merge main

.circleci/config.yml

@@ -26,7 +26,7 @@ workflows:
             tools/.* lint_only false
             configs/.* lint_only false
             .circleci/.* lint_only false
-          base-revision: dev-1.x
+          base-revision: main
           # this is the path of the configuration we should trigger once
           # path filtering and pipeline parameter value updates are
           # complete. In this case, we are using the parent dynamic

.circleci/test.yml

@@ -67,10 +67,10 @@ jobs:
             pip install git+https://github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
             mim install 'mmcv >= 2.0.0rc1'
-            pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
-            pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
-            pip install git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x
-            python -m pip install git+ssh://git@github.com/open-mmlab/mmpose.git@dev-1.x
+            pip install git+https://github.com/open-mmlab/mmpretrain.git@mmcls-1.x
+            pip install git+https://github.com/open-mmlab/mmdetection.git@main
+            pip install git+https://github.com/open-mmlab/mmsegmentation.git@main
+            python -m pip install git+ssh://git@github.com/open-mmlab/mmpose.git@main
             pip install -r requirements.txt
       - run:
           name: Build and install
@@ -103,9 +103,9 @@ jobs:
           name: Clone Repos
           command: |
             git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
-            git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
-            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification
-            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmsegmentation.git /home/circleci/mmsegmentation
+            git clone -b main --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
+            git clone -b 1.x --depth 1 https://github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification
+            git clone -b main --depth 1 https://github.com/open-mmlab/mmsegmentation.git /home/circleci/mmsegmentation
       - run:
           name: Build Docker image
           command: |
@@ -139,7 +139,7 @@ workflows:
           filters:
             branches:
               ignore:
-                - dev-1.x
+                - main
                 - 1.x
   pr_stage_test:
     when:
@@ -150,18 +150,18 @@ workflows:
           filters:
             branches:
               ignore:
-                - dev-1.x
+                - main
       - build_cpu:
           name: minimum_version_cpu
-          torch: 1.6.0
-          torchvision: 0.7.0
-          python: 3.7.9
+          torch: 1.8.1
+          torchvision: 0.9.1
+          python: 3.7.4
           requires:
             - lint
       - build_cpu:
           name: maximum_version_cpu
-          torch: 1.12.1
-          torchvision: 0.13.1
+          torch: 1.13.1
+          torchvision: 0.14.1
           python: 3.9.0
           requires:
             - lint
@@ -183,11 +183,11 @@ workflows:
     jobs:
       - build_cuda:
           name: minimum_version_gpu
-          torch: 1.6.0
+          torch: 1.8.1
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          cuda: "10.1"
+          cuda: "10.2"
           filters:
             branches:
               only:
-                - dev-1.x
+                - main

.dev_scripts/benchmark_summary_analyse.py

@@ -0,0 +1,67 @@
+import argparse
+import os
+
+import mmengine
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Analyse summary.yml generated by benchmark test')
+    parser.add_argument('file_path', help='Summary.yml path')
+    args = parser.parse_args()
+    return args
+
+
+metric_mapping = {
+    'Top 1 Accuracy': 'accuracy/top1',
+    'Top 5 Accuracy': 'accuracy/top5',
+    'box AP': 'coco/bbox_mAP',
+    'mIoU': 'mIoU'
+}
+
+
+def compare_metric(result, metric):
+    expect_val = result['expect'][metric]
+    actual_val = result['actual'].get(metric_mapping[metric], None)
+    if actual_val is None:
+        return None, None
+    if metric == 'box AP':
+        actual_val *= 100
+    decimal_bit = len(str(expect_val).split('.')[-1])
+    actual_val = round(actual_val, decimal_bit)
+    error = round(actual_val - expect_val, decimal_bit)
+    error_percent = round(abs(error) * 100 / expect_val, 3)
+    return error, error_percent
+
+
+def main():
+    args = parse_args()
+    file_path = args.file_path
+    results = mmengine.load(file_path, 'yml')
+    miss_models = dict()
+    sort_by_error = dict()
+    for k, v in results.items():
+        valid_keys = v['expect'].keys()
+        compare_res = dict()
+        for m in valid_keys:
+            error, error_percent = compare_metric(v, m)
+            if error is None:
+                continue
+            compare_res[m] = {'error': error, 'error_percent': error_percent}
+            if error != 0:
+                miss_models[k] = compare_res
+                sort_by_error[k] = error
+    sort_by_error = sorted(
+        sort_by_error.items(), key=lambda x: abs(x[1]), reverse=True)
+    miss_models_sort = dict()
+    miss_models_sort['total error models'] = len(sort_by_error)
+    for k_v in sort_by_error:
+        index = k_v[0]
+        miss_models_sort[index] = miss_models[index]
+    save_path = os.path.join(os.path.dirname(file_path), 'summary_error.yml')
+    mmengine.fileio.dump(miss_models_sort, save_path, sort_keys=False)
+    print(f'Summary analysis result saved in {save_path}')
+
+
+if __name__ == '__main__':
+    main()

.dev_scripts/benchmark_test.py

@@ -24,9 +24,9 @@
 def parse_args():
     parser = argparse.ArgumentParser(
         description="Test all models' accuracy in model-index.yml")
-    parser.add_argument(
-        'partition', type=str, help='Cluster partition to use.')
     parser.add_argument('checkpoint_root', help='Checkpoint file root path.')
+    parser.add_argument(
+        '--partition', type=str, help='Cluster partition to use.')
     parser.add_argument(
         '--job-name',
         type=str,
@@ -148,6 +148,7 @@ def create_test_job_batch(commands, model_info, args, port):
     if exists:
         print(f'{checkpoint} already exists.')
     else:
+        print(f'start downloading {fname}')
         wget.download(model_info.weights, str(checkpoint))
         print(f'\nSaved in {checkpoint}.')
 

.github/workflows/build.yml

@@ -25,22 +25,12 @@ concurrency:
 
 jobs:
   test_linux:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     strategy:
       matrix:
         python-version: [3.7]
-        torch: [1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0, 1.13.0]
+        torch: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0, 1.13.0]
         include:
-          - torch: 1.6.0
-            torch_version: 1.6
-            torchvision: 0.7.0
-          - torch: 1.7.0
-            torch_version: 1.7
-            torchvision: 0.8.1
-          - torch: 1.7.0
-            torch_version: 1.7
-            torchvision: 0.8.1
-            python-version: 3.8
           - torch: 1.8.0
             torch_version: 1.8
             torchvision: 0.9.0
@@ -103,11 +93,11 @@ jobs:
           pip install -U openmim
           mim install 'mmcv >= 2.0.0rc1'
       - name: Install MMCls
-        run: pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+        run: pip install 'mmcls>=1.0.0rc0'
       - name: Install MMDet
-        run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+        run: pip install git+https://github.com/open-mmlab/mmdetection.git@main
       - name: Install MMSeg
-        run: pip install git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x
+        run: pip install git+https://github.com/open-mmlab/mmsegmentation.git@main
       - name: Install other dependencies
         run: pip install -r requirements.txt
       - name: Build and install
@@ -119,8 +109,8 @@ jobs:
           coverage report -m
       # Upload coverage report for python3.8 && pytorch1.12.0 cpu
       - name: Upload coverage to Codecov
-        if: ${{matrix.torch == '1.12.0' && matrix.python-version == '3.8'}}
-        uses: codecov/codecov-action@v2
+        if: ${{matrix.torch == '1.13.0' && matrix.python-version == '3.8'}}
+        uses: codecov/codecov-action@v3
         with:
           file: ./coverage.xml
           flags: unittests

README.md

@@ -21,7 +21,7 @@
 <!--算法库 Badges-->
 
 [![PyPI](https://img.shields.io/pypi/v/mmrazor)](https://pypi.org/project/mmrazor)
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmrazor.readthedocs.io/en/dev-1.x/)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmrazor.readthedocs.io/en/main/)
 [![badge](https://github.com/open-mmlab/mmrazor/workflows/build/badge.svg)](https://github.com/open-mmlab/mmrazor/actions)
 [![codecov](https://codecov.io/gh/open-mmlab/mmrazor/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmrazor)
 [![license](https://img.shields.io/github/license/open-mmlab/mmrazor.svg)](https://github.com/open-mmlab/mmrazor/blob/master/LICENSE)
@@ -32,9 +32,9 @@
 
 <!--Note:请根据各算法库自身情况设置项目和链接-->
 
-[📘Documentation](https://mmrazor.readthedocs.io/en/dev-1.x/) |
-[🛠️Installation](https://mmrazor.readthedocs.io/en/dev-1.x/get_started/installation.html) |
-[👀Model Zoo](https://mmrazor.readthedocs.io/en/dev-1.x/get_started/model_zoo.html) |
+[📘Documentation](https://mmrazor.readthedocs.io/en/main/) |
+[🛠️Installation](https://mmrazor.readthedocs.io/en/main/get_started/installation.html) |
+[👀Model Zoo](https://mmrazor.readthedocs.io/en/main/get_started/model_zoo.html) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmrazor/issues/new/choose)
 
 </div>
@@ -65,12 +65,12 @@ English | [简体中文](README_zh-CN.md)
 
 ## Introduction
 
-MMRazor is a model compression toolkit for model slimming and AutoML, which includes 3 mainstream technologies:
+MMRazor is a model compression toolkit for model slimming and AutoML, which includes 4 mainstream technologies:
 
 - Neural Architecture Search (NAS)
 - Pruning
 - Knowledge Distillation (KD)
-- Quantization (come soon)
+- Quantization
 
 It is a part of the [OpenMMLab](https://openmmlab.com/) project.
 
@@ -88,22 +88,23 @@ Major features:
 
   With better modular design, developers can implement new model compression algorithms with only a few codes, or even by simply modifying config files.
 
-Below is an overview of MMRazor's design and implementation, please refer to [tutorials](https://mmrazor.readthedocs.io/en/dev-1.x/get_started/overview.html) for more details.
+About MMRazor's design and implementation, please refer to [tutorials](https://mmrazor.readthedocs.io/en/main/get_started/overview.html) for more details.
 
-<div align="center">
-  <img src="resources/design_and_implement.png" style="zoom:100%"/>
-</div>
-<br />
+## Latest Updates
 
-## What's new
+**The default branch is now `main` and the code on the branch has been upgraded to v1.0.0. The old `master` branch code now exists on the 0.x branch**
 
-MMRazor v1.0.0rc0 was released in 1/9/2022.
+MMRazor v1.0.0 was released in 2023-4-24, Major updates from 1.0.0rc2 include:
 
-Please refer to [changelog.md](/docs/en/notes/changelog.md) for more details and other release history.
+1. MMRazor quantization is released.
+2. Add a new pruning algorithm named GroupFisher.
+3. Support distilling rtmdet with MMRazor.
+
+To know more about the updates in MMRazor 1.0, please refer to [Changelog](https://mmrazor.readthedocs.io/en/main/notes/changelog.html) for more details!
 
 ## Benchmark and model zoo
 
-Results and models are available in the [model zoo](/docs/en/get_started/model_zoo.md).
+Results and models are available in the [model zoo](https://mmrazor.readthedocs.io/en/main/get_started/model_zoo.html).
 
 Supported algorithms:
 
@@ -123,6 +124,12 @@ Supported algorithms:
 
 - [x] [AutoSlim(NeurIPS'2019)](/configs/pruning/mmcls/autoslim)
 
+- [x] [L1-norm](/configs/pruning/mmcls/l1-norm)
+
+- [x] [Group Fisher](/configs/pruning/base/group_fisher)
+
+- [x] [DMCP](/configs/pruning/mmcls/dmcp)
+
 </details>
 
 <details open>
@@ -158,20 +165,31 @@ Supported algorithms:
 
 </details>
 
+<details open>
+<summary>Quantization</summary>
+
+- [x] [PTQ](/configs/quantization/ptq/base)
+
+- [x] [QAT](/configs/quantization/qat/base)
+
+- [x] [LSQ](/configs/quantization/qat/lsq)
+
+</details>
+
 ## Installation
 
 MMRazor depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMEngine](https://github.com/open-mmlab/mmengine).
 
-Please refer to [installation.md](/docs/en/get_started/installation.md) for more detailed instruction.
+Please refer to [installation.md](https://mmrazor.readthedocs.io/en/main/get_started/installation.html) for more detailed instruction.
 
 ## Getting Started
 
-Please refer to [user guides](https://mmrazor.readthedocs.io/en/dev-1.x/user_guides/index.html) for the basic usage of MMRazor. There are also [advanced guides](https://mmrazor.readthedocs.io/en/dev-1.x/advanced_guides/index.html):
+Please refer to [user guides](https://mmrazor.readthedocs.io/en/main/user_guides/index.html) for the basic usage of MMRazor. There are also [advanced guides](https://mmrazor.readthedocs.io/en/main/advanced_guides/index.html):
 
 ## Contributing
 
 We appreciate all contributions to improve MMRazor.
-Please refer to [CONTRUBUTING.md](/docs/en/notes/contribution_guide.md) for the contributing guideline.
+Please refer to [CONTRUBUTING.md](https://mmrazor.readthedocs.io/en/main/notes/contribution_guide.html) for the contributing guideline.
 
 ## Acknowledgement