Bump version to 0.10.7

* [Enhance] Support trigger ci manually

* [Fix] Fix deploy CI

.github/workflows/deploy.yml

@@ -1,6 +1,8 @@
 name: deploy
 
-on: push
+on:
+  - push
+  - workflow_dispatch
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -9,13 +11,14 @@ concurrency:
 jobs:
   build-n-publish:
     runs-on: ubuntu-latest
-    if: startsWith(github.event.ref, 'refs/tags')
+    if: |
+      startsWith(github.event.ref, 'refs/tags') || github.event_name == 'workflow_dispatch'
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10.13
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.10.13
       - name: Install wheel
         run: pip install wheel
       - name: Build MMEngine
@@ -27,13 +30,14 @@ jobs:
 
   build-n-publish-lite:
     runs-on: ubuntu-latest
-    if: startsWith(github.event.ref, 'refs/tags')
+    if: |
+      startsWith(github.event.ref, 'refs/tags') || github.event_name == 'workflow_dispatch'
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10.13
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.10.13
       - name: Install wheel
         run: pip install wheel
       - name: Build MMEngine-lite

.github/workflows/lint.yml

@@ -11,10 +11,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10.15
         uses: actions/setup-python@v2
         with:
-          python-version: 3.7
+          python-version: '3.10.15'
       - name: Install pre-commit hook
         run: |
           pip install pre-commit

.github/workflows/pr_stage_test.yml

@@ -1,5 +1,9 @@
 name: pr_stage_test
 
+env:
+  ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true
+
+
 on:
   pull_request:
     paths-ignore:
@@ -21,152 +25,114 @@ concurrency:
 jobs:
   build_cpu:
     runs-on: ubuntu-22.04
+    defaults:
+      run:
+        shell: bash -l {0}
     strategy:
       matrix:
-        python-version: [3.7]
+        python-version: ['3.9']
-        include:
+        torch: ['2.0.0']
-          - torch: 1.8.1
-            torchvision: 0.9.1
     steps:
-      - uses: actions/checkout@v3
+      - name: Check out repo
-      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/checkout@v3
-        uses: actions/setup-python@v4
+      - name: Setup conda env
+        uses: conda-incubator/setup-miniconda@v2
         with:
+          auto-update-conda: true
+          miniconda-version: "latest"
+          use-only-tar-bz2: true
+          activate-environment: test
           python-version: ${{ matrix.python-version }}
-      - name: Upgrade pip
+      - name: Update pip
-        run: python -m pip install pip --upgrade
-      - name: Upgrade wheel
-        run: python -m pip install wheel --upgrade
-      - name: Install PyTorch
-        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-      - name: Build MMEngine from source
-        run: pip install -e . -v
-      - name: Install unit tests dependencies
         run: |
-          pip install -r requirements/tests.txt
+          python -m pip install --upgrade pip wheel
-          pip install openmim
+      - name: Install dependencies
-          mim install mmcv
-      - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-          coverage xml
+          python -m pip install torch==${{matrix.torch}}
-          coverage report -m
+          python -m pip install -e . -v
-      # Upload coverage report for python3.7 && pytorch1.8.1 cpu
+          python -m pip install -r requirements/tests.txt
-      - name: Upload coverage to Codecov
+          python -m pip install openmim
+          mim install mmcv coverage
+      - name: Run unit tests with coverage
+        run: coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
+      - name: Upload Coverage to Codecov
         uses: codecov/codecov-action@v3
-        with:
-          file: ./coverage.xml
-          flags: unittests
-          env_vars: OS,PYTHON
-          name: codecov-umbrella
-          fail_ci_if_error: false
 
-  build_cu102:
+  build_gpu:
     runs-on: ubuntu-22.04
-    container:
+    defaults:
-      image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
+      run:
+        shell: bash -l {0}
     env:
       MKL_THREADING_LAYER: GNU
     strategy:
       matrix:
-        python-version: [3.7]
+        python-version: ['3.9','3.10']
+        torch: ['2.0.0','2.3.1','2.5.1']
+        cuda: ['cu118']
     steps:
-      - uses: actions/checkout@v3
+      - name: Check out repo
-      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/checkout@v3
-        uses: actions/setup-python@v4
+      - name: Setup conda env
+        uses: conda-incubator/setup-miniconda@v2
         with:
+          auto-update-conda: true
+          miniconda-version: "latest"
+          use-only-tar-bz2: true
+          activate-environment: test
           python-version: ${{ matrix.python-version }}
-      - name: Upgrade pip
+      - name: Update pip
-        run: pip install pip --upgrade
-      - name: Fetch GPG keys
         run: |
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+          python -m pip install --upgrade pip wheel
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+      - name: Install dependencies
-      - name: Install system dependencies
-        run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-      - name: Build MMEngine from source
-        run: pip install -e . -v
-      - name: Install unit tests dependencies
         run: |
-          pip install -r requirements/tests.txt
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-          pip install openmim
+          python -m pip install torch==${{matrix.torch}} --index-url https://download.pytorch.org/whl/${{matrix.cuda}}
-          mim install mmcv
+          python -m pip install -e . -v
-      - name: Run unittests and generate coverage report
+          python -m pip install -r requirements/tests.txt
-        run: |
+          python -m pip install openmim
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
+          mim install mmcv coverage
-          coverage xml
+      - name: Run unit tests with coverage
-          coverage report -m
+        run: coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
+      - name: Upload Coverage to Codecov
+        uses: codecov/codecov-action@v3
 
-  build_cu117:
+  # build_windows:
-    runs-on: ubuntu-22.04
+  #   runs-on: windows-2022
-    container:
+  #   strategy:
-      image: pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+  #     matrix:
-    strategy:
+  #       python-version: [3.9]
-      matrix:
+  #       platform: [cpu, cu111]
-        python-version: [3.9]
+  #       torch: [1.8.1]
-    steps:
+  #       torchvision: [0.9.1]
-      - uses: actions/checkout@v3
+  #       include:
-      - name: Set up Python ${{ matrix.python-version }}
+  #         - python-version: 3.8
-        uses: actions/setup-python@v4
+  #           platform: cu118
-        with:
+  #           torch: 2.1.0
-          python-version: ${{ matrix.python-version }}
+  #           torchvision: 0.16.0
-      - name: Upgrade pip
+  #   steps:
-        run: pip install pip --upgrade
+  #     - uses: actions/checkout@v3
-      - name: Fetch GPG keys
+  #     - name: Set up Python ${{ matrix.python-version }}
-        run: |
+  #       uses: actions/setup-python@v4
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+  #       with:
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+  #         python-version: ${{ matrix.python-version }}
-      - name: Install system dependencies
+  #     - name: Upgrade pip
-        run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
+  #       # Windows CI could fail If we call `pip install pip --upgrade` directly.
-      - name: Build MMEngine from source
+  #       run: python -m pip install pip wheel --upgrade
-        run: pip install -e . -v
+  #     - name: Install PyTorch
-      - name: Install unit tests dependencies
+  #       run: pip install torch==${{matrix.torch}}+${{matrix.platform}} torchvision==${{matrix.torchvision}}+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html
-        run: |
+  #     - name: Build MMEngine from source
-          pip install -r requirements/tests.txt
+  #       run: pip install -e . -v
-          pip install openmim
+  #     - name: Install unit tests dependencies
-          mim install mmcv
+  #       run: |
-      # Distributed related unit test may randomly error in PyTorch 1.13.0
+  #         pip install -r requirements/tests.txt
-      - name: Run unittests and generate coverage report
+  #         pip install openmim
-        run: |
+  #         mim install mmcv
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist/
+  #     - name: Run CPU unittests
-          coverage xml
+  #       run: pytest tests/  --ignore tests/test_dist
-          coverage report -m
+  #       if: ${{ matrix.platform == 'cpu' }}
-
+  #     - name: Run GPU unittests
-  build_windows:
+  #       # Skip testing distributed related unit tests since the memory of windows CI is limited
-    runs-on: windows-2022
+  #       run: pytest tests/ --ignore tests/test_dist --ignore tests/test_optim/test_optimizer/test_optimizer_wrapper.py --ignore tests/test_model/test_wrappers/test_model_wrapper.py --ignore tests/test_hooks/test_sync_buffers_hook.py
-    strategy:
+  #       if: ${{ matrix.platform == 'cu111' }} || ${{ matrix.platform == 'cu118' }}
-      matrix:
-        python-version: [3.7]
-        platform: [cpu, cu111]
-        torch: [1.8.1]
-        torchvision: [0.9.1]
-        include:
-          - python-version: 3.8
-            platform: cu118
-            torch: 2.1.0
-            torchvision: 0.16.0
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade pip
-        # Windows CI could fail If we call `pip install pip --upgrade` directly.
-        run: python -m pip install pip --upgrade
-      - name: Install PyTorch
-        run: pip install torch==${{matrix.torch}}+${{matrix.platform}} torchvision==${{matrix.torchvision}}+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html
-      - name: Build MMEngine from source
-        run: pip install -e . -v
-      - name: Install unit tests dependencies
-        run: |
-          pip install -r requirements/tests.txt
-          pip install openmim
-          mim install mmcv
-      - name: Run CPU unittests
-        run: pytest tests/  --ignore tests/test_dist
-        if: ${{ matrix.platform == 'cpu' }}
-      - name: Run GPU unittests
-        # Skip testing distributed related unit tests since the memory of windows CI is limited
-        run: pytest tests/ --ignore tests/test_dist --ignore tests/test_optim/test_optimizer/test_optimizer_wrapper.py --ignore tests/test_model/test_wrappers/test_model_wrapper.py --ignore tests/test_hooks/test_sync_buffers_hook.py
-        if: ${{ matrix.platform == 'cu111' }} || ${{ matrix.platform == 'cu118' }}

.pre-commit-config-zh-cn.yaml

@@ -1,7 +1,11 @@
 exclude: ^tests/data/
 repos:
-  - repo: https://gitee.com/openmmlab/mirrors-flake8
+  - repo: https://github.com/pre-commit/pre-commit
-    rev: 5.0.4
+    rev: v4.0.0
+    hooks:
+      - id: validate_manifest
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://gitee.com/openmmlab/mirrors-isort
@@ -13,7 +17,7 @@ repos:
     hooks:
       - id: yapf
   - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -55,7 +59,7 @@ repos:
         args: ["mmengine", "tests"]
       - id: remove-improper-eol-in-cn-docs
   - repo: https://gitee.com/openmmlab/mirrors-mypy
-    rev: v0.812
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
@@ -63,3 +67,4 @@ repos:
               ^examples
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests", "types-PyYAML"]

.pre-commit-config.yaml

@@ -1,7 +1,11 @@
 exclude: ^tests/data/
 repos:
+  - repo: https://github.com/pre-commit/pre-commit
+    rev: v4.0.0
+    hooks:
+      - id: validate_manifest
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
@@ -13,7 +17,7 @@ repos:
     hooks:
       - id: yapf
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -34,12 +38,8 @@ repos:
           - mdformat-openmmlab
           - mdformat_frontmatter
           - linkify-it-py
-  - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
-    hooks:
-      - id: codespell
   - repo: https://github.com/myint/docformatter
-    rev: v1.3.1
+    rev: 06907d0
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
@@ -55,7 +55,7 @@ repos:
         args: ["mmengine", "tests"]
       - id: remove-improper-eol-in-cn-docs
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.812
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
@@ -63,3 +63,4 @@ repos:
               ^examples
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests", "types-PyYAML"]

README.md

@@ -59,7 +59,7 @@ English | [简体中文](README_zh-CN.md)
 
 ## What's New
 
-v0.10.4 was released on 2024-4-23.
+v0.10.6 was released on 2025-01-13.
 
 Highlights:
 

README_zh-CN.md

@@ -59,7 +59,7 @@
 
 ## 最近进展
 
-最新版本 v0.10.4 在 2024.4.23 发布。
+最新版本 v0.10.5 在 2024.9.11 发布。
 
 版本亮点：
 

docs/en/notes/changelog.md

@@ -1,5 +1,9 @@
 # Changelog of v0.x
 
+## v0.10.5 (11/9/2024)
+
+- Fix `_is_builtin_module`. by [@HAOCHENYE](https://github.com/HAOCHENYE) in https://github.com/open-mmlab/mmengine/pull/1571
+
 ## v0.10.4 (23/4/2024)
 
 ### New Features & Enhancements

mmengine/_strategy/base.py

@@ -499,7 +499,7 @@ class BaseStrategy(metaclass=ABCMeta):
                             '"type" and "constructor" are not in '
                             f'optimizer, but got {name}={optim}')
                     optim_wrappers[name] = optim
-                return OptimWrapperDict(**optim_wrappers)
+                return OptimWrapperDict(**optim_wrappers)  # type: ignore
         else:
             raise TypeError('optimizer wrapper should be an OptimWrapper '
                             f'object or dict, but got {optim_wrapper}')

mmengine/_strategy/colossalai.py

@@ -361,7 +361,7 @@ class ColossalAIStrategy(BaseStrategy):
         map_location: Union[str, Callable] = 'default',
         callback: Optional[Callable] = None,
     ) -> dict:
-        """override this method since colossalai resume optimizer from filename
+        """Override this method since colossalai resume optimizer from filename
         directly."""
         self.logger.info(f'Resume checkpoint from {filename}')
 

mmengine/_strategy/distributed.py

@@ -53,7 +53,7 @@ class DDPStrategy(SingleDeviceStrategy):
             init_dist(launcher, backend, **kwargs)
 
     def convert_model(self, model: nn.Module) -> nn.Module:
-        """convert all ``BatchNorm`` layers in the model to ``SyncBatchNorm``
+        """Convert all ``BatchNorm`` layers in the model to ``SyncBatchNorm``
         (SyncBN) or ``mmcv.ops.sync_bn.SyncBatchNorm`` (MMSyncBN) layers.
 
         Args:

mmengine/config/config.py

@@ -393,7 +393,7 @@ class Config:
 
     def __init__(
         self,
-        cfg_dict: dict = None,
+        cfg_dict: Optional[dict] = None,
         cfg_text: Optional[str] = None,
         filename: Optional[Union[str, Path]] = None,
         env_variables: Optional[dict] = None,
@@ -1227,7 +1227,8 @@ class Config:
                 if base_code is not None:
                     base_code = ast.Expression(  # type: ignore
                         body=base_code.value)  # type: ignore
-                    base_files = eval(compile(base_code, '', mode='eval'))
+                    base_files = eval(compile(base_code, '',
+                                              mode='eval'))  # type: ignore
                 else:
                     base_files = []
         elif file_format in ('.yml', '.yaml', '.json'):
@@ -1288,7 +1289,7 @@ class Config:
     def _merge_a_into_b(a: dict,
                         b: dict,
                         allow_list_keys: bool = False) -> dict:
-        """merge dict ``a`` into dict ``b`` (non-inplace).
+        """Merge dict ``a`` into dict ``b`` (non-inplace).
 
         Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
         in-place modifications.
@@ -1358,22 +1359,22 @@ class Config:
 
     @property
     def filename(self) -> str:
-        """get file name of config."""
+        """Get file name of config."""
         return self._filename
 
     @property
     def text(self) -> str:
-        """get config text."""
+        """Get config text."""
         return self._text
 
     @property
     def env_variables(self) -> dict:
-        """get used environment variables."""
+        """Get used environment variables."""
         return self._env_variables
 
     @property
     def pretty_text(self) -> str:
-        """get formatted python config text."""
+        """Get formatted python config text."""
 
         indent = 4
 
@@ -1727,17 +1728,17 @@ class Config:
 
 
 class DictAction(Action):
-    """
+    """Argparse action to split an argument into KEY=VALUE form on the first =
-    argparse action to split an argument into KEY=VALUE form
+    and append to a dictionary.
-    on the first = and append to a dictionary. List options can
+
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
+    List options can be passed as comma separated values, i.e 'KEY=V1,V2,V3',
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
+    or with explicit brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
+    brackets to build list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
     """
 
     @staticmethod
     def _parse_int_float_bool(val: str) -> Union[int, float, bool, Any]:
-        """parse int/float/bool value in the string."""
+        """Parse int/float/bool value in the string."""
         try:
             return int(val)
         except ValueError:
@@ -1822,7 +1823,7 @@ class DictAction(Action):
                  parser: ArgumentParser,
                  namespace: Namespace,
                  values: Union[str, Sequence[Any], None],
-                 option_string: str = None):
+                 option_string: str = None):  # type: ignore
         """Parse Variables in string and add them into argparser.
 
         Args:

mmengine/config/utils.py

@@ -12,6 +12,7 @@ from mmengine.fileio import load
 from mmengine.utils import check_file_exist
 
 PYTHON_ROOT_DIR = osp.dirname(osp.dirname(sys.executable))
+SYSTEM_PYTHON_PREFIX = '/usr/lib/python'
 
 MODULE2PACKAGE = {
     'mmcls': 'mmcls',
@@ -176,7 +177,8 @@ def _is_builtin_module(module_name: str) -> bool:
         return False
     origin_path = osp.abspath(origin_path)
     if ('site-package' in origin_path or 'dist-package' in origin_path
-            or not origin_path.startswith(PYTHON_ROOT_DIR)):
+            or not origin_path.startswith(
+                (PYTHON_ROOT_DIR, SYSTEM_PYTHON_PREFIX))):
         return False
     else:
         return True

mmengine/device/utils.py

@@ -16,6 +16,12 @@ try:
 except Exception:
     IS_NPU_AVAILABLE = False
 
+try:
+    import torch_mlu  # noqa: F401
+    IS_MLU_AVAILABLE = hasattr(torch, 'mlu') and torch.mlu.is_available()
+except Exception:
+    IS_MLU_AVAILABLE = False
+
 try:
     import torch_dipu  # noqa: F401
     IS_DIPU_AVAILABLE = True
@@ -64,7 +70,7 @@ def is_npu_available() -> bool:
 
 def is_mlu_available() -> bool:
     """Returns True if Cambricon PyTorch and mlu devices exist."""
-    return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()
+    return IS_MLU_AVAILABLE
 
 
 def is_mps_available() -> bool:

mmengine/dist/utils.py

@@ -563,7 +563,7 @@ def cast_data_device(
         Tensor or list or dict: ``data`` was casted to ``device``.
     """
     if out is not None:
-        if type(data) != type(out):
+        if type(data) is not type(out):
             raise TypeError(
                 'out should be the same type with data, but got data is '
                 f'{type(data)} and out is {type(data)}')

mmengine/evaluator/metric.py

@@ -175,11 +175,11 @@ class DumpResults(BaseMetric):
         self.out_file_path = out_file_path
 
     def process(self, data_batch: Any, predictions: Sequence[dict]) -> None:
-        """transfer tensors in predictions to CPU."""
+        """Transfer tensors in predictions to CPU."""
         self.results.extend(_to_cpu(predictions))
 
     def compute_metrics(self, results: list) -> dict:
-        """dump the prediction results to a pickle file."""
+        """Dump the prediction results to a pickle file."""
         dump(results, self.out_file_path)
         print_log(
             f'Results has been saved to {self.out_file_path}.',
@@ -188,7 +188,7 @@ class DumpResults(BaseMetric):
 
 
 def _to_cpu(data: Any) -> Any:
-    """transfer all tensors and BaseDataElement to cpu."""
+    """Transfer all tensors and BaseDataElement to cpu."""
     if isinstance(data, (Tensor, BaseDataElement)):
         return data.to('cpu')
     elif isinstance(data, list):

mmengine/hooks/profiler_hook.py

@@ -233,7 +233,7 @@ class ProfilerHook(Hook):
             self._export_chrome_trace(runner)
 
     def after_train_iter(self, runner, batch_idx, data_batch, outputs):
-        """profiler will call `step` method if it is not closed."""
+        """Profiler will call `step` method if it is not closed."""
         if not self._closed:
             self.profiler.step()
         if runner.iter == self.profile_times - 1 and not self.by_epoch:

mmengine/logging/history_buffer.py

@@ -58,7 +58,7 @@ class HistoryBuffer:
         self._statistics_methods.setdefault('mean', HistoryBuffer.mean)
 
     def update(self, log_val: Union[int, float], count: int = 1) -> None:
-        """update the log history.
+        """Update the log history.
 
         If the length of the buffer exceeds ``self._max_length``, the oldest
         element will be removed from the buffer.

mmengine/logging/logger.py

@@ -443,7 +443,6 @@ def _get_host_info() -> str:
         host = f'{getuser()}@{gethostname()}'
     except Exception as e:
         warnings.warn(f'Host or user not found: {str(e)}')
-    finally:
     return host
 
 

mmengine/model/base_model/data_preprocessor.py

@@ -253,17 +253,17 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
             dict or list: Data in the same format as the model input.
         """
         data = self.cast_data(data)  # type: ignore
-        _batch_inputs = data['inputs']
+        _batch_inputs = data['inputs']  # type: ignore
         # Process data with `pseudo_collate`.
         if is_seq_of(_batch_inputs, torch.Tensor):
             batch_inputs = []
             for _batch_input in _batch_inputs:
                 # channel transform
                 if self._channel_conversion:
-                    _batch_input = _batch_input[[2, 1, 0], ...]
+                    _batch_input = _batch_input[[2, 1, 0], ...]  # type: ignore
                 # Convert to float after channel conversion to ensure
                 # efficiency
-                _batch_input = _batch_input.float()
+                _batch_input = _batch_input.float()  # type: ignore
                 # Normalization.
                 if self._enable_normalize:
                     if self.mean.shape[0] == 3:
@@ -302,7 +302,7 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
         else:
             raise TypeError('Output of `cast_data` should be a dict of '
                             'list/tuple with inputs and data_samples, '
-                            f'but got {type(data)}: {data}')
+                            f'but got {type(data)}: {data}')  # type: ignore
-        data['inputs'] = batch_inputs
+        data['inputs'] = batch_inputs  # type: ignore
-        data.setdefault('data_samples', None)
+        data.setdefault('data_samples', None)  # type: ignore
-        return data
+        return data  # type: ignore

mmengine/model/weight_init.py

@@ -119,7 +119,7 @@ def caffe2_xavier_init(module, bias=0):
 
 
 def bias_init_with_prob(prior_prob):
-    """initialize conv/fc bias value according to a given probability value."""
+    """Initialize conv/fc bias value according to a given probability value."""
     bias_init = float(-np.log((1 - prior_prob) / prior_prob))
     return bias_init
 
@@ -662,12 +662,12 @@ def trunc_normal_(tensor: Tensor,
                   std: float = 1.,
                   a: float = -2.,
                   b: float = 2.) -> Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
+    r"""Fills the input Tensor with values drawn from a truncated normal
-    normal distribution. The values are effectively drawn from the
+    distribution. The values are effectively drawn from the normal distribution
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside
-    with values outside :math:`[a, b]` redrawn until they are within
+    :math:`[a, b]` redrawn until they are within the bounds. The method used
-    the bounds. The method used for generating the random values works
+    for generating the random values works best when :math:`a \leq \text{mean}
-    best when :math:`a \leq \text{mean} \leq b`.
+    \leq b`.
 
     Modified from
     https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py

mmengine/model/wrappers/fully_sharded_distributed.py

@@ -127,7 +127,8 @@ class MMFullyShardedDataParallel(FullyShardedDataParallel):
         auto_wrap_policy: Union[str, Callable, None] = None,
         backward_prefetch: Union[str, BackwardPrefetch, None] = None,
         mixed_precision: Union[dict, MixedPrecision, None] = None,
-        param_init_fn: Union[str, Callable[[nn.Module], None]] = None,
+        param_init_fn: Union[str, Callable[
+            [nn.Module], None]] = None,  # type: ignore # noqa: E501
         use_orig_params: bool = True,
         **kwargs,
     ):
@@ -362,7 +363,7 @@ class MMFullyShardedDataParallel(FullyShardedDataParallel):
             optim: torch.optim.Optimizer,
             group: Optional[dist.ProcessGroup] = None,
         ) -> Dict[str, Any]:
-            """copied from pytorch 2.0.1 which has fixed some bugs."""
+            """Copied from pytorch 2.0.1 which has fixed some bugs."""
             state_dict_settings = FullyShardedDataParallel.get_state_dict_type(
                 model)
             return FullyShardedDataParallel._optim_state_dict_impl(
@@ -384,7 +385,7 @@ class MMFullyShardedDataParallel(FullyShardedDataParallel):
             state_dict_config: Optional[StateDictConfig] = None,
             optim_state_dict_config: Optional[OptimStateDictConfig] = None,
         ) -> StateDictSettings:
-            """copied from pytorch 2.0.1 which has fixed some bugs."""
+            """Copied from pytorch 2.0.1 which has fixed some bugs."""
             import torch.distributed.fsdp._traversal_utils as traversal_utils
             _state_dict_type_to_config = {
                 StateDictType.FULL_STATE_DICT: FullStateDictConfig,

mmengine/optim/optimizer/apex_optimizer_wrapper.py

@@ -123,8 +123,7 @@ class ApexOptimWrapper(OptimWrapper):
         self._inner_count += 1
 
     def state_dict(self) -> dict:
-        """Get the state dictionary of :attr:`optimizer` and
+        """Get the state dictionary of :attr:`optimizer` and :attr:`apex_amp`.
-        :attr:`apex_amp`.
 
         Based on the state dictionary of the optimizer, the returned state
         dictionary will add a key named "apex_amp".

mmengine/optim/optimizer/builder.py

@@ -25,6 +25,10 @@ def register_torch_optimizers() -> List[str]:
         _optim = getattr(torch.optim, module_name)
         if inspect.isclass(_optim) and issubclass(_optim,
                                                   torch.optim.Optimizer):
+            if module_name == 'Adafactor':
+                OPTIMIZERS.register_module(
+                    name='TorchAdafactor', module=_optim)
+            else:
                 OPTIMIZERS.register_module(module=_optim)
             torch_optimizers.append(module_name)
     return torch_optimizers

mmengine/optim/optimizer/default_constructor.py

@@ -131,7 +131,7 @@ class DefaultOptimWrapperConstructor:
         self._validate_cfg()
 
     def _validate_cfg(self) -> None:
-        """verify the correctness of the config."""
+        """Verify the correctness of the config."""
         if not isinstance(self.paramwise_cfg, dict):
             raise TypeError('paramwise_cfg should be None or a dict, '
                             f'but got {type(self.paramwise_cfg)}')
@@ -155,7 +155,7 @@ class DefaultOptimWrapperConstructor:
                 raise ValueError('base_wd should not be None')
 
     def _is_in(self, param_group: dict, param_group_list: list) -> bool:
-        """check whether the `param_group` is in the`param_group_list`"""
+        """Check whether the `param_group` is in the`param_group_list`"""
         assert is_list_of(param_group_list, dict)
         param = set(param_group['params'])
         param_set = set()

mmengine/optim/optimizer/optimizer_wrapper_dict.py

@@ -161,8 +161,7 @@ class OptimWrapperDict(OptimWrapper):
             self.optim_wrappers[name].load_state_dict(_state_dict)
 
     def items(self) -> Iterator[Tuple[str, OptimWrapper]]:
-        """A generator to get the name and corresponding
+        """A generator to get the name and corresponding :obj:`OptimWrapper`"""
-        :obj:`OptimWrapper`"""
         yield from self.optim_wrappers.items()
 
     def values(self) -> Iterator[OptimWrapper]:

mmengine/optim/scheduler/lr_scheduler.py

@@ -223,13 +223,13 @@ class PolyLR(LRSchedulerMixin, PolyParamScheduler):
 
 @PARAM_SCHEDULERS.register_module()
 class OneCycleLR(LRSchedulerMixin, OneCycleParamScheduler):
-    r"""Sets the learning rate of each parameter group according to the
+    r"""Sets the learning rate of each parameter group according to the 1cycle
-    1cycle learning rate policy. The 1cycle policy anneals the learning
+    learning rate policy. The 1cycle policy anneals the learning rate from an
-    rate from an initial learning rate to some maximum learning rate and then
+    initial learning rate to some maximum learning rate and then from that
-    from that maximum learning rate to some minimum learning rate much lower
+    maximum learning rate to some minimum learning rate much lower than the
-    than the initial learning rate.
+    initial learning rate. This policy was initially described in the paper
-    This policy was initially described in the paper `Super-Convergence:
+    `Super-Convergence: Very Fast Training of Neural Networks Using Large
-    Very Fast Training of Neural Networks Using Large Learning Rates`_.
+    Learning Rates`_.
 
     The 1cycle learning rate policy changes the learning rate after every
     batch. `step` should be called after a batch has been used for training.

mmengine/optim/scheduler/param_scheduler.py

@@ -565,9 +565,9 @@ class ExponentialParamScheduler(_ParamScheduler):
 
 @PARAM_SCHEDULERS.register_module()
 class CosineAnnealingParamScheduler(_ParamScheduler):
-    r"""Set the parameter value of each parameter group using a cosine
+    r"""Set the parameter value of each parameter group using a cosine annealing
-    annealing schedule, where :math:`\eta_{max}` is set to the initial value
+    schedule, where :math:`\eta_{max}` is set to the initial value and
-    and :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
+    :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
 
     .. math::
         \begin{aligned}
@@ -617,7 +617,7 @@ class CosineAnnealingParamScheduler(_ParamScheduler):
 
     .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
         https://arxiv.org/abs/1608.03983
-    """
+    """  # noqa: E501
 
     def __init__(self,
                  optimizer: Union[Optimizer, BaseOptimWrapper],
@@ -890,13 +890,13 @@ class PolyParamScheduler(_ParamScheduler):
 
 @PARAM_SCHEDULERS.register_module()
 class OneCycleParamScheduler(_ParamScheduler):
-    r"""Sets the parameters of each parameter group according to the
+    r"""Sets the parameters of each parameter group according to the 1cycle
-    1cycle learning rate policy. The 1cycle policy anneals the learning
+    learning rate policy. The 1cycle policy anneals the learning rate from an
-    rate from an initial learning rate to some maximum learning rate and then
+    initial learning rate to some maximum learning rate and then from that
-    from that maximum learning rate to some minimum learning rate much lower
+    maximum learning rate to some minimum learning rate much lower than the
-    than the initial learning rate.
+    initial learning rate. This policy was initially described in the paper
-    This policy was initially described in the paper `Super-Convergence:
+    `Super-Convergence: Very Fast Training of Neural Networks Using Large
-    Very Fast Training of Neural Networks Using Large Learning Rates`_.
+    Learning Rates`_.
 
     The 1cycle learning rate policy changes the learning rate after every
     batch. `step` should be called after a batch has been used for training.

mmengine/registry/build_functions.py

@@ -4,7 +4,7 @@ import logging
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 from mmengine.config import Config, ConfigDict
-from mmengine.utils import ManagerMixin
+from mmengine.utils import ManagerMixin, digit_version
 from .registry import Registry
 
 if TYPE_CHECKING:
@@ -232,6 +232,21 @@ def build_model_from_cfg(
         return build_from_cfg(cfg, registry, default_args)
 
 
+def build_optimizer_from_cfg(
+        cfg: Union[dict, ConfigDict, Config],
+        registry: Registry,
+        default_args: Optional[Union[dict, ConfigDict, Config]] = None) -> Any:
+    import torch
+
+    from ..logging import print_log
+    if 'type' in cfg \
+            and 'Adafactor' == cfg['type'] \
+            and digit_version(torch.__version__) >= digit_version('2.5.0'):
+        print_log(
+            'the torch version of Adafactor is registered as TorchAdafactor')
+    return build_from_cfg(cfg, registry, default_args)
+
+
 def build_scheduler_from_cfg(
     cfg: Union[dict, ConfigDict, Config],
     registry: Registry,

mmengine/registry/default_scope.py

@@ -81,7 +81,7 @@ class DefaultScope(ManagerMixin):
     @classmethod
     @contextmanager
     def overwrite_default_scope(cls, scope_name: Optional[str]) -> Generator:
-        """overwrite the current default scope with `scope_name`"""
+        """Overwrite the current default scope with `scope_name`"""
         if scope_name is None:
             yield
         else:

mmengine/registry/registry.py

@@ -332,7 +332,7 @@ class Registry:
         return root
 
     def import_from_location(self) -> None:
-        """import modules from the pre-defined locations in self._location."""
+        """Import modules from the pre-defined locations in self._location."""
         if not self._imported:
             # Avoid circular import
             from ..logging import print_log

mmengine/registry/root.py

@@ -6,8 +6,8 @@ More datails can be found at
 https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.
 """
 
-from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
+from .build_functions import (build_model_from_cfg, build_optimizer_from_cfg,
-                              build_scheduler_from_cfg)
+                              build_runner_from_cfg, build_scheduler_from_cfg)
 from .registry import Registry
 
 # manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner`
@@ -35,7 +35,7 @@ MODEL_WRAPPERS = Registry('model_wrapper')
 WEIGHT_INITIALIZERS = Registry('weight initializer')
 
 # mangage all kinds of optimizers like `SGD` and `Adam`
-OPTIMIZERS = Registry('optimizer')
+OPTIMIZERS = Registry('optimizer', build_func=build_optimizer_from_cfg)
 # manage optimizer wrapper
 OPTIM_WRAPPERS = Registry('optim_wrapper')
 # manage constructors that customize the optimization hyperparameters.

mmengine/registry/utils.py

@@ -109,7 +109,7 @@ def init_default_scope(scope: str) -> None:
     if current_scope.scope_name != scope:  # type: ignore
         print_log(
             'The current default scope '  # type: ignore
-            f'"{current_scope.scope_name}" is not "{scope}", '
+            f'"{current_scope.scope_name}" is not "{scope}", '  # type: ignore
             '`init_default_scope` will force set the current'
             f'default scope to "{scope}".',
             logger='current',

mmengine/runner/_flexible_runner.py

@@ -540,7 +540,7 @@ class FlexibleRunner:
 
     @property
     def hooks(self):
-        """list[:obj:`Hook`]: A list of registered hooks."""
+        """List[:obj:`Hook`]: A list of registered hooks."""
         return self._hooks
 
     @property
@@ -1117,7 +1117,7 @@ class FlexibleRunner:
         return '\n'.join(stage_hook_infos)
 
     def load_or_resume(self):
-        """load or resume checkpoint."""
+        """Load or resume checkpoint."""
         if self._has_loaded:
             return None
 
@@ -1539,7 +1539,7 @@ class FlexibleRunner:
         file_client_args: Optional[dict] = None,
         save_optimizer: bool = True,
         save_param_scheduler: bool = True,
-        meta: dict = None,
+        meta: Optional[dict] = None,
         by_epoch: bool = True,
         backend_args: Optional[dict] = None,
     ):

mmengine/runner/checkpoint.py

@@ -309,7 +309,7 @@ class CheckpointLoader:
 
     @classmethod
     def load_checkpoint(cls, filename, map_location=None, logger='current'):
-        """load checkpoint through URL scheme path.
+        """Load checkpoint through URL scheme path.
 
         Args:
             filename (str): checkpoint file name with given prefix
@@ -332,7 +332,7 @@ class CheckpointLoader:
 
 @CheckpointLoader.register_scheme(prefixes='')
 def load_from_local(filename, map_location):
-    """load checkpoint by local file path.
+    """Load checkpoint by local file path.
 
     Args:
         filename (str): local checkpoint file path
@@ -353,7 +353,7 @@ def load_from_http(filename,
                    map_location=None,
                    model_dir=None,
                    progress=os.isatty(0)):
-    """load checkpoint through HTTP or HTTPS scheme path. In distributed
+    """Load checkpoint through HTTP or HTTPS scheme path. In distributed
     setting, this function only download checkpoint at local rank 0.
 
     Args:
@@ -386,7 +386,7 @@ def load_from_http(filename,
 
 @CheckpointLoader.register_scheme(prefixes='pavi://')
 def load_from_pavi(filename, map_location=None):
-    """load checkpoint through the file path prefixed with pavi. In distributed
+    """Load checkpoint through the file path prefixed with pavi. In distributed
     setting, this function download ckpt at all ranks to different temporary
     directories.
 
@@ -419,7 +419,7 @@ def load_from_pavi(filename, map_location=None):
 @CheckpointLoader.register_scheme(
     prefixes=[r'(\S+\:)?s3://', r'(\S+\:)?petrel://'])
 def load_from_ceph(filename, map_location=None, backend='petrel'):
-    """load checkpoint through the file path prefixed with s3.  In distributed
+    """Load checkpoint through the file path prefixed with s3.  In distributed
     setting, this function download ckpt at all ranks to different temporary
     directories.
 
@@ -441,7 +441,7 @@ def load_from_ceph(filename, map_location=None, backend='petrel'):
 
 @CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://'))
 def load_from_torchvision(filename, map_location=None):
-    """load checkpoint through the file path prefixed with modelzoo or
+    """Load checkpoint through the file path prefixed with modelzoo or
     torchvision.
 
     Args:
@@ -467,7 +467,7 @@ def load_from_torchvision(filename, map_location=None):
 
 @CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://'))
 def load_from_openmmlab(filename, map_location=None):
-    """load checkpoint through the file path prefixed with open-mmlab or
+    """Load checkpoint through the file path prefixed with open-mmlab or
     openmmlab.
 
     Args:
@@ -510,7 +510,7 @@ def load_from_openmmlab(filename, map_location=None):
 
 @CheckpointLoader.register_scheme(prefixes='mmcls://')
 def load_from_mmcls(filename, map_location=None):
-    """load checkpoint through the file path prefixed with mmcls.
+    """Load checkpoint through the file path prefixed with mmcls.
 
     Args:
         filename (str): checkpoint file path with mmcls prefix

mmengine/runner/loops.py

@@ -8,8 +8,10 @@ import torch
 from torch.utils.data import DataLoader
 
 from mmengine.evaluator import Evaluator
-from mmengine.logging import print_log
+from mmengine.logging import HistoryBuffer, print_log
 from mmengine.registry import LOOPS
+from mmengine.structures import BaseDataElement
+from mmengine.utils import is_list_of
 from .amp import autocast
 from .base_loop import BaseLoop
 from .utils import calc_dynamic_intervals
@@ -363,17 +365,26 @@ class ValLoop(BaseLoop):
                 logger='current',
                 level=logging.WARNING)
         self.fp16 = fp16
+        self.val_loss: Dict[str, HistoryBuffer] = dict()
 
     def run(self) -> dict:
         """Launch validation."""
         self.runner.call_hook('before_val')
         self.runner.call_hook('before_val_epoch')
         self.runner.model.eval()
+
+        # clear val loss
+        self.val_loss.clear()
         for idx, data_batch in enumerate(self.dataloader):
             self.run_iter(idx, data_batch)
 
         # compute metrics
         metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
+
+        if self.val_loss:
+            loss_dict = _parse_losses(self.val_loss, 'val')
+            metrics.update(loss_dict)
+
         self.runner.call_hook('after_val_epoch', metrics=metrics)
         self.runner.call_hook('after_val')
         return metrics
@@ -391,6 +402,9 @@ class ValLoop(BaseLoop):
         # outputs should be sequence of BaseDataElement
         with autocast(enabled=self.fp16):
             outputs = self.runner.model.val_step(data_batch)
+
+        outputs, self.val_loss = _update_losses(outputs, self.val_loss)
+
         self.evaluator.process(data_samples=outputs, data_batch=data_batch)
         self.runner.call_hook(
             'after_val_iter',
@@ -435,17 +449,26 @@ class TestLoop(BaseLoop):
                 logger='current',
                 level=logging.WARNING)
         self.fp16 = fp16
+        self.test_loss: Dict[str, HistoryBuffer] = dict()
 
     def run(self) -> dict:
         """Launch test."""
         self.runner.call_hook('before_test')
         self.runner.call_hook('before_test_epoch')
         self.runner.model.eval()
+
+        # clear test loss
+        self.test_loss.clear()
         for idx, data_batch in enumerate(self.dataloader):
             self.run_iter(idx, data_batch)
 
         # compute metrics
         metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
+
+        if self.test_loss:
+            loss_dict = _parse_losses(self.test_loss, 'test')
+            metrics.update(loss_dict)
+
         self.runner.call_hook('after_test_epoch', metrics=metrics)
         self.runner.call_hook('after_test')
         return metrics
@@ -462,9 +485,66 @@ class TestLoop(BaseLoop):
         # predictions should be sequence of BaseDataElement
         with autocast(enabled=self.fp16):
             outputs = self.runner.model.test_step(data_batch)
+
+        outputs, self.test_loss = _update_losses(outputs, self.test_loss)
+
         self.evaluator.process(data_samples=outputs, data_batch=data_batch)
         self.runner.call_hook(
             'after_test_iter',
             batch_idx=idx,
             data_batch=data_batch,
             outputs=outputs)
+
+
+def _parse_losses(losses: Dict[str, HistoryBuffer],
+                  stage: str) -> Dict[str, float]:
+    """Parses the raw losses of the network.
+
+    Args:
+        losses (dict): raw losses of the network.
+        stage (str): The stage of loss, e.g., 'val' or 'test'.
+
+    Returns:
+        dict[str, float]: The key is the loss name, and the value is the
+        average loss.
+    """
+    all_loss = 0
+    loss_dict: Dict[str, float] = dict()
+
+    for loss_name, loss_value in losses.items():
+        avg_loss = loss_value.mean()
+        loss_dict[loss_name] = avg_loss
+        if 'loss' in loss_name:
+            all_loss += avg_loss
+
+    loss_dict[f'{stage}_loss'] = all_loss
+    return loss_dict
+
+
+def _update_losses(outputs: list, losses: dict) -> Tuple[list, dict]:
+    """Update and record the losses of the network.
+
+    Args:
+        outputs (list): The outputs of the network.
+        losses (dict): The losses of the network.
+
+    Returns:
+        list: The updated outputs of the network.
+        dict: The updated losses of the network.
+    """
+    if isinstance(outputs[-1],
+                  BaseDataElement) and outputs[-1].keys() == ['loss']:
+        loss = outputs[-1].loss  # type: ignore
+        outputs = outputs[:-1]
+    else:
+        loss = dict()
+
+    for loss_name, loss_value in loss.items():
+        if loss_name not in losses:
+            losses[loss_name] = HistoryBuffer()
+        if isinstance(loss_value, torch.Tensor):
+            losses[loss_name].update(loss_value.item())
+        elif is_list_of(loss_value, torch.Tensor):
+            for loss_value_i in loss_value:
+                losses[loss_name].update(loss_value_i.item())
+    return outputs, losses

mmengine/runner/runner.py

@@ -579,7 +579,7 @@ class Runner:
 
     @property
     def hooks(self):
-        """list[:obj:`Hook`]: A list of registered hooks."""
+        """List[:obj:`Hook`]: A list of registered hooks."""
         return self._hooks
 
     @property
@@ -720,7 +720,7 @@ class Runner:
 
     def build_logger(self,
                      log_level: Union[int, str] = 'INFO',
-                     log_file: str = None,
+                     log_file: Optional[str] = None,
                      **kwargs) -> MMLogger:
         """Build a global asscessable MMLogger.
 
@@ -1677,7 +1677,7 @@ class Runner:
         return '\n'.join(stage_hook_infos)
 
     def load_or_resume(self) -> None:
-        """load or resume checkpoint."""
+        """Load or resume checkpoint."""
         if self._has_loaded:
             return None
 

mmengine/structures/base_data_element.py

@@ -387,7 +387,7 @@ class BaseDataElement:
         return dict(self.metainfo_items())
 
     def __setattr__(self, name: str, value: Any):
-        """setattr is only used to set data."""
+        """Setattr is only used to set data."""
         if name in ('_metainfo_fields', '_data_fields'):
             if not hasattr(self, name):
                 super().__setattr__(name, value)

mmengine/structures/instance_data.py

@@ -135,7 +135,7 @@ class InstanceData(BaseDataElement):
     """
 
     def __setattr__(self, name: str, value: Sized):
-        """setattr is only used to set data.
+        """Setattr is only used to set data.
 
         The value must have the attribute of `__len__` and have the same length
         of `InstanceData`.

mmengine/testing/_internal/distributed.py

@@ -92,10 +92,25 @@ class MultiProcessTestCase(TestCase):
     # Constructor patches current instance test method to
     # assume the role of the main process and join its subprocesses,
     # or run the underlying test function.
-    def __init__(self, method_name: str = 'runTest') -> None:
+    def __init__(self,
+                 method_name: str = 'runTest',
+                 methodName: str = 'runTest') -> None:
+        # methodName is the correct naming in unittest
+        # and testslide uses keyword arguments.
+        # So we need to use both to 1) not break BC and, 2) support testslide.
+        if methodName != 'runTest':
+            method_name = methodName
         super().__init__(method_name)
+        try:
             fn = getattr(self, method_name)
             setattr(self, method_name, self.join_or_run(fn))
+        except AttributeError as e:
+            if methodName != 'runTest':
+                # we allow instantiation with no explicit method name
+                # but not an *incorrect* or missing method name
+                raise ValueError(
+                    f'no such test method in {self.__class__}: {methodName}'
+                ) from e
 
     def setUp(self) -> None:
         super().setUp()

mmengine/utils/dl_utils/hub.py

@@ -57,6 +57,7 @@ if TORCH_VERSION != 'parrots' and digit_version(TORCH_VERSION) < digit_version(
                  check_hash=False,
                  file_name=None):
         r"""Loads the Torch serialized object at the given URL.
+
         If downloaded file is a zip file, it will be automatically decompressed
         If the object is already present in `model_dir`, it's deserialized and
         returned.

mmengine/utils/dl_utils/time_counter.py

@@ -67,7 +67,7 @@ class TimeCounter:
 
         instance.log_interval = log_interval
         instance.warmup_interval = warmup_interval
-        instance.with_sync = with_sync
+        instance.with_sync = with_sync  # type: ignore
         instance.tag = tag
         instance.logger = logger
 
@@ -127,7 +127,7 @@ class TimeCounter:
         self.print_time(elapsed)
 
     def print_time(self, elapsed: Union[int, float]) -> None:
-        """print times per count."""
+        """Print times per count."""
         if self.__count >= self.warmup_interval:
             self.__pure_inf_time += elapsed
 

mmengine/utils/misc.py

@@ -131,7 +131,7 @@ def tuple_cast(inputs, dst_type):
 
 def is_seq_of(seq: Any,
               expected_type: Union[Type, tuple],
-              seq_type: Type = None) -> bool:
+              seq_type: Optional[Type] = None) -> bool:
     """Check whether it is a sequence of some type.
 
     Args:

mmengine/utils/package_utils.py

@@ -69,11 +69,11 @@ def get_installed_path(package: str) -> str:
         else:
             raise e
 
-    possible_path = osp.join(pkg.location, package)
+    possible_path = osp.join(pkg.location, package)  # type: ignore
     if osp.exists(possible_path):
         return possible_path
     else:
-        return osp.join(pkg.location, package2module(package))
+        return osp.join(pkg.location, package2module(package))  # type: ignore
 
 
 def package2module(package: str):

mmengine/utils/progressbar.py

@@ -3,7 +3,7 @@ import sys
 from collections.abc import Iterable
 from multiprocessing import Pool
 from shutil import get_terminal_size
-from typing import Callable, Sequence
+from typing import Callable, Optional, Sequence
 
 from .timer import Timer
 
@@ -54,7 +54,7 @@ class ProgressBar:
         self.timer = Timer()
 
     def update(self, num_tasks: int = 1):
-        """update progressbar.
+        """Update progressbar.
 
         Args:
             num_tasks (int): Update step size.
@@ -142,8 +142,8 @@ def init_pool(process_num, initializer=None, initargs=None):
 def track_parallel_progress(func: Callable,
                             tasks: Sequence,
                             nproc: int,
-                            initializer: Callable = None,
+                            initializer: Optional[Callable] = None,
-                            initargs: tuple = None,
+                            initargs: Optional[tuple] = None,
                             bar_width: int = 50,
                             chunksize: int = 1,
                             skip_first: bool = False,

mmengine/utils/progressbar_rich.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from multiprocessing import Pool
-from typing import Callable, Iterable, Sized
+from typing import Callable, Iterable, Optional, Sized
 
 from rich.progress import (BarColumn, MofNCompleteColumn, Progress, Task,
                            TaskProgressColumn, TextColumn, TimeRemainingColumn)
@@ -47,7 +47,7 @@ def _tasks_with_index(tasks):
 
 def track_progress_rich(func: Callable,
                         tasks: Iterable = tuple(),
-                        task_num: int = None,
+                        task_num: Optional[int] = None,
                         nproc: int = 1,
                         chunksize: int = 1,
                         description: str = 'Processing',

mmengine/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '0.10.4'
+__version__ = '0.10.7'
 
 
 def parse_version_info(version_str):

mmengine/visualization/vis_backend.py

@@ -161,7 +161,7 @@ class BaseVisBackend(metaclass=ABCMeta):
         pass
 
     def close(self) -> None:
-        """close an opened object."""
+        """Close an opened object."""
         pass
 
 
@@ -314,7 +314,7 @@ class LocalVisBackend(BaseVisBackend):
 
     def _dump(self, value_dict: dict, file_path: str,
               file_format: str) -> None:
-        """dump dict to file.
+        """Dump dict to file.
 
         Args:
            value_dict (dict) : The dict data to saved.
@@ -505,7 +505,7 @@ class WandbVisBackend(BaseVisBackend):
         self._wandb.log(scalar_dict, commit=self._commit)
 
     def close(self) -> None:
-        """close an opened wandb object."""
+        """Close an opened wandb object."""
         if hasattr(self, '_wandb'):
             self._wandb.join()
 
@@ -629,7 +629,7 @@ class TensorboardVisBackend(BaseVisBackend):
             self.add_scalar(key, value, step)
 
     def close(self):
-        """close an opened tensorboard object."""
+        """Close an opened tensorboard object."""
         if hasattr(self, '_tensorboard'):
             self._tensorboard.close()
 
@@ -1135,7 +1135,7 @@ class NeptuneVisBackend(BaseVisBackend):
             self._neptune[k].append(v, step=step)
 
     def close(self) -> None:
-        """close an opened object."""
+        """Close an opened object."""
         if hasattr(self, '_neptune'):
             self._neptune.stop()
 
@@ -1282,7 +1282,7 @@ class DVCLiveVisBackend(BaseVisBackend):
             self.add_scalar(key, value, step, **kwargs)
 
     def close(self) -> None:
-        """close an opened dvclive object."""
+        """Close an opened dvclive object."""
         if not hasattr(self, '_dvclive'):
             return
 

mmengine/visualization/visualizer.py

@@ -356,7 +356,7 @@ class Visualizer(ManagerMixin):
 
     @master_only
     def get_backend(self, name) -> 'BaseVisBackend':
-        """get vis backend by name.
+        """Get vis backend by name.
 
         Args:
             name (str): The name of vis backend
@@ -1145,7 +1145,7 @@ class Visualizer(ManagerMixin):
         pass
 
     def close(self) -> None:
-        """close an opened object."""
+        """Close an opened object."""
         for vis_backend in self._vis_backends.values():
             vis_backend.close()
 

tests/test_config/test_config.py

@@ -843,8 +843,8 @@ class TestConfig:
         assert cfg_dict['item4'] == 'test'
         assert '_delete_' not in cfg_dict['item1']
 
-        assert type(cfg_dict['item1']) == ConfigDict
+        assert type(cfg_dict['item1']) is ConfigDict
-        assert type(cfg_dict['item2']) == ConfigDict
+        assert type(cfg_dict['item2']) is ConfigDict
 
     def _merge_intermediate_variable(self):
 

tests/test_model/test_averaged_model.py

@@ -13,7 +13,8 @@ from mmengine.testing import assert_allclose
 class TestAveragedModel(TestCase):
     """Test the AveragedModel class.
 
-    Some test cases are referenced from https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
+    Some test cases are referenced from
+    https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
     """  # noqa: E501
 
     def _test_swa_model(self, net_device, avg_device):

tests/test_optim/test_scheduler/test_lr_scheduler.py

@@ -102,7 +102,7 @@ class TestLRScheduler(TestCase):
                 rtol=0)
 
     def test_scheduler_before_optim_warning(self):
-        """warns if scheduler is used before optimizer."""
+        """Warns if scheduler is used before optimizer."""
 
         def call_sch_before_optim():
             scheduler = StepLR(self.optimizer, gamma=0.1, step_size=3)

tests/test_optim/test_scheduler/test_momentum_scheduler.py

@@ -120,7 +120,7 @@ class TestMomentumScheduler(TestCase):
                 rtol=0)
 
     def test_scheduler_before_optim_warning(self):
-        """warns if scheduler is used before optimizer."""
+        """Warns if scheduler is used before optimizer."""
 
         def call_sch_before_optim():
             scheduler = StepMomentum(self.optimizer, gamma=0.1, step_size=3)

tests/test_optim/test_scheduler/test_param_scheduler.py

@@ -127,7 +127,7 @@ class TestParameterScheduler(TestCase):
                 rtol=0)
 
     def test_scheduler_before_optim_warning(self):
-        """warns if scheduler is used before optimizer."""
+        """Warns if scheduler is used before optimizer."""
 
         def call_sch_before_optim():
             scheduler = StepParamScheduler(

tests/test_runner/test_checkpoint.py

@@ -251,7 +251,7 @@ def test_load_checkpoint_metadata():
 
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                   *args, **kwargs):
-            """load checkpoints."""
+            """Load checkpoints."""
 
             # Names of some parameters in has been changed.
             version = local_metadata.get('version', None)

tests/test_runner/test_runner.py

@@ -2226,7 +2226,7 @@ class TestRunner(TestCase):
 
         @HOOKS.register_module(force=True)
         class TestWarmupHook(Hook):
-            """test custom train loop."""
+            """Test custom train loop."""
 
             def before_warmup_iter(self, runner, data_batch=None):
                 before_warmup_iter_results.append('before')

tests/test_structures/test_data_element.py

@@ -64,7 +64,7 @@ class TestBaseDataElement(TestCase):
         return metainfo, data
 
     def is_equal(self, x, y):
-        assert type(x) == type(y)
+        assert type(x) is type(y)
         if isinstance(
                 x, (int, float, str, list, tuple, dict, set, BaseDataElement)):
             return x == y
@@ -141,7 +141,7 @@ class TestBaseDataElement(TestCase):
 
         # test new() with no arguments
         new_instances = instances.new()
-        assert type(new_instances) == type(instances)
+        assert type(new_instances) is type(instances)
         # After deepcopy, the address of new data'element will be same as
         # origin, but when change new data' element will not effect the origin
         # element and will have new address
@@ -154,7 +154,7 @@ class TestBaseDataElement(TestCase):
         # test new() with arguments
         metainfo, data = self.setup_data()
         new_instances = instances.new(metainfo=metainfo, **data)
-        assert type(new_instances) == type(instances)
+        assert type(new_instances) is type(instances)
         assert id(new_instances.gt_instances) != id(instances.gt_instances)
         _, new_data = self.setup_data()
         new_instances.set_data(new_data)
@@ -168,7 +168,7 @@ class TestBaseDataElement(TestCase):
         metainfo, data = self.setup_data()
         instances = BaseDataElement(metainfo=metainfo, **data)
         new_instances = instances.clone()
-        assert type(new_instances) == type(instances)
+        assert type(new_instances) is type(instances)
 
     def test_set_metainfo(self):
         metainfo, _ = self.setup_data()

tests/test_visualizer/test_visualizer.py

@@ -45,7 +45,7 @@ class MockVisBackend:
         self._add_scalars = True
 
     def close(self) -> None:
-        """close an opened object."""
+        """Close an opened object."""
         self._close = True