Rename the package name to `mmpretrain`.

.dev_scripts/benchmark_regression/1-benchmark_valid.py

@@ -17,10 +17,10 @@ from modelindex.load_model_index import load
 from rich.console import Console
 from rich.table import Table
 
-from mmcls.apis import init_model
+from mmpretrain.apis import init_model
-from mmcls.datasets import CIFAR10, CIFAR100, ImageNet
+from mmpretrain.datasets import CIFAR10, CIFAR100, ImageNet
-from mmcls.utils import register_all_modules
+from mmpretrain.utils import register_all_modules
-from mmcls.visualization import ClsVisualizer
+from mmpretrain.visualization import ClsVisualizer
 
 console = Console()
 MMCLS_ROOT = Path(__file__).absolute().parents[2]

.dev_scripts/benchmark_regression/4-benchmark_speed.py

@@ -18,9 +18,9 @@ from modelindex.load_model_index import load
 from rich.console import Console
 from rich.table import Table
 
-from mmcls.datasets.builder import build_dataloader
+from mmpretrain.datasets.builder import build_dataloader
-from mmcls.datasets.pipelines import Compose
+from mmpretrain.datasets.pipelines import Compose
-from mmcls.models.builder import build_classifier
+from mmpretrain.models.builder import build_classifier
 
 console = Console()
 MMCLS_ROOT = Path(__file__).absolute().parents[2]

.dev_scripts/ckpt_tree.py

@@ -47,7 +47,7 @@ def ckpt_to_state_dict(checkpoint, key=None):
     if key is not None:
         state_dict = checkpoint[key]
     elif 'state_dict' in checkpoint:
-        # try mmcls style
+        # try mmpretrain style
         state_dict = checkpoint['state_dict']
     elif 'model' in checkpoint:
         state_dict = checkpoint['model']
@@ -149,7 +149,7 @@ def main():
     if args.path.suffix in ['.json', '.py', '.yml']:
         from mmengine.runner import get_state_dict
 
-        from mmcls.apis import init_model
+        from mmpretrain.apis import init_model
         model = init_model(args.path, device='cpu')
         state_dict = get_state_dict(model)
     else:

.dev_scripts/compare_init.py

@@ -55,7 +55,7 @@ def state_dict_from_cfg_or_ckpt(path, state_key=None):
     if path.suffix in ['.json', '.py', '.yml']:
         from mmengine.runner import get_state_dict
 
-        from mmcls.apis import init_model
+        from mmpretrain.apis import init_model
         model = init_model(path, device='cpu')
         model.init_weights()
         return get_state_dict(model)

.dev_scripts/fill_metafile.py

@@ -84,8 +84,8 @@ def get_flops(config_path):
     from mmengine.dataset import Compose
     from mmengine.registry import DefaultScope
 
-    import mmcls.datasets  # noqa: F401
+    import mmpretrain.datasets  # noqa: F401
-    from mmcls.apis import init_model
+    from mmpretrain.apis import init_model
 
     cfg = Config.fromfile(config_path)
 
@@ -98,7 +98,7 @@ def get_flops(config_path):
             # The image shape of CIFAR is (32, 32, 3)
             test_dataset.pipeline.insert(1, dict(type='Resize', scale=32))
 
-        with DefaultScope.overwrite_default_scope('mmcls'):
+        with DefaultScope.overwrite_default_scope('mmpretrain'):
             data = Compose(test_dataset.pipeline)({
                 'img':
                 np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8)

.gitignore

@@ -127,7 +127,7 @@ venv.bak/
 /work_dirs
 /projects/*/work_dirs
 /projects/*/data
-/mmcls/.mim
+/mmpretrain/.mim
 .DS_Store
 
 # Pytorch

MANIFEST.in

@@ -1,4 +1,4 @@
 include requirements/*.txt
-include mmcls/.mim/model-index.yml
+include mmpretrain/.mim/model-index.yml
-recursive-include mmcls/.mim/configs *.py *.yml
+recursive-include mmpretrain/.mim/configs *.py *.yml
-recursive-include mmcls/.mim/tools *.py *.sh
+recursive-include mmpretrain/.mim/tools *.py *.sh

configs/_base_/default_runtime.py

@@ -1,5 +1,5 @@
-# defaults to use registries in mmcls
+# defaults to use registries in mmpretrain
-default_scope = 'mmcls'
+default_scope = 'mmpretrain'
 
 # configure default hooks
 default_hooks = dict(

configs/_base_/models/replknet-31B_in1k.py

@@ -1,4 +1,4 @@
-from mmcls.models import build_classifier
+from mmpretrain.models import build_classifier
 
 model = dict(
     type='ImageClassifier',

configs/csra/resnet101-csra_1xb16_voc07-448px.py

@@ -2,11 +2,11 @@ _base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py']
 
 # Pre-trained Checkpoint Path
 checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth'  # noqa
-# If you want to use the pre-trained weight of ResNet101-CutMix from
+# If you want to use the pre-trained weight of ResNet101-CutMix from the
-# the originary repo(https://github.com/Kevinz-code/CSRA). Script of
+# originary repo(https://github.com/Kevinz-code/CSRA). Script of
-# 'tools/convert_models/torchvision_to_mmcls.py' can help you convert weight
+# 'tools/model_converters/torchvision_to_mmpretrain.py' can help you convert
-# into mmcls format. The mAP result would hit 95.5 by using the weight.
+# weight into mmpretrain format. The mAP result would hit 95.5 by using the
-# checkpoint = 'PATH/TO/PRE-TRAINED_WEIGHT'
+# weight. checkpoint = 'PATH/TO/PRE-TRAINED_WEIGHT'
 
 # model settings
 model = dict(

configs/lenet/lenet5_mnist.py

@@ -51,7 +51,7 @@ val_cfg = dict()
 test_cfg = dict()
 
 # runtime settings
-default_scope = 'mmcls'
+default_scope = 'mmpretrain'
 
 default_hooks = dict(
     # record the time of every iteration.

demo/image_demo.py

@@ -4,7 +4,7 @@ from argparse import ArgumentParser
 from mmengine.fileio import dump
 from rich import print_json
 
-from mmcls.apis import ImageClassificationInferencer
+from mmpretrain.apis import ImageClassificationInferencer
 
 
 def main():
@@ -30,7 +30,7 @@ def main():
         raise ValueError(
             f'Unavailable model "{args.model}", you can specify find a model '
             'name or a config file or find a model name from '
-            'https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html#all-checkpoints'  # noqa: E501
+            'https://mmpretrain.readthedocs.io/en/1.x/modelzoo_statistics.html#all-checkpoints'  # noqa: E501
         )
     result = inferencer(args.img, show=args.show, show_dir=args.show_dir)[0]
     # show the results

docs/en/conf.py

@@ -22,12 +22,12 @@ sys.path.insert(0, os.path.abspath('../../'))
 
 # -- Project information -----------------------------------------------------
 
-project = 'MMClassification'
+project = 'MMPretrain'
 copyright = '2020, OpenMMLab'
-author = 'MMClassification Authors'
+author = 'MMPretrain Authors'
 
 # The full version, including alpha/beta/rc tags
-version_file = '../../mmcls/version.py'
+version_file = '../../mmpretrain/version.py'
 
 
 def get_version():
@@ -92,25 +92,25 @@ html_theme_options = {
     'menu': [
         {
             'name': 'GitHub',
-            'url': 'https://github.com/open-mmlab/mmclassification'
+            'url': 'https://github.com/open-mmlab/mmpretrain'
         },
         {
             'name': 'Colab Tutorials',
             'children': [
                 {'name': 'Train and inference with shell commands',
-                 'url': 'https://colab.research.google.com/github/mzr1996/mmclassification-tutorial/blob/master/1.x/MMClassification_tools.ipynb'},
+                 'url': 'https://colab.research.google.com/github/mzr1996/mmpretrain-tutorial/blob/master/1.x/MMPretrain_tools.ipynb'},
                 {'name': 'Train and inference with Python APIs',
-                 'url': 'https://colab.research.google.com/github/mzr1996/mmclassification-tutorial/blob/master/1.x/MMClassification_python.ipynb'},
+                 'url': 'https://colab.research.google.com/github/mzr1996/mmpretrain-tutorial/blob/master/1.x/MMPretrain_python.ipynb'},
             ]
         },
         {
             'name': 'Version',
             'children': [
-                {'name': 'MMClassification 0.x',
+                {'name': 'MMPretrain 0.x',
-                 'url': 'https://mmclassification.readthedocs.io/en/latest/',
+                 'url': 'https://mmpretrain.readthedocs.io/en/latest/',
                  'description': 'master branch'},
-                {'name': 'MMClassification 1.x',
+                {'name': 'MMPretrain 1.x',
-                 'url': 'https://mmclassification.readthedocs.io/en/dev-1.x/',
+                 'url': 'https://mmpretrain.readthedocs.io/en/dev-1.x/',
                  'description': '1.x branch'},
             ],
         }
@@ -138,7 +138,7 @@ html_js_files = [
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'mmclsdoc'
+htmlhelp_basename = 'mmpretraindoc'
 
 # -- Options for LaTeX output ------------------------------------------------
 
@@ -160,16 +160,14 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (root_doc, 'mmcls.tex', 'MMClassification Documentation', author,
+    (root_doc, 'mmpretrain.tex', 'MMPretrain Documentation', author, 'manual'),
-     'manual'),
 ]
 
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [(root_doc, 'mmcls', 'MMClassification Documentation', [author], 1)
+man_pages = [(root_doc, 'mmpretrain', 'MMPretrain Documentation', [author], 1)]
-             ]
 
 # -- Options for Texinfo output ----------------------------------------------
 
@@ -177,7 +175,7 @@ man_pages = [(root_doc, 'mmcls', 'MMClassification Documentation', [author], 1)
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (root_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls',
+    (root_doc, 'mmpretrain', 'MMPretrain Documentation', author, 'mmpretrain',
      'OpenMMLab image classification toolbox and benchmark.', 'Miscellaneous'),
 ]
 

docs/en/stat.py

@@ -9,7 +9,7 @@ from tabulate import tabulate
 
 MMCLS_ROOT = Path(__file__).absolute().parents[2]
 PAPERS_ROOT = Path('papers')  # Path to save generated paper pages.
-GITHUB_PREFIX = 'https://github.com/open-mmlab/mmclassification/blob/1.x/'
+GITHUB_PREFIX = 'https://github.com/open-mmlab/mmpretrain/blob/1.x/'
 MODELZOO_TEMPLATE = """
 # Model Zoo Summary
 

docs/zh_CN/conf.py

@@ -22,12 +22,12 @@ sys.path.insert(0, os.path.abspath('../..'))
 
 # -- Project information -----------------------------------------------------
 
-project = 'MMClassification'
+project = 'MMPretrain'
 copyright = '2020, OpenMMLab'
-author = 'MMClassification Authors'
+author = 'MMPretrain Authors'
 
 # The full version, including alpha/beta/rc tags
-version_file = '../../mmcls/version.py'
+version_file = '../../mmpretrain/version.py'
 
 
 def get_version():
@@ -92,25 +92,25 @@ html_theme_options = {
     'menu': [
         {
             'name': 'GitHub',
-            'url': 'https://github.com/open-mmlab/mmclassification'
+            'url': 'https://github.com/open-mmlab/mmpretrain'
         },
         {
             'name': 'Colab 教程',
             'children': [
                 {'name': '用命令行工具训练和推理',
-                 'url': 'https://colab.research.google.com/github/mzr1996/mmclassification-tutorial/blob/master/1.x/MMClassification_tools.ipynb'},
+                 'url': 'https://colab.research.google.com/github/mzr1996/mmpretrain-tutorial/blob/master/1.x/MMPretrain_tools.ipynb'},
                 {'name': '用 Python API 训练和推理',
-                 'url': 'https://colab.research.google.com/github/mzr1996/mmclassification-tutorial/blob/master/1.x/MMClassification_python.ipynb'},
+                 'url': 'https://colab.research.google.com/github/mzr1996/mmpretrain-tutorial/blob/master/1.x/MMPretrain_python.ipynb'},
             ]
         },
         {
             'name': 'Version',
             'children': [
-                {'name': 'MMClassification 0.x',
+                {'name': 'MMPretrain 0.x',
-                 'url': 'https://mmclassification.readthedocs.io/zh_CN/latest/',
+                 'url': 'https://mmpretrain.readthedocs.io/zh_CN/latest/',
                  'description': 'master branch'},
-                {'name': 'MMClassification 1.x',
+                {'name': 'MMPretrain 1.x',
-                 'url': 'https://mmclassification.readthedocs.io/zh_CN/dev-1.x/',
+                 'url': 'https://mmpretrain.readthedocs.io/zh_CN/dev-1.x/',
                  'description': '1.x branch'},
             ],
         }
@@ -138,7 +138,7 @@ html_js_files = [
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'mmclsdoc'
+htmlhelp_basename = 'mmpretraindoc'
 
 # -- Options for LaTeX output ------------------------------------------------
 
@@ -164,16 +164,14 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (root_doc, 'mmcls.tex', 'MMClassification Documentation', author,
+    (root_doc, 'mmpretrain.tex', 'MMPretrain Documentation', author, 'manual'),
-     'manual'),
 ]
 
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [(root_doc, 'mmcls', 'MMClassification Documentation', [author], 1)
+man_pages = [(root_doc, 'mmpretrain', 'MMPretrain Documentation', [author], 1)]
-             ]
 
 # -- Options for Texinfo output ----------------------------------------------
 
@@ -181,7 +179,7 @@ man_pages = [(root_doc, 'mmcls', 'MMClassification Documentation', [author], 1)
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (root_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls',
+    (root_doc, 'mmpretrain', 'MMPretrain Documentation', author, 'mmpretrain',
      'OpenMMLab image classification toolbox and benchmark.', 'Miscellaneous'),
 ]
 

docs/zh_CN/stat.py

@@ -9,7 +9,7 @@ from tabulate import tabulate
 
 MMCLS_ROOT = Path(__file__).absolute().parents[2]
 PAPERS_ROOT = Path('papers')  # Path to save generated paper pages.
-GITHUB_PREFIX = 'https://github.com/open-mmlab/mmclassification/blob/1.x/'
+GITHUB_PREFIX = 'https://github.com/open-mmlab/mmpretrain/blob/1.x/'
 MODELZOO_TEMPLATE = """
 # 模型库统计
 

mmcls/utils/setup_env.py

@@ -1,39 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import datetime
-import warnings
-
-from mmengine import DefaultScope
-
-
-def register_all_modules(init_default_scope: bool = True) -> None:
-    """Register all modules in mmcls into the registries.
-
-    Args:
-        init_default_scope (bool): Whether initialize the mmcls default scope.
-            If True, the global default scope will be set to `mmcls`, and all
-            registries will build modules from mmcls's registry node. To
-            understand more about the registry, please refer to
-            https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md
-            Defaults to True.
-    """  # noqa
-    import mmcls.datasets  # noqa: F401,F403
-    import mmcls.engine  # noqa: F401,F403
-    import mmcls.evaluation  # noqa: F401,F403
-    import mmcls.models  # noqa: F401,F403
-    import mmcls.structures  # noqa: F401,F403
-    import mmcls.visualization  # noqa: F401,F403
-
-    if not init_default_scope:
-        return
-
-    current_scope = DefaultScope.get_current_instance()
-    if current_scope is None:
-        DefaultScope.get_instance('mmcls', scope_name='mmcls')
-    elif current_scope.scope_name != 'mmcls':
-        warnings.warn(f'The current default scope "{current_scope.scope_name}"'
-                      ' is not "mmcls", `register_all_modules` will force the '
-                      'current default scope to be "mmcls". If this is not '
-                      'expected, please set `init_default_scope=False`.')
-        # avoid name conflict
-        new_instance_name = f'mmcls-{datetime.datetime.now()}'
-        DefaultScope.get_instance(new_instance_name, scope_name='mmcls')

mmpretrain/apis/inference.py

@@ -11,8 +11,8 @@ from mmengine.infer import BaseInferencer
 from mmengine.model import BaseModel
 from mmengine.runner import load_checkpoint
 
-from mmcls.registry import TRANSFORMS
+from mmpretrain.registry import TRANSFORMS
-from mmcls.structures import ClsDataSample
+from mmpretrain.structures import ClsDataSample
 from .model import get_model, init_model, list_models
 
 ModelType = Union[BaseModel, str, Config]
@@ -63,7 +63,7 @@ class ImageClassificationInferencer(BaseInferencer):
     Example:
         1. Use a pre-trained model in MMClassification to inference an image.
 
-           >>> from mmcls import ImageClassificationInferencer
+           >>> from mmpretrain import ImageClassificationInferencer
            >>> inferencer = ImageClassificationInferencer('resnet50_8xb32_in1k')
            >>> inferencer('demo/demo.JPEG')
            [{'pred_score': array([...]),
@@ -74,7 +74,7 @@ class ImageClassificationInferencer(BaseInferencer):
         2. Use a config file and checkpoint to inference multiple images on GPU,
            and save the visualization results in a folder.
 
-           >>> from mmcls import ImageClassificationInferencer
+           >>> from mmpretrain import ImageClassificationInferencer
            >>> inferencer = ImageClassificationInferencer(
                    model='configs/resnet/resnet50_8xb32_in1k.py',
                    weights='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth',
@@ -107,7 +107,7 @@ class ImageClassificationInferencer(BaseInferencer):
         else:
             raise TypeError(
                 'The `model` can be a name of model and you can use '
-                '`mmcls.list_models` to get an available name. It can '
+                '`mmpretrain.list_models` to get an available name. It can '
                 'also be a Config object or a path to the config file.')
 
         model.eval()
@@ -185,7 +185,7 @@ class ImageClassificationInferencer(BaseInferencer):
             return None
 
         if self.visualizer is None:
-            from mmcls.visualization import ClsVisualizer
+            from mmpretrain.visualization import ClsVisualizer
             self.visualizer = ClsVisualizer()
             if self.classes is not None:
                 self.visualizer._dataset_meta = dict(classes=self.classes)

mmpretrain/apis/model.py

@@ -15,7 +15,7 @@ from modelindex.models.Model import Model
 class ModelHub:
     """A hub to host the meta information of all pre-defined models."""
     _models_dict = {}
-    __mmcls_registered = False
+    __mmpretrain_registered = False
 
     @classmethod
     def register_model_index(cls,
@@ -52,7 +52,7 @@ class ModelHub:
         Returns:
             modelindex.models.Model: The metainfo of the specified model.
         """
-        cls._register_mmcls_models()
+        cls._register_mmpretrain_models()
         # lazy load config
         metainfo = copy.deepcopy(cls._models_dict.get(model_name.lower()))
         if metainfo is None:
@@ -75,15 +75,15 @@ class ModelHub:
         return config_path
 
     @classmethod
-    def _register_mmcls_models(cls):
+    def _register_mmpretrain_models(cls):
-        # register models in mmcls
+        # register models in mmpretrain
-        if not cls.__mmcls_registered:
+        if not cls.__mmpretrain_registered:
             from mmengine.utils import get_installed_path
-            mmcls_root = Path(get_installed_path('mmcls'))
+            mmpretrain_root = Path(get_installed_path('mmpretrain'))
-            model_index_path = mmcls_root / '.mim' / 'model-index.yml'
+            model_index_path = mmpretrain_root / '.mim' / 'model-index.yml'
             ModelHub.register_model_index(
-                model_index_path, config_prefix=mmcls_root / '.mim')
+                model_index_path, config_prefix=mmpretrain_root / '.mim')
-            cls.__mmcls_registered = True
+            cls.__mmpretrain_registered = True
 
     @classmethod
     def has(cls, model_name):
@@ -118,7 +118,7 @@ def init_model(config, checkpoint=None, device=None, **kwargs):
     config.model.setdefault('data_preprocessor',
                             config.get('data_preprocessor', None))
 
-    from mmcls.registry import MODELS
+    from mmpretrain.registry import MODELS
 
     model = MODELS.build(config.model)
     if checkpoint is not None:
@@ -130,13 +130,13 @@ def init_model(config, checkpoint=None, device=None, **kwargs):
             # Don't set CLASSES if the model is headless.
             pass
         elif 'dataset_meta' in checkpoint.get('meta', {}):
-            # mmcls 1.x
+            # mmpretrain 1.x
             model.CLASSES = checkpoint['meta']['dataset_meta'].get('classes')
         elif 'CLASSES' in checkpoint.get('meta', {}):
-            # mmcls < 1.x
+            # mmpretrain < 1.x or mmselfsup < 1.x
             model.CLASSES = checkpoint['meta']['CLASSES']
         else:
-            from mmcls.datasets.categories import IMAGENET_CATEGORIES
+            from mmpretrain.datasets.categories import IMAGENET_CATEGORIES
             warnings.simplefilter('once')
             warnings.warn('Class names are not saved in the checkpoint\'s '
                           'meta data, use imagenet by default.')
@@ -165,7 +165,7 @@ def get_model(model_name, pretrained=False, device=None, **kwargs):
         Get a ResNet-50 model and extract images feature:
 
         >>> import torch
-        >>> from mmcls import get_model
+        >>> from mmpretrain import get_model
         >>> inputs = torch.rand(16, 3, 224, 224)
         >>> model = get_model('resnet50_8xb32_in1k', pretrained=True, backbone=dict(out_indices=(0, 1, 2, 3)))
         >>> feats = model.extract_feat(inputs)
@@ -178,7 +178,7 @@ def get_model(model_name, pretrained=False, device=None, **kwargs):
 
         Get Swin-Transformer model with pre-trained weights and inference:
 
-        >>> from mmcls import get_model, inference_model
+        >>> from mmpretrain import get_model, inference_model
         >>> model = get_model('swin-base_16xb64_in1k', pretrained=True)
         >>> result = inference_model(model, 'demo/demo.JPEG')
         >>> print(result['pred_class'])
@@ -201,7 +201,7 @@ def get_model(model_name, pretrained=False, device=None, **kwargs):
 
 
 def list_models(pattern=None) -> List[str]:
-    """List all models available in MMClassification.
+    """List all models available in MMPretrain.
 
     Args:
         pattern (str | None): A wildcard pattern to match model names.
@@ -212,12 +212,12 @@ def list_models(pattern=None) -> List[str]:
     Examples:
         List all models:
 
-        >>> from mmcls import list_models
+        >>> from mmpretrain import list_models
         >>> print(list_models())
 
         List ResNet-50 models on ImageNet-1k dataset:
 
-        >>> from mmcls import list_models
+        >>> from mmpretrain import list_models
         >>> print(list_models('resnet*in1k'))
         ['resnet50_8xb32_in1k',
          'resnet50_8xb32-fp16_in1k',
@@ -225,7 +225,7 @@ def list_models(pattern=None) -> List[str]:
          'resnet50_8xb256-rsb-a2-300e_in1k',
          'resnet50_8xb256-rsb-a3-100e_in1k']
     """
-    ModelHub._register_mmcls_models()
+    ModelHub._register_mmpretrain_models()
     if pattern is None:
         return sorted(list(ModelHub._models_dict.keys()))
     # Always match keys with any postfix.

mmpretrain/datasets/base_dataset.py

@@ -7,7 +7,7 @@ import mmengine
 import numpy as np
 from mmengine.dataset import BaseDataset as _BaseDataset
 
-from mmcls.registry import DATASETS, TRANSFORMS
+from mmpretrain.registry import DATASETS, TRANSFORMS
 
 
 def expanduser(path):

mmpretrain/datasets/builder.py

@@ -1,12 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 
 
 def build_dataset(cfg):
     """Build dataset.
 
     Examples:
-        >>> from mmcls.datasets import build_dataset
+        >>> from mmpretrain.datasets import build_dataset
         >>> mnist_train = build_dataset(
         ...     dict(type='MNIST', data_prefix='data/mnist/', test_mode=False))
         >>> print(mnist_train)

mmpretrain/datasets/cifar.py

@@ -7,7 +7,7 @@ import numpy as np
 from mmengine.fileio import (LocalBackend, exists, get, get_file_backend,
                              join_path)
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import BaseDataset
 from .categories import CIFAR10_CATEGORIES, CIFAR100_CATEGORIES
 from .utils import check_md5, download_and_extract_archive

mmpretrain/datasets/cub.py

@@ -3,7 +3,7 @@ from typing import List
 
 from mmengine import get_file_backend, list_from_file
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import BaseDataset
 from .categories import CUB_CATEGORIES
 
@@ -51,7 +51,7 @@ class CUB(BaseDataset):
 
 
     Examples:
-        >>> from mmcls.datasets import CUB
+        >>> from mmpretrain.datasets import CUB
         >>> cub_train_cfg = dict(data_root='data/CUB_200_2011', test_mode=True)
         >>> cub_train = CUB(**cub_train_cfg)
         >>> cub_train

mmpretrain/datasets/custom.py

@@ -5,7 +5,7 @@ from mmengine.fileio import (BaseStorageBackend, get_file_backend,
                              list_from_file)
 from mmengine.logging import MMLogger
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import BaseDataset
 
 

mmpretrain/datasets/dataset_wrappers.py

@@ -4,7 +4,7 @@ import copy
 import numpy as np
 from mmengine.dataset import BaseDataset, force_full_init
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 
 
 @DATASETS.register_module()

mmpretrain/datasets/imagenet.py

@@ -3,7 +3,7 @@ from typing import Optional, Union
 
 from mmengine.logging import MMLogger
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .categories import IMAGENET_CATEGORIES
 from .custom import CustomDataset
 

mmpretrain/datasets/inshop.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmengine import get_file_backend, list_from_file
 
-from mmcls.datasets.base_dataset import BaseDataset
+from mmpretrain.registry import DATASETS
-from mmcls.registry import DATASETS
+from .base_dataset import BaseDataset
 
 
 @DATASETS.register_module()
@@ -35,7 +35,7 @@ class InShop(BaseDataset):
         **kwargs: Other keyword arguments in :class:`BaseDataset`.
 
     Examples:
-        >>> from mmcls.datasets import InShop
+        >>> from mmpretrain.datasets import InShop
         >>>
         >>> # build train InShop dataset
         >>> inshop_train_cfg = dict(data_root='data/inshop', split='train')

mmpretrain/datasets/mnist.py

@@ -8,7 +8,7 @@ import numpy as np
 import torch
 from mmengine.fileio import LocalBackend, exists, get_file_backend, join_path
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import BaseDataset
 from .categories import FASHIONMNIST_CATEGORITES, MNIST_CATEGORITES
 from .utils import (download_and_extract_archive, open_maybe_compressed_file,

mmpretrain/datasets/multi_label.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import List
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import BaseDataset
 
 

mmpretrain/datasets/multi_task.py

@@ -60,7 +60,7 @@ class MultiTaskDataset:
     Assume we put our dataset in the ``data/mydataset`` folder in the
     repository and organize it as the below format: ::
 
-        mmclassification/
+        mmpretrain/
         └── data
             └── mydataset
                 ├── annotation
@@ -81,7 +81,7 @@ class MultiTaskDataset:
 
     .. code:: python
 
-        >>> from mmcls.datasets import build_dataset
+        >>> from mmpretrain.datasets import build_dataset
         >>> train_cfg = dict(
         ...     type="MultiTaskDataset",
         ...     ann_file="annotation/train.json",
@@ -94,7 +94,7 @@ class MultiTaskDataset:
 
     Or we can put all files in the same folder: ::
 
-        mmclassification/
+        mmpretrain/
         └── data
             └── mydataset
                  ├── train.json
@@ -109,7 +109,7 @@ class MultiTaskDataset:
 
     .. code:: python
 
-        >>> from mmcls.datasets import build_dataset
+        >>> from mmpretrain.datasets import build_dataset
         >>> train_cfg = dict(
         ...     type="MultiTaskDataset",
         ...     ann_file="train.json",
@@ -133,8 +133,8 @@ class MultiTaskDataset:
             ``data_root`` for the ``"img_path"`` field in the annotation file.
             Defaults to None.
         pipeline (Sequence[dict]): A list of dict, where each element
-            represents a operation defined in :mod:`mmcls.datasets.pipelines`.
+            represents a operation defined in
-            Defaults to an empty tuple.
+            :mod:`mmpretrain.datasets.pipelines`. Defaults to an empty tuple.
         test_mode (bool): in train mode or test mode. Defaults to False.
         file_client_args (dict, optional): Arguments to instantiate a
             FileClient. See :class:`mmengine.fileio.FileClient` for details.

mmpretrain/datasets/samplers/repeat_aug.py

@@ -5,7 +5,7 @@ import torch
 from mmengine.dist import get_dist_info, is_main_process, sync_random_seed
 from torch.utils.data import Sampler
 
-from mmcls.registry import DATA_SAMPLERS
+from mmpretrain.registry import DATA_SAMPLERS
 
 
 @DATA_SAMPLERS.register_module()

mmpretrain/datasets/transforms/auto_augment.py

@@ -11,7 +11,7 @@ from mmcv.transforms import BaseTransform, Compose, RandomChoice
 from mmcv.transforms.utils import cache_randomness
 from mmengine.utils import is_list_of, is_seq_of
 
-from mmcls.registry import TRANSFORMS
+from mmpretrain.registry import TRANSFORMS
 
 
 def merge_hparams(policy: dict, hparams: dict) -> dict:
@@ -125,7 +125,7 @@ class RandAugment(BaseTransform):
         time, and magnitude_level of every policy is 6 (total is 10 by default)
 
         >>> import numpy as np
-        >>> from mmcls.datasets import RandAugment
+        >>> from mmpretrain.datasets import RandAugment
         >>> transform = RandAugment(
         ...     policies='timm_increasing',
         ...     num_policies=2,

mmpretrain/datasets/transforms/formatting.py

@@ -9,8 +9,8 @@ from mmcv.transforms import BaseTransform
 from mmengine.utils import is_str
 from PIL import Image
 
-from mmcls.registry import TRANSFORMS
+from mmpretrain.registry import TRANSFORMS
-from mmcls.structures import ClsDataSample, MultiTaskDataSample
+from mmpretrain.structures import ClsDataSample, MultiTaskDataSample
 
 
 def to_tensor(data):
@@ -53,8 +53,8 @@ class PackClsInputs(BaseTransform):
     **Added Keys:**
 
     - inputs (:obj:`torch.Tensor`): The forward data of models.
-    - data_samples (:obj:`~mmcls.structures.ClsDataSample`): The annotation
+    - data_samples (:obj:`~mmpretrain.structures.ClsDataSample`): The
-      info of the sample.
+      annotation info of the sample.
 
     Args:
         meta_keys (Sequence[str]): The meta keys to be saved in the

mmpretrain/datasets/transforms/processing.py

@@ -11,7 +11,7 @@ import numpy as np
 from mmcv.transforms import BaseTransform
 from mmcv.transforms.utils import cache_randomness
 
-from mmcls.registry import TRANSFORMS
+from mmpretrain.registry import TRANSFORMS
 
 try:
     import albumentations
@@ -1008,7 +1008,7 @@ class Lighting(BaseTransform):
         return repr_str
 
 
-# 'Albu' is used in previous versions of mmcls, here is for compatibility
+# 'Albu' is used in previous versions of mmpretrain, here is for compatibility
 # users can use both 'Albumentations' and 'Albu'.
 @TRANSFORMS.register_module(['Albumentations', 'Albu'])
 class Albumentations(BaseTransform):
@@ -1055,13 +1055,13 @@ class Albumentations(BaseTransform):
 
     Args:
         transforms (List[Dict]): List of albumentations transform configs.
-        keymap (Optional[Dict]): Mapping of mmcls to albumentations fields,
+        keymap (Optional[Dict]): Mapping of mmpretrain to albumentations
-            in format {'input key':'albumentation-style key'}. Defaults to
+            fields, in format {'input key':'albumentation-style key'}.
-            None.
+            Defaults to None.
 
     Example:
         >>> import mmcv
-        >>> from mmcls.datasets import Albumentations
+        >>> from mmpretrain.datasets import Albumentations
         >>> transforms = [
         ...     dict(
         ...         type='ShiftScaleRotate',

mmpretrain/datasets/voc.py

@@ -4,7 +4,7 @@ from typing import List, Optional, Union
 
 from mmengine import get_file_backend, list_from_file
 
-from mmcls.registry import DATASETS
+from mmpretrain.registry import DATASETS
 from .base_dataset import expanduser
 from .categories import VOC2007_CATEGORIES
 from .multi_label import MultiLabelDataset

mmpretrain/engine/hooks/class_num_check_hook.py

@@ -2,7 +2,7 @@
 from mmengine.hooks import Hook
 from mmengine.utils import is_seq_of
 
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
 
 
 @HOOKS.register_module()

mmpretrain/engine/hooks/ema_hook.py

@@ -8,7 +8,7 @@ from mmengine.hooks import EMAHook as BaseEMAHook
 from mmengine.logging import MMLogger
 from mmengine.runner import Runner
 
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
 
 
 @HOOKS.register_module()

mmpretrain/engine/hooks/margin_head_hooks.py

@@ -3,8 +3,8 @@ import numpy as np
 from mmengine.hooks import Hook
 from mmengine.model import is_model_wrapper
 
-from mmcls.models.heads import ArcFaceClsHead
+from mmpretrain.models.heads import ArcFaceClsHead
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
 
 
 @HOOKS.register_module()

mmpretrain/engine/hooks/precise_bn_hook.py

@@ -20,7 +20,7 @@ from torch.nn.modules.batchnorm import _BatchNorm
 from torch.nn.modules.instancenorm import _InstanceNorm
 from torch.utils.data import DataLoader
 
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
 
 DATA_BATCH = Optional[Sequence[dict]]
 

mmpretrain/engine/hooks/retriever_hooks.py

@@ -4,8 +4,8 @@ import warnings
 from mmengine.hooks import Hook
 from mmengine.model import is_model_wrapper
 
-from mmcls.models import BaseRetriever
+from mmpretrain.models import BaseRetriever
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
 
 
 @HOOKS.register_module()
@@ -27,5 +27,6 @@ class PrepareProtoBeforeValLoopHook(Hook):
                 model.prepare_prototype()
         else:
             warnings.warn(
-                'Only the `mmcls.models.retrievers.BaseRetriever` can execute '
+                'Only the `mmpretrain.models.retrievers.BaseRetriever` '
-                f'`PrepareRetrieverPrototypeHook`, but got `{type(model)}`')
+                'can execute `PrepareRetrieverPrototypeHook`, but got '
+                f'`{type(model)}`')

mmpretrain/engine/hooks/switch_recipe_hook.py

@@ -6,8 +6,8 @@ from mmcv.transforms import Compose
 from mmengine.hooks import Hook
 from mmengine.model import is_model_wrapper
 
-from mmcls.models.utils import RandomBatchAugment
+from mmpretrain.models.utils import RandomBatchAugment
-from mmcls.registry import HOOKS, MODEL_WRAPPERS, MODELS
+from mmpretrain.registry import HOOKS, MODEL_WRAPPERS, MODELS
 
 
 @HOOKS.register_module()
@@ -25,9 +25,9 @@ class SwitchRecipeHook(Hook):
               train dataset. If not specified, keep the original settings.
             - ``batch_augments`` (dict | None, optional): The new batch
               augmentations of during training. See :mod:`Batch Augmentations
-              <mmcls.models.utils.batch_augments>` for more details. If None,
+              <mmpretrain.models.utils.batch_augments>` for more details.
-              disable batch augmentations. If not specified, keep the original
+              If None, disable batch augmentations. If not specified, keep the
-              settings.
+              original settings.
             - ``loss`` (dict, optional): The new loss module config. If not
               specified, keep the original settings.
 

mmpretrain/engine/hooks/visualization_hook.py

@@ -8,8 +8,8 @@ from mmengine.hooks import Hook
 from mmengine.runner import EpochBasedTrainLoop, Runner
 from mmengine.visualization import Visualizer
 
-from mmcls.registry import HOOKS
+from mmpretrain.registry import HOOKS
-from mmcls.structures import ClsDataSample
+from mmpretrain.structures import ClsDataSample
 
 
 @HOOKS.register_module()
@@ -30,7 +30,7 @@ class VisualizationHook(Hook):
             in the testing process. If None, handle with the backends of the
             visualizer. Defaults to None.
         **kwargs: other keyword arguments of
-            :meth:`mmcls.visualization.ClsVisualizer.add_datasample`.
+            :meth:`mmpretrain.visualization.ClsVisualizer.add_datasample`.
     """
 
     def __init__(self,

mmpretrain/engine/optimizers/adan_t.py

@@ -19,7 +19,7 @@ import torch
 from torch import Tensor
 from torch.optim.optimizer import Optimizer
 
-from mmcls.registry import OPTIMIZERS
+from mmpretrain.registry import OPTIMIZERS
 
 
 @OPTIMIZERS.register_module()

mmpretrain/engine/optimizers/lamb.py

@@ -61,7 +61,7 @@ import math
 import torch
 from torch.optim import Optimizer
 
-from mmcls.registry import OPTIMIZERS
+from mmpretrain.registry import OPTIMIZERS
 
 
 @OPTIMIZERS.register_module()

mmpretrain/evaluation/__init__.py

@@ -1,2 +1,3 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .functional import *  # noqa: F401,F403
 from .metrics import *  # noqa: F401,F403

mmpretrain/evaluation/metrics/multi_label.py

@@ -7,7 +7,7 @@ from mmengine.evaluator import BaseMetric
 from mmengine.logging import MMLogger
 from mmengine.structures import LabelData
 
-from mmcls.registry import METRICS
+from mmpretrain.registry import METRICS
 from .single_label import _precision_recall_f1_support, to_tensor
 
 
@@ -77,7 +77,7 @@ class MultiLabelMetric(BaseMetric):
 
     Examples:
         >>> import torch
-        >>> from mmcls.evaluation import MultiLabelMetric
+        >>> from mmpretrain.evaluation import MultiLabelMetric
         >>> # ------ The Basic Usage for category indices labels -------
         >>> y_pred = [[0], [1], [0, 1], [3]]
         >>> y_true = [[0, 3], [0, 2], [1], [3]]
@@ -114,7 +114,7 @@ class MultiLabelMetric(BaseMetric):
         (tensor(62.5000), tensor(31.2500), tensor(39.1667), tensor(8))
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.structures import ClsDataSample
+        >>> from mmpretrain.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_sampels = [
         ...     ClsDataSample().set_pred_score(pred).set_gt_score(gt)
@@ -466,7 +466,7 @@ class AveragePrecision(BaseMetric):
 
     Examples:
         >>> import torch
-        >>> from mmcls.evaluation import AveragePrecision
+        >>> from mmpretrain.evaluation import AveragePrecision
         >>> # --------- The Basic Usage for one-hot pred scores ---------
         >>> y_pred = torch.Tensor([[0.9, 0.8, 0.3, 0.2],
         ...                        [0.1, 0.2, 0.2, 0.1],
@@ -479,7 +479,7 @@ class AveragePrecision(BaseMetric):
         >>> AveragePrecision.calculate(y_pred, y_true)
         tensor(70.833)
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.structures import ClsDataSample
+        >>> from mmpretrain.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_samples = [
         ...     ClsDataSample().set_pred_score(i).set_gt_score(j)

mmpretrain/evaluation/metrics/multi_task.py

@@ -3,7 +3,7 @@ from typing import Dict, Sequence
 
 from mmengine.evaluator import BaseMetric
 
-from mmcls.registry import METRICS
+from mmpretrain.registry import METRICS
 
 
 @METRICS.register_module()
@@ -14,7 +14,7 @@ class MultiTasksMetric(BaseMetric):
             and the values is a list of the metric corresponds to this task
     Examples:
         >>> import torch
-        >>> from mmcls.evaluation import MultiTasksMetric
+        >>> from mmpretrain.evaluation import MultiTasksMetric
         # -------------------- The Basic Usage --------------------
         >>>task_metrics = {
             'task0': [dict(type='Accuracy', topk=(1, ))],

mmpretrain/evaluation/metrics/retrieval.py

@@ -8,7 +8,7 @@ from mmengine.evaluator import BaseMetric
 from mmengine.structures import LabelData
 from mmengine.utils import is_seq_of
 
-from mmcls.registry import METRICS
+from mmpretrain.registry import METRICS
 from .single_label import to_tensor
 
 
@@ -33,7 +33,7 @@ class RetrievalRecall(BaseMetric):
         Use in the code:
 
         >>> import torch
-        >>> from mmcls.evaluation import RetrievalRecall
+        >>> from mmpretrain.evaluation import RetrievalRecall
         >>> # -------------------- The Basic Usage --------------------
         >>> y_pred = [[0], [1], [2], [3]]
         >>> y_true = [[0, 1], [2], [1], [0, 3]]
@@ -48,7 +48,7 @@ class RetrievalRecall(BaseMetric):
         [tensor(9.3000), tensor(48.4000)]
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.structures import ClsDataSample
+        >>> from mmpretrain.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_samples = [
         ...     ClsDataSample().set_gt_label([0, 1]).set_pred_score(

mmpretrain/evaluation/metrics/single_label.py

@@ -8,7 +8,7 @@ import torch
 import torch.nn.functional as F
 from mmengine.evaluator import BaseMetric
 
-from mmcls.registry import METRICS
+from mmpretrain.registry import METRICS
 
 
 def to_tensor(value):
@@ -91,7 +91,7 @@ class Accuracy(BaseMetric):
 
     Examples:
         >>> import torch
-        >>> from mmcls.evaluation import Accuracy
+        >>> from mmpretrain.evaluation import Accuracy
         >>> # -------------------- The Basic Usage --------------------
         >>> y_pred = [0, 2, 1, 3]
         >>> y_true = [0, 1, 2, 3]
@@ -104,7 +104,7 @@ class Accuracy(BaseMetric):
         [[tensor([9.9000])], [tensor([51.5000])]]
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.structures import ClsDataSample
+        >>> from mmpretrain.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_samples = [
         ...     ClsDataSample().set_gt_label(0).set_pred_score(torch.rand(10))
@@ -343,7 +343,7 @@ class SingleLabelMetric(BaseMetric):
 
     Examples:
         >>> import torch
-        >>> from mmcls.evaluation import SingleLabelMetric
+        >>> from mmpretrain.evaluation import SingleLabelMetric
         >>> # -------------------- The Basic Usage --------------------
         >>> y_pred = [0, 1, 1, 3]
         >>> y_true = [0, 2, 1, 3]
@@ -358,7 +358,7 @@ class SingleLabelMetric(BaseMetric):
          (tensor(10.), tensor(0.5500), tensor(1.0427), tensor(1000))]
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.structures import ClsDataSample
+        >>> from mmpretrain.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_samples = [
         ...     ClsDataSample().set_gt_label(i%5).set_pred_score(torch.rand(5))
@@ -606,7 +606,7 @@ class ConfusionMatrix(BaseMetric):
         1. The basic usage.
 
         >>> import torch
-        >>> from mmcls.evaluation import ConfusionMatrix
+        >>> from mmpretrain.evaluation import ConfusionMatrix
         >>> y_pred = [0, 1, 1, 3]
         >>> y_true = [0, 2, 1, 3]
         >>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)

mmpretrain/evaluation/metrics/voc_multi_label.py

@@ -3,7 +3,7 @@ from typing import Optional, Sequence
 
 from mmengine.structures import LabelData
 
-from mmcls.registry import METRICS
+from mmpretrain.registry import METRICS
 from .multi_label import AveragePrecision, MultiLabelMetric
 
 

mmpretrain/models/__init__.py

@@ -8,6 +8,8 @@ from .heads import *  # noqa: F401,F403
 from .losses import *  # noqa: F401,F403
 from .necks import *  # noqa: F401,F403
 from .retrievers import *  # noqa: F401,F403
+from .selfsup import *  # noqa: F401,F403
+from .target_generators import *  # noqa: F401,F403
 from .tta import *  # noqa: F401,F403
 from .utils import *  # noqa: F401,F403
 

mmpretrain/models/backbones/alexnet.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch.nn as nn
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/beit.py

@@ -9,7 +9,7 @@ from mmcv.cnn.bricks.drop import build_dropout
 from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
 from mmengine.model import BaseModule, ModuleList
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import (BEiTAttention, resize_pos_embed,
                      resize_relative_position_bias_table, to_2tuple)
 from .vision_transformer import TransformerEncoderLayer, VisionTransformer

mmpretrain/models/backbones/conformer.py

@@ -10,7 +10,7 @@ from mmcv.cnn.bricks.transformer import AdaptivePadding
 from mmengine.model import BaseModule
 from mmengine.model.weight_init import trunc_normal_
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 from .vision_transformer import TransformerEncoderLayer
 

mmpretrain/models/backbones/convmixer.py

@@ -7,7 +7,7 @@ from mmcv.cnn.bricks import (Conv2dAdaptivePadding, build_activation_layer,
                              build_norm_layer)
 from mmengine.utils import digit_version
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/convnext.py

@@ -9,7 +9,7 @@ import torch.utils.checkpoint as cp
 from mmcv.cnn.bricks import DropPath
 from mmengine.model import BaseModule, ModuleList, Sequential
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import GRN, build_norm_layer
 from .base_backbone import BaseBackbone
 

mmpretrain/models/backbones/cspnet.py

@@ -9,7 +9,7 @@ from mmcv.cnn.bricks import DropPath
 from mmengine.model import BaseModule, Sequential
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import to_ntuple
 from .resnet import Bottleneck as ResNetBottleneck
 from .resnext import Bottleneck as ResNeXtBottleneck
@@ -278,8 +278,8 @@ class CSPNet(BaseModule):
         >>> from functools import partial
         >>> import torch
         >>> import torch.nn as nn
-        >>> from mmcls.models import CSPNet
+        >>> from mmpretrain.models import CSPNet
-        >>> from mmcls.models.backbones.resnet import Bottleneck
+        >>> from mmpretrain.models.backbones.resnet import Bottleneck
         >>>
         >>> # A simple example to build CSPNet.
         >>> arch = dict(
@@ -427,7 +427,7 @@ class CSPDarkNet(CSPNet):
             Default: None.
 
     Example:
-        >>> from mmcls.models import CSPDarkNet
+        >>> from mmpretrain.models import CSPDarkNet
         >>> import torch
         >>> model = CSPDarkNet(depth=53, out_indices=(0, 1, 2, 3, 4))
         >>> model.eval()
@@ -523,7 +523,7 @@ class CSPResNet(CSPNet):
         init_cfg (dict or list[dict], optional): Initialization config dict.
             Default: None.
     Example:
-        >>> from mmcls.models import CSPResNet
+        >>> from mmpretrain.models import CSPResNet
         >>> import torch
         >>> model = CSPResNet(depth=50, out_indices=(0, 1, 2, 3))
         >>> model.eval()
@@ -645,7 +645,7 @@ class CSPResNeXt(CSPResNet):
         init_cfg (dict or list[dict], optional): Initialization config dict.
             Default: None.
     Example:
-        >>> from mmcls.models import CSPResNeXt
+        >>> from mmpretrain.models import CSPResNeXt
         >>> import torch
         >>> model = CSPResNeXt(depth=50, out_indices=(0, 1, 2, 3))
         >>> model.eval()

mmpretrain/models/backbones/davit.py

@@ -12,8 +12,8 @@ from mmengine.model import BaseModule, ModuleList
 from mmengine.utils import to_2tuple
 from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.models.backbones.base_backbone import BaseBackbone
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import ShiftWindowMSA
 
 

mmpretrain/models/backbones/deit.py

@@ -3,7 +3,7 @@ import torch
 import torch.nn as nn
 from mmengine.model.weight_init import trunc_normal_
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .vision_transformer import VisionTransformer
 
 

mmpretrain/models/backbones/deit3.py

@@ -10,7 +10,7 @@ from mmengine.model import BaseModule, ModuleList, Sequential
 from mmengine.utils import deprecated_api_warning
 from torch import nn
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import LayerScale, MultiheadAttention, resize_pos_embed, to_2tuple
 from .vision_transformer import VisionTransformer
 

mmpretrain/models/backbones/densenet.py

@@ -10,7 +10,7 @@ import torch.utils.checkpoint as cp
 from mmcv.cnn.bricks import build_activation_layer, build_norm_layer
 from torch.jit.annotations import List
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/edgenext.py

@@ -8,7 +8,7 @@ import torch.nn as nn
 from mmcv.cnn.bricks import DropPath
 from mmengine.model import BaseModule, ModuleList, Sequential
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import (ChannelMultiheadAttention, PositionEncodingFourier,
                      build_norm_layer)
 from .base_backbone import BaseBackbone

mmpretrain/models/backbones/efficientformer.py

@@ -8,7 +8,7 @@ from mmcv.cnn.bricks import (ConvModule, DropPath, build_activation_layer,
                              build_norm_layer)
 from mmengine.model import BaseModule, ModuleList, Sequential
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import LayerScale
 from .base_backbone import BaseBackbone
 from .poolformer import Pooling
@@ -376,7 +376,7 @@ class EfficientFormer(BaseBackbone):
             Defaults to None.
 
     Example:
-        >>> from mmcls.models import EfficientFormer
+        >>> from mmpretrain.models import EfficientFormer
         >>> import torch
         >>> inputs = torch.rand((1, 3, 224, 224))
         >>> # build EfficientFormer backbone for classification task

mmpretrain/models/backbones/efficientnet.py

@@ -9,9 +9,9 @@ import torch.utils.checkpoint as cp
 from mmcv.cnn.bricks import ConvModule, DropPath
 from mmengine.model import BaseModule, Sequential
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.models.backbones.base_backbone import BaseBackbone
-from mmcls.models.utils import InvertedResidual, SELayer, make_divisible
+from mmpretrain.models.utils import InvertedResidual, SELayer, make_divisible
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 
 
 class EdgeResidual(BaseModule):

mmpretrain/models/backbones/efficientnet_v2.py

@@ -7,10 +7,10 @@ from mmcv.cnn.bricks import ConvModule, DropPath
 from mmengine.model import Sequential
 from torch import Tensor
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.registry import MODELS
-from mmcls.models.backbones.efficientnet import EdgeResidual as FusedMBConv
+from ..utils import InvertedResidual as MBConv
-from mmcls.models.utils import InvertedResidual as MBConv
+from .base_backbone import BaseBackbone
-from mmcls.registry import MODELS
+from .efficientnet import EdgeResidual as FusedMBConv
 
 
 class EnhancedConvModule(ConvModule):

mmpretrain/models/backbones/hornet.py

@@ -16,8 +16,8 @@ import torch.nn.functional as F
 import torch.utils.checkpoint as checkpoint
 from mmcv.cnn.bricks import DropPath
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.models.backbones.base_backbone import BaseBackbone
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import LayerScale
 
 

mmpretrain/models/backbones/hrnet.py

@@ -4,7 +4,7 @@ from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmengine.model import BaseModule, ModuleList, Sequential
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import BasicBlock, Bottleneck, ResLayer, get_expansion
 
 
@@ -232,7 +232,7 @@ class HRNet(BaseModule):
 
     Example:
         >>> import torch
-        >>> from mmcls.models import HRNet
+        >>> from mmpretrain.models import HRNet
         >>> extra = dict(
         >>>     stage1=dict(
         >>>         num_modules=1,

mmpretrain/models/backbones/inception_v3.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 from mmcv.cnn import build_conv_layer
 from mmengine.model import BaseModule
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 
@@ -389,7 +389,7 @@ class InceptionV3(BaseBackbone):
 
     Example:
         >>> import torch
-        >>> from mmcls.models import build_backbone
+        >>> from mmpretrain.models import build_backbone
         >>>
         >>> inputs = torch.rand(2, 3, 299, 299)
         >>> cfg = dict(type='InceptionV3', num_classes=100)

mmpretrain/models/backbones/lenet.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch.nn as nn
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/levit.py

@@ -7,8 +7,8 @@ from mmcv.cnn import build_activation_layer, fuse_conv_bn
 from mmcv.cnn.bricks import DropPath
 from mmengine.model import BaseModule, ModuleList, Sequential
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.models.backbones.base_backbone import BaseBackbone
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import build_norm_layer
 
 

mmpretrain/models/backbones/mixmim.py

@@ -9,11 +9,10 @@ from mmengine.model import BaseModule
 from torch import nn
 from torch.utils.checkpoint import checkpoint
 
-from mmcls.models.backbones.base_backbone import BaseBackbone
+from mmpretrain.registry import MODELS
-from mmcls.models.backbones.vision_transformer import TransformerEncoderLayer
+from ..utils import WindowMSA, to_2tuple
-from mmcls.models.utils.attention import WindowMSA
+from .base_backbone import BaseBackbone
-from mmcls.models.utils.helpers import to_2tuple
+from .vision_transformer import TransformerEncoderLayer
-from mmcls.registry import MODELS
 
 
 class MixMIMWindowAttention(WindowMSA):

mmpretrain/models/backbones/mlp_mixer.py

@@ -6,7 +6,7 @@ from mmcv.cnn import build_norm_layer
 from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
 from mmengine.model import BaseModule, ModuleList
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import to_2tuple
 from .base_backbone import BaseBackbone
 

mmpretrain/models/backbones/mobilenet_v2.py

@@ -5,8 +5,8 @@ from mmcv.cnn import ConvModule
 from mmengine.model import BaseModule
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.models.utils import make_divisible
+from mmpretrain.models.utils import make_divisible
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/mobilenet_v3.py

@@ -2,7 +2,7 @@
 from mmcv.cnn import ConvModule
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils import InvertedResidual
 from .base_backbone import BaseBackbone
 

mmpretrain/models/backbones/mobileone.py

@@ -9,7 +9,7 @@ from mmcv.cnn import build_activation_layer, build_conv_layer, build_norm_layer
 from mmengine.model import BaseModule, ModuleList, Sequential
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils.se_layer import SELayer
 from .base_backbone import BaseBackbone
 
@@ -302,7 +302,7 @@ class MobileOne(BaseBackbone):
         init_cfg (dict or list[dict], optional): Initialization config dict.
 
     Example:
-        >>> from mmcls.models import MobileOne
+        >>> from mmpretrain.models import MobileOne
         >>> import torch
         >>> x = torch.rand(1, 3, 224, 224)
         >>> model = MobileOne("s0", out_indices=(0, 1, 2, 3))

mmpretrain/models/backbones/mobilevit.py

@@ -7,7 +7,7 @@ import torch.nn.functional as F
 from mmcv.cnn import ConvModule, build_norm_layer
 from torch import nn
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 from .mobilenet_v2 import InvertedResidual
 from .vision_transformer import TransformerEncoderLayer

mmpretrain/models/backbones/mvit.py

@@ -489,7 +489,7 @@ class MViT(BaseBackbone):
 
     Examples:
         >>> import torch
-        >>> from mmcls.models import build_backbone
+        >>> from mmpretrain.models import build_backbone
         >>>
         >>> cfg = dict(type='MViT', arch='tiny', out_scales=[0, 1, 2, 3])
         >>> model = build_backbone(cfg)

mmpretrain/models/backbones/poolformer.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 from mmcv.cnn.bricks import DropPath, build_activation_layer, build_norm_layer
 from mmengine.model import BaseModule
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/regnet.py

@@ -3,7 +3,7 @@ import numpy as np
 import torch.nn as nn
 from mmcv.cnn import build_conv_layer, build_norm_layer
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import ResNet
 from .resnext import Bottleneck
 
@@ -43,7 +43,7 @@ class RegNet(ResNet):
             in resblocks to let them behave as identity. Default: True.
 
     Example:
-        >>> from mmcls.models import RegNet
+        >>> from mmpretrain.models import RegNet
         >>> import torch
         >>> self = RegNet(
                 arch=dict(

mmpretrain/models/backbones/replknet.py

@@ -7,7 +7,7 @@ from mmcv.cnn.bricks import DropPath
 from mmengine.model import BaseModule
 from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 

mmpretrain/models/backbones/repmlp.py

@@ -8,8 +8,8 @@ from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
 from mmcv.cnn.bricks.transformer import PatchEmbed as _PatchEmbed
 from mmengine.model import BaseModule, ModuleList, Sequential
 
-from mmcls.models.utils import SELayer, to_2tuple
+from mmpretrain.models.utils import SELayer, to_2tuple
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 
 
 def fuse_bn(conv_or_fc, bn):
@@ -88,7 +88,7 @@ class PatchEmbed(_PatchEmbed):
 
 
 class GlobalPerceptron(SELayer):
-    """GlobalPerceptron implemented by using ``mmcls.modes.SELayer``.
+    """GlobalPerceptron implemented by using ``mmpretrain.modes.SELayer``.
 
     Args:
         input_channels (int): The number of input (and output) channels

mmpretrain/models/backbones/repvgg.py

@@ -8,7 +8,7 @@ from mmengine.model import BaseModule, Sequential
 from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
 from torch import nn
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from ..utils.se_layer import SELayer
 from .base_backbone import BaseBackbone
 

mmpretrain/models/backbones/res2net.py

@@ -7,7 +7,7 @@ import torch.utils.checkpoint as cp
 from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmengine.model import ModuleList, Sequential
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import Bottleneck as _Bottleneck
 from .resnet import ResNet
 
@@ -258,7 +258,7 @@ class Res2Net(ResNet):
             Defaults to None.
 
     Example:
-        >>> from mmcls.models import Res2Net
+        >>> from mmpretrain.models import Res2Net
         >>> import torch
         >>> model = Res2Net(depth=50,
         ...                 scales=4,

mmpretrain/models/backbones/resnest.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint as cp
 from mmcv.cnn import build_conv_layer, build_norm_layer
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import Bottleneck as _Bottleneck
 from .resnet import ResLayer, ResNetV1d
 

mmpretrain/models/backbones/resnet.py

@@ -9,7 +9,7 @@ from mmengine.model import BaseModule
 from mmengine.model.weight_init import constant_init
 from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .base_backbone import BaseBackbone
 
 eps = 1.0e-5
@@ -438,7 +438,7 @@ class ResNet(BaseBackbone):
             in resblocks to let them behave as identity. Default: True.
 
     Example:
-        >>> from mmcls.models import ResNet
+        >>> from mmpretrain.models import ResNet
         >>> import torch
         >>> self = ResNet(depth=18)
         >>> self.eval()

mmpretrain/models/backbones/resnet_cifar.py

@@ -2,7 +2,7 @@
 import torch.nn as nn
 from mmcv.cnn import build_conv_layer, build_norm_layer
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import ResNet
 
 

mmpretrain/models/backbones/resnext.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn import build_conv_layer, build_norm_layer
 
-from mmcls.registry import MODELS
+from mmpretrain.registry import MODELS
 from .resnet import Bottleneck as _Bottleneck
 from .resnet import ResLayer, ResNet