From a97a3b050458ca71619ba5cb0c227dd113a2b368 Mon Sep 17 00:00:00 2001 From: Songki Choi Date: Fri, 17 Mar 2023 16:28:48 +0900 Subject: [PATCH] Bump up to v1.1.0rc1 Signed-off-by: Songki Choi --- CHANGELOG.md | 25 +++++++++++++++++++ README.md | 14 +++++------ otx/__init__.py | 2 +- .../exportable_code/demo/requirements.txt | 2 +- tox.ini | 4 +-- 5 files changed, 36 insertions(+), 11 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7c22002aaa1..eb70ff38ded 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,31 @@ All notable changes to this project will be documented in this file. +## \[v1.1.0\] + +### New features + +- Integrate multi-gpu training for semi-supervised learning and self-supervised learning (#1534) +- Add Semi-SL multilabel classification algorithm (#1805) +- Add train-type parameter to otx train (#1874) +- Add MoViNet template for action classification (#1742) +- Add embedding of inference configuration to IR for classification (#1842) +- Enable VOC dataset in OTX (#1862) +- Add in-memory caching in dataloader (#1694) + +### 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 diff --git a/README.md b/README.md index a5f78ca9fdb..90f85242387 100644 --- a/README.md +++ b/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/v1.1.0/guide/get_started/quick_start_guide/index.html) • +[Documentation](https://openvinotoolkit.github.io/training_extensions/releases/v1.1.0/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/v1.1.0/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** @@ -59,9 +59,9 @@ 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/v1.1.0/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/v1.1.0/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. --- @@ -69,7 +69,7 @@ OpenVINO™ Training Extensions will provide the following features in coming re ### 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/v1.1.0/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/v1.1.0/guide/get_started/quick_start_guide/cli_commands.html). --- diff --git a/otx/__init__.py b/otx/__init__.py index 5b1ea9b4f72..71b1d0995c8 100644 --- a/otx/__init__.py +++ b/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 diff --git a/otx/api/usecases/exportable_code/demo/requirements.txt b/otx/api/usecases/exportable_code/demo/requirements.txt index 2d13ad05327..5b1bd43c42a 100644 --- a/otx/api/usecases/exportable_code/demo/requirements.txt +++ b/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 diff --git a/tox.ini b/tox.ini index 558965b1394..b63089fb958 100644 --- a/tox.ini +++ b/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}