Bump up version to 1.1.0rc1

Signed-off-by: Songki Choi <[email protected]>

Author: Songki Choi

CHANGELOG.md

@@ -2,6 +2,32 @@
 
 All notable changes to this project will be documented in this file.
 
+## \[v1.1.0\]
+
+### New features
+
+- Add FP16 IR export support (#1683)
+- Add in-memory caching in dataloader (#1694)
+- Add MoViNet template for action classification (#1742)
+- Add Semi-SL multilabel classification algorithm (#1805)
+- Integrate multi-gpu training for semi-supervised learning and self-supervised learning (#1534)
+- Add train-type parameter to otx train (#1874)
+- Add embedding of inference configuration to IR for classification (#1842)
+- Enable VOC dataset in OTX (#1862)
+
+### Enhancements
+
+- Parametrize saliency maps dumping in export (#1888)
+- Bring mmdeploy to action recognition model export & Test optimization of action tasks (#1848)
+- Update backbone lists (#1835)
+
+### Bug fixes
+
+- Handle unpickable update_progress_callback (#1892)
+- Dataset Adapter: Avoid duplicated annotation and permit empty image (#1873)
+- Arrange scale between bbox preds and bbox targets in ATSS (#1880)
+- Fix label mismatch of evaluation and validation with large dataset in semantic segmentation (#1851)
+
 ## \[v1.0.1\]
 
 ### Enhancements

README.md

@@ -5,8 +5,8 @@
 ---
 
 [Key Features](#key-features) •
-[Quick Start](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/quick_start_guide/index.html) •
-[Documentation](https://openvinotoolkit.github.io/training_extensions/latest/index.html) •
+[Quick Start](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/get_started/quick_start_guide/index.html) •
+[Documentation](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/index.html) •
 [License](#license)
 
 [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
@@ -49,7 +49,7 @@ OpenVINO™ Training Extensions supports the following computer vision tasks:
 - **Action recognition** including action classification and detection
 - **Anomaly recognition** tasks including anomaly classification, detection and segmentation
 
-OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/algorithms/index.html):
+OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/explanation/algorithms/index.html):
 
 - **Supervised**, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
 - **Semi-supervised learning**
@@ -59,17 +59,17 @@ OpenVINO™ Training Extensions will provide the following features in coming re
 
 - **Distributed training** to accelerate the training process when you have multiple GPUs
 - **Half-precision training** to save GPUs memory and use larger batch sizes
-- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
+- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
 - OpenVINO™ Training Extensions uses [Datumaro](https://openvinotoolkit.github.io/datumaro/docs/) as the backend to hadle datasets. Thanks to that, OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We constantly working to extend supported formats to give more freedom of datasets format choice.
-- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
+- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
 
 ---
 
 ## Getting Started
 
 ### Installation
 
-Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/quick_start_guide/installation.html).
+Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/get_started/quick_start_guide/installation.html).
 
 ### OpenVINO™ Training Extensions CLI Commands
 
@@ -83,7 +83,7 @@ Please refer to the [installation guide](https://openvinotoolkit.github.io/train
 - `otx demo` allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real-life scenario.
 - `otx explain` runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions.
 
-You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/quick_start_guide/cli_commands.html).
+You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/releases/1.1.0/guide/get_started/quick_start_guide/cli_commands.html).
 
 ---
 

otx/__init__.py

@@ -3,7 +3,7 @@
 # Copyright (C) 2021-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "1.1.0rc0"
+__version__ = "1.1.0rc1"
 # NOTE: Sync w/ otx/api/usecases/exportable_code/demo/requirements.txt on release
 
 MMCLS_AVAILABLE = True

otx/api/usecases/exportable_code/demo/requirements.txt

@@ -1,3 +1,3 @@
 openmodelzoo-modelapi==2022.3.0
-otx @ git+https://github.com/openvinotoolkit/training_extensions/@3faaa782718d8d02e6303fba004c9123ee37d76a#egg=otx
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@128154fd7d58d6ef996c46b58cb8432f7110e0ca#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

tox.ini

@@ -176,8 +176,8 @@ allowlist_externals =
 commands =
     rm -rf ./dist
     python -m build --sdist
-    python -m pip install dist/otx-1.0.0.tar.gz[full]
-    # python -m pip install otx[full]==1.0.0
+    python -m pip install dist/otx-1.1.0rc1.tar.gz[full]
+    # python -m pip install otx[full]==1.1.0rc1
     pytest {posargs:tests/unit tests/integration/cli}