[Feature] Add Part1 of data transform (#1736)

* [Feature] Add Part1 of data transform

* api.rst transform->transforms

* fix typo

* fix typo

* rename test_processing into test_transforms_processing for avoiding same name

* fix mypy

* fix comment

* fix comment

docs/en/api.rst

@@ -48,7 +48,7 @@ ops
 .. automodule:: mmcv.ops
     :members:
 
-transform
+transforms
 ---------
-.. automodule:: mmcv.transform
+.. automodule:: mmcv.transforms
     :members:

mmcv/__init__.py

@@ -3,7 +3,7 @@
 from .arraymisc import *
 from .fileio import *
 from .image import *
-from .transform import *
+from .transforms import *
 from .utils import *
 from .version import *
 from .video import *

mmcv/transforms/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from .builder import TRANSFORMS
+from .loading import LoadAnnotation, LoadImageFromFile
+from .processing import Normalize, Pad, Resize
 from .wrappers import ApplyToMultiple, Compose, RandomChoice, Remap
 
-__all__ = ['TRANSFORMS', 'ApplyToMultiple', 'Compose', 'RandomChoice', 'Remap']
+__all__ = [
+    'TRANSFORMS', 'ApplyToMultiple', 'Compose', 'RandomChoice', 'Remap',
+    'LoadImageFromFile', 'LoadAnnotation', 'Normalize', 'Resize', 'Pad'
+]

mmcv/transforms/loading.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+import mmcv
+from .base import BaseTransform
+from .builder import TRANSFORMS
+
+
+@TRANSFORMS.register_module()
+class LoadImageFromFile(BaseTransform):
+    """Load an image from file.
+
+    Required Keys:
+
+    - img_path
+
+    Modified Keys:
+
+    - img
+    - width
+    - height
+    - ori_width
+    - ori_height
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+        color_type (str): The flag argument for :func:``mmcv.imfrombytes``.
+            Defaults to 'color'.
+        imdecode_backend (str): The image decoding backend type. The backend
+            argument for :func:``mmcv.imfrombytes``.
+            See :func:``mmcv.imfrombytes`` for details.
+            Defaults to 'cv2'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(
+        self,
+        to_float32: bool = False,
+        color_type: str = 'color',
+        imdecode_backend: str = 'cv2',
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
+        self.to_float32 = to_float32
+        self.color_type = color_type
+        self.imdecode_backend = imdecode_backend
+        self.file_client_args = file_client_args.copy()
+        self.file_client = mmcv.FileClient(**self.file_client_args)
+
+    def transform(self, results: dict) -> dict:
+        """Functions to load image.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+
+        filename = results['img_path']
+        img_bytes = self.file_client.get(filename)
+        img = mmcv.imfrombytes(
+            img_bytes, flag=self.color_type, backend=self.imdecode_backend)
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['img'] = img
+        height, width = img.shape[:2]
+        results['height'] = height
+        results['width'] = width
+        results['ori_height'] = height
+        results['ori_width'] = width
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"color_type='{self.color_type}', "
+                    f"imdecode_backend='{self.imdecode_backend}', "
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
+
+
+class LoadAnnotation(BaseTransform):
+    """Load and process the ``instances`` and ``seg_map`` annotation provided
+    by dataset.
+
+    The annotation format is as the following:
+
+    .. code-block:: python
+
+        {
+            'instances':
+            [
+                {
+                # List of 4 numbers representing the bounding box of the
+                # instance, in (x1, y1, x2, y2) order.
+                'bbox': [x1, y1, x2, y2],
+
+                # Label of image classification.
+                'bbox_label': 1,
+
+                # Used in key point detection.
+                # Can only load the format of [x1, y1, v1,…, xn, yn, vn]. v[i]
+                # means the visibility of this keypoint. n must be equal to the
+                # number of keypoint categories.
+                'keypoints': [x1, y1, v1, ..., xn, yn, vn]
+                }
+            ]
+            # Filename of semantic or panoptic segmentation ground truth file.
+            'seg_map': 'a/b/c'
+        }
+
+    After this module, the annotation has been changed to the format below:
+
+    .. code-block:: python
+
+        {
+            # In (x1, y1, x2, y2) order, float type. N is the number of bboxes
+            # in an image
+            'gt_bboxes': np.ndarray(N, 4)
+             # In int type.
+            'gt_bboxes_labels': np.ndarray(N, )
+             # In uint8 type.
+            'gt_semantic_seg': np.ndarray (H, W)
+             # in (x, y, v) order, float type.
+            'gt_keypoints': np.ndarray(N, NK, 3)
+        }
+
+    Required Keys:
+
+    - instances
+
+      - bbox (optional)
+      - bbox_label
+      - keypoints (optional)
+
+    - seg_map (optional)
+
+    Added Keys:
+
+    - gt_bboxes
+    - gt_bboxes_labels
+    - gt_semantic_seg
+    - gt_keypoints
+
+    Args:
+        with_bbox (bool): Whether to parse and load the bbox annotation.
+            Defaults to True.
+        with_label (bool): Whether to parse and load the label annotation.
+            Defaults to True.
+        with_seg (bool): Whether to parse and load the semantic segmentation
+            annotation. Defaults to False.
+        with_kps (bool): Whether to parse and load the keypoints annotation.
+            Defaults to False.
+        imdecode_backend (str): The image decoding backend type. The backend
+            argument for :func:``mmcv.imfrombytes``.
+            See :fun:``mmcv.imfrombytes`` for details.
+            Defaults to 'cv2'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:``mmcv.fileio.FileClient`` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(
+        self,
+        with_bbox: bool = True,
+        with_label: bool = True,
+        with_seg: bool = False,
+        with_kps: bool = False,
+        imdecode_backend: str = 'cv2',
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
+        super().__init__()
+        self.with_bbox = with_bbox
+        self.with_label = with_label
+        self.with_seg = with_seg
+        self.with_kps = with_kps
+        self.imdecode_backend = imdecode_backend
+        self.file_client_args = file_client_args.copy()
+        self.file_client = mmcv.FileClient(**self.file_client_args)
+
+    def _load_bboxes(self, results: dict) -> None:
+        """Private function to load bounding box annotations.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+        Returns:
+            dict: The dict contains loaded bounding box annotations.
+        """
+        gt_bboxes = []
+        for instance in results['instances']:
+            gt_bboxes.append(instance['bbox'])
+        results['gt_bboxes'] = np.array(gt_bboxes)
+
+    def _load_labels(self, results: dict) -> None:
+        """Private function to load label annotations.
+
+        Args:
+            results (dict): Result dict from :obj :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded label annotations.
+        """
+        gt_bboxes_labels = []
+        for instance in results['instances']:
+            gt_bboxes_labels.append(instance['bbox_label'])
+        results['gt_bboxes_labels'] = np.array(gt_bboxes_labels)
+
+    def _load_semantic_seg(self, results: dict) -> None:
+        """Private function to load semantic segmentation annotations.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded semantic segmentation annotations.
+        """
+
+        img_bytes = self.file_client.get(results['seg_map'])
+        results['gt_semantic_seg'] = mmcv.imfrombytes(
+            img_bytes, flag='unchanged',
+            backend=self.imdecode_backend).squeeze()
+
+    def _load_kps(self, results: dict) -> None:
+        """Private function to load keypoints annotations.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+        Returns:
+            dict: The dict contains loaded keypoints annotations.
+        """
+        gt_keypoints = []
+        for instance in results['instances']:
+            gt_keypoints.append(instance['keypoints'])
+        results['gt_keypoints'] = np.array(gt_keypoints).reshape(
+            (len(gt_keypoints), -1, 3))
+
+    def transform(self, results: dict) -> dict:
+        """Function to load multiple types annotations.
+
+        Args:
+            results (dict): Result dict from :obj:``mmcv.BaseDataset``.
+
+        Returns:
+            dict: The dict contains loaded bounding box, label and
+            semantic segmentation and keypoints annotations.
+        """
+
+        if self.with_bbox:
+            self._load_bboxes(results)
+        if self.with_label:
+            self._load_labels(results)
+        if self.with_seg:
+            self._load_semantic_seg(results)
+        if self.with_kps:
+            self._load_kps(results)
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(with_bbox={self.with_bbox}, '
+        repr_str += f'with_label={self.with_label}, '
+        repr_str += f'with_seg={self.with_seg}, '
+        repr_str += f'with_kps={self.with_kps}, '
+        repr_str += f"imdecode_backend='{self.imdecode_backend}', "
+        repr_str += f'file_client_args={self.file_client_args})'
+        return repr_str

mmcv/transforms/processing.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional, Sequence, Tuple, Union
+
+import numpy as np
+
+import mmcv
+from mmcv.image.geometric import _scale_size
+from .base import BaseTransform
+from .builder import TRANSFORMS
+
+
+@TRANSFORMS.register_module()
+class Normalize(BaseTransform):
+    """Normalize the image.
+
+    Required Keys:
+
+    - img
+
+    Added Keys:
+
+    - img_norm_cfg
+
+      - mean
+      - std
+      - to_rgb
+
+
+    Args:
+        mean (sequence): Mean values of 3 channels.
+        std (sequence): Std values of 3 channels.
+        to_rgb (bool): Whether to convert the image from BGR to RGB before
+            normlizing the image. If ``to_rgb=True``, the order of mean and std
+            should be RGB. If ``to_rgb=False``, the order of mean and std
+            should be BGR. Defaults to True.
+    """
+
+    def __init__(self,
+                 mean: Sequence[float],
+                 std: Sequence[float],
+                 to_rgb: bool = True) -> None:
+        self.mean = np.array(mean, dtype=np.float32)
+        self.std = np.array(std, dtype=np.float32)
+        self.to_rgb = to_rgb
+
+    def transform(self, results: dict) -> dict:
+        """Function to normalize images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Normalized results, key 'img_norm_cfg' key is added in to
+            result dict.
+        """
+
+        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
+                                          self.to_rgb)
+        results['img_norm_cfg'] = dict(
+            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Resize(BaseTransform):
+    """Resize images & bbox & seg & keypoints.
+
+    This transform resizes the input image according to ``scale`` or
+    ``scale_factor``. Bboxes, seg map and keypoints are then resized with the
+    same scale factor.
+    if ``scale`` and ``scale_factor`` are both set, it will use ``scale`` to
+    resize.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (optional)
+    - gt_semantic_seg (optional)
+    - gt_keypoints (optional)
+
+    Modified Keys:
+
+    - img
+    - gt_bboxes
+    - gt_semantic_seg
+    - gt_keypoints
+    - height
+    - width
+
+    Added Keys:
+
+    - scale
+    - scale_factor
+    - keep_ratio
+
+    Args:
+        scale (int or tuple): Images scales for resizing. Defaults to None
+        scale_factor (float or tuple[float]): Scale factors for resizing.
+            Defaults to None.
+        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
+            image. Defaults to False.
+        clip_object_border (bool, optional): Whether to clip the objects
+            outside the border of the image. In some dataset like MOT17, the gt
+            bboxes are allowed to cross the border of images. Therefore, we
+            don't need to clip the gt bboxes in these cases. Defaults to True.
+        backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
+            These two backends generates slightly different results. Defaults
+            to 'cv2'.
+        interpolation (str): Interpolation method, accepted values are
+            "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2'
+            backend, "nearest", "bilinear" for 'pillow' backend. Defaults
+            to 'cv2'.
+    """
+
+    def __init__(self,
+                 scale: Optional[Union[int, Tuple[int, int]]] = None,
+                 scale_factor: Optional[Union[float, Tuple[float,
+                                                           float]]] = None,
+                 keep_ratio: bool = False,
+                 clip_object_border: bool = True,
+                 backend: str = 'cv2',
+                 interpolation='bilinear') -> None:
+        assert scale is not None or scale_factor is not None, (
+            '`scale` and'
+            '`scale_factor` can not both be `None`')
+        if scale is None:
+            self.scale = None
+        else:
+            if isinstance(scale, int):
+                self.scale = (scale, scale)
+            else:
+                self.scale = scale
+
+        self.backend = backend
+        self.interpolation = interpolation
+        self.keep_ratio = keep_ratio
+        self.clip_object_border = clip_object_border
+        if scale_factor is None:
+            self.scale_factor = None
+        elif isinstance(scale_factor, float):
+            self.scale_factor = (scale_factor, scale_factor)
+        elif isinstance(scale_factor, tuple):
+            assert (len(scale_factor)) == 2
+            self.scale_factor = scale_factor
+        else:
+            raise TypeError(
+                f'expect scale_factor is float or Tuple(float), but'
+                f'get {type(scale_factor)}')
+
+    def _resize_img(self, results: dict) -> None:
+        """Resize images with ``results['scale']``."""
+
+        if results.get('img', None) is not None:
+            if self.keep_ratio:
+                img, scale_factor = mmcv.imrescale(
+                    results['img'],
+                    results['scale'],
+                    interpolation=self.interpolation,
+                    return_scale=True,
+                    backend=self.backend)
+                # the w_scale and h_scale has minor difference
+                # a real fix should be done in the mmcv.imrescale in the future
+                new_h, new_w = img.shape[:2]
+                h, w = results['img'].shape[:2]
+                w_scale = new_w / w
+                h_scale = new_h / h
+            else:
+                img, w_scale, h_scale = mmcv.imresize(
+                    results['img'],
+                    results['scale'],
+                    interpolation=self.interpolation,
+                    return_scale=True,
+                    backend=self.backend)
+            results['img'] = img
+            results['height'], results['width'] = img.shape[:2]
+            results['scale'] = img.shape[:2][::-1]
+            results['scale_factor'] = (w_scale, h_scale)
+            results['keep_ratio'] = self.keep_ratio
+
+    def _resize_bboxes(self, results: dict) -> None:
+        """Resize bounding boxes with ``results['scale_factor']``."""
+        if results.get('gt_bboxes', None) is not None:
+            bboxes = results['gt_bboxes'] * np.tile(
+                np.array(results['scale_factor']), 2)
+            if self.clip_object_border:
+                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, results['width'])
+                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0,
+                                          results['height'])
+            results['gt_bboxes'] = bboxes
+
+    def _resize_seg(self, results: dict) -> None:
+        """Resize semantic segmentation map with ``results['scale']``."""
+        if results.get('gt_semantic_seg', None) is not None:
+            if self.keep_ratio:
+                gt_seg = mmcv.imrescale(
+                    results['gt_semantic_seg'],
+                    results['scale'],
+                    interpolation=self.interpolation,
+                    backend=self.backend)
+            else:
+                gt_seg = mmcv.imresize(
+                    results['gt_semantic_seg'],
+                    results['scale'],
+                    interpolation=self.interpolation,
+                    backend=self.backend)
+            results['gt_semantic_seg'] = gt_seg
+
+    def _resize_keypoints(self, results: dict) -> None:
+        """Resize keypoints with ``results['scale_factor']``."""
+        if results.get('gt_keypoints', None) is not None:
+            keypoints = results['gt_keypoints']
+
+            keypoints[:, :, :2] = keypoints[:, :, :2] * np.array(
+                results['scale_factor'])
+            if self.clip_object_border:
+                keypoints[:, :, 0] = np.clip(keypoints[:, :, 0], 0,
+                                             results['width'])
+                keypoints[:, :, 1] = np.clip(keypoints[:, :, 1], 0,
+                                             results['height'])
+            results['gt_keypoints'] = keypoints
+
+    def transform(self, results: dict) -> dict:
+        """Transform function to resize images, bounding boxes, semantic
+        segmentation map and keypoints.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+        Returns:
+            dict: Resized results, 'img', 'gt_bboxes', 'gt_semantic_seg',
+            'gt_keypoints', 'scale', 'scale_factor', 'height', 'width',
+            and 'keep_ratio' keys are updated in result dict.
+        """
+
+        if self.scale:
+            results['scale'] = self.scale
+        else:
+            img_shape = results['img'].shape[:2]
+            results['scale'] = _scale_size(img_shape[::-1], self.scale_factor)
+        self._resize_img(results)
+        self._resize_bboxes(results)
+        self._resize_seg(results)
+        self._resize_keypoints(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(scale={self.scale}, '
+        repr_str += f'scale_factor={self.scale_factor}, '
+        repr_str += f'keep_ratio={self.keep_ratio}, '
+        repr_str += f'clip_object_border={self.clip_object_border}), '
+        repr_str += f'backend={self.backend}), '
+        repr_str += f'interpolation={self.interpolation})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Pad(BaseTransform):
+    """Pad the image & segmentation map.
+
+    There are three padding modes: (1) pad to a fixed size and (2) pad to the
+    minimum size that is divisible by some number. and (3)pad to square. Also,
+    pad to square and pad to the minimum size can be used as the same time.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (optional)
+    - gt_semantic_seg (optional)
+
+    Modified Keys:
+
+    - img
+    - gt_semantic_seg
+    - height
+    - width
+
+    Added Keys:
+
+    - pad_shape
+    - pad_fixed_size
+    - pad_size_divisor
+
+    Args:
+        size (tuple, optional): Fixed padding size.
+            Expected padding shape (h, w)Defaults to None.
+        size_divisor (int, optional): The divisor of padded size. Defaults to
+            None.
+        pad_to_square (bool): Whether to pad the image into a square.
+            Currently only used for YOLOX. Defaults to False.
+        pad_val (int or dict, optional): A dict for padding value.
+            if ``type(pad_val) == int``, the val to pad seg is 255. Defaults to
+            ``dict(img=0, seg=255)``.
+        padding_mode (str): Type of padding. Should be: constant, edge,
+            reflect or symmetric. Defaults to 'constant'.
+
+            - constant: pads with a constant value, this value is specified
+              with pad_val.
+            - edge: pads with the last value at the edge of the image.
+            - reflect: pads with reflection of image without repeating the last
+              value on the edge. For example, padding [1, 2, 3, 4] with 2
+              elements on both sides in reflect mode will result in
+              [3, 2, 1, 2, 3, 4, 3, 2].
+            - symmetric: pads with reflection of image repeating the last value
+              on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
+              both sides in symmetric mode will result in
+              [2, 1, 1, 2, 3, 4, 4, 3]
+    """
+
+    def __init__(self,
+                 size: Optional[Tuple[int, int]] = None,
+                 size_divisor: Optional[int] = None,
+                 pad_to_square: bool = False,
+                 pad_val: Union[int, dict] = dict(img=0, seg=255),
+                 padding_mode: str = 'constant') -> None:
+        self.size = size
+        self.size_divisor = size_divisor
+        if isinstance(pad_val, int):
+            pad_val = dict(img=pad_val, seg=255)
+        assert isinstance(pad_val, dict), 'pad_val '
+        self.pad_val = pad_val
+        self.pad_to_square = pad_to_square
+
+        if pad_to_square:
+            assert size is None, \
+                'The size and size_divisor must be None ' \
+                'when pad2square is True'
+        else:
+            assert size is not None or size_divisor is not None, \
+                'only one of size and size_divisor should be valid'
+            assert size is None or size_divisor is None
+        assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
+        self.padding_mode = padding_mode
+
+    def _pad_img(self, results: dict) -> None:
+        """Pad images according to ``self.size``."""
+        pad_val = self.pad_val.get('img', 0)
+
+        size = None
+        if self.pad_to_square:
+            max_size = max(results['img'].shape[:2])
+            size = (max_size, max_size)
+        if self.size_divisor is not None:
+            if size is None:
+                size = (results['img'].shape[0], results['img'].shape[1])
+            pad_h = int(np.ceil(
+                size[0] / self.size_divisor)) * self.size_divisor
+            pad_w = int(np.ceil(
+                size[1] / self.size_divisor)) * self.size_divisor
+            size = (pad_h, pad_w)
+        elif self.size is not None:
+            size = self.size[::-1]
+        padded_img = mmcv.impad(
+            results['img'],
+            shape=size,
+            pad_val=pad_val,
+            padding_mode=self.padding_mode)
+
+        results['img'] = padded_img
+        results['pad_shape'] = padded_img.shape
+        results['pad_fixed_size'] = self.size
+        results['pad_size_divisor'] = self.size_divisor
+        results['height'] = padded_img.shape[0]
+        results['width'] = padded_img.shape[1]
+
+    def _pad_seg(self, results: dict) -> None:
+        """Pad semantic segmentation map according to
+        ``results['pad_shape']``."""
+        if results.get('gt_semantic_seg', None) is not None:
+            pad_val = self.pad_val.get('seg', 255)
+
+            results['gt_semantic_seg'] = mmcv.impad(
+                results['gt_semantic_seg'],
+                shape=results['pad_shape'][:2],
+                pad_val=pad_val,
+                padding_mode=self.padding_mode)
+
+    def transform(self, results: dict) -> dict:
+        """Call function to pad images, masks, semantic segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        self._pad_img(results)
+        self._pad_seg(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(size={self.size}, '
+        repr_str += f'size_divisor={self.size_divisor}, '
+        repr_str += f'pad_to_square={self.pad_to_square}, '
+        repr_str += f'pad_val={self.pad_val}), '
+        repr_str += f'padding_mode={self.padding_mode})'
+        return repr_str

tests/test_transforms/test_transforms_loading.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+
+import numpy as np
+
+from mmcv.transforms import LoadAnnotation, LoadImageFromFile
+
+
+class TestLoadImageFromFile:
+
+    def test_load_img(self):
+        data_prefix = osp.join(osp.dirname(__file__), '../data')
+
+        results = dict(img_path=osp.join(data_prefix, 'color.jpg'))
+        transform = LoadImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['img_path'] == osp.join(data_prefix, 'color.jpg')
+        assert results['img'].shape == (300, 400, 3)
+        assert results['img'].dtype == np.uint8
+        assert results['height'] == 300
+        assert results['width'] == 400
+        assert results['ori_height'] == 300
+        assert results['ori_width'] == 400
+        assert repr(transform) == transform.__class__.__name__ + \
+            "(to_float32=False, color_type='color', " + \
+            "imdecode_backend='cv2', file_client_args={'backend': 'disk'})"
+
+        # to_float32
+        transform = LoadImageFromFile(to_float32=True)
+        results = transform(copy.deepcopy(results))
+        assert results['img'].dtype == np.float32
+
+        # gray image
+        results = dict(img_path=osp.join(data_prefix, 'grayscale.jpg'))
+        transform = LoadImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['img'].shape == (300, 400, 3)
+        assert results['img'].dtype == np.uint8
+
+        transform = LoadImageFromFile(color_type='unchanged')
+        results = transform(copy.deepcopy(results))
+        assert results['img'].shape == (300, 400)
+        assert results['img'].dtype == np.uint8
+
+
+class TestLoadAnnotation:
+
+    def setup_class(cls):
+        data_prefix = osp.join(osp.dirname(__file__), '../data')
+        seg_map = osp.join(data_prefix, 'grayscale.jpg')
+        cls.results = {
+            'seg_map':
+            seg_map,
+            'instances': [{
+                'bbox': [0, 0, 10, 20],
+                'bbox_label': 1,
+                'keypoints': [1, 2, 3]
+            }, {
+                'bbox': [10, 10, 110, 120],
+                'bbox_label': 2,
+                'keypoints': [4, 5, 6]
+            }]
+        }
+
+    def test_load_bboxes(self):
+        transform = LoadAnnotation(
+            with_bbox=True,
+            with_label=False,
+            with_seg=False,
+            with_kps=False,
+        )
+        results = transform(copy.deepcopy(self.results))
+        assert 'gt_bboxes' in results
+        assert (results['gt_bboxes'] == np.array([[0, 0, 10, 20],
+                                                  [10, 10, 110, 120]])).all()
+
+    def test_load_labels(self):
+        transform = LoadAnnotation(
+            with_bbox=False,
+            with_label=True,
+            with_seg=False,
+            with_kps=False,
+        )
+        results = transform(copy.deepcopy(self.results))
+        assert 'gt_bboxes_labels' in results
+        assert (results['gt_bboxes_labels'] == np.array([1, 2])).all()
+
+    def test_load_kps(self):
+        transform = LoadAnnotation(
+            with_bbox=False,
+            with_label=False,
+            with_seg=False,
+            with_kps=True,
+        )
+        results = transform(copy.deepcopy(self.results))
+        assert 'gt_keypoints' in results
+        assert (results['gt_keypoints'] == np.array([[[1, 2, 3]],
+                                                     [[4, 5, 6]]])).all()
+
+    def test_load_seg_map(self):
+        transform = LoadAnnotation(
+            with_bbox=False,
+            with_label=False,
+            with_seg=True,
+            with_kps=False,
+        )
+        results = transform(copy.deepcopy(self.results))
+        assert 'gt_semantic_seg' in results
+        assert results['gt_semantic_seg'].shape[:2] == (300, 400)
+
+    def test_repr(self):
+        transform = LoadAnnotation(
+            with_bbox=True,
+            with_label=False,
+            with_seg=False,
+            with_kps=False,
+        )
+        assert repr(transform) == ('LoadAnnotation(with_bbox=True, '
+                                   'with_label=False, with_seg=False, '
+                                   "with_kps=False, imdecode_backend='cv2', "
+                                   "file_client_args={'backend': 'disk'})")

tests/test_transforms/test_transforms_processing.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+
+import numpy as np
+import pytest
+
+import mmcv
+from mmcv.transforms import Normalize, Pad, Resize
+
+
+class TestNormalize:
+
+    def test_normalize(self):
+        img_norm_cfg = dict(
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True)
+        transform = Normalize(**img_norm_cfg)
+        results = dict()
+        img = mmcv.imread(
+            osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+        original_img = copy.deepcopy(img)
+        results['img'] = img
+        results = transform(results)
+        mean = np.array(img_norm_cfg['mean'])
+        std = np.array(img_norm_cfg['std'])
+        converted_img = (original_img[..., ::-1] - mean) / std
+        assert np.allclose(results['img'], converted_img)
+
+    def test_repr(self):
+        img_norm_cfg = dict(
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True)
+        transform = Normalize(**img_norm_cfg)
+        assert repr(transform) == ('Normalize(mean=[123.675 116.28  103.53 ], '
+                                   'std=[58.395 57.12  57.375], to_rgb=True)')
+
+
+class TestResize:
+
+    def test_resize(self):
+        data_info = dict(
+            img=np.random.random((1333, 800, 3)),
+            gt_semantic_seg=np.random.random((1333, 800, 3)),
+            gt_bboxes=np.array([[0, 0, 112, 112]]),
+            gt_keypoints=np.array([[[20, 50, 1]]]))
+
+        with pytest.raises(AssertionError):
+            transform = Resize(scale=None, scale_factor=None)
+        with pytest.raises(TypeError):
+            transform = Resize(scale_factor=[])
+        # test scale is int
+        transform = Resize(scale=2000)
+        results = transform(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2000, 2000)
+        assert results['scale_factor'] == (2000 / 800, 2000 / 1333)
+
+        # test scale is tuple
+        transform = Resize(scale=(2000, 2000))
+        results = transform(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2000, 2000)
+        assert results['scale_factor'] == (2000 / 800, 2000 / 1333)
+
+        # test scale_factor is float
+        transform = Resize(scale_factor=2.0)
+        results = transform(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2666, 1600)
+        assert results['scale_factor'] == (2.0, 2.0)
+
+        # test scale_factor is tuple
+        transform = Resize(scale_factor=(1.5, 2))
+        results = transform(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2666, 1200)
+        assert results['scale_factor'] == (1.5, 2)
+
+        # test keep_ratio is True
+        transform = Resize(scale=(2000, 2000), keep_ratio=True)
+        results = transform(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2000, 1200)
+        assert results['scale'] == (1200, 2000)
+        assert results['scale_factor'] == (1200 / 800, 2000 / 1333)
+
+        # test resize_bboxes/seg/kps
+        transform = Resize(scale_factor=(1.5, 2))
+        results = transform(copy.deepcopy(data_info))
+        assert (results['gt_bboxes'] == np.array([[0, 0, 168, 224]])).all()
+        assert (results['gt_keypoints'] == np.array([[[30, 100, 1]]])).all()
+        assert results['gt_semantic_seg'].shape[:2] == (2666, 1200)
+
+        # test clip_object_border = False
+        data_info = dict(
+            img=np.random.random((300, 400, 3)),
+            gt_bboxes=np.array([[200, 150, 600, 450]]))
+        transform = Resize(scale=(200, 150), clip_object_border=False)
+        results = transform(data_info)
+        assert (results['gt_bboxes'] == np.array([100, 75, 300, 225])).all()
+
+    def test_repr(self):
+        transform = Resize(scale=(2000, 2000), keep_ratio=True)
+        assert repr(transform) == ('Resize(scale=(2000, 2000), '
+                                   'scale_factor=None, keep_ratio=True, '
+                                   'clip_object_border=True), backend=cv2), '
+                                   'interpolation=bilinear)')
+
+
+class TestPad:
+
+    def test_pad(self):
+        # test size and size_divisor are both set
+        with pytest.raises(AssertionError):
+            Pad(size=(10, 10), size_divisor=2)
+
+        # test size and size_divisor are both None
+        with pytest.raises(AssertionError):
+            Pad(size=None, size_divisor=None)
+
+        # test size and pad_to_square are both None
+        with pytest.raises(AssertionError):
+            Pad(size=(10, 10), pad_to_square=True)
+
+        # test pad_val is not int or tuple
+        with pytest.raises(AssertionError):
+            Pad(size=(10, 10), pad_val=[])
+
+        # test padding_mode is not 'constant', 'edge', 'reflect' or 'symmetric'
+        with pytest.raises(AssertionError):
+            Pad(size=(10, 10), padding_mode='edg')
+
+        data_info = dict(
+            img=np.random.random((1333, 800, 3)),
+            gt_semantic_seg=np.random.random((1333, 800, 3)),
+            gt_bboxes=np.array([[0, 0, 112, 112]]),
+            gt_keypoints=np.array([[[20, 50, 1]]]))
+
+        # test pad img / gt_semantic_seg with size
+        trans = Pad(size=(1200, 2000))
+        results = trans(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (2000, 1200)
+        assert results['gt_semantic_seg'].shape[:2] == (2000, 1200)
+
+        # test pad img/gt_semantic_seg with size_divisor
+        trans = Pad(size_divisor=11)
+        results = trans(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (1342, 803)
+        assert results['gt_semantic_seg'].shape[:2] == (1342, 803)
+
+        # test pad img/gt_semantic_seg with pad_to_square
+        trans = Pad(pad_to_square=True)
+        results = trans(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (1333, 1333)
+        assert results['gt_semantic_seg'].shape[:2] == (1333, 1333)
+
+        # test pad img/gt_semantic_seg with pad_to_square and size_divisor
+        trans = Pad(pad_to_square=True, size_divisor=11)
+        results = trans(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (1342, 1342)
+        assert results['gt_semantic_seg'].shape[:2] == (1342, 1342)
+
+        # test pad img/gt_semantic_seg with pad_to_square and size_divisor
+        trans = Pad(pad_to_square=True, size_divisor=11)
+        results = trans(copy.deepcopy(data_info))
+        assert results['img'].shape[:2] == (1342, 1342)
+        assert results['gt_semantic_seg'].shape[:2] == (1342, 1342)
+
+        # test padding_mode
+        new_img = np.ones((1333, 800, 3))
+        data_info['img'] = new_img
+        trans = Pad(pad_to_square=True, padding_mode='edge')
+        results = trans(copy.deepcopy(data_info))
+        assert (results['img'] == np.ones((1333, 1333, 3))).all()
+
+        # test pad_val
+        new_img = np.zeros((1333, 800, 3))
+        data_info['img'] = new_img
+        trans = Pad(pad_to_square=True, pad_val=0)
+        results = trans(copy.deepcopy(data_info))
+        assert (results['img'] == np.zeros((1333, 1333, 3))).all()
+
+    def test_repr(self):
+        trans = Pad(pad_to_square=True, size_divisor=11, padding_mode='edge')
+        assert repr(trans) == (
+            'Pad(size=None, size_divisor=11, pad_to_square=True, '
+            "pad_val={'img': 0, 'seg': 255}), padding_mode=edge)")

tests/test_transform/test_transform_wrapper.py → tests/test_transforms/test_transforms_wrapper.py

@@ -4,10 +4,10 @@ import warnings
 import numpy as np
 import pytest
 
-from mmcv.transform.base import BaseTransform
-from mmcv.transform.builder import TRANSFORMS
-from mmcv.transform.utils import cache_random_params, cacheable_method
-from mmcv.transform.wrappers import (ApplyToMultiple, Compose, RandomChoice,
+from mmcv.transforms.base import BaseTransform
+from mmcv.transforms.builder import TRANSFORMS
+from mmcv.transforms.utils import cache_random_params, cacheable_method
+from mmcv.transforms.wrappers import (ApplyToMultiple, Compose, RandomChoice,
                                       Remap)