fix ci (#284)

* fix ci for circle ci

* fix bug in test_metafiles

* add  pr_stage_test for github ci

* add multiple version

* fix ut

* fix lint

* Temporarily skip dataset UT

* update github ci

* add github lint ci

* install wheel

* remove timm from requirements

* install wheel when test on windows

* fix error

* fix bug

* remove github windows ci

* fix device error of arch_params when DsnasDDP

* fix CRD dataset ut

* fix scope error

* rm test_cuda in workflows of github

* [Doc] fix typos in en/usr_guides

Co-authored-by: liukai <liukai@pjlab.org.cn>
Co-authored-by: pppppM <gjf_mail@126.com>
Co-authored-by: gaoyang07 <1546308416@qq.com>
Co-authored-by: huangpengsheng <huangpengsheng@sensetime.com>
Co-authored-by: SheffieldCao <1751899@tongji.edu.cn>

.circleci/test.yml

@@ -26,7 +26,6 @@ jobs:
           command: |
             pip install interrogate
             interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 80 mmrazor
-
   build_cpu:
     parameters:
       # The python version must match available image tags in
@@ -37,8 +36,6 @@ jobs:
         type: string
       torchvision:
         type: string
-      mmcv:
-        type: string
     docker:
       - image: cimg/python:<< parameters.python >>
     resource_class: large
@@ -58,20 +55,21 @@ jobs:
           name: Install PyTorch
           command: |
             python -V
-            python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+            pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
       - when:
           condition:
-            equal: [ "3.9.0", << parameters.python >> ]
+            equal: ["3.9.0", << parameters.python >>]
           steps:
             - run: pip install "protobuf <= 3.20.1" && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake
       - run:
           name: Install mmrazor dependencies
           command: |
-            python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main
-            python -m pip install << parameters.mmcv >>
-            python -m pip install git+ssh://git@github.com/open-mmlab/mmclassification.git@dev-1.x
-            python -m pip install git+ssh://git@github.com/open-mmlab/mmdetection.git@dev-3.x
-            python -m pip install git+ssh://git@github.com/open-mmlab/mmsegmentation.git@dev-1.x
+            pip install git+https://github.com/open-mmlab/mmengine.git@main
+            pip install -U openmim
+            mim install 'mmcv >= 2.0.0rc1'
+            pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+            pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+            pip install git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x
             pip install -r requirements.txt
       - run:
           name: Build and install
@@ -80,10 +78,9 @@ jobs:
       - run:
           name: Run unittests
           command: |
-            python -m coverage run --branch --source mmrazor -m pytest tests/
-            python -m coverage xml
-            python -m coverage report -m
-
+            coverage run --branch --source mmrazor -m pytest tests/
+            coverage xml
+            coverage report -m
   build_cuda:
     parameters:
       torch:
@@ -94,8 +91,6 @@ jobs:
       cudnn:
         type: integer
         default: 7
-      mmcv:
-        type: string
     machine:
       image: ubuntu-2004-cuda-11.4:202110-01
       # docker_layer_caching: true
@@ -103,13 +98,13 @@ jobs:
     steps:
       - checkout
       - run:
-          # CLoning repos in VM since Docker doesn't have access to the private key
+          # Cloning repos in VM since Docker doesn't have access to the private key
           name: Clone Repos
           command: |
-            git clone -b main --depth 1 ssh://git@github.com/open-mmlab/mmengine.git /home/circleci/mmengine
-            git clone -b dev-3.x --depth 1 ssh://git@github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
-            git clone -b dev-1.x --depth 1 ssh://git@github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification
-            git clone -b dev-1.x --depth 1 ssh://git@github.com/open-mmlab/mmsegmentation.git /home/circleci/mmsegmentation
+            git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
+            git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
+            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification
+            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmsegmentation.git /home/circleci/mmsegmentation
       - run:
           name: Build Docker image
           command: |
@@ -117,10 +112,10 @@ jobs:
             docker run --gpus all -t -d -v /home/circleci/project:/mmrazor -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmdetection:/mmdetection -v /home/circleci/mmclassification:/mmclassification -v /home/circleci/mmsegmentation:/mmsegmentation -w /mmrazor --name mmrazor mmrazor:gpu
       - run:
           name: Install mmrazor dependencies
-          # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html
           command: |
             docker exec mmrazor pip install -e /mmengine
-            docker exec mmrazor pip install << parameters.mmcv >>
+            docker exec mmrazor pip install -U openmim
+            docker exec mmrazor mim install 'mmcv >= 2.0.0rc1'
             docker exec mmrazor pip install -e /mmdetection
             docker exec mmrazor pip install -e /mmclassification
             docker exec mmrazor pip install -e /mmsegmentation
@@ -132,7 +127,7 @@ jobs:
       - run:
           name: Run unittests
           command: |
-            docker exec mmrazor python -m pytest tests/
+            docker exec mmrazor pytest tests/
 
 workflows:
   pr_stage_lint:
@@ -144,10 +139,10 @@ workflows:
             branches:
               ignore:
                 - dev-1.x
+                - 1.x
   pr_stage_test:
     when:
-      not:
-        << pipeline.parameters.lint_only >>
+      not: << pipeline.parameters.lint_only >>
     jobs:
       - lint:
           name: lint
@@ -159,16 +154,14 @@ workflows:
           name: minimum_version_cpu
           torch: 1.6.0
           torchvision: 0.7.0
-          python: 3.6.9  # The lowest python 3.6.x version available on CircleCI images
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.6.0/mmcv_full-2.0.0rc1-cp36-cp36m-manylinux1_x86_64.whl
+          python: 3.6.9 # The lowest python 3.6.x version available on CircleCI images
           requires:
             - lint
       - build_cpu:
           name: maximum_version_cpu
-          torch: 1.9.0
-          torchvision: 0.10.0
+          torch: 1.12.1
+          torchvision: 0.13.1
           python: 3.9.0
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.9.0/mmcv_full-2.0.0rc1-cp39-cp39-manylinux1_x86_64.whl
           requires:
             - minimum_version_cpu
       - hold:
@@ -181,20 +174,17 @@ workflows:
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
           cuda: "10.2"
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu102/torch1.8.0/mmcv_full-2.0.0rc1-cp37-cp37m-manylinux1_x86_64.whl
           requires:
             - hold
   merge_stage_test:
     when:
-      not:
-        << pipeline.parameters.lint_only >>
+      not: << pipeline.parameters.lint_only >>
     jobs:
       - build_cuda:
           name: minimum_version_gpu
           torch: 1.6.0
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu101/torch1.6.0/mmcv_full-2.0.0rc1-cp37-cp37m-manylinux1_x86_64.whl
           cuda: "10.1"
           filters:
             branches:

.github/workflows/build.yml

@@ -0,0 +1,159 @@
+name: build
+
+on:
+  push:
+    paths-ignore:
+      - "README.md"
+      - "README_zh-CN.md"
+      - "model-index.yml"
+      - "configs/**"
+      - "docs/**"
+      - ".dev_scripts/**"
+
+  pull_request:
+    paths-ignore:
+      - "README.md"
+      - "README_zh-CN.md"
+      - "docs/**"
+      - "demo/**"
+      - ".dev_scripts/**"
+      - ".circleci/**"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test_linux:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
+        include:
+          - torch: 1.6.0
+            torch_version: 1.6
+            torchvision: 0.7.0
+          - torch: 1.7.0
+            torch_version: 1.7
+            torchvision: 0.8.1
+          - torch: 1.7.0
+            torch_version: 1.7
+            torchvision: 0.8.1
+            python-version: 3.8
+          - torch: 1.8.0
+            torch_version: 1.8
+            torchvision: 0.9.0
+          - torch: 1.8.0
+            torch_version: 1.8
+            torchvision: 0.9.0
+            python-version: 3.8
+          - torch: 1.9.0
+            torch_version: 1.9
+            torchvision: 0.10.0
+          - torch: 1.9.0
+            torch_version: 1.9
+            torchvision: 0.10.0
+            python-version: 3.8
+          - torch: 1.10.0
+            torch_version: 1.10
+            torchvision: 0.11.0
+          - torch: 1.10.0
+            torch_version: 1.10
+            torchvision: 0.11.0
+            python-version: 3.8
+          - torch: 1.11.0
+            torch_version: 1.11
+            torchvision: 0.12.0
+          - torch: 1.11.0
+            torch_version: 1.11
+            torchvision: 0.12.0
+            python-version: 3.8
+          - torch: 1.12.0
+            torch_version: 1.12
+            torchvision: 0.13.0
+          - torch: 1.12.0
+            torch_version: 1.12
+            torchvision: 0.13.0
+            python-version: 3.8
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: |
+          pip install pip --upgrade
+          pip install wheel
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMEngine
+        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
+      - name: Install MMCV
+        run: |
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+      - name: Install MMCls
+        run: pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+      - name: Install MMDet
+        run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+      - name: Install MMSeg
+        run: pip install git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x
+      - name: Install other dependencies
+        run: pip install -r requirements.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmrazor -m pytest tests/
+          coverage xml
+          coverage report -m
+      # Upload coverage report for python3.8 && pytorch1.12.0 cpu
+      - name: Upload coverage to Codecov
+        if: ${{matrix.torch == '1.12.0' && matrix.python-version == '3.8'}}
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+  # test_windows:
+  #   runs-on: ${{ matrix.os }}
+  #   strategy:
+  #     matrix:
+  #       os: [windows-2022]
+  #       python: [3.7]
+  #       platform: [cpu]
+  #   steps:
+  #     - uses: actions/checkout@v2
+  #     - name: Set up Python ${{ matrix.python-version }}
+  #       uses: actions/setup-python@v2
+  #       with:
+  #         python-version: ${{ matrix.python-version }}
+  #     - name: Upgrade pip
+  #       run: |
+  #         pip install pip --upgrade
+  #         pip install wheel
+  #     - name: Install lmdb
+  #       run: pip install lmdb
+  #     - name: Install PyTorch
+  #       run: pip install torch==1.8.1+${{matrix.platform}} torchvision==0.9.1+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+  #     - name: Install mmrazor dependencies
+  #       run: |
+  #         pip install git+https://github.com/open-mmlab/mmengine.git@main
+  #         pip install -U openmim
+  #         mim install 'mmcv >= 2.0.0rc1'
+  #         pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+  #         pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+  #         pip install git+https://github.com/open-mmlab/mmsegmentation.git@dev-1.x
+  #         pip install -r requirements.txt
+  #     - name: Build and install
+  #       run: |
+  #         pip install -e .
+  #     - name: Run unittests and generate coverage report
+  #       run: |
+  #         pytest tests/

.github/workflows/lint.yml

@@ -0,0 +1,27 @@
+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files
+      - name: Check docstring coverage
+        run: |
+          pip install interrogate
+          interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmrazor

configs/nas/mmcls/darts/metafile.yml

@@ -24,5 +24,5 @@ Models:
         Metrics:
           Top 1 Accuracy: 97.32
           Top 5 Accuracy: 99.94
-    Config: configs/nas/mmcls/darts/darts_subnet_1xb96_cifar10_2.0.py
+    Config: configs/nas/darts/darts_subnet_1xb96_cifar10_2.0.py
     Weights: https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921.pth

docs/en/user_guides/2_train_different_types_algorithms.md

@@ -86,7 +86,7 @@ For example, the default `_channel_cfg_paths` is set in the config below.
 
 ```Python
 python ./tools/train.py \
-  configs/pruning/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-530M \
+  configs/pruning/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-530M.py \
   --work-dir your_work_dir
 ```
 

mmrazor/datasets/crd_dataset_wrapper.py

@@ -74,7 +74,7 @@ class CRDDataset:
         #   e.g. [2, 3, 5].
         num_classes: int = self.num_classes  # type: ignore
         if num_classes is None:
-            num_classes = len(self.dataset.CLASSES)
+            num_classes = max(self.dataset.get_gt_labels()) + 1
 
         if not self.dataset.test_mode:  # type: ignore
             # Parse info.

mmrazor/models/task_modules/tracer/parsers.py

@@ -118,13 +118,20 @@ def parse_cat(tracer, grad_fn, module2name, param2module, cur_path,
         >>> # ``out`` is obtained by concatenating two tensors
     """
     parents = grad_fn.next_functions
+    concat_id = '_'.join([str(id(p)) for p in parents])
+    concat_id_list = [str(id(p)) for p in parents]
+    concat_id_list.sort()
+    concat_id = '_'.join(concat_id_list)
+    name = f'concat_{concat_id}'
+
+    visited[name] = True
     sub_path_lists = list()
-    for i, parent in enumerate(parents):
+    for _, parent in enumerate(parents):
         sub_path_list = PathList()
         tracer.backward_trace(parent, module2name, param2module, Path(),
                               sub_path_list, visited, shared_module)
         sub_path_lists.append(sub_path_list)
-    cur_path.append(PathConcatNode('CatNode', sub_path_lists))
+    cur_path.append(PathConcatNode(name, sub_path_lists))
 
     result_paths.append(copy.deepcopy(cur_path))
     cur_path.pop(-1)

requirements/optional.txt

@@ -1,3 +1,3 @@
 albumentations>=0.3.2
 scipy
-timm
+# timm

tests/test_datasets/test_datasets.py

@@ -6,6 +6,7 @@ import tempfile
 from unittest import TestCase
 
 import numpy as np
+from mmcls.registry import DATASETS as CLS_DATASETS
 
 from mmrazor.registry import DATASETS
 from mmrazor.utils import register_all_modules
@@ -15,7 +16,8 @@ ASSETS_ROOT = osp.abspath(osp.join(osp.dirname(__file__), '../data/dataset'))
 
 
 class Test_CRD_CIFAR10(TestCase):
-    DATASET_TYPE = 'CRD_CIFAR10'
+    ORI_DATASET_TYPE = 'CIFAR10'
+    DATASET_TYPE = 'CRDDataset'
 
     @classmethod
     def setUpClass(cls) -> None:
@@ -24,10 +26,11 @@ class Test_CRD_CIFAR10(TestCase):
         tmpdir = tempfile.TemporaryDirectory()
         cls.tmpdir = tmpdir
         data_prefix = tmpdir.name
-        cls.DEFAULT_ARGS = dict(
+        cls.ORI_DEFAULT_ARGS = dict(
             data_prefix=data_prefix, pipeline=[], test_mode=False)
+        cls.DEFAULT_ARGS = dict(neg_num=1, percent=0.5)
 
-        dataset_class = DATASETS.get(cls.DATASET_TYPE)
+        dataset_class = CLS_DATASETS.get(cls.ORI_DATASET_TYPE)
         base_folder = osp.join(data_prefix, dataset_class.base_folder)
         os.mkdir(base_folder)
 
@@ -65,25 +68,16 @@ class Test_CRD_CIFAR10(TestCase):
         dataset_class = DATASETS.get(self.DATASET_TYPE)
 
         # Test overriding metainfo by `metainfo` argument
-        cfg = {**self.DEFAULT_ARGS, 'metainfo': {'classes': ('bus', 'car')}}
+        ori_cfg = {
+            **self.ORI_DEFAULT_ARGS, 'metainfo': {
+                'classes': ('bus', 'car')
+            },
+            'type': self.ORI_DATASET_TYPE,
+            '_scope_': 'mmcls'
+        }
+        cfg = {'dataset': ori_cfg, **self.DEFAULT_ARGS}
         dataset = dataset_class(**cfg)
-        self.assertEqual(dataset.CLASSES, ('bus', 'car'))
-
-        # Test overriding metainfo by `classes` argument
-        cfg = {**self.DEFAULT_ARGS, 'classes': ['bus', 'car']}
-        dataset = dataset_class(**cfg)
-        self.assertEqual(dataset.CLASSES, ('bus', 'car'))
-
-        classes_file = osp.join(ASSETS_ROOT, 'classes.txt')
-        cfg = {**self.DEFAULT_ARGS, 'classes': classes_file}
-        dataset = dataset_class(**cfg)
-        self.assertEqual(dataset.CLASSES, ('bus', 'car'))
-        self.assertEqual(dataset.class_to_idx, {'bus': 0, 'car': 1})
-
-        # Test invalid classes
-        cfg = {**self.DEFAULT_ARGS, 'classes': dict(classes=1)}
-        with self.assertRaisesRegex(ValueError, "type <class 'dict'>"):
-            dataset_class(**cfg)
+        self.assertEqual(dataset.dataset.CLASSES, ('bus', 'car'))
 
     @classmethod
     def tearDownClass(cls):
@@ -91,4 +85,4 @@ class Test_CRD_CIFAR10(TestCase):
 
 
 class Test_CRD_CIFAR100(Test_CRD_CIFAR10):
-    DATASET_TYPE = 'CRD_CIFAR100'
+    ORI_DATASET_TYPE = 'CIFAR100'

tests/test_datasets/test_transforms/test_formatting.py

@@ -6,7 +6,7 @@ import unittest
 import numpy as np
 import torch
 from mmcls.structures import ClsDataSample
-from mmengine.data import LabelData
+from mmengine.structures import LabelData
 
 from mmrazor.datasets.transforms import PackCRDClsInputs
 
@@ -34,7 +34,7 @@ class TestPackClsInputs(unittest.TestCase):
             'img': rng.rand(300, 400),
             'gt_label': rng.randint(3, ),
             # TODO.
-            'contrast_sample_idxs': rng.randint()
+            'contrast_sample_idxs': rng.randint(3, )
         }
         self.meta_keys = ('sample_idx', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor', 'flip')
@@ -44,13 +44,13 @@ class TestPackClsInputs(unittest.TestCase):
         results = transform(copy.deepcopy(self.results1))
         self.assertIn('inputs', results)
         self.assertIsInstance(results['inputs'], torch.Tensor)
-        self.assertIn('data_sample', results)
-        self.assertIsInstance(results['data_sample'], ClsDataSample)
+        self.assertIn('data_samples', results)
+        self.assertIsInstance(results['data_samples'], ClsDataSample)
 
-        data_sample = results['data_sample']
+        data_sample = results['data_samples']
         self.assertIsInstance(data_sample.gt_label, LabelData)
 
     def test_repr(self):
         transform = PackCRDClsInputs(meta_keys=self.meta_keys)
         self.assertEqual(
-            repr(transform), f'PackClsInputs(meta_keys={self.meta_keys})')
+            repr(transform), f'PackCRDClsInputs(meta_keys={self.meta_keys})')

tests/test_models/test_algorithms/test_autoslim.py

@@ -18,7 +18,8 @@ MUTATOR_TYPE = Union[torch.nn.Module, Dict]
 DISTILLER_TYPE = Union[torch.nn.Module, Dict]
 
 ARCHITECTURE_CFG = dict(
-    type='mmcls.ImageClassifier',
+    _scope_='mmcls',
+    type='ImageClassifier',
     backbone=dict(type='MobileNetV2', widen_factor=1.5),
     neck=dict(type='GlobalAveragePooling'),
     head=dict(

tests/test_models/test_algorithms/test_dsnas.py

@@ -170,19 +170,17 @@ class TestDsnasDDP(TestDsnas):
         os.environ['MASTER_PORT'] = '12345'
 
         # initialize the process group
-        if torch.cuda.is_available():
-            backend = 'nccl'
-            cls.device = 'cuda'
-        else:
-            backend = 'gloo'
+        backend = 'nccl' if torch.cuda.is_available() else 'gloo'
         dist.init_process_group(backend, rank=0, world_size=1)
 
     def prepare_model(self, device_ids=None) -> Dsnas:
-        model = ToyDiffModule().to(self.device)
-        mutator = DiffModuleMutator().to(self.device)
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+        model = ToyDiffModule()
+        mutator = DiffModuleMutator()
         mutator.prepare_from_supernet(model)
 
-        algo = Dsnas(model, mutator)
+        algo = Dsnas(model, mutator).to(self.device)
 
         return DsnasDDP(
             module=algo, find_unused_parameters=True, device_ids=device_ids)
@@ -199,24 +197,19 @@ class TestDsnasDDP(TestDsnas):
 
     @patch('mmengine.logging.message_hub.MessageHub.get_info')
     def test_dsnasddp_train_step(self, mock_get_info) -> None:
-        model = ToyDiffModule()
-        mutator = DiffModuleMutator()
-        mutator.prepare_from_supernet(model)
+        ddp_model = self.prepare_model()
         mock_get_info.return_value = 2
 
-        algo = Dsnas(model, mutator)
-        ddp_model = DsnasDDP(module=algo, find_unused_parameters=True)
         data = self._prepare_fake_data()
         optim_wrapper = build_optim_wrapper(ddp_model, self.OPTIM_WRAPPER_CFG)
         loss = ddp_model.train_step(data, optim_wrapper)
 
         self.assertIsNotNone(loss)
 
-        algo = Dsnas(model, mutator)
-        ddp_model = DsnasDDP(module=algo, find_unused_parameters=True)
+        ddp_model = self.prepare_model()
         optim_wrapper_dict = OptimWrapperDict(
-            architecture=OptimWrapper(SGD(model.parameters(), lr=0.1)),
-            mutator=OptimWrapper(SGD(model.parameters(), lr=0.01)))
+            architecture=OptimWrapper(SGD(ddp_model.parameters(), lr=0.1)),
+            mutator=OptimWrapper(SGD(ddp_model.parameters(), lr=0.01)))
         loss = ddp_model.train_step(data, optim_wrapper_dict)
 
         self.assertIsNotNone(loss)

tests/test_models/test_algorithms/test_slimmable_network.py

@@ -15,7 +15,8 @@ from mmengine.optim import build_optim_wrapper
 from mmrazor.models.algorithms import SlimmableNetwork, SlimmableNetworkDDP
 
 MODEL_CFG = dict(
-    type='mmcls.ImageClassifier',
+    _scope_='mmcls',
+    type='ImageClassifier',
     backbone=dict(type='MobileNetV2', widen_factor=1.5),
     neck=dict(type='GlobalAveragePooling'),
     head=dict(

tests/test_models/test_losses/test_distillation_losses.py

@@ -2,7 +2,7 @@
 from unittest import TestCase
 
 import torch
-from mmengine.data import BaseDataElement
+from mmengine.structures import BaseDataElement
 
 from mmrazor import digit_version
 from mmrazor.models import (ABLoss, ActivationLoss, ATLoss, CRDLoss, DKDLoss,

tests/test_models/test_mutators/test_classical_models/test_mbv2_channel_mutator.py

@@ -15,6 +15,7 @@ from mmrazor.registry import MODELS
 from ..utils import load_and_merge_channel_cfgs
 
 MODEL_CFG = dict(
+    _scope_='mmcls',
     type='mmcls.ImageClassifier',
     backbone=dict(type='MobileNetV2', widen_factor=1.5),
     neck=dict(type='GlobalAveragePooling'),