250 (72.9280) lr 2.7103e-04 eta 4:23:04
+epoch [40/50] batch [925/1000] time 1.605 (1.566) data 0.001 (0.002) loss 1.2432 (1.0804) acc 75.0000 (72.9122) lr 2.7103e-04 eta 4:22:56
+epoch [40/50] batch [930/1000] time 1.572 (1.566) data 0.001 (0.002) loss 1.2236 (1.0806) acc 59.3750 (72.9066) lr 2.7103e-04 eta 4:22:48
+epoch [40/50] batch [935/1000] time 1.576 (1.566) data 0.000 (0.002) loss 1.2959 (1.0808) acc 65.6250 (72.9044) lr 2.7103e-04 eta 4:22:41
+epoch [40/50] batch [940/1000] time 1.552 (1.566) data 0.000 (0.002) loss 0.7466 (1.0804) acc 78.1250 (72.9156) lr 2.7103e-04 eta 4:22:33
+epoch [40/50] batch [945/1000] time 1.539 (1.566) data 0.000 (0.002) loss 0.9092 (1.0815) acc 81.2500 (72.9134) lr 2.7103e-04 eta 4:22:24
+epoch [40/50] batch [950/1000] time 1.547 (1.566) data 0.000 (0.002) loss 0.9556 (1.0811) acc 65.6250 (72.9145) lr 2.7103e-04 eta 4:22:15
+epoch [40/50] batch [955/1000] time 1.715 (1.566) data 0.000 (0.002) loss 1.4092 (1.0814) acc 65.6250 (72.8959) lr 2.7103e-04 eta 4:22:09
+epoch [40/50] batch [960/1000] time 1.553 (1.566) data 0.000 (0.002) loss 1.2305 (1.0816) acc 65.6250 (72.8939) lr 2.7103e-04 eta 4:22:01
+epoch [40/50] batch [965/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.4404 (1.0812) acc 59.3750 (72.9210) lr 2.7103e-04 eta 4:21:53
+epoch [40/50] batch [970/1000] time 1.537 (1.566) data 0.000 (0.002) loss 0.6792 (1.0796) acc 87.5000 (72.9639) lr 2.7103e-04 eta 4:21:45
+epoch [40/50] batch [975/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.8154 (1.0810) acc 56.2500 (72.9199) lr 2.7103e-04 eta 4:21:38
+epoch [40/50] batch [980/1000] time 1.570 (1.566) data 0.000 (0.002) loss 1.2178 (1.0807) acc 65.6250 (72.9050) lr 2.7103e-04 eta 4:21:30
+epoch [40/50] batch [985/1000] time 1.555 (1.566) data 0.001 (0.002) loss 0.7100 (1.0798) acc 78.1250 (72.9156) lr 2.7103e-04 eta 4:21:22
+epoch [40/50] batch [990/1000] time 1.552 (1.566) data 0.000 (0.002) loss 1.0586 (1.0798) acc 71.8750 (72.9135) lr 2.7103e-04 eta 4:21:13
+epoch [40/50] batch [995/1000] time 1.541 (1.566) data 0.000 (0.002) loss 1.0762 (1.0805) acc 78.1250 (72.9083) lr 2.7103e-04 eta 4:21:05
+epoch [40/50] batch [1000/1000] time 1.695 (1.566) data 0.000 (0.001) loss 0.9175 (1.0813) acc 84.3750 (72.8906) lr 2.2949e-04 eta 4:20:59
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,399
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [41/50] batch [5/1000] time 1.541 (1.700) data 0.000 (0.206) loss 0.4487 (1.1647) acc 87.5000 (75.6250) lr 2.2949e-04 eta 4:43:06
+epoch [41/50] batch [10/1000] time 1.552 (1.633) data 0.000 (0.103) loss 0.8735 (1.1973) acc 78.1250 (74.6875) lr 2.2949e-04 eta 4:31:54
+epoch [41/50] batch [15/1000] time 1.558 (1.610) data 0.001 (0.069) loss 0.7847 (1.0888) acc 75.0000 (74.3750) lr 2.2949e-04 eta 4:27:55
+epoch [41/50] batch [20/1000] time 1.551 (1.596) data 0.000 (0.052) loss 1.0859 (1.0669) acc 68.7500 (73.7500) lr 2.2949e-04 eta 4:25:31
+epoch [41/50] batch [25/1000] time 1.559 (1.590) data 0.000 (0.042) loss 0.9409 (1.1090) acc 84.3750 (74.0000) lr 2.2949e-04 eta 4:24:17
+epoch [41/50] batch [30/1000] time 1.800 (1.593) data 0.001 (0.035) loss 1.0996 (1.1038) acc 68.7500 (73.6458) lr 2.2949e-04 eta 4:24:43
+epoch [41/50] batch [35/1000] time 1.547 (1.590) data 0.001 (0.030) loss 1.2363 (1.0961) acc 71.8750 (73.3929) lr 2.2949e-04 eta 4:23:59
+epoch [41/50] batch [40/1000] time 1.584 (1.587) data 0.001 (0.026) loss 1.4258 (1.1052) acc 75.0000 (73.2031) lr 2.2949e-04 eta 4:23:27
+epoch [41/50] batch [45/1000] time 1.565 (1.584) data 0.001 (0.023) loss 0.9194 (1.1097) acc 78.1250 (72.9167) lr 2.2949e-04 eta 4:22:45
+epoch [41/50] batch [50/1000] time 1.574 (1.581) data 0.001 (0.021) loss 0.7847 (1.0998) acc 75.0000 (73.1875) lr 2.2949e-04 eta 4:22:08
+epoch [41/50] batch [55/1000] time 1.571 (1.579) data 0.000 (0.019) loss 0.8506 (1.1079) acc 75.0000 (73.0682) lr 2.2949e-04 eta 4:21:44
+epoch [41/50] batch [60/1000] time 1.567 (1.577) data 0.000 (0.018) loss 1.5830 (1.1060) acc 59.3750 (72.9688) lr 2.2949e-04 eta 4:21:17
+epoch [41/50] batch [65/1000] time 1.560 (1.576) data 0.000 (0.016) loss 1.6357 (1.1173) acc 75.0000 (73.1250) lr 2.2949e-04 eta 4:20:55
+epoch [41/50] batch [70/1000] time 1.556 (1.574) data 0.000 (0.015) loss 0.7920 (1.1160) acc 81.2500 (73.2589) lr 2.2949e-04 eta 4:20:29
+epoch [41/50] batch [75/1000] time 1.571 (1.573) data 0.000 (0.014) loss 0.9932 (1.1245) acc 78.1250 (72.7500) lr 2.2949e-04 eta 4:20:15
+epoch [41/50] batch [80/1000] time 1.582 (1.573) data 0.001 (0.013) loss 0.4580 (1.1405) acc 81.2500 (72.1875) lr 2.2949e-04 eta 4:20:01
+epoch [41/50] batch [85/1000] time 1.547 (1.572) data 0.001 (0.013) loss 1.2090 (1.1496) acc 68.7500 (72.1324) lr 2.2949e-04 eta 4:19:45
+epoch [41/50] batch [90/1000] time 1.590 (1.571) data 0.000 (0.012) loss 1.1846 (1.1404) acc 75.0000 (72.4306) lr 2.2949e-04 eta 4:19:31
+epoch [41/50] batch [95/1000] time 1.544 (1.573) data 0.001 (0.011) loss 1.1514 (1.1368) acc 71.8750 (72.4671) lr 2.2949e-04 eta 4:19:36
+epoch [41/50] batch [100/1000] time 1.580 (1.572) data 0.000 (0.011) loss 0.8516 (1.1324) acc 81.2500 (72.5938) lr 2.2949e-04 eta 4:19:26
+epoch [41/50] batch [105/1000] time 1.533 (1.571) data 0.000 (0.010) loss 1.6592 (1.1274) acc 65.6250 (72.6786) lr 2.2949e-04 eta 4:19:07
+epoch [41/50] batch [110/1000] time 1.555 (1.571) data 0.001 (0.010) loss 1.0674 (1.1190) acc 68.7500 (72.7841) lr 2.2949e-04 eta 4:18:54
+epoch [41/50] batch [115/1000] time 1.557 (1.570) data 0.000 (0.009) loss 1.2959 (1.1197) acc 68.7500 (72.6630) lr 2.2949e-04 eta 4:18:37
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+epoch [41/50] batch [685/1000] time 1.566 (1.565) data 0.000 (0.002) loss 0.6328 (1.0915) acc 84.3750 (72.7874) lr 2.2949e-04 eta 4:02:56
+epoch [41/50] batch [690/1000] time 1.572 (1.565) data 0.000 (0.002) loss 1.0654 (1.0907) acc 78.1250 (72.7989) lr 2.2949e-04 eta 4:02:48
+epoch [41/50] batch [695/1000] time 1.544 (1.565) data 0.001 (0.002) loss 1.1650 (1.0916) acc 62.5000 (72.7608) lr 2.2949e-04 eta 4:02:39
+epoch [41/50] batch [700/1000] time 1.562 (1.565) data 0.000 (0.002) loss 1.0928 (1.0913) acc 65.6250 (72.7679) lr 2.2949e-04 eta 4:02:32
+epoch [41/50] batch [705/1000] time 1.585 (1.565) data 0.000 (0.002) loss 0.4487 (1.0931) acc 90.6250 (72.7216) lr 2.2949e-04 eta 4:02:25
+epoch [41/50] batch [710/1000] time 1.567 (1.565) data 0.000 (0.002) loss 0.9258 (1.0922) acc 78.1250 (72.7421) lr 2.2949e-04 eta 4:02:18
+epoch [41/50] batch [715/1000] time 1.586 (1.565) data 0.000 (0.002) loss 0.8843 (1.0909) acc 75.0000 (72.7448) lr 2.2949e-04 eta 4:02:10
+epoch [41/50] batch [720/1000] time 1.581 (1.565) data 0.001 (0.002) loss 0.6362 (1.0908) acc 87.5000 (72.7257) lr 2.2949e-04 eta 4:02:03
+epoch [41/50] batch [725/1000] time 1.541 (1.565) data 0.000 (0.002) loss 1.5938 (1.0915) acc 71.8750 (72.7026) lr 2.2949e-04 eta 4:01:55
+epoch [41/50] batch [730/1000] time 1.532 (1.565) data 0.001 (0.002) loss 0.5762 (1.0910) acc 87.5000 (72.7183) lr 2.2949e-04 eta 4:01:45
+epoch [41/50] batch [735/1000] time 1.564 (1.565) data 0.000 (0.002) loss 1.4629 (1.0913) acc 75.0000 (72.7253) lr 2.2949e-04 eta 4:01:37
+epoch [41/50] batch [740/1000] time 1.571 (1.565) data 0.001 (0.002) loss 0.3674 (1.0897) acc 90.6250 (72.7703) lr 2.2949e-04 eta 4:01:29
+epoch [41/50] batch [745/1000] time 1.559 (1.565) data 0.001 (0.002) loss 0.4954 (1.0890) acc 87.5000 (72.8020) lr 2.2949e-04 eta 4:01:24
+epoch [41/50] batch [750/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.6211 (1.0873) acc 84.3750 (72.8250) lr 2.2949e-04 eta 4:01:16
+epoch [41/50] batch [755/1000] time 1.562 (1.565) data 0.000 (0.002) loss 1.1816 (1.0875) acc 65.6250 (72.8311) lr 2.2949e-04 eta 4:01:08
+epoch [41/50] batch [760/1000] time 1.563 (1.565) data 0.000 (0.002) loss 0.9717 (1.0872) acc 81.2500 (72.8577) lr 2.2949e-04 eta 4:00:59
+epoch [41/50] batch [765/1000] time 1.580 (1.565) data 0.000 (0.002) loss 1.6523 (1.0873) acc 68.7500 (72.8472) lr 2.2949e-04 eta 4:00:52
+epoch [41/50] batch [770/1000] time 1.562 (1.565) data 0.000 (0.002) loss 0.6519 (1.0860) acc 78.1250 (72.8693) lr 2.2949e-04 eta 4:00:44
+epoch [41/50] batch [775/1000] time 1.543 (1.565) data 0.000 (0.002) loss 1.0996 (1.0853) acc 59.3750 (72.8427) lr 2.2949e-04 eta 4:00:35
+epoch [41/50] batch [780/1000] time 1.565 (1.565) data 0.001 (0.002) loss 0.9854 (1.0850) acc 78.1250 (72.8446) lr 2.2949e-04 eta 4:00:27
+epoch [41/50] batch [785/1000] time 1.723 (1.565) data 0.001 (0.002) loss 1.1602 (1.0843) acc 78.1250 (72.8543) lr 2.2949e-04 eta 4:00:20
+epoch [41/50] batch [790/1000] time 1.554 (1.565) data 0.001 (0.002) loss 1.3125 (1.0836) acc 71.8750 (72.8600) lr 2.2949e-04 eta 4:00:12
+epoch [41/50] batch [795/1000] time 1.564 (1.565) data 0.001 (0.002) loss 1.2529 (1.0840) acc 59.3750 (72.8420) lr 2.2949e-04 eta 4:00:05
+epoch [41/50] batch [800/1000] time 1.535 (1.565) data 0.001 (0.002) loss 1.5098 (1.0843) acc 68.7500 (72.8281) lr 2.2949e-04 eta 3:59:56
+epoch [41/50] batch [805/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.0654 (1.0849) acc 78.1250 (72.8028) lr 2.2949e-04 eta 3:59:47
+epoch [41/50] batch [810/1000] time 1.548 (1.565) data 0.000 (0.002) loss 1.6865 (1.0855) acc 59.3750 (72.8009) lr 2.2949e-04 eta 3:59:39
+epoch [41/50] batch [815/1000] time 1.536 (1.565) data 0.001 (0.002) loss 1.1904 (1.0867) acc 65.6250 (72.7569) lr 2.2949e-04 eta 3:59:31
+epoch [41/50] batch [820/1000] time 1.552 (1.565) data 0.000 (0.002) loss 0.4507 (1.0859) acc 90.6250 (72.7973) lr 2.2949e-04 eta 3:59:23
+epoch [41/50] batch [825/1000] time 1.566 (1.565) data 0.000 (0.002) loss 0.8940 (1.0867) acc 84.3750 (72.7652) lr 2.2949e-04 eta 3:59:14
+epoch [41/50] batch [830/1000] time 1.545 (1.564) data 0.000 (0.002) loss 0.9976 (1.0862) acc 65.6250 (72.7560) lr 2.2949e-04 eta 3:59:06
+epoch [41/50] batch [835/1000] time 1.550 (1.564) data 0.000 (0.002) loss 1.0898 (1.0860) acc 68.7500 (72.7507) lr 2.2949e-04 eta 3:58:57
+epoch [41/50] batch [840/1000] time 1.549 (1.564) data 0.000 (0.002) loss 1.4844 (1.0869) acc 62.5000 (72.7455) lr 2.2949e-04 eta 3:58:50
+epoch [41/50] batch [845/1000] time 1.572 (1.564) data 0.001 (0.002) loss 1.0137 (1.0874) acc 71.8750 (72.7404) lr 2.2949e-04 eta 3:58:42
+epoch [41/50] batch [850/1000] time 1.565 (1.565) data 0.000 (0.002) loss 0.8740 (1.0868) acc 71.8750 (72.7500) lr 2.2949e-04 eta 3:58:36
+epoch [41/50] batch [855/1000] time 1.559 (1.565) data 0.000 (0.002) loss 1.1729 (1.0874) acc 75.0000 (72.7449) lr 2.2949e-04 eta 3:58:28
+epoch [41/50] batch [860/1000] time 1.545 (1.565) data 0.000 (0.002) loss 1.0459 (1.0875) acc 68.7500 (72.7253) lr 2.2949e-04 eta 3:58:20
+epoch [41/50] batch [865/1000] time 1.558 (1.565) data 0.000 (0.002) loss 0.9277 (1.0871) acc 81.2500 (72.7421) lr 2.2949e-04 eta 3:58:12
+epoch [41/50] batch [870/1000] time 1.574 (1.565) data 0.001 (0.002) loss 0.8745 (1.0875) acc 78.1250 (72.7550) lr 2.2949e-04 eta 3:58:05
+epoch [41/50] batch [875/1000] time 1.574 (1.565) data 0.000 (0.002) loss 0.9775 (1.0888) acc 71.8750 (72.7321) lr 2.2949e-04 eta 3:57:57
+epoch [41/50] batch [880/1000] time 1.547 (1.565) data 0.000 (0.002) loss 1.5156 (1.0898) acc 65.6250 (72.7095) lr 2.2949e-04 eta 3:57:49
+epoch [41/50] batch [885/1000] time 1.565 (1.565) data 0.001 (0.002) loss 1.1514 (1.0893) acc 75.0000 (72.7366) lr 2.2949e-04 eta 3:57:41
+epoch [41/50] batch [890/1000] time 1.563 (1.565) data 0.001 (0.002) loss 1.1387 (1.0882) acc 75.0000 (72.7353) lr 2.2949e-04 eta 3:57:33
+epoch [41/50] batch [895/1000] time 1.532 (1.565) data 0.001 (0.002) loss 0.7930 (1.0879) acc 81.2500 (72.7270) lr 2.2949e-04 eta 3:57:27
+epoch [41/50] batch [900/1000] time 1.578 (1.565) data 0.001 (0.002) loss 1.1768 (1.0879) acc 78.1250 (72.7396) lr 2.2949e-04 eta 3:57:19
+epoch [41/50] batch [905/1000] time 1.559 (1.565) data 0.001 (0.002) loss 0.9722 (1.0873) acc 78.1250 (72.7279) lr 2.2949e-04 eta 3:57:10
+epoch [41/50] batch [910/1000] time 1.559 (1.565) data 0.001 (0.002) loss 0.6440 (1.0862) acc 87.5000 (72.7644) lr 2.2949e-04 eta 3:57:02
+epoch [41/50] batch [915/1000] time 1.570 (1.565) data 0.001 (0.002) loss 0.6646 (1.0855) acc 81.2500 (72.7937) lr 2.2949e-04 eta 3:56:54
+epoch [41/50] batch [920/1000] time 1.539 (1.565) data 0.001 (0.002) loss 1.2070 (1.0863) acc 75.0000 (72.7785) lr 2.2949e-04 eta 3:56:46
+epoch [41/50] batch [925/1000] time 1.570 (1.565) data 0.000 (0.002) loss 1.7715 (1.0864) acc 62.5000 (72.7703) lr 2.2949e-04 eta 3:56:38
+epoch [41/50] batch [930/1000] time 1.564 (1.565) data 0.000 (0.002) loss 0.9199 (1.0867) acc 78.1250 (72.7890) lr 2.2949e-04 eta 3:56:30
+epoch [41/50] batch [935/1000] time 1.549 (1.565) data 0.001 (0.002) loss 1.0508 (1.0872) acc 75.0000 (72.7741) lr 2.2949e-04 eta 3:56:22
+epoch [41/50] batch [940/1000] time 1.590 (1.565) data 0.000 (0.002) loss 1.2119 (1.0859) acc 75.0000 (72.7959) lr 2.2949e-04 eta 3:56:16
+epoch [41/50] batch [945/1000] time 1.575 (1.565) data 0.000 (0.002) loss 0.8628 (1.0848) acc 75.0000 (72.8075) lr 2.2949e-04 eta 3:56:08
+epoch [41/50] batch [950/1000] time 1.552 (1.565) data 0.000 (0.002) loss 1.1592 (1.0846) acc 62.5000 (72.8158) lr 2.2949e-04 eta 3:56:00
+epoch [41/50] batch [955/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.8716 (1.0842) acc 71.8750 (72.8076) lr 2.2949e-04 eta 3:55:52
+epoch [41/50] batch [960/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.6753 (1.0837) acc 84.3750 (72.8385) lr 2.2949e-04 eta 3:55:44
+epoch [41/50] batch [965/1000] time 1.575 (1.565) data 0.000 (0.002) loss 1.4844 (1.0840) acc 68.7500 (72.8530) lr 2.2949e-04 eta 3:55:36
+epoch [41/50] batch [970/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.6445 (1.0829) acc 81.2500 (72.8705) lr 2.2949e-04 eta 3:55:27
+epoch [41/50] batch [975/1000] time 1.586 (1.565) data 0.000 (0.002) loss 0.9570 (1.0839) acc 71.8750 (72.8494) lr 2.2949e-04 eta 3:55:19
+epoch [41/50] batch [980/1000] time 1.560 (1.565) data 0.000 (0.002) loss 0.9395 (1.0831) acc 78.1250 (72.8699) lr 2.2949e-04 eta 3:55:11
+epoch [41/50] batch [985/1000] time 1.558 (1.564) data 0.001 (0.002) loss 0.7583 (1.0823) acc 81.2500 (72.8807) lr 2.2949e-04 eta 3:55:03
+epoch [41/50] batch [990/1000] time 1.553 (1.564) data 0.000 (0.002) loss 0.9468 (1.0824) acc 75.0000 (72.8788) lr 2.2949e-04 eta 3:54:55
+epoch [41/50] batch [995/1000] time 1.542 (1.564) data 0.000 (0.002) loss 1.2598 (1.0812) acc 71.8750 (72.8957) lr 2.2949e-04 eta 3:54:46
+epoch [41/50] batch [1000/1000] time 1.691 (1.564) data 0.001 (0.002) loss 1.1689 (1.0806) acc 62.5000 (72.9125) lr 1.9098e-04 eta 3:54:39
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,400
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [42/50] batch [5/1000] time 1.569 (1.715) data 0.001 (0.209) loss 1.1084 (1.0374) acc 71.8750 (73.7500) lr 1.9098e-04 eta 4:17:09
+epoch [42/50] batch [10/1000] time 1.564 (1.632) data 0.000 (0.105) loss 0.9697 (1.0807) acc 78.1250 (74.6875) lr 1.9098e-04 eta 4:04:30
+epoch [42/50] batch [15/1000] time 1.556 (1.607) data 0.000 (0.070) loss 1.7471 (1.2029) acc 62.5000 (72.2917) lr 1.9098e-04 eta 4:00:42
+epoch [42/50] batch [20/1000] time 1.544 (1.613) data 0.001 (0.053) loss 1.2275 (1.1369) acc 62.5000 (72.9688) lr 1.9098e-04 eta 4:01:29
+epoch [42/50] batch [25/1000] time 1.563 (1.605) data 0.001 (0.042) loss 1.0205 (1.0551) acc 71.8750 (74.3750) lr 1.9098e-04 eta 4:00:05
+epoch [42/50] batch [30/1000] time 1.552 (1.597) data 0.000 (0.035) loss 1.1699 (1.0558) acc 68.7500 (73.9583) lr 1.9098e-04 eta 3:58:46
+epoch [42/50] batch [35/1000] time 1.569 (1.592) data 0.001 (0.030) loss 1.4072 (1.0877) acc 71.8750 (72.9464) lr 1.9098e-04 eta 3:57:49
+epoch [42/50] batch [40/1000] time 1.572 (1.589) data 0.001 (0.027) loss 0.9624 (1.0890) acc 71.8750 (72.3438) lr 1.9098e-04 eta 3:57:15
+epoch [42/50] batch [45/1000] time 1.537 (1.584) data 0.001 (0.024) loss 1.3232 (1.0893) acc 68.7500 (72.1528) lr 1.9098e-04 eta 3:56:23
+epoch [42/50] batch [50/1000] time 1.567 (1.582) data 0.001 (0.021) loss 0.9629 (1.0993) acc 78.1250 (72.1250) lr 1.9098e-04 eta 3:55:58
+epoch [42/50] batch [55/1000] time 1.546 (1.579) data 0.001 (0.019) loss 1.2188 (1.1054) acc 68.7500 (72.0455) lr 1.9098e-04 eta 3:55:27
+epoch [42/50] batch [60/1000] time 1.561 (1.579) data 0.001 (0.018) loss 1.3750 (1.0996) acc 65.6250 (72.4479) lr 1.9098e-04 eta 3:55:18
+epoch [42/50] batch [65/1000] time 1.547 (1.577) data 0.000 (0.017) loss 0.7656 (1.1003) acc 78.1250 (72.5481) lr 1.9098e-04 eta 3:54:51
+epoch [42/50] batch [70/1000] time 1.564 (1.577) data 0.001 (0.016) loss 0.5244 (1.0859) acc 84.3750 (72.7679) lr 1.9098e-04 eta 3:54:44
+epoch [42/50] batch [75/1000] time 1.563 (1.576) data 0.000 (0.014) loss 1.1328 (1.0727) acc 75.0000 (73.1250) lr 1.9098e-04 eta 3:54:25
+epoch [42/50] batch [80/1000] time 1.560 (1.576) data 0.000 (0.014) loss 1.4502 (1.0760) acc 68.7500 (73.0078) lr 1.9098e-04 eta 3:54:18
+epoch [42/50] batch [85/1000] time 1.579 (1.576) data 0.001 (0.013) loss 1.0488 (1.0652) acc 71.8750 (72.9779) lr 1.9098e-04 eta 3:54:05
+epoch [42/50] batch [90/1000] time 1.534 (1.575) data 0.000 (0.012) loss 0.9038 (1.0610) acc 65.6250 (72.7431) lr 1.9098e-04 eta 3:53:55
+epoch [42/50] batch [95/1000] time 1.581 (1.575) data 0.001 (0.012) loss 0.9570 (1.0513) acc 78.1250 (72.7632) lr 1.9098e-04 eta 3:53:43
+epoch [42/50] batch [100/1000] time 1.554 (1.575) data 0.000 (0.011) loss 0.9644 (1.0485) acc 84.3750 (72.9688) lr 1.9098e-04 eta 3:53:33
+epoch [42/50] batch [105/1000] time 1.555 (1.574) data 0.000 (0.011) loss 0.8462 (1.0478) acc 71.8750 (72.7679) lr 1.9098e-04 eta 3:53:22
+epoch [42/50] batch [110/1000] time 1.546 (1.574) data 0.000 (0.010) loss 0.2983 (1.0439) acc 93.7500 (72.8977) lr 1.9098e-04 eta 3:53:09
+epoch [42/50] batch [115/1000] time 1.573 (1.573) data 0.000 (0.010) loss 1.5078 (1.0476) acc 71.8750 (72.9348) lr 1.9098e-04 eta 3:52:58
+epoch [42/50] batch [120/1000] time 1.548 (1.573) data 0.000 (0.009) loss 0.9053 (1.0475) acc 84.3750 (72.9948) lr 1.9098e-04 eta 3:52:45
+epoch [42/50] batch [125/1000] time 1.549 (1.574) data 0.001 (0.009) loss 1.1533 (1.0635) acc 78.1250 (72.7250) lr 1.9098e-04 eta 3:52:46
+epoch [42/50] batch [130/1000] time 1.534 (1.573) data 0.001 (0.009) loss 1.2617 (1.0673) acc 65.6250 (72.5721) lr 1.9098e-04 eta 3:52:31
+epoch [42/50] batch [135/1000] time 1.538 (1.572) data 0.001 (0.008) loss 0.6680 (1.0615) acc 87.5000 (72.7546) lr 1.9098e-04 eta 3:52:19
+epoch [42/50] batch [140/1000] time 1.552 (1.572) data 0.000 (0.008) loss 0.5884 (1.0519) acc 87.5000 (73.0134) lr 1.9098e-04 eta 3:52:06
+epoch [42/50] batch [145/1000] time 1.578 (1.571) data 0.000 (0.008) loss 1.4766 (1.0578) acc 62.5000 (72.9741) lr 1.9098e-04 eta 3:51:52
+epoch [42/50] batch [150/1000] time 1.583 (1.571) data 0.000 (0.008) loss 0.9790 (1.0591) acc 78.1250 (72.9792) lr 1.9098e-04 eta 3:51:42
+epoch [42/50] batch [155/1000] time 1.582 (1.571) data 0.001 (0.007) loss 0.8169 (1.0522) acc 75.0000 (73.1250) lr 1.9098e-04 eta 3:51:32
+epoch [42/50] batch [160/1000] time 1.583 (1.571) data 0.001 (0.007) loss 1.0479 (1.0536) acc 78.1250 (73.1250) lr 1.9098e-04 eta 3:51:23
+epoch [42/50] batch [165/1000] time 1.581 (1.571) data 0.000 (0.007) loss 1.6191 (1.0590) acc 62.5000 (72.9924) lr 1.9098e-04 eta 3:51:15
+epoch [42/50] batch [170/1000] time 1.570 (1.571) data 0.000 (0.007) loss 1.0654 (1.0532) acc 68.7500 (73.1985) lr 1.9098e-04 eta 3:51:13
+epoch [42/50] batch [175/1000] time 1.543 (1.571) data 0.000 (0.007) loss 1.2568 (1.0523) acc 59.3750 (73.1964) lr 1.9098e-04 eta 3:51:01
+epoch [42/50] batch [180/1000] time 1.585 (1.571) data 0.000 (0.006) loss 1.3027 (1.0566) acc 62.5000 (73.0903) lr 1.9098e-04 eta 3:50:54
+epoch [42/50] batch [185/1000] time 1.552 (1.570) data 0.000 (0.006) loss 1.2256 (1.0559) acc 71.8750 (73.0574) lr 1.9098e-04 eta 3:50:41
+epoch [42/50] batch [190/1000] time 1.545 (1.570) data 0.000 (0.006) loss 1.1543 (1.0617) acc 71.8750 (73.0099) lr 1.9098e-04 eta 3:50:28
+epoch [42/50] batch [195/1000] time 1.598 (1.570) data 0.001 (0.006) loss 1.2949 (1.0624) acc 68.7500 (73.0609) lr 1.9098e-04 eta 3:50:20
+epoch [42/50] batch [200/1000] time 1.556 (1.569) data 0.000 (0.006) loss 1.1299 (1.0621) acc 59.3750 (73.0156) lr 1.9098e-04 eta 3:50:10
+epoch [42/50] batch [205/1000] time 1.552 (1.569) data 0.000 (0.006) loss 1.4990 (1.0667) acc 65.6250 (72.9268) lr 1.9098e-04 eta 3:50:01
+epoch [42/50] batch [210/1000] time 1.561 (1.570) data 0.000 (0.005) loss 1.0664 (1.0718) acc 68.7500 (72.8571) lr 1.9098e-04 eta 3:49:58
+epoch [42/50] batch [215/1000] time 1.550 (1.570) data 0.000 (0.005) loss 0.9341 (1.0702) acc 65.6250 (72.8488) lr 1.9098e-04 eta 3:49:49
+epoch [42/50] batch [220/1000] time 1.546 (1.570) data 0.000 (0.005) loss 0.7139 (1.0658) acc 78.1250 (72.9261) lr 1.9098e-04 eta 3:49:40
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+epoch [42/50] batch [780/1000] time 1.567 (1.566) data 0.001 (0.002) loss 1.1045 (1.0806) acc 81.2500 (73.0970) lr 1.9098e-04 eta 3:34:29
+epoch [42/50] batch [785/1000] time 1.567 (1.566) data 0.000 (0.002) loss 1.1650 (1.0800) acc 71.8750 (73.1051) lr 1.9098e-04 eta 3:34:21
+epoch [42/50] batch [790/1000] time 1.558 (1.566) data 0.000 (0.002) loss 0.8662 (1.0803) acc 75.0000 (73.0973) lr 1.9098e-04 eta 3:34:13
+epoch [42/50] batch [795/1000] time 1.562 (1.565) data 0.000 (0.002) loss 0.9907 (1.0799) acc 71.8750 (73.0857) lr 1.9098e-04 eta 3:34:04
+epoch [42/50] batch [800/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.1152 (1.0800) acc 59.3750 (73.0547) lr 1.9098e-04 eta 3:33:57
+epoch [42/50] batch [805/1000] time 1.564 (1.565) data 0.001 (0.002) loss 1.2490 (1.0810) acc 71.8750 (73.0512) lr 1.9098e-04 eta 3:33:48
+epoch [42/50] batch [810/1000] time 1.562 (1.565) data 0.000 (0.002) loss 1.3662 (1.0825) acc 65.6250 (73.0131) lr 1.9098e-04 eta 3:33:40
+epoch [42/50] batch [815/1000] time 1.556 (1.566) data 0.001 (0.002) loss 0.7026 (1.0821) acc 78.1250 (73.0061) lr 1.9098e-04 eta 3:33:34
+epoch [42/50] batch [820/1000] time 1.547 (1.566) data 0.001 (0.002) loss 1.2178 (1.0822) acc 68.7500 (72.9878) lr 1.9098e-04 eta 3:33:26
+epoch [42/50] batch [825/1000] time 1.572 (1.566) data 0.000 (0.002) loss 0.8828 (1.0827) acc 68.7500 (72.9924) lr 1.9098e-04 eta 3:33:18
+epoch [42/50] batch [830/1000] time 1.554 (1.566) data 0.001 (0.002) loss 1.2979 (1.0827) acc 68.7500 (72.9970) lr 1.9098e-04 eta 3:33:10
+epoch [42/50] batch [835/1000] time 1.547 (1.566) data 0.001 (0.002) loss 0.6616 (1.0808) acc 78.1250 (73.0352) lr 1.9098e-04 eta 3:33:03
+epoch [42/50] batch [840/1000] time 1.577 (1.566) data 0.001 (0.002) loss 1.1172 (1.0819) acc 68.7500 (73.0097) lr 1.9098e-04 eta 3:32:55
+epoch [42/50] batch [845/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.0947 (1.0820) acc 71.8750 (73.0141) lr 1.9098e-04 eta 3:32:47
+epoch [42/50] batch [850/1000] time 1.566 (1.566) data 0.000 (0.002) loss 1.1670 (1.0821) acc 71.8750 (72.9890) lr 1.9098e-04 eta 3:32:39
+epoch [42/50] batch [855/1000] time 1.547 (1.566) data 0.000 (0.002) loss 1.3447 (1.0820) acc 62.5000 (72.9898) lr 1.9098e-04 eta 3:32:31
+epoch [42/50] batch [860/1000] time 1.572 (1.566) data 0.001 (0.002) loss 1.1416 (1.0833) acc 71.8750 (72.9433) lr 1.9098e-04 eta 3:32:23
+epoch [42/50] batch [865/1000] time 1.580 (1.566) data 0.001 (0.002) loss 0.6846 (1.0827) acc 75.0000 (72.9371) lr 1.9098e-04 eta 3:32:15
+epoch [42/50] batch [870/1000] time 1.555 (1.566) data 0.001 (0.002) loss 0.9536 (1.0827) acc 71.8750 (72.9274) lr 1.9098e-04 eta 3:32:07
+epoch [42/50] batch [875/1000] time 1.549 (1.566) data 0.000 (0.002) loss 0.9321 (1.0828) acc 84.3750 (72.9429) lr 1.9098e-04 eta 3:31:59
+epoch [42/50] batch [880/1000] time 1.550 (1.566) data 0.001 (0.002) loss 1.7070 (1.0835) acc 71.8750 (72.9190) lr 1.9098e-04 eta 3:31:52
+epoch [42/50] batch [885/1000] time 1.572 (1.566) data 0.000 (0.002) loss 0.9883 (1.0834) acc 75.0000 (72.9237) lr 1.9098e-04 eta 3:31:44
+epoch [42/50] batch [890/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.9092 (1.0824) acc 78.1250 (72.9459) lr 1.9098e-04 eta 3:31:37
+epoch [42/50] batch [895/1000] time 1.565 (1.566) data 0.001 (0.002) loss 1.1387 (1.0817) acc 59.3750 (72.9679) lr 1.9098e-04 eta 3:31:29
+epoch [42/50] batch [900/1000] time 1.558 (1.565) data 0.001 (0.002) loss 1.2324 (1.0816) acc 71.8750 (72.9757) lr 1.9098e-04 eta 3:31:20
+epoch [42/50] batch [905/1000] time 1.580 (1.566) data 0.001 (0.002) loss 1.1230 (1.0826) acc 81.2500 (72.9593) lr 1.9098e-04 eta 3:31:12
+epoch [42/50] batch [910/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.8027 (1.0822) acc 75.0000 (72.9533) lr 1.9098e-04 eta 3:31:05
+epoch [42/50] batch [915/1000] time 1.559 (1.565) data 0.001 (0.002) loss 1.2549 (1.0815) acc 65.6250 (72.9713) lr 1.9098e-04 eta 3:30:56
+epoch [42/50] batch [920/1000] time 1.583 (1.565) data 0.001 (0.002) loss 0.8311 (1.0811) acc 81.2500 (73.0095) lr 1.9098e-04 eta 3:30:48
+epoch [42/50] batch [925/1000] time 1.558 (1.566) data 0.001 (0.002) loss 0.8022 (1.0817) acc 75.0000 (72.9899) lr 1.9098e-04 eta 3:30:42
+epoch [42/50] batch [930/1000] time 1.533 (1.566) data 0.001 (0.002) loss 0.9321 (1.0813) acc 71.8750 (72.9906) lr 1.9098e-04 eta 3:30:33
+epoch [42/50] batch [935/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.8779 (1.0811) acc 71.8750 (73.0047) lr 1.9098e-04 eta 3:30:25
+epoch [42/50] batch [940/1000] time 1.570 (1.566) data 0.001 (0.002) loss 1.6084 (1.0811) acc 65.6250 (72.9953) lr 1.9098e-04 eta 3:30:18
+epoch [42/50] batch [945/1000] time 1.561 (1.566) data 0.001 (0.002) loss 1.0957 (1.0812) acc 68.7500 (72.9828) lr 1.9098e-04 eta 3:30:10
+epoch [42/50] batch [950/1000] time 1.561 (1.566) data 0.001 (0.002) loss 1.2441 (1.0824) acc 71.8750 (72.9737) lr 1.9098e-04 eta 3:30:02
+epoch [42/50] batch [955/1000] time 1.552 (1.566) data 0.000 (0.002) loss 0.8970 (1.0825) acc 75.0000 (72.9679) lr 1.9098e-04 eta 3:29:54
+epoch [42/50] batch [960/1000] time 1.575 (1.566) data 0.001 (0.002) loss 1.4717 (1.0826) acc 75.0000 (72.9720) lr 1.9098e-04 eta 3:29:46
+epoch [42/50] batch [965/1000] time 1.556 (1.566) data 0.001 (0.002) loss 0.9971 (1.0822) acc 75.0000 (72.9890) lr 1.9098e-04 eta 3:29:40
+epoch [42/50] batch [970/1000] time 1.573 (1.566) data 0.000 (0.002) loss 0.7983 (1.0819) acc 81.2500 (72.9929) lr 1.9098e-04 eta 3:29:32
+epoch [42/50] batch [975/1000] time 1.558 (1.566) data 0.000 (0.002) loss 0.7798 (1.0823) acc 84.3750 (72.9936) lr 1.9098e-04 eta 3:29:24
+epoch [42/50] batch [980/1000] time 1.532 (1.566) data 0.000 (0.002) loss 1.7490 (1.0834) acc 68.7500 (72.9815) lr 1.9098e-04 eta 3:29:16
+epoch [42/50] batch [985/1000] time 1.541 (1.566) data 0.001 (0.002) loss 0.9971 (1.0836) acc 68.7500 (72.9759) lr 1.9098e-04 eta 3:29:08
+epoch [42/50] batch [990/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.3398 (1.0841) acc 62.5000 (72.9672) lr 1.9098e-04 eta 3:28:59
+epoch [42/50] batch [995/1000] time 1.591 (1.566) data 0.000 (0.002) loss 1.5947 (1.0849) acc 65.6250 (72.9585) lr 1.9098e-04 eta 3:28:51
+epoch [42/50] batch [1000/1000] time 1.562 (1.565) data 0.000 (0.002) loss 0.9590 (1.0851) acc 75.0000 (72.9313) lr 1.5567e-04 eta 3:28:43
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,333
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.2%
+epoch [43/50] batch [5/1000] time 1.545 (1.694) data 0.000 (0.196) loss 1.6094 (1.0738) acc 53.1250 (68.1250) lr 1.5567e-04 eta 3:45:40
+epoch [43/50] batch [10/1000] time 1.538 (1.623) data 0.000 (0.098) loss 0.9756 (1.0037) acc 84.3750 (70.9375) lr 1.5567e-04 eta 3:36:06
+epoch [43/50] batch [15/1000] time 1.589 (1.606) data 0.001 (0.066) loss 0.5371 (0.9270) acc 87.5000 (73.5417) lr 1.5567e-04 eta 3:33:46
+epoch [43/50] batch [20/1000] time 1.574 (1.598) data 0.001 (0.049) loss 0.9658 (0.9839) acc 71.8750 (72.5000) lr 1.5567e-04 eta 3:32:29
+epoch [43/50] batch [25/1000] time 1.554 (1.590) data 0.001 (0.040) loss 0.7607 (1.0216) acc 81.2500 (72.1250) lr 1.5567e-04 eta 3:31:23
+epoch [43/50] batch [30/1000] time 1.584 (1.589) data 0.001 (0.033) loss 1.0195 (1.0000) acc 75.0000 (72.6042) lr 1.5567e-04 eta 3:31:03
+epoch [43/50] batch [35/1000] time 1.820 (1.593) data 0.000 (0.028) loss 0.7485 (0.9876) acc 75.0000 (72.6786) lr 1.5567e-04 eta 3:31:25
+epoch [43/50] batch [40/1000] time 1.534 (1.588) data 0.000 (0.025) loss 1.7529 (1.0097) acc 65.6250 (73.0469) lr 1.5567e-04 eta 3:30:38
+epoch [43/50] batch [45/1000] time 1.540 (1.584) data 0.000 (0.022) loss 0.8066 (1.0108) acc 75.0000 (73.1250) lr 1.5567e-04 eta 3:29:57
+epoch [43/50] batch [50/1000] time 1.572 (1.582) data 0.000 (0.020) loss 0.9131 (1.0272) acc 71.8750 (73.1875) lr 1.5567e-04 eta 3:29:37
+epoch [43/50] batch [55/1000] time 1.568 (1.579) data 0.001 (0.018) loss 0.6113 (1.0109) acc 81.2500 (73.3523) lr 1.5567e-04 eta 3:29:09
+epoch [43/50] batch [60/1000] time 1.571 (1.578) data 0.001 (0.017) loss 0.9873 (1.0024) acc 65.6250 (73.5417) lr 1.5567e-04 eta 3:28:51
+epoch [43/50] batch [65/1000] time 1.581 (1.577) data 0.000 (0.016) loss 0.5962 (1.0103) acc 84.3750 (73.4135) lr 1.5567e-04 eta 3:28:37
+epoch [43/50] batch [70/1000] time 1.552 (1.576) data 0.000 (0.014) loss 1.4600 (1.0152) acc 62.5000 (73.6161) lr 1.5567e-04 eta 3:28:19
+epoch [43/50] batch [75/1000] time 1.573 (1.575) data 0.000 (0.014) loss 1.0889 (1.0031) acc 71.8750 (73.7917) lr 1.5567e-04 eta 3:28:00
+epoch [43/50] batch [80/1000] time 1.558 (1.576) data 0.000 (0.013) loss 0.7319 (0.9938) acc 71.8750 (73.8281) lr 1.5567e-04 eta 3:28:02
+epoch [43/50] batch [85/1000] time 1.564 (1.575) data 0.001 (0.012) loss 1.6279 (1.0006) acc 68.7500 (73.6765) lr 1.5567e-04 eta 3:27:44
+epoch [43/50] batch [90/1000] time 1.550 (1.574) data 0.000 (0.011) loss 0.9365 (1.0065) acc 75.0000 (73.4028) lr 1.5567e-04 eta 3:27:31
+epoch [43/50] batch [95/1000] time 1.584 (1.574) data 0.000 (0.011) loss 0.9121 (1.0270) acc 75.0000 (72.9934) lr 1.5567e-04 eta 3:27:22
+epoch [43/50] batch [100/1000] time 1.557 (1.573) data 0.000 (0.010) loss 1.6807 (1.0339) acc 71.8750 (73.0000) lr 1.5567e-04 eta 3:27:05
+epoch [43/50] batch [105/1000] time 1.564 (1.572) data 0.000 (0.010) loss 1.1064 (1.0322) acc 68.7500 (73.0952) lr 1.5567e-04 eta 3:26:53
+epoch [43/50] batch [110/1000] time 1.551 (1.572) data 0.000 (0.009) loss 0.7861 (1.0353) acc 78.1250 (73.2102) lr 1.5567e-04 eta 3:26:40
+epoch [43/50] batch [115/1000] time 1.551 (1.572) data 0.000 (0.009) loss 1.1631 (1.0411) acc 65.6250 (73.2609) lr 1.5567e-04 eta 3:26:31
+epoch [43/50] batch [120/1000] time 1.541 (1.571) data 0.001 (0.009) loss 1.2520 (1.0375) acc 71.8750 (73.2552) lr 1.5567e-04 eta 3:26:18
+epoch [43/50] batch [125/1000] time 1.581 (1.571) data 0.000 (0.008) loss 0.9771 (1.0294) acc 81.2500 (73.5500) lr 1.5567e-04 eta 3:26:07
+epoch [43/50] batch [130/1000] time 1.555 (1.570) data 0.000 (0.008) loss 0.9307 (1.0346) acc 71.8750 (73.4135) lr 1.5567e-04 eta 3:25:57
+epoch [43/50] batch [135/1000] time 1.561 (1.570) data 0.001 (0.008) loss 0.6191 (1.0257) acc 75.0000 (73.4954) lr 1.5567e-04 eta 3:25:48
+epoch [43/50] batch [140/1000] time 1.552 (1.570) data 0.001 (0.007) loss 1.5195 (1.0180) acc 62.5000 (73.7946) lr 1.5567e-04 eta 3:25:38
+epoch [43/50] batch [145/1000] time 1.573 (1.571) data 0.000 (0.007) loss 1.2090 (1.0196) acc 68.7500 (73.7069) lr 1.5567e-04 eta 3:25:37
+epoch [43/50] batch [150/1000] time 1.572 (1.570) data 0.001 (0.007) loss 0.9131 (1.0258) acc 71.8750 (73.5417) lr 1.5567e-04 eta 3:25:27
+epoch [43/50] batch [155/1000] time 1.560 (1.570) data 0.001 (0.007) loss 0.6895 (1.0308) acc 84.3750 (73.5282) lr 1.5567e-04 eta 3:25:18
+epoch [43/50] batch [160/1000] time 1.572 (1.570) data 0.001 (0.007) loss 0.9888 (1.0422) acc 78.1250 (73.2617) lr 1.5567e-04 eta 3:25:07
+epoch [43/50] batch [165/1000] time 1.553 (1.570) data 0.001 (0.006) loss 0.9438 (1.0434) acc 65.6250 (73.1818) lr 1.5567e-04 eta 3:24:57
+epoch [43/50] batch [170/1000] time 1.587 (1.569) data 0.000 (0.006) loss 1.2520 (1.0416) acc 68.7500 (73.2169) lr 1.5567e-04 eta 3:24:48
+epoch [43/50] batch [175/1000] time 1.563 (1.569) data 0.000 (0.006) loss 0.9097 (1.0489) acc 78.1250 (73.1786) lr 1.5567e-04 eta 3:24:39
+epoch [43/50] batch [180/1000] time 1.584 (1.569) data 0.001 (0.006) loss 1.1006 (1.0492) acc 81.2500 (73.2639) lr 1.5567e-04 eta 3:24:33
+epoch [43/50] batch [185/1000] time 1.551 (1.569) data 0.000 (0.006) loss 1.3535 (1.0475) acc 68.7500 (73.2939) lr 1.5567e-04 eta 3:24:21
+epoch [43/50] batch [190/1000] time 1.549 (1.570) data 0.001 (0.006) loss 1.2109 (1.0510) acc 71.8750 (73.2895) lr 1.5567e-04 eta 3:24:21
+epoch [43/50] batch [195/1000] time 1.560 (1.570) data 0.001 (0.006) loss 0.9561 (1.0503) acc 75.0000 (73.3494) lr 1.5567e-04 eta 3:24:10
+epoch [43/50] batch [200/1000] time 1.558 (1.569) data 0.000 (0.005) loss 1.3789 (1.0514) acc 71.8750 (73.3281) lr 1.5567e-04 eta 3:24:00
+epoch [43/50] batch [205/1000] time 1.571 (1.569) data 0.001 (0.005) loss 1.4014 (1.0557) acc 62.5000 (73.2317) lr 1.5567e-04 eta 3:23:53
+epoch [43/50] batch [210/1000] time 1.563 (1.569) data 0.000 (0.005) loss 1.0420 (1.0558) acc 68.7500 (73.1696) lr 1.5567e-04 eta 3:23:44
+epoch [43/50] batch [215/1000] time 1.559 (1.569) data 0.000 (0.005) loss 1.0068 (1.0516) acc 75.0000 (73.2558) lr 1.5567e-04 eta 3:23:34
+epoch [43/50] batch [220/1000] time 1.555 (1.569) data 0.001 (0.005) loss 0.7612 (1.0506) acc 81.2500 (73.2955) lr 1.5567e-04 eta 3:23:23
+epoch [43/50] batch [225/1000] time 1.573 (1.569) data 0.000 (0.005) loss 1.1895 (1.0522) acc 78.1250 (73.3472) lr 1.5567e-04 eta 3:23:18
+epoch [43/50] batch [230/1000] time 1.549 (1.570) data 0.001 (0.005) loss 0.6426 (1.0571) acc 75.0000 (73.1929) lr 1.5567e-04 eta 3:23:15
+epoch [43/50] batch [235/1000] time 1.541 (1.569) data 0.000 (0.005) loss 1.6875 (1.0597) acc 71.8750 (73.2048) lr 1.5567e-04 eta 3:23:05
+epoch [43/50] batch [240/1000] time 1.558 (1.569) data 0.001 (0.005) loss 1.4033 (1.0665) acc 71.8750 (73.0859) lr 1.5567e-04 eta 3:22:53
+epoch [43/50] batch [245/1000] time 1.589 (1.569) data 0.000 (0.004) loss 0.9321 (1.0704) acc 78.1250 (72.9847) lr 1.5567e-04 eta 3:22:45
+epoch [43/50] batch [250/1000] time 1.562 (1.569) data 0.000 (0.004) loss 1.0029 (1.0712) acc 65.6250 (72.9625) lr 1.5567e-04 eta 3:22:36
+epoch [43/50] batch [255/1000] time 1.549 (1.568) data 0.000 (0.004) loss 0.9844 (1.0687) acc 75.0000 (72.9779) lr 1.5567e-04 eta 3:22:26
+epoch [43/50] batch [260/1000] time 1.544 (1.568) data 0.000 (0.004) loss 1.1240 (1.0667) acc 75.0000 (73.0048) lr 1.5567e-04 eta 3:22:18
+epoch [43/50] batch [265/1000] time 1.588 (1.568) data 0.001 (0.004) loss 0.6011 (1.0679) acc 90.6250 (73.0307) lr 1.5567e-04 eta 3:22:10
+epoch [43/50] batch [270/1000] time 1.554 (1.568) data 0.000 (0.004) loss 1.3047 (1.0659) acc 65.6250 (73.0787) lr 1.5567e-04 eta 3:22:00
+epoch [43/50] batch [275/1000] time 1.594 (1.568) data 0.001 (0.004) loss 0.8291 (1.0647) acc 78.1250 (73.1136) lr 1.5567e-04 eta 3:21:52
+epoch [43/50] batch [280/1000] time 1.559 (1.568) data 0.001 (0.004) loss 1.0137 (1.0622) acc 65.6250 (73.1473) lr 1.5567e-04 eta 3:21:44
+epoch [43/50] batch [285/1000] time 1.556 (1.568) data 0.001 (0.004) loss 0.7876 (1.0611) acc 84.3750 (73.1908) lr 1.5567e-04 eta 3:21:36
+epoch [43/50] batch [290/1000] time 1.557 (1.568) data 0.001 (0.004) loss 1.2988 (1.0625) acc 65.6250 (73.1466) lr 1.5567e-04 eta 3:21:26
+epoch [43/50] batch [295/1000] time 1.548 (1.568) data 0.000 (0.004) loss 0.8892 (1.0645) acc 59.3750 (73.1250) lr 1.5567e-04 eta 3:21:22
+epoch [43/50] batch [300/1000] time 1.569 (1.568) data 0.001 (0.004) loss 0.8022 (1.0644) acc 71.8750 (73.0729) lr 1.5567e-04 eta 3:21:13
+epoch [43/50] batch [305/1000] time 1.541 (1.568) data 0.001 (0.004) loss 1.2861 (1.0615) acc 68.7500 (73.0840) lr 1.5567e-04 eta 3:21:04
+epoch [43/50] batch [310/1000] time 1.566 (1.568) data 0.000 (0.004) loss 0.9048 (1.0618) acc 78.1250 (73.0746) lr 1.5567e-04 eta 3:20:56
+epoch [43/50] batch [315/1000] time 1.589 (1.568) data 0.000 (0.004) loss 1.6797 (1.0646) acc 68.7500 (73.1052) lr 1.5567e-04 eta 3:20:48
+epoch [43/50] batch [320/1000] time 1.538 (1.568) data 0.001 (0.004) loss 1.0488 (1.0674) acc 62.5000 (73.0566) lr 1.5567e-04 eta 3:20:41
+epoch [43/50] batch [325/1000] time 1.548 (1.568) data 0.000 (0.004) loss 1.3076 (1.0666) acc 62.5000 (73.0481) lr 1.5567e-04 eta 3:20:31
+epoch [43/50] batch [330/1000] time 1.557 (1.568) data 0.000 (0.003) loss 0.9976 (1.0658) acc 65.6250 (73.0019) lr 1.5567e-04 eta 3:20:23
+epoch [43/50] batch [335/1000] time 1.543 (1.567) data 0.000 (0.003) loss 0.8813 (1.0686) acc 78.1250 (72.9757) lr 1.5567e-04 eta 3:20:13
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+epoch [43/50] batch [890/1000] time 1.546 (1.564) data 0.000 (0.002) loss 1.4834 (1.0884) acc 71.8750 (72.6896) lr 1.5567e-04 eta 3:05:19
+epoch [43/50] batch [895/1000] time 1.581 (1.564) data 0.001 (0.002) loss 1.2842 (1.0888) acc 59.3750 (72.6466) lr 1.5567e-04 eta 3:05:12
+epoch [43/50] batch [900/1000] time 1.557 (1.564) data 0.001 (0.002) loss 0.7769 (1.0890) acc 84.3750 (72.6354) lr 1.5567e-04 eta 3:05:05
+epoch [43/50] batch [905/1000] time 1.565 (1.564) data 0.001 (0.002) loss 1.4658 (1.0894) acc 71.8750 (72.6209) lr 1.5567e-04 eta 3:04:57
+epoch [43/50] batch [910/1000] time 1.539 (1.564) data 0.000 (0.002) loss 2.0176 (1.0911) acc 56.2500 (72.5927) lr 1.5567e-04 eta 3:04:49
+epoch [43/50] batch [915/1000] time 1.586 (1.564) data 0.000 (0.002) loss 1.6387 (1.0909) acc 62.5000 (72.5888) lr 1.5567e-04 eta 3:04:41
+epoch [43/50] batch [920/1000] time 1.569 (1.564) data 0.000 (0.002) loss 1.0820 (1.0907) acc 65.6250 (72.5951) lr 1.5567e-04 eta 3:04:33
+epoch [43/50] batch [925/1000] time 1.540 (1.564) data 0.000 (0.002) loss 0.7852 (1.0900) acc 78.1250 (72.6182) lr 1.5567e-04 eta 3:04:25
+epoch [43/50] batch [930/1000] time 1.591 (1.564) data 0.000 (0.002) loss 0.9365 (1.0895) acc 78.1250 (72.6310) lr 1.5567e-04 eta 3:04:17
+epoch [43/50] batch [935/1000] time 1.547 (1.564) data 0.001 (0.002) loss 1.0605 (1.0889) acc 65.6250 (72.6437) lr 1.5567e-04 eta 3:04:09
+epoch [43/50] batch [940/1000] time 1.570 (1.564) data 0.000 (0.002) loss 1.2471 (1.0891) acc 71.8750 (72.6363) lr 1.5567e-04 eta 3:04:02
+epoch [43/50] batch [945/1000] time 1.569 (1.564) data 0.001 (0.002) loss 0.6362 (1.0876) acc 87.5000 (72.6819) lr 1.5567e-04 eta 3:03:55
+epoch [43/50] batch [950/1000] time 1.577 (1.564) data 0.000 (0.002) loss 0.8589 (1.0878) acc 75.0000 (72.6678) lr 1.5567e-04 eta 3:03:47
+epoch [43/50] batch [955/1000] time 1.573 (1.564) data 0.000 (0.002) loss 0.7700 (1.0876) acc 75.0000 (72.6767) lr 1.5567e-04 eta 3:03:39
+epoch [43/50] batch [960/1000] time 1.549 (1.564) data 0.001 (0.002) loss 1.3359 (1.0860) acc 62.5000 (72.7018) lr 1.5567e-04 eta 3:03:31
+epoch [43/50] batch [965/1000] time 1.542 (1.564) data 0.000 (0.001) loss 1.1953 (1.0861) acc 68.7500 (72.6846) lr 1.5567e-04 eta 3:03:23
+epoch [43/50] batch [970/1000] time 1.550 (1.564) data 0.001 (0.001) loss 1.1904 (1.0858) acc 65.6250 (72.6804) lr 1.5567e-04 eta 3:03:15
+epoch [43/50] batch [975/1000] time 1.577 (1.564) data 0.001 (0.001) loss 1.5410 (1.0867) acc 68.7500 (72.6538) lr 1.5567e-04 eta 3:03:07
+epoch [43/50] batch [980/1000] time 1.580 (1.564) data 0.001 (0.001) loss 0.9536 (1.0874) acc 68.7500 (72.6212) lr 1.5567e-04 eta 3:02:59
+epoch [43/50] batch [985/1000] time 1.537 (1.564) data 0.001 (0.001) loss 0.4348 (1.0861) acc 84.3750 (72.6428) lr 1.5567e-04 eta 3:02:52
+epoch [43/50] batch [990/1000] time 1.570 (1.564) data 0.000 (0.001) loss 1.0781 (1.0865) acc 62.5000 (72.6294) lr 1.5567e-04 eta 3:02:45
+epoch [43/50] batch [995/1000] time 1.551 (1.564) data 0.000 (0.001) loss 1.2217 (1.0864) acc 65.6250 (72.6225) lr 1.5567e-04 eta 3:02:37
+epoch [43/50] batch [1000/1000] time 1.569 (1.564) data 0.000 (0.001) loss 0.6567 (1.0852) acc 71.8750 (72.6500) lr 1.2369e-04 eta 3:02:29
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,401
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar
+epoch [44/50] batch [5/1000] time 1.558 (1.714) data 0.000 (0.212) loss 0.7197 (0.8562) acc 78.1250 (78.7500) lr 1.2369e-04 eta 3:19:52
+epoch [44/50] batch [10/1000] time 1.548 (1.640) data 0.001 (0.106) loss 1.0684 (1.0427) acc 68.7500 (74.3750) lr 1.2369e-04 eta 3:11:04
+epoch [44/50] batch [15/1000] time 1.566 (1.613) data 0.000 (0.071) loss 0.9155 (1.0370) acc 75.0000 (75.0000) lr 1.2369e-04 eta 3:07:49
+epoch [44/50] batch [20/1000] time 1.571 (1.599) data 0.001 (0.053) loss 0.4973 (1.0187) acc 87.5000 (76.0938) lr 1.2369e-04 eta 3:06:03
+epoch [44/50] batch [25/1000] time 1.566 (1.593) data 0.001 (0.043) loss 1.1953 (1.0516) acc 75.0000 (75.5000) lr 1.2369e-04 eta 3:05:10
+epoch [44/50] batch [30/1000] time 1.572 (1.588) data 0.000 (0.036) loss 1.0391 (1.0200) acc 71.8750 (75.7292) lr 1.2369e-04 eta 3:04:28
+epoch [44/50] batch [35/1000] time 1.828 (1.593) data 0.000 (0.031) loss 0.8125 (1.0475) acc 81.2500 (75.3571) lr 1.2369e-04 eta 3:04:55
+epoch [44/50] batch [40/1000] time 1.547 (1.588) data 0.000 (0.027) loss 1.1250 (1.0423) acc 68.7500 (75.3906) lr 1.2369e-04 eta 3:04:13
+epoch [44/50] batch [45/1000] time 1.546 (1.584) data 0.001 (0.024) loss 0.9331 (1.0598) acc 68.7500 (74.3750) lr 1.2369e-04 eta 3:03:36
+epoch [44/50] batch [50/1000] time 1.561 (1.582) data 0.000 (0.022) loss 0.4504 (1.0463) acc 90.6250 (74.8125) lr 1.2369e-04 eta 3:03:17
+epoch [44/50] batch [55/1000] time 1.555 (1.580) data 0.001 (0.020) loss 0.7788 (1.0446) acc 78.1250 (74.7727) lr 1.2369e-04 eta 3:02:54
+epoch [44/50] batch [60/1000] time 1.545 (1.577) data 0.001 (0.018) loss 1.1826 (1.0479) acc 71.8750 (74.2188) lr 1.2369e-04 eta 3:02:22
+epoch [44/50] batch [65/1000] time 1.531 (1.574) data 0.000 (0.017) loss 1.9619 (1.0597) acc 59.3750 (73.7981) lr 1.2369e-04 eta 3:01:56
+epoch [44/50] batch [70/1000] time 1.587 (1.573) data 0.001 (0.016) loss 0.8608 (1.0566) acc 71.8750 (73.6607) lr 1.2369e-04 eta 3:01:42
+epoch [44/50] batch [75/1000] time 1.576 (1.572) data 0.001 (0.015) loss 0.7285 (1.0400) acc 84.3750 (73.7500) lr 1.2369e-04 eta 3:01:29
+epoch [44/50] batch [80/1000] time 1.564 (1.572) data 0.000 (0.014) loss 1.1387 (1.0528) acc 81.2500 (73.7500) lr 1.2369e-04 eta 3:01:18
+epoch [44/50] batch [85/1000] time 1.544 (1.573) data 0.001 (0.013) loss 1.4854 (1.0516) acc 68.7500 (73.7868) lr 1.2369e-04 eta 3:01:17
+epoch [44/50] batch [90/1000] time 1.576 (1.572) data 0.000 (0.012) loss 1.0811 (1.0566) acc 71.8750 (73.4722) lr 1.2369e-04 eta 3:01:03
+epoch [44/50] batch [95/1000] time 1.549 (1.571) data 0.001 (0.012) loss 0.9990 (1.0635) acc 78.1250 (73.4211) lr 1.2369e-04 eta 3:00:50
+epoch [44/50] batch [100/1000] time 1.558 (1.571) data 0.000 (0.011) loss 1.3164 (1.0655) acc 62.5000 (73.4375) lr 1.2369e-04 eta 3:00:42
+epoch [44/50] batch [105/1000] time 1.560 (1.571) data 0.001 (0.011) loss 0.5371 (1.0576) acc 84.3750 (73.7500) lr 1.2369e-04 eta 3:00:35
+epoch [44/50] batch [110/1000] time 1.571 (1.571) data 0.001 (0.010) loss 1.0908 (1.0580) acc 68.7500 (73.8920) lr 1.2369e-04 eta 3:00:25
+epoch [44/50] batch [115/1000] time 1.571 (1.571) data 0.001 (0.010) loss 0.8667 (1.0615) acc 81.2500 (73.8315) lr 1.2369e-04 eta 3:00:14
+epoch [44/50] batch [120/1000] time 1.563 (1.570) data 0.001 (0.009) loss 0.7764 (1.0562) acc 78.1250 (74.1406) lr 1.2369e-04 eta 3:00:04
+epoch [44/50] batch [125/1000] time 1.547 (1.570) data 0.000 (0.009) loss 0.8521 (1.0498) acc 71.8750 (74.1500) lr 1.2369e-04 eta 2:59:52
+epoch [44/50] batch [130/1000] time 1.549 (1.569) data 0.001 (0.009) loss 1.0723 (1.0538) acc 68.7500 (73.9663) lr 1.2369e-04 eta 2:59:40
+epoch [44/50] batch [135/1000] time 1.563 (1.569) data 0.001 (0.008) loss 1.3301 (1.0548) acc 65.6250 (73.9583) lr 1.2369e-04 eta 2:59:29
+epoch [44/50] batch [140/1000] time 1.552 (1.569) data 0.001 (0.008) loss 1.8887 (1.0614) acc 65.6250 (73.9062) lr 1.2369e-04 eta 2:59:21
+epoch [44/50] batch [145/1000] time 1.551 (1.568) data 0.000 (0.008) loss 1.2461 (1.0656) acc 75.0000 (73.8793) lr 1.2369e-04 eta 2:59:10
+epoch [44/50] batch [150/1000] time 1.552 (1.568) data 0.001 (0.008) loss 1.3799 (1.0743) acc 71.8750 (73.7917) lr 1.2369e-04 eta 2:59:02
+epoch [44/50] batch [155/1000] time 1.580 (1.568) data 0.001 (0.007) loss 1.9014 (1.0945) acc 56.2500 (73.3871) lr 1.2369e-04 eta 2:58:54
+epoch [44/50] batch [160/1000] time 1.577 (1.568) data 0.000 (0.007) loss 1.1025 (1.0956) acc 65.6250 (73.2617) lr 1.2369e-04 eta 2:58:46
+epoch [44/50] batch [165/1000] time 1.561 (1.568) data 0.000 (0.007) loss 1.0566 (1.0959) acc 68.7500 (73.1818) lr 1.2369e-04 eta 2:58:39
+epoch [44/50] batch [170/1000] time 1.592 (1.568) data 0.001 (0.007) loss 1.9600 (1.1052) acc 62.5000 (73.0515) lr 1.2369e-04 eta 2:58:29
+epoch [44/50] batch [175/1000] time 1.554 (1.568) data 0.001 (0.007) loss 1.0049 (1.1042) acc 84.3750 (73.0893) lr 1.2369e-04 eta 2:58:22
+epoch [44/50] batch [180/1000] time 1.545 (1.568) data 0.000 (0.006) loss 0.7866 (1.0962) acc 84.3750 (73.3333) lr 1.2369e-04 eta 2:58:12
+epoch [44/50] batch [185/1000] time 1.722 (1.568) data 0.000 (0.006) loss 1.0654 (1.0983) acc 62.5000 (73.1588) lr 1.2369e-04 eta 2:58:06
+epoch [44/50] batch [190/1000] time 1.592 (1.568) data 0.000 (0.006) loss 1.1318 (1.0957) acc 65.6250 (73.1250) lr 1.2369e-04 eta 2:58:00
+epoch [44/50] batch [195/1000] time 1.567 (1.568) data 0.001 (0.006) loss 1.3838 (1.1035) acc 68.7500 (73.0288) lr 1.2369e-04 eta 2:57:53
+epoch [44/50] batch [200/1000] time 1.529 (1.568) data 0.000 (0.006) loss 0.7427 (1.1028) acc 90.6250 (73.0781) lr 1.2369e-04 eta 2:57:42
+epoch [44/50] batch [205/1000] time 1.563 (1.568) data 0.000 (0.006) loss 1.1113 (1.1071) acc 71.8750 (72.9878) lr 1.2369e-04 eta 2:57:33
+epoch [44/50] batch [210/1000] time 1.545 (1.568) data 0.000 (0.006) loss 1.3467 (1.1119) acc 62.5000 (72.8274) lr 1.2369e-04 eta 2:57:23
+epoch [44/50] batch [215/1000] time 1.560 (1.567) data 0.000 (0.005) loss 0.7773 (1.1082) acc 71.8750 (72.8488) lr 1.2369e-04 eta 2:57:14
+epoch [44/50] batch [220/1000] time 1.577 (1.568) data 0.000 (0.005) loss 1.3193 (1.1097) acc 75.0000 (72.8693) lr 1.2369e-04 eta 2:57:08
+epoch [44/50] batch [225/1000] time 1.549 (1.567) data 0.000 (0.005) loss 1.1729 (1.1096) acc 78.1250 (72.8194) lr 1.2369e-04 eta 2:56:58
+epoch [44/50] batch [230/1000] time 1.674 (1.568) data 0.000 (0.005) loss 0.8257 (1.1070) acc 81.2500 (72.7310) lr 1.2369e-04 eta 2:56:54
+epoch [44/50] batch [235/1000] time 1.527 (1.567) data 0.000 (0.005) loss 1.3047 (1.1046) acc 59.3750 (72.7926) lr 1.2369e-04 eta 2:56:43
+epoch [44/50] batch [240/1000] time 1.563 (1.567) data 0.001 (0.005) loss 0.9194 (1.1036) acc 75.0000 (72.8516) lr 1.2369e-04 eta 2:56:32
+epoch [44/50] batch [245/1000] time 1.555 (1.567) data 0.000 (0.005) loss 1.3232 (1.1029) acc 68.7500 (72.8571) lr 1.2369e-04 eta 2:56:24
+epoch [44/50] batch [250/1000] time 1.535 (1.567) data 0.000 (0.005) loss 0.8984 (1.0995) acc 71.8750 (72.8500) lr 1.2369e-04 eta 2:56:15
+epoch [44/50] batch [255/1000] time 1.539 (1.566) data 0.000 (0.005) loss 1.4131 (1.0958) acc 62.5000 (72.9289) lr 1.2369e-04 eta 2:56:05
+epoch [44/50] batch [260/1000] time 1.555 (1.566) data 0.001 (0.005) loss 0.6558 (1.0897) acc 78.1250 (73.1010) lr 1.2369e-04 eta 2:55:56
+epoch [44/50] batch [265/1000] time 1.543 (1.566) data 0.000 (0.004) loss 1.2891 (1.0902) acc 62.5000 (73.0425) lr 1.2369e-04 eta 2:55:48
+epoch [44/50] batch [270/1000] time 1.536 (1.566) data 0.000 (0.004) loss 1.2930 (1.0904) acc 68.7500 (73.0324) lr 1.2369e-04 eta 2:55:39
+epoch [44/50] batch [275/1000] time 1.570 (1.567) data 0.000 (0.004) loss 1.2881 (1.0901) acc 75.0000 (73.0227) lr 1.2369e-04 eta 2:55:34
+epoch [44/50] batch [280/1000] time 1.572 (1.567) data 0.000 (0.004) loss 1.1699 (1.0889) acc 71.8750 (73.0357) lr 1.2369e-04 eta 2:55:28
+epoch [44/50] batch [285/1000] time 1.564 (1.567) data 0.000 (0.004) loss 1.6885 (1.0908) acc 56.2500 (73.0044) lr 1.2369e-04 eta 2:55:19
+epoch [44/50] batch [290/1000] time 1.546 (1.566) data 0.000 (0.004) loss 0.6357 (1.0904) acc 81.2500 (72.9310) lr 1.2369e-04 eta 2:55:11
+epoch [44/50] batch [295/1000] time 1.574 (1.566) data 0.001 (0.004) loss 1.2861 (1.0909) acc 65.6250 (72.9237) lr 1.2369e-04 eta 2:55:02
+epoch [44/50] batch [300/1000] time 1.568 (1.566) data 0.000 (0.004) loss 1.3574 (1.0899) acc 65.6250 (72.9792) lr 1.2369e-04 eta 2:54:55
+epoch [44/50] batch [305/1000] time 1.588 (1.566) data 0.000 (0.004) loss 0.8345 (1.0872) acc 75.0000 (72.9508) lr 1.2369e-04 eta 2:54:47
+epoch [44/50] batch [310/1000] time 1.543 (1.566) data 0.000 (0.004) loss 0.8140 (1.0833) acc 78.1250 (73.0343) lr 1.2369e-04 eta 2:54:38
+epoch [44/50] batch [315/1000] time 1.559 (1.566) data 0.001 (0.004) loss 0.6577 (1.0808) acc 78.1250 (73.0655) lr 1.2369e-04 eta 2:54:30
+epoch [44/50] batch [320/1000] time 1.569 (1.566) data 0.000 (0.004) loss 0.7441 (1.0815) acc 78.1250 (73.1055) lr 1.2369e-04 eta 2:54:21
+epoch [44/50] batch [325/1000] time 1.536 (1.566) data 0.001 (0.004) loss 1.0029 (1.0811) acc 75.0000 (73.1154) lr 1.2369e-04 eta 2:54:11
+epoch [44/50] batch [330/1000] time 1.549 (1.566) data 0.000 (0.004) loss 0.8027 (1.0778) acc 81.2500 (73.2386) lr 1.2369e-04 eta 2:54:04
+epoch [44/50] batch [335/1000] time 1.565 (1.566) data 0.000 (0.004) loss 1.0186 (1.0787) acc 75.0000 (73.2183) lr 1.2369e-04 eta 2:53:57
+epoch [44/50] batch [340/1000] time 1.567 (1.566) data 0.000 (0.004) loss 0.8145 (1.0786) acc 84.3750 (73.2353) lr 1.2369e-04 eta 2:53:51
+epoch [44/50] batch [345/1000] time 1.549 (1.566) data 0.001 (0.004) loss 0.5708 (1.0786) acc 84.3750 (73.2518) lr 1.2369e-04 eta 2:53:43
+epoch [44/50] batch [350/1000] time 1.562 (1.566) data 0.000 (0.003) loss 1.5732 (1.0787) acc 65.6250 (73.2143) lr 1.2369e-04 eta 2:53:34
+epoch [44/50] batch [355/1000] time 1.550 (1.566) data 0.000 (0.003) loss 1.5527 (1.0830) acc 62.5000 (73.0898) lr 1.2369e-04 eta 2:53:25
+epoch [44/50] batch [360/1000] time 1.551 (1.566) data 0.000 (0.003) loss 0.6025 (1.0809) acc 84.3750 (73.1424) lr 1.2369e-04 eta 2:53:17
+epoch [44/50] batch [365/1000] time 1.555 (1.566) data 0.000 (0.003) loss 1.8154 (1.0789) acc 53.1250 (73.2021) lr 1.2369e-04 eta 2:53:10
+epoch [44/50] batch [370/1000] time 1.555 (1.566) data 0.000 (0.003) loss 1.1211 (1.0788) acc 71.8750 (73.1503) lr 1.2369e-04 eta 2:53:01
+epoch [44/50] batch [375/1000] time 1.575 (1.566) data 0.001 (0.003) loss 0.7168 (1.0773) acc 75.0000 (73.1167) lr 1.2369e-04 eta 2:52:53
+epoch [44/50] batch [380/1000] time 1.556 (1.566) data 0.001 (0.003) loss 0.5327 (1.0744) acc 75.0000 (73.1497) lr 1.2369e-04 eta 2:52:45
+epoch [44/50] batch [385/1000] time 1.579 (1.566) data 0.000 (0.003) loss 0.9448 (1.0735) acc 75.0000 (73.1818) lr 1.2369e-04 eta 2:52:40
+epoch [44/50] batch [390/1000] time 1.581 (1.566) data 0.000 (0.003) loss 1.0068 (1.0735) acc 68.7500 (73.1731) lr 1.2369e-04 eta 2:52:33
+epoch [44/50] batch [395/1000] time 1.570 (1.566) data 0.000 (0.003) loss 0.8828 (1.0758) acc 81.2500 (73.1804) lr 1.2369e-04 eta 2:52:25
+epoch [44/50] batch [400/1000] time 1.524 (1.566) data 0.001 (0.003) loss 1.0684 (1.0748) acc 71.8750 (73.1641) lr 1.2369e-04 eta 2:52:16
+epoch [44/50] batch [405/1000] time 1.566 (1.566) data 0.001 (0.003) loss 1.2959 (1.0762) acc 68.7500 (73.0941) lr 1.2369e-04 eta 2:52:08
+epoch [44/50] batch [410/1000] time 1.546 (1.566) data 0.000 (0.003) loss 1.1816 (1.0783) acc 71.8750 (73.0793) lr 1.2369e-04 eta 2:52:00
+epoch [44/50] batch [415/1000] time 1.576 (1.566) data 0.000 (0.003) loss 1.4766 (1.0812) acc 62.5000 (72.9669) lr 1.2369e-04 eta 2:51:52
+epoch [44/50] batch [420/1000] time 1.577 (1.566) data 0.000 (0.003) loss 0.9702 (1.0828) acc 71.8750 (72.9539) lr 1.2369e-04 eta 2:51:45
+epoch [44/50] batch [425/1000] time 1.555 (1.566) data 0.000 (0.003) loss 0.6406 (1.0822) acc 87.5000 (72.9044) lr 1.2369e-04 eta 2:51:39
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+epoch [44/50] batch [985/1000] time 1.719 (1.564) data 0.001 (0.002) loss 1.2012 (1.0741) acc 78.1250 (73.0711) lr 1.2369e-04 eta 2:36:49
+epoch [44/50] batch [990/1000] time 1.583 (1.564) data 0.001 (0.002) loss 0.8467 (1.0746) acc 78.1250 (73.0682) lr 1.2369e-04 eta 2:36:41
+epoch [44/50] batch [995/1000] time 1.552 (1.564) data 0.000 (0.002) loss 1.0645 (1.0757) acc 75.0000 (73.0685) lr 1.2369e-04 eta 2:36:33
+epoch [44/50] batch [1000/1000] time 1.555 (1.564) data 0.000 (0.002) loss 1.0498 (1.0762) acc 78.1250 (73.0625) lr 9.5173e-05 eta 2:36:25
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,380
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.3%
+epoch [45/50] batch [5/1000] time 1.581 (1.703) data 0.001 (0.186) loss 1.6680 (1.1404) acc 62.5000 (70.0000) lr 9.5173e-05 eta 2:50:08
+epoch [45/50] batch [10/1000] time 1.550 (1.627) data 0.001 (0.093) loss 0.9707 (1.0150) acc 78.1250 (72.1875) lr 9.5173e-05 eta 2:42:24
+epoch [45/50] batch [15/1000] time 1.556 (1.605) data 0.001 (0.062) loss 0.7544 (1.0583) acc 75.0000 (71.4583) lr 9.5173e-05 eta 2:40:06
+epoch [45/50] batch [20/1000] time 1.541 (1.607) data 0.001 (0.047) loss 0.7769 (1.0566) acc 84.3750 (71.8750) lr 9.5173e-05 eta 2:40:11
+epoch [45/50] batch [25/1000] time 1.541 (1.598) data 0.001 (0.038) loss 1.1074 (1.0728) acc 71.8750 (70.8750) lr 9.5173e-05 eta 2:39:06
+epoch [45/50] batch [30/1000] time 1.568 (1.591) data 0.001 (0.031) loss 1.3604 (1.0768) acc 75.0000 (71.6667) lr 9.5173e-05 eta 2:38:20
+epoch [45/50] batch [35/1000] time 1.592 (1.588) data 0.000 (0.027) loss 1.1250 (1.0882) acc 75.0000 (71.9643) lr 9.5173e-05 eta 2:37:51
+epoch [45/50] batch [40/1000] time 1.546 (1.584) data 0.001 (0.024) loss 1.0332 (1.0786) acc 75.0000 (71.7969) lr 9.5173e-05 eta 2:37:20
+epoch [45/50] batch [45/1000] time 1.528 (1.581) data 0.000 (0.021) loss 1.1621 (1.0847) acc 75.0000 (71.7361) lr 9.5173e-05 eta 2:36:54
+epoch [45/50] batch [50/1000] time 1.568 (1.579) data 0.000 (0.019) loss 1.0537 (1.0875) acc 71.8750 (72.0000) lr 9.5173e-05 eta 2:36:34
+epoch [45/50] batch [55/1000] time 1.552 (1.577) data 0.001 (0.017) loss 1.0342 (1.0870) acc 68.7500 (71.5909) lr 9.5173e-05 eta 2:36:12
+epoch [45/50] batch [60/1000] time 1.571 (1.579) data 0.001 (0.016) loss 1.0117 (1.0860) acc 68.7500 (71.4062) lr 9.5173e-05 eta 2:36:18
+epoch [45/50] batch [65/1000] time 1.596 (1.578) data 0.001 (0.015) loss 1.7158 (1.0877) acc 62.5000 (71.6827) lr 9.5173e-05 eta 2:36:08
+epoch [45/50] batch [70/1000] time 1.569 (1.578) data 0.000 (0.014) loss 1.3359 (1.0765) acc 71.8750 (72.1429) lr 9.5173e-05 eta 2:35:55
+epoch [45/50] batch [75/1000] time 1.550 (1.577) data 0.000 (0.013) loss 1.1338 (1.0818) acc 56.2500 (71.7917) lr 9.5173e-05 eta 2:35:41
+epoch [45/50] batch [80/1000] time 1.537 (1.575) data 0.000 (0.012) loss 0.4666 (1.0711) acc 84.3750 (72.2266) lr 9.5173e-05 eta 2:35:25
+epoch [45/50] batch [85/1000] time 1.547 (1.574) data 0.001 (0.011) loss 1.0166 (1.0710) acc 81.2500 (72.3529) lr 9.5173e-05 eta 2:35:12
+epoch [45/50] batch [90/1000] time 1.549 (1.574) data 0.001 (0.011) loss 1.3906 (1.0916) acc 65.6250 (72.1181) lr 9.5173e-05 eta 2:35:01
+epoch [45/50] batch [95/1000] time 1.558 (1.573) data 0.001 (0.010) loss 0.5728 (1.0938) acc 81.2500 (72.0395) lr 9.5173e-05 eta 2:34:50
+epoch [45/50] batch [100/1000] time 1.535 (1.572) data 0.001 (0.010) loss 1.9912 (1.1012) acc 56.2500 (72.0000) lr 9.5173e-05 eta 2:34:35
+epoch [45/50] batch [105/1000] time 1.573 (1.572) data 0.001 (0.009) loss 1.3828 (1.1111) acc 65.6250 (71.7857) lr 9.5173e-05 eta 2:34:28
+epoch [45/50] batch [110/1000] time 1.569 (1.572) data 0.001 (0.009) loss 1.2480 (1.1112) acc 78.1250 (71.8466) lr 9.5173e-05 eta 2:34:18
+epoch [45/50] batch [115/1000] time 1.581 (1.572) data 0.001 (0.009) loss 1.2119 (1.1030) acc 71.8750 (72.0924) lr 9.5173e-05 eta 2:34:09
+epoch [45/50] batch [120/1000] time 1.576 (1.572) data 0.001 (0.008) loss 1.9092 (1.1095) acc 50.0000 (71.9010) lr 9.5173e-05 eta 2:34:02
+epoch [45/50] batch [125/1000] time 1.541 (1.573) data 0.000 (0.008) loss 1.0039 (1.1076) acc 78.1250 (71.9250) lr 9.5173e-05 eta 2:34:00
+epoch [45/50] batch [130/1000] time 1.543 (1.572) data 0.000 (0.008) loss 1.1396 (1.1055) acc 81.2500 (72.0913) lr 9.5173e-05 eta 2:33:49
+epoch [45/50] batch [135/1000] time 1.529 (1.572) data 0.001 (0.007) loss 1.5938 (1.1066) acc 68.7500 (72.1296) lr 9.5173e-05 eta 2:33:37
+epoch [45/50] batch [140/1000] time 1.558 (1.571) data 0.001 (0.007) loss 1.1875 (1.1088) acc 62.5000 (72.1875) lr 9.5173e-05 eta 2:33:28
+epoch [45/50] batch [145/1000] time 1.572 (1.571) data 0.001 (0.007) loss 1.0283 (1.0997) acc 81.2500 (72.5000) lr 9.5173e-05 eta 2:33:19
+epoch [45/50] batch [150/1000] time 1.568 (1.571) data 0.001 (0.007) loss 1.1514 (1.1062) acc 81.2500 (72.5208) lr 9.5173e-05 eta 2:33:10
+epoch [45/50] batch [155/1000] time 1.572 (1.570) data 0.000 (0.006) loss 1.2041 (1.1156) acc 75.0000 (72.3992) lr 9.5173e-05 eta 2:32:58
+epoch [45/50] batch [160/1000] time 1.558 (1.570) data 0.000 (0.006) loss 0.8218 (1.1140) acc 75.0000 (72.3633) lr 9.5173e-05 eta 2:32:51
+epoch [45/50] batch [165/1000] time 1.581 (1.570) data 0.000 (0.006) loss 1.5342 (1.1193) acc 75.0000 (72.2727) lr 9.5173e-05 eta 2:32:42
+epoch [45/50] batch [170/1000] time 1.575 (1.571) data 0.001 (0.006) loss 0.8960 (1.1111) acc 78.1250 (72.3897) lr 9.5173e-05 eta 2:32:40
+epoch [45/50] batch [175/1000] time 1.542 (1.571) data 0.000 (0.006) loss 1.1904 (1.1086) acc 62.5000 (72.2679) lr 9.5173e-05 eta 2:32:29
+epoch [45/50] batch [180/1000] time 1.563 (1.570) data 0.001 (0.006) loss 1.2686 (1.1051) acc 71.8750 (72.3438) lr 9.5173e-05 eta 2:32:17
+epoch [45/50] batch [185/1000] time 1.564 (1.570) data 0.000 (0.006) loss 1.2969 (1.1054) acc 68.7500 (72.2973) lr 9.5173e-05 eta 2:32:07
+epoch [45/50] batch [190/1000] time 1.549 (1.569) data 0.000 (0.005) loss 0.7148 (1.1059) acc 81.2500 (72.2862) lr 9.5173e-05 eta 2:31:57
+epoch [45/50] batch [195/1000] time 1.550 (1.569) data 0.001 (0.005) loss 1.2021 (1.1035) acc 65.6250 (72.3718) lr 9.5173e-05 eta 2:31:48
+epoch [45/50] batch [200/1000] time 1.580 (1.569) data 0.001 (0.005) loss 1.1963 (1.1035) acc 65.6250 (72.3750) lr 9.5173e-05 eta 2:31:39
+epoch [45/50] batch [205/1000] time 1.546 (1.569) data 0.000 (0.005) loss 1.7100 (1.1100) acc 56.2500 (72.2104) lr 9.5173e-05 eta 2:31:30
+epoch [45/50] batch [210/1000] time 1.717 (1.569) data 0.001 (0.005) loss 1.8086 (1.1144) acc 53.1250 (72.1577) lr 9.5173e-05 eta 2:31:26
+epoch [45/50] batch [215/1000] time 1.571 (1.569) data 0.001 (0.005) loss 1.1836 (1.1175) acc 65.6250 (72.0349) lr 9.5173e-05 eta 2:31:17
+epoch [45/50] batch [220/1000] time 1.581 (1.569) data 0.000 (0.005) loss 1.0684 (1.1141) acc 78.1250 (72.1023) lr 9.5173e-05 eta 2:31:10
+epoch [45/50] batch [225/1000] time 1.550 (1.569) data 0.001 (0.005) loss 1.4580 (1.1149) acc 59.3750 (71.9583) lr 9.5173e-05 eta 2:31:01
+epoch [45/50] batch [230/1000] time 1.561 (1.569) data 0.001 (0.005) loss 1.8867 (1.1173) acc 59.3750 (71.9429) lr 9.5173e-05 eta 2:30:55
+epoch [45/50] batch [235/1000] time 1.567 (1.570) data 0.000 (0.004) loss 1.1123 (1.1175) acc 59.3750 (71.8883) lr 9.5173e-05 eta 2:30:48
+epoch [45/50] batch [240/1000] time 1.524 (1.569) data 0.000 (0.004) loss 1.0859 (1.1166) acc 62.5000 (71.9010) lr 9.5173e-05 eta 2:30:39
+epoch [45/50] batch [245/1000] time 1.555 (1.569) data 0.000 (0.004) loss 1.2139 (1.1170) acc 78.1250 (71.9133) lr 9.5173e-05 eta 2:30:29
+epoch [45/50] batch [250/1000] time 1.565 (1.569) data 0.000 (0.004) loss 0.6128 (1.1173) acc 81.2500 (71.8750) lr 9.5173e-05 eta 2:30:19
+epoch [45/50] batch [255/1000] time 1.566 (1.569) data 0.001 (0.004) loss 0.5269 (1.1145) acc 84.3750 (71.8995) lr 9.5173e-05 eta 2:30:12
+epoch [45/50] batch [260/1000] time 1.576 (1.569) data 0.000 (0.004) loss 1.1182 (1.1122) acc 68.7500 (71.9231) lr 9.5173e-05 eta 2:30:04
+epoch [45/50] batch [265/1000] time 1.548 (1.569) data 0.000 (0.004) loss 0.9399 (1.1084) acc 75.0000 (72.0283) lr 9.5173e-05 eta 2:29:56
+epoch [45/50] batch [270/1000] time 1.574 (1.569) data 0.000 (0.004) loss 1.0039 (1.1088) acc 75.0000 (72.0602) lr 9.5173e-05 eta 2:29:47
+epoch [45/50] batch [275/1000] time 1.560 (1.569) data 0.000 (0.004) loss 1.6318 (1.1142) acc 65.6250 (72.0114) lr 9.5173e-05 eta 2:29:42
+epoch [45/50] batch [280/1000] time 1.551 (1.569) data 0.001 (0.004) loss 0.7964 (1.1128) acc 75.0000 (72.0536) lr 9.5173e-05 eta 2:29:33
+epoch [45/50] batch [285/1000] time 1.574 (1.569) data 0.001 (0.004) loss 0.4167 (1.1119) acc 87.5000 (72.0066) lr 9.5173e-05 eta 2:29:24
+epoch [45/50] batch [290/1000] time 1.593 (1.568) data 0.000 (0.004) loss 0.6846 (1.1093) acc 81.2500 (72.1121) lr 9.5173e-05 eta 2:29:15
+epoch [45/50] batch [295/1000] time 1.584 (1.569) data 0.000 (0.004) loss 0.8379 (1.1056) acc 78.1250 (72.1928) lr 9.5173e-05 eta 2:29:08
+epoch [45/50] batch [300/1000] time 1.584 (1.569) data 0.000 (0.004) loss 0.9863 (1.1006) acc 68.7500 (72.3438) lr 9.5173e-05 eta 2:29:01
+epoch [45/50] batch [305/1000] time 1.558 (1.568) data 0.000 (0.004) loss 1.1162 (1.1006) acc 68.7500 (72.2746) lr 9.5173e-05 eta 2:28:52
+epoch [45/50] batch [310/1000] time 1.578 (1.568) data 0.000 (0.003) loss 1.0459 (1.1006) acc 75.0000 (72.1774) lr 9.5173e-05 eta 2:28:43
+epoch [45/50] batch [315/1000] time 1.565 (1.568) data 0.000 (0.003) loss 1.1396 (1.0978) acc 71.8750 (72.1925) lr 9.5173e-05 eta 2:28:34
+epoch [45/50] batch [320/1000] time 1.534 (1.568) data 0.000 (0.003) loss 1.3682 (1.0988) acc 68.7500 (72.2266) lr 9.5173e-05 eta 2:28:28
+epoch [45/50] batch [325/1000] time 1.590 (1.568) data 0.000 (0.003) loss 1.2930 (1.0971) acc 68.7500 (72.2788) lr 9.5173e-05 eta 2:28:20
+epoch [45/50] batch [330/1000] time 1.545 (1.568) data 0.001 (0.003) loss 1.7881 (1.0980) acc 50.0000 (72.2538) lr 9.5173e-05 eta 2:28:12
+epoch [45/50] batch [335/1000] time 1.556 (1.568) data 0.000 (0.003) loss 0.5806 (1.0947) acc 84.3750 (72.2761) lr 9.5173e-05 eta 2:28:03
+epoch [45/50] batch [340/1000] time 1.574 (1.568) data 0.001 (0.003) loss 1.0166 (1.0926) acc 78.1250 (72.3070) lr 9.5173e-05 eta 2:27:54
+epoch [45/50] batch [345/1000] time 1.574 (1.568) data 0.000 (0.003) loss 1.0420 (1.0953) acc 75.0000 (72.2645) lr 9.5173e-05 eta 2:27:45
+epoch [45/50] batch [350/1000] time 1.562 (1.568) data 0.001 (0.003) loss 0.7041 (1.0943) acc 71.8750 (72.2679) lr 9.5173e-05 eta 2:27:37
+epoch [45/50] batch [355/1000] time 1.549 (1.568) data 0.001 (0.003) loss 1.0088 (1.0914) acc 71.8750 (72.3151) lr 9.5173e-05 eta 2:27:28
+epoch [45/50] batch [360/1000] time 1.534 (1.568) data 0.000 (0.003) loss 1.5049 (1.0932) acc 71.8750 (72.3003) lr 9.5173e-05 eta 2:27:21
+epoch [45/50] batch [365/1000] time 1.557 (1.568) data 0.001 (0.003) loss 1.0674 (1.0976) acc 71.8750 (72.2603) lr 9.5173e-05 eta 2:27:14
+epoch [45/50] batch [370/1000] time 1.554 (1.568) data 0.000 (0.003) loss 1.0127 (1.0987) acc 68.7500 (72.2213) lr 9.5173e-05 eta 2:27:07
+epoch [45/50] batch [375/1000] time 1.560 (1.568) data 0.001 (0.003) loss 1.2881 (1.0959) acc 81.2500 (72.3500) lr 9.5173e-05 eta 2:26:58
+epoch [45/50] batch [380/1000] time 1.555 (1.568) data 0.000 (0.003) loss 1.1611 (1.0988) acc 65.6250 (72.2615) lr 9.5173e-05 eta 2:26:50
+epoch [45/50] batch [385/1000] time 1.539 (1.568) data 0.000 (0.003) loss 1.3398 (1.0979) acc 65.6250 (72.3133) lr 9.5173e-05 eta 2:26:41
+epoch [45/50] batch [390/1000] time 1.556 (1.567) data 0.000 (0.003) loss 0.8521 (1.0971) acc 68.7500 (72.3638) lr 9.5173e-05 eta 2:26:33
+epoch [45/50] batch [395/1000] time 1.580 (1.567) data 0.001 (0.003) loss 0.5669 (1.0926) acc 84.3750 (72.4684) lr 9.5173e-05 eta 2:26:25
+epoch [45/50] batch [400/1000] time 1.532 (1.567) data 0.001 (0.003) loss 0.9404 (1.0925) acc 75.0000 (72.4609) lr 9.5173e-05 eta 2:26:15
+epoch [45/50] batch [405/1000] time 1.536 (1.567) data 0.001 (0.003) loss 1.6787 (1.0959) acc 53.1250 (72.4151) lr 9.5173e-05 eta 2:26:07
+epoch [45/50] batch [410/1000] time 1.568 (1.567) data 0.000 (0.003) loss 1.3252 (1.0955) acc 68.7500 (72.4771) lr 9.5173e-05 eta 2:25:58
+epoch [45/50] batch [415/1000] time 1.569 (1.567) data 0.000 (0.003) loss 1.3594 (1.0969) acc 68.7500 (72.4398) lr 9.5173e-05 eta 2:25:50
+epoch [45/50] batch [420/1000] time 1.582 (1.567) data 0.000 (0.003) loss 1.1162 (1.0987) acc 81.2500 (72.4330) lr 9.5173e-05 eta 2:25:42
+epoch [45/50] batch [425/1000] time 1.589 (1.567) data 0.001 (0.003) loss 1.4023 (1.0976) acc 62.5000 (72.4412) lr 9.5173e-05 eta 2:25:37
+epoch [45/50] batch [430/1000] time 1.564 (1.567) data 0.001 (0.003) loss 0.7847 (1.0963) acc 68.7500 (72.4055) lr 9.5173e-05 eta 2:25:29
+epoch [45/50] batch [435/1000] time 1.557 (1.567) data 0.000 (0.003) loss 1.0801 (1.0977) acc 75.0000 (72.4210) lr 9.5173e-05 eta 2:25:21
+epoch [45/50] batch [440/1000] time 1.565 (1.567) data 0.001 (0.003) loss 1.1680 (1.0988) acc 62.5000 (72.3651) lr 9.5173e-05 eta 2:25:13
+epoch [45/50] batch [445/1000] time 1.561 (1.567) data 0.000 (0.003) loss 1.0527 (1.1003) acc 81.2500 (72.3666) lr 9.5173e-05 eta 2:25:04
+epoch [45/50] batch [450/1000] time 1.585 (1.567) data 0.000 (0.003) loss 1.3643 (1.1015) acc 71.8750 (72.3750) lr 9.5173e-05 eta 2:24:56
+epoch [45/50] batch [455/1000] time 1.561 (1.567) data 0.000 (0.003) loss 0.7734 (1.0987) acc 78.1250 (72.4245) lr 9.5173e-05 eta 2:24:49
+epoch [45/50] batch [460/1000] time 1.579 (1.567) data 0.000 (0.003) loss 0.8677 (1.0975) acc 81.2500 (72.4796) lr 9.5173e-05 eta 2:24:41
+epoch [45/50] batch [465/1000] time 1.550 (1.567) data 0.001 (0.002) loss 1.4688 (1.0967) acc 71.8750 (72.5269) lr 9.5173e-05 eta 2:24:33
+epoch [45/50] batch [470/1000] time 1.569 (1.567) data 0.001 (0.002) loss 1.3037 (1.0957) acc 75.0000 (72.5598) lr 9.5173e-05 eta 2:24:26
+epoch [45/50] batch [475/1000] time 1.571 (1.567) data 0.000 (0.002) loss 1.4053 (1.0969) acc 59.3750 (72.5395) lr 9.5173e-05 eta 2:24:18
+epoch [45/50] batch [480/1000] time 1.560 (1.567) data 0.001 (0.002) loss 1.6191 (1.0989) acc 71.8750 (72.5000) lr 9.5173e-05 eta 2:24:09
+epoch [45/50] batch [485/1000] time 1.574 (1.567) data 0.000 (0.002) loss 1.1104 (1.0997) acc 68.7500 (72.4871) lr 9.5173e-05 eta 2:24:01
+epoch [45/50] batch [490/1000] time 1.576 (1.567) data 0.000 (0.002) loss 0.6826 (1.1006) acc 81.2500 (72.4872) lr 9.5173e-05 eta 2:23:53
+epoch [45/50] batch [495/1000] time 1.590 (1.567) data 0.001 (0.002) loss 0.4685 (1.0995) acc 90.6250 (72.5063) lr 9.5173e-05 eta 2:23:45
+epoch [45/50] batch [500/1000] time 1.563 (1.567) data 0.000 (0.002) loss 1.1553 (1.1022) acc 81.2500 (72.4625) lr 9.5173e-05 eta 2:23:38
+epoch [45/50] batch [505/1000] time 1.568 (1.567) data 0.000 (0.002) loss 2.0703 (1.1040) acc 62.5000 (72.4567) lr 9.5173e-05 eta 2:23:30
+epoch [45/50] batch [510/1000] time 1.551 (1.567) data 0.000 (0.002) loss 0.9141 (1.1032) acc 81.2500 (72.4755) lr 9.5173e-05 eta 2:23:22
+epoch [45/50] batch [515/1000] time 1.581 (1.567) data 0.001 (0.002) loss 0.9849 (1.1019) acc 78.1250 (72.5303) lr 9.5173e-05 eta 2:23:15
+epoch [45/50] batch [520/1000] time 1.561 (1.567) data 0.001 (0.002) loss 0.8359 (1.1004) acc 75.0000 (72.5601) lr 9.5173e-05 eta 2:23:07
+epoch [45/50] batch [525/1000] time 1.569 (1.567) data 0.001 (0.002) loss 0.7734 (1.1010) acc 75.0000 (72.5238) lr 9.5173e-05 eta 2:22:59
+epoch [45/50] batch [530/1000] time 1.562 (1.567) data 0.001 (0.002) loss 1.0322 (1.0994) acc 68.7500 (72.5413) lr 9.5173e-05 eta 2:22:52
+epoch [45/50] batch [535/1000] time 1.564 (1.567) data 0.001 (0.002) loss 1.4092 (1.0990) acc 62.5000 (72.5409) lr 9.5173e-05 eta 2:22:43
+epoch [45/50] batch [540/1000] time 1.559 (1.567) data 0.001 (0.002) loss 1.3604 (1.1005) acc 62.5000 (72.4942) lr 9.5173e-05 eta 2:22:35
+epoch [45/50] batch [545/1000] time 1.558 (1.567) data 0.001 (0.002) loss 1.4043 (1.1000) acc 62.5000 (72.5229) lr 9.5173e-05 eta 2:22:27
+epoch [45/50] batch [550/1000] time 1.586 (1.567) data 0.001 (0.002) loss 1.0986 (1.0999) acc 68.7500 (72.5057) lr 9.5173e-05 eta 2:22:20
+epoch [45/50] batch [555/1000] time 1.605 (1.567) data 0.000 (0.002) loss 0.9346 (1.0988) acc 78.1250 (72.5225) lr 9.5173e-05 eta 2:22:12
+epoch [45/50] batch [560/1000] time 1.546 (1.567) data 0.000 (0.002) loss 1.0830 (1.0974) acc 78.1250 (72.5725) lr 9.5173e-05 eta 2:22:04
+epoch [45/50] batch [565/1000] time 1.579 (1.567) data 0.001 (0.002) loss 0.9761 (1.0959) acc 75.0000 (72.6162) lr 9.5173e-05 eta 2:21:56
+epoch [45/50] batch [570/1000] time 1.536 (1.567) data 0.001 (0.002) loss 1.1133 (1.0962) acc 68.7500 (72.5822) lr 9.5173e-05 eta 2:21:48
+epoch [45/50] batch [575/1000] time 1.750 (1.567) data 0.001 (0.002) loss 0.9565 (1.0966) acc 81.2500 (72.6141) lr 9.5173e-05 eta 2:21:42
+epoch [45/50] batch [580/1000] time 1.582 (1.567) data 0.000 (0.002) loss 0.5566 (1.0954) acc 78.1250 (72.6401) lr 9.5173e-05 eta 2:21:33
+epoch [45/50] batch [585/1000] time 1.569 (1.567) data 0.001 (0.002) loss 1.4980 (1.0958) acc 71.8750 (72.6335) lr 9.5173e-05 eta 2:21:25
+epoch [45/50] batch [590/1000] time 1.567 (1.567) data 0.000 (0.002) loss 1.2031 (1.0952) acc 68.7500 (72.6483) lr 9.5173e-05 eta 2:21:17
+epoch [45/50] batch [595/1000] time 1.558 (1.567) data 0.001 (0.002) loss 0.9312 (1.0946) acc 75.0000 (72.6786) lr 9.5173e-05 eta 2:21:09
+epoch [45/50] batch [600/1000] time 1.533 (1.567) data 0.001 (0.002) loss 1.0518 (1.0959) acc 75.0000 (72.6667) lr 9.5173e-05 eta 2:21:01
+epoch [45/50] batch [605/1000] time 1.543 (1.567) data 0.001 (0.002) loss 1.0186 (1.0951) acc 75.0000 (72.6705) lr 9.5173e-05 eta 2:20:53
+epoch [45/50] batch [610/1000] time 1.535 (1.567) data 0.000 (0.002) loss 1.0156 (1.0954) acc 75.0000 (72.6588) lr 9.5173e-05 eta 2:20:44
+epoch [45/50] batch [615/1000] time 1.558 (1.567) data 0.001 (0.002) loss 0.8330 (1.0941) acc 87.5000 (72.6931) lr 9.5173e-05 eta 2:20:37
+epoch [45/50] batch [620/1000] time 1.740 (1.567) data 0.000 (0.002) loss 0.8066 (1.0933) acc 81.2500 (72.7218) lr 9.5173e-05 eta 2:20:30
+epoch [45/50] batch [625/1000] time 1.562 (1.567) data 0.001 (0.002) loss 0.8530 (1.0919) acc 78.1250 (72.7800) lr 9.5173e-05 eta 2:20:23
+epoch [45/50] batch [630/1000] time 1.562 (1.567) data 0.000 (0.002) loss 1.4600 (1.0925) acc 65.6250 (72.7976) lr 9.5173e-05 eta 2:20:15
+epoch [45/50] batch [635/1000] time 1.543 (1.567) data 0.000 (0.002) loss 0.9189 (1.0928) acc 75.0000 (72.8100) lr 9.5173e-05 eta 2:20:06
+epoch [45/50] batch [640/1000] time 1.586 (1.567) data 0.001 (0.002) loss 0.7959 (1.0921) acc 68.7500 (72.8027) lr 9.5173e-05 eta 2:19:59
+epoch [45/50] batch [645/1000] time 1.574 (1.567) data 0.000 (0.002) loss 1.1846 (1.0903) acc 71.8750 (72.8198) lr 9.5173e-05 eta 2:19:51
+epoch [45/50] batch [650/1000] time 1.570 (1.567) data 0.000 (0.002) loss 0.7476 (1.0892) acc 81.2500 (72.8558) lr 9.5173e-05 eta 2:19:43
+epoch [45/50] batch [655/1000] time 1.570 (1.567) data 0.000 (0.002) loss 0.8975 (1.0886) acc 81.2500 (72.9008) lr 9.5173e-05 eta 2:19:35
+epoch [45/50] batch [660/1000] time 1.556 (1.567) data 0.000 (0.002) loss 0.7749 (1.0871) acc 81.2500 (72.9214) lr 9.5173e-05 eta 2:19:27
+epoch [45/50] batch [665/1000] time 1.566 (1.567) data 0.000 (0.002) loss 1.0166 (1.0882) acc 84.3750 (72.9182) lr 9.5173e-05 eta 2:19:20
+epoch [45/50] batch [670/1000] time 1.574 (1.567) data 0.001 (0.002) loss 0.5889 (1.0881) acc 81.2500 (72.9151) lr 9.5173e-05 eta 2:19:12
+epoch [45/50] batch [675/1000] time 1.559 (1.567) data 0.000 (0.002) loss 1.4365 (1.0893) acc 59.3750 (72.8750) lr 9.5173e-05 eta 2:19:03
+epoch [45/50] batch [680/1000] time 1.556 (1.567) data 0.001 (0.002) loss 0.6875 (1.0887) acc 78.1250 (72.8906) lr 9.5173e-05 eta 2:18:56
+epoch [45/50] batch [685/1000] time 1.555 (1.567) data 0.000 (0.002) loss 1.2490 (1.0894) acc 65.6250 (72.8604) lr 9.5173e-05 eta 2:18:48
+epoch [45/50] batch [690/1000] time 1.581 (1.567) data 0.000 (0.002) loss 1.2939 (1.0891) acc 68.7500 (72.8759) lr 9.5173e-05 eta 2:18:40
+epoch [45/50] batch [695/1000] time 1.577 (1.567) data 0.001 (0.002) loss 0.8848 (1.0889) acc 84.3750 (72.9047) lr 9.5173e-05 eta 2:18:32
+epoch [45/50] batch [700/1000] time 1.563 (1.567) data 0.000 (0.002) loss 1.3799 (1.0896) acc 65.6250 (72.8929) lr 9.5173e-05 eta 2:18:24
+epoch [45/50] batch [705/1000] time 1.556 (1.567) data 0.000 (0.002) loss 0.7515 (1.0900) acc 84.3750 (72.8989) lr 9.5173e-05 eta 2:18:16
+epoch [45/50] batch [710/1000] time 1.547 (1.567) data 0.000 (0.002) loss 1.1758 (1.0904) acc 68.7500 (72.8873) lr 9.5173e-05 eta 2:18:07
+epoch [45/50] batch [715/1000] time 1.549 (1.567) data 0.001 (0.002) loss 1.4199 (1.0895) acc 71.8750 (72.9108) lr 9.5173e-05 eta 2:17:59
+epoch [45/50] batch [720/1000] time 1.564 (1.567) data 0.000 (0.002) loss 0.8745 (1.0873) acc 71.8750 (72.9470) lr 9.5173e-05 eta 2:17:51
+epoch [45/50] batch [725/1000] time 1.554 (1.567) data 0.000 (0.002) loss 0.6455 (1.0874) acc 75.0000 (72.9353) lr 9.5173e-05 eta 2:17:43
+epoch [45/50] batch [730/1000] time 1.573 (1.567) data 0.000 (0.002) loss 0.8721 (1.0862) acc 75.0000 (72.9666) lr 9.5173e-05 eta 2:17:36
+epoch [45/50] batch [735/1000] time 1.567 (1.567) data 0.001 (0.002) loss 1.0820 (1.0857) acc 68.7500 (72.9592) lr 9.5173e-05 eta 2:17:28
+epoch [45/50] batch [740/1000] time 1.555 (1.567) data 0.000 (0.002) loss 1.0195 (1.0861) acc 78.1250 (72.9434) lr 9.5173e-05 eta 2:17:20
+epoch [45/50] batch [745/1000] time 1.564 (1.567) data 0.000 (0.002) loss 0.6401 (1.0862) acc 78.1250 (72.9237) lr 9.5173e-05 eta 2:17:12
+epoch [45/50] batch [750/1000] time 1.551 (1.567) data 0.000 (0.002) loss 0.9985 (1.0865) acc 71.8750 (72.8958) lr 9.5173e-05 eta 2:17:04
+epoch [45/50] batch [755/1000] time 1.579 (1.567) data 0.000 (0.002) loss 1.1953 (1.0859) acc 75.0000 (72.9098) lr 9.5173e-05 eta 2:16:56
+epoch [45/50] batch [760/1000] time 1.548 (1.567) data 0.001 (0.002) loss 0.8472 (1.0860) acc 78.1250 (72.9482) lr 9.5173e-05 eta 2:16:48
+epoch [45/50] batch [765/1000] time 1.560 (1.567) data 0.000 (0.002) loss 1.3330 (1.0861) acc 68.7500 (72.9575) lr 9.5173e-05 eta 2:16:40
+epoch [45/50] batch [770/1000] time 1.560 (1.567) data 0.000 (0.002) loss 0.7871 (1.0855) acc 71.8750 (72.9667) lr 9.5173e-05 eta 2:16:32
+epoch [45/50] batch [775/1000] time 1.581 (1.567) data 0.000 (0.002) loss 1.4209 (1.0869) acc 65.6250 (72.9153) lr 9.5173e-05 eta 2:16:26
+epoch [45/50] batch [780/1000] time 1.548 (1.567) data 0.001 (0.002) loss 1.3193 (1.0864) acc 71.8750 (72.9327) lr 9.5173e-05 eta 2:16:18
+epoch [45/50] batch [785/1000] time 1.554 (1.567) data 0.000 (0.002) loss 1.4062 (1.0871) acc 62.5000 (72.8941) lr 9.5173e-05 eta 2:16:09
+epoch [45/50] batch [790/1000] time 1.554 (1.567) data 0.001 (0.002) loss 0.5688 (1.0863) acc 81.2500 (72.9153) lr 9.5173e-05 eta 2:16:01
+epoch [45/50] batch [795/1000] time 1.598 (1.567) data 0.001 (0.002) loss 1.2451 (1.0862) acc 68.7500 (72.9009) lr 9.5173e-05 eta 2:15:53
+epoch [45/50] batch [800/1000] time 1.568 (1.567) data 0.000 (0.002) loss 1.1172 (1.0862) acc 71.8750 (72.8984) lr 9.5173e-05 eta 2:15:45
+epoch [45/50] batch [805/1000] time 1.587 (1.567) data 0.001 (0.002) loss 0.8062 (1.0858) acc 81.2500 (72.9076) lr 9.5173e-05 eta 2:15:38
+epoch [45/50] batch [810/1000] time 1.560 (1.567) data 0.001 (0.002) loss 1.4795 (1.0858) acc 59.3750 (72.9090) lr 9.5173e-05 eta 2:15:30
+epoch [45/50] batch [815/1000] time 1.570 (1.567) data 0.001 (0.002) loss 0.4758 (1.0869) acc 84.3750 (72.8911) lr 9.5173e-05 eta 2:15:23
+epoch [45/50] batch [820/1000] time 1.561 (1.567) data 0.000 (0.002) loss 1.0391 (1.0876) acc 75.0000 (72.8659) lr 9.5173e-05 eta 2:15:15
+epoch [45/50] batch [825/1000] time 1.585 (1.567) data 0.001 (0.002) loss 1.4863 (1.0886) acc 65.6250 (72.8333) lr 9.5173e-05 eta 2:15:07
+epoch [45/50] batch [830/1000] time 1.559 (1.567) data 0.000 (0.002) loss 0.7520 (1.0871) acc 78.1250 (72.8652) lr 9.5173e-05 eta 2:14:59
+epoch [45/50] batch [835/1000] time 1.578 (1.567) data 0.001 (0.002) loss 1.3594 (1.0878) acc 68.7500 (72.8481) lr 9.5173e-05 eta 2:14:51
+epoch [45/50] batch [840/1000] time 1.577 (1.567) data 0.000 (0.002) loss 0.7710 (1.0866) acc 81.2500 (72.8757) lr 9.5173e-05 eta 2:14:43
+epoch [45/50] batch [845/1000] time 1.577 (1.567) data 0.001 (0.002) loss 1.5029 (1.0872) acc 71.8750 (72.8883) lr 9.5173e-05 eta 2:14:35
+epoch [45/50] batch [850/1000] time 1.560 (1.567) data 0.000 (0.002) loss 1.2158 (1.0873) acc 75.0000 (72.8750) lr 9.5173e-05 eta 2:14:28
+epoch [45/50] batch [855/1000] time 1.576 (1.567) data 0.000 (0.002) loss 0.9590 (1.0885) acc 75.0000 (72.8436) lr 9.5173e-05 eta 2:14:20
+epoch [45/50] batch [860/1000] time 1.564 (1.567) data 0.000 (0.002) loss 1.5400 (1.0909) acc 65.6250 (72.8089) lr 9.5173e-05 eta 2:14:12
+epoch [45/50] batch [865/1000] time 1.551 (1.567) data 0.000 (0.002) loss 1.0332 (1.0902) acc 71.8750 (72.8288) lr 9.5173e-05 eta 2:14:04
+epoch [45/50] batch [870/1000] time 1.562 (1.567) data 0.001 (0.002) loss 1.0771 (1.0910) acc 75.0000 (72.8233) lr 9.5173e-05 eta 2:13:56
+epoch [45/50] batch [875/1000] time 1.559 (1.566) data 0.001 (0.002) loss 0.9277 (1.0915) acc 71.8750 (72.8071) lr 9.5173e-05 eta 2:13:48
+epoch [45/50] batch [880/1000] time 1.563 (1.567) data 0.001 (0.002) loss 1.1504 (1.0907) acc 65.6250 (72.7876) lr 9.5173e-05 eta 2:13:41
+epoch [45/50] batch [885/1000] time 1.563 (1.567) data 0.001 (0.002) loss 1.1289 (1.0906) acc 81.2500 (72.7931) lr 9.5173e-05 eta 2:13:33
+epoch [45/50] batch [890/1000] time 1.563 (1.567) data 0.000 (0.002) loss 1.0811 (1.0908) acc 75.0000 (72.7949) lr 9.5173e-05 eta 2:13:25
+epoch [45/50] batch [895/1000] time 1.552 (1.567) data 0.000 (0.002) loss 0.9185 (1.0894) acc 75.0000 (72.8317) lr 9.5173e-05 eta 2:13:17
+epoch [45/50] batch [900/1000] time 1.558 (1.567) data 0.000 (0.002) loss 1.0869 (1.0894) acc 71.8750 (72.8472) lr 9.5173e-05 eta 2:13:09
+epoch [45/50] batch [905/1000] time 1.579 (1.566) data 0.000 (0.002) loss 1.1436 (1.0894) acc 68.7500 (72.8453) lr 9.5173e-05 eta 2:13:01
+epoch [45/50] batch [910/1000] time 1.559 (1.566) data 0.000 (0.002) loss 0.6616 (1.0885) acc 84.3750 (72.8743) lr 9.5173e-05 eta 2:12:53
+epoch [45/50] batch [915/1000] time 1.579 (1.567) data 0.000 (0.002) loss 1.0166 (1.0879) acc 75.0000 (72.8893) lr 9.5173e-05 eta 2:12:45
+epoch [45/50] batch [920/1000] time 1.556 (1.567) data 0.001 (0.001) loss 1.5195 (1.0883) acc 65.6250 (72.8872) lr 9.5173e-05 eta 2:12:37
+epoch [45/50] batch [925/1000] time 1.559 (1.567) data 0.001 (0.001) loss 1.4053 (1.0883) acc 68.7500 (72.8851) lr 9.5173e-05 eta 2:12:30
+epoch [45/50] batch [930/1000] time 1.573 (1.567) data 0.001 (0.001) loss 0.9141 (1.0874) acc 78.1250 (72.9066) lr 9.5173e-05 eta 2:12:22
+epoch [45/50] batch [935/1000] time 1.571 (1.567) data 0.001 (0.001) loss 1.2764 (1.0881) acc 71.8750 (72.8977) lr 9.5173e-05 eta 2:12:15
+epoch [45/50] batch [940/1000] time 1.582 (1.567) data 0.000 (0.001) loss 0.9448 (1.0885) acc 68.7500 (72.8856) lr 9.5173e-05 eta 2:12:06
+epoch [45/50] batch [945/1000] time 1.557 (1.567) data 0.001 (0.001) loss 0.9971 (1.0879) acc 62.5000 (72.8770) lr 9.5173e-05 eta 2:11:59
+epoch [45/50] batch [950/1000] time 1.561 (1.567) data 0.001 (0.001) loss 1.1270 (1.0866) acc 75.0000 (72.8947) lr 9.5173e-05 eta 2:11:51
+epoch [45/50] batch [955/1000] time 1.542 (1.566) data 0.000 (0.001) loss 1.1992 (1.0871) acc 71.8750 (72.9123) lr 9.5173e-05 eta 2:11:42
+epoch [45/50] batch [960/1000] time 1.570 (1.566) data 0.001 (0.001) loss 1.2979 (1.0866) acc 68.7500 (72.9102) lr 9.5173e-05 eta 2:11:35
+epoch [45/50] batch [965/1000] time 1.689 (1.567) data 0.000 (0.001) loss 0.5664 (1.0857) acc 78.1250 (72.8983) lr 9.5173e-05 eta 2:11:27
+epoch [45/50] batch [970/1000] time 1.558 (1.567) data 0.001 (0.001) loss 0.7197 (1.0849) acc 78.1250 (72.9059) lr 9.5173e-05 eta 2:11:19
+epoch [45/50] batch [975/1000] time 1.558 (1.567) data 0.000 (0.001) loss 1.2402 (1.0852) acc 75.0000 (72.8814) lr 9.5173e-05 eta 2:11:11
+epoch [45/50] batch [980/1000] time 1.578 (1.567) data 0.000 (0.001) loss 0.9917 (1.0838) acc 71.8750 (72.9114) lr 9.5173e-05 eta 2:11:04
+epoch [45/50] batch [985/1000] time 1.549 (1.567) data 0.001 (0.001) loss 1.5342 (1.0837) acc 59.3750 (72.9156) lr 9.5173e-05 eta 2:10:56
+epoch [45/50] batch [990/1000] time 1.547 (1.567) data 0.000 (0.001) loss 0.9365 (1.0831) acc 78.1250 (72.9198) lr 9.5173e-05 eta 2:10:48
+epoch [45/50] batch [995/1000] time 1.540 (1.566) data 0.000 (0.001) loss 0.8027 (1.0839) acc 78.1250 (72.8957) lr 9.5173e-05 eta 2:10:39
+epoch [45/50] batch [1000/1000] time 1.555 (1.566) data 0.001 (0.001) loss 0.7886 (1.0841) acc 81.2500 (72.9094) lr 7.0224e-05 eta 2:10:32
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,387
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+epoch [46/50] batch [5/1000] time 1.531 (1.694) data 0.000 (0.194) loss 1.2939 (0.9167) acc 62.5000 (74.3750) lr 7.0224e-05 eta 2:21:00
+epoch [46/50] batch [10/1000] time 1.539 (1.624) data 0.000 (0.097) loss 1.4336 (1.0947) acc 65.6250 (73.1250) lr 7.0224e-05 eta 2:15:03
+epoch [46/50] batch [15/1000] time 1.569 (1.604) data 0.001 (0.065) loss 1.2061 (1.0596) acc 68.7500 (73.3333) lr 7.0224e-05 eta 2:13:17
+epoch [46/50] batch [20/1000] time 1.564 (1.594) data 0.000 (0.049) loss 1.2939 (1.0421) acc 62.5000 (73.1250) lr 7.0224e-05 eta 2:12:19
+epoch [46/50] batch [25/1000] time 1.565 (1.590) data 0.001 (0.039) loss 0.6582 (1.0385) acc 71.8750 (72.8750) lr 7.0224e-05 eta 2:11:48
+epoch [46/50] batch [30/1000] time 1.552 (1.584) data 0.000 (0.033) loss 1.1826 (1.0404) acc 78.1250 (73.4375) lr 7.0224e-05 eta 2:11:11
+epoch [46/50] batch [35/1000] time 1.531 (1.581) data 0.001 (0.028) loss 0.8887 (1.0768) acc 75.0000 (72.3214) lr 7.0224e-05 eta 2:10:50
+epoch [46/50] batch [40/1000] time 1.563 (1.586) data 0.000 (0.025) loss 0.9082 (1.0685) acc 78.1250 (72.8125) lr 7.0224e-05 eta 2:11:06
+epoch [46/50] batch [45/1000] time 1.552 (1.583) data 0.000 (0.022) loss 1.3330 (1.0905) acc 53.1250 (72.6389) lr 7.0224e-05 eta 2:10:43
+epoch [46/50] batch [50/1000] time 1.554 (1.582) data 0.000 (0.020) loss 1.0693 (1.0793) acc 71.8750 (72.5625) lr 7.0224e-05 eta 2:10:30
+epoch [46/50] batch [55/1000] time 1.571 (1.581) data 0.000 (0.018) loss 1.2461 (1.0707) acc 75.0000 (72.7841) lr 7.0224e-05 eta 2:10:19
+epoch [46/50] batch [60/1000] time 1.567 (1.580) data 0.001 (0.017) loss 1.2949 (1.0735) acc 65.6250 (72.4479) lr 7.0224e-05 eta 2:10:05
+epoch [46/50] batch [65/1000] time 1.577 (1.579) data 0.000 (0.015) loss 1.2676 (1.0771) acc 65.6250 (72.4519) lr 7.0224e-05 eta 2:09:52
+epoch [46/50] batch [70/1000] time 1.541 (1.578) data 0.001 (0.014) loss 0.9966 (1.0720) acc 78.1250 (72.5446) lr 7.0224e-05 eta 2:09:38
+epoch [46/50] batch [75/1000] time 1.545 (1.576) data 0.001 (0.013) loss 0.7666 (1.0717) acc 78.1250 (72.7083) lr 7.0224e-05 eta 2:09:21
+epoch [46/50] batch [80/1000] time 1.565 (1.575) data 0.000 (0.013) loss 1.6963 (1.0824) acc 65.6250 (72.6562) lr 7.0224e-05 eta 2:09:08
+epoch [46/50] batch [85/1000] time 1.567 (1.577) data 0.000 (0.012) loss 0.7827 (1.0755) acc 75.0000 (72.8309) lr 7.0224e-05 eta 2:09:09
+epoch [46/50] batch [90/1000] time 1.567 (1.576) data 0.000 (0.011) loss 1.5215 (1.0736) acc 59.3750 (72.6042) lr 7.0224e-05 eta 2:08:57
+epoch [46/50] batch [95/1000] time 1.567 (1.575) data 0.000 (0.011) loss 1.0156 (1.0746) acc 75.0000 (72.6316) lr 7.0224e-05 eta 2:08:46
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+epoch [46/50] batch [650/1000] time 1.555 (1.566) data 0.000 (0.002) loss 1.4668 (1.0682) acc 75.0000 (73.1538) lr 7.0224e-05 eta 1:53:30
+epoch [46/50] batch [655/1000] time 1.562 (1.565) data 0.001 (0.002) loss 0.6133 (1.0672) acc 81.2500 (73.1823) lr 7.0224e-05 eta 1:53:21
+epoch [46/50] batch [660/1000] time 1.557 (1.565) data 0.001 (0.002) loss 0.9795 (1.0670) acc 75.0000 (73.1723) lr 7.0224e-05 eta 1:53:13
+epoch [46/50] batch [665/1000] time 1.560 (1.565) data 0.001 (0.002) loss 0.7173 (1.0663) acc 78.1250 (73.1767) lr 7.0224e-05 eta 1:53:05
+epoch [46/50] batch [670/1000] time 1.562 (1.565) data 0.001 (0.002) loss 0.8096 (1.0667) acc 71.8750 (73.1343) lr 7.0224e-05 eta 1:52:57
+epoch [46/50] batch [675/1000] time 1.571 (1.565) data 0.000 (0.002) loss 0.7891 (1.0676) acc 71.8750 (73.1157) lr 7.0224e-05 eta 1:52:50
+epoch [46/50] batch [680/1000] time 1.533 (1.565) data 0.001 (0.002) loss 0.7300 (1.0671) acc 81.2500 (73.1434) lr 7.0224e-05 eta 1:52:42
+epoch [46/50] batch [685/1000] time 1.570 (1.565) data 0.001 (0.002) loss 0.4216 (1.0647) acc 90.6250 (73.2071) lr 7.0224e-05 eta 1:52:34
+epoch [46/50] batch [690/1000] time 1.555 (1.565) data 0.000 (0.002) loss 1.1143 (1.0649) acc 65.6250 (73.2111) lr 7.0224e-05 eta 1:52:27
+epoch [46/50] batch [695/1000] time 1.553 (1.565) data 0.000 (0.002) loss 1.2480 (1.0666) acc 68.7500 (73.1790) lr 7.0224e-05 eta 1:52:18
+epoch [46/50] batch [700/1000] time 1.566 (1.565) data 0.000 (0.002) loss 0.8188 (1.0659) acc 78.1250 (73.1920) lr 7.0224e-05 eta 1:52:10
+epoch [46/50] batch [705/1000] time 1.549 (1.565) data 0.001 (0.002) loss 1.3984 (1.0658) acc 65.6250 (73.2048) lr 7.0224e-05 eta 1:52:02
+epoch [46/50] batch [710/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.9766 (1.0659) acc 56.2500 (73.1998) lr 7.0224e-05 eta 1:51:54
+epoch [46/50] batch [715/1000] time 1.558 (1.565) data 0.001 (0.002) loss 1.0693 (1.0662) acc 75.0000 (73.2037) lr 7.0224e-05 eta 1:51:46
+epoch [46/50] batch [720/1000] time 1.572 (1.565) data 0.000 (0.002) loss 1.1982 (1.0665) acc 78.1250 (73.2075) lr 7.0224e-05 eta 1:51:39
+epoch [46/50] batch [725/1000] time 1.600 (1.565) data 0.001 (0.002) loss 0.9023 (1.0661) acc 78.1250 (73.2198) lr 7.0224e-05 eta 1:51:32
+epoch [46/50] batch [730/1000] time 1.553 (1.565) data 0.000 (0.002) loss 1.1309 (1.0643) acc 78.1250 (73.2620) lr 7.0224e-05 eta 1:51:24
+epoch [46/50] batch [735/1000] time 1.597 (1.566) data 0.001 (0.002) loss 1.5977 (1.0640) acc 68.7500 (73.2781) lr 7.0224e-05 eta 1:51:17
+epoch [46/50] batch [740/1000] time 1.546 (1.566) data 0.000 (0.002) loss 1.0264 (1.0633) acc 75.0000 (73.2897) lr 7.0224e-05 eta 1:51:09
+epoch [46/50] batch [745/1000] time 1.577 (1.566) data 0.000 (0.002) loss 1.1670 (1.0643) acc 65.6250 (73.2760) lr 7.0224e-05 eta 1:51:01
+epoch [46/50] batch [750/1000] time 1.540 (1.566) data 0.000 (0.002) loss 1.0146 (1.0650) acc 75.0000 (73.2500) lr 7.0224e-05 eta 1:50:53
+epoch [46/50] batch [755/1000] time 1.557 (1.565) data 0.000 (0.002) loss 0.6157 (1.0640) acc 84.3750 (73.2823) lr 7.0224e-05 eta 1:50:45
+epoch [46/50] batch [760/1000] time 1.540 (1.565) data 0.001 (0.002) loss 1.7051 (1.0654) acc 62.5000 (73.2525) lr 7.0224e-05 eta 1:50:37
+epoch [46/50] batch [765/1000] time 1.540 (1.565) data 0.000 (0.002) loss 0.8101 (1.0651) acc 78.1250 (73.2843) lr 7.0224e-05 eta 1:50:29
+epoch [46/50] batch [770/1000] time 1.542 (1.565) data 0.001 (0.002) loss 0.6074 (1.0641) acc 81.2500 (73.3076) lr 7.0224e-05 eta 1:50:21
+epoch [46/50] batch [775/1000] time 1.582 (1.565) data 0.001 (0.002) loss 1.4297 (1.0655) acc 75.0000 (73.2984) lr 7.0224e-05 eta 1:50:13
+epoch [46/50] batch [780/1000] time 1.536 (1.565) data 0.000 (0.002) loss 0.8169 (1.0653) acc 71.8750 (73.3093) lr 7.0224e-05 eta 1:50:05
+epoch [46/50] batch [785/1000] time 1.547 (1.565) data 0.001 (0.002) loss 0.8330 (1.0652) acc 81.2500 (73.3240) lr 7.0224e-05 eta 1:49:57
+epoch [46/50] batch [790/1000] time 1.559 (1.565) data 0.001 (0.002) loss 1.4453 (1.0661) acc 68.7500 (73.3070) lr 7.0224e-05 eta 1:49:50
+epoch [46/50] batch [795/1000] time 1.584 (1.566) data 0.000 (0.002) loss 2.1895 (1.0682) acc 59.3750 (73.2704) lr 7.0224e-05 eta 1:49:43
+epoch [46/50] batch [800/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.6709 (1.0683) acc 84.3750 (73.2773) lr 7.0224e-05 eta 1:49:35
+epoch [46/50] batch [805/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.3962 (1.0681) acc 90.6250 (73.2764) lr 7.0224e-05 eta 1:49:27
+epoch [46/50] batch [810/1000] time 1.545 (1.565) data 0.000 (0.002) loss 1.4854 (1.0692) acc 59.3750 (73.2446) lr 7.0224e-05 eta 1:49:19
+epoch [46/50] batch [815/1000] time 1.553 (1.565) data 0.000 (0.002) loss 1.7119 (1.0707) acc 68.7500 (73.2324) lr 7.0224e-05 eta 1:49:10
+epoch [46/50] batch [820/1000] time 1.572 (1.565) data 0.000 (0.002) loss 0.9326 (1.0694) acc 68.7500 (73.2622) lr 7.0224e-05 eta 1:49:03
+epoch [46/50] batch [825/1000] time 1.557 (1.565) data 0.000 (0.002) loss 1.2295 (1.0689) acc 68.7500 (73.2765) lr 7.0224e-05 eta 1:48:55
+epoch [46/50] batch [830/1000] time 1.561 (1.565) data 0.001 (0.002) loss 1.0107 (1.0691) acc 75.0000 (73.2681) lr 7.0224e-05 eta 1:48:47
+epoch [46/50] batch [835/1000] time 1.539 (1.565) data 0.000 (0.002) loss 1.0479 (1.0684) acc 75.0000 (73.2784) lr 7.0224e-05 eta 1:48:39
+epoch [46/50] batch [840/1000] time 1.591 (1.565) data 0.000 (0.002) loss 1.3330 (1.0684) acc 71.8750 (73.2850) lr 7.0224e-05 eta 1:48:32
+epoch [46/50] batch [845/1000] time 1.565 (1.565) data 0.000 (0.002) loss 0.7231 (1.0668) acc 75.0000 (73.3321) lr 7.0224e-05 eta 1:48:24
+epoch [46/50] batch [850/1000] time 1.543 (1.565) data 0.001 (0.002) loss 0.9917 (1.0657) acc 68.7500 (73.3309) lr 7.0224e-05 eta 1:48:15
+epoch [46/50] batch [855/1000] time 1.566 (1.565) data 0.000 (0.002) loss 1.1748 (1.0678) acc 68.7500 (73.2822) lr 7.0224e-05 eta 1:48:07
+epoch [46/50] batch [860/1000] time 1.590 (1.565) data 0.001 (0.002) loss 0.9790 (1.0682) acc 84.3750 (73.3031) lr 7.0224e-05 eta 1:48:00
+epoch [46/50] batch [865/1000] time 1.534 (1.565) data 0.000 (0.002) loss 1.0615 (1.0673) acc 81.2500 (73.3345) lr 7.0224e-05 eta 1:47:51
+epoch [46/50] batch [870/1000] time 1.542 (1.565) data 0.001 (0.002) loss 1.4102 (1.0667) acc 75.0000 (73.3621) lr 7.0224e-05 eta 1:47:43
+epoch [46/50] batch [875/1000] time 1.551 (1.565) data 0.001 (0.002) loss 1.4092 (1.0671) acc 68.7500 (73.3393) lr 7.0224e-05 eta 1:47:35
+epoch [46/50] batch [880/1000] time 1.563 (1.565) data 0.000 (0.002) loss 0.7412 (1.0653) acc 84.3750 (73.3771) lr 7.0224e-05 eta 1:47:27
+epoch [46/50] batch [885/1000] time 1.565 (1.565) data 0.000 (0.002) loss 1.1270 (1.0648) acc 75.0000 (73.3898) lr 7.0224e-05 eta 1:47:20
+epoch [46/50] batch [890/1000] time 1.538 (1.565) data 0.000 (0.002) loss 0.7070 (1.0650) acc 90.6250 (73.3883) lr 7.0224e-05 eta 1:47:12
+epoch [46/50] batch [895/1000] time 1.553 (1.565) data 0.000 (0.002) loss 0.9419 (1.0644) acc 65.6250 (73.3834) lr 7.0224e-05 eta 1:47:04
+epoch [46/50] batch [900/1000] time 1.540 (1.565) data 0.000 (0.002) loss 1.2246 (1.0654) acc 68.7500 (73.3785) lr 7.0224e-05 eta 1:46:56
+epoch [46/50] batch [905/1000] time 1.572 (1.565) data 0.001 (0.002) loss 1.1924 (1.0654) acc 78.1250 (73.3874) lr 7.0224e-05 eta 1:46:48
+epoch [46/50] batch [910/1000] time 1.590 (1.565) data 0.001 (0.002) loss 1.1162 (1.0652) acc 68.7500 (73.3654) lr 7.0224e-05 eta 1:46:40
+epoch [46/50] batch [915/1000] time 1.531 (1.565) data 0.000 (0.002) loss 1.1387 (1.0660) acc 71.8750 (73.3402) lr 7.0224e-05 eta 1:46:32
+epoch [46/50] batch [920/1000] time 1.547 (1.565) data 0.000 (0.002) loss 0.8687 (1.0660) acc 75.0000 (73.3220) lr 7.0224e-05 eta 1:46:24
+epoch [46/50] batch [925/1000] time 1.561 (1.565) data 0.000 (0.002) loss 0.9712 (1.0662) acc 75.0000 (73.3209) lr 7.0224e-05 eta 1:46:16
+epoch [46/50] batch [930/1000] time 1.560 (1.565) data 0.000 (0.002) loss 1.2949 (1.0658) acc 68.7500 (73.3333) lr 7.0224e-05 eta 1:46:08
+epoch [46/50] batch [935/1000] time 1.551 (1.565) data 0.001 (0.002) loss 0.9780 (1.0673) acc 81.2500 (73.3189) lr 7.0224e-05 eta 1:46:00
+epoch [46/50] batch [940/1000] time 1.560 (1.564) data 0.000 (0.002) loss 0.9385 (1.0669) acc 75.0000 (73.3178) lr 7.0224e-05 eta 1:45:51
+epoch [46/50] batch [945/1000] time 1.720 (1.565) data 0.000 (0.002) loss 0.8530 (1.0664) acc 68.7500 (73.3135) lr 7.0224e-05 eta 1:45:44
+epoch [46/50] batch [950/1000] time 1.523 (1.565) data 0.000 (0.002) loss 1.4492 (1.0672) acc 65.6250 (73.3191) lr 7.0224e-05 eta 1:45:36
+epoch [46/50] batch [955/1000] time 1.567 (1.565) data 0.000 (0.001) loss 0.9751 (1.0677) acc 71.8750 (73.3344) lr 7.0224e-05 eta 1:45:28
+epoch [46/50] batch [960/1000] time 1.559 (1.565) data 0.000 (0.001) loss 1.1895 (1.0678) acc 68.7500 (73.3171) lr 7.0224e-05 eta 1:45:20
+epoch [46/50] batch [965/1000] time 1.548 (1.565) data 0.001 (0.001) loss 1.3584 (1.0682) acc 62.5000 (73.2966) lr 7.0224e-05 eta 1:45:12
+epoch [46/50] batch [970/1000] time 1.541 (1.564) data 0.001 (0.001) loss 0.9287 (1.0690) acc 78.1250 (73.2796) lr 7.0224e-05 eta 1:45:04
+epoch [46/50] batch [975/1000] time 1.562 (1.564) data 0.000 (0.001) loss 1.3809 (1.0698) acc 65.6250 (73.2628) lr 7.0224e-05 eta 1:44:56
+epoch [46/50] batch [980/1000] time 1.552 (1.564) data 0.000 (0.001) loss 1.3154 (1.0707) acc 68.7500 (73.2398) lr 7.0224e-05 eta 1:44:48
+epoch [46/50] batch [985/1000] time 1.540 (1.564) data 0.001 (0.001) loss 0.5469 (1.0706) acc 81.2500 (73.2329) lr 7.0224e-05 eta 1:44:40
+epoch [46/50] batch [990/1000] time 1.735 (1.564) data 0.000 (0.001) loss 1.0830 (1.0713) acc 71.8750 (73.2197) lr 7.0224e-05 eta 1:44:33
+epoch [46/50] batch [995/1000] time 1.559 (1.564) data 0.000 (0.001) loss 2.0098 (1.0714) acc 62.5000 (73.2161) lr 7.0224e-05 eta 1:44:25
+epoch [46/50] batch [1000/1000] time 1.553 (1.564) data 0.000 (0.001) loss 0.8594 (1.0717) acc 75.0000 (73.2125) lr 4.8943e-05 eta 1:44:17
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,375
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.3%
+epoch [47/50] batch [5/1000] time 1.574 (1.696) data 0.001 (0.185) loss 0.9556 (0.8328) acc 81.2500 (79.3750) lr 4.8943e-05 eta 1:52:53
+epoch [47/50] batch [10/1000] time 1.548 (1.633) data 0.000 (0.093) loss 1.2578 (1.0708) acc 68.7500 (74.0625) lr 4.8943e-05 eta 1:48:35
+epoch [47/50] batch [15/1000] time 1.564 (1.610) data 0.000 (0.062) loss 1.5410 (1.1118) acc 68.7500 (73.1250) lr 4.8943e-05 eta 1:46:54
+epoch [47/50] batch [20/1000] time 1.565 (1.599) data 0.000 (0.047) loss 0.6221 (1.0771) acc 78.1250 (72.9688) lr 4.8943e-05 eta 1:46:04
+epoch [47/50] batch [25/1000] time 1.563 (1.602) data 0.000 (0.037) loss 1.1914 (1.1256) acc 65.6250 (72.2500) lr 4.8943e-05 eta 1:46:07
+epoch [47/50] batch [30/1000] time 1.576 (1.595) data 0.001 (0.031) loss 1.1494 (1.1337) acc 68.7500 (72.7083) lr 4.8943e-05 eta 1:45:33
+epoch [47/50] batch [35/1000] time 1.547 (1.591) data 0.001 (0.027) loss 0.5278 (1.1105) acc 84.3750 (72.9464) lr 4.8943e-05 eta 1:45:09
+epoch [47/50] batch [40/1000] time 1.555 (1.587) data 0.001 (0.024) loss 0.9595 (1.1254) acc 71.8750 (72.5781) lr 4.8943e-05 eta 1:44:43
+epoch [47/50] batch [45/1000] time 1.542 (1.583) data 0.001 (0.021) loss 1.1377 (1.1510) acc 71.8750 (72.2917) lr 4.8943e-05 eta 1:44:21
+epoch [47/50] batch [50/1000] time 1.568 (1.581) data 0.000 (0.019) loss 1.3057 (1.1601) acc 62.5000 (72.3750) lr 4.8943e-05 eta 1:44:03
+epoch [47/50] batch [55/1000] time 1.557 (1.580) data 0.001 (0.017) loss 0.9609 (1.1580) acc 62.5000 (72.3295) lr 4.8943e-05 eta 1:43:51
+epoch [47/50] batch [60/1000] time 1.546 (1.578) data 0.001 (0.016) loss 1.1133 (1.1548) acc 71.8750 (72.3958) lr 4.8943e-05 eta 1:43:36
+epoch [47/50] batch [65/1000] time 1.568 (1.577) data 0.001 (0.015) loss 0.6362 (1.1329) acc 71.8750 (72.5962) lr 4.8943e-05 eta 1:43:26
+epoch [47/50] batch [70/1000] time 1.570 (1.575) data 0.000 (0.014) loss 1.0615 (1.1150) acc 78.1250 (72.9911) lr 4.8943e-05 eta 1:43:11
+epoch [47/50] batch [75/1000] time 1.553 (1.574) data 0.000 (0.013) loss 1.3096 (1.0900) acc 68.7500 (73.4583) lr 4.8943e-05 eta 1:42:59
+epoch [47/50] batch [80/1000] time 1.537 (1.573) data 0.000 (0.012) loss 1.2373 (1.0825) acc 65.6250 (73.4766) lr 4.8943e-05 eta 1:42:46
+epoch [47/50] batch [85/1000] time 1.553 (1.572) data 0.001 (0.011) loss 1.1445 (1.0671) acc 65.6250 (73.7868) lr 4.8943e-05 eta 1:42:35
+epoch [47/50] batch [90/1000] time 1.570 (1.573) data 0.001 (0.011) loss 1.0576 (1.0802) acc 71.8750 (73.4722) lr 4.8943e-05 eta 1:42:32
+epoch [47/50] batch [95/1000] time 1.543 (1.573) data 0.001 (0.010) loss 1.2070 (1.0759) acc 68.7500 (73.5197) lr 4.8943e-05 eta 1:42:21
+epoch [47/50] batch [100/1000] time 1.574 (1.572) data 0.000 (0.010) loss 1.1279 (1.0724) acc 75.0000 (73.5938) lr 4.8943e-05 eta 1:42:12
+epoch [47/50] batch [105/1000] time 1.574 (1.572) data 0.001 (0.009) loss 1.2666 (1.0773) acc 62.5000 (73.3929) lr 4.8943e-05 eta 1:42:04
+epoch [47/50] batch [110/1000] time 1.570 (1.572) data 0.001 (0.009) loss 0.6743 (1.0746) acc 81.2500 (73.3523) lr 4.8943e-05 eta 1:41:55
+epoch [47/50] batch [115/1000] time 1.546 (1.572) data 0.001 (0.009) loss 1.2051 (1.0737) acc 78.1250 (73.3424) lr 4.8943e-05 eta 1:41:45
+epoch [47/50] batch [120/1000] time 1.550 (1.571) data 0.000 (0.008) loss 1.2070 (1.0775) acc 71.8750 (73.3333) lr 4.8943e-05 eta 1:41:36
+epoch [47/50] batch [125/1000] time 1.606 (1.571) data 0.001 (0.008) loss 1.3145 (1.0897) acc 75.0000 (73.1500) lr 4.8943e-05 eta 1:41:27
+epoch [47/50] batch [130/1000] time 1.553 (1.571) data 0.000 (0.008) loss 1.1621 (1.0987) acc 65.6250 (73.0529) lr 4.8943e-05 eta 1:41:18
+epoch [47/50] batch [135/1000] time 1.562 (1.571) data 0.000 (0.007) loss 1.2979 (1.0983) acc 65.6250 (72.9398) lr 4.8943e-05 eta 1:41:12
+epoch [47/50] batch [140/1000] time 1.542 (1.571) data 0.000 (0.007) loss 0.4290 (1.0912) acc 87.5000 (73.1250) lr 4.8943e-05 eta 1:41:04
+epoch [47/50] batch [145/1000] time 1.559 (1.571) data 0.001 (0.007) loss 1.2051 (1.0895) acc 62.5000 (73.1034) lr 4.8943e-05 eta 1:40:55
+epoch [47/50] batch [150/1000] time 1.557 (1.570) data 0.001 (0.007) loss 1.0332 (1.0844) acc 75.0000 (73.2292) lr 4.8943e-05 eta 1:40:45
+epoch [47/50] batch [155/1000] time 1.563 (1.570) data 0.000 (0.007) loss 1.1309 (1.0840) acc 75.0000 (73.2056) lr 4.8943e-05 eta 1:40:37
+epoch [47/50] batch [160/1000] time 1.571 (1.570) data 0.001 (0.006) loss 0.7354 (1.0826) acc 75.0000 (73.1641) lr 4.8943e-05 eta 1:40:29
+epoch [47/50] batch [165/1000] time 1.580 (1.570) data 0.000 (0.006) loss 0.7891 (1.0792) acc 75.0000 (73.1629) lr 4.8943e-05 eta 1:40:21
+epoch [47/50] batch [170/1000] time 1.541 (1.570) data 0.001 (0.006) loss 1.6006 (1.0799) acc 56.2500 (73.1801) lr 4.8943e-05 eta 1:40:11
+epoch [47/50] batch [175/1000] time 1.737 (1.570) data 0.001 (0.006) loss 0.5288 (1.0731) acc 81.2500 (73.3393) lr 4.8943e-05 eta 1:40:06
+epoch [47/50] batch [180/1000] time 1.588 (1.570) data 0.001 (0.006) loss 0.6016 (1.0741) acc 81.2500 (73.3854) lr 4.8943e-05 eta 1:39:57
+epoch [47/50] batch [185/1000] time 1.552 (1.570) data 0.000 (0.006) loss 0.8813 (1.0701) acc 78.1250 (73.4459) lr 4.8943e-05 eta 1:39:48
+epoch [47/50] batch [190/1000] time 1.560 (1.569) data 0.000 (0.005) loss 1.7363 (1.0769) acc 56.2500 (73.3717) lr 4.8943e-05 eta 1:39:38
+epoch [47/50] batch [195/1000] time 1.549 (1.569) data 0.000 (0.005) loss 1.5098 (1.0744) acc 56.2500 (73.3333) lr 4.8943e-05 eta 1:39:30
+epoch [47/50] batch [200/1000] time 1.558 (1.569) data 0.000 (0.005) loss 1.1055 (1.0686) acc 75.0000 (73.4688) lr 4.8943e-05 eta 1:39:21
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+epoch [47/50] batch [755/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.1572 (1.0742) acc 62.5000 (72.9553) lr 4.8943e-05 eta 1:24:41
+epoch [47/50] batch [760/1000] time 1.531 (1.566) data 0.000 (0.002) loss 1.1201 (1.0727) acc 68.7500 (72.9934) lr 4.8943e-05 eta 1:24:33
+epoch [47/50] batch [765/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.5786 (1.0720) acc 87.5000 (72.9984) lr 4.8943e-05 eta 1:24:25
+epoch [47/50] batch [770/1000] time 1.564 (1.566) data 0.000 (0.002) loss 1.8115 (1.0720) acc 53.1250 (73.0032) lr 4.8943e-05 eta 1:24:17
+epoch [47/50] batch [775/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.7734 (1.0703) acc 75.0000 (73.0202) lr 4.8943e-05 eta 1:24:09
+epoch [47/50] batch [780/1000] time 1.569 (1.566) data 0.001 (0.002) loss 0.9873 (1.0706) acc 68.7500 (73.0208) lr 4.8943e-05 eta 1:24:02
+epoch [47/50] batch [785/1000] time 1.544 (1.566) data 0.001 (0.002) loss 0.5649 (1.0696) acc 81.2500 (73.0255) lr 4.8943e-05 eta 1:23:54
+epoch [47/50] batch [790/1000] time 1.570 (1.566) data 0.001 (0.002) loss 0.9995 (1.0707) acc 75.0000 (72.9984) lr 4.8943e-05 eta 1:23:46
+epoch [47/50] batch [795/1000] time 1.554 (1.566) data 0.001 (0.002) loss 1.0928 (1.0700) acc 78.1250 (73.0110) lr 4.8943e-05 eta 1:23:38
+epoch [47/50] batch [800/1000] time 1.578 (1.566) data 0.001 (0.002) loss 0.8203 (1.0701) acc 81.2500 (73.0078) lr 4.8943e-05 eta 1:23:30
+epoch [47/50] batch [805/1000] time 1.539 (1.566) data 0.001 (0.002) loss 1.9326 (1.0712) acc 59.3750 (72.9930) lr 4.8943e-05 eta 1:23:22
+epoch [47/50] batch [810/1000] time 1.545 (1.566) data 0.001 (0.002) loss 1.2100 (1.0709) acc 78.1250 (73.0093) lr 4.8943e-05 eta 1:23:14
+epoch [47/50] batch [815/1000] time 1.574 (1.566) data 0.000 (0.002) loss 0.7964 (1.0704) acc 81.2500 (73.0138) lr 4.8943e-05 eta 1:23:06
+epoch [47/50] batch [820/1000] time 1.564 (1.566) data 0.001 (0.002) loss 0.9434 (1.0696) acc 71.8750 (73.0069) lr 4.8943e-05 eta 1:22:58
+epoch [47/50] batch [825/1000] time 1.550 (1.566) data 0.000 (0.002) loss 1.6260 (1.0698) acc 59.3750 (72.9962) lr 4.8943e-05 eta 1:22:50
+epoch [47/50] batch [830/1000] time 1.574 (1.566) data 0.000 (0.002) loss 0.3889 (1.0692) acc 90.6250 (73.0233) lr 4.8943e-05 eta 1:22:43
+epoch [47/50] batch [835/1000] time 1.546 (1.566) data 0.000 (0.002) loss 1.1230 (1.0693) acc 65.6250 (73.0277) lr 4.8943e-05 eta 1:22:35
+epoch [47/50] batch [840/1000] time 1.541 (1.565) data 0.000 (0.002) loss 0.9834 (1.0693) acc 78.1250 (73.0506) lr 4.8943e-05 eta 1:22:26
+epoch [47/50] batch [845/1000] time 1.600 (1.566) data 0.001 (0.002) loss 1.1523 (1.0701) acc 75.0000 (73.0325) lr 4.8943e-05 eta 1:22:19
+epoch [47/50] batch [850/1000] time 1.545 (1.566) data 0.001 (0.002) loss 1.7471 (1.0722) acc 62.5000 (73.0037) lr 4.8943e-05 eta 1:22:11
+epoch [47/50] batch [855/1000] time 1.575 (1.566) data 0.001 (0.002) loss 0.3154 (1.0711) acc 87.5000 (73.0044) lr 4.8943e-05 eta 1:22:03
+epoch [47/50] batch [860/1000] time 1.561 (1.566) data 0.001 (0.002) loss 1.4521 (1.0714) acc 71.8750 (72.9942) lr 4.8943e-05 eta 1:21:55
+epoch [47/50] batch [865/1000] time 1.574 (1.566) data 0.001 (0.002) loss 1.1523 (1.0709) acc 62.5000 (72.9877) lr 4.8943e-05 eta 1:21:48
+epoch [47/50] batch [870/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.0684 (1.0694) acc 84.3750 (73.0280) lr 4.8943e-05 eta 1:21:40
+epoch [47/50] batch [875/1000] time 1.556 (1.565) data 0.001 (0.002) loss 1.1777 (1.0690) acc 68.7500 (73.0321) lr 4.8943e-05 eta 1:21:32
+epoch [47/50] batch [880/1000] time 1.592 (1.565) data 0.001 (0.002) loss 0.7534 (1.0689) acc 81.2500 (73.0220) lr 4.8943e-05 eta 1:21:24
+epoch [47/50] batch [885/1000] time 1.554 (1.565) data 0.000 (0.002) loss 1.1436 (1.0693) acc 71.8750 (73.0367) lr 4.8943e-05 eta 1:21:16
+epoch [47/50] batch [890/1000] time 1.570 (1.566) data 0.000 (0.002) loss 0.5015 (1.0678) acc 81.2500 (73.0758) lr 4.8943e-05 eta 1:21:09
+epoch [47/50] batch [895/1000] time 1.571 (1.566) data 0.000 (0.002) loss 0.8481 (1.0677) acc 87.5000 (73.0901) lr 4.8943e-05 eta 1:21:01
+epoch [47/50] batch [900/1000] time 1.586 (1.566) data 0.000 (0.002) loss 1.2695 (1.0680) acc 65.6250 (73.0729) lr 4.8943e-05 eta 1:20:53
+epoch [47/50] batch [905/1000] time 1.565 (1.566) data 0.000 (0.002) loss 1.3584 (1.0686) acc 50.0000 (73.0352) lr 4.8943e-05 eta 1:20:45
+epoch [47/50] batch [910/1000] time 1.551 (1.566) data 0.000 (0.002) loss 1.1738 (1.0676) acc 62.5000 (73.0391) lr 4.8943e-05 eta 1:20:37
+epoch [47/50] batch [915/1000] time 1.570 (1.566) data 0.000 (0.002) loss 1.0977 (1.0684) acc 75.0000 (73.0157) lr 4.8943e-05 eta 1:20:29
+epoch [47/50] batch [920/1000] time 1.548 (1.566) data 0.000 (0.002) loss 1.2188 (1.0692) acc 75.0000 (73.0129) lr 4.8943e-05 eta 1:20:21
+epoch [47/50] batch [925/1000] time 1.542 (1.565) data 0.000 (0.002) loss 1.4033 (1.0702) acc 68.7500 (72.9966) lr 4.8943e-05 eta 1:20:13
+epoch [47/50] batch [930/1000] time 1.728 (1.566) data 0.001 (0.001) loss 0.8359 (1.0700) acc 75.0000 (73.0040) lr 4.8943e-05 eta 1:20:06
+epoch [47/50] batch [935/1000] time 1.582 (1.566) data 0.001 (0.001) loss 1.3262 (1.0707) acc 59.3750 (72.9947) lr 4.8943e-05 eta 1:19:58
+epoch [47/50] batch [940/1000] time 1.584 (1.566) data 0.001 (0.001) loss 1.1680 (1.0700) acc 71.8750 (73.0286) lr 4.8943e-05 eta 1:19:51
+epoch [47/50] batch [945/1000] time 1.582 (1.566) data 0.001 (0.001) loss 1.3262 (1.0703) acc 75.0000 (73.0324) lr 4.8943e-05 eta 1:19:43
+epoch [47/50] batch [950/1000] time 1.560 (1.566) data 0.000 (0.001) loss 0.7646 (1.0715) acc 81.2500 (73.0099) lr 4.8943e-05 eta 1:19:35
+epoch [47/50] batch [955/1000] time 1.574 (1.566) data 0.000 (0.001) loss 1.2041 (1.0732) acc 71.8750 (72.9876) lr 4.8943e-05 eta 1:19:27
+epoch [47/50] batch [960/1000] time 1.536 (1.566) data 0.001 (0.001) loss 0.9390 (1.0727) acc 71.8750 (72.9980) lr 4.8943e-05 eta 1:19:19
+epoch [47/50] batch [965/1000] time 1.545 (1.566) data 0.001 (0.001) loss 0.6216 (1.0727) acc 84.3750 (73.0084) lr 4.8943e-05 eta 1:19:11
+epoch [47/50] batch [970/1000] time 1.537 (1.566) data 0.000 (0.001) loss 1.0225 (1.0731) acc 71.8750 (73.0026) lr 4.8943e-05 eta 1:19:04
+epoch [47/50] batch [975/1000] time 1.576 (1.566) data 0.000 (0.001) loss 1.1357 (1.0733) acc 71.8750 (73.0096) lr 4.8943e-05 eta 1:18:56
+epoch [47/50] batch [980/1000] time 1.568 (1.566) data 0.001 (0.001) loss 1.4844 (1.0735) acc 68.7500 (73.0006) lr 4.8943e-05 eta 1:18:48
+epoch [47/50] batch [985/1000] time 1.551 (1.566) data 0.001 (0.001) loss 0.9824 (1.0747) acc 78.1250 (72.9886) lr 4.8943e-05 eta 1:18:40
+epoch [47/50] batch [990/1000] time 1.543 (1.566) data 0.000 (0.001) loss 0.4468 (1.0741) acc 90.6250 (72.9924) lr 4.8943e-05 eta 1:18:32
+epoch [47/50] batch [995/1000] time 1.563 (1.566) data 0.000 (0.001) loss 1.1211 (1.0748) acc 68.7500 (72.9805) lr 4.8943e-05 eta 1:18:25
+epoch [47/50] batch [1000/1000] time 1.569 (1.566) data 0.000 (0.001) loss 1.0752 (1.0750) acc 75.0000 (72.9688) lr 3.1417e-05 eta 1:18:17
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,383
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.3%
+epoch [48/50] batch [5/1000] time 1.571 (1.699) data 0.000 (0.189) loss 1.2422 (1.1694) acc 68.7500 (66.8750) lr 3.1417e-05 eta 1:24:47
+epoch [48/50] batch [10/1000] time 1.569 (1.633) data 0.000 (0.095) loss 1.0469 (1.0943) acc 78.1250 (70.6250) lr 3.1417e-05 eta 1:21:22
+epoch [48/50] batch [15/1000] time 1.569 (1.634) data 0.000 (0.063) loss 0.9712 (1.0634) acc 71.8750 (71.2500) lr 3.1417e-05 eta 1:21:16
+epoch [48/50] batch [20/1000] time 1.547 (1.618) data 0.001 (0.048) loss 1.5117 (1.1425) acc 65.6250 (70.6250) lr 3.1417e-05 eta 1:20:20
+epoch [48/50] batch [25/1000] time 1.579 (1.609) data 0.001 (0.038) loss 0.5591 (1.0919) acc 84.3750 (71.1250) lr 3.1417e-05 eta 1:19:47
+epoch [48/50] batch [30/1000] time 1.570 (1.603) data 0.001 (0.032) loss 0.7119 (1.0588) acc 84.3750 (72.6042) lr 3.1417e-05 eta 1:19:20
+epoch [48/50] batch [35/1000] time 1.579 (1.597) data 0.000 (0.027) loss 0.8628 (1.0591) acc 78.1250 (73.2143) lr 3.1417e-05 eta 1:18:55
+epoch [48/50] batch [40/1000] time 1.559 (1.593) data 0.000 (0.024) loss 0.9736 (1.0619) acc 75.0000 (72.9688) lr 3.1417e-05 eta 1:18:34
+epoch [48/50] batch [45/1000] time 1.530 (1.588) data 0.000 (0.021) loss 0.2778 (1.0140) acc 90.6250 (73.9583) lr 3.1417e-05 eta 1:18:13
+epoch [48/50] batch [50/1000] time 1.560 (1.586) data 0.000 (0.019) loss 1.0781 (1.0264) acc 78.1250 (73.8750) lr 3.1417e-05 eta 1:17:57
+epoch [48/50] batch [55/1000] time 1.572 (1.584) data 0.000 (0.018) loss 0.4934 (1.0370) acc 78.1250 (73.9773) lr 3.1417e-05 eta 1:17:45
+epoch [48/50] batch [60/1000] time 1.561 (1.586) data 0.001 (0.016) loss 2.0586 (1.0428) acc 56.2500 (73.5938) lr 3.1417e-05 eta 1:17:42
+epoch [48/50] batch [65/1000] time 1.553 (1.585) data 0.001 (0.015) loss 0.7417 (1.0297) acc 81.2500 (73.9904) lr 3.1417e-05 eta 1:17:31
+epoch [48/50] batch [70/1000] time 1.557 (1.583) data 0.000 (0.014) loss 1.0332 (1.0309) acc 68.7500 (73.8839) lr 3.1417e-05 eta 1:17:17
+epoch [48/50] batch [75/1000] time 1.559 (1.581) data 0.000 (0.013) loss 0.9473 (1.0321) acc 78.1250 (73.7500) lr 3.1417e-05 eta 1:17:03
+epoch [48/50] batch [80/1000] time 1.571 (1.579) data 0.001 (0.012) loss 1.2549 (1.0406) acc 68.7500 (73.5156) lr 3.1417e-05 eta 1:16:51
+epoch [48/50] batch [85/1000] time 1.550 (1.578) data 0.000 (0.012) loss 1.3086 (1.0431) acc 65.6250 (73.5662) lr 3.1417e-05 eta 1:16:41
+epoch [48/50] batch [90/1000] time 1.576 (1.578) data 0.001 (0.011) loss 1.1543 (1.0415) acc 65.6250 (73.6111) lr 3.1417e-05 eta 1:16:32
+epoch [48/50] batch [95/1000] time 1.573 (1.578) data 0.001 (0.010) loss 0.7817 (1.0370) acc 78.1250 (73.6184) lr 3.1417e-05 eta 1:16:23
+epoch [48/50] batch [100/1000] time 1.538 (1.577) data 0.000 (0.010) loss 1.6309 (1.0393) acc 59.3750 (73.5625) lr 3.1417e-05 eta 1:16:11
+epoch [48/50] batch [105/1000] time 1.568 (1.576) data 0.000 (0.009) loss 1.2998 (1.0365) acc 68.7500 (73.6607) lr 3.1417e-05 eta 1:16:01
+epoch [48/50] batch [110/1000] time 1.533 (1.575) data 0.000 (0.009) loss 1.1211 (1.0369) acc 78.1250 (73.8068) lr 3.1417e-05 eta 1:15:50
+epoch [48/50] batch [115/1000] time 1.560 (1.573) data 0.000 (0.009) loss 1.2051 (1.0381) acc 71.8750 (73.8315) lr 3.1417e-05 eta 1:15:38
+epoch [48/50] batch [120/1000] time 1.586 (1.575) data 0.001 (0.008) loss 1.1113 (1.0423) acc 59.3750 (73.5938) lr 3.1417e-05 eta 1:15:34
+epoch [48/50] batch [125/1000] time 1.583 (1.574) data 0.000 (0.008) loss 1.4180 (1.0465) acc 65.6250 (73.5250) lr 3.1417e-05 eta 1:15:24
+epoch [48/50] batch [130/1000] time 1.579 (1.574) data 0.000 (0.008) loss 0.7734 (1.0532) acc 71.8750 (73.3894) lr 3.1417e-05 eta 1:15:16
+epoch [48/50] batch [135/1000] time 1.577 (1.573) data 0.000 (0.007) loss 1.3799 (1.0621) acc 53.1250 (73.0324) lr 3.1417e-05 eta 1:15:07
+epoch [48/50] batch [140/1000] time 1.541 (1.573) data 0.001 (0.007) loss 0.7905 (1.0603) acc 78.1250 (73.1027) lr 3.1417e-05 eta 1:14:58
+epoch [48/50] batch [145/1000] time 1.561 (1.573) data 0.000 (0.007) loss 1.4238 (1.0586) acc 62.5000 (73.1250) lr 3.1417e-05 eta 1:14:49
+epoch [48/50] batch [150/1000] time 1.570 (1.572) data 0.000 (0.007) loss 0.9873 (1.0567) acc 71.8750 (73.2083) lr 3.1417e-05 eta 1:14:40
+epoch [48/50] batch [155/1000] time 1.569 (1.572) data 0.000 (0.007) loss 0.6025 (1.0533) acc 78.1250 (73.1452) lr 3.1417e-05 eta 1:14:31
+epoch [48/50] batch [160/1000] time 1.533 (1.571) data 0.001 (0.006) loss 1.6807 (1.0549) acc 68.7500 (73.2227) lr 3.1417e-05 eta 1:14:22
+epoch [48/50] batch [165/1000] time 1.554 (1.572) data 0.000 (0.006) loss 1.2715 (1.0535) acc 71.8750 (73.2386) lr 3.1417e-05 eta 1:14:16
+epoch [48/50] batch [170/1000] time 1.581 (1.572) data 0.001 (0.006) loss 0.8062 (1.0498) acc 75.0000 (73.3640) lr 3.1417e-05 eta 1:14:08
+epoch [48/50] batch [175/1000] time 1.572 (1.572) data 0.001 (0.006) loss 0.7632 (1.0460) acc 78.1250 (73.4464) lr 3.1417e-05 eta 1:13:59
+epoch [48/50] batch [180/1000] time 1.554 (1.571) data 0.001 (0.006) loss 1.6777 (1.0491) acc 59.3750 (73.4201) lr 3.1417e-05 eta 1:13:51
+epoch [48/50] batch [185/1000] time 1.556 (1.571) data 0.000 (0.006) loss 0.8623 (1.0453) acc 78.1250 (73.4628) lr 3.1417e-05 eta 1:13:43
+epoch [48/50] batch [190/1000] time 1.593 (1.571) data 0.000 (0.005) loss 1.3086 (1.0430) acc 62.5000 (73.5033) lr 3.1417e-05 eta 1:13:35
+epoch [48/50] batch [195/1000] time 1.567 (1.571) data 0.001 (0.005) loss 1.1201 (1.0499) acc 68.7500 (73.3494) lr 3.1417e-05 eta 1:13:26
+epoch [48/50] batch [200/1000] time 1.547 (1.571) data 0.001 (0.005) loss 0.9902 (1.0534) acc 75.0000 (73.2656) lr 3.1417e-05 eta 1:13:17
+epoch [48/50] batch [205/1000] time 1.586 (1.570) data 0.000 (0.005) loss 1.0322 (1.0583) acc 65.6250 (73.0793) lr 3.1417e-05 eta 1:13:09
+epoch [48/50] batch [210/1000] time 1.588 (1.571) data 0.001 (0.005) loss 0.9644 (1.0618) acc 68.7500 (73.0208) lr 3.1417e-05 eta 1:13:03
+epoch [48/50] batch [215/1000] time 1.567 (1.571) data 0.000 (0.005) loss 1.3398 (1.0629) acc 65.6250 (72.9651) lr 3.1417e-05 eta 1:12:55
+epoch [48/50] batch [220/1000] time 1.549 (1.571) data 0.000 (0.005) loss 1.2705 (1.0617) acc 78.1250 (73.1108) lr 3.1417e-05 eta 1:12:47
+epoch [48/50] batch [225/1000] time 1.595 (1.571) data 0.001 (0.005) loss 0.8848 (1.0694) acc 75.0000 (73.0278) lr 3.1417e-05 eta 1:12:39
+epoch [48/50] batch [230/1000] time 1.574 (1.571) data 0.000 (0.005) loss 1.5381 (1.0754) acc 59.3750 (72.9484) lr 3.1417e-05 eta 1:12:31
+epoch [48/50] batch [235/1000] time 1.585 (1.571) data 0.000 (0.005) loss 0.6011 (1.0722) acc 84.3750 (73.0452) lr 3.1417e-05 eta 1:12:23
+epoch [48/50] batch [240/1000] time 1.549 (1.570) data 0.000 (0.004) loss 1.1201 (1.0727) acc 68.7500 (73.1250) lr 3.1417e-05 eta 1:12:14
+epoch [48/50] batch [245/1000] time 1.555 (1.570) data 0.001 (0.004) loss 1.3115 (1.0776) acc 78.1250 (73.0995) lr 3.1417e-05 eta 1:12:06
+epoch [48/50] batch [250/1000] time 1.574 (1.570) data 0.000 (0.004) loss 1.2490 (1.0766) acc 65.6250 (73.1125) lr 3.1417e-05 eta 1:11:58
+epoch [48/50] batch [255/1000] time 1.574 (1.570) data 0.000 (0.004) loss 1.5488 (1.0762) acc 65.6250 (73.1127) lr 3.1417e-05 eta 1:11:50
+epoch [48/50] batch [260/1000] time 1.548 (1.570) data 0.000 (0.004) loss 0.5630 (1.0741) acc 78.1250 (73.1490) lr 3.1417e-05 eta 1:11:41
+epoch [48/50] batch [265/1000] time 1.550 (1.570) data 0.000 (0.004) loss 1.2959 (1.0800) acc 68.7500 (72.9835) lr 3.1417e-05 eta 1:11:33
+epoch [48/50] batch [270/1000] time 1.727 (1.570) data 0.001 (0.004) loss 0.4924 (1.0756) acc 87.5000 (73.1481) lr 3.1417e-05 eta 1:11:26
+epoch [48/50] batch [275/1000] time 1.582 (1.570) data 0.000 (0.004) loss 1.1357 (1.0771) acc 62.5000 (73.1023) lr 3.1417e-05 eta 1:11:18
+epoch [48/50] batch [280/1000] time 1.594 (1.570) data 0.000 (0.004) loss 1.1289 (1.0789) acc 65.6250 (73.0246) lr 3.1417e-05 eta 1:11:10
+epoch [48/50] batch [285/1000] time 1.560 (1.570) data 0.000 (0.004) loss 0.5293 (1.0840) acc 81.2500 (72.9386) lr 3.1417e-05 eta 1:11:02
+epoch [48/50] batch [290/1000] time 1.549 (1.570) data 0.000 (0.004) loss 1.2051 (1.0843) acc 71.8750 (72.8879) lr 3.1417e-05 eta 1:10:54
+epoch [48/50] batch [295/1000] time 1.554 (1.570) data 0.000 (0.004) loss 0.7876 (1.0828) acc 75.0000 (72.9131) lr 3.1417e-05 eta 1:10:45
+epoch [48/50] batch [300/1000] time 1.552 (1.569) data 0.001 (0.004) loss 0.6245 (1.0831) acc 84.3750 (72.9271) lr 3.1417e-05 eta 1:10:37
+epoch [48/50] batch [305/1000] time 1.558 (1.569) data 0.001 (0.004) loss 0.8047 (1.0828) acc 78.1250 (72.9611) lr 3.1417e-05 eta 1:10:29
+epoch [48/50] batch [310/1000] time 1.523 (1.569) data 0.001 (0.004) loss 0.6021 (1.0810) acc 81.2500 (72.9234) lr 3.1417e-05 eta 1:10:21
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+epoch [48/50] batch [860/1000] time 1.559 (1.567) data 0.001 (0.002) loss 0.8589 (1.0665) acc 81.2500 (73.1650) lr 3.1417e-05 eta 0:55:53
+epoch [48/50] batch [865/1000] time 1.570 (1.567) data 0.001 (0.002) loss 1.4375 (1.0677) acc 62.5000 (73.1358) lr 3.1417e-05 eta 0:55:45
+epoch [48/50] batch [870/1000] time 1.572 (1.567) data 0.000 (0.002) loss 1.8203 (1.0680) acc 50.0000 (73.1106) lr 3.1417e-05 eta 0:55:37
+epoch [48/50] batch [875/1000] time 1.577 (1.567) data 0.000 (0.002) loss 0.7871 (1.0687) acc 81.2500 (73.1214) lr 3.1417e-05 eta 0:55:29
+epoch [48/50] batch [880/1000] time 1.547 (1.567) data 0.000 (0.002) loss 0.8774 (1.0677) acc 81.2500 (73.1286) lr 3.1417e-05 eta 0:55:21
+epoch [48/50] batch [885/1000] time 1.554 (1.567) data 0.000 (0.002) loss 1.2939 (1.0678) acc 78.1250 (73.1285) lr 3.1417e-05 eta 0:55:13
+epoch [48/50] batch [890/1000] time 1.569 (1.567) data 0.001 (0.002) loss 0.7310 (1.0676) acc 81.2500 (73.1320) lr 3.1417e-05 eta 0:55:05
+epoch [48/50] batch [895/1000] time 1.597 (1.567) data 0.001 (0.002) loss 1.5947 (1.0677) acc 62.5000 (73.1145) lr 3.1417e-05 eta 0:54:58
+epoch [48/50] batch [900/1000] time 1.567 (1.567) data 0.001 (0.002) loss 1.4424 (1.0689) acc 68.7500 (73.1007) lr 3.1417e-05 eta 0:54:50
+epoch [48/50] batch [905/1000] time 1.589 (1.567) data 0.001 (0.002) loss 1.0098 (1.0682) acc 65.6250 (73.1008) lr 3.1417e-05 eta 0:54:42
+epoch [48/50] batch [910/1000] time 1.546 (1.567) data 0.000 (0.002) loss 0.7783 (1.0688) acc 84.3750 (73.0941) lr 3.1417e-05 eta 0:54:34
+epoch [48/50] batch [915/1000] time 1.567 (1.567) data 0.001 (0.002) loss 1.4746 (1.0691) acc 62.5000 (73.0908) lr 3.1417e-05 eta 0:54:26
+epoch [48/50] batch [920/1000] time 1.562 (1.567) data 0.000 (0.002) loss 1.0166 (1.0693) acc 71.8750 (73.0978) lr 3.1417e-05 eta 0:54:19
+epoch [48/50] batch [925/1000] time 1.572 (1.567) data 0.000 (0.002) loss 1.5732 (1.0694) acc 65.6250 (73.1250) lr 3.1417e-05 eta 0:54:11
+epoch [48/50] batch [930/1000] time 1.589 (1.567) data 0.001 (0.002) loss 1.3818 (1.0707) acc 68.7500 (73.0813) lr 3.1417e-05 eta 0:54:03
+epoch [48/50] batch [935/1000] time 1.544 (1.567) data 0.001 (0.001) loss 1.0186 (1.0709) acc 81.2500 (73.0882) lr 3.1417e-05 eta 0:53:55
+epoch [48/50] batch [940/1000] time 1.557 (1.567) data 0.000 (0.001) loss 1.6006 (1.0724) acc 62.5000 (73.0818) lr 3.1417e-05 eta 0:53:48
+epoch [48/50] batch [945/1000] time 1.587 (1.567) data 0.000 (0.001) loss 1.5762 (1.0728) acc 68.7500 (73.0754) lr 3.1417e-05 eta 0:53:40
+epoch [48/50] batch [950/1000] time 1.571 (1.567) data 0.001 (0.001) loss 0.7231 (1.0725) acc 87.5000 (73.0921) lr 3.1417e-05 eta 0:53:32
+epoch [48/50] batch [955/1000] time 1.549 (1.567) data 0.000 (0.001) loss 1.0596 (1.0727) acc 78.1250 (73.0955) lr 3.1417e-05 eta 0:53:24
+epoch [48/50] batch [960/1000] time 1.566 (1.567) data 0.000 (0.001) loss 1.1738 (1.0735) acc 68.7500 (73.0469) lr 3.1417e-05 eta 0:53:16
+epoch [48/50] batch [965/1000] time 1.564 (1.567) data 0.000 (0.001) loss 1.6143 (1.0741) acc 59.3750 (73.0343) lr 3.1417e-05 eta 0:53:09
+epoch [48/50] batch [970/1000] time 1.567 (1.567) data 0.000 (0.001) loss 0.9316 (1.0739) acc 75.0000 (73.0445) lr 3.1417e-05 eta 0:53:01
+epoch [48/50] batch [975/1000] time 1.566 (1.567) data 0.000 (0.001) loss 0.8916 (1.0731) acc 75.0000 (73.0641) lr 3.1417e-05 eta 0:52:53
+epoch [48/50] batch [980/1000] time 1.578 (1.567) data 0.000 (0.001) loss 1.2012 (1.0731) acc 78.1250 (73.0676) lr 3.1417e-05 eta 0:52:45
+epoch [48/50] batch [985/1000] time 1.560 (1.567) data 0.001 (0.001) loss 1.2812 (1.0726) acc 71.8750 (73.0679) lr 3.1417e-05 eta 0:52:38
+epoch [48/50] batch [990/1000] time 1.592 (1.567) data 0.000 (0.001) loss 0.9502 (1.0714) acc 75.0000 (73.0808) lr 3.1417e-05 eta 0:52:30
+epoch [48/50] batch [995/1000] time 1.537 (1.567) data 0.000 (0.001) loss 1.0762 (1.0703) acc 75.0000 (73.1250) lr 3.1417e-05 eta 0:52:22
+epoch [48/50] batch [1000/1000] time 1.539 (1.567) data 0.000 (0.001) loss 1.1553 (1.0701) acc 68.7500 (73.1250) lr 1.7713e-05 eta 0:52:14
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,389
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.3%
+epoch [49/50] batch [5/1000] time 1.559 (1.702) data 0.000 (0.193) loss 0.9688 (1.1866) acc 71.8750 (71.8750) lr 1.7713e-05 eta 0:56:36
+epoch [49/50] batch [10/1000] time 1.553 (1.632) data 0.001 (0.097) loss 0.9238 (1.2225) acc 84.3750 (69.6875) lr 1.7713e-05 eta 0:54:08
+epoch [49/50] batch [15/1000] time 1.556 (1.611) data 0.001 (0.065) loss 1.3984 (1.3448) acc 71.8750 (67.7083) lr 1.7713e-05 eta 0:53:18
+epoch [49/50] batch [20/1000] time 1.567 (1.602) data 0.001 (0.049) loss 0.7661 (1.2846) acc 81.2500 (69.6875) lr 1.7713e-05 eta 0:52:51
+epoch [49/50] batch [25/1000] time 1.586 (1.595) data 0.001 (0.039) loss 0.8765 (1.2207) acc 78.1250 (70.7500) lr 1.7713e-05 eta 0:52:30
+epoch [49/50] batch [30/1000] time 1.568 (1.590) data 0.000 (0.033) loss 1.5967 (1.2297) acc 68.7500 (70.5208) lr 1.7713e-05 eta 0:52:13
+epoch [49/50] batch [35/1000] time 1.554 (1.595) data 0.000 (0.028) loss 0.8169 (1.1774) acc 84.3750 (71.5179) lr 1.7713e-05 eta 0:52:13
+epoch [49/50] batch [40/1000] time 1.571 (1.590) data 0.000 (0.025) loss 0.4878 (1.1352) acc 81.2500 (72.2656) lr 1.7713e-05 eta 0:51:57
+epoch [49/50] batch [45/1000] time 1.562 (1.587) data 0.000 (0.022) loss 1.0566 (1.1270) acc 81.2500 (72.5694) lr 1.7713e-05 eta 0:51:43
+epoch [49/50] batch [50/1000] time 1.567 (1.585) data 0.000 (0.020) loss 1.2617 (1.1387) acc 71.8750 (72.2500) lr 1.7713e-05 eta 0:51:31
+epoch [49/50] batch [55/1000] time 1.583 (1.583) data 0.001 (0.018) loss 0.9868 (1.1217) acc 78.1250 (72.5568) lr 1.7713e-05 eta 0:51:19
+epoch [49/50] batch [60/1000] time 1.569 (1.581) data 0.000 (0.017) loss 0.7524 (1.0946) acc 90.6250 (73.3333) lr 1.7713e-05 eta 0:51:08
+epoch [49/50] batch [65/1000] time 1.573 (1.580) data 0.001 (0.015) loss 1.5205 (1.0896) acc 71.8750 (73.6538) lr 1.7713e-05 eta 0:50:57
+epoch [49/50] batch [70/1000] time 1.573 (1.580) data 0.001 (0.014) loss 1.0166 (1.0815) acc 71.8750 (73.8393) lr 1.7713e-05 eta 0:50:49
+epoch [49/50] batch [75/1000] time 1.542 (1.579) data 0.001 (0.013) loss 0.8853 (1.0807) acc 78.1250 (74.0417) lr 1.7713e-05 eta 0:50:38
+epoch [49/50] batch [80/1000] time 1.570 (1.580) data 0.000 (0.013) loss 0.4189 (1.0627) acc 87.5000 (74.1797) lr 1.7713e-05 eta 0:50:32
+epoch [49/50] batch [85/1000] time 1.563 (1.579) data 0.001 (0.012) loss 1.1836 (1.0658) acc 78.1250 (74.1912) lr 1.7713e-05 eta 0:50:22
+epoch [49/50] batch [90/1000] time 1.554 (1.577) data 0.000 (0.011) loss 0.8911 (1.0763) acc 81.2500 (74.1667) lr 1.7713e-05 eta 0:50:12
+epoch [49/50] batch [95/1000] time 1.579 (1.577) data 0.001 (0.011) loss 0.7773 (1.0765) acc 71.8750 (74.0789) lr 1.7713e-05 eta 0:50:04
+epoch [49/50] batch [100/1000] time 1.551 (1.577) data 0.001 (0.010) loss 1.2002 (1.0767) acc 68.7500 (74.0938) lr 1.7713e-05 eta 0:49:55
+epoch [49/50] batch [105/1000] time 1.551 (1.576) data 0.001 (0.010) loss 0.6816 (1.0739) acc 78.1250 (73.9881) lr 1.7713e-05 eta 0:49:46
+epoch [49/50] batch [110/1000] time 1.569 (1.576) data 0.001 (0.009) loss 1.9414 (1.0784) acc 56.2500 (73.8920) lr 1.7713e-05 eta 0:49:37
+epoch [49/50] batch [115/1000] time 1.542 (1.575) data 0.000 (0.009) loss 0.4084 (1.0732) acc 93.7500 (73.9402) lr 1.7713e-05 eta 0:49:28
+epoch [49/50] batch [120/1000] time 1.558 (1.574) data 0.001 (0.009) loss 0.6001 (1.0756) acc 90.6250 (73.8281) lr 1.7713e-05 eta 0:49:18
+epoch [49/50] batch [125/1000] time 1.579 (1.574) data 0.000 (0.008) loss 1.1895 (1.0786) acc 65.6250 (73.7250) lr 1.7713e-05 eta 0:49:10
+epoch [49/50] batch [130/1000] time 1.545 (1.573) data 0.000 (0.008) loss 0.8340 (1.0803) acc 75.0000 (73.6779) lr 1.7713e-05 eta 0:49:01
+epoch [49/50] batch [135/1000] time 1.573 (1.573) data 0.001 (0.008) loss 1.0156 (1.0782) acc 71.8750 (73.6574) lr 1.7713e-05 eta 0:48:53
+epoch [49/50] batch [140/1000] time 1.577 (1.574) data 0.000 (0.007) loss 1.3525 (1.0776) acc 68.7500 (73.7946) lr 1.7713e-05 eta 0:48:47
+epoch [49/50] batch [145/1000] time 1.548 (1.574) data 0.000 (0.007) loss 1.0537 (1.0815) acc 78.1250 (73.9224) lr 1.7713e-05 eta 0:48:38
+epoch [49/50] batch [150/1000] time 1.575 (1.573) data 0.000 (0.007) loss 0.9443 (1.0744) acc 68.7500 (73.9583) lr 1.7713e-05 eta 0:48:30
+epoch [49/50] batch [155/1000] time 1.554 (1.572) data 0.000 (0.007) loss 1.4795 (1.0803) acc 68.7500 (73.8306) lr 1.7713e-05 eta 0:48:20
+epoch [49/50] batch [160/1000] time 1.577 (1.572) data 0.000 (0.007) loss 0.8369 (1.0779) acc 78.1250 (73.9453) lr 1.7713e-05 eta 0:48:12
+epoch [49/50] batch [165/1000] time 1.536 (1.572) data 0.000 (0.006) loss 1.1553 (1.0866) acc 71.8750 (73.8258) lr 1.7713e-05 eta 0:48:04
+epoch [49/50] batch [170/1000] time 1.559 (1.572) data 0.000 (0.006) loss 1.3506 (1.0854) acc 71.8750 (73.7868) lr 1.7713e-05 eta 0:47:56
+epoch [49/50] batch [175/1000] time 1.568 (1.571) data 0.001 (0.006) loss 0.7607 (1.0891) acc 71.8750 (73.6429) lr 1.7713e-05 eta 0:47:47
+epoch [49/50] batch [180/1000] time 1.560 (1.571) data 0.000 (0.006) loss 1.3516 (1.0874) acc 65.6250 (73.6285) lr 1.7713e-05 eta 0:47:38
+epoch [49/50] batch [185/1000] time 1.561 (1.571) data 0.000 (0.006) loss 0.9106 (1.0850) acc 78.1250 (73.5980) lr 1.7713e-05 eta 0:47:31
+epoch [49/50] batch [190/1000] time 1.593 (1.571) data 0.001 (0.006) loss 0.7319 (1.0808) acc 81.2500 (73.6678) lr 1.7713e-05 eta 0:47:24
+epoch [49/50] batch [195/1000] time 1.558 (1.571) data 0.001 (0.005) loss 1.3242 (1.0854) acc 71.8750 (73.6378) lr 1.7713e-05 eta 0:47:15
+epoch [49/50] batch [200/1000] time 1.590 (1.571) data 0.000 (0.005) loss 1.0811 (1.0878) acc 65.6250 (73.4844) lr 1.7713e-05 eta 0:47:07
+epoch [49/50] batch [205/1000] time 1.544 (1.571) data 0.000 (0.005) loss 1.2354 (1.0843) acc 62.5000 (73.4756) lr 1.7713e-05 eta 0:46:59
+epoch [49/50] batch [210/1000] time 1.551 (1.570) data 0.001 (0.005) loss 0.8770 (1.0844) acc 81.2500 (73.3780) lr 1.7713e-05 eta 0:46:50
+epoch [49/50] batch [215/1000] time 1.575 (1.570) data 0.000 (0.005) loss 2.2148 (1.0928) acc 53.1250 (73.2413) lr 1.7713e-05 eta 0:46:42
+epoch [49/50] batch [220/1000] time 1.554 (1.570) data 0.000 (0.005) loss 1.3535 (1.0890) acc 71.8750 (73.2812) lr 1.7713e-05 eta 0:46:34
+epoch [49/50] batch [225/1000] time 1.548 (1.569) data 0.000 (0.005) loss 1.3164 (1.0914) acc 62.5000 (73.2222) lr 1.7713e-05 eta 0:46:25
+epoch [49/50] batch [230/1000] time 1.570 (1.570) data 0.000 (0.005) loss 0.8115 (1.0947) acc 81.2500 (73.1793) lr 1.7713e-05 eta 0:46:19
+epoch [49/50] batch [235/1000] time 1.547 (1.570) data 0.001 (0.005) loss 1.2441 (1.0992) acc 65.6250 (73.1250) lr 1.7713e-05 eta 0:46:11
+epoch [49/50] batch [240/1000] time 1.576 (1.570) data 0.000 (0.005) loss 1.0928 (1.1007) acc 75.0000 (73.0859) lr 1.7713e-05 eta 0:46:02
+epoch [49/50] batch [245/1000] time 1.561 (1.570) data 0.001 (0.004) loss 0.9419 (1.1047) acc 78.1250 (73.0612) lr 1.7713e-05 eta 0:45:54
+epoch [49/50] batch [250/1000] time 1.563 (1.569) data 0.001 (0.004) loss 1.2871 (1.1126) acc 75.0000 (73.0250) lr 1.7713e-05 eta 0:45:46
+epoch [49/50] batch [255/1000] time 1.591 (1.569) data 0.000 (0.004) loss 1.0938 (1.1109) acc 75.0000 (73.0270) lr 1.7713e-05 eta 0:45:38
+epoch [49/50] batch [260/1000] time 1.573 (1.570) data 0.000 (0.004) loss 1.2549 (1.1079) acc 75.0000 (73.1130) lr 1.7713e-05 eta 0:45:31
+epoch [49/50] batch [265/1000] time 1.574 (1.569) data 0.000 (0.004) loss 1.8135 (1.1131) acc 65.6250 (72.9363) lr 1.7713e-05 eta 0:45:22
+epoch [49/50] batch [270/1000] time 1.553 (1.569) data 0.000 (0.004) loss 1.0039 (1.1107) acc 78.1250 (73.0093) lr 1.7713e-05 eta 0:45:14
+epoch [49/50] batch [275/1000] time 1.588 (1.569) data 0.000 (0.004) loss 1.1309 (1.1074) acc 71.8750 (73.0568) lr 1.7713e-05 eta 0:45:06
+epoch [49/50] batch [280/1000] time 1.588 (1.569) data 0.000 (0.004) loss 1.1055 (1.1054) acc 65.6250 (73.1027) lr 1.7713e-05 eta 0:44:58
+epoch [49/50] batch [285/1000] time 1.571 (1.569) data 0.000 (0.004) loss 0.7456 (1.1039) acc 78.1250 (73.1250) lr 1.7713e-05 eta 0:44:50
+epoch [49/50] batch [290/1000] time 1.745 (1.570) data 0.000 (0.004) loss 0.9463 (1.1029) acc 68.7500 (73.1250) lr 1.7713e-05 eta 0:44:44
+epoch [49/50] batch [295/1000] time 1.567 (1.569) data 0.000 (0.004) loss 1.2803 (1.1035) acc 71.8750 (73.0826) lr 1.7713e-05 eta 0:44:35
+epoch [49/50] batch [300/1000] time 1.549 (1.569) data 0.001 (0.004) loss 0.7808 (1.1016) acc 84.3750 (73.1354) lr 1.7713e-05 eta 0:44:27
+epoch [49/50] batch [305/1000] time 1.550 (1.569) data 0.001 (0.004) loss 1.2246 (1.0994) acc 75.0000 (73.1967) lr 1.7713e-05 eta 0:44:20
+epoch [49/50] batch [310/1000] time 1.539 (1.569) data 0.000 (0.004) loss 0.9473 (1.0970) acc 84.3750 (73.2661) lr 1.7713e-05 eta 0:44:12
+epoch [49/50] batch [315/1000] time 1.574 (1.569) data 0.000 (0.004) loss 0.8750 (1.0968) acc 81.2500 (73.2937) lr 1.7713e-05 eta 0:44:04
+epoch [49/50] batch [320/1000] time 1.560 (1.569) data 0.000 (0.004) loss 0.8804 (1.0923) acc 81.2500 (73.4180) lr 1.7713e-05 eta 0:43:55
+epoch [49/50] batch [325/1000] time 1.573 (1.569) data 0.001 (0.003) loss 0.8008 (1.0915) acc 78.1250 (73.3942) lr 1.7713e-05 eta 0:43:47
+epoch [49/50] batch [330/1000] time 1.545 (1.569) data 0.001 (0.003) loss 1.5332 (1.0932) acc 71.8750 (73.3807) lr 1.7713e-05 eta 0:43:40
+epoch [49/50] batch [335/1000] time 1.752 (1.570) data 0.000 (0.003) loss 0.6763 (1.0912) acc 84.3750 (73.4049) lr 1.7713e-05 eta 0:43:33
+epoch [49/50] batch [340/1000] time 1.566 (1.570) data 0.001 (0.003) loss 1.1660 (1.0924) acc 68.7500 (73.3732) lr 1.7713e-05 eta 0:43:25
+epoch [49/50] batch [345/1000] time 1.583 (1.570) data 0.000 (0.003) loss 0.8438 (1.0930) acc 84.3750 (73.3514) lr 1.7713e-05 eta 0:43:17
+epoch [49/50] batch [350/1000] time 1.540 (1.569) data 0.000 (0.003) loss 0.7383 (1.0937) acc 84.3750 (73.3214) lr 1.7713e-05 eta 0:43:09
+epoch [49/50] batch [355/1000] time 1.559 (1.569) data 0.000 (0.003) loss 0.9341 (1.0924) acc 71.8750 (73.2570) lr 1.7713e-05 eta 0:43:01
+epoch [49/50] batch [360/1000] time 1.530 (1.569) data 0.000 (0.003) loss 0.9043 (1.0915) acc 78.1250 (73.2639) lr 1.7713e-05 eta 0:42:53
+epoch [49/50] batch [365/1000] time 1.590 (1.569) data 0.001 (0.003) loss 0.7168 (1.0919) acc 84.3750 (73.2449) lr 1.7713e-05 eta 0:42:45
+epoch [49/50] batch [370/1000] time 1.575 (1.569) data 0.005 (0.003) loss 0.7544 (1.0886) acc 78.1250 (73.2601) lr 1.7713e-05 eta 0:42:37
+epoch [49/50] batch [375/1000] time 1.550 (1.569) data 0.001 (0.003) loss 1.3203 (1.0889) acc 71.8750 (73.2667) lr 1.7713e-05 eta 0:42:29
+epoch [49/50] batch [380/1000] time 1.563 (1.569) data 0.000 (0.003) loss 1.0303 (1.0878) acc 68.7500 (73.2812) lr 1.7713e-05 eta 0:42:22
+epoch [49/50] batch [385/1000] time 1.536 (1.569) data 0.001 (0.003) loss 0.9121 (1.0871) acc 87.5000 (73.3442) lr 1.7713e-05 eta 0:42:13
+epoch [49/50] batch [390/1000] time 1.565 (1.569) data 0.000 (0.003) loss 1.3633 (1.0874) acc 68.7500 (73.3734) lr 1.7713e-05 eta 0:42:05
+epoch [49/50] batch [395/1000] time 1.580 (1.569) data 0.000 (0.003) loss 0.6699 (1.0861) acc 71.8750 (73.4019) lr 1.7713e-05 eta 0:41:57
+epoch [49/50] batch [400/1000] time 1.571 (1.569) data 0.000 (0.003) loss 1.0781 (1.0853) acc 68.7500 (73.3828) lr 1.7713e-05 eta 0:41:49
+epoch [49/50] batch [405/1000] time 1.564 (1.569) data 0.000 (0.003) loss 0.6763 (1.0812) acc 81.2500 (73.4182) lr 1.7713e-05 eta 0:41:41
+epoch [49/50] batch [410/1000] time 1.555 (1.568) data 0.001 (0.003) loss 1.1416 (1.0820) acc 62.5000 (73.3841) lr 1.7713e-05 eta 0:41:33
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+epoch [49/50] batch [965/1000] time 1.565 (1.566) data 0.001 (0.001) loss 0.9263 (1.0782) acc 75.0000 (72.9501) lr 1.7713e-05 eta 0:27:00
+epoch [49/50] batch [970/1000] time 1.557 (1.565) data 0.000 (0.001) loss 1.0342 (1.0785) acc 71.8750 (72.9671) lr 1.7713e-05 eta 0:26:52
+epoch [49/50] batch [975/1000] time 1.555 (1.565) data 0.000 (0.001) loss 1.1553 (1.0788) acc 65.6250 (72.9647) lr 1.7713e-05 eta 0:26:44
+epoch [49/50] batch [980/1000] time 1.560 (1.565) data 0.000 (0.001) loss 1.0566 (1.0782) acc 65.6250 (72.9592) lr 1.7713e-05 eta 0:26:36
+epoch [49/50] batch [985/1000] time 1.571 (1.566) data 0.001 (0.001) loss 1.4277 (1.0789) acc 71.8750 (72.9600) lr 1.7713e-05 eta 0:26:29
+epoch [49/50] batch [990/1000] time 1.573 (1.566) data 0.000 (0.001) loss 1.3027 (1.0800) acc 65.6250 (72.9261) lr 1.7713e-05 eta 0:26:21
+epoch [49/50] batch [995/1000] time 1.574 (1.566) data 0.000 (0.001) loss 1.2842 (1.0801) acc 68.7500 (72.9303) lr 1.7713e-05 eta 0:26:13
+epoch [49/50] batch [1000/1000] time 1.568 (1.566) data 0.000 (0.001) loss 0.7983 (1.0799) acc 78.1250 (72.9562) lr 7.8853e-06 eta 0:26:05
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,383
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.3%
+epoch [50/50] batch [5/1000] time 1.564 (1.718) data 0.000 (0.203) loss 0.6777 (0.9286) acc 75.0000 (73.1250) lr 7.8853e-06 eta 0:28:29
+epoch [50/50] batch [10/1000] time 1.565 (1.638) data 0.000 (0.102) loss 0.7246 (0.9001) acc 78.1250 (74.6875) lr 7.8853e-06 eta 0:27:01
+epoch [50/50] batch [15/1000] time 1.572 (1.610) data 0.001 (0.068) loss 0.8516 (0.9186) acc 65.6250 (74.5833) lr 7.8853e-06 eta 0:26:25
+epoch [50/50] batch [20/1000] time 1.565 (1.598) data 0.000 (0.051) loss 1.2949 (1.0081) acc 68.7500 (73.2812) lr 7.8853e-06 eta 0:26:06
+epoch [50/50] batch [25/1000] time 1.558 (1.593) data 0.001 (0.041) loss 1.2461 (0.9855) acc 68.7500 (74.6250) lr 7.8853e-06 eta 0:25:53
+epoch [50/50] batch [30/1000] time 1.561 (1.587) data 0.001 (0.034) loss 0.9736 (0.9946) acc 78.1250 (74.4792) lr 7.8853e-06 eta 0:25:39
+epoch [50/50] batch [35/1000] time 1.557 (1.592) data 0.000 (0.029) loss 0.8730 (1.0334) acc 87.5000 (73.9286) lr 7.8853e-06 eta 0:25:36
+epoch [50/50] batch [40/1000] time 1.556 (1.588) data 0.000 (0.026) loss 1.0771 (1.0226) acc 59.3750 (73.5156) lr 7.8853e-06 eta 0:25:24
+epoch [50/50] batch [45/1000] time 1.565 (1.587) data 0.001 (0.023) loss 1.1689 (1.0556) acc 68.7500 (72.7083) lr 7.8853e-06 eta 0:25:15
+epoch [50/50] batch [50/1000] time 1.572 (1.585) data 0.000 (0.021) loss 1.4580 (1.0893) acc 68.7500 (72.3125) lr 7.8853e-06 eta 0:25:05
+epoch [50/50] batch [55/1000] time 1.551 (1.582) data 0.001 (0.019) loss 1.4424 (1.0964) acc 78.1250 (72.1023) lr 7.8853e-06 eta 0:24:54
+epoch [50/50] batch [60/1000] time 1.563 (1.580) data 0.000 (0.017) loss 0.9194 (1.1029) acc 75.0000 (72.2396) lr 7.8853e-06 eta 0:24:44
+epoch [50/50] batch [65/1000] time 1.562 (1.578) data 0.001 (0.016) loss 1.0938 (1.0900) acc 68.7500 (72.3077) lr 7.8853e-06 eta 0:24:35
+epoch [50/50] batch [70/1000] time 1.573 (1.577) data 0.001 (0.015) loss 1.0693 (1.0940) acc 78.1250 (72.5893) lr 7.8853e-06 eta 0:24:26
+epoch [50/50] batch [75/1000] time 1.549 (1.575) data 0.001 (0.014) loss 1.8477 (1.0879) acc 50.0000 (72.6250) lr 7.8853e-06 eta 0:24:17
+epoch [50/50] batch [80/1000] time 1.534 (1.576) data 0.001 (0.013) loss 1.2744 (1.0866) acc 62.5000 (72.5000) lr 7.8853e-06 eta 0:24:09
+epoch [50/50] batch [85/1000] time 1.552 (1.575) data 0.001 (0.012) loss 1.1074 (1.0875) acc 71.8750 (72.6471) lr 7.8853e-06 eta 0:24:00
+epoch [50/50] batch [90/1000] time 1.567 (1.574) data 0.001 (0.012) loss 1.4258 (1.0985) acc 62.5000 (72.4653) lr 7.8853e-06 eta 0:23:52
+epoch [50/50] batch [95/1000] time 1.563 (1.573) data 0.000 (0.011) loss 1.2871 (1.1060) acc 65.6250 (72.3355) lr 7.8853e-06 eta 0:23:43
+epoch [50/50] batch [100/1000] time 1.550 (1.572) data 0.001 (0.011) loss 1.0420 (1.1040) acc 78.1250 (72.2812) lr 7.8853e-06 eta 0:23:35
+epoch [50/50] batch [105/1000] time 1.541 (1.572) data 0.001 (0.010) loss 0.5884 (1.0901) acc 84.3750 (72.4702) lr 7.8853e-06 eta 0:23:27
+epoch [50/50] batch [110/1000] time 1.573 (1.572) data 0.001 (0.010) loss 1.2471 (1.0815) acc 68.7500 (72.7273) lr 7.8853e-06 eta 0:23:19
+epoch [50/50] batch [115/1000] time 1.592 (1.572) data 0.000 (0.009) loss 0.7803 (1.0838) acc 62.5000 (72.4457) lr 7.8853e-06 eta 0:23:10
+epoch [50/50] batch [120/1000] time 1.748 (1.573) data 0.000 (0.009) loss 1.0742 (1.0871) acc 68.7500 (72.4219) lr 7.8853e-06 eta 0:23:04
+epoch [50/50] batch [125/1000] time 1.584 (1.573) data 0.001 (0.009) loss 0.9429 (1.0800) acc 78.1250 (72.5750) lr 7.8853e-06 eta 0:22:56
+epoch [50/50] batch [130/1000] time 1.549 (1.573) data 0.000 (0.008) loss 0.6768 (1.0744) acc 81.2500 (72.6683) lr 7.8853e-06 eta 0:22:48
+epoch [50/50] batch [135/1000] time 1.576 (1.572) data 0.001 (0.008) loss 1.4512 (1.0681) acc 71.8750 (72.8704) lr 7.8853e-06 eta 0:22:39
+epoch [50/50] batch [140/1000] time 1.568 (1.572) data 0.000 (0.008) loss 1.2070 (1.0743) acc 78.1250 (72.8125) lr 7.8853e-06 eta 0:22:32
+epoch [50/50] batch [145/1000] time 1.569 (1.572) data 0.000 (0.007) loss 0.7646 (1.0715) acc 84.3750 (72.9957) lr 7.8853e-06 eta 0:22:23
+epoch [50/50] batch [150/1000] time 1.578 (1.571) data 0.000 (0.007) loss 1.0010 (1.0665) acc 71.8750 (73.0625) lr 7.8853e-06 eta 0:22:15
+epoch [50/50] batch [155/1000] time 1.547 (1.571) data 0.000 (0.007) loss 1.5293 (1.0696) acc 75.0000 (73.1653) lr 7.8853e-06 eta 0:22:07
+epoch [50/50] batch [160/1000] time 1.552 (1.571) data 0.000 (0.007) loss 1.0410 (1.0773) acc 68.7500 (72.9492) lr 7.8853e-06 eta 0:21:59
+epoch [50/50] batch [165/1000] time 1.545 (1.570) data 0.001 (0.007) loss 0.9424 (1.0773) acc 75.0000 (72.9545) lr 7.8853e-06 eta 0:21:51
+epoch [50/50] batch [170/1000] time 1.562 (1.570) data 0.000 (0.006) loss 1.7695 (1.0829) acc 62.5000 (72.8860) lr 7.8853e-06 eta 0:21:42
+epoch [50/50] batch [175/1000] time 1.593 (1.570) data 0.000 (0.006) loss 1.5498 (1.0786) acc 59.3750 (72.9107) lr 7.8853e-06 eta 0:21:34
+epoch [50/50] batch [180/1000] time 1.551 (1.569) data 0.000 (0.006) loss 0.5684 (1.0779) acc 84.3750 (72.9514) lr 7.8853e-06 eta 0:21:26
+epoch [50/50] batch [185/1000] time 1.563 (1.570) data 0.001 (0.006) loss 1.1162 (1.0774) acc 71.8750 (72.9392) lr 7.8853e-06 eta 0:21:19
+epoch [50/50] batch [190/1000] time 1.579 (1.570) data 0.001 (0.006) loss 1.0498 (1.0783) acc 75.0000 (72.8783) lr 7.8853e-06 eta 0:21:11
+epoch [50/50] batch [195/1000] time 1.548 (1.569) data 0.000 (0.006) loss 1.3662 (1.0787) acc 75.0000 (72.8045) lr 7.8853e-06 eta 0:21:03
+epoch [50/50] batch [200/1000] time 1.576 (1.569) data 0.000 (0.006) loss 0.5459 (1.0729) acc 93.7500 (72.8906) lr 7.8853e-06 eta 0:20:55
+epoch [50/50] batch [205/1000] time 1.555 (1.569) data 0.000 (0.005) loss 0.8931 (1.0781) acc 75.0000 (72.8659) lr 7.8853e-06 eta 0:20:47
+epoch [50/50] batch [210/1000] time 1.588 (1.568) data 0.000 (0.005) loss 1.3525 (1.0799) acc 65.6250 (72.8125) lr 7.8853e-06 eta 0:20:39
+epoch [50/50] batch [215/1000] time 1.592 (1.568) data 0.001 (0.005) loss 1.0283 (1.0800) acc 71.8750 (72.7762) lr 7.8853e-06 eta 0:20:31
+epoch [50/50] batch [220/1000] time 1.568 (1.568) data 0.000 (0.005) loss 0.8496 (1.0805) acc 84.3750 (72.7415) lr 7.8853e-06 eta 0:20:23
+epoch [50/50] batch [225/1000] time 1.555 (1.568) data 0.000 (0.005) loss 0.9253 (1.0795) acc 75.0000 (72.7639) lr 7.8853e-06 eta 0:20:15
+epoch [50/50] batch [230/1000] time 1.544 (1.569) data 0.000 (0.005) loss 1.2188 (1.0850) acc 62.5000 (72.6087) lr 7.8853e-06 eta 0:20:07
+epoch [50/50] batch [235/1000] time 1.555 (1.569) data 0.000 (0.005) loss 0.4836 (1.0818) acc 87.5000 (72.6862) lr 7.8853e-06 eta 0:19:59
+epoch [50/50] batch [240/1000] time 1.562 (1.568) data 0.000 (0.005) loss 1.2295 (1.0880) acc 65.6250 (72.4349) lr 7.8853e-06 eta 0:19:51
+epoch [50/50] batch [245/1000] time 1.535 (1.568) data 0.001 (0.005) loss 1.3018 (1.0848) acc 65.6250 (72.4490) lr 7.8853e-06 eta 0:19:44
+epoch [50/50] batch [250/1000] time 1.551 (1.568) data 0.000 (0.005) loss 0.9829 (1.0835) acc 68.7500 (72.4500) lr 7.8853e-06 eta 0:19:35
+epoch [50/50] batch [255/1000] time 1.559 (1.568) data 0.000 (0.004) loss 0.5801 (1.0850) acc 81.2500 (72.4387) lr 7.8853e-06 eta 0:19:27
+epoch [50/50] batch [260/1000] time 1.543 (1.568) data 0.000 (0.004) loss 0.7041 (1.0849) acc 87.5000 (72.4519) lr 7.8853e-06 eta 0:19:20
+epoch [50/50] batch [265/1000] time 1.554 (1.567) data 0.000 (0.004) loss 0.9453 (1.0824) acc 75.0000 (72.5118) lr 7.8853e-06 eta 0:19:12
+epoch [50/50] batch [270/1000] time 1.566 (1.567) data 0.000 (0.004) loss 1.0684 (1.0859) acc 78.1250 (72.4306) lr 7.8853e-06 eta 0:19:04
+epoch [50/50] batch [275/1000] time 1.548 (1.568) data 0.000 (0.004) loss 0.8257 (1.0849) acc 87.5000 (72.4886) lr 7.8853e-06 eta 0:18:56
+epoch [50/50] batch [280/1000] time 1.564 (1.568) data 0.001 (0.004) loss 0.5381 (1.0868) acc 84.3750 (72.3996) lr 7.8853e-06 eta 0:18:48
+epoch [50/50] batch [285/1000] time 1.579 (1.567) data 0.000 (0.004) loss 1.2246 (1.0897) acc 65.6250 (72.3465) lr 7.8853e-06 eta 0:18:40
+epoch [50/50] batch [290/1000] time 1.573 (1.567) data 0.000 (0.004) loss 0.7295 (1.0868) acc 81.2500 (72.4138) lr 7.8853e-06 eta 0:18:32
+epoch [50/50] batch [295/1000] time 1.576 (1.567) data 0.000 (0.004) loss 0.7300 (1.0888) acc 75.0000 (72.3729) lr 7.8853e-06 eta 0:18:24
+epoch [50/50] batch [300/1000] time 1.545 (1.567) data 0.000 (0.004) loss 1.1582 (1.0886) acc 78.1250 (72.4062) lr 7.8853e-06 eta 0:18:16
+epoch [50/50] batch [305/1000] time 1.526 (1.567) data 0.000 (0.004) loss 1.4062 (1.0897) acc 68.7500 (72.4693) lr 7.8853e-06 eta 0:18:08
+epoch [50/50] batch [310/1000] time 1.559 (1.567) data 0.000 (0.004) loss 0.8623 (1.0917) acc 75.0000 (72.4395) lr 7.8853e-06 eta 0:18:00
+epoch [50/50] batch [315/1000] time 1.566 (1.567) data 0.001 (0.004) loss 0.9297 (1.0868) acc 75.0000 (72.5496) lr 7.8853e-06 eta 0:17:53
+epoch [50/50] batch [320/1000] time 1.562 (1.566) data 0.000 (0.004) loss 1.3691 (1.0873) acc 68.7500 (72.5684) lr 7.8853e-06 eta 0:17:45
+epoch [50/50] batch [325/1000] time 1.557 (1.566) data 0.001 (0.004) loss 1.2246 (1.0883) acc 68.7500 (72.5865) lr 7.8853e-06 eta 0:17:37
+epoch [50/50] batch [330/1000] time 1.549 (1.566) data 0.000 (0.004) loss 1.0830 (1.0882) acc 65.6250 (72.5473) lr 7.8853e-06 eta 0:17:29
+epoch [50/50] batch [335/1000] time 1.543 (1.567) data 0.001 (0.004) loss 0.7710 (1.0878) acc 78.1250 (72.6026) lr 7.8853e-06 eta 0:17:22
+epoch [50/50] batch [340/1000] time 1.569 (1.567) data 0.001 (0.003) loss 1.0020 (1.0865) acc 81.2500 (72.6379) lr 7.8853e-06 eta 0:17:14
+epoch [50/50] batch [345/1000] time 1.549 (1.567) data 0.000 (0.003) loss 1.0801 (1.0875) acc 68.7500 (72.5906) lr 7.8853e-06 eta 0:17:06
+epoch [50/50] batch [350/1000] time 1.528 (1.567) data 0.000 (0.003) loss 1.2227 (1.0867) acc 59.3750 (72.5357) lr 7.8853e-06 eta 0:16:58
+epoch [50/50] batch [355/1000] time 1.552 (1.567) data 0.001 (0.003) loss 1.0205 (1.0874) acc 75.0000 (72.5440) lr 7.8853e-06 eta 0:16:50
+epoch [50/50] batch [360/1000] time 1.589 (1.567) data 0.000 (0.003) loss 0.5459 (1.0858) acc 90.6250 (72.6128) lr 7.8853e-06 eta 0:16:42
+epoch [50/50] batch [365/1000] time 1.567 (1.567) data 0.001 (0.003) loss 1.2852 (1.0859) acc 62.5000 (72.5771) lr 7.8853e-06 eta 0:16:34
+epoch [50/50] batch [370/1000] time 1.553 (1.566) data 0.000 (0.003) loss 1.2705 (1.0848) acc 62.5000 (72.5591) lr 7.8853e-06 eta 0:16:26
+epoch [50/50] batch [375/1000] time 1.540 (1.566) data 0.000 (0.003) loss 1.5322 (1.0848) acc 59.3750 (72.5333) lr 7.8853e-06 eta 0:16:18
+epoch [50/50] batch [380/1000] time 1.565 (1.567) data 0.000 (0.003) loss 1.2578 (1.0837) acc 62.5000 (72.5493) lr 7.8853e-06 eta 0:16:11
+epoch [50/50] batch [385/1000] time 1.570 (1.566) data 0.000 (0.003) loss 1.2568 (1.0822) acc 68.7500 (72.5731) lr 7.8853e-06 eta 0:16:03
+epoch [50/50] batch [390/1000] time 1.549 (1.566) data 0.000 (0.003) loss 1.2559 (1.0836) acc 65.6250 (72.5721) lr 7.8853e-06 eta 0:15:55
+epoch [50/50] batch [395/1000] time 1.563 (1.566) data 0.000 (0.003) loss 1.4160 (1.0844) acc 78.1250 (72.6028) lr 7.8853e-06 eta 0:15:47
+epoch [50/50] batch [400/1000] time 1.551 (1.566) data 0.000 (0.003) loss 0.4646 (1.0841) acc 84.3750 (72.6562) lr 7.8853e-06 eta 0:15:39
+epoch [50/50] batch [405/1000] time 1.577 (1.566) data 0.000 (0.003) loss 0.5991 (1.0828) acc 87.5000 (72.7083) lr 7.8853e-06 eta 0:15:31
+epoch [50/50] batch [410/1000] time 1.560 (1.566) data 0.000 (0.003) loss 1.2842 (1.0821) acc 71.8750 (72.7591) lr 7.8853e-06 eta 0:15:24
+epoch [50/50] batch [415/1000] time 1.549 (1.566) data 0.001 (0.003) loss 0.8105 (1.0809) acc 84.3750 (72.7711) lr 7.8853e-06 eta 0:15:16
+epoch [50/50] batch [420/1000] time 1.548 (1.566) data 0.000 (0.003) loss 1.3008 (1.0809) acc 71.8750 (72.7753) lr 7.8853e-06 eta 0:15:08
+epoch [50/50] batch [425/1000] time 1.552 (1.566) data 0.001 (0.003) loss 1.6201 (1.0798) acc 65.6250 (72.8382) lr 7.8853e-06 eta 0:15:00
+epoch [50/50] batch [430/1000] time 1.528 (1.566) data 0.000 (0.003) loss 1.1152 (1.0815) acc 71.8750 (72.8561) lr 7.8853e-06 eta 0:14:52
+epoch [50/50] batch [435/1000] time 1.568 (1.566) data 0.000 (0.003) loss 0.6997 (1.0826) acc 81.2500 (72.8520) lr 7.8853e-06 eta 0:14:44
+epoch [50/50] batch [440/1000] time 1.572 (1.566) data 0.000 (0.003) loss 1.3154 (1.0830) acc 71.8750 (72.8267) lr 7.8853e-06 eta 0:14:37
+epoch [50/50] batch [445/1000] time 1.545 (1.566) data 0.000 (0.003) loss 0.6631 (1.0813) acc 78.1250 (72.8511) lr 7.8853e-06 eta 0:14:29
+epoch [50/50] batch [450/1000] time 1.553 (1.566) data 0.000 (0.003) loss 1.1582 (1.0815) acc 65.6250 (72.7917) lr 7.8853e-06 eta 0:14:21
+epoch [50/50] batch [455/1000] time 1.560 (1.566) data 0.001 (0.003) loss 1.0898 (1.0816) acc 75.0000 (72.8297) lr 7.8853e-06 eta 0:14:13
+epoch [50/50] batch [460/1000] time 1.570 (1.566) data 0.000 (0.003) loss 1.7441 (1.0819) acc 62.5000 (72.8261) lr 7.8853e-06 eta 0:14:05
+epoch [50/50] batch [465/1000] time 1.574 (1.566) data 0.000 (0.003) loss 1.0010 (1.0804) acc 78.1250 (72.9032) lr 7.8853e-06 eta 0:13:57
+epoch [50/50] batch [470/1000] time 1.567 (1.566) data 0.000 (0.003) loss 1.0918 (1.0800) acc 78.1250 (72.8923) lr 7.8853e-06 eta 0:13:49
+epoch [50/50] batch [475/1000] time 1.556 (1.566) data 0.000 (0.003) loss 1.4980 (1.0800) acc 59.3750 (72.8750) lr 7.8853e-06 eta 0:13:42
+epoch [50/50] batch [480/1000] time 1.547 (1.566) data 0.000 (0.003) loss 0.8604 (1.0796) acc 78.1250 (72.8385) lr 7.8853e-06 eta 0:13:34
+epoch [50/50] batch [485/1000] time 1.736 (1.566) data 0.000 (0.003) loss 0.7754 (1.0791) acc 84.3750 (72.8673) lr 7.8853e-06 eta 0:13:26
+epoch [50/50] batch [490/1000] time 1.595 (1.566) data 0.001 (0.003) loss 1.8789 (1.0795) acc 59.3750 (72.8827) lr 7.8853e-06 eta 0:13:18
+epoch [50/50] batch [495/1000] time 1.566 (1.566) data 0.000 (0.003) loss 0.6426 (1.0785) acc 87.5000 (72.9167) lr 7.8853e-06 eta 0:13:10
+epoch [50/50] batch [500/1000] time 1.581 (1.566) data 0.000 (0.002) loss 1.0635 (1.0784) acc 75.0000 (72.9437) lr 7.8853e-06 eta 0:13:03
+epoch [50/50] batch [505/1000] time 1.586 (1.566) data 0.000 (0.002) loss 1.5293 (1.0792) acc 53.1250 (72.8899) lr 7.8853e-06 eta 0:12:55
+epoch [50/50] batch [510/1000] time 1.586 (1.566) data 0.000 (0.002) loss 1.3057 (1.0789) acc 78.1250 (72.9105) lr 7.8853e-06 eta 0:12:47
+epoch [50/50] batch [515/1000] time 1.551 (1.566) data 0.000 (0.002) loss 1.5693 (1.0795) acc 56.2500 (72.8883) lr 7.8853e-06 eta 0:12:39
+epoch [50/50] batch [520/1000] time 1.550 (1.566) data 0.000 (0.002) loss 1.3008 (1.0793) acc 75.0000 (72.8846) lr 7.8853e-06 eta 0:12:31
+epoch [50/50] batch [525/1000] time 1.579 (1.566) data 0.000 (0.002) loss 1.1357 (1.0797) acc 65.6250 (72.8512) lr 7.8853e-06 eta 0:12:24
+epoch [50/50] batch [530/1000] time 1.721 (1.567) data 0.001 (0.002) loss 1.1523 (1.0775) acc 68.7500 (72.8715) lr 7.8853e-06 eta 0:12:16
+epoch [50/50] batch [535/1000] time 1.555 (1.567) data 0.001 (0.002) loss 1.0986 (1.0769) acc 78.1250 (72.8914) lr 7.8853e-06 eta 0:12:08
+epoch [50/50] batch [540/1000] time 1.562 (1.566) data 0.000 (0.002) loss 1.2764 (1.0758) acc 65.6250 (72.8935) lr 7.8853e-06 eta 0:12:00
+epoch [50/50] batch [545/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.9902 (1.0756) acc 81.2500 (72.9014) lr 7.8853e-06 eta 0:11:52
+epoch [50/50] batch [550/1000] time 1.573 (1.566) data 0.000 (0.002) loss 0.9731 (1.0734) acc 75.0000 (72.9432) lr 7.8853e-06 eta 0:11:44
+epoch [50/50] batch [555/1000] time 1.572 (1.566) data 0.000 (0.002) loss 0.6289 (1.0720) acc 75.0000 (72.9505) lr 7.8853e-06 eta 0:11:37
+epoch [50/50] batch [560/1000] time 1.563 (1.566) data 0.001 (0.002) loss 0.8696 (1.0710) acc 75.0000 (72.9297) lr 7.8853e-06 eta 0:11:29
+epoch [50/50] batch [565/1000] time 1.587 (1.566) data 0.000 (0.002) loss 1.3652 (1.0712) acc 62.5000 (72.9259) lr 7.8853e-06 eta 0:11:21
+epoch [50/50] batch [570/1000] time 1.549 (1.566) data 0.000 (0.002) loss 0.9160 (1.0698) acc 75.0000 (72.9441) lr 7.8853e-06 eta 0:11:13
+epoch [50/50] batch [575/1000] time 1.542 (1.566) data 0.000 (0.002) loss 1.2744 (1.0679) acc 75.0000 (73.0272) lr 7.8853e-06 eta 0:11:05
+epoch [50/50] batch [580/1000] time 1.569 (1.566) data 0.000 (0.002) loss 0.9712 (1.0686) acc 81.2500 (73.0334) lr 7.8853e-06 eta 0:10:57
+epoch [50/50] batch [585/1000] time 1.557 (1.566) data 0.001 (0.002) loss 0.8423 (1.0685) acc 78.1250 (73.0449) lr 7.8853e-06 eta 0:10:50
+epoch [50/50] batch [590/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.2646 (1.0688) acc 78.1250 (73.0773) lr 7.8853e-06 eta 0:10:42
+epoch [50/50] batch [595/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.8589 (1.0693) acc 78.1250 (73.0725) lr 7.8853e-06 eta 0:10:34
+epoch [50/50] batch [600/1000] time 1.527 (1.566) data 0.000 (0.002) loss 1.1191 (1.0696) acc 68.7500 (73.0677) lr 7.8853e-06 eta 0:10:26
+epoch [50/50] batch [605/1000] time 1.536 (1.566) data 0.000 (0.002) loss 1.2188 (1.0696) acc 56.2500 (73.0682) lr 7.8853e-06 eta 0:10:18
+epoch [50/50] batch [610/1000] time 1.576 (1.566) data 0.001 (0.002) loss 1.1641 (1.0676) acc 65.6250 (73.0994) lr 7.8853e-06 eta 0:10:10
+epoch [50/50] batch [615/1000] time 1.540 (1.566) data 0.000 (0.002) loss 1.2832 (1.0687) acc 75.0000 (73.0894) lr 7.8853e-06 eta 0:10:02
+epoch [50/50] batch [620/1000] time 1.556 (1.566) data 0.000 (0.002) loss 1.3730 (1.0695) acc 65.6250 (73.0595) lr 7.8853e-06 eta 0:09:54
+epoch [50/50] batch [625/1000] time 1.577 (1.566) data 0.001 (0.002) loss 1.0713 (1.0688) acc 78.1250 (73.0850) lr 7.8853e-06 eta 0:09:47
+epoch [50/50] batch [630/1000] time 1.573 (1.566) data 0.001 (0.002) loss 1.0537 (1.0691) acc 71.8750 (73.0704) lr 7.8853e-06 eta 0:09:39
+epoch [50/50] batch [635/1000] time 1.540 (1.566) data 0.000 (0.002) loss 1.0732 (1.0697) acc 65.6250 (73.0610) lr 7.8853e-06 eta 0:09:31
+epoch [50/50] batch [640/1000] time 1.578 (1.566) data 0.000 (0.002) loss 0.8198 (1.0691) acc 78.1250 (73.0762) lr 7.8853e-06 eta 0:09:23
+epoch [50/50] batch [645/1000] time 1.561 (1.566) data 0.001 (0.002) loss 0.7603 (1.0667) acc 81.2500 (73.1008) lr 7.8853e-06 eta 0:09:15
+epoch [50/50] batch [650/1000] time 1.561 (1.566) data 0.000 (0.002) loss 1.5254 (1.0674) acc 62.5000 (73.0913) lr 7.8853e-06 eta 0:09:08
+epoch [50/50] batch [655/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.9697 (1.0701) acc 75.0000 (73.0630) lr 7.8853e-06 eta 0:09:00
+epoch [50/50] batch [660/1000] time 1.571 (1.566) data 0.001 (0.002) loss 0.9951 (1.0726) acc 71.8750 (73.0208) lr 7.8853e-06 eta 0:08:52
+epoch [50/50] batch [665/1000] time 1.571 (1.566) data 0.000 (0.002) loss 0.8115 (1.0724) acc 71.8750 (72.9981) lr 7.8853e-06 eta 0:08:44
+epoch [50/50] batch [670/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.8735 (1.0713) acc 81.2500 (73.0317) lr 7.8853e-06 eta 0:08:36
+epoch [50/50] batch [675/1000] time 1.589 (1.566) data 0.000 (0.002) loss 1.5166 (1.0717) acc 65.6250 (73.0278) lr 7.8853e-06 eta 0:08:28
+epoch [50/50] batch [680/1000] time 1.562 (1.566) data 0.001 (0.002) loss 0.9185 (1.0713) acc 71.8750 (73.0515) lr 7.8853e-06 eta 0:08:21
+epoch [50/50] batch [685/1000] time 1.545 (1.566) data 0.001 (0.002) loss 1.1885 (1.0708) acc 68.7500 (73.0566) lr 7.8853e-06 eta 0:08:13
+epoch [50/50] batch [690/1000] time 1.554 (1.566) data 0.000 (0.002) loss 0.6230 (1.0709) acc 81.2500 (73.0616) lr 7.8853e-06 eta 0:08:05
+epoch [50/50] batch [695/1000] time 1.559 (1.566) data 0.000 (0.002) loss 0.9224 (1.0701) acc 71.8750 (73.0621) lr 7.8853e-06 eta 0:07:57
+epoch [50/50] batch [700/1000] time 1.558 (1.566) data 0.001 (0.002) loss 1.2695 (1.0716) acc 71.8750 (73.0625) lr 7.8853e-06 eta 0:07:49
+epoch [50/50] batch [705/1000] time 1.551 (1.566) data 0.000 (0.002) loss 1.8408 (1.0722) acc 62.5000 (73.0585) lr 7.8853e-06 eta 0:07:41
+epoch [50/50] batch [710/1000] time 1.563 (1.566) data 0.000 (0.002) loss 1.3545 (1.0725) acc 68.7500 (73.0194) lr 7.8853e-06 eta 0:07:34
+epoch [50/50] batch [715/1000] time 1.561 (1.566) data 0.001 (0.002) loss 0.9990 (1.0734) acc 65.6250 (73.0070) lr 7.8853e-06 eta 0:07:26
+epoch [50/50] batch [720/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.6348 (1.0736) acc 53.1250 (73.0035) lr 7.8853e-06 eta 0:07:18
+epoch [50/50] batch [725/1000] time 1.554 (1.566) data 0.001 (0.002) loss 0.7690 (1.0724) acc 71.8750 (73.0431) lr 7.8853e-06 eta 0:07:10
+epoch [50/50] batch [730/1000] time 1.555 (1.566) data 0.000 (0.002) loss 0.4810 (1.0713) acc 84.3750 (73.0865) lr 7.8853e-06 eta 0:07:02
+epoch [50/50] batch [735/1000] time 1.557 (1.566) data 0.001 (0.002) loss 1.3604 (1.0723) acc 71.8750 (73.0782) lr 7.8853e-06 eta 0:06:54
+epoch [50/50] batch [740/1000] time 1.580 (1.566) data 0.001 (0.002) loss 0.6548 (1.0714) acc 81.2500 (73.1039) lr 7.8853e-06 eta 0:06:47
+epoch [50/50] batch [745/1000] time 1.582 (1.566) data 0.001 (0.002) loss 1.3291 (1.0707) acc 75.0000 (73.1376) lr 7.8853e-06 eta 0:06:39
+epoch [50/50] batch [750/1000] time 1.545 (1.566) data 0.000 (0.002) loss 1.5938 (1.0724) acc 62.5000 (73.0792) lr 7.8853e-06 eta 0:06:31
+epoch [50/50] batch [755/1000] time 1.570 (1.566) data 0.000 (0.002) loss 1.2754 (1.0728) acc 75.0000 (73.0629) lr 7.8853e-06 eta 0:06:23
+epoch [50/50] batch [760/1000] time 1.566 (1.566) data 0.000 (0.002) loss 0.6982 (1.0710) acc 78.1250 (73.1003) lr 7.8853e-06 eta 0:06:15
+epoch [50/50] batch [765/1000] time 1.582 (1.566) data 0.000 (0.002) loss 1.3672 (1.0707) acc 59.3750 (73.0923) lr 7.8853e-06 eta 0:06:08
+epoch [50/50] batch [770/1000] time 1.580 (1.566) data 0.000 (0.002) loss 0.9966 (1.0705) acc 84.3750 (73.1169) lr 7.8853e-06 eta 0:06:00
+epoch [50/50] batch [775/1000] time 1.554 (1.566) data 0.000 (0.002) loss 1.5312 (1.0703) acc 59.3750 (73.1331) lr 7.8853e-06 eta 0:05:52
+epoch [50/50] batch [780/1000] time 1.534 (1.566) data 0.000 (0.002) loss 1.7314 (1.0711) acc 62.5000 (73.1170) lr 7.8853e-06 eta 0:05:44
+epoch [50/50] batch [785/1000] time 1.545 (1.566) data 0.000 (0.002) loss 1.6562 (1.0705) acc 53.1250 (73.1091) lr 7.8853e-06 eta 0:05:36
+epoch [50/50] batch [790/1000] time 1.519 (1.566) data 0.001 (0.002) loss 1.1436 (1.0694) acc 78.1250 (73.1290) lr 7.8853e-06 eta 0:05:28
+epoch [50/50] batch [795/1000] time 1.545 (1.566) data 0.000 (0.002) loss 1.1865 (1.0698) acc 65.6250 (73.1250) lr 7.8853e-06 eta 0:05:21
+epoch [50/50] batch [800/1000] time 1.562 (1.566) data 0.000 (0.002) loss 1.5068 (1.0698) acc 56.2500 (73.1133) lr 7.8853e-06 eta 0:05:13
+epoch [50/50] batch [805/1000] time 1.563 (1.566) data 0.000 (0.002) loss 0.8252 (1.0689) acc 84.3750 (73.1211) lr 7.8853e-06 eta 0:05:05
+epoch [50/50] batch [810/1000] time 1.560 (1.566) data 0.001 (0.002) loss 0.9990 (1.0701) acc 65.6250 (73.0787) lr 7.8853e-06 eta 0:04:57
+epoch [50/50] batch [815/1000] time 1.579 (1.566) data 0.000 (0.002) loss 0.7119 (1.0691) acc 78.1250 (73.0828) lr 7.8853e-06 eta 0:04:49
+epoch [50/50] batch [820/1000] time 1.557 (1.566) data 0.001 (0.002) loss 1.1680 (1.0687) acc 71.8750 (73.0907) lr 7.8853e-06 eta 0:04:41
+epoch [50/50] batch [825/1000] time 1.568 (1.566) data 0.000 (0.002) loss 1.3418 (1.0687) acc 65.6250 (73.0871) lr 7.8853e-06 eta 0:04:34
+epoch [50/50] batch [830/1000] time 1.550 (1.566) data 0.000 (0.002) loss 0.8164 (1.0689) acc 78.1250 (73.0761) lr 7.8853e-06 eta 0:04:26
+epoch [50/50] batch [835/1000] time 1.550 (1.566) data 0.001 (0.002) loss 0.8809 (1.0677) acc 71.8750 (73.0726) lr 7.8853e-06 eta 0:04:18
+epoch [50/50] batch [840/1000] time 1.556 (1.566) data 0.001 (0.002) loss 1.3174 (1.0682) acc 71.8750 (73.0729) lr 7.8853e-06 eta 0:04:10
+epoch [50/50] batch [845/1000] time 1.547 (1.566) data 0.000 (0.002) loss 0.7095 (1.0684) acc 75.0000 (73.0214) lr 7.8853e-06 eta 0:04:02
+epoch [50/50] batch [850/1000] time 1.581 (1.566) data 0.000 (0.002) loss 0.8779 (1.0684) acc 71.8750 (73.0147) lr 7.8853e-06 eta 0:03:54
+epoch [50/50] batch [855/1000] time 1.557 (1.566) data 0.001 (0.002) loss 0.7856 (1.0675) acc 81.2500 (73.0190) lr 7.8853e-06 eta 0:03:47
+epoch [50/50] batch [860/1000] time 1.551 (1.566) data 0.001 (0.002) loss 1.7520 (1.0676) acc 53.1250 (73.0269) lr 7.8853e-06 eta 0:03:39
+epoch [50/50] batch [865/1000] time 1.546 (1.566) data 0.000 (0.002) loss 0.7539 (1.0685) acc 81.2500 (73.0202) lr 7.8853e-06 eta 0:03:31
+epoch [50/50] batch [870/1000] time 1.564 (1.566) data 0.000 (0.002) loss 1.6504 (1.0688) acc 56.2500 (73.0029) lr 7.8853e-06 eta 0:03:23
+epoch [50/50] batch [875/1000] time 1.744 (1.566) data 0.001 (0.002) loss 1.8262 (1.0686) acc 59.3750 (73.0036) lr 7.8853e-06 eta 0:03:15
+epoch [50/50] batch [880/1000] time 1.536 (1.566) data 0.000 (0.002) loss 1.9727 (1.0697) acc 62.5000 (72.9759) lr 7.8853e-06 eta 0:03:07
+epoch [50/50] batch [885/1000] time 1.545 (1.566) data 0.001 (0.002) loss 0.8506 (1.0691) acc 75.0000 (72.9908) lr 7.8853e-06 eta 0:03:00
+epoch [50/50] batch [890/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.8564 (1.0708) acc 59.3750 (72.9529) lr 7.8853e-06 eta 0:02:52
+epoch [50/50] batch [895/1000] time 1.553 (1.566) data 0.000 (0.002) loss 0.7871 (1.0700) acc 78.1250 (72.9644) lr 7.8853e-06 eta 0:02:44
+epoch [50/50] batch [900/1000] time 1.547 (1.566) data 0.000 (0.002) loss 0.8916 (1.0696) acc 75.0000 (72.9826) lr 7.8853e-06 eta 0:02:36
+epoch [50/50] batch [905/1000] time 1.560 (1.566) data 0.000 (0.002) loss 1.3662 (1.0705) acc 62.5000 (72.9454) lr 7.8853e-06 eta 0:02:28
+epoch [50/50] batch [910/1000] time 1.581 (1.566) data 0.001 (0.002) loss 1.4375 (1.0720) acc 65.6250 (72.9224) lr 7.8853e-06 eta 0:02:20
+epoch [50/50] batch [915/1000] time 1.544 (1.566) data 0.000 (0.002) loss 0.4211 (1.0712) acc 90.6250 (72.9577) lr 7.8853e-06 eta 0:02:13
+epoch [50/50] batch [920/1000] time 1.540 (1.566) data 0.000 (0.002) loss 1.1621 (1.0710) acc 68.7500 (72.9552) lr 7.8853e-06 eta 0:02:05
+epoch [50/50] batch [925/1000] time 1.573 (1.566) data 0.000 (0.002) loss 1.0742 (1.0708) acc 65.6250 (72.9764) lr 7.8853e-06 eta 0:01:57
+epoch [50/50] batch [930/1000] time 1.584 (1.566) data 0.001 (0.002) loss 0.6138 (1.0708) acc 81.2500 (73.0074) lr 7.8853e-06 eta 0:01:49
+epoch [50/50] batch [935/1000] time 1.576 (1.566) data 0.000 (0.002) loss 1.5156 (1.0722) acc 71.8750 (72.9980) lr 7.8853e-06 eta 0:01:41
+epoch [50/50] batch [940/1000] time 1.565 (1.566) data 0.001 (0.002) loss 1.2344 (1.0719) acc 75.0000 (72.9887) lr 7.8853e-06 eta 0:01:33
+epoch [50/50] batch [945/1000] time 1.559 (1.566) data 0.000 (0.002) loss 0.6201 (1.0711) acc 84.3750 (72.9993) lr 7.8853e-06 eta 0:01:26
+epoch [50/50] batch [950/1000] time 1.561 (1.566) data 0.001 (0.002) loss 1.8535 (1.0719) acc 53.1250 (72.9605) lr 7.8853e-06 eta 0:01:18
+epoch [50/50] batch [955/1000] time 1.541 (1.566) data 0.000 (0.002) loss 2.2500 (1.0736) acc 53.1250 (72.9385) lr 7.8853e-06 eta 0:01:10
+epoch [50/50] batch [960/1000] time 1.565 (1.566) data 0.000 (0.002) loss 1.0967 (1.0738) acc 68.7500 (72.9362) lr 7.8853e-06 eta 0:01:02
+epoch [50/50] batch [965/1000] time 1.558 (1.566) data 0.000 (0.002) loss 1.3193 (1.0746) acc 65.6250 (72.9372) lr 7.8853e-06 eta 0:00:54
+epoch [50/50] batch [970/1000] time 1.585 (1.566) data 0.000 (0.002) loss 1.4512 (1.0735) acc 75.0000 (72.9832) lr 7.8853e-06 eta 0:00:46
+epoch [50/50] batch [975/1000] time 1.569 (1.566) data 0.000 (0.002) loss 1.0928 (1.0716) acc 75.0000 (73.0064) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [980/1000] time 1.566 (1.566) data 0.001 (0.002) loss 1.4277 (1.0711) acc 59.3750 (73.0070) lr 7.8853e-06 eta 0:00:31
+epoch [50/50] batch [985/1000] time 1.574 (1.566) data 0.001 (0.002) loss 1.4570 (1.0719) acc 68.7500 (73.0076) lr 7.8853e-06 eta 0:00:23
+epoch [50/50] batch [990/1000] time 1.554 (1.566) data 0.000 (0.001) loss 1.1445 (1.0714) acc 78.1250 (73.0271) lr 7.8853e-06 eta 0:00:15
+epoch [50/50] batch [995/1000] time 1.555 (1.566) data 0.000 (0.001) loss 1.4424 (1.0721) acc 68.7500 (73.0276) lr 7.8853e-06 eta 0:00:07
+epoch [50/50] batch [1000/1000] time 1.575 (1.566) data 0.000 (0.001) loss 1.7236 (1.0738) acc 65.6250 (73.0062) lr 1.9733e-06 eta 0:00:00
+Evaluate on the *val* set
+=> result
+* total: 50,000
+* correct: 39,372
+* accuracy: 78.7%
+* error: 21.3%
+* macro_f1: 78.3%
+Checkpoint saved to output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the model with the best val performance
+Loading weights to prompt_learner from "output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model-best.pth.tar" (epoch = 43)
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 39,401
+* accuracy: 78.8%
+* error: 21.2%
+* macro_f1: 78.4%
+Elapsed: 1 day, 3:08:10
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
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+model.pth.tar-50
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_bestval_ep50_32shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,5342 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 525.125.06
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 43 bits physical, 48 bits virtual
+CPU(s): 256
+On-line CPU(s) list: 0-255
+Thread(s) per core: 2
+Core(s) per socket: 64
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: AuthenticAMD
+CPU family: 23
+Model: 49
+Model name: AMD EPYC 7H12 64-Core Processor
+Stepping: 0
+Frequency boost: enabled
+CPU MHz: 1493.800
+CPU max MHz: 2600.0000
+CPU min MHz: 1500.0000
+BogoMIPS: 5200.20
+Virtualization: AMD-V
+L1d cache: 4 MiB
+L1i cache: 4 MiB
+L2 cache: 64 MiB
+L3 cache: 512 MiB
+NUMA node0 CPU(s): 0-63,128-191
+NUMA node1 CPU(s): 64-127,192-255
+Vulnerability Gather data sampling: Not affected
+Vulnerability Itlb multihit: Not affected
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Mmio stale data: Not affected
+Vulnerability Retbleed: Vulnerable
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Not affected
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca sme sev sev_es
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/500] time 0.899 (4.360) data 0.000 (0.173) loss 2.5977 (3.2941) acc 37.5000 (35.6250) lr 1.0000e-05 eta 1 day, 6:16:25
+epoch [1/50] batch [10/500] time 0.881 (2.619) data 0.000 (0.087) loss 2.7363 (3.0844) acc 43.7500 (40.0000) lr 1.0000e-05 eta 18:10:39
+epoch [1/50] batch [15/500] time 0.902 (2.046) data 0.000 (0.058) loss 2.2129 (2.7855) acc 50.0000 (44.5833) lr 1.0000e-05 eta 14:12:04
+epoch [1/50] batch [20/500] time 0.860 (1.755) data 0.000 (0.044) loss 2.5312 (2.6287) acc 50.0000 (48.2812) lr 1.0000e-05 eta 12:10:41
+epoch [1/50] batch [25/500] time 0.900 (1.583) data 0.000 (0.035) loss 1.8916 (2.5268) acc 56.2500 (49.1250) lr 1.0000e-05 eta 10:58:52
+epoch [1/50] batch [30/500] time 0.887 (1.469) data 0.000 (0.029) loss 1.5869 (2.4073) acc 65.6250 (51.1458) lr 1.0000e-05 eta 10:11:17
+epoch [1/50] batch [35/500] time 0.880 (1.386) data 0.000 (0.025) loss 1.3887 (2.3064) acc 59.3750 (51.7857) lr 1.0000e-05 eta 9:36:50
+epoch [1/50] batch [40/500] time 0.878 (1.323) data 0.000 (0.022) loss 2.0977 (2.2665) acc 56.2500 (52.4219) lr 1.0000e-05 eta 9:10:25
+epoch [1/50] batch [45/500] time 0.872 (1.275) data 0.000 (0.019) loss 2.7969 (2.2371) acc 43.7500 (52.7778) lr 1.0000e-05 eta 8:50:24
+epoch [1/50] batch [50/500] time 0.882 (1.237) data 0.000 (0.018) loss 1.8838 (2.1899) acc 56.2500 (53.0000) lr 1.0000e-05 eta 8:34:34
+epoch [1/50] batch [55/500] time 0.867 (1.204) data 0.000 (0.016) loss 1.3047 (2.1260) acc 78.1250 (54.2614) lr 1.0000e-05 eta 8:20:44
+epoch [1/50] batch [60/500] time 0.900 (1.176) data 0.000 (0.015) loss 1.0781 (2.0804) acc 68.7500 (54.6875) lr 1.0000e-05 eta 8:09:01
+epoch [1/50] batch [65/500] time 0.884 (1.154) data 0.000 (0.014) loss 1.3164 (2.0520) acc 59.3750 (54.9038) lr 1.0000e-05 eta 7:59:43
+epoch [1/50] batch [70/500] time 0.878 (1.135) data 0.000 (0.013) loss 2.0781 (2.0320) acc 62.5000 (55.1339) lr 1.0000e-05 eta 7:51:26
+epoch [1/50] batch [75/500] time 0.881 (1.117) data 0.000 (0.012) loss 1.3320 (1.9989) acc 65.6250 (55.6667) lr 1.0000e-05 eta 7:44:07
+epoch [1/50] batch [80/500] time 0.895 (1.103) data 0.000 (0.011) loss 1.0059 (1.9745) acc 68.7500 (56.3672) lr 1.0000e-05 eta 7:38:06
+epoch [1/50] batch [85/500] time 0.872 (1.090) data 0.000 (0.010) loss 1.5674 (1.9596) acc 71.8750 (56.8015) lr 1.0000e-05 eta 7:32:39
+epoch [1/50] batch [90/500] time 0.867 (1.078) data 0.000 (0.010) loss 2.5039 (1.9504) acc 50.0000 (56.7708) lr 1.0000e-05 eta 7:27:42
+epoch [1/50] batch [95/500] time 0.886 (1.069) data 0.000 (0.009) loss 1.5625 (1.9373) acc 65.6250 (57.2368) lr 1.0000e-05 eta 7:23:44
+epoch [1/50] batch [100/500] time 0.884 (1.060) data 0.000 (0.009) loss 1.6953 (1.9140) acc 62.5000 (57.6562) lr 1.0000e-05 eta 7:19:46
+epoch [1/50] batch [105/500] time 0.882 (1.051) data 0.000 (0.008) loss 1.8291 (1.9147) acc 68.7500 (57.5595) lr 1.0000e-05 eta 7:15:55
+epoch [1/50] batch [110/500] time 0.877 (1.043) data 0.000 (0.008) loss 1.5049 (1.8879) acc 68.7500 (58.2955) lr 1.0000e-05 eta 7:12:36
+epoch [1/50] batch [115/500] time 0.887 (1.036) data 0.000 (0.008) loss 1.9424 (1.8724) acc 53.1250 (58.5326) lr 1.0000e-05 eta 7:09:45
+epoch [1/50] batch [120/500] time 0.874 (1.029) data 0.000 (0.007) loss 2.4902 (1.8673) acc 40.6250 (58.5417) lr 1.0000e-05 eta 7:06:53
+epoch [1/50] batch [125/500] time 0.869 (1.024) data 0.000 (0.007) loss 2.1250 (1.8601) acc 46.8750 (58.6500) lr 1.0000e-05 eta 7:04:35
+epoch [1/50] batch [130/500] time 0.888 (1.019) data 0.000 (0.007) loss 1.8857 (1.8600) acc 62.5000 (58.5817) lr 1.0000e-05 eta 7:02:13
+epoch [1/50] batch [135/500] time 0.887 (1.014) data 0.000 (0.007) loss 1.8594 (1.8513) acc 56.2500 (58.6574) lr 1.0000e-05 eta 7:00:01
+epoch [1/50] batch [140/500] time 0.879 (1.009) data 0.000 (0.006) loss 1.6777 (1.8531) acc 56.2500 (58.4821) lr 1.0000e-05 eta 6:58:07
+epoch [1/50] batch [145/500] time 0.921 (1.006) data 0.000 (0.006) loss 1.5420 (1.8516) acc 62.5000 (58.3621) lr 1.0000e-05 eta 6:56:33
+epoch [1/50] batch [150/500] time 0.882 (1.002) data 0.000 (0.006) loss 2.3789 (1.8469) acc 43.7500 (58.5208) lr 1.0000e-05 eta 6:54:56
+epoch [1/50] batch [155/500] time 0.880 (0.998) data 0.000 (0.006) loss 1.3662 (1.8344) acc 59.3750 (58.6089) lr 1.0000e-05 eta 6:53:23
+epoch [1/50] batch [160/500] time 0.910 (0.996) data 0.000 (0.006) loss 0.8252 (1.8259) acc 81.2500 (58.6914) lr 1.0000e-05 eta 6:52:19
+epoch [1/50] batch [165/500] time 0.902 (0.993) data 0.000 (0.005) loss 1.6650 (1.8150) acc 50.0000 (58.8258) lr 1.0000e-05 eta 6:50:56
+epoch [1/50] batch [170/500] time 0.880 (0.990) data 0.000 (0.005) loss 1.0439 (1.8018) acc 71.8750 (59.0809) lr 1.0000e-05 eta 6:49:32
+epoch [1/50] batch [175/500] time 0.880 (0.987) data 0.000 (0.005) loss 1.9434 (1.7959) acc 59.3750 (59.1250) lr 1.0000e-05 eta 6:48:19
+epoch [1/50] batch [180/500] time 0.878 (0.984) data 0.000 (0.005) loss 1.8682 (1.7893) acc 59.3750 (59.1493) lr 1.0000e-05 eta 6:47:02
+epoch [1/50] batch [185/500] time 0.882 (0.982) data 0.000 (0.005) loss 1.3232 (1.7843) acc 65.6250 (59.2230) lr 1.0000e-05 eta 6:45:55
+epoch [1/50] batch [190/500] time 0.865 (0.979) data 0.000 (0.005) loss 1.3945 (1.7734) acc 68.7500 (59.4901) lr 1.0000e-05 eta 6:44:51
+epoch [1/50] batch [195/500] time 0.898 (0.977) data 0.001 (0.005) loss 1.6094 (1.7689) acc 59.3750 (59.5833) lr 1.0000e-05 eta 6:43:50
+epoch [1/50] batch [200/500] time 0.898 (0.975) data 0.001 (0.005) loss 1.1592 (1.7607) acc 68.7500 (59.7188) lr 1.0000e-05 eta 6:42:50
+epoch [1/50] batch [205/500] time 0.877 (0.973) data 0.000 (0.004) loss 1.0078 (1.7480) acc 68.7500 (59.8780) lr 1.0000e-05 eta 6:42:03
+epoch [1/50] batch [210/500] time 0.899 (0.971) data 0.000 (0.004) loss 1.6758 (1.7416) acc 68.7500 (60.0595) lr 1.0000e-05 eta 6:41:01
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+epoch [50/50] batch [220/500] time 0.919 (0.889) data 0.000 (0.004) loss 0.8218 (1.0445) acc 81.2500 (73.3523) lr 7.8853e-06 eta 0:04:09
+epoch [50/50] batch [225/500] time 0.896 (0.890) data 0.000 (0.004) loss 1.4238 (1.0424) acc 65.6250 (73.3333) lr 7.8853e-06 eta 0:04:04
+epoch [50/50] batch [230/500] time 0.880 (0.890) data 0.000 (0.003) loss 0.8193 (1.0427) acc 81.2500 (73.3424) lr 7.8853e-06 eta 0:04:00
+epoch [50/50] batch [235/500] time 0.897 (0.890) data 0.000 (0.003) loss 0.6768 (1.0457) acc 78.1250 (73.3511) lr 7.8853e-06 eta 0:03:55
+epoch [50/50] batch [240/500] time 0.873 (0.889) data 0.000 (0.003) loss 0.7178 (1.0439) acc 81.2500 (73.4375) lr 7.8853e-06 eta 0:03:51
+epoch [50/50] batch [245/500] time 0.879 (0.890) data 0.000 (0.003) loss 1.0576 (1.0443) acc 71.8750 (73.4694) lr 7.8853e-06 eta 0:03:46
+epoch [50/50] batch [250/500] time 0.855 (0.890) data 0.000 (0.003) loss 0.6836 (1.0460) acc 78.1250 (73.4000) lr 7.8853e-06 eta 0:03:42
+epoch [50/50] batch [255/500] time 0.884 (0.889) data 0.000 (0.003) loss 1.0205 (1.0462) acc 65.6250 (73.3456) lr 7.8853e-06 eta 0:03:37
+epoch [50/50] batch [260/500] time 0.889 (0.889) data 0.000 (0.003) loss 1.1221 (1.0477) acc 62.5000 (73.2692) lr 7.8853e-06 eta 0:03:33
+epoch [50/50] batch [265/500] time 0.870 (0.889) data 0.000 (0.003) loss 1.1758 (1.0454) acc 75.0000 (73.3373) lr 7.8853e-06 eta 0:03:28
+epoch [50/50] batch [270/500] time 0.892 (0.889) data 0.000 (0.003) loss 0.6284 (1.0449) acc 78.1250 (73.2870) lr 7.8853e-06 eta 0:03:24
+epoch [50/50] batch [275/500] time 0.876 (0.889) data 0.000 (0.003) loss 1.0527 (1.0439) acc 81.2500 (73.3409) lr 7.8853e-06 eta 0:03:19
+epoch [50/50] batch [280/500] time 0.880 (0.889) data 0.000 (0.003) loss 0.8892 (1.0427) acc 71.8750 (73.3259) lr 7.8853e-06 eta 0:03:15
+epoch [50/50] batch [285/500] time 0.851 (0.889) data 0.000 (0.003) loss 0.8530 (1.0456) acc 75.0000 (73.2018) lr 7.8853e-06 eta 0:03:11
+epoch [50/50] batch [290/500] time 0.895 (0.889) data 0.000 (0.003) loss 0.8555 (1.0469) acc 81.2500 (73.1358) lr 7.8853e-06 eta 0:03:06
+epoch [50/50] batch [295/500] time 0.854 (0.889) data 0.000 (0.003) loss 1.2783 (1.0480) acc 75.0000 (73.1780) lr 7.8853e-06 eta 0:03:02
+epoch [50/50] batch [300/500] time 0.866 (0.889) data 0.000 (0.003) loss 0.6821 (1.0446) acc 90.6250 (73.2917) lr 7.8853e-06 eta 0:02:57
+epoch [50/50] batch [305/500] time 0.863 (0.888) data 0.000 (0.003) loss 1.0146 (1.0463) acc 81.2500 (73.2889) lr 7.8853e-06 eta 0:02:53
+epoch [50/50] batch [310/500] time 0.871 (0.888) data 0.000 (0.003) loss 0.9561 (1.0485) acc 71.8750 (73.2460) lr 7.8853e-06 eta 0:02:48
+epoch [50/50] batch [315/500] time 0.874 (0.888) data 0.000 (0.003) loss 1.5576 (1.0492) acc 62.5000 (73.2540) lr 7.8853e-06 eta 0:02:44
+epoch [50/50] batch [320/500] time 0.861 (0.888) data 0.000 (0.003) loss 0.7373 (1.0474) acc 87.5000 (73.3203) lr 7.8853e-06 eta 0:02:39
+epoch [50/50] batch [325/500] time 0.914 (0.888) data 0.000 (0.003) loss 1.7461 (1.0522) acc 65.6250 (73.2692) lr 7.8853e-06 eta 0:02:35
+epoch [50/50] batch [330/500] time 0.866 (0.888) data 0.000 (0.002) loss 0.7881 (1.0547) acc 81.2500 (73.2481) lr 7.8853e-06 eta 0:02:30
+epoch [50/50] batch [335/500] time 0.901 (0.888) data 0.000 (0.002) loss 0.4353 (1.0531) acc 93.7500 (73.3022) lr 7.8853e-06 eta 0:02:26
+epoch [50/50] batch [340/500] time 0.898 (0.888) data 0.000 (0.002) loss 0.8335 (1.0554) acc 81.2500 (73.2812) lr 7.8853e-06 eta 0:02:22
+epoch [50/50] batch [345/500] time 0.874 (0.888) data 0.000 (0.002) loss 0.9746 (1.0563) acc 78.1250 (73.1975) lr 7.8853e-06 eta 0:02:17
+epoch [50/50] batch [350/500] time 0.873 (0.888) data 0.000 (0.002) loss 0.7119 (1.0551) acc 71.8750 (73.1696) lr 7.8853e-06 eta 0:02:13
+epoch [50/50] batch [355/500] time 0.891 (0.887) data 0.000 (0.002) loss 1.0996 (1.0559) acc 75.0000 (73.1426) lr 7.8853e-06 eta 0:02:08
+epoch [50/50] batch [360/500] time 0.898 (0.887) data 0.000 (0.002) loss 0.9941 (1.0548) acc 78.1250 (73.1684) lr 7.8853e-06 eta 0:02:04
+epoch [50/50] batch [365/500] time 0.861 (0.887) data 0.000 (0.002) loss 1.5703 (1.0553) acc 71.8750 (73.1678) lr 7.8853e-06 eta 0:01:59
+epoch [50/50] batch [370/500] time 0.900 (0.887) data 0.000 (0.002) loss 0.9814 (1.0516) acc 68.7500 (73.2348) lr 7.8853e-06 eta 0:01:55
+epoch [50/50] batch [375/500] time 0.894 (0.887) data 0.000 (0.002) loss 2.1621 (1.0522) acc 59.3750 (73.2667) lr 7.8853e-06 eta 0:01:50
+epoch [50/50] batch [380/500] time 0.865 (0.887) data 0.000 (0.002) loss 1.4688 (1.0521) acc 65.6250 (73.2977) lr 7.8853e-06 eta 0:01:46
+epoch [50/50] batch [385/500] time 0.888 (0.887) data 0.000 (0.002) loss 0.7954 (1.0531) acc 81.2500 (73.3198) lr 7.8853e-06 eta 0:01:42
+epoch [50/50] batch [390/500] time 0.875 (0.887) data 0.000 (0.002) loss 0.6636 (1.0527) acc 78.1250 (73.3173) lr 7.8853e-06 eta 0:01:37
+epoch [50/50] batch [395/500] time 0.892 (0.887) data 0.000 (0.002) loss 0.7432 (1.0526) acc 78.1250 (73.3228) lr 7.8853e-06 eta 0:01:33
+epoch [50/50] batch [400/500] time 0.896 (0.887) data 0.000 (0.002) loss 1.2910 (1.0532) acc 68.7500 (73.3438) lr 7.8853e-06 eta 0:01:28
+epoch [50/50] batch [405/500] time 0.899 (0.887) data 0.000 (0.002) loss 1.4844 (1.0531) acc 65.6250 (73.3410) lr 7.8853e-06 eta 0:01:24
+epoch [50/50] batch [410/500] time 0.912 (0.887) data 0.000 (0.002) loss 0.7090 (1.0505) acc 78.1250 (73.4223) lr 7.8853e-06 eta 0:01:19
+epoch [50/50] batch [415/500] time 0.914 (0.888) data 0.000 (0.002) loss 1.0820 (1.0497) acc 78.1250 (73.4864) lr 7.8853e-06 eta 0:01:15
+epoch [50/50] batch [420/500] time 0.865 (0.888) data 0.000 (0.002) loss 0.6450 (1.0479) acc 81.2500 (73.5045) lr 7.8853e-06 eta 0:01:11
+epoch [50/50] batch [425/500] time 0.877 (0.888) data 0.000 (0.002) loss 0.3677 (1.0458) acc 90.6250 (73.5809) lr 7.8853e-06 eta 0:01:06
+epoch [50/50] batch [430/500] time 0.909 (0.888) data 0.000 (0.002) loss 1.3506 (1.0465) acc 71.8750 (73.5756) lr 7.8853e-06 eta 0:01:02
+epoch [50/50] batch [435/500] time 0.851 (0.888) data 0.000 (0.002) loss 1.3906 (1.0462) acc 56.2500 (73.5632) lr 7.8853e-06 eta 0:00:57
+epoch [50/50] batch [440/500] time 0.895 (0.887) data 0.000 (0.002) loss 0.6631 (1.0476) acc 71.8750 (73.5440) lr 7.8853e-06 eta 0:00:53
+epoch [50/50] batch [445/500] time 0.883 (0.887) data 0.000 (0.002) loss 1.2051 (1.0457) acc 68.7500 (73.5885) lr 7.8853e-06 eta 0:00:48
+epoch [50/50] batch [450/500] time 0.911 (0.887) data 0.000 (0.002) loss 1.3496 (1.0489) acc 71.8750 (73.5694) lr 7.8853e-06 eta 0:00:44
+epoch [50/50] batch [455/500] time 0.876 (0.887) data 0.000 (0.002) loss 0.7402 (1.0482) acc 75.0000 (73.5646) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [460/500] time 0.918 (0.887) data 0.000 (0.002) loss 1.0596 (1.0478) acc 75.0000 (73.5462) lr 7.8853e-06 eta 0:00:35
+epoch [50/50] batch [465/500] time 0.885 (0.887) data 0.000 (0.002) loss 0.7373 (1.0466) acc 84.3750 (73.5954) lr 7.8853e-06 eta 0:00:31
+epoch [50/50] batch [470/500] time 0.886 (0.887) data 0.000 (0.002) loss 1.7842 (1.0493) acc 71.8750 (73.5638) lr 7.8853e-06 eta 0:00:26
+epoch [50/50] batch [475/500] time 0.872 (0.887) data 0.000 (0.002) loss 1.1602 (1.0486) acc 81.2500 (73.6382) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [480/500] time 0.885 (0.887) data 0.000 (0.002) loss 1.0117 (1.0495) acc 78.1250 (73.6589) lr 7.8853e-06 eta 0:00:17
+epoch [50/50] batch [485/500] time 0.923 (0.887) data 0.000 (0.002) loss 0.7983 (1.0481) acc 78.1250 (73.7242) lr 7.8853e-06 eta 0:00:13
+epoch [50/50] batch [490/500] time 0.880 (0.887) data 0.000 (0.002) loss 0.7788 (1.0482) acc 75.0000 (73.6990) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [495/500] time 0.884 (0.887) data 0.000 (0.002) loss 0.9722 (1.0476) acc 78.1250 (73.7311) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [500/500] time 0.875 (0.887) data 0.000 (0.002) loss 0.8242 (1.0479) acc 78.1250 (73.7250) lr 1.9733e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 38,975
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+Elapsed: 6:13:37
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..e772079e
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698948244.ckb-gpu-a.998015.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698948244.ckb-gpu-a.998015.0
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698948244.ckb-gpu-a.998015.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
new file mode 100644
index 00000000..bea00dc2
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,5342 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 525.125.06
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 43 bits physical, 48 bits virtual
+CPU(s): 256
+On-line CPU(s) list: 0-255
+Thread(s) per core: 2
+Core(s) per socket: 64
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: AuthenticAMD
+CPU family: 23
+Model: 49
+Model name: AMD EPYC 7H12 64-Core Processor
+Stepping: 0
+Frequency boost: enabled
+CPU MHz: 1579.755
+CPU max MHz: 2600.0000
+CPU min MHz: 1500.0000
+BogoMIPS: 5200.20
+Virtualization: AMD-V
+L1d cache: 4 MiB
+L1i cache: 4 MiB
+L2 cache: 64 MiB
+L3 cache: 512 MiB
+NUMA node0 CPU(s): 0-63,128-191
+NUMA node1 CPU(s): 64-127,192-255
+Vulnerability Gather data sampling: Not affected
+Vulnerability Itlb multihit: Not affected
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Mmio stale data: Not affected
+Vulnerability Retbleed: Vulnerable
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Not affected
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca sme sev sev_es
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/500] time 0.884 (1.684) data 0.000 (0.139) loss 2.6719 (3.0027) acc 50.0000 (44.3750) lr 1.0000e-05 eta 11:41:19
+epoch [1/50] batch [10/500] time 0.876 (1.282) data 0.000 (0.069) loss 2.1621 (2.6791) acc 53.1250 (47.5000) lr 1.0000e-05 eta 8:54:02
+epoch [1/50] batch [15/500] time 0.893 (1.150) data 0.000 (0.046) loss 2.3926 (2.6344) acc 53.1250 (47.5000) lr 1.0000e-05 eta 7:58:41
+epoch [1/50] batch [20/500] time 0.885 (1.084) data 0.000 (0.035) loss 2.6055 (2.5419) acc 40.6250 (48.4375) lr 1.0000e-05 eta 7:31:16
+epoch [1/50] batch [25/500] time 0.917 (1.047) data 0.000 (0.028) loss 2.2500 (2.4515) acc 46.8750 (49.6250) lr 1.0000e-05 eta 7:15:56
+epoch [1/50] batch [30/500] time 0.900 (1.019) data 0.000 (0.023) loss 1.9385 (2.4207) acc 56.2500 (49.8958) lr 1.0000e-05 eta 7:04:08
+epoch [1/50] batch [35/500] time 0.915 (1.000) data 0.000 (0.020) loss 1.7520 (2.3321) acc 56.2500 (51.2500) lr 1.0000e-05 eta 6:56:12
+epoch [1/50] batch [40/500] time 0.872 (0.986) data 0.000 (0.018) loss 2.2129 (2.2769) acc 50.0000 (51.8750) lr 1.0000e-05 eta 6:50:14
+epoch [1/50] batch [45/500] time 0.885 (0.974) data 0.000 (0.016) loss 1.5869 (2.2609) acc 59.3750 (52.2917) lr 1.0000e-05 eta 6:45:00
+epoch [1/50] batch [50/500] time 0.880 (0.965) data 0.000 (0.014) loss 1.3604 (2.2211) acc 71.8750 (53.1250) lr 1.0000e-05 eta 6:41:14
+epoch [1/50] batch [55/500] time 0.856 (0.957) data 0.000 (0.013) loss 2.5371 (2.1803) acc 40.6250 (53.8068) lr 1.0000e-05 eta 6:38:02
+epoch [1/50] batch [60/500] time 0.887 (0.951) data 0.000 (0.012) loss 1.6885 (2.1439) acc 59.3750 (54.4792) lr 1.0000e-05 eta 6:35:27
+epoch [1/50] batch [65/500] time 0.883 (0.945) data 0.000 (0.011) loss 1.3691 (2.1062) acc 71.8750 (55.2404) lr 1.0000e-05 eta 6:32:45
+epoch [1/50] batch [70/500] time 0.848 (0.940) data 0.000 (0.010) loss 1.3203 (2.0913) acc 68.7500 (55.5804) lr 1.0000e-05 eta 6:30:28
+epoch [1/50] batch [75/500] time 0.862 (0.934) data 0.000 (0.009) loss 1.7529 (2.0629) acc 56.2500 (55.7917) lr 1.0000e-05 eta 6:28:11
+epoch [1/50] batch [80/500] time 0.876 (0.930) data 0.000 (0.009) loss 1.4912 (2.0368) acc 68.7500 (56.4453) lr 1.0000e-05 eta 6:26:25
+epoch [1/50] batch [85/500] time 0.887 (0.928) data 0.000 (0.008) loss 2.0371 (2.0134) acc 40.6250 (56.5809) lr 1.0000e-05 eta 6:25:19
+epoch [1/50] batch [90/500] time 0.862 (0.925) data 0.000 (0.008) loss 1.4316 (2.0049) acc 59.3750 (56.4236) lr 1.0000e-05 eta 6:24:00
+epoch [1/50] batch [95/500] time 0.893 (0.923) data 0.000 (0.008) loss 0.9624 (1.9725) acc 75.0000 (56.9079) lr 1.0000e-05 eta 6:22:57
+epoch [1/50] batch [100/500] time 0.894 (0.920) data 0.000 (0.007) loss 1.8184 (1.9715) acc 59.3750 (57.0625) lr 1.0000e-05 eta 6:21:59
+epoch [1/50] batch [105/500] time 0.876 (0.918) data 0.000 (0.007) loss 1.8506 (1.9651) acc 59.3750 (57.4107) lr 1.0000e-05 eta 6:20:59
+epoch [1/50] batch [110/500] time 0.896 (0.917) data 0.000 (0.007) loss 1.3242 (1.9542) acc 75.0000 (57.5852) lr 1.0000e-05 eta 6:20:24
+epoch [1/50] batch [115/500] time 0.888 (0.916) data 0.000 (0.006) loss 2.3340 (1.9418) acc 56.2500 (57.7717) lr 1.0000e-05 eta 6:19:46
+epoch [1/50] batch [120/500] time 0.872 (0.915) data 0.000 (0.006) loss 2.0195 (1.9297) acc 46.8750 (58.0469) lr 1.0000e-05 eta 6:19:20
+epoch [1/50] batch [125/500] time 0.865 (0.913) data 0.000 (0.006) loss 1.2373 (1.9106) acc 71.8750 (58.4250) lr 1.0000e-05 eta 6:18:40
+epoch [1/50] batch [130/500] time 0.864 (0.912) data 0.000 (0.006) loss 1.9961 (1.9037) acc 59.3750 (58.5817) lr 1.0000e-05 eta 6:17:57
+epoch [1/50] batch [135/500] time 0.858 (0.910) data 0.001 (0.005) loss 1.7275 (1.8902) acc 56.2500 (58.7963) lr 1.0000e-05 eta 6:17:13
+epoch [1/50] batch [140/500] time 0.878 (0.909) data 0.000 (0.005) loss 2.3594 (1.8775) acc 53.1250 (58.9509) lr 1.0000e-05 eta 6:16:32
+epoch [1/50] batch [145/500] time 0.881 (0.908) data 0.000 (0.005) loss 1.8564 (1.8715) acc 56.2500 (58.8793) lr 1.0000e-05 eta 6:16:12
+epoch [1/50] batch [150/500] time 0.861 (0.907) data 0.000 (0.005) loss 1.9980 (1.8628) acc 53.1250 (59.1667) lr 1.0000e-05 eta 6:15:38
+epoch [1/50] batch [155/500] time 0.878 (0.906) data 0.000 (0.005) loss 2.0977 (1.8558) acc 56.2500 (59.3347) lr 1.0000e-05 eta 6:15:17
+epoch [1/50] batch [160/500] time 0.861 (0.906) data 0.000 (0.005) loss 1.4668 (1.8541) acc 56.2500 (59.1797) lr 1.0000e-05 eta 6:15:08
+epoch [1/50] batch [165/500] time 0.874 (0.906) data 0.000 (0.004) loss 1.7119 (1.8491) acc 53.1250 (59.1856) lr 1.0000e-05 eta 6:14:57
+epoch [1/50] batch [170/500] time 0.880 (0.906) data 0.000 (0.004) loss 1.7588 (1.8374) acc 62.5000 (59.3566) lr 1.0000e-05 eta 6:14:48
+epoch [1/50] batch [175/500] time 0.918 (0.906) data 0.000 (0.004) loss 2.0781 (1.8385) acc 59.3750 (59.4107) lr 1.0000e-05 eta 6:14:45
+epoch [1/50] batch [180/500] time 0.889 (0.905) data 0.000 (0.004) loss 0.9956 (1.8300) acc 75.0000 (59.5486) lr 1.0000e-05 eta 6:14:28
+epoch [1/50] batch [185/500] time 0.900 (0.905) data 0.000 (0.004) loss 1.1094 (1.8253) acc 65.6250 (59.6284) lr 1.0000e-05 eta 6:14:10
+epoch [1/50] batch [190/500] time 0.885 (0.904) data 0.000 (0.004) loss 1.1211 (1.8156) acc 71.8750 (59.7697) lr 1.0000e-05 eta 6:13:44
+epoch [1/50] batch [195/500] time 0.864 (0.903) data 0.000 (0.004) loss 2.0332 (1.8151) acc 62.5000 (59.8237) lr 1.0000e-05 eta 6:13:21
+epoch [1/50] batch [200/500] time 0.876 (0.903) data 0.000 (0.004) loss 1.7812 (1.8054) acc 65.6250 (59.9844) lr 1.0000e-05 eta 6:13:03
+epoch [1/50] batch [205/500] time 0.897 (0.903) data 0.000 (0.004) loss 1.7979 (1.7951) acc 62.5000 (60.0762) lr 1.0000e-05 eta 6:13:11
+epoch [1/50] batch [210/500] time 0.896 (0.903) data 0.000 (0.004) loss 1.6221 (1.7956) acc 62.5000 (60.0595) lr 1.0000e-05 eta 6:13:01
+epoch [1/50] batch [215/500] time 0.903 (0.903) data 0.000 (0.003) loss 1.2305 (1.7844) acc 65.6250 (60.2762) lr 1.0000e-05 eta 6:12:52
+epoch [1/50] batch [220/500] time 0.885 (0.903) data 0.000 (0.003) loss 2.3125 (1.7787) acc 56.2500 (60.4403) lr 1.0000e-05 eta 6:12:46
+epoch [1/50] batch [225/500] time 0.911 (0.902) data 0.000 (0.003) loss 1.5068 (1.7711) acc 65.6250 (60.5556) lr 1.0000e-05 eta 6:12:31
+epoch [1/50] batch [230/500] time 0.877 (0.902) data 0.000 (0.003) loss 1.1816 (1.7646) acc 75.0000 (60.7337) lr 1.0000e-05 eta 6:12:12
+epoch [1/50] batch [235/500] time 0.911 (0.902) data 0.000 (0.003) loss 1.4854 (1.7569) acc 62.5000 (60.8112) lr 1.0000e-05 eta 6:12:06
+epoch [1/50] batch [240/500] time 0.873 (0.902) data 0.000 (0.003) loss 1.3838 (1.7569) acc 68.7500 (60.8854) lr 1.0000e-05 eta 6:12:01
+epoch [1/50] batch [245/500] time 0.911 (0.901) data 0.000 (0.003) loss 1.0498 (1.7544) acc 75.0000 (60.9184) lr 1.0000e-05 eta 6:11:50
+epoch [1/50] batch [250/500] time 0.880 (0.901) data 0.000 (0.003) loss 1.2861 (1.7467) acc 59.3750 (61.0000) lr 1.0000e-05 eta 6:11:36
+epoch [1/50] batch [255/500] time 0.893 (0.901) data 0.000 (0.003) loss 1.6689 (1.7407) acc 71.8750 (61.0907) lr 1.0000e-05 eta 6:11:23
+epoch [1/50] batch [260/500] time 0.877 (0.900) data 0.000 (0.003) loss 1.8457 (1.7376) acc 68.7500 (61.2260) lr 1.0000e-05 eta 6:11:15
+epoch [1/50] batch [265/500] time 0.894 (0.900) data 0.000 (0.003) loss 2.2324 (1.7313) acc 46.8750 (61.3679) lr 1.0000e-05 eta 6:11:07
+epoch [1/50] batch [270/500] time 0.903 (0.900) data 0.000 (0.003) loss 1.3438 (1.7195) acc 71.8750 (61.6204) lr 1.0000e-05 eta 6:10:55
+epoch [1/50] batch [275/500] time 0.866 (0.900) data 0.000 (0.003) loss 1.4639 (1.7159) acc 68.7500 (61.6591) lr 1.0000e-05 eta 6:10:41
+epoch [1/50] batch [280/500] time 0.889 (0.899) data 0.000 (0.003) loss 1.2188 (1.7089) acc 65.6250 (61.7969) lr 1.0000e-05 eta 6:10:30
+epoch [1/50] batch [285/500] time 0.867 (0.899) data 0.000 (0.003) loss 1.1113 (1.7032) acc 71.8750 (61.9408) lr 1.0000e-05 eta 6:10:17
+epoch [1/50] batch [290/500] time 0.906 (0.899) data 0.000 (0.003) loss 1.0664 (1.6993) acc 65.6250 (61.9612) lr 1.0000e-05 eta 6:10:04
+epoch [1/50] batch [295/500] time 0.863 (0.898) data 0.000 (0.003) loss 1.1455 (1.6939) acc 65.6250 (62.0339) lr 1.0000e-05 eta 6:09:52
+epoch [1/50] batch [300/500] time 0.866 (0.898) data 0.000 (0.003) loss 1.6172 (1.6872) acc 56.2500 (62.0208) lr 1.0000e-05 eta 6:09:39
+epoch [1/50] batch [305/500] time 0.871 (0.898) data 0.000 (0.003) loss 1.3730 (1.6857) acc 71.8750 (62.0492) lr 1.0000e-05 eta 6:09:35
+epoch [1/50] batch [310/500] time 0.872 (0.898) data 0.000 (0.002) loss 0.8818 (1.6798) acc 78.1250 (62.1774) lr 1.0000e-05 eta 6:09:26
+epoch [1/50] batch [315/500] time 0.906 (0.898) data 0.000 (0.002) loss 1.0029 (1.6796) acc 65.6250 (62.1726) lr 1.0000e-05 eta 6:09:15
+epoch [1/50] batch [320/500] time 0.880 (0.897) data 0.000 (0.002) loss 1.0869 (1.6703) acc 65.6250 (62.3242) lr 1.0000e-05 eta 6:09:09
+epoch [1/50] batch [325/500] time 0.877 (0.897) data 0.000 (0.002) loss 1.5264 (1.6672) acc 65.6250 (62.3942) lr 1.0000e-05 eta 6:08:55
+epoch [1/50] batch [330/500] time 0.883 (0.897) data 0.001 (0.002) loss 1.6621 (1.6667) acc 59.3750 (62.3674) lr 1.0000e-05 eta 6:08:44
+epoch [1/50] batch [335/500] time 0.916 (0.897) data 0.000 (0.002) loss 1.8838 (1.6611) acc 65.6250 (62.4347) lr 1.0000e-05 eta 6:08:43
+epoch [1/50] batch [340/500] time 0.915 (0.897) data 0.000 (0.002) loss 0.6094 (1.6591) acc 87.5000 (62.5184) lr 1.0000e-05 eta 6:08:38
+epoch [1/50] batch [345/500] time 0.887 (0.897) data 0.000 (0.002) loss 1.0771 (1.6564) acc 71.8750 (62.5634) lr 1.0000e-05 eta 6:08:34
+epoch [1/50] batch [350/500] time 0.912 (0.897) data 0.000 (0.002) loss 1.5244 (1.6549) acc 68.7500 (62.5982) lr 1.0000e-05 eta 6:08:35
+epoch [1/50] batch [355/500] time 0.861 (0.897) data 0.000 (0.002) loss 1.2002 (1.6517) acc 71.8750 (62.6408) lr 1.0000e-05 eta 6:08:29
+epoch [1/50] batch [360/500] time 0.871 (0.897) data 0.000 (0.002) loss 1.1689 (1.6477) acc 71.8750 (62.6736) lr 1.0000e-05 eta 6:08:14
+epoch [1/50] batch [365/500] time 0.888 (0.896) data 0.000 (0.002) loss 1.4883 (1.6436) acc 59.3750 (62.6712) lr 1.0000e-05 eta 6:08:04
+epoch [1/50] batch [370/500] time 0.905 (0.896) data 0.000 (0.002) loss 1.4648 (1.6414) acc 59.3750 (62.7196) lr 1.0000e-05 eta 6:07:58
+epoch [1/50] batch [375/500] time 0.911 (0.896) data 0.000 (0.002) loss 2.1250 (1.6374) acc 53.1250 (62.7500) lr 1.0000e-05 eta 6:07:51
+epoch [1/50] batch [380/500] time 0.879 (0.896) data 0.000 (0.002) loss 1.3408 (1.6347) acc 75.0000 (62.7632) lr 1.0000e-05 eta 6:07:42
+epoch [1/50] batch [385/500] time 0.875 (0.896) data 0.000 (0.002) loss 1.7363 (1.6326) acc 62.5000 (62.8084) lr 1.0000e-05 eta 6:07:33
+epoch [1/50] batch [390/500] time 0.890 (0.896) data 0.000 (0.002) loss 1.1152 (1.6298) acc 71.8750 (62.9407) lr 1.0000e-05 eta 6:07:23
+epoch [1/50] batch [395/500] time 0.884 (0.896) data 0.000 (0.002) loss 0.9424 (1.6234) acc 71.8750 (63.0617) lr 1.0000e-05 eta 6:07:18
+epoch [1/50] batch [400/500] time 0.900 (0.896) data 0.001 (0.002) loss 1.6748 (1.6199) acc 59.3750 (63.1797) lr 1.0000e-05 eta 6:07:14
+epoch [1/50] batch [405/500] time 0.867 (0.896) data 0.000 (0.002) loss 0.9951 (1.6166) acc 59.3750 (63.1944) lr 1.0000e-05 eta 6:07:08
+epoch [1/50] batch [410/500] time 0.884 (0.895) data 0.000 (0.002) loss 1.7021 (1.6145) acc 62.5000 (63.2088) lr 1.0000e-05 eta 6:06:58
+epoch [1/50] batch [415/500] time 0.902 (0.895) data 0.000 (0.002) loss 1.7559 (1.6136) acc 62.5000 (63.2756) lr 1.0000e-05 eta 6:06:50
+epoch [1/50] batch [420/500] time 0.857 (0.895) data 0.000 (0.002) loss 1.8350 (1.6125) acc 53.1250 (63.2812) lr 1.0000e-05 eta 6:06:38
+epoch [1/50] batch [425/500] time 0.880 (0.895) data 0.000 (0.002) loss 1.9307 (1.6128) acc 62.5000 (63.2868) lr 1.0000e-05 eta 6:06:31
+epoch [1/50] batch [430/500] time 0.918 (0.895) data 0.000 (0.002) loss 1.2988 (1.6113) acc 75.0000 (63.3285) lr 1.0000e-05 eta 6:06:25
+epoch [1/50] batch [435/500] time 0.905 (0.895) data 0.000 (0.002) loss 1.1514 (1.6075) acc 71.8750 (63.4124) lr 1.0000e-05 eta 6:06:20
+epoch [1/50] batch [440/500] time 0.881 (0.895) data 0.000 (0.002) loss 1.1328 (1.6046) acc 71.8750 (63.4588) lr 1.0000e-05 eta 6:06:12
+epoch [1/50] batch [445/500] time 0.991 (0.895) data 0.000 (0.002) loss 1.1445 (1.6021) acc 75.0000 (63.5253) lr 1.0000e-05 eta 6:06:11
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+epoch [49/50] batch [455/500] time 0.863 (0.889) data 0.000 (0.002) loss 0.7847 (0.9844) acc 81.2500 (74.9863) lr 1.7713e-05 eta 0:08:04
+epoch [49/50] batch [460/500] time 0.896 (0.889) data 0.000 (0.002) loss 1.3652 (0.9850) acc 75.0000 (74.9660) lr 1.7713e-05 eta 0:07:59
+epoch [49/50] batch [465/500] time 0.868 (0.889) data 0.000 (0.002) loss 1.1289 (0.9866) acc 78.1250 (74.9597) lr 1.7713e-05 eta 0:07:55
+epoch [49/50] batch [470/500] time 0.904 (0.889) data 0.000 (0.002) loss 0.9976 (0.9861) acc 75.0000 (74.9601) lr 1.7713e-05 eta 0:07:50
+epoch [49/50] batch [475/500] time 0.915 (0.889) data 0.000 (0.002) loss 1.2070 (0.9849) acc 65.6250 (74.9803) lr 1.7713e-05 eta 0:07:46
+epoch [49/50] batch [480/500] time 0.908 (0.889) data 0.000 (0.002) loss 0.6929 (0.9829) acc 78.1250 (75.0456) lr 1.7713e-05 eta 0:07:42
+epoch [49/50] batch [485/500] time 0.895 (0.889) data 0.000 (0.002) loss 0.8452 (0.9833) acc 75.0000 (75.0129) lr 1.7713e-05 eta 0:07:37
+epoch [49/50] batch [490/500] time 0.895 (0.889) data 0.000 (0.002) loss 1.1621 (0.9849) acc 81.2500 (74.9745) lr 1.7713e-05 eta 0:07:33
+epoch [49/50] batch [495/500] time 0.862 (0.888) data 0.000 (0.002) loss 1.2715 (0.9841) acc 65.6250 (74.9811) lr 1.7713e-05 eta 0:07:28
+epoch [49/50] batch [500/500] time 0.891 (0.888) data 0.000 (0.002) loss 1.1982 (0.9852) acc 68.7500 (74.9688) lr 7.8853e-06 eta 0:07:24
+epoch [50/50] batch [5/500] time 0.894 (1.054) data 0.000 (0.135) loss 0.5654 (0.8583) acc 78.1250 (75.6250) lr 7.8853e-06 eta 0:08:41
+epoch [50/50] batch [10/500] time 0.887 (0.971) data 0.000 (0.067) loss 1.0693 (0.9325) acc 71.8750 (75.0000) lr 7.8853e-06 eta 0:07:55
+epoch [50/50] batch [15/500] time 0.871 (0.940) data 0.000 (0.045) loss 0.9917 (0.9297) acc 78.1250 (73.7500) lr 7.8853e-06 eta 0:07:36
+epoch [50/50] batch [20/500] time 0.867 (0.928) data 0.000 (0.034) loss 1.7197 (0.9906) acc 68.7500 (73.9062) lr 7.8853e-06 eta 0:07:25
+epoch [50/50] batch [25/500] time 0.876 (0.916) data 0.000 (0.027) loss 0.9053 (0.9874) acc 71.8750 (74.5000) lr 7.8853e-06 eta 0:07:14
+epoch [50/50] batch [30/500] time 0.899 (0.911) data 0.000 (0.023) loss 1.6289 (1.0049) acc 62.5000 (74.1667) lr 7.8853e-06 eta 0:07:08
+epoch [50/50] batch [35/500] time 0.897 (0.909) data 0.000 (0.019) loss 0.9829 (0.9972) acc 71.8750 (74.3750) lr 7.8853e-06 eta 0:07:02
+epoch [50/50] batch [40/500] time 0.882 (0.908) data 0.000 (0.017) loss 0.6396 (0.9820) acc 81.2500 (74.4531) lr 7.8853e-06 eta 0:06:57
+epoch [50/50] batch [45/500] time 0.883 (0.904) data 0.000 (0.015) loss 0.6328 (0.9923) acc 84.3750 (74.5833) lr 7.8853e-06 eta 0:06:51
+epoch [50/50] batch [50/500] time 0.885 (0.903) data 0.000 (0.014) loss 0.8911 (0.9981) acc 68.7500 (74.4375) lr 7.8853e-06 eta 0:06:46
+epoch [50/50] batch [55/500] time 0.875 (0.901) data 0.000 (0.012) loss 1.3350 (1.0012) acc 62.5000 (74.5455) lr 7.8853e-06 eta 0:06:40
+epoch [50/50] batch [60/500] time 0.900 (0.900) data 0.000 (0.011) loss 1.4766 (0.9975) acc 71.8750 (74.6875) lr 7.8853e-06 eta 0:06:35
+epoch [50/50] batch [65/500] time 0.870 (0.899) data 0.000 (0.011) loss 0.7612 (0.9927) acc 81.2500 (74.6154) lr 7.8853e-06 eta 0:06:30
+epoch [50/50] batch [70/500] time 0.854 (0.897) data 0.000 (0.010) loss 0.8184 (0.9929) acc 65.6250 (74.5089) lr 7.8853e-06 eta 0:06:25
+epoch [50/50] batch [75/500] time 0.903 (0.896) data 0.000 (0.009) loss 1.5127 (1.0183) acc 65.6250 (74.2500) lr 7.8853e-06 eta 0:06:20
+epoch [50/50] batch [80/500] time 0.892 (0.897) data 0.000 (0.009) loss 1.4502 (1.0454) acc 62.5000 (73.6328) lr 7.8853e-06 eta 0:06:16
+epoch [50/50] batch [85/500] time 0.861 (0.896) data 0.000 (0.008) loss 1.3350 (1.0501) acc 62.5000 (73.6029) lr 7.8853e-06 eta 0:06:11
+epoch [50/50] batch [90/500] time 0.872 (0.894) data 0.000 (0.008) loss 1.1748 (1.0431) acc 65.6250 (73.5764) lr 7.8853e-06 eta 0:06:06
+epoch [50/50] batch [95/500] time 0.886 (0.894) data 0.000 (0.007) loss 0.9097 (1.0388) acc 81.2500 (73.8158) lr 7.8853e-06 eta 0:06:02
+epoch [50/50] batch [100/500] time 0.873 (0.893) data 0.000 (0.007) loss 1.3545 (1.0357) acc 75.0000 (74.0000) lr 7.8853e-06 eta 0:05:57
+epoch [50/50] batch [105/500] time 0.872 (0.893) data 0.000 (0.007) loss 0.7686 (1.0253) acc 78.1250 (74.2262) lr 7.8853e-06 eta 0:05:52
+epoch [50/50] batch [110/500] time 0.910 (0.892) data 0.000 (0.006) loss 1.2480 (1.0244) acc 59.3750 (73.9489) lr 7.8853e-06 eta 0:05:48
+epoch [50/50] batch [115/500] time 0.859 (0.892) data 0.000 (0.006) loss 1.0820 (1.0261) acc 71.8750 (73.8315) lr 7.8853e-06 eta 0:05:43
+epoch [50/50] batch [120/500] time 0.890 (0.891) data 0.000 (0.006) loss 0.8896 (1.0276) acc 75.0000 (73.7240) lr 7.8853e-06 eta 0:05:38
+epoch [50/50] batch [125/500] time 0.908 (0.891) data 0.000 (0.006) loss 1.7373 (1.0227) acc 62.5000 (73.8000) lr 7.8853e-06 eta 0:05:34
+epoch [50/50] batch [130/500] time 0.913 (0.891) data 0.000 (0.005) loss 1.0898 (1.0181) acc 62.5000 (73.7981) lr 7.8853e-06 eta 0:05:29
+epoch [50/50] batch [135/500] time 0.884 (0.892) data 0.000 (0.005) loss 1.3730 (1.0206) acc 71.8750 (73.8194) lr 7.8853e-06 eta 0:05:25
+epoch [50/50] batch [140/500] time 0.920 (0.892) data 0.000 (0.005) loss 1.4043 (1.0192) acc 56.2500 (73.7723) lr 7.8853e-06 eta 0:05:21
+epoch [50/50] batch [145/500] time 0.971 (0.892) data 0.000 (0.005) loss 1.1533 (1.0169) acc 71.8750 (73.8362) lr 7.8853e-06 eta 0:05:16
+epoch [50/50] batch [150/500] time 0.871 (0.892) data 0.000 (0.005) loss 1.4072 (1.0204) acc 68.7500 (73.7917) lr 7.8853e-06 eta 0:05:12
+epoch [50/50] batch [155/500] time 0.881 (0.892) data 0.000 (0.005) loss 1.2080 (1.0189) acc 71.8750 (73.8710) lr 7.8853e-06 eta 0:05:07
+epoch [50/50] batch [160/500] time 0.890 (0.891) data 0.000 (0.004) loss 0.4243 (1.0184) acc 87.5000 (73.8281) lr 7.8853e-06 eta 0:05:03
+epoch [50/50] batch [165/500] time 0.903 (0.891) data 0.000 (0.004) loss 1.1455 (1.0191) acc 75.0000 (73.9394) lr 7.8853e-06 eta 0:04:58
+epoch [50/50] batch [170/500] time 0.886 (0.891) data 0.000 (0.004) loss 1.5498 (1.0167) acc 68.7500 (74.0625) lr 7.8853e-06 eta 0:04:54
+epoch [50/50] batch [175/500] time 0.853 (0.891) data 0.000 (0.004) loss 0.7773 (1.0140) acc 81.2500 (74.1250) lr 7.8853e-06 eta 0:04:49
+epoch [50/50] batch [180/500] time 0.903 (0.891) data 0.000 (0.004) loss 0.6875 (1.0130) acc 81.2500 (74.1493) lr 7.8853e-06 eta 0:04:45
+epoch [50/50] batch [185/500] time 0.914 (0.892) data 0.000 (0.004) loss 1.0234 (1.0139) acc 78.1250 (74.1216) lr 7.8853e-06 eta 0:04:40
+epoch [50/50] batch [190/500] time 0.877 (0.891) data 0.000 (0.004) loss 1.1875 (1.0177) acc 75.0000 (74.0461) lr 7.8853e-06 eta 0:04:36
+epoch [50/50] batch [195/500] time 0.877 (0.891) data 0.000 (0.004) loss 1.1182 (1.0159) acc 78.1250 (74.1506) lr 7.8853e-06 eta 0:04:31
+epoch [50/50] batch [200/500] time 0.877 (0.891) data 0.000 (0.004) loss 1.3516 (1.0211) acc 59.3750 (73.9219) lr 7.8853e-06 eta 0:04:27
+epoch [50/50] batch [205/500] time 0.870 (0.890) data 0.000 (0.003) loss 0.7129 (1.0149) acc 78.1250 (74.0396) lr 7.8853e-06 eta 0:04:22
+epoch [50/50] batch [210/500] time 0.851 (0.890) data 0.001 (0.003) loss 0.9893 (1.0168) acc 75.0000 (74.0327) lr 7.8853e-06 eta 0:04:18
+epoch [50/50] batch [215/500] time 0.889 (0.890) data 0.000 (0.003) loss 0.6431 (1.0106) acc 84.3750 (74.1424) lr 7.8853e-06 eta 0:04:13
+epoch [50/50] batch [220/500] time 0.863 (0.889) data 0.000 (0.003) loss 1.0957 (1.0065) acc 65.6250 (74.2045) lr 7.8853e-06 eta 0:04:09
+epoch [50/50] batch [225/500] time 0.899 (0.889) data 0.000 (0.003) loss 0.9448 (1.0081) acc 75.0000 (74.1389) lr 7.8853e-06 eta 0:04:04
+epoch [50/50] batch [230/500] time 0.846 (0.889) data 0.000 (0.003) loss 0.8906 (1.0052) acc 71.8750 (74.1712) lr 7.8853e-06 eta 0:04:00
+epoch [50/50] batch [235/500] time 0.892 (0.889) data 0.000 (0.003) loss 1.6758 (1.0093) acc 53.1250 (74.1356) lr 7.8853e-06 eta 0:03:55
+epoch [50/50] batch [240/500] time 0.893 (0.889) data 0.000 (0.003) loss 1.2080 (1.0150) acc 71.8750 (74.0625) lr 7.8853e-06 eta 0:03:51
+epoch [50/50] batch [245/500] time 0.888 (0.890) data 0.000 (0.003) loss 0.9756 (1.0163) acc 81.2500 (74.0561) lr 7.8853e-06 eta 0:03:46
+epoch [50/50] batch [250/500] time 0.912 (0.890) data 0.000 (0.003) loss 1.0273 (1.0184) acc 68.7500 (74.0250) lr 7.8853e-06 eta 0:03:42
+epoch [50/50] batch [255/500] time 0.857 (0.889) data 0.000 (0.003) loss 1.0234 (1.0222) acc 75.0000 (73.9951) lr 7.8853e-06 eta 0:03:37
+epoch [50/50] batch [260/500] time 0.892 (0.889) data 0.000 (0.003) loss 0.4268 (1.0221) acc 90.6250 (73.9663) lr 7.8853e-06 eta 0:03:33
+epoch [50/50] batch [265/500] time 0.887 (0.889) data 0.000 (0.003) loss 0.6875 (1.0206) acc 84.3750 (74.0920) lr 7.8853e-06 eta 0:03:28
+epoch [50/50] batch [270/500] time 0.899 (0.889) data 0.000 (0.003) loss 0.6611 (1.0183) acc 87.5000 (74.2130) lr 7.8853e-06 eta 0:03:24
+epoch [50/50] batch [275/500] time 0.872 (0.889) data 0.000 (0.003) loss 1.0713 (1.0221) acc 65.6250 (74.1818) lr 7.8853e-06 eta 0:03:19
+epoch [50/50] batch [280/500] time 0.903 (0.889) data 0.000 (0.003) loss 0.6704 (1.0214) acc 81.2500 (74.2411) lr 7.8853e-06 eta 0:03:15
+epoch [50/50] batch [285/500] time 0.868 (0.889) data 0.000 (0.003) loss 0.7622 (1.0213) acc 81.2500 (74.1886) lr 7.8853e-06 eta 0:03:11
+epoch [50/50] batch [290/500] time 0.863 (0.889) data 0.000 (0.003) loss 0.7261 (1.0186) acc 71.8750 (74.2026) lr 7.8853e-06 eta 0:03:06
+epoch [50/50] batch [295/500] time 0.879 (0.889) data 0.000 (0.003) loss 1.0146 (1.0186) acc 68.7500 (74.2161) lr 7.8853e-06 eta 0:03:02
+epoch [50/50] batch [300/500] time 0.892 (0.889) data 0.000 (0.002) loss 1.1055 (1.0239) acc 71.8750 (74.1042) lr 7.8853e-06 eta 0:02:57
+epoch [50/50] batch [305/500] time 0.854 (0.889) data 0.000 (0.002) loss 0.8281 (1.0263) acc 68.7500 (74.0676) lr 7.8853e-06 eta 0:02:53
+epoch [50/50] batch [310/500] time 0.892 (0.889) data 0.000 (0.002) loss 1.2998 (1.0282) acc 65.6250 (74.0020) lr 7.8853e-06 eta 0:02:48
+epoch [50/50] batch [315/500] time 0.904 (0.888) data 0.000 (0.002) loss 0.9629 (1.0276) acc 75.0000 (74.0179) lr 7.8853e-06 eta 0:02:44
+epoch [50/50] batch [320/500] time 0.892 (0.888) data 0.000 (0.002) loss 1.6641 (1.0290) acc 56.2500 (73.9746) lr 7.8853e-06 eta 0:02:39
+epoch [50/50] batch [325/500] time 0.889 (0.888) data 0.000 (0.002) loss 0.8501 (1.0279) acc 68.7500 (73.9904) lr 7.8853e-06 eta 0:02:35
+epoch [50/50] batch [330/500] time 0.882 (0.888) data 0.000 (0.002) loss 1.2471 (1.0281) acc 65.6250 (73.9299) lr 7.8853e-06 eta 0:02:31
+epoch [50/50] batch [335/500] time 0.882 (0.888) data 0.000 (0.002) loss 0.6079 (1.0250) acc 84.3750 (74.0299) lr 7.8853e-06 eta 0:02:26
+epoch [50/50] batch [340/500] time 0.869 (0.888) data 0.000 (0.002) loss 1.1094 (1.0276) acc 75.0000 (74.0257) lr 7.8853e-06 eta 0:02:22
+epoch [50/50] batch [345/500] time 0.897 (0.888) data 0.000 (0.002) loss 0.6455 (1.0262) acc 78.1250 (74.0761) lr 7.8853e-06 eta 0:02:17
+epoch [50/50] batch [350/500] time 0.887 (0.888) data 0.000 (0.002) loss 0.8701 (1.0293) acc 78.1250 (74.0625) lr 7.8853e-06 eta 0:02:13
+epoch [50/50] batch [355/500] time 0.888 (0.888) data 0.000 (0.002) loss 1.5654 (1.0306) acc 71.8750 (74.0669) lr 7.8853e-06 eta 0:02:08
+epoch [50/50] batch [360/500] time 0.893 (0.888) data 0.000 (0.002) loss 1.3486 (1.0299) acc 62.5000 (74.0365) lr 7.8853e-06 eta 0:02:04
+epoch [50/50] batch [365/500] time 0.892 (0.888) data 0.000 (0.002) loss 0.9985 (1.0282) acc 78.1250 (74.0582) lr 7.8853e-06 eta 0:01:59
+epoch [50/50] batch [370/500] time 0.883 (0.888) data 0.000 (0.002) loss 1.1602 (1.0284) acc 75.0000 (74.0118) lr 7.8853e-06 eta 0:01:55
+epoch [50/50] batch [375/500] time 0.849 (0.888) data 0.000 (0.002) loss 1.2012 (1.0306) acc 71.8750 (74.0083) lr 7.8853e-06 eta 0:01:50
+epoch [50/50] batch [380/500] time 0.868 (0.888) data 0.000 (0.002) loss 1.1650 (1.0297) acc 75.0000 (74.0296) lr 7.8853e-06 eta 0:01:46
+epoch [50/50] batch [385/500] time 0.902 (0.888) data 0.000 (0.002) loss 1.1270 (1.0315) acc 75.0000 (73.9854) lr 7.8853e-06 eta 0:01:42
+epoch [50/50] batch [390/500] time 0.896 (0.888) data 0.000 (0.002) loss 0.8828 (1.0311) acc 75.0000 (73.9904) lr 7.8853e-06 eta 0:01:37
+epoch [50/50] batch [395/500] time 0.896 (0.888) data 0.000 (0.002) loss 0.7803 (1.0304) acc 75.0000 (74.0032) lr 7.8853e-06 eta 0:01:33
+epoch [50/50] batch [400/500] time 0.885 (0.888) data 0.000 (0.002) loss 0.9004 (1.0315) acc 81.2500 (74.0000) lr 7.8853e-06 eta 0:01:28
+epoch [50/50] batch [405/500] time 0.884 (0.888) data 0.000 (0.002) loss 0.9351 (1.0298) acc 71.8750 (74.0046) lr 7.8853e-06 eta 0:01:24
+epoch [50/50] batch [410/500] time 0.871 (0.888) data 0.000 (0.002) loss 0.9648 (1.0270) acc 71.8750 (74.0473) lr 7.8853e-06 eta 0:01:19
+epoch [50/50] batch [415/500] time 0.862 (0.888) data 0.000 (0.002) loss 0.7793 (1.0267) acc 81.2500 (74.0889) lr 7.8853e-06 eta 0:01:15
+epoch [50/50] batch [420/500] time 0.890 (0.888) data 0.000 (0.002) loss 0.6846 (1.0244) acc 78.1250 (74.1295) lr 7.8853e-06 eta 0:01:11
+epoch [50/50] batch [425/500] time 0.866 (0.888) data 0.000 (0.002) loss 1.3350 (1.0264) acc 71.8750 (74.0956) lr 7.8853e-06 eta 0:01:06
+epoch [50/50] batch [430/500] time 0.883 (0.888) data 0.000 (0.002) loss 1.0352 (1.0279) acc 75.0000 (74.0698) lr 7.8853e-06 eta 0:01:02
+epoch [50/50] batch [435/500] time 0.910 (0.888) data 0.000 (0.002) loss 1.4219 (1.0277) acc 71.8750 (74.1020) lr 7.8853e-06 eta 0:00:57
+epoch [50/50] batch [440/500] time 0.912 (0.888) data 0.000 (0.002) loss 0.8687 (1.0267) acc 81.2500 (74.1548) lr 7.8853e-06 eta 0:00:53
+epoch [50/50] batch [445/500] time 0.887 (0.888) data 0.000 (0.002) loss 0.7344 (1.0252) acc 78.1250 (74.1643) lr 7.8853e-06 eta 0:00:48
+epoch [50/50] batch [450/500] time 0.921 (0.888) data 0.000 (0.002) loss 0.6040 (1.0216) acc 84.3750 (74.2431) lr 7.8853e-06 eta 0:00:44
+epoch [50/50] batch [455/500] time 0.896 (0.888) data 0.000 (0.002) loss 0.8164 (1.0207) acc 78.1250 (74.2445) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [460/500] time 0.893 (0.888) data 0.000 (0.002) loss 0.8198 (1.0197) acc 75.0000 (74.2459) lr 7.8853e-06 eta 0:00:35
+epoch [50/50] batch [465/500] time 0.882 (0.888) data 0.000 (0.002) loss 0.8301 (1.0183) acc 75.0000 (74.2540) lr 7.8853e-06 eta 0:00:31
+epoch [50/50] batch [470/500] time 0.890 (0.888) data 0.000 (0.002) loss 0.7725 (1.0183) acc 81.2500 (74.2553) lr 7.8853e-06 eta 0:00:26
+epoch [50/50] batch [475/500] time 0.857 (0.888) data 0.000 (0.002) loss 1.0381 (1.0183) acc 71.8750 (74.2697) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [480/500] time 0.896 (0.888) data 0.000 (0.002) loss 0.8540 (1.0184) acc 84.3750 (74.2643) lr 7.8853e-06 eta 0:00:17
+epoch [50/50] batch [485/500] time 0.883 (0.888) data 0.000 (0.002) loss 0.9023 (1.0185) acc 81.2500 (74.2461) lr 7.8853e-06 eta 0:00:13
+epoch [50/50] batch [490/500] time 0.882 (0.888) data 0.000 (0.002) loss 1.0029 (1.0191) acc 81.2500 (74.2921) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [495/500] time 0.879 (0.888) data 0.000 (0.002) loss 0.8647 (1.0191) acc 78.1250 (74.3245) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [500/500] time 0.857 (0.888) data 0.000 (0.002) loss 0.9663 (1.0188) acc 78.1250 (74.3375) lr 1.9733e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 38,987
+* accuracy: 78.0%
+* error: 22.0%
+* macro_f1: 77.5%
+Elapsed: 6:12:36
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
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index 00000000..a9d493d3
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@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698970684.ckb-gpu-a.1343964.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698970684.ckb-gpu-a.1343964.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/log.txt
new file mode 100644
index 00000000..88627ea0
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/log.txt
@@ -0,0 +1,5342 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.16.3
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 525.125.06
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 43 bits physical, 48 bits virtual
+CPU(s): 256
+On-line CPU(s) list: 0-255
+Thread(s) per core: 2
+Core(s) per socket: 64
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: AuthenticAMD
+CPU family: 23
+Model: 49
+Model name: AMD EPYC 7H12 64-Core Processor
+Stepping: 0
+Frequency boost: enabled
+CPU MHz: 1499.981
+CPU max MHz: 2600.0000
+CPU min MHz: 1500.0000
+BogoMIPS: 5200.20
+Virtualization: AMD-V
+L1d cache: 4 MiB
+L1i cache: 4 MiB
+L2 cache: 64 MiB
+L3 cache: 512 MiB
+NUMA node0 CPU(s): 0-63,128-191
+NUMA node1 CPU(s): 64-127,192-255
+Vulnerability Gather data sampling: Not affected
+Vulnerability Itlb multihit: Not affected
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Mmio stale data: Not affected
+Vulnerability Retbleed: Vulnerable
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Not affected
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca sme sev sev_es
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/50] batch [5/500] time 0.886 (1.684) data 0.000 (0.133) loss 2.3535 (2.9648) acc 50.0000 (42.5000) lr 1.0000e-05 eta 11:41:34
+epoch [1/50] batch [10/500] time 0.883 (1.278) data 0.000 (0.067) loss 2.1328 (2.6693) acc 59.3750 (47.8125) lr 1.0000e-05 eta 8:52:20
+epoch [1/50] batch [15/500] time 0.870 (1.139) data 0.000 (0.045) loss 1.8613 (2.4141) acc 46.8750 (50.0000) lr 1.0000e-05 eta 7:54:18
+epoch [1/50] batch [20/500] time 0.886 (1.076) data 0.000 (0.033) loss 2.0859 (2.3501) acc 46.8750 (50.0000) lr 1.0000e-05 eta 7:27:56
+epoch [1/50] batch [25/500] time 0.887 (1.038) data 0.000 (0.027) loss 1.4404 (2.2324) acc 71.8750 (52.1250) lr 1.0000e-05 eta 7:12:08
+epoch [1/50] batch [30/500] time 0.881 (1.013) data 0.000 (0.022) loss 1.3496 (2.1673) acc 65.6250 (53.2292) lr 1.0000e-05 eta 7:01:28
+epoch [1/50] batch [35/500] time 0.874 (0.992) data 0.000 (0.019) loss 1.2822 (2.1096) acc 68.7500 (53.9286) lr 1.0000e-05 eta 6:52:57
+epoch [1/50] batch [40/500] time 0.879 (0.979) data 0.000 (0.017) loss 2.2305 (2.0646) acc 50.0000 (54.6094) lr 1.0000e-05 eta 6:47:08
+epoch [1/50] batch [45/500] time 0.883 (0.969) data 0.000 (0.015) loss 1.4131 (1.9933) acc 53.1250 (55.8333) lr 1.0000e-05 eta 6:42:57
+epoch [1/50] batch [50/500] time 0.891 (0.962) data 0.000 (0.014) loss 1.7188 (1.9862) acc 56.2500 (55.6875) lr 1.0000e-05 eta 6:39:59
+epoch [1/50] batch [55/500] time 0.868 (0.955) data 0.000 (0.012) loss 2.0117 (1.9549) acc 53.1250 (56.1932) lr 1.0000e-05 eta 6:37:11
+epoch [1/50] batch [60/500] time 0.885 (0.949) data 0.000 (0.011) loss 1.7812 (1.9369) acc 62.5000 (56.6146) lr 1.0000e-05 eta 6:34:30
+epoch [1/50] batch [65/500] time 0.871 (0.944) data 0.000 (0.010) loss 0.9346 (1.8873) acc 71.8750 (57.4519) lr 1.0000e-05 eta 6:32:25
+epoch [1/50] batch [70/500] time 0.856 (0.938) data 0.000 (0.010) loss 1.4199 (1.8519) acc 65.6250 (57.9464) lr 1.0000e-05 eta 6:29:53
+epoch [1/50] batch [75/500] time 0.870 (0.934) data 0.000 (0.009) loss 1.8916 (1.8255) acc 59.3750 (58.6667) lr 1.0000e-05 eta 6:28:08
+epoch [1/50] batch [80/500] time 0.875 (0.930) data 0.000 (0.009) loss 1.1973 (1.8047) acc 56.2500 (59.0234) lr 1.0000e-05 eta 6:26:18
+epoch [1/50] batch [85/500] time 0.851 (0.927) data 0.000 (0.008) loss 1.7314 (1.8044) acc 59.3750 (58.6765) lr 1.0000e-05 eta 6:24:58
+epoch [1/50] batch [90/500] time 0.886 (0.925) data 0.000 (0.008) loss 1.2559 (1.7867) acc 56.2500 (58.8889) lr 1.0000e-05 eta 6:23:58
+epoch [1/50] batch [95/500] time 0.874 (0.922) data 0.000 (0.007) loss 1.3574 (1.7706) acc 71.8750 (59.2105) lr 1.0000e-05 eta 6:22:53
+epoch [1/50] batch [100/500] time 0.838 (0.920) data 0.000 (0.007) loss 1.1436 (1.7552) acc 68.7500 (59.5000) lr 1.0000e-05 eta 6:21:45
+epoch [1/50] batch [105/500] time 0.870 (0.918) data 0.000 (0.007) loss 1.5820 (1.7363) acc 68.7500 (59.9107) lr 1.0000e-05 eta 6:20:42
+epoch [1/50] batch [110/500] time 0.883 (0.916) data 0.000 (0.006) loss 1.3232 (1.7167) acc 78.1250 (60.4261) lr 1.0000e-05 eta 6:19:57
+epoch [1/50] batch [115/500] time 0.876 (0.914) data 0.000 (0.006) loss 1.1436 (1.7071) acc 71.8750 (60.7065) lr 1.0000e-05 eta 6:19:14
+epoch [1/50] batch [120/500] time 0.888 (0.913) data 0.000 (0.006) loss 1.3359 (1.6979) acc 59.3750 (60.8854) lr 1.0000e-05 eta 6:18:43
+epoch [1/50] batch [125/500] time 0.856 (0.912) data 0.000 (0.006) loss 1.1484 (1.6866) acc 68.7500 (61.1750) lr 1.0000e-05 eta 6:18:00
+epoch [1/50] batch [130/500] time 0.871 (0.910) data 0.000 (0.005) loss 1.6211 (1.6868) acc 62.5000 (61.2500) lr 1.0000e-05 eta 6:17:22
+epoch [1/50] batch [135/500] time 0.879 (0.909) data 0.000 (0.005) loss 1.7900 (1.6813) acc 56.2500 (61.4815) lr 1.0000e-05 eta 6:16:52
+epoch [1/50] batch [140/500] time 0.863 (0.908) data 0.000 (0.005) loss 1.6768 (1.6732) acc 59.3750 (61.5625) lr 1.0000e-05 eta 6:16:17
+epoch [1/50] batch [145/500] time 0.912 (0.907) data 0.000 (0.005) loss 1.7451 (1.6701) acc 62.5000 (61.6164) lr 1.0000e-05 eta 6:15:46
+epoch [1/50] batch [150/500] time 0.888 (0.907) data 0.000 (0.005) loss 1.4463 (1.6643) acc 62.5000 (61.8125) lr 1.0000e-05 eta 6:15:26
+epoch [1/50] batch [155/500] time 0.906 (0.906) data 0.000 (0.005) loss 1.2051 (1.6573) acc 68.7500 (61.8548) lr 1.0000e-05 eta 6:15:13
+epoch [1/50] batch [160/500] time 0.876 (0.906) data 0.000 (0.004) loss 1.4961 (1.6522) acc 68.7500 (61.9922) lr 1.0000e-05 eta 6:14:54
+epoch [1/50] batch [165/500] time 0.914 (0.905) data 0.000 (0.004) loss 1.2881 (1.6441) acc 53.1250 (62.0455) lr 1.0000e-05 eta 6:14:42
+epoch [1/50] batch [170/500] time 0.895 (0.906) data 0.000 (0.004) loss 1.9189 (1.6441) acc 62.5000 (62.0588) lr 1.0000e-05 eta 6:14:48
+epoch [1/50] batch [175/500] time 0.867 (0.905) data 0.000 (0.004) loss 0.9766 (1.6301) acc 75.0000 (62.1964) lr 1.0000e-05 eta 6:14:27
+epoch [1/50] batch [180/500] time 0.875 (0.904) data 0.000 (0.004) loss 1.6416 (1.6304) acc 62.5000 (62.3958) lr 1.0000e-05 eta 6:14:02
+epoch [1/50] batch [185/500] time 0.900 (0.904) data 0.000 (0.004) loss 1.8340 (1.6263) acc 62.5000 (62.5845) lr 1.0000e-05 eta 6:13:52
+epoch [1/50] batch [190/500] time 0.883 (0.903) data 0.000 (0.004) loss 1.7725 (1.6252) acc 71.8750 (62.6974) lr 1.0000e-05 eta 6:13:33
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+epoch [50/50] batch [240/500] time 0.883 (0.889) data 0.000 (0.004) loss 0.9966 (1.0230) acc 68.7500 (74.4141) lr 7.8853e-06 eta 0:03:51
+epoch [50/50] batch [245/500] time 0.885 (0.889) data 0.000 (0.003) loss 0.8672 (1.0270) acc 75.0000 (74.2602) lr 7.8853e-06 eta 0:03:46
+epoch [50/50] batch [250/500] time 0.899 (0.889) data 0.000 (0.003) loss 0.9111 (1.0292) acc 71.8750 (74.1875) lr 7.8853e-06 eta 0:03:42
+epoch [50/50] batch [255/500] time 0.880 (0.888) data 0.000 (0.003) loss 0.5938 (1.0275) acc 90.6250 (74.1912) lr 7.8853e-06 eta 0:03:37
+epoch [50/50] batch [260/500] time 0.976 (0.889) data 0.000 (0.003) loss 0.5479 (1.0235) acc 81.2500 (74.3029) lr 7.8853e-06 eta 0:03:33
+epoch [50/50] batch [265/500] time 0.886 (0.888) data 0.000 (0.003) loss 0.7329 (1.0197) acc 81.2500 (74.4222) lr 7.8853e-06 eta 0:03:28
+epoch [50/50] batch [270/500] time 0.852 (0.888) data 0.000 (0.003) loss 0.6299 (1.0176) acc 84.3750 (74.5602) lr 7.8853e-06 eta 0:03:24
+epoch [50/50] batch [275/500] time 0.906 (0.889) data 0.000 (0.003) loss 1.2500 (1.0187) acc 65.6250 (74.5000) lr 7.8853e-06 eta 0:03:19
+epoch [50/50] batch [280/500] time 0.859 (0.889) data 0.000 (0.003) loss 0.8413 (1.0204) acc 78.1250 (74.4420) lr 7.8853e-06 eta 0:03:15
+epoch [50/50] batch [285/500] time 0.900 (0.889) data 0.000 (0.003) loss 0.9761 (1.0197) acc 68.7500 (74.4298) lr 7.8853e-06 eta 0:03:11
+epoch [50/50] batch [290/500] time 0.881 (0.889) data 0.000 (0.003) loss 1.2617 (1.0203) acc 75.0000 (74.4720) lr 7.8853e-06 eta 0:03:06
+epoch [50/50] batch [295/500] time 0.888 (0.889) data 0.000 (0.003) loss 0.7168 (1.0192) acc 84.3750 (74.4809) lr 7.8853e-06 eta 0:03:02
+epoch [50/50] batch [300/500] time 0.878 (0.888) data 0.000 (0.003) loss 0.3928 (1.0183) acc 93.7500 (74.5417) lr 7.8853e-06 eta 0:02:57
+epoch [50/50] batch [305/500] time 0.865 (0.889) data 0.000 (0.003) loss 1.2617 (1.0221) acc 62.5000 (74.4057) lr 7.8853e-06 eta 0:02:53
+epoch [50/50] batch [310/500] time 0.878 (0.889) data 0.000 (0.003) loss 1.2236 (1.0242) acc 59.3750 (74.2944) lr 7.8853e-06 eta 0:02:48
+epoch [50/50] batch [315/500] time 0.880 (0.889) data 0.000 (0.003) loss 0.6465 (1.0224) acc 84.3750 (74.3254) lr 7.8853e-06 eta 0:02:44
+epoch [50/50] batch [320/500] time 0.866 (0.888) data 0.000 (0.003) loss 1.1729 (1.0246) acc 75.0000 (74.3164) lr 7.8853e-06 eta 0:02:39
+epoch [50/50] batch [325/500] time 0.910 (0.888) data 0.000 (0.003) loss 1.4521 (1.0266) acc 75.0000 (74.2885) lr 7.8853e-06 eta 0:02:35
+epoch [50/50] batch [330/500] time 0.866 (0.888) data 0.000 (0.003) loss 1.4150 (1.0259) acc 71.8750 (74.2992) lr 7.8853e-06 eta 0:02:30
+epoch [50/50] batch [335/500] time 0.868 (0.888) data 0.000 (0.003) loss 1.0039 (1.0296) acc 71.8750 (74.2444) lr 7.8853e-06 eta 0:02:26
+epoch [50/50] batch [340/500] time 0.869 (0.888) data 0.000 (0.003) loss 0.7695 (1.0300) acc 84.3750 (74.2647) lr 7.8853e-06 eta 0:02:22
+epoch [50/50] batch [345/500] time 0.868 (0.888) data 0.000 (0.003) loss 0.8403 (1.0309) acc 75.0000 (74.1848) lr 7.8853e-06 eta 0:02:17
+epoch [50/50] batch [350/500] time 0.861 (0.887) data 0.000 (0.002) loss 1.0820 (1.0318) acc 75.0000 (74.1696) lr 7.8853e-06 eta 0:02:13
+epoch [50/50] batch [355/500] time 0.868 (0.887) data 0.000 (0.002) loss 1.2852 (1.0345) acc 71.8750 (74.0493) lr 7.8853e-06 eta 0:02:08
+epoch [50/50] batch [360/500] time 0.909 (0.887) data 0.000 (0.002) loss 0.8677 (1.0345) acc 78.1250 (74.0799) lr 7.8853e-06 eta 0:02:04
+epoch [50/50] batch [365/500] time 0.885 (0.887) data 0.000 (0.002) loss 1.1846 (1.0339) acc 62.5000 (74.0582) lr 7.8853e-06 eta 0:01:59
+epoch [50/50] batch [370/500] time 0.891 (0.887) data 0.000 (0.002) loss 0.9053 (1.0332) acc 81.2500 (74.1132) lr 7.8853e-06 eta 0:01:55
+epoch [50/50] batch [375/500] time 0.909 (0.888) data 0.000 (0.002) loss 0.7832 (1.0316) acc 81.2500 (74.1333) lr 7.8853e-06 eta 0:01:50
+epoch [50/50] batch [380/500] time 0.880 (0.888) data 0.000 (0.002) loss 0.9209 (1.0281) acc 78.1250 (74.2188) lr 7.8853e-06 eta 0:01:46
+epoch [50/50] batch [385/500] time 0.873 (0.888) data 0.000 (0.002) loss 1.0312 (1.0258) acc 68.7500 (74.2614) lr 7.8853e-06 eta 0:01:42
+epoch [50/50] batch [390/500] time 0.849 (0.887) data 0.001 (0.002) loss 1.1807 (1.0234) acc 71.8750 (74.3189) lr 7.8853e-06 eta 0:01:37
+epoch [50/50] batch [395/500] time 0.875 (0.887) data 0.000 (0.002) loss 1.1494 (1.0261) acc 68.7500 (74.2405) lr 7.8853e-06 eta 0:01:33
+epoch [50/50] batch [400/500] time 0.894 (0.887) data 0.000 (0.002) loss 1.0176 (1.0271) acc 78.1250 (74.2422) lr 7.8853e-06 eta 0:01:28
+epoch [50/50] batch [405/500] time 0.885 (0.888) data 0.000 (0.002) loss 1.4971 (1.0259) acc 65.6250 (74.3133) lr 7.8853e-06 eta 0:01:24
+epoch [50/50] batch [410/500] time 0.872 (0.888) data 0.000 (0.002) loss 0.7031 (1.0242) acc 75.0000 (74.3521) lr 7.8853e-06 eta 0:01:19
+epoch [50/50] batch [415/500] time 0.898 (0.888) data 0.000 (0.002) loss 1.0791 (1.0258) acc 65.6250 (74.3072) lr 7.8853e-06 eta 0:01:15
+epoch [50/50] batch [420/500] time 0.893 (0.887) data 0.000 (0.002) loss 0.9976 (1.0279) acc 62.5000 (74.3080) lr 7.8853e-06 eta 0:01:10
+epoch [50/50] batch [425/500] time 0.886 (0.887) data 0.000 (0.002) loss 0.4375 (1.0277) acc 90.6250 (74.3088) lr 7.8853e-06 eta 0:01:06
+epoch [50/50] batch [430/500] time 0.881 (0.887) data 0.000 (0.002) loss 1.4160 (1.0291) acc 65.6250 (74.2587) lr 7.8853e-06 eta 0:01:02
+epoch [50/50] batch [435/500] time 0.891 (0.887) data 0.000 (0.002) loss 0.9116 (1.0286) acc 78.1250 (74.2672) lr 7.8853e-06 eta 0:00:57
+epoch [50/50] batch [440/500] time 0.873 (0.887) data 0.000 (0.002) loss 1.0273 (1.0294) acc 71.8750 (74.2116) lr 7.8853e-06 eta 0:00:53
+epoch [50/50] batch [445/500] time 0.891 (0.887) data 0.000 (0.002) loss 1.0371 (1.0290) acc 68.7500 (74.2135) lr 7.8853e-06 eta 0:00:48
+epoch [50/50] batch [450/500] time 0.868 (0.887) data 0.000 (0.002) loss 0.5454 (1.0298) acc 84.3750 (74.2083) lr 7.8853e-06 eta 0:00:44
+epoch [50/50] batch [455/500] time 0.889 (0.887) data 0.000 (0.002) loss 1.7773 (1.0320) acc 62.5000 (74.1690) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [460/500] time 0.870 (0.887) data 0.000 (0.002) loss 0.9399 (1.0316) acc 68.7500 (74.1644) lr 7.8853e-06 eta 0:00:35
+epoch [50/50] batch [465/500] time 0.859 (0.887) data 0.000 (0.002) loss 1.4922 (1.0343) acc 65.6250 (74.0927) lr 7.8853e-06 eta 0:00:31
+epoch [50/50] batch [470/500] time 0.875 (0.887) data 0.000 (0.002) loss 0.8403 (1.0355) acc 78.1250 (74.1090) lr 7.8853e-06 eta 0:00:26
+epoch [50/50] batch [475/500] time 0.905 (0.887) data 0.000 (0.002) loss 1.0254 (1.0343) acc 78.1250 (74.1053) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [480/500] time 0.912 (0.887) data 0.000 (0.002) loss 0.9800 (1.0332) acc 68.7500 (74.1081) lr 7.8853e-06 eta 0:00:17
+epoch [50/50] batch [485/500] time 0.871 (0.887) data 0.000 (0.002) loss 1.5898 (1.0337) acc 65.6250 (74.0915) lr 7.8853e-06 eta 0:00:13
+epoch [50/50] batch [490/500] time 0.884 (0.887) data 0.000 (0.002) loss 0.6924 (1.0333) acc 84.3750 (74.1071) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [495/500] time 0.895 (0.887) data 0.000 (0.002) loss 0.6357 (1.0333) acc 84.3750 (74.1162) lr 7.8853e-06 eta 0:00:04
+epoch [50/50] batch [500/500] time 0.893 (0.887) data 0.000 (0.002) loss 0.9790 (1.0338) acc 78.1250 (74.0563) lr 1.9733e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 39,082
+* accuracy: 78.2%
+* error: 21.8%
+* macro_f1: 77.7%
+Elapsed: 6:12:38
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..da6abaad
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698993062.ckb-gpu-a.1686533.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698993062.ckb-gpu-a.1686533.0
new file mode 100644
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698993062.ckb-gpu-a.1686533.0 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..30b32f66
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.012
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/31] time 1.340 (2.159) data 0.000 (0.165) loss 3.2520 (3.2875) acc 34.3750 (34.3750) lr 1.0000e-05 eta 0:55:36
+epoch [1/50] batch [10/31] time 1.344 (1.752) data 0.000 (0.082) loss 3.3301 (3.0771) acc 34.3750 (36.2500) lr 1.0000e-05 eta 0:44:57
+epoch [1/50] batch [15/31] time 1.354 (1.618) data 0.000 (0.055) loss 2.5000 (2.8858) acc 50.0000 (40.2083) lr 1.0000e-05 eta 0:41:23
+epoch [1/50] batch [20/31] time 1.355 (1.552) data 0.000 (0.041) loss 3.4648 (2.8345) acc 34.3750 (42.1875) lr 1.0000e-05 eta 0:39:35
+epoch [1/50] batch [25/31] time 1.377 (1.515) data 0.000 (0.033) loss 2.0664 (2.6438) acc 65.6250 (45.3750) lr 1.0000e-05 eta 0:38:30
+epoch [1/50] batch [30/31] time 1.363 (1.489) data 0.000 (0.028) loss 2.5957 (2.5393) acc 43.7500 (46.8750) lr 1.0000e-05 eta 0:37:43
+epoch [2/50] batch [5/31] time 1.366 (1.539) data 0.000 (0.176) loss 1.4180 (1.7137) acc 62.5000 (62.5000) lr 2.0000e-03 eta 0:38:50
+epoch [2/50] batch [10/31] time 1.358 (1.450) data 0.000 (0.088) loss 1.3145 (1.6289) acc 62.5000 (62.5000) lr 2.0000e-03 eta 0:36:27
+epoch [2/50] batch [15/31] time 1.347 (1.421) data 0.000 (0.059) loss 1.9111 (1.5553) acc 68.7500 (64.7917) lr 2.0000e-03 eta 0:35:36
+epoch [2/50] batch [20/31] time 1.360 (1.403) data 0.000 (0.044) loss 1.3291 (1.5576) acc 62.5000 (64.5312) lr 2.0000e-03 eta 0:35:03
+epoch [2/50] batch [25/31] time 1.362 (1.394) data 0.000 (0.035) loss 1.2266 (1.5649) acc 65.6250 (64.5000) lr 2.0000e-03 eta 0:34:42
+epoch [2/50] batch [30/31] time 1.355 (1.386) data 0.000 (0.030) loss 1.4561 (1.5499) acc 71.8750 (64.8958) lr 2.0000e-03 eta 0:34:23
+epoch [3/50] batch [5/31] time 1.358 (1.533) data 0.000 (0.176) loss 1.3398 (1.4328) acc 78.1250 (68.1250) lr 1.9980e-03 eta 0:37:53
+epoch [3/50] batch [10/31] time 1.345 (1.444) data 0.000 (0.088) loss 1.1631 (1.4499) acc 68.7500 (67.8125) lr 1.9980e-03 eta 0:35:34
+epoch [3/50] batch [15/31] time 1.350 (1.414) data 0.000 (0.059) loss 0.9927 (1.3484) acc 68.7500 (67.5000) lr 1.9980e-03 eta 0:34:42
+epoch [3/50] batch [20/31] time 1.343 (1.401) data 0.000 (0.044) loss 1.4072 (1.3630) acc 68.7500 (67.3438) lr 1.9980e-03 eta 0:34:15
+epoch [3/50] batch [25/31] time 1.369 (1.393) data 0.000 (0.036) loss 1.1680 (1.3121) acc 75.0000 (68.1250) lr 1.9980e-03 eta 0:33:57
+epoch [3/50] batch [30/31] time 1.360 (1.386) data 0.000 (0.030) loss 1.7451 (1.3339) acc 46.8750 (67.6042) lr 1.9980e-03 eta 0:33:41
+epoch [4/50] batch [5/31] time 1.366 (1.546) data 0.000 (0.182) loss 0.8345 (1.1864) acc 75.0000 (73.1250) lr 1.9921e-03 eta 0:37:24
+epoch [4/50] batch [10/31] time 1.371 (1.453) data 0.001 (0.091) loss 0.8857 (1.1329) acc 65.6250 (71.8750) lr 1.9921e-03 eta 0:35:01
+epoch [4/50] batch [15/31] time 1.358 (1.420) data 0.000 (0.061) loss 0.9395 (1.1826) acc 81.2500 (71.2500) lr 1.9921e-03 eta 0:34:07
+epoch [4/50] batch [20/31] time 1.361 (1.405) data 0.000 (0.046) loss 1.1533 (1.2199) acc 71.8750 (70.9375) lr 1.9921e-03 eta 0:33:39
+epoch [4/50] batch [25/31] time 1.372 (1.395) data 0.001 (0.037) loss 1.0127 (1.2429) acc 75.0000 (69.6250) lr 1.9921e-03 eta 0:33:18
+epoch [4/50] batch [30/31] time 1.371 (1.390) data 0.000 (0.031) loss 1.0830 (1.2102) acc 75.0000 (69.8958) lr 1.9921e-03 eta 0:33:03
+epoch [5/50] batch [5/31] time 1.338 (1.608) data 0.000 (0.211) loss 1.0117 (0.9483) acc 78.1250 (75.6250) lr 1.9823e-03 eta 0:38:05
+epoch [5/50] batch [10/31] time 1.360 (1.482) data 0.000 (0.106) loss 1.1328 (1.0974) acc 68.7500 (73.7500) lr 1.9823e-03 eta 0:34:58
+epoch [5/50] batch [15/31] time 1.393 (1.444) data 0.000 (0.071) loss 0.5415 (1.0721) acc 81.2500 (73.5417) lr 1.9823e-03 eta 0:33:57
+epoch [5/50] batch [20/31] time 1.356 (1.424) data 0.000 (0.053) loss 0.9604 (1.1032) acc 81.2500 (73.4375) lr 1.9823e-03 eta 0:33:22
+epoch [5/50] batch [25/31] time 1.364 (1.413) data 0.000 (0.042) loss 1.7881 (1.1727) acc 68.7500 (72.7500) lr 1.9823e-03 eta 0:32:59
+epoch [5/50] batch [30/31] time 1.352 (1.404) data 0.000 (0.035) loss 1.3682 (1.2318) acc 68.7500 (70.9375) lr 1.9823e-03 eta 0:32:40
+epoch [6/50] batch [5/31] time 1.374 (1.561) data 0.000 (0.190) loss 1.1152 (1.0459) acc 78.1250 (73.7500) lr 1.9686e-03 eta 0:36:10
+epoch [6/50] batch [10/31] time 1.363 (1.459) data 0.000 (0.095) loss 1.8027 (1.1712) acc 59.3750 (73.7500) lr 1.9686e-03 eta 0:33:41
+epoch [6/50] batch [15/31] time 1.367 (1.425) data 0.000 (0.064) loss 1.0078 (1.1298) acc 78.1250 (73.9583) lr 1.9686e-03 eta 0:32:47
+epoch [6/50] batch [20/31] time 1.346 (1.416) data 0.000 (0.048) loss 0.8770 (1.1262) acc 81.2500 (72.9688) lr 1.9686e-03 eta 0:32:26
+epoch [6/50] batch [25/31] time 1.370 (1.405) data 0.000 (0.038) loss 1.1260 (1.1283) acc 71.8750 (72.3750) lr 1.9686e-03 eta 0:32:05
+epoch [6/50] batch [30/31] time 1.362 (1.397) data 0.000 (0.032) loss 1.1230 (1.1538) acc 71.8750 (72.3958) lr 1.9686e-03 eta 0:31:47
+epoch [7/50] batch [5/31] time 1.360 (1.553) data 0.000 (0.196) loss 1.2998 (1.3525) acc 71.8750 (66.2500) lr 1.9511e-03 eta 0:35:11
+epoch [7/50] batch [10/31] time 1.361 (1.461) data 0.000 (0.098) loss 0.8594 (1.1873) acc 71.8750 (70.3125) lr 1.9511e-03 eta 0:32:58
+epoch [7/50] batch [15/31] time 1.364 (1.428) data 0.000 (0.066) loss 0.8711 (1.1880) acc 71.8750 (70.2083) lr 1.9511e-03 eta 0:32:05
+epoch [7/50] batch [20/31] time 1.386 (1.413) data 0.000 (0.049) loss 1.2910 (1.1432) acc 68.7500 (71.0938) lr 1.9511e-03 eta 0:31:39
+epoch [7/50] batch [25/31] time 1.365 (1.405) data 0.000 (0.040) loss 1.2490 (1.1700) acc 68.7500 (70.2500) lr 1.9511e-03 eta 0:31:21
+epoch [7/50] batch [30/31] time 1.341 (1.402) data 0.000 (0.033) loss 1.9756 (1.1959) acc 59.3750 (70.1042) lr 1.9511e-03 eta 0:31:09
+epoch [8/50] batch [5/31] time 1.369 (1.539) data 0.000 (0.179) loss 1.3301 (1.0374) acc 65.6250 (71.8750) lr 1.9298e-03 eta 0:34:03
+epoch [8/50] batch [10/31] time 1.346 (1.446) data 0.000 (0.090) loss 1.2617 (1.1095) acc 71.8750 (70.3125) lr 1.9298e-03 eta 0:31:53
+epoch [8/50] batch [15/31] time 1.375 (1.416) data 0.000 (0.060) loss 0.8193 (1.0007) acc 75.0000 (72.9167) lr 1.9298e-03 eta 0:31:06
+epoch [8/50] batch [20/31] time 1.370 (1.401) data 0.000 (0.045) loss 0.6870 (1.0486) acc 78.1250 (72.5000) lr 1.9298e-03 eta 0:30:39
+epoch [8/50] batch [25/31] time 1.343 (1.392) data 0.000 (0.036) loss 1.0088 (1.0666) acc 78.1250 (71.8750) lr 1.9298e-03 eta 0:30:20
+epoch [8/50] batch [30/31] time 1.361 (1.387) data 0.000 (0.030) loss 0.8423 (1.0700) acc 78.1250 (72.0833) lr 1.9298e-03 eta 0:30:07
+epoch [9/50] batch [5/31] time 1.371 (1.546) data 0.000 (0.179) loss 1.7012 (1.0435) acc 62.5000 (73.7500) lr 1.9048e-03 eta 0:33:25
+epoch [9/50] batch [10/31] time 1.348 (1.466) data 0.000 (0.090) loss 1.9766 (1.1021) acc 65.6250 (74.0625) lr 1.9048e-03 eta 0:31:34
+epoch [9/50] batch [15/31] time 1.369 (1.430) data 0.000 (0.060) loss 1.6387 (1.1207) acc 62.5000 (72.9167) lr 1.9048e-03 eta 0:30:40
+epoch [9/50] batch [20/31] time 1.349 (1.413) data 0.000 (0.045) loss 1.4365 (1.1682) acc 53.1250 (70.6250) lr 1.9048e-03 eta 0:30:10
+epoch [9/50] batch [25/31] time 1.354 (1.402) data 0.000 (0.036) loss 1.3086 (1.1999) acc 68.7500 (70.0000) lr 1.9048e-03 eta 0:29:49
+epoch [9/50] batch [30/31] time 1.386 (1.396) data 0.000 (0.030) loss 1.9121 (1.2061) acc 56.2500 (70.1042) lr 1.9048e-03 eta 0:29:35
+epoch [10/50] batch [5/31] time 1.351 (1.549) data 0.001 (0.190) loss 0.9009 (1.0769) acc 81.2500 (73.1250) lr 1.8763e-03 eta 0:32:41
+epoch [10/50] batch [10/31] time 1.353 (1.451) data 0.000 (0.095) loss 1.5010 (1.0623) acc 62.5000 (73.7500) lr 1.8763e-03 eta 0:30:30
+epoch [10/50] batch [15/31] time 1.480 (1.428) data 0.000 (0.064) loss 1.5811 (1.1082) acc 65.6250 (72.9167) lr 1.8763e-03 eta 0:29:53
+epoch [10/50] batch [20/31] time 1.370 (1.411) data 0.001 (0.048) loss 1.0186 (1.0976) acc 78.1250 (73.1250) lr 1.8763e-03 eta 0:29:25
+epoch [10/50] batch [25/31] time 1.374 (1.403) data 0.000 (0.038) loss 1.0605 (1.0710) acc 78.1250 (73.5000) lr 1.8763e-03 eta 0:29:08
+epoch [10/50] batch [30/31] time 1.347 (1.396) data 0.000 (0.032) loss 0.9409 (1.0455) acc 81.2500 (73.3333) lr 1.8763e-03 eta 0:28:52
+epoch [11/50] batch [5/31] time 1.355 (1.553) data 0.000 (0.190) loss 1.1709 (1.1305) acc 62.5000 (68.1250) lr 1.8443e-03 eta 0:31:58
+epoch [11/50] batch [10/31] time 1.374 (1.457) data 0.000 (0.095) loss 1.6191 (1.1599) acc 65.6250 (69.3750) lr 1.8443e-03 eta 0:29:52
+epoch [11/50] batch [15/31] time 1.359 (1.423) data 0.000 (0.064) loss 1.0225 (1.0258) acc 84.3750 (73.9583) lr 1.8443e-03 eta 0:29:03
+epoch [11/50] batch [20/31] time 1.363 (1.409) data 0.000 (0.048) loss 0.9629 (1.0050) acc 65.6250 (74.8438) lr 1.8443e-03 eta 0:28:38
+epoch [11/50] batch [25/31] time 1.340 (1.404) data 0.000 (0.038) loss 1.5127 (1.0279) acc 62.5000 (74.2500) lr 1.8443e-03 eta 0:28:25
+epoch [11/50] batch [30/31] time 1.347 (1.397) data 0.000 (0.032) loss 1.1797 (1.0381) acc 81.2500 (73.7500) lr 1.8443e-03 eta 0:28:10
+epoch [12/50] batch [5/31] time 1.371 (1.539) data 0.000 (0.171) loss 1.4658 (1.2498) acc 78.1250 (71.2500) lr 1.8090e-03 eta 0:30:52
+epoch [12/50] batch [10/31] time 1.378 (1.451) data 0.000 (0.086) loss 1.0674 (1.0673) acc 71.8750 (74.0625) lr 1.8090e-03 eta 0:29:00
+epoch [12/50] batch [15/31] time 1.350 (1.420) data 0.000 (0.057) loss 0.8467 (1.0803) acc 71.8750 (73.3333) lr 1.8090e-03 eta 0:28:15
+epoch [12/50] batch [20/31] time 1.352 (1.402) data 0.000 (0.043) loss 0.8022 (1.0629) acc 81.2500 (74.5312) lr 1.8090e-03 eta 0:27:46
+epoch [12/50] batch [25/31] time 1.346 (1.393) data 0.000 (0.035) loss 1.0439 (1.1255) acc 78.1250 (72.7500) lr 1.8090e-03 eta 0:27:29
+epoch [12/50] batch [30/31] time 1.349 (1.387) data 0.000 (0.029) loss 0.7979 (1.1042) acc 71.8750 (72.3958) lr 1.8090e-03 eta 0:27:14
+epoch [13/50] batch [5/31] time 1.372 (1.576) data 0.000 (0.181) loss 1.2158 (1.2566) acc 78.1250 (69.3750) lr 1.7705e-03 eta 0:30:48
+epoch [13/50] batch [10/31] time 1.367 (1.472) data 0.000 (0.091) loss 1.1592 (1.1075) acc 68.7500 (71.8750) lr 1.7705e-03 eta 0:28:38
+epoch [13/50] batch [15/31] time 1.353 (1.436) data 0.000 (0.061) loss 0.8374 (1.0870) acc 75.0000 (73.7500) lr 1.7705e-03 eta 0:27:49
+epoch [13/50] batch [20/31] time 1.370 (1.419) data 0.000 (0.046) loss 0.9287 (1.0919) acc 75.0000 (72.8125) lr 1.7705e-03 eta 0:27:23
+epoch [13/50] batch [25/31] time 1.361 (1.407) data 0.000 (0.037) loss 2.1445 (1.1528) acc 62.5000 (71.5000) lr 1.7705e-03 eta 0:27:02
+epoch [13/50] batch [30/31] time 1.366 (1.399) data 0.000 (0.030) loss 0.6733 (1.1225) acc 84.3750 (72.0833) lr 1.7705e-03 eta 0:26:46
+epoch [14/50] batch [5/31] time 1.349 (1.618) data 0.000 (0.254) loss 0.7236 (0.8346) acc 68.7500 (75.0000) lr 1.7290e-03 eta 0:30:47
+epoch [14/50] batch [10/31] time 1.370 (1.487) data 0.000 (0.127) loss 0.6982 (0.8976) acc 81.2500 (76.5625) lr 1.7290e-03 eta 0:28:11
+epoch [14/50] batch [15/31] time 1.372 (1.447) data 0.000 (0.085) loss 1.0635 (1.0551) acc 75.0000 (74.5833) lr 1.7290e-03 eta 0:27:18
+epoch [14/50] batch [20/31] time 1.354 (1.435) data 0.000 (0.064) loss 0.9604 (1.0096) acc 68.7500 (74.6875) lr 1.7290e-03 eta 0:26:56
+epoch [14/50] batch [25/31] time 1.357 (1.420) data 0.000 (0.051) loss 0.4807 (1.0377) acc 87.5000 (73.8750) lr 1.7290e-03 eta 0:26:33
+epoch [14/50] batch [30/31] time 1.361 (1.411) data 0.000 (0.043) loss 0.6240 (1.0655) acc 81.2500 (73.7500) lr 1.7290e-03 eta 0:26:16
+epoch [15/50] batch [5/31] time 1.375 (1.539) data 0.000 (0.172) loss 0.5845 (0.8232) acc 81.2500 (78.7500) lr 1.6845e-03 eta 0:28:30
+epoch [15/50] batch [10/31] time 1.357 (1.451) data 0.000 (0.086) loss 0.8228 (0.9502) acc 65.6250 (75.6250) lr 1.6845e-03 eta 0:26:44
+epoch [15/50] batch [15/31] time 1.373 (1.423) data 0.000 (0.058) loss 1.1836 (0.9904) acc 68.7500 (73.9583) lr 1.6845e-03 eta 0:26:06
+epoch [15/50] batch [20/31] time 1.360 (1.405) data 0.000 (0.043) loss 0.6201 (0.9986) acc 87.5000 (73.9062) lr 1.6845e-03 eta 0:25:39
+epoch [15/50] batch [25/31] time 1.374 (1.398) data 0.000 (0.035) loss 1.1504 (0.9979) acc 78.1250 (73.8750) lr 1.6845e-03 eta 0:25:25
+epoch [15/50] batch [30/31] time 1.378 (1.394) data 0.000 (0.029) loss 1.1641 (1.0080) acc 84.3750 (74.0625) lr 1.6845e-03 eta 0:25:13
+epoch [16/50] batch [5/31] time 1.370 (1.545) data 0.000 (0.179) loss 1.0693 (0.8639) acc 68.7500 (76.2500) lr 1.6374e-03 eta 0:27:48
+epoch [16/50] batch [10/31] time 1.360 (1.454) data 0.000 (0.090) loss 0.9648 (0.9537) acc 81.2500 (75.0000) lr 1.6374e-03 eta 0:26:02
+epoch [16/50] batch [15/31] time 1.358 (1.421) data 0.000 (0.060) loss 1.2783 (0.9713) acc 65.6250 (75.2083) lr 1.6374e-03 eta 0:25:20
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+epoch [35/50] batch [20/31] time 1.374 (1.413) data 0.000 (0.040) loss 0.4209 (0.6829) acc 90.6250 (85.0000) lr 5.1825e-04 eta 0:11:12
+epoch [35/50] batch [25/31] time 1.347 (1.402) data 0.000 (0.032) loss 0.7856 (0.6900) acc 78.1250 (84.1250) lr 5.1825e-04 eta 0:11:00
+epoch [35/50] batch [30/31] time 1.352 (1.396) data 0.000 (0.027) loss 0.7393 (0.7072) acc 75.0000 (83.5417) lr 5.1825e-04 eta 0:10:50
+epoch [36/50] batch [5/31] time 1.386 (1.530) data 0.000 (0.160) loss 0.6338 (0.7847) acc 84.3750 (81.8750) lr 4.6417e-04 eta 0:11:43
+epoch [36/50] batch [10/31] time 1.364 (1.446) data 0.000 (0.080) loss 0.2192 (0.6029) acc 96.8750 (86.2500) lr 4.6417e-04 eta 0:10:58
+epoch [36/50] batch [15/31] time 1.352 (1.415) data 0.000 (0.054) loss 0.8115 (0.7403) acc 87.5000 (83.7500) lr 4.6417e-04 eta 0:10:36
+epoch [36/50] batch [20/31] time 1.358 (1.410) data 0.000 (0.040) loss 0.9043 (0.7901) acc 75.0000 (81.7188) lr 4.6417e-04 eta 0:10:27
+epoch [36/50] batch [25/31] time 1.343 (1.399) data 0.000 (0.032) loss 0.5605 (0.8048) acc 87.5000 (81.2500) lr 4.6417e-04 eta 0:10:15
+epoch [36/50] batch [30/31] time 1.340 (1.392) data 0.000 (0.027) loss 0.7505 (0.8019) acc 78.1250 (81.3542) lr 4.6417e-04 eta 0:10:05
+epoch [37/50] batch [5/31] time 1.343 (1.517) data 0.000 (0.154) loss 0.9321 (0.6248) acc 75.0000 (86.8750) lr 4.1221e-04 eta 0:10:50
+epoch [37/50] batch [10/31] time 1.360 (1.435) data 0.000 (0.077) loss 0.6934 (0.6264) acc 87.5000 (87.8125) lr 4.1221e-04 eta 0:10:08
+epoch [37/50] batch [15/31] time 1.366 (1.410) data 0.000 (0.052) loss 0.2822 (0.6181) acc 93.7500 (87.2917) lr 4.1221e-04 eta 0:09:50
+epoch [37/50] batch [20/31] time 1.350 (1.395) data 0.000 (0.039) loss 1.2031 (0.6996) acc 75.0000 (85.3125) lr 4.1221e-04 eta 0:09:37
+epoch [37/50] batch [25/31] time 1.362 (1.388) data 0.000 (0.031) loss 0.4360 (0.6718) acc 87.5000 (85.6250) lr 4.1221e-04 eta 0:09:27
+epoch [37/50] batch [30/31] time 1.358 (1.385) data 0.000 (0.026) loss 0.6929 (0.6753) acc 84.3750 (85.0000) lr 4.1221e-04 eta 0:09:19
+epoch [38/50] batch [5/31] time 1.357 (1.534) data 0.000 (0.166) loss 0.4871 (0.5136) acc 84.3750 (84.3750) lr 3.6258e-04 eta 0:10:10
+epoch [38/50] batch [10/31] time 1.342 (1.443) data 0.000 (0.083) loss 0.5322 (0.5161) acc 84.3750 (85.0000) lr 3.6258e-04 eta 0:09:27
+epoch [38/50] batch [15/31] time 1.354 (1.414) data 0.000 (0.056) loss 0.3340 (0.6272) acc 93.7500 (84.3750) lr 3.6258e-04 eta 0:09:08
+epoch [38/50] batch [20/31] time 1.367 (1.400) data 0.000 (0.042) loss 0.5889 (0.6158) acc 81.2500 (84.3750) lr 3.6258e-04 eta 0:08:56
+epoch [38/50] batch [25/31] time 1.365 (1.392) data 0.000 (0.034) loss 0.6172 (0.6375) acc 87.5000 (85.0000) lr 3.6258e-04 eta 0:08:46
+epoch [38/50] batch [30/31] time 1.370 (1.387) data 0.000 (0.028) loss 0.4006 (0.6727) acc 93.7500 (84.4792) lr 3.6258e-04 eta 0:08:37
+epoch [39/50] batch [5/31] time 1.354 (1.596) data 0.000 (0.237) loss 0.4221 (0.6093) acc 81.2500 (83.7500) lr 3.1545e-04 eta 0:09:45
+epoch [39/50] batch [10/31] time 1.372 (1.480) data 0.000 (0.118) loss 0.9253 (0.6603) acc 78.1250 (82.8125) lr 3.1545e-04 eta 0:08:55
+epoch [39/50] batch [15/31] time 1.359 (1.439) data 0.000 (0.079) loss 0.5771 (0.6698) acc 84.3750 (82.5000) lr 3.1545e-04 eta 0:08:33
+epoch [39/50] batch [20/31] time 1.520 (1.425) data 0.000 (0.059) loss 0.3345 (0.6379) acc 90.6250 (83.4375) lr 3.1545e-04 eta 0:08:21
+epoch [39/50] batch [25/31] time 1.360 (1.413) data 0.000 (0.048) loss 0.4636 (0.6119) acc 84.3750 (83.7500) lr 3.1545e-04 eta 0:08:10
+epoch [39/50] batch [30/31] time 1.386 (1.406) data 0.000 (0.040) loss 0.4927 (0.6208) acc 87.5000 (84.0625) lr 3.1545e-04 eta 0:08:00
+epoch [40/50] batch [5/31] time 1.356 (1.531) data 0.000 (0.162) loss 0.7192 (0.6792) acc 87.5000 (85.0000) lr 2.7103e-04 eta 0:08:34
+epoch [40/50] batch [10/31] time 1.365 (1.446) data 0.001 (0.081) loss 1.5010 (0.8282) acc 71.8750 (81.8750) lr 2.7103e-04 eta 0:07:58
+epoch [40/50] batch [15/31] time 1.353 (1.416) data 0.000 (0.054) loss 0.7271 (0.7403) acc 90.6250 (83.5417) lr 2.7103e-04 eta 0:07:41
+epoch [40/50] batch [20/31] time 1.356 (1.402) data 0.000 (0.041) loss 0.4612 (0.7264) acc 87.5000 (83.1250) lr 2.7103e-04 eta 0:07:30
+epoch [40/50] batch [25/31] time 1.350 (1.395) data 0.000 (0.033) loss 0.6265 (0.7287) acc 87.5000 (83.2500) lr 2.7103e-04 eta 0:07:20
+epoch [40/50] batch [30/31] time 1.364 (1.388) data 0.000 (0.027) loss 1.0703 (0.7686) acc 75.0000 (82.7083) lr 2.7103e-04 eta 0:07:11
+epoch [41/50] batch [5/31] time 1.353 (1.523) data 0.000 (0.156) loss 0.4106 (0.5667) acc 93.7500 (83.1250) lr 2.2949e-04 eta 0:07:44
+epoch [41/50] batch [10/31] time 1.340 (1.432) data 0.000 (0.078) loss 0.3313 (0.5844) acc 90.6250 (83.1250) lr 2.2949e-04 eta 0:07:09
+epoch [41/50] batch [15/31] time 1.351 (1.408) data 0.000 (0.052) loss 0.3828 (0.5737) acc 96.8750 (85.2083) lr 2.2949e-04 eta 0:06:55
+epoch [41/50] batch [20/31] time 1.364 (1.397) data 0.000 (0.039) loss 0.7964 (0.6286) acc 87.5000 (85.1562) lr 2.2949e-04 eta 0:06:45
+epoch [41/50] batch [25/31] time 1.354 (1.388) data 0.000 (0.032) loss 0.7129 (0.6551) acc 84.3750 (85.2500) lr 2.2949e-04 eta 0:06:35
+epoch [41/50] batch [30/31] time 1.373 (1.384) data 0.000 (0.026) loss 0.4146 (0.6632) acc 84.3750 (84.6875) lr 2.2949e-04 eta 0:06:27
+epoch [42/50] batch [5/31] time 1.363 (1.528) data 0.000 (0.160) loss 1.2539 (0.8131) acc 65.6250 (83.1250) lr 1.9098e-04 eta 0:06:58
+epoch [42/50] batch [10/31] time 1.344 (1.452) data 0.000 (0.080) loss 0.7490 (0.8107) acc 81.2500 (83.4375) lr 1.9098e-04 eta 0:06:30
+epoch [42/50] batch [15/31] time 1.363 (1.419) data 0.000 (0.054) loss 0.9121 (0.7101) acc 84.3750 (84.7917) lr 1.9098e-04 eta 0:06:14
+epoch [42/50] batch [20/31] time 1.353 (1.403) data 0.000 (0.040) loss 0.4937 (0.6584) acc 93.7500 (85.4688) lr 1.9098e-04 eta 0:06:03
+epoch [42/50] batch [25/31] time 1.359 (1.394) data 0.000 (0.032) loss 0.7808 (0.6880) acc 78.1250 (84.6250) lr 1.9098e-04 eta 0:05:54
+epoch [42/50] batch [30/31] time 1.348 (1.387) data 0.000 (0.027) loss 1.0010 (0.6984) acc 75.0000 (84.3750) lr 1.9098e-04 eta 0:05:45
+epoch [43/50] batch [5/31] time 1.353 (1.545) data 0.000 (0.185) loss 0.8589 (0.5351) acc 84.3750 (84.3750) lr 1.5567e-04 eta 0:06:15
+epoch [43/50] batch [10/31] time 1.370 (1.451) data 0.001 (0.093) loss 0.9268 (0.5684) acc 81.2500 (84.6875) lr 1.5567e-04 eta 0:05:45
+epoch [43/50] batch [15/31] time 1.492 (1.429) data 0.000 (0.062) loss 0.6475 (0.5674) acc 84.3750 (85.4167) lr 1.5567e-04 eta 0:05:32
+epoch [43/50] batch [20/31] time 1.351 (1.413) data 0.001 (0.047) loss 0.4697 (0.5759) acc 90.6250 (85.6250) lr 1.5567e-04 eta 0:05:22
+epoch [43/50] batch [25/31] time 1.354 (1.400) data 0.000 (0.037) loss 0.9736 (0.6313) acc 81.2500 (85.5000) lr 1.5567e-04 eta 0:05:12
+epoch [43/50] batch [30/31] time 1.360 (1.393) data 0.000 (0.031) loss 0.7437 (0.6604) acc 81.2500 (84.2708) lr 1.5567e-04 eta 0:05:03
+epoch [44/50] batch [5/31] time 1.347 (1.527) data 0.000 (0.164) loss 0.5854 (0.5700) acc 87.5000 (86.2500) lr 1.2369e-04 eta 0:05:23
+epoch [44/50] batch [10/31] time 1.384 (1.446) data 0.000 (0.082) loss 0.3213 (0.5784) acc 87.5000 (86.8750) lr 1.2369e-04 eta 0:04:59
+epoch [44/50] batch [15/31] time 1.355 (1.416) data 0.000 (0.055) loss 0.5913 (0.5830) acc 84.3750 (86.8750) lr 1.2369e-04 eta 0:04:45
+epoch [44/50] batch [20/31] time 1.358 (1.402) data 0.000 (0.041) loss 0.6777 (0.6566) acc 84.3750 (85.1562) lr 1.2369e-04 eta 0:04:36
+epoch [44/50] batch [25/31] time 1.346 (1.397) data 0.000 (0.033) loss 0.5444 (0.6493) acc 90.6250 (85.3750) lr 1.2369e-04 eta 0:04:28
+epoch [44/50] batch [30/31] time 1.360 (1.391) data 0.000 (0.028) loss 0.5508 (0.6347) acc 93.7500 (85.9375) lr 1.2369e-04 eta 0:04:20
+epoch [45/50] batch [5/31] time 1.365 (1.543) data 0.000 (0.174) loss 1.0723 (0.8200) acc 81.2500 (80.0000) lr 9.5173e-05 eta 0:04:39
+epoch [45/50] batch [10/31] time 1.394 (1.454) data 0.000 (0.087) loss 0.5918 (0.8144) acc 87.5000 (81.5625) lr 9.5173e-05 eta 0:04:15
+epoch [45/50] batch [15/31] time 1.360 (1.424) data 0.000 (0.058) loss 0.6689 (0.7740) acc 84.3750 (82.2917) lr 9.5173e-05 eta 0:04:03
+epoch [45/50] batch [20/31] time 1.346 (1.405) data 0.000 (0.044) loss 0.3132 (0.7056) acc 93.7500 (83.9062) lr 9.5173e-05 eta 0:03:53
+epoch [45/50] batch [25/31] time 1.353 (1.396) data 0.000 (0.035) loss 0.7993 (0.7100) acc 84.3750 (84.1250) lr 9.5173e-05 eta 0:03:44
+epoch [45/50] batch [30/31] time 1.371 (1.390) data 0.000 (0.029) loss 0.7197 (0.7463) acc 71.8750 (83.2292) lr 9.5173e-05 eta 0:03:36
+epoch [46/50] batch [5/31] time 1.393 (1.566) data 0.001 (0.169) loss 0.6909 (0.6827) acc 87.5000 (85.6250) lr 7.0224e-05 eta 0:03:54
+epoch [46/50] batch [10/31] time 1.363 (1.464) data 0.000 (0.085) loss 0.4412 (0.5646) acc 81.2500 (87.8125) lr 7.0224e-05 eta 0:03:32
+epoch [46/50] batch [15/31] time 1.359 (1.428) data 0.001 (0.057) loss 0.4761 (0.5907) acc 90.6250 (87.7083) lr 7.0224e-05 eta 0:03:19
+epoch [46/50] batch [20/31] time 1.373 (1.410) data 0.000 (0.043) loss 1.0176 (0.6998) acc 81.2500 (85.6250) lr 7.0224e-05 eta 0:03:10
+epoch [46/50] batch [25/31] time 1.387 (1.399) data 0.000 (0.034) loss 0.8896 (0.7250) acc 84.3750 (85.0000) lr 7.0224e-05 eta 0:03:01
+epoch [46/50] batch [30/31] time 1.371 (1.393) data 0.000 (0.028) loss 1.0137 (0.7039) acc 71.8750 (84.1667) lr 7.0224e-05 eta 0:02:54
+epoch [47/50] batch [5/31] time 1.353 (1.543) data 0.000 (0.180) loss 0.3164 (0.5798) acc 96.8750 (87.5000) lr 4.8943e-05 eta 0:03:03
+epoch [47/50] batch [10/31] time 1.365 (1.457) data 0.000 (0.090) loss 0.3396 (0.6585) acc 100.0000 (87.8125) lr 4.8943e-05 eta 0:02:46
+epoch [47/50] batch [15/31] time 1.357 (1.423) data 0.000 (0.060) loss 0.5327 (0.6760) acc 93.7500 (86.4583) lr 4.8943e-05 eta 0:02:35
+epoch [47/50] batch [20/31] time 1.361 (1.415) data 0.000 (0.045) loss 1.8213 (0.7415) acc 71.8750 (85.0000) lr 4.8943e-05 eta 0:02:27
+epoch [47/50] batch [25/31] time 1.378 (1.406) data 0.000 (0.036) loss 0.4988 (0.7645) acc 87.5000 (84.0000) lr 4.8943e-05 eta 0:02:19
+epoch [47/50] batch [30/31] time 1.376 (1.398) data 0.000 (0.030) loss 1.0781 (0.7441) acc 78.1250 (84.4792) lr 4.8943e-05 eta 0:02:11
+epoch [48/50] batch [5/31] time 1.348 (1.544) data 0.000 (0.171) loss 1.1416 (0.7943) acc 81.2500 (82.5000) lr 3.1417e-05 eta 0:02:15
+epoch [48/50] batch [10/31] time 1.344 (1.450) data 0.000 (0.086) loss 0.4219 (0.6777) acc 81.2500 (82.1875) lr 3.1417e-05 eta 0:02:00
+epoch [48/50] batch [15/31] time 1.360 (1.420) data 0.000 (0.057) loss 0.5894 (0.6531) acc 90.6250 (83.5417) lr 3.1417e-05 eta 0:01:50
+epoch [48/50] batch [20/31] time 1.371 (1.406) data 0.000 (0.043) loss 0.3237 (0.6431) acc 93.7500 (84.2188) lr 3.1417e-05 eta 0:01:42
+epoch [48/50] batch [25/31] time 1.360 (1.397) data 0.000 (0.035) loss 0.8301 (0.6769) acc 84.3750 (83.0000) lr 3.1417e-05 eta 0:01:34
+epoch [48/50] batch [30/31] time 1.368 (1.391) data 0.000 (0.029) loss 0.5938 (0.6646) acc 90.6250 (83.7500) lr 3.1417e-05 eta 0:01:27
+epoch [49/50] batch [5/31] time 1.347 (1.504) data 0.001 (0.156) loss 0.6641 (0.7141) acc 93.7500 (86.2500) lr 1.7713e-05 eta 0:01:25
+epoch [49/50] batch [10/31] time 1.362 (1.435) data 0.000 (0.078) loss 0.9292 (0.6986) acc 87.5000 (86.2500) lr 1.7713e-05 eta 0:01:14
+epoch [49/50] batch [15/31] time 1.374 (1.407) data 0.000 (0.052) loss 0.4053 (0.6653) acc 87.5000 (86.2500) lr 1.7713e-05 eta 0:01:06
+epoch [49/50] batch [20/31] time 1.354 (1.394) data 0.000 (0.039) loss 0.6465 (0.6729) acc 87.5000 (85.9375) lr 1.7713e-05 eta 0:00:58
+epoch [49/50] batch [25/31] time 1.362 (1.388) data 0.000 (0.031) loss 0.6372 (0.6985) acc 78.1250 (85.0000) lr 1.7713e-05 eta 0:00:51
+epoch [49/50] batch [30/31] time 1.350 (1.383) data 0.000 (0.026) loss 0.3113 (0.6837) acc 93.7500 (84.8958) lr 1.7713e-05 eta 0:00:44
+epoch [50/50] batch [5/31] time 1.375 (1.554) data 0.000 (0.183) loss 0.7314 (0.6158) acc 81.2500 (83.1250) lr 7.8853e-06 eta 0:00:40
+epoch [50/50] batch [10/31] time 1.367 (1.460) data 0.000 (0.092) loss 1.0088 (0.6608) acc 81.2500 (83.7500) lr 7.8853e-06 eta 0:00:30
+epoch [50/50] batch [15/31] time 1.390 (1.429) data 0.000 (0.061) loss 0.8169 (0.6978) acc 84.3750 (83.9583) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [20/31] time 1.359 (1.420) data 0.000 (0.046) loss 0.3306 (0.6946) acc 87.5000 (84.0625) lr 7.8853e-06 eta 0:00:15
+epoch [50/50] batch [25/31] time 1.369 (1.408) data 0.000 (0.037) loss 0.6626 (0.7117) acc 81.2500 (83.5000) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [30/31] time 1.372 (1.400) data 0.000 (0.031) loss 1.1152 (0.7395) acc 81.2500 (83.3333) lr 7.8853e-06 eta 0:00:01
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 35,855
+* accuracy: 71.7%
+* error: 28.3%
+* macro_f1: 70.9%
+Elapsed: 0:41:39
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
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Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698414340.ckb-gpu-lambda.180810.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698414340.ckb-gpu-lambda.180810.0
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
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+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1788.053
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/31] time 1.339 (2.197) data 0.000 (0.177) loss 2.6309 (3.1812) acc 50.0000 (38.7500) lr 1.0000e-05 eta 0:56:33
+epoch [1/50] batch [10/31] time 1.348 (1.774) data 0.000 (0.089) loss 2.1016 (2.9586) acc 53.1250 (42.1875) lr 1.0000e-05 eta 0:45:32
+epoch [1/50] batch [15/31] time 1.364 (1.635) data 0.000 (0.059) loss 2.1426 (2.7659) acc 68.7500 (45.4167) lr 1.0000e-05 eta 0:41:50
+epoch [1/50] batch [20/31] time 1.353 (1.567) data 0.000 (0.045) loss 1.6680 (2.6183) acc 50.0000 (46.5625) lr 1.0000e-05 eta 0:39:58
+epoch [1/50] batch [25/31] time 1.357 (1.525) data 0.000 (0.036) loss 2.6172 (2.5639) acc 50.0000 (48.1250) lr 1.0000e-05 eta 0:38:45
+epoch [1/50] batch [30/31] time 1.371 (1.497) data 0.000 (0.030) loss 1.8232 (2.4429) acc 59.3750 (50.1042) lr 1.0000e-05 eta 0:37:55
+epoch [2/50] batch [5/31] time 1.364 (1.541) data 0.001 (0.176) loss 1.2686 (1.7197) acc 65.6250 (61.8750) lr 2.0000e-03 eta 0:38:53
+epoch [2/50] batch [10/31] time 1.363 (1.453) data 0.000 (0.088) loss 1.6875 (1.6651) acc 59.3750 (63.1250) lr 2.0000e-03 eta 0:36:33
+epoch [2/50] batch [15/31] time 1.384 (1.424) data 0.000 (0.059) loss 2.5840 (1.7930) acc 46.8750 (61.2500) lr 2.0000e-03 eta 0:35:41
+epoch [2/50] batch [20/31] time 1.365 (1.408) data 0.000 (0.044) loss 2.2246 (1.7135) acc 59.3750 (62.3438) lr 2.0000e-03 eta 0:35:10
+epoch [2/50] batch [25/31] time 1.360 (1.399) data 0.000 (0.035) loss 2.1699 (1.6595) acc 53.1250 (63.5000) lr 2.0000e-03 eta 0:34:50
+epoch [2/50] batch [30/31] time 1.361 (1.394) data 0.000 (0.030) loss 1.2598 (1.5757) acc 68.7500 (64.6875) lr 2.0000e-03 eta 0:34:35
+epoch [3/50] batch [5/31] time 1.375 (1.552) data 0.000 (0.179) loss 1.3213 (1.3980) acc 68.7500 (61.8750) lr 1.9980e-03 eta 0:38:20
+epoch [3/50] batch [10/31] time 1.384 (1.463) data 0.000 (0.090) loss 0.8296 (1.1829) acc 75.0000 (67.8125) lr 1.9980e-03 eta 0:36:02
+epoch [3/50] batch [15/31] time 1.349 (1.429) data 0.000 (0.060) loss 1.1035 (1.1757) acc 75.0000 (68.9583) lr 1.9980e-03 eta 0:35:04
+epoch [3/50] batch [20/31] time 1.359 (1.411) data 0.000 (0.045) loss 0.8706 (1.2156) acc 78.1250 (68.7500) lr 1.9980e-03 eta 0:34:31
+epoch [3/50] batch [25/31] time 1.362 (1.401) data 0.000 (0.036) loss 1.2256 (1.2496) acc 68.7500 (68.2500) lr 1.9980e-03 eta 0:34:09
+epoch [3/50] batch [30/31] time 1.354 (1.394) data 0.000 (0.030) loss 1.5312 (1.3330) acc 65.6250 (67.1875) lr 1.9980e-03 eta 0:33:52
+epoch [4/50] batch [5/31] time 1.348 (1.546) data 0.001 (0.180) loss 1.2080 (1.3455) acc 62.5000 (65.0000) lr 1.9921e-03 eta 0:37:25
+epoch [4/50] batch [10/31] time 1.354 (1.452) data 0.000 (0.090) loss 1.5068 (1.2898) acc 59.3750 (65.9375) lr 1.9921e-03 eta 0:35:01
+epoch [4/50] batch [15/31] time 1.363 (1.423) data 0.000 (0.060) loss 1.3164 (1.2254) acc 68.7500 (68.1250) lr 1.9921e-03 eta 0:34:11
+epoch [4/50] batch [20/31] time 1.374 (1.408) data 0.000 (0.045) loss 1.0312 (1.1816) acc 59.3750 (67.8125) lr 1.9921e-03 eta 0:33:43
+epoch [4/50] batch [25/31] time 1.364 (1.399) data 0.000 (0.036) loss 1.1445 (1.2537) acc 65.6250 (66.7500) lr 1.9921e-03 eta 0:33:22
+epoch [4/50] batch [30/31] time 1.376 (1.393) data 0.000 (0.030) loss 1.1035 (1.2443) acc 71.8750 (67.7083) lr 1.9921e-03 eta 0:33:07
+epoch [5/50] batch [5/31] time 1.354 (1.570) data 0.000 (0.176) loss 0.8760 (1.0461) acc 78.1250 (75.0000) lr 1.9823e-03 eta 0:37:10
+epoch [5/50] batch [10/31] time 1.371 (1.467) data 0.000 (0.088) loss 1.2490 (1.1023) acc 65.6250 (73.4375) lr 1.9823e-03 eta 0:34:37
+epoch [5/50] batch [15/31] time 1.354 (1.431) data 0.000 (0.059) loss 1.3379 (1.1615) acc 71.8750 (72.0833) lr 1.9823e-03 eta 0:33:39
+epoch [5/50] batch [20/31] time 1.363 (1.414) data 0.000 (0.044) loss 0.7480 (1.1119) acc 87.5000 (72.5000) lr 1.9823e-03 eta 0:33:07
+epoch [5/50] batch [25/31] time 1.377 (1.403) data 0.000 (0.036) loss 1.1807 (1.1258) acc 71.8750 (71.8750) lr 1.9823e-03 eta 0:32:45
+epoch [5/50] batch [30/31] time 1.373 (1.397) data 0.000 (0.030) loss 0.7158 (1.1297) acc 81.2500 (71.8750) lr 1.9823e-03 eta 0:32:30
+epoch [6/50] batch [5/31] time 1.362 (1.564) data 0.000 (0.190) loss 1.4688 (1.2365) acc 65.6250 (71.8750) lr 1.9686e-03 eta 0:36:13
+epoch [6/50] batch [10/31] time 1.363 (1.459) data 0.001 (0.095) loss 1.1426 (1.1428) acc 68.7500 (71.2500) lr 1.9686e-03 eta 0:33:40
+epoch [6/50] batch [15/31] time 1.360 (1.424) data 0.000 (0.064) loss 1.0586 (1.1300) acc 81.2500 (72.2917) lr 1.9686e-03 eta 0:32:44
+epoch [6/50] batch [20/31] time 1.488 (1.414) data 0.000 (0.048) loss 1.0439 (1.1024) acc 68.7500 (72.1875) lr 1.9686e-03 eta 0:32:24
+epoch [6/50] batch [25/31] time 1.373 (1.404) data 0.000 (0.038) loss 1.0908 (1.1245) acc 75.0000 (72.2500) lr 1.9686e-03 eta 0:32:03
+epoch [6/50] batch [30/31] time 1.349 (1.395) data 0.000 (0.032) loss 1.6514 (1.1931) acc 75.0000 (71.2500) lr 1.9686e-03 eta 0:31:44
+epoch [7/50] batch [5/31] time 1.364 (1.537) data 0.000 (0.180) loss 0.8696 (1.1160) acc 75.0000 (71.2500) lr 1.9511e-03 eta 0:34:49
+epoch [7/50] batch [10/31] time 1.374 (1.455) data 0.000 (0.090) loss 0.2893 (0.9391) acc 90.6250 (74.3750) lr 1.9511e-03 eta 0:32:49
+epoch [7/50] batch [15/31] time 1.361 (1.421) data 0.001 (0.060) loss 1.1846 (1.0136) acc 65.6250 (73.9583) lr 1.9511e-03 eta 0:31:57
+epoch [7/50] batch [20/31] time 1.360 (1.406) data 0.000 (0.045) loss 0.6582 (1.0456) acc 81.2500 (73.1250) lr 1.9511e-03 eta 0:31:30
+epoch [7/50] batch [25/31] time 1.350 (1.396) data 0.000 (0.036) loss 1.1006 (1.0727) acc 81.2500 (73.0000) lr 1.9511e-03 eta 0:31:09
+epoch [7/50] batch [30/31] time 1.341 (1.390) data 0.000 (0.030) loss 1.6680 (1.1117) acc 68.7500 (72.1875) lr 1.9511e-03 eta 0:30:54
+epoch [8/50] batch [5/31] time 1.383 (1.547) data 0.000 (0.175) loss 0.8936 (1.2207) acc 75.0000 (68.7500) lr 1.9298e-03 eta 0:34:14
+epoch [8/50] batch [10/31] time 1.360 (1.455) data 0.000 (0.088) loss 1.4834 (1.1402) acc 65.6250 (70.9375) lr 1.9298e-03 eta 0:32:05
+epoch [8/50] batch [15/31] time 1.363 (1.426) data 0.000 (0.059) loss 1.2197 (1.0936) acc 68.7500 (71.8750) lr 1.9298e-03 eta 0:31:19
+epoch [8/50] batch [20/31] time 1.390 (1.413) data 0.000 (0.044) loss 0.8135 (1.0998) acc 75.0000 (72.1875) lr 1.9298e-03 eta 0:30:54
+epoch [8/50] batch [25/31] time 1.375 (1.405) data 0.000 (0.035) loss 0.7212 (1.1034) acc 87.5000 (72.2500) lr 1.9298e-03 eta 0:30:37
+epoch [8/50] batch [30/31] time 1.367 (1.397) data 0.000 (0.030) loss 1.2295 (1.1330) acc 65.6250 (71.6667) lr 1.9298e-03 eta 0:30:19
+epoch [9/50] batch [5/31] time 1.361 (1.554) data 0.001 (0.179) loss 1.8262 (1.5525) acc 62.5000 (70.0000) lr 1.9048e-03 eta 0:33:34
+epoch [9/50] batch [10/31] time 1.372 (1.463) data 0.000 (0.090) loss 0.8940 (1.3555) acc 81.2500 (70.3125) lr 1.9048e-03 eta 0:31:29
+epoch [9/50] batch [15/31] time 1.359 (1.431) data 0.001 (0.060) loss 0.6133 (1.2098) acc 81.2500 (72.7083) lr 1.9048e-03 eta 0:30:41
+epoch [9/50] batch [20/31] time 1.354 (1.416) data 0.000 (0.045) loss 1.5742 (1.1588) acc 59.3750 (73.1250) lr 1.9048e-03 eta 0:30:14
+epoch [9/50] batch [25/31] time 1.349 (1.409) data 0.000 (0.036) loss 0.9971 (1.1587) acc 75.0000 (72.3750) lr 1.9048e-03 eta 0:29:58
+epoch [9/50] batch [30/31] time 1.352 (1.401) data 0.000 (0.030) loss 0.6372 (1.1231) acc 84.3750 (73.0208) lr 1.9048e-03 eta 0:29:41
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+epoch [47/50] batch [15/31] time 1.367 (1.427) data 0.000 (0.057) loss 0.6655 (0.6403) acc 84.3750 (85.4167) lr 4.8943e-05 eta 0:02:35
+epoch [47/50] batch [20/31] time 1.394 (1.414) data 0.000 (0.043) loss 0.2094 (0.6658) acc 100.0000 (85.4688) lr 4.8943e-05 eta 0:02:27
+epoch [47/50] batch [25/31] time 1.353 (1.404) data 0.000 (0.034) loss 0.6538 (0.6742) acc 84.3750 (84.7500) lr 4.8943e-05 eta 0:02:19
+epoch [47/50] batch [30/31] time 1.357 (1.397) data 0.000 (0.029) loss 1.0996 (0.6986) acc 81.2500 (84.4792) lr 4.8943e-05 eta 0:02:11
+epoch [48/50] batch [5/31] time 1.380 (1.542) data 0.000 (0.162) loss 0.6299 (0.6461) acc 87.5000 (83.1250) lr 3.1417e-05 eta 0:02:15
+epoch [48/50] batch [10/31] time 1.371 (1.453) data 0.001 (0.081) loss 0.5820 (0.6527) acc 84.3750 (84.3750) lr 3.1417e-05 eta 0:02:00
+epoch [48/50] batch [15/31] time 1.379 (1.425) data 0.000 (0.054) loss 1.1318 (0.6449) acc 68.7500 (83.9583) lr 3.1417e-05 eta 0:01:51
+epoch [48/50] batch [20/31] time 1.358 (1.411) data 0.000 (0.041) loss 0.4568 (0.6140) acc 87.5000 (85.3125) lr 3.1417e-05 eta 0:01:42
+epoch [48/50] batch [25/31] time 1.356 (1.403) data 0.000 (0.033) loss 0.7852 (0.6475) acc 87.5000 (85.0000) lr 3.1417e-05 eta 0:01:35
+epoch [48/50] batch [30/31] time 1.348 (1.395) data 0.000 (0.027) loss 0.3081 (0.6465) acc 93.7500 (85.0000) lr 3.1417e-05 eta 0:01:27
+epoch [49/50] batch [5/31] time 1.365 (1.545) data 0.000 (0.170) loss 0.9312 (0.6191) acc 78.1250 (85.6250) lr 1.7713e-05 eta 0:01:28
+epoch [49/50] batch [10/31] time 1.361 (1.459) data 0.000 (0.085) loss 0.5273 (0.5782) acc 84.3750 (86.8750) lr 1.7713e-05 eta 0:01:15
+epoch [49/50] batch [15/31] time 1.374 (1.441) data 0.000 (0.057) loss 0.3965 (0.6275) acc 90.6250 (85.8333) lr 1.7713e-05 eta 0:01:07
+epoch [49/50] batch [20/31] time 1.364 (1.423) data 0.000 (0.043) loss 0.7236 (0.6272) acc 87.5000 (85.6250) lr 1.7713e-05 eta 0:00:59
+epoch [49/50] batch [25/31] time 1.373 (1.412) data 0.000 (0.034) loss 0.7075 (0.6190) acc 78.1250 (85.7500) lr 1.7713e-05 eta 0:00:52
+epoch [49/50] batch [30/31] time 1.359 (1.403) data 0.000 (0.029) loss 1.0098 (0.6578) acc 75.0000 (84.8958) lr 1.7713e-05 eta 0:00:44
+epoch [50/50] batch [5/31] time 1.357 (1.525) data 0.000 (0.162) loss 0.7188 (0.7777) acc 84.3750 (80.6250) lr 7.8853e-06 eta 0:00:39
+epoch [50/50] batch [10/31] time 1.380 (1.445) data 0.001 (0.081) loss 0.4192 (0.6395) acc 90.6250 (85.6250) lr 7.8853e-06 eta 0:00:30
+epoch [50/50] batch [15/31] time 1.384 (1.420) data 0.000 (0.054) loss 0.4712 (0.6426) acc 93.7500 (85.0000) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [20/31] time 1.349 (1.402) data 0.000 (0.041) loss 0.8394 (0.6361) acc 87.5000 (85.7812) lr 7.8853e-06 eta 0:00:15
+epoch [50/50] batch [25/31] time 1.393 (1.398) data 0.001 (0.033) loss 0.4070 (0.6094) acc 90.6250 (86.5000) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [30/31] time 1.368 (1.393) data 0.000 (0.027) loss 0.8589 (0.5958) acc 84.3750 (86.6667) lr 7.8853e-06 eta 0:00:01
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 36,000
+* accuracy: 72.0%
+* error: 28.0%
+* macro_f1: 71.2%
+Elapsed: 0:41:43
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..3396ba43
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698416867.ckb-gpu-lambda.218936.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698416867.ckb-gpu-lambda.218936.0
new file mode 100644
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diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
new file mode 100644
index 00000000..d88af7b8
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/log.txt
@@ -0,0 +1,639 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '1']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 3
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 1
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3
+RESUME:
+SEED: 3
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.018
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Loading preprocessed few-shot data from /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_1-seed_3.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 1,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard)
+epoch [1/50] batch [5/31] time 1.344 (2.171) data 0.000 (0.164) loss 2.2090 (2.8641) acc 53.1250 (50.6250) lr 1.0000e-05 eta 0:55:54
+epoch [1/50] batch [10/31] time 1.355 (1.761) data 0.000 (0.082) loss 1.9395 (2.6137) acc 56.2500 (53.7500) lr 1.0000e-05 eta 0:45:12
+epoch [1/50] batch [15/31] time 1.350 (1.627) data 0.000 (0.055) loss 2.8848 (2.5189) acc 46.8750 (52.0833) lr 1.0000e-05 eta 0:41:37
+epoch [1/50] batch [20/31] time 1.360 (1.559) data 0.000 (0.041) loss 1.7744 (2.4144) acc 71.8750 (53.5938) lr 1.0000e-05 eta 0:39:45
+epoch [1/50] batch [25/31] time 1.356 (1.518) data 0.000 (0.033) loss 1.8799 (2.2971) acc 59.3750 (54.0000) lr 1.0000e-05 eta 0:38:34
+epoch [1/50] batch [30/31] time 1.361 (1.493) data 0.000 (0.028) loss 1.9287 (2.2614) acc 46.8750 (53.6458) lr 1.0000e-05 eta 0:37:49
+epoch [2/50] batch [5/31] time 1.375 (1.541) data 0.000 (0.173) loss 1.5967 (1.4787) acc 65.6250 (66.8750) lr 2.0000e-03 eta 0:38:53
+epoch [2/50] batch [10/31] time 1.348 (1.451) data 0.001 (0.087) loss 2.0352 (1.5800) acc 56.2500 (64.3750) lr 2.0000e-03 eta 0:36:29
+epoch [2/50] batch [15/31] time 1.351 (1.424) data 0.000 (0.058) loss 1.1982 (1.4487) acc 71.8750 (66.4583) lr 2.0000e-03 eta 0:35:41
+epoch [2/50] batch [20/31] time 1.363 (1.410) data 0.000 (0.044) loss 1.2295 (1.4687) acc 71.8750 (65.6250) lr 2.0000e-03 eta 0:35:13
+epoch [2/50] batch [25/31] time 1.363 (1.401) data 0.000 (0.035) loss 0.4741 (1.4165) acc 84.3750 (66.6250) lr 2.0000e-03 eta 0:34:53
+epoch [2/50] batch [30/31] time 1.338 (1.394) data 0.000 (0.029) loss 1.0176 (1.3847) acc 78.1250 (66.4583) lr 2.0000e-03 eta 0:34:36
+epoch [3/50] batch [5/31] time 1.341 (1.554) data 0.001 (0.192) loss 1.0811 (1.4348) acc 62.5000 (63.7500) lr 1.9980e-03 eta 0:38:24
+epoch [3/50] batch [10/31] time 1.367 (1.453) data 0.000 (0.096) loss 0.6226 (1.2843) acc 78.1250 (66.8750) lr 1.9980e-03 eta 0:35:47
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+epoch [40/50] batch [15/31] time 1.347 (1.421) data 0.000 (0.058) loss 1.2822 (0.7696) acc 71.8750 (82.9167) lr 2.7103e-04 eta 0:07:43
+epoch [40/50] batch [20/31] time 1.366 (1.405) data 0.000 (0.044) loss 0.4683 (0.7388) acc 90.6250 (83.7500) lr 2.7103e-04 eta 0:07:31
+epoch [40/50] batch [25/31] time 1.370 (1.398) data 0.000 (0.035) loss 0.6025 (0.7200) acc 78.1250 (84.0000) lr 2.7103e-04 eta 0:07:21
+epoch [40/50] batch [30/31] time 1.396 (1.394) data 0.000 (0.029) loss 0.5200 (0.7135) acc 90.6250 (83.6458) lr 2.7103e-04 eta 0:07:13
+epoch [41/50] batch [5/31] time 1.368 (1.542) data 0.000 (0.154) loss 0.9458 (0.7002) acc 78.1250 (83.1250) lr 2.2949e-04 eta 0:07:50
+epoch [41/50] batch [10/31] time 1.370 (1.452) data 0.000 (0.077) loss 0.9302 (0.7808) acc 71.8750 (81.2500) lr 2.2949e-04 eta 0:07:15
+epoch [41/50] batch [15/31] time 1.359 (1.422) data 0.001 (0.052) loss 0.2471 (0.6696) acc 96.8750 (83.7500) lr 2.2949e-04 eta 0:06:59
+epoch [41/50] batch [20/31] time 1.362 (1.406) data 0.000 (0.039) loss 0.5269 (0.6434) acc 84.3750 (84.2188) lr 2.2949e-04 eta 0:06:47
+epoch [41/50] batch [25/31] time 1.368 (1.394) data 0.000 (0.031) loss 0.8066 (0.6244) acc 78.1250 (84.5000) lr 2.2949e-04 eta 0:06:37
+epoch [41/50] batch [30/31] time 1.348 (1.389) data 0.001 (0.026) loss 0.9160 (0.6512) acc 75.0000 (84.3750) lr 2.2949e-04 eta 0:06:28
+epoch [42/50] batch [5/31] time 1.362 (1.537) data 0.001 (0.174) loss 1.0625 (0.7685) acc 75.0000 (81.2500) lr 1.9098e-04 eta 0:07:01
+epoch [42/50] batch [10/31] time 1.374 (1.448) data 0.000 (0.087) loss 0.7002 (0.6819) acc 84.3750 (83.1250) lr 1.9098e-04 eta 0:06:29
+epoch [42/50] batch [15/31] time 1.363 (1.417) data 0.001 (0.058) loss 0.9146 (0.7701) acc 84.3750 (81.2500) lr 1.9098e-04 eta 0:06:14
+epoch [42/50] batch [20/31] time 1.360 (1.404) data 0.000 (0.044) loss 0.3889 (0.7077) acc 87.5000 (82.9688) lr 1.9098e-04 eta 0:06:03
+epoch [42/50] batch [25/31] time 1.370 (1.397) data 0.000 (0.035) loss 1.0879 (0.7479) acc 71.8750 (82.3750) lr 1.9098e-04 eta 0:05:54
+epoch [42/50] batch [30/31] time 1.360 (1.392) data 0.000 (0.029) loss 0.6860 (0.7446) acc 87.5000 (82.6042) lr 1.9098e-04 eta 0:05:46
+epoch [43/50] batch [5/31] time 1.364 (1.537) data 0.000 (0.169) loss 0.7168 (0.5835) acc 87.5000 (88.1250) lr 1.5567e-04 eta 0:06:13
+epoch [43/50] batch [10/31] time 1.365 (1.450) data 0.001 (0.085) loss 0.3188 (0.6941) acc 90.6250 (86.5625) lr 1.5567e-04 eta 0:05:44
+epoch [43/50] batch [15/31] time 1.353 (1.422) data 0.001 (0.057) loss 0.4817 (0.6191) acc 87.5000 (87.2917) lr 1.5567e-04 eta 0:05:31
+epoch [43/50] batch [20/31] time 1.387 (1.406) data 0.000 (0.043) loss 0.1838 (0.6278) acc 96.8750 (86.0938) lr 1.5567e-04 eta 0:05:20
+epoch [43/50] batch [25/31] time 1.341 (1.399) data 0.000 (0.034) loss 0.9404 (0.6569) acc 87.5000 (85.2500) lr 1.5567e-04 eta 0:05:11
+epoch [43/50] batch [30/31] time 1.371 (1.393) data 0.000 (0.029) loss 0.2668 (0.6654) acc 96.8750 (85.1042) lr 1.5567e-04 eta 0:05:03
+epoch [44/50] batch [5/31] time 1.374 (1.573) data 0.000 (0.172) loss 1.2715 (0.8109) acc 81.2500 (84.3750) lr 1.2369e-04 eta 0:05:33
+epoch [44/50] batch [10/31] time 1.367 (1.469) data 0.001 (0.086) loss 1.1387 (0.8833) acc 78.1250 (81.5625) lr 1.2369e-04 eta 0:05:04
+epoch [44/50] batch [15/31] time 1.385 (1.435) data 0.000 (0.058) loss 0.6387 (0.8218) acc 87.5000 (83.3333) lr 1.2369e-04 eta 0:04:49
+epoch [44/50] batch [20/31] time 1.359 (1.419) data 0.000 (0.043) loss 0.7095 (0.8076) acc 81.2500 (82.8125) lr 1.2369e-04 eta 0:04:39
+epoch [44/50] batch [25/31] time 1.379 (1.410) data 0.000 (0.035) loss 0.7524 (0.8098) acc 87.5000 (82.7500) lr 1.2369e-04 eta 0:04:30
+epoch [44/50] batch [30/31] time 1.357 (1.403) data 0.000 (0.029) loss 0.7153 (0.7745) acc 81.2500 (83.5417) lr 1.2369e-04 eta 0:04:22
+epoch [45/50] batch [5/31] time 1.378 (1.546) data 0.001 (0.168) loss 0.3740 (0.5265) acc 96.8750 (91.2500) lr 9.5173e-05 eta 0:04:39
+epoch [45/50] batch [10/31] time 1.361 (1.453) data 0.001 (0.084) loss 0.7480 (0.6542) acc 81.2500 (86.2500) lr 9.5173e-05 eta 0:04:15
+epoch [45/50] batch [15/31] time 1.355 (1.430) data 0.000 (0.056) loss 0.7222 (0.6557) acc 75.0000 (85.0000) lr 9.5173e-05 eta 0:04:04
+epoch [45/50] batch [20/31] time 1.348 (1.413) data 0.000 (0.042) loss 0.6523 (0.7049) acc 81.2500 (83.4375) lr 9.5173e-05 eta 0:03:54
+epoch [45/50] batch [25/31] time 1.353 (1.400) data 0.000 (0.034) loss 0.6812 (0.6774) acc 75.0000 (83.6250) lr 9.5173e-05 eta 0:03:45
+epoch [45/50] batch [30/31] time 1.360 (1.393) data 0.000 (0.028) loss 1.1201 (0.7064) acc 71.8750 (82.7083) lr 9.5173e-05 eta 0:03:37
+epoch [46/50] batch [5/31] time 1.382 (1.548) data 0.001 (0.173) loss 0.6895 (0.7261) acc 87.5000 (81.8750) lr 7.0224e-05 eta 0:03:52
+epoch [46/50] batch [10/31] time 1.356 (1.452) data 0.000 (0.087) loss 0.5161 (0.6446) acc 87.5000 (84.6875) lr 7.0224e-05 eta 0:03:30
+epoch [46/50] batch [15/31] time 1.349 (1.423) data 0.001 (0.058) loss 0.6035 (0.6144) acc 84.3750 (86.2500) lr 7.0224e-05 eta 0:03:19
+epoch [46/50] batch [20/31] time 1.496 (1.415) data 0.000 (0.044) loss 0.5181 (0.6703) acc 84.3750 (84.8438) lr 7.0224e-05 eta 0:03:11
+epoch [46/50] batch [25/31] time 1.372 (1.403) data 0.000 (0.035) loss 1.1426 (0.7124) acc 84.3750 (84.1250) lr 7.0224e-05 eta 0:03:02
+epoch [46/50] batch [30/31] time 1.364 (1.395) data 0.000 (0.029) loss 0.3047 (0.7047) acc 90.6250 (83.9583) lr 7.0224e-05 eta 0:02:54
+epoch [47/50] batch [5/31] time 1.378 (1.533) data 0.000 (0.162) loss 0.4790 (0.7105) acc 90.6250 (83.1250) lr 4.8943e-05 eta 0:03:02
+epoch [47/50] batch [10/31] time 1.372 (1.452) data 0.000 (0.081) loss 0.9678 (0.7792) acc 84.3750 (82.1875) lr 4.8943e-05 eta 0:02:45
+epoch [47/50] batch [15/31] time 1.361 (1.421) data 0.001 (0.054) loss 0.7559 (0.7316) acc 84.3750 (83.3333) lr 4.8943e-05 eta 0:02:34
+epoch [47/50] batch [20/31] time 1.378 (1.407) data 0.000 (0.041) loss 1.3027 (0.7734) acc 75.0000 (82.8125) lr 4.8943e-05 eta 0:02:26
+epoch [47/50] batch [25/31] time 1.372 (1.400) data 0.000 (0.033) loss 0.6958 (0.7290) acc 84.3750 (83.5000) lr 4.8943e-05 eta 0:02:18
+epoch [47/50] batch [30/31] time 1.370 (1.394) data 0.000 (0.027) loss 0.8804 (0.7330) acc 78.1250 (83.4375) lr 4.8943e-05 eta 0:02:11
+epoch [48/50] batch [5/31] time 1.351 (1.530) data 0.000 (0.166) loss 1.1260 (0.8375) acc 71.8750 (80.6250) lr 3.1417e-05 eta 0:02:14
+epoch [48/50] batch [10/31] time 1.361 (1.448) data 0.000 (0.083) loss 0.9106 (0.7042) acc 78.1250 (82.8125) lr 3.1417e-05 eta 0:02:00
+epoch [48/50] batch [15/31] time 1.364 (1.422) data 0.000 (0.056) loss 0.8066 (0.6822) acc 84.3750 (83.9583) lr 3.1417e-05 eta 0:01:50
+epoch [48/50] batch [20/31] time 1.369 (1.408) data 0.000 (0.042) loss 0.2722 (0.6889) acc 93.7500 (83.2812) lr 3.1417e-05 eta 0:01:42
+epoch [48/50] batch [25/31] time 1.367 (1.399) data 0.000 (0.034) loss 0.7773 (0.6799) acc 87.5000 (84.0000) lr 3.1417e-05 eta 0:01:35
+epoch [48/50] batch [30/31] time 1.372 (1.394) data 0.000 (0.028) loss 0.4861 (0.6694) acc 90.6250 (84.0625) lr 3.1417e-05 eta 0:01:27
+epoch [49/50] batch [5/31] time 1.349 (1.533) data 0.000 (0.161) loss 0.8228 (0.7031) acc 84.3750 (87.5000) lr 1.7713e-05 eta 0:01:27
+epoch [49/50] batch [10/31] time 1.367 (1.447) data 0.001 (0.081) loss 0.5732 (0.6880) acc 87.5000 (85.9375) lr 1.7713e-05 eta 0:01:15
+epoch [49/50] batch [15/31] time 1.362 (1.427) data 0.000 (0.054) loss 0.6060 (0.6962) acc 84.3750 (85.0000) lr 1.7713e-05 eta 0:01:07
+epoch [49/50] batch [20/31] time 1.351 (1.409) data 0.000 (0.041) loss 0.2756 (0.6428) acc 93.7500 (86.2500) lr 1.7713e-05 eta 0:00:59
+epoch [49/50] batch [25/31] time 1.367 (1.399) data 0.000 (0.032) loss 0.4299 (0.6455) acc 87.5000 (85.6250) lr 1.7713e-05 eta 0:00:51
+epoch [49/50] batch [30/31] time 1.353 (1.391) data 0.000 (0.027) loss 0.9780 (0.6742) acc 71.8750 (84.7917) lr 1.7713e-05 eta 0:00:44
+epoch [50/50] batch [5/31] time 1.386 (1.542) data 0.000 (0.171) loss 0.4329 (0.5537) acc 96.8750 (88.7500) lr 7.8853e-06 eta 0:00:40
+epoch [50/50] batch [10/31] time 1.344 (1.448) data 0.001 (0.086) loss 0.5361 (0.6722) acc 84.3750 (85.0000) lr 7.8853e-06 eta 0:00:30
+epoch [50/50] batch [15/31] time 1.346 (1.415) data 0.000 (0.057) loss 0.8652 (0.7430) acc 81.2500 (83.7500) lr 7.8853e-06 eta 0:00:22
+epoch [50/50] batch [20/31] time 1.355 (1.399) data 0.000 (0.043) loss 0.4609 (0.7021) acc 90.6250 (84.2188) lr 7.8853e-06 eta 0:00:15
+epoch [50/50] batch [25/31] time 1.348 (1.392) data 0.000 (0.034) loss 0.4346 (0.7164) acc 87.5000 (83.7500) lr 7.8853e-06 eta 0:00:08
+epoch [50/50] batch [30/31] time 1.364 (1.387) data 0.000 (0.029) loss 1.1328 (0.7366) acc 78.1250 (83.3333) lr 7.8853e-06 eta 0:00:01
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 36,046
+* accuracy: 72.1%
+* error: 27.9%
+* macro_f1: 71.4%
+Elapsed: 0:41:42
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
new file mode 100644
index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..bc6c62d3
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698419398.ckb-gpu-lambda.257100.0 b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698419398.ckb-gpu-lambda.257100.0
new file mode 100644
index 00000000..03cb6f49
Binary files /dev/null and b/python/ClipDetection/CoOp/output/imagenet/CoOp/vit_l14_ep50_1shots/nctx16_cscFalse_ctpend/seed3/tensorboard/events.out.tfevents.1698419398.ckb-gpu-lambda.257100.0 differ
diff --git a/python/ClipDetection/CoOp/requirements.txt b/python/ClipDetection/CoOp/requirements.txt
new file mode 100644
index 00000000..a7a7778b
--- /dev/null
+++ b/python/ClipDetection/CoOp/requirements.txt
@@ -0,0 +1,3 @@
+ftfy
+regex
+tqdm
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
new file mode 100644
index 00000000..9b268b8e
--- /dev/null
+++ b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt
@@ -0,0 +1,5340 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 1
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1
+RESUME:
+SEED: 1
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.024
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Creating a 16-shot dataset
+Saving preprocessed few-shot data to /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_1.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard)
+epoch [1/50] batch [5/500] time 1.336 (2.296) data 0.000 (0.324) loss 2.5957 (3.2949) acc 37.5000 (35.6250) lr 1.0000e-05 eta 15:56:16
+epoch [1/50] batch [10/500] time 1.355 (1.822) data 0.000 (0.162) loss 2.7363 (3.0840) acc 43.7500 (40.0000) lr 1.0000e-05 eta 12:38:55
+epoch [1/50] batch [15/500] time 1.371 (1.668) data 0.000 (0.108) loss 2.2109 (2.7854) acc 50.0000 (44.5833) lr 1.0000e-05 eta 11:34:39
+epoch [1/50] batch [20/500] time 1.348 (1.589) data 0.001 (0.081) loss 2.5312 (2.6285) acc 50.0000 (48.4375) lr 1.0000e-05 eta 11:01:38
+epoch [1/50] batch [25/500] time 1.348 (1.541) data 0.000 (0.065) loss 1.8896 (2.5264) acc 56.2500 (49.1250) lr 1.0000e-05 eta 10:41:23
+epoch [1/50] batch [30/500] time 1.363 (1.510) data 0.000 (0.054) loss 1.5879 (2.4069) acc 65.6250 (51.1458) lr 1.0000e-05 eta 10:28:19
+epoch [1/50] batch [35/500] time 1.369 (1.488) data 0.000 (0.047) loss 1.3916 (2.3061) acc 59.3750 (51.7857) lr 1.0000e-05 eta 10:19:17
+epoch [1/50] batch [40/500] time 1.343 (1.471) data 0.000 (0.041) loss 2.0996 (2.2664) acc 56.2500 (52.4219) lr 1.0000e-05 eta 10:11:56
+epoch [1/50] batch [45/500] time 1.362 (1.458) data 0.000 (0.036) loss 2.7988 (2.2370) acc 43.7500 (52.7778) lr 1.0000e-05 eta 10:06:18
+epoch [1/50] batch [50/500] time 1.348 (1.447) data 0.000 (0.033) loss 1.8828 (2.1898) acc 59.3750 (53.0625) lr 1.0000e-05 eta 10:01:41
+epoch [1/50] batch [55/500] time 1.352 (1.439) data 0.000 (0.030) loss 1.3057 (2.1259) acc 78.1250 (54.3182) lr 1.0000e-05 eta 9:58:03
+epoch [1/50] batch [60/500] time 1.350 (1.432) data 0.000 (0.027) loss 1.0781 (2.0803) acc 68.7500 (54.7396) lr 1.0000e-05 eta 9:55:06
+epoch [1/50] batch [65/500] time 1.357 (1.427) data 0.000 (0.025) loss 1.3174 (2.0519) acc 59.3750 (54.9519) lr 1.0000e-05 eta 9:52:54
+epoch [1/50] batch [70/500] time 1.327 (1.422) data 0.000 (0.023) loss 2.0801 (2.0319) acc 62.5000 (55.1786) lr 1.0000e-05 eta 9:50:42
+epoch [1/50] batch [75/500] time 1.330 (1.417) data 0.000 (0.022) loss 1.3330 (1.9988) acc 65.6250 (55.7083) lr 1.0000e-05 eta 9:48:30
+epoch [1/50] batch [80/500] time 1.368 (1.413) data 0.000 (0.021) loss 1.0049 (1.9743) acc 68.7500 (56.4062) lr 1.0000e-05 eta 9:46:59
+epoch [1/50] batch [85/500] time 1.374 (1.411) data 0.000 (0.019) loss 1.5674 (1.9595) acc 71.8750 (56.8015) lr 1.0000e-05 eta 9:45:53
+epoch [1/50] batch [90/500] time 1.365 (1.408) data 0.000 (0.018) loss 2.5020 (1.9502) acc 50.0000 (56.7708) lr 1.0000e-05 eta 9:44:37
+epoch [1/50] batch [95/500] time 1.364 (1.406) data 0.000 (0.017) loss 1.5625 (1.9372) acc 65.6250 (57.2368) lr 1.0000e-05 eta 9:43:29
+epoch [1/50] batch [100/500] time 1.372 (1.403) data 0.000 (0.017) loss 1.6943 (1.9139) acc 62.5000 (57.6562) lr 1.0000e-05 eta 9:42:26
+epoch [1/50] batch [105/500] time 1.351 (1.401) data 0.000 (0.016) loss 1.8271 (1.9145) acc 68.7500 (57.5595) lr 1.0000e-05 eta 9:41:27
+epoch [1/50] batch [110/500] time 1.353 (1.399) data 0.000 (0.015) loss 1.5078 (1.8877) acc 68.7500 (58.3239) lr 1.0000e-05 eta 9:40:32
+epoch [1/50] batch [115/500] time 1.367 (1.398) data 0.000 (0.014) loss 1.9453 (1.8722) acc 53.1250 (58.5598) lr 1.0000e-05 eta 9:39:53
+epoch [1/50] batch [120/500] time 1.363 (1.397) data 0.001 (0.014) loss 2.4883 (1.8671) acc 40.6250 (58.5677) lr 1.0000e-05 eta 9:39:08
+epoch [1/50] batch [125/500] time 1.336 (1.395) data 0.000 (0.013) loss 2.1230 (1.8599) acc 46.8750 (58.7000) lr 1.0000e-05 eta 9:38:09
+epoch [1/50] batch [130/500] time 1.361 (1.395) data 0.000 (0.013) loss 1.8857 (1.8599) acc 62.5000 (58.6298) lr 1.0000e-05 eta 9:38:03
+epoch [1/50] batch [135/500] time 1.362 (1.393) data 0.000 (0.012) loss 1.8574 (1.8511) acc 56.2500 (58.7037) lr 1.0000e-05 eta 9:37:26
+epoch [1/50] batch [140/500] time 1.350 (1.392) data 0.000 (0.012) loss 1.6787 (1.8529) acc 56.2500 (58.5268) lr 1.0000e-05 eta 9:36:41
+epoch [1/50] batch [145/500] time 1.371 (1.391) data 0.000 (0.012) loss 1.5439 (1.8515) acc 62.5000 (58.4052) lr 1.0000e-05 eta 9:36:10
+epoch [1/50] batch [150/500] time 1.363 (1.390) data 0.000 (0.011) loss 2.3809 (1.8468) acc 43.7500 (58.5625) lr 1.0000e-05 eta 9:35:41
+epoch [1/50] batch [155/500] time 1.364 (1.389) data 0.000 (0.011) loss 1.3672 (1.8343) acc 59.3750 (58.6492) lr 1.0000e-05 eta 9:35:07
+epoch [1/50] batch [160/500] time 1.355 (1.388) data 0.000 (0.010) loss 0.8252 (1.8258) acc 81.2500 (58.7305) lr 1.0000e-05 eta 9:34:35
+epoch [1/50] batch [165/500] time 1.359 (1.387) data 0.000 (0.010) loss 1.6670 (1.8149) acc 53.1250 (58.8826) lr 1.0000e-05 eta 9:33:57
+epoch [1/50] batch [170/500] time 1.372 (1.386) data 0.000 (0.010) loss 1.0439 (1.8017) acc 71.8750 (59.1360) lr 1.0000e-05 eta 9:33:34
+epoch [1/50] batch [175/500] time 1.367 (1.385) data 0.000 (0.010) loss 1.9434 (1.7958) acc 59.3750 (59.1786) lr 1.0000e-05 eta 9:33:14
+epoch [1/50] batch [180/500] time 1.355 (1.385) data 0.000 (0.009) loss 1.8701 (1.7892) acc 59.3750 (59.1840) lr 1.0000e-05 eta 9:32:45
+epoch [1/50] batch [185/500] time 1.348 (1.384) data 0.000 (0.009) loss 1.3232 (1.7843) acc 65.6250 (59.2568) lr 1.0000e-05 eta 9:32:15
+epoch [1/50] batch [190/500] time 1.358 (1.383) data 0.000 (0.009) loss 1.3926 (1.7733) acc 68.7500 (59.5230) lr 1.0000e-05 eta 9:31:57
+epoch [1/50] batch [195/500] time 1.360 (1.383) data 0.000 (0.009) loss 1.6104 (1.7688) acc 59.3750 (59.6314) lr 1.0000e-05 eta 9:31:33
+epoch [1/50] batch [200/500] time 1.355 (1.382) data 0.000 (0.008) loss 1.1621 (1.7606) acc 68.7500 (59.7656) lr 1.0000e-05 eta 9:31:09
+epoch [1/50] batch [205/500] time 1.343 (1.381) data 0.000 (0.008) loss 1.0078 (1.7479) acc 68.7500 (59.9390) lr 1.0000e-05 eta 9:30:40
+epoch [1/50] batch [210/500] time 1.366 (1.380) data 0.000 (0.008) loss 1.6758 (1.7415) acc 68.7500 (60.1190) lr 1.0000e-05 eta 9:30:16
+epoch [1/50] batch [215/500] time 1.350 (1.379) data 0.000 (0.008) loss 1.3613 (1.7380) acc 75.0000 (60.2471) lr 1.0000e-05 eta 9:29:49
+epoch [1/50] batch [220/500] time 1.368 (1.379) data 0.000 (0.008) loss 1.2627 (1.7310) acc 65.6250 (60.4261) lr 1.0000e-05 eta 9:29:29
+epoch [1/50] batch [225/500] time 1.370 (1.379) data 0.000 (0.008) loss 2.0684 (1.7332) acc 59.3750 (60.4167) lr 1.0000e-05 eta 9:29:18
+epoch [1/50] batch [230/500] time 1.373 (1.379) data 0.000 (0.007) loss 1.6084 (1.7276) acc 56.2500 (60.4348) lr 1.0000e-05 eta 9:29:14
+epoch [1/50] batch [235/500] time 1.360 (1.379) data 0.000 (0.007) loss 1.5430 (1.7261) acc 62.5000 (60.4654) lr 1.0000e-05 eta 9:29:00
+epoch [1/50] batch [240/500] time 1.354 (1.378) data 0.000 (0.007) loss 1.5811 (1.7237) acc 65.6250 (60.5859) lr 1.0000e-05 eta 9:28:35
+epoch [1/50] batch [245/500] time 1.342 (1.377) data 0.000 (0.007) loss 1.4033 (1.7219) acc 68.7500 (60.6378) lr 1.0000e-05 eta 9:28:16
+epoch [1/50] batch [250/500] time 1.359 (1.377) data 0.000 (0.007) loss 1.4551 (1.7234) acc 68.7500 (60.6125) lr 1.0000e-05 eta 9:27:59
+epoch [1/50] batch [255/500] time 1.352 (1.377) data 0.000 (0.007) loss 1.5400 (1.7234) acc 59.3750 (60.6618) lr 1.0000e-05 eta 9:27:43
+epoch [1/50] batch [260/500] time 1.352 (1.376) data 0.000 (0.007) loss 2.3906 (1.7175) acc 53.1250 (60.7933) lr 1.0000e-05 eta 9:27:31
+epoch [1/50] batch [265/500] time 1.369 (1.376) data 0.000 (0.006) loss 1.7559 (1.7127) acc 65.6250 (60.9552) lr 1.0000e-05 eta 9:27:16
+epoch [1/50] batch [270/500] time 1.353 (1.376) data 0.000 (0.006) loss 1.3486 (1.7093) acc 65.6250 (61.0301) lr 1.0000e-05 eta 9:27:01
+epoch [1/50] batch [275/500] time 1.356 (1.376) data 0.000 (0.006) loss 1.8271 (1.7078) acc 71.8750 (61.0682) lr 1.0000e-05 eta 9:26:55
+epoch [1/50] batch [280/500] time 1.370 (1.376) data 0.000 (0.006) loss 2.8652 (1.7116) acc 46.8750 (61.0491) lr 1.0000e-05 eta 9:26:45
+epoch [1/50] batch [285/500] time 1.365 (1.375) data 0.001 (0.006) loss 1.1494 (1.7097) acc 75.0000 (61.1294) lr 1.0000e-05 eta 9:26:34
+epoch [1/50] batch [290/500] time 1.358 (1.375) data 0.000 (0.006) loss 2.1191 (1.7120) acc 59.3750 (61.1422) lr 1.0000e-05 eta 9:26:20
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+epoch [1/50] batch [300/500] time 1.366 (1.375) data 0.000 (0.006) loss 1.2188 (1.7096) acc 68.7500 (61.1354) lr 1.0000e-05 eta 9:26:00
+epoch [1/50] batch [305/500] time 1.377 (1.375) data 0.000 (0.006) loss 1.2275 (1.7028) acc 68.7500 (61.2500) lr 1.0000e-05 eta 9:25:49
+epoch [1/50] batch [310/500] time 1.351 (1.375) data 0.000 (0.006) loss 1.5947 (1.6971) acc 65.6250 (61.3105) lr 1.0000e-05 eta 9:25:39
+epoch [1/50] batch [315/500] time 1.351 (1.374) data 0.000 (0.006) loss 2.0039 (1.6965) acc 59.3750 (61.3591) lr 1.0000e-05 eta 9:25:27
+epoch [1/50] batch [320/500] time 1.361 (1.374) data 0.000 (0.005) loss 1.1045 (1.6944) acc 78.1250 (61.4453) lr 1.0000e-05 eta 9:25:13
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+epoch [1/50] batch [340/500] time 1.375 (1.373) data 0.000 (0.005) loss 0.9780 (1.6801) acc 78.1250 (61.7647) lr 1.0000e-05 eta 9:24:24
+epoch [1/50] batch [345/500] time 1.352 (1.374) data 0.000 (0.005) loss 1.6279 (1.6789) acc 59.3750 (61.7663) lr 1.0000e-05 eta 9:24:25
+epoch [1/50] batch [350/500] time 1.356 (1.373) data 0.000 (0.005) loss 1.7861 (1.6800) acc 65.6250 (61.8036) lr 1.0000e-05 eta 9:24:13
+epoch [1/50] batch [355/500] time 1.372 (1.373) data 0.001 (0.005) loss 1.4678 (1.6768) acc 62.5000 (61.8310) lr 1.0000e-05 eta 9:24:00
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+epoch [1/50] batch [365/500] time 1.370 (1.373) data 0.000 (0.005) loss 1.0889 (1.6704) acc 68.7500 (61.8921) lr 1.0000e-05 eta 9:23:37
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+epoch [1/50] batch [380/500] time 1.364 (1.372) data 0.000 (0.005) loss 1.2100 (1.6659) acc 71.8750 (61.8914) lr 1.0000e-05 eta 9:23:10
+epoch [1/50] batch [385/500] time 1.484 (1.373) data 0.000 (0.005) loss 1.1992 (1.6654) acc 62.5000 (61.9075) lr 1.0000e-05 eta 9:23:06
+epoch [1/50] batch [390/500] time 1.349 (1.373) data 0.000 (0.005) loss 2.3633 (1.6621) acc 53.1250 (62.0353) lr 1.0000e-05 eta 9:23:00
+epoch [1/50] batch [395/500] time 1.380 (1.373) data 0.000 (0.004) loss 0.9912 (1.6581) acc 75.0000 (62.1282) lr 1.0000e-05 eta 9:22:52
+epoch [1/50] batch [400/500] time 1.352 (1.372) data 0.000 (0.004) loss 1.9746 (1.6596) acc 46.8750 (62.0938) lr 1.0000e-05 eta 9:22:43
+epoch [1/50] batch [405/500] time 1.358 (1.372) data 0.000 (0.004) loss 1.0908 (1.6587) acc 68.7500 (62.0988) lr 1.0000e-05 eta 9:22:34
+epoch [1/50] batch [410/500] time 1.364 (1.372) data 0.000 (0.004) loss 1.1006 (1.6530) acc 75.0000 (62.2332) lr 1.0000e-05 eta 9:22:25
+epoch [1/50] batch [415/500] time 1.485 (1.373) data 0.000 (0.004) loss 1.3232 (1.6466) acc 65.6250 (62.3494) lr 1.0000e-05 eta 9:22:23
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+epoch [50/50] batch [20/500] time 1.342 (1.398) data 0.000 (0.042) loss 0.7046 (1.0124) acc 71.8750 (72.3438) lr 7.8853e-06 eta 0:11:11
+epoch [50/50] batch [25/500] time 1.345 (1.390) data 0.000 (0.034) loss 0.9751 (1.0036) acc 71.8750 (71.7500) lr 7.8853e-06 eta 0:11:00
+epoch [50/50] batch [30/500] time 1.383 (1.386) data 0.000 (0.028) loss 1.4023 (1.0186) acc 68.7500 (72.0833) lr 7.8853e-06 eta 0:10:51
+epoch [50/50] batch [35/500] time 1.348 (1.382) data 0.000 (0.024) loss 1.7012 (1.0178) acc 65.6250 (72.5893) lr 7.8853e-06 eta 0:10:42
+epoch [50/50] batch [40/500] time 1.342 (1.378) data 0.000 (0.021) loss 1.4336 (1.0197) acc 68.7500 (72.8125) lr 7.8853e-06 eta 0:10:33
+epoch [50/50] batch [45/500] time 1.356 (1.377) data 0.000 (0.019) loss 0.8169 (1.0425) acc 84.3750 (72.6389) lr 7.8853e-06 eta 0:10:26
+epoch [50/50] batch [50/500] time 1.343 (1.375) data 0.000 (0.017) loss 0.6675 (1.0107) acc 93.7500 (73.8125) lr 7.8853e-06 eta 0:10:18
+epoch [50/50] batch [55/500] time 1.373 (1.375) data 0.000 (0.015) loss 0.7480 (1.0058) acc 81.2500 (74.0341) lr 7.8853e-06 eta 0:10:11
+epoch [50/50] batch [60/500] time 1.369 (1.377) data 0.000 (0.014) loss 0.8838 (0.9989) acc 78.1250 (74.4271) lr 7.8853e-06 eta 0:10:05
+epoch [50/50] batch [65/500] time 1.357 (1.375) data 0.000 (0.013) loss 1.0020 (1.0090) acc 81.2500 (74.2308) lr 7.8853e-06 eta 0:09:58
+epoch [50/50] batch [70/500] time 1.345 (1.374) data 0.000 (0.012) loss 1.3975 (1.0163) acc 71.8750 (74.1964) lr 7.8853e-06 eta 0:09:50
+epoch [50/50] batch [75/500] time 1.342 (1.373) data 0.000 (0.011) loss 1.3643 (1.0172) acc 59.3750 (73.8750) lr 7.8853e-06 eta 0:09:43
+epoch [50/50] batch [80/500] time 1.347 (1.372) data 0.000 (0.011) loss 1.3770 (1.0219) acc 68.7500 (73.7109) lr 7.8853e-06 eta 0:09:36
+epoch [50/50] batch [85/500] time 1.373 (1.371) data 0.000 (0.010) loss 1.2051 (1.0297) acc 65.6250 (73.5294) lr 7.8853e-06 eta 0:09:29
+epoch [50/50] batch [90/500] time 1.356 (1.370) data 0.000 (0.010) loss 1.2246 (1.0403) acc 68.7500 (73.4722) lr 7.8853e-06 eta 0:09:21
+epoch [50/50] batch [95/500] time 1.371 (1.370) data 0.000 (0.009) loss 1.4697 (1.0535) acc 56.2500 (73.0921) lr 7.8853e-06 eta 0:09:14
+epoch [50/50] batch [100/500] time 1.373 (1.370) data 0.000 (0.009) loss 0.9453 (1.0608) acc 68.7500 (72.7500) lr 7.8853e-06 eta 0:09:07
+epoch [50/50] batch [105/500] time 1.353 (1.371) data 0.000 (0.008) loss 1.2617 (1.0680) acc 65.6250 (72.6488) lr 7.8853e-06 eta 0:09:01
+epoch [50/50] batch [110/500] time 1.342 (1.370) data 0.000 (0.008) loss 1.5879 (1.0613) acc 68.7500 (72.8693) lr 7.8853e-06 eta 0:08:54
+epoch [50/50] batch [115/500] time 1.350 (1.370) data 0.000 (0.008) loss 0.7173 (1.0630) acc 81.2500 (73.0707) lr 7.8853e-06 eta 0:08:47
+epoch [50/50] batch [120/500] time 1.345 (1.369) data 0.000 (0.007) loss 0.8218 (1.0542) acc 75.0000 (73.0469) lr 7.8853e-06 eta 0:08:40
+epoch [50/50] batch [125/500] time 1.365 (1.369) data 0.000 (0.007) loss 0.8896 (1.0536) acc 71.8750 (73.0250) lr 7.8853e-06 eta 0:08:33
+epoch [50/50] batch [130/500] time 1.369 (1.369) data 0.000 (0.007) loss 0.7524 (1.0476) acc 75.0000 (73.1010) lr 7.8853e-06 eta 0:08:26
+epoch [50/50] batch [135/500] time 1.360 (1.368) data 0.000 (0.006) loss 1.2197 (1.0472) acc 59.3750 (73.1481) lr 7.8853e-06 eta 0:08:19
+epoch [50/50] batch [140/500] time 1.363 (1.368) data 0.000 (0.006) loss 0.7334 (1.0417) acc 75.0000 (73.3259) lr 7.8853e-06 eta 0:08:12
+epoch [50/50] batch [145/500] time 1.348 (1.368) data 0.000 (0.006) loss 0.7153 (1.0384) acc 84.3750 (73.4483) lr 7.8853e-06 eta 0:08:05
+epoch [50/50] batch [150/500] time 1.393 (1.368) data 0.000 (0.006) loss 1.1064 (1.0406) acc 68.7500 (73.4583) lr 7.8853e-06 eta 0:07:58
+epoch [50/50] batch [155/500] time 1.370 (1.368) data 0.000 (0.006) loss 0.8516 (1.0465) acc 78.1250 (73.3468) lr 7.8853e-06 eta 0:07:52
+epoch [50/50] batch [160/500] time 1.355 (1.368) data 0.000 (0.006) loss 0.7715 (1.0471) acc 75.0000 (73.3594) lr 7.8853e-06 eta 0:07:45
+epoch [50/50] batch [165/500] time 1.347 (1.368) data 0.000 (0.005) loss 0.4839 (1.0450) acc 87.5000 (73.4659) lr 7.8853e-06 eta 0:07:38
+epoch [50/50] batch [170/500] time 1.365 (1.368) data 0.001 (0.005) loss 1.1631 (1.0412) acc 75.0000 (73.5662) lr 7.8853e-06 eta 0:07:31
+epoch [50/50] batch [175/500] time 1.362 (1.367) data 0.000 (0.005) loss 0.6245 (1.0380) acc 71.8750 (73.5357) lr 7.8853e-06 eta 0:07:24
+epoch [50/50] batch [180/500] time 1.349 (1.367) data 0.000 (0.005) loss 0.6211 (1.0297) acc 84.3750 (73.7153) lr 7.8853e-06 eta 0:07:17
+epoch [50/50] batch [185/500] time 1.361 (1.367) data 0.000 (0.005) loss 1.2949 (1.0344) acc 68.7500 (73.5980) lr 7.8853e-06 eta 0:07:10
+epoch [50/50] batch [190/500] time 1.371 (1.366) data 0.000 (0.005) loss 0.6489 (1.0338) acc 84.3750 (73.5526) lr 7.8853e-06 eta 0:07:03
+epoch [50/50] batch [195/500] time 1.359 (1.367) data 0.000 (0.005) loss 1.3213 (1.0368) acc 71.8750 (73.5417) lr 7.8853e-06 eta 0:06:56
+epoch [50/50] batch [200/500] time 1.368 (1.367) data 0.000 (0.004) loss 1.2061 (1.0434) acc 75.0000 (73.2656) lr 7.8853e-06 eta 0:06:50
+epoch [50/50] batch [205/500] time 1.386 (1.368) data 0.000 (0.004) loss 1.1338 (1.0435) acc 81.2500 (73.2927) lr 7.8853e-06 eta 0:06:43
+epoch [50/50] batch [210/500] time 1.342 (1.367) data 0.000 (0.004) loss 1.0293 (1.0429) acc 78.1250 (73.2887) lr 7.8853e-06 eta 0:06:36
+epoch [50/50] batch [215/500] time 1.336 (1.367) data 0.000 (0.004) loss 0.5586 (1.0398) acc 84.3750 (73.3721) lr 7.8853e-06 eta 0:06:29
+epoch [50/50] batch [220/500] time 1.364 (1.367) data 0.000 (0.004) loss 0.9473 (1.0380) acc 75.0000 (73.4233) lr 7.8853e-06 eta 0:06:22
+epoch [50/50] batch [225/500] time 1.381 (1.367) data 0.000 (0.004) loss 1.4551 (1.0358) acc 59.3750 (73.4306) lr 7.8853e-06 eta 0:06:15
+epoch [50/50] batch [230/500] time 1.341 (1.366) data 0.000 (0.004) loss 0.8125 (1.0353) acc 84.3750 (73.4375) lr 7.8853e-06 eta 0:06:08
+epoch [50/50] batch [235/500] time 1.354 (1.366) data 0.000 (0.004) loss 0.5781 (1.0379) acc 87.5000 (73.4574) lr 7.8853e-06 eta 0:06:02
+epoch [50/50] batch [240/500] time 1.364 (1.366) data 0.000 (0.004) loss 0.7192 (1.0366) acc 81.2500 (73.5026) lr 7.8853e-06 eta 0:05:55
+epoch [50/50] batch [245/500] time 1.348 (1.366) data 0.000 (0.004) loss 1.0508 (1.0361) acc 68.7500 (73.4949) lr 7.8853e-06 eta 0:05:48
+epoch [50/50] batch [250/500] time 1.391 (1.366) data 0.000 (0.004) loss 0.6147 (1.0377) acc 81.2500 (73.4500) lr 7.8853e-06 eta 0:05:41
+epoch [50/50] batch [255/500] time 1.351 (1.366) data 0.000 (0.004) loss 1.0508 (1.0374) acc 71.8750 (73.4559) lr 7.8853e-06 eta 0:05:34
+epoch [50/50] batch [260/500] time 1.355 (1.366) data 0.000 (0.004) loss 1.0215 (1.0384) acc 62.5000 (73.3894) lr 7.8853e-06 eta 0:05:27
+epoch [50/50] batch [265/500] time 1.362 (1.366) data 0.000 (0.003) loss 1.2666 (1.0370) acc 78.1250 (73.4316) lr 7.8853e-06 eta 0:05:21
+epoch [50/50] batch [270/500] time 1.359 (1.366) data 0.000 (0.003) loss 0.6768 (1.0379) acc 90.6250 (73.4722) lr 7.8853e-06 eta 0:05:14
+epoch [50/50] batch [275/500] time 1.354 (1.366) data 0.000 (0.003) loss 1.0977 (1.0372) acc 78.1250 (73.5568) lr 7.8853e-06 eta 0:05:07
+epoch [50/50] batch [280/500] time 1.387 (1.366) data 0.000 (0.003) loss 0.8521 (1.0356) acc 75.0000 (73.5826) lr 7.8853e-06 eta 0:05:00
+epoch [50/50] batch [285/500] time 1.368 (1.366) data 0.000 (0.003) loss 0.8364 (1.0380) acc 68.7500 (73.4320) lr 7.8853e-06 eta 0:04:53
+epoch [50/50] batch [290/500] time 1.361 (1.366) data 0.000 (0.003) loss 0.8350 (1.0380) acc 78.1250 (73.3944) lr 7.8853e-06 eta 0:04:46
+epoch [50/50] batch [295/500] time 1.333 (1.366) data 0.000 (0.003) loss 1.3760 (1.0395) acc 65.6250 (73.3792) lr 7.8853e-06 eta 0:04:40
+epoch [50/50] batch [300/500] time 1.368 (1.366) data 0.000 (0.003) loss 0.6738 (1.0351) acc 87.5000 (73.4688) lr 7.8853e-06 eta 0:04:33
+epoch [50/50] batch [305/500] time 1.348 (1.366) data 0.000 (0.003) loss 1.0322 (1.0370) acc 75.0000 (73.5041) lr 7.8853e-06 eta 0:04:26
+epoch [50/50] batch [310/500] time 1.355 (1.366) data 0.000 (0.003) loss 1.0186 (1.0387) acc 71.8750 (73.4677) lr 7.8853e-06 eta 0:04:19
+epoch [50/50] batch [315/500] time 1.345 (1.366) data 0.000 (0.003) loss 1.6377 (1.0389) acc 65.6250 (73.4722) lr 7.8853e-06 eta 0:04:12
+epoch [50/50] batch [320/500] time 1.365 (1.366) data 0.000 (0.003) loss 0.7979 (1.0388) acc 84.3750 (73.5254) lr 7.8853e-06 eta 0:04:05
+epoch [50/50] batch [325/500] time 1.347 (1.366) data 0.000 (0.003) loss 1.8965 (1.0439) acc 62.5000 (73.4615) lr 7.8853e-06 eta 0:03:58
+epoch [50/50] batch [330/500] time 1.363 (1.365) data 0.000 (0.003) loss 0.6958 (1.0464) acc 84.3750 (73.4375) lr 7.8853e-06 eta 0:03:52
+epoch [50/50] batch [335/500] time 1.341 (1.365) data 0.000 (0.003) loss 0.3496 (1.0450) acc 87.5000 (73.5168) lr 7.8853e-06 eta 0:03:45
+epoch [50/50] batch [340/500] time 1.387 (1.365) data 0.000 (0.003) loss 0.7085 (1.0465) acc 84.3750 (73.4651) lr 7.8853e-06 eta 0:03:38
+epoch [50/50] batch [345/500] time 1.489 (1.366) data 0.000 (0.003) loss 1.0693 (1.0477) acc 71.8750 (73.3786) lr 7.8853e-06 eta 0:03:31
+epoch [50/50] batch [350/500] time 1.361 (1.365) data 0.000 (0.003) loss 0.7822 (1.0465) acc 75.0000 (73.3929) lr 7.8853e-06 eta 0:03:24
+epoch [50/50] batch [355/500] time 1.364 (1.365) data 0.000 (0.003) loss 0.9878 (1.0472) acc 78.1250 (73.4155) lr 7.8853e-06 eta 0:03:17
+epoch [50/50] batch [360/500] time 1.361 (1.365) data 0.000 (0.003) loss 1.0059 (1.0464) acc 78.1250 (73.4549) lr 7.8853e-06 eta 0:03:11
+epoch [50/50] batch [365/500] time 1.366 (1.365) data 0.000 (0.003) loss 1.5508 (1.0467) acc 62.5000 (73.4161) lr 7.8853e-06 eta 0:03:04
+epoch [50/50] batch [370/500] time 1.357 (1.365) data 0.000 (0.003) loss 1.0508 (1.0432) acc 65.6250 (73.4459) lr 7.8853e-06 eta 0:02:57
+epoch [50/50] batch [375/500] time 1.362 (1.365) data 0.000 (0.003) loss 2.0977 (1.0444) acc 59.3750 (73.4667) lr 7.8853e-06 eta 0:02:50
+epoch [50/50] batch [380/500] time 1.362 (1.365) data 0.000 (0.003) loss 1.5547 (1.0453) acc 68.7500 (73.4868) lr 7.8853e-06 eta 0:02:43
+epoch [50/50] batch [385/500] time 1.352 (1.365) data 0.000 (0.003) loss 0.6372 (1.0470) acc 78.1250 (73.4740) lr 7.8853e-06 eta 0:02:36
+epoch [50/50] batch [390/500] time 1.356 (1.365) data 0.000 (0.002) loss 0.4758 (1.0456) acc 84.3750 (73.4856) lr 7.8853e-06 eta 0:02:30
+epoch [50/50] batch [395/500] time 1.344 (1.365) data 0.000 (0.002) loss 0.8574 (1.0458) acc 78.1250 (73.4731) lr 7.8853e-06 eta 0:02:23
+epoch [50/50] batch [400/500] time 1.344 (1.365) data 0.000 (0.002) loss 1.2051 (1.0461) acc 68.7500 (73.5000) lr 7.8853e-06 eta 0:02:16
+epoch [50/50] batch [405/500] time 1.351 (1.365) data 0.000 (0.002) loss 1.4756 (1.0458) acc 62.5000 (73.5417) lr 7.8853e-06 eta 0:02:09
+epoch [50/50] batch [410/500] time 1.374 (1.365) data 0.000 (0.002) loss 0.6509 (1.0433) acc 71.8750 (73.6052) lr 7.8853e-06 eta 0:02:02
+epoch [50/50] batch [415/500] time 1.360 (1.365) data 0.000 (0.002) loss 1.1953 (1.0426) acc 78.1250 (73.6596) lr 7.8853e-06 eta 0:01:55
+epoch [50/50] batch [420/500] time 1.368 (1.365) data 0.000 (0.002) loss 0.5513 (1.0415) acc 81.2500 (73.6830) lr 7.8853e-06 eta 0:01:49
+epoch [50/50] batch [425/500] time 1.352 (1.365) data 0.000 (0.002) loss 0.3328 (1.0389) acc 90.6250 (73.7794) lr 7.8853e-06 eta 0:01:42
+epoch [50/50] batch [430/500] time 1.359 (1.365) data 0.000 (0.002) loss 1.3740 (1.0399) acc 71.8750 (73.7791) lr 7.8853e-06 eta 0:01:35
+epoch [50/50] batch [435/500] time 1.347 (1.364) data 0.000 (0.002) loss 1.2969 (1.0393) acc 59.3750 (73.7859) lr 7.8853e-06 eta 0:01:28
+epoch [50/50] batch [440/500] time 1.358 (1.364) data 0.000 (0.002) loss 0.6968 (1.0403) acc 75.0000 (73.7642) lr 7.8853e-06 eta 0:01:21
+epoch [50/50] batch [445/500] time 1.347 (1.364) data 0.000 (0.002) loss 1.2129 (1.0389) acc 71.8750 (73.7992) lr 7.8853e-06 eta 0:01:15
+epoch [50/50] batch [450/500] time 1.355 (1.364) data 0.000 (0.002) loss 1.3301 (1.0414) acc 68.7500 (73.7778) lr 7.8853e-06 eta 0:01:08
+epoch [50/50] batch [455/500] time 1.357 (1.364) data 0.000 (0.002) loss 0.7915 (1.0410) acc 78.1250 (73.8049) lr 7.8853e-06 eta 0:01:01
+epoch [50/50] batch [460/500] time 1.351 (1.364) data 0.000 (0.002) loss 1.1152 (1.0409) acc 68.7500 (73.7704) lr 7.8853e-06 eta 0:00:54
+epoch [50/50] batch [465/500] time 1.373 (1.364) data 0.000 (0.002) loss 0.6621 (1.0388) acc 81.2500 (73.8105) lr 7.8853e-06 eta 0:00:47
+epoch [50/50] batch [470/500] time 1.341 (1.364) data 0.000 (0.002) loss 1.6699 (1.0416) acc 68.7500 (73.7500) lr 7.8853e-06 eta 0:00:40
+epoch [50/50] batch [475/500] time 1.369 (1.364) data 0.000 (0.002) loss 1.1982 (1.0413) acc 71.8750 (73.7697) lr 7.8853e-06 eta 0:00:34
+epoch [50/50] batch [480/500] time 1.378 (1.364) data 0.000 (0.002) loss 1.0557 (1.0426) acc 81.2500 (73.7826) lr 7.8853e-06 eta 0:00:27
+epoch [50/50] batch [485/500] time 1.377 (1.364) data 0.001 (0.002) loss 0.7363 (1.0414) acc 75.0000 (73.8338) lr 7.8853e-06 eta 0:00:20
+epoch [50/50] batch [490/500] time 1.371 (1.364) data 0.000 (0.002) loss 0.8311 (1.0422) acc 75.0000 (73.8138) lr 7.8853e-06 eta 0:00:13
+epoch [50/50] batch [495/500] time 1.369 (1.364) data 0.000 (0.002) loss 0.9443 (1.0414) acc 84.3750 (73.8699) lr 7.8853e-06 eta 0:00:06
+epoch [50/50] batch [500/500] time 1.372 (1.364) data 0.000 (0.002) loss 0.8145 (1.0416) acc 84.3750 (73.8500) lr 1.9733e-06 eta 0:00:00
+Checkpoint saved to output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
+Finish training
+Deploy the last-epoch model
+Evaluate on the *test* set
+=> result
+* total: 50,000
+* correct: 38,940
+* accuracy: 77.9%
+* error: 22.1%
+* macro_f1: 77.4%
+Elapsed: 9:33:52
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
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index 00000000..a9d493d3
--- /dev/null
+++ b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/checkpoint
@@ -0,0 +1 @@
+model.pth.tar-50
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50
new file mode 100644
index 00000000..4a2eaf00
Binary files /dev/null and b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 differ
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698431659.ckb-gpu-lambda.380665.0 b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698431659.ckb-gpu-lambda.380665.0
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Binary files /dev/null and b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1698431659.ckb-gpu-lambda.380665.0 differ
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
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+++ b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt
@@ -0,0 +1,2029 @@
+***************
+** Arguments **
+***************
+backbone:
+config_file: configs/trainers/CoOp/vit_l14_ep50.yaml
+dataset_config_file: configs/datasets/imagenet.yaml
+eval_only: False
+head:
+load_epoch: None
+model_dir:
+no_train: False
+opts: ['TRAINER.COOP.N_CTX', '16', 'TRAINER.COOP.CSC', 'False', 'TRAINER.COOP.CLASS_TOKEN_POSITION', 'end', 'DATASET.NUM_SHOTS', '16']
+output_dir: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+resume:
+root: /ckb-nfs/home/zcafego/
+seed: 2
+source_domains: None
+target_domains: None
+trainer: CoOp
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+ K_TRANSFORMS: 1
+ NUM_WORKERS: 8
+ RETURN_IMG0: False
+ TEST:
+ BATCH_SIZE: 100
+ SAMPLER: SequentialSampler
+ TRAIN_U:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAME_AS_X: True
+ SAMPLER: RandomSampler
+ TRAIN_X:
+ BATCH_SIZE: 32
+ N_DOMAIN: 0
+ N_INS: 16
+ SAMPLER: RandomSampler
+DATASET:
+ ALL_AS_UNLABELED: False
+ CIFAR_C_LEVEL: 1
+ CIFAR_C_TYPE:
+ NAME: ImageNet
+ NUM_LABELED: -1
+ NUM_SHOTS: 16
+ ROOT: /ckb-nfs/home/zcafego/
+ SOURCE_DOMAINS: ()
+ STL10_FOLD: -1
+ SUBSAMPLE_CLASSES: all
+ TARGET_DOMAINS: ()
+ VAL_PERCENT: 0.1
+INPUT:
+ COLORJITTER_B: 0.4
+ COLORJITTER_C: 0.4
+ COLORJITTER_H: 0.1
+ COLORJITTER_S: 0.4
+ CROP_PADDING: 4
+ CUTOUT_LEN: 16
+ CUTOUT_N: 1
+ GB_K: 21
+ GB_P: 0.5
+ GN_MEAN: 0.0
+ GN_STD: 0.15
+ INTERPOLATION: bicubic
+ NO_TRANSFORM: False
+ PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+ PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+ RANDAUGMENT_M: 10
+ RANDAUGMENT_N: 2
+ RGS_P: 0.2
+ RRCROP_SCALE: (0.08, 1.0)
+ SIZE: (224, 224)
+ TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
+MODEL:
+ BACKBONE:
+ NAME: ViT-L/14
+ PRETRAINED: True
+ HEAD:
+ ACTIVATION: relu
+ BN: True
+ DROPOUT: 0.0
+ HIDDEN_LAYERS: ()
+ NAME:
+ INIT_WEIGHTS:
+OPTIM:
+ ADAM_BETA1: 0.9
+ ADAM_BETA2: 0.999
+ BASE_LR_MULT: 0.1
+ GAMMA: 0.1
+ LR: 0.002
+ LR_SCHEDULER: cosine
+ MAX_EPOCH: 50
+ MOMENTUM: 0.9
+ NAME: sgd
+ NEW_LAYERS: ()
+ RMSPROP_ALPHA: 0.99
+ SGD_DAMPNING: 0
+ SGD_NESTEROV: False
+ STAGED_LR: False
+ STEPSIZE: (-1,)
+ WARMUP_CONS_LR: 1e-05
+ WARMUP_EPOCH: 1
+ WARMUP_MIN_LR: 1e-05
+ WARMUP_RECOUNT: True
+ WARMUP_TYPE: constant
+ WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2
+RESUME:
+SEED: 2
+TEST:
+ COMPUTE_CMAT: False
+ EVALUATOR: Classification
+ FINAL_MODEL: last_step
+ NO_TEST: False
+ PER_CLASS_RESULT: False
+ SPLIT: test
+TRAIN:
+ CHECKPOINT_FREQ: 0
+ COUNT_ITER: train_x
+ PRINT_FREQ: 5
+TRAINER:
+ CDAC:
+ CLASS_LR_MULTI: 10
+ P_THRESH: 0.95
+ RAMPUP_COEF: 30
+ RAMPUP_ITRS: 1000
+ STRONG_TRANSFORMS: ()
+ TOPK_MATCH: 5
+ COCOOP:
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ COOP:
+ CLASS_TOKEN_POSITION: end
+ CSC: False
+ CTX_INIT:
+ N_CTX: 16
+ PREC: fp16
+ CROSSGRAD:
+ ALPHA_D: 0.5
+ ALPHA_F: 0.5
+ EPS_D: 1.0
+ EPS_F: 1.0
+ DAEL:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DAELDG:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 0.5
+ DDAIG:
+ ALPHA: 0.5
+ CLAMP: False
+ CLAMP_MAX: 1.0
+ CLAMP_MIN: -1.0
+ G_ARCH:
+ LMDA: 0.3
+ WARMUP: 0
+ DOMAINMIX:
+ ALPHA: 1.0
+ BETA: 1.0
+ TYPE: crossdomain
+ ENTMIN:
+ LMDA: 0.001
+ FIXMATCH:
+ CONF_THRE: 0.95
+ STRONG_TRANSFORMS: ()
+ WEIGHT_U: 1.0
+ M3SDA:
+ LMDA: 0.5
+ N_STEP_F: 4
+ MCD:
+ N_STEP_F: 4
+ MEANTEACHER:
+ EMA_ALPHA: 0.999
+ RAMPUP: 5
+ WEIGHT_U: 1.0
+ MIXMATCH:
+ MIXUP_BETA: 0.75
+ RAMPUP: 20000
+ TEMP: 2.0
+ WEIGHT_U: 100.0
+ MME:
+ LMDA: 0.1
+ NAME: CoOp
+ SE:
+ CONF_THRE: 0.95
+ EMA_ALPHA: 0.999
+ RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.1.0
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 20.04.6 LTS (x86_64)
+GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
+Clang version: 10.0.0-4ubuntu1
+CMake version: version 3.23.2
+Libc version: glibc-2.31
+
+Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
+Is CUDA available: True
+CUDA runtime version: Could not collect
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration:
+GPU 0: Tesla V100-SXM2-32GB
+GPU 1: Tesla V100-SXM2-32GB
+GPU 2: Tesla V100-SXM2-32GB
+GPU 3: Tesla V100-SXM2-32GB
+
+Nvidia driver version: 510.73.05
+cuDNN version: Probably one of the following:
+/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
+/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
+/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture: x86_64
+CPU op-mode(s): 32-bit, 64-bit
+Byte Order: Little Endian
+Address sizes: 46 bits physical, 48 bits virtual
+CPU(s): 64
+On-line CPU(s) list: 0-63
+Thread(s) per core: 2
+Core(s) per socket: 16
+Socket(s): 2
+NUMA node(s): 2
+Vendor ID: GenuineIntel
+CPU family: 6
+Model: 85
+Model name: Intel(R) Xeon(R) Gold 6242 CPU @ 2.80GHz
+Stepping: 7
+CPU MHz: 1200.106
+CPU max MHz: 3900.0000
+CPU min MHz: 1200.0000
+BogoMIPS: 5600.00
+Virtualization: VT-x
+L1d cache: 1 MiB
+L1i cache: 1 MiB
+L2 cache: 32 MiB
+L3 cache: 44 MiB
+NUMA node0 CPU(s): 0-15,32-47
+NUMA node1 CPU(s): 16-31,48-63
+Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
+Vulnerability L1tf: Not affected
+Vulnerability Mds: Not affected
+Vulnerability Meltdown: Not affected
+Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
+Vulnerability Srbds: Not affected
+Vulnerability Tsx async abort: Mitigation; TSX disabled
+Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
+
+Versions of relevant libraries:
+[pip3] flake8==3.7.9
+[pip3] numpy==1.24.3
+[pip3] torch==2.1.0
+[pip3] torchvision==0.8.2
+[pip3] triton==2.1.0
+[pip3] tritonclient==2.33.0
+[conda] blas 1.0 mkl
+[conda] cudatoolkit 11.8.0 h6a678d5_0
+[conda] ffmpeg 4.3 hf484d3e_0 pytorch
+[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
+[conda] mkl 2023.1.0 h213fc3f_46343
+[conda] mkl-service 2.4.0 py38h5eee18b_1
+[conda] mkl_fft 1.3.8 py38h5eee18b_0
+[conda] mkl_random 1.2.4 py38hdb19cb5_0
+[conda] numpy 1.24.3 py38hf6e8229_1
+[conda] numpy-base 1.24.3 py38h060ed82_1
+[conda] pytorch 2.1.0 py3.8_cuda11.8_cudnn8.7.0_0 pytorch
+[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
+[conda] pytorch-mutex 1.0 cuda pytorch
+[conda] torch 2.1.0 pypi_0 pypi
+[conda] torchtriton 2.1.0 py38 pytorch
+[conda] torchvision 0.16.0 py38_cu118 pytorch
+[conda] triton 2.1.0 pypi_0 pypi
+ Pillow (10.0.1)
+
+Loading trainer: CoOp
+Loading dataset: ImageNet
+Creating a 16-shot dataset
+Saving preprocessed few-shot data to /ckb-nfs/home/zcafego/imagenet/split_fewshot/shot_16-seed_2.pkl
+Building transform_train
++ random resized crop (size=(224, 224), scale=(0.08, 1.0))
++ random flip
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+--------- --------
+Dataset ImageNet
+# classes 1,000
+# train_x 16,000
+# val 50,000
+# test 50,000
+--------- --------
+Loading CLIP (backbone: ViT-L/14)
+Building custom CLIP
+Initializing a generic context
+Initial context: "X X X X X X X X X X X X X X X X"
+Number of context words (tokens): 16
+Turning off gradients in both the image and the text encoder
+Multiple GPUs detected (n_gpus=2), use all of them!
+Loading evaluator: Classification
+No checkpoint found, train from scratch
+Initialize tensorboard (log_dir=output/imagenet/CoOp/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard)
+epoch [1/50] batch [5/500] time 1.353 (2.380) data 0.000 (0.337) loss 2.6680 (3.0004) acc 46.8750 (43.1250) lr 1.0000e-05 eta 16:31:27
+epoch [1/50] batch [10/500] time 1.367 (1.874) data 0.000 (0.169) loss 2.1602 (2.6768) acc 53.1250 (46.8750) lr 1.0000e-05 eta 13:00:24
+epoch [1/50] batch [15/500] time 1.348 (1.701) data 0.000 (0.113) loss 2.3926 (2.6326) acc 53.1250 (47.0833) lr 1.0000e-05 eta 11:48:27
+epoch [1/50] batch [20/500] time 1.366 (1.617) data 0.000 (0.085) loss 2.6055 (2.5407) acc 40.6250 (48.1250) lr 1.0000e-05 eta 11:13:22
+epoch [1/50] batch [25/500] time 1.369 (1.567) data 0.000 (0.068) loss 2.2500 (2.4505) acc 46.8750 (49.3750) lr 1.0000e-05 eta 10:52:18
+epoch [1/50] batch [30/500] time 1.358 (1.532) data 0.000 (0.056) loss 1.9404 (2.4199) acc 56.2500 (49.6875) lr 1.0000e-05 eta 10:37:40
+epoch [1/50] batch [35/500] time 1.362 (1.508) data 0.000 (0.048) loss 1.7510 (2.3313) acc 56.2500 (51.0714) lr 1.0000e-05 eta 10:27:25
+epoch [1/50] batch [40/500] time 1.365 (1.490) data 0.000 (0.042) loss 2.2148 (2.2764) acc 50.0000 (51.7188) lr 1.0000e-05 eta 10:19:54
+epoch [1/50] batch [45/500] time 1.361 (1.476) data 0.000 (0.038) loss 1.5869 (2.2606) acc 59.3750 (52.1528) lr 1.0000e-05 eta 10:13:52
+epoch [1/50] batch [50/500] time 1.354 (1.464) data 0.001 (0.034) loss 1.3623 (2.2209) acc 71.8750 (53.0000) lr 1.0000e-05 eta 10:08:47
+epoch [1/50] batch [55/500] time 1.376 (1.455) data 0.000 (0.031) loss 2.5352 (2.1800) acc 40.6250 (53.6932) lr 1.0000e-05 eta 10:05:03
+epoch [1/50] batch [60/500] time 1.364 (1.447) data 0.000 (0.028) loss 1.6846 (2.1436) acc 59.3750 (54.3750) lr 1.0000e-05 eta 10:01:32
+epoch [1/50] batch [65/500] time 1.365 (1.441) data 0.000 (0.026) loss 1.3691 (2.1060) acc 68.7500 (55.0962) lr 1.0000e-05 eta 9:58:45
+epoch [1/50] batch [70/500] time 1.363 (1.435) data 0.000 (0.024) loss 1.3213 (2.0912) acc 68.7500 (55.4464) lr 1.0000e-05 eta 9:56:22
+epoch [1/50] batch [75/500] time 1.371 (1.430) data 0.000 (0.023) loss 1.7529 (2.0628) acc 56.2500 (55.6667) lr 1.0000e-05 eta 9:54:10
+epoch [1/50] batch [80/500] time 1.375 (1.426) data 0.000 (0.021) loss 1.4932 (2.0367) acc 68.7500 (56.3281) lr 1.0000e-05 eta 9:52:14
+epoch [1/50] batch [85/500] time 1.364 (1.422) data 0.001 (0.020) loss 2.0371 (2.0133) acc 40.6250 (56.4706) lr 1.0000e-05 eta 9:50:25
+epoch [1/50] batch [90/500] time 1.364 (1.419) data 0.000 (0.019) loss 1.4316 (2.0048) acc 59.3750 (56.3194) lr 1.0000e-05 eta 9:49:02
+epoch [1/50] batch [95/500] time 1.370 (1.416) data 0.000 (0.018) loss 0.9624 (1.9725) acc 75.0000 (56.8092) lr 1.0000e-05 eta 9:47:41
+epoch [1/50] batch [100/500] time 1.384 (1.414) data 0.000 (0.017) loss 1.8203 (1.9715) acc 59.3750 (56.9688) lr 1.0000e-05 eta 9:46:44
+epoch [1/50] batch [105/500] time 1.364 (1.411) data 0.000 (0.016) loss 1.8516 (1.9651) acc 59.3750 (57.3214) lr 1.0000e-05 eta 9:45:35
+epoch [1/50] batch [110/500] time 1.361 (1.410) data 0.000 (0.016) loss 1.3242 (1.9543) acc 75.0000 (57.5000) lr 1.0000e-05 eta 9:44:42
+epoch [1/50] batch [115/500] time 1.373 (1.408) data 0.000 (0.015) loss 2.3379 (1.9418) acc 56.2500 (57.6902) lr 1.0000e-05 eta 9:43:45
+epoch [1/50] batch [120/500] time 1.380 (1.406) data 0.000 (0.014) loss 2.0215 (1.9298) acc 46.8750 (57.9688) lr 1.0000e-05 eta 9:42:59
+epoch [1/50] batch [125/500] time 1.351 (1.404) data 0.000 (0.014) loss 1.2373 (1.9107) acc 71.8750 (58.3500) lr 1.0000e-05 eta 9:42:15
+epoch [1/50] batch [130/500] time 1.364 (1.404) data 0.000 (0.013) loss 1.9961 (1.9038) acc 59.3750 (58.5096) lr 1.0000e-05 eta 9:42:08
+epoch [1/50] batch [135/500] time 1.373 (1.403) data 0.000 (0.013) loss 1.7256 (1.8903) acc 56.2500 (58.7269) lr 1.0000e-05 eta 9:41:27
+epoch [1/50] batch [140/500] time 1.394 (1.402) data 0.000 (0.012) loss 2.3574 (1.8775) acc 53.1250 (58.8839) lr 1.0000e-05 eta 9:40:59
+epoch [1/50] batch [145/500] time 1.377 (1.401) data 0.000 (0.012) loss 1.8555 (1.8715) acc 56.2500 (58.8362) lr 1.0000e-05 eta 9:40:27
+epoch [1/50] batch [150/500] time 1.366 (1.400) data 0.000 (0.012) loss 1.9990 (1.8628) acc 53.1250 (59.1458) lr 1.0000e-05 eta 9:39:54
+epoch [1/50] batch [155/500] time 1.363 (1.399) data 0.000 (0.011) loss 2.0996 (1.8558) acc 59.3750 (59.3347) lr 1.0000e-05 eta 9:39:21
+epoch [1/50] batch [160/500] time 1.370 (1.398) data 0.000 (0.011) loss 1.4707 (1.8541) acc 56.2500 (59.1797) lr 1.0000e-05 eta 9:38:51
+epoch [1/50] batch [165/500] time 1.350 (1.397) data 0.000 (0.011) loss 1.7148 (1.8492) acc 53.1250 (59.1856) lr 1.0000e-05 eta 9:38:21
+epoch [1/50] batch [170/500] time 1.380 (1.396) data 0.000 (0.010) loss 1.7578 (1.8375) acc 62.5000 (59.3566) lr 1.0000e-05 eta 9:37:53
+epoch [1/50] batch [175/500] time 1.369 (1.395) data 0.000 (0.010) loss 2.0742 (1.8386) acc 59.3750 (59.4107) lr 1.0000e-05 eta 9:37:20
+epoch [1/50] batch [180/500] time 1.369 (1.395) data 0.000 (0.010) loss 0.9971 (1.8301) acc 75.0000 (59.5486) lr 1.0000e-05 eta 9:36:52
+epoch [1/50] batch [185/500] time 1.367 (1.394) data 0.000 (0.009) loss 1.1113 (1.8254) acc 65.6250 (59.6284) lr 1.0000e-05 eta 9:36:24
+epoch [1/50] batch [190/500] time 1.377 (1.393) data 0.000 (0.009) loss 1.1191 (1.8157) acc 71.8750 (59.7862) lr 1.0000e-05 eta 9:35:56
+epoch [1/50] batch [195/500] time 1.354 (1.392) data 0.000 (0.009) loss 2.0312 (1.8152) acc 62.5000 (59.8397) lr 1.0000e-05 eta 9:35:31
+epoch [1/50] batch [200/500] time 1.365 (1.392) data 0.000 (0.009) loss 1.7812 (1.8054) acc 65.6250 (60.0156) lr 1.0000e-05 eta 9:35:09
+epoch [1/50] batch [205/500] time 1.339 (1.391) data 0.000 (0.009) loss 1.7959 (1.7951) acc 62.5000 (60.1067) lr 1.0000e-05 eta 9:34:42
+epoch [1/50] batch [210/500] time 1.360 (1.390) data 0.000 (0.008) loss 1.6230 (1.7956) acc 62.5000 (60.0744) lr 1.0000e-05 eta 9:34:17
+epoch [1/50] batch [215/500] time 1.352 (1.389) data 0.000 (0.008) loss 1.2314 (1.7844) acc 65.6250 (60.2907) lr 1.0000e-05 eta 9:33:55
+epoch [1/50] batch [220/500] time 1.392 (1.389) data 0.000 (0.008) loss 2.3105 (1.7787) acc 56.2500 (60.4545) lr 1.0000e-05 eta 9:33:42
+epoch [1/50] batch [225/500] time 1.374 (1.389) data 0.000 (0.008) loss 1.5068 (1.7711) acc 65.6250 (60.5694) lr 1.0000e-05 eta 9:33:27
+epoch [1/50] batch [230/500] time 1.362 (1.389) data 0.000 (0.008) loss 1.1777 (1.7646) acc 75.0000 (60.7473) lr 1.0000e-05 eta 9:33:16
+epoch [1/50] batch [235/500] time 1.365 (1.388) data 0.000 (0.008) loss 1.4883 (1.7569) acc 62.5000 (60.8245) lr 1.0000e-05 eta 9:32:53
+epoch [1/50] batch [240/500] time 1.381 (1.388) data 0.000 (0.007) loss 1.3818 (1.7569) acc 71.8750 (60.9245) lr 1.0000e-05 eta 9:32:38
+epoch [1/50] batch [245/500] time 1.351 (1.387) data 0.000 (0.007) loss 1.0498 (1.7544) acc 75.0000 (60.9566) lr 1.0000e-05 eta 9:32:20
+epoch [1/50] batch [250/500] time 1.389 (1.387) data 0.000 (0.007) loss 1.2861 (1.7467) acc 59.3750 (61.0375) lr 1.0000e-05 eta 9:32:06
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+epoch [17/50] batch [105/500] time 1.367 (1.375) data 0.000 (0.009) loss 1.0156 (1.0858) acc 81.2500 (72.2321) lr 1.5878e-03 eta 6:27:05
+epoch [17/50] batch [110/500] time 1.370 (1.374) data 0.000 (0.008) loss 1.3740 (1.0916) acc 59.3750 (72.1875) lr 1.5878e-03 eta 6:26:43
+epoch [17/50] batch [115/500] time 1.364 (1.374) data 0.001 (0.008) loss 1.2432 (1.0984) acc 65.6250 (72.1467) lr 1.5878e-03 eta 6:26:33
+epoch [17/50] batch [120/500] time 1.378 (1.373) data 0.001 (0.008) loss 1.0518 (1.1004) acc 71.8750 (72.1094) lr 1.5878e-03 eta 6:26:16
+epoch [17/50] batch [125/500] time 1.379 (1.373) data 0.000 (0.007) loss 1.7881 (1.1120) acc 68.7500 (71.9000) lr 1.5878e-03 eta 6:26:04
+epoch [17/50] batch [130/500] time 1.363 (1.373) data 0.001 (0.007) loss 1.2285 (1.1084) acc 68.7500 (71.8510) lr 1.5878e-03 eta 6:25:55
+epoch [17/50] batch [135/500] time 1.352 (1.372) data 0.000 (0.007) loss 1.0752 (1.1049) acc 75.0000 (71.7593) lr 1.5878e-03 eta 6:25:39
+epoch [17/50] batch [140/500] time 1.380 (1.372) data 0.000 (0.007) loss 1.0723 (1.1065) acc 75.0000 (71.9420) lr 1.5878e-03 eta 6:25:28
+epoch [17/50] batch [145/500] time 1.373 (1.372) data 0.001 (0.006) loss 0.7935 (1.1001) acc 71.8750 (72.0905) lr 1.5878e-03 eta 6:25:19
+epoch [17/50] batch [150/500] time 1.354 (1.372) data 0.001 (0.006) loss 0.9170 (1.0984) acc 84.3750 (72.2500) lr 1.5878e-03 eta 6:25:12
+epoch [17/50] batch [155/500] time 1.370 (1.371) data 0.000 (0.006) loss 1.5674 (1.0948) acc 68.7500 (72.3589) lr 1.5878e-03 eta 6:25:02
+epoch [17/50] batch [160/500] time 1.369 (1.371) data 0.000 (0.006) loss 0.7935 (1.0943) acc 75.0000 (72.4023) lr 1.5878e-03 eta 6:24:49
+epoch [17/50] batch [165/500] time 1.369 (1.371) data 0.000 (0.006) loss 1.0342 (1.0987) acc 65.6250 (72.2727) lr 1.5878e-03 eta 6:24:36
+epoch [17/50] batch [170/500] time 1.366 (1.370) data 0.001 (0.006) loss 1.2266 (1.0927) acc 65.6250 (72.3713) lr 1.5878e-03 eta 6:24:23
+epoch [17/50] batch [175/500] time 1.366 (1.370) data 0.000 (0.005) loss 0.7690 (1.0931) acc 78.1250 (72.2857) lr 1.5878e-03 eta 6:24:14
+epoch [17/50] batch [180/500] time 1.368 (1.371) data 0.000 (0.005) loss 0.7910 (1.0869) acc 75.0000 (72.3264) lr 1.5878e-03 eta 6:24:11
+epoch [17/50] batch [185/500] time 1.362 (1.370) data 0.001 (0.005) loss 1.1572 (1.0845) acc 65.6250 (72.2973) lr 1.5878e-03 eta 6:24:03
+epoch [17/50] batch [190/500] time 1.519 (1.371) data 0.001 (0.005) loss 1.1660 (1.0822) acc 62.5000 (72.2697) lr 1.5878e-03 eta 6:24:05
+epoch [17/50] batch [195/500] time 1.344 (1.370) data 0.001 (0.005) loss 1.0400 (1.0847) acc 75.0000 (72.2276) lr 1.5878e-03 eta 6:23:50
+epoch [17/50] batch [200/500] time 1.364 (1.370) data 0.001 (0.005) loss 1.1260 (1.0862) acc 62.5000 (72.2188) lr 1.5878e-03 eta 6:23:43
+epoch [17/50] batch [205/500] time 1.362 (1.370) data 0.001 (0.005) loss 1.4717 (1.0903) acc 71.8750 (72.1951) lr 1.5878e-03 eta 6:23:36
+epoch [17/50] batch [210/500] time 1.353 (1.370) data 0.000 (0.005) loss 1.4531 (1.0977) acc 75.0000 (72.0387) lr 1.5878e-03 eta 6:23:27
+epoch [17/50] batch [215/500] time 1.374 (1.370) data 0.001 (0.005) loss 0.8711 (1.0939) acc 78.1250 (72.0785) lr 1.5878e-03 eta 6:23:19
+epoch [17/50] batch [220/500] time 1.363 (1.370) data 0.000 (0.004) loss 1.1328 (1.0929) acc 75.0000 (72.0597) lr 1.5878e-03 eta 6:23:09
+epoch [17/50] batch [225/500] time 1.360 (1.370) data 0.000 (0.004) loss 1.2070 (1.0928) acc 68.7500 (72.1250) lr 1.5878e-03 eta 6:23:01
+epoch [17/50] batch [230/500] time 1.356 (1.370) data 0.000 (0.004) loss 1.5508 (1.0992) acc 59.3750 (72.0109) lr 1.5878e-03 eta 6:22:47
+epoch [17/50] batch [235/500] time 1.353 (1.370) data 0.001 (0.004) loss 0.5430 (1.0957) acc 81.2500 (72.1144) lr 1.5878e-03 eta 6:22:44
+epoch [17/50] batch [240/500] time 1.370 (1.370) data 0.000 (0.004) loss 1.0811 (1.0955) acc 71.8750 (72.1615) lr 1.5878e-03 eta 6:22:39
+epoch [17/50] batch [245/500] time 1.372 (1.370) data 0.000 (0.004) loss 1.2471 (1.0947) acc 65.6250 (72.1684) lr 1.5878e-03 eta 6:22:33
+epoch [17/50] batch [250/500] time 1.350 (1.370) data 0.000 (0.004) loss 0.8711 (1.0921) acc 81.2500 (72.1875) lr 1.5878e-03 eta 6:22:22
+epoch [17/50] batch [255/500] time 1.354 (1.369) data 0.000 (0.004) loss 0.9106 (1.0884) acc 84.3750 (72.2794) lr 1.5878e-03 eta 6:22:10
+epoch [17/50] batch [260/500] time 1.381 (1.369) data 0.000 (0.004) loss 0.6816 (1.0880) acc 75.0000 (72.2957) lr 1.5878e-03 eta 6:22:01
+epoch [17/50] batch [265/500] time 1.364 (1.369) data 0.000 (0.004) loss 1.0635 (1.0843) acc 75.0000 (72.3821) lr 1.5878e-03 eta 6:21:50
+epoch [17/50] batch [270/500] time 1.368 (1.369) data 0.000 (0.004) loss 1.6201 (1.0865) acc 62.5000 (72.3727) lr 1.5878e-03 eta 6:21:41
+epoch [17/50] batch [275/500] time 1.380 (1.369) data 0.000 (0.004) loss 0.8726 (1.0808) acc 71.8750 (72.5000) lr 1.5878e-03 eta 6:21:35
+epoch [17/50] batch [280/500] time 1.369 (1.369) data 0.000 (0.004) loss 0.8901 (1.0798) acc 84.3750 (72.5781) lr 1.5878e-03 eta 6:21:29
+epoch [17/50] batch [285/500] time 1.359 (1.369) data 0.000 (0.004) loss 0.8813 (1.0813) acc 81.2500 (72.5439) lr 1.5878e-03 eta 6:21:18
+epoch [17/50] batch [290/500] time 1.365 (1.369) data 0.000 (0.003) loss 0.7954 (1.0819) acc 87.5000 (72.5323) lr 1.5878e-03 eta 6:21:09
+epoch [17/50] batch [295/500] time 1.344 (1.368) data 0.000 (0.003) loss 1.2217 (1.0822) acc 62.5000 (72.5106) lr 1.5878e-03 eta 6:20:59
+epoch [17/50] batch [300/500] time 1.353 (1.368) data 0.000 (0.003) loss 1.0010 (1.0828) acc 78.1250 (72.5208) lr 1.5878e-03 eta 6:20:49
+epoch [17/50] batch [305/500] time 1.366 (1.368) data 0.001 (0.003) loss 0.8823 (1.0819) acc 78.1250 (72.5922) lr 1.5878e-03 eta 6:20:42
+epoch [17/50] batch [310/500] time 1.371 (1.368) data 0.000 (0.003) loss 1.1475 (1.0805) acc 81.2500 (72.6613) lr 1.5878e-03 eta 6:20:37
+epoch [17/50] batch [315/500] time 1.361 (1.368) data 0.000 (0.003) loss 0.5098 (1.0770) acc 90.6250 (72.7778) lr 1.5878e-03 eta 6:20:30
+epoch [17/50] batch [320/500] time 1.371 (1.368) data 0.000 (0.003) loss 0.5874 (1.0791) acc 78.1250 (72.6758) lr 1.5878e-03 eta 6:20:22
+epoch [17/50] batch [325/500] time 1.366 (1.368) data 0.001 (0.003) loss 0.8667 (1.0792) acc 84.3750 (72.7308) lr 1.5878e-03 eta 6:20:14
+epoch [17/50] batch [330/500] time 1.369 (1.368) data 0.001 (0.003) loss 0.9224 (1.0785) acc 65.6250 (72.6705) lr 1.5878e-03 eta 6:20:05
+epoch [17/50] batch [335/500] time 1.351 (1.368) data 0.000 (0.003) loss 1.4775 (1.0789) acc 65.6250 (72.6772) lr 1.5878e-03 eta 6:20:04
+epoch [17/50] batch [340/500] time 1.367 (1.368) data 0.000 (0.003) loss 0.8564 (1.0793) acc 75.0000 (72.6195) lr 1.5878e-03 eta 6:19:55
+epoch [17/50] batch [345/500] time 1.366 (1.368) data 0.000 (0.003) loss 0.9966 (1.0772) acc 71.8750 (72.6721) lr 1.5878e-03 eta 6:19:47
+epoch [17/50] batch [350/500] time 1.362 (1.368) data 0.000 (0.003) loss 0.9062 (1.0800) acc 78.1250 (72.5893) lr 1.5878e-03 eta 6:19:39
+epoch [17/50] batch [355/500] time 1.363 (1.368) data 0.000 (0.003) loss 1.5879 (1.0824) acc 62.5000 (72.5792) lr 1.5878e-03 eta 6:19:30
+epoch [17/50] batch [360/500] time 1.388 (1.368) data 0.000 (0.003) loss 1.2656 (1.0849) acc 75.0000 (72.5694) lr 1.5878e-03 eta 6:19:24
+epoch [17/50] batch [365/500] time 1.350 (1.368) data 0.000 (0.003) loss 0.8149 (1.0847) acc 87.5000 (72.5685) lr 1.5878e-03 eta 6:19:14
+epoch [17/50] batch [370/500] time 1.339 (1.368) data 0.000 (0.003) loss 0.7573 (1.0846) acc 78.1250 (72.5929) lr 1.5878e-03 eta 6:19:03
+epoch [17/50] batch [375/500] time 1.363 (1.368) data 0.000 (0.003) loss 0.6636 (1.0817) acc 78.1250 (72.6333) lr 1.5878e-03 eta 6:18:55
+epoch [17/50] batch [380/500] time 1.352 (1.368) data 0.000 (0.003) loss 0.8472 (1.0798) acc 75.0000 (72.6316) lr 1.5878e-03 eta 6:18:52
+epoch [17/50] batch [385/500] time 1.365 (1.368) data 0.001 (0.003) loss 1.2412 (1.0798) acc 59.3750 (72.5649) lr 1.5878e-03 eta 6:18:45
+epoch [17/50] batch [390/500] time 1.372 (1.368) data 0.000 (0.003) loss 1.7988 (1.0868) acc 53.1250 (72.4439) lr 1.5878e-03 eta 6:18:37
+epoch [17/50] batch [395/500] time 1.384 (1.368) data 0.001 (0.003) loss 0.6279 (1.0855) acc 75.0000 (72.4842) lr 1.5878e-03 eta 6:18:32
+epoch [17/50] batch [400/500] time 1.366 (1.368) data 0.001 (0.003) loss 1.1670 (1.0870) acc 71.8750 (72.4609) lr 1.5878e-03 eta 6:18:24
+epoch [17/50] batch [405/500] time 1.352 (1.368) data 0.001 (0.003) loss 1.5029 (1.0880) acc 62.5000 (72.4228) lr 1.5878e-03 eta 6:18:16
+epoch [17/50] batch [410/500] time 1.360 (1.368) data 0.000 (0.003) loss 0.6812 (1.0881) acc 81.2500 (72.4543) lr 1.5878e-03 eta 6:18:08
+epoch [17/50] batch [415/500] time 1.358 (1.368) data 0.000 (0.003) loss 0.9517 (1.0894) acc 68.7500 (72.4473) lr 1.5878e-03 eta 6:18:01
+epoch [17/50] batch [420/500] time 1.361 (1.368) data 0.000 (0.003) loss 0.6875 (1.0900) acc 78.1250 (72.4628) lr 1.5878e-03 eta 6:17:56
+epoch [17/50] batch [425/500] time 1.361 (1.368) data 0.000 (0.003) loss 1.5469 (1.0920) acc 62.5000 (72.4265) lr 1.5878e-03 eta 6:17:48
+epoch [17/50] batch [430/500] time 1.349 (1.368) data 0.000 (0.003) loss 1.8291 (1.0934) acc 59.3750 (72.4346) lr 1.5878e-03 eta 6:17:39
+epoch [17/50] batch [435/500] time 1.374 (1.367) data 0.000 (0.002) loss 1.1230 (1.0927) acc 81.2500 (72.4928) lr 1.5878e-03 eta 6:17:32
+epoch [17/50] batch [440/500] time 1.367 (1.367) data 0.000 (0.002) loss 1.0283 (1.0899) acc 81.2500 (72.5639) lr 1.5878e-03 eta 6:17:23
+epoch [17/50] batch [445/500] time 1.351 (1.367) data 0.000 (0.002) loss 1.0879 (1.0890) acc 78.1250 (72.5913) lr 1.5878e-03 eta 6:17:15
+epoch [17/50] batch [450/500] time 1.357 (1.367) data 0.000 (0.002) loss 0.8413 (1.0914) acc 68.7500 (72.5417) lr 1.5878e-03 eta 6:17:05
+epoch [17/50] batch [455/500] time 1.345 (1.367) data 0.000 (0.002) loss 1.0967 (1.0940) acc 78.1250 (72.4657) lr 1.5878e-03 eta 6:16:56
+epoch [17/50] batch [460/500] time 1.353 (1.367) data 0.000 (0.002) loss 1.0225 (1.0932) acc 75.0000 (72.4864) lr 1.5878e-03 eta 6:16:48
+epoch [17/50] batch [465/500] time 1.356 (1.367) data 0.000 (0.002) loss 1.2480 (1.0934) acc 68.7500 (72.5134) lr 1.5878e-03 eta 6:16:40
+epoch [17/50] batch [470/500] time 1.361 (1.367) data 0.000 (0.002) loss 1.6299 (1.0918) acc 62.5000 (72.5532) lr 1.5878e-03 eta 6:16:31
+epoch [17/50] batch [475/500] time 1.370 (1.367) data 0.000 (0.002) loss 1.4355 (1.0935) acc 68.7500 (72.5066) lr 1.5878e-03 eta 6:16:23
+epoch [17/50] batch [480/500] time 1.363 (1.367) data 0.000 (0.002) loss 0.7661 (1.0938) acc 81.2500 (72.5326) lr 1.5878e-03 eta 6:16:19
+epoch [17/50] batch [485/500] time 1.353 (1.367) data 0.001 (0.002) loss 0.5933 (1.0942) acc 84.3750 (72.5451) lr 1.5878e-03 eta 6:16:11
+epoch [17/50] batch [490/500] time 1.377 (1.367) data 0.000 (0.002) loss 1.2627 (1.0941) acc 56.2500 (72.5383) lr 1.5878e-03 eta 6:16:04
+epoch [17/50] batch [495/500] time 1.345 (1.367) data 0.000 (0.002) loss 1.0967 (1.0953) acc 68.7500 (72.5000) lr 1.5878e-03 eta 6:15:56
+epoch [17/50] batch [500/500] time 1.363 (1.367) data 0.000 (0.002) loss 1.4971 (1.0976) acc 62.5000 (72.4313) lr 1.5358e-03 eta 6:15:50
diff --git a/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698466111.ckb-gpu-lambda.856979.0 b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698466111.ckb-gpu-lambda.856979.0
new file mode 100644
index 00000000..a8a58611
Binary files /dev/null and b/python/ClipDetection/CoOp/saved_outputs/vit_l14_ep50_16shots/nctx16_cscFalse_ctpend/seed2/tensorboard/events.out.tfevents.1698466111.ckb-gpu-lambda.856979.0 differ
diff --git a/python/ClipDetection/CoOp/train.py b/python/ClipDetection/CoOp/train.py
new file mode 100644
index 00000000..cc939fb5
--- /dev/null
+++ b/python/ClipDetection/CoOp/train.py
@@ -0,0 +1,238 @@
+################################################################
+# CHANGES MADE TO FILE #
+# ------------------------------------------------------------ #
+# Created get_trainer() function. #
+# - Functions like main() but doesn't initialize logger and #
+# returns trainer object from build_trainer(cfg) call #
+# #
+# - trainer.load_model() and trainer.train() are called in #
+# clip_component.py #
+# #
+# - extend_cfg() has added cuda parameter to set model #
+# precision to fp32 or fp16 #
+################################################################
+
+
+import argparse
+import torch
+
+from dassl.utils import setup_logger, set_random_seed, collect_env_info
+from dassl.config import get_cfg_default
+from dassl.engine import build_trainer
+
+# custom
+# import CoOp.datasets.oxford_pets
+# import CoOp.datasets.oxford_flowers
+# import CoOp.datasets.fgvc_aircraft
+# import CoOp.datasets.dtd
+# import CoOp.datasets.eurosat
+# import CoOp.datasets.stanford_cars
+# import CoOp.datasets.food101
+# import CoOp.datasets.sun397
+# import CoOp.datasets.caltech101
+# import CoOp.datasets.ucf101
+# import CoOp.datasets.cococrops
+# import CoOp.datasets.imagenet
+
+# import CoOp.datasets.imagenet_sketch
+# import CoOp.datasets.imagenetv2
+# import CoOp.datasets.imagenet_a
+# import CoOp.datasets.imagenet_r
+
+import CoOp.trainers.coop
+# import CoOp.trainers.cocoop
+# import CoOp.trainers.zsclip
+
+import os
+
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+
+
+def print_args(args, cfg):
+ print("***************")
+ print("** Arguments **")
+ print("***************")
+ optkeys = list(args.__dict__.keys())
+ optkeys.sort()
+ for key in optkeys:
+ print("{}: {}".format(key, args.__dict__[key]))
+ print("************")
+ print("** Config **")
+ print("************")
+ print(cfg)
+
+
+def reset_cfg(cfg, args):
+ if args.root:
+ cfg.DATASET.ROOT = args.root
+
+ if args.output_dir:
+ cfg.OUTPUT_DIR = args.output_dir
+
+ if args.resume:
+ cfg.RESUME = args.resume
+
+ if args.seed:
+ cfg.SEED = args.seed
+
+ if args.source_domains:
+ cfg.DATASET.SOURCE_DOMAINS = args.source_domains
+
+ if args.target_domains:
+ cfg.DATASET.TARGET_DOMAINS = args.target_domains
+
+ if args.transforms:
+ cfg.INPUT.TRANSFORMS = args.transforms
+
+ if args.trainer:
+ cfg.TRAINER.NAME = args.trainer
+
+ if args.backbone:
+ cfg.MODEL.BACKBONE.NAME = args.backbone
+
+ if args.head:
+ cfg.MODEL.HEAD.NAME = args.head
+
+
+def extend_cfg(cfg, cuda=True):
+ """
+ Add new config variables.
+
+ E.g.
+ from yacs.config import CfgNode as CN
+ cfg.TRAINER.MY_MODEL = CN()
+ cfg.TRAINER.MY_MODEL.PARAM_A = 1.
+ cfg.TRAINER.MY_MODEL.PARAM_B = 0.5
+ cfg.TRAINER.MY_MODEL.PARAM_C = False
+ """
+ from yacs.config import CfgNode as CN
+
+ cfg.TRAINER.COOP = CN()
+ cfg.TRAINER.COOP.N_CTX = 16 # number of context vectors
+ cfg.TRAINER.COOP.CSC = False # class-specific context
+ cfg.TRAINER.COOP.CTX_INIT = "" # initialization words
+ if cuda:
+ cfg.TRAINER.COOP.PREC = "fp16" # fp16, fp32, amp
+ else:
+ cfg.TRAINER.COOP.PREC = "fp32"
+ cfg.TRAINER.COOP.CLASS_TOKEN_POSITION = "end" # 'middle' or 'end' or 'front'
+
+ cfg.TRAINER.COCOOP = CN()
+ cfg.TRAINER.COCOOP.N_CTX = 16 # number of context vectors
+ cfg.TRAINER.COCOOP.CTX_INIT = "" # initialization words
+ cfg.TRAINER.COCOOP.PREC = "fp16" # fp16, fp32, amp
+
+ cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new
+
+
+def setup_cfg(args):
+ cfg = get_cfg_default()
+ extend_cfg(cfg, args.cuda)
+
+ # 1. From the dataset config file
+ if args.dataset_config_file:
+ cfg.merge_from_file(args.dataset_config_file)
+
+ # 2. From the method config file
+ if args.config_file:
+ cfg.merge_from_file(args.config_file)
+
+ # 3. From input arguments
+ reset_cfg(cfg, args)
+
+ # 4. From optional input arguments
+ cfg.merge_from_list(args.opts)
+
+ cfg.freeze()
+
+ return cfg
+
+def get_trainer(args, classnames=[], device_id=-1):
+ cfg = setup_cfg(args)
+ if cfg.SEED >= 0:
+ set_random_seed(cfg.SEED)
+
+ if torch.cuda.is_available() and cfg.USE_CUDA:
+ torch.backends.cudnn.benchmark = True
+
+ return build_trainer(cfg, classnames, device_id)
+
+def main(args, image=None):
+ cfg = setup_cfg(args)
+ if cfg.SEED >= 0:
+ print("Setting fixed seed: {}".format(cfg.SEED))
+ set_random_seed(cfg.SEED)
+ # setup_logger(cfg.OUTPUT_DIR)
+
+ if torch.cuda.is_available() and cfg.USE_CUDA:
+ torch.backends.cudnn.benchmark = True
+
+ print_args(args, cfg)
+ print("Collecting env info ...")
+ print("** System info **\n{}\n".format(collect_env_info()))
+
+ trainer = build_trainer(cfg)
+
+ if args.eval_only:
+ trainer.load_model(args.model_dir, epoch=args.load_epoch)
+ return trainer.test(image=image)
+
+ if not args.no_train:
+ trainer.train()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--root", type=str, default="", help="path to dataset")
+ parser.add_argument("--output-dir", type=str, default="", help="output directory")
+ parser.add_argument(
+ "--resume",
+ type=str,
+ default="",
+ help="checkpoint directory (from which the training resumes)",
+ )
+ parser.add_argument(
+ "--seed", type=int, default=-1, help="only positive value enables a fixed seed"
+ )
+ parser.add_argument(
+ "--source-domains", type=str, nargs="+", help="source domains for DA/DG"
+ )
+ parser.add_argument(
+ "--target-domains", type=str, nargs="+", help="target domains for DA/DG"
+ )
+ parser.add_argument(
+ "--transforms", type=str, nargs="+", help="data augmentation methods"
+ )
+ parser.add_argument(
+ "--config-file", type=str, default="", help="path to config file"
+ )
+ parser.add_argument(
+ "--dataset-config-file",
+ type=str,
+ default="",
+ help="path to config file for dataset setup",
+ )
+ parser.add_argument("--trainer", type=str, default="", help="name of trainer")
+ parser.add_argument("--backbone", type=str, default="", help="name of CNN backbone")
+ parser.add_argument("--head", type=str, default="", help="name of head")
+ parser.add_argument("--eval-only", action="store_true", help="evaluation only")
+ parser.add_argument(
+ "--model-dir",
+ type=str,
+ default="",
+ help="load model from this directory for eval-only mode",
+ )
+ parser.add_argument(
+ "--load-epoch", type=int, help="load model weights at this epoch for evaluation"
+ )
+ parser.add_argument(
+ "--no-train", action="store_true", help="do not call trainer.train()"
+ )
+ parser.add_argument(
+ "opts",
+ default=None,
+ nargs=argparse.REMAINDER,
+ help="modify config options using the command-line",
+ )
+ args = parser.parse_args()
+ main(args)
diff --git a/python/ClipDetection/CoOp/trainers/__init__.py b/python/ClipDetection/CoOp/trainers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/python/ClipDetection/CoOp/trainers/cocoop.py b/python/ClipDetection/CoOp/trainers/cocoop.py
new file mode 100644
index 00000000..51508c88
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/cocoop.py
@@ -0,0 +1,315 @@
+import os.path as osp
+from collections import OrderedDict
+import math
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.cuda.amp import GradScaler, autocast
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+from dassl.utils import load_pretrained_weights, load_checkpoint
+from dassl.optim import build_optimizer, build_lr_scheduler
+
+from clip import clip
+from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+_tokenizer = _Tokenizer()
+
+
+def load_clip_to_cpu(cfg):
+ backbone_name = cfg.MODEL.BACKBONE.NAME
+ url = clip._MODELS[backbone_name]
+ model_path = clip._download(url)
+
+ try:
+ # loading JIT archive
+ model = torch.jit.load(model_path, map_location="cpu").eval()
+ state_dict = None
+
+ except RuntimeError:
+ state_dict = torch.load(model_path, map_location="cpu")
+
+ model = clip.build_model(state_dict or model.state_dict())
+
+ return model
+
+
+class TextEncoder(nn.Module):
+ def __init__(self, clip_model):
+ super().__init__()
+ self.transformer = clip_model.transformer
+ self.positional_embedding = clip_model.positional_embedding
+ self.ln_final = clip_model.ln_final
+ self.text_projection = clip_model.text_projection
+ self.dtype = clip_model.dtype
+
+ def forward(self, prompts, tokenized_prompts):
+ x = prompts + self.positional_embedding.type(self.dtype)
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.ln_final(x).type(self.dtype)
+
+ # x.shape = [batch_size, n_ctx, transformer.width]
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection
+
+ return x
+
+
+class PromptLearner(nn.Module):
+ def __init__(self, cfg, classnames, clip_model):
+ super().__init__()
+ n_cls = len(classnames)
+ n_ctx = cfg.TRAINER.COCOOP.N_CTX
+ ctx_init = cfg.TRAINER.COCOOP.CTX_INIT
+ dtype = clip_model.dtype
+ ctx_dim = clip_model.ln_final.weight.shape[0]
+ vis_dim = clip_model.visual.output_dim
+ clip_imsize = clip_model.visual.input_resolution
+ cfg_imsize = cfg.INPUT.SIZE[0]
+ assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})"
+
+ if ctx_init:
+ # use given words to initialize context vectors
+ ctx_init = ctx_init.replace("_", " ")
+ n_ctx = len(ctx_init.split(" "))
+ prompt = clip.tokenize(ctx_init)
+ with torch.no_grad():
+ embedding = clip_model.token_embedding(prompt).type(dtype)
+ ctx_vectors = embedding[0, 1 : 1 + n_ctx, :]
+ prompt_prefix = ctx_init
+ else:
+ # random initialization
+ ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype)
+ nn.init.normal_(ctx_vectors, std=0.02)
+ prompt_prefix = " ".join(["X"] * n_ctx)
+
+ print(f'Initial context: "{prompt_prefix}"')
+ print(f"Number of context words (tokens): {n_ctx}")
+
+ self.ctx = nn.Parameter(ctx_vectors)
+
+ self.meta_net = nn.Sequential(OrderedDict([
+ ("linear1", nn.Linear(vis_dim, vis_dim // 16)),
+ ("relu", nn.ReLU(inplace=True)),
+ ("linear2", nn.Linear(vis_dim // 16, ctx_dim))
+ ]))
+
+ if cfg.TRAINER.COCOOP.PREC == "fp16":
+ self.meta_net.half()
+
+ classnames = [name.replace("_", " ") for name in classnames]
+ name_lens = [len(_tokenizer.encode(name)) for name in classnames]
+ prompts = [prompt_prefix + " " + name + "." for name in classnames]
+
+ tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) # (n_cls, n_tkn)
+ with torch.no_grad():
+ embedding = clip_model.token_embedding(tokenized_prompts).type(dtype)
+
+ # These token vectors will be saved when in save_model(),
+ # but they should be ignored in load_model() as we want to use
+ # those computed using the current class names
+ self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS
+ self.register_buffer("token_suffix", embedding[:, 1 + n_ctx :, :]) # CLS, EOS
+
+ self.n_cls = n_cls
+ self.n_ctx = n_ctx
+ self.tokenized_prompts = tokenized_prompts # torch.Tensor
+ self.name_lens = name_lens
+
+ def construct_prompts(self, ctx, prefix, suffix, label=None):
+ # dim0 is either batch_size (during training) or n_cls (during testing)
+ # ctx: context tokens, with shape of (dim0, n_ctx, ctx_dim)
+ # prefix: the sos token, with shape of (n_cls, 1, ctx_dim)
+ # suffix: remaining tokens, with shape of (n_cls, *, ctx_dim)
+
+ if label is not None:
+ prefix = prefix[label]
+ suffix = suffix[label]
+
+ prompts = torch.cat(
+ [
+ prefix, # (dim0, 1, dim)
+ ctx, # (dim0, n_ctx, dim)
+ suffix, # (dim0, *, dim)
+ ],
+ dim=1,
+ )
+
+ return prompts
+
+ def forward(self, im_features):
+ prefix = self.token_prefix
+ suffix = self.token_suffix
+ ctx = self.ctx # (n_ctx, ctx_dim)
+ bias = self.meta_net(im_features) # (batch, ctx_dim)
+ bias = bias.unsqueeze(1) # (batch, 1, ctx_dim)
+ ctx = ctx.unsqueeze(0) # (1, n_ctx, ctx_dim)
+ ctx_shifted = ctx + bias # (batch, n_ctx, ctx_dim)
+
+ # Use instance-conditioned context tokens for all classes
+ prompts = []
+ for ctx_shifted_i in ctx_shifted:
+ ctx_i = ctx_shifted_i.unsqueeze(0).expand(self.n_cls, -1, -1)
+ pts_i = self.construct_prompts(ctx_i, prefix, suffix) # (n_cls, n_tkn, ctx_dim)
+ prompts.append(pts_i)
+ prompts = torch.stack(prompts)
+
+ return prompts
+
+
+class CustomCLIP(nn.Module):
+ def __init__(self, cfg, classnames, clip_model):
+ super().__init__()
+ self.prompt_learner = PromptLearner(cfg, classnames, clip_model)
+ self.tokenized_prompts = self.prompt_learner.tokenized_prompts
+ self.image_encoder = clip_model.visual
+ self.text_encoder = TextEncoder(clip_model)
+ self.logit_scale = clip_model.logit_scale
+ self.dtype = clip_model.dtype
+
+ def forward(self, image, label=None):
+ tokenized_prompts = self.tokenized_prompts
+ logit_scale = self.logit_scale.exp()
+
+ image_features = self.image_encoder(image.type(self.dtype))
+ image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+
+ prompts = self.prompt_learner(image_features)
+
+ logits = []
+ for pts_i, imf_i in zip(prompts, image_features):
+ text_features = self.text_encoder(pts_i, tokenized_prompts)
+ text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+ l_i = logit_scale * imf_i @ text_features.t()
+ logits.append(l_i)
+ logits = torch.stack(logits)
+
+ if self.prompt_learner.training:
+ return F.cross_entropy(logits, label)
+
+ return logits
+
+
+@TRAINER_REGISTRY.register()
+class CoCoOp(TrainerX):
+ def check_cfg(self, cfg):
+ assert cfg.TRAINER.COCOOP.PREC in ["fp16", "fp32", "amp"]
+
+ def build_model(self):
+ cfg = self.cfg
+ classnames = self.dm.dataset.classnames
+
+ print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})")
+ clip_model = load_clip_to_cpu(cfg)
+
+ if cfg.TRAINER.COCOOP.PREC == "fp32" or cfg.TRAINER.COCOOP.PREC == "amp":
+ # CLIP's default precision is fp16
+ clip_model.float()
+
+ print("Building custom CLIP")
+ self.model = CustomCLIP(cfg, classnames, clip_model)
+
+ print("Turning off gradients in both the image and the text encoder")
+ name_to_update = "prompt_learner"
+
+ for name, param in self.model.named_parameters():
+ if name_to_update not in name:
+ param.requires_grad_(False)
+
+ # Double check
+ enabled = set()
+ for name, param in self.model.named_parameters():
+ if param.requires_grad:
+ enabled.add(name)
+ print(f"Parameters to be updated: {enabled}")
+
+ if cfg.MODEL.INIT_WEIGHTS:
+ load_pretrained_weights(self.model.prompt_learner, cfg.MODEL.INIT_WEIGHTS)
+
+ self.model.to(self.device)
+ # NOTE: only give prompt_learner to the optimizer
+ self.optim = build_optimizer(self.model.prompt_learner, cfg.OPTIM)
+ self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)
+ self.register_model("prompt_learner", self.model.prompt_learner, self.optim, self.sched)
+
+ self.scaler = GradScaler() if cfg.TRAINER.COCOOP.PREC == "amp" else None
+
+ # Note that multi-gpu training could be slow because CLIP's size is
+ # big, which slows down the copy operation in DataParallel
+ device_count = torch.cuda.device_count()
+ if device_count > 1:
+ print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!")
+ self.model = nn.DataParallel(self.model)
+
+ def forward_backward(self, batch):
+ image, label = self.parse_batch_train(batch)
+
+ model = self.model
+ optim = self.optim
+ scaler = self.scaler
+
+ prec = self.cfg.TRAINER.COCOOP.PREC
+ if prec == "amp":
+ with autocast():
+ loss = model(image, label)
+ optim.zero_grad()
+ scaler.scale(loss).backward()
+ scaler.step(optim)
+ scaler.update()
+ else:
+ loss = model(image, label)
+ optim.zero_grad()
+ loss.backward()
+ optim.step()
+
+ loss_summary = {"loss": loss.item()}
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch):
+ input = batch["img"]
+ label = batch["label"]
+ input = input.to(self.device)
+ label = label.to(self.device)
+ return input, label
+
+ def load_model(self, directory, epoch=None):
+ if not directory:
+ print("Note that load_model() is skipped as no pretrained model is given")
+ return
+
+ names = self.get_model_names()
+
+ # By default, the best model is loaded
+ model_file = "model-best.pth.tar"
+
+ if epoch is not None:
+ model_file = "model.pth.tar-" + str(epoch)
+
+ for name in names:
+ model_path = osp.join(directory, name, model_file)
+
+ if not osp.exists(model_path):
+ raise FileNotFoundError('Model not found at "{}"'.format(model_path))
+
+ checkpoint = load_checkpoint(model_path)
+ state_dict = checkpoint["state_dict"]
+ epoch = checkpoint["epoch"]
+
+ # Ignore fixed token vectors
+ if "token_prefix" in state_dict:
+ del state_dict["token_prefix"]
+
+ if "token_suffix" in state_dict:
+ del state_dict["token_suffix"]
+
+ print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch))
+ # set strict=False
+ self._models[name].load_state_dict(state_dict, strict=False)
diff --git a/python/ClipDetection/CoOp/trainers/coop.py b/python/ClipDetection/CoOp/trainers/coop.py
new file mode 100644
index 00000000..d0bca081
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/coop.py
@@ -0,0 +1,326 @@
+################################################################
+# CHANGES MADE TO FILE #
+# ------------------------------------------------------------ #
+# Parameter classnames=[] added to CoOp class __init__. #
+# - Used to bypass need for DataManager object. #
+# #
+################################################################
+
+import os.path as osp
+import random
+import numpy as np
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.cuda.amp import GradScaler, autocast
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+from dassl.utils import load_pretrained_weights, load_checkpoint
+from dassl.optim import build_optimizer, build_lr_scheduler
+
+from clip import clip
+from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+_tokenizer = _Tokenizer()
+
+
+def load_clip_to_cpu(cfg):
+ backbone_name = cfg.MODEL.BACKBONE.NAME
+ url = clip._MODELS[backbone_name]
+ model_path = clip._download(url, "/models")
+
+ try:
+ # loading JIT archive
+ model = torch.jit.load(model_path, map_location="cpu").eval()
+ state_dict = None
+
+ except RuntimeError:
+ state_dict = torch.load(model_path, map_location="cpu")
+
+ model = clip.build_model(state_dict or model.state_dict())
+
+ return model
+
+
+class TextEncoder(nn.Module):
+ def __init__(self, clip_model):
+ super().__init__()
+ self.transformer = clip_model.transformer
+ self.positional_embedding = clip_model.positional_embedding
+ self.ln_final = clip_model.ln_final
+ self.text_projection = clip_model.text_projection
+ self.dtype = clip_model.dtype
+
+ def forward(self, prompts, tokenized_prompts):
+ x = prompts + self.positional_embedding.type(self.dtype)
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.ln_final(x).type(self.dtype)
+
+ # x.shape = [batch_size, n_ctx, transformer.width]
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection
+
+ return x
+
+
+class PromptLearner(nn.Module):
+ def __init__(self, cfg, classnames, clip_model):
+ super().__init__()
+ n_cls = len(classnames)
+ n_ctx = cfg.TRAINER.COOP.N_CTX
+ ctx_init = cfg.TRAINER.COOP.CTX_INIT
+ dtype = clip_model.dtype
+ ctx_dim = clip_model.ln_final.weight.shape[0]
+ clip_imsize = clip_model.visual.input_resolution
+ cfg_imsize = cfg.INPUT.SIZE[0]
+ assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})"
+
+ if ctx_init:
+ # use given words to initialize context vectors
+ ctx_init = ctx_init.replace("_", " ")
+ n_ctx = len(ctx_init.split(" "))
+ prompt = clip.tokenize(ctx_init)
+ with torch.no_grad():
+ embedding = clip_model.token_embedding(prompt).type(dtype)
+ ctx_vectors = embedding[0, 1 : 1 + n_ctx, :]
+ prompt_prefix = ctx_init
+
+ else:
+ # random initialization
+ if cfg.TRAINER.COOP.CSC:
+ ctx_vectors = torch.empty(n_cls, n_ctx, ctx_dim, dtype=dtype)
+ else:
+ ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype)
+ nn.init.normal_(ctx_vectors, std=0.02)
+ prompt_prefix = " ".join(["X"] * n_ctx)
+
+
+ self.ctx = nn.Parameter(ctx_vectors) # to be optimized
+
+ classnames = [name.replace("_", " ") for name in classnames]
+ name_lens = [len(_tokenizer.encode(name)) for name in classnames]
+ prompts = [prompt_prefix + " " + name + "." for name in classnames]
+
+ tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts])
+ with torch.no_grad():
+ embedding = clip_model.token_embedding(tokenized_prompts).type(dtype)
+
+ # These token vectors will be saved when in save_model(),
+ # but they should be ignored in load_model() as we want to use
+ # those computed using the current class names
+ self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS
+ self.register_buffer("token_suffix", embedding[:, 1 + n_ctx :, :]) # CLS, EOS
+
+ self.n_cls = n_cls
+ self.n_ctx = n_ctx
+ self.tokenized_prompts = tokenized_prompts # torch.Tensor
+ self.name_lens = name_lens
+ self.class_token_position = cfg.TRAINER.COOP.CLASS_TOKEN_POSITION
+
+ def forward(self):
+ ctx = self.ctx
+ if ctx.dim() == 2:
+ ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1)
+
+ prefix = self.token_prefix
+ suffix = self.token_suffix
+
+ if self.class_token_position == "end":
+ prompts = torch.cat(
+ [
+ prefix, # (n_cls, 1, dim)
+ ctx, # (n_cls, n_ctx, dim)
+ suffix, # (n_cls, *, dim)
+ ],
+ dim=1,
+ )
+
+ elif self.class_token_position == "middle":
+ half_n_ctx = self.n_ctx // 2
+ prompts = []
+ for i in range(self.n_cls):
+ name_len = self.name_lens[i]
+ prefix_i = prefix[i : i + 1, :, :]
+ class_i = suffix[i : i + 1, :name_len, :]
+ suffix_i = suffix[i : i + 1, name_len:, :]
+ ctx_i_half1 = ctx[i : i + 1, :half_n_ctx, :]
+ ctx_i_half2 = ctx[i : i + 1, half_n_ctx:, :]
+ prompt = torch.cat(
+ [
+ prefix_i, # (1, 1, dim)
+ ctx_i_half1, # (1, n_ctx//2, dim)
+ class_i, # (1, name_len, dim)
+ ctx_i_half2, # (1, n_ctx//2, dim)
+ suffix_i, # (1, *, dim)
+ ],
+ dim=1,
+ )
+ prompts.append(prompt)
+ prompts = torch.cat(prompts, dim=0)
+
+ elif self.class_token_position == "front":
+ prompts = []
+ for i in range(self.n_cls):
+ name_len = self.name_lens[i]
+ prefix_i = prefix[i : i + 1, :, :]
+ class_i = suffix[i : i + 1, :name_len, :]
+ suffix_i = suffix[i : i + 1, name_len:, :]
+ ctx_i = ctx[i : i + 1, :, :]
+ prompt = torch.cat(
+ [
+ prefix_i, # (1, 1, dim)
+ class_i, # (1, name_len, dim)
+ ctx_i, # (1, n_ctx, dim)
+ suffix_i, # (1, *, dim)
+ ],
+ dim=1,
+ )
+ prompts.append(prompt)
+ prompts = torch.cat(prompts, dim=0)
+
+ else:
+ raise ValueError
+
+ return prompts
+
+
+class CustomCLIP(nn.Module):
+ def __init__(self, cfg, classnames, clip_model):
+ super().__init__()
+ self.prompt_learner = PromptLearner(cfg, classnames, clip_model)
+ self.tokenized_prompts = self.prompt_learner.tokenized_prompts
+ self.image_encoder = clip_model.visual
+ self.text_encoder = TextEncoder(clip_model)
+ self.logit_scale = clip_model.logit_scale
+ self.dtype = clip_model.dtype
+
+ def forward(self, image):
+ image_features = self.image_encoder(image.type(self.dtype))
+
+ prompts = self.prompt_learner()
+ tokenized_prompts = self.tokenized_prompts
+ text_features = self.text_encoder(prompts, tokenized_prompts)
+
+ image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+ text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+
+ logit_scale = self.logit_scale.exp()
+ logits = logit_scale * image_features @ text_features.t()
+
+ return logits, image_features
+
+
+@TRAINER_REGISTRY.register()
+class CoOp(TrainerX):
+ """Context Optimization (CoOp).
+
+ Learning to Prompt for Vision-Language Models
+ https://arxiv.org/abs/2109.01134
+ """
+
+ def check_cfg(self, cfg):
+ assert cfg.TRAINER.COOP.PREC in ["fp16", "fp32", "amp"]
+
+ def build_model(self, classnames=[]):
+ cfg = self.cfg
+ classnames = classnames
+
+ clip_model = load_clip_to_cpu(cfg)
+
+ if cfg.TRAINER.COOP.PREC == "fp32" or cfg.TRAINER.COOP.PREC == "amp":
+ # CLIP's default precision is fp16
+ clip_model.float()
+
+ self.model = CustomCLIP(cfg, classnames, clip_model)
+
+ for name, param in self.model.named_parameters():
+ if "prompt_learner" not in name:
+ param.requires_grad_(False)
+
+ if cfg.MODEL.INIT_WEIGHTS:
+ load_pretrained_weights(self.model.prompt_learner, cfg.MODEL.INIT_WEIGHTS)
+
+ self.model.to(self.device)
+ # NOTE: only give prompt_learner to the optimizer
+ self.optim = build_optimizer(self.model.prompt_learner, cfg.OPTIM)
+ self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)
+ self.register_model("prompt_learner", self.model.prompt_learner, self.optim, self.sched)
+
+ self.scaler = GradScaler() if cfg.TRAINER.COOP.PREC == "amp" else None
+
+ # Note that multi-gpu training could be slow because CLIP's size is
+ # big, which slows down the copy operation in DataParallel
+ # device_count = torch.cuda.device_count()
+ # if device_count > 1:
+ # self.model = nn.DataParallel(self.model)
+
+ def forward_backward(self, batch):
+ image, label = self.parse_batch_train(batch)
+
+ prec = self.cfg.TRAINER.COOP.PREC
+ if prec == "amp":
+ with autocast():
+ output = self.model(image)
+ loss = F.cross_entropy(output, label)
+ self.optim.zero_grad()
+ self.scaler.scale(loss).backward()
+ self.scaler.step(self.optim)
+ self.scaler.update()
+ else:
+ output = self.model(image)
+ loss = F.cross_entropy(output, label)
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss": loss.item(),
+ "acc": compute_accuracy(output, label)[0].item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch):
+ input = batch["img"]
+ label = batch["label"]
+ input = input.to(self.device)
+ label = label.to(self.device)
+ return input, label
+
+ def load_model(self, directory, epoch=None):
+ if not directory:
+ print("Note that load_model() is skipped as no pretrained model is given")
+ return
+
+ names = self.get_model_names()
+
+ # By default, the best model is loaded
+ model_file = "model-best.pth.tar"
+
+ if epoch is not None:
+ model_file = "model.pth.tar-" + str(epoch)
+
+ for name in names:
+ model_path = osp.join(directory, name, model_file)
+
+ if not osp.exists(model_path):
+ raise FileNotFoundError('Model not found at "{}"'.format(model_path))
+
+ checkpoint = load_checkpoint(model_path)
+ state_dict = checkpoint["state_dict"]
+ epoch = checkpoint["epoch"]
+
+ # Ignore fixed token vectors
+ if "token_prefix" in state_dict:
+ del state_dict["token_prefix"]
+
+ if "token_suffix" in state_dict:
+ del state_dict["token_suffix"]
+
+ # set strict=False
+ self._models[name].load_state_dict(state_dict, strict=False)
diff --git a/python/ClipDetection/CoOp/trainers/custom_generator.txt b/python/ClipDetection/CoOp/trainers/custom_generator.txt
new file mode 100644
index 00000000..5558e084
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/custom_generator.txt
@@ -0,0 +1,11275 @@
+Parameter containing:
+tensor(4.6052)Parameter containing:
+tensor([[-0.0260, -0.0138, -0.0155, ..., 0.0092, 0.0369, 0.0030],
+ [ 0.0144, -0.0042, -0.0056, ..., 0.0071, -0.0027, 0.0038],
+ [-0.0149, 0.0385, 0.0246, ..., -0.0042, 0.0160, 0.0317],
+ ...,
+ [-0.0068, 0.0134, -0.0277, ..., -0.0073, 0.0196, -0.0100],
+ [-0.0111, 0.0476, 0.0128, ..., -0.0119, -0.0163, -0.0131],
+ [ 0.0333, -0.0335, 0.0196, ..., 0.0075, -0.0094, -0.0114]])Parameter containing:
+tensor([ 0.0138, 0.2357, -0.1285, ..., 0.0171, -0.3332, -0.2366])Parameter containing:
+tensor([[ 0.0019, 0.0479, -0.0149, ..., 0.0005, -0.0558, -0.0460],
+ [ 0.0114, -0.0413, 0.0357, ..., 0.0271, -0.0313, -0.0383],
+ [-0.0026, -0.0340, -0.0006, ..., 0.0216, -0.0294, -0.0423],
+ ...,
+ [-0.0038, -0.0350, -0.0048, ..., -0.0228, -0.0328, -0.0412],
+ [-0.0046, -0.0360, -0.0026, ..., -0.0350, -0.0355, -0.0353],
+ [-0.0073, -0.0287, -0.0144, ..., -0.0202, -0.0272, -0.0360]])Parameter containing:
+tensor([[ 0.0224, -0.0139, -0.0072, ..., -0.0058, -0.0078, 0.0139],
+ [ 0.0186, 0.0084, 0.0400, ..., -0.0149, -0.0241, -0.0003],
+ [ 0.0075, -0.0007, 0.0195, ..., -0.0062, -0.0083, 0.0156],
+ ...,
+ [ 0.0121, -0.0165, -0.0144, ..., -0.0066, 0.0088, 0.0027],
+ [-0.0164, -0.0100, -0.0053, ..., -0.0005, -0.0001, -0.0075],
+ [ 0.0092, 0.0048, 0.0069, ..., 0.0054, -0.0162, 0.0262]])Parameter containing:
+tensor([[[[ 2.5284e-02, 1.0597e-02, 7.1678e-03, ..., 2.3422e-02,
+ 2.1683e-02, 4.8637e-03],
+ [ 1.3748e-02, -6.2103e-03, -4.8103e-03, ..., 1.6418e-02,
+ 7.0114e-03, -1.3161e-02],
+ [ 1.0048e-02, 2.1286e-03, 2.2945e-03, ..., 5.5695e-03,
+ 5.0468e-03, -1.2604e-02],
+ ...,
+ [-1.0101e-02, -2.3854e-04, -5.4588e-03, ..., -1.9226e-02,
+ -2.4017e-02, -2.4765e-02],
+ [-3.4752e-03, -1.0979e-02, -1.3603e-02, ..., -7.6408e-03,
+ 1.5583e-03, -4.4365e-03],
+ [-2.1469e-02, -4.3182e-02, -3.0121e-02, ..., -5.2147e-03,
+ 3.7346e-03, -6.8016e-03]],
+
+ [[ 1.5930e-02, -4.9095e-03, -1.2283e-02, ..., 2.5879e-02,
+ 2.4048e-02, 5.6458e-03],
+ [ 2.1019e-03, -2.4185e-02, -2.6337e-02, ..., 1.5297e-02,
+ 5.2605e-03, -1.5121e-02],
+ [ 5.1956e-03, -7.2556e-03, -9.4376e-03, ..., 7.9193e-03,
+ 5.4703e-03, -1.2398e-02],
+ ...,
+ [-4.2267e-03, 5.9624e-03, -6.2656e-04, ..., 3.8528e-03,
+ 4.2963e-04, -5.4207e-03],
+ [-2.8496e-03, -1.1482e-02, -1.3680e-02, ..., 1.5129e-02,
+ 2.3285e-02, 1.2856e-02],
+ [-2.7740e-02, -4.9561e-02, -3.1158e-02, ..., 1.2787e-02,
+ 1.7975e-02, 6.4516e-04]],
+
+ [[ 1.6403e-02, -2.0084e-03, -4.8714e-03, ..., 1.6159e-02,
+ 1.1337e-02, 5.2719e-03],
+ [ 1.8549e-03, -2.1622e-02, -2.4734e-02, ..., 6.0081e-03,
+ -4.9477e-03, -8.3389e-03],
+ [ 4.8523e-03, -1.0818e-02, -1.5015e-02, ..., 6.0272e-04,
+ -2.3615e-04, -7.6065e-03],
+ ...,
+ [ 2.4033e-03, 2.6741e-03, -8.2016e-03, ..., -1.0231e-02,
+ -1.0254e-02, -7.4234e-03],
+ [ 8.2626e-03, -3.1586e-03, -9.0256e-03, ..., -3.5248e-03,
+ 6.7329e-03, 5.1842e-03],
+ [-1.0529e-02, -2.6947e-02, -1.5656e-02, ..., 1.6518e-03,
+ 6.4774e-03, 2.7132e-04]]],
+
+
+ [[[ 1.5366e-02, 2.6184e-02, 5.8479e-03, ..., 8.4534e-03,
+ -9.0027e-03, 2.0325e-02],
+ [-1.8753e-02, -7.4615e-03, -1.6830e-02, ..., 2.9640e-03,
+ -1.9193e-05, 1.5640e-02],
+ [-2.4765e-02, -1.2184e-02, 1.7405e-03, ..., -2.6291e-02,
+ -2.8641e-02, -3.6869e-03],
+ ...,
+ [ 7.4539e-03, -6.8169e-03, 1.4931e-02, ..., 1.4824e-02,
+ -5.6839e-03, -6.2599e-03],
+ [ 6.2408e-03, -8.2016e-03, 4.1229e-02, ..., -5.0664e-06,
+ -2.8336e-02, -1.9409e-02],
+ [ 1.7120e-02, -1.1139e-02, 6.1279e-02, ..., -4.5490e-04,
+ 7.2899e-03, 4.6967e-02]],
+
+ [[ 2.1149e-02, 3.3386e-02, 1.0483e-02, ..., 6.6109e-03,
+ -1.1864e-02, 1.7838e-02],
+ [-1.5022e-02, -8.8882e-04, -9.4604e-03, ..., 4.7722e-03,
+ 3.3522e-04, 1.4709e-02],
+ [-2.3026e-02, -6.3400e-03, 1.1215e-02, ..., -2.9251e-02,
+ -3.2776e-02, -7.0419e-03],
+ ...,
+ [ 5.5275e-03, -1.1826e-02, 7.7248e-03, ..., 1.1215e-02,
+ -1.1208e-02, -9.9030e-03],
+ [ 2.2125e-03, -1.5572e-02, 3.5980e-02, ..., -4.5929e-03,
+ -3.7567e-02, -2.6779e-02],
+ [ 1.0384e-02, -2.4033e-02, 5.2917e-02, ..., -1.1375e-02,
+ -4.0016e-03, 4.0253e-02]],
+
+ [[ 1.0483e-02, 2.2339e-02, 8.9121e-04, ..., 5.2719e-03,
+ -1.2917e-02, 1.7471e-02],
+ [-2.5070e-02, -1.1597e-02, -1.9104e-02, ..., 4.4594e-03,
+ 4.0364e-04, 1.5610e-02],
+ [-3.3417e-02, -1.8112e-02, -1.3227e-03, ..., -2.8519e-02,
+ -3.0121e-02, -6.7444e-03],
+ ...,
+ [ 4.9820e-03, -1.0445e-02, 1.0681e-02, ..., 1.3405e-02,
+ -8.7509e-03, -8.8196e-03],
+ [ 2.5711e-03, -1.3268e-02, 4.1168e-02, ..., 9.7275e-04,
+ -3.0792e-02, -2.5375e-02],
+ [ 8.9951e-03, -2.1439e-02, 5.3528e-02, ..., -6.4163e-03,
+ -4.1795e-04, 3.9398e-02]]],
+
+
+ [[[ 7.2441e-03, 3.7231e-03, -2.4662e-03, ..., 1.0353e-02,
+ 1.4267e-02, 1.9363e-02],
+ [-3.0270e-03, -3.2539e-03, -1.2878e-02, ..., 9.7847e-04,
+ 5.2299e-03, 6.8626e-03],
+ [-4.3182e-03, 5.6915e-03, -3.1910e-03, ..., 8.4114e-04,
+ 2.2297e-03, 7.1373e-03],
+ ...,
+ [ 4.4632e-03, 3.8757e-03, -2.0063e-04, ..., 1.5976e-02,
+ 1.4221e-02, 1.2756e-02],
+ [ 2.5146e-02, 1.4793e-02, 5.1003e-03, ..., 2.2858e-02,
+ 2.2186e-02, 2.3026e-02],
+ [ 3.0807e-02, 2.6031e-02, 1.4259e-02, ..., 2.5116e-02,
+ 2.1759e-02, 2.4887e-02]],
+
+ [[ 6.9695e-03, 5.0888e-03, -2.8915e-03, ..., 1.7868e-02,
+ 1.9669e-02, 2.9037e-02],
+ [-2.8973e-03, -1.2035e-03, -1.1116e-02, ..., 5.5542e-03,
+ 5.9547e-03, 1.3420e-02],
+ [-9.8190e-03, 4.3716e-03, 2.3806e-04, ..., 1.1253e-03,
+ -8.7976e-04, 9.4681e-03],
+ ...,
+ [ 6.1417e-03, 5.1804e-03, 2.1095e-03, ..., 2.4979e-02,
+ 2.5146e-02, 2.7710e-02],
+ [ 3.1128e-02, 2.0096e-02, 8.0948e-03, ..., 3.3722e-02,
+ 3.3295e-02, 4.0405e-02],
+ [ 3.7659e-02, 3.2166e-02, 1.8311e-02, ..., 4.2542e-02,
+ 3.9429e-02, 4.6356e-02]],
+
+ [[ 1.7014e-02, 1.5358e-02, 1.1269e-02, ..., 2.1378e-02,
+ 2.1317e-02, 3.0075e-02],
+ [ 7.4120e-03, 7.8087e-03, 1.1091e-03, ..., 7.4654e-03,
+ 7.7209e-03, 1.2947e-02],
+ [-5.4646e-04, 1.1208e-02, 6.4545e-03, ..., 4.1313e-03,
+ 3.2539e-03, 9.7275e-03],
+ ...,
+ [ 3.3531e-03, 2.0325e-04, 1.3704e-03, ..., 7.8087e-03,
+ 7.9422e-03, 1.4809e-02],
+ [ 1.6571e-02, 2.9163e-03, 4.2105e-04, ..., 1.1787e-02,
+ 1.1337e-02, 1.8753e-02],
+ [ 1.9714e-02, 1.0704e-02, 2.9335e-03, ..., 2.1042e-02,
+ 1.5457e-02, 2.2263e-02]]],
+
+
+ ...,
+
+
+ [[[-3.1614e-04, -6.5041e-04, -6.0844e-04, ..., 6.5207e-05,
+ 2.8062e-04, -5.1928e-04],
+ [-5.2452e-06, -9.8610e-04, -9.5367e-04, ..., 1.9908e-05,
+ -1.0675e-04, -8.3148e-05],
+ [-9.5606e-04, -6.4993e-04, -1.2035e-03, ..., -6.1035e-04,
+ -4.2439e-04, 6.3181e-04],
+ ...,
+ [-7.1907e-04, -6.2132e-04, 1.0270e-04, ..., -3.2485e-05,
+ -7.7963e-04, -7.9155e-04],
+ [-9.8991e-04, 6.4433e-05, -1.2598e-03, ..., -8.0490e-04,
+ -1.2980e-03, -1.2064e-03],
+ [-2.8110e-04, -5.8031e-04, -2.4199e-04, ..., -5.1558e-05,
+ 4.4203e-04, 1.4377e-04]],
+
+ [[ 5.6839e-04, 1.9491e-05, 2.8157e-04, ..., 1.6952e-04,
+ 9.6035e-04, -5.6601e-04],
+ [ 9.8038e-04, 2.3961e-05, 4.3941e-04, ..., 3.5739e-04,
+ 7.8630e-04, -6.2466e-04],
+ [-2.5654e-04, 3.8624e-04, 1.7090e-03, ..., 6.6614e-04,
+ 6.1607e-04, 7.3719e-04],
+ ...,
+ [ 5.9319e-04, 4.7755e-04, 4.7016e-04, ..., 1.0605e-03,
+ 6.6137e-04, 3.1066e-04],
+ [ 8.3494e-04, 4.7708e-04, -1.0042e-03, ..., 6.4945e-04,
+ -2.4092e-04, 3.6502e-04],
+ [ 4.7803e-04, -3.4690e-04, 6.3467e-04, ..., 2.3830e-04,
+ 1.9407e-04, 4.0698e-04]],
+
+ [[ 2.0623e-04, -7.5936e-05, -6.9094e-04, ..., -2.5582e-04,
+ -5.5313e-04, -5.7125e-04],
+ [-9.0122e-05, 3.5214e-04, 2.0063e-04, ..., -2.6512e-04,
+ 1.1653e-04, 5.8317e-04],
+ [-9.5224e-04, -3.9577e-04, -3.9458e-04, ..., 2.1636e-04,
+ 6.0797e-05, 1.7786e-04],
+ ...,
+ [ 4.9019e-04, -1.6594e-04, 5.3120e-04, ..., 3.1352e-04,
+ 9.8825e-05, 5.7650e-04],
+ [ 7.5400e-05, 4.0960e-04, -6.8998e-04, ..., 1.8597e-04,
+ 1.9622e-04, -3.3689e-04],
+ [-1.4269e-04, -2.5558e-04, 2.9540e-04, ..., 2.1315e-04,
+ -2.9826e-04, 4.0221e-04]]],
+
+
+ [[[ 1.2306e-02, 1.8921e-02, 5.3024e-03, ..., 1.1612e-02,
+ 6.5956e-03, 2.7069e-02],
+ [ 1.1261e-02, 2.9709e-02, 1.3695e-02, ..., -8.9722e-03,
+ -1.7639e-02, -3.2501e-03],
+ [ 2.1103e-02, 3.1342e-02, 1.7731e-02, ..., -1.1185e-02,
+ -2.7451e-02, -5.5275e-03],
+ ...,
+ [ 3.7292e-02, 2.5757e-02, 6.7863e-03, ..., 1.8631e-02,
+ 2.8793e-02, 3.6560e-02],
+ [ 1.9577e-02, -5.3711e-03, -2.1255e-02, ..., -1.6953e-02,
+ -2.3621e-02, 4.6463e-03],
+ [ 1.3992e-02, -2.7130e-02, -5.1117e-02, ..., -1.2520e-02,
+ -4.0009e-02, 1.3618e-02]],
+
+ [[ 1.7109e-03, 9.4223e-03, -2.4147e-03, ..., 8.3694e-03,
+ 3.3112e-03, 2.3117e-02],
+ [ 1.1692e-03, 2.3514e-02, 1.1520e-02, ..., -8.2321e-03,
+ -1.8555e-02, -6.4278e-03],
+ [ 1.0735e-02, 2.6749e-02, 1.8997e-02, ..., -1.1795e-02,
+ -3.0396e-02, -9.2773e-03],
+ ...,
+ [ 3.4821e-02, 2.1423e-02, 8.1253e-04, ..., 1.6235e-02,
+ 2.6367e-02, 3.4302e-02],
+ [ 1.4656e-02, -1.1101e-02, -2.7344e-02, ..., -2.0676e-02,
+ -3.1250e-02, -1.2932e-03],
+ [ 5.8136e-03, -3.8971e-02, -6.3354e-02, ..., -2.1881e-02,
+ -5.2307e-02, 4.1885e-03]],
+
+ [[-1.0658e-02, -1.8530e-03, -8.5220e-03, ..., 4.6959e-03,
+ -1.9407e-03, 1.7426e-02],
+ [-1.3008e-02, 1.1108e-02, 5.3177e-03, ..., -8.9722e-03,
+ -2.1408e-02, -9.2850e-03],
+ [-3.2902e-03, 1.4580e-02, 1.3863e-02, ..., -1.2299e-02,
+ -2.9846e-02, -1.2985e-02],
+ ...,
+ [ 3.2806e-02, 2.2476e-02, 6.9771e-03, ..., 1.0704e-02,
+ 1.9516e-02, 2.4567e-02],
+ [ 1.3817e-02, -6.0501e-03, -1.4580e-02, ..., -2.2476e-02,
+ -3.2013e-02, -9.6893e-03],
+ [ 5.8556e-03, -3.2196e-02, -5.1910e-02, ..., -2.4429e-02,
+ -5.2979e-02, -3.0937e-03]]],
+
+
+ [[[ 2.2598e-02, -7.3586e-03, -2.9099e-02, ..., -2.2873e-02,
+ 8.5068e-03, -4.8706e-02],
+ [ 1.7410e-02, -3.1433e-02, -4.2816e-02, ..., -6.2675e-03,
+ 9.4528e-03, -3.8910e-02],
+ [ 2.2125e-02, -1.5839e-02, -4.1351e-02, ..., 4.6021e-02,
+ 2.4017e-02, -1.1345e-02],
+ ...,
+ [ 2.8290e-02, 3.7964e-02, 4.1656e-02, ..., 2.4734e-02,
+ -2.2011e-03, -1.9989e-02],
+ [-1.5671e-02, -2.0996e-02, -2.9182e-03, ..., 2.0828e-02,
+ 7.9803e-03, 1.4175e-02],
+ [-3.1624e-03, -9.1400e-03, 7.2937e-03, ..., 1.6663e-02,
+ 1.3590e-03, 1.6647e-02]],
+
+ [[ 2.2675e-02, -8.0872e-03, -3.0746e-02, ..., -1.9989e-02,
+ 1.6220e-02, -4.3518e-02],
+ [ 1.6678e-02, -3.2532e-02, -4.2694e-02, ..., -6.4468e-04,
+ 1.8555e-02, -3.2135e-02],
+ [ 2.0767e-02, -1.6098e-02, -3.9978e-02, ..., 5.0598e-02,
+ 2.9999e-02, -5.6038e-03],
+ ...,
+ [ 4.2328e-02, 5.0476e-02, 4.9988e-02, ..., 2.2064e-02,
+ -1.8721e-03, -1.5190e-02],
+ [-4.8981e-03, -1.0933e-02, 6.5994e-03, ..., 1.9073e-02,
+ 7.9498e-03, 2.0065e-02],
+ [ 4.9896e-03, -1.7853e-03, 1.5068e-02, ..., 1.0445e-02,
+ -2.7905e-03, 1.9196e-02]],
+
+ [[ 7.0305e-03, -1.8372e-02, -3.5797e-02, ..., -1.5244e-02,
+ 2.1683e-02, -3.0380e-02],
+ [-1.7321e-04, -4.1534e-02, -4.5563e-02, ..., 4.7989e-03,
+ 2.4796e-02, -1.7990e-02],
+ [ 2.1000e-03, -2.8732e-02, -4.5746e-02, ..., 5.0171e-02,
+ 3.4485e-02, 4.2267e-03],
+ ...,
+ [ 3.7415e-02, 4.6143e-02, 4.9500e-02, ..., 2.0111e-02,
+ 4.0741e-03, -6.3667e-03],
+ [-5.8479e-03, -9.4757e-03, 1.2398e-02, ..., 2.1317e-02,
+ 1.5762e-02, 2.5894e-02],
+ [ 2.3136e-03, -7.0858e-04, 1.7914e-02, ..., 1.1047e-02,
+ 2.1496e-03, 2.2278e-02]]]])Parameter containing:
+tensor([0.3311, 0.0032, 0.1610, ..., 2.1922, 0.0050, 0.0039])Parameter containing:
+tensor([-0.0045, -0.0452, -0.0475, ..., 0.0402, -0.1402, -0.0132])Parameter containing:
+tensor([[-7.0632e-05, -1.6510e-04, -7.0930e-05, ..., 4.5090e-03,
+ -2.9160e-02, -7.8201e-05],
+ [-1.3733e-04, 1.2165e-04, 4.2319e-05, ..., -1.6594e-03,
+ 3.1433e-02, 7.4446e-05],
+ [ 4.8018e-04, 7.7963e-04, -1.0991e-04, ..., -1.6846e-02,
+ 4.2999e-02, 1.5199e-04],
+ ...,
+ [ 2.1267e-04, 4.1032e-04, -7.2420e-05, ..., 4.8027e-03,
+ -1.7338e-03, -6.6102e-05],
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+ -7.0724e-03, 2.1725e-03],
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+ [ 2.6455e-03, 1.1078e-02, -1.6968e-02, ..., -1.1044e-03,
+ -6.0959e-03, -3.0914e-02],
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+ -4.2877e-03, 1.9012e-02]])Parameter containing:
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+ -1.2718e-02, -1.2207e-02],
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+ 5.6446e-05, -1.8692e-02],
+ [ 6.8626e-03, 1.8829e-02, 1.0094e-02, ..., -3.1891e-03,
+ 9.3765e-03, -3.5896e-03],
+ ...,
+ [ 5.6763e-03, 3.4389e-03, -8.8310e-04, ..., 5.5847e-03,
+ -3.6240e-03, -4.8103e-03],
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+ -2.5177e-02, -2.7252e-02]])Parameter containing:
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+ [-0.0110, -0.0084, 0.0369, ..., -0.0102, 0.0071, 0.0117],
+ ...,
+ [ 0.0205, -0.0055, -0.0127, ..., -0.0057, -0.0044, 0.0095],
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+ ...,
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+ ...,
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+ -1.7151e-02, -2.6047e-02],
+ ...,
+ [-2.9617e-02, -7.5722e-03, 8.4043e-06, ..., -1.1253e-02,
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+ -1.3786e-02, 1.5656e-02]])Parameter containing:
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+ ...,
+ [-0.0045, 0.0165, 0.0076, ..., 0.0143, -0.0025, -0.0003],
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+ [ 0.0109, -0.0224, -0.0055, ..., -0.0055, -0.0267, -0.0187],
+ ...,
+ [-0.0167, 0.0103, -0.0058, ..., -0.0046, -0.0215, 0.0237],
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+ [ 0.0196, -0.0219, 0.0057, ..., 0.0070, -0.0059, -0.0075],
+ ...,
+ [-0.0068, -0.0123, 0.0011, ..., 0.0024, -0.0069, -0.0181],
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+ -4.6844e-03, -1.2360e-02],
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+ 1.2108e-02, 1.5915e-02]])Parameter containing:
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+ ...,
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+ -2.9087e-03, -5.4131e-03],
+ ...,
+ [-1.4465e-02, 1.2436e-02, -1.3103e-03, ..., -8.3694e-03,
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+ -6.9504e-03, -1.4145e-02]])Parameter containing:
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+ ...,
+ [ 0.0077, -0.0031, 0.0343, ..., -0.0061, 0.0099, -0.0152],
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+ ...,
+ [ 0.0138, 0.0236, -0.0157, ..., 0.0255, -0.0269, -0.0320],
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+ ...,
+ [ 0.0026, 0.0136, -0.0086, ..., 0.0001, -0.0151, -0.0018],
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+ 1.0445e-02, 9.1171e-03],
+ ...,
+ [-2.1515e-02, -1.4336e-02, -3.9558e-03, ..., 2.7351e-03,
+ -3.2997e-03, 2.3087e-02],
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+ 6.0081e-03, 1.7471e-02]])Parameter containing:
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+ -8.7272e-02, -5.9155e-02, -1.8755e-02, 8.3795e-03, -1.3303e-01,
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+ -1.5702e-01, -1.6632e-01, -7.6623e-02, -1.2067e-01, 9.7476e-02,
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+ -2.4697e-02, -2.3285e-01, -1.8711e-01, 5.9722e-02, -5.0565e-03,
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+ -7.4359e-02, 5.9318e-02, 4.5689e-02, -3.4782e-01, 1.2438e-02,
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+ -1.5170e-01, -1.7022e-01, -1.8604e-01, 7.0737e-02, -2.3663e-01,
+ -3.6244e-02, -2.3101e-01, -2.2293e-01, 8.8521e-03, -7.0427e-02,
+ -2.2006e-01, 6.1665e-02, -1.2758e-01, -1.3123e-01, 1.3888e-02,
+ -2.7413e-01, -3.8097e-02, -3.4511e-01, -2.7228e-01, 8.0634e-02,
+ -1.5312e-02, -3.7472e-03, 2.0875e-02, 1.5117e-01, -2.4863e-01,
+ -3.2693e-01, -1.0401e-01, -1.4831e-01, -1.8991e-01, -1.6961e-01,
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+ -1.2943e-01, -1.7973e-01, 1.0068e-01, -1.7821e-01, -1.9093e-01,
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+ -2.5148e-01, -1.0391e-01, -1.6442e-01, -1.2958e-01, -6.3028e-02,
+ -7.3926e-02, -9.9090e-02, 1.4622e-02, -3.2253e-01, -2.1039e-01,
+ -3.5321e-02, -6.1373e-02, -4.3052e-03, -2.5899e-01, 1.4603e-01,
+ -6.2891e-02, 3.2609e-02, -8.3760e-02, -7.8426e-02, -5.5548e-02,
+ -2.1703e-01, -7.2742e-02, -1.0241e-01, -1.5250e-01, -5.3758e-03,
+ 1.6436e-01, -1.6233e-01, -1.1661e-01, -2.6216e-01, -3.3025e-01,
+ -1.5915e-01, -3.5974e-01, -1.6534e-01, 8.1741e-03, 1.2124e-01,
+ -7.8771e-02, -2.6709e-01, -1.5131e-01, 1.1832e-01, -9.7288e-02,
+ 1.5229e-01, -1.3003e-01, -3.0911e-01, -8.6667e-02, -6.7893e-02,
+ -1.5559e-01, -1.3761e-01, -4.8186e-02, -1.4222e-01, -5.8575e-02,
+ -4.5176e-01, -2.7698e-01, -1.8527e-01, 1.3501e-01, 1.4931e-02,
+ -5.2130e-01, -2.6890e-01, 9.5427e-02])
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/trainers/custom_generator_cuda.txt b/python/ClipDetection/CoOp/trainers/custom_generator_cuda.txt
new file mode 100644
index 00000000..1428fa4b
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/custom_generator_cuda.txt
@@ -0,0 +1,11696 @@
+Parameter containing:
+tensor(4.6052, device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-3.6102e-02, 8.2932e-03, 1.1726e-02, ..., 1.1253e-03,
+ -4.8218e-03, 1.7639e-02],
+ [ 3.5629e-03, -1.0719e-02, 2.6947e-02, ..., 6.7596e-03,
+ 9.3536e-03, 4.5252e-04],
+ [-1.8234e-02, -1.9272e-02, -4.8523e-03, ..., -1.6937e-02,
+ 3.1796e-03, -6.8932e-03],
+ ...,
+ [-2.7466e-02, -4.8752e-03, 2.0004e-02, ..., 1.2712e-03,
+ -2.6382e-02, 2.4521e-02],
+ [ 1.2375e-02, -1.9409e-02, 4.3678e-03, ..., 1.6769e-02,
+ -3.3844e-02, -1.2253e-02],
+ [ 2.2934e-02, 8.4534e-03, 3.3875e-02, ..., -3.8853e-03,
+ 2.7120e-05, -7.8354e-03]], dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0138, 0.2357, -0.1285, ..., 0.0171, -0.3332, -0.2366],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0019, 0.0479, -0.0149, ..., 0.0005, -0.0558, -0.0460],
+ [ 0.0114, -0.0413, 0.0357, ..., 0.0271, -0.0313, -0.0383],
+ [-0.0026, -0.0340, -0.0006, ..., 0.0216, -0.0294, -0.0423],
+ ...,
+ [-0.0038, -0.0350, -0.0048, ..., -0.0228, -0.0328, -0.0412],
+ [-0.0046, -0.0360, -0.0026, ..., -0.0350, -0.0355, -0.0353],
+ [-0.0073, -0.0287, -0.0144, ..., -0.0202, -0.0272, -0.0360]],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0224, -0.0139, -0.0072, ..., -0.0058, -0.0078, 0.0139],
+ [ 0.0186, 0.0084, 0.0400, ..., -0.0149, -0.0241, -0.0003],
+ [ 0.0075, -0.0007, 0.0195, ..., -0.0062, -0.0083, 0.0156],
+ ...,
+ [ 0.0121, -0.0165, -0.0144, ..., -0.0066, 0.0088, 0.0027],
+ [-0.0164, -0.0100, -0.0053, ..., -0.0005, -0.0001, -0.0075],
+ [ 0.0092, 0.0048, 0.0069, ..., 0.0054, -0.0162, 0.0262]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[[[ 2.5284e-02, 1.0597e-02, 7.1678e-03, ..., 2.3422e-02,
+ 2.1683e-02, 4.8637e-03],
+ [ 1.3748e-02, -6.2103e-03, -4.8103e-03, ..., 1.6418e-02,
+ 7.0114e-03, -1.3161e-02],
+ [ 1.0048e-02, 2.1286e-03, 2.2945e-03, ..., 5.5695e-03,
+ 5.0468e-03, -1.2604e-02],
+ ...,
+ [-1.0101e-02, -2.3854e-04, -5.4588e-03, ..., -1.9226e-02,
+ -2.4017e-02, -2.4765e-02],
+ [-3.4752e-03, -1.0979e-02, -1.3603e-02, ..., -7.6408e-03,
+ 1.5583e-03, -4.4365e-03],
+ [-2.1469e-02, -4.3182e-02, -3.0121e-02, ..., -5.2147e-03,
+ 3.7346e-03, -6.8016e-03]],
+
+ [[ 1.5930e-02, -4.9095e-03, -1.2283e-02, ..., 2.5879e-02,
+ 2.4048e-02, 5.6458e-03],
+ [ 2.1019e-03, -2.4185e-02, -2.6337e-02, ..., 1.5297e-02,
+ 5.2605e-03, -1.5121e-02],
+ [ 5.1956e-03, -7.2556e-03, -9.4376e-03, ..., 7.9193e-03,
+ 5.4703e-03, -1.2398e-02],
+ ...,
+ [-4.2267e-03, 5.9624e-03, -6.2656e-04, ..., 3.8528e-03,
+ 4.2963e-04, -5.4207e-03],
+ [-2.8496e-03, -1.1482e-02, -1.3680e-02, ..., 1.5129e-02,
+ 2.3285e-02, 1.2856e-02],
+ [-2.7740e-02, -4.9561e-02, -3.1158e-02, ..., 1.2787e-02,
+ 1.7975e-02, 6.4516e-04]],
+
+ [[ 1.6403e-02, -2.0084e-03, -4.8714e-03, ..., 1.6159e-02,
+ 1.1337e-02, 5.2719e-03],
+ [ 1.8549e-03, -2.1622e-02, -2.4734e-02, ..., 6.0081e-03,
+ -4.9477e-03, -8.3389e-03],
+ [ 4.8523e-03, -1.0818e-02, -1.5015e-02, ..., 6.0272e-04,
+ -2.3615e-04, -7.6065e-03],
+ ...,
+ [ 2.4033e-03, 2.6741e-03, -8.2016e-03, ..., -1.0231e-02,
+ -1.0254e-02, -7.4234e-03],
+ [ 8.2626e-03, -3.1586e-03, -9.0256e-03, ..., -3.5248e-03,
+ 6.7329e-03, 5.1842e-03],
+ [-1.0529e-02, -2.6947e-02, -1.5656e-02, ..., 1.6518e-03,
+ 6.4774e-03, 2.7132e-04]]],
+
+
+ [[[ 1.5366e-02, 2.6184e-02, 5.8479e-03, ..., 8.4534e-03,
+ -9.0027e-03, 2.0325e-02],
+ [-1.8753e-02, -7.4615e-03, -1.6830e-02, ..., 2.9640e-03,
+ -1.9193e-05, 1.5640e-02],
+ [-2.4765e-02, -1.2184e-02, 1.7405e-03, ..., -2.6291e-02,
+ -2.8641e-02, -3.6869e-03],
+ ...,
+ [ 7.4539e-03, -6.8169e-03, 1.4931e-02, ..., 1.4824e-02,
+ -5.6839e-03, -6.2599e-03],
+ [ 6.2408e-03, -8.2016e-03, 4.1229e-02, ..., -5.0664e-06,
+ -2.8336e-02, -1.9409e-02],
+ [ 1.7120e-02, -1.1139e-02, 6.1279e-02, ..., -4.5490e-04,
+ 7.2899e-03, 4.6967e-02]],
+
+ [[ 2.1149e-02, 3.3386e-02, 1.0483e-02, ..., 6.6109e-03,
+ -1.1864e-02, 1.7838e-02],
+ [-1.5022e-02, -8.8882e-04, -9.4604e-03, ..., 4.7722e-03,
+ 3.3522e-04, 1.4709e-02],
+ [-2.3026e-02, -6.3400e-03, 1.1215e-02, ..., -2.9251e-02,
+ -3.2776e-02, -7.0419e-03],
+ ...,
+ [ 5.5275e-03, -1.1826e-02, 7.7248e-03, ..., 1.1215e-02,
+ -1.1208e-02, -9.9030e-03],
+ [ 2.2125e-03, -1.5572e-02, 3.5980e-02, ..., -4.5929e-03,
+ -3.7567e-02, -2.6779e-02],
+ [ 1.0384e-02, -2.4033e-02, 5.2917e-02, ..., -1.1375e-02,
+ -4.0016e-03, 4.0253e-02]],
+
+ [[ 1.0483e-02, 2.2339e-02, 8.9121e-04, ..., 5.2719e-03,
+ -1.2917e-02, 1.7471e-02],
+ [-2.5070e-02, -1.1597e-02, -1.9104e-02, ..., 4.4594e-03,
+ 4.0364e-04, 1.5610e-02],
+ [-3.3417e-02, -1.8112e-02, -1.3227e-03, ..., -2.8519e-02,
+ -3.0121e-02, -6.7444e-03],
+ ...,
+ [ 4.9820e-03, -1.0445e-02, 1.0681e-02, ..., 1.3405e-02,
+ -8.7509e-03, -8.8196e-03],
+ [ 2.5711e-03, -1.3268e-02, 4.1168e-02, ..., 9.7275e-04,
+ -3.0792e-02, -2.5375e-02],
+ [ 8.9951e-03, -2.1439e-02, 5.3528e-02, ..., -6.4163e-03,
+ -4.1795e-04, 3.9398e-02]]],
+
+
+ [[[ 7.2441e-03, 3.7231e-03, -2.4662e-03, ..., 1.0353e-02,
+ 1.4267e-02, 1.9363e-02],
+ [-3.0270e-03, -3.2539e-03, -1.2878e-02, ..., 9.7847e-04,
+ 5.2299e-03, 6.8626e-03],
+ [-4.3182e-03, 5.6915e-03, -3.1910e-03, ..., 8.4114e-04,
+ 2.2297e-03, 7.1373e-03],
+ ...,
+ [ 4.4632e-03, 3.8757e-03, -2.0063e-04, ..., 1.5976e-02,
+ 1.4221e-02, 1.2756e-02],
+ [ 2.5146e-02, 1.4793e-02, 5.1003e-03, ..., 2.2858e-02,
+ 2.2186e-02, 2.3026e-02],
+ [ 3.0807e-02, 2.6031e-02, 1.4259e-02, ..., 2.5116e-02,
+ 2.1759e-02, 2.4887e-02]],
+
+ [[ 6.9695e-03, 5.0888e-03, -2.8915e-03, ..., 1.7868e-02,
+ 1.9669e-02, 2.9037e-02],
+ [-2.8973e-03, -1.2035e-03, -1.1116e-02, ..., 5.5542e-03,
+ 5.9547e-03, 1.3420e-02],
+ [-9.8190e-03, 4.3716e-03, 2.3806e-04, ..., 1.1253e-03,
+ -8.7976e-04, 9.4681e-03],
+ ...,
+ [ 6.1417e-03, 5.1804e-03, 2.1095e-03, ..., 2.4979e-02,
+ 2.5146e-02, 2.7710e-02],
+ [ 3.1128e-02, 2.0096e-02, 8.0948e-03, ..., 3.3722e-02,
+ 3.3295e-02, 4.0405e-02],
+ [ 3.7659e-02, 3.2166e-02, 1.8311e-02, ..., 4.2542e-02,
+ 3.9429e-02, 4.6356e-02]],
+
+ [[ 1.7014e-02, 1.5358e-02, 1.1269e-02, ..., 2.1378e-02,
+ 2.1317e-02, 3.0075e-02],
+ [ 7.4120e-03, 7.8087e-03, 1.1091e-03, ..., 7.4654e-03,
+ 7.7209e-03, 1.2947e-02],
+ [-5.4646e-04, 1.1208e-02, 6.4545e-03, ..., 4.1313e-03,
+ 3.2539e-03, 9.7275e-03],
+ ...,
+ [ 3.3531e-03, 2.0325e-04, 1.3704e-03, ..., 7.8087e-03,
+ 7.9422e-03, 1.4809e-02],
+ [ 1.6571e-02, 2.9163e-03, 4.2105e-04, ..., 1.1787e-02,
+ 1.1337e-02, 1.8753e-02],
+ [ 1.9714e-02, 1.0704e-02, 2.9335e-03, ..., 2.1042e-02,
+ 1.5457e-02, 2.2263e-02]]],
+
+
+ ...,
+
+
+ [[[-3.1614e-04, -6.5041e-04, -6.0844e-04, ..., 6.5207e-05,
+ 2.8062e-04, -5.1928e-04],
+ [-5.2452e-06, -9.8610e-04, -9.5367e-04, ..., 1.9908e-05,
+ -1.0675e-04, -8.3148e-05],
+ [-9.5606e-04, -6.4993e-04, -1.2035e-03, ..., -6.1035e-04,
+ -4.2439e-04, 6.3181e-04],
+ ...,
+ [-7.1907e-04, -6.2132e-04, 1.0270e-04, ..., -3.2485e-05,
+ -7.7963e-04, -7.9155e-04],
+ [-9.8991e-04, 6.4433e-05, -1.2598e-03, ..., -8.0490e-04,
+ -1.2980e-03, -1.2064e-03],
+ [-2.8110e-04, -5.8031e-04, -2.4199e-04, ..., -5.1558e-05,
+ 4.4203e-04, 1.4377e-04]],
+
+ [[ 5.6839e-04, 1.9491e-05, 2.8157e-04, ..., 1.6952e-04,
+ 9.6035e-04, -5.6601e-04],
+ [ 9.8038e-04, 2.3961e-05, 4.3941e-04, ..., 3.5739e-04,
+ 7.8630e-04, -6.2466e-04],
+ [-2.5654e-04, 3.8624e-04, 1.7090e-03, ..., 6.6614e-04,
+ 6.1607e-04, 7.3719e-04],
+ ...,
+ [ 5.9319e-04, 4.7755e-04, 4.7016e-04, ..., 1.0605e-03,
+ 6.6137e-04, 3.1066e-04],
+ [ 8.3494e-04, 4.7708e-04, -1.0042e-03, ..., 6.4945e-04,
+ -2.4092e-04, 3.6502e-04],
+ [ 4.7803e-04, -3.4690e-04, 6.3467e-04, ..., 2.3830e-04,
+ 1.9407e-04, 4.0698e-04]],
+
+ [[ 2.0623e-04, -7.5936e-05, -6.9094e-04, ..., -2.5582e-04,
+ -5.5313e-04, -5.7125e-04],
+ [-9.0122e-05, 3.5214e-04, 2.0063e-04, ..., -2.6512e-04,
+ 1.1653e-04, 5.8317e-04],
+ [-9.5224e-04, -3.9577e-04, -3.9458e-04, ..., 2.1636e-04,
+ 6.0797e-05, 1.7786e-04],
+ ...,
+ [ 4.9019e-04, -1.6594e-04, 5.3120e-04, ..., 3.1352e-04,
+ 9.8825e-05, 5.7650e-04],
+ [ 7.5400e-05, 4.0960e-04, -6.8998e-04, ..., 1.8597e-04,
+ 1.9622e-04, -3.3689e-04],
+ [-1.4269e-04, -2.5558e-04, 2.9540e-04, ..., 2.1315e-04,
+ -2.9826e-04, 4.0221e-04]]],
+
+
+ [[[ 1.2306e-02, 1.8921e-02, 5.3024e-03, ..., 1.1612e-02,
+ 6.5956e-03, 2.7069e-02],
+ [ 1.1261e-02, 2.9709e-02, 1.3695e-02, ..., -8.9722e-03,
+ -1.7639e-02, -3.2501e-03],
+ [ 2.1103e-02, 3.1342e-02, 1.7731e-02, ..., -1.1185e-02,
+ -2.7451e-02, -5.5275e-03],
+ ...,
+ [ 3.7292e-02, 2.5757e-02, 6.7863e-03, ..., 1.8631e-02,
+ 2.8793e-02, 3.6560e-02],
+ [ 1.9577e-02, -5.3711e-03, -2.1255e-02, ..., -1.6953e-02,
+ -2.3621e-02, 4.6463e-03],
+ [ 1.3992e-02, -2.7130e-02, -5.1117e-02, ..., -1.2520e-02,
+ -4.0009e-02, 1.3618e-02]],
+
+ [[ 1.7109e-03, 9.4223e-03, -2.4147e-03, ..., 8.3694e-03,
+ 3.3112e-03, 2.3117e-02],
+ [ 1.1692e-03, 2.3514e-02, 1.1520e-02, ..., -8.2321e-03,
+ -1.8555e-02, -6.4278e-03],
+ [ 1.0735e-02, 2.6749e-02, 1.8997e-02, ..., -1.1795e-02,
+ -3.0396e-02, -9.2773e-03],
+ ...,
+ [ 3.4821e-02, 2.1423e-02, 8.1253e-04, ..., 1.6235e-02,
+ 2.6367e-02, 3.4302e-02],
+ [ 1.4656e-02, -1.1101e-02, -2.7344e-02, ..., -2.0676e-02,
+ -3.1250e-02, -1.2932e-03],
+ [ 5.8136e-03, -3.8971e-02, -6.3354e-02, ..., -2.1881e-02,
+ -5.2307e-02, 4.1885e-03]],
+
+ [[-1.0658e-02, -1.8530e-03, -8.5220e-03, ..., 4.6959e-03,
+ -1.9407e-03, 1.7426e-02],
+ [-1.3008e-02, 1.1108e-02, 5.3177e-03, ..., -8.9722e-03,
+ -2.1408e-02, -9.2850e-03],
+ [-3.2902e-03, 1.4580e-02, 1.3863e-02, ..., -1.2299e-02,
+ -2.9846e-02, -1.2985e-02],
+ ...,
+ [ 3.2806e-02, 2.2476e-02, 6.9771e-03, ..., 1.0704e-02,
+ 1.9516e-02, 2.4567e-02],
+ [ 1.3817e-02, -6.0501e-03, -1.4580e-02, ..., -2.2476e-02,
+ -3.2013e-02, -9.6893e-03],
+ [ 5.8556e-03, -3.2196e-02, -5.1910e-02, ..., -2.4429e-02,
+ -5.2979e-02, -3.0937e-03]]],
+
+
+ [[[ 2.2598e-02, -7.3586e-03, -2.9099e-02, ..., -2.2873e-02,
+ 8.5068e-03, -4.8706e-02],
+ [ 1.7410e-02, -3.1433e-02, -4.2816e-02, ..., -6.2675e-03,
+ 9.4528e-03, -3.8910e-02],
+ [ 2.2125e-02, -1.5839e-02, -4.1351e-02, ..., 4.6021e-02,
+ 2.4017e-02, -1.1345e-02],
+ ...,
+ [ 2.8290e-02, 3.7964e-02, 4.1656e-02, ..., 2.4734e-02,
+ -2.2011e-03, -1.9989e-02],
+ [-1.5671e-02, -2.0996e-02, -2.9182e-03, ..., 2.0828e-02,
+ 7.9803e-03, 1.4175e-02],
+ [-3.1624e-03, -9.1400e-03, 7.2937e-03, ..., 1.6663e-02,
+ 1.3590e-03, 1.6647e-02]],
+
+ [[ 2.2675e-02, -8.0872e-03, -3.0746e-02, ..., -1.9989e-02,
+ 1.6220e-02, -4.3518e-02],
+ [ 1.6678e-02, -3.2532e-02, -4.2694e-02, ..., -6.4468e-04,
+ 1.8555e-02, -3.2135e-02],
+ [ 2.0767e-02, -1.6098e-02, -3.9978e-02, ..., 5.0598e-02,
+ 2.9999e-02, -5.6038e-03],
+ ...,
+ [ 4.2328e-02, 5.0476e-02, 4.9988e-02, ..., 2.2064e-02,
+ -1.8721e-03, -1.5190e-02],
+ [-4.8981e-03, -1.0933e-02, 6.5994e-03, ..., 1.9073e-02,
+ 7.9498e-03, 2.0065e-02],
+ [ 4.9896e-03, -1.7853e-03, 1.5068e-02, ..., 1.0445e-02,
+ -2.7905e-03, 1.9196e-02]],
+
+ [[ 7.0305e-03, -1.8372e-02, -3.5797e-02, ..., -1.5244e-02,
+ 2.1683e-02, -3.0380e-02],
+ [-1.7321e-04, -4.1534e-02, -4.5563e-02, ..., 4.7989e-03,
+ 2.4796e-02, -1.7990e-02],
+ [ 2.1000e-03, -2.8732e-02, -4.5746e-02, ..., 5.0171e-02,
+ 3.4485e-02, 4.2267e-03],
+ ...,
+ [ 3.7415e-02, 4.6143e-02, 4.9500e-02, ..., 2.0111e-02,
+ 4.0741e-03, -6.3667e-03],
+ [-5.8479e-03, -9.4757e-03, 1.2398e-02, ..., 2.1317e-02,
+ 1.5762e-02, 2.5894e-02],
+ [ 2.3136e-03, -7.0858e-04, 1.7914e-02, ..., 1.1047e-02,
+ 2.1496e-03, 2.2278e-02]]]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([0.3311, 0.0032, 0.1610, ..., 2.1922, 0.0050, 0.0039], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0045, -0.0452, -0.0475, ..., 0.0402, -0.1402, -0.0132],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-7.0632e-05, -1.6510e-04, -7.0930e-05, ..., 4.5090e-03,
+ -2.9160e-02, -7.8201e-05],
+ [-1.3733e-04, 1.2165e-04, 4.2319e-05, ..., -1.6594e-03,
+ 3.1433e-02, 7.4446e-05],
+ [ 4.8018e-04, 7.7963e-04, -1.0991e-04, ..., -1.6846e-02,
+ 4.2999e-02, 1.5199e-04],
+ ...,
+ [ 2.1267e-04, 4.1032e-04, -7.2420e-05, ..., 4.8027e-03,
+ -1.7338e-03, -6.6102e-05],
+ [ 3.0518e-04, -4.4405e-05, -2.2709e-04, ..., 1.1551e-02,
+ 3.3436e-03, 7.4685e-05],
+ [-2.8849e-05, 4.5919e-04, 9.3341e-05, ..., -1.1314e-02,
+ 3.7670e-03, -7.7844e-05]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 1.5674, -1.6143, -0.8208, ..., 0.0115, 0.0107, -0.0043],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-6.7596e-03, 8.8043e-03, -7.9422e-03, ..., -8.6441e-03,
+ -8.7433e-03, 3.5553e-03],
+ [ 1.2077e-02, 5.8784e-03, 1.1253e-02, ..., -3.7060e-03,
+ 2.0008e-03, 3.8319e-03],
+ [-5.2032e-03, 2.6913e-03, 1.2894e-02, ..., 6.4812e-03,
+ -3.0398e-05, -4.2796e-04],
+ ...,
+ [-4.5037e-04, -2.5063e-03, -3.2768e-03, ..., -3.2768e-03,
+ -1.9409e-02, 9.2545e-03],
+ [-7.3624e-03, 2.8419e-03, -7.9193e-03, ..., 4.0627e-04,
+ -1.3866e-03, -6.7186e-04],
+ [ 9.0408e-03, 1.5287e-03, 1.6737e-03, ..., 2.4242e-03,
+ -3.7575e-03, 4.9667e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0262, -0.0654, 0.0032, ..., 0.1761, -0.0446, 0.0023],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([6.1186e-04, 2.0990e-03, 3.0166e-05, ..., 6.9025e-01, 3.5588e-01,
+ 1.4703e-04], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 1.3605e-04, 8.3127e-04, -2.0098e-05, ..., -3.6831e-01,
+ 1.7861e-01, 7.4003e-05], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 3.6597e-04, 2.6047e-05, 1.1921e-07, ..., -6.6109e-03,
+ -1.1740e-03, -6.4468e-04],
+ [ 5.3291e-03, 1.3710e-02, -3.5620e-04, ..., -3.8052e-03,
+ -2.5225e-04, 6.0730e-03],
+ [ 1.3428e-03, 1.2884e-03, -1.9073e-06, ..., -2.8549e-02,
+ -1.1930e-03, 1.4906e-03],
+ ...,
+ [-2.4994e-02, -1.0262e-02, 2.3067e-04, ..., -2.0103e-03,
+ -1.2665e-02, 6.2332e-03],
+ [ 3.2401e-04, 9.3758e-05, -5.9605e-08, ..., -6.0234e-03,
+ -7.3862e-04, -6.4611e-04],
+ [ 1.1129e-03, -2.3117e-02, -2.7061e-04, ..., -4.4365e-03,
+ 3.5744e-03, -7.4997e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.6826, -0.3132, -0.8076, ..., -0.2167, -0.6543, -0.3040],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0043, -0.0023, 0.0041, ..., 0.0116, -0.0049, -0.0073],
+ [ 0.0018, 0.0191, -0.0102, ..., -0.0261, 0.0026, 0.0206],
+ [ 0.0039, -0.0002, -0.0028, ..., 0.0029, 0.0038, -0.0151],
+ ...,
+ [ 0.0021, -0.0003, -0.0034, ..., 0.0033, 0.0015, 0.0089],
+ [-0.0059, 0.0078, 0.0069, ..., -0.0005, -0.0060, 0.0020],
+ [ 0.0003, -0.0039, -0.0022, ..., -0.0094, 0.0005, 0.0039]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0184, -0.1008, 0.0398, ..., -0.0965, -0.1080, -0.0237],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.8353e-01, 5.9135e-01, 3.3711e-06, ..., 2.0198e+00, 7.7565e-01,
+ 2.9745e-01], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([2.1628e-02, 2.1650e-01, 2.3350e-04, ..., 2.6387e-01, 4.4878e-01,
+ 5.1503e-02], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0033, 0.0007, -0.0002, ..., 0.0078, -0.0240, 0.0078],
+ [ 0.0070, 0.0033, 0.0170, ..., -0.0062, 0.0080, 0.0055],
+ [ 0.0102, -0.0102, -0.0003, ..., 0.0024, 0.0164, 0.0043],
+ ...,
+ [ 0.0113, 0.0003, -0.0048, ..., 0.0002, 0.0042, -0.0065],
+ [-0.0144, -0.0119, 0.0076, ..., -0.0037, 0.0036, 0.0072],
+ [-0.0012, -0.0020, 0.0010, ..., -0.0066, -0.0222, -0.0007]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-1.0248e-01, 8.7988e-01, 1.4414e+00, ..., -1.0862e-03,
+ -4.0474e-03, 2.2471e-04], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([[-4.9829e-04, -2.1194e-02, -1.9908e-05, ..., -1.1253e-02,
+ 1.1993e-02, 1.0979e-04],
+ [-5.0068e-04, 7.5417e-03, -4.4131e-04, ..., -2.8553e-03,
+ 1.1459e-02, -3.0899e-03],
+ [ 2.7752e-03, -5.4703e-03, -1.1978e-02, ..., -3.8319e-03,
+ -1.0222e-04, -5.6686e-03],
+ ...,
+ [ 5.3825e-03, -1.8539e-02, 8.3313e-02, ..., -2.1317e-02,
+ -9.7198e-03, 1.5419e-02],
+ [-9.5062e-03, -2.0390e-03, 5.9166e-03, ..., 8.5144e-03,
+ -4.4022e-03, 6.3820e-03],
+ [-2.8553e-03, 6.8321e-03, -9.3508e-04, ..., 5.5199e-03,
+ 4.7264e-03, -4.1389e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0203, -0.0213, 0.0256, ..., -0.0386, -0.0219, -0.0045],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.3628, 0.7462, 0.0949, ..., 0.8510, 0.4239, 0.2627], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0484, -0.1306, 0.0199, ..., -0.1032, -0.0533, 0.0084],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0025, 0.0060, 0.0152, ..., -0.0063, 0.0478, -0.0812],
+ [ 0.0002, -0.0075, 0.0009, ..., -0.0011, -0.0030, 0.0037],
+ [ 0.0053, -0.0222, 0.0008, ..., -0.0101, 0.0178, -0.0035],
+ ...,
+ [ 0.0026, -0.0111, 0.0018, ..., -0.0058, -0.0008, 0.0039],
+ [-0.0072, 0.0112, 0.0018, ..., 0.0027, -0.0154, -0.0180],
+ [ 0.0151, 0.0001, 0.0326, ..., -0.0002, -0.0062, -0.0225]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1219, -0.6836, -0.5273, ..., -0.7568, -0.0984, -0.3079],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 1.8396e-03, -9.6321e-04, -1.6800e-02, ..., -3.0613e-03,
+ -9.5901e-03, -3.4103e-03],
+ [-1.5350e-02, 6.2675e-03, 1.4854e-02, ..., 6.7291e-03,
+ -9.3937e-05, -6.2218e-03],
+ [ 5.9891e-03, -4.2915e-04, 1.0605e-02, ..., -5.6076e-03,
+ -2.0447e-03, 5.9662e-03],
+ ...,
+ [ 2.9125e-03, -2.3937e-03, 4.5738e-03, ..., 1.6699e-03,
+ 6.7043e-04, 5.3139e-03],
+ [ 9.0456e-04, -1.3828e-03, 1.1587e-03, ..., -1.1549e-03,
+ 4.4975e-03, -5.7945e-03],
+ [ 3.0212e-02, 3.7136e-03, 1.1283e-04, ..., 4.8065e-03,
+ 1.2444e-02, 5.4054e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0242, -0.0644, 0.0790, ..., -0.0809, -0.1028, -0.0834],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.4126, 0.9839, 0.1912, ..., 0.7707, 0.5578, 0.5130], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0194, -0.1082, -0.0194, ..., 0.0886, 0.1335, 0.0285],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-6.4240e-03, -2.0248e-02, -2.0676e-02, ..., 1.1930e-03,
+ 2.8778e-02, -5.5267e-02],
+ [-2.8038e-03, 1.3485e-03, -1.9196e-02, ..., 9.4748e-04,
+ -1.9562e-02, -2.9373e-03],
+ [-5.8861e-03, -4.8141e-03, 5.3825e-03, ..., -1.8219e-02,
+ -2.0416e-02, -9.6283e-03],
+ ...,
+ [-2.5009e-02, 1.1108e-02, 1.0498e-02, ..., 4.8447e-03,
+ 1.2636e-05, 2.5177e-03],
+ [ 1.0887e-02, 1.1696e-02, 1.1856e-02, ..., 2.7962e-03,
+ -4.8447e-03, -6.4964e-03],
+ [-8.6746e-03, 2.5177e-03, -4.9591e-03, ..., 2.8553e-03,
+ -8.6136e-03, 4.2229e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.1929, -0.0773, -0.0911, ..., 0.1083, 0.0064, 0.0453],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-2.2720e-02, 2.8229e-03, -3.1710e-04, ..., 2.3804e-02,
+ -2.3819e-02, 1.4458e-02],
+ [-1.9178e-03, 6.7055e-05, -1.0406e-02, ..., -7.9041e-03,
+ -4.2076e-03, -6.3286e-03],
+ [-1.2703e-02, -6.1874e-03, -1.0422e-02, ..., -1.6769e-02,
+ -6.2981e-03, -1.8578e-03],
+ ...,
+ [-1.6136e-03, 9.8228e-04, -7.8888e-03, ..., -6.7940e-03,
+ -2.7447e-03, -2.1706e-03],
+ [ 5.8823e-03, -3.4351e-03, 1.2810e-02, ..., -1.3399e-03,
+ 1.7090e-03, 7.6027e-03],
+ [ 1.0025e-02, 6.5842e-03, 1.1444e-02, ..., -5.9242e-03,
+ -1.4353e-03, -3.4161e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0180, -0.0964, 0.0243, ..., -0.0159, -0.0454, -0.0301],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.5055, 0.3092, 0.3977, ..., 1.4209, 0.4980, 0.3574], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0747, -0.0122, 0.0623, ..., -0.0418, 0.0183, -0.0493],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0067, -0.0268, 0.0142, ..., -0.0009, 0.0202, -0.0156],
+ [ 0.0101, 0.0105, -0.0008, ..., 0.0012, -0.0004, 0.0251],
+ [-0.0059, 0.0096, 0.0011, ..., -0.0045, 0.0005, 0.0131],
+ ...,
+ [ 0.0016, -0.0027, -0.0004, ..., 0.0003, -0.0022, -0.0065],
+ [ 0.0005, 0.0114, 0.0169, ..., 0.0032, 0.0011, 0.0202],
+ [-0.0124, 0.0076, -0.0112, ..., 0.0046, -0.0065, -0.0068]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2451, -0.3083, -0.4565, ..., -0.1675, -0.2117, -0.5532],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 6.9380e-04, -4.9591e-03, 3.3998e-04, ..., -5.6534e-03,
+ 4.0131e-03, 1.9054e-03],
+ [ 2.4014e-03, -1.7365e-02, -4.1771e-03, ..., -9.8419e-04,
+ 1.3916e-02, -2.5787e-03],
+ [-2.0340e-02, 7.0419e-03, 4.9667e-03, ..., 9.6846e-04,
+ -1.9730e-02, 7.8964e-04],
+ ...,
+ [-7.2746e-03, 9.3412e-04, 2.4259e-04, ..., -6.7294e-05,
+ 1.0061e-03, 3.1109e-03],
+ [-1.4820e-03, -6.7673e-03, -1.0185e-03, ..., 3.6182e-03,
+ -1.1826e-02, 2.4719e-02],
+ [ 6.9389e-03, 3.9864e-03, -3.3212e-04, ..., 1.5701e-02,
+ 7.3318e-03, 7.0572e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0455, -0.0800, -0.0028, ..., -0.0156, -0.1378, -0.0312],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.6342, 0.5487, 0.3780, ..., 1.3511, 0.4005, 0.4882], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0342, -0.0825, -0.0966, ..., -0.0490, 0.0846, -0.2136],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 2.7573e-02, 1.0155e-02, 5.2223e-03, ..., 6.3057e-03,
+ -8.5449e-03, -1.4496e-02],
+ [ 4.0741e-03, 1.8341e-02, -4.6654e-03, ..., -6.0539e-03,
+ -2.0538e-02, 5.9052e-03],
+ [ 6.1989e-05, -9.3613e-03, 4.7445e-04, ..., 1.0582e-02,
+ 9.0256e-03, -1.5945e-02],
+ ...,
+ [ 2.3632e-03, 1.7147e-03, 1.2856e-02, ..., 1.9665e-03,
+ 1.4906e-03, -5.8441e-03],
+ [-1.9121e-03, 1.6052e-02, 7.6561e-03, ..., 2.6722e-03,
+ -5.3329e-03, -3.0499e-03],
+ [-1.9257e-02, -6.6910e-03, 1.0643e-02, ..., -2.6035e-03,
+ 6.3744e-03, 3.3646e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.1843, -0.5454, -0.1458, ..., -0.0142, 0.0038, 0.0057],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-2.3544e-02, 3.3112e-03, -1.4915e-03, ..., -7.5264e-03,
+ 1.7456e-02, 1.1635e-02],
+ [ 6.1531e-03, -3.9154e-02, -3.7251e-03, ..., -2.8820e-03,
+ -2.1454e-02, 1.2619e-02],
+ [ 5.9624e-03, -9.4299e-03, 1.4954e-02, ..., -1.4839e-02,
+ 7.3280e-03, -1.1848e-02],
+ ...,
+ [ 4.7982e-05, 4.2915e-03, -1.1238e-02, ..., -1.1238e-02,
+ 1.3962e-03, -1.3695e-03],
+ [-7.3586e-03, -1.0338e-02, -1.3638e-04, ..., 2.1240e-02,
+ 1.3512e-02, -2.4395e-03],
+ [-1.8524e-02, -1.1511e-03, -6.6681e-03, ..., -3.1424e-04,
+ -3.4256e-03, 3.2120e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0142, -0.0583, 0.0198, ..., 0.0195, -0.1207, 0.0172],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.7431, 0.3526, 0.6107, ..., 2.2615, 0.5052, 0.3920], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0007, 0.0244, 0.0183, ..., -0.1535, -0.0343, 0.0142],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 2.4109e-03, 3.2253e-03, -4.0948e-05, ..., -2.7924e-03,
+ -6.2485e-03, 6.4964e-03],
+ [ 1.8692e-03, -1.9464e-03, -2.5692e-03, ..., -8.8959e-03,
+ -5.5275e-03, -7.2517e-03],
+ [-4.8370e-03, -2.5986e-02, -6.8359e-03, ..., -7.1068e-03,
+ 1.1925e-02, 3.2806e-03],
+ ...,
+ [ 6.9885e-03, 3.9635e-03, -1.4124e-03, ..., -4.8065e-03,
+ -1.8377e-03, 8.5258e-04],
+ [-2.0752e-02, -1.8066e-02, -5.8937e-03, ..., -8.4991e-03,
+ -1.3115e-02, -6.9733e-03],
+ [-2.3849e-02, 7.0190e-03, -5.0430e-03, ..., -1.0780e-02,
+ -5.9013e-03, -8.5068e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0958, -0.1884, -0.1593, ..., -0.2017, -0.3232, -0.3743],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0077, -0.0076, 0.0042, ..., 0.0185, 0.0244, 0.0145],
+ [ 0.0102, 0.0012, -0.0082, ..., -0.0322, -0.0016, 0.0077],
+ [-0.0055, -0.0099, -0.0081, ..., 0.0024, 0.0082, 0.0235],
+ ...,
+ [ 0.0053, 0.0035, 0.0003, ..., -0.0044, -0.0019, -0.0058],
+ [-0.0026, 0.0178, 0.0062, ..., 0.0020, -0.0052, -0.0042],
+ [-0.0041, 0.0082, 0.0150, ..., -0.0024, 0.0150, -0.0076]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0141, -0.0796, 0.0049, ..., 0.0710, -0.1786, 0.0413],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.9759, 0.5307, 0.6513, ..., 0.0107, 0.5041, 0.5372], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0589, 0.0290, 0.0214, ..., 0.3877, -0.0775, -0.1199],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0074, 0.0006, -0.0156, ..., -0.0098, -0.0449, -0.0081],
+ [-0.0049, -0.0510, 0.0051, ..., -0.0058, -0.0286, 0.0074],
+ [ 0.0070, 0.0021, 0.0135, ..., 0.0238, 0.0070, -0.0351],
+ ...,
+ [ 0.0131, -0.0296, -0.0192, ..., -0.0009, 0.0007, 0.0007],
+ [ 0.0140, 0.0147, -0.0112, ..., 0.0018, 0.0341, -0.0212],
+ [ 0.0168, -0.0181, -0.0056, ..., 0.0013, -0.0197, -0.0118]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.4622, -0.0086, 0.2756, ..., 0.0269, 0.0068, -0.0022],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0027, -0.0052, 0.0031, ..., -0.0015, -0.0176, -0.0188],
+ [-0.0074, 0.0147, 0.0008, ..., 0.0245, -0.0242, 0.0237],
+ [ 0.0041, 0.0037, 0.0004, ..., 0.0177, 0.0124, 0.0109],
+ ...,
+ [-0.0073, 0.0058, -0.0050, ..., -0.0073, -0.0063, 0.0020],
+ [ 0.0157, -0.0413, 0.0109, ..., 0.0118, -0.0392, 0.0283],
+ [ 0.0064, 0.0013, -0.0097, ..., 0.0003, 0.0149, 0.0117]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0089, -0.0779, 0.0223, ..., -0.0115, -0.1759, 0.0235],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.7499, 0.4987, 0.7858, ..., 1.1598, 0.6024, 0.5770], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0150, -0.0119, 0.0050, ..., -0.1037, 0.0333, -0.0361],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0077, -0.0109, -0.0022, ..., -0.0063, 0.0133, 0.0150],
+ [-0.0096, 0.0191, 0.0149, ..., -0.0078, 0.0161, 0.0103],
+ [-0.0020, 0.0116, 0.0042, ..., -0.0045, 0.0149, 0.0007],
+ ...,
+ [ 0.0186, 0.0082, 0.0246, ..., -0.0084, 0.0029, -0.0158],
+ [ 0.0175, -0.0043, 0.0002, ..., -0.0078, 0.0047, -0.0143],
+ [-0.0011, -0.0010, 0.0262, ..., -0.0082, -0.0047, -0.0202]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2437, -0.3796, -0.5195, ..., -0.2163, -0.4231, -0.2202],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0169, 0.0065, -0.0019, ..., -0.0216, 0.0189, 0.0012],
+ [ 0.0048, -0.0130, 0.0052, ..., -0.0211, -0.0036, -0.0101],
+ [ 0.0040, -0.0051, -0.0002, ..., -0.0073, -0.0107, -0.0037],
+ ...,
+ [-0.0058, -0.0022, 0.0002, ..., -0.0073, -0.0007, 0.0026],
+ [-0.0098, -0.0155, 0.0002, ..., 0.0191, 0.0043, 0.0222],
+ [-0.0067, 0.0011, 0.0009, ..., 0.0020, -0.0060, 0.0049]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0154, -0.0523, -0.0401, ..., 0.1025, -0.1436, -0.0176],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1220, 0.5171, 1.0746, ..., 0.0111, 0.6744, 0.7526], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0830, -0.0924, -0.0048, ..., 0.1117, -0.0385, -0.0674],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-1.6815e-02, 3.0869e-02, 9.5444e-03, ..., -7.8125e-03,
+ 1.6342e-02, -1.0109e-02],
+ [-1.2794e-02, 7.7095e-03, -3.9101e-03, ..., -1.1053e-03,
+ -5.3482e-03, -1.1383e-02],
+ [ 1.6956e-03, -1.2161e-02, -4.4136e-03, ..., -1.5106e-03,
+ -1.3565e-02, 6.5117e-03],
+ ...,
+ [-1.2596e-02, 1.0803e-02, 5.0116e-04, ..., -3.5954e-04,
+ -3.2578e-03, -5.4300e-05],
+ [-1.4236e-02, -4.2572e-03, 1.3161e-02, ..., 1.7285e-05,
+ -3.1860e-02, -1.3054e-02],
+ [ 1.2398e-02, 5.1737e-05, 2.3148e-02, ..., -2.7866e-03,
+ -4.3144e-03, -2.5146e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0057, -1.4600, 0.3438, ..., -0.0042, -0.0107, -0.0046],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 6.3477e-03, 1.2695e-02, -6.6872e-03, ..., 1.6868e-05,
+ 1.6006e-02, -1.3306e-02],
+ [-5.9090e-03, 8.4877e-04, -1.6708e-02, ..., -2.0477e-02,
+ -1.0666e-02, -1.1078e-02],
+ [ 2.4246e-02, 1.2558e-02, -1.6769e-02, ..., 6.4697e-03,
+ 1.2642e-02, -1.8021e-02],
+ ...,
+ [ 1.6754e-02, 5.8670e-03, -1.1282e-03, ..., -1.5726e-03,
+ 1.8406e-03, 1.1803e-02],
+ [ 5.7335e-03, 7.0724e-03, 1.3092e-02, ..., -1.1902e-02,
+ 1.6022e-02, -1.1311e-03],
+ [ 2.0809e-03, -2.6493e-03, -3.7041e-03, ..., -4.9400e-03,
+ 6.8893e-03, 1.5732e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0091, -0.0477, -0.0098, ..., -0.0483, -0.1364, 0.0059],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.8486, 0.6437, 0.8933, ..., 1.2490, 0.7166, 0.8544], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0071, 0.0669, -0.0529, ..., -0.1688, -0.0491, 0.0438],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 5.3062e-03, 1.2688e-02, 1.0406e-02, ..., -2.4281e-03,
+ -4.9362e-03, 1.8988e-03],
+ [-2.9793e-03, -1.5764e-03, 1.6724e-02, ..., 1.1415e-03,
+ -2.2034e-02, -9.2392e-03],
+ [-3.9673e-02, -1.2040e-05, 2.4188e-04, ..., 2.8877e-03,
+ -3.9101e-03, -2.3239e-02],
+ ...,
+ [ 8.1329e-03, 1.3290e-02, 2.1637e-02, ..., -6.3057e-03,
+ -4.2686e-03, -1.4544e-03],
+ [ 2.7435e-02, 6.1798e-03, 1.0468e-02, ..., 3.2425e-05,
+ 4.9400e-03, -9.4604e-03],
+ [ 1.3458e-02, 7.5836e-03, -1.2062e-02, ..., 4.9925e-04,
+ -9.8419e-03, -1.8356e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.4192, -0.2394, -0.3069, ..., -0.3665, -0.2556, -0.1316],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0160, -0.0017, -0.0088, ..., 0.0165, -0.0056, 0.0135],
+ [ 0.0050, 0.0226, 0.0044, ..., 0.0111, 0.0021, 0.0038],
+ [ 0.0148, -0.0083, 0.0003, ..., 0.0085, 0.0015, -0.0004],
+ ...,
+ [-0.0031, -0.0009, -0.0014, ..., 0.0004, -0.0025, -0.0012],
+ [-0.0146, -0.0036, 0.0007, ..., 0.0108, -0.0012, -0.0406],
+ [ 0.0060, 0.0041, -0.0141, ..., -0.0118, 0.0065, -0.0112]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0417, 0.0127, -0.0229, ..., 0.0725, -0.0144, -0.0360],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1527, 0.7259, 1.1281, ..., 1.0935, 0.8785, 1.1066], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0413, -0.1707, -0.0308, ..., 0.0418, -0.2141, -0.0075],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0110, -0.0251, -0.0094, ..., 0.0972, 0.0119, -0.0125],
+ [-0.0140, -0.0233, 0.0010, ..., -0.1094, -0.0082, -0.0113],
+ [-0.0004, 0.0120, 0.0042, ..., 0.0518, 0.0182, 0.0130],
+ ...,
+ [-0.0162, 0.0175, -0.0176, ..., 0.0016, -0.0075, 0.0305],
+ [ 0.0311, -0.0070, -0.0240, ..., -0.0003, -0.0044, -0.0165],
+ [-0.0102, -0.0211, 0.0222, ..., -0.0004, -0.0292, -0.0076]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.2430, 0.3398, -0.1389, ..., 0.0082, -0.0049, 0.0183],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0019, 0.0045, 0.0063, ..., 0.0163, -0.0224, 0.0068],
+ [-0.0041, 0.0050, -0.0084, ..., -0.0048, -0.0007, 0.0138],
+ [-0.0173, 0.0011, 0.0050, ..., 0.0098, 0.0256, -0.0074],
+ ...,
+ [ 0.0102, -0.0012, -0.0010, ..., -0.0045, 0.0045, 0.0039],
+ [ 0.0035, -0.0075, 0.0118, ..., -0.0043, -0.0048, 0.0198],
+ [ 0.0175, -0.0080, -0.0061, ..., -0.0388, -0.0020, 0.0159]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0218, 0.0022, 0.0177, ..., 0.0566, -0.0418, -0.0156],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.8877, 0.8024, 1.0279, ..., 2.0427, 0.9536, 0.9729], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1278, -0.0779, -0.0511, ..., -0.2266, -0.0554, 0.0418],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0282, 0.0064, 0.0033, ..., -0.0064, 0.0038, 0.0184],
+ [ 0.0007, 0.0181, 0.0098, ..., -0.0025, -0.0096, 0.0182],
+ [-0.0056, 0.0077, 0.0009, ..., -0.0078, -0.0058, -0.0323],
+ ...,
+ [-0.0288, 0.0303, 0.0033, ..., 0.0016, -0.0074, 0.0192],
+ [-0.0051, -0.0323, -0.0066, ..., -0.0045, 0.0333, 0.0005],
+ [ 0.0188, 0.0207, 0.0077, ..., -0.0080, -0.0315, -0.0182]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1138, -0.1492, -0.4014, ..., -0.2352, -0.3323, -0.2046],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-1.9287e-02, 2.2621e-03, 6.0921e-03, ..., -3.4676e-03,
+ -8.6060e-03, -2.0447e-03],
+ [-6.7711e-05, -3.6869e-03, -5.7602e-03, ..., -1.7380e-02,
+ 3.8025e-02, -1.9588e-03],
+ [-1.2627e-02, -4.6158e-03, -6.8207e-03, ..., 1.4572e-03,
+ -6.2037e-04, -1.3741e-02],
+ ...,
+ [ 3.5152e-03, 2.8687e-03, -8.9417e-03, ..., -8.0633e-04,
+ 7.1335e-03, 3.8662e-03],
+ [ 1.1139e-02, 7.1411e-03, 7.1297e-03, ..., -1.9293e-03,
+ -5.5265e-04, 3.8330e-02],
+ [-8.9264e-03, -5.6114e-03, 2.1210e-03, ..., -1.2589e-02,
+ -8.9493e-03, 8.3389e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.0068, 0.0181, -0.0552, ..., 0.1211, -0.0751, -0.1089],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1916, 0.9694, 1.2653, ..., 0.1731, 0.9097, 1.1966], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0955, -0.1844, -0.0546, ..., 0.2570, -0.0544, 0.0379],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0093, -0.0152, -0.0200, ..., 0.0346, -0.0043, -0.0287],
+ [ 0.0025, -0.0165, -0.0050, ..., -0.0740, -0.0039, -0.0172],
+ [-0.0126, 0.0090, 0.0117, ..., -0.0017, 0.0034, 0.0126],
+ ...,
+ [-0.0276, -0.0107, -0.0004, ..., -0.0036, 0.0028, 0.0067],
+ [ 0.0289, -0.0022, -0.0177, ..., -0.0029, 0.0003, -0.0052],
+ [-0.0118, 0.0090, 0.0049, ..., -0.0104, 0.0250, 0.0115]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-1.8496, 0.1801, 2.3359, ..., 0.0398, -0.0217, -0.1345],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0018, -0.0264, 0.0078, ..., 0.0173, -0.0076, -0.0041],
+ [ 0.0135, -0.0083, 0.0026, ..., 0.0076, 0.0072, -0.0242],
+ [ 0.0054, 0.0058, -0.0234, ..., -0.0210, -0.0069, 0.0223],
+ ...,
+ [-0.0025, 0.0097, -0.0013, ..., 0.0089, 0.0019, 0.0197],
+ [ 0.0045, -0.0037, 0.0037, ..., 0.0008, 0.0096, -0.0237],
+ [-0.0100, 0.0123, 0.0061, ..., -0.0153, -0.0145, 0.0152]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0037, 0.0258, -0.0091, ..., -0.0498, -0.0065, -0.0458],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1915, 1.0973, 1.2475, ..., 1.4018, 1.1544, 1.1824], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0225, -0.0913, 0.0974, ..., -0.2996, -0.0410, 0.0070],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0168, 0.0096, 0.0042, ..., -0.0020, -0.0020, 0.0089],
+ [-0.0215, 0.0329, 0.0130, ..., 0.0041, 0.0160, 0.0035],
+ [-0.0176, -0.0188, 0.0220, ..., 0.0037, -0.0368, 0.0167],
+ ...,
+ [ 0.0086, -0.0059, -0.0079, ..., 0.0015, -0.0030, -0.0178],
+ [-0.0288, -0.0067, 0.0123, ..., -0.0054, -0.0138, -0.0072],
+ [-0.0190, 0.0143, -0.0290, ..., -0.0286, -0.0196, -0.0011]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1221, -0.2141, -0.4116, ..., -0.1118, -0.1777, -0.3623],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0010, 0.0136, -0.0347, ..., -0.0224, 0.0056, -0.0153],
+ [-0.0027, -0.0350, 0.0204, ..., 0.0106, -0.0202, -0.0021],
+ [ 0.0073, -0.0276, -0.0020, ..., 0.0134, 0.0046, -0.0155],
+ ...,
+ [-0.0004, -0.0072, 0.0019, ..., -0.0059, 0.0039, 0.0084],
+ [ 0.0117, -0.0049, -0.0148, ..., -0.0053, 0.0066, -0.0098],
+ [ 0.0130, 0.0172, 0.0037, ..., 0.0183, -0.0211, -0.0070]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0200, 0.0231, -0.0658, ..., 0.1027, -0.0781, -0.1132],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2565, 1.1066, 1.2045, ..., 0.5890, 1.0264, 1.2907], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0063, -0.0652, 0.0874, ..., 0.1717, 0.1017, -0.0355],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0141, 0.0224, 0.0066, ..., -0.0503, -0.0308, -0.0002],
+ [-0.0106, -0.0263, 0.0116, ..., -0.0196, -0.0080, 0.0482],
+ [ 0.0088, -0.0083, 0.0067, ..., 0.0172, -0.0225, 0.0448],
+ ...,
+ [ 0.0117, 0.0198, 0.0119, ..., 0.0007, 0.0045, -0.0206],
+ [ 0.0123, -0.0125, 0.0020, ..., 0.0034, 0.0106, -0.0007],
+ [ 0.0226, -0.0011, -0.0222, ..., 0.0048, -0.0005, -0.0066]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0543, -0.0735, 0.2413, ..., -0.0484, -0.1190, 0.0173],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0229, 0.0144, -0.0111, ..., -0.0034, 0.0119, -0.0192],
+ [-0.0048, -0.0063, 0.0103, ..., -0.0061, 0.0145, 0.0030],
+ [ 0.0077, 0.0203, 0.0148, ..., -0.0084, -0.0068, 0.0304],
+ ...,
+ [-0.0003, 0.0012, 0.0053, ..., 0.0082, -0.0035, 0.0224],
+ [-0.0080, 0.0013, 0.0045, ..., 0.0091, -0.0064, -0.0116],
+ [-0.0171, 0.0154, -0.0227, ..., -0.0176, 0.0146, -0.0069]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0177, 0.0686, -0.0156, ..., -0.0817, 0.0255, 0.0177],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1690, 1.1532, 1.1559, ..., 1.5800, 1.1703, 1.2291], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0074, 0.0918, -0.0353, ..., -0.3273, -0.1143, -0.0546],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0204, -0.0119, -0.0051, ..., -0.0030, -0.0053, 0.0117],
+ [-0.0035, -0.0211, 0.0029, ..., -0.0038, 0.0121, 0.0023],
+ [ 0.0126, -0.0055, 0.0038, ..., 0.0006, 0.0247, 0.0077],
+ ...,
+ [ 0.0121, 0.0132, -0.0259, ..., 0.0031, 0.0226, 0.0040],
+ [-0.0022, 0.0106, -0.0208, ..., -0.0026, 0.0163, -0.0018],
+ [-0.0326, 0.0187, 0.0123, ..., -0.0007, -0.0089, 0.0122]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3628, -0.2209, -0.1646, ..., -0.2522, -0.2683, -0.2517],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0055, 0.0073, 0.0131, ..., -0.0030, -0.0204, -0.0067],
+ [-0.0153, 0.0021, 0.0061, ..., 0.0199, -0.0058, -0.0234],
+ [-0.0043, 0.0070, 0.0054, ..., 0.0016, 0.0075, -0.0185],
+ ...,
+ [-0.0034, -0.0019, 0.0044, ..., -0.0031, -0.0046, 0.0004],
+ [-0.0026, -0.0267, -0.0127, ..., 0.0038, -0.0151, 0.0075],
+ [-0.0029, -0.0212, -0.0195, ..., 0.0119, 0.0086, -0.0139]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0283, 0.0284, -0.0328, ..., 0.0670, -0.0050, -0.0489],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2720, 1.2516, 1.2042, ..., 0.7531, 1.0650, 1.2413], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0019, -0.0140, 0.0240, ..., 0.2147, -0.1253, -0.2114],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0038, -0.0111, 0.0217, ..., -0.0341, 0.0049, 0.0043],
+ [ 0.0020, 0.0047, 0.0051, ..., -0.0009, -0.0141, 0.0165],
+ [-0.0086, 0.0055, 0.0177, ..., 0.0030, -0.0044, -0.0111],
+ ...,
+ [ 0.0037, 0.0199, -0.0006, ..., -0.0081, 0.0196, -0.0002],
+ [-0.0116, 0.0020, -0.0122, ..., 0.0042, -0.0016, -0.0110],
+ [-0.0201, 0.0025, -0.0230, ..., -0.0041, 0.0287, 0.0105]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.8447, 0.0093, -1.0840, ..., -0.0142, 0.0109, 0.0013],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0003, 0.0259, 0.0041, ..., 0.0052, -0.0108, 0.0274],
+ [ 0.0064, 0.0003, -0.0086, ..., -0.0271, 0.0063, 0.0018],
+ [-0.0234, 0.0012, 0.0170, ..., 0.0238, 0.0096, 0.0125],
+ ...,
+ [ 0.0077, 0.0320, 0.0242, ..., 0.0052, 0.0005, 0.0047],
+ [ 0.0064, 0.0084, 0.0002, ..., -0.0008, 0.0042, -0.0140],
+ [ 0.0204, -0.0061, -0.0246, ..., 0.0237, -0.0045, 0.0173]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0204, 0.0182, -0.0022, ..., -0.0782, 0.0405, -0.0199],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1486, 1.2062, 1.1745, ..., 1.6290, 1.1674, 1.2157], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0552, 0.0993, -0.0013, ..., -0.1784, -0.0515, -0.0148],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0086, -0.0217, 0.0128, ..., -0.0079, -0.0053, 0.0027],
+ [-0.0070, 0.0067, 0.0020, ..., -0.0125, -0.0191, -0.0117],
+ [ 0.0062, 0.0227, 0.0108, ..., 0.0066, 0.0004, 0.0018],
+ ...,
+ [ 0.0228, -0.0078, 0.0063, ..., 0.0002, 0.0019, -0.0005],
+ [-0.0191, 0.0253, 0.0069, ..., -0.0109, -0.0114, -0.0081],
+ [ 0.0292, -0.0316, -0.0293, ..., -0.0048, 0.0165, -0.0164]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.4792, -0.1467, -0.1043, ..., -0.2996, -0.2251, -0.3262],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0207, 0.0158, -0.0021, ..., 0.0083, 0.0042, 0.0273],
+ [ 0.0063, 0.0024, -0.0061, ..., 0.0069, -0.0269, 0.0042],
+ [ 0.0299, -0.0060, -0.0002, ..., -0.0130, 0.0070, -0.0297],
+ ...,
+ [-0.0122, 0.0011, -0.0082, ..., -0.0026, 0.0038, -0.0006],
+ [-0.0204, 0.0085, 0.0057, ..., 0.0096, -0.0105, 0.0216],
+ [-0.0023, 0.0328, 0.0013, ..., -0.0099, -0.0044, 0.0145]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0411, -0.0040, -0.0516, ..., 0.1114, 0.0086, -0.0609],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.3836, 1.2857, 1.2323, ..., 0.6118, 1.1779, 1.2560], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.2367, 0.0575, 0.1226, ..., 0.2404, 0.0237, -0.0258],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0172, -0.0115, -0.0176, ..., 0.0113, -0.0038, 0.0092],
+ [-0.0095, 0.0104, 0.0087, ..., 0.0204, -0.0187, -0.0189],
+ [-0.0259, -0.0040, 0.0021, ..., 0.0106, 0.0068, -0.0149],
+ ...,
+ [ 0.0015, 0.0207, -0.0071, ..., -0.0045, -0.0049, 0.0017],
+ [ 0.0381, 0.0040, -0.0079, ..., 0.0003, -0.0011, 0.0140],
+ [ 0.0094, -0.0019, -0.0035, ..., -0.0018, 0.0271, -0.0058]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0180, 0.2048, -0.1954, ..., 0.0674, -0.0071, 0.0122],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0127, -0.0093, -0.0233, ..., -0.0062, -0.0268, -0.0001],
+ [ 0.0243, -0.0188, 0.0209, ..., -0.0062, -0.0029, -0.0023],
+ [-0.0073, 0.0052, -0.0273, ..., 0.0022, 0.0090, 0.0104],
+ ...,
+ [-0.0117, 0.0100, 0.0137, ..., 0.0083, -0.0002, 0.0034],
+ [ 0.0061, 0.0166, 0.0235, ..., 0.0010, 0.0024, -0.0336],
+ [-0.0002, -0.0062, -0.0147, ..., 0.0020, -0.0193, -0.0020]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0233, 0.0391, -0.0073, ..., -0.0649, 0.0291, 0.0002],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.1998, 1.2310, 1.1488, ..., 1.5903, 1.2228, 1.3022], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0585, 0.0091, -0.0592, ..., -0.2559, -0.1667, -0.0673],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-1.1040e-02, -1.6754e-02, -2.7451e-02, ..., -4.3964e-04,
+ 6.0501e-03, 5.5552e-04],
+ [ 2.4002e-02, -2.4567e-02, 7.3128e-03, ..., -3.2883e-03,
+ 1.0437e-02, -2.3246e-05],
+ [ 8.8272e-03, 8.2474e-03, 3.9597e-03, ..., 4.3845e-04,
+ -7.0724e-03, 2.1725e-03],
+ ...,
+ [-1.2598e-03, -9.5901e-03, 1.6785e-02, ..., -1.8721e-03,
+ -4.9057e-03, 7.3891e-03],
+ [ 2.6455e-03, 1.1078e-02, -1.6968e-02, ..., -1.1044e-03,
+ -6.0959e-03, -3.0914e-02],
+ [ 1.2146e-02, 2.3819e-02, 5.0545e-04, ..., 2.2030e-03,
+ -4.2877e-03, 1.9012e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.1323, -0.2241, -0.0570, ..., -0.2708, -0.3240, -0.0825],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 9.5673e-03, -1.0620e-02, -2.4261e-02, ..., 3.1433e-02,
+ -1.2718e-02, -1.2207e-02],
+ [ 8.1787e-03, 6.4707e-04, 3.0732e-04, ..., -1.3092e-02,
+ 5.6446e-05, -1.8692e-02],
+ [ 6.8626e-03, 1.8829e-02, 1.0094e-02, ..., -3.1891e-03,
+ 9.3765e-03, -3.5896e-03],
+ ...,
+ [ 5.6763e-03, 3.4389e-03, -8.8310e-04, ..., 5.5847e-03,
+ -3.6240e-03, -4.8103e-03],
+ [ 2.6627e-03, -1.5274e-02, -6.7186e-04, ..., -2.0081e-02,
+ 1.1981e-04, 1.0040e-02],
+ [-7.2365e-03, -5.4207e-03, -3.8395e-03, ..., 5.3978e-03,
+ -2.5177e-02, -2.7252e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0388, 0.0069, -0.0129, ..., 0.0417, 0.0218, 0.0082],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.4280, 1.3614, 1.2954, ..., 1.0131, 1.1817, 1.3209], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1139, 0.0275, 0.0677, ..., 0.1797, 0.0199, -0.2525],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0128, 0.0002, 0.0248, ..., -0.0053, 0.0151, -0.0208],
+ [-0.0050, 0.0376, -0.0262, ..., 0.0018, 0.0168, 0.0039],
+ [-0.0110, -0.0084, 0.0369, ..., -0.0102, 0.0071, 0.0117],
+ ...,
+ [ 0.0205, -0.0055, -0.0127, ..., -0.0057, -0.0044, 0.0095],
+ [-0.0154, -0.0017, -0.0012, ..., 0.0026, -0.0132, 0.0012],
+ [ 0.0082, 0.0055, 0.0048, ..., -0.0060, -0.0069, 0.0101]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0060, 0.0181, 0.0104, ..., -0.0065, 0.0091, 0.0008],
+ [-0.0115, 0.0102, 0.0212, ..., 0.0144, 0.0276, -0.0077],
+ [ 0.0390, 0.0029, 0.0083, ..., -0.0165, 0.0036, -0.0177],
+ ...,
+ [ 0.0054, -0.0068, -0.0049, ..., 0.0166, -0.0177, 0.0042],
+ [ 0.0016, -0.0031, 0.0076, ..., 0.0091, 0.0008, 0.0024],
+ [ 0.0034, -0.0059, 0.0107, ..., -0.0199, 0.0139, -0.0083]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0060, 0.0679, 0.0352, ..., -0.0554, 0.0134, 0.0558],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2294, 1.2056, 1.1645, ..., 1.8344, 1.1523, 1.2639], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0175, -0.0714, -0.1254, ..., -0.2901, -0.1457, 0.1501],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-4.8256e-03, -3.2368e-03, 1.3252e-02, ..., -1.7681e-03,
+ 2.2354e-02, -1.1436e-02],
+ [ 4.0253e-02, 3.1097e-02, 1.9760e-02, ..., 5.3787e-03,
+ -2.2949e-02, 1.4923e-02],
+ [-1.9789e-05, -2.5848e-02, -1.0681e-02, ..., 1.1975e-04,
+ 1.0056e-02, 9.3384e-03],
+ ...,
+ [ 1.9211e-02, -1.5373e-02, 5.6839e-03, ..., -1.1314e-02,
+ -4.1748e-02, 1.5808e-02],
+ [ 2.8934e-03, -1.8179e-04, 8.8425e-03, ..., -2.5787e-03,
+ -1.7517e-02, -6.8169e-03],
+ [ 1.7838e-02, -6.3019e-03, -3.8700e-03, ..., 3.0651e-03,
+ -3.5019e-03, 1.3748e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.2314, -0.3215, -0.0737, ..., -0.3018, -0.1614, -0.3069],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0107, 0.0326, 0.0092, ..., -0.0069, -0.0051, -0.0002],
+ [-0.0118, 0.0024, 0.0258, ..., 0.0087, -0.0250, -0.0087],
+ [ 0.0156, 0.0077, 0.0071, ..., -0.0158, -0.0195, 0.0202],
+ ...,
+ [-0.0062, 0.0010, 0.0041, ..., -0.0098, 0.0120, 0.0015],
+ [-0.0022, 0.0381, -0.0009, ..., 0.0051, 0.0093, 0.0135],
+ [ 0.0090, -0.0229, -0.0135, ..., 0.0130, 0.0066, 0.0037]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([0.0371, 0.0197, 0.0018, ..., 0.0558, 0.0674, 0.0106], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.4988, 1.4287, 1.3546, ..., 0.9505, 1.1804, 1.4063], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1733, -0.0029, 0.0176, ..., 0.2492, 0.0635, -0.1153],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0109, -0.0005, 0.0278, ..., 0.0017, -0.0215, 0.0092],
+ [ 0.0046, -0.0021, 0.0013, ..., 0.0017, -0.0017, 0.0037],
+ [-0.0275, 0.0318, 0.0133, ..., 0.0012, 0.0040, -0.0225],
+ ...,
+ [ 0.0179, 0.0136, -0.0099, ..., 0.0038, 0.0117, 0.0026],
+ [ 0.0067, 0.0052, -0.0031, ..., -0.0122, 0.0013, -0.0083],
+ [ 0.0012, -0.0281, -0.0114, ..., -0.0038, -0.0005, -0.0021]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.2450, 1.7920, 0.0699, ..., 0.0484, -0.0464, 0.0208],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0059, 0.0109, -0.0109, ..., -0.0163, 0.0007, -0.0298],
+ [-0.0065, -0.0061, -0.0041, ..., -0.0236, -0.0090, 0.0271],
+ [ 0.0104, 0.0079, 0.0172, ..., -0.0066, 0.0009, -0.0156],
+ ...,
+ [ 0.0096, -0.0068, 0.0006, ..., -0.0150, 0.0118, 0.0032],
+ [ 0.0230, 0.0083, 0.0289, ..., -0.0219, 0.0005, 0.0038],
+ [ 0.0172, 0.0228, 0.0059, ..., 0.0067, 0.0123, -0.0109]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0267, -0.0056, -0.0026, ..., -0.0532, -0.0267, 0.0485],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2355, 1.2508, 1.2161, ..., 1.8124, 1.1440, 1.3011], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1252, -0.0353, 0.1171, ..., -0.1227, -0.0330, 0.1001],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-6.1083e-04, -1.1574e-02, 9.1705e-03, ..., -1.0834e-03,
+ 8.1482e-03, 3.4065e-03],
+ [ 1.2016e-02, -1.7960e-02, 3.3379e-03, ..., -8.6365e-03,
+ -1.7424e-03, -1.5541e-02],
+ [ 8.0948e-03, -1.1383e-02, -2.7039e-02, ..., 4.2725e-03,
+ 4.9667e-03, -2.5375e-02],
+ ...,
+ [-2.1606e-02, -2.0233e-02, -3.5381e-03, ..., 2.8253e-05,
+ -1.3222e-02, 7.2975e-03],
+ [ 2.8515e-03, 1.3855e-02, 1.0794e-04, ..., -3.3092e-03,
+ -1.4519e-02, 1.1742e-02],
+ [-1.1467e-02, 1.2001e-02, 1.0672e-03, ..., -3.9520e-03,
+ -5.1178e-02, 5.3864e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.3506, -0.3098, -0.0694, ..., -0.3074, -0.2494, -0.4229],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0110, 0.0193, -0.0116, ..., 0.0081, -0.0065, -0.0187],
+ [ 0.0120, 0.0100, 0.0045, ..., 0.0063, -0.0106, -0.0092],
+ [-0.0012, 0.0172, 0.0223, ..., 0.0052, 0.0394, 0.0099],
+ ...,
+ [-0.0025, 0.0068, -0.0081, ..., 0.0005, -0.0055, 0.0065],
+ [ 0.0112, -0.0010, -0.0028, ..., -0.0172, -0.0041, -0.0017],
+ [ 0.0086, -0.0009, 0.0137, ..., -0.0030, 0.0077, -0.0112]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0374, -0.0088, -0.0429, ..., 0.0653, -0.0126, -0.0252],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.5180, 1.3799, 1.3971, ..., 0.8399, 1.2614, 1.5007], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1863, 0.1162, 0.4045, ..., 0.2292, 0.4198, -0.0957],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0160, 0.0014, -0.0089, ..., 0.0042, 0.0289, 0.0184],
+ [-0.0010, 0.0089, -0.0017, ..., -0.0152, -0.0108, -0.0008],
+ [-0.0141, -0.0294, 0.0109, ..., -0.0025, 0.0298, 0.0266],
+ ...,
+ [ 0.0245, 0.0203, 0.0064, ..., -0.0128, 0.0092, -0.0031],
+ [ 0.0211, 0.0237, -0.0171, ..., -0.0165, -0.0070, 0.0133],
+ [-0.0094, 0.0021, 0.0247, ..., -0.0004, 0.0047, -0.0201]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2849, -0.3125, -0.2026, ..., -0.0510, -0.0885, 0.0077],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0026, -0.0256, 0.0400, ..., -0.0214, -0.0089, 0.0125],
+ [-0.0129, -0.0088, 0.0240, ..., -0.0082, -0.0172, -0.0212],
+ [-0.0200, -0.0017, 0.0146, ..., -0.0077, 0.0185, -0.0176],
+ ...,
+ [-0.0275, 0.0012, -0.0196, ..., 0.0564, 0.0609, -0.0008],
+ [ 0.0161, 0.0054, -0.0016, ..., -0.0032, 0.0063, 0.0044],
+ [-0.0055, -0.0287, -0.0144, ..., 0.0080, -0.0101, 0.0110]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0077, 0.0031, 0.0012, ..., -0.0704, 0.0297, 0.0082],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2799, 1.2512, 1.2956, ..., 2.2034, 1.1719, 1.3681], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1496, -0.0801, -0.0724, ..., -0.1659, -0.0900, 0.0350],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0067, 0.0413, 0.0378, ..., 0.0076, 0.0142, 0.0230],
+ [ 0.0130, 0.0156, 0.0179, ..., 0.0018, 0.0109, 0.0043],
+ [-0.0078, -0.0166, 0.0107, ..., -0.0015, -0.0044, 0.0111],
+ ...,
+ [ 0.0138, 0.0290, 0.0173, ..., -0.0042, 0.0035, 0.0030],
+ [ 0.0160, 0.0144, 0.0156, ..., -0.0038, -0.0043, 0.0115],
+ [-0.0084, 0.0176, 0.0231, ..., 0.0015, -0.0203, -0.0239]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2788, -0.1956, -0.3853, ..., -0.3225, -0.2610, -0.0354],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-7.0419e-03, -1.1139e-02, -1.7349e-02, ..., 1.0239e-02,
+ -1.8906e-02, 2.0798e-02],
+ [ 1.1635e-02, -8.4457e-03, -7.3700e-03, ..., 1.1932e-02,
+ -1.0246e-02, -1.2451e-02],
+ [ 1.3153e-02, -2.9282e-02, -1.9894e-03, ..., 4.7760e-03,
+ -2.7866e-03, -1.2886e-02],
+ ...,
+ [-3.2005e-03, 1.4801e-02, -3.5763e-03, ..., 6.4313e-05,
+ 1.2386e-04, 2.5702e-04],
+ [-1.8677e-02, -8.7967e-03, 9.5978e-03, ..., 4.4403e-03,
+ -1.1940e-02, 2.3422e-02],
+ [-5.8060e-03, -2.0889e-02, -1.2917e-02, ..., -4.3907e-03,
+ -7.6561e-03, 2.6611e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0329, 0.0113, -0.0181, ..., 0.0332, 0.0061, -0.0410],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.6208, 1.4989, 1.4207, ..., 0.7640, 1.2692, 1.4951], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1374, -0.0238, -0.0117, ..., 0.3347, 0.1457, -0.0975],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0034, -0.0225, -0.0031, ..., 0.0137, -0.0365, -0.0123],
+ [-0.0254, -0.0124, 0.0016, ..., -0.0371, 0.0126, -0.0146],
+ [ 0.0078, -0.0007, -0.0157, ..., 0.0026, -0.0030, 0.0202],
+ ...,
+ [ 0.0041, -0.0135, 0.0168, ..., 0.0034, -0.0226, -0.0060],
+ [-0.0034, -0.0090, 0.0047, ..., -0.0018, 0.0212, -0.0074],
+ [ 0.0030, 0.0077, -0.0017, ..., -0.0023, -0.0013, 0.0052]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.2683, -0.1324, 0.1324, ..., 0.0209, 0.0130, 0.0207],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0029, 0.0157, 0.0060, ..., 0.0021, 0.0078, -0.0089],
+ [ 0.0114, -0.0065, 0.0057, ..., 0.0285, 0.0281, 0.0063],
+ [-0.0123, -0.0026, 0.0062, ..., 0.0232, -0.0135, -0.0089],
+ ...,
+ [-0.0011, 0.0021, -0.0047, ..., -0.0017, -0.0325, -0.0199],
+ [-0.0076, -0.0072, -0.0037, ..., -0.0192, -0.0359, -0.0052],
+ [-0.0138, -0.0226, 0.0044, ..., 0.0032, 0.0111, -0.0124]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0062, -0.0194, -0.0133, ..., 0.0403, 0.0331, 0.0198],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.3324, 1.2243, 1.2615, ..., 1.9267, 1.1337, 1.3792], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1416, 0.0005, 0.0165, ..., 0.0163, -0.0729, 0.0122],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0125, -0.0005, -0.0153, ..., -0.0146, -0.0185, -0.0108],
+ [-0.0015, 0.0045, 0.0178, ..., -0.0033, 0.0042, -0.0107],
+ [-0.0038, 0.0046, 0.0096, ..., -0.0217, 0.0142, 0.0295],
+ ...,
+ [ 0.0055, -0.0117, -0.0156, ..., -0.0233, -0.0058, 0.0149],
+ [ 0.0230, -0.0006, 0.0009, ..., 0.0129, 0.0170, 0.0101],
+ [ 0.0109, 0.0066, -0.0111, ..., 0.0038, 0.0099, -0.0238]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2153, -0.2781, -0.3320, ..., -0.1223, -0.1307, -0.2898],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0037, -0.0316, 0.0164, ..., -0.0018, -0.0181, 0.0028],
+ [ 0.0187, 0.0093, 0.0047, ..., 0.0109, 0.0137, -0.0057],
+ [-0.0071, -0.0045, 0.0287, ..., 0.0160, -0.0103, 0.0014],
+ ...,
+ [-0.0109, 0.0038, -0.0134, ..., 0.0115, -0.0042, -0.0035],
+ [-0.0127, 0.0039, 0.0083, ..., 0.0040, -0.0111, 0.0036],
+ [-0.0054, -0.0070, -0.0073, ..., -0.0131, -0.0262, 0.0085]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0253, -0.0024, -0.0242, ..., 0.0956, 0.0208, -0.0150],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.6542, 1.5471, 1.5496, ..., 0.4089, 1.3961, 1.6685], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.2147, 0.1279, 0.3980, ..., 0.3844, 0.3855, -0.2151],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0031, 0.0022, -0.0024, ..., 0.0046, -0.0105, -0.0062],
+ [-0.0263, 0.0304, 0.0018, ..., -0.0321, 0.0226, 0.0128],
+ [ 0.0247, 0.0204, 0.0079, ..., -0.0015, 0.0224, -0.0038],
+ ...,
+ [-0.0034, 0.0151, -0.0085, ..., 0.0086, -0.0034, 0.0134],
+ [ 0.0203, -0.0166, 0.0061, ..., 0.0006, -0.0226, -0.0220],
+ [ 0.0190, -0.0124, -0.0086, ..., -0.0007, -0.0131, -0.0075]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-2.5195, 0.2338, -0.3826, ..., -0.0098, -0.0044, 0.0407],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0303, -0.0037, 0.0023, ..., 0.0084, 0.0081, -0.0136],
+ [-0.0157, -0.0159, 0.0211, ..., 0.0249, 0.0152, 0.0164],
+ [-0.0068, -0.0159, 0.0054, ..., 0.0163, 0.0173, 0.0204],
+ ...,
+ [-0.0006, 0.0029, -0.0114, ..., -0.0071, 0.0085, 0.0100],
+ [-0.0033, -0.0080, -0.0203, ..., -0.0054, 0.0173, 0.0025],
+ [ 0.0112, -0.0027, 0.0068, ..., 0.0061, -0.0080, 0.0069]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0108, -0.0551, 0.0180, ..., 0.0265, 0.0322, -0.0401],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2997, 1.2493, 1.3251, ..., 1.4770, 1.1521, 1.3748], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1529, 0.0496, -0.0569, ..., -0.0100, -0.0241, -0.0367],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0036, 0.0120, 0.0108, ..., -0.0241, -0.0065, -0.0097],
+ [-0.0012, -0.0218, -0.0014, ..., -0.0027, -0.0102, 0.0113],
+ [ 0.0043, 0.0098, -0.0035, ..., -0.0025, -0.0057, 0.0186],
+ ...,
+ [-0.0025, -0.0322, -0.0135, ..., -0.0106, -0.0206, -0.0133],
+ [-0.0045, 0.0262, -0.0152, ..., -0.0032, -0.0122, -0.0036],
+ [-0.0104, 0.0072, -0.0117, ..., -0.0020, 0.0015, -0.0004]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2783, -0.2571, -0.3367, ..., -0.3469, -0.2042, -0.0554],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0010, -0.0118, 0.0168, ..., 0.0077, -0.0058, 0.0077],
+ [ 0.0135, -0.0157, -0.0040, ..., -0.0081, 0.0145, -0.0026],
+ [ 0.0177, -0.0216, 0.0124, ..., 0.0067, -0.0014, -0.0008],
+ ...,
+ [-0.0105, 0.0073, -0.0061, ..., 0.0008, 0.0031, -0.0009],
+ [-0.0176, 0.0078, -0.0194, ..., -0.0242, -0.0090, -0.0050],
+ [ 0.0074, 0.0026, 0.0208, ..., 0.0214, -0.0106, 0.0054]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0367, -0.0981, -0.0668, ..., 0.0355, 0.0193, -0.0256],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.7683, 1.6419, 1.7225, ..., 0.6681, 1.5146, 1.7884], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1888, 0.3456, 0.1489, ..., 0.4134, 0.4308, -0.1223],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0099, -0.0102, 0.0307, ..., 0.0022, -0.0095, 0.0271],
+ [-0.0269, -0.0032, -0.0007, ..., -0.0077, -0.0161, -0.0114],
+ [-0.0016, 0.0004, 0.0387, ..., -0.0108, 0.0132, -0.0004],
+ ...,
+ [-0.0021, -0.0089, 0.0141, ..., 0.0007, -0.0084, 0.0113],
+ [ 0.0048, -0.0126, -0.0221, ..., -0.0024, -0.0106, 0.0105],
+ [-0.0116, -0.0002, -0.0007, ..., -0.0022, -0.0071, -0.0038]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2299, 0.2384, -0.0945, ..., 0.0501, -0.0047, -0.0003],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0283, -0.0055, 0.0025, ..., -0.0084, 0.0048, -0.0027],
+ [ 0.0133, -0.0057, -0.0082, ..., -0.0007, -0.0018, -0.0043],
+ [-0.0129, 0.0132, 0.0077, ..., -0.0180, 0.0022, -0.0036],
+ ...,
+ [ 0.0090, 0.0050, -0.0102, ..., -0.0129, -0.0157, -0.0071],
+ [ 0.0154, -0.0024, -0.0170, ..., 0.0126, -0.0098, 0.0101],
+ [ 0.0222, 0.0012, -0.0260, ..., -0.0199, -0.0145, 0.0066]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0646, -0.0642, 0.0045, ..., -0.0348, -0.0156, -0.0321],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.3934, 1.3553, 1.4854, ..., 1.8728, 1.3167, 1.4949], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0756, -0.1134, -0.0586, ..., -0.0262, -0.0903, -0.1063],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-7.9727e-03, 4.2458e-03, 1.8478e-02, ..., -1.1673e-02,
+ 1.3763e-02, -6.2256e-03],
+ [-1.7181e-02, 1.0742e-02, -4.7760e-03, ..., -3.7718e-04,
+ -2.2888e-02, -8.6594e-03],
+ [-4.2701e-04, 2.2446e-02, 1.0483e-02, ..., -4.0817e-03,
+ -1.7151e-02, -2.6047e-02],
+ ...,
+ [-2.9617e-02, -7.5722e-03, 8.4043e-06, ..., -1.1253e-02,
+ 1.6479e-02, 2.7222e-02],
+ [-1.2772e-02, 6.8283e-03, 2.5269e-02, ..., -7.0038e-03,
+ -7.5645e-03, 9.1019e-03],
+ [ 4.2176e-04, -4.2152e-03, 4.3335e-02, ..., -3.2711e-03,
+ -1.3786e-02, 1.5656e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.3418, -0.2771, -0.3467, ..., -0.3989, -0.2386, -0.2927],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0106, 0.0111, 0.0187, ..., -0.0266, 0.0003, -0.0147],
+ [ 0.0346, 0.0015, -0.0025, ..., -0.0093, 0.0119, -0.0310],
+ [-0.0043, -0.0276, 0.0013, ..., -0.0066, 0.0263, 0.0338],
+ ...,
+ [-0.0045, 0.0165, 0.0076, ..., 0.0143, -0.0025, -0.0003],
+ [ 0.0067, -0.0164, 0.0050, ..., 0.0121, -0.0008, -0.0172],
+ [-0.0008, -0.0125, -0.0156, ..., 0.0319, 0.0113, -0.0105]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0361, -0.0391, 0.0151, ..., -0.0164, 0.0040, -0.0078],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.1112, 2.0118, 2.0347, ..., 0.7085, 1.8153, 2.2010], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1631, -0.1508, 0.1484, ..., 0.4431, 0.6810, -0.3282],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0117, -0.0245, -0.0218, ..., 0.0068, -0.0019, -0.0032],
+ [ 0.0243, 0.0013, -0.0101, ..., 0.0473, -0.0216, 0.0135],
+ [ 0.0109, -0.0224, -0.0055, ..., -0.0055, -0.0267, -0.0187],
+ ...,
+ [-0.0167, 0.0103, -0.0058, ..., -0.0046, -0.0215, 0.0237],
+ [ 0.0119, -0.0105, 0.0158, ..., 0.0023, -0.0127, -0.0004],
+ [-0.0119, -0.0276, 0.0225, ..., -0.0024, -0.0047, -0.0064]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.2433, -0.1136, 0.0888, ..., -0.0050, -0.0137, 0.0093],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0302, 0.0129, -0.0099, ..., 0.0201, 0.0093, -0.0045],
+ [-0.0376, -0.0102, -0.0002, ..., -0.0104, 0.0078, -0.0009],
+ [ 0.0196, -0.0219, 0.0057, ..., 0.0070, -0.0059, -0.0075],
+ ...,
+ [-0.0068, -0.0123, 0.0011, ..., 0.0024, -0.0069, -0.0181],
+ [ 0.0018, -0.0121, -0.0095, ..., -0.0199, 0.0067, -0.0080],
+ [-0.0084, 0.0186, 0.0111, ..., -0.0047, 0.0052, 0.0088]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-7.3486e-02, 2.0966e-02, 2.3758e-02, ..., 9.0637e-03,
+ 1.1623e-05, -1.4076e-02], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([1.3803, 1.3287, 1.4781, ..., 1.5120, 1.3130, 1.4137], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.0475, -0.0935, -0.0597, ..., 0.0320, 0.0142, -0.0661],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 1.6296e-02, -1.0147e-02, 2.2263e-02, ..., 1.8875e-02,
+ -4.6844e-03, -1.2360e-02],
+ [ 1.4534e-02, -1.0414e-02, -2.5024e-02, ..., -1.7578e-02,
+ -3.4729e-02, -2.3346e-02],
+ [-2.1347e-02, 2.1301e-02, 3.8509e-03, ..., 8.6441e-03,
+ 1.4847e-02, -6.3400e-03],
+ ...,
+ [-4.7302e-03, -2.5574e-02, 7.4959e-03, ..., 3.6087e-03,
+ 1.5732e-02, -2.2202e-02],
+ [-6.3324e-04, 8.2550e-03, -1.3161e-02, ..., 5.1918e-03,
+ 2.1324e-03, 1.3359e-02],
+ [-5.0240e-03, 4.4479e-03, -1.5625e-02, ..., 1.8707e-02,
+ -4.8995e-05, 1.2718e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.2683, -0.3921, -0.3276, ..., -0.3716, -0.2025, -0.3127],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0052, -0.0143, 0.0135, ..., 0.0038, 0.0296, -0.0021],
+ [ 0.0280, -0.0070, -0.0129, ..., -0.0207, 0.0208, 0.0257],
+ [ 0.0018, -0.0234, 0.0009, ..., 0.0089, -0.0099, -0.0107],
+ ...,
+ [ 0.0084, -0.0017, 0.0058, ..., -0.0016, -0.0057, -0.0010],
+ [ 0.0047, -0.0170, -0.0032, ..., 0.0134, -0.0184, 0.0449],
+ [-0.0015, -0.0398, -0.0143, ..., -0.0135, 0.0247, 0.0222]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0358, 0.0342, 0.0543, ..., 0.0743, -0.0069, 0.0033],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.5167, 2.3499, 2.4777, ..., 0.5123, 2.0356, 2.4509], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.2338, -0.0299, 0.1534, ..., 0.4063, 0.7359, -0.2059],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0072, -0.0024, -0.0093, ..., 0.0206, -0.0025, -0.0194],
+ [ 0.0068, 0.0083, 0.0218, ..., -0.0244, -0.0298, 0.0023],
+ [ 0.0273, 0.0015, -0.0178, ..., -0.0870, 0.0066, -0.0008],
+ ...,
+ [ 0.0076, -0.0371, -0.0106, ..., 0.0053, -0.0015, -0.0093],
+ [-0.0015, 0.0093, -0.0339, ..., -0.0024, 0.0004, -0.0021],
+ [ 0.0035, -0.0088, 0.0025, ..., -0.0005, 0.0056, -0.0153]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0615, 0.0997, -0.5298, ..., 0.0029, -0.0045, -0.0547],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0042, -0.0015, 0.0274, ..., 0.0097, -0.0203, 0.0125],
+ [-0.0057, 0.0277, 0.0067, ..., 0.0058, -0.0193, 0.0007],
+ [-0.0005, 0.0042, 0.0217, ..., 0.0109, 0.0060, 0.0009],
+ ...,
+ [ 0.0093, -0.0028, -0.0129, ..., 0.0005, 0.0210, -0.0072],
+ [ 0.0155, 0.0005, 0.0134, ..., -0.0217, -0.0046, 0.0098],
+ [ 0.0043, -0.0210, -0.0279, ..., -0.0082, -0.0022, 0.0044]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0168, 0.0077, -0.0467, ..., 0.0064, -0.0126, -0.0271],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.4813, 1.5299, 1.5828, ..., 1.5154, 1.4352, 1.5897], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1628, -0.0896, -0.0374, ..., -0.0098, -0.0610, -0.1625],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0061, -0.0101, 0.0220, ..., 0.0076, -0.0179, -0.0062],
+ [ 0.0287, 0.0189, 0.0143, ..., -0.0079, 0.0128, -0.0096],
+ [-0.0176, 0.0025, -0.0220, ..., -0.0191, -0.0070, -0.0005],
+ ...,
+ [ 0.0012, -0.0170, -0.0051, ..., -0.0094, -0.0273, 0.0126],
+ [ 0.0056, -0.0026, 0.0170, ..., 0.0264, -0.0188, -0.0084],
+ [ 0.0042, 0.0020, 0.0170, ..., -0.0107, -0.0194, -0.0005]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2793, -0.3450, -0.2959, ..., -0.1840, -0.1981, -0.2493],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-1.0500e-03, -1.4481e-02, 9.7084e-04, ..., 3.5362e-03,
+ -7.4148e-04, 2.0218e-02],
+ [ 1.8265e-02, -1.7059e-02, -6.9523e-04, ..., -7.4577e-04,
+ -8.8272e-03, -1.0271e-03],
+ [ 6.8474e-03, 8.5602e-03, -2.2079e-02, ..., 1.6556e-02,
+ -1.0653e-03, -2.1194e-02],
+ ...,
+ [ 2.0035e-02, -9.2239e-03, 1.4229e-02, ..., -6.3858e-03,
+ -7.1640e-03, -2.1927e-02],
+ [ 1.5144e-02, -9.1791e-06, 7.7324e-03, ..., -7.3395e-03,
+ 3.1433e-03, 9.2697e-03],
+ [ 7.2021e-03, 2.0950e-02, 8.4610e-03, ..., 9.9106e-03,
+ -2.2316e-03, -6.6261e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([0.0066, 0.0104, 0.0044, ..., 0.0064, 0.0797, 0.0699], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.6710, 2.5634, 2.7691, ..., 0.6788, 2.2533, 2.7433], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0355, 0.2774, 0.4173, ..., 0.5667, 0.5320, -0.4676],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0145, -0.0110, 0.0256, ..., -0.0594, -0.0049, 0.0130],
+ [-0.0188, -0.0083, -0.0112, ..., 0.0231, -0.0353, 0.0120],
+ [ 0.0106, 0.0018, -0.0003, ..., 0.0022, 0.0061, 0.0072],
+ ...,
+ [ 0.0017, 0.0005, 0.0002, ..., 0.0049, -0.0219, -0.0394],
+ [ 0.0120, 0.0053, -0.0002, ..., -0.0002, 0.0002, -0.0100],
+ [-0.0057, 0.0138, 0.0124, ..., -0.0036, -0.0128, 0.0019]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2327, 0.0399, -0.0326, ..., -0.0056, 0.0197, 0.0396],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0253, 0.0045, 0.0054, ..., 0.0114, -0.0120, 0.0098],
+ [ 0.0248, -0.0201, -0.0091, ..., -0.0043, -0.0027, 0.0147],
+ [ 0.0056, 0.0186, -0.0143, ..., -0.0139, -0.0035, -0.0077],
+ ...,
+ [ 0.0012, -0.0047, -0.0184, ..., 0.0032, -0.0123, 0.0104],
+ [ 0.0084, -0.0137, 0.0252, ..., 0.0189, -0.0143, 0.0102],
+ [-0.0266, -0.0148, -0.0076, ..., 0.0242, -0.0059, 0.0166]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0083, 0.0090, -0.0957, ..., -0.0067, 0.0007, -0.0046],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.5726, 1.4541, 1.5816, ..., 1.7312, 1.4169, 1.5937], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1270, -0.2203, -0.0099, ..., -0.0846, -0.0867, -0.1574],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0049, 0.0010, -0.0008, ..., 0.0092, -0.0068, -0.0089],
+ [-0.0189, 0.0112, -0.0008, ..., -0.0095, -0.0150, 0.0131],
+ [-0.0017, -0.0340, 0.0049, ..., -0.0096, 0.0049, -0.0091],
+ ...,
+ [ 0.0015, -0.0103, -0.0238, ..., -0.0044, -0.0164, -0.0042],
+ [ 0.0059, -0.0020, -0.0025, ..., 0.0057, 0.0186, 0.0068],
+ [ 0.0137, 0.0040, -0.0026, ..., -0.0155, 0.0179, -0.0174]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1598, -0.3298, -0.3064, ..., -0.3005, -0.3159, -0.1328],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-5.0659e-03, -1.4755e-02, 2.9678e-02, ..., -1.4786e-02,
+ 3.0472e-02, 1.2962e-02],
+ [ 8.6517e-03, -2.0859e-02, -1.2672e-02, ..., 1.1673e-02,
+ -2.9373e-02, 4.4823e-03],
+ [-1.6266e-02, 4.0253e-02, -6.0081e-03, ..., 2.8193e-05,
+ -1.5068e-02, -1.5480e-02],
+ ...,
+ [-1.2833e-02, 1.1993e-02, -5.6553e-04, ..., 5.5046e-03,
+ 1.6586e-02, -7.4272e-03],
+ [ 2.9144e-02, 8.1482e-03, -1.4267e-02, ..., 2.8549e-02,
+ 3.4962e-03, -7.6218e-03],
+ [ 2.3270e-02, -1.7654e-02, -1.4374e-02, ..., -3.7155e-03,
+ -1.8509e-02, -3.0289e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.0115, 0.0317, -0.0131, ..., -0.0563, -0.0150, 0.0325],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.7785, 2.7386, 2.7390, ..., 0.8678, 2.4946, 2.8710], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.1475, -0.0199, 0.2092, ..., 0.4587, 0.5408, -0.2745],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0120, -0.0190, -0.0403, ..., -0.0117, 0.0023, 0.0093],
+ [ 0.0185, 0.0156, 0.0064, ..., -0.0211, 0.0304, 0.0128],
+ [-0.0041, -0.0232, -0.0050, ..., -0.0144, -0.0013, 0.0115],
+ ...,
+ [-0.0279, -0.0333, 0.0062, ..., -0.0130, -0.0025, 0.0134],
+ [ 0.0011, 0.0101, 0.0281, ..., -0.0020, 0.0121, 0.0017],
+ [-0.0162, 0.0049, -0.0176, ..., 0.0049, 0.0010, -0.0232]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0367, -0.0407, -0.0178, ..., 0.0190, 0.0422, 0.0333],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0003, -0.0005, -0.0012, ..., 0.0035, 0.0030, -0.0334],
+ [-0.0137, -0.0057, 0.0201, ..., -0.0077, -0.0303, -0.0032],
+ [-0.0225, -0.0056, 0.0133, ..., 0.0120, -0.0059, -0.0108],
+ ...,
+ [ 0.0131, 0.0180, -0.0046, ..., 0.0325, -0.0201, -0.0211],
+ [-0.0181, 0.0204, -0.0102, ..., -0.0033, 0.0038, -0.0071],
+ [-0.0171, -0.0187, 0.0197, ..., 0.0140, -0.0235, -0.0155]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0430, 0.0264, -0.0948, ..., -0.0741, -0.0225, -0.0398],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.6421, 1.5236, 1.6723, ..., 1.8496, 1.4900, 1.6385], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.0308, -0.3167, -0.0299, ..., -0.0403, -0.0753, -0.2397],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0262, -0.0157, 0.0032, ..., -0.0235, -0.0293, -0.0457],
+ [ 0.0032, -0.0053, 0.0187, ..., 0.0109, 0.0186, 0.0035],
+ [ 0.0222, -0.0173, 0.0017, ..., -0.0083, 0.0020, 0.0136],
+ ...,
+ [ 0.0419, 0.0184, -0.0148, ..., 0.0049, 0.0197, -0.0116],
+ [ 0.0028, -0.0195, 0.0098, ..., 0.0172, -0.0162, -0.0341],
+ [ 0.0033, -0.0130, -0.0162, ..., 0.0044, -0.0219, -0.0079]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3088, -0.1615, -0.2668, ..., -0.2515, -0.2261, -0.2349],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0020, -0.0129, -0.0182, ..., 0.0192, -0.0327, -0.0179],
+ [ 0.0028, -0.0038, -0.0319, ..., -0.0060, 0.0162, 0.0273],
+ [ 0.0273, -0.0120, -0.0101, ..., -0.0013, -0.0036, -0.0072],
+ ...,
+ [ 0.0023, -0.0175, -0.0372, ..., 0.0134, 0.0053, 0.0087],
+ [ 0.0148, -0.0124, -0.0037, ..., 0.0165, -0.0067, 0.0085],
+ [-0.0011, 0.0023, 0.0334, ..., -0.0055, -0.0134, 0.0073]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.1197, -0.0195, 0.1592, ..., -0.0289, -0.0276, -0.0573],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([3.1758, 3.2323, 3.2080, ..., 1.1438, 2.6186, 3.2341], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([-0.3706, 0.0532, -0.3175, ..., 0.1710, 0.3347, -0.2024],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 2.1271e-02, 1.1780e-02, -6.7997e-04, ..., 6.0921e-03,
+ -3.5896e-03, -7.0524e-04],
+ [ 5.5809e-03, -1.0429e-02, -1.2751e-03, ..., 2.9259e-03,
+ 3.8683e-05, 2.1801e-03],
+ [-2.0950e-02, 3.2496e-04, -2.4063e-02, ..., -6.4819e-02,
+ -3.2349e-03, -4.4746e-03],
+ ...,
+ [ 1.0742e-02, -3.3913e-03, -1.1414e-02, ..., -2.3003e-03,
+ 2.1942e-02, 2.1652e-02],
+ [-1.3786e-02, 1.0185e-02, -4.3068e-03, ..., 9.9850e-04,
+ 7.5111e-03, 2.2797e-02],
+ [ 4.1842e-05, 1.5434e-02, -5.5361e-04, ..., 8.8730e-03,
+ 1.2108e-02, 1.5915e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [ 0.0062, -0.0193, 0.0082, ..., 0.0038, 0.0258, -0.0131],
+ ...,
+ [ 0.0200, 0.0005, -0.0168, ..., -0.0123, -0.0038, 0.0042],
+ [ 0.0073, 0.0172, -0.0105, ..., 0.0243, 0.0107, 0.0142],
+ [ 0.0281, 0.0236, 0.0068, ..., -0.0044, -0.0162, -0.0067]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
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+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [ 0.0138, 0.0011, 0.0013, ..., 0.0371, 0.0046, 0.0199],
+ ...,
+ [ 0.0202, 0.0034, 0.0115, ..., -0.0043, -0.0131, -0.0177],
+ [ 0.0054, 0.0023, 0.0239, ..., -0.0273, 0.0084, -0.0098],
+ [ 0.0170, 0.0049, -0.0137, ..., -0.0036, -0.0199, 0.0079]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [ 0.0023, -0.0021, 0.0147, ..., -0.0046, 0.0072, 0.0077],
+ ...,
+ [-0.0114, -0.0102, 0.0014, ..., 0.0213, 0.0042, -0.0110],
+ [ 0.0055, -0.0080, -0.0157, ..., -0.0074, -0.0366, 0.0046],
+ [ 0.0316, 0.0016, 0.0092, ..., -0.0057, -0.0119, -0.0157]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
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+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [-0.0014, -0.0324, -0.0615, ..., 0.0012, -0.0250, 0.0104],
+ ...,
+ [ 0.0085, -0.0075, 0.0259, ..., -0.0010, 0.0261, -0.0071],
+ [-0.0081, 0.0082, 0.0233, ..., 0.0083, -0.0072, 0.0106],
+ [-0.0114, 0.0072, 0.0143, ..., -0.0098, -0.0095, 0.0079]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -2.6584e-04, 1.1932e-02],
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+ -1.2825e-02, -8.9407e-07],
+ [ 7.9193e-03, -1.8036e-02, 1.1223e-02, ..., -3.8147e-02,
+ -2.9087e-03, -5.4131e-03],
+ ...,
+ [-1.4465e-02, 1.2436e-02, -1.3103e-03, ..., -8.3694e-03,
+ 1.7273e-02, -9.1934e-04],
+ [-3.6774e-03, 3.2272e-03, 2.3682e-02, ..., -4.9706e-03,
+ 7.7705e-03, -1.4359e-02],
+ [ 1.0548e-03, -1.9181e-04, -1.6556e-02, ..., -1.1215e-02,
+ -6.9504e-03, -1.4145e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
+tensor([0.3892, 0.2379, 0.1540, ..., 0.6268, 0.2169, 0.1550], device='cuda:1',
+ requires_grad=True)Parameter containing:
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+ [ 0.0157, -0.0013, -0.0331, ..., 0.0240, -0.0025, -0.0044],
+ [ 0.0204, 0.0067, -0.0038, ..., -0.0049, 0.0252, -0.0015],
+ ...,
+ [ 0.0077, -0.0031, 0.0343, ..., -0.0061, 0.0099, -0.0152],
+ [-0.0005, 0.0071, -0.0140, ..., -0.0133, -0.0071, 0.0009],
+ [-0.0012, 0.0004, 0.0090, ..., -0.0095, -0.0076, -0.0047]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [ 0.0050, 0.0103, 0.0122, ..., 0.0073, -0.0067, -0.0138],
+ [-0.0014, 0.0265, 0.0125, ..., -0.0273, 0.0040, -0.0028],
+ ...,
+ [ 0.0138, 0.0236, -0.0157, ..., 0.0255, -0.0269, -0.0320],
+ [ 0.0021, -0.0071, 0.0076, ..., -0.0042, 0.0137, -0.0034],
+ [-0.0111, 0.0175, -0.0121, ..., -0.0294, -0.0013, -0.0084]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1592, -0.0064, 0.1971, ..., 0.0551, -0.0191, 0.0068],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.5678, 2.3816, 2.5756, ..., 1.8250, 2.4113, 2.7505], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([ 0.1836, 0.3324, -0.2291, ..., -0.1089, 0.5930, -0.2813],
+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [-0.0170, 0.0098, 0.0225, ..., -0.0176, -0.0123, -0.0115],
+ [-0.0039, -0.0008, 0.0077, ..., 0.0050, 0.0103, -0.0090],
+ ...,
+ [ 0.0026, 0.0136, -0.0086, ..., 0.0001, -0.0151, -0.0018],
+ [ 0.0049, 0.0023, -0.0202, ..., 0.0277, 0.0162, -0.0295],
+ [-0.0040, 0.0099, -0.0184, ..., -0.0405, -0.0316, -0.0159]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 7.5607e-03, 2.2369e-02],
+ [ 2.1725e-03, 1.5160e-02, -1.8494e-02, ..., 6.2218e-03,
+ -9.6977e-05, 1.4214e-02],
+ [-7.4997e-03, 1.7151e-02, -1.4481e-02, ..., -2.2156e-02,
+ 1.0445e-02, 9.1171e-03],
+ ...,
+ [-2.1515e-02, -1.4336e-02, -3.9558e-03, ..., 2.7351e-03,
+ -3.2997e-03, 2.3087e-02],
+ [ 1.9348e-02, 1.7441e-02, 4.3488e-03, ..., 6.1913e-03,
+ -1.8509e-02, 2.2385e-02],
+ [ 1.6891e-02, 7.5951e-03, -1.6037e-02, ..., -4.5509e-03,
+ 6.0081e-03, 1.7471e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.2128, 0.1381, 0.1891, ..., 0.0071, 0.0607, -0.0499],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
+tensor([-0.0080, -0.0616, -0.0676, ..., -0.0960, -0.1752, -0.1096],
+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [ 0.0117, 0.0109, -0.0199, ..., 0.0012, 0.0099, -0.0078],
+ [-0.0045, -0.0047, -0.0147, ..., -0.0005, -0.0079, -0.0103],
+ ...,
+ [-0.0171, -0.0022, -0.0138, ..., 0.0246, -0.0203, -0.0171],
+ [ 0.0086, 0.0179, -0.0107, ..., -0.0160, -0.0177, -0.0097],
+ [ 0.0428, -0.0029, -0.0069, ..., -0.0147, 0.0129, 0.0242]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [ 0.0019, -0.0100, -0.0021, ..., 0.0078, 0.0061, -0.0132],
+ [-0.0086, -0.0003, 0.0180, ..., 0.0017, 0.0049, 0.0218],
+ ...,
+ [-0.0206, -0.0111, -0.0025, ..., -0.0035, 0.0097, 0.0248],
+ [ 0.0121, -0.0078, 0.0101, ..., -0.0093, 0.0092, -0.0375],
+ [ 0.0031, -0.0039, 0.0001, ..., -0.0069, 0.0013, 0.0023]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.0218, -0.1331, -0.1234, ..., -0.1169, 0.0630, 0.0916],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
+tensor([-0.0204, -0.0891, 0.0739, ..., 0.0297, 0.1517, -0.2596],
+ device='cuda:1', requires_grad=True)Parameter containing:
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+ requires_grad=True)Parameter containing:
+tensor([-0.0061, 0.1510, -0.0549, ..., 0.2748, 0.0765, 0.0091],
+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [ 0.0042, 0.0029, 0.0002, ..., 0.0010, 0.0015, -0.0012],
+ [ 0.0018, 0.0007, -0.0012, ..., -0.0029, -0.0009, 0.0026],
+ ...,
+ [ 0.0216, 0.0055, -0.0101, ..., -0.0065, -0.0029, 0.0037],
+ [ 0.0188, 0.0073, -0.0077, ..., -0.0025, -0.0009, 0.0057],
+ [ 0.0330, 0.0281, 0.0289, ..., 0.0160, 0.0102, -0.0310]],
+ device='cuda:1', requires_grad=True)Parameter containing:
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+ [-0.0054, -0.0049, 0.0055, ..., 0.0239, 0.0171, -0.0071],
+ [ 0.0032, 0.0101, -0.0155, ..., 0.0070, -0.0119, -0.0098],
+ ...,
+ [-0.0112, 0.0009, 0.0023, ..., -0.0169, -0.0096, -0.0147],
+ [ 0.0080, 0.0086, 0.0201, ..., -0.0108, -0.0191, 0.0043],
+ [-0.0168, -0.0018, -0.0156, ..., 0.0095, 0.0383, 0.0007]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0139, 0.0147, -0.0089, ..., -0.0349, -0.0042, -0.0188],
+ [-0.0586, -0.0059, -0.0179, ..., 0.0012, -0.0068, 0.0254],
+ [-0.0211, -0.0321, 0.0308, ..., -0.0189, 0.0091, 0.0066],
+ ...,
+ [-0.0217, -0.0089, -0.0143, ..., -0.0153, 0.0053, 0.0016],
+ [-0.0086, -0.0083, -0.0049, ..., 0.0208, -0.0048, -0.0041],
+ [-0.0087, -0.0024, 0.0105, ..., -0.0037, -0.0148, 0.0030]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2406, 0.1490, 0.4639, ..., -0.0241, 0.0349, -0.0144],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -6.3782e-03, 1.3786e-02],
+ [-9.3231e-03, 9.2888e-04, -1.3893e-02, ..., -1.1345e-02,
+ 1.7748e-03, -8.9569e-03],
+ [ 5.6648e-04, 1.0345e-02, 8.1718e-05, ..., 1.3893e-02,
+ 7.3791e-05, 6.1369e-04],
+ ...,
+ [-3.0212e-02, 3.7193e-03, 1.2009e-02, ..., 7.0229e-03,
+ 8.0566e-03, 1.4572e-02],
+ [ 6.4421e-04, -1.0941e-02, -6.3133e-03, ..., 5.6953e-03,
+ -7.6637e-03, -2.9297e-03],
+ [-4.3526e-03, 4.7607e-03, -6.6528e-03, ..., 7.3853e-03,
+ 4.3716e-03, 7.4348e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
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+ 4.2877e-03, -3.8681e-03, -4.8370e-02, -2.2751e-02, -2.1248e-03,
+ 1.4526e-02, -3.3607e-03, 4.7058e-02, 1.4565e-02, -6.0730e-02,
+ -1.1035e-01, -2.3251e-03, 2.5635e-02, -6.1523e-02, -1.0469e+00,
+ -3.0869e-02, 6.7078e-02, -1.4503e-02, 2.6855e-02, -7.4646e-02,
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+ -2.9648e-02, -1.3227e-03, 2.5940e-02, 6.6650e-02, 5.8899e-02,
+ -2.0615e-02, -3.5889e-02, 1.6830e-02, 1.0229e-01, 5.3040e-02,
+ 3.8280e-03, -2.3972e-02, 1.2512e-01, -4.3579e-02, 4.3335e-02,
+ 2.9175e-02, -2.9160e-02, -3.9093e-02, -2.2247e-02, 9.2850e-03,
+ -9.8511e-02, 3.3478e-02, -4.2023e-02, -3.2043e-02, -4.7394e-02,
+ 1.1938e-01, 5.7709e-02, 1.2903e-01, -2.9327e-02, -1.1314e-02,
+ 7.3090e-03, 3.3844e-02, 2.8290e-02, -1.6266e-02, 7.0740e-02,
+ -7.3486e-02, 5.4413e-02, -6.0120e-03, 5.1651e-03, -1.4600e-01,
+ -3.6896e-02, 2.1088e-02, 3.6914e-01, 9.0393e-02, -5.8517e-03,
+ 8.8318e-02, -8.3847e-03, 9.6512e-03, -3.3783e-02, -4.0710e-02,
+ 3.9703e-02, -2.7776e-04, -3.7262e-02, 8.9539e-02, -7.3853e-02,
+ -3.6743e-02, 1.1426e-01, 5.7335e-03, 6.6589e-02, 3.0502e-02,
+ 2.4170e-02, 6.7017e-02, -4.7363e-02, 1.1696e-02, -5.0568e-02,
+ 3.9001e-02, 1.2695e-02, -3.1647e-02, -4.1016e-02, -6.2683e-02,
+ -2.7084e-02, -2.3511e-01, 2.4002e-02, 1.0413e-01, 1.2520e-02,
+ 1.4908e-02, -8.5693e-02, -6.4575e-02, -2.4414e-02, -4.9408e-02,
+ 3.0045e-02, 2.5436e-02, -4.7333e-02, -3.4576e-02, -2.5772e-02,
+ 2.5345e-02, 7.3669e-02, 2.6398e-02, -1.2976e-01, 5.1544e-02,
+ 6.9199e-03, -6.0028e-02, -8.6792e-02, 1.3252e-02, 1.9196e-02,
+ -1.3283e-02, 1.0910e-02, 3.8025e-02, 7.4120e-03, -2.3865e-02,
+ -3.4882e-02, 4.7731e-04, -7.3059e-02, -1.1017e-02, -5.8685e-02,
+ -2.5238e-02, -2.3773e-02, 5.0201e-02, -2.6428e-02, -5.1361e-02,
+ -7.4219e-02, 4.5624e-02, 5.3192e-02, 1.3208e-01, 4.1931e-02,
+ 1.5083e-02, -1.1676e-01, 7.9895e-02, 6.4209e-02, 1.0178e-02,
+ 6.6681e-03, 8.0490e-03, -2.8870e-02, -6.2790e-03, -4.5357e-03,
+ -7.2266e-02, -6.2744e-02, -4.0955e-02, 1.5533e-02, -2.6749e-02,
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+ -2.1301e-02, 5.0415e-02, 1.6006e-02, 1.5850e-03, -7.7362e-03,
+ -2.8809e-02, -1.2871e-02, -1.6708e-02, -1.0777e-03, 2.6367e-02,
+ -7.3395e-03, -1.2238e-02, 2.3804e-02, -1.8433e-02, 5.7640e-03,
+ -3.2379e-02, -2.2598e-02, 1.7105e-02, 2.0096e-02, -6.7871e-02,
+ 3.6926e-02, -3.5248e-02, 1.6699e-01, 4.4495e-02, 1.0643e-02,
+ 6.1829e-02, -5.8960e-02, -2.0401e-02, 1.4259e-02, 1.8372e-02,
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+ 9.9413e-02, -6.2378e-02, -8.8212e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0402, 0.0049, 0.0031, ..., 0.0076, -0.0040, -0.0004],
+ [ 0.0320, -0.0247, 0.0270, ..., 0.0014, -0.0266, -0.0196],
+ [-0.0072, 0.0229, 0.0050, ..., -0.0068, -0.0446, -0.0313],
+ ...,
+ [ 0.0280, -0.0149, 0.0136, ..., 0.0182, -0.0120, -0.0161],
+ [ 0.0343, -0.0128, -0.0234, ..., 0.0229, -0.0218, 0.0272],
+ [ 0.0184, 0.0124, 0.0135, ..., -0.0094, 0.0302, -0.0117]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3799, -0.4065, -0.2979, ..., -0.4219, -0.3420, -0.1925],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0009, 0.0018, 0.0037, ..., -0.0094, 0.0236, 0.0011],
+ [ 0.0007, 0.0022, -0.0113, ..., -0.0333, 0.0027, 0.0064],
+ [ 0.0013, -0.0087, 0.0208, ..., 0.0051, 0.0020, 0.0045],
+ ...,
+ [ 0.0153, -0.0221, 0.0076, ..., -0.0112, 0.0199, -0.0161],
+ [-0.0092, -0.0176, 0.0055, ..., -0.0182, 0.0059, 0.0039],
+ [-0.0012, -0.0012, -0.0088, ..., -0.0243, 0.0233, -0.0009]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 7.7896e-03, -3.2593e-02, 2.3365e-03, -2.7428e-03, -1.7853e-02,
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+ 1.2192e-02, -2.8833e-01, 8.6212e-03, -3.7537e-02, -2.2629e-02,
+ -2.6428e-02, 6.6566e-03, -1.2238e-02, 8.9645e-03, 2.0905e-02,
+ -5.8098e-03, -7.1899e-02, -1.3962e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.5466, 1.6287, 1.4620, 1.5152, 1.6963, 1.5352, 1.7028, 1.6495, 1.6754,
+ 1.5446, 1.7198, 1.6647, 1.7217, 1.7013, 1.5661, 1.5963, 1.7138, 1.6526,
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+ 6.2924e-01, 5.0303e-01, -1.4128e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0033, -0.0180, 0.0063, ..., 0.0171, 0.0053, 0.0176],
+ [ 0.0050, 0.0288, 0.0542, ..., 0.0377, 0.0121, -0.0257],
+ [ 0.0002, -0.0528, 0.0353, ..., 0.0037, 0.0121, 0.0060],
+ ...,
+ [ 0.0066, 0.0045, 0.0136, ..., 0.0031, 0.0118, -0.0052],
+ [-0.0037, 0.0018, -0.0075, ..., 0.0004, -0.0041, 0.0008],
+ [-0.0034, 0.0127, -0.0073, ..., 0.0064, -0.0214, -0.0094]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.3430, -0.0836, 0.0424, ..., -0.0128, -0.0226, 0.0145],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0056, -0.0199, 0.0132, ..., -0.0029, 0.0242, -0.0021],
+ [ 0.0218, 0.0037, 0.0028, ..., -0.0120, 0.0019, 0.0024],
+ [ 0.0007, -0.0039, -0.0249, ..., -0.0206, 0.0182, 0.0032],
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+ [ 0.0174, -0.0362, -0.0287, ..., -0.0099, -0.0143, 0.0133]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -2.5925e-02, -3.2410e-02, 7.2266e-02, -4.3671e-02, 9.2010e-03,
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+ -2.4963e-02, -4.7607e-03, 7.7515e-03, -2.6108e-02, 7.8430e-03,
+ -2.4933e-02, -4.3518e-02, 3.5839e-03, 2.2598e-02, 1.8845e-02,
+ -1.0582e-02, -1.5945e-02, 3.8834e-03, 2.5909e-02, 6.1249e-02,
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+ -4.7852e-02, -3.6346e-02, 1.0300e-02, -1.5511e-02, -3.6133e-02,
+ -4.7821e-02, -1.3428e-02, -6.1493e-03, -8.7051e-03, 2.0996e-02,
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+ -4.9194e-02, 1.4847e-02, 2.1591e-03, -1.6916e-04, 1.1780e-02,
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+ -5.1239e-02, 1.7960e-02, 9.1019e-03, -3.3142e-02, -1.5480e-02,
+ -1.3832e-02, -1.0956e-01, -3.7975e-03, -5.7343e-02, -1.4809e-02,
+ -9.6893e-03, 4.9866e-02, -5.1880e-02, 4.2175e-02, -2.3911e-02,
+ 1.9703e-03, -2.2034e-02, -3.8261e-03], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 1.0362, 1.0344, 1.1302, 1.1394, 1.1145, 0.9793, 1.2097, 1.0334, 1.2245,
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+ 1.1139, 1.0485, 1.1752, 1.1343, 1.1493, 1.1050, 1.0833, 1.0571, 1.1405,
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+ 1.1468, 1.0984, 1.0819, 1.0745, 1.1147, 1.0569, 1.1257, 1.1980, 1.2418,
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+ 1.1944, 1.0511, 1.1842, 1.1555, 1.0770, 1.0528, 1.0551, 1.1226, 1.1294,
+ 1.0864, 1.1444, 1.0887, 1.0853, 1.2392, 1.0416, 1.0600, 1.1678, 1.1766,
+ 1.1449, 1.1450, 1.2752, 1.1177, 1.1140, 1.1532, 1.1915, 1.1941, 1.0838,
+ 1.1238, 0.9961, 1.0554, 1.1288, 1.1269, 1.1024, 1.1493, 1.1472, 1.1067,
+ 1.1309, 1.0237, 1.1736], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 1.2977e-01, 3.4533e-02, -1.8398e-01, 1.2173e-02, 5.4290e-02,
+ 7.0416e-02, 6.9125e-02, 6.3581e-02, -6.3971e-02, 2.2436e-02,
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+ -2.4700e-03, 2.0580e-02, 1.5001e-01, -4.6368e-02, -8.9924e-02,
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+ -4.2307e-02, 1.8172e-02, 2.4058e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0344, 0.0046, 0.0019, ..., -0.0018, 0.0054, -0.0178],
+ [-0.0100, 0.0007, 0.0120, ..., -0.0170, 0.0012, -0.0188],
+ [-0.0169, 0.0147, -0.0102, ..., 0.0031, -0.0298, 0.0021],
+ ...,
+ [ 0.0024, 0.0114, 0.0381, ..., 0.0197, -0.0068, 0.0028],
+ [-0.0170, -0.0138, 0.0048, ..., 0.0125, -0.0223, 0.0095],
+ [-0.0003, -0.0298, -0.0086, ..., -0.0083, 0.0122, -0.0196]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2842, -0.3364, 0.0483, ..., -0.4465, -0.3184, -0.2751],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0024, 0.0274, 0.0246, ..., 0.0208, 0.0061, 0.0094],
+ [-0.0033, 0.0003, -0.0214, ..., 0.0064, 0.0232, 0.0025],
+ [-0.0203, 0.0050, -0.0124, ..., 0.0002, -0.0194, -0.0300],
+ ...,
+ [-0.0107, 0.0104, 0.0014, ..., 0.0129, -0.0087, 0.0057],
+ [-0.0105, -0.0092, 0.0100, ..., 0.0361, -0.0151, -0.0012],
+ [ 0.0143, 0.0048, 0.0093, ..., 0.0324, -0.0147, -0.0111]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 1.7771, 1.7942, 1.8408], device='cuda:1', requires_grad=True)Parameter containing:
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+ -7.7916e-02, -6.8427e-01, -8.1858e-03, 1.2579e-01, 2.1868e-01,
+ -6.8004e-01, 7.4477e-01, 3.0428e-01, 4.4441e-02, -5.2122e-01,
+ -8.5566e-01, -7.7845e-02, -4.6011e-01, -4.8875e-01, -6.0817e-01,
+ -4.2802e-01, -1.0616e-01, -6.4764e-01, 5.8157e-01, 9.6697e-02,
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+ 3.3541e-01, 4.0628e-01, -6.7456e-01, 8.9355e-01, -5.9333e-02,
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+ -4.0588e-02, 6.0938e-01, 3.0536e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0334, 0.0285, 0.0295, ..., 0.0152, -0.0162, -0.0086],
+ [-0.0067, 0.0443, -0.0088, ..., 0.0052, 0.0228, -0.0394],
+ [-0.0080, -0.0090, -0.0276, ..., -0.0169, -0.0212, 0.0219],
+ ...,
+ [-0.0186, 0.0049, -0.0079, ..., -0.0045, 0.0192, -0.0271],
+ [ 0.0003, 0.0048, -0.0178, ..., -0.0006, -0.0150, 0.0126],
+ [ 0.0191, -0.0278, 0.0059, ..., 0.0222, 0.0142, 0.0118]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.4333, 0.1654, -0.0519, ..., -0.0249, 0.0006, 0.0306],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0058, 0.0067, 0.0045, ..., -0.0050, 0.0077, -0.0065],
+ [ 0.0011, -0.0229, -0.0150, ..., 0.0013, 0.0057, 0.0196],
+ [-0.0234, 0.0136, -0.0235, ..., 0.0329, -0.0069, 0.0318],
+ ...,
+ [ 0.0086, 0.0077, -0.0036, ..., -0.0093, -0.0244, 0.0068],
+ [ 0.0283, 0.0173, 0.0116, ..., -0.0010, 0.0039, -0.0024],
+ [ 0.0225, 0.0120, 0.0018, ..., -0.0170, 0.0129, -0.0031]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 2.5513e-02, 4.2145e-02, 7.9422e-03], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.2778, 1.4128, 1.3243, 1.2121, 1.4286, 1.2285, 1.2714, 1.2086, 1.2280,
+ 1.2027, 1.3056, 1.3433, 1.3571, 1.2871, 1.2546, 1.3233, 1.2960, 1.4360,
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+ -2.4353e-03, -2.0962e-02, -1.2389e-02, -3.2438e-02, -7.5809e-02,
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+ -1.8654e-02, -8.5054e-02, 1.4421e-02, 6.8078e-02, -5.8056e-02,
+ -4.4167e-02, -1.4837e-02, -7.9102e-02, -4.1090e-02, 7.0457e-02,
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+ -4.1965e-02, 8.5480e-02, -2.1664e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0202, 0.0051, -0.0099, ..., 0.0150, -0.0118, -0.0252],
+ [ 0.0279, -0.0216, -0.0171, ..., -0.0218, 0.0069, 0.0059],
+ [-0.0100, 0.0249, 0.0076, ..., 0.0068, -0.0119, 0.0081],
+ ...,
+ [ 0.0039, -0.0400, -0.0170, ..., 0.0191, -0.0038, 0.0145],
+ [ 0.0186, -0.0087, 0.0062, ..., 0.0125, -0.0135, -0.0063],
+ [ 0.0241, -0.0023, -0.0027, ..., 0.0083, 0.0031, 0.0143]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1687, -0.1522, -0.1874, ..., -0.3894, -0.2622, -0.3452],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0104, 0.0026, -0.0121, ..., 0.0043, -0.0197, -0.0084],
+ [-0.0017, -0.0006, 0.0162, ..., -0.0014, 0.0029, 0.0020],
+ [-0.0115, 0.0131, -0.0065, ..., -0.0152, 0.0059, 0.0125],
+ ...,
+ [-0.0005, 0.0133, -0.0086, ..., 0.0159, -0.0166, 0.0221],
+ [-0.0144, -0.0031, 0.0204, ..., 0.0199, 0.0079, -0.0012],
+ [-0.0021, -0.0152, -0.0143, ..., 0.0090, -0.0025, -0.0068]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -6.3171e-02, 2.3773e-02, 7.6050e-02, 3.6392e-03, 5.1117e-03,
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+ -5.4321e-02, 5.0995e-02, -6.7139e-02, 1.6037e-02, 4.4373e-02,
+ -3.3600e-02, -5.5328e-02, -3.9978e-02, 6.6223e-02, 4.3121e-02,
+ -9.3262e-02, -1.2390e-02, 1.1337e-02, 1.2619e-02, 4.5204e-03,
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+ -3.3844e-02, 2.0676e-02, 7.3624e-04, -3.8818e-02, 1.3878e-02,
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+ 2.2873e-02, 3.6804e-02, 1.8997e-03, 1.3168e-02, 5.0316e-03,
+ -2.5654e-03, 3.4851e-02, -5.0201e-03, 1.0445e-02, 3.4485e-02,
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+ -7.4844e-03, 6.0272e-02, -2.4399e-02, 3.3691e-02, 1.4244e-02,
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+ -3.5461e-02, -2.6962e-02, -8.1787e-02, -2.7451e-02, 1.2024e-02,
+ -4.1992e-02, 8.1635e-03, 6.4430e-03, -3.7689e-02, 5.9601e-02,
+ -2.4536e-02, 6.6589e-02, 7.8552e-02, 8.3130e-02, 1.6815e-02,
+ -1.2283e-02, 3.2074e-02, 1.6693e-02, -8.3008e-02, -1.6525e-02,
+ -4.8920e-02, 1.9150e-02, -4.1748e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([2.0257, 2.0344, 1.9511, 1.9693, 1.9585, 1.9219, 2.0683, 1.9555, 1.9977,
+ 2.0715, 2.0045, 2.0027, 2.0430, 2.0124, 2.0187, 1.8801, 1.9952, 2.1130,
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+ 1.9623, 2.0737, 1.9730, 1.9533, 2.1365, 2.0299, 1.8659, 1.9766, 2.1460,
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+ 2.2126, 2.3055, 1.8294, 1.9801, 2.0337, 2.0238, 2.0515, 2.3617, 2.0011,
+ 1.9341, 1.9198, 2.0020], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 2.1501e-01, 7.4704e-01, -4.3778e-01, -1.9189e-01, -1.9360e-01,
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+ -7.0129e-01, -2.6266e-01, -2.6017e-01, -7.0782e-01, 4.3546e-01,
+ -5.1003e-01, 1.1640e-01, -1.5618e-01, 2.6692e-01, 1.1821e-01,
+ -4.6812e-01, -5.0343e-01, 2.3759e-01, 8.6180e-01, 1.1069e-01,
+ -1.8956e-01, 2.4460e-01, 5.6212e-01, -1.0961e-01, -6.8296e-01,
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+ -6.3780e-01, -5.2782e-01, -2.7818e-01, 3.5333e-01, 8.6573e-01,
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+ -2.0664e-01, -3.6145e-01, -3.0133e-01, 1.7422e+00, 2.1885e-01,
+ 1.6592e-01, 3.7474e-01, 4.0835e-01], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-1.8555e-02, 1.2207e-02, -1.6556e-02, ..., 9.8953e-03,
+ 1.6815e-02, -1.8707e-02],
+ [ 3.0487e-02, 3.2715e-02, -3.0022e-03, ..., 3.5187e-02,
+ 3.5980e-02, -5.8136e-03],
+ [ 2.0390e-03, -2.0386e-02, 1.7670e-02, ..., 2.3132e-02,
+ 4.0550e-03, 1.1375e-02],
+ ...,
+ [-5.3101e-03, 2.4445e-02, -1.9531e-02, ..., -1.0094e-02,
+ -1.0544e-02, 2.3727e-03],
+ [-1.3418e-03, 4.7874e-03, 1.2207e-02, ..., 7.7553e-03,
+ -6.1214e-05, -1.3153e-02],
+ [ 9.2850e-03, 7.7629e-03, -1.5533e-02, ..., 1.3306e-02,
+ 5.0316e-03, 2.2507e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.1748, -0.0695, -0.2499, ..., -0.0291, 0.0082, 0.0654],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0072, 0.0056, 0.0144, ..., 0.0010, 0.0068, -0.0195],
+ [-0.0204, 0.0330, -0.0089, ..., -0.0183, 0.0075, 0.0104],
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+ ...,
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+ [-0.0321, -0.0037, 0.0038, ..., 0.0033, -0.0005, 0.0031],
+ [ 0.0155, 0.0208, -0.0011, ..., 0.0005, -0.0043, -0.0342]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 1.4253, 1.2813, 1.3836], device='cuda:1', requires_grad=True)Parameter containing:
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+ -1.6496e-02, -2.5456e-02, -1.3371e-02, -1.5350e-02, -3.9309e-02,
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+ -7.5973e-03, -1.0846e-01, -3.1029e-03, 5.0573e-02, -5.4467e-02,
+ -4.5049e-02, -1.6193e-02, -4.6469e-02, -5.5992e-02, 4.7240e-02,
+ -3.7008e-03, 1.1970e-01, 1.0794e-01, 4.3454e-02, -7.2692e-02,
+ 1.9468e-02, 4.4535e-02, 5.7215e-02, -6.2108e-02, -7.0441e-02,
+ -7.9148e-02, 8.5440e-02, 1.7515e-04], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0181, -0.0053, 0.0376, ..., 0.0159, 0.0007, -0.0079],
+ [-0.0007, 0.0249, 0.0235, ..., -0.0029, -0.0186, 0.0282],
+ [-0.0062, 0.0307, 0.0169, ..., -0.0004, -0.0213, -0.0294],
+ ...,
+ [-0.0041, -0.0177, 0.0085, ..., 0.0139, -0.0345, 0.0094],
+ [-0.0312, -0.0024, 0.0006, ..., -0.0078, 0.0215, -0.0030],
+ [ 0.0234, -0.0085, -0.0076, ..., 0.0165, -0.0413, 0.0310]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3335, -0.4612, -0.1525, ..., -0.2974, -0.4580, -0.3103],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0056, 0.0015, -0.0063, ..., -0.0112, -0.0193, -0.0046],
+ [ 0.0023, 0.0101, 0.0025, ..., 0.0093, 0.0138, 0.0203],
+ [-0.0024, 0.0070, -0.0107, ..., 0.0100, -0.0153, 0.0128],
+ ...,
+ [-0.0022, -0.0275, 0.0059, ..., 0.0156, 0.0155, 0.0103],
+ [ 0.0172, 0.0090, 0.0030, ..., -0.0214, -0.0010, -0.0127],
+ [-0.0244, 0.0601, 0.0131, ..., 0.0105, -0.0049, 0.0170]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-1.4061e-02, 5.6396e-02, -6.3660e-02, -1.5656e-02, -7.0129e-02,
+ 2.0599e-02, 4.9377e-02, -3.9101e-03, -9.5215e-02, -2.7008e-02,
+ -6.2294e-03, -5.4504e-02, -3.6987e-02, 1.1467e-02, -6.0364e-02,
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+ -3.0930e-02, 1.3420e-02, 2.3880e-02, -3.1372e-02, 5.2948e-02,
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+ -1.0480e-01, 8.2474e-03, -3.4485e-02, 3.0014e-02, 3.0609e-02,
+ -3.0533e-02, 8.3801e-02, 2.3941e-02, 5.3024e-03, -6.8481e-02,
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+ 2.0746, 2.0915, 2.0639, 2.0736, 2.0799, 2.2220, 2.2648, 1.9870, 2.1855,
+ 2.0254, 2.0939, 1.9822, 1.9375, 2.1553, 2.1249, 2.1436, 2.0041, 2.0035,
+ 2.0451, 1.9603, 2.1117, 2.2223, 2.1545, 2.0233, 2.1566, 1.9525, 1.9624,
+ 2.0093, 2.0608, 2.1441, 2.0819, 2.0554, 2.0715, 2.0770, 2.0988, 2.3386,
+ 2.0327, 2.0376, 2.1202, 2.1253, 2.0856, 2.0563, 1.7493, 2.0553, 1.9948,
+ 2.0059, 2.2029, 2.0470, 2.1513, 2.2328, 2.0887, 2.1264, 2.0788, 1.9652,
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+ 2.0207, 1.9886, 2.1628], device='cuda:1', requires_grad=True)Parameter containing:
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+ -0.4591, -0.4535, 0.6669, 0.6368, -0.5369, 0.5936, -0.4235, -0.6996,
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+ -0.2835, 0.0946, -0.1392, 0.5883, 0.1297, 0.4315, 0.7669, 0.1802,
+ -0.5833, -0.1598, 0.4056, -0.2835, 2.4506, -0.0757, 0.6105, -0.2125,
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+ -0.5151, 0.3451, 0.6331, -0.0027, 0.3818, -0.2817, -0.4590, 0.6107,
+ -0.6834, 0.0148, -0.3686, -0.6853, 0.1435, -0.0316, 0.7759, 0.4233,
+ -0.0109, 0.9338, -0.3701, 0.0630, -0.1912, -0.1769, -0.5478, -0.2753,
+ -0.8406, -0.5571, 0.0083, -0.0551, 0.2625, 0.2223, -0.6563, 0.7482,
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+ -0.2622, -0.2401, 0.4067, 0.6801, -0.5707, -0.6888, 0.3839, -0.2854,
+ -0.6706, 0.2172, 0.2885, -0.1456, 0.4580, 0.3836, -0.4238, -0.6184,
+ -0.0139, 0.5807, 0.6330, -0.3544, -0.4924, 0.5683, 0.4040, -0.2675,
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+ -0.0751, -0.5416, -0.7117, 0.3526, -0.3994, -0.0146, -1.1012, -0.0727,
+ -0.4971, 0.3637, 0.2655, 0.4278, 0.6634, -0.0713, -0.0099, -0.1449,
+ -0.4338, -0.1937, -0.6498, -0.7250, 0.2271, -0.6612, 0.1220, 0.3243,
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+ 0.2829, -0.6148, 0.3372, 0.9084, 0.3225, -0.3238, -0.5079, -0.4441,
+ -0.5670, 0.3065, -0.0379, 0.6865, -0.7325, -0.6104, -0.6129, 0.3126,
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+ -0.8835, -0.3817, 0.8097, -0.0851, 1.0992, -0.4803, 0.3294, -0.1898,
+ 0.3222, 0.3128, 0.1108, 0.1788, -0.4207, -0.2907, 0.8966, -0.0309,
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+ 0.3616, -0.1017, 0.3762, -0.3327, 0.1810, 0.3345, -1.0147, 0.9662,
+ -0.1385, 0.2843, 0.4133, -0.6416, -0.2800, 0.3751, -0.6579, -0.4096,
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+ -0.5432, -0.1476, -0.7711, 0.6847, 0.2413, 0.2893, 0.7736, -0.4954,
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+ -0.2374, -0.0454, -0.0685, -0.5255, 0.1050, -0.1814, 0.0709, -0.4543,
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+ -0.4014, 0.5753, 0.3237, -0.0531, -0.2725, 0.4342, 0.4947, -0.5230,
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+ 0.3727, 0.7445, 0.7685, 0.2294, 0.6002, -0.2364, -0.0361, 0.8746,
+ 0.5983, 0.7713, -0.0492, 0.0072, 0.3269, 0.4310, 0.1407, -0.4384,
+ -0.1729, -0.3326, 0.4758, -0.4218, 0.8020, -0.0350, -0.7215, 0.4019,
+ 0.9356, 0.5021, 0.5510, -0.3667, 0.4088, 0.5787, 0.5401, 0.5139,
+ 0.2937, 2.5777, -0.4828, -0.2321, -0.1922, 0.7759, 0.4836, -0.4644,
+ 0.1484, -0.9385, -0.7380, -0.1577, -0.8349, -0.3073, -0.5824, 0.5791,
+ -0.0707, 0.2943, -0.5056, -0.0182, 0.1451, 0.0616, 0.4046, 0.0973,
+ 0.4898, 0.7257, -0.2495, -0.0735, -0.8476, 0.2016, -0.0924, -0.2625,
+ -0.2927, -0.5473, -0.0670, -0.2398, -0.1042, 0.2342, -0.6123, -0.7766,
+ -0.2891, -0.1510, -0.3887, -0.8447, 0.3705, -0.3663, 0.3751, -0.3333,
+ 0.2739, -0.1393, -0.5013, -0.0648, 0.8232, 1.0130, -0.2885, 0.4336,
+ 0.4970, -0.2387, 0.1380, -0.7007, -0.7673, -0.3192, 0.2573, -0.4747,
+ -0.4189, -0.1628, -0.6715, 0.0745, 0.3072, 0.8509, -0.2102, -0.5690,
+ -0.4548, -0.0489, -0.0070, -0.0870, -0.2811, -0.6708, 0.6954, 0.4769,
+ -0.4482, -0.5612, -0.2993, 0.9320, 0.3225, -0.2130, 0.4533, 0.4963],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0083, -0.0071, -0.0021, ..., -0.0010, 0.0090, 0.0052],
+ [ 0.0555, -0.0439, -0.0148, ..., 0.0150, -0.0063, 0.0137],
+ [ 0.0143, 0.0434, 0.0090, ..., -0.0173, 0.0094, -0.0070],
+ ...,
+ [-0.0136, -0.0252, 0.0119, ..., -0.0044, 0.0303, 0.0039],
+ [ 0.0051, -0.0150, -0.0075, ..., -0.0273, -0.0061, -0.0200],
+ [ 0.0049, -0.0019, 0.0238, ..., 0.0028, -0.0135, -0.0199]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 1.1367, -0.1210, 0.1425, ..., -0.0217, -0.0073, -0.0175],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0033, 0.0186, -0.0174, ..., -0.0052, 0.0126, 0.0130],
+ [-0.0088, 0.0030, 0.0068, ..., 0.0046, -0.0482, 0.0263],
+ [-0.0032, 0.0138, -0.0109, ..., 0.0132, 0.0237, 0.0135],
+ ...,
+ [ 0.0033, -0.0081, -0.0091, ..., 0.0204, -0.0066, 0.0058],
+ [-0.0299, 0.0074, -0.0033, ..., -0.0114, 0.0144, 0.0044],
+ [ 0.0031, 0.0146, -0.0029, ..., 0.0022, -0.0339, -0.0151]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-4.5128e-03, 1.0891e-03, -3.3478e-02, -1.2718e-02, -6.7078e-02,
+ 6.7139e-02, 6.8726e-02, -1.5411e-02, 4.0192e-02, 3.3264e-02,
+ 2.8030e-02, -1.7822e-02, -5.0354e-02, -3.2593e-02, 8.6975e-03,
+ 1.7487e-02, -1.7090e-03, 4.8798e-02, -1.4435e-02, -4.2175e-02,
+ 2.5803e-02, 2.9007e-02, 2.6627e-03, -1.9150e-02, 3.9734e-02,
+ -5.0354e-02, -2.0618e-03, 2.3621e-02, 1.5732e-02, 2.6825e-02,
+ 1.3304e-04, 1.1345e-02, -7.6218e-03, -5.8632e-03, 2.7752e-03,
+ 3.4485e-02, -7.0251e-02, -5.3925e-02, -1.8692e-02, 4.6722e-02,
+ -7.8659e-03, -8.9264e-03, 2.0721e-02, -5.7182e-03, 1.3725e-02,
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+ -7.1618e-02, 8.7326e-02, -1.1954e-04], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0018, -0.0035, 0.0047, ..., -0.0104, 0.0071, -0.0345],
+ [-0.0061, -0.0015, -0.0073, ..., -0.0022, -0.0246, -0.0080],
+ [-0.0041, -0.0014, -0.0101, ..., -0.0074, 0.0381, -0.0051],
+ ...,
+ [ 0.0136, 0.0115, -0.0352, ..., -0.0105, 0.0295, 0.0026],
+ [ 0.0275, 0.0076, -0.0009, ..., 0.0037, -0.0102, -0.0048],
+ [-0.0325, -0.0094, -0.0329, ..., 0.0203, 0.0127, 0.0209]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3206, 0.0299, -0.2832, ..., -0.3774, -0.0878, -0.3206],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [ 0.0061, 0.0051, 0.0010, ..., -0.0092, -0.0083, -0.0078],
+ [ 0.0028, 0.0019, -0.0141, ..., -0.0223, 0.0200, -0.0158],
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+ [-0.0170, 0.0005, 0.0025, ..., -0.0155, 0.0081, 0.0126]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -2.3514e-02, 9.7885e-03, 4.0588e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 2.0942, 2.1266, 2.2787], device='cuda:1', requires_grad=True)Parameter containing:
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+ -1.0792e-01, 9.2764e-01, -1.7870e-01, 4.6413e-01, -2.7450e-01,
+ -5.8849e-01, -4.7641e-01, -1.3057e-01, 5.1836e-01, 2.7529e-02,
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+ 3.1585e-01, 2.0645e-01, 6.1529e-01], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0039, -0.0192, -0.0103, ..., 0.0052, 0.0099, 0.0056],
+ [-0.0152, -0.0052, -0.0067, ..., -0.0012, -0.0065, -0.0135],
+ [ 0.0042, 0.0065, 0.0006, ..., 0.0062, 0.0171, 0.0149],
+ ...,
+ [-0.0145, -0.0176, 0.0174, ..., -0.0138, -0.0058, -0.0098],
+ [ 0.0048, 0.0245, 0.0091, ..., -0.0059, 0.0057, -0.0145],
+ [-0.0083, 0.0048, -0.0003, ..., -0.0104, 0.0195, 0.0123]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3210, 0.2307, -0.0475, ..., 0.0169, -0.0356, -0.0140],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 5.7831e-03, -8.9035e-03, 5.7757e-05, ..., 6.5651e-03,
+ -8.6365e-03, -6.7825e-03],
+ [-1.5414e-04, -4.9782e-03, 2.2430e-02, ..., -2.0050e-02,
+ -1.9369e-03, 7.0000e-03],
+ [-1.2711e-02, -7.1526e-03, -3.1647e-02, ..., 6.2637e-03,
+ -2.0340e-02, 1.4626e-02],
+ ...,
+ [-6.7062e-03, 5.0068e-04, -8.3008e-03, ..., 3.5477e-03,
+ 2.7447e-03, -2.1606e-02],
+ [ 2.0172e-02, -1.5497e-03, -1.4412e-02, ..., 5.5504e-04,
+ -1.2497e-02, 7.7095e-03],
+ [-5.1003e-03, 1.3168e-02, -4.6082e-03, ..., -8.7051e-03,
+ -2.3022e-03, 1.5236e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 2.8961e-02, 1.2787e-02, 9.3918e-03, -4.4594e-03, -2.9327e-02,
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+ 1.5531, 1.4182, 1.4350], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 9.5040e-02, 6.2264e-02, -1.1090e-01, -1.8604e-02, 8.0698e-02,
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+ -3.6957e-02, -5.1987e-03, -5.0143e-02, -5.8339e-02, 7.5306e-02,
+ -2.9533e-02, 7.2287e-02, 9.0682e-02, 2.2872e-02, -5.3548e-02,
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+ -7.9938e-02, 1.0629e-01, -3.4558e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-2.1896e-02, 2.0416e-02, 2.3441e-03, ..., 1.0908e-04,
+ 6.9656e-03, 1.4427e-02],
+ [ 3.4351e-03, -6.1264e-03, 2.0737e-02, ..., -1.4359e-02,
+ -2.4033e-02, -1.4053e-02],
+ [ 1.8631e-02, -2.3880e-02, -5.8861e-03, ..., 5.5122e-03,
+ -3.2663e-04, -2.0580e-03],
+ ...,
+ [-7.4425e-03, -9.9869e-03, 1.6281e-02, ..., 2.2583e-02,
+ 1.5378e-05, 1.3184e-02],
+ [ 7.1678e-03, -2.3453e-02, 1.8890e-02, ..., 1.1101e-02,
+ 3.5305e-03, 3.5629e-03],
+ [-1.4053e-02, -1.7029e-02, -9.1400e-03, ..., -1.0704e-02,
+ 1.3428e-02, 3.3951e-04]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.3743, -0.4089, -0.3171, ..., -0.2976, -0.0021, -0.3103],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0040, 0.0537, 0.0006, ..., -0.0110, 0.0033, 0.0026],
+ [ 0.0102, -0.0116, -0.0276, ..., -0.0249, -0.0013, 0.0136],
+ [-0.0247, 0.0174, -0.0146, ..., -0.0083, -0.0184, -0.0121],
+ ...,
+ [-0.0018, -0.0283, 0.0097, ..., 0.0254, -0.0131, 0.0048],
+ [ 0.0004, 0.0063, -0.0341, ..., -0.0153, 0.0024, 0.0111],
+ [-0.0007, 0.0055, -0.0035, ..., -0.0027, -0.0048, -0.0002]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 5.4077e-02, 1.3013e-01, -5.7983e-02, 1.5717e-02, 3.2715e-02,
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+ -4.1402e-01, -4.9207e-01, -4.5646e-01, 7.1663e-02, 3.1954e-01,
+ 2.2474e-01, 3.8796e-01, 6.4554e-01], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0420, -0.0440, -0.0347, ..., -0.0244, -0.0091, 0.0145],
+ [-0.0261, 0.0071, 0.0178, ..., 0.0025, -0.0036, -0.0314],
+ [ 0.0267, -0.0007, 0.0216, ..., 0.0241, 0.0350, -0.0015],
+ ...,
+ [-0.0045, -0.0047, 0.0123, ..., -0.0035, 0.0097, 0.0152],
+ [-0.0158, -0.0261, -0.0006, ..., -0.0156, -0.0045, 0.0177],
+ [-0.0226, -0.0010, -0.0124, ..., 0.0051, 0.0012, 0.0042]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 0.1199, -0.1290, 0.1978, ..., -0.0250, 0.0124, -0.0064],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-5.5428e-03, -2.7206e-02, 2.7370e-03, ..., 2.2766e-02,
+ -3.1643e-03, -1.5764e-03],
+ [ 2.0981e-02, -1.9547e-02, 1.0455e-04, ..., -1.9424e-02,
+ -8.0032e-03, 3.5324e-03],
+ [ 1.8829e-02, 1.2306e-02, -3.6640e-03, ..., -1.7288e-02,
+ 6.9389e-03, 1.1398e-02],
+ ...,
+ [-1.0307e-02, 1.9897e-02, 5.8711e-05, ..., -1.7059e-02,
+ 9.9411e-03, 2.1317e-02],
+ [-2.5986e-02, -2.5024e-02, 1.0292e-02, ..., 2.4624e-03,
+ -7.9422e-03, -1.5936e-03],
+ [ 1.5373e-02, 1.6113e-02, -3.0041e-03, ..., 5.8517e-03,
+ 7.4081e-03, 2.3529e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
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+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -3.3771e-02, 1.0841e-01, 2.3453e-02, -1.4740e-02, 6.4261e-02,
+ -5.0851e-02, 1.1362e-01, 7.0055e-02, 4.6831e-02, 3.7477e-02,
+ -1.3111e-02, -9.9276e-03, 6.9019e-03, -7.9165e-02, -3.6014e-03,
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+ -2.3436e-02, -2.2548e-01, 1.1270e-01, 5.3056e-02, 9.3794e-02,
+ 1.0745e-01, 3.8244e-02, 6.4040e-02, 1.0307e-01, -9.8231e-03,
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+ -3.8501e-02, -1.6234e-01, -2.3968e-02, 4.3340e-02, -4.9714e-02,
+ -2.6754e-02, -1.0560e-03, -1.0699e-01, -2.9853e-02, 7.2143e-02,
+ -1.1260e-02, 4.7919e-02, 9.6402e-02, 4.0698e-02, -4.5639e-02,
+ -2.8596e-02, 4.3692e-02, 7.4926e-02, -6.2533e-02, -8.9408e-02,
+ -6.3402e-02, 1.1697e-01, 7.2318e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0048, 0.0208, 0.0301, ..., 0.0115, 0.0131, 0.0097],
+ [ 0.0114, -0.0127, 0.0137, ..., 0.0169, 0.0023, 0.0218],
+ [-0.0232, -0.0296, 0.0010, ..., 0.0052, -0.0218, 0.0328],
+ ...,
+ [-0.0138, -0.0232, -0.0012, ..., -0.0105, -0.0009, 0.0167],
+ [-0.0087, 0.0149, -0.0075, ..., 0.0247, -0.0048, -0.0043],
+ [-0.0146, 0.0123, 0.0197, ..., -0.0132, 0.0005, -0.0090]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2957, -0.3293, -0.3479, ..., -0.1686, -0.4126, -0.3352],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-2.0264e-02, 2.8854e-02, 4.7760e-03, ..., -4.8904e-03,
+ -3.3169e-03, -2.8595e-02],
+ [ 7.9193e-03, -1.0986e-02, 8.4000e-03, ..., 2.7145e-02,
+ 3.1189e-02, -2.6474e-02],
+ [ 1.8585e-02, -1.3618e-02, -1.1322e-02, ..., 6.1989e-03,
+ -1.4870e-02, -5.5194e-05],
+ ...,
+ [ 1.0979e-02, 2.5269e-02, -1.1635e-03, ..., 2.2926e-03,
+ 2.9037e-02, -2.4094e-02],
+ [ 1.6174e-02, 2.0721e-02, 5.5618e-03, ..., -1.0529e-03,
+ 6.1226e-03, 1.5610e-02],
+ [-1.6403e-02, 1.9646e-03, -7.2136e-03, ..., -3.4119e-02,
+ -3.3054e-03, -1.8219e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 7.6416e-02, 3.9429e-02, -1.3733e-01, 5.8136e-02, 3.9581e-02,
+ 7.4158e-02, 5.7098e-02, -3.6793e-03, -6.1531e-03, 1.2093e-02,
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+ 1.0063e-02, 8.3252e-02, 1.8173e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 2.0837, 2.2015, 2.3503], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 1.3281e-01, 1.0489e+00, -4.3994e-01, -3.5076e-01, -1.5734e-01,
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+ 9.7501e-02, 2.7520e-01, 1.0619e+00], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0090, -0.0285, -0.0117, ..., -0.0334, -0.0124, -0.0016],
+ [ 0.0073, -0.0109, -0.0155, ..., -0.0185, 0.0384, -0.0127],
+ [ 0.0053, 0.0201, 0.0105, ..., -0.0385, -0.0188, -0.0073],
+ ...,
+ [-0.0101, 0.0020, -0.0220, ..., 0.0291, -0.0050, 0.0291],
+ [ 0.0129, 0.0045, 0.0251, ..., 0.0085, 0.0212, -0.0309],
+ [-0.0195, -0.0013, 0.0205, ..., 0.0047, -0.0370, 0.0062]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2406, 0.2976, 0.3762, ..., -0.0151, 0.0414, 0.0243],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [-0.0301, -0.0264, -0.0079, ..., -0.0047, -0.0026, 0.0025],
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+ [ 0.0045, 0.0113, 0.0006, ..., -0.0065, -0.0201, -0.0050]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -7.2486e-02, 1.0574e-01, -4.5374e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0249, 0.0004, -0.0134, ..., 0.0097, -0.0221, -0.0155],
+ [-0.0316, 0.0126, -0.0031, ..., -0.0116, 0.0157, 0.0038],
+ [-0.0350, 0.0108, 0.0050, ..., -0.0090, -0.0208, -0.0072],
+ ...,
+ [-0.0134, -0.0048, 0.0264, ..., -0.0219, -0.0065, -0.0021],
+ [ 0.0038, 0.0062, -0.0022, ..., -0.0090, -0.0054, -0.0187],
+ [ 0.0026, 0.0050, 0.0312, ..., -0.0390, 0.0172, -0.0119]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3540, -0.2788, -0.3865, ..., -0.3696, -0.3518, -0.2264],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 0.0172, -0.0035, -0.0072, ..., 0.0195, 0.0084, -0.0046],
+ [ 0.0164, 0.0169, -0.0219, ..., 0.0049, 0.0127, 0.0043],
+ [-0.0011, -0.0046, 0.0048, ..., 0.0056, -0.0323, -0.0245],
+ ...,
+ [ 0.0166, 0.0074, 0.0161, ..., -0.0444, -0.0176, -0.0061],
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+ [-0.0192, -0.0033, 0.0053, ..., 0.0050, 0.0029, -0.0080]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -1.0490e-02, -2.8820e-03, -1.4297e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -0.2936, 0.0707, -0.0800, 0.5583, 0.2862, 0.4679, 0.6217, 0.6404],
+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[-0.0240, 0.0029, -0.0312, ..., 0.0232, 0.0232, -0.0007],
+ [-0.0009, 0.0125, -0.0428, ..., 0.0167, -0.0114, 0.0172],
+ [-0.0243, 0.0004, -0.0028, ..., -0.0064, 0.0121, 0.0166],
+ ...,
+ [ 0.0119, 0.0008, -0.0014, ..., -0.0109, 0.0003, -0.0192],
+ [-0.0027, -0.0135, 0.0034, ..., 0.0144, 0.0325, -0.0189],
+ [ 0.0063, 0.0089, -0.0012, ..., 0.0233, -0.0183, -0.0119]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.0339, 0.5952, -0.3469, ..., 0.0100, -0.0171, 0.0073],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 1.6983e-02, 1.5869e-02, 2.5711e-02, ..., 1.8282e-03,
+ -1.1787e-02, -2.0477e-02],
+ [-4.1723e-06, 4.6234e-03, -1.7273e-02, ..., -1.3374e-02,
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+ [-9.9716e-03, -4.3945e-03, 2.9068e-03, ..., 1.9684e-02,
+ -2.8351e-02, -2.8290e-02],
+ ...,
+ [-2.1042e-02, -2.2217e-02, 2.5452e-02, ..., -4.4417e-04,
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+ [ 6.3667e-03, -1.1711e-02, -3.2842e-05, ..., -1.2466e-02,
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+ [ 1.1337e-02, 1.0567e-02, 2.4395e-03, ..., -1.5053e-02,
+ -5.5428e-03, -7.4120e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
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+ 1.7542, 1.6884, 1.7213], device='cuda:1', requires_grad=True)Parameter containing:
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+ -1.7048e-02, -1.7593e-01, 8.0783e-03, 3.7989e-02, -4.0361e-02,
+ -2.3227e-02, -2.0796e-02, -1.1312e-01, -4.6048e-02, 9.6961e-02,
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+ -2.2783e-02, 6.6940e-02, 5.3477e-02, -1.4346e-01, -9.9305e-02,
+ -5.6176e-02, 1.2165e-01, 1.0727e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0076, 0.0140, -0.0179, ..., -0.0190, -0.0001, 0.0083],
+ [-0.0082, -0.0062, -0.0340, ..., 0.0008, 0.0212, -0.0008],
+ [-0.0030, -0.0002, 0.0044, ..., 0.0062, -0.0090, 0.0150],
+ ...,
+ [-0.0003, 0.0026, -0.0107, ..., 0.0015, 0.0198, 0.0079],
+ [-0.0139, 0.0263, 0.0082, ..., -0.0135, 0.0330, -0.0161],
+ [ 0.0057, 0.0020, -0.0367, ..., 0.0184, -0.0093, 0.0296]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2454, -0.3401, -0.3860, ..., -0.3416, -0.3689, -0.1425],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0122, 0.0114, 0.0196, ..., 0.0045, 0.0157, 0.0007],
+ [ 0.0119, 0.0089, -0.0186, ..., -0.0101, -0.0171, -0.0015],
+ [ 0.0058, -0.0237, 0.0086, ..., -0.0162, 0.0190, -0.0067],
+ ...,
+ [-0.0058, 0.0016, 0.0089, ..., -0.0329, -0.0035, 0.0069],
+ [-0.0245, 0.0052, 0.0177, ..., -0.0051, 0.0200, 0.0037],
+ [ 0.0006, -0.0077, 0.0039, ..., 0.0064, 0.0152, -0.0266]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 4.8370e-02, 1.8768e-02, -7.0095e-04], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 2.2017, 2.3095, 2.3668], device='cuda:1', requires_grad=True)Parameter containing:
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+ device='cuda:1', requires_grad=True)Parameter containing:
+tensor([[ 0.0218, 0.0299, 0.0232, ..., 0.0067, -0.0137, 0.0305],
+ [-0.0040, -0.0037, 0.0077, ..., 0.0148, 0.0173, -0.0006],
+ [ 0.0255, 0.0141, 0.0116, ..., -0.0026, 0.0090, -0.0149],
+ ...,
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+ [ 0.0077, -0.0093, 0.0101, ..., 0.0183, 0.0176, -0.0251],
+ [-0.0074, 0.0018, -0.0270, ..., -0.0280, -0.0008, 0.0160]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3987, 0.2491, 0.1892, ..., 0.0093, 0.0336, -0.0070],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 1.6482, 1.6091, 1.6466], device='cuda:1', requires_grad=True)Parameter containing:
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+ -4.7636e-02, -1.5777e-01, 3.0539e-02, 1.1327e-01, -7.4810e-03,
+ -1.7076e-03, -1.1211e-01, -8.4320e-02, -1.8981e-02, 1.2201e-01,
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+ -4.6553e-03, 5.7443e-02, 1.1077e-01, -1.2032e-01, -1.3606e-01,
+ -4.3071e-02, 1.1801e-01, -1.3836e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0171, 0.0130, 0.0185, ..., 0.0060, -0.0017, -0.0105],
+ [-0.0364, 0.0154, -0.0142, ..., 0.0086, 0.0068, 0.0017],
+ [-0.0113, -0.0242, 0.0130, ..., 0.0225, 0.0031, -0.0107],
+ ...,
+ [ 0.0166, 0.0227, -0.0178, ..., -0.0059, 0.0132, -0.0079],
+ [ 0.0282, 0.0054, 0.0172, ..., 0.0009, 0.0006, 0.0101],
+ [ 0.0041, 0.0177, -0.0183, ..., 0.0003, 0.0102, 0.0056]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.2388, -0.3625, -0.0865, ..., -0.3342, -0.2629, -0.1206],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[ 1.4420e-02, -3.2654e-02, -8.9569e-03, ..., 8.2855e-03,
+ 1.0498e-02, -1.5457e-02],
+ [-7.6370e-03, -2.0157e-02, 1.2436e-02, ..., 4.3762e-02,
+ 3.8452e-02, -2.3422e-02],
+ [ 1.2445e-04, 2.7905e-03, -6.9084e-03, ..., -7.2594e-03,
+ 1.1620e-02, 1.2497e-02],
+ ...,
+ [ 2.7823e-04, 7.8583e-03, -2.6993e-02, ..., 2.8183e-02,
+ -4.7226e-03, 4.9896e-03],
+ [-6.7711e-03, -6.6414e-03, -4.2305e-03, ..., 5.4321e-03,
+ 1.3855e-02, 1.0252e-05],
+ [ 2.7084e-03, 7.5684e-03, -7.6370e-03, ..., 2.0428e-03,
+ -1.5198e-02, -6.1722e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 1.6052e-02, 1.0201e-02, -8.6594e-03, -4.4342e-02, -7.0618e-02,
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+ 1.2549e-01, -1.4820e-01, 6.9585e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 0.0030, -0.0047, 0.0065, ..., 0.0104, -0.0140, 0.0053],
+ [-0.0176, 0.0191, -0.0227, ..., 0.0217, 0.0145, -0.0007],
+ [ 0.0033, -0.0146, 0.0133, ..., 0.0050, -0.0265, -0.0137],
+ ...,
+ [-0.0195, -0.0042, -0.0021, ..., -0.0063, 0.0234, -0.0025],
+ [-0.0185, 0.0035, -0.0008, ..., 0.0019, 0.0356, 0.0087],
+ [-0.0246, -0.0227, -0.0082, ..., -0.0005, -0.0009, 0.0117]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.1141, 0.1932, 0.1205, ..., -0.0247, 0.0140, 0.0328],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0028, -0.0143, 0.0161, ..., 0.0151, 0.0104, -0.0198],
+ [-0.0164, -0.0346, 0.0067, ..., 0.0199, -0.0166, -0.0236],
+ [-0.0236, -0.0092, -0.0233, ..., -0.0062, -0.0015, 0.0028],
+ ...,
+ [ 0.0289, 0.0172, -0.0065, ..., -0.0083, -0.0195, 0.0067],
+ [ 0.0268, -0.0132, 0.0347, ..., 0.0141, 0.0156, -0.0042],
+ [-0.0458, 0.0232, -0.0022, ..., -0.0111, 0.0161, 0.0254]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -3.6713e-02, 8.4167e-02, -8.8867e-02, 7.8918e-02, 1.6525e-02,
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+ 7.7820e-02, -1.1420e-01, 8.2031e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.8626, 1.8031, 1.7707, 1.7813, 1.5411, 1.7607, 1.7641, 1.6653, 1.7079,
+ 1.7684, 1.8017, 1.6956, 1.6986, 1.7498, 1.8004, 1.8909, 1.7765, 1.8687,
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+ -8.3394e-02, 3.5046e-02, 1.2930e-02, -1.7214e-01, 9.5684e-02,
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+ -5.6036e-02, -1.1804e-01, 3.8965e-02, 1.1923e-01, -3.0770e-02,
+ -6.4427e-03, -4.9890e-02, -9.9548e-02, 9.1358e-03, 1.5483e-01,
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+ -2.0951e-02, 7.2451e-02, 1.5202e-01, -1.3042e-01, -1.3775e-01,
+ -7.0584e-02, 1.7459e-01, -1.6711e-03], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[-0.0193, 0.0156, -0.0011, ..., 0.0403, -0.0181, -0.0006],
+ [-0.0301, 0.0043, -0.0099, ..., -0.0052, 0.0136, -0.0069],
+ [-0.0016, 0.0183, 0.0016, ..., -0.0064, -0.0117, 0.0118],
+ ...,
+ [ 0.0070, 0.0163, -0.0119, ..., 0.0026, -0.0226, 0.0221],
+ [ 0.0004, 0.0120, 0.0207, ..., -0.0106, 0.0029, 0.0323],
+ [ 0.0079, -0.0005, 0.0047, ..., -0.0068, -0.0219, 0.0219]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([-0.3269, -0.2380, -0.3926, ..., -0.2299, 0.2595, -0.2932],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0171, 0.0118, 0.0016, ..., -0.0016, 0.0133, -0.0409],
+ [-0.0224, -0.0010, -0.0217, ..., 0.0149, 0.0045, -0.0212],
+ [ 0.0286, 0.0206, -0.0153, ..., -0.0103, -0.0133, -0.0120],
+ ...,
+ [-0.0076, -0.0130, 0.0111, ..., 0.0085, -0.0125, 0.0113],
+ [-0.0247, -0.0079, 0.0172, ..., 0.0136, -0.0062, -0.0172],
+ [ 0.0397, -0.0172, -0.0138, ..., 0.0265, 0.0010, 0.0029]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ -9.4604e-02, 9.8953e-03, 6.1554e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 2.3097, 2.3412, 2.3888], device='cuda:1', requires_grad=True)Parameter containing:
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+ -1.2025e-01, -2.1390e-01, -1.7674e-02, 3.4328e-01, 3.1743e-02,
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+ 8.3802e-02, -1.1817e-02, 1.2176e-01], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 2.9816e-02, 4.1199e-03, 8.1406e-03, ..., 1.6235e-02,
+ -2.6321e-02, 4.1542e-03],
+ [-9.5673e-03, -3.6621e-02, -5.4779e-03, ..., -1.4587e-02,
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+ [ 7.9727e-03, -1.2749e-02, 1.3336e-02, ..., -3.6591e-02,
+ -5.0735e-04, -1.6289e-03],
+ ...,
+ [ 2.0859e-02, -7.8630e-04, -1.1818e-02, ..., 7.7069e-05,
+ -3.9337e-02, -8.6823e-03],
+ [-9.7809e-03, -6.9389e-03, -4.0497e-02, ..., 1.0925e-02,
+ -5.8136e-03, 1.8625e-03],
+ [-2.3834e-02, -9.3536e-03, -4.1656e-03, ..., 1.7807e-02,
+ -1.5495e-02, -1.8188e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([ 0.1714, 0.2435, 0.2001, ..., 0.0595, 0.0106, -0.0736],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ [-0.0030, -0.0152, 0.0158, ..., 0.0002, 0.0506, -0.0149],
+ [ 0.0067, -0.0117, -0.0151, ..., -0.0057, -0.0125, -0.0014],
+ ...,
+ [ 0.0228, 0.0216, 0.0058, ..., 0.0212, -0.0056, 0.0391],
+ [ 0.0132, 0.0172, -0.0291, ..., -0.0060, -0.0128, -0.0266],
+ [-0.0192, -0.0129, 0.0062, ..., -0.0020, 0.0054, -0.0218]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
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+ 1.9264, 1.8954, 1.8412, 1.9310, 1.8819, 1.8354, 0.6095, 1.8538, 1.8894,
+ 1.8347, 1.8615, 1.8613, 1.8770, 1.8895, 1.8258, 1.8161, 1.9044, 1.8602,
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+ 1.8577, 1.7747, 1.9154, 1.7346, 1.8922, 1.9370, 1.8342, 1.9007, 1.7837,
+ 1.9888, 1.8941, 1.8775, 1.9283, 1.8973, 2.0281, 1.4591, 2.0369, 1.9378,
+ 1.8734, 1.9433, 1.8871, 1.7934, 1.9058, 1.8899, 1.8908, 1.9713, 1.8724,
+ 1.9331, 1.9974, 1.8825, 1.9038, 1.8145, 1.9421, 1.9098, 1.9045, 1.9809,
+ 1.9200, 1.8320, 1.7140, 1.9192, 1.8688, 1.8621, 1.9538, 1.8416, 1.8669,
+ 1.8944, 1.7779, 1.8887, 1.8708, 1.8072, 1.9348, 1.8595, 1.9091, 1.9632,
+ 1.9195, 1.8811, 1.8306], device='cuda:1', requires_grad=True)Parameter containing:
+tensor([ 1.4977e-01, 9.0828e-02, -1.1229e-01, -2.4072e-02, 2.1253e-01,
+ 2.2673e-01, 1.5718e-01, 2.1828e-01, -2.4588e-01, -8.8377e-02,
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+ -3.9823e-02, -3.1859e-01, 1.5124e-01, 1.4009e-01, 5.0370e-02,
+ 8.3453e-02, 9.9049e-02, 2.4573e-01, 1.9968e-01, 5.8656e-02,
+ -5.6640e-02, 4.0581e-02, 2.6943e-02, -1.3497e-04, -1.2463e-01,
+ 5.2881e-02, -1.2241e-01, 8.8162e-03, 1.3377e-01, 5.5711e-03,
+ -3.2902e-02, -1.4677e-01, -4.2712e-02, 1.2307e-01, 8.0042e-02,
+ -3.1782e-02, -1.3426e-01, 2.1633e-02, 1.0981e-02, 2.2117e-01,
+ -1.3080e-02, 8.9470e-02, 1.8250e-01, -2.3044e-01, -5.8739e-02,
+ -3.1466e-02, 6.1521e-02, 2.5686e-01, -6.6933e-02, -2.3293e-01,
+ -2.6690e-02, 2.1225e-01, 7.8747e-02], device='cuda:1',
+ requires_grad=True)Parameter containing:
+tensor([[ 4.3831e-03, -1.7395e-02, 3.9558e-03, ..., -1.6966e-03,
+ 1.0384e-02, 3.0640e-02],
+ [-2.2182e-03, -2.1912e-02, 5.6572e-03, ..., 2.1820e-02,
+ 7.8583e-03, 1.6800e-02],
+ [ 5.5084e-03, -1.9119e-02, -4.4098e-03, ..., 2.0035e-02,
+ -1.3260e-02, 1.9684e-03],
+ ...,
+ [ 4.6631e-02, -5.4359e-05, -3.4523e-03, ..., 4.8828e-03,
+ 2.2156e-02, -1.1864e-02],
+ [ 8.8787e-04, -8.1482e-03, 1.7776e-02, ..., -1.6052e-02,
+ 3.0502e-02, 9.1400e-03],
+ [ 1.3641e-02, 7.2708e-03, 3.8815e-03, ..., -3.4828e-03,
+ 2.0161e-03, -3.3054e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)Parameter containing:
+tensor([-0.1271, -0.1493, -0.2289, ..., -0.5405, -0.3594, -0.0424],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([[-0.0115, -0.0157, -0.0200, ..., -0.0466, -0.0021, -0.0165],
+ [ 0.0092, -0.0032, -0.0108, ..., 0.0121, -0.0015, 0.0188],
+ [ 0.0030, -0.0024, 0.0109, ..., -0.0146, -0.0017, -0.0030],
+ ...,
+ [-0.0107, -0.0003, 0.0144, ..., -0.0054, 0.0235, 0.0117],
+ [ 0.0090, 0.0042, 0.0030, ..., 0.0037, 0.0125, -0.0172],
+ [-0.0052, -0.0043, 0.0161, ..., 0.0261, 0.0152, -0.0026]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([ 2.5586e-01, -2.6196e-01, 2.3486e-01, 1.3916e-01, -1.3599e-03,
+ 4.4922e-02, 1.1615e-01, 1.1955e-02, -2.2461e-01, 1.1938e-01,
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+ -1.1322e-01, 3.1433e-02, -1.7426e-02, 2.8857e-01, -9.8145e-02,
+ -2.0264e-01, 8.4595e-02, 7.5928e-02, -5.0781e-02, 1.3879e-01,
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+ -1.5991e-02, -7.6538e-02, -4.0100e-02, 8.8745e-02, 2.7173e-01,
+ -7.8186e-02, 7.9285e-02, 2.8214e-02, 1.0638e-01, -4.3396e-02,
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+ -2.7319e-01, 2.6221e-01, -2.4612e-02, 5.6580e-02, 2.6953e-01,
+ 1.0156e-01, 7.1655e-02, 6.6452e-03, 4.6356e-02, 8.2947e-02,
+ 1.0986e-02, -1.8567e-01, -2.7026e-01, 9.7412e-02, 6.3416e-02,
+ -1.0809e-01, 2.5684e-01, 5.9418e-02, 5.4047e-02, 2.0657e-03,
+ -8.6914e-02, 6.0425e-02, -1.0840e-01, -1.6711e-01, -1.5234e-01,
+ 2.5879e-01, -5.5786e-02, -7.8430e-02, 5.5878e-02, -3.2812e-01,
+ -1.9934e-01, 9.6313e-02, -1.3062e-01, 1.6736e-01, 1.7151e-01,
+ 8.5815e-02, -1.1182e-01, -1.5137e-02, -4.1931e-02, -6.9336e-02,
+ -4.3823e-02, -4.1809e-02, 7.6904e-02, -3.6157e-01, -6.3660e-02,
+ -1.6382e-01, 2.4658e-02, 4.3774e-01, -4.3774e-01, -1.1328e-01,
+ 8.6121e-02, 4.8401e-02, -6.8542e-02, -1.1786e-01, -1.8677e-01,
+ 2.4097e-01, 1.3098e-01, 2.5928e-01, -1.8005e-01, -6.6650e-02,
+ 1.5649e-01, 9.4604e-02, -6.0059e-02], device='cuda:1',
+ dtype=torch.float16, requires_grad=True)Parameter containing:
+tensor([1.7453, 1.7916, 1.7866, 1.7060, 2.2256, 1.7691, 1.6459, 1.7028, 1.7940,
+ 1.7137, 1.7359, 1.6988, 1.7650, 1.6460, 1.9754, 1.7098, 1.6823, 1.6809,
+ 1.7675, 2.8573, 1.7179, 1.7905, 1.7851, 1.7294, 1.7422, 1.7077, 1.7375,
+ 1.6869, 1.8025, 1.6808, 1.6921, 1.7139, 1.7179, 1.7785, 1.7144, 1.6573,
+ 1.7561, 1.7219, 1.8840, 1.8551, 1.7527, 1.7285, 1.8142, 1.7218, 1.7085,
+ 1.7747, 1.7440, 1.6605, 1.7240, 1.6341, 1.6871, 1.7110, 1.8223, 1.7324,
+ 1.6996, 1.9939, 1.6452, 1.5798, 1.6484, 1.6495, 1.7104, 1.6899, 1.7505,
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+ -3.5778e-02, 1.1070e-01, 1.7500e-01], device='cuda:1',
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+ 0.9786, 0.9825, 0.9780], device='cuda:1', requires_grad=True)Parameter containing:
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+ -1.4017e-01, -2.0010e-01, -1.3645e-01, -5.3604e-02, 9.3507e-02,
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+ -2.8346e-01, -2.3274e-01, -1.6795e-01, -3.2785e-01, 3.7867e-02,
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+ -9.7641e-02, -2.6895e-01, -1.6988e-01, -1.9597e-01, -1.6472e-01,
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+ -1.3753e-01, -1.1721e-01, -1.3412e-01, -1.3196e-01, 3.5904e-02,
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+ -2.3271e-01, -2.4277e-02, -2.7056e-01, -3.3887e-01, -2.0596e-01,
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+ -3.2807e-02, -2.2663e-01, -3.0295e-01, 9.7512e-03, -1.7489e-01,
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+ -2.9450e-01, -1.2680e-01, 8.3230e-02, -2.6408e-01, -4.8992e-02,
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+ -1.1585e-01, -3.7202e-02, 2.2169e-01, -1.1157e-01, -1.3117e-01,
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+ -1.6886e-01, -4.3418e-01, -1.8379e-01, -1.7249e-01, -4.3381e-02,
+ -3.0515e-01, -1.3860e-02, -1.3408e-01, 2.3386e-02, -1.9684e-01,
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+ -5.7091e-02, -2.6378e-01, -1.8482e-01, 2.4157e-02, -3.0910e-01,
+ -9.7387e-02, -1.3443e-01, -1.3120e-01, -4.1030e-02, -2.8421e-01,
+ -1.1970e-01, 2.6584e-01, -2.9540e-01, -4.1858e-01, -4.4225e-02,
+ -3.3776e-02, 5.1072e-02, -1.0784e-01, 2.4809e-03, -1.0006e-02,
+ -1.6900e-01, -1.6756e-01, 7.1867e-02, -1.4536e-01, -3.2948e-01,
+ -4.8964e-02, 2.5080e-02, -2.9177e-01, -2.3546e-01, -9.6734e-02,
+ 5.2769e-02, 5.9821e-02, -7.1729e-02, -5.2216e-02, 4.6748e-02,
+ -1.0374e-01, -3.3411e-01, 1.9977e-02, -1.9944e-01, -1.4249e-01,
+ -4.2926e-01, -2.0550e-01, -1.2332e-01, 2.4972e-02, -3.1156e-01,
+ -3.5618e-02, 1.4841e-01, -6.8435e-02, -8.9134e-02, -4.3101e-01,
+ 3.2034e-02, -5.7796e-02, -1.0815e-01, 7.9357e-02, 6.0425e-02,
+ 9.0992e-02, -8.4117e-02, -1.6460e-01, -1.8860e-01, -1.1188e-01,
+ -1.6019e-02, -2.7539e-01, -3.0461e-02, 3.6393e-02, -5.5778e-02,
+ -2.3261e-01, 3.3813e-03, -7.2618e-02, 1.3152e-01, 2.5424e-02,
+ -2.3474e-01, 1.2788e-01, 1.0549e-01, -3.9438e-01, -1.4477e-01,
+ -8.7272e-02, -5.9155e-02, -1.8755e-02, 8.3795e-03, -1.3303e-01,
+ 3.5993e-02, -4.8489e-01, -9.8144e-02, -2.0187e-01, -1.7780e-01,
+ -1.5702e-01, -1.6632e-01, -7.6623e-02, -1.2067e-01, 9.7476e-02,
+ -1.4865e-01, 3.9298e-02, -8.1964e-02, -5.7571e-02, -1.6357e-02,
+ -2.4697e-02, -2.3285e-01, -1.8711e-01, 5.9722e-02, -5.0565e-03,
+ 1.6085e-01, -5.6193e-02, -1.2748e-01, -1.7989e-01, 1.6912e-02,
+ -7.4359e-02, 5.9318e-02, 4.5689e-02, -3.4782e-01, 1.2438e-02,
+ 4.9462e-03, -7.6542e-02, -1.7623e-03, -3.1946e-01, -8.4081e-02,
+ 4.2274e-02, -2.0708e-01, -1.0376e-01, -3.2503e-01, -4.9442e-02,
+ -1.5170e-01, -1.7022e-01, -1.8604e-01, 7.0737e-02, -2.3663e-01,
+ -3.6244e-02, -2.3101e-01, -2.2293e-01, 8.8521e-03, -7.0427e-02,
+ -2.2006e-01, 6.1665e-02, -1.2758e-01, -1.3123e-01, 1.3888e-02,
+ -2.7413e-01, -3.8097e-02, -3.4511e-01, -2.7228e-01, 8.0634e-02,
+ -1.5312e-02, -3.7472e-03, 2.0875e-02, 1.5117e-01, -2.4863e-01,
+ -3.2693e-01, -1.0401e-01, -1.4831e-01, -1.8991e-01, -1.6961e-01,
+ 1.1688e-01, 6.0180e-02, -5.3109e-02, -3.7527e-01, 1.3247e-01,
+ -1.2943e-01, -1.7973e-01, 1.0068e-01, -1.7821e-01, -1.9093e-01,
+ 2.9122e-02, -5.7265e-01, -1.4149e-01, -7.8197e-02, -2.9365e-01,
+ -2.5148e-01, -1.0391e-01, -1.6442e-01, -1.2958e-01, -6.3028e-02,
+ -7.3926e-02, -9.9090e-02, 1.4622e-02, -3.2253e-01, -2.1039e-01,
+ -3.5321e-02, -6.1373e-02, -4.3052e-03, -2.5899e-01, 1.4603e-01,
+ -6.2891e-02, 3.2609e-02, -8.3760e-02, -7.8426e-02, -5.5548e-02,
+ -2.1703e-01, -7.2742e-02, -1.0241e-01, -1.5250e-01, -5.3758e-03,
+ 1.6436e-01, -1.6233e-01, -1.1661e-01, -2.6216e-01, -3.3025e-01,
+ -1.5915e-01, -3.5974e-01, -1.6534e-01, 8.1741e-03, 1.2124e-01,
+ -7.8771e-02, -2.6709e-01, -1.5131e-01, 1.1832e-01, -9.7288e-02,
+ 1.5229e-01, -1.3003e-01, -3.0911e-01, -8.6667e-02, -6.7893e-02,
+ -1.5559e-01, -1.3761e-01, -4.8186e-02, -1.4222e-01, -5.8575e-02,
+ -4.5176e-01, -2.7698e-01, -1.8527e-01, 1.3501e-01, 1.4931e-02,
+ -5.2130e-01, -2.6890e-01, 9.5427e-02], device='cuda:1',
+ requires_grad=True)
\ No newline at end of file
diff --git a/python/ClipDetection/CoOp/trainers/custom_named_generator_cuda.txt b/python/ClipDetection/CoOp/trainers/custom_named_generator_cuda.txt
new file mode 100644
index 00000000..1d5546c8
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/custom_named_generator_cuda.txt
@@ -0,0 +1,12587 @@
+torch.Size([])
+Parameter containing:
+tensor(4.6052, device='cuda:1', requires_grad=True)
+torch.Size([16, 768])
+Parameter containing:
+tensor([[-3.6102e-02, 8.2932e-03, 1.1726e-02, ..., 1.1253e-03,
+ -4.8218e-03, 1.7639e-02],
+ [ 3.5629e-03, -1.0719e-02, 2.6947e-02, ..., 6.7596e-03,
+ 9.3536e-03, 4.5252e-04],
+ [-1.8234e-02, -1.9272e-02, -4.8523e-03, ..., -1.6937e-02,
+ 3.1796e-03, -6.8932e-03],
+ ...,
+ [-2.7466e-02, -4.8752e-03, 2.0004e-02, ..., 1.2712e-03,
+ -2.6382e-02, 2.4521e-02],
+ [ 1.2375e-02, -1.9409e-02, 4.3678e-03, ..., 1.6769e-02,
+ -3.3844e-02, -1.2253e-02],
+ [ 2.2934e-02, 8.4534e-03, 3.3875e-02, ..., -3.8853e-03,
+ 2.7120e-05, -7.8354e-03]], dtype=torch.float16, requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([ 0.0138, 0.2357, -0.1285, ..., 0.0171, -0.3332, -0.2366],
+ device='cuda:1', requires_grad=True)
+torch.Size([257, 1024])
+Parameter containing:
+tensor([[ 0.0019, 0.0479, -0.0149, ..., 0.0005, -0.0558, -0.0460],
+ [ 0.0114, -0.0413, 0.0357, ..., 0.0271, -0.0313, -0.0383],
+ [-0.0026, -0.0340, -0.0006, ..., 0.0216, -0.0294, -0.0423],
+ ...,
+ [-0.0038, -0.0350, -0.0048, ..., -0.0228, -0.0328, -0.0412],
+ [-0.0046, -0.0360, -0.0026, ..., -0.0350, -0.0355, -0.0353],
+ [-0.0073, -0.0287, -0.0144, ..., -0.0202, -0.0272, -0.0360]],
+ device='cuda:1', requires_grad=True)
+torch.Size([1024, 768])
+Parameter containing:
+tensor([[ 0.0224, -0.0139, -0.0072, ..., -0.0058, -0.0078, 0.0139],
+ [ 0.0186, 0.0084, 0.0400, ..., -0.0149, -0.0241, -0.0003],
+ [ 0.0075, -0.0007, 0.0195, ..., -0.0062, -0.0083, 0.0156],
+ ...,
+ [ 0.0121, -0.0165, -0.0144, ..., -0.0066, 0.0088, 0.0027],
+ [-0.0164, -0.0100, -0.0053, ..., -0.0005, -0.0001, -0.0075],
+ [ 0.0092, 0.0048, 0.0069, ..., 0.0054, -0.0162, 0.0262]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024, 3, 14, 14])
+Parameter containing:
+tensor([[[[ 2.5284e-02, 1.0597e-02, 7.1678e-03, ..., 2.3422e-02,
+ 2.1683e-02, 4.8637e-03],
+ [ 1.3748e-02, -6.2103e-03, -4.8103e-03, ..., 1.6418e-02,
+ 7.0114e-03, -1.3161e-02],
+ [ 1.0048e-02, 2.1286e-03, 2.2945e-03, ..., 5.5695e-03,
+ 5.0468e-03, -1.2604e-02],
+ ...,
+ [-1.0101e-02, -2.3854e-04, -5.4588e-03, ..., -1.9226e-02,
+ -2.4017e-02, -2.4765e-02],
+ [-3.4752e-03, -1.0979e-02, -1.3603e-02, ..., -7.6408e-03,
+ 1.5583e-03, -4.4365e-03],
+ [-2.1469e-02, -4.3182e-02, -3.0121e-02, ..., -5.2147e-03,
+ 3.7346e-03, -6.8016e-03]],
+
+ [[ 1.5930e-02, -4.9095e-03, -1.2283e-02, ..., 2.5879e-02,
+ 2.4048e-02, 5.6458e-03],
+ [ 2.1019e-03, -2.4185e-02, -2.6337e-02, ..., 1.5297e-02,
+ 5.2605e-03, -1.5121e-02],
+ [ 5.1956e-03, -7.2556e-03, -9.4376e-03, ..., 7.9193e-03,
+ 5.4703e-03, -1.2398e-02],
+ ...,
+ [-4.2267e-03, 5.9624e-03, -6.2656e-04, ..., 3.8528e-03,
+ 4.2963e-04, -5.4207e-03],
+ [-2.8496e-03, -1.1482e-02, -1.3680e-02, ..., 1.5129e-02,
+ 2.3285e-02, 1.2856e-02],
+ [-2.7740e-02, -4.9561e-02, -3.1158e-02, ..., 1.2787e-02,
+ 1.7975e-02, 6.4516e-04]],
+
+ [[ 1.6403e-02, -2.0084e-03, -4.8714e-03, ..., 1.6159e-02,
+ 1.1337e-02, 5.2719e-03],
+ [ 1.8549e-03, -2.1622e-02, -2.4734e-02, ..., 6.0081e-03,
+ -4.9477e-03, -8.3389e-03],
+ [ 4.8523e-03, -1.0818e-02, -1.5015e-02, ..., 6.0272e-04,
+ -2.3615e-04, -7.6065e-03],
+ ...,
+ [ 2.4033e-03, 2.6741e-03, -8.2016e-03, ..., -1.0231e-02,
+ -1.0254e-02, -7.4234e-03],
+ [ 8.2626e-03, -3.1586e-03, -9.0256e-03, ..., -3.5248e-03,
+ 6.7329e-03, 5.1842e-03],
+ [-1.0529e-02, -2.6947e-02, -1.5656e-02, ..., 1.6518e-03,
+ 6.4774e-03, 2.7132e-04]]],
+
+
+ [[[ 1.5366e-02, 2.6184e-02, 5.8479e-03, ..., 8.4534e-03,
+ -9.0027e-03, 2.0325e-02],
+ [-1.8753e-02, -7.4615e-03, -1.6830e-02, ..., 2.9640e-03,
+ -1.9193e-05, 1.5640e-02],
+ [-2.4765e-02, -1.2184e-02, 1.7405e-03, ..., -2.6291e-02,
+ -2.8641e-02, -3.6869e-03],
+ ...,
+ [ 7.4539e-03, -6.8169e-03, 1.4931e-02, ..., 1.4824e-02,
+ -5.6839e-03, -6.2599e-03],
+ [ 6.2408e-03, -8.2016e-03, 4.1229e-02, ..., -5.0664e-06,
+ -2.8336e-02, -1.9409e-02],
+ [ 1.7120e-02, -1.1139e-02, 6.1279e-02, ..., -4.5490e-04,
+ 7.2899e-03, 4.6967e-02]],
+
+ [[ 2.1149e-02, 3.3386e-02, 1.0483e-02, ..., 6.6109e-03,
+ -1.1864e-02, 1.7838e-02],
+ [-1.5022e-02, -8.8882e-04, -9.4604e-03, ..., 4.7722e-03,
+ 3.3522e-04, 1.4709e-02],
+ [-2.3026e-02, -6.3400e-03, 1.1215e-02, ..., -2.9251e-02,
+ -3.2776e-02, -7.0419e-03],
+ ...,
+ [ 5.5275e-03, -1.1826e-02, 7.7248e-03, ..., 1.1215e-02,
+ -1.1208e-02, -9.9030e-03],
+ [ 2.2125e-03, -1.5572e-02, 3.5980e-02, ..., -4.5929e-03,
+ -3.7567e-02, -2.6779e-02],
+ [ 1.0384e-02, -2.4033e-02, 5.2917e-02, ..., -1.1375e-02,
+ -4.0016e-03, 4.0253e-02]],
+
+ [[ 1.0483e-02, 2.2339e-02, 8.9121e-04, ..., 5.2719e-03,
+ -1.2917e-02, 1.7471e-02],
+ [-2.5070e-02, -1.1597e-02, -1.9104e-02, ..., 4.4594e-03,
+ 4.0364e-04, 1.5610e-02],
+ [-3.3417e-02, -1.8112e-02, -1.3227e-03, ..., -2.8519e-02,
+ -3.0121e-02, -6.7444e-03],
+ ...,
+ [ 4.9820e-03, -1.0445e-02, 1.0681e-02, ..., 1.3405e-02,
+ -8.7509e-03, -8.8196e-03],
+ [ 2.5711e-03, -1.3268e-02, 4.1168e-02, ..., 9.7275e-04,
+ -3.0792e-02, -2.5375e-02],
+ [ 8.9951e-03, -2.1439e-02, 5.3528e-02, ..., -6.4163e-03,
+ -4.1795e-04, 3.9398e-02]]],
+
+
+ [[[ 7.2441e-03, 3.7231e-03, -2.4662e-03, ..., 1.0353e-02,
+ 1.4267e-02, 1.9363e-02],
+ [-3.0270e-03, -3.2539e-03, -1.2878e-02, ..., 9.7847e-04,
+ 5.2299e-03, 6.8626e-03],
+ [-4.3182e-03, 5.6915e-03, -3.1910e-03, ..., 8.4114e-04,
+ 2.2297e-03, 7.1373e-03],
+ ...,
+ [ 4.4632e-03, 3.8757e-03, -2.0063e-04, ..., 1.5976e-02,
+ 1.4221e-02, 1.2756e-02],
+ [ 2.5146e-02, 1.4793e-02, 5.1003e-03, ..., 2.2858e-02,
+ 2.2186e-02, 2.3026e-02],
+ [ 3.0807e-02, 2.6031e-02, 1.4259e-02, ..., 2.5116e-02,
+ 2.1759e-02, 2.4887e-02]],
+
+ [[ 6.9695e-03, 5.0888e-03, -2.8915e-03, ..., 1.7868e-02,
+ 1.9669e-02, 2.9037e-02],
+ [-2.8973e-03, -1.2035e-03, -1.1116e-02, ..., 5.5542e-03,
+ 5.9547e-03, 1.3420e-02],
+ [-9.8190e-03, 4.3716e-03, 2.3806e-04, ..., 1.1253e-03,
+ -8.7976e-04, 9.4681e-03],
+ ...,
+ [ 6.1417e-03, 5.1804e-03, 2.1095e-03, ..., 2.4979e-02,
+ 2.5146e-02, 2.7710e-02],
+ [ 3.1128e-02, 2.0096e-02, 8.0948e-03, ..., 3.3722e-02,
+ 3.3295e-02, 4.0405e-02],
+ [ 3.7659e-02, 3.2166e-02, 1.8311e-02, ..., 4.2542e-02,
+ 3.9429e-02, 4.6356e-02]],
+
+ [[ 1.7014e-02, 1.5358e-02, 1.1269e-02, ..., 2.1378e-02,
+ 2.1317e-02, 3.0075e-02],
+ [ 7.4120e-03, 7.8087e-03, 1.1091e-03, ..., 7.4654e-03,
+ 7.7209e-03, 1.2947e-02],
+ [-5.4646e-04, 1.1208e-02, 6.4545e-03, ..., 4.1313e-03,
+ 3.2539e-03, 9.7275e-03],
+ ...,
+ [ 3.3531e-03, 2.0325e-04, 1.3704e-03, ..., 7.8087e-03,
+ 7.9422e-03, 1.4809e-02],
+ [ 1.6571e-02, 2.9163e-03, 4.2105e-04, ..., 1.1787e-02,
+ 1.1337e-02, 1.8753e-02],
+ [ 1.9714e-02, 1.0704e-02, 2.9335e-03, ..., 2.1042e-02,
+ 1.5457e-02, 2.2263e-02]]],
+
+
+ ...,
+
+
+ [[[-3.1614e-04, -6.5041e-04, -6.0844e-04, ..., 6.5207e-05,
+ 2.8062e-04, -5.1928e-04],
+ [-5.2452e-06, -9.8610e-04, -9.5367e-04, ..., 1.9908e-05,
+ -1.0675e-04, -8.3148e-05],
+ [-9.5606e-04, -6.4993e-04, -1.2035e-03, ..., -6.1035e-04,
+ -4.2439e-04, 6.3181e-04],
+ ...,
+ [-7.1907e-04, -6.2132e-04, 1.0270e-04, ..., -3.2485e-05,
+ -7.7963e-04, -7.9155e-04],
+ [-9.8991e-04, 6.4433e-05, -1.2598e-03, ..., -8.0490e-04,
+ -1.2980e-03, -1.2064e-03],
+ [-2.8110e-04, -5.8031e-04, -2.4199e-04, ..., -5.1558e-05,
+ 4.4203e-04, 1.4377e-04]],
+
+ [[ 5.6839e-04, 1.9491e-05, 2.8157e-04, ..., 1.6952e-04,
+ 9.6035e-04, -5.6601e-04],
+ [ 9.8038e-04, 2.3961e-05, 4.3941e-04, ..., 3.5739e-04,
+ 7.8630e-04, -6.2466e-04],
+ [-2.5654e-04, 3.8624e-04, 1.7090e-03, ..., 6.6614e-04,
+ 6.1607e-04, 7.3719e-04],
+ ...,
+ [ 5.9319e-04, 4.7755e-04, 4.7016e-04, ..., 1.0605e-03,
+ 6.6137e-04, 3.1066e-04],
+ [ 8.3494e-04, 4.7708e-04, -1.0042e-03, ..., 6.4945e-04,
+ -2.4092e-04, 3.6502e-04],
+ [ 4.7803e-04, -3.4690e-04, 6.3467e-04, ..., 2.3830e-04,
+ 1.9407e-04, 4.0698e-04]],
+
+ [[ 2.0623e-04, -7.5936e-05, -6.9094e-04, ..., -2.5582e-04,
+ -5.5313e-04, -5.7125e-04],
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+ 1.1653e-04, 5.8317e-04],
+ [-9.5224e-04, -3.9577e-04, -3.9458e-04, ..., 2.1636e-04,
+ 6.0797e-05, 1.7786e-04],
+ ...,
+ [ 4.9019e-04, -1.6594e-04, 5.3120e-04, ..., 3.1352e-04,
+ 9.8825e-05, 5.7650e-04],
+ [ 7.5400e-05, 4.0960e-04, -6.8998e-04, ..., 1.8597e-04,
+ 1.9622e-04, -3.3689e-04],
+ [-1.4269e-04, -2.5558e-04, 2.9540e-04, ..., 2.1315e-04,
+ -2.9826e-04, 4.0221e-04]]],
+
+
+ [[[ 1.2306e-02, 1.8921e-02, 5.3024e-03, ..., 1.1612e-02,
+ 6.5956e-03, 2.7069e-02],
+ [ 1.1261e-02, 2.9709e-02, 1.3695e-02, ..., -8.9722e-03,
+ -1.7639e-02, -3.2501e-03],
+ [ 2.1103e-02, 3.1342e-02, 1.7731e-02, ..., -1.1185e-02,
+ -2.7451e-02, -5.5275e-03],
+ ...,
+ [ 3.7292e-02, 2.5757e-02, 6.7863e-03, ..., 1.8631e-02,
+ 2.8793e-02, 3.6560e-02],
+ [ 1.9577e-02, -5.3711e-03, -2.1255e-02, ..., -1.6953e-02,
+ -2.3621e-02, 4.6463e-03],
+ [ 1.3992e-02, -2.7130e-02, -5.1117e-02, ..., -1.2520e-02,
+ -4.0009e-02, 1.3618e-02]],
+
+ [[ 1.7109e-03, 9.4223e-03, -2.4147e-03, ..., 8.3694e-03,
+ 3.3112e-03, 2.3117e-02],
+ [ 1.1692e-03, 2.3514e-02, 1.1520e-02, ..., -8.2321e-03,
+ -1.8555e-02, -6.4278e-03],
+ [ 1.0735e-02, 2.6749e-02, 1.8997e-02, ..., -1.1795e-02,
+ -3.0396e-02, -9.2773e-03],
+ ...,
+ [ 3.4821e-02, 2.1423e-02, 8.1253e-04, ..., 1.6235e-02,
+ 2.6367e-02, 3.4302e-02],
+ [ 1.4656e-02, -1.1101e-02, -2.7344e-02, ..., -2.0676e-02,
+ -3.1250e-02, -1.2932e-03],
+ [ 5.8136e-03, -3.8971e-02, -6.3354e-02, ..., -2.1881e-02,
+ -5.2307e-02, 4.1885e-03]],
+
+ [[-1.0658e-02, -1.8530e-03, -8.5220e-03, ..., 4.6959e-03,
+ -1.9407e-03, 1.7426e-02],
+ [-1.3008e-02, 1.1108e-02, 5.3177e-03, ..., -8.9722e-03,
+ -2.1408e-02, -9.2850e-03],
+ [-3.2902e-03, 1.4580e-02, 1.3863e-02, ..., -1.2299e-02,
+ -2.9846e-02, -1.2985e-02],
+ ...,
+ [ 3.2806e-02, 2.2476e-02, 6.9771e-03, ..., 1.0704e-02,
+ 1.9516e-02, 2.4567e-02],
+ [ 1.3817e-02, -6.0501e-03, -1.4580e-02, ..., -2.2476e-02,
+ -3.2013e-02, -9.6893e-03],
+ [ 5.8556e-03, -3.2196e-02, -5.1910e-02, ..., -2.4429e-02,
+ -5.2979e-02, -3.0937e-03]]],
+
+
+ [[[ 2.2598e-02, -7.3586e-03, -2.9099e-02, ..., -2.2873e-02,
+ 8.5068e-03, -4.8706e-02],
+ [ 1.7410e-02, -3.1433e-02, -4.2816e-02, ..., -6.2675e-03,
+ 9.4528e-03, -3.8910e-02],
+ [ 2.2125e-02, -1.5839e-02, -4.1351e-02, ..., 4.6021e-02,
+ 2.4017e-02, -1.1345e-02],
+ ...,
+ [ 2.8290e-02, 3.7964e-02, 4.1656e-02, ..., 2.4734e-02,
+ -2.2011e-03, -1.9989e-02],
+ [-1.5671e-02, -2.0996e-02, -2.9182e-03, ..., 2.0828e-02,
+ 7.9803e-03, 1.4175e-02],
+ [-3.1624e-03, -9.1400e-03, 7.2937e-03, ..., 1.6663e-02,
+ 1.3590e-03, 1.6647e-02]],
+
+ [[ 2.2675e-02, -8.0872e-03, -3.0746e-02, ..., -1.9989e-02,
+ 1.6220e-02, -4.3518e-02],
+ [ 1.6678e-02, -3.2532e-02, -4.2694e-02, ..., -6.4468e-04,
+ 1.8555e-02, -3.2135e-02],
+ [ 2.0767e-02, -1.6098e-02, -3.9978e-02, ..., 5.0598e-02,
+ 2.9999e-02, -5.6038e-03],
+ ...,
+ [ 4.2328e-02, 5.0476e-02, 4.9988e-02, ..., 2.2064e-02,
+ -1.8721e-03, -1.5190e-02],
+ [-4.8981e-03, -1.0933e-02, 6.5994e-03, ..., 1.9073e-02,
+ 7.9498e-03, 2.0065e-02],
+ [ 4.9896e-03, -1.7853e-03, 1.5068e-02, ..., 1.0445e-02,
+ -2.7905e-03, 1.9196e-02]],
+
+ [[ 7.0305e-03, -1.8372e-02, -3.5797e-02, ..., -1.5244e-02,
+ 2.1683e-02, -3.0380e-02],
+ [-1.7321e-04, -4.1534e-02, -4.5563e-02, ..., 4.7989e-03,
+ 2.4796e-02, -1.7990e-02],
+ [ 2.1000e-03, -2.8732e-02, -4.5746e-02, ..., 5.0171e-02,
+ 3.4485e-02, 4.2267e-03],
+ ...,
+ [ 3.7415e-02, 4.6143e-02, 4.9500e-02, ..., 2.0111e-02,
+ 4.0741e-03, -6.3667e-03],
+ [-5.8479e-03, -9.4757e-03, 1.2398e-02, ..., 2.1317e-02,
+ 1.5762e-02, 2.5894e-02],
+ [ 2.3136e-03, -7.0858e-04, 1.7914e-02, ..., 1.1047e-02,
+ 2.1496e-03, 2.2278e-02]]]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([0.3311, 0.0032, 0.1610, ..., 2.1922, 0.0050, 0.0039], device='cuda:1',
+ requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([-0.0045, -0.0452, -0.0475, ..., 0.0402, -0.1402, -0.0132],
+ device='cuda:1', requires_grad=True)
+torch.Size([3072, 1024])
+Parameter containing:
+tensor([[-7.0632e-05, -1.6510e-04, -7.0930e-05, ..., 4.5090e-03,
+ -2.9160e-02, -7.8201e-05],
+ [-1.3733e-04, 1.2165e-04, 4.2319e-05, ..., -1.6594e-03,
+ 3.1433e-02, 7.4446e-05],
+ [ 4.8018e-04, 7.7963e-04, -1.0991e-04, ..., -1.6846e-02,
+ 4.2999e-02, 1.5199e-04],
+ ...,
+ [ 2.1267e-04, 4.1032e-04, -7.2420e-05, ..., 4.8027e-03,
+ -1.7338e-03, -6.6102e-05],
+ [ 3.0518e-04, -4.4405e-05, -2.2709e-04, ..., 1.1551e-02,
+ 3.3436e-03, 7.4685e-05],
+ [-2.8849e-05, 4.5919e-04, 9.3341e-05, ..., -1.1314e-02,
+ 3.7670e-03, -7.7844e-05]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([3072])
+Parameter containing:
+tensor([ 1.5674, -1.6143, -0.8208, ..., 0.0115, 0.0107, -0.0043],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024, 1024])
+Parameter containing:
+tensor([[-6.7596e-03, 8.8043e-03, -7.9422e-03, ..., -8.6441e-03,
+ -8.7433e-03, 3.5553e-03],
+ [ 1.2077e-02, 5.8784e-03, 1.1253e-02, ..., -3.7060e-03,
+ 2.0008e-03, 3.8319e-03],
+ [-5.2032e-03, 2.6913e-03, 1.2894e-02, ..., 6.4812e-03,
+ -3.0398e-05, -4.2796e-04],
+ ...,
+ [-4.5037e-04, -2.5063e-03, -3.2768e-03, ..., -3.2768e-03,
+ -1.9409e-02, 9.2545e-03],
+ [-7.3624e-03, 2.8419e-03, -7.9193e-03, ..., 4.0627e-04,
+ -1.3866e-03, -6.7186e-04],
+ [ 9.0408e-03, 1.5287e-03, 1.6737e-03, ..., 2.4242e-03,
+ -3.7575e-03, 4.9667e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([-0.0262, -0.0654, 0.0032, ..., 0.1761, -0.0446, 0.0023],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([6.1186e-04, 2.0990e-03, 3.0166e-05, ..., 6.9025e-01, 3.5588e-01,
+ 1.4703e-04], device='cuda:1', requires_grad=True)
+torch.Size([1024])
+Parameter containing:
+tensor([ 1.3605e-04, 8.3127e-04, -2.0098e-05, ..., -3.6831e-01,
+ 1.7861e-01, 7.4003e-05], device='cuda:1', requires_grad=True)
+torch.Size([4096, 1024])
+Parameter containing:
+tensor([[ 3.6597e-04, 2.6047e-05, 1.1921e-07, ..., -6.6109e-03,
+ -1.1740e-03, -6.4468e-04],
+ [ 5.3291e-03, 1.3710e-02, -3.5620e-04, ..., -3.8052e-03,
+ -2.5225e-04, 6.0730e-03],
+ [ 1.3428e-03, 1.2884e-03, -1.9073e-06, ..., -2.8549e-02,
+ -1.1930e-03, 1.4906e-03],
+ ...,
+ [-2.4994e-02, -1.0262e-02, 2.3067e-04, ..., -2.0103e-03,
+ -1.2665e-02, 6.2332e-03],
+ [ 3.2401e-04, 9.3758e-05, -5.9605e-08, ..., -6.0234e-03,
+ -7.3862e-04, -6.4611e-04],
+ [ 1.1129e-03, -2.3117e-02, -2.7061e-04, ..., -4.4365e-03,
+ 3.5744e-03, -7.4997e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([4096])
+Parameter containing:
+tensor([-0.6826, -0.3132, -0.8076, ..., -0.2167, -0.6543, -0.3040],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024, 4096])
+Parameter containing:
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+ [ 0.0018, 0.0191, -0.0102, ..., -0.0261, 0.0026, 0.0206],
+ [ 0.0039, -0.0002, -0.0028, ..., 0.0029, 0.0038, -0.0151],
+ ...,
+ [ 0.0021, -0.0003, -0.0034, ..., 0.0033, 0.0015, 0.0089],
+ [-0.0059, 0.0078, 0.0069, ..., -0.0005, -0.0060, 0.0020],
+ [ 0.0003, -0.0039, -0.0022, ..., -0.0094, 0.0005, 0.0039]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024])
+Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([1024])
+Parameter containing:
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+ 2.9745e-01], device='cuda:1', requires_grad=True)
+torch.Size([1024])
+Parameter containing:
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+ 5.1503e-02], device='cuda:1', requires_grad=True)
+torch.Size([3072, 1024])
+Parameter containing:
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+ [ 0.0070, 0.0033, 0.0170, ..., -0.0062, 0.0080, 0.0055],
+ [ 0.0102, -0.0102, -0.0003, ..., 0.0024, 0.0164, 0.0043],
+ ...,
+ [ 0.0113, 0.0003, -0.0048, ..., 0.0002, 0.0042, -0.0065],
+ [-0.0144, -0.0119, 0.0076, ..., -0.0037, 0.0036, 0.0072],
+ [-0.0012, -0.0020, 0.0010, ..., -0.0066, -0.0222, -0.0007]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([3072])
+Parameter containing:
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+ -4.0474e-03, 2.2471e-04], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([1024, 1024])
+Parameter containing:
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+ 1.1993e-02, 1.0979e-04],
+ [-5.0068e-04, 7.5417e-03, -4.4131e-04, ..., -2.8553e-03,
+ 1.1459e-02, -3.0899e-03],
+ [ 2.7752e-03, -5.4703e-03, -1.1978e-02, ..., -3.8319e-03,
+ -1.0222e-04, -5.6686e-03],
+ ...,
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+ -9.7198e-03, 1.5419e-02],
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+ 4.7264e-03, -4.1389e-03]], device='cuda:1', dtype=torch.float16,
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+ ...,
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+ -2.0447e-03, 5.9662e-03],
+ ...,
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+ 6.7043e-04, 5.3139e-03],
+ [ 9.0456e-04, -1.3828e-03, 1.1587e-03, ..., -1.1549e-03,
+ 4.4975e-03, -5.7945e-03],
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+ 1.2444e-02, 5.4054e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ -2.0416e-02, -9.6283e-03],
+ ...,
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+ -4.8447e-03, -6.4964e-03],
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+ -8.6136e-03, 4.2229e-03]], device='cuda:1', dtype=torch.float16,
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+ -6.2981e-03, -1.8578e-03],
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+ -1.4353e-03, -3.4161e-03]], device='cuda:1', dtype=torch.float16,
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+ -1.9730e-02, 7.8964e-04],
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+ -1.1826e-02, 2.4719e-02],
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+ 7.3318e-03, 7.0572e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ 9.0256e-03, -1.5945e-02],
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+ -5.3329e-03, -3.0499e-03],
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+ 7.3280e-03, -1.1848e-02],
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+ -3.4256e-03, 3.2120e-03]], device='cuda:1', dtype=torch.float16,
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+ 1.1925e-02, 3.2806e-03],
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+ requires_grad=True)
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+ -4.9362e-03, 1.8988e-03],
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+ [-3.9673e-02, -1.2040e-05, 2.4188e-04, ..., 2.8877e-03,
+ -3.9101e-03, -2.3239e-02],
+ ...,
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+ -4.2686e-03, -1.4544e-03],
+ [ 2.7435e-02, 6.1798e-03, 1.0468e-02, ..., 3.2425e-05,
+ 4.9400e-03, -9.4604e-03],
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+ -9.8419e-03, -1.8356e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ ...,
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+ ...,
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+ -8.6060e-03, -2.0447e-03],
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+ -6.2037e-04, -1.3741e-02],
+ ...,
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+ [ 0.0073, -0.0276, -0.0020, ..., 0.0134, 0.0046, -0.0155],
+ ...,
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+ [ 0.0088, -0.0083, 0.0067, ..., 0.0172, -0.0225, 0.0448],
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+ [ 0.0077, 0.0203, 0.0148, ..., -0.0084, -0.0068, 0.0304],
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+ [-0.0073, 0.0052, -0.0273, ..., 0.0022, 0.0090, 0.0104],
+ ...,
+ [-0.0117, 0.0100, 0.0137, ..., 0.0083, -0.0002, 0.0034],
+ [ 0.0061, 0.0166, 0.0235, ..., 0.0010, 0.0024, -0.0336],
+ [-0.0002, -0.0062, -0.0147, ..., 0.0020, -0.0193, -0.0020]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
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+Parameter containing:
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+Parameter containing:
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+ requires_grad=True)
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+ device='cuda:1', requires_grad=True)
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+ 6.0501e-03, 5.5552e-04],
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+ [ 8.8272e-03, 8.2474e-03, 3.9597e-03, ..., 4.3845e-04,
+ -7.0724e-03, 2.1725e-03],
+ ...,
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+ -4.9057e-03, 7.3891e-03],
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+ -6.0959e-03, -3.0914e-02],
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+ -4.2877e-03, 1.9012e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ 9.3765e-03, -3.5896e-03],
+ ...,
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+ -3.6240e-03, -4.8103e-03],
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+ -2.5177e-02, -2.7252e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ requires_grad=True)
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+ ...,
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+ ...,
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+ 1.0056e-02, 9.3384e-03],
+ ...,
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+ -4.1748e-02, 1.5808e-02],
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+ -3.5019e-03, 1.3748e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ [ 0.0156, 0.0077, 0.0071, ..., -0.0158, -0.0195, 0.0202],
+ ...,
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+ requires_grad=True)
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+ ...,
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+ ...,
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+ -1.7424e-03, -1.5541e-02],
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+ 4.9667e-03, -2.5375e-02],
+ ...,
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+ -1.3222e-02, 7.2975e-03],
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+ -1.4519e-02, 1.1742e-02],
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+ -5.1178e-02, 5.3864e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ [-0.0012, 0.0172, 0.0223, ..., 0.0052, 0.0394, 0.0099],
+ ...,
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+ [ 0.0086, -0.0009, 0.0137, ..., -0.0030, 0.0077, -0.0112]],
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+ -2.7866e-03, -1.2886e-02],
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+ requires_grad=True)
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+ ...,
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+ requires_grad=True)
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+ [ 0.0247, 0.0204, 0.0079, ..., -0.0015, 0.0224, -0.0038],
+ ...,
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+ ...,
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+ ...,
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+ [ 0.0177, -0.0216, 0.0124, ..., 0.0067, -0.0014, -0.0008],
+ ...,
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+ requires_grad=True)
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+ ...,
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+ ...,
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+ requires_grad=True)
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+ [-4.2701e-04, 2.2446e-02, 1.0483e-02, ..., -4.0817e-03,
+ -1.7151e-02, -2.6047e-02],
+ ...,
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+ 1.6479e-02, 2.7222e-02],
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+ -7.5645e-03, 9.1019e-03],
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+ -1.3786e-02, 1.5656e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ [-0.0043, -0.0276, 0.0013, ..., -0.0066, 0.0263, 0.0338],
+ ...,
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+ requires_grad=True)
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+ [ 0.0109, -0.0224, -0.0055, ..., -0.0055, -0.0267, -0.0187],
+ ...,
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+ [-0.0119, -0.0276, 0.0225, ..., -0.0024, -0.0047, -0.0064]],
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+ [ 0.0196, -0.0219, 0.0057, ..., 0.0070, -0.0059, -0.0075],
+ ...,
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+ [-0.0084, 0.0186, 0.0111, ..., -0.0047, 0.0052, 0.0088]],
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+ 1.1623e-05, -1.4076e-02], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ -4.6844e-03, -1.2360e-02],
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+ -3.4729e-02, -2.3346e-02],
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+ 1.4847e-02, -6.3400e-03],
+ ...,
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+ [-6.3324e-04, 8.2550e-03, -1.3161e-02, ..., 5.1918e-03,
+ 2.1324e-03, 1.3359e-02],
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+ -4.8995e-05, 1.2718e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ [ 0.0018, -0.0234, 0.0009, ..., 0.0089, -0.0099, -0.0107],
+ ...,
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+ [ 0.0273, 0.0015, -0.0178, ..., -0.0870, 0.0066, -0.0008],
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+ ...,
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+ -1.0653e-03, -2.1194e-02],
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+ requires_grad=True)
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+ [-0.0017, -0.0340, 0.0049, ..., -0.0096, 0.0049, -0.0091],
+ ...,
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+ -1.5068e-02, -1.5480e-02],
+ ...,
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+ -1.8509e-02, -3.0289e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ requires_grad=True)
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+ ...,
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+ ...,
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+ ...,
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+ -3.5896e-03, -7.0524e-04],
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+ -3.2349e-03, -4.4746e-03],
+ ...,
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+ [-1.3786e-02, 1.0185e-02, -4.3068e-03, ..., 9.9850e-04,
+ 7.5111e-03, 2.2797e-02],
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+ 1.2108e-02, 1.5915e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ -2.9087e-03, -5.4131e-03],
+ ...,
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+ -6.9504e-03, -1.4145e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ [ 0.0204, 0.0067, -0.0038, ..., -0.0049, 0.0252, -0.0015],
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+ requires_grad=True)
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+ dtype=torch.float16, requires_grad=True)
+torch.Size([768])
+Parameter containing:
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+ -4.1965e-02, 8.5480e-02, -2.1664e-03], device='cuda:1',
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+ [ 0.0279, -0.0216, -0.0171, ..., -0.0218, 0.0069, 0.0059],
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+ [ 0.0186, -0.0087, 0.0062, ..., 0.0125, -0.0135, -0.0063],
+ [ 0.0241, -0.0023, -0.0027, ..., 0.0083, 0.0031, 0.0143]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
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+ 1.6815e-02, -1.8707e-02],
+ [ 3.0487e-02, 3.2715e-02, -3.0022e-03, ..., 3.5187e-02,
+ 3.5980e-02, -5.8136e-03],
+ [ 2.0390e-03, -2.0386e-02, 1.7670e-02, ..., 2.3132e-02,
+ 4.0550e-03, 1.1375e-02],
+ ...,
+ [-5.3101e-03, 2.4445e-02, -1.9531e-02, ..., -1.0094e-02,
+ -1.0544e-02, 2.3727e-03],
+ [-1.3418e-03, 4.7874e-03, 1.2207e-02, ..., 7.7553e-03,
+ -6.1214e-05, -1.3153e-02],
+ [ 9.2850e-03, 7.7629e-03, -1.5533e-02, ..., 1.3306e-02,
+ 5.0316e-03, 2.2507e-03]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
+torch.Size([2304])
+Parameter containing:
+tensor([ 0.1748, -0.0695, -0.2499, ..., -0.0291, 0.0082, 0.0654],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([768, 768])
+Parameter containing:
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+ [-0.0204, 0.0330, -0.0089, ..., -0.0183, 0.0075, 0.0104],
+ [-0.0120, -0.0031, 0.0017, ..., -0.0134, 0.0086, -0.0073],
+ ...,
+ [-0.0075, -0.0018, -0.0179, ..., -0.0070, -0.0049, -0.0116],
+ [-0.0321, -0.0037, 0.0038, ..., 0.0033, -0.0005, 0.0031],
+ [ 0.0155, 0.0208, -0.0011, ..., 0.0005, -0.0043, -0.0342]],
+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([768])
+Parameter containing:
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+ dtype=torch.float16, requires_grad=True)
+torch.Size([768])
+Parameter containing:
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+torch.Size([768])
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([2304])
+Parameter containing:
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([768, 768])
+Parameter containing:
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+ -8.6365e-03, -6.7825e-03],
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+ -2.0340e-02, 1.4626e-02],
+ ...,
+ [-6.7062e-03, 5.0068e-04, -8.3008e-03, ..., 3.5477e-03,
+ 2.7447e-03, -2.1606e-02],
+ [ 2.0172e-02, -1.5497e-03, -1.4412e-02, ..., 5.5504e-04,
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+ -2.3022e-03, 1.5236e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([768, 3072])
+Parameter containing:
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+ -3.3169e-03, -2.8595e-02],
+ [ 7.9193e-03, -1.0986e-02, 8.4000e-03, ..., 2.7145e-02,
+ 3.1189e-02, -2.6474e-02],
+ [ 1.8585e-02, -1.3618e-02, -1.1322e-02, ..., 6.1989e-03,
+ -1.4870e-02, -5.5194e-05],
+ ...,
+ [ 1.0979e-02, 2.5269e-02, -1.1635e-03, ..., 2.2926e-03,
+ 2.9037e-02, -2.4094e-02],
+ [ 1.6174e-02, 2.0721e-02, 5.5618e-03, ..., -1.0529e-03,
+ 6.1226e-03, 1.5610e-02],
+ [-1.6403e-02, 1.9646e-03, -7.2136e-03, ..., -3.4119e-02,
+ -3.3054e-03, -1.8219e-02]], device='cuda:1', dtype=torch.float16,
+ requires_grad=True)
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+ dtype=torch.float16, requires_grad=True)
+torch.Size([768])
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
+torch.Size([768, 768])
+Parameter containing:
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+ ...,
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+ device='cuda:1', dtype=torch.float16, requires_grad=True)
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+ dtype=torch.float16, requires_grad=True)
+torch.Size([768])
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+ -4.3071e-02, 1.1801e-01, -1.3836e-02], device='cuda:1',
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+ -2.6690e-02, 2.1225e-01, 7.8747e-02], device='cuda:1',
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+ [-2.2182e-03, -2.1912e-02, 5.6572e-03, ..., 2.1820e-02,
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+ -1.3260e-02, 1.9684e-03],
+ ...,
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+ 2.0161e-03, -3.3054e-03]], device='cuda:1', dtype=torch.float16,
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+ 4.4178e-02, 2.9294e-02, 1.8558e-01, -9.1317e-02, 2.0792e-02,
+ -1.6872e-01, -2.2066e-01, -6.8652e-02, 6.6996e-02, -2.0110e-01,
+ -1.2881e-02, -4.1882e-02, -1.6867e-01, -4.1276e-02, -1.1632e-01,
+ -2.4713e-01, -2.1714e-01, -2.5670e-01, -7.6108e-02, 7.5738e-02,
+ -5.2782e-02, -2.1995e-01, -2.1114e-01, -8.2967e-02, -2.9250e-01,
+ -1.1585e-01, -3.7202e-02, 2.2169e-01, -1.1157e-01, -1.3117e-01,
+ -4.5070e-01, 9.1724e-02, -2.5536e-01, -1.1099e-01, -1.4028e-01,
+ 2.8116e-02, -5.5589e-02, -1.1969e-01, -8.9219e-02, 4.4426e-02,
+ 1.8124e-01, 6.9828e-02, -2.9449e-01, -3.8633e-01, -3.2289e-01,
+ -1.5161e-01, 7.4736e-02, -3.2911e-01, 9.0841e-02, -9.4956e-02,
+ -1.6886e-01, -4.3418e-01, -1.8379e-01, -1.7249e-01, -4.3381e-02,
+ -3.0515e-01, -1.3860e-02, -1.3408e-01, 2.3386e-02, -1.9684e-01,
+ 1.1179e-01, -1.0725e-01, -5.1684e-02, 9.9985e-03, -8.1149e-02,
+ -5.7091e-02, -2.6378e-01, -1.8482e-01, 2.4157e-02, -3.0910e-01,
+ -9.7387e-02, -1.3443e-01, -1.3120e-01, -4.1030e-02, -2.8421e-01,
+ -1.1970e-01, 2.6584e-01, -2.9540e-01, -4.1858e-01, -4.4225e-02,
+ -3.3776e-02, 5.1072e-02, -1.0784e-01, 2.4809e-03, -1.0006e-02,
+ -1.6900e-01, -1.6756e-01, 7.1867e-02, -1.4536e-01, -3.2948e-01,
+ -4.8964e-02, 2.5080e-02, -2.9177e-01, -2.3546e-01, -9.6734e-02,
+ 5.2769e-02, 5.9821e-02, -7.1729e-02, -5.2216e-02, 4.6748e-02,
+ -1.0374e-01, -3.3411e-01, 1.9977e-02, -1.9944e-01, -1.4249e-01,
+ -4.2926e-01, -2.0550e-01, -1.2332e-01, 2.4972e-02, -3.1156e-01,
+ -3.5618e-02, 1.4841e-01, -6.8435e-02, -8.9134e-02, -4.3101e-01,
+ 3.2034e-02, -5.7796e-02, -1.0815e-01, 7.9357e-02, 6.0425e-02,
+ 9.0992e-02, -8.4117e-02, -1.6460e-01, -1.8860e-01, -1.1188e-01,
+ -1.6019e-02, -2.7539e-01, -3.0461e-02, 3.6393e-02, -5.5778e-02,
+ -2.3261e-01, 3.3813e-03, -7.2618e-02, 1.3152e-01, 2.5424e-02,
+ -2.3474e-01, 1.2788e-01, 1.0549e-01, -3.9438e-01, -1.4477e-01,
+ -8.7272e-02, -5.9155e-02, -1.8755e-02, 8.3795e-03, -1.3303e-01,
+ 3.5993e-02, -4.8489e-01, -9.8144e-02, -2.0187e-01, -1.7780e-01,
+ -1.5702e-01, -1.6632e-01, -7.6623e-02, -1.2067e-01, 9.7476e-02,
+ -1.4865e-01, 3.9298e-02, -8.1964e-02, -5.7571e-02, -1.6357e-02,
+ -2.4697e-02, -2.3285e-01, -1.8711e-01, 5.9722e-02, -5.0565e-03,
+ 1.6085e-01, -5.6193e-02, -1.2748e-01, -1.7989e-01, 1.6912e-02,
+ -7.4359e-02, 5.9318e-02, 4.5689e-02, -3.4782e-01, 1.2438e-02,
+ 4.9462e-03, -7.6542e-02, -1.7623e-03, -3.1946e-01, -8.4081e-02,
+ 4.2274e-02, -2.0708e-01, -1.0376e-01, -3.2503e-01, -4.9442e-02,
+ -1.5170e-01, -1.7022e-01, -1.8604e-01, 7.0737e-02, -2.3663e-01,
+ -3.6244e-02, -2.3101e-01, -2.2293e-01, 8.8521e-03, -7.0427e-02,
+ -2.2006e-01, 6.1665e-02, -1.2758e-01, -1.3123e-01, 1.3888e-02,
+ -2.7413e-01, -3.8097e-02, -3.4511e-01, -2.7228e-01, 8.0634e-02,
+ -1.5312e-02, -3.7472e-03, 2.0875e-02, 1.5117e-01, -2.4863e-01,
+ -3.2693e-01, -1.0401e-01, -1.4831e-01, -1.8991e-01, -1.6961e-01,
+ 1.1688e-01, 6.0180e-02, -5.3109e-02, -3.7527e-01, 1.3247e-01,
+ -1.2943e-01, -1.7973e-01, 1.0068e-01, -1.7821e-01, -1.9093e-01,
+ 2.9122e-02, -5.7265e-01, -1.4149e-01, -7.8197e-02, -2.9365e-01,
+ -2.5148e-01, -1.0391e-01, -1.6442e-01, -1.2958e-01, -6.3028e-02,
+ -7.3926e-02, -9.9090e-02, 1.4622e-02, -3.2253e-01, -2.1039e-01,
+ -3.5321e-02, -6.1373e-02, -4.3052e-03, -2.5899e-01, 1.4603e-01,
+ -6.2891e-02, 3.2609e-02, -8.3760e-02, -7.8426e-02, -5.5548e-02,
+ -2.1703e-01, -7.2742e-02, -1.0241e-01, -1.5250e-01, -5.3758e-03,
+ 1.6436e-01, -1.6233e-01, -1.1661e-01, -2.6216e-01, -3.3025e-01,
+ -1.5915e-01, -3.5974e-01, -1.6534e-01, 8.1741e-03, 1.2124e-01,
+ -7.8771e-02, -2.6709e-01, -1.5131e-01, 1.1832e-01, -9.7288e-02,
+ 1.5229e-01, -1.3003e-01, -3.0911e-01, -8.6667e-02, -6.7893e-02,
+ -1.5559e-01, -1.3761e-01, -4.8186e-02, -1.4222e-01, -5.8575e-02,
+ -4.5176e-01, -2.7698e-01, -1.8527e-01, 1.3501e-01, 1.4931e-02,
+ -5.2130e-01, -2.6890e-01, 9.5427e-02], device='cuda:1',
+ requires_grad=True)
diff --git a/python/ClipDetection/CoOp/trainers/imagenet_templates.py b/python/ClipDetection/CoOp/trainers/imagenet_templates.py
new file mode 100644
index 00000000..560c5a50
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/imagenet_templates.py
@@ -0,0 +1,94 @@
+# source: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb
+
+IMAGENET_TEMPLATES = [
+ "a bad photo of a {}.",
+ "a photo of many {}.",
+ "a sculpture of a {}.",
+ "a photo of the hard to see {}.",
+ "a low resolution photo of the {}.",
+ "a rendering of a {}.",
+ "graffiti of a {}.",
+ "a bad photo of the {}.",
+ "a cropped photo of the {}.",
+ "a tattoo of a {}.",
+ "the embroidered {}.",
+ "a photo of a hard to see {}.",
+ "a bright photo of a {}.",
+ "a photo of a clean {}.",
+ "a photo of a dirty {}.",
+ "a dark photo of the {}.",
+ "a drawing of a {}.",
+ "a photo of my {}.",
+ "the plastic {}.",
+ "a photo of the cool {}.",
+ "a close-up photo of a {}.",
+ "a black and white photo of the {}.",
+ "a painting of the {}.",
+ "a painting of a {}.",
+ "a pixelated photo of the {}.",
+ "a sculpture of the {}.",
+ "a bright photo of the {}.",
+ "a cropped photo of a {}.",
+ "a plastic {}.",
+ "a photo of the dirty {}.",
+ "a jpeg corrupted photo of a {}.",
+ "a blurry photo of the {}.",
+ "a photo of the {}.",
+ "a good photo of the {}.",
+ "a rendering of the {}.",
+ "a {} in a video game.",
+ "a photo of one {}.",
+ "a doodle of a {}.",
+ "a close-up photo of the {}.",
+ "a photo of a {}.",
+ "the origami {}.",
+ "the {} in a video game.",
+ "a sketch of a {}.",
+ "a doodle of the {}.",
+ "a origami {}.",
+ "a low resolution photo of a {}.",
+ "the toy {}.",
+ "a rendition of the {}.",
+ "a photo of the clean {}.",
+ "a photo of a large {}.",
+ "a rendition of a {}.",
+ "a photo of a nice {}.",
+ "a photo of a weird {}.",
+ "a blurry photo of a {}.",
+ "a cartoon {}.",
+ "art of a {}.",
+ "a sketch of the {}.",
+ "a embroidered {}.",
+ "a pixelated photo of a {}.",
+ "itap of the {}.",
+ "a jpeg corrupted photo of the {}.",
+ "a good photo of a {}.",
+ "a plushie {}.",
+ "a photo of the nice {}.",
+ "a photo of the small {}.",
+ "a photo of the weird {}.",
+ "the cartoon {}.",
+ "art of the {}.",
+ "a drawing of the {}.",
+ "a photo of the large {}.",
+ "a black and white photo of a {}.",
+ "the plushie {}.",
+ "a dark photo of a {}.",
+ "itap of a {}.",
+ "graffiti of the {}.",
+ "a toy {}.",
+ "itap of my {}.",
+ "a photo of a cool {}.",
+ "a photo of a small {}.",
+ "a tattoo of the {}.",
+]
+
+IMAGENET_TEMPLATES_SELECT = [
+ "itap of a {}.",
+ "a bad photo of the {}.",
+ "a origami {}.",
+ "a photo of the large {}.",
+ "a {} in a video game.",
+ "art of the {}.",
+ "a photo of the small {}.",
+]
diff --git a/python/ClipDetection/CoOp/trainers/zsclip.py b/python/ClipDetection/CoOp/trainers/zsclip.py
new file mode 100644
index 00000000..f9391188
--- /dev/null
+++ b/python/ClipDetection/CoOp/trainers/zsclip.py
@@ -0,0 +1,99 @@
+import torch
+import torch.nn as nn
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.optim import build_optimizer, build_lr_scheduler
+
+from clip import clip
+from clip.model import convert_weights
+
+from .coop import load_clip_to_cpu
+from .imagenet_templates import IMAGENET_TEMPLATES, IMAGENET_TEMPLATES_SELECT
+
+CUSTOM_TEMPLATES = {
+ "OxfordPets": "a photo of a {}, a type of pet.",
+ "OxfordFlowers": "a photo of a {}, a type of flower.",
+ "FGVCAircraft": "a photo of a {}, a type of aircraft.",
+ "DescribableTextures": "{} texture.",
+ "EuroSAT": "a centered satellite photo of {}.",
+ "StanfordCars": "a photo of a {}.",
+ "Food101": "a photo of {}, a type of food.",
+ "SUN397": "a photo of a {}.",
+ "Caltech101": "a photo of a {}.",
+ "UCF101": "a photo of a person doing {}.",
+ "ImageNet": "a photo of a {}.",
+ "ImageNetSketch": "a photo of a {}.",
+ "ImageNetV2": "a photo of a {}.",
+ "ImageNetA": "a photo of a {}.",
+ "ImageNetR": "a photo of a {}.",
+}
+
+
+@TRAINER_REGISTRY.register()
+class ZeroshotCLIP(TrainerX):
+ def build_model(self):
+ cfg = self.cfg
+ classnames = self.dm.dataset.classnames
+
+ print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})")
+ clip_model = load_clip_to_cpu(cfg)
+ clip_model.to(self.device)
+
+ temp = CUSTOM_TEMPLATES[cfg.DATASET.NAME]
+ prompts = [temp.format(c.replace("_", " ")) for c in classnames]
+ print(f"Prompts: {prompts}")
+ prompts = torch.cat([clip.tokenize(p) for p in prompts])
+ prompts = prompts.to(self.device)
+
+ with torch.no_grad():
+ text_features = clip_model.encode_text(prompts)
+ text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+
+ self.text_features = text_features
+ self.clip_model = clip_model
+
+ def model_inference(self, image):
+ image_features = self.clip_model.encode_image(image)
+ image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+ logit_scale = self.clip_model.logit_scale.exp()
+ logits = logit_scale * image_features @ self.text_features.t()
+ return logits
+
+
+@TRAINER_REGISTRY.register()
+class ZeroshotCLIP2(ZeroshotCLIP):
+ """Prompt ensembling."""
+
+ # templates = IMAGENET_TEMPLATES
+ templates = IMAGENET_TEMPLATES_SELECT
+
+ def build_model(self):
+ cfg = self.cfg
+ classnames = self.dm.dataset.classnames
+
+ print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})")
+ clip_model = load_clip_to_cpu(cfg)
+ clip_model.to(self.device)
+
+ for params in clip_model.parameters():
+ params.requires_grad_(False)
+
+ # add custom-made prompt
+ if cfg.DATASET.NAME != "ImageNet":
+ self.templates += [CUSTOM_TEMPLATES[cfg.DATASET.NAME]]
+
+ num_temp = len(self.templates)
+ print(f"Prompt ensembling (n={num_temp})")
+
+ mean_text_features = 0
+ for i, temp in enumerate(self.templates):
+ prompts = [temp.format(c.replace("_", " ")) for c in classnames]
+ prompts = torch.cat([clip.tokenize(p) for p in prompts]).to(self.device)
+ text_features = clip_model.encode_text(prompts)
+ text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+ mean_text_features = mean_text_features + text_features
+ mean_text_features = mean_text_features / num_temp
+ mean_text_features = mean_text_features / mean_text_features.norm(dim=-1, keepdim=True)
+
+ self.text_features = mean_text_features
+ self.clip_model = clip_model
diff --git a/python/ClipDetection/Dassl.pytorch/.flake8 b/python/ClipDetection/Dassl.pytorch/.flake8
new file mode 100644
index 00000000..ac13c77e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/.flake8
@@ -0,0 +1,24 @@
+[flake8]
+ignore =
+ # At least two spaces before inline comment
+ E261,
+ # Line lengths are recommended to be no greater than 79 characters
+ E501,
+ # Missing whitespace around arithmetic operator
+ E226,
+ # Blank line contains whitespace
+ W293,
+ # Do not use bare 'except'
+ E722,
+ # Line break after binary operator
+ W504,
+ # Too many leading '#' for block comment
+ E266,
+ # Line break before binary operator
+ W503,
+ # Continuation line over-indented for hanging indent
+ E126,
+ # Module level import not at top of file
+ E402
+max-line-length = 79
+exclude = __init__.py, build
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/.gitignore b/python/ClipDetection/Dassl.pytorch/.gitignore
new file mode 100644
index 00000000..e8bcb640
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/.gitignore
@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# OS X
+.DS_Store
+.Spotlight-V100
+.Trashes
+._*
+
+# This project
+output/
+debug/
diff --git a/python/ClipDetection/Dassl.pytorch/.isort.cfg b/python/ClipDetection/Dassl.pytorch/.isort.cfg
new file mode 100644
index 00000000..6b019a3d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/.isort.cfg
@@ -0,0 +1,10 @@
+[isort]
+line_length=79
+multi_line_output=6
+length_sort=true
+known_standard_library=numpy,setuptools
+known_myself=dassl
+known_third_party=matplotlib,cv2,torch,torchvision,PIL,yacs,scipy,gdown
+no_lines_before=STDLIB,THIRDPARTY
+sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER
+default_section=FIRSTPARTY
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/.style.yapf b/python/ClipDetection/Dassl.pytorch/.style.yapf
new file mode 100644
index 00000000..5d8b5f5c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/.style.yapf
@@ -0,0 +1,7 @@
+[style]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+DEDENT_CLOSING_BRACKETS = true
+SPACES_BEFORE_COMMENT = 2
+ARITHMETIC_PRECEDENCE_INDICATION = true
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/DATASETS.md b/python/ClipDetection/Dassl.pytorch/DATASETS.md
new file mode 100644
index 00000000..27ad5099
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/DATASETS.md
@@ -0,0 +1,318 @@
+# How to Install Datasets
+
+`$DATA` denotes the location where datasets are installed, e.g.
+
+```
+$DATA/
+|–– office31/
+|–– office_home/
+|–– visda17/
+```
+
+[Domain Adaptation](#domain-adaptation)
+- [Office-31](#office-31)
+- [Office-Home](#office-home)
+- [VisDA17](#visda17)
+- [CIFAR10-STL10](#cifar10-stl10)
+- [Digit-5](#digit-5)
+- [DomainNet](#domainnet)
+- [miniDomainNet](#miniDomainNet)
+
+[Domain Generalization](#domain-generalization)
+- [PACS](#pacs)
+- [VLCS](#vlcs)
+- [Office-Home-DG](#office-home-dg)
+- [Digits-DG](#digits-dg)
+- [Digit-Single](#digit-single)
+- [CIFAR-10-C](#cifar-10-c)
+- [CIFAR-100-C](#cifar-100-c)
+- [WILDS](#wilds)
+
+[Semi-Supervised Learning](#semi-supervised-learning)
+- [CIFAR10/100 and SVHN](#cifar10100-and-svhn)
+- [STL10](#stl10)
+
+## Domain Adaptation
+
+### Office-31
+
+Download link: https://people.eecs.berkeley.edu/~jhoffman/domainadapt/#datasets_code.
+
+File structure:
+
+```
+office31/
+|–– amazon/
+| |–– back_pack/
+| |–– bike/
+| |–– ...
+|–– dslr/
+| |–– back_pack/
+| |–– bike/
+| |–– ...
+|–– webcam/
+| |–– back_pack/
+| |–– bike/
+| |–– ...
+```
+
+Note that within each domain folder you need to move all class folders out of the `images/` folder and then delete the `images/` folder.
+
+### Office-Home
+
+Download link: http://hemanthdv.org/OfficeHome-Dataset/.
+
+File structure:
+
+```
+office_home/
+|–– art/
+|–– clipart/
+|–– product/
+|–– real_world/
+```
+
+### VisDA17
+
+Download link: http://ai.bu.edu/visda-2017/.
+
+The dataset can also be downloaded using our script at `datasets/da/visda17.sh`. Run the following command in your terminal under `Dassl.pytorch/datasets/da`,
+
+```bash
+sh visda17.sh $DATA
+```
+
+Once the download is finished, the file structure will look like
+
+```
+visda17/
+|–– train/
+|–– test/
+|–– validation/
+```
+
+### CIFAR10-STL10
+
+Run the following command in your terminal under `Dassl.pytorch/datasets/da`,
+
+```bash
+python cifar_stl.py $DATA/cifar_stl
+```
+
+This will create a folder named `cifar_stl` under `$DATA`. The file structure will look like
+
+```
+cifar_stl/
+|–– cifar/
+| |–– train/
+| |–– test/
+|–– stl/
+| |–– train/
+| |–– test/
+```
+
+Note that only 9 classes shared by both datasets are kept.
+
+### Digit-5
+
+Create a folder `$DATA/digit5` and download to this folder the dataset from [here](https://github.com/VisionLearningGroup/VisionLearningGroup.github.io/tree/master/M3SDA/code_MSDA_digit#digit-five-download). This should give you
+
+```
+digit5/
+|–– Digit-Five/
+```
+
+Then, run the following command in your terminal under `Dassl.pytorch/datasets/da`,
+
+```bash
+python digit5.py $DATA/digit5
+```
+
+This will extract the data and organize the file structure as
+
+```
+digit5/
+|–– Digit-Five/
+|–– mnist/
+|–– mnist_m/
+|–– usps/
+|–– svhn/
+|–– syn/
+```
+
+### DomainNet
+
+Download link: http://ai.bu.edu/M3SDA/. (Please download the cleaned version of split files)
+
+File structure:
+
+```
+domainnet/
+|–– clipart/
+|–– infograph/
+|–– painting/
+|–– quickdraw/
+|–– real/
+|–– sketch/
+|–– splits/
+| |–– clipart_train.txt
+| |–– clipart_test.txt
+| |–– ...
+```
+
+### miniDomainNet
+
+You need to download the DomainNet dataset first. The miniDomainNet's split files can be downloaded at this [google drive](https://drive.google.com/open?id=15rrLDCrzyi6ZY-1vJar3u7plgLe4COL7). After the zip file is extracted, you should have the folder `$DATA/domainnet/splits_mini/`.
+
+## Domain Generalization
+
+### PACS
+
+Download link: [google drive](https://drive.google.com/open?id=1m4X4fROCCXMO0lRLrr6Zz9Vb3974NWhE).
+
+File structure:
+
+```
+pacs/
+|–– images/
+|–– splits/
+```
+
+You do not necessarily have to manually download this dataset. Once you run ``tools/train.py``, the code will detect if the dataset exists or not and automatically download the dataset to ``$DATA`` if missing. This also applies to VLCS, Office-Home-DG, and Digits-DG.
+
+### VLCS
+
+Download link: [google drive](https://drive.google.com/file/d/1r0WL5DDqKfSPp9E3tRENwHaXNs1olLZd/view?usp=sharing) (credit to https://github.com/fmcarlucci/JigenDG#vlcs)
+
+File structure:
+
+```
+VLCS/
+|–– CALTECH/
+|–– LABELME/
+|–– PASCAL/
+|–– SUN/
+```
+
+### Office-Home-DG
+
+Download link: [google drive](https://drive.google.com/open?id=1gkbf_KaxoBws-GWT3XIPZ7BnkqbAxIFa).
+
+File structure:
+
+```
+office_home_dg/
+|–– art/
+|–– clipart/
+|–– product/
+|–– real_world/
+```
+
+### Digits-DG
+
+Download link: [google driv](https://drive.google.com/open?id=15V7EsHfCcfbKgsDmzQKj_DfXt_XYp_P7).
+
+File structure:
+
+```
+digits_dg/
+|–– mnist/
+|–– mnist_m/
+|–– svhn/
+|–– syn/
+```
+
+### Digit-Single
+Follow the steps for [Digit-5](#digit-5) to organize the dataset.
+
+### CIFAR-10-C
+
+First download the CIFAR-10-C dataset from https://zenodo.org/record/2535967#.YFxHEWQzb0o to, e.g., $DATA, and extract the file under the same directory. Then, navigate to `Dassl.pytorch/datasets/dg` and run the following command in your terminal
+```bash
+python cifar_c.py $DATA/CIFAR-10-C
+```
+where the first argument denotes the path to the (uncompressed) CIFAR-10-C dataset.
+
+The script will extract images from the `.npy` files and save them to `cifar10_c/` created under $DATA. The file structure will look like
+```
+cifar10_c/
+|–– brightness/
+| |–– 1/ # 5 intensity levels in total
+| |–– 2/
+| |–– 3/
+| |–– 4/
+| |–– 5/
+|–– ... # 19 corruption types in total
+```
+
+Note that `cifar10_c/` only contains the test images. The training images are the normal CIFAR-10 images. See [CIFAR10/100 and SVHN](#cifar10100-and-svhn) for how to prepare the CIFAR-10 dataset.
+
+### CIFAR-100-C
+
+First download the CIFAR-100-C dataset from https://zenodo.org/record/3555552#.YFxpQmQzb0o to, e.g., $DATA, and extract the file under the same directory. Then, navigate to `Dassl.pytorch/datasets/dg` and run the following command in your terminal
+```bash
+python cifar_c.py $DATA/CIFAR-100-C
+```
+where the first argument denotes the path to the (uncompressed) CIFAR-100-C dataset.
+
+The script will extract images from the `.npy` files and save them to `cifar100_c/` created under $DATA. The file structure will look like
+```
+cifar100_c/
+|–– brightness/
+| |–– 1/ # 5 intensity levels in total
+| |–– 2/
+| |–– 3/
+| |–– 4/
+| |–– 5/
+|–– ... # 19 corruption types in total
+```
+
+Note that `cifar100_c/` only contains the test images. The training images are the normal CIFAR-100 images. See [CIFAR10/100 and SVHN](#cifar10100-and-svhn) for how to prepare the CIFAR-100 dataset.
+
+### WILDS
+
+No action is required to preprocess WILDS's datasets. The code will automatically download the data.
+
+## Semi-Supervised Learning
+
+### CIFAR10/100 and SVHN
+
+Run the following command in your terminal under `Dassl.pytorch/datasets/ssl`,
+
+```bash
+python cifar10_cifar100_svhn.py $DATA
+```
+
+This will create three folders under `$DATA`, i.e.
+
+```
+cifar10/
+|–– train/
+|–– test/
+cifar100/
+|–– train/
+|–– test/
+svhn/
+|–– train/
+|–– test/
+```
+
+### STL10
+
+Run the following command in your terminal under `Dassl.pytorch/datasets/ssl`,
+
+```bash
+python stl10.py $DATA/stl10
+```
+
+This will create a folder named `stl10` under `$DATA` and extract the data into three folders, i.e. `train`, `test` and `unlabeled`. Then, download from http://ai.stanford.edu/~acoates/stl10/ the "Binary files" and extract it under `stl10`.
+
+The file structure will look like
+
+```
+stl10/
+|–– train/
+|–– test/
+|–– unlabeled/
+|–– stl10_binary/
+```
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/LICENSE b/python/ClipDetection/Dassl.pytorch/LICENSE
new file mode 100644
index 00000000..69196145
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Kaiyang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/python/ClipDetection/Dassl.pytorch/MODIFICATIONS b/python/ClipDetection/Dassl.pytorch/MODIFICATIONS
new file mode 100644
index 00000000..451e41d4
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/MODIFICATIONS
@@ -0,0 +1,4 @@
+The following files have been modified:
+
+./dassl/config/defaults.py
+./dassl/engine/trainer.py
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/README.md b/python/ClipDetection/Dassl.pytorch/README.md
new file mode 100644
index 00000000..6f3ec6f3
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/README.md
@@ -0,0 +1,279 @@
+# Dassl
+
+## Introduction
+
+Dassl is a [PyTorch](https://pytorch.org) toolbox initially developed for our project [Domain Adaptive Ensemble Learning (DAEL)](https://arxiv.org/abs/2003.07325) to support research in domain adaptation and generalization---since in DAEL we study how to unify these two problems in a single learning framework. Given that domain adaptation is closely related to semi-supervised learning---both study how to exploit unlabeled data---we also incorporate components that support research for the latter.
+
+Why the name "Dassl"? Dassl combines the initials of domain adaptation (DA) and semi-supervised learning (SSL), which sounds natural and informative.
+
+Dassl has a modular design and unified interfaces, allowing fast prototyping and experimentation of new DA/DG/SSL methods. With Dassl, a new method can be implemented with only a few lines of code. Don't believe? Take a look at the [engine](https://github.com/KaiyangZhou/Dassl.pytorch/tree/master/dassl/engine) folder, which contains the implementations of many existing methods (then you will come back and star this repo). :-)
+
+Basically, Dassl is perfect for doing research in the following areas:
+- Domain adaptation
+- Domain generalization
+- Semi-supervised learning
+
+BUT, thanks to the neat design, Dassl can also be used as a codebase to develop any deep learning projects, like [this](https://github.com/KaiyangZhou/CoOp). :-)
+
+A drawback of Dassl is that it doesn't (yet? hmm) support distributed multi-GPU training (Dassl uses `DataParallel` to wrap a model, which is less efficient than `DistributedDataParallel`).
+
+We don't provide detailed documentations for Dassl, unlike another [project](https://kaiyangzhou.github.io/deep-person-reid/) of ours. This is because Dassl is developed for research purpose and as a researcher, we think it's important to be able to read source code and we highly encourage you to do so---definitely not because we are lazy. :-)
+
+## What's new
+- **[Oct 2022]** New paper "[On-Device Domain Generalization](https://arxiv.org/abs/2209.07521)" is out! Code, models and datasets: https://github.com/KaiyangZhou/on-device-dg.
+
+
+ More
+
+- **[Jun 2022]** `v0.6.0`: Make `cfg.TRAINER.METHOD_NAME` consistent with the method class name.
+- **[Jun 2022]** A new domain adaptation method [CDAC (CVPR'21)](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Cross-Domain_Adaptive_Clustering_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.pdf) is added by [Shreejal Trivedi](https://github.com/shreejalt). See [here](https://github.com/KaiyangZhou/Dassl.pytorch/pull/44) for more details.
+- **[Jun 2022]** Adds three datasets from the [WILDS](https://wilds.stanford.edu/) benchmark: iWildCam, FMoW and Camelyon17. See [here](https://github.com/KaiyangZhou/Dassl.pytorch/commit/7f7eab8e22f6e176b97a539100eca12d6a403909) for more details.
+- **[May 2022]** A new domain generalization method [DDG](https://arxiv.org/abs/2205.13913) developed by [Zhishu Sun](https://github.com/siaimes) and to appear at IJCAI'22 is added to this repo. See [here](https://github.com/MetaVisionLab/DDG) for more details.
+- **[Mar 2022]** A new domain generalization method [EFDM](https://arxiv.org/abs/2203.07740) developed by [Yabin Zhang (PolyU)](https://ybzh.github.io/) and to appear at CVPR'22 is added to this repo. See [here](https://github.com/KaiyangZhou/Dassl.pytorch/pull/36) for more details.
+- **[Feb 2022]** In case you don't know, a class in the painting domain of DomainNet (the official splits) only has test images (no training images), which could affect performance. See section 4.a in our [paper](https://arxiv.org/abs/2003.07325) for more details.
+- **[Oct 2021]** `v0.5.0`: **Important changes** made to `transforms.py`. 1) `center_crop` becomes a default transform in testing (applied after resizing the smaller edge to a certain size to keep the image aspect ratio). 2) For training, `Resize(cfg.INPUT.SIZE)` is deactivated when `random_crop` or `random_resized_crop` is used. These changes won't make any difference to the training transforms used in existing config files, nor to the testing transforms unless the raw images are not squared (the only difference is that now the image aspect ratio is respected).
+- **[Oct 2021]** `v0.4.3`: Copy the attributes in `self.dm` (data manager) to `SimpleTrainer` and make `self.dm` optional, which means from now on, you can build data loaders from any source you like rather than being forced to use `DataManager`.
+- **[Sep 2021]** `v0.4.2`: An important update is to set `drop_last=is_train and len(data_source)>=batch_size` when constructing a data loader to avoid 0-length.
+
+
+
+## Overview
+
+Dassl has implemented the following methods:
+
+- Single-source domain adaptation
+ - [Cross Domain Adaptive Clustering for Semi Supervised Domain Adaptation (CVPR'21)](https://arxiv.org/pdf/2104.09415.pdf) [[dassl/engine/da/cdac.py](dassl/engine/da/cdac.py)]
+ - [Semi-supervised Domain Adaptation via Minimax Entropy (ICCV'19)](https://arxiv.org/abs/1904.06487) [[dassl/engine/da/mme.py](dassl/engine/da/mme.py)]
+ - [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (CVPR'18)](https://arxiv.org/abs/1712.02560https://arxiv.org/abs/1712.02560) [[dassl/engine/da/mcd.py](dassl/engine/da/mcd.py)]
+ - [Self-ensembling for visual domain adaptation (ICLR'18)](https://arxiv.org/abs/1706.05208) [[dassl/engine/da/self_ensembling.py](dassl/engine/da/self_ensembling.py)]
+ - [Revisiting Batch Normalization For Practical Domain Adaptation (ICLR-W'17)](https://arxiv.org/abs/1603.04779) [[dassl/engine/da/adabn.py](dassl/engine/da/adabn.py)]
+ - [Adversarial Discriminative Domain Adaptation (CVPR'17)](https://arxiv.org/abs/1702.05464) [[dassl/engine/da/adda.py](dassl/engine/da/adda.py)]
+ - [Domain-Adversarial Training of Neural Networks (JMLR'16) ](https://arxiv.org/abs/1505.07818) [[dassl/engine/da/dann.py](dassl/engine/da/dann.py)]
+
+- Multi-source domain adaptation
+ - [Domain Aadaptive Ensemble Learning](https://arxiv.org/abs/2003.07325) [[dassl/engine/da/dael.py](dassl/engine/da/dael.py)]
+ - [Moment Matching for Multi-Source Domain Adaptation (ICCV'19)](https://arxiv.org/abs/1812.01754) [[dassl/engine/da/m3sda.py](dassl/engine/da/m3sda.py)]
+
+- Domain generalization
+ - [Dynamic Domain Generalization (IJCAI'22)](https://arxiv.org/abs/2205.13913) [[dassl/modeling/backbone/resnet_dynamic.py](dassl/modeling/backbone/resnet_dynamic.py)] [[dassl/engine/dg/domain_mix.py](dassl/engine/dg/domain_mix.py)]
+ - [Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization (CVPR'22)](https://arxiv.org/abs/2203.07740) [[dassl/modeling/ops/efdmix.py](dassl/modeling/ops/efdmix.py)]
+ - [Domain Generalization with MixStyle (ICLR'21)](https://openreview.net/forum?id=6xHJ37MVxxp) [[dassl/modeling/ops/mixstyle.py](dassl/modeling/ops/mixstyle.py)]
+ - [Deep Domain-Adversarial Image Generation for Domain Generalisation (AAAI'20)](https://arxiv.org/abs/2003.06054) [[dassl/engine/dg/ddaig.py](dassl/engine/dg/ddaig.py)]
+ - [Generalizing Across Domains via Cross-Gradient Training (ICLR'18)](https://arxiv.org/abs/1804.10745) [[dassl/engine/dg/crossgrad.py](dassl/engine/dg/crossgrad.py)]
+
+- Semi-supervised learning
+ - [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://arxiv.org/abs/2001.07685) [[dassl/engine/ssl/fixmatch.py](dassl/engine/ssl/fixmatch.py)]
+ - [MixMatch: A Holistic Approach to Semi-Supervised Learning (NeurIPS'19)](https://arxiv.org/abs/1905.02249) [[dassl/engine/ssl/mixmatch.py](dassl/engine/ssl/mixmatch.py)]
+ - [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results (NeurIPS'17)](https://arxiv.org/abs/1703.01780) [[dassl/engine/ssl/mean_teacher.py](dassl/engine/ssl/mean_teacher.py)]
+ - [Semi-supervised Learning by Entropy Minimization (NeurIPS'04)](http://papers.nips.cc/paper/2740-semi-supervised-learning-by-entropy-minimization.pdf) [[dassl/engine/ssl/entmin.py](dassl/engine/ssl/entmin.py)]
+
+*Feel free to make a [PR](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork) to add your methods here to make it easier for others to benchmark!*
+
+Dassl supports the following datasets:
+
+- Domain adaptation
+ - [Office-31](https://scalable.mpi-inf.mpg.de/files/2013/04/saenko_eccv_2010.pdf)
+ - [Office-Home](http://hemanthdv.org/OfficeHome-Dataset/)
+ - [VisDA17](http://ai.bu.edu/visda-2017/)
+ - [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html)-[STL10](https://cs.stanford.edu/~acoates/stl10/)
+ - [Digit-5](https://github.com/VisionLearningGroup/VisionLearningGroup.github.io/tree/master/M3SDA/code_MSDA_digit#digit-five-download)
+ - [DomainNet](http://ai.bu.edu/M3SDA/)
+ - [miniDomainNet](https://arxiv.org/abs/2003.07325)
+
+- Domain generalization
+ - [PACS](https://arxiv.org/abs/1710.03077)
+ - [VLCS](https://people.csail.mit.edu/torralba/publications/datasets_cvpr11.pdf)
+ - [Office-Home](http://hemanthdv.org/OfficeHome-Dataset/)
+ - [Digits-DG](https://arxiv.org/abs/2003.06054)
+ - [Digit-Single](https://arxiv.org/abs/1805.12018)
+ - [CIFAR-10-C](https://arxiv.org/abs/1807.01697)
+ - [CIFAR-100-C](https://arxiv.org/abs/1807.01697)
+ - [iWildCam-WILDS](https://wilds.stanford.edu/datasets/#iwildcam)
+ - [Camelyon17-WILDS](https://wilds.stanford.edu/datasets/#camelyon17)
+ - [FMoW-WILDS](https://wilds.stanford.edu/datasets/#fmow)
+
+- Semi-supervised learning
+ - [CIFAR10/100](https://www.cs.toronto.edu/~kriz/cifar.html.)
+ - [SVHN](http://ufldl.stanford.edu/housenumbers/)
+ - [STL10](https://cs.stanford.edu/~acoates/stl10/)
+
+## Get started
+
+### Installation
+
+Make sure [conda](https://www.anaconda.com/distribution/) is installed properly.
+
+```bash
+# Clone this repo
+git clone https://github.com/KaiyangZhou/Dassl.pytorch.git
+cd Dassl.pytorch/
+
+# Create a conda environment
+conda create -y -n dassl python=3.8
+
+# Activate the environment
+conda activate dassl
+
+# Install torch (requires version >= 1.8.1) and torchvision
+# Please refer to https://pytorch.org/ if you need a different cuda version
+conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Install this library (no need to re-build if the source code is modified)
+python setup.py develop
+```
+
+Follow the instructions in [DATASETS.md](./DATASETS.md) to preprocess the datasets.
+
+### Training
+
+The main interface is implemented in `tools/train.py`, which basically does
+
+1. initialize the config with `cfg = setup_cfg(args)` where `args` contains the command-line input (see `tools/train.py` for the list of input arguments);
+2. instantiate a `trainer` with `build_trainer(cfg)` which loads the dataset and builds a deep neural network model;
+3. call `trainer.train()` for training and evaluating the model.
+
+Below we provide an example for training a source-only baseline on the popular domain adaptation dataset, Office-31,
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/train.py \
+--root $DATA \
+--trainer SourceOnly \
+--source-domains amazon \
+--target-domains webcam \
+--dataset-config-file configs/datasets/da/office31.yaml \
+--config-file configs/trainers/da/source_only/office31.yaml \
+--output-dir output/source_only_office31
+```
+
+`$DATA` denotes the location where datasets are installed. `--dataset-config-file` loads the common setting for the dataset (Office-31 in this case) such as image size and model architecture. `--config-file` loads the algorithm-specific setting such as hyper-parameters and optimization parameters.
+
+To use multiple sources, namely the multi-source domain adaptation task, one just needs to add more sources to `--source-domains`. For instance, to train a source-only baseline on miniDomainNet, one can do
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/train.py \
+--root $DATA \
+--trainer SourceOnly \
+--source-domains clipart painting real \
+--target-domains sketch \
+--dataset-config-file configs/datasets/da/mini_domainnet.yaml \
+--config-file configs/trainers/da/source_only/mini_domainnet.yaml \
+--output-dir output/source_only_minidn
+```
+
+After the training finishes, the model weights will be saved under the specified output directory, along with a log file and a tensorboard file for visualization.
+
+To print out the results saved in the log file (so you do not need to exhaustively go through all log files and calculate the mean/std by yourself), you can use `tools/parse_test_res.py`. The instruction can be found in the code.
+
+For other trainers such as `MCD`, you can set `--trainer MCD` while keeping the config file unchanged, i.e. using the same training parameters as `SourceOnly` (in the simplest case). To modify the hyper-parameters in MCD, like `N_STEP_F` (number of steps to update the feature extractor), you can append `TRAINER.MCD.N_STEP_F 4` to the existing input arguments (otherwise the default value will be used). Alternatively, you can create a new `.yaml` config file to store your custom setting. See [here](https://github.com/KaiyangZhou/Dassl.pytorch/blob/master/dassl/config/defaults.py#L176) for a complete list of algorithm-specific hyper-parameters.
+
+### Test
+Model testing can be done by using `--eval-only`, which asks the code to run `trainer.test()`. You also need to provide the trained model and specify which model file (i.e. saved at which epoch) to use. For example, to use `model.pth.tar-20` saved at `output/source_only_office31/model`, you can do
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/train.py \
+--root $DATA \
+--trainer SourceOnly \
+--source-domains amazon \
+--target-domains webcam \
+--dataset-config-file configs/datasets/da/office31.yaml \
+--config-file configs/trainers/da/source_only/office31.yaml \
+--output-dir output/source_only_office31_test \
+--eval-only \
+--model-dir output/source_only_office31 \
+--load-epoch 20
+```
+
+Note that `--model-dir` takes as input the directory path which was specified in `--output-dir` in the training stage.
+
+### Write a new trainer
+A good practice is to go through `dassl/engine/trainer.py` to get familar with the base trainer classes, which provide generic functions and training loops. To write a trainer class for domain adaptation or semi-supervised learning, the new class can subclass `TrainerXU`. For domain generalization, the new class can subclass `TrainerX`. In particular, `TrainerXU` and `TrainerX` mainly differ in whether using a data loader for unlabeled data. With the base classes, a new trainer may only need to implement the `forward_backward()` method, which performs loss computation and model update. See `dassl/enigne/da/source_only.py` for example.
+
+### Add a new backbone/head/network
+`backbone` corresponds to a convolutional neural network model which performs feature extraction. `head` (which is an optional module) is mounted on top of `backbone` for further processing, which can be, for example, a MLP. `backbone` and `head` are basic building blocks for constructing a `SimpleNet()` (see `dassl/engine/trainer.py`) which serves as the primary model for a task. `network` contains custom neural network models, such as an image generator.
+
+To add a new module, namely a backbone/head/network, you need to first register the module using the corresponding `registry`, i.e. `BACKBONE_REGISTRY` for `backbone`, `HEAD_REGISTRY` for `head` and `NETWORK_RESIGTRY` for `network`. Note that for a new `backbone`, we require the model to subclass `Backbone` as defined in `dassl/modeling/backbone/backbone.py` and specify the `self._out_features` attribute.
+
+We provide an example below for how to add a new `backbone`.
+```python
+from dassl.modeling import Backbone, BACKBONE_REGISTRY
+
+class MyBackbone(Backbone):
+
+ def __init__(self):
+ super().__init__()
+ # Create layers
+ self.conv = ...
+
+ self._out_features = 2048
+
+ def forward(self, x):
+ # Extract and return features
+
+@BACKBONE_REGISTRY.register()
+def my_backbone(**kwargs):
+ return MyBackbone()
+```
+Then, you can set `MODEL.BACKBONE.NAME` to `my_backbone` to use your own architecture. For more details, please refer to the source code in `dassl/modeling`.
+
+### Add a dataset
+An example code structure is shown below. Make sure you subclass `DatasetBase` and register the dataset with `@DATASET_REGISTRY.register()`. All you need is to load `train_x`, `train_u` (optional), `val` (optional) and `test`, among which `train_u` and `val` could be `None` or simply ignored. Each of these variables contains a list of `Datum` objects. A `Datum` object (implemented [here](https://github.com/KaiyangZhou/Dassl.pytorch/blob/master/dassl/data/datasets/base_dataset.py#L12)) contains information for a single image, like `impath` (string) and `label` (int).
+
+```python
+from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
+
+@DATASET_REGISTRY.register()
+class NewDataset(DatasetBase):
+
+ dataset_dir = ''
+
+ def __init__(self, cfg):
+
+ train_x = ...
+ train_u = ... # optional, can be None
+ val = ... # optional, can be None
+ test = ...
+
+ super().__init__(train_x=train_x, train_u=train_u, val=val, test=test)
+```
+
+We suggest you take a look at the datasets code in some projects like [this](https://github.com/KaiyangZhou/CoOp), which is built on top of Dassl.
+
+## Relevant Research
+
+We would like to share here our research relevant to Dassl.
+
+- [On-Device Domain Generalization](https://arxiv.org/abs/2209.07521)
+- [Domain Generalization: A Survey](https://arxiv.org/abs/2103.02503) (TPAMI 2022)
+- [Domain Adaptive Ensemble Learning](https://arxiv.org/abs/2003.07325) (TIP 2021)
+- [MixStyle Neural Networks for Domain Generalization and Adaptation](https://arxiv.org/abs/2107.02053)
+- [Semi-Supervised Domain Generalization with Stochastic StyleMatch](https://arxiv.org/abs/2106.00592)
+- [Domain Generalization with MixStyle](https://openreview.net/forum?id=6xHJ37MVxxp) (ICLR 2021)
+- [Learning to Generate Novel Domains for Domain Generalization](https://arxiv.org/abs/2007.03304) (ECCV 2020)
+- [Deep Domain-Adversarial Image Generation for Domain Generalisation](https://arxiv.org/abs/2003.06054) (AAAI 2020)
+
+## Citation
+
+If you find this code useful to your research, please give credit to the following paper
+
+```
+@article{zhou2022domain,
+ title={Domain generalization: A survey},
+ author={Zhou, Kaiyang and Liu, Ziwei and Qiao, Yu and Xiang, Tao and Loy, Chen Change},
+ journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+ year={2022},
+ publisher={IEEE}
+}
+
+@article{zhou2021domain,
+ title={Domain adaptive ensemble learning},
+ author={Zhou, Kaiyang and Yang, Yongxin and Qiao, Yu and Xiang, Tao},
+ journal={IEEE Transactions on Image Processing},
+ volume={30},
+ pages={8008--8018},
+ year={2021},
+ publisher={IEEE}
+}
+```
diff --git a/python/ClipDetection/Dassl.pytorch/configs/README.md b/python/ClipDetection/Dassl.pytorch/configs/README.md
new file mode 100644
index 00000000..18b90a46
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/README.md
@@ -0,0 +1 @@
+The `datasets/` folder contains dataset-specific config files which define the standard protocols (e.g., image size, data augmentation, network architecture) used by most papers. The `trainers/` folder contains method-specific config files which define optimization algorithms (e.g., optimizer, epoch) and hyperparameter settings.
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/cifar_stl.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/cifar_stl.yaml
new file mode 100644
index 00000000..52c086fa
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/cifar_stl.yaml
@@ -0,0 +1,7 @@
+INPUT:
+ SIZE: (32, 32)
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "CIFARSTL"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/digit5.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/digit5.yaml
new file mode 100644
index 00000000..f754d643
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/digit5.yaml
@@ -0,0 +1,12 @@
+INPUT:
+ SIZE: (32, 32)
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+ TRANSFORMS: ["normalize"]
+
+DATASET:
+ NAME: "Digit5"
+
+MODEL:
+ BACKBONE:
+ NAME: "cnn_digit5_m3sda"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/domainnet.yaml
new file mode 100644
index 00000000..075f9232
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/domainnet.yaml
@@ -0,0 +1,10 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "DomainNet"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet101"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/mini_domainnet.yaml
new file mode 100644
index 00000000..cfb34d8a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/mini_domainnet.yaml
@@ -0,0 +1,10 @@
+INPUT:
+ SIZE: (96, 96)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "miniDomainNet"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet18"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office31.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office31.yaml
new file mode 100644
index 00000000..77cca035
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office31.yaml
@@ -0,0 +1,14 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "Office31"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet50"
+ HEAD:
+ NAME: "mlp"
+ HIDDEN_LAYERS: [256]
+ DROPOUT: 0.
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office_home.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office_home.yaml
new file mode 100644
index 00000000..7e181fda
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/office_home.yaml
@@ -0,0 +1,5 @@
+INPUT:
+ SIZE: (224, 224)
+
+DATASET:
+ NAME: "OfficeHome"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/da/visda17.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/visda17.yaml
new file mode 100644
index 00000000..d54f2f63
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/da/visda17.yaml
@@ -0,0 +1,13 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "center_crop", "normalize"]
+
+DATASET:
+ NAME: "VisDA17"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet101"
+
+TEST:
+ PER_CLASS_RESULT: True
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/camelyon17.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/camelyon17.yaml
new file mode 100644
index 00000000..11a2c4d7
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/camelyon17.yaml
@@ -0,0 +1,6 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"]
+
+DATASET:
+ NAME: "Camelyon17"
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar100_c.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar100_c.yaml
new file mode 100644
index 00000000..c4b7f917
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar100_c.yaml
@@ -0,0 +1,14 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "CIFAR100C"
+ CIFAR_C_TYPE: "fog"
+ CIFAR_C_LEVEL: 5
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_16_4"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar10_c.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar10_c.yaml
new file mode 100644
index 00000000..ec5702ed
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/cifar10_c.yaml
@@ -0,0 +1,14 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "CIFAR10C"
+ CIFAR_C_TYPE: "fog"
+ CIFAR_C_LEVEL: 5
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_16_4"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digit_single.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digit_single.yaml
new file mode 100644
index 00000000..a6bacbb2
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digit_single.yaml
@@ -0,0 +1,12 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "DigitSingle"
+
+MODEL:
+ BACKBONE:
+ NAME: "cnn_digitsingle"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digits_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digits_dg.yaml
new file mode 100644
index 00000000..ca25e213
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/digits_dg.yaml
@@ -0,0 +1,12 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "DigitsDG"
+
+MODEL:
+ BACKBONE:
+ NAME: "cnn_digitsdg"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/fmow.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/fmow.yaml
new file mode 100644
index 00000000..825ee809
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/fmow.yaml
@@ -0,0 +1,6 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"]
+
+DATASET:
+ NAME: "FMoW"
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/iwildcam.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/iwildcam.yaml
new file mode 100644
index 00000000..c8aa2eb5
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/iwildcam.yaml
@@ -0,0 +1,6 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"]
+
+DATASET:
+ NAME: "IWildCam"
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/office_home_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/office_home_dg.yaml
new file mode 100644
index 00000000..0835973c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/office_home_dg.yaml
@@ -0,0 +1,11 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "OfficeHomeDG"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet18"
+ PRETRAINED: True
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/pacs.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/pacs.yaml
new file mode 100644
index 00000000..682ab1c8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/pacs.yaml
@@ -0,0 +1,11 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "PACS"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet18"
+ PRETRAINED: True
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/vlcs.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/vlcs.yaml
new file mode 100644
index 00000000..0c8804cf
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/dg/vlcs.yaml
@@ -0,0 +1,11 @@
+INPUT:
+ SIZE: (224, 224)
+ TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+ NAME: "VLCS"
+
+MODEL:
+ BACKBONE:
+ NAME: "resnet18"
+ PRETRAINED: True
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar10.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar10.yaml
new file mode 100644
index 00000000..63b6a1df
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar10.yaml
@@ -0,0 +1,14 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+ NAME: "CIFAR10"
+ NUM_LABELED: 4000
+ VAL_PERCENT: 0.
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_28_2"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar100.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar100.yaml
new file mode 100644
index 00000000..6230a881
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/cifar100.yaml
@@ -0,0 +1,15 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+ CROP_PADDING: 4
+
+DATASET:
+ NAME: "CIFAR100"
+ NUM_LABELED: 10000
+ VAL_PERCENT: 0.
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_28_2"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/stl10.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/stl10.yaml
new file mode 100644
index 00000000..7b11df12
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/stl10.yaml
@@ -0,0 +1,14 @@
+INPUT:
+ SIZE: (96, 96)
+ TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+ CROP_PADDING: 4
+
+DATASET:
+ NAME: "STL10"
+ STL10_FOLD: 0
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_28_2"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/svhn.yaml b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/svhn.yaml
new file mode 100644
index 00000000..cd3b527b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/datasets/ssl/svhn.yaml
@@ -0,0 +1,15 @@
+INPUT:
+ SIZE: (32, 32)
+ TRANSFORMS: ["random_crop", "normalize"]
+ PIXEL_MEAN: [0.5, 0.5, 0.5]
+ PIXEL_STD: [0.5, 0.5, 0.5]
+ CROP_PADDING: 4
+
+DATASET:
+ NAME: "SVHN"
+ NUM_LABELED: 1000
+ VAL_PERCENT: 0.
+
+MODEL:
+ BACKBONE:
+ NAME: "wide_resnet_28_2"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/digit5.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/digit5.yaml
new file mode 100644
index 00000000..04ba3467
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/digit5.yaml
@@ -0,0 +1,20 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomSampler"
+ BATCH_SIZE: 64
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 192
+ TEST:
+ BATCH_SIZE: 256
+ K_TRANSFORMS: 2
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.001
+ MAX_EPOCH: 90
+ RAMPUP_ITRS: 10000
+
+TRAINER:
+ CDAC:
+ STRONG_TRANSFORMS: ["randaugment", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/domainnet.yaml
new file mode 100644
index 00000000..e5fd5593
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/domainnet.yaml
@@ -0,0 +1,20 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 30
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 6
+ TEST:
+ BATCH_SIZE: 30
+ K_TRANSFORMS: 2
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.001
+ MAX_EPOCH: 90
+ RAMPUP_ITRS: 10000
+
+TRAINER:
+ CDAC:
+ STRONG_TRANSFORMS: ["randaugment", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/mini_domainnet.yaml
new file mode 100644
index 00000000..cb4f9c12
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/cdac/mini_domainnet.yaml
@@ -0,0 +1,21 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 64
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 192
+ TEST:
+ BATCH_SIZE: 200
+ K_TRANSFORMS: 2
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.001
+ MAX_EPOCH: 60
+ RAMPUP_ITRS: 10000
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ CDAC:
+ STRONG_TRANSFORMS: ["randaugment", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/digit5.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/digit5.yaml
new file mode 100644
index 00000000..d83bfe42
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/digit5.yaml
@@ -0,0 +1,20 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 256
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 256
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [30]
+ MAX_EPOCH: 30
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ DAEL:
+ STRONG_TRANSFORMS: ["randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/domainnet.yaml
new file mode 100644
index 00000000..fc7cd211
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/domainnet.yaml
@@ -0,0 +1,19 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 30
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 6
+ TEST:
+ BATCH_SIZE: 30
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ MAX_EPOCH: 40
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ DAEL:
+ STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/mini_domainnet.yaml
new file mode 100644
index 00000000..708ddcba
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/dael/mini_domainnet.yaml
@@ -0,0 +1,19 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 192
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 200
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.005
+ MAX_EPOCH: 60
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ DAEL:
+ STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/digit5.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/digit5.yaml
new file mode 100644
index 00000000..a70887b0
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/digit5.yaml
@@ -0,0 +1,16 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 256
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 256
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [30]
+ MAX_EPOCH: 30
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/domainnet.yaml
new file mode 100644
index 00000000..5abaa12a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/domainnet.yaml
@@ -0,0 +1,15 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 30
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 6
+ TEST:
+ BATCH_SIZE: 30
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ MAX_EPOCH: 40
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/mini_domainnet.yaml
new file mode 100644
index 00000000..6edf3e3c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/m3sda/mini_domainnet.yaml
@@ -0,0 +1,15 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 192
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 200
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.005
+ MAX_EPOCH: 60
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/digit5.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/digit5.yaml
new file mode 100644
index 00000000..64ce348e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/digit5.yaml
@@ -0,0 +1,12 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 256
+ TEST:
+ BATCH_SIZE: 256
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [30]
+ MAX_EPOCH: 30
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/mini_domainnet.yaml
new file mode 100644
index 00000000..bd8471eb
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/mini_domainnet.yaml
@@ -0,0 +1,11 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 128
+ TEST:
+ BATCH_SIZE: 128
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.005
+ MAX_EPOCH: 60
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/office31.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/office31.yaml
new file mode 100644
index 00000000..8fb73ee1
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/office31.yaml
@@ -0,0 +1,11 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 32
+ TEST:
+ BATCH_SIZE: 32
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ STEPSIZE: [20]
+ MAX_EPOCH: 20
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/visda17.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/visda17.yaml
new file mode 100644
index 00000000..3c25fb09
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/da/source_only/visda17.yaml
@@ -0,0 +1,15 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 32
+ TEST:
+ BATCH_SIZE: 32
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.0001
+ STEPSIZE: [2]
+ MAX_EPOCH: 2
+
+TRAIN:
+ PRINT_FREQ: 50
+ COUNT_ITER: "train_u"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/digits_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/digits_dg.yaml
new file mode 100644
index 00000000..45304313
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/digits_dg.yaml
@@ -0,0 +1,16 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 120
+ TEST:
+ BATCH_SIZE: 100
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [20]
+ MAX_EPOCH: 50
+
+TRAINER:
+ DAELDG:
+ STRONG_TRANSFORMS: ["randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/office_home_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/office_home_dg.yaml
new file mode 100644
index 00000000..8b17f5a7
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/office_home_dg.yaml
@@ -0,0 +1,16 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 30
+ TEST:
+ BATCH_SIZE: 100
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ MAX_EPOCH: 40
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ DAELDG:
+ STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/pacs.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/pacs.yaml
new file mode 100644
index 00000000..8b17f5a7
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/daeldg/pacs.yaml
@@ -0,0 +1,16 @@
+DATALOADER:
+ TRAIN_X:
+ SAMPLER: "RandomDomainSampler"
+ BATCH_SIZE: 30
+ TEST:
+ BATCH_SIZE: 100
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.002
+ MAX_EPOCH: 40
+ LR_SCHEDULER: "cosine"
+
+TRAINER:
+ DAELDG:
+ STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/digits_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/digits_dg.yaml
new file mode 100644
index 00000000..8ee80302
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/digits_dg.yaml
@@ -0,0 +1,20 @@
+INPUT:
+ PIXEL_MEAN: [0., 0., 0.]
+ PIXEL_STD: [1., 1., 1.]
+
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 128
+ TEST:
+ BATCH_SIZE: 128
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [20]
+ MAX_EPOCH: 50
+
+TRAINER:
+ DDAIG:
+ G_ARCH: "fcn_3x32_gctx"
+ LMDA: 0.3
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/office_home_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/office_home_dg.yaml
new file mode 100644
index 00000000..b55f8100
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/office_home_dg.yaml
@@ -0,0 +1,21 @@
+INPUT:
+ PIXEL_MEAN: [0., 0., 0.]
+ PIXEL_STD: [1., 1., 1.]
+
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 16
+ TEST:
+ BATCH_SIZE: 16
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.0005
+ STEPSIZE: [20]
+ MAX_EPOCH: 25
+
+TRAINER:
+ DDAIG:
+ G_ARCH: "fcn_3x64_gctx"
+ WARMUP: 3
+ LMDA: 0.3
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/pacs.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/pacs.yaml
new file mode 100644
index 00000000..b55f8100
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/ddaig/pacs.yaml
@@ -0,0 +1,21 @@
+INPUT:
+ PIXEL_MEAN: [0., 0., 0.]
+ PIXEL_STD: [1., 1., 1.]
+
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 16
+ TEST:
+ BATCH_SIZE: 16
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.0005
+ STEPSIZE: [20]
+ MAX_EPOCH: 25
+
+TRAINER:
+ DDAIG:
+ G_ARCH: "fcn_3x64_gctx"
+ WARMUP: 3
+ LMDA: 0.3
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/digits_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/digits_dg.yaml
new file mode 100644
index 00000000..8b73fbea
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/digits_dg.yaml
@@ -0,0 +1,15 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 128
+ TEST:
+ BATCH_SIZE: 100
+ NUM_WORKERS: 8
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [20]
+ MAX_EPOCH: 50
+
+TRAIN:
+ PRINT_FREQ: 20
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/mini_domainnet.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/mini_domainnet.yaml
new file mode 100644
index 00000000..bd8471eb
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/mini_domainnet.yaml
@@ -0,0 +1,11 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 128
+ TEST:
+ BATCH_SIZE: 128
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.005
+ MAX_EPOCH: 60
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/office_home_dg.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/office_home_dg.yaml
new file mode 100644
index 00000000..43f62142
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/office_home_dg.yaml
@@ -0,0 +1,11 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 100
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.001
+ MAX_EPOCH: 50
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/pacs.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/pacs.yaml
new file mode 100644
index 00000000..43f62142
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/dg/vanilla/pacs.yaml
@@ -0,0 +1,11 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 64
+ TEST:
+ BATCH_SIZE: 100
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.001
+ MAX_EPOCH: 50
+ LR_SCHEDULER: "cosine"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/configs/trainers/ssl/fixmatch/cifar10.yaml b/python/ClipDetection/Dassl.pytorch/configs/trainers/ssl/fixmatch/cifar10.yaml
new file mode 100644
index 00000000..a03fc6c9
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/configs/trainers/ssl/fixmatch/cifar10.yaml
@@ -0,0 +1,23 @@
+DATALOADER:
+ TRAIN_X:
+ BATCH_SIZE: 64
+ TRAIN_U:
+ SAME_AS_X: False
+ BATCH_SIZE: 448
+ TEST:
+ BATCH_SIZE: 500
+
+OPTIM:
+ NAME: "sgd"
+ LR: 0.05
+ STEPSIZE: [4000]
+ MAX_EPOCH: 4000
+ LR_SCHEDULER: "cosine"
+
+TRAIN:
+ COUNT_ITER: "train_u"
+ PRINT_FREQ: 10
+
+TRAINER:
+ FIXMATCH:
+ STRONG_TRANSFORMS: ["random_flip", "randaugment_fixmatch", "normalize", "cutout"]
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/__init__.py
new file mode 100644
index 00000000..225e3ca0
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/__init__.py
@@ -0,0 +1,18 @@
+"""
+Dassl
+------
+PyTorch toolbox for domain adaptation and semi-supervised learning.
+
+URL: https://github.com/KaiyangZhou/Dassl.pytorch
+
+@article{zhou2020domain,
+ title={Domain Adaptive Ensemble Learning},
+ author={Zhou, Kaiyang and Yang, Yongxin and Qiao, Yu and Xiang, Tao},
+ journal={arXiv preprint arXiv:2003.07325},
+ year={2020}
+}
+"""
+
+__version__ = "0.6.3"
+__author__ = "Kaiyang Zhou"
+__homepage__ = "https://kaiyangzhou.github.io/"
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/config/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/config/__init__.py
new file mode 100644
index 00000000..d745fbab
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/config/__init__.py
@@ -0,0 +1,21 @@
+from .defaults import _C as cfg_default
+
+
+def get_cfg_default():
+ return cfg_default.clone()
+
+
+def clean_cfg(cfg, trainer):
+ """Remove unused trainers (configs).
+
+ Aim: Only show relevant information when calling print(cfg).
+
+ Args:
+ cfg (_C): cfg instance.
+ trainer (str): trainer name.
+ """
+ keys = list(cfg.TRAINER.keys())
+ for key in keys:
+ if key == "NAME" or key == trainer.upper():
+ continue
+ cfg.TRAINER.pop(key, None)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/config/defaults.py b/python/ClipDetection/Dassl.pytorch/dassl/config/defaults.py
new file mode 100644
index 00000000..f6dcf143
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/config/defaults.py
@@ -0,0 +1,313 @@
+################################################################
+# CHANGES MADE TO FILE #
+# ------------------------------------------------------------ #
+# Changed _C.VERBOSE = False from True #
+# #
+################################################################
+
+from yacs.config import CfgNode as CN
+
+###########################
+# Config definition
+###########################
+
+_C = CN()
+
+_C.VERSION = 1
+
+# Directory to save the output files (like log.txt and model weights)
+_C.OUTPUT_DIR = "./output"
+# Path to a directory where the files were saved previously
+_C.RESUME = ""
+# Set seed to negative value to randomize everything
+# Set seed to positive value to use a fixed seed
+_C.SEED = -1
+_C.USE_CUDA = True
+# Print detailed information
+# E.g. trainer, dataset, and backbone
+_C.VERBOSE = False
+
+###########################
+# Input
+###########################
+_C.INPUT = CN()
+_C.INPUT.SIZE = (224, 224)
+# Mode of interpolation in resize functions
+_C.INPUT.INTERPOLATION = "bilinear"
+# For available choices please refer to transforms.py
+_C.INPUT.TRANSFORMS = ()
+# If True, tfm_train and tfm_test will be None
+_C.INPUT.NO_TRANSFORM = False
+# Mean and std (default: ImageNet)
+_C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406]
+_C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225]
+# Random crop
+_C.INPUT.CROP_PADDING = 4
+# Random resized crop
+_C.INPUT.RRCROP_SCALE = (0.08, 1.0)
+# Cutout
+_C.INPUT.CUTOUT_N = 1
+_C.INPUT.CUTOUT_LEN = 16
+# Gaussian noise
+_C.INPUT.GN_MEAN = 0.0
+_C.INPUT.GN_STD = 0.15
+# RandomAugment
+_C.INPUT.RANDAUGMENT_N = 2
+_C.INPUT.RANDAUGMENT_M = 10
+# ColorJitter (brightness, contrast, saturation, hue)
+_C.INPUT.COLORJITTER_B = 0.4
+_C.INPUT.COLORJITTER_C = 0.4
+_C.INPUT.COLORJITTER_S = 0.4
+_C.INPUT.COLORJITTER_H = 0.1
+# Random gray scale's probability
+_C.INPUT.RGS_P = 0.2
+# Gaussian blur
+_C.INPUT.GB_P = 0.5 # propability of applying this operation
+_C.INPUT.GB_K = 21 # kernel size (should be an odd number)
+
+###########################
+# Dataset
+###########################
+_C.DATASET = CN()
+# Directory where datasets are stored
+_C.DATASET.ROOT = ""
+_C.DATASET.NAME = ""
+# List of source/target domains' names (strings)
+# Do not apply to some datasets, which have pre-defined splits
+_C.DATASET.SOURCE_DOMAINS = ()
+_C.DATASET.TARGET_DOMAINS = ()
+# Number of labeled instances in total
+# Useful for the semi-supervised learning
+_C.DATASET.NUM_LABELED = -1
+# Number of images per class
+_C.DATASET.NUM_SHOTS = -1
+# Percentage of validation data (only used for SSL datasets)
+# Set to 0 if do not want to use val data
+# Using val data for hyperparameter tuning was done in Oliver et al. 2018
+_C.DATASET.VAL_PERCENT = 0.1
+# Fold index for STL-10 dataset (normal range is 0 - 9)
+# Negative number means None
+_C.DATASET.STL10_FOLD = -1
+# CIFAR-10/100-C's corruption type and intensity level
+_C.DATASET.CIFAR_C_TYPE = ""
+_C.DATASET.CIFAR_C_LEVEL = 1
+# Use all data in the unlabeled data set (e.g. FixMatch)
+_C.DATASET.ALL_AS_UNLABELED = False
+
+###########################
+# Dataloader
+###########################
+_C.DATALOADER = CN()
+_C.DATALOADER.NUM_WORKERS = 4
+# Apply transformations to an image K times (during training)
+_C.DATALOADER.K_TRANSFORMS = 1
+# img0 denotes image tensor without augmentation
+# Useful for consistency learning
+_C.DATALOADER.RETURN_IMG0 = False
+# Setting for the train_x data-loader
+_C.DATALOADER.TRAIN_X = CN()
+_C.DATALOADER.TRAIN_X.SAMPLER = "RandomSampler"
+_C.DATALOADER.TRAIN_X.BATCH_SIZE = 32
+# Parameter for RandomDomainSampler
+# 0 or -1 means sampling from all domains
+_C.DATALOADER.TRAIN_X.N_DOMAIN = 0
+# Parameter of RandomClassSampler
+# Number of instances per class
+_C.DATALOADER.TRAIN_X.N_INS = 16
+
+# Setting for the train_u data-loader
+_C.DATALOADER.TRAIN_U = CN()
+# Set to false if you want to have unique
+# data loader params for train_u
+_C.DATALOADER.TRAIN_U.SAME_AS_X = True
+_C.DATALOADER.TRAIN_U.SAMPLER = "RandomSampler"
+_C.DATALOADER.TRAIN_U.BATCH_SIZE = 32
+_C.DATALOADER.TRAIN_U.N_DOMAIN = 0
+_C.DATALOADER.TRAIN_U.N_INS = 16
+
+# Setting for the test data-loader
+_C.DATALOADER.TEST = CN()
+_C.DATALOADER.TEST.SAMPLER = "SequentialSampler"
+_C.DATALOADER.TEST.BATCH_SIZE = 32
+
+###########################
+# Model
+###########################
+_C.MODEL = CN()
+# Path to model weights (for initialization)
+_C.MODEL.INIT_WEIGHTS = ""
+_C.MODEL.BACKBONE = CN()
+_C.MODEL.BACKBONE.NAME = ""
+_C.MODEL.BACKBONE.PRETRAINED = True
+# Definition of embedding layers
+_C.MODEL.HEAD = CN()
+# If none, do not construct embedding layers, the
+# backbone's output will be passed to the classifier
+_C.MODEL.HEAD.NAME = ""
+# Structure of hidden layers (a list), e.g. [512, 512]
+# If undefined, no embedding layer will be constructed
+_C.MODEL.HEAD.HIDDEN_LAYERS = ()
+_C.MODEL.HEAD.ACTIVATION = "relu"
+_C.MODEL.HEAD.BN = True
+_C.MODEL.HEAD.DROPOUT = 0.0
+
+###########################
+# Optimization
+###########################
+_C.OPTIM = CN()
+_C.OPTIM.NAME = "adam"
+_C.OPTIM.LR = 0.0003
+_C.OPTIM.WEIGHT_DECAY = 5e-4
+_C.OPTIM.MOMENTUM = 0.9
+_C.OPTIM.SGD_DAMPNING = 0
+_C.OPTIM.SGD_NESTEROV = False
+_C.OPTIM.RMSPROP_ALPHA = 0.99
+# The following also apply to other
+# adaptive optimizers like adamw
+_C.OPTIM.ADAM_BETA1 = 0.9
+_C.OPTIM.ADAM_BETA2 = 0.999
+# STAGED_LR allows different layers to have
+# different lr, e.g. pre-trained base layers
+# can be assigned a smaller lr than the new
+# classification layer
+_C.OPTIM.STAGED_LR = False
+_C.OPTIM.NEW_LAYERS = ()
+_C.OPTIM.BASE_LR_MULT = 0.1
+# Learning rate scheduler
+_C.OPTIM.LR_SCHEDULER = "single_step"
+# -1 or 0 means the stepsize is equal to max_epoch
+_C.OPTIM.STEPSIZE = (-1, )
+_C.OPTIM.GAMMA = 0.1
+_C.OPTIM.MAX_EPOCH = 10
+# Set WARMUP_EPOCH larger than 0 to activate warmup training
+_C.OPTIM.WARMUP_EPOCH = -1
+# Either linear or constant
+_C.OPTIM.WARMUP_TYPE = "linear"
+# Constant learning rate when type=constant
+_C.OPTIM.WARMUP_CONS_LR = 1e-5
+# Minimum learning rate when type=linear
+_C.OPTIM.WARMUP_MIN_LR = 1e-5
+# Recount epoch for the next scheduler (last_epoch=-1)
+# Otherwise last_epoch=warmup_epoch
+_C.OPTIM.WARMUP_RECOUNT = True
+
+###########################
+# Train
+###########################
+_C.TRAIN = CN()
+# How often (epoch) to save model during training
+# Set to 0 or negative value to only save the last one
+_C.TRAIN.CHECKPOINT_FREQ = 0
+# How often (batch) to print training information
+_C.TRAIN.PRINT_FREQ = 10
+# Use 'train_x', 'train_u' or 'smaller_one' to count
+# the number of iterations in an epoch (for DA and SSL)
+_C.TRAIN.COUNT_ITER = "train_x"
+
+###########################
+# Test
+###########################
+_C.TEST = CN()
+_C.TEST.EVALUATOR = "Classification"
+_C.TEST.PER_CLASS_RESULT = False
+# Compute confusion matrix, which will be saved
+# to $OUTPUT_DIR/cmat.pt
+_C.TEST.COMPUTE_CMAT = False
+# If NO_TEST=True, no testing will be conducted
+_C.TEST.NO_TEST = False
+# Use test or val set for FINAL evaluation
+_C.TEST.SPLIT = "test"
+# Which model to test after training (last_step or best_val)
+# If best_val, evaluation is done every epoch (if val data
+# is unavailable, test data will be used)
+_C.TEST.FINAL_MODEL = "last_step"
+
+###########################
+# Trainer specifics
+###########################
+_C.TRAINER = CN()
+_C.TRAINER.NAME = ""
+
+######
+# DA
+######
+# MCD
+_C.TRAINER.MCD = CN()
+_C.TRAINER.MCD.N_STEP_F = 4 # number of steps to train F
+# MME
+_C.TRAINER.MME = CN()
+_C.TRAINER.MME.LMDA = 0.1 # weight for the entropy loss
+# CDAC
+_C.TRAINER.CDAC = CN()
+_C.TRAINER.CDAC.CLASS_LR_MULTI = 10
+_C.TRAINER.CDAC.RAMPUP_COEF = 30
+_C.TRAINER.CDAC.RAMPUP_ITRS = 1000
+_C.TRAINER.CDAC.TOPK_MATCH = 5
+_C.TRAINER.CDAC.P_THRESH = 0.95
+_C.TRAINER.CDAC.STRONG_TRANSFORMS = ()
+# SE (SelfEnsembling)
+_C.TRAINER.SE = CN()
+_C.TRAINER.SE.EMA_ALPHA = 0.999
+_C.TRAINER.SE.CONF_THRE = 0.95
+_C.TRAINER.SE.RAMPUP = 300
+# M3SDA
+_C.TRAINER.M3SDA = CN()
+_C.TRAINER.M3SDA.LMDA = 0.5 # weight for the moment distance loss
+_C.TRAINER.M3SDA.N_STEP_F = 4 # follow MCD
+# DAEL
+_C.TRAINER.DAEL = CN()
+_C.TRAINER.DAEL.WEIGHT_U = 0.5 # weight on the unlabeled loss
+_C.TRAINER.DAEL.CONF_THRE = 0.95 # confidence threshold
+_C.TRAINER.DAEL.STRONG_TRANSFORMS = ()
+
+######
+# DG
+######
+# CrossGrad
+_C.TRAINER.CROSSGRAD = CN()
+_C.TRAINER.CROSSGRAD.EPS_F = 1.0 # scaling parameter for D's gradients
+_C.TRAINER.CROSSGRAD.EPS_D = 1.0 # scaling parameter for F's gradients
+_C.TRAINER.CROSSGRAD.ALPHA_F = 0.5 # balancing weight for the label net's loss
+_C.TRAINER.CROSSGRAD.ALPHA_D = 0.5 # balancing weight for the domain net's loss
+# DDAIG
+_C.TRAINER.DDAIG = CN()
+_C.TRAINER.DDAIG.G_ARCH = "" # generator's architecture
+_C.TRAINER.DDAIG.LMDA = 0.3 # perturbation weight
+_C.TRAINER.DDAIG.CLAMP = False # clamp perturbation values
+_C.TRAINER.DDAIG.CLAMP_MIN = -1.0
+_C.TRAINER.DDAIG.CLAMP_MAX = 1.0
+_C.TRAINER.DDAIG.WARMUP = 0
+_C.TRAINER.DDAIG.ALPHA = 0.5 # balancing weight for the losses
+# DAELDG (the DG version of DAEL)
+_C.TRAINER.DAELDG = CN()
+_C.TRAINER.DAELDG.WEIGHT_U = 0.5 # weight on the unlabeled loss
+_C.TRAINER.DAELDG.CONF_THRE = 0.95 # confidence threshold
+_C.TRAINER.DAELDG.STRONG_TRANSFORMS = ()
+# DOMAINMIX
+_C.TRAINER.DOMAINMIX = CN()
+_C.TRAINER.DOMAINMIX.TYPE = "crossdomain"
+_C.TRAINER.DOMAINMIX.ALPHA = 1.0
+_C.TRAINER.DOMAINMIX.BETA = 1.0
+
+######
+# SSL
+######
+# EntMin
+_C.TRAINER.ENTMIN = CN()
+_C.TRAINER.ENTMIN.LMDA = 1e-3 # weight on the entropy loss
+# Mean Teacher
+_C.TRAINER.MEANTEACHER = CN()
+_C.TRAINER.MEANTEACHER.WEIGHT_U = 1.0 # weight on the unlabeled loss
+_C.TRAINER.MEANTEACHER.EMA_ALPHA = 0.999
+_C.TRAINER.MEANTEACHER.RAMPUP = 5 # epochs used to ramp up the loss_u weight
+# MixMatch
+_C.TRAINER.MIXMATCH = CN()
+_C.TRAINER.MIXMATCH.WEIGHT_U = 100.0 # weight on the unlabeled loss
+_C.TRAINER.MIXMATCH.TEMP = 2.0 # temperature for sharpening the probability
+_C.TRAINER.MIXMATCH.MIXUP_BETA = 0.75
+_C.TRAINER.MIXMATCH.RAMPUP = 20000 # steps used to ramp up the loss_u weight
+# FixMatch
+_C.TRAINER.FIXMATCH = CN()
+_C.TRAINER.FIXMATCH.WEIGHT_U = 1.0 # weight on the unlabeled loss
+_C.TRAINER.FIXMATCH.CONF_THRE = 0.95 # confidence threshold
+_C.TRAINER.FIXMATCH.STRONG_TRANSFORMS = ()
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/__init__.py
new file mode 100644
index 00000000..66ca734e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/__init__.py
@@ -0,0 +1 @@
+from .data_manager import DataManager, DatasetWrapper
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/data_manager.py b/python/ClipDetection/Dassl.pytorch/dassl/data/data_manager.py
new file mode 100644
index 00000000..0bb2b7b0
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/data_manager.py
@@ -0,0 +1,270 @@
+import torch
+import torchvision.transforms as T
+from tabulate import tabulate
+from torch.utils.data import Dataset as TorchDataset
+
+from dassl.utils import read_image
+
+from .datasets import build_dataset
+from .samplers import build_sampler
+from .transforms import INTERPOLATION_MODES, build_transform
+
+
+def build_data_loader(
+ cfg,
+ sampler_type="SequentialSampler",
+ data_source=None,
+ batch_size=64,
+ n_domain=0,
+ n_ins=2,
+ tfm=None,
+ is_train=True,
+ dataset_wrapper=None
+):
+ # Build sampler
+ sampler = build_sampler(
+ sampler_type,
+ cfg=cfg,
+ data_source=data_source,
+ batch_size=batch_size,
+ n_domain=n_domain,
+ n_ins=n_ins
+ )
+
+ if dataset_wrapper is None:
+ dataset_wrapper = DatasetWrapper
+
+ # Build data loader
+ data_loader = torch.utils.data.DataLoader(
+ dataset_wrapper(cfg, data_source, transform=tfm, is_train=is_train),
+ batch_size=batch_size,
+ sampler=sampler,
+ num_workers=cfg.DATALOADER.NUM_WORKERS,
+ drop_last=is_train and len(data_source) >= batch_size,
+ pin_memory=(torch.cuda.is_available() and cfg.USE_CUDA)
+ )
+ assert len(data_loader) > 0
+
+ return data_loader
+
+
+class DataManager:
+
+ def __init__(
+ self,
+ cfg,
+ custom_tfm_train=None,
+ custom_tfm_test=None,
+ dataset_wrapper=None
+ ):
+ # Load dataset
+ dataset = build_dataset(cfg)
+
+ # Build transform
+ if custom_tfm_train is None:
+ tfm_train = build_transform(cfg, is_train=True)
+ else:
+ print("* Using custom transform for training")
+ tfm_train = custom_tfm_train
+
+ if custom_tfm_test is None:
+ tfm_test = build_transform(cfg, is_train=False)
+ else:
+ print("* Using custom transform for testing")
+ tfm_test = custom_tfm_test
+
+ # Build train_loader_x
+ train_loader_x = build_data_loader(
+ cfg,
+ sampler_type=cfg.DATALOADER.TRAIN_X.SAMPLER,
+ data_source=dataset.train_x,
+ batch_size=cfg.DATALOADER.TRAIN_X.BATCH_SIZE,
+ n_domain=cfg.DATALOADER.TRAIN_X.N_DOMAIN,
+ n_ins=cfg.DATALOADER.TRAIN_X.N_INS,
+ tfm=tfm_train,
+ is_train=True,
+ dataset_wrapper=dataset_wrapper
+ )
+
+ # Build train_loader_u
+ train_loader_u = None
+ if dataset.train_u:
+ sampler_type_ = cfg.DATALOADER.TRAIN_U.SAMPLER
+ batch_size_ = cfg.DATALOADER.TRAIN_U.BATCH_SIZE
+ n_domain_ = cfg.DATALOADER.TRAIN_U.N_DOMAIN
+ n_ins_ = cfg.DATALOADER.TRAIN_U.N_INS
+
+ if cfg.DATALOADER.TRAIN_U.SAME_AS_X:
+ sampler_type_ = cfg.DATALOADER.TRAIN_X.SAMPLER
+ batch_size_ = cfg.DATALOADER.TRAIN_X.BATCH_SIZE
+ n_domain_ = cfg.DATALOADER.TRAIN_X.N_DOMAIN
+ n_ins_ = cfg.DATALOADER.TRAIN_X.N_INS
+
+ train_loader_u = build_data_loader(
+ cfg,
+ sampler_type=sampler_type_,
+ data_source=dataset.train_u,
+ batch_size=batch_size_,
+ n_domain=n_domain_,
+ n_ins=n_ins_,
+ tfm=tfm_train,
+ is_train=True,
+ dataset_wrapper=dataset_wrapper
+ )
+
+ # Build val_loader
+ val_loader = None
+ if dataset.val:
+ val_loader = build_data_loader(
+ cfg,
+ sampler_type=cfg.DATALOADER.TEST.SAMPLER,
+ data_source=dataset.val,
+ batch_size=cfg.DATALOADER.TEST.BATCH_SIZE,
+ tfm=tfm_test,
+ is_train=False,
+ dataset_wrapper=dataset_wrapper
+ )
+
+ # Build test_loader
+ test_loader = build_data_loader(
+ cfg,
+ sampler_type=cfg.DATALOADER.TEST.SAMPLER,
+ data_source=dataset.test,
+ batch_size=cfg.DATALOADER.TEST.BATCH_SIZE,
+ tfm=tfm_test,
+ is_train=False,
+ dataset_wrapper=dataset_wrapper
+ )
+ classification_loader = build_data_loader(
+ cfg,
+ sampler_type=cfg.DATALOADER.TEST.SAMPLER,
+ data_source=dataset.test,
+ batch_size=cfg.DATALOADER.TEST.BATCH_SIZE,
+ tfm=tfm_test,
+ is_train=False,
+ dataset_wrapper=dataset_wrapper
+ )
+
+ # Attributes
+ self._num_classes = dataset.num_classes
+ self._num_source_domains = len(cfg.DATASET.SOURCE_DOMAINS)
+ self._lab2cname = dataset.lab2cname
+
+ # Dataset and data-loaders
+ self.dataset = dataset
+ self.train_loader_x = train_loader_x
+ self.train_loader_u = train_loader_u
+ self.val_loader = val_loader
+ self.test_loader = test_loader
+
+ if cfg.VERBOSE:
+ self.show_dataset_summary(cfg)
+
+ @property
+ def num_classes(self):
+ return self._num_classes
+
+ @property
+ def num_source_domains(self):
+ return self._num_source_domains
+
+ @property
+ def lab2cname(self):
+ return self._lab2cname
+
+ def show_dataset_summary(self, cfg):
+ dataset_name = cfg.DATASET.NAME
+ source_domains = cfg.DATASET.SOURCE_DOMAINS
+ target_domains = cfg.DATASET.TARGET_DOMAINS
+
+ table = []
+ table.append(["Dataset", dataset_name])
+ if source_domains:
+ table.append(["Source", source_domains])
+ if target_domains:
+ table.append(["Target", target_domains])
+ table.append(["# classes", f"{self.num_classes:,}"])
+ table.append(["# train_x", f"{len(self.dataset.train_x):,}"])
+ if self.dataset.train_u:
+ table.append(["# train_u", f"{len(self.dataset.train_u):,}"])
+ if self.dataset.val:
+ table.append(["# val", f"{len(self.dataset.val):,}"])
+ table.append(["# test", f"{len(self.dataset.test):,}"])
+
+ print(tabulate(table))
+
+
+class DatasetWrapper(TorchDataset):
+
+ def __init__(self, cfg, data_source, transform=None, is_train=False):
+ self.cfg = cfg
+ self.data_source = data_source
+ self.transform = transform # accept list (tuple) as input
+ self.is_train = is_train
+ # Augmenting an image K>1 times is only allowed during training
+ self.k_tfm = cfg.DATALOADER.K_TRANSFORMS if is_train else 1
+ self.return_img0 = cfg.DATALOADER.RETURN_IMG0
+
+ if self.k_tfm > 1 and transform is None:
+ raise ValueError(
+ "Cannot augment the image {} times "
+ "because transform is None".format(self.k_tfm)
+ )
+
+ # Build transform that doesn't apply any data augmentation
+ interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
+ to_tensor = []
+ to_tensor += [T.Resize(cfg.INPUT.SIZE, interpolation=interp_mode)]
+ to_tensor += [T.ToTensor()]
+ if "normalize" in cfg.INPUT.TRANSFORMS:
+ normalize = T.Normalize(
+ mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
+ )
+ to_tensor += [normalize]
+ self.to_tensor = T.Compose(to_tensor)
+
+ def __len__(self):
+ return len(self.data_source)
+
+ def __getitem__(self, idx):
+ item = self.data_source[idx]
+
+ output = {
+ "label": item.label,
+ "domain": item.domain,
+ "impath": item.impath,
+ "index": idx
+ }
+
+ img0 = read_image(item.impath)
+
+ if self.transform is not None:
+ if isinstance(self.transform, (list, tuple)):
+ for i, tfm in enumerate(self.transform):
+ img = self._transform_image(tfm, img0)
+ keyname = "img"
+ if (i + 1) > 1:
+ keyname += str(i + 1)
+ output[keyname] = img
+ else:
+ img = self._transform_image(self.transform, img0)
+ output["img"] = img
+ else:
+ output["img"] = img0
+
+ if self.return_img0:
+ output["img0"] = self.to_tensor(img0) # without any augmentation
+
+ return output
+
+ def _transform_image(self, tfm, img0):
+ img_list = []
+
+ for k in range(self.k_tfm):
+ img_list.append(tfm(img0))
+
+ img = img_list
+ if len(img) == 1:
+ img = img[0]
+
+ return img
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/__init__.py
new file mode 100644
index 00000000..4f58326f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/__init__.py
@@ -0,0 +1,6 @@
+from .build import DATASET_REGISTRY, build_dataset # isort:skip
+from .base_dataset import Datum, DatasetBase # isort:skip
+
+from .da import *
+from .dg import *
+from .ssl import *
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/base_dataset.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/base_dataset.py
new file mode 100644
index 00000000..c7cafd04
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/base_dataset.py
@@ -0,0 +1,237 @@
+import os
+import random
+import os.path as osp
+import tarfile
+import zipfile
+from collections import defaultdict
+import gdown
+
+from dassl.utils import check_isfile
+
+
+class Datum:
+ """Data instance which defines the basic attributes.
+
+ Args:
+ impath (str): image path.
+ label (int): class label.
+ domain (int): domain label.
+ classname (str): class name.
+ """
+
+ def __init__(self, impath="", label=0, domain=0, classname=""):
+ assert isinstance(impath, str)
+ assert check_isfile(impath)
+
+ self._impath = impath
+ self._label = label
+ self._domain = domain
+ self._classname = classname
+
+ @property
+ def impath(self):
+ return self._impath
+
+ @property
+ def label(self):
+ return self._label
+
+ @property
+ def domain(self):
+ return self._domain
+
+ @property
+ def classname(self):
+ return self._classname
+
+
+class DatasetBase:
+ """A unified dataset class for
+ 1) domain adaptation
+ 2) domain generalization
+ 3) semi-supervised learning
+ """
+
+ dataset_dir = "" # the directory where the dataset is stored
+ domains = [] # string names of all domains
+
+ def __init__(self, train_x=None, train_u=None, val=None, test=None):
+ self._train_x = train_x # labeled training data
+ self._train_u = train_u # unlabeled training data (optional)
+ self._val = val # validation data (optional)
+ self._test = test # test data
+ self._num_classes = self.get_num_classes(train_x)
+ self._lab2cname, self._classnames = self.get_lab2cname(train_x)
+
+ @property
+ def train_x(self):
+ return self._train_x
+
+ @property
+ def train_u(self):
+ return self._train_u
+
+ @property
+ def val(self):
+ return self._val
+
+ @property
+ def test(self):
+ return self._test
+
+ @property
+ def lab2cname(self):
+ return self._lab2cname
+
+ @property
+ def classnames(self):
+ return self._classnames
+
+ @property
+ def num_classes(self):
+ return self._num_classes
+
+ @staticmethod
+ def get_num_classes(data_source):
+ """Count number of classes.
+
+ Args:
+ data_source (list): a list of Datum objects.
+ """
+ label_set = set()
+ for item in data_source:
+ label_set.add(item.label)
+ return max(label_set) + 1
+
+ @staticmethod
+ def get_lab2cname(data_source):
+ """Get a label-to-classname mapping (dict).
+
+ Args:
+ data_source (list): a list of Datum objects.
+ """
+ container = set()
+ for item in data_source:
+ container.add((item.label, item.classname))
+ mapping = {label: classname for label, classname in container}
+ labels = list(mapping.keys())
+ labels.sort()
+ classnames = [mapping[label] for label in labels]
+ return mapping, classnames
+
+ def check_input_domains(self, source_domains, target_domains):
+ assert len(source_domains) > 0, "source_domains (list) is empty"
+ assert len(target_domains) > 0, "target_domains (list) is empty"
+ self.is_input_domain_valid(source_domains)
+ self.is_input_domain_valid(target_domains)
+
+ def is_input_domain_valid(self, input_domains):
+ for domain in input_domains:
+ if domain not in self.domains:
+ raise ValueError(
+ "Input domain must belong to {}, "
+ "but got [{}]".format(self.domains, domain)
+ )
+
+ def download_data(self, url, dst, from_gdrive=True):
+ if not osp.exists(osp.dirname(dst)):
+ os.makedirs(osp.dirname(dst))
+
+ if from_gdrive:
+ gdown.download(url, dst, quiet=False)
+ else:
+ raise NotImplementedError
+
+ print("Extracting file ...")
+
+ if dst.endswith(".zip"):
+ zip_ref = zipfile.ZipFile(dst, "r")
+ zip_ref.extractall(osp.dirname(dst))
+ zip_ref.close()
+
+ elif dst.endswith(".tar"):
+ tar = tarfile.open(dst, "r:")
+ tar.extractall(osp.dirname(dst))
+ tar.close()
+
+ elif dst.endswith(".tar.gz"):
+ tar = tarfile.open(dst, "r:gz")
+ tar.extractall(osp.dirname(dst))
+ tar.close()
+
+ else:
+ raise NotImplementedError
+
+ print("File extracted to {}".format(osp.dirname(dst)))
+
+ def generate_fewshot_dataset(
+ self, *data_sources, num_shots=-1, repeat=False
+ ):
+ """Generate a few-shot dataset (typically for the training set).
+
+ This function is useful when one wants to evaluate a model
+ in a few-shot learning setting where each class only contains
+ a small number of images.
+
+ Args:
+ data_sources: each individual is a list containing Datum objects.
+ num_shots (int): number of instances per class to sample.
+ repeat (bool): repeat images if needed (default: False).
+ """
+ if num_shots < 1:
+ if len(data_sources) == 1:
+ return data_sources[0]
+ return data_sources
+
+ print(f"Creating a {num_shots}-shot dataset")
+
+ output = []
+
+ for data_source in data_sources:
+ tracker = self.split_dataset_by_label(data_source)
+ dataset = []
+
+ for label, items in tracker.items():
+ if len(items) >= num_shots:
+ sampled_items = random.sample(items, num_shots)
+ else:
+ if repeat:
+ sampled_items = random.choices(items, k=num_shots)
+ else:
+ sampled_items = items
+ dataset.extend(sampled_items)
+
+ output.append(dataset)
+
+ if len(output) == 1:
+ return output[0]
+
+ return output
+
+ def split_dataset_by_label(self, data_source):
+ """Split a dataset, i.e. a list of Datum objects,
+ into class-specific groups stored in a dictionary.
+
+ Args:
+ data_source (list): a list of Datum objects.
+ """
+ output = defaultdict(list)
+
+ for item in data_source:
+ output[item.label].append(item)
+
+ return output
+
+ def split_dataset_by_domain(self, data_source):
+ """Split a dataset, i.e. a list of Datum objects,
+ into domain-specific groups stored in a dictionary.
+
+ Args:
+ data_source (list): a list of Datum objects.
+ """
+ output = defaultdict(list)
+
+ for item in data_source:
+ output[item.domain].append(item)
+
+ return output
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/build.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/build.py
new file mode 100644
index 00000000..9de62c61
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+DATASET_REGISTRY = Registry("DATASET")
+
+
+def build_dataset(cfg):
+ avai_datasets = DATASET_REGISTRY.registered_names()
+ check_availability(cfg.DATASET.NAME, avai_datasets)
+ if cfg.VERBOSE:
+ print("Loading dataset: {}".format(cfg.DATASET.NAME))
+ return DATASET_REGISTRY.get(cfg.DATASET.NAME)(cfg)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/__init__.py
new file mode 100644
index 00000000..9c7b60f2
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/__init__.py
@@ -0,0 +1,7 @@
+from .digit5 import Digit5
+from .visda17 import VisDA17
+from .cifarstl import CIFARSTL
+from .office31 import Office31
+from .domainnet import DomainNet
+from .office_home import OfficeHome
+from .mini_domainnet import miniDomainNet
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/cifarstl.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/cifarstl.py
new file mode 100644
index 00000000..ca27eb10
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/cifarstl.py
@@ -0,0 +1,68 @@
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class CIFARSTL(DatasetBase):
+ """CIFAR-10 and STL-10.
+
+ CIFAR-10:
+ - 60,000 32x32 colour images.
+ - 10 classes, with 6,000 images per class.
+ - 50,000 training images and 10,000 test images.
+ - URL: https://www.cs.toronto.edu/~kriz/cifar.html.
+
+ STL-10:
+ - 10 classes: airplane, bird, car, cat, deer, dog, horse,
+ monkey, ship, truck.
+ - Images are 96x96 pixels, color.
+ - 500 training images (10 pre-defined folds), 800 test images
+ per class.
+ - URL: https://cs.stanford.edu/~acoates/stl10/.
+
+ Reference:
+ - Krizhevsky. Learning Multiple Layers of Features
+ from Tiny Images. Tech report.
+ - Coates et al. An Analysis of Single Layer Networks in
+ Unsupervised Feature Learning. AISTATS 2011.
+ """
+
+ dataset_dir = "cifar_stl"
+ domains = ["cifar", "stl"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, input_domains, split="train"):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ data_dir = osp.join(self.dataset_dir, dname, split)
+ class_names = listdir_nohidden(data_dir)
+
+ for class_name in class_names:
+ class_dir = osp.join(data_dir, class_name)
+ imnames = listdir_nohidden(class_dir)
+ label = int(class_name.split("_")[0])
+
+ for imname in imnames:
+ impath = osp.join(class_dir, imname)
+ item = Datum(impath=impath, label=label, domain=domain)
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/digit5.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/digit5.py
new file mode 100644
index 00000000..4320005a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/digit5.py
@@ -0,0 +1,124 @@
+import random
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+# Folder names for train and test sets
+MNIST = {"train": "train_images", "test": "test_images"}
+MNIST_M = {"train": "train_images", "test": "test_images"}
+SVHN = {"train": "train_images", "test": "test_images"}
+SYN = {"train": "train_images", "test": "test_images"}
+USPS = {"train": "train_images", "test": "test_images"}
+
+
+def read_image_list(im_dir, n_max=None, n_repeat=None):
+ items = []
+
+ for imname in listdir_nohidden(im_dir):
+ imname_noext = osp.splitext(imname)[0]
+ label = int(imname_noext.split("_")[1])
+ impath = osp.join(im_dir, imname)
+ items.append((impath, label))
+
+ if n_max is not None:
+ items = random.sample(items, n_max)
+
+ if n_repeat is not None:
+ items *= n_repeat
+
+ return items
+
+
+def load_mnist(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, MNIST[split])
+ n_max = 25000 if split == "train" else 9000
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_mnist_m(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, MNIST_M[split])
+ n_max = 25000 if split == "train" else 9000
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_svhn(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, SVHN[split])
+ n_max = 25000 if split == "train" else 9000
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_syn(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, SYN[split])
+ n_max = 25000 if split == "train" else 9000
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_usps(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, USPS[split])
+ n_repeat = 3 if split == "train" else None
+ return read_image_list(data_dir, n_repeat=n_repeat)
+
+
+@DATASET_REGISTRY.register()
+class Digit5(DatasetBase):
+ """Five digit datasets.
+
+ It contains:
+ - MNIST: hand-written digits.
+ - MNIST-M: variant of MNIST with blended background.
+ - SVHN: street view house number.
+ - SYN: synthetic digits.
+ - USPS: hand-written digits, slightly different from MNIST.
+
+ For MNIST, MNIST-M, SVHN and SYN, we randomly sample 25,000 images from
+ the training set and 9,000 images from the test set. For USPS which has only
+ 9,298 images in total, we use the entire dataset but replicate its training
+ set for 3 times so as to match the training set size of other domains.
+
+ Reference:
+ - Lecun et al. Gradient-based learning applied to document
+ recognition. IEEE 1998.
+ - Ganin et al. Domain-adversarial training of neural networks.
+ JMLR 2016.
+ - Netzer et al. Reading digits in natural images with unsupervised
+ feature learning. NIPS-W 2011.
+ """
+
+ dataset_dir = "digit5"
+ domains = ["mnist", "mnist_m", "svhn", "syn", "usps"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, input_domains, split="train"):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ func = "load_" + dname
+ domain_dir = osp.join(self.dataset_dir, dname)
+ items_d = eval(func)(domain_dir, split=split)
+
+ for impath, label in items_d:
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=str(label)
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/domainnet.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/domainnet.py
new file mode 100644
index 00000000..8a703bf1
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/domainnet.py
@@ -0,0 +1,69 @@
+import os.path as osp
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class DomainNet(DatasetBase):
+ """DomainNet.
+
+ Statistics:
+ - 6 distinct domains: Clipart, Infograph, Painting, Quickdraw,
+ Real, Sketch.
+ - Around 0.6M images.
+ - 345 categories.
+ - URL: http://ai.bu.edu/M3SDA/.
+
+ Special note: the t-shirt class (327) is missing in painting_train.txt.
+
+ Reference:
+ - Peng et al. Moment Matching for Multi-Source Domain
+ Adaptation. ICCV 2019.
+ """
+
+ dataset_dir = "domainnet"
+ domains = [
+ "clipart", "infograph", "painting", "quickdraw", "real", "sketch"
+ ]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ self.split_dir = osp.join(self.dataset_dir, "splits")
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
+ val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="test")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+ super().__init__(train_x=train_x, train_u=train_u, val=val, test=test)
+
+ def _read_data(self, input_domains, split="train"):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ filename = dname + "_" + split + ".txt"
+ split_file = osp.join(self.split_dir, filename)
+
+ with open(split_file, "r") as f:
+ lines = f.readlines()
+ for line in lines:
+ line = line.strip()
+ impath, label = line.split(" ")
+ classname = impath.split("/")[1]
+ impath = osp.join(self.dataset_dir, impath)
+ label = int(label)
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=classname
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/mini_domainnet.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/mini_domainnet.py
new file mode 100644
index 00000000..4a708691
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/mini_domainnet.py
@@ -0,0 +1,58 @@
+import os.path as osp
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class miniDomainNet(DatasetBase):
+ """A subset of DomainNet.
+
+ Reference:
+ - Peng et al. Moment Matching for Multi-Source Domain
+ Adaptation. ICCV 2019.
+ - Zhou et al. Domain Adaptive Ensemble Learning.
+ """
+
+ dataset_dir = "domainnet"
+ domains = ["clipart", "painting", "real", "sketch"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ self.split_dir = osp.join(self.dataset_dir, "splits_mini")
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, input_domains, split="train"):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ filename = dname + "_" + split + ".txt"
+ split_file = osp.join(self.split_dir, filename)
+
+ with open(split_file, "r") as f:
+ lines = f.readlines()
+ for line in lines:
+ line = line.strip()
+ impath, label = line.split(" ")
+ classname = impath.split("/")[1]
+ impath = osp.join(self.dataset_dir, impath)
+ label = int(label)
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=classname
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office31.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office31.py
new file mode 100644
index 00000000..c2daca1d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office31.py
@@ -0,0 +1,63 @@
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class Office31(DatasetBase):
+ """Office-31.
+
+ Statistics:
+ - 4,110 images.
+ - 31 classes related to office objects.
+ - 3 domains: Amazon, Webcam, Dslr.
+ - URL: https://people.eecs.berkeley.edu/~jhoffman/domainadapt/.
+
+ Reference:
+ - Saenko et al. Adapting visual category models to
+ new domains. ECCV 2010.
+ """
+
+ dataset_dir = "office31"
+ domains = ["amazon", "webcam", "dslr"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS)
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS)
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS)
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, input_domains):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ domain_dir = osp.join(self.dataset_dir, dname)
+ class_names = listdir_nohidden(domain_dir)
+ class_names.sort()
+
+ for label, class_name in enumerate(class_names):
+ class_path = osp.join(domain_dir, class_name)
+ imnames = listdir_nohidden(class_path)
+
+ for imname in imnames:
+ impath = osp.join(class_path, imname)
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=class_name
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office_home.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office_home.py
new file mode 100644
index 00000000..61996f2f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/office_home.py
@@ -0,0 +1,63 @@
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class OfficeHome(DatasetBase):
+ """Office-Home.
+
+ Statistics:
+ - Around 15,500 images.
+ - 65 classes related to office and home objects.
+ - 4 domains: Art, Clipart, Product, Real World.
+ - URL: http://hemanthdv.org/OfficeHome-Dataset/.
+
+ Reference:
+ - Venkateswara et al. Deep Hashing Network for Unsupervised
+ Domain Adaptation. CVPR 2017.
+ """
+
+ dataset_dir = "office_home"
+ domains = ["art", "clipart", "product", "real_world"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS)
+ train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS)
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS)
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, input_domains):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ domain_dir = osp.join(self.dataset_dir, dname)
+ class_names = listdir_nohidden(domain_dir)
+ class_names.sort()
+
+ for label, class_name in enumerate(class_names):
+ class_path = osp.join(domain_dir, class_name)
+ imnames = listdir_nohidden(class_path)
+
+ for imname in imnames:
+ impath = osp.join(class_path, imname)
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=class_name.lower(),
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/visda17.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/visda17.py
new file mode 100644
index 00000000..48c1045e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/da/visda17.py
@@ -0,0 +1,61 @@
+import os.path as osp
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class VisDA17(DatasetBase):
+ """VisDA17.
+
+ Focusing on simulation-to-reality domain shift.
+
+ URL: http://ai.bu.edu/visda-2017/.
+
+ Reference:
+ - Peng et al. VisDA: The Visual Domain Adaptation
+ Challenge. ArXiv 2017.
+ """
+
+ dataset_dir = "visda17"
+ domains = ["synthetic", "real"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train_x = self._read_data("synthetic")
+ train_u = self._read_data("real")
+ test = self._read_data("real")
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data(self, dname):
+ filedir = "train" if dname == "synthetic" else "validation"
+ image_list = osp.join(self.dataset_dir, filedir, "image_list.txt")
+ items = []
+ # There is only one source domain
+ domain = 0
+
+ with open(image_list, "r") as f:
+ lines = f.readlines()
+
+ for line in lines:
+ line = line.strip()
+ impath, label = line.split(" ")
+ classname = impath.split("/")[0]
+ impath = osp.join(self.dataset_dir, filedir, impath)
+ label = int(label)
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=classname
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/__init__.py
new file mode 100644
index 00000000..b94c35cd
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/__init__.py
@@ -0,0 +1,7 @@
+from .pacs import PACS
+from .vlcs import VLCS
+from .wilds import *
+from .cifar_c import CIFAR10C, CIFAR100C
+from .digits_dg import DigitsDG
+from .digit_single import DigitSingle
+from .office_home_dg import OfficeHomeDG
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/cifar_c.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/cifar_c.py
new file mode 100644
index 00000000..7d1e4f38
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/cifar_c.py
@@ -0,0 +1,123 @@
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+AVAI_C_TYPES = [
+ "brightness",
+ "contrast",
+ "defocus_blur",
+ "elastic_transform",
+ "fog",
+ "frost",
+ "gaussian_blur",
+ "gaussian_noise",
+ "glass_blur",
+ "impulse_noise",
+ "jpeg_compression",
+ "motion_blur",
+ "pixelate",
+ "saturate",
+ "shot_noise",
+ "snow",
+ "spatter",
+ "speckle_noise",
+ "zoom_blur",
+]
+
+
+@DATASET_REGISTRY.register()
+class CIFAR10C(DatasetBase):
+ """CIFAR-10 -> CIFAR-10-C.
+
+ Dataset link: https://zenodo.org/record/2535967#.YFwtV2Qzb0o
+
+ Statistics:
+ - 2 domains: the normal CIFAR-10 vs. a corrupted CIFAR-10
+ - 10 categories
+
+ Reference:
+ - Hendrycks et al. Benchmarking neural network robustness
+ to common corruptions and perturbations. ICLR 2019.
+ """
+
+ dataset_dir = ""
+ domains = ["cifar10", "cifar10_c"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = root
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+ source_domain = cfg.DATASET.SOURCE_DOMAINS[0]
+ target_domain = cfg.DATASET.TARGET_DOMAINS[0]
+ assert source_domain == self.domains[0]
+ assert target_domain == self.domains[1]
+
+ c_type = cfg.DATASET.CIFAR_C_TYPE
+ c_level = cfg.DATASET.CIFAR_C_LEVEL
+
+ if not c_type:
+ raise ValueError(
+ "Please specify DATASET.CIFAR_C_TYPE in the config file"
+ )
+
+ assert (
+ c_type in AVAI_C_TYPES
+ ), f'C_TYPE is expected to belong to {AVAI_C_TYPES}, but got "{c_type}"'
+ assert 1 <= c_level <= 5
+
+ train_dir = osp.join(self.dataset_dir, source_domain, "train")
+ test_dir = osp.join(
+ self.dataset_dir, target_domain, c_type, str(c_level)
+ )
+
+ if not osp.exists(test_dir):
+ raise ValueError
+
+ train = self._read_data(train_dir)
+ test = self._read_data(test_dir)
+
+ super().__init__(train_x=train, test=test)
+
+ def _read_data(self, data_dir):
+ class_names = listdir_nohidden(data_dir)
+ class_names.sort()
+ items = []
+
+ for label, class_name in enumerate(class_names):
+ class_dir = osp.join(data_dir, class_name)
+ imnames = listdir_nohidden(class_dir)
+
+ for imname in imnames:
+ impath = osp.join(class_dir, imname)
+ item = Datum(impath=impath, label=label, domain=0)
+ items.append(item)
+
+ return items
+
+
+@DATASET_REGISTRY.register()
+class CIFAR100C(CIFAR10C):
+ """CIFAR-100 -> CIFAR-100-C.
+
+ Dataset link: https://zenodo.org/record/3555552#.YFxpQmQzb0o
+
+ Statistics:
+ - 2 domains: the normal CIFAR-100 vs. a corrupted CIFAR-100
+ - 10 categories
+
+ Reference:
+ - Hendrycks et al. Benchmarking neural network robustness
+ to common corruptions and perturbations. ICLR 2019.
+ """
+
+ dataset_dir = ""
+ domains = ["cifar100", "cifar100_c"]
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digit_single.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digit_single.py
new file mode 100644
index 00000000..5490e92f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digit_single.py
@@ -0,0 +1,124 @@
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+# Folder names for train and test sets
+MNIST = {"train": "train_images", "test": "test_images"}
+MNIST_M = {"train": "train_images", "test": "test_images"}
+SVHN = {"train": "train_images", "test": "test_images"}
+SYN = {"train": "train_images", "test": "test_images"}
+USPS = {"train": "train_images", "test": "test_images"}
+
+
+def read_image_list(im_dir, n_max=None, n_repeat=None):
+ items = []
+
+ for imname in listdir_nohidden(im_dir):
+ imname_noext = osp.splitext(imname)[0]
+ label = int(imname_noext.split("_")[1])
+ impath = osp.join(im_dir, imname)
+ items.append((impath, label))
+
+ if n_max is not None:
+ # Note that the sampling process is NOT random,
+ # which follows that in Volpi et al. NIPS'18.
+ items = items[:n_max]
+
+ if n_repeat is not None:
+ items *= n_repeat
+
+ return items
+
+
+def load_mnist(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, MNIST[split])
+ n_max = 10000 if split == "train" else None
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_mnist_m(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, MNIST_M[split])
+ n_max = 10000 if split == "train" else None
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_svhn(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, SVHN[split])
+ n_max = 10000 if split == "train" else None
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_syn(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, SYN[split])
+ n_max = 10000 if split == "train" else None
+ return read_image_list(data_dir, n_max=n_max)
+
+
+def load_usps(dataset_dir, split="train"):
+ data_dir = osp.join(dataset_dir, USPS[split])
+ return read_image_list(data_dir)
+
+
+@DATASET_REGISTRY.register()
+class DigitSingle(DatasetBase):
+ """Digit recognition datasets for single-source domain generalization.
+
+ There are five digit datasets:
+ - MNIST: hand-written digits.
+ - MNIST-M: variant of MNIST with blended background.
+ - SVHN: street view house number.
+ - SYN: synthetic digits.
+ - USPS: hand-written digits, slightly different from MNIST.
+
+ Protocol:
+ Volpi et al. train a model using 10,000 images from MNIST and
+ evaluate the model on the test split of the other four datasets. However,
+ the code does not restrict you to only use MNIST as the source dataset.
+ Instead, you can use any dataset as the source. But note that only 10,000
+ images will be sampled from the source dataset for training.
+
+ Reference:
+ - Lecun et al. Gradient-based learning applied to document
+ recognition. IEEE 1998.
+ - Ganin et al. Domain-adversarial training of neural networks.
+ JMLR 2016.
+ - Netzer et al. Reading digits in natural images with unsupervised
+ feature learning. NIPS-W 2011.
+ - Volpi et al. Generalizing to Unseen Domains via Adversarial Data
+ Augmentation. NIPS 2018.
+ """
+
+ # Reuse the digit-5 folder instead of creating a new folder
+ dataset_dir = "digit5"
+ domains = ["mnist", "mnist_m", "svhn", "syn", "usps"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
+ val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="test")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+ super().__init__(train_x=train, val=val, test=test)
+
+ def _read_data(self, input_domains, split="train"):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ func = "load_" + dname
+ domain_dir = osp.join(self.dataset_dir, dname)
+ items_d = eval(func)(domain_dir, split=split)
+
+ for impath, label in items_d:
+ item = Datum(impath=impath, label=label, domain=domain)
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digits_dg.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digits_dg.py
new file mode 100644
index 00000000..43ccd6f4
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/digits_dg.py
@@ -0,0 +1,97 @@
+import glob
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class DigitsDG(DatasetBase):
+ """Digits-DG.
+
+ It contains 4 digit datasets:
+ - MNIST: hand-written digits.
+ - MNIST-M: variant of MNIST with blended background.
+ - SVHN: street view house number.
+ - SYN: synthetic digits.
+
+ Reference:
+ - Lecun et al. Gradient-based learning applied to document
+ recognition. IEEE 1998.
+ - Ganin et al. Domain-adversarial training of neural networks.
+ JMLR 2016.
+ - Netzer et al. Reading digits in natural images with unsupervised
+ feature learning. NIPS-W 2011.
+ - Zhou et al. Deep Domain-Adversarial Image Generation for Domain
+ Generalisation. AAAI 2020.
+ """
+
+ dataset_dir = "digits_dg"
+ domains = ["mnist", "mnist_m", "svhn", "syn"]
+ data_url = "https://drive.google.com/uc?id=15V7EsHfCcfbKgsDmzQKj_DfXt_XYp_P7"
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ if not osp.exists(self.dataset_dir):
+ dst = osp.join(root, "digits_dg.zip")
+ self.download_data(self.data_url, dst, from_gdrive=True)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train = self.read_data(
+ self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "train"
+ )
+ val = self.read_data(
+ self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "val"
+ )
+ test = self.read_data(
+ self.dataset_dir, cfg.DATASET.TARGET_DOMAINS, "all"
+ )
+
+ super().__init__(train_x=train, val=val, test=test)
+
+ @staticmethod
+ def read_data(dataset_dir, input_domains, split):
+
+ def _load_data_from_directory(directory):
+ folders = listdir_nohidden(directory)
+ folders.sort()
+ items_ = []
+
+ for label, folder in enumerate(folders):
+ impaths = glob.glob(osp.join(directory, folder, "*.jpg"))
+
+ for impath in impaths:
+ items_.append((impath, label))
+
+ return items_
+
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ if split == "all":
+ train_dir = osp.join(dataset_dir, dname, "train")
+ impath_label_list = _load_data_from_directory(train_dir)
+ val_dir = osp.join(dataset_dir, dname, "val")
+ impath_label_list += _load_data_from_directory(val_dir)
+ else:
+ split_dir = osp.join(dataset_dir, dname, split)
+ impath_label_list = _load_data_from_directory(split_dir)
+
+ for impath, label in impath_label_list:
+ class_name = impath.split("/")[-2].lower()
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=class_name
+ )
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/office_home_dg.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/office_home_dg.py
new file mode 100644
index 00000000..ef08754b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/office_home_dg.py
@@ -0,0 +1,49 @@
+import os.path as osp
+
+from ..build import DATASET_REGISTRY
+from .digits_dg import DigitsDG
+from ..base_dataset import DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class OfficeHomeDG(DatasetBase):
+ """Office-Home.
+
+ Statistics:
+ - Around 15,500 images.
+ - 65 classes related to office and home objects.
+ - 4 domains: Art, Clipart, Product, Real World.
+ - URL: http://hemanthdv.org/OfficeHome-Dataset/.
+
+ Reference:
+ - Venkateswara et al. Deep Hashing Network for Unsupervised
+ Domain Adaptation. CVPR 2017.
+ """
+
+ dataset_dir = "office_home_dg"
+ domains = ["art", "clipart", "product", "real_world"]
+ data_url = "https://drive.google.com/uc?id=1gkbf_KaxoBws-GWT3XIPZ7BnkqbAxIFa"
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ if not osp.exists(self.dataset_dir):
+ dst = osp.join(root, "office_home_dg.zip")
+ self.download_data(self.data_url, dst, from_gdrive=True)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train = DigitsDG.read_data(
+ self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "train"
+ )
+ val = DigitsDG.read_data(
+ self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "val"
+ )
+ test = DigitsDG.read_data(
+ self.dataset_dir, cfg.DATASET.TARGET_DOMAINS, "all"
+ )
+
+ super().__init__(train_x=train, val=val, test=test)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/pacs.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/pacs.py
new file mode 100644
index 00000000..e0159d49
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/pacs.py
@@ -0,0 +1,94 @@
+import os.path as osp
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class PACS(DatasetBase):
+ """PACS.
+
+ Statistics:
+ - 4 domains: Photo (1,670), Art (2,048), Cartoon
+ (2,344), Sketch (3,929).
+ - 7 categories: dog, elephant, giraffe, guitar, horse,
+ house and person.
+
+ Reference:
+ - Li et al. Deeper, broader and artier domain generalization.
+ ICCV 2017.
+ """
+
+ dataset_dir = "pacs"
+ domains = ["art_painting", "cartoon", "photo", "sketch"]
+ data_url = "https://drive.google.com/uc?id=1m4X4fROCCXMO0lRLrr6Zz9Vb3974NWhE"
+ # the following images contain errors and should be ignored
+ _error_paths = ["sketch/dog/n02103406_4068-1.png"]
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ self.image_dir = osp.join(self.dataset_dir, "images")
+ self.split_dir = osp.join(self.dataset_dir, "splits")
+
+ if not osp.exists(self.dataset_dir):
+ dst = osp.join(root, "pacs.zip")
+ self.download_data(self.data_url, dst, from_gdrive=True)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "train")
+ val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "crossval")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, "all")
+
+ super().__init__(train_x=train, val=val, test=test)
+
+ def _read_data(self, input_domains, split):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ if split == "all":
+ file_train = osp.join(
+ self.split_dir, dname + "_train_kfold.txt"
+ )
+ impath_label_list = self._read_split_pacs(file_train)
+ file_val = osp.join(
+ self.split_dir, dname + "_crossval_kfold.txt"
+ )
+ impath_label_list += self._read_split_pacs(file_val)
+ else:
+ file = osp.join(
+ self.split_dir, dname + "_" + split + "_kfold.txt"
+ )
+ impath_label_list = self._read_split_pacs(file)
+
+ for impath, label in impath_label_list:
+ classname = impath.split("/")[-2]
+ item = Datum(
+ impath=impath,
+ label=label,
+ domain=domain,
+ classname=classname
+ )
+ items.append(item)
+
+ return items
+
+ def _read_split_pacs(self, split_file):
+ items = []
+
+ with open(split_file, "r") as f:
+ lines = f.readlines()
+
+ for line in lines:
+ line = line.strip()
+ impath, label = line.split(" ")
+ if impath in self._error_paths:
+ continue
+ impath = osp.join(self.image_dir, impath)
+ label = int(label) - 1
+ items.append((impath, label))
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/vlcs.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/vlcs.py
new file mode 100644
index 00000000..77218e2f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/vlcs.py
@@ -0,0 +1,60 @@
+import glob
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class VLCS(DatasetBase):
+ """VLCS.
+
+ Statistics:
+ - 4 domains: CALTECH, LABELME, PASCAL, SUN
+ - 5 categories: bird, car, chair, dog, and person.
+
+ Reference:
+ - Torralba and Efros. Unbiased look at dataset bias. CVPR 2011.
+ """
+
+ dataset_dir = "VLCS"
+ domains = ["caltech", "labelme", "pascal", "sun"]
+ data_url = "https://drive.google.com/uc?id=1r0WL5DDqKfSPp9E3tRENwHaXNs1olLZd"
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+
+ if not osp.exists(self.dataset_dir):
+ dst = osp.join(root, "vlcs.zip")
+ self.download_data(self.data_url, dst, from_gdrive=True)
+
+ self.check_input_domains(
+ cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+ )
+
+ train = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "train")
+ val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "crossval")
+ test = self._read_data(cfg.DATASET.TARGET_DOMAINS, "test")
+
+ super().__init__(train_x=train, val=val, test=test)
+
+ def _read_data(self, input_domains, split):
+ items = []
+
+ for domain, dname in enumerate(input_domains):
+ dname = dname.upper()
+ path = osp.join(self.dataset_dir, dname, split)
+ folders = listdir_nohidden(path)
+ folders.sort()
+
+ for label, folder in enumerate(folders):
+ impaths = glob.glob(osp.join(path, folder, "*.jpg"))
+
+ for impath in impaths:
+ item = Datum(impath=impath, label=label, domain=domain)
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/__init__.py
new file mode 100644
index 00000000..2898f7cc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/__init__.py
@@ -0,0 +1,3 @@
+from .fmow import FMoW
+from .iwildcam import IWildCam
+from .camelyon17 import Camelyon17
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/camelyon17.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/camelyon17.py
new file mode 100644
index 00000000..fade5ebc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/camelyon17.py
@@ -0,0 +1,24 @@
+from dassl.data.datasets import DATASET_REGISTRY
+
+from .wilds_base import WILDSBase
+
+
+@DATASET_REGISTRY.register()
+class Camelyon17(WILDSBase):
+ """Tumor tissue recognition.
+
+ 2 classes (whether a given region of tissue contains tumor tissue).
+
+ Reference:
+ - Bandi et al. "From detection of individual metastases to classification of lymph
+ node status at the patient level: the CAMELYON17 challenge." TMI 2021.
+ - Koh et al. "Wilds: A benchmark of in-the-wild distribution shifts." ICML 2021.
+ """
+
+ dataset_dir = "camelyon17_v1.0"
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+
+ def load_classnames(self):
+ return {0: "healthy tissue", 1: "tumor tissue"}
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/fmow.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/fmow.py
new file mode 100644
index 00000000..d7398e05
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/fmow.py
@@ -0,0 +1,57 @@
+import os.path as osp
+
+from dassl.data.datasets import DATASET_REGISTRY
+
+from .wilds_base import WILDSBase
+
+CATEGORIES = [
+ "airport", "airport_hangar", "airport_terminal", "amusement_park",
+ "aquaculture", "archaeological_site", "barn", "border_checkpoint",
+ "burial_site", "car_dealership", "construction_site", "crop_field", "dam",
+ "debris_or_rubble", "educational_institution", "electric_substation",
+ "factory_or_powerplant", "fire_station", "flooded_road", "fountain",
+ "gas_station", "golf_course", "ground_transportation_station", "helipad",
+ "hospital", "impoverished_settlement", "interchange", "lake_or_pond",
+ "lighthouse", "military_facility", "multi-unit_residential",
+ "nuclear_powerplant", "office_building", "oil_or_gas_facility", "park",
+ "parking_lot_or_garage", "place_of_worship", "police_station", "port",
+ "prison", "race_track", "railway_bridge", "recreational_facility",
+ "road_bridge", "runway", "shipyard", "shopping_mall",
+ "single-unit_residential", "smokestack", "solar_farm", "space_facility",
+ "stadium", "storage_tank", "surface_mine", "swimming_pool", "toll_booth",
+ "tower", "tunnel_opening", "waste_disposal", "water_treatment_facility",
+ "wind_farm", "zoo"
+]
+
+
+@DATASET_REGISTRY.register()
+class FMoW(WILDSBase):
+ """Satellite imagery classification.
+
+ 62 classes (building or land use categories).
+
+ Reference:
+ - Christie et al. "Functional Map of the World." CVPR 2018.
+ - Koh et al. "Wilds: A benchmark of in-the-wild distribution shifts." ICML 2021.
+ """
+
+ dataset_dir = "fmow_v1.1"
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+
+ def get_image_path(self, dataset, idx):
+ idx = dataset.full_idxs[idx]
+ image_name = f"rgb_img_{idx}.png"
+ image_path = osp.join(self.dataset_dir, "images", image_name)
+ return image_path
+
+ def get_domain(self, dataset, idx):
+ # number of regions: 5 or 6
+ # number of years: 16
+ region_id = int(dataset.metadata_array[idx][0])
+ year_id = int(dataset.metadata_array[idx][1])
+ return region_id*16 + year_id
+
+ def load_classnames(self):
+ return {i: cat for i, cat in enumerate(CATEGORIES)}
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/iwildcam.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/iwildcam.py
new file mode 100644
index 00000000..3d1f016c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/iwildcam.py
@@ -0,0 +1,32 @@
+import os.path as osp
+import pandas as pd
+
+from dassl.data.datasets import DATASET_REGISTRY
+
+from .wilds_base import WILDSBase
+
+
+@DATASET_REGISTRY.register()
+class IWildCam(WILDSBase):
+ """Animal species recognition.
+
+ 182 classes (species).
+
+ Reference:
+ - Beery et al. "The iwildcam 2021 competition dataset." arXiv 2021.
+ - Koh et al. "Wilds: A benchmark of in-the-wild distribution shifts." ICML 2021.
+ """
+
+ dataset_dir = "iwildcam_v2.0"
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+
+ def get_image_path(self, dataset, idx):
+ image_name = dataset._input_array[idx]
+ image_path = osp.join(self.dataset_dir, "train", image_name)
+ return image_path
+
+ def load_classnames(self):
+ df = pd.read_csv(osp.join(self.dataset_dir, "categories.csv"))
+ return dict(df["name"])
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/wilds_base.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/wilds_base.py
new file mode 100644
index 00000000..33232e1e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/dg/wilds/wilds_base.py
@@ -0,0 +1,110 @@
+import logging # isort:skip
+logging.disable(logging.WARNING) # isort:skip
+
+import pickle
+import logging
+import os.path as osp
+from wilds import get_dataset as wilds_get_dataset
+
+from dassl.data.datasets import Datum, DatasetBase
+
+
+class WILDSBase(DatasetBase):
+
+ dataset_dir = ""
+ relabel_domain = True
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ name = self.dataset_dir.split("_")[0]
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ self.preloaded = osp.join(self.dataset_dir, "zhou_preloaded.pkl")
+
+ self.label_to_name = self.load_classnames()
+ assert isinstance(self.label_to_name, dict)
+
+ if osp.exists(self.preloaded):
+ with open(self.preloaded, "rb") as file:
+ dataset = pickle.load(file)
+ train = dataset["train"]
+ val = dataset["val"]
+ test = dataset["test"]
+ else:
+ dataset = wilds_get_dataset(
+ dataset=name, root_dir=root, download=True
+ )
+ subset_train = dataset.get_subset("train")
+ subset_val = dataset.get_subset("val")
+ subset_test = dataset.get_subset("test")
+
+ train = self.read_data(subset_train)
+ val = self.read_data(subset_val)
+ test = self.read_data(subset_test)
+
+ # Save time for data loading next time
+ preloaded = {"train": train, "val": val, "test": test}
+ with open(self.preloaded, "wb") as file:
+ pickle.dump(preloaded, file, protocol=pickle.HIGHEST_PROTOCOL)
+
+ # Few-shot learning
+ k = cfg.DATASET.NUM_SHOTS
+ if k > 0:
+ groups = self.split_dataset_by_domain(train)
+ groups = list(groups.values())
+ groups = self.generate_fewshot_dataset(*groups, num_shots=k)
+ train = []
+ for group in groups:
+ train.extend(group)
+
+ super().__init__(train_x=train, val=val, test=test)
+
+ def load_classnames(self):
+ raise NotImplementedError
+
+ def get_image_path(self, dataset, idx):
+ image_name = dataset._input_array[idx]
+ image_path = osp.join(self.dataset_dir, image_name)
+ return image_path
+
+ def get_label(self, dataset, idx):
+ return int(dataset.y_array[idx])
+
+ def get_domain(self, dataset, idx):
+ return int(dataset.metadata_array[idx][0])
+
+ def read_data(self, subset):
+ items = []
+ indices = subset.indices
+ dataset = subset.dataset
+
+ for idx in indices:
+ image_path = self.get_image_path(dataset, idx)
+ label = self.get_label(dataset, idx)
+ domain = self.get_domain(dataset, idx)
+ classname = self.label_to_name[label]
+ item = Datum(
+ impath=image_path,
+ label=label,
+ domain=domain,
+ classname=classname
+ )
+ items.append(item)
+
+ if self.relabel_domain:
+ domains = set([item.domain for item in items])
+ mapping = {domain: i for i, domain in enumerate(domains)}
+
+ items_new = []
+
+ for item in items:
+ item_new = Datum(
+ impath=item.impath,
+ label=item.label,
+ domain=mapping[item.domain],
+ classname=item.classname
+ )
+ items_new.append(item_new)
+
+ return items_new
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/__init__.py
new file mode 100644
index 00000000..a6607dcc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/__init__.py
@@ -0,0 +1,3 @@
+from .svhn import SVHN
+from .cifar import CIFAR10, CIFAR100
+from .stl10 import STL10
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/cifar.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/cifar.py
new file mode 100644
index 00000000..55845279
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/cifar.py
@@ -0,0 +1,108 @@
+import math
+import random
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class CIFAR10(DatasetBase):
+ """CIFAR10 for SSL.
+
+ Reference:
+ - Krizhevsky. Learning Multiple Layers of Features
+ from Tiny Images. Tech report.
+ """
+
+ dataset_dir = "cifar10"
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ train_dir = osp.join(self.dataset_dir, "train")
+ test_dir = osp.join(self.dataset_dir, "test")
+
+ assert cfg.DATASET.NUM_LABELED > 0
+
+ train_x, train_u, val = self._read_data_train(
+ train_dir, cfg.DATASET.NUM_LABELED, cfg.DATASET.VAL_PERCENT
+ )
+ test = self._read_data_test(test_dir)
+
+ if cfg.DATASET.ALL_AS_UNLABELED:
+ train_u = train_u + train_x
+
+ if len(val) == 0:
+ val = None
+
+ super().__init__(train_x=train_x, train_u=train_u, val=val, test=test)
+
+ def _read_data_train(self, data_dir, num_labeled, val_percent):
+ class_names = listdir_nohidden(data_dir)
+ class_names.sort()
+ num_labeled_per_class = num_labeled / len(class_names)
+ items_x, items_u, items_v = [], [], []
+
+ for label, class_name in enumerate(class_names):
+ class_dir = osp.join(data_dir, class_name)
+ imnames = listdir_nohidden(class_dir)
+
+ # Split into train and val following Oliver et al. 2018
+ # Set cfg.DATASET.VAL_PERCENT to 0 to not use val data
+ num_val = math.floor(len(imnames) * val_percent)
+ imnames_train = imnames[num_val:]
+ imnames_val = imnames[:num_val]
+
+ # Note we do shuffle after split
+ random.shuffle(imnames_train)
+
+ for i, imname in enumerate(imnames_train):
+ impath = osp.join(class_dir, imname)
+ item = Datum(impath=impath, label=label)
+
+ if (i + 1) <= num_labeled_per_class:
+ items_x.append(item)
+
+ else:
+ items_u.append(item)
+
+ for imname in imnames_val:
+ impath = osp.join(class_dir, imname)
+ item = Datum(impath=impath, label=label)
+ items_v.append(item)
+
+ return items_x, items_u, items_v
+
+ def _read_data_test(self, data_dir):
+ class_names = listdir_nohidden(data_dir)
+ class_names.sort()
+ items = []
+
+ for label, class_name in enumerate(class_names):
+ class_dir = osp.join(data_dir, class_name)
+ imnames = listdir_nohidden(class_dir)
+
+ for imname in imnames:
+ impath = osp.join(class_dir, imname)
+ item = Datum(impath=impath, label=label)
+ items.append(item)
+
+ return items
+
+
+@DATASET_REGISTRY.register()
+class CIFAR100(CIFAR10):
+ """CIFAR100 for SSL.
+
+ Reference:
+ - Krizhevsky. Learning Multiple Layers of Features
+ from Tiny Images. Tech report.
+ """
+
+ dataset_dir = "cifar100"
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/stl10.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/stl10.py
new file mode 100644
index 00000000..6a1f9f2d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/stl10.py
@@ -0,0 +1,87 @@
+import numpy as np
+import os.path as osp
+
+from dassl.utils import listdir_nohidden
+
+from ..build import DATASET_REGISTRY
+from ..base_dataset import Datum, DatasetBase
+
+
+@DATASET_REGISTRY.register()
+class STL10(DatasetBase):
+ """STL-10 dataset.
+
+ Description:
+ - 10 classes: airplane, bird, car, cat, deer, dog, horse,
+ monkey, ship, truck.
+ - Images are 96x96 pixels, color.
+ - 500 training images per class, 800 test images per class.
+ - 100,000 unlabeled images for unsupervised learning.
+
+ Reference:
+ - Coates et al. An Analysis of Single Layer Networks in
+ Unsupervised Feature Learning. AISTATS 2011.
+ """
+
+ dataset_dir = "stl10"
+
+ def __init__(self, cfg):
+ root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+ self.dataset_dir = osp.join(root, self.dataset_dir)
+ train_dir = osp.join(self.dataset_dir, "train")
+ test_dir = osp.join(self.dataset_dir, "test")
+ unlabeled_dir = osp.join(self.dataset_dir, "unlabeled")
+ fold_file = osp.join(
+ self.dataset_dir, "stl10_binary", "fold_indices.txt"
+ )
+
+ # Only use the first five splits
+ assert 0 <= cfg.DATASET.STL10_FOLD <= 4
+
+ train_x = self._read_data_train(
+ train_dir, cfg.DATASET.STL10_FOLD, fold_file
+ )
+ train_u = self._read_data_all(unlabeled_dir)
+ test = self._read_data_all(test_dir)
+
+ if cfg.DATASET.ALL_AS_UNLABELED:
+ train_u = train_u + train_x
+
+ super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+ def _read_data_train(self, data_dir, fold, fold_file):
+ imnames = listdir_nohidden(data_dir)
+ imnames.sort()
+ items = []
+
+ list_idx = list(range(len(imnames)))
+ if fold >= 0:
+ with open(fold_file, "r") as f:
+ str_idx = f.read().splitlines()[fold]
+ list_idx = np.fromstring(str_idx, dtype=np.uint8, sep=" ")
+
+ for i in list_idx:
+ imname = imnames[i]
+ impath = osp.join(data_dir, imname)
+ label = osp.splitext(imname)[0].split("_")[1]
+ label = int(label)
+ item = Datum(impath=impath, label=label)
+ items.append(item)
+
+ return items
+
+ def _read_data_all(self, data_dir):
+ imnames = listdir_nohidden(data_dir)
+ items = []
+
+ for imname in imnames:
+ impath = osp.join(data_dir, imname)
+ label = osp.splitext(imname)[0].split("_")[1]
+ if label == "none":
+ label = -1
+ else:
+ label = int(label)
+ item = Datum(impath=impath, label=label)
+ items.append(item)
+
+ return items
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/svhn.py b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/svhn.py
new file mode 100644
index 00000000..15e0de56
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/datasets/ssl/svhn.py
@@ -0,0 +1,17 @@
+from .cifar import CIFAR10
+from ..build import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class SVHN(CIFAR10):
+ """SVHN for SSL.
+
+ Reference:
+ - Netzer et al. Reading Digits in Natural Images with
+ Unsupervised Feature Learning. NIPS-W 2011.
+ """
+
+ dataset_dir = "svhn"
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/samplers.py b/python/ClipDetection/Dassl.pytorch/dassl/data/samplers.py
new file mode 100644
index 00000000..562bfbca
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/samplers.py
@@ -0,0 +1,205 @@
+import copy
+import numpy as np
+import random
+from collections import defaultdict
+from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler
+
+
+class RandomDomainSampler(Sampler):
+ """Randomly samples N domains each with K images
+ to form a minibatch of size N*K.
+
+ Args:
+ data_source (list): list of Datums.
+ batch_size (int): batch size.
+ n_domain (int): number of domains to sample in a minibatch.
+ """
+
+ def __init__(self, data_source, batch_size, n_domain):
+ self.data_source = data_source
+
+ # Keep track of image indices for each domain
+ self.domain_dict = defaultdict(list)
+ for i, item in enumerate(data_source):
+ self.domain_dict[item.domain].append(i)
+ self.domains = list(self.domain_dict.keys())
+
+ # Make sure each domain has equal number of images
+ if n_domain is None or n_domain <= 0:
+ n_domain = len(self.domains)
+ assert batch_size % n_domain == 0
+ self.n_img_per_domain = batch_size // n_domain
+
+ self.batch_size = batch_size
+ # n_domain denotes number of domains sampled in a minibatch
+ self.n_domain = n_domain
+ self.length = len(list(self.__iter__()))
+
+ def __iter__(self):
+ domain_dict = copy.deepcopy(self.domain_dict)
+ final_idxs = []
+ stop_sampling = False
+
+ while not stop_sampling:
+ selected_domains = random.sample(self.domains, self.n_domain)
+
+ for domain in selected_domains:
+ idxs = domain_dict[domain]
+ selected_idxs = random.sample(idxs, self.n_img_per_domain)
+ final_idxs.extend(selected_idxs)
+
+ for idx in selected_idxs:
+ domain_dict[domain].remove(idx)
+
+ remaining = len(domain_dict[domain])
+ if remaining < self.n_img_per_domain:
+ stop_sampling = True
+
+ return iter(final_idxs)
+
+ def __len__(self):
+ return self.length
+
+
+class SeqDomainSampler(Sampler):
+ """Sequential domain sampler, which randomly samples K
+ images from each domain to form a minibatch.
+
+ Args:
+ data_source (list): list of Datums.
+ batch_size (int): batch size.
+ """
+
+ def __init__(self, data_source, batch_size):
+ self.data_source = data_source
+
+ # Keep track of image indices for each domain
+ self.domain_dict = defaultdict(list)
+ for i, item in enumerate(data_source):
+ self.domain_dict[item.domain].append(i)
+ self.domains = list(self.domain_dict.keys())
+ self.domains.sort()
+
+ # Make sure each domain has equal number of images
+ n_domain = len(self.domains)
+ assert batch_size % n_domain == 0
+ self.n_img_per_domain = batch_size // n_domain
+
+ self.batch_size = batch_size
+ # n_domain denotes number of domains sampled in a minibatch
+ self.n_domain = n_domain
+ self.length = len(list(self.__iter__()))
+
+ def __iter__(self):
+ domain_dict = copy.deepcopy(self.domain_dict)
+ final_idxs = []
+ stop_sampling = False
+
+ while not stop_sampling:
+ for domain in self.domains:
+ idxs = domain_dict[domain]
+ selected_idxs = random.sample(idxs, self.n_img_per_domain)
+ final_idxs.extend(selected_idxs)
+
+ for idx in selected_idxs:
+ domain_dict[domain].remove(idx)
+
+ remaining = len(domain_dict[domain])
+ if remaining < self.n_img_per_domain:
+ stop_sampling = True
+
+ return iter(final_idxs)
+
+ def __len__(self):
+ return self.length
+
+
+class RandomClassSampler(Sampler):
+ """Randomly samples N classes each with K instances to
+ form a minibatch of size N*K.
+
+ Modified from https://github.com/KaiyangZhou/deep-person-reid.
+
+ Args:
+ data_source (list): list of Datums.
+ batch_size (int): batch size.
+ n_ins (int): number of instances per class to sample in a minibatch.
+ """
+
+ def __init__(self, data_source, batch_size, n_ins):
+ if batch_size < n_ins:
+ raise ValueError(
+ "batch_size={} must be no less "
+ "than n_ins={}".format(batch_size, n_ins)
+ )
+
+ self.data_source = data_source
+ self.batch_size = batch_size
+ self.n_ins = n_ins
+ self.ncls_per_batch = self.batch_size // self.n_ins
+ self.index_dic = defaultdict(list)
+ for index, item in enumerate(data_source):
+ self.index_dic[item.label].append(index)
+ self.labels = list(self.index_dic.keys())
+ assert len(self.labels) >= self.ncls_per_batch
+
+ # estimate number of images in an epoch
+ self.length = len(list(self.__iter__()))
+
+ def __iter__(self):
+ batch_idxs_dict = defaultdict(list)
+
+ for label in self.labels:
+ idxs = copy.deepcopy(self.index_dic[label])
+ if len(idxs) < self.n_ins:
+ idxs = np.random.choice(idxs, size=self.n_ins, replace=True)
+ random.shuffle(idxs)
+ batch_idxs = []
+ for idx in idxs:
+ batch_idxs.append(idx)
+ if len(batch_idxs) == self.n_ins:
+ batch_idxs_dict[label].append(batch_idxs)
+ batch_idxs = []
+
+ avai_labels = copy.deepcopy(self.labels)
+ final_idxs = []
+
+ while len(avai_labels) >= self.ncls_per_batch:
+ selected_labels = random.sample(avai_labels, self.ncls_per_batch)
+ for label in selected_labels:
+ batch_idxs = batch_idxs_dict[label].pop(0)
+ final_idxs.extend(batch_idxs)
+ if len(batch_idxs_dict[label]) == 0:
+ avai_labels.remove(label)
+
+ return iter(final_idxs)
+
+ def __len__(self):
+ return self.length
+
+
+def build_sampler(
+ sampler_type,
+ cfg=None,
+ data_source=None,
+ batch_size=32,
+ n_domain=0,
+ n_ins=16
+):
+ if sampler_type == "RandomSampler":
+ return RandomSampler(data_source)
+
+ elif sampler_type == "SequentialSampler":
+ return SequentialSampler(data_source)
+
+ elif sampler_type == "RandomDomainSampler":
+ return RandomDomainSampler(data_source, batch_size, n_domain)
+
+ elif sampler_type == "SeqDomainSampler":
+ return SeqDomainSampler(data_source, batch_size)
+
+ elif sampler_type == "RandomClassSampler":
+ return RandomClassSampler(data_source, batch_size, n_ins)
+
+ else:
+ raise ValueError("Unknown sampler type: {}".format(sampler_type))
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/__init__.py
new file mode 100644
index 00000000..02c05d67
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/__init__.py
@@ -0,0 +1 @@
+from .transforms import INTERPOLATION_MODES, build_transform
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/autoaugment.py b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/autoaugment.py
new file mode 100644
index 00000000..2e14fcee
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/autoaugment.py
@@ -0,0 +1,273 @@
+"""
+Source: https://github.com/DeepVoltaire/AutoAugment
+"""
+import numpy as np
+import random
+from PIL import Image, ImageOps, ImageEnhance
+
+
+class ImageNetPolicy:
+ """Randomly choose one of the best 24 Sub-policies on ImageNet.
+
+ Example:
+ >>> policy = ImageNetPolicy()
+ >>> transformed = policy(image)
+
+ Example as a PyTorch Transform:
+ >>> transform=transforms.Compose([
+ >>> transforms.Resize(256),
+ >>> ImageNetPolicy(),
+ >>> transforms.ToTensor()])
+ """
+
+ def __init__(self, fillcolor=(128, 128, 128)):
+ self.policies = [
+ SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
+ SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+ SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
+ SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
+ SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+ SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
+ SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
+ SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
+ SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
+ SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
+ SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
+ SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
+ SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
+ SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+ SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+ SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
+ SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
+ SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
+ SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
+ SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
+ SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+ SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+ SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+ SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+ SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
+ ]
+
+ def __call__(self, img):
+ policy_idx = random.randint(0, len(self.policies) - 1)
+ return self.policies[policy_idx](img)
+
+ def __repr__(self):
+ return "AutoAugment ImageNet Policy"
+
+
+class CIFAR10Policy:
+ """Randomly choose one of the best 25 Sub-policies on CIFAR10.
+
+ Example:
+ >>> policy = CIFAR10Policy()
+ >>> transformed = policy(image)
+
+ Example as a PyTorch Transform:
+ >>> transform=transforms.Compose([
+ >>> transforms.Resize(256),
+ >>> CIFAR10Policy(),
+ >>> transforms.ToTensor()])
+ """
+
+ def __init__(self, fillcolor=(128, 128, 128)):
+ self.policies = [
+ SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
+ SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
+ SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
+ SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
+ SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
+ SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
+ SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
+ SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
+ SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
+ SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
+ SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
+ SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
+ SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
+ SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
+ SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
+ SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
+ SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor),
+ SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
+ SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
+ SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
+ SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
+ SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
+ SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
+ SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
+ SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor),
+ ]
+
+ def __call__(self, img):
+ policy_idx = random.randint(0, len(self.policies) - 1)
+ return self.policies[policy_idx](img)
+
+ def __repr__(self):
+ return "AutoAugment CIFAR10 Policy"
+
+
+class SVHNPolicy:
+ """Randomly choose one of the best 25 Sub-policies on SVHN.
+
+ Example:
+ >>> policy = SVHNPolicy()
+ >>> transformed = policy(image)
+
+ Example as a PyTorch Transform:
+ >>> transform=transforms.Compose([
+ >>> transforms.Resize(256),
+ >>> SVHNPolicy(),
+ >>> transforms.ToTensor()])
+ """
+
+ def __init__(self, fillcolor=(128, 128, 128)):
+ self.policies = [
+ SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
+ SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
+ SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
+ SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
+ SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
+ SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
+ SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
+ SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
+ SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
+ SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
+ SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
+ SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
+ SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
+ SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
+ SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
+ SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
+ SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
+ SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
+ SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
+ SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
+ SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
+ SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
+ SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
+ SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
+ SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor),
+ ]
+
+ def __call__(self, img):
+ policy_idx = random.randint(0, len(self.policies) - 1)
+ return self.policies[policy_idx](img)
+
+ def __repr__(self):
+ return "AutoAugment SVHN Policy"
+
+
+class SubPolicy(object):
+
+ def __init__(
+ self,
+ p1,
+ operation1,
+ magnitude_idx1,
+ p2,
+ operation2,
+ magnitude_idx2,
+ fillcolor=(128, 128, 128),
+ ):
+ ranges = {
+ "shearX": np.linspace(0, 0.3, 10),
+ "shearY": np.linspace(0, 0.3, 10),
+ "translateX": np.linspace(0, 150 / 331, 10),
+ "translateY": np.linspace(0, 150 / 331, 10),
+ "rotate": np.linspace(0, 30, 10),
+ "color": np.linspace(0.0, 0.9, 10),
+ "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+ "solarize": np.linspace(256, 0, 10),
+ "contrast": np.linspace(0.0, 0.9, 10),
+ "sharpness": np.linspace(0.0, 0.9, 10),
+ "brightness": np.linspace(0.0, 0.9, 10),
+ "autocontrast": [0] * 10,
+ "equalize": [0] * 10,
+ "invert": [0] * 10,
+ }
+
+ # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+ def rotate_with_fill(img, magnitude):
+ rot = img.convert("RGBA").rotate(magnitude)
+ return Image.composite(
+ rot, Image.new("RGBA", rot.size, (128, ) * 4), rot
+ ).convert(img.mode)
+
+ func = {
+ "shearX":
+ lambda img, magnitude: img.transform(
+ img.size,
+ Image.AFFINE,
+ (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+ Image.BICUBIC,
+ fillcolor=fillcolor,
+ ),
+ "shearY":
+ lambda img, magnitude: img.transform(
+ img.size,
+ Image.AFFINE,
+ (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+ Image.BICUBIC,
+ fillcolor=fillcolor,
+ ),
+ "translateX":
+ lambda img, magnitude: img.transform(
+ img.size,
+ Image.AFFINE,
+ (
+ 1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0,
+ 1, 0
+ ),
+ fillcolor=fillcolor,
+ ),
+ "translateY":
+ lambda img, magnitude: img.transform(
+ img.size,
+ Image.AFFINE,
+ (
+ 1, 0, 0, 0, 1, magnitude * img.size[1] * random.
+ choice([-1, 1])
+ ),
+ fillcolor=fillcolor,
+ ),
+ "rotate":
+ lambda img, magnitude: rotate_with_fill(img, magnitude),
+ "color":
+ lambda img, magnitude: ImageEnhance.Color(img).
+ enhance(1 + magnitude * random.choice([-1, 1])),
+ "posterize":
+ lambda img, magnitude: ImageOps.posterize(img, magnitude),
+ "solarize":
+ lambda img, magnitude: ImageOps.solarize(img, magnitude),
+ "contrast":
+ lambda img, magnitude: ImageEnhance.Contrast(img).
+ enhance(1 + magnitude * random.choice([-1, 1])),
+ "sharpness":
+ lambda img, magnitude: ImageEnhance.Sharpness(img).
+ enhance(1 + magnitude * random.choice([-1, 1])),
+ "brightness":
+ lambda img, magnitude: ImageEnhance.Brightness(img).
+ enhance(1 + magnitude * random.choice([-1, 1])),
+ "autocontrast":
+ lambda img, magnitude: ImageOps.autocontrast(img),
+ "equalize":
+ lambda img, magnitude: ImageOps.equalize(img),
+ "invert":
+ lambda img, magnitude: ImageOps.invert(img),
+ }
+
+ self.p1 = p1
+ self.operation1 = func[operation1]
+ self.magnitude1 = ranges[operation1][magnitude_idx1]
+ self.p2 = p2
+ self.operation2 = func[operation2]
+ self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+ def __call__(self, img):
+ if random.random() < self.p1:
+ img = self.operation1(img, self.magnitude1)
+ if random.random() < self.p2:
+ img = self.operation2(img, self.magnitude2)
+ return img
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/randaugment.py b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/randaugment.py
new file mode 100644
index 00000000..5c39ff3e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/randaugment.py
@@ -0,0 +1,363 @@
+"""
+Credit to
+1) https://github.com/ildoonet/pytorch-randaugment
+2) https://github.com/kakaobrain/fast-autoaugment
+"""
+import numpy as np
+import random
+import PIL
+import torch
+import PIL.ImageOps
+import PIL.ImageDraw
+import PIL.ImageEnhance
+from PIL import Image
+
+
+def ShearX(img, v):
+ assert -0.3 <= v <= 0.3
+ if random.random() > 0.5:
+ v = -v
+ return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
+
+
+def ShearY(img, v):
+ assert -0.3 <= v <= 0.3
+ if random.random() > 0.5:
+ v = -v
+ return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
+
+
+def TranslateX(img, v):
+ # [-150, 150] => percentage: [-0.45, 0.45]
+ assert -0.45 <= v <= 0.45
+ if random.random() > 0.5:
+ v = -v
+ v = v * img.size[0]
+ return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
+
+
+def TranslateXabs(img, v):
+ # [-150, 150] => percentage: [-0.45, 0.45]
+ assert 0 <= v
+ if random.random() > 0.5:
+ v = -v
+ return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
+
+
+def TranslateY(img, v):
+ # [-150, 150] => percentage: [-0.45, 0.45]
+ assert -0.45 <= v <= 0.45
+ if random.random() > 0.5:
+ v = -v
+ v = v * img.size[1]
+ return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
+
+
+def TranslateYabs(img, v):
+ # [-150, 150] => percentage: [-0.45, 0.45]
+ assert 0 <= v
+ if random.random() > 0.5:
+ v = -v
+ return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
+
+
+def Rotate(img, v):
+ assert -30 <= v <= 30
+ if random.random() > 0.5:
+ v = -v
+ return img.rotate(v)
+
+
+def AutoContrast(img, _):
+ return PIL.ImageOps.autocontrast(img)
+
+
+def Invert(img, _):
+ return PIL.ImageOps.invert(img)
+
+
+def Equalize(img, _):
+ return PIL.ImageOps.equalize(img)
+
+
+def Flip(img, _):
+ return PIL.ImageOps.mirror(img)
+
+
+def Solarize(img, v):
+ assert 0 <= v <= 256
+ return PIL.ImageOps.solarize(img, v)
+
+
+def SolarizeAdd(img, addition=0, threshold=128):
+ img_np = np.array(img).astype(np.int)
+ img_np = img_np + addition
+ img_np = np.clip(img_np, 0, 255)
+ img_np = img_np.astype(np.uint8)
+ img = Image.fromarray(img_np)
+ return PIL.ImageOps.solarize(img, threshold)
+
+
+def Posterize(img, v):
+ assert 4 <= v <= 8
+ v = int(v)
+ return PIL.ImageOps.posterize(img, v)
+
+
+def Contrast(img, v):
+ assert 0.0 <= v <= 2.0
+ return PIL.ImageEnhance.Contrast(img).enhance(v)
+
+
+def Color(img, v):
+ assert 0.0 <= v <= 2.0
+ return PIL.ImageEnhance.Color(img).enhance(v)
+
+
+def Brightness(img, v):
+ assert 0.0 <= v <= 2.0
+ return PIL.ImageEnhance.Brightness(img).enhance(v)
+
+
+def Sharpness(img, v):
+ assert 0.0 <= v <= 2.0
+ return PIL.ImageEnhance.Sharpness(img).enhance(v)
+
+
+def Cutout(img, v):
+ # [0, 60] => percentage: [0, 0.2]
+ assert 0.0 <= v <= 0.2
+ if v <= 0.0:
+ return img
+
+ v = v * img.size[0]
+ return CutoutAbs(img, v)
+
+
+def CutoutAbs(img, v):
+ # [0, 60] => percentage: [0, 0.2]
+ # assert 0 <= v <= 20
+ if v < 0:
+ return img
+ w, h = img.size
+ x0 = np.random.uniform(w)
+ y0 = np.random.uniform(h)
+
+ x0 = int(max(0, x0 - v/2.0))
+ y0 = int(max(0, y0 - v/2.0))
+ x1 = min(w, x0 + v)
+ y1 = min(h, y0 + v)
+
+ xy = (x0, y0, x1, y1)
+ color = (125, 123, 114)
+ # color = (0, 0, 0)
+ img = img.copy()
+ PIL.ImageDraw.Draw(img).rectangle(xy, color)
+ return img
+
+
+def SamplePairing(imgs):
+ # [0, 0.4]
+ def f(img1, v):
+ i = np.random.choice(len(imgs))
+ img2 = PIL.Image.fromarray(imgs[i])
+ return PIL.Image.blend(img1, img2, v)
+
+ return f
+
+
+def Identity(img, v):
+ return img
+
+
+class Lighting:
+ """Lighting noise (AlexNet - style PCA - based noise)."""
+
+ def __init__(self, alphastd, eigval, eigvec):
+ self.alphastd = alphastd
+ self.eigval = torch.Tensor(eigval)
+ self.eigvec = torch.Tensor(eigvec)
+
+ def __call__(self, img):
+ if self.alphastd == 0:
+ return img
+
+ alpha = img.new().resize_(3).normal_(0, self.alphastd)
+ rgb = (
+ self.eigvec.type_as(img).clone().mul(
+ alpha.view(1, 3).expand(3, 3)
+ ).mul(self.eigval.view(1, 3).expand(3, 3)).sum(1).squeeze()
+ )
+
+ return img.add(rgb.view(3, 1, 1).expand_as(img))
+
+
+class CutoutDefault:
+ """
+ Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
+ """
+
+ def __init__(self, length):
+ self.length = length
+
+ def __call__(self, img):
+ h, w = img.size(1), img.size(2)
+ mask = np.ones((h, w), np.float32)
+ y = np.random.randint(h)
+ x = np.random.randint(w)
+
+ y1 = np.clip(y - self.length // 2, 0, h)
+ y2 = np.clip(y + self.length // 2, 0, h)
+ x1 = np.clip(x - self.length // 2, 0, w)
+ x2 = np.clip(x + self.length // 2, 0, w)
+
+ mask[y1:y2, x1:x2] = 0.0
+ mask = torch.from_numpy(mask)
+ mask = mask.expand_as(img)
+ img *= mask
+ return img
+
+
+def randaugment_list():
+ # 16 oeprations and their ranges
+ # https://github.com/google-research/uda/blob/master/image/randaugment/policies.py#L57
+ # augs = [
+ # (Identity, 0., 1.0),
+ # (ShearX, 0., 0.3), # 0
+ # (ShearY, 0., 0.3), # 1
+ # (TranslateX, 0., 0.33), # 2
+ # (TranslateY, 0., 0.33), # 3
+ # (Rotate, 0, 30), # 4
+ # (AutoContrast, 0, 1), # 5
+ # (Invert, 0, 1), # 6
+ # (Equalize, 0, 1), # 7
+ # (Solarize, 0, 110), # 8
+ # (Posterize, 4, 8), # 9
+ # # (Contrast, 0.1, 1.9), # 10
+ # (Color, 0.1, 1.9), # 11
+ # (Brightness, 0.1, 1.9), # 12
+ # (Sharpness, 0.1, 1.9), # 13
+ # # (Cutout, 0, 0.2), # 14
+ # # (SamplePairing(imgs), 0, 0.4) # 15
+ # ]
+
+ # https://github.com/tensorflow/tpu/blob/8462d083dd89489a79e3200bcc8d4063bf362186/models/official/efficientnet/autoaugment.py#L505
+ augs = [
+ (AutoContrast, 0, 1),
+ (Equalize, 0, 1),
+ (Invert, 0, 1),
+ (Rotate, 0, 30),
+ (Posterize, 4, 8),
+ (Solarize, 0, 256),
+ (SolarizeAdd, 0, 110),
+ (Color, 0.1, 1.9),
+ (Contrast, 0.1, 1.9),
+ (Brightness, 0.1, 1.9),
+ (Sharpness, 0.1, 1.9),
+ (ShearX, 0.0, 0.3),
+ (ShearY, 0.0, 0.3),
+ (CutoutAbs, 0, 40),
+ (TranslateXabs, 0.0, 100),
+ (TranslateYabs, 0.0, 100),
+ ]
+
+ return augs
+
+
+def randaugment_list2():
+ augs = [
+ (AutoContrast, 0, 1),
+ (Brightness, 0.1, 1.9),
+ (Color, 0.1, 1.9),
+ (Contrast, 0.1, 1.9),
+ (Equalize, 0, 1),
+ (Identity, 0, 1),
+ (Invert, 0, 1),
+ (Posterize, 4, 8),
+ (Rotate, -30, 30),
+ (Sharpness, 0.1, 1.9),
+ (ShearX, -0.3, 0.3),
+ (ShearY, -0.3, 0.3),
+ (Solarize, 0, 256),
+ (TranslateX, -0.3, 0.3),
+ (TranslateY, -0.3, 0.3),
+ ]
+
+ return augs
+
+
+def fixmatch_list():
+ # https://arxiv.org/abs/2001.07685
+ augs = [
+ (AutoContrast, 0, 1),
+ (Brightness, 0.05, 0.95),
+ (Color, 0.05, 0.95),
+ (Contrast, 0.05, 0.95),
+ (Equalize, 0, 1),
+ (Identity, 0, 1),
+ (Posterize, 4, 8),
+ (Rotate, -30, 30),
+ (Sharpness, 0.05, 0.95),
+ (ShearX, -0.3, 0.3),
+ (ShearY, -0.3, 0.3),
+ (Solarize, 0, 256),
+ (TranslateX, -0.3, 0.3),
+ (TranslateY, -0.3, 0.3),
+ ]
+
+ return augs
+
+
+class RandAugment:
+
+ def __init__(self, n=2, m=10):
+ assert 0 <= m <= 30
+ self.n = n
+ self.m = m
+ self.augment_list = randaugment_list()
+
+ def __call__(self, img):
+ ops = random.choices(self.augment_list, k=self.n)
+
+ for op, minval, maxval in ops:
+ val = (self.m / 30) * (maxval-minval) + minval
+ img = op(img, val)
+
+ return img
+
+
+class RandAugment2:
+
+ def __init__(self, n=2, p=0.6):
+ self.n = n
+ self.p = p
+ self.augment_list = randaugment_list2()
+
+ def __call__(self, img):
+ ops = random.choices(self.augment_list, k=self.n)
+
+ for op, minval, maxval in ops:
+ if random.random() > self.p:
+ continue
+ m = random.random()
+ val = m * (maxval-minval) + minval
+ img = op(img, val)
+
+ return img
+
+
+class RandAugmentFixMatch:
+
+ def __init__(self, n=2):
+ self.n = n
+ self.augment_list = fixmatch_list()
+
+ def __call__(self, img):
+ ops = random.choices(self.augment_list, k=self.n)
+
+ for op, minval, maxval in ops:
+ m = random.random()
+ val = m * (maxval-minval) + minval
+ img = op(img, val)
+
+ return img
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/transforms.py b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/transforms.py
new file mode 100644
index 00000000..904e97aa
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/data/transforms/transforms.py
@@ -0,0 +1,319 @@
+from PIL import Image
+import numpy as np
+import random
+import torch
+import torchvision.transforms.functional as F
+from torchvision.transforms import (
+ Resize, Compose, ToTensor, Normalize, CenterCrop, RandomCrop, ColorJitter,
+ RandomApply, GaussianBlur, RandomGrayscale, RandomResizedCrop,
+ RandomHorizontalFlip
+)
+
+from .autoaugment import SVHNPolicy, CIFAR10Policy, ImageNetPolicy
+from .randaugment import RandAugment, RandAugment2, RandAugmentFixMatch
+
+AVAI_CHOICES = [
+ "random_flip",
+ "random_resized_crop",
+ "normalize",
+ "instance_norm",
+ "random_crop",
+ "random_translation",
+ "center_crop", # This has become a default operation during testing
+ "cutout",
+ "imagenet_policy",
+ "cifar10_policy",
+ "svhn_policy",
+ "randaugment",
+ "randaugment_fixmatch",
+ "randaugment2",
+ "gaussian_noise",
+ "colorjitter",
+ "randomgrayscale",
+ "gaussian_blur",
+]
+
+INTERPOLATION_MODES = {
+ "bilinear": Image.BILINEAR,
+ "bicubic": Image.BICUBIC,
+ "nearest": Image.NEAREST,
+}
+
+
+class Random2DTranslation:
+ """Given an image of (height, width), we resize it to
+ (height*1.125, width*1.125), and then perform random cropping.
+
+ Args:
+ height (int): target image height.
+ width (int): target image width.
+ p (float, optional): probability that this operation takes place.
+ Default is 0.5.
+ interpolation (int, optional): desired interpolation. Default is
+ ``torchvision.transforms.functional.InterpolationMode.BILINEAR``
+ """
+
+ def __init__(
+ self, height, width, p=0.5, interpolation=Image.BILINEAR
+ ):
+ self.height = height
+ self.width = width
+ self.p = p
+ self.interpolation = interpolation
+
+ def __call__(self, img):
+ if random.uniform(0, 1) > self.p:
+ return F.resize(
+ img=img,
+ size=[self.height, self.width],
+ interpolation=self.interpolation
+ )
+
+ new_width = int(round(self.width * 1.125))
+ new_height = int(round(self.height * 1.125))
+ resized_img = F.resize(
+ img=img,
+ size=[new_height, new_width],
+ interpolation=self.interpolation
+ )
+ x_maxrange = new_width - self.width
+ y_maxrange = new_height - self.height
+ x1 = int(round(random.uniform(0, x_maxrange)))
+ y1 = int(round(random.uniform(0, y_maxrange)))
+ croped_img = F.crop(
+ img=resized_img,
+ top=y1,
+ left=x1,
+ height=self.height,
+ width=self.width
+ )
+
+ return croped_img
+
+
+class InstanceNormalization:
+ """Normalize data using per-channel mean and standard deviation.
+
+ Reference:
+ - Ulyanov et al. Instance normalization: The missing in- gredient
+ for fast stylization. ArXiv 2016.
+ - Shu et al. A DIRT-T Approach to Unsupervised Domain Adaptation.
+ ICLR 2018.
+ """
+
+ def __init__(self, eps=1e-8):
+ self.eps = eps
+
+ def __call__(self, img):
+ C, H, W = img.shape
+ img_re = img.reshape(C, H * W)
+ mean = img_re.mean(1).view(C, 1, 1)
+ std = img_re.std(1).view(C, 1, 1)
+ return (img-mean) / (std + self.eps)
+
+
+class Cutout:
+ """Randomly mask out one or more patches from an image.
+
+ https://github.com/uoguelph-mlrg/Cutout
+
+ Args:
+ n_holes (int, optional): number of patches to cut out
+ of each image. Default is 1.
+ length (int, optinal): length (in pixels) of each square
+ patch. Default is 16.
+ """
+
+ def __init__(self, n_holes=1, length=16):
+ self.n_holes = n_holes
+ self.length = length
+
+ def __call__(self, img):
+ """
+ Args:
+ img (Tensor): tensor image of size (C, H, W).
+
+ Returns:
+ Tensor: image with n_holes of dimension
+ length x length cut out of it.
+ """
+ h = img.size(1)
+ w = img.size(2)
+
+ mask = np.ones((h, w), np.float32)
+
+ for n in range(self.n_holes):
+ y = np.random.randint(h)
+ x = np.random.randint(w)
+
+ y1 = np.clip(y - self.length // 2, 0, h)
+ y2 = np.clip(y + self.length // 2, 0, h)
+ x1 = np.clip(x - self.length // 2, 0, w)
+ x2 = np.clip(x + self.length // 2, 0, w)
+
+ mask[y1:y2, x1:x2] = 0.0
+
+ mask = torch.from_numpy(mask)
+ mask = mask.expand_as(img)
+ return img * mask
+
+
+class GaussianNoise:
+ """Add gaussian noise."""
+
+ def __init__(self, mean=0, std=0.15, p=0.5):
+ self.mean = mean
+ self.std = std
+ self.p = p
+
+ def __call__(self, img):
+ if random.uniform(0, 1) > self.p:
+ return img
+ noise = torch.randn(img.size()) * self.std + self.mean
+ return img + noise
+
+
+def build_transform(cfg, is_train=True, choices=None):
+ """Build transformation function.
+
+ Args:
+ cfg (CfgNode): config.
+ is_train (bool, optional): for training (True) or test (False).
+ Default is True.
+ choices (list, optional): list of strings which will overwrite
+ cfg.INPUT.TRANSFORMS if given. Default is None.
+ """
+ if cfg.INPUT.NO_TRANSFORM:
+ print("Note: no transform is applied!")
+ return None
+
+ if choices is None:
+ choices = cfg.INPUT.TRANSFORMS
+
+ for choice in choices:
+ assert choice in AVAI_CHOICES
+
+ target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}"
+
+ normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
+
+ if is_train:
+ return _build_transform_train(cfg, choices, target_size, normalize)
+ else:
+ return _build_transform_test(cfg, choices, target_size, normalize)
+
+
+def _build_transform_train(cfg, choices, target_size, normalize):
+ tfm_train = []
+
+ interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
+ input_size = cfg.INPUT.SIZE
+
+ # Make sure the image size matches the target size
+ conditions = []
+ conditions += ["random_crop" not in choices]
+ conditions += ["random_resized_crop" not in choices]
+ if all(conditions):
+ tfm_train += [Resize(input_size, interpolation=interp_mode)]
+
+ if "random_translation" in choices:
+ tfm_train += [Random2DTranslation(input_size[0], input_size[1])]
+
+ if "random_crop" in choices:
+ crop_padding = cfg.INPUT.CROP_PADDING
+ tfm_train += [RandomCrop(input_size, padding=crop_padding)]
+
+ if "random_resized_crop" in choices:
+ s_ = cfg.INPUT.RRCROP_SCALE
+ tfm_train += [
+ RandomResizedCrop(input_size, scale=s_, interpolation=interp_mode)
+ ]
+
+ if "random_flip" in choices:
+ tfm_train += [RandomHorizontalFlip()]
+
+ if "imagenet_policy" in choices:
+ tfm_train += [ImageNetPolicy()]
+
+ if "cifar10_policy" in choices:
+ tfm_train += [CIFAR10Policy()]
+
+ if "svhn_policy" in choices:
+ tfm_train += [SVHNPolicy()]
+
+ if "randaugment" in choices:
+ n_ = cfg.INPUT.RANDAUGMENT_N
+ m_ = cfg.INPUT.RANDAUGMENT_M
+ tfm_train += [RandAugment(n_, m_)]
+
+ if "randaugment_fixmatch" in choices:
+ n_ = cfg.INPUT.RANDAUGMENT_N
+ tfm_train += [RandAugmentFixMatch(n_)]
+
+ if "randaugment2" in choices:
+ n_ = cfg.INPUT.RANDAUGMENT_N
+ tfm_train += [RandAugment2(n_)]
+
+ if "colorjitter" in choices:
+ b_ = cfg.INPUT.COLORJITTER_B
+ c_ = cfg.INPUT.COLORJITTER_C
+ s_ = cfg.INPUT.COLORJITTER_S
+ h_ = cfg.INPUT.COLORJITTER_H
+ tfm_train += [
+ ColorJitter(
+ brightness=b_,
+ contrast=c_,
+ saturation=s_,
+ hue=h_,
+ )
+ ]
+
+ if "randomgrayscale" in choices:
+ tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)]
+
+ if "gaussian_blur" in choices:
+ gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P
+ tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)]
+
+ tfm_train += [ToTensor()]
+
+ if "cutout" in choices:
+ cutout_n = cfg.INPUT.CUTOUT_N
+ cutout_len = cfg.INPUT.CUTOUT_LEN
+ tfm_train += [Cutout(cutout_n, cutout_len)]
+
+ if "normalize" in choices:
+ tfm_train += [normalize]
+
+ if "gaussian_noise" in choices:
+ tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)]
+
+ if "instance_norm" in choices:
+ tfm_train += [InstanceNormalization()]
+
+ tfm_train = Compose(tfm_train)
+
+ return tfm_train
+
+
+def _build_transform_test(cfg, choices, target_size, normalize):
+ tfm_test = []
+
+ interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
+ input_size = cfg.INPUT.SIZE
+
+ tfm_test += [Resize(max(input_size), interpolation=interp_mode)]
+
+ tfm_test += [CenterCrop(input_size)]
+
+ tfm_test += [ToTensor()]
+
+ if "normalize" in choices:
+ tfm_test += [normalize]
+
+ if "instance_norm" in choices:
+ tfm_test += [InstanceNormalization()]
+
+ tfm_test = Compose(tfm_test)
+
+ return tfm_test
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/__init__.py
new file mode 100644
index 00000000..3cb3fb86
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/__init__.py
@@ -0,0 +1,6 @@
+from .build import TRAINER_REGISTRY, build_trainer # isort:skip
+from .trainer import TrainerX, TrainerXU, TrainerBase, SimpleTrainer, SimpleNet # isort:skip
+
+from .da import *
+from .dg import *
+from .ssl import *
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/build.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/build.py
new file mode 100644
index 00000000..47791250
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+TRAINER_REGISTRY = Registry("TRAINER")
+
+
+def build_trainer(cfg, classnames=[], device_id=-1):
+ avai_trainers = TRAINER_REGISTRY.registered_names()
+ check_availability(cfg.TRAINER.NAME, avai_trainers)
+ if cfg.VERBOSE:
+ print("Loading trainer: {}".format(cfg.TRAINER.NAME))
+ return TRAINER_REGISTRY.get(cfg.TRAINER.NAME)(cfg, classnames, device_id)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/__init__.py
new file mode 100644
index 00000000..910bf34b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/__init__.py
@@ -0,0 +1,10 @@
+from .se import SE
+from .mcd import MCD
+from .mme import MME
+from .adda import ADDA
+from .cdac import CDAC
+from .dael import DAEL
+from .dann import DANN
+from .adabn import AdaBN
+from .m3sda import M3SDA
+from .source_only import SourceOnly
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adabn.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adabn.py
new file mode 100644
index 00000000..116d8a21
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adabn.py
@@ -0,0 +1,38 @@
+import torch
+
+from dassl.utils import check_isfile
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+
+
+@TRAINER_REGISTRY.register()
+class AdaBN(TrainerXU):
+ """Adaptive Batch Normalization.
+
+ https://arxiv.org/abs/1603.04779.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.done_reset_bn_stats = False
+
+ def check_cfg(self, cfg):
+ assert check_isfile(
+ cfg.MODEL.INIT_WEIGHTS
+ ), "The weights of source model must be provided"
+
+ def before_epoch(self):
+ if not self.done_reset_bn_stats:
+ for m in self.model.modules():
+ classname = m.__class__.__name__
+ if classname.find("BatchNorm") != -1:
+ m.reset_running_stats()
+
+ self.done_reset_bn_stats = True
+
+ def forward_backward(self, batch_x, batch_u):
+ input_u = batch_u["img"].to(self.device)
+
+ with torch.no_grad():
+ self.model(input_u)
+
+ return None
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adda.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adda.py
new file mode 100644
index 00000000..a9018e78
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/adda.py
@@ -0,0 +1,85 @@
+import copy
+import torch
+import torch.nn as nn
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import check_isfile, count_num_param, open_specified_layers
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.modeling import build_head
+
+
+@TRAINER_REGISTRY.register()
+class ADDA(TrainerXU):
+ """Adversarial Discriminative Domain Adaptation.
+
+ https://arxiv.org/abs/1702.05464.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.open_layers = ["backbone"]
+ if isinstance(self.model.head, nn.Module):
+ self.open_layers.append("head")
+
+ self.source_model = copy.deepcopy(self.model)
+ self.source_model.eval()
+ for param in self.source_model.parameters():
+ param.requires_grad_(False)
+
+ self.build_critic()
+
+ self.bce = nn.BCEWithLogitsLoss()
+
+ def check_cfg(self, cfg):
+ assert check_isfile(
+ cfg.MODEL.INIT_WEIGHTS
+ ), "The weights of source model must be provided"
+
+ def build_critic(self):
+ cfg = self.cfg
+
+ print("Building critic network")
+ fdim = self.model.fdim
+ critic_body = build_head(
+ "mlp",
+ verbose=cfg.VERBOSE,
+ in_features=fdim,
+ hidden_layers=[fdim, fdim // 2],
+ activation="leaky_relu",
+ )
+ self.critic = nn.Sequential(critic_body, nn.Linear(fdim // 2, 1))
+ print("# params: {:,}".format(count_num_param(self.critic)))
+ self.critic.to(self.device)
+ self.optim_c = build_optimizer(self.critic, cfg.OPTIM)
+ self.sched_c = build_lr_scheduler(self.optim_c, cfg.OPTIM)
+ self.register_model("critic", self.critic, self.optim_c, self.sched_c)
+
+ def forward_backward(self, batch_x, batch_u):
+ open_specified_layers(self.model, self.open_layers)
+ input_x, _, input_u = self.parse_batch_train(batch_x, batch_u)
+ domain_x = torch.ones(input_x.shape[0], 1).to(self.device)
+ domain_u = torch.zeros(input_u.shape[0], 1).to(self.device)
+
+ _, feat_x = self.source_model(input_x, return_feature=True)
+ _, feat_u = self.model(input_u, return_feature=True)
+
+ logit_xd = self.critic(feat_x)
+ logit_ud = self.critic(feat_u.detach())
+
+ loss_critic = self.bce(logit_xd, domain_x)
+ loss_critic += self.bce(logit_ud, domain_u)
+ self.model_backward_and_update(loss_critic, "critic")
+
+ logit_ud = self.critic(feat_u)
+ loss_model = self.bce(logit_ud, 1 - domain_u)
+ self.model_backward_and_update(loss_model, "model")
+
+ loss_summary = {
+ "loss_critic": loss_critic.item(),
+ "loss_model": loss_model.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/cdac.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/cdac.py
new file mode 100644
index 00000000..ed846597
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/cdac.py
@@ -0,0 +1,275 @@
+import numpy as np
+from functools import partial
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.optim.lr_scheduler import LambdaLR
+
+from dassl.data import DataManager
+from dassl.optim import build_optimizer
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling.ops import ReverseGrad
+from dassl.engine.trainer import SimpleNet
+from dassl.data.transforms.transforms import build_transform
+
+
+def custom_scheduler(iter, max_iter=None, alpha=10, beta=0.75, init_lr=0.001):
+ """Custom LR Annealing
+
+ https://arxiv.org/pdf/1409.7495.pdf
+ """
+ if max_iter is None:
+ return init_lr
+ return (1 + float(iter / max_iter) * alpha)**(-1.0 * beta)
+
+
+class AAC(nn.Module):
+
+ def forward(self, sim_mat, prob_u, prob_us):
+
+ P = prob_u.matmul(prob_us.t())
+
+ loss = -(
+ sim_mat * torch.log(P + 1e-7) +
+ (1.-sim_mat) * torch.log(1. - P + 1e-7)
+ )
+ return loss.mean()
+
+
+class Prototypes(nn.Module):
+
+ def __init__(self, fdim, num_classes, temp=0.05):
+ super().__init__()
+ self.prototypes = nn.Linear(fdim, num_classes, bias=False)
+ self.temp = temp
+ self.revgrad = ReverseGrad()
+
+ def forward(self, x, reverse=False):
+ if reverse:
+ x = self.revgrad(x)
+ x = F.normalize(x, p=2, dim=1)
+ out = self.prototypes(x)
+ out = out / self.temp
+ return out
+
+
+@TRAINER_REGISTRY.register()
+class CDAC(TrainerXU):
+ """Cross Domain Adaptive Clustering.
+
+ https://arxiv.org/pdf/2104.09415.pdf
+ """
+
+ def __init__(self, cfg):
+ self.rampup_coef = cfg.TRAINER.CDAC.RAMPUP_COEF
+ self.rampup_iters = cfg.TRAINER.CDAC.RAMPUP_ITRS
+ self.lr_multi = cfg.TRAINER.CDAC.CLASS_LR_MULTI
+ self.topk = cfg.TRAINER.CDAC.TOPK_MATCH
+ self.p_thresh = cfg.TRAINER.CDAC.P_THRESH
+ self.aac_criterion = AAC()
+ super().__init__(cfg)
+
+ def check_cfg(self, cfg):
+ assert len(
+ cfg.TRAINER.CDAC.STRONG_TRANSFORMS
+ ) > 0, "Strong augmentations are necessary to run CDAC"
+ assert cfg.DATALOADER.K_TRANSFORMS == 2, "CDAC needs two strong augmentations of the same image."
+
+ def build_data_loader(self):
+
+ cfg = self.cfg
+ tfm_train = build_transform(cfg, is_train=True)
+ custom_tfm_train = [tfm_train]
+ choices = cfg.TRAINER.CDAC.STRONG_TRANSFORMS
+ tfm_train_strong = build_transform(cfg, is_train=True, choices=choices)
+ custom_tfm_train += [tfm_train_strong]
+ self.dm = DataManager(self.cfg, custom_tfm_train=custom_tfm_train)
+ self.train_loader_x = self.dm.train_loader_x
+ self.train_loader_u = self.dm.train_loader_u
+ self.val_loader = self.dm.val_loader
+ self.test_loader = self.dm.test_loader
+ self.num_classes = self.dm.num_classes
+ self.lab2cname = self.dm.lab2cname
+
+ def build_model(self):
+ cfg = self.cfg
+
+ # Custom LR Scheduler for CDAC
+ if self.cfg.TRAIN.COUNT_ITER == "train_x":
+ self.num_batches = len(self.train_loader_x)
+ elif self.cfg.TRAIN.COUNT_ITER == "train_u":
+ self.num_batches = len(self.len_train_loader_u)
+ elif self.cfg.TRAIN.COUNT_ITER == "smaller_one":
+ self.num_batches = min(
+ len(self.train_loader_x), len(self.train_loader_u)
+ )
+ self.max_iter = self.max_epoch * self.num_batches
+ print("Max Iterations: %d" % self.max_iter)
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ custom_lr_F = partial(
+ custom_scheduler, max_iter=self.max_iter, init_lr=cfg.OPTIM.LR
+ )
+ self.sched_F = LambdaLR(self.optim_F, custom_lr_F)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+
+ print("Building C")
+ self.C = Prototypes(self.F.fdim, self.num_classes)
+ self.C.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.C)))
+ self.optim_C = build_optimizer(self.C, cfg.OPTIM)
+
+ # Multiply the learning rate of C by lr_multi
+ for group_param in self.optim_C.param_groups:
+ group_param['lr'] *= self.lr_multi
+ custom_lr_C = partial(
+ custom_scheduler,
+ max_iter=self.max_iter,
+ init_lr=cfg.OPTIM.LR * self.lr_multi
+ )
+ self.sched_C = LambdaLR(self.optim_C, custom_lr_C)
+ self.register_model("C", self.C, self.optim_C, self.sched_C)
+
+ def assess_y_pred_quality(self, y_pred, y_true, mask):
+ n_masked_correct = (y_pred.eq(y_true).float() * mask).sum()
+ acc_thre = n_masked_correct / (mask.sum() + 1e-5)
+ acc_raw = y_pred.eq(y_true).sum() / y_pred.numel() # raw accuracy
+ keep_rate = mask.sum() / mask.numel()
+ output = {
+ "acc_thre": acc_thre,
+ "acc_raw": acc_raw,
+ "keep_rate": keep_rate
+ }
+ return output
+
+ def forward_backward(self, batch_x, batch_u):
+
+ current_itr = self.epoch * self.num_batches + self.batch_idx
+
+ input_x, label_x, input_u, input_us, input_us2, label_u = self.parse_batch_train(
+ batch_x, batch_u
+ )
+
+ # Paper Reference Eq. 2 - Supervised Loss
+
+ feat_x = self.F(input_x)
+ logit_x = self.C(feat_x)
+ loss_x = F.cross_entropy(logit_x, label_x)
+
+ self.model_backward_and_update(loss_x)
+
+ feat_u = self.F(input_u)
+ feat_us = self.F(input_us)
+ feat_us2 = self.F(input_us2)
+
+ # Paper Reference Eq.3 - Adversarial Adaptive Loss
+ logit_u = self.C(feat_u, reverse=True)
+ logit_us = self.C(feat_us, reverse=True)
+ prob_u, prob_us = F.softmax(logit_u, dim=1), F.softmax(logit_us, dim=1)
+
+ # Get similarity matrix s_ij
+ sim_mat = self.get_similarity_matrix(feat_u, self.topk, self.device)
+
+ aac_loss = (-1. * self.aac_criterion(sim_mat, prob_u, prob_us))
+
+ # Paper Reference Eq. 4 - Pseudo label Loss
+ logit_u = self.C(feat_u)
+ logit_us = self.C(feat_us)
+ logit_us2 = self.C(feat_us2)
+ prob_u, prob_us, prob_us2 = F.softmax(
+ logit_u, dim=1
+ ), F.softmax(
+ logit_us, dim=1
+ ), F.softmax(
+ logit_us2, dim=1
+ )
+ prob_u = prob_u.detach()
+ max_probs, max_idx = torch.max(prob_u, dim=-1)
+ mask = max_probs.ge(self.p_thresh).float()
+ p_u_stats = self.assess_y_pred_quality(max_idx, label_u, mask)
+
+ pl_loss = (
+ F.cross_entropy(logit_us2, max_idx, reduction='none') * mask
+ ).mean()
+
+ # Paper Reference Eq. 8 - Consistency Loss
+ cons_multi = self.sigmoid_rampup(
+ current_itr=current_itr, rampup_itr=self.rampup_iters
+ ) * self.rampup_coef
+ cons_loss = cons_multi * F.mse_loss(prob_us, prob_us2)
+
+ loss_u = aac_loss + pl_loss + cons_loss
+
+ self.model_backward_and_update(loss_u)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ "aac_loss": aac_loss.item(),
+ "pl_loss": pl_loss.item(),
+ "cons_loss": cons_loss.item(),
+ "p_u_pred_acc": p_u_stats["acc_raw"],
+ "p_u_pred_acc_thre": p_u_stats["acc_thre"],
+ "p_u_pred_keep": p_u_stats["keep_rate"]
+ }
+
+ # Update LR after every iteration as mentioned in the paper
+
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+
+ input_x = batch_x["img"][0]
+ label_x = batch_x["label"]
+
+ input_u = batch_u["img"][0]
+ input_us = batch_u["img2"][0]
+ input_us2 = batch_u["img2"][1]
+ label_u = batch_u["label"]
+
+ input_x = input_x.to(self.device)
+ label_x = label_x.to(self.device)
+
+ input_u = input_u.to(self.device)
+ input_us = input_us.to(self.device)
+ input_us2 = input_us2.to(self.device)
+ label_u = label_u.to(self.device)
+
+ return input_x, label_x, input_u, input_us, input_us2, label_u
+
+ def model_inference(self, input):
+ return self.C(self.F(input))
+
+ @staticmethod
+ def get_similarity_matrix(feat, topk, device):
+
+ feat_d = feat.detach()
+
+ feat_d = torch.sort(
+ torch.argsort(feat_d, dim=1, descending=True)[:, :topk], dim=1
+ )[0]
+ sim_mat = torch.zeros((feat_d.shape[0], feat_d.shape[0])).to(device)
+ for row in range(feat_d.shape[0]):
+ sim_mat[row, torch.all(feat_d == feat_d[row, :], dim=1)] = 1
+ return sim_mat
+
+ @staticmethod
+ def sigmoid_rampup(current_itr, rampup_itr):
+ """Exponential Rampup
+ https://arxiv.org/abs/1610.02242
+ """
+ if rampup_itr == 0:
+ return 1.0
+ else:
+ var = np.clip(current_itr, 0.0, rampup_itr)
+ phase = 1.0 - var/rampup_itr
+ return float(np.exp(-5.0 * phase * phase))
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dael.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dael.py
new file mode 100644
index 00000000..458df7da
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dael.py
@@ -0,0 +1,210 @@
+import torch
+import torch.nn as nn
+
+from dassl.data import DataManager
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.engine.trainer import SimpleNet
+from dassl.data.transforms import build_transform
+from dassl.modeling.ops.utils import create_onehot
+
+
+class Experts(nn.Module):
+
+ def __init__(self, n_source, fdim, num_classes):
+ super().__init__()
+ self.linears = nn.ModuleList(
+ [nn.Linear(fdim, num_classes) for _ in range(n_source)]
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, i, x):
+ x = self.linears[i](x)
+ x = self.softmax(x)
+ return x
+
+
+@TRAINER_REGISTRY.register()
+class DAEL(TrainerXU):
+ """Domain Adaptive Ensemble Learning.
+
+ https://arxiv.org/abs/2003.07325.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ n_domain = cfg.DATALOADER.TRAIN_X.N_DOMAIN
+ batch_size = cfg.DATALOADER.TRAIN_X.BATCH_SIZE
+ if n_domain <= 0:
+ n_domain = self.num_source_domains
+ self.split_batch = batch_size // n_domain
+ self.n_domain = n_domain
+
+ self.weight_u = cfg.TRAINER.DAEL.WEIGHT_U
+ self.conf_thre = cfg.TRAINER.DAEL.CONF_THRE
+
+ def check_cfg(self, cfg):
+ assert cfg.DATALOADER.TRAIN_X.SAMPLER == "RandomDomainSampler"
+ assert not cfg.DATALOADER.TRAIN_U.SAME_AS_X
+ assert len(cfg.TRAINER.DAEL.STRONG_TRANSFORMS) > 0
+
+ def build_data_loader(self):
+ cfg = self.cfg
+ tfm_train = build_transform(cfg, is_train=True)
+ custom_tfm_train = [tfm_train]
+ choices = cfg.TRAINER.DAEL.STRONG_TRANSFORMS
+ tfm_train_strong = build_transform(cfg, is_train=True, choices=choices)
+ custom_tfm_train += [tfm_train_strong]
+ dm = DataManager(self.cfg, custom_tfm_train=custom_tfm_train)
+ self.train_loader_x = dm.train_loader_x
+ self.train_loader_u = dm.train_loader_u
+ self.val_loader = dm.val_loader
+ self.test_loader = dm.test_loader
+ self.num_classes = dm.num_classes
+ self.num_source_domains = dm.num_source_domains
+ self.lab2cname = dm.lab2cname
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+ fdim = self.F.fdim
+
+ print("Building E")
+ self.E = Experts(self.num_source_domains, fdim, self.num_classes)
+ self.E.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.E)))
+ self.optim_E = build_optimizer(self.E, cfg.OPTIM)
+ self.sched_E = build_lr_scheduler(self.optim_E, cfg.OPTIM)
+ self.register_model("E", self.E, self.optim_E, self.sched_E)
+
+ def forward_backward(self, batch_x, batch_u):
+ parsed_data = self.parse_batch_train(batch_x, batch_u)
+ input_x, input_x2, label_x, domain_x, input_u, input_u2 = parsed_data
+
+ input_x = torch.split(input_x, self.split_batch, 0)
+ input_x2 = torch.split(input_x2, self.split_batch, 0)
+ label_x = torch.split(label_x, self.split_batch, 0)
+ domain_x = torch.split(domain_x, self.split_batch, 0)
+ domain_x = [d[0].item() for d in domain_x]
+
+ # Generate pseudo label
+ with torch.no_grad():
+ feat_u = self.F(input_u)
+ pred_u = []
+ for k in range(self.num_source_domains):
+ pred_uk = self.E(k, feat_u)
+ pred_uk = pred_uk.unsqueeze(1)
+ pred_u.append(pred_uk)
+ pred_u = torch.cat(pred_u, 1) # (B, K, C)
+ # Get the highest probability and index (label) for each expert
+ experts_max_p, experts_max_idx = pred_u.max(2) # (B, K)
+ # Get the most confident expert
+ max_expert_p, max_expert_idx = experts_max_p.max(1) # (B)
+ pseudo_label_u = []
+ for i, experts_label in zip(max_expert_idx, experts_max_idx):
+ pseudo_label_u.append(experts_label[i])
+ pseudo_label_u = torch.stack(pseudo_label_u, 0)
+ pseudo_label_u = create_onehot(pseudo_label_u, self.num_classes)
+ pseudo_label_u = pseudo_label_u.to(self.device)
+ label_u_mask = (max_expert_p >= self.conf_thre).float()
+
+ loss_x = 0
+ loss_cr = 0
+ acc_x = 0
+
+ feat_x = [self.F(x) for x in input_x]
+ feat_x2 = [self.F(x) for x in input_x2]
+ feat_u2 = self.F(input_u2)
+
+ for feat_xi, feat_x2i, label_xi, i in zip(
+ feat_x, feat_x2, label_x, domain_x
+ ):
+ cr_s = [j for j in domain_x if j != i]
+
+ # Learning expert
+ pred_xi = self.E(i, feat_xi)
+ loss_x += (-label_xi * torch.log(pred_xi + 1e-5)).sum(1).mean()
+ expert_label_xi = pred_xi.detach()
+ acc_x += compute_accuracy(pred_xi.detach(),
+ label_xi.max(1)[1])[0].item()
+
+ # Consistency regularization
+ cr_pred = []
+ for j in cr_s:
+ pred_j = self.E(j, feat_x2i)
+ pred_j = pred_j.unsqueeze(1)
+ cr_pred.append(pred_j)
+ cr_pred = torch.cat(cr_pred, 1)
+ cr_pred = cr_pred.mean(1)
+ loss_cr += ((cr_pred - expert_label_xi)**2).sum(1).mean()
+
+ loss_x /= self.n_domain
+ loss_cr /= self.n_domain
+ acc_x /= self.n_domain
+
+ # Unsupervised loss
+ pred_u = []
+ for k in range(self.num_source_domains):
+ pred_uk = self.E(k, feat_u2)
+ pred_uk = pred_uk.unsqueeze(1)
+ pred_u.append(pred_uk)
+ pred_u = torch.cat(pred_u, 1)
+ pred_u = pred_u.mean(1)
+ l_u = (-pseudo_label_u * torch.log(pred_u + 1e-5)).sum(1)
+ loss_u = (l_u * label_u_mask).mean()
+
+ loss = 0
+ loss += loss_x
+ loss += loss_cr
+ loss += loss_u * self.weight_u
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": acc_x,
+ "loss_cr": loss_cr.item(),
+ "loss_u": loss_u.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"]
+ input_x2 = batch_x["img2"]
+ label_x = batch_x["label"]
+ domain_x = batch_x["domain"]
+ input_u = batch_u["img"]
+ input_u2 = batch_u["img2"]
+
+ label_x = create_onehot(label_x, self.num_classes)
+
+ input_x = input_x.to(self.device)
+ input_x2 = input_x2.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u = input_u.to(self.device)
+ input_u2 = input_u2.to(self.device)
+
+ return input_x, input_x2, label_x, domain_x, input_u, input_u2
+
+ def model_inference(self, input):
+ f = self.F(input)
+ p = []
+ for k in range(self.num_source_domains):
+ p_k = self.E(k, f)
+ p_k = p_k.unsqueeze(1)
+ p.append(p_k)
+ p = torch.cat(p, 1)
+ p = p.mean(1)
+ return p
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dann.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dann.py
new file mode 100644
index 00000000..64bb3f7d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/dann.py
@@ -0,0 +1,78 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling import build_head
+from dassl.modeling.ops import ReverseGrad
+
+
+@TRAINER_REGISTRY.register()
+class DANN(TrainerXU):
+ """Domain-Adversarial Neural Networks.
+
+ https://arxiv.org/abs/1505.07818.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.build_critic()
+ self.ce = nn.CrossEntropyLoss()
+ self.bce = nn.BCEWithLogitsLoss()
+
+ def build_critic(self):
+ cfg = self.cfg
+
+ print("Building critic network")
+ fdim = self.model.fdim
+ critic_body = build_head(
+ "mlp",
+ verbose=cfg.VERBOSE,
+ in_features=fdim,
+ hidden_layers=[fdim, fdim],
+ activation="leaky_relu",
+ )
+ self.critic = nn.Sequential(critic_body, nn.Linear(fdim, 1))
+ print("# params: {:,}".format(count_num_param(self.critic)))
+ self.critic.to(self.device)
+ self.optim_c = build_optimizer(self.critic, cfg.OPTIM)
+ self.sched_c = build_lr_scheduler(self.optim_c, cfg.OPTIM)
+ self.register_model("critic", self.critic, self.optim_c, self.sched_c)
+ self.revgrad = ReverseGrad()
+
+ def forward_backward(self, batch_x, batch_u):
+ input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
+ domain_x = torch.ones(input_x.shape[0], 1).to(self.device)
+ domain_u = torch.zeros(input_u.shape[0], 1).to(self.device)
+
+ global_step = self.batch_idx + self.epoch * self.num_batches
+ progress = global_step / (self.max_epoch * self.num_batches)
+ lmda = 2 / (1 + np.exp(-10 * progress)) - 1
+
+ logit_x, feat_x = self.model(input_x, return_feature=True)
+ _, feat_u = self.model(input_u, return_feature=True)
+
+ loss_x = self.ce(logit_x, label_x)
+
+ feat_x = self.revgrad(feat_x, grad_scaling=lmda)
+ feat_u = self.revgrad(feat_u, grad_scaling=lmda)
+ output_xd = self.critic(feat_x)
+ output_ud = self.critic(feat_u)
+ loss_d = self.bce(output_xd, domain_x) + self.bce(output_ud, domain_u)
+
+ loss = loss_x + loss_d
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+ "loss_d": loss_d.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/m3sda.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/m3sda.py
new file mode 100644
index 00000000..59b5673f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/m3sda.py
@@ -0,0 +1,208 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.engine.trainer import SimpleNet
+
+
+class PairClassifiers(nn.Module):
+
+ def __init__(self, fdim, num_classes):
+ super().__init__()
+ self.c1 = nn.Linear(fdim, num_classes)
+ self.c2 = nn.Linear(fdim, num_classes)
+
+ def forward(self, x):
+ z1 = self.c1(x)
+ if not self.training:
+ return z1
+ z2 = self.c2(x)
+ return z1, z2
+
+
+@TRAINER_REGISTRY.register()
+class M3SDA(TrainerXU):
+ """Moment Matching for Multi-Source Domain Adaptation.
+
+ https://arxiv.org/abs/1812.01754.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ n_domain = cfg.DATALOADER.TRAIN_X.N_DOMAIN
+ batch_size = cfg.DATALOADER.TRAIN_X.BATCH_SIZE
+ if n_domain <= 0:
+ n_domain = self.num_source_domains
+ self.split_batch = batch_size // n_domain
+ self.n_domain = n_domain
+
+ self.n_step_F = cfg.TRAINER.M3SDA.N_STEP_F
+ self.lmda = cfg.TRAINER.M3SDA.LMDA
+
+ def check_cfg(self, cfg):
+ assert cfg.DATALOADER.TRAIN_X.SAMPLER == "RandomDomainSampler"
+ assert not cfg.DATALOADER.TRAIN_U.SAME_AS_X
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+ fdim = self.F.fdim
+
+ print("Building C")
+ self.C = nn.ModuleList(
+ [
+ PairClassifiers(fdim, self.num_classes)
+ for _ in range(self.num_source_domains)
+ ]
+ )
+ self.C.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.C)))
+ self.optim_C = build_optimizer(self.C, cfg.OPTIM)
+ self.sched_C = build_lr_scheduler(self.optim_C, cfg.OPTIM)
+ self.register_model("C", self.C, self.optim_C, self.sched_C)
+
+ def forward_backward(self, batch_x, batch_u):
+ parsed = self.parse_batch_train(batch_x, batch_u)
+ input_x, label_x, domain_x, input_u = parsed
+
+ input_x = torch.split(input_x, self.split_batch, 0)
+ label_x = torch.split(label_x, self.split_batch, 0)
+ domain_x = torch.split(domain_x, self.split_batch, 0)
+ domain_x = [d[0].item() for d in domain_x]
+
+ # Step A
+ loss_x = 0
+ feat_x = []
+
+ for x, y, d in zip(input_x, label_x, domain_x):
+ f = self.F(x)
+ z1, z2 = self.C[d](f)
+ loss_x += F.cross_entropy(z1, y) + F.cross_entropy(z2, y)
+
+ feat_x.append(f)
+
+ loss_x /= self.n_domain
+
+ feat_u = self.F(input_u)
+ loss_msda = self.moment_distance(feat_x, feat_u)
+
+ loss_step_A = loss_x + loss_msda * self.lmda
+ self.model_backward_and_update(loss_step_A)
+
+ # Step B
+ with torch.no_grad():
+ feat_u = self.F(input_u)
+
+ loss_x, loss_dis = 0, 0
+
+ for x, y, d in zip(input_x, label_x, domain_x):
+ with torch.no_grad():
+ f = self.F(x)
+ z1, z2 = self.C[d](f)
+ loss_x += F.cross_entropy(z1, y) + F.cross_entropy(z2, y)
+
+ z1, z2 = self.C[d](feat_u)
+ p1 = F.softmax(z1, 1)
+ p2 = F.softmax(z2, 1)
+ loss_dis += self.discrepancy(p1, p2)
+
+ loss_x /= self.n_domain
+ loss_dis /= self.n_domain
+
+ loss_step_B = loss_x - loss_dis
+ self.model_backward_and_update(loss_step_B, "C")
+
+ # Step C
+ for _ in range(self.n_step_F):
+ feat_u = self.F(input_u)
+
+ loss_dis = 0
+
+ for d in domain_x:
+ z1, z2 = self.C[d](feat_u)
+ p1 = F.softmax(z1, 1)
+ p2 = F.softmax(z2, 1)
+ loss_dis += self.discrepancy(p1, p2)
+
+ loss_dis /= self.n_domain
+ loss_step_C = loss_dis
+
+ self.model_backward_and_update(loss_step_C, "F")
+
+ loss_summary = {
+ "loss_step_A": loss_step_A.item(),
+ "loss_step_B": loss_step_B.item(),
+ "loss_step_C": loss_step_C.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def moment_distance(self, x, u):
+ # x (list): a list of feature matrix.
+ # u (torch.Tensor): feature matrix.
+ x_mean = [xi.mean(0) for xi in x]
+ u_mean = u.mean(0)
+ dist1 = self.pairwise_distance(x_mean, u_mean)
+
+ x_var = [xi.var(0) for xi in x]
+ u_var = u.var(0)
+ dist2 = self.pairwise_distance(x_var, u_var)
+
+ return (dist1+dist2) / 2
+
+ def pairwise_distance(self, x, u):
+ # x (list): a list of feature vector.
+ # u (torch.Tensor): feature vector.
+ dist = 0
+ count = 0
+
+ for xi in x:
+ dist += self.euclidean(xi, u)
+ count += 1
+
+ for i in range(len(x) - 1):
+ for j in range(i + 1, len(x)):
+ dist += self.euclidean(x[i], x[j])
+ count += 1
+
+ return dist / count
+
+ def euclidean(self, input1, input2):
+ return ((input1 - input2)**2).sum().sqrt()
+
+ def discrepancy(self, y1, y2):
+ return (y1 - y2).abs().mean()
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"]
+ label_x = batch_x["label"]
+ domain_x = batch_x["domain"]
+ input_u = batch_u["img"]
+
+ input_x = input_x.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u = input_u.to(self.device)
+
+ return input_x, label_x, domain_x, input_u
+
+ def model_inference(self, input):
+ f = self.F(input)
+ p = 0
+ for C_i in self.C:
+ z = C_i(f)
+ p += F.softmax(z, 1)
+ p = p / len(self.C)
+ return p
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mcd.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mcd.py
new file mode 100644
index 00000000..174a2e05
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mcd.py
@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.engine.trainer import SimpleNet
+
+
+@TRAINER_REGISTRY.register()
+class MCD(TrainerXU):
+ """Maximum Classifier Discrepancy.
+
+ https://arxiv.org/abs/1712.02560.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.n_step_F = cfg.TRAINER.MCD.N_STEP_F
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+ fdim = self.F.fdim
+
+ print("Building C1")
+ self.C1 = nn.Linear(fdim, self.num_classes)
+ self.C1.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.C1)))
+ self.optim_C1 = build_optimizer(self.C1, cfg.OPTIM)
+ self.sched_C1 = build_lr_scheduler(self.optim_C1, cfg.OPTIM)
+ self.register_model("C1", self.C1, self.optim_C1, self.sched_C1)
+
+ print("Building C2")
+ self.C2 = nn.Linear(fdim, self.num_classes)
+ self.C2.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.C2)))
+ self.optim_C2 = build_optimizer(self.C2, cfg.OPTIM)
+ self.sched_C2 = build_lr_scheduler(self.optim_C2, cfg.OPTIM)
+ self.register_model("C2", self.C2, self.optim_C2, self.sched_C2)
+
+ def forward_backward(self, batch_x, batch_u):
+ parsed = self.parse_batch_train(batch_x, batch_u)
+ input_x, label_x, input_u = parsed
+
+ # Step A
+ feat_x = self.F(input_x)
+ logit_x1 = self.C1(feat_x)
+ logit_x2 = self.C2(feat_x)
+ loss_x1 = F.cross_entropy(logit_x1, label_x)
+ loss_x2 = F.cross_entropy(logit_x2, label_x)
+ loss_step_A = loss_x1 + loss_x2
+ self.model_backward_and_update(loss_step_A)
+
+ # Step B
+ with torch.no_grad():
+ feat_x = self.F(input_x)
+ logit_x1 = self.C1(feat_x)
+ logit_x2 = self.C2(feat_x)
+ loss_x1 = F.cross_entropy(logit_x1, label_x)
+ loss_x2 = F.cross_entropy(logit_x2, label_x)
+ loss_x = loss_x1 + loss_x2
+
+ with torch.no_grad():
+ feat_u = self.F(input_u)
+ pred_u1 = F.softmax(self.C1(feat_u), 1)
+ pred_u2 = F.softmax(self.C2(feat_u), 1)
+ loss_dis = self.discrepancy(pred_u1, pred_u2)
+
+ loss_step_B = loss_x - loss_dis
+ self.model_backward_and_update(loss_step_B, ["C1", "C2"])
+
+ # Step C
+ for _ in range(self.n_step_F):
+ feat_u = self.F(input_u)
+ pred_u1 = F.softmax(self.C1(feat_u), 1)
+ pred_u2 = F.softmax(self.C2(feat_u), 1)
+ loss_step_C = self.discrepancy(pred_u1, pred_u2)
+ self.model_backward_and_update(loss_step_C, "F")
+
+ loss_summary = {
+ "loss_step_A": loss_step_A.item(),
+ "loss_step_B": loss_step_B.item(),
+ "loss_step_C": loss_step_C.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def discrepancy(self, y1, y2):
+ return (y1 - y2).abs().mean()
+
+ def model_inference(self, input):
+ feat = self.F(input)
+ return self.C1(feat)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mme.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mme.py
new file mode 100644
index 00000000..fd7775c6
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/mme.py
@@ -0,0 +1,86 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling.ops import ReverseGrad
+from dassl.engine.trainer import SimpleNet
+
+
+class Prototypes(nn.Module):
+
+ def __init__(self, fdim, num_classes, temp=0.05):
+ super().__init__()
+ self.prototypes = nn.Linear(fdim, num_classes, bias=False)
+ self.temp = temp
+
+ def forward(self, x):
+ x = F.normalize(x, p=2, dim=1)
+ out = self.prototypes(x)
+ out = out / self.temp
+ return out
+
+
+@TRAINER_REGISTRY.register()
+class MME(TrainerXU):
+ """Minimax Entropy.
+
+ https://arxiv.org/abs/1904.06487.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.lmda = cfg.TRAINER.MME.LMDA
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+
+ print("Building C")
+ self.C = Prototypes(self.F.fdim, self.num_classes)
+ self.C.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.C)))
+ self.optim_C = build_optimizer(self.C, cfg.OPTIM)
+ self.sched_C = build_lr_scheduler(self.optim_C, cfg.OPTIM)
+ self.register_model("C", self.C, self.optim_C, self.sched_C)
+
+ self.revgrad = ReverseGrad()
+
+ def forward_backward(self, batch_x, batch_u):
+ input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
+
+ feat_x = self.F(input_x)
+ logit_x = self.C(feat_x)
+ loss_x = F.cross_entropy(logit_x, label_x)
+ self.model_backward_and_update(loss_x)
+
+ feat_u = self.F(input_u)
+ feat_u = self.revgrad(feat_u)
+ logit_u = self.C(feat_u)
+ prob_u = F.softmax(logit_u, 1)
+ loss_u = -(-prob_u * torch.log(prob_u + 1e-5)).sum(1).mean()
+ self.model_backward_and_update(loss_u * self.lmda)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def model_inference(self, input):
+ return self.C(self.F(input))
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/se.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/se.py
new file mode 100644
index 00000000..b0f498a3
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/se.py
@@ -0,0 +1,78 @@
+import copy
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling.ops.utils import sigmoid_rampup, ema_model_update
+
+
+@TRAINER_REGISTRY.register()
+class SE(TrainerXU):
+ """Self-ensembling for visual domain adaptation.
+
+ https://arxiv.org/abs/1706.05208.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.ema_alpha = cfg.TRAINER.SE.EMA_ALPHA
+ self.conf_thre = cfg.TRAINER.SE.CONF_THRE
+ self.rampup = cfg.TRAINER.SE.RAMPUP
+
+ self.teacher = copy.deepcopy(self.model)
+ self.teacher.train()
+ for param in self.teacher.parameters():
+ param.requires_grad_(False)
+
+ def check_cfg(self, cfg):
+ assert cfg.DATALOADER.K_TRANSFORMS == 2
+
+ def forward_backward(self, batch_x, batch_u):
+ global_step = self.batch_idx + self.epoch * self.num_batches
+ parsed = self.parse_batch_train(batch_x, batch_u)
+ input_x, label_x, input_u1, input_u2 = parsed
+
+ logit_x = self.model(input_x)
+ loss_x = F.cross_entropy(logit_x, label_x)
+
+ prob_u = F.softmax(self.model(input_u1), 1)
+ t_prob_u = F.softmax(self.teacher(input_u2), 1)
+ loss_u = ((prob_u - t_prob_u)**2).sum(1)
+
+ if self.conf_thre:
+ max_prob = t_prob_u.max(1)[0]
+ mask = (max_prob > self.conf_thre).float()
+ loss_u = (loss_u * mask).mean()
+ else:
+ weight_u = sigmoid_rampup(global_step, self.rampup)
+ loss_u = loss_u.mean() * weight_u
+
+ loss = loss_x + loss_u
+ self.model_backward_and_update(loss)
+
+ ema_alpha = min(1 - 1 / (global_step+1), self.ema_alpha)
+ ema_model_update(self.model, self.teacher, ema_alpha)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"][0]
+ label_x = batch_x["label"]
+ input_u = batch_u["img"]
+ input_u1, input_u2 = input_u
+
+ input_x = input_x.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u1 = input_u1.to(self.device)
+ input_u2 = input_u2.to(self.device)
+
+ return input_x, label_x, input_u1, input_u2
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/da/source_only.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/source_only.py
new file mode 100644
index 00000000..2e7d9a68
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/da/source_only.py
@@ -0,0 +1,34 @@
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+
+
+@TRAINER_REGISTRY.register()
+class SourceOnly(TrainerXU):
+ """Baseline model for domain adaptation, which is
+ trained using source data only.
+ """
+
+ def forward_backward(self, batch_x, batch_u):
+ input, label = self.parse_batch_train(batch_x, batch_u)
+ output = self.model(input)
+ loss = F.cross_entropy(output, label)
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss": loss.item(),
+ "acc": compute_accuracy(output, label)[0].item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input = batch_x["img"]
+ label = batch_x["label"]
+ input = input.to(self.device)
+ label = label.to(self.device)
+ return input, label
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/__init__.py
new file mode 100644
index 00000000..23146a4a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/__init__.py
@@ -0,0 +1,5 @@
+from .ddaig import DDAIG
+from .daeldg import DAELDG
+from .vanilla import Vanilla
+from .crossgrad import CrossGrad
+from .domain_mix import DomainMix
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/crossgrad.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/crossgrad.py
new file mode 100644
index 00000000..ad9a6bd5
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/crossgrad.py
@@ -0,0 +1,83 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.engine.trainer import SimpleNet
+
+
+@TRAINER_REGISTRY.register()
+class CrossGrad(TrainerX):
+ """Cross-gradient training.
+
+ https://arxiv.org/abs/1804.10745.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.eps_f = cfg.TRAINER.CROSSGRAD.EPS_F
+ self.eps_d = cfg.TRAINER.CROSSGRAD.EPS_D
+ self.alpha_f = cfg.TRAINER.CROSSGRAD.ALPHA_F
+ self.alpha_d = cfg.TRAINER.CROSSGRAD.ALPHA_D
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, self.num_classes)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+
+ print("Building D")
+ self.D = SimpleNet(cfg, cfg.MODEL, self.num_source_domains)
+ self.D.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.D)))
+ self.optim_D = build_optimizer(self.D, cfg.OPTIM)
+ self.sched_D = build_lr_scheduler(self.optim_D, cfg.OPTIM)
+ self.register_model("D", self.D, self.optim_D, self.sched_D)
+
+ def forward_backward(self, batch):
+ input, label, domain = self.parse_batch_train(batch)
+
+ input.requires_grad = True
+
+ # Compute domain perturbation
+ loss_d = F.cross_entropy(self.D(input), domain)
+ loss_d.backward()
+ grad_d = torch.clamp(input.grad.data, min=-0.1, max=0.1)
+ input_d = input.data + self.eps_f * grad_d
+
+ # Compute label perturbation
+ input.grad.data.zero_()
+ loss_f = F.cross_entropy(self.F(input), label)
+ loss_f.backward()
+ grad_f = torch.clamp(input.grad.data, min=-0.1, max=0.1)
+ input_f = input.data + self.eps_d * grad_f
+
+ input = input.detach()
+
+ # Update label net
+ loss_f1 = F.cross_entropy(self.F(input), label)
+ loss_f2 = F.cross_entropy(self.F(input_d), label)
+ loss_f = (1 - self.alpha_f) * loss_f1 + self.alpha_f * loss_f2
+ self.model_backward_and_update(loss_f, "F")
+
+ # Update domain net
+ loss_d1 = F.cross_entropy(self.D(input), domain)
+ loss_d2 = F.cross_entropy(self.D(input_f), domain)
+ loss_d = (1 - self.alpha_d) * loss_d1 + self.alpha_d * loss_d2
+ self.model_backward_and_update(loss_d, "D")
+
+ loss_summary = {"loss_f": loss_f.item(), "loss_d": loss_d.item()}
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def model_inference(self, input):
+ return self.F(input)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/daeldg.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/daeldg.py
new file mode 100644
index 00000000..8d6d11c4
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/daeldg.py
@@ -0,0 +1,168 @@
+import torch
+import torch.nn as nn
+
+from dassl.data import DataManager
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+from dassl.engine.trainer import SimpleNet
+from dassl.data.transforms import build_transform
+from dassl.modeling.ops.utils import create_onehot
+
+
+class Experts(nn.Module):
+
+ def __init__(self, n_source, fdim, num_classes):
+ super().__init__()
+ self.linears = nn.ModuleList(
+ [nn.Linear(fdim, num_classes) for _ in range(n_source)]
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, i, x):
+ x = self.linears[i](x)
+ x = self.softmax(x)
+ return x
+
+
+@TRAINER_REGISTRY.register()
+class DAELDG(TrainerX):
+ """Domain Adaptive Ensemble Learning.
+
+ DG version: only use labeled source data.
+
+ https://arxiv.org/abs/2003.07325.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ n_domain = cfg.DATALOADER.TRAIN_X.N_DOMAIN
+ batch_size = cfg.DATALOADER.TRAIN_X.BATCH_SIZE
+ if n_domain <= 0:
+ n_domain = self.num_source_domains
+ self.split_batch = batch_size // n_domain
+ self.n_domain = n_domain
+ self.conf_thre = cfg.TRAINER.DAELDG.CONF_THRE
+
+ def check_cfg(self, cfg):
+ assert cfg.DATALOADER.TRAIN_X.SAMPLER == "RandomDomainSampler"
+ assert len(cfg.TRAINER.DAELDG.STRONG_TRANSFORMS) > 0
+
+ def build_data_loader(self):
+ cfg = self.cfg
+ tfm_train = build_transform(cfg, is_train=True)
+ custom_tfm_train = [tfm_train]
+ choices = cfg.TRAINER.DAELDG.STRONG_TRANSFORMS
+ tfm_train_strong = build_transform(cfg, is_train=True, choices=choices)
+ custom_tfm_train += [tfm_train_strong]
+ dm = DataManager(self.cfg, custom_tfm_train=custom_tfm_train)
+ self.train_loader_x = dm.train_loader_x
+ self.train_loader_u = dm.train_loader_u
+ self.val_loader = dm.val_loader
+ self.test_loader = dm.test_loader
+ self.num_classes = dm.num_classes
+ self.num_source_domains = dm.num_source_domains
+ self.lab2cname = dm.lab2cname
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, 0)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+ fdim = self.F.fdim
+
+ print("Building E")
+ self.E = Experts(self.num_source_domains, fdim, self.num_classes)
+ self.E.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.E)))
+ self.optim_E = build_optimizer(self.E, cfg.OPTIM)
+ self.sched_E = build_lr_scheduler(self.optim_E, cfg.OPTIM)
+ self.register_model("E", self.E, self.optim_E, self.sched_E)
+
+ def forward_backward(self, batch):
+ parsed_data = self.parse_batch_train(batch)
+ input, input2, label, domain = parsed_data
+
+ input = torch.split(input, self.split_batch, 0)
+ input2 = torch.split(input2, self.split_batch, 0)
+ label = torch.split(label, self.split_batch, 0)
+ domain = torch.split(domain, self.split_batch, 0)
+ domain = [d[0].item() for d in domain]
+
+ loss_x = 0
+ loss_cr = 0
+ acc = 0
+
+ feat = [self.F(x) for x in input]
+ feat2 = [self.F(x) for x in input2]
+
+ for feat_i, feat2_i, label_i, i in zip(feat, feat2, label, domain):
+ cr_s = [j for j in domain if j != i]
+
+ # Learning expert
+ pred_i = self.E(i, feat_i)
+ loss_x += (-label_i * torch.log(pred_i + 1e-5)).sum(1).mean()
+ expert_label_i = pred_i.detach()
+ acc += compute_accuracy(pred_i.detach(),
+ label_i.max(1)[1])[0].item()
+
+ # Consistency regularization
+ cr_pred = []
+ for j in cr_s:
+ pred_j = self.E(j, feat2_i)
+ pred_j = pred_j.unsqueeze(1)
+ cr_pred.append(pred_j)
+ cr_pred = torch.cat(cr_pred, 1)
+ cr_pred = cr_pred.mean(1)
+ loss_cr += ((cr_pred - expert_label_i)**2).sum(1).mean()
+
+ loss_x /= self.n_domain
+ loss_cr /= self.n_domain
+ acc /= self.n_domain
+
+ loss = 0
+ loss += loss_x
+ loss += loss_cr
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc": acc,
+ "loss_cr": loss_cr.item()
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch):
+ input = batch["img"]
+ input2 = batch["img2"]
+ label = batch["label"]
+ domain = batch["domain"]
+
+ label = create_onehot(label, self.num_classes)
+
+ input = input.to(self.device)
+ input2 = input2.to(self.device)
+ label = label.to(self.device)
+
+ return input, input2, label, domain
+
+ def model_inference(self, input):
+ f = self.F(input)
+ p = []
+ for k in range(self.num_source_domains):
+ p_k = self.E(k, f)
+ p_k = p_k.unsqueeze(1)
+ p.append(p_k)
+ p = torch.cat(p, 1)
+ p = p.mean(1)
+ return p
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/ddaig.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/ddaig.py
new file mode 100644
index 00000000..b7fbd973
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/ddaig.py
@@ -0,0 +1,107 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.modeling import build_network
+from dassl.engine.trainer import SimpleNet
+
+
+@TRAINER_REGISTRY.register()
+class DDAIG(TrainerX):
+ """Deep Domain-Adversarial Image Generation.
+
+ https://arxiv.org/abs/2003.06054.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.lmda = cfg.TRAINER.DDAIG.LMDA
+ self.clamp = cfg.TRAINER.DDAIG.CLAMP
+ self.clamp_min = cfg.TRAINER.DDAIG.CLAMP_MIN
+ self.clamp_max = cfg.TRAINER.DDAIG.CLAMP_MAX
+ self.warmup = cfg.TRAINER.DDAIG.WARMUP
+ self.alpha = cfg.TRAINER.DDAIG.ALPHA
+
+ def build_model(self):
+ cfg = self.cfg
+
+ print("Building F")
+ self.F = SimpleNet(cfg, cfg.MODEL, self.num_classes)
+ self.F.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.F)))
+ self.optim_F = build_optimizer(self.F, cfg.OPTIM)
+ self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
+ self.register_model("F", self.F, self.optim_F, self.sched_F)
+
+ print("Building D")
+ self.D = SimpleNet(cfg, cfg.MODEL, self.num_source_domains)
+ self.D.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.D)))
+ self.optim_D = build_optimizer(self.D, cfg.OPTIM)
+ self.sched_D = build_lr_scheduler(self.optim_D, cfg.OPTIM)
+ self.register_model("D", self.D, self.optim_D, self.sched_D)
+
+ print("Building G")
+ self.G = build_network(cfg.TRAINER.DDAIG.G_ARCH, verbose=cfg.VERBOSE)
+ self.G.to(self.device)
+ print("# params: {:,}".format(count_num_param(self.G)))
+ self.optim_G = build_optimizer(self.G, cfg.OPTIM)
+ self.sched_G = build_lr_scheduler(self.optim_G, cfg.OPTIM)
+ self.register_model("G", self.G, self.optim_G, self.sched_G)
+
+ def forward_backward(self, batch):
+ input, label, domain = self.parse_batch_train(batch)
+
+ #############
+ # Update G
+ #############
+ input_p = self.G(input, lmda=self.lmda)
+ if self.clamp:
+ input_p = torch.clamp(
+ input_p, min=self.clamp_min, max=self.clamp_max
+ )
+ loss_g = 0
+ # Minimize label loss
+ loss_g += F.cross_entropy(self.F(input_p), label)
+ # Maximize domain loss
+ loss_g -= F.cross_entropy(self.D(input_p), domain)
+ self.model_backward_and_update(loss_g, "G")
+
+ # Perturb data with new G
+ with torch.no_grad():
+ input_p = self.G(input, lmda=self.lmda)
+ if self.clamp:
+ input_p = torch.clamp(
+ input_p, min=self.clamp_min, max=self.clamp_max
+ )
+
+ #############
+ # Update F
+ #############
+ loss_f = F.cross_entropy(self.F(input), label)
+ if (self.epoch + 1) > self.warmup:
+ loss_fp = F.cross_entropy(self.F(input_p), label)
+ loss_f = (1.0 - self.alpha) * loss_f + self.alpha * loss_fp
+ self.model_backward_and_update(loss_f, "F")
+
+ #############
+ # Update D
+ #############
+ loss_d = F.cross_entropy(self.D(input), domain)
+ self.model_backward_and_update(loss_d, "D")
+
+ loss_summary = {
+ "loss_g": loss_g.item(),
+ "loss_f": loss_f.item(),
+ "loss_d": loss_d.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def model_inference(self, input):
+ return self.F(input)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/domain_mix.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/domain_mix.py
new file mode 100644
index 00000000..654f2706
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/domain_mix.py
@@ -0,0 +1,81 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+
+__all__ = ["DomainMix"]
+
+
+@TRAINER_REGISTRY.register()
+class DomainMix(TrainerX):
+ """DomainMix.
+
+ Dynamic Domain Generalization.
+
+ https://github.com/MetaVisionLab/DDG
+ """
+
+ def __init__(self, cfg):
+ super(DomainMix, self).__init__(cfg)
+ self.mix_type = cfg.TRAINER.DOMAINMIX.TYPE
+ self.alpha = cfg.TRAINER.DOMAINMIX.ALPHA
+ self.beta = cfg.TRAINER.DOMAINMIX.BETA
+ self.dist_beta = torch.distributions.Beta(self.alpha, self.beta)
+
+ def forward_backward(self, batch):
+ images, label_a, label_b, lam = self.parse_batch_train(batch)
+ output = self.model(images)
+ loss = lam * F.cross_entropy(
+ output, label_a
+ ) + (1-lam) * F.cross_entropy(output, label_b)
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss": loss.item(),
+ "acc": compute_accuracy(output, label_a)[0].item()
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch):
+ images = batch["img"]
+ target = batch["label"]
+ domain = batch["domain"]
+ images = images.to(self.device)
+ target = target.to(self.device)
+ domain = domain.to(self.device)
+ images, target_a, target_b, lam = self.domain_mix(
+ images, target, domain
+ )
+ return images, target_a, target_b, lam
+
+ def domain_mix(self, x, target, domain):
+ lam = (
+ self.dist_beta.rsample((1, ))
+ if self.alpha > 0 else torch.tensor(1)
+ ).to(x.device)
+
+ # random shuffle
+ perm = torch.randperm(x.size(0), dtype=torch.int64, device=x.device)
+ if self.mix_type == "crossdomain":
+ domain_list = torch.unique(domain)
+ if len(domain_list) > 1:
+ for idx in domain_list:
+ cnt_a = torch.sum(domain == idx)
+ idx_b = (domain != idx).nonzero().squeeze(-1)
+ cnt_b = idx_b.shape[0]
+ perm_b = torch.ones(cnt_b).multinomial(
+ num_samples=cnt_a, replacement=bool(cnt_a > cnt_b)
+ )
+ perm[domain == idx] = idx_b[perm_b]
+ elif self.mix_type != "random":
+ raise NotImplementedError(
+ f"Chooses {'random', 'crossdomain'}, but got {self.mix_type}."
+ )
+ mixed_x = lam*x + (1-lam) * x[perm, :]
+ target_a, target_b = target, target[perm]
+ return mixed_x, target_a, target_b, lam
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/vanilla.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/vanilla.py
new file mode 100644
index 00000000..e35f30a1
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/dg/vanilla.py
@@ -0,0 +1,35 @@
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+
+
+@TRAINER_REGISTRY.register()
+class Vanilla(TrainerX):
+ """Vanilla model.
+
+ A.k.a. Empirical Risk Minimization, or ERM.
+ """
+
+ def forward_backward(self, batch):
+ input, target = self.parse_batch_train(batch)
+ output = self.model(input)
+ loss = F.cross_entropy(output, target)
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss": loss.item(),
+ "acc": compute_accuracy(output, target)[0].item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch):
+ input = batch["img"]
+ target = batch["label"]
+ input = input.to(self.device)
+ target = target.to(self.device)
+ return input, target
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/__init__.py
new file mode 100644
index 00000000..46fa781f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/__init__.py
@@ -0,0 +1,5 @@
+from .entmin import EntMin
+from .fixmatch import FixMatch
+from .mixmatch import MixMatch
+from .mean_teacher import MeanTeacher
+from .sup_baseline import SupBaseline
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/entmin.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/entmin.py
new file mode 100644
index 00000000..a17186a8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/entmin.py
@@ -0,0 +1,41 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+
+
+@TRAINER_REGISTRY.register()
+class EntMin(TrainerXU):
+ """Entropy Minimization.
+
+ http://papers.nips.cc/paper/2740-semi-supervised-learning-by-entropy-minimization.pdf.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.lmda = cfg.TRAINER.ENTMIN.LMDA
+
+ def forward_backward(self, batch_x, batch_u):
+ input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
+
+ output_x = self.model(input_x)
+ loss_x = F.cross_entropy(output_x, label_x)
+
+ output_u = F.softmax(self.model(input_u), 1)
+ loss_u = (-output_u * torch.log(output_u + 1e-5)).sum(1).mean()
+
+ loss = loss_x + loss_u * self.lmda
+
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(output_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/fixmatch.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/fixmatch.py
new file mode 100644
index 00000000..be6001f8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/fixmatch.py
@@ -0,0 +1,112 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.data import DataManager
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.data.transforms import build_transform
+
+
+@TRAINER_REGISTRY.register()
+class FixMatch(TrainerXU):
+ """FixMatch: Simplifying Semi-Supervised Learning with
+ Consistency and Confidence.
+
+ https://arxiv.org/abs/2001.07685.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.weight_u = cfg.TRAINER.FIXMATCH.WEIGHT_U
+ self.conf_thre = cfg.TRAINER.FIXMATCH.CONF_THRE
+
+ def check_cfg(self, cfg):
+ assert len(cfg.TRAINER.FIXMATCH.STRONG_TRANSFORMS) > 0
+
+ def build_data_loader(self):
+ cfg = self.cfg
+ tfm_train = build_transform(cfg, is_train=True)
+ custom_tfm_train = [tfm_train]
+ choices = cfg.TRAINER.FIXMATCH.STRONG_TRANSFORMS
+ tfm_train_strong = build_transform(cfg, is_train=True, choices=choices)
+ custom_tfm_train += [tfm_train_strong]
+ self.dm = DataManager(self.cfg, custom_tfm_train=custom_tfm_train)
+ self.train_loader_x = self.dm.train_loader_x
+ self.train_loader_u = self.dm.train_loader_u
+ self.val_loader = self.dm.val_loader
+ self.test_loader = self.dm.test_loader
+ self.num_classes = self.dm.num_classes
+
+ def assess_y_pred_quality(self, y_pred, y_true, mask):
+ n_masked_correct = (y_pred.eq(y_true).float() * mask).sum()
+ acc_thre = n_masked_correct / (mask.sum() + 1e-5)
+ acc_raw = y_pred.eq(y_true).sum() / y_pred.numel() # raw accuracy
+ keep_rate = mask.sum() / mask.numel()
+ output = {
+ "acc_thre": acc_thre,
+ "acc_raw": acc_raw,
+ "keep_rate": keep_rate
+ }
+ return output
+
+ def forward_backward(self, batch_x, batch_u):
+ parsed_data = self.parse_batch_train(batch_x, batch_u)
+ input_x, input_x2, label_x, input_u, input_u2, label_u = parsed_data
+ input_u = torch.cat([input_x, input_u], 0)
+ input_u2 = torch.cat([input_x2, input_u2], 0)
+ n_x = input_x.size(0)
+
+ # Generate pseudo labels
+ with torch.no_grad():
+ output_u = F.softmax(self.model(input_u), 1)
+ max_prob, label_u_pred = output_u.max(1)
+ mask_u = (max_prob >= self.conf_thre).float()
+
+ # Evaluate pseudo labels' accuracy
+ y_u_pred_stats = self.assess_y_pred_quality(
+ label_u_pred[n_x:], label_u, mask_u[n_x:]
+ )
+
+ # Supervised loss
+ output_x = self.model(input_x)
+ loss_x = F.cross_entropy(output_x, label_x)
+
+ # Unsupervised loss
+ output_u = self.model(input_u2)
+ loss_u = F.cross_entropy(output_u, label_u_pred, reduction="none")
+ loss_u = (loss_u * mask_u).mean()
+
+ loss = loss_x + loss_u * self.weight_u
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(output_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ "y_u_pred_acc_raw": y_u_pred_stats["acc_raw"],
+ "y_u_pred_acc_thre": y_u_pred_stats["acc_thre"],
+ "y_u_pred_keep": y_u_pred_stats["keep_rate"],
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"]
+ input_x2 = batch_x["img2"]
+ label_x = batch_x["label"]
+ input_u = batch_u["img"]
+ input_u2 = batch_u["img2"]
+ # label_u is used only for evaluating pseudo labels' accuracy
+ label_u = batch_u["label"]
+
+ input_x = input_x.to(self.device)
+ input_x2 = input_x2.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u = input_u.to(self.device)
+ input_u2 = input_u2.to(self.device)
+ label_u = label_u.to(self.device)
+
+ return input_x, input_x2, label_x, input_u, input_u2, label_u
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mean_teacher.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mean_teacher.py
new file mode 100644
index 00000000..054dc490
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mean_teacher.py
@@ -0,0 +1,54 @@
+import copy
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling.ops.utils import sigmoid_rampup, ema_model_update
+
+
+@TRAINER_REGISTRY.register()
+class MeanTeacher(TrainerXU):
+ """Mean teacher.
+
+ https://arxiv.org/abs/1703.01780.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.weight_u = cfg.TRAINER.MEANTEACHER.WEIGHT_U
+ self.ema_alpha = cfg.TRAINER.MEANTEACHER.EMA_ALPHA
+ self.rampup = cfg.TRAINER.MEANTEACHER.RAMPUP
+
+ self.teacher = copy.deepcopy(self.model)
+ self.teacher.train()
+ for param in self.teacher.parameters():
+ param.requires_grad_(False)
+
+ def forward_backward(self, batch_x, batch_u):
+ input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
+
+ logit_x = self.model(input_x)
+ loss_x = F.cross_entropy(logit_x, label_x)
+
+ target_u = F.softmax(self.teacher(input_u), 1)
+ prob_u = F.softmax(self.model(input_u), 1)
+ loss_u = ((prob_u - target_u)**2).sum(1).mean()
+
+ weight_u = self.weight_u * sigmoid_rampup(self.epoch, self.rampup)
+ loss = loss_x + loss_u*weight_u
+ self.model_backward_and_update(loss)
+
+ global_step = self.batch_idx + self.epoch * self.num_batches
+ ema_alpha = min(1 - 1 / (global_step+1), self.ema_alpha)
+ ema_model_update(self.model, self.teacher, ema_alpha)
+
+ loss_summary = {
+ "loss_x": loss_x.item(),
+ "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+ "loss_u": loss_u.item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mixmatch.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mixmatch.py
new file mode 100644
index 00000000..6bb24e16
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/mixmatch.py
@@ -0,0 +1,98 @@
+import torch
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.modeling.ops import mixup
+from dassl.modeling.ops.utils import (
+ sharpen_prob, create_onehot, linear_rampup, shuffle_index
+)
+
+
+@TRAINER_REGISTRY.register()
+class MixMatch(TrainerXU):
+ """MixMatch: A Holistic Approach to Semi-Supervised Learning.
+
+ https://arxiv.org/abs/1905.02249.
+ """
+
+ def __init__(self, cfg):
+ super().__init__(cfg)
+ self.weight_u = cfg.TRAINER.MIXMATCH.WEIGHT_U
+ self.temp = cfg.TRAINER.MIXMATCH.TEMP
+ self.beta = cfg.TRAINER.MIXMATCH.MIXUP_BETA
+ self.rampup = cfg.TRAINER.MIXMATCH.RAMPUP
+
+ def check_cfg(self, cfg):
+ assert cfg.DATALOADER.K_TRANSFORMS > 1
+
+ def forward_backward(self, batch_x, batch_u):
+ input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
+ num_x = input_x.shape[0]
+
+ global_step = self.batch_idx + self.epoch * self.num_batches
+ weight_u = self.weight_u * linear_rampup(global_step, self.rampup)
+
+ # Generate pseudo-label for unlabeled data
+ with torch.no_grad():
+ output_u = 0
+ for input_ui in input_u:
+ output_ui = F.softmax(self.model(input_ui), 1)
+ output_u += output_ui
+ output_u /= len(input_u)
+ label_u = sharpen_prob(output_u, self.temp)
+ label_u = [label_u] * len(input_u)
+ label_u = torch.cat(label_u, 0)
+ input_u = torch.cat(input_u, 0)
+
+ # Combine and shuffle labeled and unlabeled data
+ input_xu = torch.cat([input_x, input_u], 0)
+ label_xu = torch.cat([label_x, label_u], 0)
+ input_xu, label_xu = shuffle_index(input_xu, label_xu)
+
+ # Mixup
+ input_x, label_x = mixup(
+ input_x,
+ input_xu[:num_x],
+ label_x,
+ label_xu[:num_x],
+ self.beta,
+ preserve_order=True,
+ )
+
+ input_u, label_u = mixup(
+ input_u,
+ input_xu[num_x:],
+ label_u,
+ label_xu[num_x:],
+ self.beta,
+ preserve_order=True,
+ )
+
+ # Compute losses
+ output_x = F.softmax(self.model(input_x), 1)
+ loss_x = (-label_x * torch.log(output_x + 1e-5)).sum(1).mean()
+
+ output_u = F.softmax(self.model(input_u), 1)
+ loss_u = ((label_u - output_u)**2).mean()
+
+ loss = loss_x + loss_u*weight_u
+ self.model_backward_and_update(loss)
+
+ loss_summary = {"loss_x": loss_x.item(), "loss_u": loss_u.item()}
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"][0]
+ label_x = batch_x["label"]
+ label_x = create_onehot(label_x, self.num_classes)
+ input_u = batch_u["img"]
+
+ input_x = input_x.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u = [input_ui.to(self.device) for input_ui in input_u]
+
+ return input_x, label_x, input_u
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/sup_baseline.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/sup_baseline.py
new file mode 100644
index 00000000..b2f5228e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/ssl/sup_baseline.py
@@ -0,0 +1,32 @@
+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+
+
+@TRAINER_REGISTRY.register()
+class SupBaseline(TrainerXU):
+ """Supervised Baseline."""
+
+ def forward_backward(self, batch_x, batch_u):
+ input, label = self.parse_batch_train(batch_x, batch_u)
+ output = self.model(input)
+ loss = F.cross_entropy(output, label)
+ self.model_backward_and_update(loss)
+
+ loss_summary = {
+ "loss": loss.item(),
+ "acc": compute_accuracy(output, label)[0].item(),
+ }
+
+ if (self.batch_idx + 1) == self.num_batches:
+ self.update_lr()
+
+ return loss_summary
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input = batch_x["img"]
+ label = batch_x["label"]
+ input = input.to(self.device)
+ label = label.to(self.device)
+ return input, label
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/engine/trainer.py b/python/ClipDetection/Dassl.pytorch/dassl/engine/trainer.py
new file mode 100644
index 00000000..92142c96
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/engine/trainer.py
@@ -0,0 +1,652 @@
+##################################################################
+# OpenMPF Modifications #
+# -------------------------------------------------------------- #
+# Parameter classnames=[] added to SimpleTrainer class __init__. #
+# - Used to bypass need for DataManager object. #
+# #
+# Prevent SimpleTrainer from creating DataLoader or Evaluator #
+# #
+# Simplify SimpleTrainer.test() by simply returning model #
+# inference call on single image passed to it as parameter #
+# #
+##################################################################
+
+import time
+import numpy as np
+import os.path as osp
+import datetime
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from torch.utils.tensorboard import SummaryWriter
+
+from dassl.data import DataManager
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import (
+ MetricMeter, AverageMeter, tolist_if_not, count_num_param, load_checkpoint,
+ save_checkpoint, mkdir_if_missing, resume_from_checkpoint,
+ load_pretrained_weights
+)
+from dassl.modeling import build_head, build_backbone
+from dassl.evaluation import build_evaluator
+
+
+class SimpleNet(nn.Module):
+ """A simple neural network composed of a CNN backbone
+ and optionally a head such as mlp for classification.
+ """
+
+ def __init__(self, cfg, model_cfg, num_classes, **kwargs):
+ super().__init__()
+ self.backbone = build_backbone(
+ model_cfg.BACKBONE.NAME,
+ verbose=cfg.VERBOSE,
+ pretrained=model_cfg.BACKBONE.PRETRAINED,
+ **kwargs,
+ )
+ fdim = self.backbone.out_features
+
+ self.head = None
+ if model_cfg.HEAD.NAME and model_cfg.HEAD.HIDDEN_LAYERS:
+ self.head = build_head(
+ model_cfg.HEAD.NAME,
+ verbose=cfg.VERBOSE,
+ in_features=fdim,
+ hidden_layers=model_cfg.HEAD.HIDDEN_LAYERS,
+ activation=model_cfg.HEAD.ACTIVATION,
+ bn=model_cfg.HEAD.BN,
+ dropout=model_cfg.HEAD.DROPOUT,
+ **kwargs,
+ )
+ fdim = self.head.out_features
+
+ self.classifier = None
+ if num_classes > 0:
+ self.classifier = nn.Linear(fdim, num_classes)
+
+ self._fdim = fdim
+
+ @property
+ def fdim(self):
+ return self._fdim
+
+ def forward(self, x, return_feature=False):
+ f = self.backbone(x)
+ if self.head is not None:
+ f = self.head(f)
+
+ if self.classifier is None:
+ return f
+
+ y = self.classifier(f)
+
+ if return_feature:
+ return y, f
+
+ return y
+
+
+class TrainerBase:
+ """Base class for iterative trainer."""
+
+ def __init__(self):
+ self._models = OrderedDict()
+ self._optims = OrderedDict()
+ self._scheds = OrderedDict()
+ self._writer = None
+
+ def register_model(self, name="model", model=None, optim=None, sched=None):
+ if self.__dict__.get("_models") is None:
+ raise AttributeError(
+ "Cannot assign model before super().__init__() call"
+ )
+
+ if self.__dict__.get("_optims") is None:
+ raise AttributeError(
+ "Cannot assign optim before super().__init__() call"
+ )
+
+ if self.__dict__.get("_scheds") is None:
+ raise AttributeError(
+ "Cannot assign sched before super().__init__() call"
+ )
+
+ assert name not in self._models, "Found duplicate model names"
+
+ self._models[name] = model
+ self._optims[name] = optim
+ self._scheds[name] = sched
+
+ def get_model_names(self, names=None):
+ names_real = list(self._models.keys())
+ if names is not None:
+ names = tolist_if_not(names)
+ for name in names:
+ assert name in names_real
+ return names
+ else:
+ return names_real
+
+ def save_model(
+ self, epoch, directory, is_best=False, val_result=None, model_name=""
+ ):
+ names = self.get_model_names()
+
+ for name in names:
+ model_dict = self._models[name].state_dict()
+
+ optim_dict = None
+ if self._optims[name] is not None:
+ optim_dict = self._optims[name].state_dict()
+
+ sched_dict = None
+ if self._scheds[name] is not None:
+ sched_dict = self._scheds[name].state_dict()
+
+ save_checkpoint(
+ {
+ "state_dict": model_dict,
+ "epoch": epoch + 1,
+ "optimizer": optim_dict,
+ "scheduler": sched_dict,
+ "val_result": val_result
+ },
+ osp.join(directory, name),
+ is_best=is_best,
+ model_name=model_name,
+ )
+
+ def resume_model_if_exist(self, directory):
+ names = self.get_model_names()
+ file_missing = False
+
+ for name in names:
+ path = osp.join(directory, name)
+ if not osp.exists(path):
+ file_missing = True
+ break
+
+ if file_missing:
+ print("No checkpoint found, train from scratch")
+ return 0
+
+ print(f"Found checkpoint at {directory} (will resume training)")
+
+ for name in names:
+ path = osp.join(directory, name)
+ start_epoch = resume_from_checkpoint(
+ path, self._models[name], self._optims[name],
+ self._scheds[name]
+ )
+
+ return start_epoch
+
+ def load_model(self, directory, epoch=None):
+ if not directory:
+ print(
+ "Note that load_model() is skipped as no pretrained "
+ "model is given (ignore this if it's done on purpose)"
+ )
+ return
+
+ names = self.get_model_names()
+
+ # By default, the best model is loaded
+ model_file = "model-best.pth.tar"
+
+ if epoch is not None:
+ model_file = "model.pth.tar-" + str(epoch)
+
+ for name in names:
+ model_path = osp.join(directory, name, model_file)
+
+ if not osp.exists(model_path):
+ raise FileNotFoundError(f"No model at {model_path}")
+
+ checkpoint = load_checkpoint(model_path)
+ state_dict = checkpoint["state_dict"]
+ epoch = checkpoint["epoch"]
+ val_result = checkpoint["val_result"]
+ print(
+ f"Load {model_path} to {name} (epoch={epoch}, val_result={val_result:.1f})"
+ )
+ self._models[name].load_state_dict(state_dict)
+
+ def set_model_mode(self, mode="train", names=None):
+ names = self.get_model_names(names)
+
+ for name in names:
+ if mode == "train":
+ self._models[name].train()
+ elif mode in ["test", "eval"]:
+ self._models[name].eval()
+ else:
+ raise KeyError
+
+ def update_lr(self, names=None):
+ names = self.get_model_names(names)
+
+ for name in names:
+ if self._scheds[name] is not None:
+ self._scheds[name].step()
+
+ def detect_anomaly(self, loss):
+ if not torch.isfinite(loss).all():
+ raise FloatingPointError("Loss is infinite or NaN!")
+
+ def init_writer(self, log_dir):
+ if self.__dict__.get("_writer") is None or self._writer is None:
+ print(f"Initialize tensorboard (log_dir={log_dir})")
+ self._writer = SummaryWriter(log_dir=log_dir)
+
+ def close_writer(self):
+ if self._writer is not None:
+ self._writer.close()
+
+ def write_scalar(self, tag, scalar_value, global_step=None):
+ if self._writer is None:
+ # Do nothing if writer is not initialized
+ # Note that writer is only used when training is needed
+ pass
+ else:
+ self._writer.add_scalar(tag, scalar_value, global_step)
+
+ def train(self, start_epoch, max_epoch):
+ """Generic training loops."""
+ self.start_epoch = start_epoch
+ self.max_epoch = max_epoch
+
+ self.before_train()
+ for self.epoch in range(self.start_epoch, self.max_epoch):
+ self.before_epoch()
+ self.run_epoch()
+ self.after_epoch()
+ self.after_train()
+
+ def before_train(self):
+ pass
+
+ def after_train(self):
+ pass
+
+ def before_epoch(self):
+ pass
+
+ def after_epoch(self):
+ pass
+
+ def run_epoch(self):
+ raise NotImplementedError
+
+ def test(self):
+ raise NotImplementedError
+
+ def parse_batch_train(self, batch):
+ raise NotImplementedError
+
+ def parse_batch_test(self, batch):
+ raise NotImplementedError
+
+ def forward_backward(self, batch):
+ raise NotImplementedError
+
+ def model_inference(self, input):
+ raise NotImplementedError
+
+ def model_zero_grad(self, names=None):
+ names = self.get_model_names(names)
+ for name in names:
+ if self._optims[name] is not None:
+ self._optims[name].zero_grad()
+
+ def model_backward(self, loss):
+ self.detect_anomaly(loss)
+ loss.backward()
+
+ def model_update(self, names=None):
+ names = self.get_model_names(names)
+ for name in names:
+ if self._optims[name] is not None:
+ self._optims[name].step()
+
+ def model_backward_and_update(self, loss, names=None):
+ self.model_zero_grad(names)
+ self.model_backward(loss)
+ self.model_update(names)
+
+
+class SimpleTrainer(TrainerBase):
+ """A simple trainer class implementing generic functions."""
+
+ def __init__(self, cfg, classnames=[], device_id=-1):
+ super().__init__()
+ self.check_cfg(cfg)
+
+ if torch.cuda.is_available() and cfg.USE_CUDA and device_id >= 0:
+ self.device = torch.device(f"cuda:{device_id}")
+ else:
+ self.device = torch.device("cpu")
+
+ # Save as attributes some frequently used variables
+ self.start_epoch = self.epoch = 0
+ self.max_epoch = cfg.OPTIM.MAX_EPOCH
+ self.output_dir = cfg.OUTPUT_DIR
+
+ self.cfg = cfg
+ # self.build_data_loader()
+ self.build_model(classnames)
+ # self.evaluator = build_evaluator(cfg, lab2cname=self.lab2cname)
+ self.best_result = -np.inf
+
+ def check_cfg(self, cfg):
+ """Check whether some variables are set correctly for
+ the trainer (optional).
+
+ For example, a trainer might require a particular sampler
+ for training such as 'RandomDomainSampler', so it is good
+ to do the checking:
+
+ assert cfg.DATALOADER.SAMPLER_TRAIN == 'RandomDomainSampler'
+ """
+ pass
+
+ def build_data_loader(self):
+ """Create essential data-related attributes.
+
+ A re-implementation of this method must create the
+ same attributes (self.dm is optional).
+ """
+ dm = DataManager(self.cfg)
+
+ self.train_loader_x = dm.train_loader_x
+ self.train_loader_u = dm.train_loader_u # optional, can be None
+ self.val_loader = dm.val_loader # optional, can be None
+ self.test_loader = dm.test_loader
+
+ self.num_classes = dm.num_classes
+ self.num_source_domains = dm.num_source_domains
+ self.lab2cname = dm.lab2cname # dict {label: classname}
+
+ self.dm = dm
+
+ def build_model(self):
+ """Build and register model.
+
+ The default builds a classification model along with its
+ optimizer and scheduler.
+
+ Custom trainers can re-implement this method if necessary.
+ """
+ cfg = self.cfg
+
+ print("Building model")
+ self.model = SimpleNet(cfg, cfg.MODEL, self.num_classes)
+ if cfg.MODEL.INIT_WEIGHTS:
+ load_pretrained_weights(self.model, cfg.MODEL.INIT_WEIGHTS)
+ self.model.to(self.device)
+ print(f"# params: {count_num_param(self.model):,}")
+ self.optim = build_optimizer(self.model, cfg.OPTIM)
+ self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)
+ self.register_model("model", self.model, self.optim, self.sched)
+
+ # device_count = torch.cuda.device_count()
+ # if device_count > 1 and cfg.USE_CUDA:
+ # print(f"Detected {device_count} GPUs (use nn.DataParallel)")
+ # self.model = nn.DataParallel(self.model)
+
+ def train(self):
+ super().train(self.start_epoch, self.max_epoch)
+
+ def before_train(self):
+ directory = self.cfg.OUTPUT_DIR
+ if self.cfg.RESUME:
+ directory = self.cfg.RESUME
+ self.start_epoch = self.resume_model_if_exist(directory)
+
+ # Initialize summary writer
+ writer_dir = osp.join(self.output_dir, "tensorboard")
+ mkdir_if_missing(writer_dir)
+ self.init_writer(writer_dir)
+
+ # Remember the starting time (for computing the elapsed time)
+ self.time_start = time.time()
+
+ def after_train(self):
+ print("Finish training")
+
+ do_test = not self.cfg.TEST.NO_TEST
+ if do_test:
+ if self.cfg.TEST.FINAL_MODEL == "best_val":
+ print("Deploy the model with the best val performance")
+ self.load_model(self.output_dir)
+ else:
+ print("Deploy the last-epoch model")
+ self.test()
+
+ # Show elapsed time
+ elapsed = round(time.time() - self.time_start)
+ elapsed = str(datetime.timedelta(seconds=elapsed))
+ print(f"Elapsed: {elapsed}")
+
+ # Close writer
+ self.close_writer()
+
+ def after_epoch(self):
+ last_epoch = (self.epoch + 1) == self.max_epoch
+ do_test = not self.cfg.TEST.NO_TEST
+ meet_checkpoint_freq = (
+ (self.epoch + 1) % self.cfg.TRAIN.CHECKPOINT_FREQ == 0
+ if self.cfg.TRAIN.CHECKPOINT_FREQ > 0 else False
+ )
+
+ if do_test and self.cfg.TEST.FINAL_MODEL == "best_val":
+ curr_result = self.test(split="val")
+ is_best = curr_result > self.best_result
+ if is_best:
+ self.best_result = curr_result
+ self.save_model(
+ self.epoch,
+ self.output_dir,
+ val_result=curr_result,
+ model_name="model-best.pth.tar"
+ )
+
+ if meet_checkpoint_freq or last_epoch:
+ self.save_model(self.epoch, self.output_dir)
+
+ @torch.no_grad()
+ def test(self, images=None, split=None):
+ """A generic testing pipeline."""
+ self.set_model_mode("eval")
+ # self.evaluator.reset()
+
+ # if split is None:
+ # split = self.cfg.TEST.SPLIT
+
+ # if split == "val" and self.val_loader is not None:
+ # data_loader = self.val_loader
+ # else:
+ # split = "test" # in case val_loader is None
+ # data_loader = self.test_loader
+
+ # print(f"Evaluate on the *{split}* set")
+ images = images.to(self.device)
+ return self.model_inference(images)
+
+ for batch_idx, batch in enumerate(tqdm(data_loader)):
+ input, label = self.parse_batch_test(batch)
+ output = self.model_inference(input)
+ self.evaluator.process(output, label)
+
+ results = self.evaluator.evaluate()
+
+ for k, v in results.items():
+ tag = f"{split}/{k}"
+ self.write_scalar(tag, v, self.epoch)
+
+ return list(results.values())[0]
+
+ def model_inference(self, input):
+ return self.model(input)
+
+ def parse_batch_test(self, batch):
+ input = batch["img"]
+ label = batch["label"]
+
+ input = input.to(self.device)
+ label = label.to(self.device)
+
+ return input, label
+
+ def get_current_lr(self, names=None):
+ names = self.get_model_names(names)
+ name = names[0]
+ return self._optims[name].param_groups[0]["lr"]
+
+
+class TrainerXU(SimpleTrainer):
+ """A base trainer using both labeled and unlabeled data.
+
+ In the context of domain adaptation, labeled and unlabeled data
+ come from source and target domains respectively.
+
+ When it comes to semi-supervised learning, all data comes from the
+ same domain.
+ """
+
+ def run_epoch(self):
+ self.set_model_mode("train")
+ losses = MetricMeter()
+ batch_time = AverageMeter()
+ data_time = AverageMeter()
+
+ # Decide to iterate over labeled or unlabeled dataset
+ len_train_loader_x = len(self.train_loader_x)
+ len_train_loader_u = len(self.train_loader_u)
+ if self.cfg.TRAIN.COUNT_ITER == "train_x":
+ self.num_batches = len_train_loader_x
+ elif self.cfg.TRAIN.COUNT_ITER == "train_u":
+ self.num_batches = len_train_loader_u
+ elif self.cfg.TRAIN.COUNT_ITER == "smaller_one":
+ self.num_batches = min(len_train_loader_x, len_train_loader_u)
+ else:
+ raise ValueError
+
+ train_loader_x_iter = iter(self.train_loader_x)
+ train_loader_u_iter = iter(self.train_loader_u)
+
+ end = time.time()
+ for self.batch_idx in range(self.num_batches):
+ try:
+ batch_x = next(train_loader_x_iter)
+ except StopIteration:
+ train_loader_x_iter = iter(self.train_loader_x)
+ batch_x = next(train_loader_x_iter)
+
+ try:
+ batch_u = next(train_loader_u_iter)
+ except StopIteration:
+ train_loader_u_iter = iter(self.train_loader_u)
+ batch_u = next(train_loader_u_iter)
+
+ data_time.update(time.time() - end)
+ loss_summary = self.forward_backward(batch_x, batch_u)
+ batch_time.update(time.time() - end)
+ losses.update(loss_summary)
+
+ meet_freq = (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0
+ only_few_batches = self.num_batches < self.cfg.TRAIN.PRINT_FREQ
+ if meet_freq or only_few_batches:
+ nb_remain = 0
+ nb_remain += self.num_batches - self.batch_idx - 1
+ nb_remain += (
+ self.max_epoch - self.epoch - 1
+ ) * self.num_batches
+ eta_seconds = batch_time.avg * nb_remain
+ eta = str(datetime.timedelta(seconds=int(eta_seconds)))
+
+ info = []
+ info += [f"epoch [{self.epoch + 1}/{self.max_epoch}]"]
+ info += [f"batch [{self.batch_idx + 1}/{self.num_batches}]"]
+ info += [f"time {batch_time.val:.3f} ({batch_time.avg:.3f})"]
+ info += [f"data {data_time.val:.3f} ({data_time.avg:.3f})"]
+ info += [f"{losses}"]
+ info += [f"lr {self.get_current_lr():.4e}"]
+ info += [f"eta {eta}"]
+ print(" ".join(info))
+
+ n_iter = self.epoch * self.num_batches + self.batch_idx
+ for name, meter in losses.meters.items():
+ self.write_scalar("train/" + name, meter.avg, n_iter)
+ self.write_scalar("train/lr", self.get_current_lr(), n_iter)
+
+ end = time.time()
+
+ def parse_batch_train(self, batch_x, batch_u):
+ input_x = batch_x["img"]
+ label_x = batch_x["label"]
+ input_u = batch_u["img"]
+
+ input_x = input_x.to(self.device)
+ label_x = label_x.to(self.device)
+ input_u = input_u.to(self.device)
+
+ return input_x, label_x, input_u
+
+
+class TrainerX(SimpleTrainer):
+ """A base trainer using labeled data only."""
+
+ def run_epoch(self):
+ self.set_model_mode("train")
+ losses = MetricMeter()
+ batch_time = AverageMeter()
+ data_time = AverageMeter()
+ self.num_batches = len(self.train_loader_x)
+
+ end = time.time()
+ for self.batch_idx, batch in enumerate(self.train_loader_x):
+ data_time.update(time.time() - end)
+ loss_summary = self.forward_backward(batch)
+ batch_time.update(time.time() - end)
+ losses.update(loss_summary)
+
+ meet_freq = (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0
+ only_few_batches = self.num_batches < self.cfg.TRAIN.PRINT_FREQ
+ if meet_freq or only_few_batches:
+ nb_remain = 0
+ nb_remain += self.num_batches - self.batch_idx - 1
+ nb_remain += (
+ self.max_epoch - self.epoch - 1
+ ) * self.num_batches
+ eta_seconds = batch_time.avg * nb_remain
+ eta = str(datetime.timedelta(seconds=int(eta_seconds)))
+
+ info = []
+ info += [f"epoch [{self.epoch + 1}/{self.max_epoch}]"]
+ info += [f"batch [{self.batch_idx + 1}/{self.num_batches}]"]
+ info += [f"time {batch_time.val:.3f} ({batch_time.avg:.3f})"]
+ info += [f"data {data_time.val:.3f} ({data_time.avg:.3f})"]
+ info += [f"{losses}"]
+ info += [f"lr {self.get_current_lr():.4e}"]
+ info += [f"eta {eta}"]
+ print(" ".join(info))
+
+ n_iter = self.epoch * self.num_batches + self.batch_idx
+ for name, meter in losses.meters.items():
+ self.write_scalar("train/" + name, meter.avg, n_iter)
+ self.write_scalar("train/lr", self.get_current_lr(), n_iter)
+
+ end = time.time()
+
+ def parse_batch_train(self, batch):
+ input = batch["img"]
+ label = batch["label"]
+ domain = batch["domain"]
+
+ input = input.to(self.device)
+ label = label.to(self.device)
+ domain = domain.to(self.device)
+
+ return input, label, domain
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/evaluation/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/__init__.py
new file mode 100644
index 00000000..59a024f5
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/__init__.py
@@ -0,0 +1,3 @@
+from .build import build_evaluator, EVALUATOR_REGISTRY # isort:skip
+
+from .evaluator import EvaluatorBase, Classification
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/evaluation/build.py b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/build.py
new file mode 100644
index 00000000..3132a3f4
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+EVALUATOR_REGISTRY = Registry("EVALUATOR")
+
+
+def build_evaluator(cfg, **kwargs):
+ avai_evaluators = EVALUATOR_REGISTRY.registered_names()
+ check_availability(cfg.TEST.EVALUATOR, avai_evaluators)
+ if cfg.VERBOSE:
+ print("Loading evaluator: {}".format(cfg.TEST.EVALUATOR))
+ return EVALUATOR_REGISTRY.get(cfg.TEST.EVALUATOR)(cfg, **kwargs)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/evaluation/evaluator.py b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/evaluator.py
new file mode 100644
index 00000000..eef37975
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/evaluation/evaluator.py
@@ -0,0 +1,125 @@
+import numpy as np
+import os.path as osp
+from collections import OrderedDict, defaultdict
+import torch
+from sklearn.metrics import f1_score, confusion_matrix
+
+from .build import EVALUATOR_REGISTRY
+
+
+class EvaluatorBase:
+ """Base evaluator."""
+
+ def __init__(self, cfg):
+ self.cfg = cfg
+
+ def reset(self):
+ raise NotImplementedError
+
+ def process(self, mo, gt):
+ raise NotImplementedError
+
+ def evaluate(self):
+ raise NotImplementedError
+
+
+@EVALUATOR_REGISTRY.register()
+class Classification(EvaluatorBase):
+ """Evaluator for classification."""
+
+ def __init__(self, cfg, lab2cname=None, **kwargs):
+ super().__init__(cfg)
+ self._lab2cname = lab2cname
+ self._correct = 0
+ self._total = 0
+ self._per_class_res = None
+ self._y_true = []
+ self._y_pred = []
+ if cfg.TEST.PER_CLASS_RESULT:
+ assert lab2cname is not None
+ self._per_class_res = defaultdict(list)
+
+ def reset(self):
+ self._correct = 0
+ self._total = 0
+ self._y_true = []
+ self._y_pred = []
+ if self._per_class_res is not None:
+ self._per_class_res = defaultdict(list)
+
+ def process(self, mo, gt):
+ # mo (torch.Tensor): model output [batch, num_classes]
+ # gt (torch.LongTensor): ground truth [batch]
+ pred = mo.max(1)[1]
+ matches = pred.eq(gt).float()
+ self._correct += int(matches.sum().item())
+ self._total += gt.shape[0]
+
+ self._y_true.extend(gt.data.cpu().numpy().tolist())
+ self._y_pred.extend(pred.data.cpu().numpy().tolist())
+
+ if self._per_class_res is not None:
+ for i, label in enumerate(gt):
+ label = label.item()
+ matches_i = int(matches[i].item())
+ self._per_class_res[label].append(matches_i)
+
+ def evaluate(self):
+ results = OrderedDict()
+ acc = 100.0 * self._correct / self._total
+ err = 100.0 - acc
+ macro_f1 = 100.0 * f1_score(
+ self._y_true,
+ self._y_pred,
+ average="macro",
+ labels=np.unique(self._y_true)
+ )
+
+ # The first value will be returned by trainer.test()
+ results["accuracy"] = acc
+ results["error_rate"] = err
+ results["macro_f1"] = macro_f1
+
+ print(
+ "=> result\n"
+ f"* total: {self._total:,}\n"
+ f"* correct: {self._correct:,}\n"
+ f"* accuracy: {acc:.1f}%\n"
+ f"* error: {err:.1f}%\n"
+ f"* macro_f1: {macro_f1:.1f}%"
+ )
+
+ if self._per_class_res is not None:
+ labels = list(self._per_class_res.keys())
+ labels.sort()
+
+ print("=> per-class result")
+ accs = []
+
+ for label in labels:
+ classname = self._lab2cname[label]
+ res = self._per_class_res[label]
+ correct = sum(res)
+ total = len(res)
+ acc = 100.0 * correct / total
+ accs.append(acc)
+ print(
+ f"* class: {label} ({classname})\t"
+ f"total: {total:,}\t"
+ f"correct: {correct:,}\t"
+ f"acc: {acc:.1f}%"
+ )
+ mean_acc = np.mean(accs)
+ print(f"* average: {mean_acc:.1f}%")
+
+ results["perclass_accuracy"] = mean_acc
+
+ if self.cfg.TEST.COMPUTE_CMAT:
+ cmat = confusion_matrix(
+ self._y_true, self._y_pred, normalize="true"
+ )
+ save_path = osp.join(self.cfg.OUTPUT_DIR, "cmat.pt")
+ torch.save(cmat, save_path)
+ print(f"Confusion matrix is saved to {save_path}")
+
+ return results
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/metrics/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/metrics/__init__.py
new file mode 100644
index 00000000..c2b37de8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/metrics/__init__.py
@@ -0,0 +1,4 @@
+from .accuracy import compute_accuracy
+from .distance import (
+ cosine_distance, compute_distance_matrix, euclidean_squared_distance
+)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/metrics/accuracy.py b/python/ClipDetection/Dassl.pytorch/dassl/metrics/accuracy.py
new file mode 100644
index 00000000..a8ed0ae5
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/metrics/accuracy.py
@@ -0,0 +1,30 @@
+def compute_accuracy(output, target, topk=(1, )):
+ """Computes the accuracy over the k top predictions for
+ the specified values of k.
+
+ Args:
+ output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
+ target (torch.LongTensor): ground truth labels with shape (batch_size).
+ topk (tuple, optional): accuracy at top-k will be computed. For example,
+ topk=(1, 5) means accuracy at top-1 and top-5 will be computed.
+
+ Returns:
+ list: accuracy at top-k.
+ """
+ maxk = max(topk)
+ batch_size = target.size(0)
+
+ if isinstance(output, (tuple, list)):
+ output = output[0]
+
+ _, pred = output.topk(maxk, 1, True, True)
+ pred = pred.t()
+ correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+ res = []
+ for k in topk:
+ correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+ acc = correct_k.mul_(100.0 / batch_size)
+ res.append(acc)
+
+ return res
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/metrics/distance.py b/python/ClipDetection/Dassl.pytorch/dassl/metrics/distance.py
new file mode 100644
index 00000000..80568151
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/metrics/distance.py
@@ -0,0 +1,77 @@
+"""
+Source: https://github.com/KaiyangZhou/deep-person-reid
+"""
+import torch
+from torch.nn import functional as F
+
+
+def compute_distance_matrix(input1, input2, metric="euclidean"):
+ """A wrapper function for computing distance matrix.
+
+ Each input matrix has the shape (n_data, feature_dim).
+
+ Args:
+ input1 (torch.Tensor): 2-D feature matrix.
+ input2 (torch.Tensor): 2-D feature matrix.
+ metric (str, optional): "euclidean" or "cosine".
+ Default is "euclidean".
+
+ Returns:
+ torch.Tensor: distance matrix.
+ """
+ # check input
+ assert isinstance(input1, torch.Tensor)
+ assert isinstance(input2, torch.Tensor)
+ assert input1.dim() == 2, "Expected 2-D tensor, but got {}-D".format(
+ input1.dim()
+ )
+ assert input2.dim() == 2, "Expected 2-D tensor, but got {}-D".format(
+ input2.dim()
+ )
+ assert input1.size(1) == input2.size(1)
+
+ if metric == "euclidean":
+ distmat = euclidean_squared_distance(input1, input2)
+ elif metric == "cosine":
+ distmat = cosine_distance(input1, input2)
+ else:
+ raise ValueError(
+ "Unknown distance metric: {}. "
+ 'Please choose either "euclidean" or "cosine"'.format(metric)
+ )
+
+ return distmat
+
+
+def euclidean_squared_distance(input1, input2):
+ """Computes euclidean squared distance.
+
+ Args:
+ input1 (torch.Tensor): 2-D feature matrix.
+ input2 (torch.Tensor): 2-D feature matrix.
+
+ Returns:
+ torch.Tensor: distance matrix.
+ """
+ m, n = input1.size(0), input2.size(0)
+ mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)
+ mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
+ distmat = mat1 + mat2
+ distmat.addmm_(1, -2, input1, input2.t())
+ return distmat
+
+
+def cosine_distance(input1, input2):
+ """Computes cosine distance.
+
+ Args:
+ input1 (torch.Tensor): 2-D feature matrix.
+ input2 (torch.Tensor): 2-D feature matrix.
+
+ Returns:
+ torch.Tensor: distance matrix.
+ """
+ input1_normed = F.normalize(input1, p=2, dim=1)
+ input2_normed = F.normalize(input2, p=2, dim=1)
+ distmat = 1 - torch.mm(input1_normed, input2_normed.t())
+ return distmat
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/__init__.py
new file mode 100644
index 00000000..88466b9b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/__init__.py
@@ -0,0 +1,3 @@
+from .head import HEAD_REGISTRY, build_head
+from .network import NETWORK_REGISTRY, build_network
+from .backbone import BACKBONE_REGISTRY, Backbone, build_backbone
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/__init__.py
new file mode 100644
index 00000000..8e6dc684
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/__init__.py
@@ -0,0 +1,23 @@
+from .build import build_backbone, BACKBONE_REGISTRY # isort:skip
+from .backbone import Backbone # isort:skip
+
+from .vgg import vgg16
+from .resnet import (
+ resnet18, resnet34, resnet50, resnet101, resnet152, resnet18_ms_l1,
+ resnet50_ms_l1, resnet18_ms_l12, resnet50_ms_l12, resnet101_ms_l1,
+ resnet18_ms_l123, resnet50_ms_l123, resnet101_ms_l12, resnet101_ms_l123,
+ resnet18_efdmix_l1, resnet50_efdmix_l1, resnet18_efdmix_l12,
+ resnet50_efdmix_l12, resnet101_efdmix_l1, resnet18_efdmix_l123,
+ resnet50_efdmix_l123, resnet101_efdmix_l12, resnet101_efdmix_l123
+)
+from .alexnet import alexnet
+from .wide_resnet import wide_resnet_16_4, wide_resnet_28_2
+from .cnn_digitsdg import cnn_digitsdg
+from .efficientnet import (
+ efficientnet_b0, efficientnet_b1, efficientnet_b2, efficientnet_b3,
+ efficientnet_b4, efficientnet_b5, efficientnet_b6, efficientnet_b7
+)
+from .resnet_dynamic import *
+from .cnn_digitsingle import cnn_digitsingle
+from .preact_resnet18 import preact_resnet18
+from .cnn_digit5_m3sda import cnn_digit5_m3sda
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/alexnet.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/alexnet.py
new file mode 100644
index 00000000..2daff243
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/alexnet.py
@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+model_urls = {
+ "alexnet": "https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth",
+}
+
+
+class AlexNet(Backbone):
+
+ def __init__(self):
+ super().__init__()
+ self.features = nn.Sequential(
+ nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ nn.Conv2d(64, 192, kernel_size=5, padding=2),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ nn.Conv2d(192, 384, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(384, 256, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(256, 256, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ )
+ self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
+ # Note that self.classifier outputs features rather than logits
+ self.classifier = nn.Sequential(
+ nn.Dropout(),
+ nn.Linear(256 * 6 * 6, 4096),
+ nn.ReLU(inplace=True),
+ nn.Dropout(),
+ nn.Linear(4096, 4096),
+ nn.ReLU(inplace=True),
+ )
+
+ self._out_features = 4096
+
+ def forward(self, x):
+ x = self.features(x)
+ x = self.avgpool(x)
+ x = torch.flatten(x, 1)
+ return self.classifier(x)
+
+
+def init_pretrained_weights(model, model_url):
+ pretrain_dict = model_zoo.load_url(model_url)
+ model.load_state_dict(pretrain_dict, strict=False)
+
+
+@BACKBONE_REGISTRY.register()
+def alexnet(pretrained=True, **kwargs):
+ model = AlexNet()
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["alexnet"])
+
+ return model
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/backbone.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/backbone.py
new file mode 100644
index 00000000..b544d945
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/backbone.py
@@ -0,0 +1,17 @@
+import torch.nn as nn
+
+
+class Backbone(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+
+ def forward(self):
+ pass
+
+ @property
+ def out_features(self):
+ """Output feature dimension."""
+ if self.__dict__.get("_out_features") is None:
+ return None
+ return self._out_features
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/build.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/build.py
new file mode 100644
index 00000000..61f4e4fe
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+BACKBONE_REGISTRY = Registry("BACKBONE")
+
+
+def build_backbone(name, verbose=True, **kwargs):
+ avai_backbones = BACKBONE_REGISTRY.registered_names()
+ check_availability(name, avai_backbones)
+ if verbose:
+ print("Backbone: {}".format(name))
+ return BACKBONE_REGISTRY.get(name)(**kwargs)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digit5_m3sda.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digit5_m3sda.py
new file mode 100644
index 00000000..deabded8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digit5_m3sda.py
@@ -0,0 +1,58 @@
+"""
+Reference
+
+https://github.com/VisionLearningGroup/VisionLearningGroup.github.io/tree/master/M3SDA
+"""
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class FeatureExtractor(Backbone):
+
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=5, stride=1, padding=2)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.conv2 = nn.Conv2d(64, 64, kernel_size=5, stride=1, padding=2)
+ self.bn2 = nn.BatchNorm2d(64)
+ self.conv3 = nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2)
+ self.bn3 = nn.BatchNorm2d(128)
+ self.fc1 = nn.Linear(8192, 3072)
+ self.bn1_fc = nn.BatchNorm1d(3072)
+ self.fc2 = nn.Linear(3072, 2048)
+ self.bn2_fc = nn.BatchNorm1d(2048)
+
+ self._out_features = 2048
+
+ def _check_input(self, x):
+ H, W = x.shape[2:]
+ assert (
+ H == 32 and W == 32
+ ), "Input to network must be 32x32, " "but got {}x{}".format(H, W)
+
+ def forward(self, x):
+ self._check_input(x)
+ x = F.relu(self.bn1(self.conv1(x)))
+ x = F.max_pool2d(x, stride=2, kernel_size=3, padding=1)
+ x = F.relu(self.bn2(self.conv2(x)))
+ x = F.max_pool2d(x, stride=2, kernel_size=3, padding=1)
+ x = F.relu(self.bn3(self.conv3(x)))
+ x = x.view(x.size(0), 8192)
+ x = F.relu(self.bn1_fc(self.fc1(x)))
+ x = F.dropout(x, training=self.training)
+ x = F.relu(self.bn2_fc(self.fc2(x)))
+ return x
+
+
+@BACKBONE_REGISTRY.register()
+def cnn_digit5_m3sda(**kwargs):
+ """
+ This architecture was used for the Digit-5 dataset in:
+
+ - Peng et al. Moment Matching for Multi-Source
+ Domain Adaptation. ICCV 2019.
+ """
+ return FeatureExtractor()
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsdg.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsdg.py
new file mode 100644
index 00000000..c68044f3
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsdg.py
@@ -0,0 +1,61 @@
+import torch.nn as nn
+from torch.nn import functional as F
+
+from dassl.utils import init_network_weights
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class Convolution(nn.Module):
+
+ def __init__(self, c_in, c_out):
+ super().__init__()
+ self.conv = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1)
+ self.relu = nn.ReLU(True)
+
+ def forward(self, x):
+ return self.relu(self.conv(x))
+
+
+class ConvNet(Backbone):
+
+ def __init__(self, c_hidden=64):
+ super().__init__()
+ self.conv1 = Convolution(3, c_hidden)
+ self.conv2 = Convolution(c_hidden, c_hidden)
+ self.conv3 = Convolution(c_hidden, c_hidden)
+ self.conv4 = Convolution(c_hidden, c_hidden)
+
+ self._out_features = 2**2 * c_hidden
+
+ def _check_input(self, x):
+ H, W = x.shape[2:]
+ assert (
+ H == 32 and W == 32
+ ), "Input to network must be 32x32, " "but got {}x{}".format(H, W)
+
+ def forward(self, x):
+ self._check_input(x)
+ x = self.conv1(x)
+ x = F.max_pool2d(x, 2)
+ x = self.conv2(x)
+ x = F.max_pool2d(x, 2)
+ x = self.conv3(x)
+ x = F.max_pool2d(x, 2)
+ x = self.conv4(x)
+ x = F.max_pool2d(x, 2)
+ return x.view(x.size(0), -1)
+
+
+@BACKBONE_REGISTRY.register()
+def cnn_digitsdg(**kwargs):
+ """
+ This architecture was used for DigitsDG dataset in:
+
+ - Zhou et al. Deep Domain-Adversarial Image Generation
+ for Domain Generalisation. AAAI 2020.
+ """
+ model = ConvNet(c_hidden=64)
+ init_network_weights(model, init_type="kaiming")
+ return model
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsingle.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsingle.py
new file mode 100644
index 00000000..0c5101ce
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/cnn_digitsingle.py
@@ -0,0 +1,56 @@
+"""
+This model is built based on
+https://github.com/ricvolpi/generalize-unseen-domains/blob/master/model.py
+"""
+import torch.nn as nn
+from torch.nn import functional as F
+
+from dassl.utils import init_network_weights
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class CNN(Backbone):
+
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(3, 64, 5)
+ self.conv2 = nn.Conv2d(64, 128, 5)
+ self.fc3 = nn.Linear(5 * 5 * 128, 1024)
+ self.fc4 = nn.Linear(1024, 1024)
+
+ self._out_features = 1024
+
+ def _check_input(self, x):
+ H, W = x.shape[2:]
+ assert (
+ H == 32 and W == 32
+ ), "Input to network must be 32x32, " "but got {}x{}".format(H, W)
+
+ def forward(self, x):
+ self._check_input(x)
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+
+ x = x.view(x.size(0), -1)
+
+ x = self.fc3(x)
+ x = F.relu(x)
+
+ x = self.fc4(x)
+ x = F.relu(x)
+
+ return x
+
+
+@BACKBONE_REGISTRY.register()
+def cnn_digitsingle(**kwargs):
+ model = CNN()
+ init_network_weights(model, init_type="kaiming")
+ return model
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/__init__.py
new file mode 100644
index 00000000..20ee4333
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/__init__.py
@@ -0,0 +1,12 @@
+"""
+Source: https://github.com/lukemelas/EfficientNet-PyTorch.
+"""
+__version__ = "0.6.4"
+from .model import (
+ EfficientNet, efficientnet_b0, efficientnet_b1, efficientnet_b2,
+ efficientnet_b3, efficientnet_b4, efficientnet_b5, efficientnet_b6,
+ efficientnet_b7
+)
+from .utils import (
+ BlockArgs, BlockDecoder, GlobalParams, efficientnet, get_model_params
+)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py
new file mode 100755
index 00000000..ed01261d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py
@@ -0,0 +1,371 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .utils import (
+ Swish, MemoryEfficientSwish, drop_connect, round_filters, round_repeats,
+ get_model_params, efficientnet_params, get_same_padding_conv2d,
+ load_pretrained_weights, calculate_output_image_size
+)
+from ..build import BACKBONE_REGISTRY
+from ..backbone import Backbone
+
+
+class MBConvBlock(nn.Module):
+ """
+ Mobile Inverted Residual Bottleneck Block
+
+ Args:
+ block_args (namedtuple): BlockArgs, see above
+ global_params (namedtuple): GlobalParam, see above
+
+ Attributes:
+ has_se (bool): Whether the block contains a Squeeze and Excitation layer.
+ """
+
+ def __init__(self, block_args, global_params, image_size=None):
+ super().__init__()
+ self._block_args = block_args
+ self._bn_mom = 1 - global_params.batch_norm_momentum
+ self._bn_eps = global_params.batch_norm_epsilon
+ self.has_se = (self._block_args.se_ratio is
+ not None) and (0 < self._block_args.se_ratio <= 1)
+ self.id_skip = block_args.id_skip # skip connection and drop connect
+
+ # Expansion phase
+ inp = self._block_args.input_filters # number of input channels
+ oup = (
+ self._block_args.input_filters * self._block_args.expand_ratio
+ ) # number of output channels
+ if self._block_args.expand_ratio != 1:
+ Conv2d = get_same_padding_conv2d(image_size=image_size)
+ self._expand_conv = Conv2d(
+ in_channels=inp, out_channels=oup, kernel_size=1, bias=False
+ )
+ self._bn0 = nn.BatchNorm2d(
+ num_features=oup, momentum=self._bn_mom, eps=self._bn_eps
+ )
+ # image_size = calculate_output_image_size(image_size, 1) <-- this would do nothing
+
+ # Depthwise convolution phase
+ k = self._block_args.kernel_size
+ s = self._block_args.stride
+ Conv2d = get_same_padding_conv2d(image_size=image_size)
+ self._depthwise_conv = Conv2d(
+ in_channels=oup,
+ out_channels=oup,
+ groups=oup, # groups makes it depthwise
+ kernel_size=k,
+ stride=s,
+ bias=False,
+ )
+ self._bn1 = nn.BatchNorm2d(
+ num_features=oup, momentum=self._bn_mom, eps=self._bn_eps
+ )
+ image_size = calculate_output_image_size(image_size, s)
+
+ # Squeeze and Excitation layer, if desired
+ if self.has_se:
+ Conv2d = get_same_padding_conv2d(image_size=(1, 1))
+ num_squeezed_channels = max(
+ 1,
+ int(
+ self._block_args.input_filters * self._block_args.se_ratio
+ )
+ )
+ self._se_reduce = Conv2d(
+ in_channels=oup,
+ out_channels=num_squeezed_channels,
+ kernel_size=1
+ )
+ self._se_expand = Conv2d(
+ in_channels=num_squeezed_channels,
+ out_channels=oup,
+ kernel_size=1
+ )
+
+ # Output phase
+ final_oup = self._block_args.output_filters
+ Conv2d = get_same_padding_conv2d(image_size=image_size)
+ self._project_conv = Conv2d(
+ in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False
+ )
+ self._bn2 = nn.BatchNorm2d(
+ num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps
+ )
+ self._swish = MemoryEfficientSwish()
+
+ def forward(self, inputs, drop_connect_rate=None):
+ """
+ :param inputs: input tensor
+ :param drop_connect_rate: drop connect rate (float, between 0 and 1)
+ :return: output of block
+ """
+
+ # Expansion and Depthwise Convolution
+ x = inputs
+ if self._block_args.expand_ratio != 1:
+ x = self._swish(self._bn0(self._expand_conv(inputs)))
+ x = self._swish(self._bn1(self._depthwise_conv(x)))
+
+ # Squeeze and Excitation
+ if self.has_se:
+ x_squeezed = F.adaptive_avg_pool2d(x, 1)
+ x_squeezed = self._se_expand(
+ self._swish(self._se_reduce(x_squeezed))
+ )
+ x = torch.sigmoid(x_squeezed) * x
+
+ x = self._bn2(self._project_conv(x))
+
+ # Skip connection and drop connect
+ input_filters, output_filters = (
+ self._block_args.input_filters,
+ self._block_args.output_filters,
+ )
+ if (
+ self.id_skip and self._block_args.stride == 1
+ and input_filters == output_filters
+ ):
+ if drop_connect_rate:
+ x = drop_connect(
+ x, p=drop_connect_rate, training=self.training
+ )
+ x = x + inputs # skip connection
+ return x
+
+ def set_swish(self, memory_efficient=True):
+ """Sets swish function as memory efficient (for training) or standard (for export)"""
+ self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
+
+
+class EfficientNet(Backbone):
+ """
+ An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods
+
+ Args:
+ blocks_args (list): A list of BlockArgs to construct blocks
+ global_params (namedtuple): A set of GlobalParams shared between blocks
+
+ Example:
+ model = EfficientNet.from_pretrained('efficientnet-b0')
+
+ """
+
+ def __init__(self, blocks_args=None, global_params=None):
+ super().__init__()
+ assert isinstance(blocks_args, list), "blocks_args should be a list"
+ assert len(blocks_args) > 0, "block args must be greater than 0"
+ self._global_params = global_params
+ self._blocks_args = blocks_args
+
+ # Batch norm parameters
+ bn_mom = 1 - self._global_params.batch_norm_momentum
+ bn_eps = self._global_params.batch_norm_epsilon
+
+ # Get stem static or dynamic convolution depending on image size
+ image_size = global_params.image_size
+ Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)
+
+ # Stem
+ in_channels = 3 # rgb
+ out_channels = round_filters(
+ 32, self._global_params
+ ) # number of output channels
+ self._conv_stem = Conv2d(
+ in_channels, out_channels, kernel_size=3, stride=2, bias=False
+ )
+ self._bn0 = nn.BatchNorm2d(
+ num_features=out_channels, momentum=bn_mom, eps=bn_eps
+ )
+ image_size = calculate_output_image_size(image_size, 2)
+
+ # Build blocks
+ self._blocks = nn.ModuleList([])
+ for block_args in self._blocks_args:
+
+ # Update block input and output filters based on depth multiplier.
+ block_args = block_args._replace(
+ input_filters=round_filters(
+ block_args.input_filters, self._global_params
+ ),
+ output_filters=round_filters(
+ block_args.output_filters, self._global_params
+ ),
+ num_repeat=round_repeats(
+ block_args.num_repeat, self._global_params
+ ),
+ )
+
+ # The first block needs to take care of stride and filter size increase.
+ self._blocks.append(
+ MBConvBlock(
+ block_args, self._global_params, image_size=image_size
+ )
+ )
+ image_size = calculate_output_image_size(
+ image_size, block_args.stride
+ )
+ if block_args.num_repeat > 1:
+ block_args = block_args._replace(
+ input_filters=block_args.output_filters, stride=1
+ )
+ for _ in range(block_args.num_repeat - 1):
+ self._blocks.append(
+ MBConvBlock(
+ block_args, self._global_params, image_size=image_size
+ )
+ )
+ # image_size = calculate_output_image_size(image_size, block_args.stride) # ?
+
+ # Head
+ in_channels = block_args.output_filters # output of final block
+ out_channels = round_filters(1280, self._global_params)
+ Conv2d = get_same_padding_conv2d(image_size=image_size)
+ self._conv_head = Conv2d(
+ in_channels, out_channels, kernel_size=1, bias=False
+ )
+ self._bn1 = nn.BatchNorm2d(
+ num_features=out_channels, momentum=bn_mom, eps=bn_eps
+ )
+
+ # Final linear layer
+ self._avg_pooling = nn.AdaptiveAvgPool2d(1)
+ self._dropout = nn.Dropout(self._global_params.dropout_rate)
+ # self._fc = nn.Linear(out_channels, self._global_params.num_classes)
+ self._swish = MemoryEfficientSwish()
+
+ self._out_features = out_channels
+
+ def set_swish(self, memory_efficient=True):
+ """Sets swish function as memory efficient (for training) or standard (for export)"""
+ self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
+ for block in self._blocks:
+ block.set_swish(memory_efficient)
+
+ def extract_features(self, inputs):
+ """Returns output of the final convolution layer"""
+
+ # Stem
+ x = self._swish(self._bn0(self._conv_stem(inputs)))
+
+ # Blocks
+ for idx, block in enumerate(self._blocks):
+ drop_connect_rate = self._global_params.drop_connect_rate
+ if drop_connect_rate:
+ drop_connect_rate *= float(idx) / len(self._blocks)
+ x = block(x, drop_connect_rate=drop_connect_rate)
+
+ # Head
+ x = self._swish(self._bn1(self._conv_head(x)))
+
+ return x
+
+ def forward(self, inputs):
+ """
+ Calls extract_features to extract features, applies
+ final linear layer, and returns logits.
+ """
+ bs = inputs.size(0)
+ # Convolution layers
+ x = self.extract_features(inputs)
+
+ # Pooling and final linear layer
+ x = self._avg_pooling(x)
+ x = x.view(bs, -1)
+ x = self._dropout(x)
+ # x = self._fc(x)
+ return x
+
+ @classmethod
+ def from_name(cls, model_name, override_params=None):
+ cls._check_model_name_is_valid(model_name)
+ blocks_args, global_params = get_model_params(
+ model_name, override_params
+ )
+ return cls(blocks_args, global_params)
+
+ @classmethod
+ def from_pretrained(
+ cls, model_name, advprop=False, num_classes=1000, in_channels=3
+ ):
+ model = cls.from_name(
+ model_name, override_params={"num_classes": num_classes}
+ )
+ load_pretrained_weights(
+ model, model_name, load_fc=(num_classes == 1000), advprop=advprop
+ )
+ model._change_in_channels(in_channels)
+ return model
+
+ @classmethod
+ def get_image_size(cls, model_name):
+ cls._check_model_name_is_valid(model_name)
+ _, _, res, _ = efficientnet_params(model_name)
+ return res
+
+ @classmethod
+ def _check_model_name_is_valid(cls, model_name):
+ """Validates model name."""
+ valid_models = ["efficientnet-b" + str(i) for i in range(9)]
+ if model_name not in valid_models:
+ raise ValueError(
+ "model_name should be one of: " + ", ".join(valid_models)
+ )
+
+ def _change_in_channels(model, in_channels):
+ if in_channels != 3:
+ Conv2d = get_same_padding_conv2d(
+ image_size=model._global_params.image_size
+ )
+ out_channels = round_filters(32, model._global_params)
+ model._conv_stem = Conv2d(
+ in_channels, out_channels, kernel_size=3, stride=2, bias=False
+ )
+
+
+def build_efficientnet(name, pretrained):
+ if pretrained:
+ return EfficientNet.from_pretrained("efficientnet-{}".format(name))
+ else:
+ return EfficientNet.from_name("efficientnet-{}".format(name))
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b0(pretrained=True, **kwargs):
+ return build_efficientnet("b0", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b1(pretrained=True, **kwargs):
+ return build_efficientnet("b1", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b2(pretrained=True, **kwargs):
+ return build_efficientnet("b2", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b3(pretrained=True, **kwargs):
+ return build_efficientnet("b3", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b4(pretrained=True, **kwargs):
+ return build_efficientnet("b4", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b5(pretrained=True, **kwargs):
+ return build_efficientnet("b5", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b6(pretrained=True, **kwargs):
+ return build_efficientnet("b6", pretrained)
+
+
+@BACKBONE_REGISTRY.register()
+def efficientnet_b7(pretrained=True, **kwargs):
+ return build_efficientnet("b7", pretrained)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/utils.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/utils.py
new file mode 100755
index 00000000..a4205061
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/efficientnet/utils.py
@@ -0,0 +1,477 @@
+"""
+This file contains helper functions for building the model and for loading model parameters.
+These helper functions are built to mirror those in the official TensorFlow implementation.
+"""
+
+import re
+import math
+import collections
+from functools import partial
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils import model_zoo
+
+########################################################################
+############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
+########################################################################
+
+# Parameters for the entire model (stem, all blocks, and head)
+GlobalParams = collections.namedtuple(
+ "GlobalParams",
+ [
+ "batch_norm_momentum",
+ "batch_norm_epsilon",
+ "dropout_rate",
+ "num_classes",
+ "width_coefficient",
+ "depth_coefficient",
+ "depth_divisor",
+ "min_depth",
+ "drop_connect_rate",
+ "image_size",
+ ],
+)
+
+# Parameters for an individual model block
+BlockArgs = collections.namedtuple(
+ "BlockArgs",
+ [
+ "kernel_size",
+ "num_repeat",
+ "input_filters",
+ "output_filters",
+ "expand_ratio",
+ "id_skip",
+ "stride",
+ "se_ratio",
+ ],
+)
+
+# Change namedtuple defaults
+GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields)
+BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields)
+
+
+class SwishImplementation(torch.autograd.Function):
+
+ @staticmethod
+ def forward(ctx, i):
+ result = i * torch.sigmoid(i)
+ ctx.save_for_backward(i)
+ return result
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ i = ctx.saved_variables[0]
+ sigmoid_i = torch.sigmoid(i)
+ return grad_output * (sigmoid_i * (1 + i * (1-sigmoid_i)))
+
+
+class MemoryEfficientSwish(nn.Module):
+
+ def forward(self, x):
+ return SwishImplementation.apply(x)
+
+
+class Swish(nn.Module):
+
+ def forward(self, x):
+ return x * torch.sigmoid(x)
+
+
+def round_filters(filters, global_params):
+ """Calculate and round number of filters based on depth multiplier."""
+ multiplier = global_params.width_coefficient
+ if not multiplier:
+ return filters
+ divisor = global_params.depth_divisor
+ min_depth = global_params.min_depth
+ filters *= multiplier
+ min_depth = min_depth or divisor
+ new_filters = max(min_depth, int(filters + divisor/2) // divisor * divisor)
+ if new_filters < 0.9 * filters: # prevent rounding by more than 10%
+ new_filters += divisor
+ return int(new_filters)
+
+
+def round_repeats(repeats, global_params):
+ """Round number of filters based on depth multiplier."""
+ multiplier = global_params.depth_coefficient
+ if not multiplier:
+ return repeats
+ return int(math.ceil(multiplier * repeats))
+
+
+def drop_connect(inputs, p, training):
+ """Drop connect."""
+ if not training:
+ return inputs
+ batch_size = inputs.shape[0]
+ keep_prob = 1 - p
+ random_tensor = keep_prob
+ random_tensor += torch.rand(
+ [batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device
+ )
+ binary_tensor = torch.floor(random_tensor)
+ output = inputs / keep_prob * binary_tensor
+ return output
+
+
+def get_same_padding_conv2d(image_size=None):
+ """Chooses static padding if you have specified an image size, and dynamic padding otherwise.
+ Static padding is necessary for ONNX exporting of models."""
+ if image_size is None:
+ return Conv2dDynamicSamePadding
+ else:
+ return partial(Conv2dStaticSamePadding, image_size=image_size)
+
+
+def get_width_and_height_from_size(x):
+ """Obtains width and height from a int or tuple"""
+ if isinstance(x, int):
+ return x, x
+ if isinstance(x, list) or isinstance(x, tuple):
+ return x
+ else:
+ raise TypeError()
+
+
+def calculate_output_image_size(input_image_size, stride):
+ """
+ Calculates the output image size when using Conv2dSamePadding with a stride.
+ Necessary for static padding. Thanks to mannatsingh for pointing this out.
+ """
+ if input_image_size is None:
+ return None
+ image_height, image_width = get_width_and_height_from_size(
+ input_image_size
+ )
+ stride = stride if isinstance(stride, int) else stride[0]
+ image_height = int(math.ceil(image_height / stride))
+ image_width = int(math.ceil(image_width / stride))
+ return [image_height, image_width]
+
+
+class Conv2dDynamicSamePadding(nn.Conv2d):
+ """2D Convolutions like TensorFlow, for a dynamic image size"""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ dilation=1,
+ groups=1,
+ bias=True,
+ ):
+ super().__init__(
+ in_channels, out_channels, kernel_size, stride, 0, dilation,
+ groups, bias
+ )
+ self.stride = self.stride if len(self.stride
+ ) == 2 else [self.stride[0]] * 2
+
+ def forward(self, x):
+ ih, iw = x.size()[-2:]
+ kh, kw = self.weight.size()[-2:]
+ sh, sw = self.stride
+ oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
+ pad_h = max(
+ (oh-1) * self.stride[0] + (kh-1) * self.dilation[0] + 1 - ih, 0
+ )
+ pad_w = max(
+ (ow-1) * self.stride[1] + (kw-1) * self.dilation[1] + 1 - iw, 0
+ )
+ if pad_h > 0 or pad_w > 0:
+ x = F.pad(
+ x,
+ [pad_w // 2, pad_w - pad_w//2, pad_h // 2, pad_h - pad_h//2]
+ )
+ return F.conv2d(
+ x,
+ self.weight,
+ self.bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups,
+ )
+
+
+class Conv2dStaticSamePadding(nn.Conv2d):
+ """2D Convolutions like TensorFlow, for a fixed image size"""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ image_size=None,
+ **kwargs
+ ):
+ super().__init__(in_channels, out_channels, kernel_size, **kwargs)
+ self.stride = self.stride if len(self.stride
+ ) == 2 else [self.stride[0]] * 2
+
+ # Calculate padding based on image size and save it
+ assert image_size is not None
+ ih, iw = (image_size,
+ image_size) if isinstance(image_size, int) else image_size
+ kh, kw = self.weight.size()[-2:]
+ sh, sw = self.stride
+ oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
+ pad_h = max(
+ (oh-1) * self.stride[0] + (kh-1) * self.dilation[0] + 1 - ih, 0
+ )
+ pad_w = max(
+ (ow-1) * self.stride[1] + (kw-1) * self.dilation[1] + 1 - iw, 0
+ )
+ if pad_h > 0 or pad_w > 0:
+ self.static_padding = nn.ZeroPad2d(
+ (pad_w // 2, pad_w - pad_w//2, pad_h // 2, pad_h - pad_h//2)
+ )
+ else:
+ self.static_padding = Identity()
+
+ def forward(self, x):
+ x = self.static_padding(x)
+ x = F.conv2d(
+ x,
+ self.weight,
+ self.bias,
+ self.stride,
+ self.padding,
+ self.dilation,
+ self.groups,
+ )
+ return x
+
+
+class Identity(nn.Module):
+
+ def __init__(self, ):
+ super(Identity, self).__init__()
+
+ def forward(self, input):
+ return input
+
+
+########################################################################
+############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############
+########################################################################
+
+
+def efficientnet_params(model_name):
+ """Map EfficientNet model name to parameter coefficients."""
+ params_dict = {
+ # Coefficients: width,depth,res,dropout
+ "efficientnet-b0": (1.0, 1.0, 224, 0.2),
+ "efficientnet-b1": (1.0, 1.1, 240, 0.2),
+ "efficientnet-b2": (1.1, 1.2, 260, 0.3),
+ "efficientnet-b3": (1.2, 1.4, 300, 0.3),
+ "efficientnet-b4": (1.4, 1.8, 380, 0.4),
+ "efficientnet-b5": (1.6, 2.2, 456, 0.4),
+ "efficientnet-b6": (1.8, 2.6, 528, 0.5),
+ "efficientnet-b7": (2.0, 3.1, 600, 0.5),
+ "efficientnet-b8": (2.2, 3.6, 672, 0.5),
+ "efficientnet-l2": (4.3, 5.3, 800, 0.5),
+ }
+ return params_dict[model_name]
+
+
+class BlockDecoder(object):
+ """Block Decoder for readability, straight from the official TensorFlow repository"""
+
+ @staticmethod
+ def _decode_block_string(block_string):
+ """Gets a block through a string notation of arguments."""
+ assert isinstance(block_string, str)
+
+ ops = block_string.split("_")
+ options = {}
+ for op in ops:
+ splits = re.split(r"(\d.*)", op)
+ if len(splits) >= 2:
+ key, value = splits[:2]
+ options[key] = value
+
+ # Check stride
+ assert ("s" in options and len(options["s"]) == 1) or (
+ len(options["s"]) == 2 and options["s"][0] == options["s"][1]
+ )
+
+ return BlockArgs(
+ kernel_size=int(options["k"]),
+ num_repeat=int(options["r"]),
+ input_filters=int(options["i"]),
+ output_filters=int(options["o"]),
+ expand_ratio=int(options["e"]),
+ id_skip=("noskip" not in block_string),
+ se_ratio=float(options["se"]) if "se" in options else None,
+ stride=[int(options["s"][0])],
+ )
+
+ @staticmethod
+ def _encode_block_string(block):
+ """Encodes a block to a string."""
+ args = [
+ "r%d" % block.num_repeat,
+ "k%d" % block.kernel_size,
+ "s%d%d" % (block.strides[0], block.strides[1]),
+ "e%s" % block.expand_ratio,
+ "i%d" % block.input_filters,
+ "o%d" % block.output_filters,
+ ]
+ if 0 < block.se_ratio <= 1:
+ args.append("se%s" % block.se_ratio)
+ if block.id_skip is False:
+ args.append("noskip")
+ return "_".join(args)
+
+ @staticmethod
+ def decode(string_list):
+ """
+ Decodes a list of string notations to specify blocks inside the network.
+
+ :param string_list: a list of strings, each string is a notation of block
+ :return: a list of BlockArgs namedtuples of block args
+ """
+ assert isinstance(string_list, list)
+ blocks_args = []
+ for block_string in string_list:
+ blocks_args.append(BlockDecoder._decode_block_string(block_string))
+ return blocks_args
+
+ @staticmethod
+ def encode(blocks_args):
+ """
+ Encodes a list of BlockArgs to a list of strings.
+
+ :param blocks_args: a list of BlockArgs namedtuples of block args
+ :return: a list of strings, each string is a notation of block
+ """
+ block_strings = []
+ for block in blocks_args:
+ block_strings.append(BlockDecoder._encode_block_string(block))
+ return block_strings
+
+
+def efficientnet(
+ width_coefficient=None,
+ depth_coefficient=None,
+ dropout_rate=0.2,
+ drop_connect_rate=0.2,
+ image_size=None,
+ num_classes=1000,
+):
+ """Creates a efficientnet model."""
+
+ blocks_args = [
+ "r1_k3_s11_e1_i32_o16_se0.25",
+ "r2_k3_s22_e6_i16_o24_se0.25",
+ "r2_k5_s22_e6_i24_o40_se0.25",
+ "r3_k3_s22_e6_i40_o80_se0.25",
+ "r3_k5_s11_e6_i80_o112_se0.25",
+ "r4_k5_s22_e6_i112_o192_se0.25",
+ "r1_k3_s11_e6_i192_o320_se0.25",
+ ]
+ blocks_args = BlockDecoder.decode(blocks_args)
+
+ global_params = GlobalParams(
+ batch_norm_momentum=0.99,
+ batch_norm_epsilon=1e-3,
+ dropout_rate=dropout_rate,
+ drop_connect_rate=drop_connect_rate,
+ # data_format='channels_last', # removed, this is always true in PyTorch
+ num_classes=num_classes,
+ width_coefficient=width_coefficient,
+ depth_coefficient=depth_coefficient,
+ depth_divisor=8,
+ min_depth=None,
+ image_size=image_size,
+ )
+
+ return blocks_args, global_params
+
+
+def get_model_params(model_name, override_params):
+ """Get the block args and global params for a given model"""
+ if model_name.startswith("efficientnet"):
+ w, d, s, p = efficientnet_params(model_name)
+ # note: all models have drop connect rate = 0.2
+ blocks_args, global_params = efficientnet(
+ width_coefficient=w,
+ depth_coefficient=d,
+ dropout_rate=p,
+ image_size=s
+ )
+ else:
+ raise NotImplementedError(
+ "model name is not pre-defined: %s" % model_name
+ )
+ if override_params:
+ # ValueError will be raised here if override_params has fields not included in global_params.
+ global_params = global_params._replace(**override_params)
+ return blocks_args, global_params
+
+
+url_map = {
+ "efficientnet-b0":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
+ "efficientnet-b1":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
+ "efficientnet-b2":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
+ "efficientnet-b3":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
+ "efficientnet-b4":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
+ "efficientnet-b5":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
+ "efficientnet-b6":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
+ "efficientnet-b7":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth",
+}
+
+url_map_advprop = {
+ "efficientnet-b0":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth",
+ "efficientnet-b1":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth",
+ "efficientnet-b2":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth",
+ "efficientnet-b3":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth",
+ "efficientnet-b4":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth",
+ "efficientnet-b5":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth",
+ "efficientnet-b6":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth",
+ "efficientnet-b7":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth",
+ "efficientnet-b8":
+ "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth",
+}
+
+
+def load_pretrained_weights(model, model_name, load_fc=True, advprop=False):
+ """Loads pretrained weights, and downloads if loading for the first time."""
+ # AutoAugment or Advprop (different preprocessing)
+ url_map_ = url_map_advprop if advprop else url_map
+ state_dict = model_zoo.load_url(url_map_[model_name])
+ model.load_state_dict(state_dict, strict=False)
+ """
+ if load_fc:
+ model.load_state_dict(state_dict)
+ else:
+ state_dict.pop('_fc.weight')
+ state_dict.pop('_fc.bias')
+ res = model.load_state_dict(state_dict, strict=False)
+ assert set(res.missing_keys) == set(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'
+
+ print('Loaded pretrained weights for {}'.format(model_name))
+ """
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/preact_resnet18.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/preact_resnet18.py
new file mode 100644
index 00000000..8c070899
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/preact_resnet18.py
@@ -0,0 +1,135 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class PreActBlock(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, stride=1):
+ super().__init__()
+ self.bn1 = nn.BatchNorm2d(in_planes)
+ self.conv1 = nn.Conv2d(
+ in_planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False
+ )
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(
+ planes, planes, kernel_size=3, stride=1, padding=1, bias=False
+ )
+
+ if stride != 1 or in_planes != self.expansion * planes:
+ self.shortcut = nn.Sequential(
+ nn.Conv2d(
+ in_planes,
+ self.expansion * planes,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ )
+ )
+
+ def forward(self, x):
+ out = F.relu(self.bn1(x))
+ shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
+ out = self.conv1(out)
+ out = self.conv2(F.relu(self.bn2(out)))
+ out += shortcut
+ return out
+
+
+class PreActBottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, in_planes, planes, stride=1):
+ super().__init__()
+ self.bn1 = nn.BatchNorm2d(in_planes)
+ self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False
+ )
+ self.bn3 = nn.BatchNorm2d(planes)
+ self.conv3 = nn.Conv2d(
+ planes, self.expansion * planes, kernel_size=1, bias=False
+ )
+
+ if stride != 1 or in_planes != self.expansion * planes:
+ self.shortcut = nn.Sequential(
+ nn.Conv2d(
+ in_planes,
+ self.expansion * planes,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ )
+ )
+
+ def forward(self, x):
+ out = F.relu(self.bn1(x))
+ shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
+ out = self.conv1(out)
+ out = self.conv2(F.relu(self.bn2(out)))
+ out = self.conv3(F.relu(self.bn3(out)))
+ out += shortcut
+ return out
+
+
+class PreActResNet(Backbone):
+
+ def __init__(self, block, num_blocks):
+ super().__init__()
+ self.in_planes = 64
+
+ self.conv1 = nn.Conv2d(
+ 3, 64, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+ self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+ self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+
+ self._out_features = 512 * block.expansion
+
+ def _make_layer(self, block, planes, num_blocks, stride):
+ strides = [stride] + [1] * (num_blocks-1)
+ layers = []
+ for stride in strides:
+ layers.append(block(self.in_planes, planes, stride))
+ self.in_planes = planes * block.expansion
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.layer1(out)
+ out = self.layer2(out)
+ out = self.layer3(out)
+ out = self.layer4(out)
+ out = F.avg_pool2d(out, 4)
+ out = out.view(out.size(0), -1)
+ return out
+
+
+"""
+Preact-ResNet18 was used for the CIFAR10 and
+SVHN datasets (both are SSL tasks) in
+
+- Wang et al. Semi-Supervised Learning by
+Augmented Distribution Alignment. ICCV 2019.
+"""
+
+
+@BACKBONE_REGISTRY.register()
+def preact_resnet18(**kwargs):
+ return PreActResNet(PreActBlock, [2, 2, 2, 2])
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet.py
new file mode 100644
index 00000000..60b9a8c8
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet.py
@@ -0,0 +1,591 @@
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+model_urls = {
+ "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
+ "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
+ "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
+ "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
+ "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+ """3x3 convolution with padding"""
+ return nn.Conv2d(
+ in_planes,
+ out_planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False
+ )
+
+
+class BasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super().__init__()
+ self.conv1 = conv3x3(inplanes, planes, stride)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = conv3x3(planes, planes)
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super().__init__()
+ self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False
+ )
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv3 = nn.Conv2d(
+ planes, planes * self.expansion, kernel_size=1, bias=False
+ )
+ self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class ResNet(Backbone):
+
+ def __init__(
+ self,
+ block,
+ layers,
+ ms_class=None,
+ ms_layers=[],
+ ms_p=0.5,
+ ms_a=0.1,
+ **kwargs
+ ):
+ self.inplanes = 64
+ super().__init__()
+
+ # backbone network
+ self.conv1 = nn.Conv2d(
+ 3, 64, kernel_size=7, stride=2, padding=3, bias=False
+ )
+ self.bn1 = nn.BatchNorm2d(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+ self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+
+ self._out_features = 512 * block.expansion
+
+ self.mixstyle = None
+ if ms_layers:
+ self.mixstyle = ms_class(p=ms_p, alpha=ms_a)
+ for layer_name in ms_layers:
+ assert layer_name in ["layer1", "layer2", "layer3"]
+ print(
+ f"Insert {self.mixstyle.__class__.__name__} after {ms_layers}"
+ )
+ self.ms_layers = ms_layers
+
+ self._init_params()
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2d(
+ self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ ),
+ nn.BatchNorm2d(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def _init_params(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(
+ m.weight, mode="fan_out", nonlinearity="relu"
+ )
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm2d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.Linear):
+ nn.init.normal_(m.weight, 0, 0.01)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+
+ def featuremaps(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+ x = self.layer1(x)
+ if "layer1" in self.ms_layers:
+ x = self.mixstyle(x)
+ x = self.layer2(x)
+ if "layer2" in self.ms_layers:
+ x = self.mixstyle(x)
+ x = self.layer3(x)
+ if "layer3" in self.ms_layers:
+ x = self.mixstyle(x)
+ return self.layer4(x)
+
+ def forward(self, x):
+ f = self.featuremaps(x)
+ v = self.global_avgpool(f)
+ return v.view(v.size(0), -1)
+
+
+def init_pretrained_weights(model, model_url):
+ pretrain_dict = model_zoo.load_url(model_url)
+ model.load_state_dict(pretrain_dict, strict=False)
+
+
+"""
+Residual network configurations:
+--
+resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
+resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
+resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
+resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
+resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
+"""
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18(pretrained=True, **kwargs):
+ model = ResNet(block=BasicBlock, layers=[2, 2, 2, 2])
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet34(pretrained=True, **kwargs):
+ model = ResNet(block=BasicBlock, layers=[3, 4, 6, 3])
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet34"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50(pretrained=True, **kwargs):
+ model = ResNet(block=Bottleneck, layers=[3, 4, 6, 3])
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101(pretrained=True, **kwargs):
+ model = ResNet(block=Bottleneck, layers=[3, 4, 23, 3])
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet152(pretrained=True, **kwargs):
+ model = ResNet(block=Bottleneck, layers=[3, 8, 36, 3])
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet152"])
+
+ return model
+
+
+"""
+Residual networks with mixstyle
+"""
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_ms_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_ms_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_ms_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_ms_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_ms_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_ms_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_ms_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_ms_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_ms_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import MixStyle
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+"""
+Residual networks with efdmix
+"""
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_efdmix_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_efdmix_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_efdmix_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=BasicBlock,
+ layers=[2, 2, 2, 2],
+ ms_class=EFDMix,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet18"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_efdmix_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_efdmix_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_efdmix_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet50"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_efdmix_l123(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2", "layer3"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_efdmix_l12(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1", "layer2"],
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_efdmix_l1(pretrained=True, **kwargs):
+ from dassl.modeling.ops import EFDMix
+
+ model = ResNet(
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ ms_class=EFDMix,
+ ms_layers=["layer1"]
+ )
+
+ if pretrained:
+ init_pretrained_weights(model, model_urls["resnet101"])
+
+ return model
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet_dynamic.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet_dynamic.py
new file mode 100644
index 00000000..c4e08ded
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/resnet_dynamic.py
@@ -0,0 +1,734 @@
+"""
+Dynamic ResNet from `"Dynamic Domain Generalization" `_.
+"""
+
+from typing import Any, List, Type, Union, Callable, Optional
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.hub import load_state_dict_from_url
+
+from dassl.modeling.ops import MixStyle, Conv2dDynamic
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+__all__ = [
+ "resnet18_dynamic", "resnet50_dynamic", "resnet101_dynamic",
+ "resnet18_dynamic_ms_l123", "resnet18_dynamic_ms_l12",
+ "resnet18_dynamic_ms_l1", "resnet50_dynamic_ms_l123",
+ "resnet50_dynamic_ms_l12", "resnet50_dynamic_ms_l1",
+ "resnet101_dynamic_ms_l123", "resnet101_dynamic_ms_l12",
+ "resnet101_dynamic_ms_l1"
+]
+
+model_urls = {
+ "resnet18_dynamic":
+ "https://csip.fzu.edu.cn/files/models/resnet18_dynamic-074db766.pth",
+ "resnet50_dynamic":
+ "https://csip.fzu.edu.cn/files/models/resnet50_dynamic-2c3b0201.pth",
+ "resnet101_dynamic":
+ "https://csip.fzu.edu.cn/files/models/resnet101_dynamic-c5f15780.pth",
+}
+
+
+def conv3x3(
+ in_planes: int,
+ out_planes: int,
+ stride: int = 1,
+ groups: int = 1,
+ dilation: int = 1
+) -> nn.Conv2d:
+ """3x3 convolution with padding"""
+ return nn.Conv2d(
+ in_planes,
+ out_planes,
+ kernel_size=3,
+ stride=stride,
+ padding=dilation,
+ groups=groups,
+ bias=False,
+ dilation=dilation
+ )
+
+
+def conv3x3_dynamic(
+ in_planes: int,
+ out_planes: int,
+ stride: int = 1,
+ attention_in_channels: int = None
+) -> Conv2dDynamic:
+ """3x3 convolution with padding"""
+ return Conv2dDynamic(
+ in_planes,
+ out_planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False,
+ attention_in_channels=attention_in_channels
+ )
+
+
+def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
+ """1x1 convolution"""
+ return nn.Conv2d(
+ in_planes, out_planes, kernel_size=1, stride=stride, bias=False
+ )
+
+
+def load_state_dict(
+ model: nn.Module,
+ state_dict: "OrderedDict[str, Tensor]",
+ allowed_missing_keys: List = None
+):
+ r"""Copies parameters and buffers from :attr:`state_dict` into
+ this module and its descendants. If :attr:`strict` is ``True``, then
+ the keys of :attr:`state_dict` must exactly match the keys returned
+ by this module's :meth:`~torch.nn.Module.state_dict` function.
+
+ Args:
+ model (torch.nn.Module): a torch.nn.Module object where state_dict load for.
+ state_dict (dict): a dict containing parameters and
+ persistent buffers.
+ allowed_missing_keys (List, optional): not raise `RuntimeError` if missing_keys
+ equal to allowed_missing_keys.
+
+ Returns:
+ ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
+ * **missing_keys** is a list of str containing the missing keys
+ * **unexpected_keys** is a list of str containing the unexpected keys
+
+ Note:
+ If a parameter or buffer is registered as ``None`` and its corresponding key
+ exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
+ ``RuntimeError``.
+ """
+ missing_keys, unexpected_keys = model.load_state_dict(
+ state_dict, strict=allowed_missing_keys is None
+ )
+
+ msgs: List[str] = []
+ raise_error = False
+ if len(unexpected_keys) > 0:
+ raise_error = True
+ msgs.insert(
+ 0, "Unexpected key(s) in state_dict: {}. ".format(
+ ", ".join("'{}'".format(k) for k in unexpected_keys)
+ )
+ )
+ if len(missing_keys) > 0:
+ if allowed_missing_keys is None or sorted(missing_keys) != sorted(
+ allowed_missing_keys
+ ):
+ raise_error = True
+ msgs.insert(
+ 0, "Missing key(s) in state_dict: {}. ".format(
+ ", ".join("'{}'".format(k) for k in missing_keys)
+ )
+ )
+ if raise_error:
+ raise RuntimeError(
+ "Error(s) in loading state_dict for {}:\n\t{}".format(
+ model.__class__.__name__, "\n\t".join(msgs)
+ )
+ )
+ if len(msgs) > 0:
+ print(
+ "\nInfo(s) in loading state_dict for {}:\n\t{}".format(
+ model.__class__.__name__, "\n\t".join(msgs)
+ )
+ )
+
+
+class BasicBlock(nn.Module):
+ expansion: int = 1
+
+ def __init__(
+ self,
+ inplanes: int,
+ planes: int,
+ stride: int = 1,
+ downsample: Optional[nn.Module] = None,
+ groups: int = 1,
+ base_width: int = 64,
+ dilation: int = 1,
+ norm_layer: Optional[Callable[..., nn.Module]] = None
+ ) -> None:
+ super(BasicBlock, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ if groups != 1 or base_width != 64:
+ raise ValueError(
+ "BasicBlock only supports groups=1 and base_width=64"
+ )
+ if dilation > 1:
+ raise NotImplementedError(
+ "Dilation > 1 not supported in BasicBlock"
+ )
+ # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv3x3(inplanes, planes, stride)
+ self.bn1 = norm_layer(planes)
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = conv3x3(planes, planes)
+ self.bn2 = norm_layer(planes)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x: Tensor) -> Tensor:
+ identity = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+
+class Bottleneck(nn.Module):
+ # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
+ # while original implementation places the stride at the first 1x1 convolution(self.conv1)
+ # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
+ # This variant is also known as ResNet V1.5 and improves accuracy according to
+ # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+
+ expansion: int = 4
+
+ def __init__(
+ self,
+ inplanes: int,
+ planes: int,
+ stride: int = 1,
+ downsample: Optional[nn.Module] = None,
+ groups: int = 1,
+ base_width: int = 64,
+ dilation: int = 1,
+ norm_layer: Optional[Callable[..., nn.Module]] = None
+ ) -> None:
+ super(Bottleneck, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ width = int(planes * (base_width/64.)) * groups
+ # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv1x1(inplanes, width)
+ self.bn1 = norm_layer(width)
+ self.conv2 = conv3x3(width, width, stride, groups, dilation)
+ self.bn2 = norm_layer(width)
+ self.conv3 = conv1x1(width, planes * self.expansion)
+ self.bn3 = norm_layer(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x: Tensor) -> Tensor:
+ identity = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+
+class BasicBlockDynamic(nn.Module):
+ expansion: int = 1
+
+ def __init__(
+ self,
+ inplanes: int,
+ planes: int,
+ stride: int = 1,
+ downsample: Optional[nn.Module] = None,
+ groups: int = 1,
+ base_width: int = 64,
+ dilation: int = 1,
+ norm_layer: Optional[Callable[..., nn.Module]] = None
+ ) -> None:
+ super(BasicBlockDynamic, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ if groups != 1 or base_width != 64:
+ raise ValueError(
+ "BasicBlock only supports groups=1 and base_width=64"
+ )
+ if dilation > 1:
+ raise NotImplementedError(
+ "Dilation > 1 not supported in BasicBlock"
+ )
+ # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv3x3_dynamic(
+ inplanes, planes, stride, attention_in_channels=inplanes
+ )
+ self.bn1 = norm_layer(planes)
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = conv3x3_dynamic(
+ planes, planes, attention_in_channels=inplanes
+ )
+ self.bn2 = norm_layer(planes)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x: Tensor) -> Tensor:
+ identity = x
+
+ out = self.conv1(x, attention_x=x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out, attention_x=x)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+
+class BottleneckDynamic(nn.Module):
+ # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
+ # while original implementation places the stride at the first 1x1 convolution(self.conv1)
+ # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
+ # This variant is also known as ResNet V1.5 and improves accuracy according to
+ # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+
+ expansion: int = 4
+
+ def __init__(
+ self,
+ inplanes: int,
+ planes: int,
+ stride: int = 1,
+ downsample: Optional[nn.Module] = None,
+ groups: int = 1,
+ base_width: int = 64,
+ dilation: int = 1,
+ norm_layer: Optional[Callable[..., nn.Module]] = None
+ ) -> None:
+ super(BottleneckDynamic, self).__init__()
+ if groups != 1:
+ raise ValueError("BottleneckDynamic only supports groups=1")
+ if dilation > 1:
+ raise NotImplementedError(
+ "Dilation > 1 not supported in BottleneckDynamic"
+ )
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ width = int(planes * (base_width/64.)) * groups
+ # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv1x1(inplanes, width)
+ self.bn1 = norm_layer(width)
+ self.conv2 = conv3x3_dynamic(
+ width, width, stride, attention_in_channels=inplanes
+ )
+ self.bn2 = norm_layer(width)
+ self.conv3 = conv1x1(width, planes * self.expansion)
+ self.bn3 = norm_layer(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x: Tensor) -> Tensor:
+ identity = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out, attention_x=x)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+
+class ResNet(Backbone):
+
+ def __init__(
+ self,
+ block: Type[Union[BasicBlock, Bottleneck, BasicBlockDynamic,
+ BottleneckDynamic]],
+ layers: List[int],
+ has_fc: bool = True,
+ num_classes: int = 1000,
+ zero_init_residual: bool = False,
+ groups: int = 1,
+ width_per_group: int = 64,
+ replace_stride_with_dilation: Optional[List[bool]] = None,
+ norm_layer: Optional[Callable[..., nn.Module]] = None,
+ ms_class=None,
+ ms_layers=None,
+ ms_p=0.5,
+ ms_a=0.1
+ ) -> None:
+ super(ResNet, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ self._norm_layer = norm_layer
+
+ self.inplanes = 64
+ self.dilation = 1
+ if replace_stride_with_dilation is None:
+ # each element in the tuple indicates if we should replace
+ # the 2x2 stride with a dilated convolution instead
+ replace_stride_with_dilation = [False, False, False]
+ if len(replace_stride_with_dilation) != 3:
+ raise ValueError(
+ "replace_stride_with_dilation should be None "
+ "or a 3-element tuple, got {}".
+ format(replace_stride_with_dilation)
+ )
+ self.groups = groups
+ self.base_width = width_per_group
+ self.conv1 = nn.Conv2d(
+ 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
+ )
+ self.bn1 = norm_layer(self.inplanes)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(
+ block,
+ 128,
+ layers[1],
+ stride=2,
+ dilate=replace_stride_with_dilation[0]
+ )
+ self.layer3 = self._make_layer(
+ block,
+ 256,
+ layers[2],
+ stride=2,
+ dilate=replace_stride_with_dilation[1]
+ )
+ self.layer4 = self._make_layer(
+ block,
+ 512,
+ layers[3],
+ stride=2,
+ dilate=replace_stride_with_dilation[2]
+ )
+ self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+ self.has_fc = has_fc
+ self._out_features = 512 * block.expansion
+ if has_fc:
+ self.fc = nn.Linear(self.out_features, num_classes)
+ self._out_features = num_classes
+
+ if ms_class is not None and ms_layers is not None:
+ self.ms_class = ms_class(p=ms_p, alpha=ms_a)
+ for layer in ms_layers:
+ assert layer in ["layer1", "layer2", "layer3"]
+ self.ms_layers = ms_layers
+ else:
+ self.ms_class = None
+ self.ms_layers = []
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(
+ m.weight, mode="fan_out", nonlinearity="relu"
+ )
+ elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ # Zero-initialize the last BN in each residual branch,
+ # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+ # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+ if zero_init_residual:
+ for m in self.modules():
+ if isinstance(m, Bottleneck):
+ nn.init.constant_(m.bn3.weight, 0)
+ elif isinstance(m, BasicBlock):
+ nn.init.constant_(m.bn2.weight, 0)
+
+ def _make_layer(
+ self,
+ block: Type[Union[BasicBlock, Bottleneck]],
+ planes: int,
+ blocks: int,
+ stride: int = 1,
+ dilate: bool = False
+ ) -> nn.Sequential:
+ norm_layer = self._norm_layer
+ downsample = None
+ previous_dilation = self.dilation
+ if dilate:
+ self.dilation *= stride
+ stride = 1
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ conv1x1(self.inplanes, planes * block.expansion, stride),
+ norm_layer(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(
+ block(
+ self.inplanes, planes, stride, downsample, self.groups,
+ self.base_width, previous_dilation, norm_layer
+ )
+ )
+ self.inplanes = planes * block.expansion
+ for _ in range(1, blocks):
+ layers.append(
+ block(
+ self.inplanes,
+ planes,
+ groups=self.groups,
+ base_width=self.base_width,
+ dilation=self.dilation,
+ norm_layer=norm_layer
+ )
+ )
+
+ return nn.Sequential(*layers)
+
+ def _forward_impl(self, x: Tensor) -> Tensor:
+ # See note [TorchScript super()]
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ if "layer1" in self.ms_layers:
+ x = self.ms_class(x)
+ x = self.layer2(x)
+ if "layer2" in self.ms_layers:
+ x = self.ms_class(x)
+ x = self.layer3(x)
+ if "layer3" in self.ms_layers:
+ x = self.ms_class(x)
+ x = self.layer4(x)
+
+ x = self.avgpool(x)
+ x = torch.flatten(x, 1)
+ if self.has_fc:
+ x = self.fc(x)
+
+ return x
+
+ def forward(self, x: Tensor) -> Tensor:
+ return self._forward_impl(x)
+
+
+def _resnet(
+ arch: str, block: Type[Union[BasicBlock, Bottleneck, BasicBlockDynamic,
+ BottleneckDynamic]], layers: List[int],
+ pretrained: bool, progress: bool, **kwargs: Any
+) -> ResNet:
+ model = ResNet(block, layers, **kwargs)
+ if pretrained:
+ state_dict = load_state_dict_from_url(
+ model_urls[arch], progress=progress
+ )
+ # remove useless keys from sate_dict 1. no fc; 2. out_features != 1000.
+ removed_keys = model.has_fc is False or (
+ model.has_fc is True and model.out_features != 1000
+ )
+ removed_keys = ["fc.weight", "fc.bias"] if removed_keys else []
+ for key in removed_keys:
+ state_dict.pop(key)
+ # if has fc, then allow missing key, else strict load state_dict.
+ allowed_missing_keys = removed_keys if model.has_fc else None
+ load_state_dict(model, state_dict, allowed_missing_keys)
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_dynamic(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet18_dynamic",
+ BasicBlockDynamic, [2, 2, 2, 2],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_dynamic(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet50_dynamic",
+ BottleneckDynamic, [3, 4, 6, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_dynamic(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet101_dynamic",
+ BottleneckDynamic, [3, 4, 23, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_dynamic_ms_l123(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet18_dynamic",
+ BasicBlockDynamic, [2, 2, 2, 2],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_dynamic_ms_l12(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet18_dynamic",
+ BasicBlockDynamic, [2, 2, 2, 2],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet18_dynamic_ms_l1(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet18_dynamic",
+ BasicBlockDynamic, [2, 2, 2, 2],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_dynamic_ms_l123(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet50_dynamic",
+ BottleneckDynamic, [3, 4, 6, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_dynamic_ms_l12(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet50_dynamic",
+ BottleneckDynamic, [3, 4, 6, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet50_dynamic_ms_l1(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet50_dynamic",
+ BottleneckDynamic, [3, 4, 6, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_dynamic_ms_l123(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet101_dynamic",
+ BottleneckDynamic, [3, 4, 23, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2", "layer3"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_dynamic_ms_l12(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet101_dynamic",
+ BottleneckDynamic, [3, 4, 23, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1", "layer2"]
+ )
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def resnet101_dynamic_ms_l1(pretrained=True, **kwargs) -> ResNet:
+ model = _resnet(
+ "resnet101_dynamic",
+ BottleneckDynamic, [3, 4, 23, 3],
+ pretrained=pretrained,
+ progress=True,
+ has_fc=False,
+ ms_class=MixStyle,
+ ms_layers=["layer1"]
+ )
+ return model
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/vgg.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/vgg.py
new file mode 100644
index 00000000..3f91491a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/vgg.py
@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+try:
+ from torch.hub import load_state_dict_from_url
+except ImportError:
+ from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+model_urls = {
+ "vgg11": "https://download.pytorch.org/models/vgg11-bbd30ac9.pth",
+ "vgg13": "https://download.pytorch.org/models/vgg13-c768596a.pth",
+ "vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth",
+ "vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
+ "vgg11_bn": "https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
+ "vgg13_bn": "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
+ "vgg16_bn": "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
+ "vgg19_bn": "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth",
+}
+
+
+class VGG(Backbone):
+
+ def __init__(self, features, init_weights=True):
+ super().__init__()
+ self.features = features
+ self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
+ # Note that self.classifier outputs features rather than logits
+ self.classifier = nn.Sequential(
+ nn.Linear(512 * 7 * 7, 4096),
+ nn.ReLU(True),
+ nn.Dropout(),
+ nn.Linear(4096, 4096),
+ nn.ReLU(True),
+ nn.Dropout(),
+ )
+
+ self._out_features = 4096
+
+ if init_weights:
+ self._initialize_weights()
+
+ def forward(self, x):
+ x = self.features(x)
+ x = self.avgpool(x)
+ x = torch.flatten(x, 1)
+ return self.classifier(x)
+
+ def _initialize_weights(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(
+ m.weight, mode="fan_out", nonlinearity="relu"
+ )
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm2d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.Linear):
+ nn.init.normal_(m.weight, 0, 0.01)
+ nn.init.constant_(m.bias, 0)
+
+
+def make_layers(cfg, batch_norm=False):
+ layers = []
+ in_channels = 3
+ for v in cfg:
+ if v == "M":
+ layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+ else:
+ conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+ if batch_norm:
+ layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+ else:
+ layers += [conv2d, nn.ReLU(inplace=True)]
+ in_channels = v
+ return nn.Sequential(*layers)
+
+
+cfgs = {
+ "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+ "B":
+ [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+ "D": [
+ 64,
+ 64,
+ "M",
+ 128,
+ 128,
+ "M",
+ 256,
+ 256,
+ 256,
+ "M",
+ 512,
+ 512,
+ 512,
+ "M",
+ 512,
+ 512,
+ 512,
+ "M",
+ ],
+ "E": [
+ 64,
+ 64,
+ "M",
+ 128,
+ 128,
+ "M",
+ 256,
+ 256,
+ 256,
+ 256,
+ "M",
+ 512,
+ 512,
+ 512,
+ 512,
+ "M",
+ 512,
+ 512,
+ 512,
+ 512,
+ "M",
+ ],
+}
+
+
+def _vgg(arch, cfg, batch_norm, pretrained):
+ init_weights = False if pretrained else True
+ model = VGG(
+ make_layers(cfgs[cfg], batch_norm=batch_norm),
+ init_weights=init_weights
+ )
+ if pretrained:
+ state_dict = load_state_dict_from_url(model_urls[arch], progress=True)
+ model.load_state_dict(state_dict, strict=False)
+ return model
+
+
+@BACKBONE_REGISTRY.register()
+def vgg16(pretrained=True, **kwargs):
+ return _vgg("vgg16", "D", False, pretrained)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/wide_resnet.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/wide_resnet.py
new file mode 100644
index 00000000..88ea949d
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/backbone/wide_resnet.py
@@ -0,0 +1,150 @@
+"""
+Modified from https://github.com/xternalz/WideResNet-pytorch
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class BasicBlock(nn.Module):
+
+ def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
+ super().__init__()
+ self.bn1 = nn.BatchNorm2d(in_planes)
+ self.relu1 = nn.LeakyReLU(0.01, inplace=True)
+ self.conv1 = nn.Conv2d(
+ in_planes,
+ out_planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False
+ )
+ self.bn2 = nn.BatchNorm2d(out_planes)
+ self.relu2 = nn.LeakyReLU(0.01, inplace=True)
+ self.conv2 = nn.Conv2d(
+ out_planes,
+ out_planes,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False
+ )
+ self.droprate = dropRate
+ self.equalInOut = in_planes == out_planes
+ self.convShortcut = (
+ (not self.equalInOut) and nn.Conv2d(
+ in_planes,
+ out_planes,
+ kernel_size=1,
+ stride=stride,
+ padding=0,
+ bias=False,
+ ) or None
+ )
+
+ def forward(self, x):
+ if not self.equalInOut:
+ x = self.relu1(self.bn1(x))
+ else:
+ out = self.relu1(self.bn1(x))
+ out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
+ if self.droprate > 0:
+ out = F.dropout(out, p=self.droprate, training=self.training)
+ out = self.conv2(out)
+ return torch.add(x if self.equalInOut else self.convShortcut(x), out)
+
+
+class NetworkBlock(nn.Module):
+
+ def __init__(
+ self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0
+ ):
+ super().__init__()
+ self.layer = self._make_layer(
+ block, in_planes, out_planes, nb_layers, stride, dropRate
+ )
+
+ def _make_layer(
+ self, block, in_planes, out_planes, nb_layers, stride, dropRate
+ ):
+ layers = []
+ for i in range(int(nb_layers)):
+ layers.append(
+ block(
+ i == 0 and in_planes or out_planes,
+ out_planes,
+ i == 0 and stride or 1,
+ dropRate,
+ )
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.layer(x)
+
+
+class WideResNet(Backbone):
+
+ def __init__(self, depth, widen_factor, dropRate=0.0):
+ super().__init__()
+ nChannels = [
+ 16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
+ ]
+ assert (depth-4) % 6 == 0
+ n = (depth-4) / 6
+ block = BasicBlock
+ # 1st conv before any network block
+ self.conv1 = nn.Conv2d(
+ 3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False
+ )
+ # 1st block
+ self.block1 = NetworkBlock(
+ n, nChannels[0], nChannels[1], block, 1, dropRate
+ )
+ # 2nd block
+ self.block2 = NetworkBlock(
+ n, nChannels[1], nChannels[2], block, 2, dropRate
+ )
+ # 3rd block
+ self.block3 = NetworkBlock(
+ n, nChannels[2], nChannels[3], block, 2, dropRate
+ )
+ # global average pooling and classifier
+ self.bn1 = nn.BatchNorm2d(nChannels[3])
+ self.relu = nn.LeakyReLU(0.01, inplace=True)
+
+ self._out_features = nChannels[3]
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(
+ m.weight, mode="fan_out", nonlinearity="relu"
+ )
+ elif isinstance(m, nn.BatchNorm2d):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+ elif isinstance(m, nn.Linear):
+ m.bias.data.zero_()
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.block1(out)
+ out = self.block2(out)
+ out = self.block3(out)
+ out = self.relu(self.bn1(out))
+ out = F.adaptive_avg_pool2d(out, 1)
+ return out.view(out.size(0), -1)
+
+
+@BACKBONE_REGISTRY.register()
+def wide_resnet_28_2(**kwargs):
+ return WideResNet(28, 2)
+
+
+@BACKBONE_REGISTRY.register()
+def wide_resnet_16_4(**kwargs):
+ return WideResNet(16, 4)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/__init__.py
new file mode 100644
index 00000000..e76fb8cc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/__init__.py
@@ -0,0 +1,3 @@
+from .build import build_head, HEAD_REGISTRY # isort:skip
+
+from .mlp import mlp
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/build.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/build.py
new file mode 100644
index 00000000..730437b6
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+HEAD_REGISTRY = Registry("HEAD")
+
+
+def build_head(name, verbose=True, **kwargs):
+ avai_heads = HEAD_REGISTRY.registered_names()
+ check_availability(name, avai_heads)
+ if verbose:
+ print("Head: {}".format(name))
+ return HEAD_REGISTRY.get(name)(**kwargs)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/mlp.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/mlp.py
new file mode 100644
index 00000000..89aae50e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/head/mlp.py
@@ -0,0 +1,50 @@
+import functools
+import torch.nn as nn
+
+from .build import HEAD_REGISTRY
+
+
+class MLP(nn.Module):
+
+ def __init__(
+ self,
+ in_features=2048,
+ hidden_layers=[],
+ activation="relu",
+ bn=True,
+ dropout=0.0,
+ ):
+ super().__init__()
+ if isinstance(hidden_layers, int):
+ hidden_layers = [hidden_layers]
+
+ assert len(hidden_layers) > 0
+ self.out_features = hidden_layers[-1]
+
+ mlp = []
+
+ if activation == "relu":
+ act_fn = functools.partial(nn.ReLU, inplace=True)
+ elif activation == "leaky_relu":
+ act_fn = functools.partial(nn.LeakyReLU, inplace=True)
+ else:
+ raise NotImplementedError
+
+ for hidden_dim in hidden_layers:
+ mlp += [nn.Linear(in_features, hidden_dim)]
+ if bn:
+ mlp += [nn.BatchNorm1d(hidden_dim)]
+ mlp += [act_fn()]
+ if dropout > 0:
+ mlp += [nn.Dropout(dropout)]
+ in_features = hidden_dim
+
+ self.mlp = nn.Sequential(*mlp)
+
+ def forward(self, x):
+ return self.mlp(x)
+
+
+@HEAD_REGISTRY.register()
+def mlp(**kwargs):
+ return MLP(**kwargs)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/__init__.py
new file mode 100644
index 00000000..a6fcc638
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/__init__.py
@@ -0,0 +1,5 @@
+from .build import build_network, NETWORK_REGISTRY # isort:skip
+
+from .ddaig_fcn import (
+ fcn_3x32_gctx, fcn_3x64_gctx, fcn_3x32_gctx_stn, fcn_3x64_gctx_stn
+)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/build.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/build.py
new file mode 100644
index 00000000..e615314f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/build.py
@@ -0,0 +1,11 @@
+from dassl.utils import Registry, check_availability
+
+NETWORK_REGISTRY = Registry("NETWORK")
+
+
+def build_network(name, verbose=True, **kwargs):
+ avai_models = NETWORK_REGISTRY.registered_names()
+ check_availability(name, avai_models)
+ if verbose:
+ print("Network: {}".format(name))
+ return NETWORK_REGISTRY.get(name)(**kwargs)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/ddaig_fcn.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/ddaig_fcn.py
new file mode 100644
index 00000000..17e3bdd2
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/network/ddaig_fcn.py
@@ -0,0 +1,329 @@
+"""
+Credit to: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
+"""
+import functools
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .build import NETWORK_REGISTRY
+
+
+def init_network_weights(model, init_type="normal", gain=0.02):
+
+ def _init_func(m):
+ classname = m.__class__.__name__
+ if hasattr(m, "weight") and (
+ classname.find("Conv") != -1 or classname.find("Linear") != -1
+ ):
+ if init_type == "normal":
+ nn.init.normal_(m.weight.data, 0.0, gain)
+ elif init_type == "xavier":
+ nn.init.xavier_normal_(m.weight.data, gain=gain)
+ elif init_type == "kaiming":
+ nn.init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
+ elif init_type == "orthogonal":
+ nn.init.orthogonal_(m.weight.data, gain=gain)
+ else:
+ raise NotImplementedError(
+ "initialization method {} is not implemented".
+ format(init_type)
+ )
+ if hasattr(m, "bias") and m.bias is not None:
+ nn.init.constant_(m.bias.data, 0.0)
+ elif classname.find("BatchNorm2d") != -1:
+ nn.init.constant_(m.weight.data, 1.0)
+ nn.init.constant_(m.bias.data, 0.0)
+ elif classname.find("InstanceNorm2d") != -1:
+ if m.weight is not None and m.bias is not None:
+ nn.init.constant_(m.weight.data, 1.0)
+ nn.init.constant_(m.bias.data, 0.0)
+
+ model.apply(_init_func)
+
+
+def get_norm_layer(norm_type="instance"):
+ if norm_type == "batch":
+ norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+ elif norm_type == "instance":
+ norm_layer = functools.partial(
+ nn.InstanceNorm2d, affine=False, track_running_stats=False
+ )
+ elif norm_type == "none":
+ norm_layer = None
+ else:
+ raise NotImplementedError(
+ "normalization layer [%s] is not found" % norm_type
+ )
+ return norm_layer
+
+
+class ResnetBlock(nn.Module):
+
+ def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+ super().__init__()
+ self.conv_block = self.build_conv_block(
+ dim, padding_type, norm_layer, use_dropout, use_bias
+ )
+
+ def build_conv_block(
+ self, dim, padding_type, norm_layer, use_dropout, use_bias
+ ):
+ conv_block = []
+ p = 0
+ if padding_type == "reflect":
+ conv_block += [nn.ReflectionPad2d(1)]
+ elif padding_type == "replicate":
+ conv_block += [nn.ReplicationPad2d(1)]
+ elif padding_type == "zero":
+ p = 1
+ else:
+ raise NotImplementedError(
+ "padding [%s] is not implemented" % padding_type
+ )
+
+ conv_block += [
+ nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+ norm_layer(dim),
+ nn.ReLU(True),
+ ]
+ if use_dropout:
+ conv_block += [nn.Dropout(0.5)]
+
+ p = 0
+ if padding_type == "reflect":
+ conv_block += [nn.ReflectionPad2d(1)]
+ elif padding_type == "replicate":
+ conv_block += [nn.ReplicationPad2d(1)]
+ elif padding_type == "zero":
+ p = 1
+ else:
+ raise NotImplementedError(
+ "padding [%s] is not implemented" % padding_type
+ )
+ conv_block += [
+ nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+ norm_layer(dim),
+ ]
+
+ return nn.Sequential(*conv_block)
+
+ def forward(self, x):
+ return x + self.conv_block(x)
+
+
+class LocNet(nn.Module):
+ """Localization network."""
+
+ def __init__(
+ self,
+ input_nc,
+ nc=32,
+ n_blocks=3,
+ use_dropout=False,
+ padding_type="zero",
+ image_size=32,
+ ):
+ super().__init__()
+
+ backbone = []
+ backbone += [
+ nn.Conv2d(
+ input_nc, nc, kernel_size=3, stride=2, padding=1, bias=False
+ )
+ ]
+ backbone += [nn.BatchNorm2d(nc)]
+ backbone += [nn.ReLU(True)]
+ for _ in range(n_blocks):
+ backbone += [
+ ResnetBlock(
+ nc,
+ padding_type=padding_type,
+ norm_layer=nn.BatchNorm2d,
+ use_dropout=use_dropout,
+ use_bias=False,
+ )
+ ]
+ backbone += [nn.MaxPool2d(2, stride=2)]
+ self.backbone = nn.Sequential(*backbone)
+ reduced_imsize = int(image_size * 0.5**(n_blocks + 1))
+ self.fc_loc = nn.Linear(nc * reduced_imsize**2, 2 * 2)
+
+ def forward(self, x):
+ x = self.backbone(x)
+ x = x.view(x.size(0), -1)
+ x = self.fc_loc(x)
+ x = torch.tanh(x)
+ x = x.view(-1, 2, 2)
+ theta = x.data.new_zeros(x.size(0), 2, 3)
+ theta[:, :, :2] = x
+ return theta
+
+
+class FCN(nn.Module):
+ """Fully convolutional network."""
+
+ def __init__(
+ self,
+ input_nc,
+ output_nc,
+ nc=32,
+ n_blocks=3,
+ norm_layer=nn.BatchNorm2d,
+ use_dropout=False,
+ padding_type="reflect",
+ gctx=True,
+ stn=False,
+ image_size=32,
+ ):
+ super().__init__()
+
+ backbone = []
+
+ p = 0
+ if padding_type == "reflect":
+ backbone += [nn.ReflectionPad2d(1)]
+ elif padding_type == "replicate":
+ backbone += [nn.ReplicationPad2d(1)]
+ elif padding_type == "zero":
+ p = 1
+ else:
+ raise NotImplementedError
+ backbone += [
+ nn.Conv2d(
+ input_nc, nc, kernel_size=3, stride=1, padding=p, bias=False
+ )
+ ]
+ backbone += [norm_layer(nc)]
+ backbone += [nn.ReLU(True)]
+
+ for _ in range(n_blocks):
+ backbone += [
+ ResnetBlock(
+ nc,
+ padding_type=padding_type,
+ norm_layer=norm_layer,
+ use_dropout=use_dropout,
+ use_bias=False,
+ )
+ ]
+ self.backbone = nn.Sequential(*backbone)
+
+ # global context fusion layer
+ self.gctx_fusion = None
+ if gctx:
+ self.gctx_fusion = nn.Sequential(
+ nn.Conv2d(
+ 2 * nc, nc, kernel_size=1, stride=1, padding=0, bias=False
+ ),
+ norm_layer(nc),
+ nn.ReLU(True),
+ )
+
+ self.regress = nn.Sequential(
+ nn.Conv2d(
+ nc, output_nc, kernel_size=1, stride=1, padding=0, bias=True
+ ),
+ nn.Tanh(),
+ )
+
+ self.locnet = None
+ if stn:
+ self.locnet = LocNet(
+ input_nc, nc=nc, n_blocks=n_blocks, image_size=image_size
+ )
+
+ def init_loc_layer(self):
+ """Initialize the weights/bias with identity transformation."""
+ if self.locnet is not None:
+ self.locnet.fc_loc.weight.data.zero_()
+ self.locnet.fc_loc.bias.data.copy_(
+ torch.tensor([1, 0, 0, 1], dtype=torch.float)
+ )
+
+ def stn(self, x):
+ """Spatial transformer network."""
+ theta = self.locnet(x)
+ grid = F.affine_grid(theta, x.size())
+ return F.grid_sample(x, grid), theta
+
+ def forward(self, x, lmda=1.0, return_p=False, return_stn_output=False):
+ """
+ Args:
+ x (torch.Tensor): input mini-batch.
+ lmda (float): multiplier for perturbation.
+ return_p (bool): return perturbation.
+ return_stn_output (bool): return the output of stn.
+ """
+ theta = None
+ if self.locnet is not None:
+ x, theta = self.stn(x)
+ input = x
+
+ x = self.backbone(x)
+ if self.gctx_fusion is not None:
+ c = F.adaptive_avg_pool2d(x, (1, 1))
+ c = c.expand_as(x)
+ x = torch.cat([x, c], 1)
+ x = self.gctx_fusion(x)
+
+ p = self.regress(x)
+ x_p = input + lmda*p
+
+ if return_stn_output:
+ return x_p, p, input
+
+ if return_p:
+ return x_p, p
+
+ return x_p
+
+
+@NETWORK_REGISTRY.register()
+def fcn_3x32_gctx(**kwargs):
+ norm_layer = get_norm_layer(norm_type="instance")
+ net = FCN(3, 3, nc=32, n_blocks=3, norm_layer=norm_layer)
+ init_network_weights(net, init_type="normal", gain=0.02)
+ return net
+
+
+@NETWORK_REGISTRY.register()
+def fcn_3x64_gctx(**kwargs):
+ norm_layer = get_norm_layer(norm_type="instance")
+ net = FCN(3, 3, nc=64, n_blocks=3, norm_layer=norm_layer)
+ init_network_weights(net, init_type="normal", gain=0.02)
+ return net
+
+
+@NETWORK_REGISTRY.register()
+def fcn_3x32_gctx_stn(image_size=32, **kwargs):
+ norm_layer = get_norm_layer(norm_type="instance")
+ net = FCN(
+ 3,
+ 3,
+ nc=32,
+ n_blocks=3,
+ norm_layer=norm_layer,
+ stn=True,
+ image_size=image_size
+ )
+ init_network_weights(net, init_type="normal", gain=0.02)
+ net.init_loc_layer()
+ return net
+
+
+@NETWORK_REGISTRY.register()
+def fcn_3x64_gctx_stn(image_size=224, **kwargs):
+ norm_layer = get_norm_layer(norm_type="instance")
+ net = FCN(
+ 3,
+ 3,
+ nc=64,
+ n_blocks=3,
+ norm_layer=norm_layer,
+ stn=True,
+ image_size=image_size
+ )
+ init_network_weights(net, init_type="normal", gain=0.02)
+ net.init_loc_layer()
+ return net
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/__init__.py
new file mode 100644
index 00000000..44d06400
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/__init__.py
@@ -0,0 +1,18 @@
+from .mmd import MaximumMeanDiscrepancy
+from .conv import *
+from .dsbn import DSBN1d, DSBN2d
+from .mixup import mixup
+from .efdmix import (
+ EFDMix, random_efdmix, activate_efdmix, run_with_efdmix, deactivate_efdmix,
+ crossdomain_efdmix, run_without_efdmix
+)
+from .mixstyle import (
+ MixStyle, random_mixstyle, activate_mixstyle, run_with_mixstyle,
+ deactivate_mixstyle, crossdomain_mixstyle, run_without_mixstyle
+)
+from .attention import *
+from .transnorm import TransNorm1d, TransNorm2d
+from .sequential2 import Sequential2
+from .reverse_grad import ReverseGrad
+from .cross_entropy import cross_entropy
+from .optimal_transport import SinkhornDivergence, MinibatchEnergyDistance
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/attention.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/attention.py
new file mode 100644
index 00000000..16ddcdab
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/attention.py
@@ -0,0 +1,31 @@
+import torch.nn as nn
+from torch.nn import functional as F
+
+__all__ = ["Attention"]
+
+
+class Attention(nn.Module):
+ """Attention from `"Dynamic Domain Generalization" `_.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_features: int,
+ squeeze=None,
+ bias: bool = True
+ ):
+ super(Attention, self).__init__()
+ self.squeeze = squeeze if squeeze else in_channels // 16
+ assert self.squeeze > 0
+ self.avg_pool = nn.AdaptiveAvgPool2d(1)
+ self.fc1 = nn.Linear(in_channels, self.squeeze, bias=bias)
+ self.fc2 = nn.Linear(self.squeeze, out_features, bias=bias)
+ self.sf = nn.Softmax(dim=-1)
+
+ def forward(self, x):
+ x = self.avg_pool(x).view(x.shape[:-2])
+ x = self.fc1(x)
+ x = F.relu(x, inplace=True)
+ x = self.fc2(x)
+ return self.sf(x)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/conv.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/conv.py
new file mode 100644
index 00000000..fcee716f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/conv.py
@@ -0,0 +1,95 @@
+import torch.nn as nn
+
+from .attention import Attention
+
+__all__ = ["Conv2dDynamic"]
+
+
+class Conv2dDynamic(nn.Module):
+ """Conv2dDynamic from `"Dynamic Domain Generalization" `_.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int,
+ padding: int,
+ bias: bool = True,
+ squeeze: int = None,
+ attention_in_channels: int = None
+ ) -> None:
+ super(Conv2dDynamic, self).__init__()
+
+ if kernel_size // 2 != padding:
+ # Only when this condition is met, we can ensure that different
+ # kernel_size can obtain feature maps of consistent size.
+ # Let I, K, S, P, O: O = (I + 2P - K) // S + 1, if P = K // 2, then O = (I - K % 2) // S + 1
+ # This means that the output of two different Ks with the same parity can be made the same by adjusting P.
+ raise ValueError("`padding` must be equal to `kernel_size // 2`.")
+ if kernel_size % 2 == 0:
+ raise ValueError(
+ "Kernel_size must be odd now because the templates we used are odd (kernel_size=1)."
+ )
+
+ self.conv = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ bias=bias
+ )
+ self.kernel_templates = nn.ModuleDict()
+ self.kernel_templates["conv_nn"] = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ groups=min(in_channels, out_channels),
+ bias=bias
+ )
+ self.kernel_templates["conv_11"] = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=stride,
+ padding=0,
+ bias=bias
+ )
+ self.kernel_templates["conv_n1"] = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=(kernel_size, 1),
+ stride=stride,
+ padding=(padding, 0),
+ bias=bias
+ )
+ self.kernel_templates["conv_1n"] = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=(1, kernel_size),
+ stride=stride,
+ padding=(0, padding),
+ bias=bias
+ )
+ self.attention = Attention(
+ attention_in_channels if attention_in_channels else in_channels,
+ 4,
+ squeeze,
+ bias=bias
+ )
+
+ def forward(self, x, attention_x=None):
+ attention_x = x if attention_x is None else attention_x
+ y = self.attention(attention_x)
+
+ out = self.conv(x)
+
+ for i, template in enumerate(self.kernel_templates):
+ out += self.kernel_templates[template](x) * y[:,
+ i].view(-1, 1, 1, 1)
+
+ return out
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/cross_entropy.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/cross_entropy.py
new file mode 100644
index 00000000..21723b02
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/cross_entropy.py
@@ -0,0 +1,30 @@
+import torch
+from torch.nn import functional as F
+
+
+def cross_entropy(input, target, label_smooth=0, reduction="mean"):
+ """Cross entropy loss.
+
+ Args:
+ input (torch.Tensor): logit matrix with shape of (batch, num_classes).
+ target (torch.LongTensor): int label matrix.
+ label_smooth (float, optional): label smoothing hyper-parameter.
+ Default is 0.
+ reduction (str, optional): how the losses for a mini-batch
+ will be aggregated. Default is 'mean'.
+ """
+ num_classes = input.shape[1]
+ log_prob = F.log_softmax(input, dim=1)
+ zeros = torch.zeros(log_prob.size())
+ target = zeros.scatter_(1, target.unsqueeze(1).data.cpu(), 1)
+ target = target.type_as(input)
+ target = (1-label_smooth) * target + label_smooth/num_classes
+ loss = (-target * log_prob).sum(1)
+ if reduction == "mean":
+ return loss.mean()
+ elif reduction == "sum":
+ return loss.sum()
+ elif reduction == "none":
+ return loss
+ else:
+ raise ValueError
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/dsbn.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/dsbn.py
new file mode 100644
index 00000000..e3ee3550
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/dsbn.py
@@ -0,0 +1,45 @@
+import torch.nn as nn
+
+
+class _DSBN(nn.Module):
+ """Domain Specific Batch Normalization.
+
+ Args:
+ num_features (int): number of features.
+ n_domain (int): number of domains.
+ bn_type (str): type of bn. Choices are ['1d', '2d'].
+ """
+
+ def __init__(self, num_features, n_domain, bn_type):
+ super().__init__()
+ if bn_type == "1d":
+ BN = nn.BatchNorm1d
+ elif bn_type == "2d":
+ BN = nn.BatchNorm2d
+ else:
+ raise ValueError
+
+ self.bn = nn.ModuleList(BN(num_features) for _ in range(n_domain))
+
+ self.valid_domain_idxs = list(range(n_domain))
+ self.n_domain = n_domain
+ self.domain_idx = 0
+
+ def select_bn(self, domain_idx=0):
+ assert domain_idx in self.valid_domain_idxs
+ self.domain_idx = domain_idx
+
+ def forward(self, x):
+ return self.bn[self.domain_idx](x)
+
+
+class DSBN1d(_DSBN):
+
+ def __init__(self, num_features, n_domain):
+ super().__init__(num_features, n_domain, "1d")
+
+
+class DSBN2d(_DSBN):
+
+ def __init__(self, num_features, n_domain):
+ super().__init__(num_features, n_domain, "2d")
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/efdmix.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/efdmix.py
new file mode 100644
index 00000000..af58815a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/efdmix.py
@@ -0,0 +1,118 @@
+import random
+from contextlib import contextmanager
+import torch
+import torch.nn as nn
+
+
+def deactivate_efdmix(m):
+ if type(m) == EFDMix:
+ m.set_activation_status(False)
+
+
+def activate_efdmix(m):
+ if type(m) == EFDMix:
+ m.set_activation_status(True)
+
+
+def random_efdmix(m):
+ if type(m) == EFDMix:
+ m.update_mix_method("random")
+
+
+def crossdomain_efdmix(m):
+ if type(m) == EFDMix:
+ m.update_mix_method("crossdomain")
+
+
+@contextmanager
+def run_without_efdmix(model):
+ # Assume MixStyle was initially activated
+ try:
+ model.apply(deactivate_efdmix)
+ yield
+ finally:
+ model.apply(activate_efdmix)
+
+
+@contextmanager
+def run_with_efdmix(model, mix=None):
+ # Assume MixStyle was initially deactivated
+ if mix == "random":
+ model.apply(random_efdmix)
+
+ elif mix == "crossdomain":
+ model.apply(crossdomain_efdmix)
+
+ try:
+ model.apply(activate_efdmix)
+ yield
+ finally:
+ model.apply(deactivate_efdmix)
+
+
+class EFDMix(nn.Module):
+ """EFDMix.
+
+ Reference:
+ Zhang et al. Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization. CVPR 2022.
+ """
+
+ def __init__(self, p=0.5, alpha=0.1, eps=1e-6, mix="random"):
+ """
+ Args:
+ p (float): probability of using MixStyle.
+ alpha (float): parameter of the Beta distribution.
+ eps (float): scaling parameter to avoid numerical issues.
+ mix (str): how to mix.
+ """
+ super().__init__()
+ self.p = p
+ self.beta = torch.distributions.Beta(alpha, alpha)
+ self.eps = eps
+ self.alpha = alpha
+ self.mix = mix
+ self._activated = True
+
+ def __repr__(self):
+ return (
+ f"MixStyle(p={self.p}, alpha={self.alpha}, eps={self.eps}, mix={self.mix})"
+ )
+
+ def set_activation_status(self, status=True):
+ self._activated = status
+
+ def update_mix_method(self, mix="random"):
+ self.mix = mix
+
+ def forward(self, x):
+ if not self.training or not self._activated:
+ return x
+
+ if random.random() > self.p:
+ return x
+
+ B, C, W, H = x.size(0), x.size(1), x.size(2), x.size(3)
+ x_view = x.view(B, C, -1)
+ value_x, index_x = torch.sort(x_view) # sort inputs
+ lmda = self.beta.sample((B, 1, 1))
+ lmda = lmda.to(x.device)
+
+ if self.mix == "random":
+ # random shuffle
+ perm = torch.randperm(B)
+
+ elif self.mix == "crossdomain":
+ # split into two halves and swap the order
+ perm = torch.arange(B - 1, -1, -1) # inverse index
+ perm_b, perm_a = perm.chunk(2)
+ perm_b = perm_b[torch.randperm(perm_b.shape[0])]
+ perm_a = perm_a[torch.randperm(perm_a.shape[0])]
+ perm = torch.cat([perm_b, perm_a], 0)
+
+ else:
+ raise NotImplementedError
+
+ inverse_index = index_x.argsort(-1)
+ x_view_copy = value_x[perm].gather(-1, inverse_index)
+ new_x = x_view + (x_view_copy - x_view.detach()) * (1-lmda)
+ return new_x.view(B, C, W, H)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixstyle.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixstyle.py
new file mode 100644
index 00000000..34f47a89
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixstyle.py
@@ -0,0 +1,124 @@
+import random
+from contextlib import contextmanager
+import torch
+import torch.nn as nn
+
+
+def deactivate_mixstyle(m):
+ if type(m) == MixStyle:
+ m.set_activation_status(False)
+
+
+def activate_mixstyle(m):
+ if type(m) == MixStyle:
+ m.set_activation_status(True)
+
+
+def random_mixstyle(m):
+ if type(m) == MixStyle:
+ m.update_mix_method("random")
+
+
+def crossdomain_mixstyle(m):
+ if type(m) == MixStyle:
+ m.update_mix_method("crossdomain")
+
+
+@contextmanager
+def run_without_mixstyle(model):
+ # Assume MixStyle was initially activated
+ try:
+ model.apply(deactivate_mixstyle)
+ yield
+ finally:
+ model.apply(activate_mixstyle)
+
+
+@contextmanager
+def run_with_mixstyle(model, mix=None):
+ # Assume MixStyle was initially deactivated
+ if mix == "random":
+ model.apply(random_mixstyle)
+
+ elif mix == "crossdomain":
+ model.apply(crossdomain_mixstyle)
+
+ try:
+ model.apply(activate_mixstyle)
+ yield
+ finally:
+ model.apply(deactivate_mixstyle)
+
+
+class MixStyle(nn.Module):
+ """MixStyle.
+
+ Reference:
+ Zhou et al. Domain Generalization with MixStyle. ICLR 2021.
+ """
+
+ def __init__(self, p=0.5, alpha=0.1, eps=1e-6, mix="random"):
+ """
+ Args:
+ p (float): probability of using MixStyle.
+ alpha (float): parameter of the Beta distribution.
+ eps (float): scaling parameter to avoid numerical issues.
+ mix (str): how to mix.
+ """
+ super().__init__()
+ self.p = p
+ self.beta = torch.distributions.Beta(alpha, alpha)
+ self.eps = eps
+ self.alpha = alpha
+ self.mix = mix
+ self._activated = True
+
+ def __repr__(self):
+ return (
+ f"MixStyle(p={self.p}, alpha={self.alpha}, eps={self.eps}, mix={self.mix})"
+ )
+
+ def set_activation_status(self, status=True):
+ self._activated = status
+
+ def update_mix_method(self, mix="random"):
+ self.mix = mix
+
+ def forward(self, x):
+ if not self.training or not self._activated:
+ return x
+
+ if random.random() > self.p:
+ return x
+
+ B = x.size(0)
+
+ mu = x.mean(dim=[2, 3], keepdim=True)
+ var = x.var(dim=[2, 3], keepdim=True)
+ sig = (var + self.eps).sqrt()
+ mu, sig = mu.detach(), sig.detach()
+ x_normed = (x-mu) / sig
+
+ lmda = self.beta.sample((B, 1, 1, 1))
+ lmda = lmda.to(x.device)
+
+ if self.mix == "random":
+ # random shuffle
+ perm = torch.randperm(B)
+
+ elif self.mix == "crossdomain":
+ # split into two halves and swap the order
+ perm = torch.arange(B - 1, -1, -1) # inverse index
+ perm_b, perm_a = perm.chunk(2)
+ perm_b = perm_b[torch.randperm(perm_b.shape[0])]
+ perm_a = perm_a[torch.randperm(perm_a.shape[0])]
+ perm = torch.cat([perm_b, perm_a], 0)
+
+ else:
+ raise NotImplementedError
+
+ mu2, sig2 = mu[perm], sig[perm]
+ mu_mix = mu*lmda + mu2 * (1-lmda)
+ sig_mix = sig*lmda + sig2 * (1-lmda)
+
+ return x_normed*sig_mix + mu_mix
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixup.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixup.py
new file mode 100644
index 00000000..5844074a
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mixup.py
@@ -0,0 +1,23 @@
+import torch
+
+
+def mixup(x1, x2, y1, y2, beta, preserve_order=False):
+ """Mixup.
+
+ Args:
+ x1 (torch.Tensor): data with shape of (b, c, h, w).
+ x2 (torch.Tensor): data with shape of (b, c, h, w).
+ y1 (torch.Tensor): label with shape of (b, n).
+ y2 (torch.Tensor): label with shape of (b, n).
+ beta (float): hyper-parameter for Beta sampling.
+ preserve_order (bool): apply lmda=max(lmda, 1-lmda).
+ Default is False.
+ """
+ lmda = torch.distributions.Beta(beta, beta).sample([x1.shape[0], 1, 1, 1])
+ if preserve_order:
+ lmda = torch.max(lmda, 1 - lmda)
+ lmda = lmda.to(x1.device)
+ xmix = x1*lmda + x2 * (1-lmda)
+ lmda = lmda[:, :, 0, 0]
+ ymix = y1*lmda + y2 * (1-lmda)
+ return xmix, ymix
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mmd.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mmd.py
new file mode 100644
index 00000000..a23fa575
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/mmd.py
@@ -0,0 +1,91 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class MaximumMeanDiscrepancy(nn.Module):
+
+ def __init__(self, kernel_type="rbf", normalize=False):
+ super().__init__()
+ self.kernel_type = kernel_type
+ self.normalize = normalize
+
+ def forward(self, x, y):
+ # x, y: two batches of data with shape (batch, dim)
+ # MMD^2(x, y) = k(x, x') - 2k(x, y) + k(y, y')
+ if self.normalize:
+ x = F.normalize(x, dim=1)
+ y = F.normalize(y, dim=1)
+ if self.kernel_type == "linear":
+ return self.linear_mmd(x, y)
+ elif self.kernel_type == "poly":
+ return self.poly_mmd(x, y)
+ elif self.kernel_type == "rbf":
+ return self.rbf_mmd(x, y)
+ else:
+ raise NotImplementedError
+
+ def linear_mmd(self, x, y):
+ # k(x, y) = x^T y
+ k_xx = self.remove_self_distance(torch.mm(x, x.t()))
+ k_yy = self.remove_self_distance(torch.mm(y, y.t()))
+ k_xy = torch.mm(x, y.t())
+ return k_xx.mean() + k_yy.mean() - 2 * k_xy.mean()
+
+ def poly_mmd(self, x, y, alpha=1.0, c=2.0, d=2):
+ # k(x, y) = (alpha * x^T y + c)^d
+ k_xx = self.remove_self_distance(torch.mm(x, x.t()))
+ k_xx = (alpha*k_xx + c).pow(d)
+ k_yy = self.remove_self_distance(torch.mm(y, y.t()))
+ k_yy = (alpha*k_yy + c).pow(d)
+ k_xy = torch.mm(x, y.t())
+ k_xy = (alpha*k_xy + c).pow(d)
+ return k_xx.mean() + k_yy.mean() - 2 * k_xy.mean()
+
+ def rbf_mmd(self, x, y):
+ # k_xx
+ d_xx = self.euclidean_squared_distance(x, x)
+ d_xx = self.remove_self_distance(d_xx)
+ k_xx = self.rbf_kernel_mixture(d_xx)
+ # k_yy
+ d_yy = self.euclidean_squared_distance(y, y)
+ d_yy = self.remove_self_distance(d_yy)
+ k_yy = self.rbf_kernel_mixture(d_yy)
+ # k_xy
+ d_xy = self.euclidean_squared_distance(x, y)
+ k_xy = self.rbf_kernel_mixture(d_xy)
+ return k_xx.mean() + k_yy.mean() - 2 * k_xy.mean()
+
+ @staticmethod
+ def rbf_kernel_mixture(exponent, sigmas=[1, 5, 10]):
+ K = 0
+ for sigma in sigmas:
+ gamma = 1.0 / (2.0 * sigma**2)
+ K += torch.exp(-gamma * exponent)
+ return K
+
+ @staticmethod
+ def remove_self_distance(distmat):
+ tmp_list = []
+ for i, row in enumerate(distmat):
+ row1 = torch.cat([row[:i], row[i + 1:]])
+ tmp_list.append(row1)
+ return torch.stack(tmp_list)
+
+ @staticmethod
+ def euclidean_squared_distance(x, y):
+ m, n = x.size(0), y.size(0)
+ distmat = (
+ torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n) +
+ torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t()
+ )
+ # distmat.addmm_(1, -2, x, y.t())
+ distmat.addmm_(x, y.t(), beta=1, alpha=-2)
+ return distmat
+
+
+if __name__ == "__main__":
+ mmd = MaximumMeanDiscrepancy(kernel_type="rbf")
+ input1, input2 = torch.rand(3, 100), torch.rand(3, 100)
+ d = mmd(input1, input2)
+ print(d.item())
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/optimal_transport.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/optimal_transport.py
new file mode 100644
index 00000000..128ea96b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/optimal_transport.py
@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class OptimalTransport(nn.Module):
+
+ @staticmethod
+ def distance(batch1, batch2, dist_metric="cosine"):
+ if dist_metric == "cosine":
+ batch1 = F.normalize(batch1, p=2, dim=1)
+ batch2 = F.normalize(batch2, p=2, dim=1)
+ dist_mat = 1 - torch.mm(batch1, batch2.t())
+ elif dist_metric == "euclidean":
+ m, n = batch1.size(0), batch2.size(0)
+ dist_mat = (
+ torch.pow(batch1, 2).sum(dim=1, keepdim=True).expand(m, n) +
+ torch.pow(batch2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
+ )
+ dist_mat.addmm_(
+ 1, -2, batch1, batch2.t()
+ ) # squared euclidean distance
+ elif dist_metric == "fast_euclidean":
+ batch1 = batch1.unsqueeze(-2)
+ batch2 = batch2.unsqueeze(-3)
+ dist_mat = torch.sum((torch.abs(batch1 - batch2))**2, -1)
+ else:
+ raise ValueError(
+ "Unknown cost function: {}. Expected to "
+ "be one of [cosine | euclidean]".format(dist_metric)
+ )
+ return dist_mat
+
+
+class SinkhornDivergence(OptimalTransport):
+ thre = 1e-3
+
+ def __init__(
+ self,
+ dist_metric="cosine",
+ eps=0.01,
+ max_iter=5,
+ bp_to_sinkhorn=False
+ ):
+ super().__init__()
+ self.dist_metric = dist_metric
+ self.eps = eps
+ self.max_iter = max_iter
+ self.bp_to_sinkhorn = bp_to_sinkhorn
+
+ def forward(self, x, y):
+ # x, y: two batches of data with shape (batch, dim)
+ W_xy = self.transport_cost(x, y)
+ W_xx = self.transport_cost(x, x)
+ W_yy = self.transport_cost(y, y)
+ return 2*W_xy - W_xx - W_yy
+
+ def transport_cost(self, x, y, return_pi=False):
+ C = self.distance(x, y, dist_metric=self.dist_metric)
+ pi = self.sinkhorn_iterate(C, self.eps, self.max_iter, self.thre)
+ if not self.bp_to_sinkhorn:
+ pi = pi.detach()
+ cost = torch.sum(pi * C)
+ if return_pi:
+ return cost, pi
+ return cost
+
+ @staticmethod
+ def sinkhorn_iterate(C, eps, max_iter, thre):
+ nx, ny = C.shape
+ mu = torch.ones(nx, dtype=C.dtype, device=C.device) * (1.0/nx)
+ nu = torch.ones(ny, dtype=C.dtype, device=C.device) * (1.0/ny)
+ u = torch.zeros_like(mu)
+ v = torch.zeros_like(nu)
+
+ def M(_C, _u, _v):
+ """Modified cost for logarithmic updates.
+ Eq: M_{ij} = (-c_{ij} + u_i + v_j) / epsilon
+ """
+ return (-_C + _u.unsqueeze(-1) + _v.unsqueeze(-2)) / eps
+
+ real_iter = 0 # check if algorithm terminates before max_iter
+ # Sinkhorn iterations
+ for i in range(max_iter):
+ u0 = u
+ u = eps * (
+ torch.log(mu + 1e-8) - torch.logsumexp(M(C, u, v), dim=1)
+ ) + u
+ v = (
+ eps * (
+ torch.log(nu + 1e-8) -
+ torch.logsumexp(M(C, u, v).permute(1, 0), dim=1)
+ ) + v
+ )
+ err = (u - u0).abs().sum()
+ real_iter += 1
+ if err.item() < thre:
+ break
+ # Transport plan pi = diag(a)*K*diag(b)
+ return torch.exp(M(C, u, v))
+
+
+class MinibatchEnergyDistance(SinkhornDivergence):
+
+ def __init__(
+ self,
+ dist_metric="cosine",
+ eps=0.01,
+ max_iter=5,
+ bp_to_sinkhorn=False
+ ):
+ super().__init__(
+ dist_metric=dist_metric,
+ eps=eps,
+ max_iter=max_iter,
+ bp_to_sinkhorn=bp_to_sinkhorn,
+ )
+
+ def forward(self, x, y):
+ x1, x2 = torch.split(x, x.size(0) // 2, dim=0)
+ y1, y2 = torch.split(y, y.size(0) // 2, dim=0)
+ cost = 0
+ cost += self.transport_cost(x1, y1)
+ cost += self.transport_cost(x1, y2)
+ cost += self.transport_cost(x2, y1)
+ cost += self.transport_cost(x2, y2)
+ cost -= 2 * self.transport_cost(x1, x2)
+ cost -= 2 * self.transport_cost(y1, y2)
+ return cost
+
+
+if __name__ == "__main__":
+ # example: https://dfdazac.github.io/sinkhorn.html
+ import numpy as np
+
+ n_points = 5
+ a = np.array([[i, 0] for i in range(n_points)])
+ b = np.array([[i, 1] for i in range(n_points)])
+ x = torch.tensor(a, dtype=torch.float)
+ y = torch.tensor(b, dtype=torch.float)
+ sinkhorn = SinkhornDivergence(
+ dist_metric="euclidean", eps=0.01, max_iter=5
+ )
+ dist, pi = sinkhorn.transport_cost(x, y, True)
+ import pdb
+
+ pdb.set_trace()
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/reverse_grad.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/reverse_grad.py
new file mode 100644
index 00000000..34bab9db
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/reverse_grad.py
@@ -0,0 +1,34 @@
+import torch.nn as nn
+from torch.autograd import Function
+
+
+class _ReverseGrad(Function):
+
+ @staticmethod
+ def forward(ctx, input, grad_scaling):
+ ctx.grad_scaling = grad_scaling
+ return input.view_as(input)
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ grad_scaling = ctx.grad_scaling
+ return -grad_scaling * grad_output, None
+
+
+reverse_grad = _ReverseGrad.apply
+
+
+class ReverseGrad(nn.Module):
+ """Gradient reversal layer.
+
+ It acts as an identity layer in the forward,
+ but reverses the sign of the gradient in
+ the backward.
+ """
+
+ def forward(self, x, grad_scaling=1.0):
+ assert (grad_scaling >=
+ 0), "grad_scaling must be non-negative, " "but got {}".format(
+ grad_scaling
+ )
+ return reverse_grad(x, grad_scaling)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/sequential2.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/sequential2.py
new file mode 100644
index 00000000..47a83834
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/sequential2.py
@@ -0,0 +1,15 @@
+import torch.nn as nn
+
+
+class Sequential2(nn.Sequential):
+ """An alternative sequential container to nn.Sequential,
+ which accepts an arbitrary number of input arguments.
+ """
+
+ def forward(self, *inputs):
+ for module in self._modules.values():
+ if isinstance(inputs, tuple):
+ inputs = module(*inputs)
+ else:
+ inputs = module(inputs)
+ return inputs
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/transnorm.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/transnorm.py
new file mode 100644
index 00000000..453db773
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/transnorm.py
@@ -0,0 +1,138 @@
+import torch
+import torch.nn as nn
+
+
+class _TransNorm(nn.Module):
+ """Transferable normalization.
+
+ Reference:
+ - Wang et al. Transferable Normalization: Towards Improving
+ Transferability of Deep Neural Networks. NeurIPS 2019.
+
+ Args:
+ num_features (int): number of features.
+ eps (float): epsilon.
+ momentum (float): value for updating running_mean and running_var.
+ adaptive_alpha (bool): apply domain adaptive alpha.
+ """
+
+ def __init__(
+ self, num_features, eps=1e-5, momentum=0.1, adaptive_alpha=True
+ ):
+ super().__init__()
+ self.num_features = num_features
+ self.eps = eps
+ self.momentum = momentum
+ self.adaptive_alpha = adaptive_alpha
+
+ self.register_buffer("running_mean_s", torch.zeros(num_features))
+ self.register_buffer("running_var_s", torch.ones(num_features))
+ self.register_buffer("running_mean_t", torch.zeros(num_features))
+ self.register_buffer("running_var_t", torch.ones(num_features))
+
+ self.weight = nn.Parameter(torch.ones(num_features))
+ self.bias = nn.Parameter(torch.zeros(num_features))
+
+ def resnet_running_stats(self):
+ self.running_mean_s.zero_()
+ self.running_var_s.fill_(1)
+ self.running_mean_t.zero_()
+ self.running_var_t.fill_(1)
+
+ def reset_parameters(self):
+ nn.init.ones_(self.weight)
+ nn.init.zeros_(self.bias)
+
+ def _check_input(self, x):
+ raise NotImplementedError
+
+ def _compute_alpha(self, mean_s, var_s, mean_t, var_t):
+ C = self.num_features
+ ratio_s = mean_s / (var_s + self.eps).sqrt()
+ ratio_t = mean_t / (var_t + self.eps).sqrt()
+ dist = (ratio_s - ratio_t).abs()
+ dist_inv = 1 / (1+dist)
+ return C * dist_inv / dist_inv.sum()
+
+ def forward(self, input):
+ self._check_input(input)
+ C = self.num_features
+ if input.dim() == 2:
+ new_shape = (1, C)
+ elif input.dim() == 4:
+ new_shape = (1, C, 1, 1)
+ else:
+ raise ValueError
+
+ weight = self.weight.view(*new_shape)
+ bias = self.bias.view(*new_shape)
+
+ if not self.training:
+ mean_t = self.running_mean_t.view(*new_shape)
+ var_t = self.running_var_t.view(*new_shape)
+ output = (input-mean_t) / (var_t + self.eps).sqrt()
+ output = output*weight + bias
+
+ if self.adaptive_alpha:
+ mean_s = self.running_mean_s.view(*new_shape)
+ var_s = self.running_var_s.view(*new_shape)
+ alpha = self._compute_alpha(mean_s, var_s, mean_t, var_t)
+ alpha = alpha.reshape(*new_shape)
+ output = (1 + alpha.detach()) * output
+
+ return output
+
+ input_s, input_t = torch.split(input, input.shape[0] // 2, dim=0)
+
+ x_s = input_s.transpose(0, 1).reshape(C, -1)
+ mean_s = x_s.mean(1)
+ var_s = x_s.var(1)
+ self.running_mean_s.mul_(self.momentum)
+ self.running_mean_s.add_((1 - self.momentum) * mean_s.data)
+ self.running_var_s.mul_(self.momentum)
+ self.running_var_s.add_((1 - self.momentum) * var_s.data)
+ mean_s = mean_s.reshape(*new_shape)
+ var_s = var_s.reshape(*new_shape)
+ output_s = (input_s-mean_s) / (var_s + self.eps).sqrt()
+ output_s = output_s*weight + bias
+
+ x_t = input_t.transpose(0, 1).reshape(C, -1)
+ mean_t = x_t.mean(1)
+ var_t = x_t.var(1)
+ self.running_mean_t.mul_(self.momentum)
+ self.running_mean_t.add_((1 - self.momentum) * mean_t.data)
+ self.running_var_t.mul_(self.momentum)
+ self.running_var_t.add_((1 - self.momentum) * var_t.data)
+ mean_t = mean_t.reshape(*new_shape)
+ var_t = var_t.reshape(*new_shape)
+ output_t = (input_t-mean_t) / (var_t + self.eps).sqrt()
+ output_t = output_t*weight + bias
+
+ output = torch.cat([output_s, output_t], 0)
+
+ if self.adaptive_alpha:
+ alpha = self._compute_alpha(mean_s, var_s, mean_t, var_t)
+ alpha = alpha.reshape(*new_shape)
+ output = (1 + alpha.detach()) * output
+
+ return output
+
+
+class TransNorm1d(_TransNorm):
+
+ def _check_input(self, x):
+ if x.dim() != 2:
+ raise ValueError(
+ "Expected the input to be 2-D, "
+ "but got {}-D".format(x.dim())
+ )
+
+
+class TransNorm2d(_TransNorm):
+
+ def _check_input(self, x):
+ if x.dim() != 4:
+ raise ValueError(
+ "Expected the input to be 4-D, "
+ "but got {}-D".format(x.dim())
+ )
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/utils.py b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/utils.py
new file mode 100644
index 00000000..6bfcc898
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/modeling/ops/utils.py
@@ -0,0 +1,77 @@
+import numpy as np
+import torch
+
+
+def sharpen_prob(p, temperature=2):
+ """Sharpening probability with a temperature.
+
+ Args:
+ p (torch.Tensor): probability matrix (batch_size, n_classes)
+ temperature (float): temperature.
+ """
+ p = p.pow(temperature)
+ return p / p.sum(1, keepdim=True)
+
+
+def reverse_index(data, label):
+ """Reverse order."""
+ inv_idx = torch.arange(data.size(0) - 1, -1, -1).long()
+ return data[inv_idx], label[inv_idx]
+
+
+def shuffle_index(data, label):
+ """Shuffle order."""
+ rnd_idx = torch.randperm(data.shape[0])
+ return data[rnd_idx], label[rnd_idx]
+
+
+def create_onehot(label, num_classes):
+ """Create one-hot tensor.
+
+ We suggest using nn.functional.one_hot.
+
+ Args:
+ label (torch.Tensor): 1-D tensor.
+ num_classes (int): number of classes.
+ """
+ onehot = torch.zeros(label.shape[0], num_classes)
+ onehot = onehot.scatter(1, label.unsqueeze(1).data.cpu(), 1)
+ onehot = onehot.to(label.device)
+ return onehot
+
+
+def sigmoid_rampup(current, rampup_length):
+ """Exponential rampup.
+
+ Args:
+ current (int): current step.
+ rampup_length (int): maximum step.
+ """
+ assert rampup_length > 0
+ current = np.clip(current, 0.0, rampup_length)
+ phase = 1.0 - current/rampup_length
+ return float(np.exp(-5.0 * phase * phase))
+
+
+def linear_rampup(current, rampup_length):
+ """Linear rampup.
+
+ Args:
+ current (int): current step.
+ rampup_length (int): maximum step.
+ """
+ assert rampup_length > 0
+ ratio = np.clip(current / rampup_length, 0.0, 1.0)
+ return float(ratio)
+
+
+def ema_model_update(model, ema_model, alpha):
+ """Exponential moving average of model parameters.
+
+ Args:
+ model (nn.Module): model being trained.
+ ema_model (nn.Module): ema of the model.
+ alpha (float): ema decay rate.
+ """
+ for ema_param, param in zip(ema_model.parameters(), model.parameters()):
+ ema_param.data.mul_(alpha).add_(param.data, alpha=1 - alpha)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/optim/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/optim/__init__.py
new file mode 100644
index 00000000..e7ef4c04
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/optim/__init__.py
@@ -0,0 +1,2 @@
+from .optimizer import build_optimizer
+from .lr_scheduler import build_lr_scheduler
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/optim/lr_scheduler.py b/python/ClipDetection/Dassl.pytorch/dassl/optim/lr_scheduler.py
new file mode 100644
index 00000000..48d58853
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/optim/lr_scheduler.py
@@ -0,0 +1,152 @@
+"""
+Modified from https://github.com/KaiyangZhou/deep-person-reid
+"""
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+AVAI_SCHEDS = ["single_step", "multi_step", "cosine"]
+
+
+class _BaseWarmupScheduler(_LRScheduler):
+
+ def __init__(
+ self,
+ optimizer,
+ successor,
+ warmup_epoch,
+ last_epoch=-1,
+ verbose=False
+ ):
+ self.successor = successor
+ self.warmup_epoch = warmup_epoch
+ super().__init__(optimizer, last_epoch, verbose)
+
+ def get_lr(self):
+ raise NotImplementedError
+
+ def step(self, epoch=None):
+ if self.last_epoch >= self.warmup_epoch:
+ self.successor.step(epoch)
+ self._last_lr = self.successor.get_last_lr()
+ else:
+ super().step(epoch)
+
+
+class ConstantWarmupScheduler(_BaseWarmupScheduler):
+
+ def __init__(
+ self,
+ optimizer,
+ successor,
+ warmup_epoch,
+ cons_lr,
+ last_epoch=-1,
+ verbose=False
+ ):
+ self.cons_lr = cons_lr
+ super().__init__(
+ optimizer, successor, warmup_epoch, last_epoch, verbose
+ )
+
+ def get_lr(self):
+ if self.last_epoch >= self.warmup_epoch:
+ return self.successor.get_last_lr()
+ return [self.cons_lr for _ in self.base_lrs]
+
+
+class LinearWarmupScheduler(_BaseWarmupScheduler):
+
+ def __init__(
+ self,
+ optimizer,
+ successor,
+ warmup_epoch,
+ min_lr,
+ last_epoch=-1,
+ verbose=False
+ ):
+ self.min_lr = min_lr
+ super().__init__(
+ optimizer, successor, warmup_epoch, last_epoch, verbose
+ )
+
+ def get_lr(self):
+ if self.last_epoch >= self.warmup_epoch:
+ return self.successor.get_last_lr()
+ if self.last_epoch == 0:
+ return [self.min_lr for _ in self.base_lrs]
+ return [
+ lr * self.last_epoch / self.warmup_epoch for lr in self.base_lrs
+ ]
+
+
+def build_lr_scheduler(optimizer, optim_cfg):
+ """A function wrapper for building a learning rate scheduler.
+
+ Args:
+ optimizer (Optimizer): an Optimizer.
+ optim_cfg (CfgNode): optimization config.
+ """
+ lr_scheduler = optim_cfg.LR_SCHEDULER
+ stepsize = optim_cfg.STEPSIZE
+ gamma = optim_cfg.GAMMA
+ max_epoch = optim_cfg.MAX_EPOCH
+
+ if lr_scheduler not in AVAI_SCHEDS:
+ raise ValueError(
+ f"scheduler must be one of {AVAI_SCHEDS}, but got {lr_scheduler}"
+ )
+
+ if lr_scheduler == "single_step":
+ if isinstance(stepsize, (list, tuple)):
+ stepsize = stepsize[-1]
+
+ if not isinstance(stepsize, int):
+ raise TypeError(
+ "For single_step lr_scheduler, stepsize must "
+ f"be an integer, but got {type(stepsize)}"
+ )
+
+ if stepsize <= 0:
+ stepsize = max_epoch
+
+ scheduler = torch.optim.lr_scheduler.StepLR(
+ optimizer, step_size=stepsize, gamma=gamma
+ )
+
+ elif lr_scheduler == "multi_step":
+ if not isinstance(stepsize, (list, tuple)):
+ raise TypeError(
+ "For multi_step lr_scheduler, stepsize must "
+ f"be a list, but got {type(stepsize)}"
+ )
+
+ scheduler = torch.optim.lr_scheduler.MultiStepLR(
+ optimizer, milestones=stepsize, gamma=gamma
+ )
+
+ elif lr_scheduler == "cosine":
+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+ optimizer, float(max_epoch)
+ )
+
+ if optim_cfg.WARMUP_EPOCH > 0:
+ if not optim_cfg.WARMUP_RECOUNT:
+ scheduler.last_epoch = optim_cfg.WARMUP_EPOCH
+
+ if optim_cfg.WARMUP_TYPE == "constant":
+ scheduler = ConstantWarmupScheduler(
+ optimizer, scheduler, optim_cfg.WARMUP_EPOCH,
+ optim_cfg.WARMUP_CONS_LR
+ )
+
+ elif optim_cfg.WARMUP_TYPE == "linear":
+ scheduler = LinearWarmupScheduler(
+ optimizer, scheduler, optim_cfg.WARMUP_EPOCH,
+ optim_cfg.WARMUP_MIN_LR
+ )
+
+ else:
+ raise ValueError
+
+ return scheduler
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/optim/optimizer.py b/python/ClipDetection/Dassl.pytorch/dassl/optim/optimizer.py
new file mode 100644
index 00000000..5ebcd622
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/optim/optimizer.py
@@ -0,0 +1,142 @@
+"""
+Modified from https://github.com/KaiyangZhou/deep-person-reid
+"""
+import warnings
+import torch
+import torch.nn as nn
+
+from .radam import RAdam
+
+AVAI_OPTIMS = ["adam", "amsgrad", "sgd", "rmsprop", "radam", "adamw"]
+
+
+def build_optimizer(model, optim_cfg, param_groups=None):
+ """A function wrapper for building an optimizer.
+
+ Args:
+ model (nn.Module or iterable): model.
+ optim_cfg (CfgNode): optimization config.
+ param_groups: If provided, directly optimize param_groups and abandon model
+ """
+ optim = optim_cfg.NAME
+ lr = optim_cfg.LR
+ weight_decay = optim_cfg.WEIGHT_DECAY
+ momentum = optim_cfg.MOMENTUM
+ sgd_dampening = optim_cfg.SGD_DAMPNING
+ sgd_nesterov = optim_cfg.SGD_NESTEROV
+ rmsprop_alpha = optim_cfg.RMSPROP_ALPHA
+ adam_beta1 = optim_cfg.ADAM_BETA1
+ adam_beta2 = optim_cfg.ADAM_BETA2
+ staged_lr = optim_cfg.STAGED_LR
+ new_layers = optim_cfg.NEW_LAYERS
+ base_lr_mult = optim_cfg.BASE_LR_MULT
+
+ if optim not in AVAI_OPTIMS:
+ raise ValueError(
+ f"optim must be one of {AVAI_OPTIMS}, but got {optim}"
+ )
+
+ if param_groups is not None and staged_lr:
+ warnings.warn(
+ "staged_lr will be ignored, if you need to use staged_lr, "
+ "please bind it with param_groups yourself."
+ )
+
+ if param_groups is None:
+ if staged_lr:
+ if not isinstance(model, nn.Module):
+ raise TypeError(
+ "When staged_lr is True, model given to "
+ "build_optimizer() must be an instance of nn.Module"
+ )
+
+ if isinstance(model, nn.DataParallel):
+ model = model.module
+
+ if isinstance(new_layers, str):
+ if new_layers is None:
+ warnings.warn("new_layers is empty (staged_lr is useless)")
+ new_layers = [new_layers]
+
+ base_params = []
+ base_layers = []
+ new_params = []
+
+ for name, module in model.named_children():
+ if name in new_layers:
+ new_params += [p for p in module.parameters()]
+ else:
+ base_params += [p for p in module.parameters()]
+ base_layers.append(name)
+
+ param_groups = [
+ {
+ "params": base_params,
+ "lr": lr * base_lr_mult
+ },
+ {
+ "params": new_params
+ },
+ ]
+
+ else:
+ if isinstance(model, nn.Module):
+ param_groups = model.parameters()
+ else:
+ param_groups = model
+
+ if optim == "adam":
+ optimizer = torch.optim.Adam(
+ param_groups,
+ lr=lr,
+ weight_decay=weight_decay,
+ betas=(adam_beta1, adam_beta2),
+ )
+
+ elif optim == "amsgrad":
+ optimizer = torch.optim.Adam(
+ param_groups,
+ lr=lr,
+ weight_decay=weight_decay,
+ betas=(adam_beta1, adam_beta2),
+ amsgrad=True,
+ )
+
+ elif optim == "sgd":
+ optimizer = torch.optim.SGD(
+ param_groups,
+ lr=lr,
+ momentum=momentum,
+ weight_decay=weight_decay,
+ dampening=sgd_dampening,
+ nesterov=sgd_nesterov,
+ )
+
+ elif optim == "rmsprop":
+ optimizer = torch.optim.RMSprop(
+ param_groups,
+ lr=lr,
+ momentum=momentum,
+ weight_decay=weight_decay,
+ alpha=rmsprop_alpha,
+ )
+
+ elif optim == "radam":
+ optimizer = RAdam(
+ param_groups,
+ lr=lr,
+ weight_decay=weight_decay,
+ betas=(adam_beta1, adam_beta2),
+ )
+
+ elif optim == "adamw":
+ optimizer = torch.optim.AdamW(
+ param_groups,
+ lr=lr,
+ weight_decay=weight_decay,
+ betas=(adam_beta1, adam_beta2),
+ )
+ else:
+ raise NotImplementedError(f"Optimizer {optim} not implemented yet!")
+
+ return optimizer
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/optim/radam.py b/python/ClipDetection/Dassl.pytorch/dassl/optim/radam.py
new file mode 100644
index 00000000..f4c1989f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/optim/radam.py
@@ -0,0 +1,332 @@
+"""
+Imported from: https://github.com/LiyuanLucasLiu/RAdam
+
+https://arxiv.org/abs/1908.03265
+
+@article{liu2019radam,
+ title={On the Variance of the Adaptive Learning Rate and Beyond},
+ author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
+ journal={arXiv preprint arXiv:1908.03265},
+ year={2019}
+}
+"""
+import math
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ eps=1e-8,
+ weight_decay=0,
+ degenerated_to_sgd=True,
+ ):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 0: {}".format(betas[0])
+ )
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 1: {}".format(betas[1])
+ )
+
+ self.degenerated_to_sgd = degenerated_to_sgd
+ defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+ self.buffer = [[None, None, None] for ind in range(10)]
+ super(RAdam, self).__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super(RAdam, self).__setstate__(state)
+
+ def step(self, closure=None):
+
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data.float()
+ if grad.is_sparse:
+ raise RuntimeError(
+ "RAdam does not support sparse gradients"
+ )
+
+ p_data_fp32 = p.data.float()
+
+ state = self.state[p]
+
+ if len(state) == 0:
+ state["step"] = 0
+ state["exp_avg"] = torch.zeros_like(p_data_fp32)
+ state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ else:
+ state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
+ state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
+ p_data_fp32
+ )
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+ exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+ state["step"] += 1
+ buffered = self.buffer[int(state["step"] % 10)]
+ if state["step"] == buffered[0]:
+ N_sma, step_size = buffered[1], buffered[2]
+ else:
+ buffered[0] = state["step"]
+ beta2_t = beta2**state["step"]
+ N_sma_max = 2 / (1-beta2) - 1
+ N_sma = N_sma_max - 2 * state["step"
+ ] * beta2_t / (1-beta2_t)
+ buffered[1] = N_sma
+
+ # more conservative since it's an approximated value
+ if N_sma >= 5:
+ step_size = math.sqrt(
+ (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+ (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+ ) / (1 - beta1**state["step"])
+ elif self.degenerated_to_sgd:
+ step_size = 1.0 / (1 - beta1**state["step"])
+ else:
+ step_size = -1
+ buffered[2] = step_size
+
+ # more conservative since it's an approximated value
+ if N_sma >= 5:
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ -group["weight_decay"] * group["lr"], p_data_fp32
+ )
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+ p_data_fp32.addcdiv_(
+ -step_size * group["lr"], exp_avg, denom
+ )
+ p.data.copy_(p_data_fp32)
+ elif step_size > 0:
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ -group["weight_decay"] * group["lr"], p_data_fp32
+ )
+ p_data_fp32.add_(-step_size * group["lr"], exp_avg)
+ p.data.copy_(p_data_fp32)
+
+ return loss
+
+
+class PlainRAdam(Optimizer):
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ eps=1e-8,
+ weight_decay=0,
+ degenerated_to_sgd=True,
+ ):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 0: {}".format(betas[0])
+ )
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 1: {}".format(betas[1])
+ )
+
+ self.degenerated_to_sgd = degenerated_to_sgd
+ defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+
+ super(PlainRAdam, self).__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super(PlainRAdam, self).__setstate__(state)
+
+ def step(self, closure=None):
+
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data.float()
+ if grad.is_sparse:
+ raise RuntimeError(
+ "RAdam does not support sparse gradients"
+ )
+
+ p_data_fp32 = p.data.float()
+
+ state = self.state[p]
+
+ if len(state) == 0:
+ state["step"] = 0
+ state["exp_avg"] = torch.zeros_like(p_data_fp32)
+ state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ else:
+ state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
+ state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
+ p_data_fp32
+ )
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+ exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+ state["step"] += 1
+ beta2_t = beta2**state["step"]
+ N_sma_max = 2 / (1-beta2) - 1
+ N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1-beta2_t)
+
+ # more conservative since it's an approximated value
+ if N_sma >= 5:
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ -group["weight_decay"] * group["lr"], p_data_fp32
+ )
+ step_size = (
+ group["lr"] * math.sqrt(
+ (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
+ (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
+ ) / (1 - beta1**state["step"])
+ )
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+ p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+ p.data.copy_(p_data_fp32)
+ elif self.degenerated_to_sgd:
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ -group["weight_decay"] * group["lr"], p_data_fp32
+ )
+ step_size = group["lr"] / (1 - beta1**state["step"])
+ p_data_fp32.add_(-step_size, exp_avg)
+ p.data.copy_(p_data_fp32)
+
+ return loss
+
+
+class AdamW(Optimizer):
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ eps=1e-8,
+ weight_decay=0,
+ warmup=0
+ ):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 0: {}".format(betas[0])
+ )
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError(
+ "Invalid beta parameter at index 1: {}".format(betas[1])
+ )
+
+ defaults = dict(
+ lr=lr,
+ betas=betas,
+ eps=eps,
+ weight_decay=weight_decay,
+ warmup=warmup
+ )
+ super(AdamW, self).__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super(AdamW, self).__setstate__(state)
+
+ def step(self, closure=None):
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data.float()
+ if grad.is_sparse:
+ raise RuntimeError(
+ "Adam does not support sparse gradients, please consider SparseAdam instead"
+ )
+
+ p_data_fp32 = p.data.float()
+
+ state = self.state[p]
+
+ if len(state) == 0:
+ state["step"] = 0
+ state["exp_avg"] = torch.zeros_like(p_data_fp32)
+ state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ else:
+ state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
+ state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
+ p_data_fp32
+ )
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ state["step"] += 1
+
+ exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+ exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+ bias_correction1 = 1 - beta1**state["step"]
+ bias_correction2 = 1 - beta2**state["step"]
+
+ if group["warmup"] > state["step"]:
+ scheduled_lr = 1e-8 + state["step"] * group["lr"] / group[
+ "warmup"]
+ else:
+ scheduled_lr = group["lr"]
+
+ step_size = (
+ scheduled_lr * math.sqrt(bias_correction2) /
+ bias_correction1
+ )
+
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(
+ -group["weight_decay"] * scheduled_lr, p_data_fp32
+ )
+
+ p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+
+ p.data.copy_(p_data_fp32)
+
+ return loss
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/__init__.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/__init__.py
new file mode 100644
index 00000000..c47679fd
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/__init__.py
@@ -0,0 +1,5 @@
+from .tools import *
+from .logger import *
+from .meters import *
+from .registry import *
+from .torchtools import *
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/logger.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/logger.py
new file mode 100644
index 00000000..d5e680a0
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/logger.py
@@ -0,0 +1,73 @@
+import os
+import sys
+import time
+import os.path as osp
+
+from .tools import mkdir_if_missing
+
+__all__ = ["Logger", "setup_logger"]
+
+
+class Logger:
+ """Write console output to external text file.
+
+ Imported from ``_
+
+ Args:
+ fpath (str): directory to save logging file.
+
+ Examples::
+ >>> import sys
+ >>> import os.path as osp
+ >>> save_dir = 'output/experiment-1'
+ >>> log_name = 'train.log'
+ >>> sys.stdout = Logger(osp.join(save_dir, log_name))
+ """
+
+ def __init__(self, fpath=None):
+ self.console = sys.stdout
+ self.file = None
+ if fpath is not None:
+ mkdir_if_missing(osp.dirname(fpath))
+ self.file = open(fpath, "w")
+
+ def __del__(self):
+ self.close()
+
+ def __enter__(self):
+ pass
+
+ def __exit__(self, *args):
+ self.close()
+
+ def write(self, msg):
+ self.console.write(msg)
+ if self.file is not None:
+ self.file.write(msg)
+
+ def flush(self):
+ self.console.flush()
+ if self.file is not None:
+ self.file.flush()
+ os.fsync(self.file.fileno())
+
+ def close(self):
+ self.console.close()
+ if self.file is not None:
+ self.file.close()
+
+
+def setup_logger(output=None):
+ if output is None:
+ return
+
+ if output.endswith(".txt") or output.endswith(".log"):
+ fpath = output
+ else:
+ fpath = osp.join(output, "log.txt")
+
+ if osp.exists(fpath):
+ # make sure the existing log file is not over-written
+ fpath += time.strftime("-%Y-%m-%d-%H-%M-%S")
+
+ sys.stdout = Logger(fpath)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/meters.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/meters.py
new file mode 100644
index 00000000..a779b591
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/meters.py
@@ -0,0 +1,80 @@
+from collections import defaultdict
+import torch
+
+__all__ = ["AverageMeter", "MetricMeter"]
+
+
+class AverageMeter:
+ """Compute and store the average and current value.
+
+ Examples::
+ >>> # 1. Initialize a meter to record loss
+ >>> losses = AverageMeter()
+ >>> # 2. Update meter after every mini-batch update
+ >>> losses.update(loss_value, batch_size)
+ """
+
+ def __init__(self, ema=False):
+ """
+ Args:
+ ema (bool, optional): apply exponential moving average.
+ """
+ self.ema = ema
+ self.reset()
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ if isinstance(val, torch.Tensor):
+ val = val.item()
+
+ self.val = val
+ self.sum += val * n
+ self.count += n
+
+ if self.ema:
+ self.avg = self.avg * 0.9 + self.val * 0.1
+ else:
+ self.avg = self.sum / self.count
+
+
+class MetricMeter:
+ """Store the average and current value for a set of metrics.
+
+ Examples::
+ >>> # 1. Create an instance of MetricMeter
+ >>> metric = MetricMeter()
+ >>> # 2. Update using a dictionary as input
+ >>> input_dict = {'loss_1': value_1, 'loss_2': value_2}
+ >>> metric.update(input_dict)
+ >>> # 3. Convert to string and print
+ >>> print(str(metric))
+ """
+
+ def __init__(self, delimiter=" "):
+ self.meters = defaultdict(AverageMeter)
+ self.delimiter = delimiter
+
+ def update(self, input_dict):
+ if input_dict is None:
+ return
+
+ if not isinstance(input_dict, dict):
+ raise TypeError(
+ "Input to MetricMeter.update() must be a dictionary"
+ )
+
+ for k, v in input_dict.items():
+ if isinstance(v, torch.Tensor):
+ v = v.item()
+ self.meters[k].update(v)
+
+ def __str__(self):
+ output_str = []
+ for name, meter in self.meters.items():
+ output_str.append(f"{name} {meter.val:.4f} ({meter.avg:.4f})")
+ return self.delimiter.join(output_str)
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/registry.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/registry.py
new file mode 100644
index 00000000..5079784e
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/registry.py
@@ -0,0 +1,69 @@
+"""
+Modified from https://github.com/facebookresearch/fvcore
+"""
+__all__ = ["Registry"]
+
+
+class Registry:
+ """A registry providing name -> object mapping, to support
+ custom modules.
+
+ To create a registry (e.g. a backbone registry):
+
+ .. code-block:: python
+
+ BACKBONE_REGISTRY = Registry('BACKBONE')
+
+ To register an object:
+
+ .. code-block:: python
+
+ @BACKBONE_REGISTRY.register()
+ class MyBackbone(nn.Module):
+ ...
+
+ Or:
+
+ .. code-block:: python
+
+ BACKBONE_REGISTRY.register(MyBackbone)
+ """
+
+ def __init__(self, name):
+ self._name = name
+ self._obj_map = dict()
+
+ def _do_register(self, name, obj, force=False):
+ if name in self._obj_map and not force:
+ raise KeyError(
+ 'An object named "{}" was already '
+ 'registered in "{}" registry'.format(name, self._name)
+ )
+
+ self._obj_map[name] = obj
+
+ def register(self, obj=None, force=False):
+ if obj is None:
+ # Used as a decorator
+ def wrapper(fn_or_class):
+ name = fn_or_class.__name__
+ self._do_register(name, fn_or_class, force=force)
+ return fn_or_class
+
+ return wrapper
+
+ # Used as a function call
+ name = obj.__name__
+ self._do_register(name, obj, force=force)
+
+ def get(self, name):
+ if name not in self._obj_map:
+ raise KeyError(
+ 'Object name "{}" does not exist '
+ 'in "{}" registry'.format(name, self._name)
+ )
+
+ return self._obj_map[name]
+
+ def registered_names(self):
+ return list(self._obj_map.keys())
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/tools.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/tools.py
new file mode 100644
index 00000000..62d4f307
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/tools.py
@@ -0,0 +1,185 @@
+"""
+Modified from https://github.com/KaiyangZhou/deep-person-reid
+"""
+import os
+import sys
+import json
+import time
+import errno
+import numpy as np
+import random
+import os.path as osp
+import warnings
+from difflib import SequenceMatcher
+import PIL
+import torch
+from PIL import Image
+
+__all__ = [
+ "mkdir_if_missing",
+ "check_isfile",
+ "read_json",
+ "write_json",
+ "set_random_seed",
+ "download_url",
+ "read_image",
+ "collect_env_info",
+ "listdir_nohidden",
+ "get_most_similar_str_to_a_from_b",
+ "check_availability",
+ "tolist_if_not",
+]
+
+
+def mkdir_if_missing(dirname):
+ """Create dirname if it is missing."""
+ if not osp.exists(dirname):
+ try:
+ os.makedirs(dirname)
+ except OSError as e:
+ if e.errno != errno.EEXIST:
+ raise
+
+
+def check_isfile(fpath):
+ """Check if the given path is a file.
+
+ Args:
+ fpath (str): file path.
+
+ Returns:
+ bool
+ """
+ isfile = osp.isfile(fpath)
+ if not isfile:
+ warnings.warn('No file found at "{}"'.format(fpath))
+ return isfile
+
+
+def read_json(fpath):
+ """Read json file from a path."""
+ with open(fpath, "r") as f:
+ obj = json.load(f)
+ return obj
+
+
+def write_json(obj, fpath):
+ """Writes to a json file."""
+ mkdir_if_missing(osp.dirname(fpath))
+ with open(fpath, "w") as f:
+ json.dump(obj, f, indent=4, separators=(",", ": "))
+
+
+def set_random_seed(seed):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def download_url(url, dst):
+ """Download file from a url to a destination.
+
+ Args:
+ url (str): url to download file.
+ dst (str): destination path.
+ """
+ from six.moves import urllib
+
+ print('* url="{}"'.format(url))
+ print('* destination="{}"'.format(dst))
+
+ def _reporthook(count, block_size, total_size):
+ global start_time
+ if count == 0:
+ start_time = time.time()
+ return
+ duration = time.time() - start_time
+ progress_size = int(count * block_size)
+ speed = int(progress_size / (1024*duration))
+ percent = int(count * block_size * 100 / total_size)
+ sys.stdout.write(
+ "\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
+ (percent, progress_size / (1024*1024), speed, duration)
+ )
+ sys.stdout.flush()
+
+ urllib.request.urlretrieve(url, dst, _reporthook)
+ sys.stdout.write("\n")
+
+
+def read_image(path):
+ """Read image from path using ``PIL.Image``.
+
+ Args:
+ path (str): path to an image.
+
+ Returns:
+ PIL image
+ """
+ return Image.open(path).convert("RGB")
+
+
+def collect_env_info():
+ """Return env info as a string.
+
+ Code source: github.com/facebookresearch/maskrcnn-benchmark
+ """
+ from torch.utils.collect_env import get_pretty_env_info
+
+ env_str = get_pretty_env_info()
+ env_str += "\n Pillow ({})".format(PIL.__version__)
+ return env_str
+
+
+def listdir_nohidden(path, sort=False):
+ """List non-hidden items in a directory.
+
+ Args:
+ path (str): directory path.
+ sort (bool): sort the items.
+ """
+ items = [f for f in os.listdir(path) if not f.startswith(".")]
+ if sort:
+ items.sort()
+ return items
+
+
+def get_most_similar_str_to_a_from_b(a, b):
+ """Return the most similar string to a in b.
+
+ Args:
+ a (str): probe string.
+ b (list): a list of candidate strings.
+ """
+ highest_sim = 0
+ chosen = None
+ for candidate in b:
+ sim = SequenceMatcher(None, a, candidate).ratio()
+ if sim >= highest_sim:
+ highest_sim = sim
+ chosen = candidate
+ return chosen
+
+
+def check_availability(requested, available):
+ """Check if an element is available in a list.
+
+ Args:
+ requested (str): probe string.
+ available (list): a list of available strings.
+ """
+ if requested not in available:
+ psb_ans = get_most_similar_str_to_a_from_b(requested, available)
+ raise ValueError(
+ "The requested one is expected "
+ "to belong to {}, but got [{}] "
+ "(do you mean [{}]?)".format(available, requested, psb_ans)
+ )
+
+
+def tolist_if_not(x):
+ """Convert to a list."""
+ if not isinstance(x, list):
+ x = [x]
+ return x
diff --git a/python/ClipDetection/Dassl.pytorch/dassl/utils/torchtools.py b/python/ClipDetection/Dassl.pytorch/dassl/utils/torchtools.py
new file mode 100644
index 00000000..aa4dedfc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/dassl/utils/torchtools.py
@@ -0,0 +1,347 @@
+"""
+Modified from https://github.com/KaiyangZhou/deep-person-reid
+"""
+import pickle
+import shutil
+import os.path as osp
+import warnings
+from functools import partial
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+
+from .tools import mkdir_if_missing
+
+__all__ = [
+ "save_checkpoint",
+ "load_checkpoint",
+ "resume_from_checkpoint",
+ "open_all_layers",
+ "open_specified_layers",
+ "count_num_param",
+ "load_pretrained_weights",
+ "init_network_weights",
+]
+
+
+def save_checkpoint(
+ state,
+ save_dir,
+ is_best=False,
+ remove_module_from_keys=True,
+ model_name=""
+):
+ r"""Save checkpoint.
+
+ Args:
+ state (dict): dictionary.
+ save_dir (str): directory to save checkpoint.
+ is_best (bool, optional): if True, this checkpoint will be copied and named
+ ``model-best.pth.tar``. Default is False.
+ remove_module_from_keys (bool, optional): whether to remove "module."
+ from layer names. Default is True.
+ model_name (str, optional): model name to save.
+ """
+ mkdir_if_missing(save_dir)
+
+ if remove_module_from_keys:
+ # remove 'module.' in state_dict's keys
+ state_dict = state["state_dict"]
+ new_state_dict = OrderedDict()
+ for k, v in state_dict.items():
+ if k.startswith("module."):
+ k = k[7:]
+ new_state_dict[k] = v
+ state["state_dict"] = new_state_dict
+
+ # save model
+ epoch = state["epoch"]
+ if not model_name:
+ model_name = "model.pth.tar-" + str(epoch)
+ fpath = osp.join(save_dir, model_name)
+ torch.save(state, fpath)
+ print(f"Checkpoint saved to {fpath}")
+
+ # save current model name
+ checkpoint_file = osp.join(save_dir, "checkpoint")
+ checkpoint = open(checkpoint_file, "w+")
+ checkpoint.write("{}\n".format(osp.basename(fpath)))
+ checkpoint.close()
+
+ if is_best:
+ best_fpath = osp.join(osp.dirname(fpath), "model-best.pth.tar")
+ shutil.copy(fpath, best_fpath)
+ print('Best checkpoint saved to "{}"'.format(best_fpath))
+
+
+def load_checkpoint(fpath, device='cpu'):
+ r"""Load checkpoint.
+
+ ``UnicodeDecodeError`` can be well handled, which means
+ python2-saved files can be read from python3.
+
+ Args:
+ fpath (str): path to checkpoint.
+
+ Returns:
+ dict
+
+ Examples::
+ >>> fpath = 'log/my_model/model.pth.tar-10'
+ >>> checkpoint = load_checkpoint(fpath)
+ """
+ if fpath is None:
+ raise ValueError("File path is None")
+
+ if not osp.exists(fpath):
+ raise FileNotFoundError('File is not found at "{}"'.format(fpath))
+
+ map_location = device
+
+ try:
+ checkpoint = torch.load(fpath, map_location=map_location)
+
+ except UnicodeDecodeError:
+ pickle.load = partial(pickle.load, encoding="latin1")
+ pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
+ checkpoint = torch.load(
+ fpath, pickle_module=pickle, map_location=map_location
+ )
+
+ except Exception:
+ print('Unable to load checkpoint from "{}"'.format(fpath))
+ raise
+
+ return checkpoint
+
+
+def resume_from_checkpoint(fdir, model, optimizer=None, scheduler=None):
+ r"""Resume training from a checkpoint.
+
+ This will load (1) model weights and (2) ``state_dict``
+ of optimizer if ``optimizer`` is not None.
+
+ Args:
+ fdir (str): directory where the model was saved.
+ model (nn.Module): model.
+ optimizer (Optimizer, optional): an Optimizer.
+ scheduler (Scheduler, optional): an Scheduler.
+
+ Returns:
+ int: start_epoch.
+
+ Examples::
+ >>> fdir = 'log/my_model'
+ >>> start_epoch = resume_from_checkpoint(fdir, model, optimizer, scheduler)
+ """
+ with open(osp.join(fdir, "checkpoint"), "r") as checkpoint:
+ model_name = checkpoint.readlines()[0].strip("\n")
+ fpath = osp.join(fdir, model_name)
+
+ print('Loading checkpoint from "{}"'.format(fpath))
+ checkpoint = load_checkpoint(fpath)
+ model.load_state_dict(checkpoint["state_dict"])
+ print("Loaded model weights")
+
+ if optimizer is not None and "optimizer" in checkpoint.keys():
+ optimizer.load_state_dict(checkpoint["optimizer"])
+ print("Loaded optimizer")
+
+ if scheduler is not None and "scheduler" in checkpoint.keys():
+ scheduler.load_state_dict(checkpoint["scheduler"])
+ print("Loaded scheduler")
+
+ start_epoch = checkpoint["epoch"]
+ print("Previous epoch: {}".format(start_epoch))
+
+ return start_epoch
+
+
+def adjust_learning_rate(
+ optimizer,
+ base_lr,
+ epoch,
+ stepsize=20,
+ gamma=0.1,
+ linear_decay=False,
+ final_lr=0,
+ max_epoch=100,
+):
+ r"""Adjust learning rate.
+
+ Deprecated.
+ """
+ if linear_decay:
+ # linearly decay learning rate from base_lr to final_lr
+ frac_done = epoch / max_epoch
+ lr = frac_done*final_lr + (1.0-frac_done) * base_lr
+ else:
+ # decay learning rate by gamma for every stepsize
+ lr = base_lr * (gamma**(epoch // stepsize))
+
+ for param_group in optimizer.param_groups:
+ param_group["lr"] = lr
+
+
+def set_bn_to_eval(m):
+ r"""Set BatchNorm layers to eval mode."""
+ # 1. no update for running mean and var
+ # 2. scale and shift parameters are still trainable
+ classname = m.__class__.__name__
+ if classname.find("BatchNorm") != -1:
+ m.eval()
+
+
+def open_all_layers(model):
+ r"""Open all layers in model for training.
+
+ Examples::
+ >>> open_all_layers(model)
+ """
+ model.train()
+ for p in model.parameters():
+ p.requires_grad = True
+
+
+def open_specified_layers(model, open_layers):
+ r"""Open specified layers in model for training while keeping
+ other layers frozen.
+
+ Args:
+ model (nn.Module): neural net model.
+ open_layers (str or list): layers open for training.
+
+ Examples::
+ >>> # Only model.classifier will be updated.
+ >>> open_layers = 'classifier'
+ >>> open_specified_layers(model, open_layers)
+ >>> # Only model.fc and model.classifier will be updated.
+ >>> open_layers = ['fc', 'classifier']
+ >>> open_specified_layers(model, open_layers)
+ """
+ if isinstance(model, nn.DataParallel):
+ model = model.module
+
+ if isinstance(open_layers, str):
+ open_layers = [open_layers]
+
+ for layer in open_layers:
+ assert hasattr(model, layer), f"{layer} is not an attribute"
+
+ for name, module in model.named_children():
+ if name in open_layers:
+ module.train()
+ for p in module.parameters():
+ p.requires_grad = True
+ else:
+ module.eval()
+ for p in module.parameters():
+ p.requires_grad = False
+
+
+def count_num_param(model=None, params=None):
+ r"""Count number of parameters in a model.
+
+ Args:
+ model (nn.Module): network model.
+ params: network model`s params.
+ Examples::
+ >>> model_size = count_num_param(model)
+ """
+
+ if model is not None:
+ return sum(p.numel() for p in model.parameters())
+
+ if params is not None:
+ s = 0
+ for p in params:
+ if isinstance(p, dict):
+ s += p["params"].numel()
+ else:
+ s += p.numel()
+ return s
+
+ raise ValueError("model and params must provide at least one.")
+
+
+def load_pretrained_weights(model, weight_path):
+ r"""Load pretrianed weights to model.
+
+ Features::
+ - Incompatible layers (unmatched in name or size) will be ignored.
+ - Can automatically deal with keys containing "module.".
+
+ Args:
+ model (nn.Module): network model.
+ weight_path (str): path to pretrained weights.
+
+ Examples::
+ >>> weight_path = 'log/my_model/model-best.pth.tar'
+ >>> load_pretrained_weights(model, weight_path)
+ """
+ checkpoint = load_checkpoint(weight_path)
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ else:
+ state_dict = checkpoint
+
+ model_dict = model.state_dict()
+ new_state_dict = OrderedDict()
+ matched_layers, discarded_layers = [], []
+
+ for k, v in state_dict.items():
+ if k.startswith("module."):
+ k = k[7:] # discard module.
+
+ if k in model_dict and model_dict[k].size() == v.size():
+ new_state_dict[k] = v
+ matched_layers.append(k)
+ else:
+ discarded_layers.append(k)
+
+ model_dict.update(new_state_dict)
+ model.load_state_dict(model_dict)
+
+ if len(matched_layers) == 0:
+ warnings.warn(
+ f"Cannot load {weight_path} (check the key names manually)"
+ )
+ else:
+ print(f"Successfully loaded pretrained weights from {weight_path}")
+ if len(discarded_layers) > 0:
+ print(
+ f"Layers discarded due to unmatched keys or size: {discarded_layers}"
+ )
+
+
+def init_network_weights(model, init_type="normal", gain=0.02):
+
+ def _init_func(m):
+ classname = m.__class__.__name__
+
+ if hasattr(m, "weight") and (
+ classname.find("Conv") != -1 or classname.find("Linear") != -1
+ ):
+ if init_type == "normal":
+ nn.init.normal_(m.weight.data, 0.0, gain)
+ elif init_type == "xavier":
+ nn.init.xavier_normal_(m.weight.data, gain=gain)
+ elif init_type == "kaiming":
+ nn.init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
+ elif init_type == "orthogonal":
+ nn.init.orthogonal_(m.weight.data, gain=gain)
+ else:
+ raise NotImplementedError
+ if hasattr(m, "bias") and m.bias is not None:
+ nn.init.constant_(m.bias.data, 0.0)
+
+ elif classname.find("BatchNorm") != -1:
+ nn.init.constant_(m.weight.data, 1.0)
+ nn.init.constant_(m.bias.data, 0.0)
+
+ elif classname.find("InstanceNorm") != -1:
+ if m.weight is not None and m.bias is not None:
+ nn.init.constant_(m.weight.data, 1.0)
+ nn.init.constant_(m.bias.data, 0.0)
+
+ model.apply(_init_func)
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/da/cifar_stl.py b/python/ClipDetection/Dassl.pytorch/datasets/da/cifar_stl.py
new file mode 100644
index 00000000..52c16aad
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/da/cifar_stl.py
@@ -0,0 +1,95 @@
+import sys
+import pprint as pp
+import os.path as osp
+from torchvision.datasets import STL10, CIFAR10
+
+from dassl.utils import mkdir_if_missing
+
+cifar_label2name = {
+ 0: "airplane",
+ 1: "car", # the original name was 'automobile'
+ 2: "bird",
+ 3: "cat",
+ 4: "deer",
+ 5: "dog",
+ 6: "frog", # conflict class
+ 7: "horse",
+ 8: "ship",
+ 9: "truck",
+}
+
+stl_label2name = {
+ 0: "airplane",
+ 1: "bird",
+ 2: "car",
+ 3: "cat",
+ 4: "deer",
+ 5: "dog",
+ 6: "horse",
+ 7: "monkey", # conflict class
+ 8: "ship",
+ 9: "truck",
+}
+
+new_name2label = {
+ "airplane": 0,
+ "bird": 1,
+ "car": 2,
+ "cat": 3,
+ "deer": 4,
+ "dog": 5,
+ "horse": 6,
+ "ship": 7,
+ "truck": 8,
+}
+
+
+def extract_and_save_image(dataset, save_dir, discard, label2name):
+ if osp.exists(save_dir):
+ print('Folder "{}" already exists'.format(save_dir))
+ return
+
+ print('Extracting images to "{}" ...'.format(save_dir))
+ mkdir_if_missing(save_dir)
+
+ for i in range(len(dataset)):
+ img, label = dataset[i]
+ if label == discard:
+ continue
+ class_name = label2name[label]
+ label_new = new_name2label[class_name]
+ class_dir = osp.join(
+ save_dir,
+ str(label_new).zfill(3) + "_" + class_name
+ )
+ mkdir_if_missing(class_dir)
+ impath = osp.join(class_dir, str(i + 1).zfill(5) + ".jpg")
+ img.save(impath)
+
+
+def download_and_prepare(name, root, discarded_label, label2name):
+ print("Dataset: {}".format(name))
+ print("Root: {}".format(root))
+ print("Old labels:")
+ pp.pprint(label2name)
+ print("Discarded label: {}".format(discarded_label))
+ print("New labels:")
+ pp.pprint(new_name2label)
+
+ if name == "cifar":
+ train = CIFAR10(root, train=True, download=True)
+ test = CIFAR10(root, train=False)
+ else:
+ train = STL10(root, split="train", download=True)
+ test = STL10(root, split="test")
+
+ train_dir = osp.join(root, name, "train")
+ test_dir = osp.join(root, name, "test")
+
+ extract_and_save_image(train, train_dir, discarded_label, label2name)
+ extract_and_save_image(test, test_dir, discarded_label, label2name)
+
+
+if __name__ == "__main__":
+ download_and_prepare("cifar", sys.argv[1], 6, cifar_label2name)
+ download_and_prepare("stl", sys.argv[1], 7, stl_label2name)
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/da/digit5.py b/python/ClipDetection/Dassl.pytorch/datasets/da/digit5.py
new file mode 100644
index 00000000..500511dc
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/da/digit5.py
@@ -0,0 +1,131 @@
+import os
+import numpy as np
+import os.path as osp
+import argparse
+from PIL import Image
+from scipy.io import loadmat
+
+
+def mkdir_if_missing(directory):
+ if not osp.exists(directory):
+ os.makedirs(directory)
+
+
+def extract_and_save(data, label, save_dir):
+ for i, (x, y) in enumerate(zip(data, label)):
+ if x.shape[2] == 1:
+ x = np.repeat(x, 3, axis=2)
+ if y == 10:
+ y = 0
+ x = Image.fromarray(x, mode="RGB")
+ save_path = osp.join(
+ save_dir,
+ str(i + 1).zfill(6) + "_" + str(y) + ".jpg"
+ )
+ x.save(save_path)
+
+
+def load_mnist(data_dir, raw_data_dir):
+ filepath = osp.join(raw_data_dir, "mnist_data.mat")
+ data = loadmat(filepath)
+
+ train_data = np.reshape(data["train_32"], (55000, 32, 32, 1))
+ test_data = np.reshape(data["test_32"], (10000, 32, 32, 1))
+
+ train_label = np.nonzero(data["label_train"])[1]
+ test_label = np.nonzero(data["label_test"])[1]
+
+ return train_data, test_data, train_label, test_label
+
+
+def load_mnist_m(data_dir, raw_data_dir):
+ filepath = osp.join(raw_data_dir, "mnistm_with_label.mat")
+ data = loadmat(filepath)
+
+ train_data = data["train"]
+ test_data = data["test"]
+
+ train_label = np.nonzero(data["label_train"])[1]
+ test_label = np.nonzero(data["label_test"])[1]
+
+ return train_data, test_data, train_label, test_label
+
+
+def load_svhn(data_dir, raw_data_dir):
+ train = loadmat(osp.join(raw_data_dir, "svhn_train_32x32.mat"))
+ train_data = train["X"].transpose(3, 0, 1, 2)
+ train_label = train["y"][:, 0]
+
+ test = loadmat(osp.join(raw_data_dir, "svhn_test_32x32.mat"))
+ test_data = test["X"].transpose(3, 0, 1, 2)
+ test_label = test["y"][:, 0]
+
+ return train_data, test_data, train_label, test_label
+
+
+def load_syn(data_dir, raw_data_dir):
+ filepath = osp.join(raw_data_dir, "syn_number.mat")
+ data = loadmat(filepath)
+
+ train_data = data["train_data"]
+ test_data = data["test_data"]
+
+ train_label = data["train_label"][:, 0]
+ test_label = data["test_label"][:, 0]
+
+ return train_data, test_data, train_label, test_label
+
+
+def load_usps(data_dir, raw_data_dir):
+ filepath = osp.join(raw_data_dir, "usps_28x28.mat")
+ data = loadmat(filepath)["dataset"]
+
+ train_data = data[0][0].transpose(0, 2, 3, 1)
+ test_data = data[1][0].transpose(0, 2, 3, 1)
+
+ train_data *= 255
+ test_data *= 255
+
+ train_data = train_data.astype(np.uint8)
+ test_data = test_data.astype(np.uint8)
+
+ train_label = data[0][1][:, 0]
+ test_label = data[1][1][:, 0]
+
+ return train_data, test_data, train_label, test_label
+
+
+def main(data_dir):
+ data_dir = osp.abspath(osp.expanduser(data_dir))
+ raw_data_dir = osp.join(data_dir, "Digit-Five")
+
+ if not osp.exists(data_dir):
+ raise FileNotFoundError('"{}" does not exist'.format(data_dir))
+
+ datasets = ["mnist", "mnist_m", "svhn", "syn", "usps"]
+
+ for name in datasets:
+ print("Creating {}".format(name))
+
+ output = eval("load_" + name)(data_dir, raw_data_dir)
+ train_data, test_data, train_label, test_label = output
+
+ print("# train: {}".format(train_data.shape[0]))
+ print("# test: {}".format(test_data.shape[0]))
+
+ train_dir = osp.join(data_dir, name, "train_images")
+ mkdir_if_missing(train_dir)
+ test_dir = osp.join(data_dir, name, "test_images")
+ mkdir_if_missing(test_dir)
+
+ extract_and_save(train_data, train_label, train_dir)
+ extract_and_save(test_data, test_label, test_dir)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "data_dir", type=str, help="directory containing Digit-Five/"
+ )
+ args = parser.parse_args()
+ main(args.data_dir)
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/da/visda17.sh b/python/ClipDetection/Dassl.pytorch/datasets/da/visda17.sh
new file mode 100644
index 00000000..ce98d313
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/da/visda17.sh
@@ -0,0 +1,24 @@
+# ------------------------------------------------------------------------
+# ROOT is the root directory where you put your domain datasets.
+#
+# Suppose you wanna put the dataset under $DATA, which stores all the
+# domain datasets, run the following command in your terminal to
+# download VisDa17:
+#
+# $ sh visda17.sh $DATA
+#------------------------------------------------------------------------
+
+ROOT=$1
+mkdir $ROOT/visda17
+cd $ROOT/visda17
+
+wget http://csr.bu.edu/ftp/visda17/clf/train.tar
+tar xvf train.tar
+
+wget http://csr.bu.edu/ftp/visda17/clf/validation.tar
+tar xvf validation.tar
+
+wget http://csr.bu.edu/ftp/visda17/clf/test.tar
+tar xvf test.tar
+
+wget https://raw.githubusercontent.com/VisionLearningGroup/taskcv-2017-public/master/classification/data/image_list.txt -O test/image_list.txt
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/dg/cifar_c.py b/python/ClipDetection/Dassl.pytorch/datasets/dg/cifar_c.py
new file mode 100644
index 00000000..f407f858
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/dg/cifar_c.py
@@ -0,0 +1,73 @@
+"""
+This script
+- creates a folder named "cifar10_c" under the same directory as 'CIFAR-10-C'
+- extracts images from .npy files and save them as .jpg.
+"""
+import os
+import sys
+import numpy as np
+import os.path as osp
+from PIL import Image
+
+from dassl.utils import mkdir_if_missing
+
+
+def extract_and_save(images, labels, level, dst):
+ # level denotes the corruption intensity level (0-based)
+ assert 0 <= level <= 4
+
+ for i in range(10000):
+ real_i = i + level*10000
+ im = Image.fromarray(images[real_i])
+ label = int(labels[real_i])
+ category_dir = osp.join(dst, str(label).zfill(3))
+ mkdir_if_missing(category_dir)
+ save_path = osp.join(category_dir, str(i + 1).zfill(5) + ".jpg")
+ im.save(save_path)
+
+
+def main(npy_folder):
+ npy_folder = osp.abspath(osp.expanduser(npy_folder))
+ dataset_cap = osp.basename(npy_folder)
+
+ assert dataset_cap in ["CIFAR-10-C", "CIFAR-100-C"]
+
+ if dataset_cap == "CIFAR-10-C":
+ dataset = "cifar10_c"
+ else:
+ dataset = "cifar100_c"
+
+ if not osp.exists(npy_folder):
+ print('The given folder "{}" does not exist'.format(npy_folder))
+
+ root = osp.dirname(npy_folder)
+ im_folder = osp.join(root, dataset)
+
+ mkdir_if_missing(im_folder)
+
+ dirnames = os.listdir(npy_folder)
+ dirnames.remove("labels.npy")
+ if "README.txt" in dirnames:
+ dirnames.remove("README.txt")
+ assert len(dirnames) == 19
+ labels = np.load(osp.join(npy_folder, "labels.npy"))
+
+ for dirname in dirnames:
+ corruption = dirname.split(".")[0]
+ corruption_folder = osp.join(im_folder, corruption)
+ mkdir_if_missing(corruption_folder)
+
+ npy_filename = osp.join(npy_folder, dirname)
+ images = np.load(npy_filename)
+ assert images.shape[0] == 50000
+
+ for level in range(5):
+ dst = osp.join(corruption_folder, str(level + 1))
+ mkdir_if_missing(dst)
+ print('Saving images to "{}"'.format(dst))
+ extract_and_save(images, labels, level, dst)
+
+
+if __name__ == "__main__":
+ # sys.argv[1] contains the path to CIFAR-10-C or CIFAR-100-C
+ main(sys.argv[1])
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/ssl/cifar10_cifar100_svhn.py b/python/ClipDetection/Dassl.pytorch/datasets/ssl/cifar10_cifar100_svhn.py
new file mode 100644
index 00000000..ad9aa11c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/ssl/cifar10_cifar100_svhn.py
@@ -0,0 +1,50 @@
+import sys
+import os.path as osp
+from torchvision.datasets import SVHN, CIFAR10, CIFAR100
+
+from dassl.utils import mkdir_if_missing
+
+
+def extract_and_save_image(dataset, save_dir):
+ if osp.exists(save_dir):
+ print('Folder "{}" already exists'.format(save_dir))
+ return
+
+ print('Extracting images to "{}" ...'.format(save_dir))
+ mkdir_if_missing(save_dir)
+
+ for i in range(len(dataset)):
+ img, label = dataset[i]
+ class_dir = osp.join(save_dir, str(label).zfill(3))
+ mkdir_if_missing(class_dir)
+ impath = osp.join(class_dir, str(i + 1).zfill(5) + ".jpg")
+ img.save(impath)
+
+
+def download_and_prepare(name, root):
+ print("Dataset: {}".format(name))
+ print("Root: {}".format(root))
+
+ if name == "cifar10":
+ train = CIFAR10(root, train=True, download=True)
+ test = CIFAR10(root, train=False)
+ elif name == "cifar100":
+ train = CIFAR100(root, train=True, download=True)
+ test = CIFAR100(root, train=False)
+ elif name == "svhn":
+ train = SVHN(root, split="train", download=True)
+ test = SVHN(root, split="test", download=True)
+ else:
+ raise ValueError
+
+ train_dir = osp.join(root, name, "train")
+ test_dir = osp.join(root, name, "test")
+
+ extract_and_save_image(train, train_dir)
+ extract_and_save_image(test, test_dir)
+
+
+if __name__ == "__main__":
+ download_and_prepare("cifar10", sys.argv[1])
+ download_and_prepare("cifar100", sys.argv[1])
+ download_and_prepare("svhn", sys.argv[1])
diff --git a/python/ClipDetection/Dassl.pytorch/datasets/ssl/stl10.py b/python/ClipDetection/Dassl.pytorch/datasets/ssl/stl10.py
new file mode 100644
index 00000000..3f2ed2cb
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/datasets/ssl/stl10.py
@@ -0,0 +1,42 @@
+import sys
+import os.path as osp
+from torchvision.datasets import STL10
+
+from dassl.utils import mkdir_if_missing
+
+
+def extract_and_save_image(dataset, save_dir):
+ if osp.exists(save_dir):
+ print('Folder "{}" already exists'.format(save_dir))
+ return
+
+ print('Extracting images to "{}" ...'.format(save_dir))
+ mkdir_if_missing(save_dir)
+
+ for i in range(len(dataset)):
+ img, label = dataset[i]
+ if label == -1:
+ label_name = "none"
+ else:
+ label_name = str(label)
+ imname = str(i).zfill(6) + "_" + label_name + ".jpg"
+ impath = osp.join(save_dir, imname)
+ img.save(impath)
+
+
+def download_and_prepare(root):
+ train = STL10(root, split="train", download=True)
+ test = STL10(root, split="test")
+ unlabeled = STL10(root, split="unlabeled")
+
+ train_dir = osp.join(root, "train")
+ test_dir = osp.join(root, "test")
+ unlabeled_dir = osp.join(root, "unlabeled")
+
+ extract_and_save_image(train, train_dir)
+ extract_and_save_image(test, test_dir)
+ extract_and_save_image(unlabeled, unlabeled_dir)
+
+
+if __name__ == "__main__":
+ download_and_prepare(sys.argv[1])
diff --git a/python/ClipDetection/Dassl.pytorch/linter.sh b/python/ClipDetection/Dassl.pytorch/linter.sh
new file mode 100644
index 00000000..9db34f9f
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/linter.sh
@@ -0,0 +1,11 @@
+echo "Running isort"
+isort -y -sp .
+echo "Done"
+
+echo "Running yapf"
+yapf -i -r -vv -e build .
+echo "Done"
+
+echo "Running flake8"
+flake8 .
+echo "Done"
\ No newline at end of file
diff --git a/python/ClipDetection/Dassl.pytorch/requirements.txt b/python/ClipDetection/Dassl.pytorch/requirements.txt
new file mode 100644
index 00000000..d8dbbdfb
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/requirements.txt
@@ -0,0 +1,14 @@
+flake8==3.7.9
+yapf==0.29.0
+isort==4.3.21
+yacs
+gdown
+tb-nightly
+future
+scipy
+scikit-learn
+tqdm
+ftfy
+regex
+wilds==1.2.2
+tabulate
diff --git a/python/ClipDetection/Dassl.pytorch/setup.py b/python/ClipDetection/Dassl.pytorch/setup.py
new file mode 100644
index 00000000..b0cbe47b
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/setup.py
@@ -0,0 +1,48 @@
+import numpy as np
+import os.path as osp
+from setuptools import setup, find_packages
+
+
+def readme():
+ with open('README.md') as f:
+ content = f.read()
+ return content
+
+
+def find_version():
+ version_file = 'dassl/__init__.py'
+ with open(version_file, 'r') as f:
+ exec(compile(f.read(), version_file, 'exec'))
+ return locals()['__version__']
+
+
+def numpy_include():
+ try:
+ numpy_include = np.get_include()
+ except AttributeError:
+ numpy_include = np.get_numpy_include()
+ return numpy_include
+
+
+def get_requirements(filename='requirements.txt'):
+ here = osp.dirname(osp.realpath(__file__))
+ with open(osp.join(here, filename), 'r') as f:
+ requires = [line.replace('\n', '') for line in f.readlines()]
+ return requires
+
+
+setup(
+ name='dassl',
+ version=find_version(),
+ description='Dassl: Domain adaptation and semi-supervised learning',
+ author='Kaiyang Zhou',
+ license='MIT',
+ long_description=readme(),
+ url='https://github.com/KaiyangZhou/Dassl.pytorch',
+ packages=find_packages(),
+ install_requires=get_requirements(),
+ keywords=[
+ 'Domain Adaptation', 'Domain Generalization',
+ 'Semi-Supervised Learning', 'Pytorch'
+ ]
+)
diff --git a/python/ClipDetection/Dassl.pytorch/tools/parse_test_res.py b/python/ClipDetection/Dassl.pytorch/tools/parse_test_res.py
new file mode 100644
index 00000000..d5105add
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/tools/parse_test_res.py
@@ -0,0 +1,178 @@
+"""
+Goal
+---
+1. Read test results from log.txt files
+2. Compute mean and std across different folders (seeds)
+
+Usage
+---
+Assume the output files are saved under output/my_experiment,
+which contains results of different seeds, e.g.,
+
+my_experiment/
+ seed1/
+ log.txt
+ seed2/
+ log.txt
+ seed3/
+ log.txt
+
+Run the following command from the root directory:
+
+$ python tools/parse_test_res.py output/my_experiment
+
+Add --ci95 to the argument if you wanna get 95% confidence
+interval instead of standard deviation:
+
+$ python tools/parse_test_res.py output/my_experiment --ci95
+
+If my_experiment/ has the following structure,
+
+my_experiment/
+ exp-1/
+ seed1/
+ log.txt
+ ...
+ seed2/
+ log.txt
+ ...
+ seed3/
+ log.txt
+ ...
+ exp-2/
+ ...
+ exp-3/
+ ...
+
+Run
+
+$ python tools/parse_test_res.py output/my_experiment --multi-exp
+"""
+import re
+import numpy as np
+import os.path as osp
+import argparse
+from collections import OrderedDict, defaultdict
+
+from dassl.utils import check_isfile, listdir_nohidden
+
+
+def compute_ci95(res):
+ return 1.96 * np.std(res) / np.sqrt(len(res))
+
+
+def parse_function(*metrics, directory="", args=None, end_signal=None):
+ print("===")
+ print(f"Parsing files in {directory}")
+ subdirs = listdir_nohidden(directory, sort=True)
+
+ outputs = []
+
+ for subdir in subdirs:
+ fpath = osp.join(directory, subdir, "log.txt")
+ assert check_isfile(fpath)
+ good_to_go = False
+ output = OrderedDict()
+
+ with open(fpath, "r") as f:
+ lines = f.readlines()
+
+ for line in lines:
+ line = line.strip()
+
+ if line == end_signal:
+ good_to_go = True
+
+ for metric in metrics:
+ match = metric["regex"].search(line)
+ if match and good_to_go:
+ if "file" not in output:
+ output["file"] = fpath
+ num = float(match.group(1))
+ name = metric["name"]
+ output[name] = num
+
+ if output:
+ outputs.append(output)
+
+ assert len(outputs) > 0, f"Nothing found in {directory}"
+
+ metrics_results = defaultdict(list)
+ for output in outputs:
+ msg = ""
+ for key, value in output.items():
+ if isinstance(value, float):
+ msg += f"{key}: {value:.1f}%. "
+ else:
+ msg += f"{key}: {value}. "
+ if key != "file":
+ metrics_results[key].append(value)
+ print(msg)
+
+ output_results = OrderedDict()
+ for key, values in metrics_results.items():
+ avg = np.mean(values)
+ std = compute_ci95(values) if args.ci95 else np.std(values)
+ print(f"* average {key}: {avg:.1f}% +- {std:.1f}%")
+ output_results[key] = avg
+ print("===")
+
+ return output_results
+
+
+def main(args, end_signal):
+ metric = {
+ "name": args.keyword,
+ "regex": re.compile(fr"\* {args.keyword}: ([\.\deE+-]+)%"),
+ }
+
+ if args.multi_exp:
+ final_results = defaultdict(list)
+
+ for directory in listdir_nohidden(args.directory, sort=True):
+ directory = osp.join(args.directory, directory)
+ results = parse_function(
+ metric, directory=directory, args=args, end_signal=end_signal
+ )
+
+ for key, value in results.items():
+ final_results[key].append(value)
+
+ print("Average performance")
+ for key, values in final_results.items():
+ avg = np.mean(values)
+ print(f"* {key}: {avg:.1f}%")
+
+ else:
+ parse_function(
+ metric, directory=args.directory, args=args, end_signal=end_signal
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("directory", type=str, help="path to directory")
+ parser.add_argument(
+ "--ci95",
+ action="store_true",
+ help=r"compute 95\% confidence interval"
+ )
+ parser.add_argument(
+ "--test-log", action="store_true", help="parse test-only logs"
+ )
+ parser.add_argument(
+ "--multi-exp", action="store_true", help="parse multiple experiments"
+ )
+ parser.add_argument(
+ "--keyword",
+ default="accuracy",
+ type=str,
+ help="which keyword to extract"
+ )
+ args = parser.parse_args()
+
+ end_signal = "Finish training" # needs to be adapted to the latest
+ if args.test_log:
+ end_signal = "=> result"
+
+ main(args, end_signal)
diff --git a/python/ClipDetection/Dassl.pytorch/tools/replace_text.py b/python/ClipDetection/Dassl.pytorch/tools/replace_text.py
new file mode 100644
index 00000000..71761544
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/tools/replace_text.py
@@ -0,0 +1,69 @@
+"""
+Replace text in python files.
+"""
+import glob
+import os.path as osp
+import argparse
+import fileinput
+
+EXTENSION = ".py"
+
+
+def is_python_file(filename):
+ ext = osp.splitext(filename)[1]
+ return ext == EXTENSION
+
+
+def update_file(filename, text_to_search, replacement_text):
+ print("Processing {}".format(filename))
+ with fileinput.FileInput(filename, inplace=True, backup="") as file:
+ for line in file:
+ print(line.replace(text_to_search, replacement_text), end="")
+
+
+def recursive_update(directory, text_to_search, replacement_text):
+ filenames = glob.glob(osp.join(directory, "*"))
+
+ for filename in filenames:
+ if osp.isfile(filename):
+ if not is_python_file(filename):
+ continue
+ update_file(filename, text_to_search, replacement_text)
+ elif osp.isdir(filename):
+ recursive_update(filename, text_to_search, replacement_text)
+ else:
+ raise NotImplementedError
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "file_or_dir", type=str, help="path to file or directory"
+ )
+ parser.add_argument("text_to_search", type=str, help="name to be replaced")
+ parser.add_argument("replacement_text", type=str, help="new name")
+ parser.add_argument(
+ "--ext", type=str, default=".py", help="file extension"
+ )
+ args = parser.parse_args()
+
+ file_or_dir = args.file_or_dir
+ text_to_search = args.text_to_search
+ replacement_text = args.replacement_text
+ extension = args.ext
+
+ global EXTENSION
+ EXTENSION = extension
+
+ if osp.isfile(file_or_dir):
+ if not is_python_file(file_or_dir):
+ return
+ update_file(file_or_dir, text_to_search, replacement_text)
+ elif osp.isdir(file_or_dir):
+ recursive_update(file_or_dir, text_to_search, replacement_text)
+ else:
+ raise NotImplementedError
+
+
+if __name__ == "__main__":
+ main()
diff --git a/python/ClipDetection/Dassl.pytorch/tools/train.py b/python/ClipDetection/Dassl.pytorch/tools/train.py
new file mode 100644
index 00000000..106cd19c
--- /dev/null
+++ b/python/ClipDetection/Dassl.pytorch/tools/train.py
@@ -0,0 +1,191 @@
+import argparse
+import torch
+
+from dassl.utils import setup_logger, set_random_seed, collect_env_info
+from dassl.config import clean_cfg, get_cfg_default
+from dassl.engine import build_trainer
+
+
+def print_args(args, cfg):
+ print("***************")
+ print("** Arguments **")
+ print("***************")
+ optkeys = list(args.__dict__.keys())
+ optkeys.sort()
+ for key in optkeys:
+ print("{}: {}".format(key, args.__dict__[key]))
+ print("************")
+ print("** Config **")
+ print("************")
+ print(cfg)
+
+
+def reset_cfg(cfg, args):
+ if args.root:
+ cfg.DATASET.ROOT = args.root
+
+ if args.output_dir:
+ cfg.OUTPUT_DIR = args.output_dir
+
+ if args.resume:
+ cfg.RESUME = args.resume
+
+ if args.seed:
+ cfg.SEED = args.seed
+
+ if args.source_domains:
+ cfg.DATASET.SOURCE_DOMAINS = args.source_domains
+
+ if args.target_domains:
+ cfg.DATASET.TARGET_DOMAINS = args.target_domains
+
+ if args.transforms:
+ cfg.INPUT.TRANSFORMS = args.transforms
+
+ if args.trainer:
+ cfg.TRAINER.NAME = args.trainer
+
+ if args.backbone:
+ cfg.MODEL.BACKBONE.NAME = args.backbone
+
+ if args.head:
+ cfg.MODEL.HEAD.NAME = args.head
+
+
+def extend_cfg(cfg):
+ """
+ Add new config variables.
+
+ E.g.
+ from yacs.config import CfgNode as CN
+ cfg.TRAINER.MY_MODEL = CN()
+ cfg.TRAINER.MY_MODEL.PARAM_A = 1.
+ cfg.TRAINER.MY_MODEL.PARAM_B = 0.5
+ cfg.TRAINER.MY_MODEL.PARAM_C = False
+ """
+ pass
+
+
+def setup_cfg(args):
+ cfg = get_cfg_default()
+ extend_cfg(cfg)
+
+ # 1. From the dataset config file
+ if args.dataset_config_file:
+ cfg.merge_from_file(args.dataset_config_file)
+
+ # 2. From the method config file
+ if args.config_file:
+ cfg.merge_from_file(args.config_file)
+
+ # 3. From input arguments
+ reset_cfg(cfg, args)
+
+ # 4. From optional input arguments
+ cfg.merge_from_list(args.opts)
+
+ clean_cfg(cfg, args.trainer)
+ cfg.freeze()
+
+ return cfg
+
+
+def main(args):
+ cfg = setup_cfg(args)
+ if cfg.SEED >= 0:
+ print("Setting fixed seed: {}".format(cfg.SEED))
+ set_random_seed(cfg.SEED)
+ setup_logger(cfg.OUTPUT_DIR)
+
+ if torch.cuda.is_available() and cfg.USE_CUDA:
+ torch.backends.cudnn.benchmark = True
+
+ print_args(args, cfg)
+ print("Collecting env info ...")
+ print("** System info **\n{}\n".format(collect_env_info()))
+
+ trainer = build_trainer(cfg)
+
+ if args.eval_only:
+ trainer.load_model(args.model_dir, epoch=args.load_epoch)
+ trainer.test()
+ return
+
+ if not args.no_train:
+ trainer.train()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--root", type=str, default="", help="path to dataset")
+ parser.add_argument(
+ "--output-dir", type=str, default="", help="output directory"
+ )
+ parser.add_argument(
+ "--resume",
+ type=str,
+ default="",
+ help="checkpoint directory (from which the training resumes)",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=-1,
+ help="only positive value enables a fixed seed"
+ )
+ parser.add_argument(
+ "--source-domains",
+ type=str,
+ nargs="+",
+ help="source domains for DA/DG"
+ )
+ parser.add_argument(
+ "--target-domains",
+ type=str,
+ nargs="+",
+ help="target domains for DA/DG"
+ )
+ parser.add_argument(
+ "--transforms", type=str, nargs="+", help="data augmentation methods"
+ )
+ parser.add_argument(
+ "--config-file", type=str, default="", help="path to config file"
+ )
+ parser.add_argument(
+ "--dataset-config-file",
+ type=str,
+ default="",
+ help="path to config file for dataset setup",
+ )
+ parser.add_argument(
+ "--trainer", type=str, default="", help="name of trainer"
+ )
+ parser.add_argument(
+ "--backbone", type=str, default="", help="name of CNN backbone"
+ )
+ parser.add_argument("--head", type=str, default="", help="name of head")
+ parser.add_argument(
+ "--eval-only", action="store_true", help="evaluation only"
+ )
+ parser.add_argument(
+ "--model-dir",
+ type=str,
+ default="",
+ help="load model from this directory for eval-only mode",
+ )
+ parser.add_argument(
+ "--load-epoch",
+ type=int,
+ help="load model weights at this epoch for evaluation"
+ )
+ parser.add_argument(
+ "--no-train", action="store_true", help="do not call trainer.train()"
+ )
+ parser.add_argument(
+ "opts",
+ default=None,
+ nargs=argparse.REMAINDER,
+ help="modify config options using the command-line",
+ )
+ args = parser.parse_args()
+ main(args)
diff --git a/python/ClipDetection/Dockerfile b/python/ClipDetection/Dockerfile
index 681b05d3..f7b120bb 100644
--- a/python/ClipDetection/Dockerfile
+++ b/python/ClipDetection/Dockerfile
@@ -1,66 +1,79 @@
-# syntax=docker/dockerfile:experimental
-
-#############################################################################
-# NOTICE #
-# #
-# This software (or technical data) was produced for the U.S. Government #
-# under contract, and is subject to the Rights in Data-General Clause #
-# 52.227-14, Alt. IV (DEC 2007). #
-# #
-# Copyright 2024 The MITRE Corporation. All Rights Reserved. #
-#############################################################################
-
-#############################################################################
-# Copyright 2024 The MITRE Corporation #
-# #
-# Licensed under the Apache License, Version 2.0 (the "License"); #
-# you may not use this file except in compliance with the License. #
-# You may obtain a copy of the License at #
-# #
-# http://www.apache.org/licenses/LICENSE-2.0 #
-# #
-# Unless required by applicable law or agreed to in writing, software #
-# distributed under the License is distributed on an "AS IS" BASIS, #
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
-# See the License for the specific language governing permissions and #
-# limitations under the License. #
-#############################################################################
-
-ARG MODELS_REGISTRY=openmpf/
-ARG BUILD_REGISTRY
-ARG BUILD_TAG=latest
-FROM ${MODELS_REGISTRY}openmpf_clip_detection_models:8.0.0 as models
-FROM ${BUILD_REGISTRY}openmpf_python_executor_ssb:${BUILD_TAG}
-
-COPY --from=models /models/ViT-B-32.pt /models/ViT-B-32.pt
-COPY --from=models /models/ViT-L-14.pt /models/ViT-L-14.pt
-
-RUN --mount=type=tmpfs,target=/var/cache/apt \
- --mount=type=tmpfs,target=/var/lib/apt/lists \
- --mount=type=tmpfs,target=/tmp \
- apt-get update; \
- DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y wget
-
-RUN pip3 install --upgrade pip
-
-RUN pip3 install ftfy regex tqdm 'setuptools<70'
-
-RUN --mount=type=tmpfs,target=/tmp \
- mkdir /tmp/CLIP; \
- wget -O- 'https://github.com/openai/CLIP/tarball/master' \
- | tar --extract --gzip --directory /tmp/CLIP; \
- cd /tmp/CLIP/*; \
- pip3 install . 'torchvision==0.14.1'
-
-ARG RUN_TESTS=false
-
-RUN --mount=target=.,readwrite \
- install-component.sh; \
- if [ "${RUN_TESTS,,}" == true ]; then python tests/test_clip.py; fi
-
-LABEL org.label-schema.license="Apache 2.0" \
- org.label-schema.name="OpenMPF CLIP Detection" \
- org.label-schema.schema-version="1.0" \
- org.label-schema.url="https://openmpf.github.io" \
- org.label-schema.vcs-url="https://github.com/openmpf/openmpf-components" \
- org.label-schema.vendor="MITRE"
+# syntax=docker/dockerfile:experimental
+
+#############################################################################
+# NOTICE #
+# #
+# This software (or technical data) was produced for the U.S. Government #
+# under contract, and is subject to the Rights in Data-General Clause #
+# 52.227-14, Alt. IV (DEC 2007). #
+# #
+# Copyright 2024 The MITRE Corporation. All Rights Reserved. #
+#############################################################################
+
+#############################################################################
+# Copyright 2024 The MITRE Corporation #
+# #
+# Licensed under the Apache License, Version 2.0 (the "License"); #
+# you may not use this file except in compliance with the License. #
+# You may obtain a copy of the License at #
+# #
+# http://www.apache.org/licenses/LICENSE-2.0 #
+# #
+# Unless required by applicable law or agreed to in writing, software #
+# distributed under the License is distributed on an "AS IS" BASIS, #
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
+# See the License for the specific language governing permissions and #
+# limitations under the License. #
+#############################################################################
+
+ARG MODELS_REGISTRY=openmpf/
+ARG BUILD_REGISTRY
+ARG BUILD_TAG=latest
+FROM ${MODELS_REGISTRY}openmpf_clip_detection_models:9.0.0-feature as models
+FROM ${BUILD_REGISTRY}openmpf_python_executor_ssb:${BUILD_TAG}
+
+COPY --from=models /models/ViT-B-32.pt /models/ViT-B-32.pt
+COPY --from=models /models/ViT-L-14.pt /models/ViT-L-14.pt
+COPY --from=models /models/model.pth.tar-50 /models/prompt_learner/model.pth.tar-50
+
+RUN --mount=type=tmpfs,target=/var/cache/apt \
+ --mount=type=tmpfs,target=/var/lib/apt/lists \
+ --mount=type=tmpfs,target=/tmp \
+ apt-get update; \
+ DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y wget
+
+RUN pip3 install --upgrade pip
+
+RUN pip3 install ftfy regex tqdm
+
+RUN pip3 install torch==1.7.1 torchvision==0.8.2 --extra-index-url https://download.pytorch.org/whl/cu102
+
+RUN --mount=type=tmpfs,target=/tmp \
+ mkdir /tmp/CLIP; \
+ wget -O- 'https://github.com/openai/CLIP/tarball/master' \
+ | tar --extract --gzip --directory /tmp/CLIP; \
+ cd /tmp/CLIP/*; \
+ pip3 install .
+
+COPY ./Dassl.pytorch/requirements.txt /tmp/requirements.txt
+RUN pip3 install --no-cache-dir -r /tmp/requirements.txt
+
+RUN --mount=target=.,readwrite \
+ cd ./Dassl.pytorch && pip3 install .
+
+COPY ./CoOp /opt/coop_src/CoOp
+
+ARG RUN_TESTS=false
+
+RUN --mount=target=.,readwrite \
+ install-component.sh; \
+ if [ "${RUN_TESTS,,}" == true ]; then python tests/test_clip.py; fi
+
+ENV PYTHONPATH="$PYTHONPATH:/opt/coop_src"
+
+LABEL org.label-schema.license="Apache 2.0" \
+ org.label-schema.name="OpenMPF CLIP Detection" \
+ org.label-schema.schema-version="1.0" \
+ org.label-schema.url="https://openmpf.github.io" \
+ org.label-schema.vcs-url="https://github.com/openmpf/openmpf-components" \
+ org.label-schema.vendor="MITRE"
diff --git a/python/ClipDetection/LICENSE b/python/ClipDetection/LICENSE
index e6289faf..b4f90184 100644
--- a/python/ClipDetection/LICENSE
+++ b/python/ClipDetection/LICENSE
@@ -23,10 +23,19 @@ this software is being used.
This software makes use of a data model derived from third party software:
---------------------------------------------------------------------------
+------------------------------------------------------------------------------
The TensorFlow implementation of the Contrastive Language-Image
Pre-Training (CLIP) model used by this component was developed by OpenAI:
http://www.github.com/openai/CLIP
-The OpenAI CLIP model is licensed under the MIT License.
\ No newline at end of file
+The OpenAI CLIP model is licensed under the MIT License.
+
+------------------------------------------------------------------------------
+
+The Python implementations of Context Optimization (CoOp) and Dassl.pytorch
+used by this component were developed by Kaiyang Zhou and others:
+https://github.com/KaiyangZhou/CoOp
+https://github.com/KaiyangZhou/Dassl.pytorch
+
+CoOp and Dassl.pytorch are licensed under the MIT License.
\ No newline at end of file
diff --git a/python/ClipDetection/NOTICE b/python/ClipDetection/NOTICE
index ae6303f5..d16224c4 100644
--- a/python/ClipDetection/NOTICE
+++ b/python/ClipDetection/NOTICE
@@ -1,7 +1,17 @@
+<<<<<<< HEAD
+# NOTICE
+
+This software (or technical data) was produced for the U.S. Government
+under contract, and is subject to the Rights in Data-General Clause
+552.227-14, Alt. IV (DEC 2007).
+
+Copyright 2023 The MITRE Corporation. All Rights Reserved.
+=======
# NOTICE
This software (or technical data) was produced for the U.S. Government
under contract, and is subject to the Rights in Data-General Clause
552.227-14, Alt. IV (DEC 2007).
-Copyright 2024 The MITRE Corporation. All Rights Reserved.
\ No newline at end of file
+Copyright 2024 The MITRE Corporation. All Rights Reserved.
+>>>>>>> origin/develop
diff --git a/python/ClipDetection/README.md b/python/ClipDetection/README.md
index 675443b5..4899d28f 100644
--- a/python/ClipDetection/README.md
+++ b/python/ClipDetection/README.md
@@ -6,28 +6,30 @@ This repository contains source code for the OpenMPF CLIP detection component. C
The following are the properties that can be specified for the component. Each property has a default value and so none of them necessarily need to be specified for processing jobs.
-- `MODEL_NAME`: Specifies the CLIP model that is loaded and used by the component. The only supported models are 'ViT-L/14' (the default model) and 'ViT-B/32'.
+- `MODEL_NAME`: Specifies the CLIP model that is loaded and used by the component, as well as allowing the component to utilize CoOp for ImageNet classification. The only supported models are 'ViT-L/14' (the default model), 'ViT-B/32', and 'CoOp'.
- `NUMBER_OF_CLASSIFICATIONS`: Specifies how many of the top classifications you want to return. The default value is set to 1, and so you'll only see the classification with the greatest confidence.
-- `CLASSIFICATION_PATH`: If specified, this allows the user to give the component a file path to their own list of classifications in a CSV file, if the COCO or ImageNet class lists aren't of interest. See below for the formatting that's required for that file.
+- `TEMPLATE_TYPE`: There are three template files that are included in the component, with the number of templates in each being 1, 7, and 80. The one template is a basic template, while the 7 and 80 come from the OpenAI team when trying to [improve performance](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb) on the ImageNet dataset. The default value is 'openai_80', while 'openai_1' and 'openai_7' are the only other valid inputs. Also this property is overridden if a `TEMPLATE_PATH` is specified.
-- `CLASSIFICATION_LIST`: Specifies whether the user wants to use the COCO or ImageNet classification list, by specifying 'coco' or 'imagenet', respectively. By default, this is set to 'coco'. Also this property is overridden if a `CLASSIFICATION_PATH` is given.
+- `TEMPLATE_PATH`: If specified, this allows the user to give the component a file path to their own list of templates. See below for the formatting that's required for that file. The OpenAI developers admitted that the process of developing templates was a lot of trial and error, so feel free to come up with your own! Also, a value of '' is required if `MODEL_NAME`='CoOp'.
-- `TEMPLATE_PATH`: If specified, this allows the user to give the component a file path to their own list of templates. See below for the formatting that's required for that file. The OpenAI developers admitted that the process of developing templates was a lot of trial and error, so feel free to come up with your own!
+- `CLASSIFICATION_LIST`: Specifies whether the user wants to use the COCO or ImageNet classification list, by specifying 'coco' or 'imagenet', respectively. By default, this is set to 'coco'. Also this property is overridden if a `CLASSIFICATION_PATH` is given, and a value of 'imagenet' is required if `MODEL_NAME`='CoOp'.
-- `TEMPLATE_TYPE`: There are three template files that are included in the component, with the number of templates in each being 1, 7, and 80. The one template is a basic template, while the 7 and 80 come from the OpenAI team when trying to [improve performance](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb) on the ImageNet dataset. The default value is 'openai_80', while 'openai_1' and 'openai_7' are the only other valid inputs. Also this property is overridden if a `TEMPLATE_PATH` is specified.
+- `CLASSIFICATION_PATH`: If specified, this allows the user to give the component a file path to their own list of classifications in a CSV file, if the COCO or ImageNet class lists aren't of interest. See below for the formatting that's required for that file. Also, a value of '' is required if `MODEL_NAME`='CoOp'.
- `ENABLE_CROPPING`: A boolean toggle to specify if the image is to be cropped into 144 images of size 224x224 which cover all areas of the original. By default, this is set to true. This technique is described in Section 7 of the paper "[Going deeper with convolutions](https://arxiv.org/abs/1409.4842)" from Szegedy, et al.
-- `ENABLE_TRITON`: A boolean toggle to specify whether the component should use a Triton inference server to process the image job. By default this is set to false.
-
-- `INCLUDE_FEATURES`: A boolean toggle to specify whether the `FEATURE` detection property is included with each detection. By default, this is set to false.
+- `ENABLE_TRITON`: A boolean toggle to specify whether the component should use a Triton inference server to process the image job. By default this is set to false. Also, a value of false is required if `MODEL_NAME`='CoOp'.
- `TRITON_SERVER`: Specifies the Triton server `:` to use for inferencing. By default, this is set to 'clip-detection-server:8001'.
+- `INCLUDE_FEATURES`: A boolean toggle to specify whether the `FEATURE` detection property is included with each detection. By default, this is set to false.
+
- `DETECTION_FRAME_BATCH_SIZE`: Specifies the batch size when processing video files. By default, this is set to 64.
+- `CUDA_DEVICE_ID`: Specifies the ID of the CUDA device that will be used to run the models. When less than 0 CUDA will be disabled.
+
## Detection Properties
Returned `ImageLocation` objects have the following members in their `detection_properties`:
@@ -41,7 +43,7 @@ Returned `ImageLocation` objects have the following members in their `detection_
# Custom Templates
-When tuning the CLIP model, it is important to have appropriate templates for what you're trying to classify. In order to write the file, put one template on each line. Use a pair of brackets, {}, where the potential classifications need to be placed. See below for example templates.
+When tuning the CLIP model, it is important to have appropriate templates for what you're trying to classify. In order to write the file, put one template on each line. Use a pair of brackets, {}, where the class names need to be placed. See below for example templates.
```
A photograph of a {}.
A {} in an open field.
@@ -49,15 +51,19 @@ A {} in an open field.
# Custom Classifications
-The need for custom classifications arose when training on the ImageNet classifications, where any different class can have many equivalent names. For example, one of the classes is "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias". We found the model to be most performant when given a single representative class title. For this case, 'great white shark' makes the most sense. The `imagenet_classification_list.csv` file gives representative titles for each class, adapted from .ipynb files on the CLIP GitHub page.
+The need for custom classifications arose when training on the ImageNet classifications, where any different class can have many equivalent names. For example, one of the classes is "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias". We found the model to be most performant when given a single representative class title. For this case, 'great white shark' makes the most sense. The `imagenet_classification_list.csv` file gives representative titles for each class, adapted from .ipynb files on the [CLIP GitHub page](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb).
-As for the format of the CSV file, it has two columns. The first being the representative name, and the second being the full name of the class. The representative name is what goes inside the brackets, {}, of the templates, and the full name is what will be used when displaying results. Below are a couple of examples of rows from the ImageNet classifications. Note that in the first example, quotes are put around the full classification name so they're easier to read and so that those commas aren't confused for the separator.
+As for the format of the CSV file, it has two columns. The first column contains the representative name, and the second contains the full name of the class. The representative name is what goes inside the brackets, {}, of the templates, and the full name is what will be used when displaying results. Below are a couple of examples of rows from the ImageNet classifications. Note that in the first example, quotes are put around the full classification name so they're easier to read and so that those commas aren't confused for the separator.
```
tench,"tench, Tinca tinca"
kite (bird of prey),kite
magpie,magpie
```
+
+# Context Optimization (CoOp)
+[Context Optimization (CoOp)](https://github.com/KaiyangZhou/CoOp) was developed by Kaiyang Zhao et al., to adapt the CLIP model to downstream datasets via prompt learning. For the ImageNet dataset (and many others), it is [shown to improve performance](https://arxiv.org/abs/2109.01134) for classification. For this component, trained text prompts have been implemented for use on the ImageNet classes. To use CoOp, make sure that the following properties are set: `MODEL_NAME`='CoOp', `CLASSIFICATION_LIST`='imagenet', `TEMPLATE_PATH`='', `CLASSIFICATION_PATH`='', and `ENABLE_TRITON`=false.
+
# Non-Triton Performance
The table below shows the performance of this component on a NVIDIA Tesla V100 32GB GPU, for varying batch sizes with both models:
| Model Name | Batch Size | Total Time (seconds) | Average Time per Batch (seconds) | Average Images per Second |
@@ -93,7 +99,7 @@ The table below shows the performance of this component with Triton on a NVIDIA
# Future Research
* Investigate using the CLIP interrogator for determining text prompts for classification.
* Investigate methods to automate the generation of text prompts.
- * [Context Optimization (CoOp)](http://arxiv.org/abs/2109.01134) and [Conditional Context Optimization (CoCoOp)](http://arxiv.org/abs/2203.05557) models a prompt's context as a set of learnable vectors that can be optimized for the classes you're looking for, with CoCoOp improving on CoOp's ability in classifying to classes unseen by CoOp in training.
+ * [Context Optimization (CoOp)](http://arxiv.org/abs/2109.01134) and [Conditional Context Optimization (CoCoOp)](http://arxiv.org/abs/2203.05557) model a prompt's context as a set of learnable vectors that can be optimized for the classes you're looking for, with CoCoOp improving on CoOp's ability in classifying to classes unseen by CoOp in training.
# Known Issues
diff --git a/python/ClipDetection/__init__.py b/python/ClipDetection/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/python/ClipDetection/clip_component/clip_component.py b/python/ClipDetection/clip_component/clip_component.py
index 78d781ca..78fe4227 100644
--- a/python/ClipDetection/clip_component/clip_component.py
+++ b/python/ClipDetection/clip_component/clip_component.py
@@ -30,6 +30,7 @@
from pkg_resources import resource_filename
from itertools import islice
from typing import Iterable, Mapping
+import argparse
from PIL import Image
import cv2
@@ -39,6 +40,7 @@
import torchvision.transforms as T
import torchvision.transforms.functional as TF
import clip
+from CoOp.train import get_trainer
import tritonclient.grpc as grpcclient
from tritonclient.utils import InferenceServerException, triton_to_np_dtype
@@ -47,7 +49,6 @@
import mpf_component_util as mpf_util
logger = logging.getLogger('ClipComponent')
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
class ClipComponent(mpf_util.ImageReaderMixin, mpf_util.VideoCaptureMixin):
detection_type = 'CLASS'
@@ -66,7 +67,7 @@ def _get_prop(job_properties, key, default_value, accept_values=[]):
return prop
def _parse_properties(self, job_properties):
- model_name = self._get_prop(job_properties, "MODEL_NAME", "ViT-L/14", ["ViT-L/14", "ViT-B/32"])
+ model_name = self._get_prop(job_properties, "MODEL_NAME", "ViT-L/14", ["ViT-L/14", "ViT-B/32", "CoOp"])
batch_size = self._get_prop(job_properties, "DETECTION_FRAME_BATCH_SIZE", 64)
classification_list = self._get_prop(job_properties, "CLASSIFICATION_LIST", 'coco', ['coco', 'imagenet'])
classification_path = os.path.expandvars(self._get_prop(job_properties, "CLASSIFICATION_PATH", ''))
@@ -77,6 +78,7 @@ def _parse_properties(self, job_properties):
template_type = self._get_prop(job_properties, "TEMPLATE_TYPE", 'openai_80', ['openai_1', 'openai_7', 'openai_80'])
template_path = os.path.expandvars(self._get_prop(job_properties, "TEMPLATE_PATH", ''))
triton_server = self._get_prop(job_properties, "TRITON_SERVER", 'clip-detection-server:8001')
+ cuda_device_id = self._get_prop(job_properties, "CUDA_DEVICE_ID", -1)
return dict(
model_name = model_name,
@@ -89,19 +91,30 @@ def _parse_properties(self, job_properties):
num_classifications = num_classifications,
template_type = template_type,
template_path = template_path,
- triton_server = triton_server
+ triton_server = triton_server,
+ cuda_device_id = cuda_device_id
)
def get_detections_from_image_reader(self, image_job, image_reader):
logger.info("Received image job: %s", image_job)
kwargs = self._parse_properties(image_job.job_properties)
+ if kwargs['cuda_device_id'] >= torch.cuda.device_count():
+ raise mpf.DetectionException(
+ f"Invalid CUDA device ID.",
+ mpf.DetectionError.INVALID_PROPERTY
+ )
+ elif kwargs['cuda_device_id'] >= 0:
+ device = torch.device(f"cuda:{kwargs['cuda_device_id']}")
+ else:
+ device = torch.device('cpu')
+
image = image_reader.get_image()
num_detections = 0
try:
- wrapper = self._get_model_wrapper(kwargs['model_name'])
- detections = wrapper.get_detections((image,), **kwargs)
+ wrapper = self._get_model_wrapper(model_name=kwargs['model_name'], kwargs=kwargs, device=device)
+ detections = wrapper.get_detections((image,), device, **kwargs)
for detection in detections:
yield detection
num_detections += 1
@@ -134,17 +147,26 @@ def get_detections_from_video_capture(self,
video_capture: mpf_util.VideoCapture) -> Iterable[mpf.VideoTrack]:
logger.info("Received video job: %s", video_job)
kwargs = self._parse_properties(video_job.job_properties)
+ if kwargs['cuda_device_id'] >= torch.cuda.device_count():
+ raise mpf.DetectionException(
+ f"Invalid CUDA device ID.",
+ mpf.DetectionError.INVALID_PROPERTY
+ )
+ elif kwargs['cuda_device_id'] >= 0:
+ device = torch.device(f"cuda:{kwargs['cuda_device_id']}")
+ else:
+ device = torch.device('cpu')
# If processing a video where each frame is cropped into 144 images, the batch size is set to one so that the crops aren't split between batches
batch_size = 1 if kwargs['enable_cropping'] else kwargs['batch_size']
batch_gen = self._batches_from_video_capture(video_capture, batch_size)
detections = []
- wrapper = self._get_model_wrapper(kwargs['model_name'])
+ wrapper = self._get_model_wrapper(model_name=kwargs['model_name'], kwargs=kwargs, device=device)
for n, batch in batch_gen:
try:
- detections += list(islice(wrapper.get_detections(batch, **kwargs), n))
+ detections += list(islice(wrapper.get_detections(batch, device, **kwargs), n))
except Exception as e:
logger.exception(f"Job failed due to: {e}")
raise
@@ -153,14 +175,157 @@ def get_detections_from_video_capture(self,
logger.info(f"Job complete. Found {len(tracks)} tracks.")
return tracks
- def _get_model_wrapper(self, model_name):
+ def _get_model_wrapper(self, model_name, kwargs, device):
if model_name not in self._model_wrappers:
- self._model_wrappers[model_name] = ClipWrapper(model_name)
+ if model_name == "CoOp":
+ self._model_wrappers['CoOp'] = CoOpWrapper(**kwargs)
+ else:
+ self._model_wrappers[model_name] = ClipWrapper(device, model_name)
return self._model_wrappers[model_name]
+class CoOpWrapper(object):
+ def __init__(self, **kwargs):
+ if (kwargs['classification_list'] == 'coco') or (kwargs['template_path'] != '') or (kwargs['classification_path'] != '') or (kwargs['enable_triton'] == True):
+ raise mpf.DetectionException(
+ f"Properties incompatible with CoOp. Make sure that CLASSIFICATION_LIST='imagenet', TEMPLATE_PATH='', CLASSIFICATION_PATH='', and ENABLE_TRITON=False.",
+ mpf.DetectionError.INVALID_PROPERTY
+ )
+ self._manual_args = self._get_coop_args()
+ if kwargs['cuda_device_id'] >= 0:
+ self._manual_args.insert(0, '--cuda')
+
+ self.args = self._create_arg_parser(self._manual_args)
+ self._class_mapping = self._get_mapping_from_classifications(os.path.realpath(resource_filename(__name__, f'data/imagenet_classification_list.csv')))
+ self.classnames = self._class_mapping.keys()
+ # Create trainer object
+ print("Creating trainer...")
+ self.trainer = get_trainer(self.args, self.classnames, kwargs['cuda_device_id'])
+ print("Trainer created.")
+ self.trainer.load_model(self.args.model_dir, epoch = self.args.load_epoch)
+
+ def get_detections(self, images, device, **kwargs):
+ # Preprocess image
+ self._preprocessor = ImagePreprocessor(enable_cropping=False, image_size=224)
+ images = [Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) for image in images]
+ image_sizes = [image.size for image in images]
+ torch_imgs = torch.stack([self._preprocessor.preprocess(image).squeeze(0) for image in images]).to(device)
+
+ # Pass image through model
+ output, image_features = self.trainer.test(images=torch_imgs)
+
+ softmax = torch.nn.Softmax(dim=1)(output)
+ values, indices = softmax.topk(kwargs['num_classifications'])
+
+ for detection_values, detection_indices, image_size in zip(values, indices, image_sizes):
+ classification_list = []
+ classification_confidence_list = []
+ count = 0
+ for value, index in zip(detection_values, detection_indices):
+ if count >= kwargs['num_classifications']:
+ break
+ class_name = self._class_mapping[list(self._class_mapping.keys())[int(index)]]
+ if class_name not in classification_list:
+ classification_list.append(class_name)
+ classification_confidence_list.append(str(value.item()))
+ count += 1
+
+ classification_list = '; '.join(classification_list)
+ classification_confidence_list = '; '.join(classification_confidence_list)
+
+ detection_properties = {
+ "CLASSIFICATION": classification_list.split('; ')[0],
+ "CLASSIFICATION CONFIDENCE LIST": classification_confidence_list,
+ "CLASSIFICATION LIST": classification_list
+ }
+
+ if kwargs['include_features']:
+ detection_properties['FEATURE'] = base64.b64encode(image_features.cpu().numpy()).decode()
+
+ yield mpf.ImageLocation(
+ x_left_upper = 0,
+ y_left_upper = 0,
+ width = image_size[0],
+ height = image_size[1],
+ confidence = float(classification_confidence_list.split('; ')[0]),
+ detection_properties = detection_properties
+ )
+
+ def _create_arg_parser(self, manual_args):
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--root", type=str, default="", help="path to dataset")
+ parser.add_argument("--output-dir", type=str, default="", help="output directory")
+ parser.add_argument(
+ "--resume",
+ type=str,
+ default="",
+ help="checkpoint directory (from which the training resumes)",
+ )
+ parser.add_argument(
+ "--seed", type=int, default=-1, help="only positive value enables a fixed seed"
+ )
+ parser.add_argument(
+ "--source-domains", type=str, nargs="+", help="source domains for DA/DG"
+ )
+ parser.add_argument(
+ "--target-domains", type=str, nargs="+", help="target domains for DA/DG"
+ )
+ parser.add_argument(
+ "--transforms", type=str, nargs="+", help="data augmentation methods"
+ )
+ parser.add_argument(
+ "--config-file", type=str, default="", help="path to config file"
+ )
+ parser.add_argument(
+ "--dataset-config-file",
+ type=str,
+ default="",
+ help="path to config file for dataset setup",
+ )
+ parser.add_argument("--trainer", type=str, default="", help="name of trainer")
+ parser.add_argument("--backbone", type=str, default="", help="name of CNN backbone")
+ parser.add_argument("--head", type=str, default="", help="name of head")
+ parser.add_argument("--eval-only", action="store_true", help="evaluation only")
+ parser.add_argument(
+ "--model-dir",
+ type=str,
+ default="",
+ help="load model from this directory for eval-only mode",
+ )
+ parser.add_argument(
+ "--load-epoch", type=int, help="load model weights at this epoch for evaluation"
+ )
+ parser.add_argument(
+ "--no-train", action="store_true", help="do not call trainer.train()"
+ )
+ parser.add_argument(
+ "opts",
+ default=None,
+ nargs=argparse.REMAINDER,
+ help="modify config options using the command-line",
+ )
+ parser.add_argument("--cuda", action="store_true", help="enable use of CUDA.")
+ args = parser.parse_args(manual_args)
+ return args
+
+ @staticmethod
+ def _get_mapping_from_classifications(classification_path: str) -> Mapping[str, str]:
+ with open(classification_path) as csvfile:
+ mapping = {}
+ csvreader = csv.reader(csvfile)
+ for row in csvreader:
+ mapping[row[0].strip()] = row[1].strip()
+
+ return mapping
+
+ @staticmethod
+ def _get_coop_args():
+ with open(os.path.realpath(resource_filename(__name__, 'data/coop_args.txt'))) as f:
+ args = f.read().strip().split()
+ return args
+
class ClipWrapper(object):
- def __init__(self, model_name='ViT-L/14'):
+ def __init__(self, device, model_name='ViT-L/14'):
logger.info("Loading model...")
model, _ = clip.load(model_name, device=device, download_root='/models')
logger.info("Model loaded.")
@@ -179,15 +344,17 @@ def __init__(self, model_name='ViT-L/14'):
self._text_features = None
self._inferencing_server = None
+ self._device = device
- def get_detections(self, images, **kwargs) -> Iterable[mpf.ImageLocation]:
+ def get_detections(self, images, device, **kwargs) -> Iterable[mpf.ImageLocation]:
+ self._device = device
templates_changed = self._check_template_list(kwargs['template_path'], kwargs['template_type'])
self._check_class_list(kwargs['classification_path'], kwargs['classification_list'], templates_changed)
self._preprocessor = ImagePreprocessor(kwargs['enable_cropping'], self._input_resolution)
images = [Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) for image in images]
image_sizes = [image.size for image in images]
- torch_imgs = torch.stack([self._preprocessor.preprocess(image).squeeze(0) for image in images]).to(device)
+ torch_imgs = torch.stack([self._preprocessor.preprocess(image).squeeze(0) for image in images]).to(self._device)
if kwargs['enable_cropping']:
torch_imgs = torch_imgs.squeeze(0)
@@ -198,7 +365,7 @@ def get_detections(self, images, **kwargs) -> Iterable[mpf.ImageLocation]:
self._inferencing_server = CLIPInferencingServer(kwargs['triton_server'], kwargs['model_name'])
results = self._inferencing_server.get_responses(torch_imgs)
- image_features = torch.Tensor(np.copy(results)).squeeze(0).to(device=device)
+ image_features = torch.Tensor(np.copy(results)).squeeze(0).to(self._device)
else:
with torch.no_grad():
image_features = self._model.encode_image(torch_imgs).float()
@@ -206,7 +373,7 @@ def get_detections(self, images, **kwargs) -> Iterable[mpf.ImageLocation]:
with torch.no_grad():
image_features /= image_features.norm(dim=-1, keepdim=True)
- similarity = (100.0 * image_features @ self._text_features).softmax(dim=-1).to(device)
+ similarity = (100.0 * image_features @ self._text_features).softmax(dim=-1).to(self._device)
if kwargs['enable_cropping']:
similarity = torch.mean(similarity, 0).unsqueeze(0)
@@ -300,7 +467,7 @@ def _check_class_list(self, classification_path: str, classification_list: str,
try:
logger.info("Updating classifications...")
- self._class_mapping = self._get_mapping_from_classifications(classification_path)
+ self._class_mapping = self._get_mapping_from_classifications(self._classification_path)
logger.info("Classifications updated.")
except Exception:
raise mpf.DetectionException(
@@ -313,13 +480,13 @@ def _check_class_list(self, classification_path: str, classification_list: str,
text_features = []
for label in self._class_mapping.keys():
text_phrases = [template.format(label) for template in self._templates]
- text_tokens = clip.tokenize(text_phrases).to(device)
+ text_tokens = clip.tokenize(text_phrases).to(self._device)
text_embeddings = self._model.encode_text(text_tokens)
text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)
text_embedding = text_embeddings.mean(dim=0)
text_embedding /= text_embedding.norm()
text_features.append(text_embedding)
- self._text_features = torch.stack(text_features, dim=1).float().to(device)
+ self._text_features = torch.stack(text_features, dim=1).float().to(self._device)
logger.info("Text embeddings created.")
@staticmethod
diff --git a/python/ClipDetection/clip_component/data/coco_classification_list.csv b/python/ClipDetection/clip_component/data/coco_classification_list.csv
index e67ec89f..29ebda3a 100644
--- a/python/ClipDetection/clip_component/data/coco_classification_list.csv
+++ b/python/ClipDetection/clip_component/data/coco_classification_list.csv
@@ -1,80 +1,80 @@
-person,person
-bicycle,bicycle
-car,car
-motorcycle,motorcycle
-airplane,airplane
-bus,bus
-train,train
-truck,truck
-boat,boat
-traffic light,traffic light
-fire hydrant,fire hydrant
-stop sign,stop sign
-parking meter,parking meter
-bench,bench
-bird,bird
-cat,cat
-dog,dog
-horse,horse
-sheep,sheep
-cow,cow
-elephant,elephant
-bear,bear
-zebra,zebra
-giraffe,giraffe
-backpack,backpack
-umbrella,umbrella
-handbag,handbag
-tie,tie
-suitcase,suitcase
-frisbee,frisbee
-skis,skis
-snowboard,snowboard
-sports ball,sports ball
-kite,kite
-baseball bat,baseball bat
-baseball glove,baseball glove
-skateboard,skateboard
-surfboard,surfboard
-tennis racket,tennis racket
-bottle,bottle
-wine glass,wine glass
-cup,cup
-fork,fork
-knife,knife
-spoon,spoon
-bowl,bowl
-banana,banana
-apple,apple
-sandwich,sandwich
-orange,orange
-broccoli,broccoli
-carrot,carrot
-hot dog,hot dog
-pizza,pizza
-donut,donut
-cake,cake
-chair,chair
-couch,couch
-potted plant,potted plant
-bed,bed
-dining table,dining table
-toilet,toilet
-tv,tv
-laptop,laptop
-mouse,mouse
-remote,remote
-keyboard,keyboard
-cell phone,cell phone
-microwave,microwave
-oven,oven
-toaster,toaster
-sink,sink
-refrigerator,refrigerator
-book,book
-clock,clock
-vase,vase
-scissors,scissors
-teddy bear,teddy bear
-hair drier,hair drier
-toothbrush,toothbrush
+person,person
+bicycle,bicycle
+car,car
+motorcycle,motorcycle
+airplane,airplane
+bus,bus
+train,train
+truck,truck
+boat,boat
+traffic light,traffic light
+fire hydrant,fire hydrant
+stop sign,stop sign
+parking meter,parking meter
+bench,bench
+bird,bird
+cat,cat
+dog,dog
+horse,horse
+sheep,sheep
+cow,cow
+elephant,elephant
+bear,bear
+zebra,zebra
+giraffe,giraffe
+backpack,backpack
+umbrella,umbrella
+handbag,handbag
+tie,tie
+suitcase,suitcase
+frisbee,frisbee
+skis,skis
+snowboard,snowboard
+sports ball,sports ball
+kite,kite
+baseball bat,baseball bat
+baseball glove,baseball glove
+skateboard,skateboard
+surfboard,surfboard
+tennis racket,tennis racket
+bottle,bottle
+wine glass,wine glass
+cup,cup
+fork,fork
+knife,knife
+spoon,spoon
+bowl,bowl
+banana,banana
+apple,apple
+sandwich,sandwich
+orange,orange
+broccoli,broccoli
+carrot,carrot
+hot dog,hot dog
+pizza,pizza
+donut,donut
+cake,cake
+chair,chair
+couch,couch
+potted plant,potted plant
+bed,bed
+dining table,dining table
+toilet,toilet
+tv,tv
+laptop,laptop
+mouse,mouse
+remote,remote
+keyboard,keyboard
+cell phone,cell phone
+microwave,microwave
+oven,oven
+toaster,toaster
+sink,sink
+refrigerator,refrigerator
+book,book
+clock,clock
+vase,vase
+scissors,scissors
+teddy bear,teddy bear
+hair drier,hair drier
+toothbrush,toothbrush
diff --git a/python/ClipDetection/clip_component/data/coop_args.txt b/python/ClipDetection/clip_component/data/coop_args.txt
new file mode 100644
index 00000000..fda4be90
--- /dev/null
+++ b/python/ClipDetection/clip_component/data/coop_args.txt
@@ -0,0 +1 @@
+--seed 1 --trainer CoOp --config-file /opt/coop_src/CoOp/configs/trainers/CoOp/vit_l14_ep50.yaml --model-dir /models --load-epoch 50 --eval-only TRAINER.COOP.N_CTX 16 TRAINER.COOP.CSC False TRAINER.COOP.CLASS_TOKEN_POSITION end
\ No newline at end of file
diff --git a/python/ClipDetection/clip_component/data/eighty_templates.txt b/python/ClipDetection/clip_component/data/eighty_templates.txt
index 526e39c0..9026bf5e 100644
--- a/python/ClipDetection/clip_component/data/eighty_templates.txt
+++ b/python/ClipDetection/clip_component/data/eighty_templates.txt
@@ -1,80 +1,80 @@
-a bad photo of a {}.
-a photo of many {}.
-a sculpture of a {}.
-a photo of the hard to see {}.
-a low resolution photo of the {}.
-a rendering of a {}.
-graffiti of a {}.
-a bad photo of the {}.
-a cropped photo of the {}.
-a tattoo of a {}.
-the embroidered {}.
-a photo of a hard to see {}.
-a bright photo of a {}.
-a photo of a clean {}.
-a photo of a dirty {}.
-a dark photo of the {}.
-a drawing of a {}.
-a photo of my {}.
-the plastic {}.
-a photo of the cool {}.
-a close-up photo of a {}.
-a black and white photo of the {}.
-a painting of the {}.
-a painting of a {}.
-a pixelated photo of the {}.
-a sculpture of the {}.
-a bright photo of the {}.
-a cropped photo of a {}.
-a plastic {}.
-a photo of the dirty {}.
-a jpeg corrupted photo of a {}.
-a blurry photo of the {}.
-a photo of the {}.
-a good photo of the {}.
-a rendering of the {}.
-a {} in a video game.
-a photo of one {}.
-a doodle of a {}.
-a close-up photo of the {}.
-a photo of a {}.
-the origami {}.
-the {} in a video game.
-a sketch of a {}.
-a doodle of the {}.
-a origami {}.
-a low resolution photo of a {}.
-the toy {}.
-a rendition of the {}.
-a photo of the clean {}.
-a photo of a large {}.
-a rendition of a {}.
-a photo of a nice {}.
-a photo of a weird {}.
-a blurry photo of a {}.
-a cartoon {}.
-art of a {}.
-a sketch of the {}.
-a embroidered {}.
-a pixelated photo of a {}.
-itap of the {}.
-a jpeg corrupted photo of the {}.
-a good photo of a {}.
-a plushie {}.
-a photo of the nice {}.
-a photo of the small {}.
-a photo of the weird {}.
-the cartoon {}.
-art of the {}.
-a drawing of the {}.
-a photo of the large {}.
-a black and white photo of a {}.
-the plushie {}.
-a dark photo of a {}.
-itap of a {}.
-graffiti of the {}.
-a toy {}.
-itap of my {}.
-a photo of a cool {}.
-a photo of a small {}.
+a bad photo of a {}.
+a photo of many {}.
+a sculpture of a {}.
+a photo of the hard to see {}.
+a low resolution photo of the {}.
+a rendering of a {}.
+graffiti of a {}.
+a bad photo of the {}.
+a cropped photo of the {}.
+a tattoo of a {}.
+the embroidered {}.
+a photo of a hard to see {}.
+a bright photo of a {}.
+a photo of a clean {}.
+a photo of a dirty {}.
+a dark photo of the {}.
+a drawing of a {}.
+a photo of my {}.
+the plastic {}.
+a photo of the cool {}.
+a close-up photo of a {}.
+a black and white photo of the {}.
+a painting of the {}.
+a painting of a {}.
+a pixelated photo of the {}.
+a sculpture of the {}.
+a bright photo of the {}.
+a cropped photo of a {}.
+a plastic {}.
+a photo of the dirty {}.
+a jpeg corrupted photo of a {}.
+a blurry photo of the {}.
+a photo of the {}.
+a good photo of the {}.
+a rendering of the {}.
+a {} in a video game.
+a photo of one {}.
+a doodle of a {}.
+a close-up photo of the {}.
+a photo of a {}.
+the origami {}.
+the {} in a video game.
+a sketch of a {}.
+a doodle of the {}.
+a origami {}.
+a low resolution photo of a {}.
+the toy {}.
+a rendition of the {}.
+a photo of the clean {}.
+a photo of a large {}.
+a rendition of a {}.
+a photo of a nice {}.
+a photo of a weird {}.
+a blurry photo of a {}.
+a cartoon {}.
+art of a {}.
+a sketch of the {}.
+a embroidered {}.
+a pixelated photo of a {}.
+itap of the {}.
+a jpeg corrupted photo of the {}.
+a good photo of a {}.
+a plushie {}.
+a photo of the nice {}.
+a photo of the small {}.
+a photo of the weird {}.
+the cartoon {}.
+art of the {}.
+a drawing of the {}.
+a photo of the large {}.
+a black and white photo of a {}.
+the plushie {}.
+a dark photo of a {}.
+itap of a {}.
+graffiti of the {}.
+a toy {}.
+itap of my {}.
+a photo of a cool {}.
+a photo of a small {}.
a tattoo of the {}.
\ No newline at end of file
diff --git a/python/ClipDetection/clip_component/data/imagenet_classification_list.csv b/python/ClipDetection/clip_component/data/imagenet_classification_list.csv
index 6de2c296..e19b0a7d 100644
--- a/python/ClipDetection/clip_component/data/imagenet_classification_list.csv
+++ b/python/ClipDetection/clip_component/data/imagenet_classification_list.csv
@@ -1,1000 +1,1000 @@
-tench,"tench, Tinca tinca"
-goldfish,"goldfish, Carassius auratus"
-great white shark,"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias"
-tiger shark,"tiger shark, Galeocerdo cuvieri"
-hammerhead shark,"hammerhead, hammerhead shark"
-electric ray,"electric ray, crampfish, numbfish, torpedo"
-stingray,stingray
-rooster,cock
-hen,hen
-ostrich,"ostrich, Struthio camelus"
-brambling,"brambling, Fringilla montifringilla"
-goldfinch,"goldfinch, Carduelis carduelis"
-house finch,"house finch, linnet, Carpodacus mexicanus"
-junco,"junco, snowbird"
-indigo bunting,"indigo bunting, indigo finch, indigo bird, Passerina cyanea"
-American robin,"robin, American robin, Turdus migratorius"
-bulbul,bulbul
-jay,jay
-magpie,magpie
-chickadee,chickadee
-American dipper,"water ouzel, dipper"
-kite (bird of prey),kite
-bald eagle,"bald eagle, American eagle, Haliaeetus leucocephalus"
-vulture,vulture
-great grey owl,"great grey owl, great gray owl, Strix nebulosa"
-fire salamander,"European fire salamander, Salamandra salamandra"
-smooth newt,"common newt, Triturus vulgaris"
-newt,eft
-spotted salamander,"spotted salamander, Ambystoma maculatum"
-axolotl,"axolotl, mud puppy, Ambystoma mexicanum"
-American bullfrog,"bullfrog, Rana catesbeiana"
-tree frog,"tree frog, tree-frog"
-tailed frog,"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui"
-loggerhead sea turtle,"loggerhead, loggerhead turtle, Caretta caretta"
-leatherback sea turtle,"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea"
-mud turtle,mud turtle
-terrapin,terrapin
-box turtle,"box turtle, box tortoise"
-banded gecko,banded gecko
-green iguana,"common iguana, iguana, Iguana iguana"
-Carolina anole,"American chameleon, anole, Anolis carolinensis"
-desert grassland whiptail lizard,"whiptail, whiptail lizard"
-agama,agama
-frilled-necked lizard,"frilled lizard, Chlamydosaurus kingi"
-alligator lizard,alligator lizard
-Gila monster,"Gila monster, Heloderma suspectum"
-European green lizard,"green lizard, Lacerta viridis"
-chameleon,"African chameleon, Chamaeleo chamaeleon"
-Komodo dragon,"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis"
-Nile crocodile,"African crocodile, Nile crocodile, Crocodylus niloticus"
-American alligator,"American alligator, Alligator mississipiensis"
-triceratops,triceratops
-worm snake,"thunder snake, worm snake, Carphophis amoenus"
-ring-necked snake,"ringneck snake, ring-necked snake, ring snake"
-eastern hog-nosed snake,"hognose snake, puff adder, sand viper"
-smooth green snake,"green snake, grass snake"
-kingsnake,"king snake, kingsnake"
-garter snake,"garter snake, grass snake"
-water snake,water snake
-vine snake,vine snake
-night snake,"night snake, Hypsiglena torquata"
-boa constrictor,"boa constrictor, Constrictor constrictor"
-African rock python,"rock python, rock snake, Python sebae"
-Indian cobra,"Indian cobra, Naja naja"
-green mamba,green mamba
-sea snake,sea snake
-Saharan horned viper,"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus"
-eastern diamondback rattlesnake,"diamondback, diamondback rattlesnake, Crotalus adamanteus"
-sidewinder rattlesnake,"sidewinder, horned rattlesnake, Crotalus cerastes"
-trilobite,trilobite
-harvestman,"harvestman, daddy longlegs, Phalangium opilio"
-scorpion,scorpion
-yellow garden spider,"black and gold garden spider, Argiope aurantia"
-barn spider,"barn spider, Araneus cavaticus"
-European garden spider,"garden spider, Aranea diademata"
-southern black widow,"black widow, Latrodectus mactans"
-tarantula,tarantula
-wolf spider,"wolf spider, hunting spider"
-tick,tick
-centipede,centipede
-black grouse,black grouse
-ptarmigan,ptarmigan
-ruffed grouse,"ruffed grouse, partridge, Bonasa umbellus"
-prairie grouse,"prairie chicken, prairie grouse, prairie fowl"
-peafowl,peacock
-quail,quail
-partridge,partridge
-african grey parrot,"African grey, African gray, Psittacus erithacus"
-macaw,macaw
-sulphur-crested cockatoo,"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita"
-lorikeet,lorikeet
-coucal,coucal
-bee eater,bee eater
-hornbill,hornbill
-hummingbird,hummingbird
-jacamar,jacamar
-toucan,toucan
-duck,drake
-red-breasted merganser,"red-breasted merganser, Mergus serrator"
-goose,goose
-black swan,"black swan, Cygnus atratus"
-tusker,tusker
-echidna,"echidna, spiny anteater, anteater"
-platypus,"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus"
-wallaby,"wallaby, brush kangaroo"
-koala,"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus"
-wombat,wombat
-jellyfish,jellyfish
-sea anemone,"sea anemone, anemone"
-brain coral,brain coral
-flatworm,"flatworm, platyhelminth"
-nematode,"nematode, nematode worm, roundworm"
-conch,conch
-snail,snail
-slug,slug
-sea slug,"sea slug, nudibranch"
-chiton,"chiton, coat-of-mail shell, sea cradle, polyplacophore"
-chambered nautilus,"chambered nautilus, pearly nautilus, nautilus"
-Dungeness crab,"Dungeness crab, Cancer magister"
-rock crab,"rock crab, Cancer irroratus"
-fiddler crab,fiddler crab
-red king crab,"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica"
-American lobster,"American lobster, Northern lobster, Maine lobster, Homarus americanus"
-spiny lobster,"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish"
-crayfish,"crayfish, crawfish, crawdad, crawdaddy"
-hermit crab,hermit crab
-isopod,isopod
-white stork,"white stork, Ciconia ciconia"
-black stork,"black stork, Ciconia nigra"
-spoonbill,spoonbill
-flamingo,flamingo
-little blue heron,"little blue heron, Egretta caerulea"
-great egret,"American egret, great white heron, Egretta albus"
-bittern bird,bittern
-crane bird,crane bird
-limpkin,"limpkin, Aramus pictus"
-common gallinule,"European gallinule, Porphyrio porphyrio"
-American coot,"American coot, marsh hen, mud hen, water hen, Fulica americana"
-bustard,bustard
-ruddy turnstone,"ruddy turnstone, Arenaria interpres"
-dunlin,"red-backed sandpiper, dunlin, Erolia alpina"
-common redshank,"redshank, Tringa totanus"
-dowitcher,dowitcher
-oystercatcher,"oystercatcher, oyster catcher"
-pelican,pelican
-king penguin,"king penguin, Aptenodytes patagonica"
-albatross,"albatross, mollymawk"
-grey whale,"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus"
-killer whale,"killer whale, killer, orca, grampus, sea wolf, Orcinus orca"
-dugong,"dugong, Dugong dugon"
-sea lion,sea lion
-Chihuahua,Chihuahua
-Japanese Chin,Japanese spaniel
-Maltese,"Maltese dog, Maltese terrier, Maltese"
-Pekingese,"Pekinese, Pekingese, Peke"
-Shih Tzu,Shih-Tzu
-King Charles Spaniel,Blenheim spaniel
-Papillon,papillon
-toy terrier,toy terrier
-Rhodesian Ridgeback,Rhodesian ridgeback
-Afghan Hound,"Afghan hound, Afghan"
-Basset Hound,"basset, basset hound"
-Beagle,beagle
-Bloodhound,"bloodhound, sleuthhound"
-Bluetick Coonhound,bluetick
-Black and Tan Coonhound,black-and-tan coonhound
-Treeing Walker Coonhound,"Walker hound, Walker foxhound"
-English foxhound,English foxhound
-Redbone Coonhound,redbone
-borzoi,"borzoi, Russian wolfhound"
-Irish Wolfhound,Irish wolfhound
-Italian Greyhound,Italian greyhound
-Whippet,whippet
-Ibizan Hound,"Ibizan hound, Ibizan Podenco"
-Norwegian Elkhound,"Norwegian elkhound, elkhound"
-Otterhound,"otterhound, otter hound"
-Saluki,"Saluki, gazelle hound"
-Scottish Deerhound,"Scottish deerhound, deerhound"
-Weimaraner,Weimaraner
-Staffordshire Bull Terrier,"Staffordshire bullterrier, Staffordshire bull terrier"
-American Staffordshire Terrier,"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier"
-Bedlington Terrier,Bedlington terrier
-Border Terrier,Border terrier
-Kerry Blue Terrier,Kerry blue terrier
-Irish Terrier,Irish terrier
-Norfolk Terrier,Norfolk terrier
-Norwich Terrier,Norwich terrier
-Yorkshire Terrier,Yorkshire terrier
-Wire Fox Terrier,wire-haired fox terrier
-Lakeland Terrier,Lakeland terrier
-Sealyham Terrier,"Sealyham terrier, Sealyham"
-Airedale Terrier,"Airedale, Airedale terrier"
-Cairn Terrier,"cairn, cairn terrier"
-Australian Terrier,Australian terrier
-Dandie Dinmont Terrier,"Dandie Dinmont, Dandie Dinmont terrier"
-Boston Terrier,"Boston bull, Boston terrier"
-Miniature Schnauzer,miniature schnauzer
-Giant Schnauzer,giant schnauzer
-Standard Schnauzer,standard schnauzer
-Scottish Terrier,"Scotch terrier, Scottish terrier, Scottie"
-Tibetan Terrier,"Tibetan terrier, chrysanthemum dog"
-Australian Silky Terrier,"silky terrier, Sydney silky"
-Soft-coated Wheaten Terrier,soft-coated wheaten terrier
-West Highland White Terrier,West Highland white terrier
-Lhasa Apso,"Lhasa, Lhasa apso"
-Flat-Coated Retriever,flat-coated retriever
-Curly-coated Retriever,curly-coated retriever
-Golden Retriever,golden retriever
-Labrador Retriever,Labrador retriever
-Chesapeake Bay Retriever,Chesapeake Bay retriever
-German Shorthaired Pointer,German short-haired pointer
-Vizsla,"vizsla, Hungarian pointer"
-English Setter,English setter
-Irish Setter,"Irish setter, red setter"
-Gordon Setter,Gordon setter
-Brittany dog,Brittany spaniel
-Clumber Spaniel,"clumber, clumber spaniel"
-English Springer Spaniel,"English springer, English springer spaniel"
-Welsh Springer Spaniel,Welsh springer spaniel
-Cocker Spaniel,"cocker spaniel, English cocker spaniel, cocker"
-Sussex Spaniel,Sussex spaniel
-Irish Water Spaniel,Irish water spaniel
-Kuvasz,kuvasz
-Schipperke,schipperke
-Groenendael dog,groenendael
-Malinois,malinois
-Briard,briard
-Australian Kelpie,kelpie
-Komondor,komondor
-Old English Sheepdog,"Old English sheepdog, bobtail"
-Shetland Sheepdog,"Shetland sheepdog, Shetland sheep dog, Shetland"
-collie,collie
-Border Collie,Border collie
-Bouvier des Flandres dog,"Bouvier des Flandres, Bouviers des Flandres"
-Rottweiler,Rottweiler
-German Shepherd Dog,"German shepherd, German shepherd dog, German police dog, alsatian"
-Dobermann,"Doberman, Doberman pinscher"
-Miniature Pinscher,miniature pinscher
-Greater Swiss Mountain Dog,Greater Swiss Mountain dog
-Bernese Mountain Dog,Bernese mountain dog
-Appenzeller Sennenhund,Appenzeller
-Entlebucher Sennenhund,EntleBucher
-Boxer,boxer
-Bullmastiff,bull mastiff
-Tibetan Mastiff,Tibetan mastiff
-French Bulldog,French bulldog
-Great Dane,Great Dane
-St. Bernard,"Saint Bernard, St Bernard"
-husky,"Eskimo dog, husky"
-Alaskan Malamute,"malamute, malemute, Alaskan malamute"
-Siberian Husky,Siberian husky
-Dalmatian,"dalmatian, coach dog, carriage dog"
-Affenpinscher,"affenpinscher, monkey pinscher, monkey dog"
-Basenji,basenji
-pug,"pug, pug-dog"
-Leonberger,Leonberg
-Newfoundland dog,"Newfoundland, Newfoundland dog"
-Great Pyrenees dog,Great Pyrenees
-Samoyed,"Samoyed, Samoyede"
-Pomeranian,Pomeranian
-Chow Chow,"chow, chow chow"
-Keeshond,keeshond
-brussels griffon,Brabancon griffon
-Pembroke Welsh Corgi,"Pembroke, Pembroke Welsh corgi"
-Cardigan Welsh Corgi,"Cardigan, Cardigan Welsh corgi"
-Toy Poodle,toy poodle
-Miniature Poodle,miniature poodle
-Standard Poodle,standard poodle
-Mexican hairless dog (xoloitzcuintli),Mexican hairless
-grey wolf,"timber wolf, grey wolf, gray wolf, Canis lupus"
-Alaskan tundra wolf,"white wolf, Arctic wolf, Canis lupus tundrarum"
-red wolf or maned wolf,"red wolf, maned wolf, Canis rufus, Canis niger"
-coyote,"coyote, prairie wolf, brush wolf, Canis latrans"
-dingo,"dingo, warrigal, warragal, Canis dingo"
-dhole,"dhole, Cuon alpinus"
-African wild dog,"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus"
-hyena,"hyena, hyaena"
-red fox,"red fox, Vulpes vulpes"
-kit fox,"kit fox, Vulpes macrotis"
-Arctic fox,"Arctic fox, white fox, Alopex lagopus"
-grey fox,"grey fox, gray fox, Urocyon cinereoargenteus"
-tabby cat,"tabby, tabby cat"
-tiger cat,tiger cat
-Persian cat,Persian cat
-Siamese cat,"Siamese cat, Siamese"
-Egyptian Mau,Egyptian cat
-cougar,"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor"
-lynx,"lynx, catamount"
-leopard,"leopard, Panthera pardus"
-snow leopard,"snow leopard, ounce, Panthera uncia"
-jaguar,"jaguar, panther, Panthera onca, Felis onca"
-lion,"lion, king of beasts, Panthera leo"
-tiger,"tiger, Panthera tigris"
-cheetah,"cheetah, chetah, Acinonyx jubatus"
-brown bear,"brown bear, bruin, Ursus arctos"
-American black bear,"American black bear, black bear, Ursus americanus, Euarctos americanus"
-polar bear,"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus"
-sloth bear,"sloth bear, Melursus ursinus, Ursus ursinus"
-mongoose,mongoose
-meerkat,"meerkat, mierkat"
-tiger beetle,tiger beetle
-ladybug,"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle"
-ground beetle,"ground beetle, carabid beetle"
-longhorn beetle,"long-horned beetle, longicorn, longicorn beetle"
-leaf beetle,"leaf beetle, chrysomelid"
-dung beetle,dung beetle
-rhinoceros beetle,rhinoceros beetle
-weevil,weevil
-fly,fly
-bee,bee
-ant,"ant, emmet, pismire"
-grasshopper,"grasshopper, hopper"
-cricket insect,cricket
-stick insect,"walking stick, walkingstick, stick insect"
-cockroach,"cockroach, roach"
-praying mantis,"mantis, mantid"
-cicada,"cicada, cicala"
-leafhopper,leafhopper
-lacewing,"lacewing, lacewing fly"
-dragonfly,"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk"
-damselfly,damselfly
-red admiral butterfly,admiral
-ringlet butterfly,"ringlet, ringlet butterfly"
-monarch butterfly,"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus"
-small white butterfly,cabbage butterfly
-sulphur butterfly,"sulphur butterfly, sulfur butterfly"
-gossamer-winged butterfly,"lycaenid, lycaenid butterfly"
-starfish,"starfish, sea star"
-sea urchin,sea urchin
-sea cucumber,"sea cucumber, holothurian"
-cottontail rabbit,"wood rabbit, cottontail, cottontail rabbit"
-hare,hare
-Angora rabbit,"Angora, Angora rabbit"
-hamster,hamster
-porcupine,"porcupine, hedgehog"
-fox squirrel,"fox squirrel, eastern fox squirrel, Sciurus niger"
-marmot,marmot
-beaver,beaver
-guinea pig,"guinea pig, Cavia cobaya"
-common sorrel horse,sorrel
-zebra,zebra
-pig,"hog, pig, grunter, squealer, Sus scrofa"
-wild boar,"wild boar, boar, Sus scrofa"
-warthog,warthog
-hippopotamus,"hippopotamus, hippo, river horse, Hippopotamus amphibius"
-ox,ox
-water buffalo,"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis"
-bison,bison
-ram (adult male sheep),"ram, tup"
-bighorn sheep,"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis"
-Alpine ibex,"ibex, Capra ibex"
-hartebeest,hartebeest
-impala (antelope),"impala, Aepyceros melampus"
-gazelle,gazelle
-arabian camel,"Arabian camel, dromedary, Camelus dromedarius"
-llama,llama
-weasel,weasel
-mink,mink
-European polecat,"polecat, fitch, foulmart, foumart, Mustela putorius"
-black-footed ferret,"black-footed ferret, ferret, Mustela nigripes"
-otter,otter
-skunk,"skunk, polecat, wood pussy"
-badger,badger
-armadillo,armadillo
-three-toed sloth,"three-toed sloth, ai, Bradypus tridactylus"
-orangutan,"orangutan, orang, orangutang, Pongo pygmaeus"
-gorilla,"gorilla, Gorilla gorilla"
-chimpanzee,"chimpanzee, chimp, Pan troglodytes"
-gibbon,"gibbon, Hylobates lar"
-siamang,"siamang, Hylobates syndactylus, Symphalangus syndactylus"
-guenon,"guenon, guenon monkey"
-patas monkey,"patas, hussar monkey, Erythrocebus patas"
-baboon,baboon
-macaque,macaque
-langur,langur
-black-and-white colobus,"colobus, colobus monkey"
-proboscis monkey,"proboscis monkey, Nasalis larvatus"
-marmoset,marmoset
-white-headed capuchin,"capuchin, ringtail, Cebus capucinus"
-howler monkey,"howler monkey, howler"
-titi monkey,"titi, titi monkey"
-Geoffroy's spider monkey,"spider monkey, Ateles geoffroyi"
-common squirrel monkey,"squirrel monkey, Saimiri sciureus"
-ring-tailed lemur,"Madagascar cat, ring-tailed lemur, Lemur catta"
-indri,"indri, indris, Indri indri, Indri brevicaudatus"
-Asian elephant,"Indian elephant, Elephas maximus"
-African bush elephant,"African elephant, Loxodonta africana"
-red panda,"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens"
-giant panda,"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca"
-snoek fish,"barracouta, snoek"
-eel,eel
-silver salmon,"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch"
-rock beauty fish,"rock beauty, Holocanthus tricolor"
-clownfish,anemone fish
-sturgeon,sturgeon
-gar fish,"gar, garfish, garpike, billfish, Lepisosteus osseus"
-lionfish,lionfish
-pufferfish,"puffer, pufferfish, blowfish, globefish"
-abacus,abacus
-abaya,abaya
-academic gown,"academic gown, academic robe, judge's robe"
-accordion,"accordion, piano accordion, squeeze box"
-acoustic guitar,acoustic guitar
-aircraft carrier,"aircraft carrier, carrier, flattop, attack aircraft carrier"
-airliner,airliner
-airship,"airship, dirigible"
-altar,altar
-ambulance,ambulance
-amphibious vehicle,"amphibian, amphibious vehicle"
-analog clock,analog clock
-apiary,"apiary, bee house"
-apron,apron
-trash can,"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin"
-assault rifle,"assault rifle, assault gun"
-backpack,"backpack, back pack, knapsack, packsack, rucksack, haversack"
-bakery,"bakery, bakeshop, bakehouse"
-balance beam,"balance beam, beam"
-balloon,balloon
-ballpoint pen,"ballpoint, ballpoint pen, ballpen, Biro"
-Band-Aid,Band Aid
-banjo,banjo
-baluster / handrail,"bannister, banister, balustrade, balusters, handrail"
-barbell,barbell
-barber chair,barber chair
-barbershop,barbershop
-barn,barn
-barometer,barometer
-barrel,"barrel, cask"
-wheelbarrow,"barrow, garden cart, lawn cart, wheelbarrow"
-baseball,baseball
-basketball,basketball
-bassinet,bassinet
-bassoon,bassoon
-swimming cap,"bathing cap, swimming cap"
-bath towel,bath towel
-bathtub,"bathtub, bathing tub, bath, tub"
-station wagon,"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon"
-lighthouse,"beacon, lighthouse, beacon light, pharos"
-beaker,beaker
-military hat (bearskin or shako),"bearskin, busby, shako"
-beer bottle,beer bottle
-beer glass,beer glass
-bell tower,"bell cote, bell cot"
-baby bib,bib
-tandem bicycle,"bicycle-built-for-two, tandem bicycle, tandem"
-bikini,"bikini, two-piece"
-ring binder,"binder, ring-binder"
-binoculars,"binoculars, field glasses, opera glasses"
-birdhouse,birdhouse
-boathouse,boathouse
-bobsleigh,"bobsled, bobsleigh, bob"
-bolo tie,"bolo tie, bolo, bola tie, bola"
-poke bonnet,"bonnet, poke bonnet"
-bookcase,bookcase
-bookstore,"bookshop, bookstore, bookstall"
-bottle cap,bottlecap
-hunting bow,bow
-bow tie,"bow tie, bow-tie, bowtie"
-brass memorial plaque,"brass, memorial tablet, plaque"
-bra,"brassiere, bra, bandeau"
-breakwater,"breakwater, groin, groyne, mole, bulwark, seawall, jetty"
-breastplate,"breastplate, aegis, egis"
-broom,broom
-bucket,"bucket, pail"
-buckle,buckle
-bulletproof vest,bulletproof vest
-high-speed train,"bullet train, bullet"
-butcher shop,"butcher shop, meat market"
-taxicab,"cab, hack, taxi, taxicab"
-cauldron,"caldron, cauldron"
-candle,"candle, taper, wax light"
-cannon,cannon
-canoe,canoe
-can opener,"can opener, tin opener"
-cardigan,cardigan
-car mirror,car mirror
-carousel,"carousel, carrousel, merry-go-round, roundabout, whirligig"
-tool kit,"carpenter's kit, tool kit"
-cardboard box / carton,carton
-car wheel,car wheel
-automated teller machine,"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM"
-cassette,cassette
-cassette player,cassette player
-castle,castle
-catamaran,catamaran
-CD player,CD player
-cello,"cello, violoncello"
-mobile phone,"cellular telephone, cellular phone, cellphone, cell, mobile phone"
-chain,chain
-chain-link fence,chainlink fence
-chain mail,"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour"
-chainsaw,"chain saw, chainsaw"
-storage chest,chest
-chiffonier,"chiffonier, commode"
-bell or wind chime,"chime, bell, gong"
-china cabinet,"china cabinet, china closet"
-Christmas stocking,Christmas stocking
-church,"church, church building"
-movie theater,"cinema, movie theater, movie theatre, movie house, picture palace"
-cleaver,"cleaver, meat cleaver, chopper"
-cliff dwelling,cliff dwelling
-cloak,cloak
-clogs,"clog, geta, patten, sabot"
-cocktail shaker,cocktail shaker
-coffee mug,coffee mug
-coffeemaker,coffeepot
-spiral or coil,"coil, spiral, volute, whorl, helix"
-combination lock,combination lock
-computer keyboard,"computer keyboard, keypad"
-candy store,"confectionery, confectionary, candy store"
-container ship,"container ship, containership, container vessel"
-convertible,convertible
-corkscrew,"corkscrew, bottle screw"
-cornet,"cornet, horn, trumpet, trump"
-cowboy boot,cowboy boot
-cowboy hat,"cowboy hat, ten-gallon hat"
-cradle,cradle
-construction crane,construction crane
-crash helmet,crash helmet
-crate,crate
-infant bed,"crib, cot"
-Crock Pot,Crock Pot
-croquet ball,croquet ball
-crutch,crutch
-cuirass,cuirass
-dam,"dam, dike, dyke"
-desk,desk
-desktop computer,desktop computer
-rotary dial telephone,"dial telephone, dial phone"
-diaper,"diaper, nappy, napkin"
-digital clock,digital clock
-digital watch,digital watch
-dining table,"dining table, board"
-dishcloth,"dishrag, dishcloth"
-dishwasher,"dishwasher, dish washer, dishwashing machine"
-disc brake,"disk brake, disc brake"
-dock,"dock, dockage, docking facility"
-dog sled,"dogsled, dog sled, dog sleigh"
-dome,dome
-doormat,"doormat, welcome mat"
-drilling rig,"drilling platform, offshore rig"
-drum,"drum, membranophone, tympan"
-drumstick,drumstick
-dumbbell,dumbbell
-Dutch oven,Dutch oven
-electric fan,"electric fan, blower"
-electric guitar,electric guitar
-electric locomotive,electric locomotive
-entertainment center,entertainment center
-envelope,envelope
-espresso machine,espresso maker
-face powder,face powder
-feather boa,"feather boa, boa"
-filing cabinet,"file, file cabinet, filing cabinet"
-fireboat,fireboat
-fire truck,"fire engine, fire truck"
-fire screen,"fire screen, fireguard"
-flagpole,"flagpole, flagstaff"
-flute,"flute, transverse flute"
-folding chair,folding chair
-football helmet,football helmet
-forklift,forklift
-fountain,fountain
-fountain pen,fountain pen
-four-poster bed,four-poster
-freight car,freight car
-French horn,"French horn, horn"
-frying pan,"frying pan, frypan, skillet"
-fur coat,fur coat
-garbage truck,"garbage truck, dustcart"
-gas mask or respirator,"gasmask, respirator, gas helmet"
-gas pump,"gas pump, gasoline pump, petrol pump, island dispenser"
-goblet,goblet
-go-kart,go-kart
-golf ball,golf ball
-golf cart,"golfcart, golf cart"
-gondola,gondola
-gong,"gong, tam-tam"
-gown,gown
-grand piano,"grand piano, grand"
-greenhouse,"greenhouse, nursery, glasshouse"
-radiator grille,"grille, radiator grille"
-grocery store,"grocery store, grocery, food market, market"
-guillotine,guillotine
-hair clip,hair slide
-hair spray,hair spray
-half-track,half track
-hammer,hammer
-hamper,hamper
-hair dryer,"hand blower, blow dryer, blow drier, hair dryer, hair drier"
-hand-held computer,"hand-held computer, hand-held microcomputer"
-handkerchief,"handkerchief, hankie, hanky, hankey"
-hard disk drive,"hard disc, hard disk, fixed disk"
-harmonica,"harmonica, mouth organ, harp, mouth harp"
-harp,harp
-combine harvester,"harvester, reaper"
-hatchet,hatchet
-holster,holster
-home theater,"home theater, home theatre"
-honeycomb,honeycomb
-hook,"hook, claw"
-hoop skirt,"hoopskirt, crinoline"
-gymnastic horizontal bar,"horizontal bar, high bar"
-horse-drawn vehicle,"horse cart, horse-cart"
-hourglass,hourglass
-iPod,iPod
-clothes iron,"iron, smoothing iron"
-carved pumpkin,jack-o'-lantern
-jeans,"jean, blue jean, denim"
-jeep,"jeep, landrover"
-T-shirt,"jersey, T-shirt, tee shirt"
-jigsaw puzzle,jigsaw puzzle
-rickshaw,"jinrikisha, ricksha, rickshaw"
-joystick,joystick
-kimono,kimono
-knee pad,knee pad
-knot,knot
-lab coat,"lab coat, laboratory coat"
-ladle,ladle
-lampshade,"lampshade, lamp shade"
-laptop computer,"laptop, laptop computer"
-lawn mower,"lawn mower, mower"
-lens cap,"lens cap, lens cover"
-letter opener,"letter opener, paper knife, paperknife"
-library,library
-lifeboat,lifeboat
-lighter,"lighter, light, igniter, ignitor"
-limousine,"limousine, limo"
-ocean liner,"liner, ocean liner"
-lipstick,"lipstick, lip rouge"
-slip-on shoe,Loafer
-lotion,lotion
-music speaker,"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system"
-loupe magnifying glass,"loupe, jeweler's loupe"
-sawmill,"lumbermill, sawmill"
-magnetic compass,magnetic compass
-messenger bag,"mailbag, postbag"
-mailbox,"mailbox, letter box"
-tights,maillot
-one-piece bathing suit,"maillot, tank suit"
-manhole cover,manhole cover
-maraca,maraca
-marimba,"marimba, xylophone"
-mask,mask
-matchstick,matchstick
-maypole,maypole
-maze,"maze, labyrinth"
-measuring cup,measuring cup
-medicine cabinet,"medicine chest, medicine cabinet"
-megalith,"megalith, megalithic structure"
-microphone,"microphone, mike"
-microwave oven,"microwave, microwave oven"
-military uniform,military uniform
-milk can,milk can
-minibus,minibus
-miniskirt,"miniskirt, mini"
-minivan,minivan
-missile,missile
-mitten,mitten
-mixing bowl,mixing bowl
-mobile home,"mobile home, manufactured home"
-ford model t,Model T
-modem,modem
-monastery,monastery
-monitor,monitor
-moped,moped
-mortar and pestle,mortar
-graduation cap,mortarboard
-mosque,mosque
-mosquito net,mosquito net
-vespa,"motor scooter, scooter"
-mountain bike,"mountain bike, all-terrain bike, off-roader"
-tent,mountain tent
-computer mouse,"mouse, computer mouse"
-mousetrap,mousetrap
-moving van,moving van
-muzzle,muzzle
-metal nail,nail
-neck brace,neck brace
-necklace,necklace
-baby pacifier,nipple
-notebook computer,"notebook, notebook computer"
-obelisk,obelisk
-oboe,"oboe, hautboy, hautbois"
-ocarina,"ocarina, sweet potato"
-odometer,"odometer, hodometer, mileometer, milometer"
-oil filter,oil filter
-pipe organ,"organ, pipe organ"
-oscilloscope,"oscilloscope, scope, cathode-ray oscilloscope, CRO"
-overskirt,overskirt
-bullock cart,oxcart
-oxygen mask,oxygen mask
-product packet / packaging,packet
-paddle,"paddle, boat paddle"
-paddle wheel,"paddlewheel, paddle wheel"
-padlock,padlock
-paintbrush,paintbrush
-pajamas,"pajama, pyjama, pj's, jammies"
-palace,palace
-pan flute,"panpipe, pandean pipe, syrinx"
-paper towel,paper towel
-parachute,"parachute, chute"
-parallel bars,"parallel bars, bars"
-park bench,park bench
-parking meter,parking meter
-railroad car,"passenger car, coach, carriage"
-patio,"patio, terrace"
-payphone,"pay-phone, pay-station"
-pedestal,"pedestal, plinth, footstall"
-pencil case,"pencil box, pencil case"
-pencil sharpener,pencil sharpener
-perfume,"perfume, essence"
-Petri dish,Petri dish
-photocopier,photocopier
-plectrum,"pick, plectrum, plectron"
-Pickelhaube,pickelhaube
-picket fence,"picket fence, paling"
-pickup truck,"pickup, pickup truck"
-pier,pier
-piggy bank,"piggy bank, penny bank"
-pill bottle,pill bottle
-pillow,pillow
-ping-pong ball,ping-pong ball
-pinwheel,pinwheel
-pirate ship,"pirate, pirate ship"
-drink pitcher,"pitcher, ewer"
-block plane,"plane, carpenter's plane, woodworking plane"
-planetarium,planetarium
-plastic bag,plastic bag
-plate rack,plate rack
-farm plow,"plow, plough"
-plunger,"plunger, plumber's helper"
-Polaroid camera,"Polaroid camera, Polaroid Land camera"
-pole,pole
-police van,"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria"
-poncho,poncho
-pool table,"pool table, billiard table, snooker table"
-soda bottle,"pop bottle, soda bottle"
-plant pot,"pot, flowerpot"
-potter's wheel,potter's wheel
-power drill,power drill
-prayer rug,"prayer rug, prayer mat"
-printer,printer
-prison,"prison, prison house"
-missile,"projectile, missile"
-projector,projector
-hockey puck,"puck, hockey puck"
-punching bag,"punching bag, punch bag, punching ball, punchball"
-purse,purse
-quill,"quill, quill pen"
-quilt,"quilt, comforter, comfort, puff"
-race car,"racer, race car, racing car"
-racket,"racket, racquet"
-radiator,radiator
-radio,"radio, wireless"
-radio telescope,"radio telescope, radio reflector"
-rain barrel,rain barrel
-recreational vehicle,"recreational vehicle, RV, R.V."
-fishing casting reel,reel
-reflex camera,reflex camera
-refrigerator,"refrigerator, icebox"
-remote control,"remote control, remote"
-restaurant,"restaurant, eating house, eating place, eatery"
-revolver,"revolver, six-gun, six-shooter"
-rifle,rifle
-rocking chair,"rocking chair, rocker"
-rotisserie,rotisserie
-eraser,"rubber eraser, rubber, pencil eraser"
-rugby ball,rugby ball
-ruler measuring stick,"rule, ruler"
-sneaker,running shoe
-safe,safe
-safety pin,safety pin
-salt shaker,"saltshaker, salt shaker"
-sandal,sandal
-sarong,sarong
-saxophone,"sax, saxophone"
-scabbard,scabbard
-weighing scale,"scale, weighing machine"
-school bus,school bus
-schooner,schooner
-scoreboard,scoreboard
-CRT monitor,"screen, CRT screen"
-screw,screw
-screwdriver,screwdriver
-seat belt,"seat belt, seatbelt"
-sewing machine,sewing machine
-shield,"shield, buckler"
-shoe store,"shoe shop, shoe-shop, shoe store"
-shoji screen / room divider,shoji
-shopping basket,shopping basket
-shopping cart,shopping cart
-shovel,shovel
-shower cap,shower cap
-shower curtain,shower curtain
-ski,ski
-balaclava ski mask,ski mask
-sleeping bag,sleeping bag
-slide rule,"slide rule, slipstick"
-sliding door,sliding door
-slot machine,"slot, one-armed bandit"
-snorkel,snorkel
-snowmobile,snowmobile
-snowplow,"snowplow, snowplough"
-soap dispenser,soap dispenser
-soccer ball,soccer ball
-sock,sock
-solar thermal collector,"solar dish, solar collector, solar furnace"
-sombrero,sombrero
-soup bowl,soup bowl
-keyboard space bar,space bar
-space heater,space heater
-space shuttle,space shuttle
-spatula,spatula
-motorboat,speedboat
-spider web,"spider web, spider's web"
-spindle,spindle
-sports car,"sports car, sport car"
-spotlight,"spotlight, spot"
-stage,stage
-steam locomotive,steam locomotive
-through arch bridge,steel arch bridge
-steel drum,steel drum
-stethoscope,stethoscope
-scarf,stole
-stone wall,stone wall
-stopwatch,"stopwatch, stop watch"
-stove,stove
-strainer,strainer
-tram,"streetcar, tram, tramcar, trolley, trolley car"
-stretcher,stretcher
-couch,"studio couch, day bed"
-stupa,"stupa, tope"
-submarine,"submarine, pigboat, sub, U-boat"
-suit,"suit, suit of clothes"
-sundial,sundial
-sunglasses,sunglass
-sunglasses,"sunglasses, dark glasses, shades"
-sunscreen,"sunscreen, sunblock, sun blocker"
-suspension bridge,suspension bridge
-mop,"swab, swob, mop"
-sweatshirt,sweatshirt
-swim trunks / shorts,"swimming trunks, bathing trunks"
-swing,swing
-electrical switch,"switch, electric switch, electrical switch"
-syringe,syringe
-table lamp,table lamp
-tank,"tank, army tank, armored combat vehicle, armoured combat vehicle"
-tape player,tape player
-teapot,teapot
-teddy bear,"teddy, teddy bear"
-television,"television, television system"
-tennis ball,tennis ball
-thatched roof,"thatch, thatched roof"
-front curtain,"theater curtain, theatre curtain"
-thimble,thimble
-threshing machine,"thresher, thrasher, threshing machine"
-throne,throne
-tile roof,tile roof
-toaster,toaster
-tobacco shop,"tobacco shop, tobacconist shop, tobacconist"
-toilet seat,toilet seat
-torch,torch
-totem pole,totem pole
-tow truck,"tow truck, tow car, wrecker"
-toy store,toyshop
-tractor,tractor
-semi-trailer truck,"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi"
-tray,tray
-trench coat,trench coat
-tricycle,"tricycle, trike, velocipede"
-trimaran,trimaran
-tripod,tripod
-triumphal arch,triumphal arch
-trolleybus,"trolleybus, trolley coach, trackless trolley"
-trombone,trombone
-hot tub,"tub, vat"
-turnstile,turnstile
-typewriter keyboard,typewriter keyboard
-umbrella,umbrella
-unicycle,"unicycle, monocycle"
-upright piano,"upright, upright piano"
-vacuum cleaner,"vacuum, vacuum cleaner"
-vase,vase
-vaulted or arched ceiling,vault
-velvet fabric,velvet
-vending machine,vending machine
-vestment,vestment
-viaduct,viaduct
-violin,"violin, fiddle"
-volleyball,volleyball
-waffle iron,waffle iron
-wall clock,wall clock
-wallet,"wallet, billfold, notecase, pocketbook"
-wardrobe,"wardrobe, closet, press"
-military aircraft,"warplane, military plane"
-sink,"washbasin, handbasin, washbowl, lavabo, wash-hand basin"
-washing machine,"washer, automatic washer, washing machine"
-water bottle,water bottle
-water jug,water jug
-water tower,water tower
-whiskey jug,whiskey jug
-whistle,whistle
-hair wig,wig
-window screen,window screen
-window shade,window shade
-Windsor tie,Windsor tie
-wine bottle,wine bottle
-airplane wing,wing
-wok,wok
-wooden spoon,wooden spoon
-wool,"wool, woolen, woollen"
-split-rail fence,"worm fence, snake fence, snake-rail fence, Virginia fence"
-shipwreck,wreck
-sailboat,yawl
-yurt,yurt
-website,"web site, website, internet site, site"
-comic book,comic book
-crossword,"crossword puzzle, crossword"
-traffic or street sign,street sign
-traffic light,"traffic light, traffic signal, stoplight"
-dust jacket,"book jacket, dust cover, dust jacket, dust wrapper"
-menu,menu
-plate,plate
-guacamole,guacamole
-consomme,consomme
-hot pot,"hot pot, hotpot"
-trifle,trifle
-ice cream,"ice cream, icecream"
-popsicle,"ice lolly, lolly, lollipop, popsicle"
-baguette,French loaf
-bagel,"bagel, beigel"
-pretzel,pretzel
-cheeseburger,cheeseburger
-hot dog,"hotdog, hot dog, red hot"
-mashed potatoes,mashed potato
-cabbage,head cabbage
-broccoli,broccoli
-cauliflower,cauliflower
-zucchini,"zucchini, courgette"
-spaghetti squash,spaghetti squash
-acorn squash,acorn squash
-butternut squash,butternut squash
-cucumber,"cucumber, cuke"
-artichoke,"artichoke, globe artichoke"
-bell pepper,bell pepper
-cardoon,cardoon
-mushroom,mushroom
-Granny Smith apple,Granny Smith
-strawberry,strawberry
-orange,orange
-lemon,lemon
-fig,fig
-pineapple,"pineapple, ananas"
-banana,banana
-jackfruit,"jackfruit, jak, jack"
-cherimoya (custard apple),custard apple
-pomegranate,pomegranate
-hay,hay
-carbonara,carbonara
-chocolate syrup,"chocolate sauce, chocolate syrup"
-dough,dough
-meatloaf,"meat loaf, meatloaf"
-pizza,"pizza, pizza pie"
-pot pie,potpie
-burrito,burrito
-red wine,red wine
-espresso,espresso
-tea cup,cup
-eggnog,eggnog
-mountain,alp
-bubble,bubble
-cliff,"cliff, drop, drop-off"
-coral reef,coral reef
-geyser,geyser
-lakeshore,"lakeside, lakeshore"
-promontory,"promontory, headland, head, foreland"
-sandbar,"sandbar, sand bar"
-beach,"seashore, coast, seacoast, sea-coast"
-valley,"valley, vale"
-volcano,volcano
-baseball player,"ballplayer, baseball player"
-bridegroom,"groom, bridegroom"
-scuba diver,scuba diver
-rapeseed,rapeseed
-daisy,daisy
-yellow lady's slipper,"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum"
-corn,corn
-acorn,acorn
-rose hip,"hip, rose hip, rosehip"
-horse chestnut seed,"buckeye, horse chestnut, conker"
-coral fungus,coral fungus
-agaric,agaric
-gyromitra,gyromitra
-stinkhorn mushroom,"stinkhorn, carrion fungus"
-earth star fungus,earthstar
-hen of the woods mushroom,"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa"
-bolete,bolete
-corn cob,"ear, spike, capitulum"
-toilet paper,"toilet tissue, toilet paper, bathroom tissue"
+tench,"tench, Tinca tinca"
+goldfish,"goldfish, Carassius auratus"
+great white shark,"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias"
+tiger shark,"tiger shark, Galeocerdo cuvieri"
+hammerhead shark,"hammerhead, hammerhead shark"
+electric ray,"electric ray, crampfish, numbfish, torpedo"
+stingray,stingray
+rooster,cock
+hen,hen
+ostrich,"ostrich, Struthio camelus"
+brambling,"brambling, Fringilla montifringilla"
+goldfinch,"goldfinch, Carduelis carduelis"
+house finch,"house finch, linnet, Carpodacus mexicanus"
+junco,"junco, snowbird"
+indigo bunting,"indigo bunting, indigo finch, indigo bird, Passerina cyanea"
+American robin,"robin, American robin, Turdus migratorius"
+bulbul,bulbul
+jay,jay
+magpie,magpie
+chickadee,chickadee
+American dipper,"water ouzel, dipper"
+kite (bird of prey),kite
+bald eagle,"bald eagle, American eagle, Haliaeetus leucocephalus"
+vulture,vulture
+great grey owl,"great grey owl, great gray owl, Strix nebulosa"
+fire salamander,"European fire salamander, Salamandra salamandra"
+smooth newt,"common newt, Triturus vulgaris"
+newt,eft
+spotted salamander,"spotted salamander, Ambystoma maculatum"
+axolotl,"axolotl, mud puppy, Ambystoma mexicanum"
+American bullfrog,"bullfrog, Rana catesbeiana"
+tree frog,"tree frog, tree-frog"
+tailed frog,"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui"
+loggerhead sea turtle,"loggerhead, loggerhead turtle, Caretta caretta"
+leatherback sea turtle,"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea"
+mud turtle,mud turtle
+terrapin,terrapin
+box turtle,"box turtle, box tortoise"
+banded gecko,banded gecko
+green iguana,"common iguana, iguana, Iguana iguana"
+Carolina anole,"American chameleon, anole, Anolis carolinensis"
+desert grassland whiptail lizard,"whiptail, whiptail lizard"
+agama,agama
+frilled-necked lizard,"frilled lizard, Chlamydosaurus kingi"
+alligator lizard,alligator lizard
+Gila monster,"Gila monster, Heloderma suspectum"
+European green lizard,"green lizard, Lacerta viridis"
+chameleon,"African chameleon, Chamaeleo chamaeleon"
+Komodo dragon,"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis"
+Nile crocodile,"African crocodile, Nile crocodile, Crocodylus niloticus"
+American alligator,"American alligator, Alligator mississipiensis"
+triceratops,triceratops
+worm snake,"thunder snake, worm snake, Carphophis amoenus"
+ring-necked snake,"ringneck snake, ring-necked snake, ring snake"
+eastern hog-nosed snake,"hognose snake, puff adder, sand viper"
+smooth green snake,"green snake, grass snake"
+kingsnake,"king snake, kingsnake"
+garter snake,"garter snake, grass snake"
+water snake,water snake
+vine snake,vine snake
+night snake,"night snake, Hypsiglena torquata"
+boa constrictor,"boa constrictor, Constrictor constrictor"
+African rock python,"rock python, rock snake, Python sebae"
+Indian cobra,"Indian cobra, Naja naja"
+green mamba,green mamba
+sea snake,sea snake
+Saharan horned viper,"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus"
+eastern diamondback rattlesnake,"diamondback, diamondback rattlesnake, Crotalus adamanteus"
+sidewinder rattlesnake,"sidewinder, horned rattlesnake, Crotalus cerastes"
+trilobite,trilobite
+harvestman,"harvestman, daddy longlegs, Phalangium opilio"
+scorpion,scorpion
+yellow garden spider,"black and gold garden spider, Argiope aurantia"
+barn spider,"barn spider, Araneus cavaticus"
+European garden spider,"garden spider, Aranea diademata"
+southern black widow,"black widow, Latrodectus mactans"
+tarantula,tarantula
+wolf spider,"wolf spider, hunting spider"
+tick,tick
+centipede,centipede
+black grouse,black grouse
+ptarmigan,ptarmigan
+ruffed grouse,"ruffed grouse, partridge, Bonasa umbellus"
+prairie grouse,"prairie chicken, prairie grouse, prairie fowl"
+peafowl,peacock
+quail,quail
+partridge,partridge
+african grey parrot,"African grey, African gray, Psittacus erithacus"
+macaw,macaw
+sulphur-crested cockatoo,"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita"
+lorikeet,lorikeet
+coucal,coucal
+bee eater,bee eater
+hornbill,hornbill
+hummingbird,hummingbird
+jacamar,jacamar
+toucan,toucan
+duck,drake
+red-breasted merganser,"red-breasted merganser, Mergus serrator"
+goose,goose
+black swan,"black swan, Cygnus atratus"
+tusker,tusker
+echidna,"echidna, spiny anteater, anteater"
+platypus,"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus"
+wallaby,"wallaby, brush kangaroo"
+koala,"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus"
+wombat,wombat
+jellyfish,jellyfish
+sea anemone,"sea anemone, anemone"
+brain coral,brain coral
+flatworm,"flatworm, platyhelminth"
+nematode,"nematode, nematode worm, roundworm"
+conch,conch
+snail,snail
+slug,slug
+sea slug,"sea slug, nudibranch"
+chiton,"chiton, coat-of-mail shell, sea cradle, polyplacophore"
+chambered nautilus,"chambered nautilus, pearly nautilus, nautilus"
+Dungeness crab,"Dungeness crab, Cancer magister"
+rock crab,"rock crab, Cancer irroratus"
+fiddler crab,fiddler crab
+red king crab,"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica"
+American lobster,"American lobster, Northern lobster, Maine lobster, Homarus americanus"
+spiny lobster,"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish"
+crayfish,"crayfish, crawfish, crawdad, crawdaddy"
+hermit crab,hermit crab
+isopod,isopod
+white stork,"white stork, Ciconia ciconia"
+black stork,"black stork, Ciconia nigra"
+spoonbill,spoonbill
+flamingo,flamingo
+little blue heron,"little blue heron, Egretta caerulea"
+great egret,"American egret, great white heron, Egretta albus"
+bittern bird,bittern
+crane bird,crane bird
+limpkin,"limpkin, Aramus pictus"
+common gallinule,"European gallinule, Porphyrio porphyrio"
+American coot,"American coot, marsh hen, mud hen, water hen, Fulica americana"
+bustard,bustard
+ruddy turnstone,"ruddy turnstone, Arenaria interpres"
+dunlin,"red-backed sandpiper, dunlin, Erolia alpina"
+common redshank,"redshank, Tringa totanus"
+dowitcher,dowitcher
+oystercatcher,"oystercatcher, oyster catcher"
+pelican,pelican
+king penguin,"king penguin, Aptenodytes patagonica"
+albatross,"albatross, mollymawk"
+grey whale,"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus"
+killer whale,"killer whale, killer, orca, grampus, sea wolf, Orcinus orca"
+dugong,"dugong, Dugong dugon"
+sea lion,sea lion
+Chihuahua,Chihuahua
+Japanese Chin,Japanese spaniel
+Maltese,"Maltese dog, Maltese terrier, Maltese"
+Pekingese,"Pekinese, Pekingese, Peke"
+Shih Tzu,Shih-Tzu
+King Charles Spaniel,Blenheim spaniel
+Papillon,papillon
+toy terrier,toy terrier
+Rhodesian Ridgeback,Rhodesian ridgeback
+Afghan Hound,"Afghan hound, Afghan"
+Basset Hound,"basset, basset hound"
+Beagle,beagle
+Bloodhound,"bloodhound, sleuthhound"
+Bluetick Coonhound,bluetick
+Black and Tan Coonhound,black-and-tan coonhound
+Treeing Walker Coonhound,"Walker hound, Walker foxhound"
+English foxhound,English foxhound
+Redbone Coonhound,redbone
+borzoi,"borzoi, Russian wolfhound"
+Irish Wolfhound,Irish wolfhound
+Italian Greyhound,Italian greyhound
+Whippet,whippet
+Ibizan Hound,"Ibizan hound, Ibizan Podenco"
+Norwegian Elkhound,"Norwegian elkhound, elkhound"
+Otterhound,"otterhound, otter hound"
+Saluki,"Saluki, gazelle hound"
+Scottish Deerhound,"Scottish deerhound, deerhound"
+Weimaraner,Weimaraner
+Staffordshire Bull Terrier,"Staffordshire bullterrier, Staffordshire bull terrier"
+American Staffordshire Terrier,"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier"
+Bedlington Terrier,Bedlington terrier
+Border Terrier,Border terrier
+Kerry Blue Terrier,Kerry blue terrier
+Irish Terrier,Irish terrier
+Norfolk Terrier,Norfolk terrier
+Norwich Terrier,Norwich terrier
+Yorkshire Terrier,Yorkshire terrier
+Wire Fox Terrier,wire-haired fox terrier
+Lakeland Terrier,Lakeland terrier
+Sealyham Terrier,"Sealyham terrier, Sealyham"
+Airedale Terrier,"Airedale, Airedale terrier"
+Cairn Terrier,"cairn, cairn terrier"
+Australian Terrier,Australian terrier
+Dandie Dinmont Terrier,"Dandie Dinmont, Dandie Dinmont terrier"
+Boston Terrier,"Boston bull, Boston terrier"
+Miniature Schnauzer,miniature schnauzer
+Giant Schnauzer,giant schnauzer
+Standard Schnauzer,standard schnauzer
+Scottish Terrier,"Scotch terrier, Scottish terrier, Scottie"
+Tibetan Terrier,"Tibetan terrier, chrysanthemum dog"
+Australian Silky Terrier,"silky terrier, Sydney silky"
+Soft-coated Wheaten Terrier,soft-coated wheaten terrier
+West Highland White Terrier,West Highland white terrier
+Lhasa Apso,"Lhasa, Lhasa apso"
+Flat-Coated Retriever,flat-coated retriever
+Curly-coated Retriever,curly-coated retriever
+Golden Retriever,golden retriever
+Labrador Retriever,Labrador retriever
+Chesapeake Bay Retriever,Chesapeake Bay retriever
+German Shorthaired Pointer,German short-haired pointer
+Vizsla,"vizsla, Hungarian pointer"
+English Setter,English setter
+Irish Setter,"Irish setter, red setter"
+Gordon Setter,Gordon setter
+Brittany dog,Brittany spaniel
+Clumber Spaniel,"clumber, clumber spaniel"
+English Springer Spaniel,"English springer, English springer spaniel"
+Welsh Springer Spaniel,Welsh springer spaniel
+Cocker Spaniel,"cocker spaniel, English cocker spaniel, cocker"
+Sussex Spaniel,Sussex spaniel
+Irish Water Spaniel,Irish water spaniel
+Kuvasz,kuvasz
+Schipperke,schipperke
+Groenendael dog,groenendael
+Malinois,malinois
+Briard,briard
+Australian Kelpie,kelpie
+Komondor,komondor
+Old English Sheepdog,"Old English sheepdog, bobtail"
+Shetland Sheepdog,"Shetland sheepdog, Shetland sheep dog, Shetland"
+collie,collie
+Border Collie,Border collie
+Bouvier des Flandres dog,"Bouvier des Flandres, Bouviers des Flandres"
+Rottweiler,Rottweiler
+German Shepherd Dog,"German shepherd, German shepherd dog, German police dog, alsatian"
+Dobermann,"Doberman, Doberman pinscher"
+Miniature Pinscher,miniature pinscher
+Greater Swiss Mountain Dog,Greater Swiss Mountain dog
+Bernese Mountain Dog,Bernese mountain dog
+Appenzeller Sennenhund,Appenzeller
+Entlebucher Sennenhund,EntleBucher
+Boxer,boxer
+Bullmastiff,bull mastiff
+Tibetan Mastiff,Tibetan mastiff
+French Bulldog,French bulldog
+Great Dane,Great Dane
+St. Bernard,"Saint Bernard, St Bernard"
+husky,"Eskimo dog, husky"
+Alaskan Malamute,"malamute, malemute, Alaskan malamute"
+Siberian Husky,Siberian husky
+Dalmatian,"dalmatian, coach dog, carriage dog"
+Affenpinscher,"affenpinscher, monkey pinscher, monkey dog"
+Basenji,basenji
+pug,"pug, pug-dog"
+Leonberger,Leonberg
+Newfoundland dog,"Newfoundland, Newfoundland dog"
+Great Pyrenees dog,Great Pyrenees
+Samoyed,"Samoyed, Samoyede"
+Pomeranian,Pomeranian
+Chow Chow,"chow, chow chow"
+Keeshond,keeshond
+brussels griffon,Brabancon griffon
+Pembroke Welsh Corgi,"Pembroke, Pembroke Welsh corgi"
+Cardigan Welsh Corgi,"Cardigan, Cardigan Welsh corgi"
+Toy Poodle,toy poodle
+Miniature Poodle,miniature poodle
+Standard Poodle,standard poodle
+Mexican hairless dog (xoloitzcuintli),Mexican hairless
+grey wolf,"timber wolf, grey wolf, gray wolf, Canis lupus"
+Alaskan tundra wolf,"white wolf, Arctic wolf, Canis lupus tundrarum"
+red wolf or maned wolf,"red wolf, maned wolf, Canis rufus, Canis niger"
+coyote,"coyote, prairie wolf, brush wolf, Canis latrans"
+dingo,"dingo, warrigal, warragal, Canis dingo"
+dhole,"dhole, Cuon alpinus"
+African wild dog,"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus"
+hyena,"hyena, hyaena"
+red fox,"red fox, Vulpes vulpes"
+kit fox,"kit fox, Vulpes macrotis"
+Arctic fox,"Arctic fox, white fox, Alopex lagopus"
+grey fox,"grey fox, gray fox, Urocyon cinereoargenteus"
+tabby cat,"tabby, tabby cat"
+tiger cat,tiger cat
+Persian cat,Persian cat
+Siamese cat,"Siamese cat, Siamese"
+Egyptian Mau,Egyptian cat
+cougar,"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor"
+lynx,"lynx, catamount"
+leopard,"leopard, Panthera pardus"
+snow leopard,"snow leopard, ounce, Panthera uncia"
+jaguar,"jaguar, panther, Panthera onca, Felis onca"
+lion,"lion, king of beasts, Panthera leo"
+tiger,"tiger, Panthera tigris"
+cheetah,"cheetah, chetah, Acinonyx jubatus"
+brown bear,"brown bear, bruin, Ursus arctos"
+American black bear,"American black bear, black bear, Ursus americanus, Euarctos americanus"
+polar bear,"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus"
+sloth bear,"sloth bear, Melursus ursinus, Ursus ursinus"
+mongoose,mongoose
+meerkat,"meerkat, mierkat"
+tiger beetle,tiger beetle
+ladybug,"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle"
+ground beetle,"ground beetle, carabid beetle"
+longhorn beetle,"long-horned beetle, longicorn, longicorn beetle"
+leaf beetle,"leaf beetle, chrysomelid"
+dung beetle,dung beetle
+rhinoceros beetle,rhinoceros beetle
+weevil,weevil
+fly,fly
+bee,bee
+ant,"ant, emmet, pismire"
+grasshopper,"grasshopper, hopper"
+cricket insect,cricket
+stick insect,"walking stick, walkingstick, stick insect"
+cockroach,"cockroach, roach"
+praying mantis,"mantis, mantid"
+cicada,"cicada, cicala"
+leafhopper,leafhopper
+lacewing,"lacewing, lacewing fly"
+dragonfly,"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk"
+damselfly,damselfly
+red admiral butterfly,admiral
+ringlet butterfly,"ringlet, ringlet butterfly"
+monarch butterfly,"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus"
+small white butterfly,cabbage butterfly
+sulphur butterfly,"sulphur butterfly, sulfur butterfly"
+gossamer-winged butterfly,"lycaenid, lycaenid butterfly"
+starfish,"starfish, sea star"
+sea urchin,sea urchin
+sea cucumber,"sea cucumber, holothurian"
+cottontail rabbit,"wood rabbit, cottontail, cottontail rabbit"
+hare,hare
+Angora rabbit,"Angora, Angora rabbit"
+hamster,hamster
+porcupine,"porcupine, hedgehog"
+fox squirrel,"fox squirrel, eastern fox squirrel, Sciurus niger"
+marmot,marmot
+beaver,beaver
+guinea pig,"guinea pig, Cavia cobaya"
+common sorrel horse,sorrel
+zebra,zebra
+pig,"hog, pig, grunter, squealer, Sus scrofa"
+wild boar,"wild boar, boar, Sus scrofa"
+warthog,warthog
+hippopotamus,"hippopotamus, hippo, river horse, Hippopotamus amphibius"
+ox,ox
+water buffalo,"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis"
+bison,bison
+ram (adult male sheep),"ram, tup"
+bighorn sheep,"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis"
+Alpine ibex,"ibex, Capra ibex"
+hartebeest,hartebeest
+impala (antelope),"impala, Aepyceros melampus"
+gazelle,gazelle
+arabian camel,"Arabian camel, dromedary, Camelus dromedarius"
+llama,llama
+weasel,weasel
+mink,mink
+European polecat,"polecat, fitch, foulmart, foumart, Mustela putorius"
+black-footed ferret,"black-footed ferret, ferret, Mustela nigripes"
+otter,otter
+skunk,"skunk, polecat, wood pussy"
+badger,badger
+armadillo,armadillo
+three-toed sloth,"three-toed sloth, ai, Bradypus tridactylus"
+orangutan,"orangutan, orang, orangutang, Pongo pygmaeus"
+gorilla,"gorilla, Gorilla gorilla"
+chimpanzee,"chimpanzee, chimp, Pan troglodytes"
+gibbon,"gibbon, Hylobates lar"
+siamang,"siamang, Hylobates syndactylus, Symphalangus syndactylus"
+guenon,"guenon, guenon monkey"
+patas monkey,"patas, hussar monkey, Erythrocebus patas"
+baboon,baboon
+macaque,macaque
+langur,langur
+black-and-white colobus,"colobus, colobus monkey"
+proboscis monkey,"proboscis monkey, Nasalis larvatus"
+marmoset,marmoset
+white-headed capuchin,"capuchin, ringtail, Cebus capucinus"
+howler monkey,"howler monkey, howler"
+titi monkey,"titi, titi monkey"
+Geoffroy's spider monkey,"spider monkey, Ateles geoffroyi"
+common squirrel monkey,"squirrel monkey, Saimiri sciureus"
+ring-tailed lemur,"Madagascar cat, ring-tailed lemur, Lemur catta"
+indri,"indri, indris, Indri indri, Indri brevicaudatus"
+Asian elephant,"Indian elephant, Elephas maximus"
+African bush elephant,"African elephant, Loxodonta africana"
+red panda,"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens"
+giant panda,"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca"
+snoek fish,"barracouta, snoek"
+eel,eel
+silver salmon,"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch"
+rock beauty fish,"rock beauty, Holocanthus tricolor"
+clownfish,anemone fish
+sturgeon,sturgeon
+gar fish,"gar, garfish, garpike, billfish, Lepisosteus osseus"
+lionfish,lionfish
+pufferfish,"puffer, pufferfish, blowfish, globefish"
+abacus,abacus
+abaya,abaya
+academic gown,"academic gown, academic robe, judge's robe"
+accordion,"accordion, piano accordion, squeeze box"
+acoustic guitar,acoustic guitar
+aircraft carrier,"aircraft carrier, carrier, flattop, attack aircraft carrier"
+airliner,airliner
+airship,"airship, dirigible"
+altar,altar
+ambulance,ambulance
+amphibious vehicle,"amphibian, amphibious vehicle"
+analog clock,analog clock
+apiary,"apiary, bee house"
+apron,apron
+trash can,"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin"
+assault rifle,"assault rifle, assault gun"
+backpack,"backpack, back pack, knapsack, packsack, rucksack, haversack"
+bakery,"bakery, bakeshop, bakehouse"
+balance beam,"balance beam, beam"
+balloon,balloon
+ballpoint pen,"ballpoint, ballpoint pen, ballpen, Biro"
+Band-Aid,Band Aid
+banjo,banjo
+baluster / handrail,"bannister, banister, balustrade, balusters, handrail"
+barbell,barbell
+barber chair,barber chair
+barbershop,barbershop
+barn,barn
+barometer,barometer
+barrel,"barrel, cask"
+wheelbarrow,"barrow, garden cart, lawn cart, wheelbarrow"
+baseball,baseball
+basketball,basketball
+bassinet,bassinet
+bassoon,bassoon
+swimming cap,"bathing cap, swimming cap"
+bath towel,bath towel
+bathtub,"bathtub, bathing tub, bath, tub"
+station wagon,"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon"
+lighthouse,"beacon, lighthouse, beacon light, pharos"
+beaker,beaker
+military hat (bearskin or shako),"bearskin, busby, shako"
+beer bottle,beer bottle
+beer glass,beer glass
+bell tower,"bell cote, bell cot"
+baby bib,bib
+tandem bicycle,"bicycle-built-for-two, tandem bicycle, tandem"
+bikini,"bikini, two-piece"
+ring binder,"binder, ring-binder"
+binoculars,"binoculars, field glasses, opera glasses"
+birdhouse,birdhouse
+boathouse,boathouse
+bobsleigh,"bobsled, bobsleigh, bob"
+bolo tie,"bolo tie, bolo, bola tie, bola"
+poke bonnet,"bonnet, poke bonnet"
+bookcase,bookcase
+bookstore,"bookshop, bookstore, bookstall"
+bottle cap,bottlecap
+hunting bow,bow
+bow tie,"bow tie, bow-tie, bowtie"
+brass memorial plaque,"brass, memorial tablet, plaque"
+bra,"brassiere, bra, bandeau"
+breakwater,"breakwater, groin, groyne, mole, bulwark, seawall, jetty"
+breastplate,"breastplate, aegis, egis"
+broom,broom
+bucket,"bucket, pail"
+buckle,buckle
+bulletproof vest,bulletproof vest
+high-speed train,"bullet train, bullet"
+butcher shop,"butcher shop, meat market"
+taxicab,"cab, hack, taxi, taxicab"
+cauldron,"caldron, cauldron"
+candle,"candle, taper, wax light"
+cannon,cannon
+canoe,canoe
+can opener,"can opener, tin opener"
+cardigan,cardigan
+car mirror,car mirror
+carousel,"carousel, carrousel, merry-go-round, roundabout, whirligig"
+tool kit,"carpenter's kit, tool kit"
+cardboard box / carton,carton
+car wheel,car wheel
+automated teller machine,"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM"
+cassette,cassette
+cassette player,cassette player
+castle,castle
+catamaran,catamaran
+CD player,CD player
+cello,"cello, violoncello"
+mobile phone,"cellular telephone, cellular phone, cellphone, cell, mobile phone"
+chain,chain
+chain-link fence,chainlink fence
+chain mail,"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour"
+chainsaw,"chain saw, chainsaw"
+storage chest,chest
+chiffonier,"chiffonier, commode"
+bell or wind chime,"chime, bell, gong"
+china cabinet,"china cabinet, china closet"
+Christmas stocking,Christmas stocking
+church,"church, church building"
+movie theater,"cinema, movie theater, movie theatre, movie house, picture palace"
+cleaver,"cleaver, meat cleaver, chopper"
+cliff dwelling,cliff dwelling
+cloak,cloak
+clogs,"clog, geta, patten, sabot"
+cocktail shaker,cocktail shaker
+coffee mug,coffee mug
+coffeemaker,coffeepot
+spiral or coil,"coil, spiral, volute, whorl, helix"
+combination lock,combination lock
+computer keyboard,"computer keyboard, keypad"
+candy store,"confectionery, confectionary, candy store"
+container ship,"container ship, containership, container vessel"
+convertible,convertible
+corkscrew,"corkscrew, bottle screw"
+cornet,"cornet, horn, trumpet, trump"
+cowboy boot,cowboy boot
+cowboy hat,"cowboy hat, ten-gallon hat"
+cradle,cradle
+construction crane,construction crane
+crash helmet,crash helmet
+crate,crate
+infant bed,"crib, cot"
+Crock Pot,Crock Pot
+croquet ball,croquet ball
+crutch,crutch
+cuirass,cuirass
+dam,"dam, dike, dyke"
+desk,desk
+desktop computer,desktop computer
+rotary dial telephone,"dial telephone, dial phone"
+diaper,"diaper, nappy, napkin"
+digital clock,digital clock
+digital watch,digital watch
+dining table,"dining table, board"
+dishcloth,"dishrag, dishcloth"
+dishwasher,"dishwasher, dish washer, dishwashing machine"
+disc brake,"disk brake, disc brake"
+dock,"dock, dockage, docking facility"
+dog sled,"dogsled, dog sled, dog sleigh"
+dome,dome
+doormat,"doormat, welcome mat"
+drilling rig,"drilling platform, offshore rig"
+drum,"drum, membranophone, tympan"
+drumstick,drumstick
+dumbbell,dumbbell
+Dutch oven,Dutch oven
+electric fan,"electric fan, blower"
+electric guitar,electric guitar
+electric locomotive,electric locomotive
+entertainment center,entertainment center
+envelope,envelope
+espresso machine,espresso maker
+face powder,face powder
+feather boa,"feather boa, boa"
+filing cabinet,"file, file cabinet, filing cabinet"
+fireboat,fireboat
+fire truck,"fire engine, fire truck"
+fire screen,"fire screen, fireguard"
+flagpole,"flagpole, flagstaff"
+flute,"flute, transverse flute"
+folding chair,folding chair
+football helmet,football helmet
+forklift,forklift
+fountain,fountain
+fountain pen,fountain pen
+four-poster bed,four-poster
+freight car,freight car
+French horn,"French horn, horn"
+frying pan,"frying pan, frypan, skillet"
+fur coat,fur coat
+garbage truck,"garbage truck, dustcart"
+gas mask or respirator,"gasmask, respirator, gas helmet"
+gas pump,"gas pump, gasoline pump, petrol pump, island dispenser"
+goblet,goblet
+go-kart,go-kart
+golf ball,golf ball
+golf cart,"golfcart, golf cart"
+gondola,gondola
+gong,"gong, tam-tam"
+gown,gown
+grand piano,"grand piano, grand"
+greenhouse,"greenhouse, nursery, glasshouse"
+radiator grille,"grille, radiator grille"
+grocery store,"grocery store, grocery, food market, market"
+guillotine,guillotine
+hair clip,hair slide
+hair spray,hair spray
+half-track,half track
+hammer,hammer
+hamper,hamper
+hair dryer,"hand blower, blow dryer, blow drier, hair dryer, hair drier"
+hand-held computer,"hand-held computer, hand-held microcomputer"
+handkerchief,"handkerchief, hankie, hanky, hankey"
+hard disk drive,"hard disc, hard disk, fixed disk"
+harmonica,"harmonica, mouth organ, harp, mouth harp"
+harp,harp
+combine harvester,"harvester, reaper"
+hatchet,hatchet
+holster,holster
+home theater,"home theater, home theatre"
+honeycomb,honeycomb
+hook,"hook, claw"
+hoop skirt,"hoopskirt, crinoline"
+gymnastic horizontal bar,"horizontal bar, high bar"
+horse-drawn vehicle,"horse cart, horse-cart"
+hourglass,hourglass
+iPod,iPod
+clothes iron,"iron, smoothing iron"
+carved pumpkin,jack-o'-lantern
+jeans,"jean, blue jean, denim"
+jeep,"jeep, landrover"
+T-shirt,"jersey, T-shirt, tee shirt"
+jigsaw puzzle,jigsaw puzzle
+rickshaw,"jinrikisha, ricksha, rickshaw"
+joystick,joystick
+kimono,kimono
+knee pad,knee pad
+knot,knot
+lab coat,"lab coat, laboratory coat"
+ladle,ladle
+lampshade,"lampshade, lamp shade"
+laptop computer,"laptop, laptop computer"
+lawn mower,"lawn mower, mower"
+lens cap,"lens cap, lens cover"
+letter opener,"letter opener, paper knife, paperknife"
+library,library
+lifeboat,lifeboat
+lighter,"lighter, light, igniter, ignitor"
+limousine,"limousine, limo"
+ocean liner,"liner, ocean liner"
+lipstick,"lipstick, lip rouge"
+slip-on shoe,Loafer
+lotion,lotion
+music speaker,"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system"
+loupe magnifying glass,"loupe, jeweler's loupe"
+sawmill,"lumbermill, sawmill"
+magnetic compass,magnetic compass
+messenger bag,"mailbag, postbag"
+mailbox,"mailbox, letter box"
+tights,maillot
+one-piece bathing suit,"maillot, tank suit"
+manhole cover,manhole cover
+maraca,maraca
+marimba,"marimba, xylophone"
+mask,mask
+matchstick,matchstick
+maypole,maypole
+maze,"maze, labyrinth"
+measuring cup,measuring cup
+medicine cabinet,"medicine chest, medicine cabinet"
+megalith,"megalith, megalithic structure"
+microphone,"microphone, mike"
+microwave oven,"microwave, microwave oven"
+military uniform,military uniform
+milk can,milk can
+minibus,minibus
+miniskirt,"miniskirt, mini"
+minivan,minivan
+missile,missile
+mitten,mitten
+mixing bowl,mixing bowl
+mobile home,"mobile home, manufactured home"
+ford model t,Model T
+modem,modem
+monastery,monastery
+monitor,monitor
+moped,moped
+mortar and pestle,mortar
+graduation cap,mortarboard
+mosque,mosque
+mosquito net,mosquito net
+vespa,"motor scooter, scooter"
+mountain bike,"mountain bike, all-terrain bike, off-roader"
+tent,mountain tent
+computer mouse,"mouse, computer mouse"
+mousetrap,mousetrap
+moving van,moving van
+muzzle,muzzle
+metal nail,nail
+neck brace,neck brace
+necklace,necklace
+baby pacifier,nipple
+notebook computer,"notebook, notebook computer"
+obelisk,obelisk
+oboe,"oboe, hautboy, hautbois"
+ocarina,"ocarina, sweet potato"
+odometer,"odometer, hodometer, mileometer, milometer"
+oil filter,oil filter
+pipe organ,"organ, pipe organ"
+oscilloscope,"oscilloscope, scope, cathode-ray oscilloscope, CRO"
+overskirt,overskirt
+bullock cart,oxcart
+oxygen mask,oxygen mask
+product packet / packaging,packet
+paddle,"paddle, boat paddle"
+paddle wheel,"paddlewheel, paddle wheel"
+padlock,padlock
+paintbrush,paintbrush
+pajamas,"pajama, pyjama, pj's, jammies"
+palace,palace
+pan flute,"panpipe, pandean pipe, syrinx"
+paper towel,paper towel
+parachute,"parachute, chute"
+parallel bars,"parallel bars, bars"
+park bench,park bench
+parking meter,parking meter
+railroad car,"passenger car, coach, carriage"
+patio,"patio, terrace"
+payphone,"pay-phone, pay-station"
+pedestal,"pedestal, plinth, footstall"
+pencil case,"pencil box, pencil case"
+pencil sharpener,pencil sharpener
+perfume,"perfume, essence"
+Petri dish,Petri dish
+photocopier,photocopier
+plectrum,"pick, plectrum, plectron"
+Pickelhaube,pickelhaube
+picket fence,"picket fence, paling"
+pickup truck,"pickup, pickup truck"
+pier,pier
+piggy bank,"piggy bank, penny bank"
+pill bottle,pill bottle
+pillow,pillow
+ping-pong ball,ping-pong ball
+pinwheel,pinwheel
+pirate ship,"pirate, pirate ship"
+drink pitcher,"pitcher, ewer"
+block plane,"plane, carpenter's plane, woodworking plane"
+planetarium,planetarium
+plastic bag,plastic bag
+plate rack,plate rack
+farm plow,"plow, plough"
+plunger,"plunger, plumber's helper"
+Polaroid camera,"Polaroid camera, Polaroid Land camera"
+pole,pole
+police van,"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria"
+poncho,poncho
+pool table,"pool table, billiard table, snooker table"
+soda bottle,"pop bottle, soda bottle"
+plant pot,"pot, flowerpot"
+potter's wheel,potter's wheel
+power drill,power drill
+prayer rug,"prayer rug, prayer mat"
+printer,printer
+prison,"prison, prison house"
+missile,"projectile, missile"
+projector,projector
+hockey puck,"puck, hockey puck"
+punching bag,"punching bag, punch bag, punching ball, punchball"
+purse,purse
+quill,"quill, quill pen"
+quilt,"quilt, comforter, comfort, puff"
+race car,"racer, race car, racing car"
+racket,"racket, racquet"
+radiator,radiator
+radio,"radio, wireless"
+radio telescope,"radio telescope, radio reflector"
+rain barrel,rain barrel
+recreational vehicle,"recreational vehicle, RV, R.V."
+fishing casting reel,reel
+reflex camera,reflex camera
+refrigerator,"refrigerator, icebox"
+remote control,"remote control, remote"
+restaurant,"restaurant, eating house, eating place, eatery"
+revolver,"revolver, six-gun, six-shooter"
+rifle,rifle
+rocking chair,"rocking chair, rocker"
+rotisserie,rotisserie
+eraser,"rubber eraser, rubber, pencil eraser"
+rugby ball,rugby ball
+ruler measuring stick,"rule, ruler"
+sneaker,running shoe
+safe,safe
+safety pin,safety pin
+salt shaker,"saltshaker, salt shaker"
+sandal,sandal
+sarong,sarong
+saxophone,"sax, saxophone"
+scabbard,scabbard
+weighing scale,"scale, weighing machine"
+school bus,school bus
+schooner,schooner
+scoreboard,scoreboard
+CRT monitor,"screen, CRT screen"
+screw,screw
+screwdriver,screwdriver
+seat belt,"seat belt, seatbelt"
+sewing machine,sewing machine
+shield,"shield, buckler"
+shoe store,"shoe shop, shoe-shop, shoe store"
+shoji screen / room divider,shoji
+shopping basket,shopping basket
+shopping cart,shopping cart
+shovel,shovel
+shower cap,shower cap
+shower curtain,shower curtain
+ski,ski
+balaclava ski mask,ski mask
+sleeping bag,sleeping bag
+slide rule,"slide rule, slipstick"
+sliding door,sliding door
+slot machine,"slot, one-armed bandit"
+snorkel,snorkel
+snowmobile,snowmobile
+snowplow,"snowplow, snowplough"
+soap dispenser,soap dispenser
+soccer ball,soccer ball
+sock,sock
+solar thermal collector,"solar dish, solar collector, solar furnace"
+sombrero,sombrero
+soup bowl,soup bowl
+keyboard space bar,space bar
+space heater,space heater
+space shuttle,space shuttle
+spatula,spatula
+motorboat,speedboat
+spider web,"spider web, spider's web"
+spindle,spindle
+sports car,"sports car, sport car"
+spotlight,"spotlight, spot"
+stage,stage
+steam locomotive,steam locomotive
+through arch bridge,steel arch bridge
+steel drum,steel drum
+stethoscope,stethoscope
+scarf,stole
+stone wall,stone wall
+stopwatch,"stopwatch, stop watch"
+stove,stove
+strainer,strainer
+tram,"streetcar, tram, tramcar, trolley, trolley car"
+stretcher,stretcher
+couch,"studio couch, day bed"
+stupa,"stupa, tope"
+submarine,"submarine, pigboat, sub, U-boat"
+suit,"suit, suit of clothes"
+sundial,sundial
+sunglasses,sunglass
+sunglasses,"sunglasses, dark glasses, shades"
+sunscreen,"sunscreen, sunblock, sun blocker"
+suspension bridge,suspension bridge
+mop,"swab, swob, mop"
+sweatshirt,sweatshirt
+swim trunks / shorts,"swimming trunks, bathing trunks"
+swing,swing
+electrical switch,"switch, electric switch, electrical switch"
+syringe,syringe
+table lamp,table lamp
+tank,"tank, army tank, armored combat vehicle, armoured combat vehicle"
+tape player,tape player
+teapot,teapot
+teddy bear,"teddy, teddy bear"
+television,"television, television system"
+tennis ball,tennis ball
+thatched roof,"thatch, thatched roof"
+front curtain,"theater curtain, theatre curtain"
+thimble,thimble
+threshing machine,"thresher, thrasher, threshing machine"
+throne,throne
+tile roof,tile roof
+toaster,toaster
+tobacco shop,"tobacco shop, tobacconist shop, tobacconist"
+toilet seat,toilet seat
+torch,torch
+totem pole,totem pole
+tow truck,"tow truck, tow car, wrecker"
+toy store,toyshop
+tractor,tractor
+semi-trailer truck,"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi"
+tray,tray
+trench coat,trench coat
+tricycle,"tricycle, trike, velocipede"
+trimaran,trimaran
+tripod,tripod
+triumphal arch,triumphal arch
+trolleybus,"trolleybus, trolley coach, trackless trolley"
+trombone,trombone
+hot tub,"tub, vat"
+turnstile,turnstile
+typewriter keyboard,typewriter keyboard
+umbrella,umbrella
+unicycle,"unicycle, monocycle"
+upright piano,"upright, upright piano"
+vacuum cleaner,"vacuum, vacuum cleaner"
+vase,vase
+vaulted or arched ceiling,vault
+velvet fabric,velvet
+vending machine,vending machine
+vestment,vestment
+viaduct,viaduct
+violin,"violin, fiddle"
+volleyball,volleyball
+waffle iron,waffle iron
+wall clock,wall clock
+wallet,"wallet, billfold, notecase, pocketbook"
+wardrobe,"wardrobe, closet, press"
+military aircraft,"warplane, military plane"
+sink,"washbasin, handbasin, washbowl, lavabo, wash-hand basin"
+washing machine,"washer, automatic washer, washing machine"
+water bottle,water bottle
+water jug,water jug
+water tower,water tower
+whiskey jug,whiskey jug
+whistle,whistle
+hair wig,wig
+window screen,window screen
+window shade,window shade
+Windsor tie,Windsor tie
+wine bottle,wine bottle
+airplane wing,wing
+wok,wok
+wooden spoon,wooden spoon
+wool,"wool, woolen, woollen"
+split-rail fence,"worm fence, snake fence, snake-rail fence, Virginia fence"
+shipwreck,wreck
+sailboat,yawl
+yurt,yurt
+website,"web site, website, internet site, site"
+comic book,comic book
+crossword,"crossword puzzle, crossword"
+traffic or street sign,street sign
+traffic light,"traffic light, traffic signal, stoplight"
+dust jacket,"book jacket, dust cover, dust jacket, dust wrapper"
+menu,menu
+plate,plate
+guacamole,guacamole
+consomme,consomme
+hot pot,"hot pot, hotpot"
+trifle,trifle
+ice cream,"ice cream, icecream"
+popsicle,"ice lolly, lolly, lollipop, popsicle"
+baguette,French loaf
+bagel,"bagel, beigel"
+pretzel,pretzel
+cheeseburger,cheeseburger
+hot dog,"hotdog, hot dog, red hot"
+mashed potatoes,mashed potato
+cabbage,head cabbage
+broccoli,broccoli
+cauliflower,cauliflower
+zucchini,"zucchini, courgette"
+spaghetti squash,spaghetti squash
+acorn squash,acorn squash
+butternut squash,butternut squash
+cucumber,"cucumber, cuke"
+artichoke,"artichoke, globe artichoke"
+bell pepper,bell pepper
+cardoon,cardoon
+mushroom,mushroom
+Granny Smith apple,Granny Smith
+strawberry,strawberry
+orange,orange
+lemon,lemon
+fig,fig
+pineapple,"pineapple, ananas"
+banana,banana
+jackfruit,"jackfruit, jak, jack"
+cherimoya (custard apple),custard apple
+pomegranate,pomegranate
+hay,hay
+carbonara,carbonara
+chocolate syrup,"chocolate sauce, chocolate syrup"
+dough,dough
+meatloaf,"meat loaf, meatloaf"
+pizza,"pizza, pizza pie"
+pot pie,potpie
+burrito,burrito
+red wine,red wine
+espresso,espresso
+tea cup,cup
+eggnog,eggnog
+mountain,alp
+bubble,bubble
+cliff,"cliff, drop, drop-off"
+coral reef,coral reef
+geyser,geyser
+lakeshore,"lakeside, lakeshore"
+promontory,"promontory, headland, head, foreland"
+sandbar,"sandbar, sand bar"
+beach,"seashore, coast, seacoast, sea-coast"
+valley,"valley, vale"
+volcano,volcano
+baseball player,"ballplayer, baseball player"
+bridegroom,"groom, bridegroom"
+scuba diver,scuba diver
+rapeseed,rapeseed
+daisy,daisy
+yellow lady's slipper,"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum"
+corn,corn
+acorn,acorn
+rose hip,"hip, rose hip, rosehip"
+horse chestnut seed,"buckeye, horse chestnut, conker"
+coral fungus,coral fungus
+agaric,agaric
+gyromitra,gyromitra
+stinkhorn mushroom,"stinkhorn, carrion fungus"
+earth star fungus,earthstar
+hen of the woods mushroom,"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa"
+bolete,bolete
+corn cob,"ear, spike, capitulum"
+toilet paper,"toilet tissue, toilet paper, bathroom tissue"
diff --git a/python/ClipDetection/clip_component/data/seven_templates.txt b/python/ClipDetection/clip_component/data/seven_templates.txt
index 2ba4ff13..c941f180 100644
--- a/python/ClipDetection/clip_component/data/seven_templates.txt
+++ b/python/ClipDetection/clip_component/data/seven_templates.txt
@@ -1,7 +1,7 @@
-itap of a {}.
-a bad photo of the {}.
-an origami {}.
-a photo of the large {}.
-a {} in a video game.
-art of the {}.
+itap of a {}.
+a bad photo of the {}.
+an origami {}.
+a photo of the large {}.
+a {} in a video game.
+art of the {}.
a photo of the small {}.
\ No newline at end of file
diff --git a/python/ClipDetection/plugin-files/descriptor/descriptor.json b/python/ClipDetection/plugin-files/descriptor/descriptor.json
index e722fd36..befab3d0 100644
--- a/python/ClipDetection/plugin-files/descriptor/descriptor.json
+++ b/python/ClipDetection/plugin-files/descriptor/descriptor.json
@@ -23,7 +23,7 @@
"properties": [
{
"name": "MODEL_NAME",
- "description": "Specifies which CLIP model to load for inferencing. The available models are 'ViT-L/14' and 'ViT-B/32'.",
+ "description": "Specifies which CLIP model to load for inferencing. The available models are 'ViT-L/14', 'ViT-B/32', and 'CoOp'.",
"type": "STRING",
"defaultValue": "ViT-L/14"
},
@@ -40,20 +40,20 @@
"defaultValue": "openai_80"
},
{
- "name": "CLASSIFICATION_LIST",
- "description": "Specifies the classification list that will be tokenized for the text encoder (supports 'imagenet' and 'coco'). By default, the COCO classifications will be used.",
+ "name": "TEMPLATE_PATH",
+ "description": "Optionally specifies a path to a custom text file containing templates for use in the CLIP model. Include a single {} where each classification is to be inserted. If MODEL_NAME=='CoOp', then '' is the only supported value.",
"type": "STRING",
- "defaultValue": "coco"
+ "defaultValue": ""
},
{
- "name": "CLASSIFICATION_PATH",
- "description": "Optionally specifies a path to a custom csv file containing two names for each classification: one is the full name to display and the other to enter into the CLIP text encoding.",
+ "name": "CLASSIFICATION_LIST",
+ "description": "Specifies the classification list that will be tokenized for the text encoder (supports 'imagenet' and 'coco'). By default, the COCO classifications will be used. If MODEL_NAME=='CoOp', then 'imagenet' is the only supported value.",
"type": "STRING",
- "defaultValue": ""
+ "defaultValue": "coco"
},
{
- "name": "TEMPLATE_PATH",
- "description": "Optionally specifies a path to a custom text file containing templates for use in the CLIP model. Include a single {} where each classification is to be inserted.",
+ "name": "CLASSIFICATION_PATH",
+ "description": "Optionally specifies a path to a custom csv file containing two names for each classification: one is the full name to display and the other to enter into the CLIP text encoding. If MODEL_NAME=='CoOp', then '' is the only supported value.",
"type": "STRING",
"defaultValue": ""
},
@@ -65,13 +65,7 @@
},
{
"name": "ENABLE_TRITON",
- "description": "If true, inferencing will be performed via a configured Triton inference server.",
- "type": "BOOLEAN",
- "defaultValue": "false"
- },
- {
- "name": "INCLUDE_FEATURES",
- "description": "If true, the detection will have a detection property, FEATURE, which contains the base64-encoded version of the feature vector.",
+ "description": "If true, inferencing will be performed via a configured Triton inference server. If MODEL_NAME=='CoOp', then 'false' is the only supported value.",
"type": "BOOLEAN",
"defaultValue": "false"
},
@@ -81,11 +75,23 @@
"type": "STRING",
"defaultValue": "clip-detection-server:8001"
},
+ {
+ "name": "INCLUDE_FEATURES",
+ "description": "If true, the detection will have a detection property, FEATURE, which contains the base64-encoded version of the feature vector.",
+ "type": "BOOLEAN",
+ "defaultValue": "false"
+ },
{
"name": "DETECTION_FRAME_BATCH_SIZE",
"description": "Number of frames to batch inference when processing video. GPU VRAM dependant. If ENABLE_CROPPING is set to true, then the value will be ignored and set to 1.",
"type": "INT",
"defaultValue": "64"
+ },
+ {
+ "name": "CUDA_DEVICE_ID",
+ "description": "ID of CUDA device (typically 0) that will be used to run the models. When less than 0 CUDA will be disabled.",
+ "type": "INT",
+ "propertiesKey": "detection.cuda.device.id"
}
]
}
diff --git a/python/ClipDetection/setup.cfg b/python/ClipDetection/setup.cfg
index d2f4d8eb..e563032a 100644
--- a/python/ClipDetection/setup.cfg
+++ b/python/ClipDetection/setup.cfg
@@ -40,5 +40,5 @@ mpf.exported_component =
component = clip_component.clip_component:ClipComponent
[options.package_data]
-clip_component = data/imagenet_classification_list.csv, data/coco_classification_list.csv, data/eighty_templates.txt, data/seven_templates.txt, data/one_template.txt
+clip_component = data/imagenet_classification_list.csv, data/coco_classification_list.csv, data/eighty_templates.txt, data/seven_templates.txt, data/one_template.txt, data/coop_args.txt
diff --git a/python/ClipDetection/tests/__init__.py b/python/ClipDetection/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/python/ClipDetection/tests/data/NOTICE b/python/ClipDetection/tests/data/NOTICE
index 0cee4e82..405aecad 100644
--- a/python/ClipDetection/tests/data/NOTICE
+++ b/python/ClipDetection/tests/data/NOTICE
@@ -1,17 +1,17 @@
-# dog.jpg
-# Public Domain
-
-# collie.jpg
-# Public Domain
-
-# riot.jpg
-# Public Domain
-
-# test_video.mp4
-# Custom created file from public domain images
-
-# violence_classes.csv
-# Custom created file for testing CLASSIFICATION_PATH
-
-# violence_templates.txt
+# dog.jpg
+# Public Domain
+
+# collie.jpg
+# Public Domain
+
+# riot.jpg
+# Public Domain
+
+# test_video.mp4
+# Custom created file from public domain images
+
+# violence_classes.csv
+# Custom created file for testing CLASSIFICATION_PATH
+
+# violence_templates.txt
# Custom created file for testing TEMPLATE_PATH
\ No newline at end of file
diff --git a/python/ClipDetection/tests/data/rollup.csv b/python/ClipDetection/tests/data/rollup.csv
index d251e69c..62276c46 100644
--- a/python/ClipDetection/tests/data/rollup.csv
+++ b/python/ClipDetection/tests/data/rollup.csv
@@ -1,7 +1,7 @@
-dog,indoor animal
-cat,indoor animal
-lion,wild animal
-sedan,vehicle
-truck,vehicle
-guitar,musical instrument
+dog,indoor animal
+cat,indoor animal
+lion,wild animal
+sedan,vehicle
+truck,vehicle
+guitar,musical instrument
house,building
\ No newline at end of file
diff --git a/python/ClipDetection/tests/data/violence_classes.csv b/python/ClipDetection/tests/data/violence_classes.csv
index b5adadaa..963a9177 100644
--- a/python/ClipDetection/tests/data/violence_classes.csv
+++ b/python/ClipDetection/tests/data/violence_classes.csv
@@ -1,4 +1,4 @@
-peaceful,peaceful scene
-safe,safe scene
-violent,violent scene
+peaceful,peaceful scene
+safe,safe scene
+violent,violent scene
dangerous,dangerous scene
\ No newline at end of file
diff --git a/python/ClipDetection/tests/data/violence_templates.txt b/python/ClipDetection/tests/data/violence_templates.txt
index b3330db8..fae6e652 100644
--- a/python/ClipDetection/tests/data/violence_templates.txt
+++ b/python/ClipDetection/tests/data/violence_templates.txt
@@ -1,3 +1,3 @@
-a {} scene.
-photo of a {} scene.
+a {} scene.
+photo of a {} scene.
people in a {} environment.
\ No newline at end of file
diff --git a/python/ClipDetection/tests/test_clip.py b/python/ClipDetection/tests/test_clip.py
index 5d673b77..4ece799c 100644
--- a/python/ClipDetection/tests/test_clip.py
+++ b/python/ClipDetection/tests/test_clip.py
@@ -38,7 +38,6 @@
logging.basicConfig(level=logging.DEBUG)
class TestClip(unittest.TestCase):
-
def test_image_file(self):
job = mpf.ImageJob(
job_name='test-image',
diff --git a/python/ClipDetection/triton_server/models/vit_b_32.config.pbtxt b/python/ClipDetection/triton_server/models/vit_b_32.config.pbtxt
index 32249761..a842ec56 100644
--- a/python/ClipDetection/triton_server/models/vit_b_32.config.pbtxt
+++ b/python/ClipDetection/triton_server/models/vit_b_32.config.pbtxt
@@ -1,28 +1,28 @@
-name: "vit_b_32"
-default_model_filename: "vit_b_32.pt"
-backend: "pytorch"
-max_batch_size: 2048
-input [
- {
- name: "image_input"
- data_type: TYPE_FP32
- dims: [3, 224, 224]
- }
-]
-output [
- {
- name: "feature_vector__0"
- data_type: TYPE_FP32
- dims: [512]
- }
-]
-parameters [
- {
- key: "INFERENCE_MODE"
- value: {string_value: "true"}
- },
- {
- key: "ENABLE_NVFUSER"
- value: {string_value: "true"}
- }
+name: "vit_b_32"
+default_model_filename: "vit_b_32.pt"
+backend: "pytorch"
+max_batch_size: 2048
+input [
+ {
+ name: "image_input"
+ data_type: TYPE_FP32
+ dims: [3, 224, 224]
+ }
+]
+output [
+ {
+ name: "feature_vector__0"
+ data_type: TYPE_FP32
+ dims: [512]
+ }
+]
+parameters [
+ {
+ key: "INFERENCE_MODE"
+ value: {string_value: "true"}
+ },
+ {
+ key: "ENABLE_NVFUSER"
+ value: {string_value: "true"}
+ }
]
\ No newline at end of file
diff --git a/python/ClipDetection/triton_server/models/vit_l_14.config.pbtxt b/python/ClipDetection/triton_server/models/vit_l_14.config.pbtxt
index 3431bfa7..4ac33b95 100644
--- a/python/ClipDetection/triton_server/models/vit_l_14.config.pbtxt
+++ b/python/ClipDetection/triton_server/models/vit_l_14.config.pbtxt
@@ -1,28 +1,28 @@
-name: "vit_l_14"
-default_model_filename: "vit_l_14.pt"
-backend: "pytorch"
-max_batch_size: 2048
-input [
- {
- name: "image_input"
- data_type: TYPE_FP32
- dims: [3, 224, 224]
- }
-]
-output [
- {
- name: "feature_vector__0"
- data_type: TYPE_FP32
- dims: [512]
- }
-]
-parameters [
- {
- key: "INFERENCE_MODE"
- value: {string_value: "true"}
- },
- {
- key: "ENABLE_NVFUSER"
- value: {string_value: "true"}
- }
+name: "vit_l_14"
+default_model_filename: "vit_l_14.pt"
+backend: "pytorch"
+max_batch_size: 2048
+input [
+ {
+ name: "image_input"
+ data_type: TYPE_FP32
+ dims: [3, 224, 224]
+ }
+]
+output [
+ {
+ name: "feature_vector__0"
+ data_type: TYPE_FP32
+ dims: [512]
+ }
+]
+parameters [
+ {
+ key: "INFERENCE_MODE"
+ value: {string_value: "true"}
+ },
+ {
+ key: "ENABLE_NVFUSER"
+ value: {string_value: "true"}
+ }
]
\ No newline at end of file