[Improve] Rename `mmcls.data` to `mmcls.structures`. (#941)

mmcls/datasets/pipelines/formatting.py

@@ -9,8 +9,8 @@ from mmcv.parallel import DataContainer as DC
 from mmcv.transforms.base import BaseTransform
 from PIL import Image
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import TRANSFORMS
+from mmcls.structures import ClsDataSample
 
 
 def to_tensor(data):

mmcls/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.data import ClsDataSample
 from mmcls.registry import HOOKS
+from mmcls.structures import ClsDataSample
 
 
 @HOOKS.register_module()

mmcls/evaluation/metrics/multi_label.py

@@ -93,7 +93,7 @@ class MultiLabelMetric(BaseMetric):
         (tensor(62.5000), tensor(31.2500), tensor(39.1667), tensor(8))
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> # The `data_batch` won't be used in this case, just use a fake.
         >>> data_batch = [
@@ -457,7 +457,7 @@ class AveragePrecision(BaseMetric):
         >>> AveragePrecision.calculate(y_pred, y_true)
         tensor(70.833)
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> # The `data_batch` won't be used in this case, just use a fake.
         >>> data_batch = [

mmcls/evaluation/metrics/single_label.py

@@ -86,7 +86,7 @@ class Accuracy(BaseMetric):
         [[tensor([9.9000])], [tensor([51.5000])]]
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_batch = [{
         ...     'inputs': None,  # In this example, the `inputs` is not used.
@@ -335,7 +335,7 @@ class SingleLabelMetric(BaseMetric):
           tensor(1000))]
         >>>
         >>> # ------------------- Use with Evalutor -------------------
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>> from mmengine.evaluator import Evaluator
         >>> data_batch = [{
         ...     'inputs': None,  # In this example, the `inputs` is not used.

mmcls/models/classifiers/image.py

@@ -3,8 +3,8 @@ from typing import List, Optional
 
 import torch
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from .base import BaseClassifier
 
 
@@ -100,7 +100,7 @@ class ImageClassifier(BaseClassifier):
 
             - If ``mode="tensor"``, return a tensor or a tuple of tensor.
             - If ``mode="predict"``, return a list of
-              :obj:`mmcls.data.ClsDataSample`.
+              :obj:`mmcls.structures.ClsDataSample`.
             - If ``mode="loss"``, return a dict of tensor.
         """
         if mode == 'tensor':

mmcls/models/heads/cls_head.py

@@ -4,9 +4,9 @@ from typing import List, Optional, Tuple, Union
 import torch
 import torch.nn.functional as F
 
-from mmcls.data import ClsDataSample
 from mmcls.evaluation.metrics import Accuracy
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from .base_head import BaseHead
 
 

mmcls/models/heads/conformer_head.py

@@ -4,9 +4,9 @@ from typing import List, Sequence, Tuple
 import torch
 import torch.nn as nn
 
-from mmcls.data import ClsDataSample
 from mmcls.evaluation.metrics import Accuracy
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from .cls_head import ClsHead
 
 

mmcls/models/heads/multi_label_cls_head.py

@@ -4,8 +4,8 @@ from typing import Dict, List, Optional, Tuple
 import torch
 from mmengine.data import LabelData
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from .base_head import BaseHead
 
 

mmcls/models/utils/batch_augments/mixup.py

@@ -5,8 +5,8 @@ import numpy as np
 import torch
 from mmengine.data import LabelData
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import BATCH_AUGMENTS
+from mmcls.structures import ClsDataSample
 
 
 @BATCH_AUGMENTS.register_module()

mmcls/structures/cls_data_sample.py

@@ -70,7 +70,7 @@ class ClsDataSample(BaseDataElement):
 
     Examples:
         >>> import torch
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>>
         >>> img_meta = dict(img_shape=(960, 720), num_classes=5)
         >>> data_sample = ClsDataSample(metainfo=img_meta)

mmcls/utils/setup_env.py

@@ -73,11 +73,11 @@ def register_all_modules(init_default_scope: bool = True) -> None:
             https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md
             Defaults to True.
     """  # noqa
-    import mmcls.data  # noqa: F401,F403
     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:

mmcls/visualization/cls_visualizer.py

@@ -6,8 +6,8 @@ import numpy as np
 from mmengine import Visualizer
 from mmengine.dist import master_only
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import VISUALIZERS
+from mmcls.structures import ClsDataSample
 
 
 def _get_adaptive_scale(img_shape: Tuple[int, int],
@@ -57,7 +57,7 @@ class ClsVisualizer(Visualizer):
         >>> import mmcv
         >>> from pathlib import Path
         >>> from mmcls.visualization import ClsVisualizer
-        >>> from mmcls.data import ClsDataSample
+        >>> from mmcls.structures import ClsDataSample
         >>> # Example image
         >>> img = mmcv.imread("./demo/bird.JPEG", channel_order='rgb')
         >>> # Example annotation

tests/test_datasets/test_pipelines/test_formatting.py

@@ -7,8 +7,8 @@ import numpy as np
 import torch
 from mmengine.data import LabelData
 
-from mmcls.data import ClsDataSample
 from mmcls.datasets.pipelines import PackClsInputs
+from mmcls.structures import ClsDataSample
 
 
 class TestPackClsInputs(unittest.TestCase):

tests/test_engine/test_hooks/test_visualization_hook.py

@@ -7,9 +7,9 @@ from unittest.mock import ANY, MagicMock, patch
 import torch
 from mmengine.runner import EpochBasedTrainLoop, IterBasedTrainLoop
 
-from mmcls.data import ClsDataSample
 from mmcls.engine import VisualizationHook
 from mmcls.registry import HOOKS
+from mmcls.structures import ClsDataSample
 from mmcls.utils import register_all_modules
 from mmcls.visualization import ClsVisualizer
 

tests/test_evaluation/test_metrics/test_multi_label.py

@@ -6,8 +6,8 @@ import sklearn.metrics
 import torch
 from mmengine.evaluator import Evaluator
 
-from mmcls.data import ClsDataSample
 from mmcls.evaluation.metrics import AveragePrecision, MultiLabelMetric
+from mmcls.structures import ClsDataSample
 from mmcls.utils import register_all_modules
 
 register_all_modules()

tests/test_evaluation/test_metrics/test_single_label.py

@@ -5,9 +5,9 @@ from unittest import TestCase
 import numpy as np
 import torch
 
-from mmcls.data import ClsDataSample
 from mmcls.evaluation.metrics import Accuracy, SingleLabelMetric
 from mmcls.registry import METRICS
+from mmcls.structures import ClsDataSample
 
 
 class TestAccuracy(TestCase):

tests/test_models/test_classifiers.py

@@ -5,9 +5,9 @@ from unittest.mock import MagicMock
 import torch
 from mmengine import ConfigDict
 
-from mmcls.data import ClsDataSample
 from mmcls.models import ImageClassifier
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from mmcls.utils import register_all_modules
 
 register_all_modules()

tests/test_models/test_heads.py

@@ -7,8 +7,8 @@ import numpy as np
 import torch
 from mmengine import is_seq_of
 
-from mmcls.data import ClsDataSample
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from mmcls.utils import register_all_modules
 
 register_all_modules()

tests/test_models/test_utils/test_batch_augments.py

@@ -5,9 +5,9 @@ from unittest.mock import MagicMock, patch
 import numpy as np
 import torch
 
-from mmcls.data import ClsDataSample
 from mmcls.models import Mixup, RandomBatchAugment
 from mmcls.registry import BATCH_AUGMENTS
+from mmcls.structures import ClsDataSample
 
 
 class TestRandomBatchAugment(TestCase):

tests/test_models/test_utils/test_data_preprocessor.py

@@ -3,9 +3,9 @@ from unittest import TestCase
 
 import torch
 
-from mmcls.data import ClsDataSample
 from mmcls.models import ClsDataPreprocessor, RandomBatchAugment
 from mmcls.registry import MODELS
+from mmcls.structures import ClsDataSample
 from mmcls.utils import register_all_modules
 
 register_all_modules()

tests/test_structures/test_datasample.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from mmengine.data import LabelData
 
-from mmcls.data import ClsDataSample
+from mmcls.structures import ClsDataSample
 
 
 class TestClsDataSample(TestCase):

tests/test_visualizations/test_visualizer.py

@@ -7,7 +7,7 @@ from unittest.mock import patch
 import numpy as np
 import torch
 
-from mmcls.data import ClsDataSample
+from mmcls.structures import ClsDataSample
 from mmcls.visualization import ClsVisualizer