Bump version to 0.10.7

[fix] fix ci (#1636 )
[Fix] remove torch dependencies in `build_function.py` (#1632 )
2025-03-04 20:20:27 +08:00 · 2025-02-27 15:13:38 +08:00 · 2025-02-18 16:38:51 +08:00 · 2025-01-15 18:11:05 +08:00 · 2025-01-15 18:07:34 +08:00 · 2025-01-15 17:58:23 +08:00
59 changed files with 393 additions and 290 deletions
--- a/.github/workflows/deploy.yml
+++ b/.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
-      - name: Set up Python 3.7
-        uses: actions/setup-python@v2
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10.13
+        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
-      - name: Set up Python 3.7
-        uses: actions/setup-python@v2
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10.13
+        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
--- a/.github/workflows/lint.yml
+++ b/.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
--- a/.github/workflows/pr_stage_test.yml
+++ b/.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]
-        include:
-          - torch: 1.8.1
-            torchvision: 0.9.1
+        python-version: ['3.9']
+        torch: ['2.0.0']
    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - name: Check out repo
+        uses: actions/checkout@v3
+      - 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: ${{ matrix.python-version }}
-      - name: Upgrade pip
-        run: python -m pip install pip --upgrade
-      - name: Upgrade wheel
-        run: python -m pip install wheel --upgrade
-      - name: Install PyTorch
-        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-      - name: Build MMEngine from source
-        run: pip install -e . -v
-      - name: Install unit tests dependencies
+      - name: Update pip
        run: |
-          pip install -r requirements/tests.txt
-          pip install openmim
-          mim install mmcv
-      - name: Run unittests and generate coverage report
+          python -m pip install --upgrade pip wheel
+      - name: Install dependencies
        run: |
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
-          coverage xml
-          coverage report -m
-      # Upload coverage report for python3.7 && pytorch1.8.1 cpu
-      - name: Upload coverage to Codecov
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
+          python -m pip install torch==${{matrix.torch}}
+          python -m pip install -e . -v
+          python -m pip install -r requirements/tests.txt
+          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:
-      image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
+    defaults:
+      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: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - name: Check out repo
+        uses: actions/checkout@v3
+      - 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: ${{ matrix.python-version }}
-      - name: Upgrade pip
-        run: pip install pip --upgrade
-      - name: Fetch GPG keys
+      - name: Update pip
        run: |
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
-      - name: Install system dependencies
-        run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-      - name: Build MMEngine from source
-        run: pip install -e . -v
-      - name: Install unit tests dependencies
+          python -m pip install --upgrade pip wheel
+      - name: Install dependencies
        run: |
-          pip install -r requirements/tests.txt
-          pip install openmim
-          mim install mmcv
-      - name: Run unittests and generate coverage report
-        run: |
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist
-          coverage xml
-          coverage report -m
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
+          python -m pip install torch==${{matrix.torch}} --index-url https://download.pytorch.org/whl/${{matrix.cuda}}
+          python -m pip install -e . -v
+          python -m pip install -r requirements/tests.txt
+          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

-  build_cu117:
-    runs-on: ubuntu-22.04
-    container:
-      image: pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
-    strategy:
-      matrix:
-        python-version: [3.9]
-    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
-        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
-          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
-      - name: Install system dependencies
-        run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
-      - 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
-      # Distributed related unit test may randomly error in PyTorch 1.13.0
-      - name: Run unittests and generate coverage report
-        run: |
-          coverage run --branch --source mmengine -m pytest tests/  --ignore tests/test_dist/
-          coverage xml
-          coverage report -m
-
-  build_windows:
-    runs-on: windows-2022
-    strategy:
-      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' }}
+  # build_windows:
+  #   runs-on: windows-2022
+  #   strategy:
+  #     matrix:
+  #       python-version: [3.9]
+  #       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: ${{ matrix.python-version }}
+  #     - name: Upgrade pip
+  #       # Windows CI could fail If we call `pip install pip --upgrade` directly.
+  #       run: python -m pip install pip wheel --upgrade
+  #     - name: Install PyTorch
+  #       run: pip install torch==${{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' }}
--- a/.pre-commit-config-zh-cn.yaml
+++ b/.pre-commit-config-zh-cn.yaml
@ -1,7 +1,11 @@
 exclude: ^tests/data/
 repos:
-  - repo: https://gitee.com/openmmlab/mirrors-flake8
-    rev: 5.0.4
+  - repo: https://github.com/pre-commit/pre-commit
+    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"]
--- a/.pre-commit-config.yaml
+++ b/.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"]
--- a/README.md
+++ b/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:

--- a/README_zh-CN.md
+++ b/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：

--- a/docs/en/notes/changelog.md
+++ b/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
--- a/mmengine/_strategy/base.py
+++ b/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}')
--- a/mmengine/_strategy/colossalai.py
+++ b/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}')

--- a/mmengine/_strategy/distributed.py
+++ b/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:
--- a/mmengine/config/config.py
+++ b/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 = and append to a dictionary. List options can
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
+    """Argparse action to split an argument into KEY=VALUE form 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 brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested
+    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:
--- a/mmengine/config/utils.py
+++ b/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
--- a/mmengine/device/utils.py
+++ b/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:
--- a/mmengine/dist/utils.py
+++ b/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)}')
--- a/mmengine/evaluator/metric.py
+++ b/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):
--- a/mmengine/hooks/profiler_hook.py
+++ b/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:
--- a/mmengine/logging/history_buffer.py
+++ b/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.
--- a/mmengine/logging/logger.py
+++ b/mmengine/logging/logger.py
@ -443,8 +443,7 @@ 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
+    return host


 def _get_logging_file_handlers() -> Dict:
--- a/mmengine/model/base_model/data_preprocessor.py
+++ b/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}')
-        data['inputs'] = batch_inputs
-        data.setdefault('data_samples', None)
-        return data
+                            f'but got {type(data)}: {data}')  # type: ignore
+        data['inputs'] = batch_inputs  # type: ignore
+        data.setdefault('data_samples', None)  # type: ignore
+        return data  # type: ignore
--- a/mmengine/model/weight_init.py
+++ b/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
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
+    r"""Fills the input Tensor with values drawn from a truncated normal
+    distribution. The values are effectively drawn from the normal distribution
+    :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside
+    :math:`[a, b]` redrawn until they are within the bounds. The method used
+    for generating the random values works best when :math:`a \leq \text{mean}
+    \leq b`.

    Modified from
    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
--- a/mmengine/model/wrappers/fully_sharded_distributed.py
+++ b/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,
--- a/mmengine/optim/optimizer/apex_optimizer_wrapper.py
+++ b/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
-        :attr:`apex_amp`.
+        """Get the state dictionary of :attr:`optimizer` and :attr:`apex_amp`.

        Based on the state dictionary of the optimizer, the returned state
        dictionary will add a key named "apex_amp".
--- a/mmengine/optim/optimizer/builder.py
+++ b/mmengine/optim/optimizer/builder.py
@ -25,7 +25,11 @@ def register_torch_optimizers() -> List[str]:
        _optim = getattr(torch.optim, module_name)
        if inspect.isclass(_optim) and issubclass(_optim,
                                                  torch.optim.Optimizer):
-            OPTIMIZERS.register_module(module=_optim)
+            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

--- a/mmengine/optim/optimizer/default_constructor.py
+++ b/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()
--- a/mmengine/optim/optimizer/optimizer_wrapper_dict.py
+++ b/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
-        :obj:`OptimWrapper`"""
+        """A generator to get the name and corresponding :obj:`OptimWrapper`"""
        yield from self.optim_wrappers.items()

    def values(self) -> Iterator[OptimWrapper]:
--- a/mmengine/optim/scheduler/lr_scheduler.py
+++ b/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
-    1cycle learning rate policy. The 1cycle policy anneals the learning
-    rate from an initial learning rate to some maximum learning rate and then
-    from that maximum learning rate to some minimum learning rate much lower
-    than the initial learning rate.
-    This policy was initially described in the paper `Super-Convergence:
-    Very Fast Training of Neural Networks Using Large Learning Rates`_.
+    r"""Sets the learning rate of each parameter group according to the 1cycle
+    learning rate policy. The 1cycle policy anneals the learning rate from an
+    initial learning rate to some maximum learning rate and then from that
+    maximum learning rate to some minimum learning rate much lower than the
+    initial learning rate. This policy was initially described in the paper
+    `Super-Convergence: Very Fast Training of Neural Networks Using Large
+    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.
--- a/mmengine/optim/scheduler/param_scheduler.py
+++ b/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
-    annealing schedule, where :math:`\eta_{max}` is set to the initial value
-    and :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
+    r"""Set the parameter value of each parameter group using a cosine annealing
+    schedule, where :math:`\eta_{max}` is set to the initial value and
+    :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
-    1cycle learning rate policy. The 1cycle policy anneals the learning
-    rate from an initial learning rate to some maximum learning rate and then
-    from that maximum learning rate to some minimum learning rate much lower
-    than the initial learning rate.
-    This policy was initially described in the paper `Super-Convergence:
-    Very Fast Training of Neural Networks Using Large Learning Rates`_.
+    r"""Sets the parameters of each parameter group according to the 1cycle
+    learning rate policy. The 1cycle policy anneals the learning rate from an
+    initial learning rate to some maximum learning rate and then from that
+    maximum learning rate to some minimum learning rate much lower than the
+    initial learning rate. This policy was initially described in the paper
+    `Super-Convergence: Very Fast Training of Neural Networks Using Large
+    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.
--- a/mmengine/registry/build_functions.py
+++ b/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,
--- a/mmengine/registry/default_scope.py
+++ b/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:
--- a/mmengine/registry/registry.py
+++ b/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
--- a/mmengine/registry/root.py
+++ b/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,
-                              build_scheduler_from_cfg)
+from .build_functions import (build_model_from_cfg, build_optimizer_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.
--- a/mmengine/registry/utils.py
+++ b/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',
--- a/mmengine/runner/_flexible_runner.py
+++ b/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,
    ):
--- a/mmengine/runner/checkpoint.py
+++ b/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
--- a/mmengine/runner/loops.py
+++ b/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
--- a/mmengine/runner/runner.py
+++ b/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

--- a/mmengine/structures/base_data_element.py
+++ b/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)
--- a/mmengine/structures/instance_data.py
+++ b/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`.
--- a/mmengine/testing/_internal/distributed.py
+++ b/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)
-        fn = getattr(self, method_name)
-        setattr(self, method_name, self.join_or_run(fn))
+        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()
--- a/mmengine/utils/dl_utils/hub.py
+++ b/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.
--- a/mmengine/utils/dl_utils/time_counter.py
+++ b/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

--- a/mmengine/utils/misc.py
+++ b/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:
--- a/mmengine/utils/package_utils.py
+++ b/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):
--- a/mmengine/utils/progressbar.py
+++ b/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,
-                            initargs: tuple = None,
+                            initializer: Optional[Callable] = None,
+                            initargs: Optional[tuple] = None,
                            bar_width: int = 50,
                            chunksize: int = 1,
                            skip_first: bool = False,
--- a/mmengine/utils/progressbar_rich.py
+++ b/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',
--- a/mmengine/version.py
+++ b/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):
--- a/mmengine/visualization/vis_backend.py
+++ b/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

--- a/mmengine/visualization/visualizer.py
+++ b/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()

--- a/tests/test_config/test_config.py
+++ b/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['item2']) == ConfigDict
+        assert type(cfg_dict['item1']) is ConfigDict
+        assert type(cfg_dict['item2']) is ConfigDict

    def _merge_intermediate_variable(self):

--- a/tests/test_fileio/test_backends/test_petrel_backend.py
+++ b/tests/test_fileio/test_backends/test_petrel_backend.py
@ -300,8 +300,8 @@ except ImportError:
                get_inputs.append(filepath)

            with build_temporary_directory() as tmp_dir, \
-                 patch.object(backend, 'put', side_effect=put),\
-                 patch.object(backend, 'get', side_effect=get),\
+                 patch.object(backend, 'put', side_effect=put), \
+                 patch.object(backend, 'get', side_effect=get), \
                 patch.object(backend, 'exists', return_value=False):
                tmp_dir = tmp_dir.replace('\\', '/')
                dst = f'{tmp_dir}/dir'
@ -351,7 +351,7 @@ except ImportError:

            with build_temporary_directory() as tmp_dir, \
                 patch.object(backend, 'copyfile_from_local',
-                              side_effect=copyfile_from_local),\
+                              side_effect=copyfile_from_local), \
                 patch.object(backend, 'exists', return_value=False):
                backend.copytree_from_local(tmp_dir, self.petrel_dir)

@ -427,7 +427,7 @@ except ImportError:
            def remove(filepath):
                inputs.append(filepath)

-            with build_temporary_directory() as tmp_dir,\
+            with build_temporary_directory() as tmp_dir, \
                 patch.object(backend, 'remove', side_effect=remove):
                backend.rmtree(tmp_dir)

--- a/tests/test_model/test_averaged_model.py
+++ b/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):
--- a/tests/test_optim/test_scheduler/test_lr_scheduler.py
+++ b/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)
--- a/tests/test_optim/test_scheduler/test_momentum_scheduler.py
+++ b/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)
--- a/tests/test_optim/test_scheduler/test_param_scheduler.py
+++ b/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(
--- a/tests/test_runner/test_checkpoint.py
+++ b/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)
--- a/tests/test_runner/test_runner.py
+++ b/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')
--- a/tests/test_structures/test_data_element.py
+++ b/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()
--- a/tests/test_visualizer/test_visualizer.py
+++ b/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
Author	SHA1	Message	Date
Mashiro	390ba2fbb2	Bump version to 0.10.7	2025-03-04 20:20:27 +08:00
Epiphany	d620552c2c	[fix] fix ci (#1636 )	2025-02-27 15:13:38 +08:00
Mashiro	41fa84a9a9	[Fix] remove torch dependencies in `build_function.py` (#1632 )	2025-02-18 16:38:51 +08:00
Mashiro	698782f920	[Fix] Fix deploy ci (#1628 ) * [Enhance] Support trigger ci manually * [Fix] Fix deploy CI	2025-01-15 18:11:05 +08:00
Mashiro	e60ab1dde3	[Enhance] Support trigger ci manually (#1627 )	2025-01-15 18:07:34 +08:00
Mashiro	8ec837814e	[Enhance] Support trigger ci manually (#1626 )	2025-01-15 17:58:23 +08:00
Qian Zhao	a4475f5eea	Update deploy.yml (#1625 )	2025-01-15 17:34:07 +08:00
Mashiro	a8c74c346d	Bump version to v0.10.6 (#1623 )	2025-01-13 19:20:26 +08:00
Epiphany	9124ebf7a2	[Enhance] ensure type in cfg (#1602 ) * ensure type in cfg * change import level	2024-11-06 20:26:47 +08:00
Epiphany	2e0ab7a922	[Fix] fix Adafactor optim on torch2.5 and fix compatibility (#1600 ) * fix Adafactor opptim on torch2.5 and fix compatibility * fix runtest error	2024-11-05 21:22:46 +08:00
Epiphany	fc59364d64	[Fix] fix error when pytest>=8.2 (#1601 )	2024-11-05 20:43:17 +08:00
Tibor Reiss	4183cf0829	Fix return in finally (#1596 )	2024-11-04 14:39:25 +08:00
Mashiro	cc3b74b5e8	[Fix] Fix lint (#1598 ) * [Fix] Fix lint * [Fix] Fix lint * Update mmengine/dist/utils.py Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> --------- Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>	2024-11-02 22:23:51 +08:00
Mashiro	c9b59962d6	Yehc/bump version to v0.10.5 (#1572 ) * fix check builtin module * bump version to v0.10.5 * bump version to v0.10.5	2024-09-20 15:42:06 +08:00
Mashiro	5e736b143b	fix check builtin module (#1571 )	2024-09-11 18:45:24 +08:00
Chris Jiang	85c83ba616	Update is_mlu_available (#1537 ) * Update is_mlu_available to adapt torch_mlu main, the torch.is_mlu_available method is removed * Update utils.py * Update utils.py	2024-05-30 10:07:45 +08:00
fanqiNO1	d1f1aabf81	[Feature] Support calculating loss during validation (#1503 )	2024-05-17 15:27:53 +08:00