diff --git a/README.md b/README.md index 0670eb18..c3df5718 100644 --- a/README.md +++ b/README.md @@ -106,6 +106,7 @@ Congratulations! You are done! Now you can train your model with your favorite f | **Train** clothes binary segmentation by @ternaus | [Repo](https://github.com/ternaus/cloths_segmentation) | | | **Load and inference** pretrained Segformer | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/segformer_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/segformer_inference_pretrained.ipynb) | | **Load and inference** pretrained DPT | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/dpt_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/dpt_inference_pretrained.ipynb) | +| **Load and inference** pretrained DPT | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb) | | **Save and load** models locally / to HuggingFace Hub |[Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb) | **Export** trained model to ONNX | [Notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) | @@ -113,18 +114,20 @@ Congratulations! You are done! Now you can train your model with your favorite f ## 📦 Models and encoders ### Architectures - - Unet [[paper](https://arxiv.org/abs/1505.04597)] [[docs](https://smp.readthedocs.io/en/latest/models.html#unet)] - - Unet++ [[paper](https://arxiv.org/pdf/1807.10165.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id2)] - - MAnet [[paper](https://ieeexplore.ieee.org/abstract/document/9201310)] [[docs](https://smp.readthedocs.io/en/latest/models.html#manet)] - - Linknet [[paper](https://arxiv.org/abs/1707.03718)] [[docs](https://smp.readthedocs.io/en/latest/models.html#linknet)] - - FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#fpn)] - - PSPNet [[paper](https://arxiv.org/abs/1612.01105)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pspnet)] - - PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)] - - DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)] - - DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)] - - UPerNet [[paper](https://arxiv.org/abs/1807.10221)] [[docs](https://smp.readthedocs.io/en/latest/models.html#upernet)] - - Segformer [[paper](https://arxiv.org/abs/2105.15203)] [[docs](https://smp.readthedocs.io/en/latest/models.html#segformer)] - - DPT [[paper](https://arxiv.org/abs/2103.13413)] [[docs](https://smp.readthedocs.io/en/latest/models.html#dpt)] +| Architecture | Paper | Documentation | Checkpoints | +|--------------|-------|---------------|------------| +| Unet | [paper](https://arxiv.org/abs/1505.04597) | [docs](https://smp.readthedocs.io/en/latest/models.html#unet) | | +| Unet++ | [paper](https://arxiv.org/pdf/1807.10165.pdf) | [docs](https://smp.readthedocs.io/en/latest/models.html#unetplusplus) | | +| MAnet | [paper](https://ieeexplore.ieee.org/abstract/document/9201310) | [docs](https://smp.readthedocs.io/en/latest/models.html#manet) | | +| Linknet | [paper](https://arxiv.org/abs/1707.03718) | [docs](https://smp.readthedocs.io/en/latest/models.html#linknet) | | +| FPN | [paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf) | [docs](https://smp.readthedocs.io/en/latest/models.html#fpn) | | +| PSPNet | [paper](https://arxiv.org/abs/1612.01105) | [docs](https://smp.readthedocs.io/en/latest/models.html#pspnet) | | +| PAN | [paper](https://arxiv.org/abs/1805.10180) | [docs](https://smp.readthedocs.io/en/latest/models.html#pan) | | +| DeepLabV3 | [paper](https://arxiv.org/abs/1706.05587) | [docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3) | | +| DeepLabV3+ | [paper](https://arxiv.org/abs/1802.02611) | [docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3plus) | | +| UPerNet | [paper](https://arxiv.org/abs/1807.10221) | [docs](https://smp.readthedocs.io/en/latest/models.html#upernet) | [checkpoints](https://huggingface.co/collections/smp-hub/upernet-67fadcdbe08418c6ea94f768) | +| Segformer | [paper](https://arxiv.org/abs/2105.15203) | [docs](https://smp.readthedocs.io/en/latest/models.html#segformer) | [checkpoints](https://huggingface.co/collections/smp-hub/segformer-6749eb4923dea2c355f29a1f) | +| DPT | [paper](https://arxiv.org/abs/2103.13413) | [docs](https://smp.readthedocs.io/en/latest/models.html#dpt) | [checkpoints](https://huggingface.co/collections/smp-hub/dpt-67f30487327c0599a0c62d68) | ### Encoders diff --git a/examples/upernet_inference_pretrained.ipynb b/examples/upernet_inference_pretrained.ipynb new file mode 100644 index 00000000..aa644858 --- /dev/null +++ b/examples/upernet_inference_pretrained.ipynb @@ -0,0 +1,153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make sure you have the latest version of the libraries\n", + "!pip install -U segmentation-models-pytorch\n", + "!pip install albumentations matplotlib requests pillow" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/projects/segmentation_models.pytorch/.venv/lib/python3.10/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 requests\n", + "import numpy as np\n", + "import albumentations as A\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import torch\n", + "import segmentation_models_pytorch as smp\n", + "\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocessing:\n", + " Compose([\n", + " Resize(p=1.0, height=512, width=512, interpolation=1, mask_interpolation=0),\n", + " Normalize(p=1.0, mean=(123.675, 116.28, 103.53), std=(58.395, 57.12, 57.375), max_pixel_value=1.0, normalization='standard'),\n", + "], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# More checkpoints can be found here:\n", + "# https://huggingface.co/collections/smp-hub/upernet-67fadcdbe08418c6ea94f768\n", + "checkpoint = \"smp-hub/upernet-swin-tiny\"\n", + "\n", + "# Load pretrained model and preprocessing function\n", + "model = smp.from_pretrained(checkpoint).eval().to(device)\n", + "preprocessing = A.Compose.from_pretrained(checkpoint)\n", + "print(\"Preprocessing:\\n\", preprocessing)\n", + "\n", + "# Load image\n", + "url = \"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg\"\n", + "image = Image.open(requests.get(url, stream=True).raw)\n", + "\n", + "# Preprocess image\n", + "image = np.array(image)\n", + "normalized_image = preprocessing(image=image)[\"image\"]\n", + "input_tensor = torch.as_tensor(normalized_image)\n", + "input_tensor = input_tensor.permute(2, 0, 1).unsqueeze(0) # HWC -> BCHW\n", + "input_tensor = input_tensor.to(device)\n", + "\n", + "# Perform inference\n", + "with torch.no_grad():\n", + " output_mask = model(input_tensor)\n", + "\n", + "# Postprocess mask\n", + "mask = torch.nn.functional.interpolate(\n", + " output_mask, size=image.shape[:2], mode=\"bilinear\", align_corners=False\n", + ")\n", + "mask = mask[0].argmax(0).cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(121)\n", + "plt.axis(\"off\")\n", + "plt.imshow(image)\n", + "plt.title(\"Input Image\")\n", + "\n", + "plt.subplot(122)\n", + "plt.axis(\"off\")\n", + "plt.imshow(mask)\n", + "plt.title(\"Output Mask\")\n", + "\n", + "plt.show()" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements/required.txt b/requirements/required.txt index 64cf1be7..bdb4d9e3 100644 --- a/requirements/required.txt +++ b/requirements/required.txt @@ -1,6 +1,6 @@ huggingface_hub==0.30.2 numpy==2.2.4 -pillow==11.2.0 +pillow==11.2.1 safetensors==0.5.3 timm==1.0.15 torch==2.6.0 diff --git a/scripts/models-conversions/upernet-hf-to-smp.py b/scripts/models-conversions/upernet-hf-to-smp.py new file mode 100644 index 00000000..8cd3162f --- /dev/null +++ b/scripts/models-conversions/upernet-hf-to-smp.py @@ -0,0 +1,249 @@ +import re +import torch +import albumentations as A +import segmentation_models_pytorch as smp +from huggingface_hub import hf_hub_download, HfApi +from collections import defaultdict + +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# fmt: off +CONVNEXT_MAPPING = { + r"backbone.embeddings.patch_embeddings.(weight|bias)": r"encoder.model.stem_0.\1", + r"backbone.embeddings.layernorm.(weight|bias)": r"encoder.model.stem_1.\1", + r"backbone.encoder.stages.(\d+).layers.(\d+).layer_scale_parameter": r"encoder.model.stages_\1.blocks.\2.gamma", + r"backbone.encoder.stages.(\d+).layers.(\d+).dwconv.(weight|bias)": r"encoder.model.stages_\1.blocks.\2.conv_dw.\3", + r"backbone.encoder.stages.(\d+).layers.(\d+).layernorm.(weight|bias)": r"encoder.model.stages_\1.blocks.\2.norm.\3", + r"backbone.encoder.stages.(\d+).layers.(\d+).pwconv(\d+).(weight|bias)": r"encoder.model.stages_\1.blocks.\2.mlp.fc\3.\4", + r"backbone.encoder.stages.(\d+).downsampling_layer.(\d+).(weight|bias)": r"encoder.model.stages_\1.downsample.\2.\3", +} + +SWIN_MAPPING = { + r"backbone.embeddings.patch_embeddings.projection": r"encoder.model.patch_embed.proj", + r"backbone.embeddings.norm": r"encoder.model.patch_embed.norm", + r"backbone.encoder.layers.(\d+).blocks.(\d+).layernorm_before": r"encoder.model.layers_\1.blocks.\2.norm1", + r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.self.relative_position_bias_table": r"encoder.model.layers_\1.blocks.\2.attn.relative_position_bias_table", + r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.self.(query|key|value)": r"encoder.model.layers_\1.blocks.\2.attn.\3", + r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.output.dense": r"encoder.model.layers_\1.blocks.\2.attn.proj", + r"backbone.encoder.layers.(\d+).blocks.(\d+).layernorm_after": r"encoder.model.layers_\1.blocks.\2.norm2", + r"backbone.encoder.layers.(\d+).blocks.(\d+).intermediate.dense": r"encoder.model.layers_\1.blocks.\2.mlp.fc1", + r"backbone.encoder.layers.(\d+).blocks.(\d+).output.dense": r"encoder.model.layers_\1.blocks.\2.mlp.fc2", + r"backbone.encoder.layers.(\d+).downsample.reduction": lambda x: f"encoder.model.layers_{1 + int(x.group(1))}.downsample.reduction", + r"backbone.encoder.layers.(\d+).downsample.norm": lambda x: f"encoder.model.layers_{1 + int(x.group(1))}.downsample.norm", +} + +DECODER_MAPPING = { + + # started from 1 in hf + r"backbone.hidden_states_norms.stage(\d+)": lambda x: f"decoder.feature_norms.{int(x.group(1)) - 1}", + + r"decode_head.psp_modules.(\d+).(\d+).conv.weight": r"decoder.psp.blocks.\1.\2.0.weight", + r"decode_head.psp_modules.(\d+).(\d+).batch_norm": r"decoder.psp.blocks.\1.\2.1", + r"decode_head.bottleneck.conv.weight": r"decoder.psp.out_conv.0.weight", + r"decode_head.bottleneck.batch_norm": r"decoder.psp.out_conv.1", + + # fpn blocks are in reverse order (3 blocks total, so 2 - i) + r"decode_head.lateral_convs.(\d+).conv.weight": lambda x: f"decoder.fpn_lateral_blocks.{2 - int(x.group(1))}.conv_norm_relu.0.weight", + r"decode_head.lateral_convs.(\d+).batch_norm": lambda x: f"decoder.fpn_lateral_blocks.{2 - int(x.group(1))}.conv_norm_relu.1", + r"decode_head.fpn_convs.(\d+).conv.weight": lambda x: f"decoder.fpn_conv_blocks.{2 - int(x.group(1))}.0.weight", + r"decode_head.fpn_convs.(\d+).batch_norm": lambda x: f"decoder.fpn_conv_blocks.{2 - int(x.group(1))}.1", + + r"decode_head.fpn_bottleneck.conv.weight": r"decoder.fusion_block.0.weight", + r"decode_head.fpn_bottleneck.batch_norm": r"decoder.fusion_block.1", + r"decode_head.classifier": r"segmentation_head.0", +} +# fmt: on + +PRETRAINED_CHECKPOINTS = { + "convnext-tiny": { + "repo_id": "openmmlab/upernet-convnext-tiny", + "encoder_name": "tu-convnext_tiny", + "decoder_channels": 512, + "classes": 150, + "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, + }, + "convnext-small": { + "repo_id": "openmmlab/upernet-convnext-small", + "encoder_name": "tu-convnext_small", + "decoder_channels": 512, + "classes": 150, + "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, + }, + "convnext-base": { + "repo_id": "openmmlab/upernet-convnext-base", + "encoder_name": "tu-convnext_base", + "decoder_channels": 512, + "classes": 150, + "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, + }, + "convnext-large": { + "repo_id": "openmmlab/upernet-convnext-large", + "encoder_name": "tu-convnext_large", + "decoder_channels": 512, + "classes": 150, + "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, + }, + "convnext-xlarge": { + "repo_id": "openmmlab/upernet-convnext-xlarge", + "encoder_name": "tu-convnext_xlarge", + "decoder_channels": 512, + "classes": 150, + "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, + }, + "swin-tiny": { + "repo_id": "openmmlab/upernet-swin-tiny", + "encoder_name": "tu-swin_tiny_patch4_window7_224", + "decoder_channels": 512, + "classes": 150, + "extra_kwargs": {"img_size": 512}, + "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, + }, + "swin-small": { + "repo_id": "openmmlab/upernet-swin-small", + "encoder_name": "tu-swin_small_patch4_window7_224", + "decoder_channels": 512, + "classes": 150, + "extra_kwargs": {"img_size": 512}, + "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, + }, + "swin-large": { + "repo_id": "openmmlab/upernet-swin-large", + "encoder_name": "tu-swin_large_patch4_window12_384", + "decoder_channels": 512, + "classes": 150, + "extra_kwargs": {"img_size": 512}, + "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, + }, +} + + +def convert_old_keys_to_new_keys(state_dict_keys: dict, keys_mapping: dict): + """ + This function should be applied only once, on the concatenated keys to efficiently rename using + the key mappings. + """ + output_dict = {} + if state_dict_keys is not None: + old_text = "\n".join(state_dict_keys) + new_text = old_text + for pattern, replacement in keys_mapping.items(): + if replacement is None: + new_text = re.sub(pattern, "", new_text) # an empty line + continue + new_text = re.sub(pattern, replacement, new_text) + output_dict = dict(zip(old_text.split("\n"), new_text.split("\n"))) + return output_dict + + +def group_qkv_layers(state_dict: dict) -> dict: + """Find corresponding layer names for query, key and value layers and stack them in a single layer""" + + state_dict = state_dict.copy() # shallow copy + + result = defaultdict(dict) + layer_names = list(state_dict.keys()) + qkv_names = ["query", "key", "value"] + for layer_name in layer_names: + for pattern in qkv_names: + if pattern in layer_name: + new_key = layer_name.replace(pattern, "qkv") + result[new_key][pattern] = state_dict.pop(layer_name) + break + + # merge them all + for new_key, patterns in result.items(): + state_dict[new_key] = torch.cat( + [patterns[qkv_name] for qkv_name in qkv_names], dim=0 + ) + + return state_dict + + +def convert_model(model_name: str, push_to_hub: bool = False): + params = PRETRAINED_CHECKPOINTS[model_name] + + print(f"Converting model: {model_name}") + print(f"Downloading weights from: {params['repo_id']}") + + hf_weights_path = hf_hub_download( + repo_id=params["repo_id"], filename="pytorch_model.bin" + ) + hf_state_dict = torch.load(hf_weights_path, weights_only=True) + print(f"Loaded HuggingFace state dict with {len(hf_state_dict)} keys") + + # Rename keys + keys_mapping = convert_old_keys_to_new_keys(hf_state_dict.keys(), params["mapping"]) + + smp_state_dict = {} + for old_key, new_key in keys_mapping.items(): + smp_state_dict[new_key] = hf_state_dict[old_key] + + # remove aux head + smp_state_dict = { + k: v for k, v in smp_state_dict.items() if "auxiliary_head." not in k + } + + # [swin] group qkv layers and remove `relative_position_index` + smp_state_dict = group_qkv_layers(smp_state_dict) + smp_state_dict = { + k: v for k, v in smp_state_dict.items() if "relative_position_index" not in k + } + + # Create model + print(f"Creating SMP UPerNet model with encoder: {params['encoder_name']}") + extra_kwargs = params.get("extra_kwargs", {}) + smp_model = smp.UPerNet( + encoder_name=params["encoder_name"], + encoder_weights=None, + decoder_channels=params["decoder_channels"], + classes=params["classes"], + **extra_kwargs, + ) + + print("Loading weights into SMP model...") + smp_model.load_state_dict(smp_state_dict, strict=True) + + # Check we can run the model + print("Verifying model with test inference...") + smp_model.eval() + sample = torch.ones(1, 3, 512, 512) + with torch.no_grad(): + output = smp_model(sample) + print(f"Test inference successful. Output shape: {output.shape}") + + # Save model with preprocessing + smp_repo_id = f"smp-hub/upernet-{model_name}" + print(f"Saving model to: {smp_repo_id}") + smp_model.save_pretrained(save_directory=smp_repo_id) + + transform = A.Compose( + [ + A.Resize(512, 512), + A.Normalize( + mean=(123.675, 116.28, 103.53), + std=(58.395, 57.12, 57.375), + max_pixel_value=1.0, + ), + ] + ) + transform.save_pretrained(save_directory=smp_repo_id) + + if push_to_hub: + print(f"Pushing model to HuggingFace Hub: {smp_repo_id}") + api = HfApi() + if not api.repo_exists(smp_repo_id): + api.create_repo(repo_id=smp_repo_id, repo_type="model") + api.upload_folder( + repo_id=smp_repo_id, + folder_path=smp_repo_id, + repo_type="model", + ) + + print(f"Conversion of {model_name} completed successfully!") + + +if __name__ == "__main__": + print(f"Starting conversion of {len(PRETRAINED_CHECKPOINTS)} UPerNet models") + for model_name in PRETRAINED_CHECKPOINTS.keys(): + convert_model(model_name, push_to_hub=True) + print("All conversions completed!") diff --git a/segmentation_models_pytorch/base/initialization.py b/segmentation_models_pytorch/base/initialization.py index 4bea4aa6..cf518edd 100644 --- a/segmentation_models_pytorch/base/initialization.py +++ b/segmentation_models_pytorch/base/initialization.py @@ -8,7 +8,9 @@ def initialize_decoder(module): if m.bias is not None: nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.BatchNorm2d): + elif isinstance( + m, (nn.BatchNorm2d, nn.LayerNorm, nn.GroupNorm, nn.InstanceNorm2d) + ): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) diff --git a/segmentation_models_pytorch/base/model.py b/segmentation_models_pytorch/base/model.py index 67f9422a..9b0db714 100644 --- a/segmentation_models_pytorch/base/model.py +++ b/segmentation_models_pytorch/base/model.py @@ -87,9 +87,7 @@ def predict(self, x): """ if self.training: self.eval() - - x = self.forward(x) - + x = self(x) return x def load_state_dict(self, state_dict, **kwargs): diff --git a/segmentation_models_pytorch/decoders/upernet/decoder.py b/segmentation_models_pytorch/decoders/upernet/decoder.py index 810778f3..435927df 100644 --- a/segmentation_models_pytorch/decoders/upernet/decoder.py +++ b/segmentation_models_pytorch/decoders/upernet/decoder.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Union, Sequence +from typing import Any, Dict, Union, Sequence, List import torch import torch.nn as nn @@ -22,7 +22,7 @@ def __init__( nn.AdaptiveAvgPool2d(size), md.Conv2dReLU( in_channels, - in_channels // len(sizes), + out_channels, kernel_size=1, use_norm=use_norm, ), @@ -31,50 +31,67 @@ def __init__( ] ) self.out_conv = md.Conv2dReLU( - in_channels=in_channels * 2, + in_channels=in_channels + len(sizes) * out_channels, out_channels=out_channels, - kernel_size=1, + kernel_size=3, + padding=1, use_norm="batchnorm", ) - def forward(self, x): - _, _, height, width = x.shape - out = [x] + [ - F.interpolate( - block(x), size=(height, width), mode="bilinear", align_corners=False + def forward(self, feature: torch.Tensor) -> torch.Tensor: + _, _, height, width = feature.shape + pyramid_features = [feature] + for block in self.blocks: + pooled_feature = block(feature) + resized_feature = F.interpolate( + pooled_feature, + size=(height, width), + mode="bilinear", + align_corners=False, ) - for block in self.blocks - ] - out = self.out_conv(torch.cat(out, dim=1)) - return out + pyramid_features.append(resized_feature) + fused_feature = self.out_conv(torch.cat(pyramid_features, dim=1)) + return fused_feature + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.permute(0, 2, 3, 1) # to channels_last + normed_x = nn.functional.layer_norm( + x, self.normalized_shape, self.weight, self.bias, self.eps + ) + normed_x = normed_x.permute(0, 3, 1, 2) # to channels_first + return normed_x -class FPNBlock(nn.Module): +class FPNLateralBlock(nn.Module): def __init__( self, - skip_channels: int, - pyramid_channels: int, + lateral_channels: int, + out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() - self.skip_conv = ( - md.Conv2dReLU( - skip_channels, - pyramid_channels, - kernel_size=1, - use_norm=use_norm, - ) - if skip_channels != 0 - else nn.Identity() + self.conv_norm_relu = md.Conv2dReLU( + lateral_channels, + out_channels, + kernel_size=1, + use_norm=use_norm, ) - def forward(self, x, skip): - _, channels, height, width = skip.shape - x = F.interpolate(x, size=(height, width), mode="bilinear", align_corners=False) - if channels != 0: - skip = self.skip_conv(skip) - x = x + skip - return x + def forward( + self, state_feature: torch.Tensor, lateral_feature: torch.Tensor + ) -> torch.Tensor: + # 1. Apply block to encoder feature + lateral_feature = self.conv_norm_relu(lateral_feature) + # 2. Upsample encoder feature to the "state" feature resolution + _, _, height, width = lateral_feature.shape + state_feature = F.interpolate( + state_feature, size=(height, width), mode="bilinear", align_corners=False + ) + # 3. Sum state and encoder features + fused_feature = state_feature + lateral_feature + return fused_feature class UPerNetDecoder(nn.Module): @@ -82,8 +99,7 @@ def __init__( self, encoder_channels: Sequence[int], encoder_depth: int = 5, - pyramid_channels: int = 256, - segmentation_channels: int = 64, + decoder_channels: int = 256, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() @@ -95,51 +111,101 @@ def __init__( ) ) - encoder_channels = encoder_channels[::-1] + # Encoder channels for input features starting from the highest resolution + # [1, 1/2, 1/4, 1/8, 1/16, ...] for num_features = encoder_depth + 1, + # but we use only [1/4, 1/8, 1/16, ...] for UPerNet + encoder_channels = encoder_channels[2:] + + self.feature_norms = nn.ModuleList( + [LayerNorm2d(channels, eps=1e-6) for channels in encoder_channels] + ) # PSP Module + lowest_resolution_feature_channels = encoder_channels[-1] self.psp = PSPModule( - in_channels=encoder_channels[0], - out_channels=pyramid_channels, + in_channels=lowest_resolution_feature_channels, + out_channels=decoder_channels, sizes=(1, 2, 3, 6), use_norm=use_norm, ) # FPN Module - self.fpn_stages = nn.ModuleList( - [FPNBlock(ch, pyramid_channels) for ch in encoder_channels[1:]] - ) + # we skip lower resolution feature maps + reverse the order + # [1/4, 1/8, 1/16, 1/32] -> [1/16, 1/8, 1/4] + lateral_channels = encoder_channels[:-1][::-1] + self.fpn_lateral_blocks = nn.ModuleList([]) + self.fpn_conv_blocks = nn.ModuleList([]) + for channels in lateral_channels: + block = FPNLateralBlock( + lateral_channels=channels, + out_channels=decoder_channels, + use_norm=use_norm, + ) + self.fpn_lateral_blocks.append(block) + conv_block = md.Conv2dReLU( + in_channels=decoder_channels, + out_channels=decoder_channels, + kernel_size=3, + padding=1, + use_norm=use_norm, + ) + self.fpn_conv_blocks.append(conv_block) - self.fpn_bottleneck = md.Conv2dReLU( - in_channels=(len(encoder_channels) - 1) * pyramid_channels, - out_channels=segmentation_channels, + num_blocks_to_fuse = len(self.fpn_conv_blocks) + 1 # +1 for the PSP module + self.fusion_block = md.Conv2dReLU( + in_channels=num_blocks_to_fuse * decoder_channels, + out_channels=decoder_channels, kernel_size=3, padding=1, use_norm=use_norm, ) - def forward(self, features): - output_size = features[0].shape[2:] - target_size = [size // 4 for size in output_size] + def forward(self, features: List[torch.Tensor]) -> torch.Tensor: + """ + Args: + features (List[torch.Tensor]): + features with: [1, 1/2, 1/4, 1/8, 1/16, ...] spatial resolutions, + where the first feature is the highest resolution and the number + of features is equal to encoder_depth + 1. + """ + + # skip 1/1 and 1/2 resolution features + features = features[2:] - features = features[1:] # remove first skip with same spatial resolution - features = features[::-1] # reverse channels to start from head of encoder + # normalize feature maps + for i, norm in enumerate(self.feature_norms): + features[i] = norm(features[i]) - psp_out = self.psp(features[0]) + # pass lowest resolution feature to PSP module + psp_out = self.psp(features[-1]) + # skip lowest features for FPN + reverse the order + # [1/4, 1/8, 1/16, 1/32] -> [1/16, 1/8, 1/4] + fpn_lateral_features = features[:-1][::-1] fpn_features = [psp_out] - for feature, stage in zip(features[1:], self.fpn_stages): - fpn_feature = stage(fpn_features[-1], feature) + for i, block in enumerate(self.fpn_lateral_blocks): + # 1. for each encoder (skip) feature we apply 1x1 ConvNormRelu, + # 2. upsample latest fpn feature to it's resolution + # 3. sum them together + lateral_feature = fpn_lateral_features[i] + state_feature = fpn_features[-1] + fpn_feature = block(state_feature, lateral_feature) fpn_features.append(fpn_feature) + # Apply FPN conv blocks, but skip PSP module + for i, conv_block in enumerate(self.fpn_conv_blocks, start=1): + fpn_features[i] = conv_block(fpn_features[i]) + # Resize all FPN features to 1/4 of the original resolution. resized_fpn_features = [] + target_size = fpn_features[-1].shape[2:] # 1/4 of the original resolution for feature in fpn_features: resized_feature = F.interpolate( feature, size=target_size, mode="bilinear", align_corners=False ) resized_fpn_features.append(resized_feature) - output = self.fpn_bottleneck(torch.cat(resized_fpn_features, dim=1)) - + # reverse and concatenate + stacked_features = torch.cat(resized_fpn_features[::-1], dim=1) + output = self.fusion_block(stacked_features) return output diff --git a/segmentation_models_pytorch/decoders/upernet/model.py b/segmentation_models_pytorch/decoders/upernet/model.py index c68f8ad0..54f578b3 100644 --- a/segmentation_models_pytorch/decoders/upernet/model.py +++ b/segmentation_models_pytorch/decoders/upernet/model.py @@ -63,20 +63,18 @@ class UPerNet(SegmentationModel): """ - _is_torch_scriptable = False - @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", - decoder_pyramid_channels: int = 256, - decoder_segmentation_channels: int = 64, + decoder_channels: int = 256, decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, + upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): @@ -93,17 +91,16 @@ def __init__( self.decoder = UPerNetDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, - pyramid_channels=decoder_pyramid_channels, - segmentation_channels=decoder_segmentation_channels, + decoder_channels=decoder_channels, use_norm=decoder_use_norm, ) self.segmentation_head = SegmentationHead( - in_channels=decoder_segmentation_channels, + in_channels=decoder_channels, out_channels=classes, activation=activation, kernel_size=1, - upsampling=4, + upsampling=upsampling, ) if aux_params is not None: diff --git a/segmentation_models_pytorch/encoders/timm_universal.py b/segmentation_models_pytorch/encoders/timm_universal.py index 299178f2..138b2ef8 100644 --- a/segmentation_models_pytorch/encoders/timm_universal.py +++ b/segmentation_models_pytorch/encoders/timm_universal.py @@ -130,9 +130,9 @@ def __init__( # Most transformer-like models use out_indices=(0, 1, 2, 3) for depth=5. common_kwargs["out_indices"] = tuple(range(depth - 1)) - self.model = timm.create_model( - name, **_merge_kwargs_no_duplicates(common_kwargs, kwargs) - ) + timm_model_kwargs = _merge_kwargs_no_duplicates(common_kwargs, kwargs) + self.model = timm.create_model(name, **timm_model_kwargs) + # Add a dummy output channel (0) to align with traditional encoder structures. self._out_channels = ( [in_channels] + [0] + self.model.feature_info.channels() diff --git a/tests/models/base.py b/tests/models/base.py index 717bc801..2f317348 100644 --- a/tests/models/base.py +++ b/tests/models/base.py @@ -252,10 +252,11 @@ def test_compile(self): compiled_model(sample) @pytest.mark.torch_export - def test_torch_export(self): + def test_torch_export(self, eps=1e-5): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") + torch.manual_seed(42) sample = self._get_sample().to(default_device) model = self.get_default_model() model.eval() @@ -271,7 +272,7 @@ def test_torch_export(self): exported_output = exported_model.module().forward(sample) self.assertEqual(eager_output.shape, exported_output.shape) - torch.testing.assert_close(eager_output, exported_output) + torch.testing.assert_close(eager_output, exported_output, rtol=eps, atol=eps) @pytest.mark.torch_script def test_torch_script(self): diff --git a/tests/models/test_upernet.py b/tests/models/test_upernet.py index 1c23406b..a69062ae 100644 --- a/tests/models/test_upernet.py +++ b/tests/models/test_upernet.py @@ -1,3 +1,5 @@ +import pytest + from tests.models import base @@ -6,3 +8,7 @@ class TestUnetModel(base.BaseModelTester): files_for_diff = [r"decoders/upernet/", r"base/"] default_batch_size = 2 + + @pytest.mark.torch_export + def test_torch_export(self): + super().test_torch_export(eps=1e-3)