update codes

mmdet/datasets/transforms/text_transformers.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+
+from mmcv.transforms import BaseTransform
+
+from mmdet.registry import TRANSFORMS
+from mmdet.structures.bbox import BaseBoxes
+
+try:
+    from transformers import AutoTokenizer
+    from transformers import BertModel as HFBertModel
+except ImportError:
+    AutoTokenizer = None
+    HFBertModel = None
+
+import random
+import re
+
+import numpy as np
+
+
+def clean_name(name):
+    name = re.sub(r'\(.*\)', '', name)
+    name = re.sub(r'_', ' ', name)
+    name = re.sub(r'  ', ' ', name)
+    name = name.lower()
+    return name
+
+
+def check_for_positive_overflow(gt_bboxes, gt_labels, text, tokenizer,
+                                max_tokens):
+    # Check if we have too many positive labels
+    # generate a caption by appending the positive labels
+    positive_label_list = np.unique(gt_labels).tolist()
+    # random shuffule so we can sample different annotations
+    # at different epochs
+    random.shuffle(positive_label_list)
+
+    kept_lables = []
+    length = 0
+
+    for index, label in enumerate(positive_label_list):
+
+        label_text = clean_name(text[str(label)]) + '. '
+
+        tokenized = tokenizer.tokenize(label_text)
+
+        length += len(tokenized)
+
+        if length > max_tokens:
+            break
+        else:
+            kept_lables.append(label)
+
+    keep_box_index = []
+    keep_gt_labels = []
+    for i in range(len(gt_labels)):
+        if gt_labels[i] in kept_lables:
+            keep_box_index.append(i)
+            keep_gt_labels.append(gt_labels[i])
+
+    return gt_bboxes[keep_box_index], np.array(
+        keep_gt_labels, dtype=np.long), length
+
+
+def generate_senetence_given_labels(positive_label_list, negative_label_list,
+                                    text):
+    label_to_positions = {}
+
+    label_list = negative_label_list + positive_label_list
+
+    random.shuffle(label_list)
+
+    pheso_caption = ''
+
+    label_remap_dict = {}
+    for index, label in enumerate(label_list):
+
+        start_index = len(pheso_caption)
+
+        pheso_caption += clean_name(text[str(label)])
+
+        end_index = len(pheso_caption)
+
+        if label in positive_label_list:
+            label_to_positions[index] = [[start_index, end_index]]
+            label_remap_dict[int(label)] = index
+
+        # if index != len(label_list) - 1:
+        #     pheso_caption += '. '
+        pheso_caption += '. '
+
+    return label_to_positions, pheso_caption, label_remap_dict
+
+
+@TRANSFORMS.register_module()
+class RandomSamplingNegPos(BaseTransform):
+
+    def __init__(self,
+                 tokenizer_name,
+                 num_sample_negative=85,
+                 max_tokens=256,
+                 full_sampling_prob=0.5,
+                 label_map_file=None):
+        if AutoTokenizer is None:
+            raise RuntimeError(
+                'transformers is not installed, please install it by: '
+                'pip install transformers.')
+
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+        self.num_sample_negative = num_sample_negative
+        self.full_sampling_prob = full_sampling_prob
+        self.max_tokens = max_tokens
+        self.label_map = None
+        if label_map_file:
+            with open(label_map_file, 'r') as file:
+                self.label_map = json.load(file)
+
+    def transform(self, results: dict) -> dict:
+        if 'phrases' in results:
+            return self.vg_aug(results)
+        else:
+            return self.od_aug(results)
+
+    def vg_aug(self, results):
+        gt_bboxes = results['gt_bboxes']
+        if isinstance(gt_bboxes, BaseBoxes):
+            gt_bboxes = gt_bboxes.tensor
+        gt_labels = results['gt_bboxes_labels']
+        text = results['text'].lower().strip()
+        if not text.endswith('.'):
+            text = text + '. '
+
+        phrases = results['phrases']
+        # TODO: add neg
+        positive_label_list = np.unique(gt_labels).tolist()
+        label_to_positions = {}
+        for label in positive_label_list:
+            label_to_positions[label] = phrases[label]['tokens_positive']
+
+        results['gt_bboxes'] = gt_bboxes
+        results['gt_bboxes_labels'] = gt_labels
+
+        results['text'] = text
+        results['tokens_positive'] = label_to_positions
+        return results
+
+    def od_aug(self, results):
+        gt_bboxes = results['gt_bboxes']
+        if isinstance(gt_bboxes, BaseBoxes):
+            gt_bboxes = gt_bboxes.tensor
+        gt_labels = results['gt_bboxes_labels']
+
+        if 'text' not in results:
+            assert self.label_map is not None
+            text = self.label_map
+        else:
+            text = results['text']
+
+        original_box_num = len(gt_labels)
+        # If the category name is in the format of 'a/b' (in object365),
+        # we randomly select one of them.
+        for key, value in text.items():
+            if '/' in value:
+                text[key] = random.choice(value.split('/')).strip()
+
+        gt_bboxes, gt_labels, positive_caption_length = \
+            check_for_positive_overflow(gt_bboxes, gt_labels,
+                                        text, self.tokenizer, self.max_tokens)
+
+        if len(gt_bboxes) < original_box_num:
+            print('WARNING: removed {} boxes due to positive caption overflow'.
+                  format(original_box_num - len(gt_bboxes)))
+
+        valid_negative_indexes = list(text.keys())
+
+        positive_label_list = np.unique(gt_labels).tolist()
+        full_negative = self.num_sample_negative
+
+        if full_negative > len(valid_negative_indexes):
+            full_negative = len(valid_negative_indexes)
+
+        outer_prob = random.random()
+
+        if outer_prob < self.full_sampling_prob:
+            # c. probability_full: add both all positive and all negatives
+            num_negatives = full_negative
+        else:
+            if random.random() < 1.0:
+                num_negatives = np.random.choice(max(1, full_negative)) + 1
+            else:
+                num_negatives = full_negative
+
+        # Keep some negatives
+        negative_label_list = set()
+        if num_negatives != -1:
+            if num_negatives > len(valid_negative_indexes):
+                num_negatives = len(valid_negative_indexes)
+
+            for i in np.random.choice(
+                    valid_negative_indexes, size=num_negatives, replace=False):
+                if int(i) not in positive_label_list:
+                    negative_label_list.add(i)
+
+        random.shuffle(positive_label_list)
+
+        negative_label_list = list(negative_label_list)
+        random.shuffle(negative_label_list)
+
+        negative_max_length = self.max_tokens - positive_caption_length
+        screened_negative_label_list = []
+
+        for negative_label in negative_label_list:
+            label_text = clean_name(text[str(negative_label)]) + '. '
+
+            tokenized = self.tokenizer.tokenize(label_text)
+
+            negative_max_length -= len(tokenized)
+
+            if negative_max_length > 0:
+                screened_negative_label_list.append(negative_label)
+            else:
+                break
+        negative_label_list = screened_negative_label_list
+        label_to_positions, pheso_caption, label_remap_dict = \
+            generate_senetence_given_labels(positive_label_list,
+                                            negative_label_list, text)
+
+        # label remap
+        if len(gt_labels) > 0:
+            gt_labels = np.vectorize(lambda x: label_remap_dict[x])(gt_labels)
+
+        results['gt_bboxes'] = gt_bboxes
+        results['gt_bboxes_labels'] = gt_labels
+
+        results['text'] = pheso_caption
+        results['tokens_positive'] = label_to_positions
+
+        return results
+
+
+@TRANSFORMS.register_module()
+class LoadTextAnnotations(BaseTransform):
+
+    def transform(self, results: dict) -> dict:
+        if 'phrases' in results:
+            tokens_positive = [
+                phrase['tokens_positive']
+                for phrase in results['phrases'].values()
+            ]
+            results['tokens_positive'] = tokens_positive
+        else:
+            text = results['text']
+            results['text'] = list(text.values())
+        return results

mmdet/datasets/transforms/transformers_glip.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+from mmcv.transforms import BaseTransform
+
+from mmdet.registry import TRANSFORMS
+from mmdet.structures.bbox import HorizontalBoxes, autocast_box_type
+from .transforms import RandomFlip
+
+
+@TRANSFORMS.register_module()
+class GTBoxSubOne_GLIP(BaseTransform):
+    """Subtract 1 from the x2 and y2 coordinates of the gt_bboxes."""
+
+    def transform(self, results: dict) -> dict:
+        if 'gt_bboxes' in results:
+            gt_bboxes = results['gt_bboxes']
+            if isinstance(gt_bboxes, np.ndarray):
+                gt_bboxes[:, 2:] -= 1
+                results['gt_bboxes'] = gt_bboxes
+            elif isinstance(gt_bboxes, HorizontalBoxes):
+                gt_bboxes = results['gt_bboxes'].tensor
+                gt_bboxes[:, 2:] -= 1
+                results['gt_bboxes'] = HorizontalBoxes(gt_bboxes)
+            else:
+                raise NotImplementedError
+        return results
+
+
+@TRANSFORMS.register_module()
+class RandomFlip_GLIP(RandomFlip):
+    """Flip the image & bboxes & masks & segs horizontally or vertically.
+
+    When using horizontal flipping, the corresponding bbox x-coordinate needs
+    to be additionally subtracted by one.
+    """
+
+    @autocast_box_type()
+    def _flip(self, results: dict) -> None:
+        """Flip images, bounding boxes, and semantic segmentation map."""
+        # flip image
+        results['img'] = mmcv.imflip(
+            results['img'], direction=results['flip_direction'])
+
+        img_shape = results['img'].shape[:2]
+
+        # flip bboxes
+        if results.get('gt_bboxes', None) is not None:
+            results['gt_bboxes'].flip_(img_shape, results['flip_direction'])
+            # Only change this line
+            if results['flip_direction'] == 'horizontal':
+                results['gt_bboxes'].translate_([-1, 0])
+
+        # TODO: check it
+        # flip masks
+        if results.get('gt_masks', None) is not None:
+            results['gt_masks'] = results['gt_masks'].flip(
+                results['flip_direction'])
+
+        # flip segs
+        if results.get('gt_seg_map', None) is not None:
+            results['gt_seg_map'] = mmcv.imflip(
+                results['gt_seg_map'], direction=results['flip_direction'])
+
+        # record homography matrix for flip
+        self._record_homography_matrix(results)

mmdet/datasets/transforms/transforms.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import inspect
+import math
+import warnings
+from typing import List, Optional, Sequence, Tuple, Union
+
+import cv2
+import mmcv
+import numpy as np
+from mmcv.image import imresize
+from mmcv.image.geometric import _scale_size
+from mmcv.transforms import BaseTransform
+from mmcv.transforms import Pad as MMCV_Pad
+from mmcv.transforms import RandomFlip as MMCV_RandomFlip
+from mmcv.transforms import Resize as MMCV_Resize
+from mmcv.transforms.utils import avoid_cache_randomness, cache_randomness
+from mmengine.dataset import BaseDataset
+from mmengine.utils import is_str
+from numpy import random
+
+from mmdet.registry import TRANSFORMS
+from mmdet.structures.bbox import HorizontalBoxes, autocast_box_type
+from mmdet.structures.mask import BitmapMasks, PolygonMasks
+from mmdet.utils import log_img_scale
+
+try:
+    from imagecorruptions import corrupt
+except ImportError:
+    corrupt = None
+
+try:
+    import albumentations
+    from albumentations import Compose
+except ImportError:
+    albumentations = None
+    Compose = None
+
+Number = Union[int, float]
+
+
+def _fixed_scale_size(
+    size: Tuple[int, int],
+    scale: Union[float, int, tuple],
+) -> Tuple[int, int]:
+    """Rescale a size by a ratio.
+
+    Args:
+        size (tuple[int]): (w, h).
+        scale (float | tuple(float)): Scaling factor.
+
+    Returns:
+        tuple[int]: scaled size.
+    """
+    if isinstance(scale, (float, int)):
+        scale = (scale, scale)
+    w, h = size
+    # don't need o.5 offset
+    return int(w * float(scale[0])), int(h * float(scale[1]))
+
+
+def rescale_size(old_size: tuple,
+                 scale: Union[float, int, tuple],
+                 return_scale: bool = False) -> tuple:
+    """Calculate the new size to be rescaled to.
+
+    Args:
+        old_size (tuple[int]): The old size (w, h) of image.
+        scale (float | tuple[int]): The scaling factor or maximum size.
+            If it is a float number, then the image will be rescaled by this
+            factor, else if it is a tuple of 2 integers, then the image will
+            be rescaled as large as possible within the scale.
+        return_scale (bool): Whether to return the scaling factor besides the
+            rescaled image size.
+
+    Returns:
+        tuple[int]: The new rescaled image size.
+    """
+    w, h = old_size
+    if isinstance(scale, (float, int)):
+        if scale <= 0:
+            raise ValueError(f'Invalid scale {scale}, must be positive.')
+        scale_factor = scale
+    elif isinstance(scale, tuple):
+        max_long_edge = max(scale)
+        max_short_edge = min(scale)
+        scale_factor = min(max_long_edge / max(h, w),
+                           max_short_edge / min(h, w))
+    else:
+        raise TypeError(
+            f'Scale must be a number or tuple of int, but got {type(scale)}')
+    # only change this
+    new_size = _fixed_scale_size((w, h), scale_factor)
+
+    if return_scale:
+        return new_size, scale_factor
+    else:
+        return new_size
+
+
+def imrescale(
+    img: np.ndarray,
+    scale: Union[float, Tuple[int, int]],
+    return_scale: bool = False,
+    interpolation: str = 'bilinear',
+    backend: Optional[str] = None
+) -> Union[np.ndarray, Tuple[np.ndarray, float]]:
+    """Resize image while keeping the aspect ratio.
+
+    Args:
+        img (ndarray): The input image.
+        scale (float | tuple[int]): The scaling factor or maximum size.
+            If it is a float number, then the image will be rescaled by this
+            factor, else if it is a tuple of 2 integers, then the image will
+            be rescaled as large as possible within the scale.
+        return_scale (bool): Whether to return the scaling factor besides the
+            rescaled image.
+        interpolation (str): Same as :func:`resize`.
+        backend (str | None): Same as :func:`resize`.
+
+    Returns:
+        ndarray: The rescaled image.
+    """
+    h, w = img.shape[:2]
+    new_size, scale_factor = rescale_size((w, h), scale, return_scale=True)
+    rescaled_img = imresize(
+        img, new_size, interpolation=interpolation, backend=backend)
+    if return_scale:
+        return rescaled_img, scale_factor
+    else:
+        return rescaled_img
+
+
+@TRANSFORMS.register_module()
+class Resize(MMCV_Resize):
+    """Resize images & bbox & seg.
+
+    This transform resizes the input image according to ``scale`` or
+    ``scale_factor``. Bboxes, masks, and seg map 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 (BaseBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes
+    - gt_masks
+    - gt_seg_map
+
+
+    Added Keys:
+
+    - scale
+    - scale_factor
+    - keep_ratio
+    - homography_matrix
+
+    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): 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 'bilinear'.
+    """
+
+    def _resize_masks(self, results: dict) -> None:
+        """Resize masks with ``results['scale']``"""
+        if results.get('gt_masks', None) is not None:
+            if self.keep_ratio:
+                results['gt_masks'] = results['gt_masks'].rescale(
+                    results['scale'])
+            else:
+                results['gt_masks'] = results['gt_masks'].resize(
+                    results['img_shape'])
+
+    def _resize_bboxes(self, results: dict) -> None:
+        """Resize bounding boxes with ``results['scale_factor']``."""
+        if results.get('gt_bboxes', None) is not None:
+            results['gt_bboxes'].rescale_(results['scale_factor'])
+            if self.clip_object_border:
+                results['gt_bboxes'].clip_(results['img_shape'])
+
+    def _record_homography_matrix(self, results: dict) -> None:
+        """Record the homography matrix for the Resize."""
+        w_scale, h_scale = results['scale_factor']
+        homography_matrix = np.array(
+            [[w_scale, 0, 0], [0, h_scale, 0], [0, 0, 1]], dtype=np.float32)
+        if results.get('homography_matrix', None) is None:
+            results['homography_matrix'] = homography_matrix
+        else:
+            results['homography_matrix'] = homography_matrix @ results[
+                'homography_matrix']
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to resize images, bounding boxes and semantic
+        segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+        Returns:
+            dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',
+            '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_masks(results)
+        self._resize_seg(results)
+        self._record_homography_matrix(results)
+        return results
+
+    def __repr__(self) -> str:
+        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 FixScaleResize(Resize):
+    """Compared to Resize, FixScaleResize fixes the scaling issue when
+    `keep_ratio=true`."""
+
+    def _resize_img(self, results):
+        """Resize images with ``results['scale']``."""
+        if results.get('img', None) is not None:
+            if self.keep_ratio:
+                img, scale_factor = imrescale(
+                    results['img'],
+                    results['scale'],
+                    interpolation=self.interpolation,
+                    return_scale=True,
+                    backend=self.backend)
+                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['img_shape'] = img.shape[:2]
+            results['scale_factor'] = (w_scale, h_scale)
+            results['keep_ratio'] = self.keep_ratio
+
+
+@TRANSFORMS.register_module()
+class ResizeShortestEdge(BaseTransform):
+    """Resize the image and mask while keeping the aspect ratio unchanged.
+
+    Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/augmentation_impl.py#L130 # noqa:E501
+
+    This transform attempts to scale the shorter edge to the given
+    `scale`, as long as the longer edge does not exceed `max_size`.
+    If `max_size` is reached, then downscale so that the longer
+    edge does not exceed `max_size`.
+
+    Required Keys:
+        - img
+        - gt_seg_map (optional)
+    Modified Keys:
+        - img
+        - img_shape
+        - gt_seg_map (optional))
+    Added Keys:
+        - scale
+        - scale_factor
+        - keep_ratio
+
+    Args:
+        scale (Union[int, Tuple[int, int]]): The target short edge length.
+            If it's tuple, will select the min value as the short edge length.
+        max_size (int): The maximum allowed longest edge length.
+    """
+
+    def __init__(self,
+                 scale: Union[int, Tuple[int, int]],
+                 max_size: Optional[int] = None,
+                 resize_type: str = 'Resize',
+                 **resize_kwargs) -> None:
+        super().__init__()
+        self.scale = scale
+        self.max_size = max_size
+
+        self.resize_cfg = dict(type=resize_type, **resize_kwargs)
+        self.resize = TRANSFORMS.build({'scale': 0, **self.resize_cfg})
+
+    def _get_output_shape(
+            self, img: np.ndarray,
+            short_edge_length: Union[int, Tuple[int, int]]) -> Tuple[int, int]:
+        """Compute the target image shape with the given `short_edge_length`.
+
+        Args:
+            img (np.ndarray): The input image.
+            short_edge_length (Union[int, Tuple[int, int]]): The target short
+                edge length. If it's tuple, will select the min value as the
+                short edge length.
+        """
+        h, w = img.shape[:2]
+        if isinstance(short_edge_length, int):
+            size = short_edge_length * 1.0
+        elif isinstance(short_edge_length, tuple):
+            size = min(short_edge_length) * 1.0
+        scale = size / min(h, w)
+        if h < w:
+            new_h, new_w = size, scale * w
+        else:
+            new_h, new_w = scale * h, size
+
+        if self.max_size and max(new_h, new_w) > self.max_size:
+            scale = self.max_size * 1.0 / max(new_h, new_w)
+            new_h *= scale
+            new_w *= scale
+
+        new_h = int(new_h + 0.5)
+        new_w = int(new_w + 0.5)
+        return new_w, new_h
+
+    def transform(self, results: dict) -> dict:
+        self.resize.scale = self._get_output_shape(results['img'], self.scale)
+        return self.resize(results)
+
+
+@TRANSFORMS.register_module()
+class FixShapeResize(Resize):
+    """Resize images & bbox & seg to the specified size.
+
+    This transform resizes the input image according to ``width`` and
+    ``height``. Bboxes, masks, and seg map are then resized
+    with the same parameters.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes
+    - gt_masks
+    - gt_seg_map
+
+
+    Added Keys:
+
+    - scale
+    - scale_factor
+    - keep_ratio
+    - homography_matrix
+
+    Args:
+        width (int): width for resizing.
+        height (int): height for resizing.
+            Defaults to None.
+        pad_val (Number | dict[str, Number], optional): Padding value for if
+            the pad_mode is "constant".  If it is a single number, the value
+            to pad the image is the number and to pad the semantic
+            segmentation map is 255. If it is a dict, it should have the
+            following keys:
+
+            - img: The value to pad the image.
+            - seg: The value to pad the semantic segmentation map.
+            Defaults to dict(img=0, seg=255).
+        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
+            image. Defaults to False.
+        clip_object_border (bool): 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 'bilinear'.
+    """
+
+    def __init__(self,
+                 width: int,
+                 height: int,
+                 pad_val: Union[Number, dict] = dict(img=0, seg=255),
+                 keep_ratio: bool = False,
+                 clip_object_border: bool = True,
+                 backend: str = 'cv2',
+                 interpolation: str = 'bilinear') -> None:
+        assert width is not None and height is not None, (
+            '`width` and'
+            '`height` can not be `None`')
+
+        self.width = width
+        self.height = height
+        self.scale = (width, height)
+
+        self.backend = backend
+        self.interpolation = interpolation
+        self.keep_ratio = keep_ratio
+        self.clip_object_border = clip_object_border
+
+        if keep_ratio is True:
+            # padding to the fixed size when keep_ratio=True
+            self.pad_transform = Pad(size=self.scale, pad_val=pad_val)
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to resize images, bounding boxes and semantic
+        segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+        Returns:
+            dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',
+            'scale', 'scale_factor', 'height', 'width', and 'keep_ratio' keys
+            are updated in result dict.
+        """
+        img = results['img']
+        h, w = img.shape[:2]
+        if self.keep_ratio:
+            scale_factor = min(self.width / w, self.height / h)
+            results['scale_factor'] = (scale_factor, scale_factor)
+            real_w, real_h = int(w * float(scale_factor) +
+                                 0.5), int(h * float(scale_factor) + 0.5)
+            img, scale_factor = mmcv.imrescale(
+                results['img'], (real_w, real_h),
+                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
+            results['img'] = img
+            results['img_shape'] = img.shape[:2]
+            results['keep_ratio'] = self.keep_ratio
+            results['scale'] = (real_w, real_h)
+        else:
+            results['scale'] = (self.width, self.height)
+            results['scale_factor'] = (self.width / w, self.height / h)
+            super()._resize_img(results)
+
+        self._resize_bboxes(results)
+        self._resize_masks(results)
+        self._resize_seg(results)
+        self._record_homography_matrix(results)
+        if self.keep_ratio:
+            self.pad_transform(results)
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(width={self.width}, height={self.height}, '
+        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 RandomFlip(MMCV_RandomFlip):
+    """Flip the image & bbox & mask & segmentation map. Added or Updated keys:
+    flip, flip_direction, img, gt_bboxes, and gt_seg_map. There are 3 flip
+    modes:
+
+     - ``prob`` is float, ``direction`` is string: the image will be
+         ``direction``ly flipped with probability of ``prob`` .
+         E.g., ``prob=0.5``, ``direction='horizontal'``,
+         then image will be horizontally flipped with probability of 0.5.
+     - ``prob`` is float, ``direction`` is list of string: the image will
+         be ``direction[i]``ly flipped with probability of
+         ``prob/len(direction)``.
+         E.g., ``prob=0.5``, ``direction=['horizontal', 'vertical']``,
+         then image will be horizontally flipped with probability of 0.25,
+         vertically with probability of 0.25.
+     - ``prob`` is list of float, ``direction`` is list of string:
+         given ``len(prob) == len(direction)``, the image will
+         be ``direction[i]``ly flipped with probability of ``prob[i]``.
+         E.g., ``prob=[0.3, 0.5]``, ``direction=['horizontal',
+         'vertical']``, then image will be horizontally flipped with
+         probability of 0.3, vertically with probability of 0.5.
+
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - gt_bboxes
+    - gt_masks
+    - gt_seg_map
+
+    Added Keys:
+
+    - flip
+    - flip_direction
+    - homography_matrix
+
+
+    Args:
+         prob (float | list[float], optional): The flipping probability.
+             Defaults to None.
+         direction(str | list[str]): The flipping direction. Options
+             If input is a list, the length must equal ``prob``. Each
+             element in ``prob`` indicates the flip probability of
+             corresponding direction. Defaults to 'horizontal'.
+    """
+
+    def _record_homography_matrix(self, results: dict) -> None:
+        """Record the homography matrix for the RandomFlip."""
+        cur_dir = results['flip_direction']
+        h, w = results['img'].shape[:2]
+
+        if cur_dir == 'horizontal':
+            homography_matrix = np.array([[-1, 0, w], [0, 1, 0], [0, 0, 1]],
+                                         dtype=np.float32)
+        elif cur_dir == 'vertical':
+            homography_matrix = np.array([[1, 0, 0], [0, -1, h], [0, 0, 1]],
+                                         dtype=np.float32)
+        elif cur_dir == 'diagonal':
+            homography_matrix = np.array([[-1, 0, w], [0, -1, h], [0, 0, 1]],
+                                         dtype=np.float32)
+        else:
+            homography_matrix = np.eye(3, dtype=np.float32)
+
+        if results.get('homography_matrix', None) is None:
+            results['homography_matrix'] = homography_matrix
+        else:
+            results['homography_matrix'] = homography_matrix @ results[
+                'homography_matrix']
+
+    @autocast_box_type()
+    def _flip(self, results: dict) -> None:
+        """Flip images, bounding boxes, and semantic segmentation map."""
+        # flip image
+        results['img'] = mmcv.imflip(
+            results['img'], direction=results['flip_direction'])
+
+        img_shape = results['img'].shape[:2]
+
+        # flip bboxes
+        if results.get('gt_bboxes', None) is not None:
+            results['gt_bboxes'].flip_(img_shape, results['flip_direction'])
+
+        # flip masks
+        if results.get('gt_masks', None) is not None:
+            results['gt_masks'] = results['gt_masks'].flip(
+                results['flip_direction'])
+
+        # flip segs
+        if results.get('gt_seg_map', None) is not None:
+            results['gt_seg_map'] = mmcv.imflip(
+                results['gt_seg_map'], direction=results['flip_direction'])
+
+        # record homography matrix for flip
+        self._record_homography_matrix(results)
+
+
+@TRANSFORMS.register_module()
+class RandomShift(BaseTransform):
+    """Shift the image and box given shift pixels and probability.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32])
+    - gt_bboxes_labels (np.int64)
+    - gt_ignore_flags (bool) (optional)
+
+    Modified Keys:
+
+    - img
+    - gt_bboxes
+    - gt_bboxes_labels
+    - gt_ignore_flags (bool) (optional)
+
+    Args:
+        prob (float): Probability of shifts. Defaults to 0.5.
+        max_shift_px (int): The max pixels for shifting. Defaults to 32.
+        filter_thr_px (int): The width and height threshold for filtering.
+            The bbox and the rest of the targets below the width and
+            height threshold will be filtered. Defaults to 1.
+    """
+
+    def __init__(self,
+                 prob: float = 0.5,
+                 max_shift_px: int = 32,
+                 filter_thr_px: int = 1) -> None:
+        assert 0 <= prob <= 1
+        assert max_shift_px >= 0
+        self.prob = prob
+        self.max_shift_px = max_shift_px
+        self.filter_thr_px = int(filter_thr_px)
+
+    @cache_randomness
+    def _random_prob(self) -> float:
+        return random.uniform(0, 1)
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to random shift images, bounding boxes.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Shift results.
+        """
+        if self._random_prob() < self.prob:
+            img_shape = results['img'].shape[:2]
+
+            random_shift_x = random.randint(-self.max_shift_px,
+                                            self.max_shift_px)
+            random_shift_y = random.randint(-self.max_shift_px,
+                                            self.max_shift_px)
+            new_x = max(0, random_shift_x)
+            ori_x = max(0, -random_shift_x)
+            new_y = max(0, random_shift_y)
+            ori_y = max(0, -random_shift_y)
+
+            # TODO: support mask and semantic segmentation maps.
+            bboxes = results['gt_bboxes'].clone()
+            bboxes.translate_([random_shift_x, random_shift_y])
+
+            # clip border
+            bboxes.clip_(img_shape)
+
+            # remove invalid bboxes
+            valid_inds = (bboxes.widths > self.filter_thr_px).numpy() & (
+                bboxes.heights > self.filter_thr_px).numpy()
+            # If the shift does not contain any gt-bbox area, skip this
+            # image.
+            if not valid_inds.any():
+                return results
+            bboxes = bboxes[valid_inds]
+            results['gt_bboxes'] = bboxes
+            results['gt_bboxes_labels'] = results['gt_bboxes_labels'][
+                valid_inds]
+
+            if results.get('gt_ignore_flags', None) is not None:
+                results['gt_ignore_flags'] = \
+                    results['gt_ignore_flags'][valid_inds]
+
+            # shift img
+            img = results['img']
+            new_img = np.zeros_like(img)
+            img_h, img_w = img.shape[:2]
+            new_h = img_h - np.abs(random_shift_y)
+            new_w = img_w - np.abs(random_shift_x)
+            new_img[new_y:new_y + new_h, new_x:new_x + new_w] \
+                = img[ori_y:ori_y + new_h, ori_x:ori_x + new_w]
+            results['img'] = new_img
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(prob={self.prob}, '
+        repr_str += f'max_shift_px={self.max_shift_px}, '
+        repr_str += f'filter_thr_px={self.filter_thr_px})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Pad(MMCV_Pad):
+    """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 (BaseBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_masks
+    - gt_seg_map
+
+    Added Keys:
+
+    - pad_shape
+    - pad_fixed_size
+    - pad_size_divisor
+
+    Args:
+        size (tuple, optional): Fixed padding size.
+            Expected padding shape (width, height). 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 (Number | dict[str, Number], optional) - Padding value for if
+            the pad_mode is "constant".  If it is a single number, the value
+            to pad the image is the number and to pad the semantic
+            segmentation map is 255. If it is a dict, it should have the
+            following keys:
+
+            - img: The value to pad the image.
+            - seg: The value to pad the semantic segmentation map.
+            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 _pad_masks(self, results: dict) -> None:
+        """Pad masks according to ``results['pad_shape']``."""
+        if results.get('gt_masks', None) is not None:
+            pad_val = self.pad_val.get('masks', 0)
+            pad_shape = results['pad_shape'][:2]
+            results['gt_masks'] = results['gt_masks'].pad(
+                pad_shape, pad_val=pad_val)
+
+    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)
+        self._pad_masks(results)
+        return results
+
+
+@TRANSFORMS.register_module()
+class RandomCrop(BaseTransform):
+    """Random crop the image & bboxes & masks.
+
+    The absolute ``crop_size`` is sampled based on ``crop_type`` and
+    ``image_size``, then the cropped results are generated.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_masks (optional)
+    - gt_ignore_flags (optional)
+    - gt_seg_map (optional)
+    - gt_instances_ids (options, only used in MOT/VIS)
+
+    Added Keys:
+
+    - homography_matrix
+
+    Args:
+        crop_size (tuple): The relative ratio or absolute pixels of
+            (width, height).
+        crop_type (str, optional): One of "relative_range", "relative",
+            "absolute", "absolute_range". "relative" randomly crops
+            (h * crop_size[0], w * crop_size[1]) part from an input of size
+            (h, w). "relative_range" uniformly samples relative crop size from
+            range [crop_size[0], 1] and [crop_size[1], 1] for height and width
+            respectively. "absolute" crops from an input with absolute size
+            (crop_size[0], crop_size[1]). "absolute_range" uniformly samples
+            crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w
+            in range [crop_size[0], min(w, crop_size[1])].
+            Defaults to "absolute".
+        allow_negative_crop (bool, optional): Whether to allow a crop that does
+            not contain any bbox area. Defaults to False.
+        recompute_bbox (bool, optional): Whether to re-compute the boxes based
+            on cropped instance masks. Defaults to False.
+        bbox_clip_border (bool, optional): Whether clip the objects outside
+            the border of the image. Defaults to True.
+
+    Note:
+        - If the image is smaller than the absolute crop size, return the
+            original image.
+        - The keys for bboxes, labels and masks must be aligned. That is,
+          ``gt_bboxes`` corresponds to ``gt_labels`` and ``gt_masks``, and
+          ``gt_bboxes_ignore`` corresponds to ``gt_labels_ignore`` and
+          ``gt_masks_ignore``.
+        - If the crop does not contain any gt-bbox region and
+          ``allow_negative_crop`` is set to False, skip this image.
+    """
+
+    def __init__(self,
+                 crop_size: tuple,
+                 crop_type: str = 'absolute',
+                 allow_negative_crop: bool = False,
+                 recompute_bbox: bool = False,
+                 bbox_clip_border: bool = True) -> None:
+        if crop_type not in [
+                'relative_range', 'relative', 'absolute', 'absolute_range'
+        ]:
+            raise ValueError(f'Invalid crop_type {crop_type}.')
+        if crop_type in ['absolute', 'absolute_range']:
+            assert crop_size[0] > 0 and crop_size[1] > 0
+            assert isinstance(crop_size[0], int) and isinstance(
+                crop_size[1], int)
+            if crop_type == 'absolute_range':
+                assert crop_size[0] <= crop_size[1]
+        else:
+            assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1
+        self.crop_size = crop_size
+        self.crop_type = crop_type
+        self.allow_negative_crop = allow_negative_crop
+        self.bbox_clip_border = bbox_clip_border
+        self.recompute_bbox = recompute_bbox
+
+    def _crop_data(self, results: dict, crop_size: Tuple[int, int],
+                   allow_negative_crop: bool) -> Union[dict, None]:
+        """Function to randomly crop images, bounding boxes, masks, semantic
+        segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            crop_size (Tuple[int, int]): Expected absolute size after
+                cropping, (h, w).
+            allow_negative_crop (bool): Whether to allow a crop that does not
+                contain any bbox area.
+
+        Returns:
+            results (Union[dict, None]): Randomly cropped results, 'img_shape'
+                key in result dict is updated according to crop size. None will
+                be returned when there is no valid bbox after cropping.
+        """
+        assert crop_size[0] > 0 and crop_size[1] > 0
+        img = results['img']
+        margin_h = max(img.shape[0] - crop_size[0], 0)
+        margin_w = max(img.shape[1] - crop_size[1], 0)
+        offset_h, offset_w = self._rand_offset((margin_h, margin_w))
+        crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
+        crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
+
+        # Record the homography matrix for the RandomCrop
+        homography_matrix = np.array(
+            [[1, 0, -offset_w], [0, 1, -offset_h], [0, 0, 1]],
+            dtype=np.float32)
+        if results.get('homography_matrix', None) is None:
+            results['homography_matrix'] = homography_matrix
+        else:
+            results['homography_matrix'] = homography_matrix @ results[
+                'homography_matrix']
+
+        # crop the image
+        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
+        img_shape = img.shape
+        results['img'] = img
+        results['img_shape'] = img_shape[:2]
+
+        # crop bboxes accordingly and clip to the image boundary
+        if results.get('gt_bboxes', None) is not None:
+            bboxes = results['gt_bboxes']
+            bboxes.translate_([-offset_w, -offset_h])
+            if self.bbox_clip_border:
+                bboxes.clip_(img_shape[:2])
+            valid_inds = bboxes.is_inside(img_shape[:2]).numpy()
+            # If the crop does not contain any gt-bbox area and
+            # allow_negative_crop is False, skip this image.
+            if (not valid_inds.any() and not allow_negative_crop):
+                return None
+
+            results['gt_bboxes'] = bboxes[valid_inds]
+
+            if results.get('gt_ignore_flags', None) is not None:
+                results['gt_ignore_flags'] = \
+                    results['gt_ignore_flags'][valid_inds]
+
+            if results.get('gt_bboxes_labels', None) is not None:
+                results['gt_bboxes_labels'] = \
+                    results['gt_bboxes_labels'][valid_inds]
+
+            if results.get('gt_masks', None) is not None:
+                results['gt_masks'] = results['gt_masks'][
+                    valid_inds.nonzero()[0]].crop(
+                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
+                if self.recompute_bbox:
+                    results['gt_bboxes'] = results['gt_masks'].get_bboxes(
+                        type(results['gt_bboxes']))
+
+            # We should remove the instance ids corresponding to invalid boxes.
+            if results.get('gt_instances_ids', None) is not None:
+                results['gt_instances_ids'] = \
+                    results['gt_instances_ids'][valid_inds]
+
+        # crop semantic seg
+        if results.get('gt_seg_map', None) is not None:
+            results['gt_seg_map'] = results['gt_seg_map'][crop_y1:crop_y2,
+                                                          crop_x1:crop_x2]
+
+        return results
+
+    @cache_randomness
+    def _rand_offset(self, margin: Tuple[int, int]) -> Tuple[int, int]:
+        """Randomly generate crop offset.
+
+        Args:
+            margin (Tuple[int, int]): The upper bound for the offset generated
+                randomly.
+
+        Returns:
+            Tuple[int, int]: The random offset for the crop.
+        """
+        margin_h, margin_w = margin
+        offset_h = np.random.randint(0, margin_h + 1)
+        offset_w = np.random.randint(0, margin_w + 1)
+
+        return offset_h, offset_w
+
+    @cache_randomness
+    def _get_crop_size(self, image_size: Tuple[int, int]) -> Tuple[int, int]:
+        """Randomly generates the absolute crop size based on `crop_type` and
+        `image_size`.
+
+        Args:
+            image_size (Tuple[int, int]): (h, w).
+
+        Returns:
+            crop_size (Tuple[int, int]): (crop_h, crop_w) in absolute pixels.
+        """
+        h, w = image_size
+        if self.crop_type == 'absolute':
+            return min(self.crop_size[1], h), min(self.crop_size[0], w)
+        elif self.crop_type == 'absolute_range':
+            crop_h = np.random.randint(
+                min(h, self.crop_size[0]),
+                min(h, self.crop_size[1]) + 1)
+            crop_w = np.random.randint(
+                min(w, self.crop_size[0]),
+                min(w, self.crop_size[1]) + 1)
+            return crop_h, crop_w
+        elif self.crop_type == 'relative':
+            crop_w, crop_h = self.crop_size
+            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
+        else:
+            # 'relative_range'
+            crop_size = np.asarray(self.crop_size, dtype=np.float32)
+            crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)
+            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> Union[dict, None]:
+        """Transform function to randomly crop images, bounding boxes, masks,
+        semantic segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            results (Union[dict, None]): Randomly cropped results, 'img_shape'
+                key in result dict is updated according to crop size. None will
+                be returned when there is no valid bbox after cropping.
+        """
+        image_size = results['img'].shape[:2]
+        crop_size = self._get_crop_size(image_size)
+        results = self._crop_data(results, crop_size, self.allow_negative_crop)
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(crop_size={self.crop_size}, '
+        repr_str += f'crop_type={self.crop_type}, '
+        repr_str += f'allow_negative_crop={self.allow_negative_crop}, '
+        repr_str += f'recompute_bbox={self.recompute_bbox}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class SegRescale(BaseTransform):
+    """Rescale semantic segmentation maps.
+
+    This transform rescale the ``gt_seg_map`` according to ``scale_factor``.
+
+    Required Keys:
+
+    - gt_seg_map
+
+    Modified Keys:
+
+    - gt_seg_map
+
+    Args:
+        scale_factor (float): The scale factor of the final output. Defaults
+            to 1.
+        backend (str): Image rescale backend, choices are 'cv2' and 'pillow'.
+            These two backends generates slightly different results. Defaults
+            to 'cv2'.
+    """
+
+    def __init__(self, scale_factor: float = 1, backend: str = 'cv2') -> None:
+        self.scale_factor = scale_factor
+        self.backend = backend
+
+    def transform(self, results: dict) -> dict:
+        """Transform function to scale the semantic segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with semantic segmentation map scaled.
+        """
+        if self.scale_factor != 1:
+            results['gt_seg_map'] = mmcv.imrescale(
+                results['gt_seg_map'],
+                self.scale_factor,
+                interpolation='nearest',
+                backend=self.backend)
+
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(scale_factor={self.scale_factor}, '
+        repr_str += f'backend={self.backend})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class PhotoMetricDistortion(BaseTransform):
+    """Apply photometric distortion to image sequentially, every transformation
+    is applied with a probability of 0.5. The position of random contrast is in
+    second or second to last.
+
+    1. random brightness
+    2. random contrast (mode 0)
+    3. convert color from BGR to HSV
+    4. random saturation
+    5. random hue
+    6. convert color from HSV to BGR
+    7. random contrast (mode 1)
+    8. randomly swap channels
+
+    Required Keys:
+
+    - img (np.uint8)
+
+    Modified Keys:
+
+    - img (np.float32)
+
+    Args:
+        brightness_delta (int): delta of brightness.
+        contrast_range (sequence): range of contrast.
+        saturation_range (sequence): range of saturation.
+        hue_delta (int): delta of hue.
+    """
+
+    def __init__(self,
+                 brightness_delta: int = 32,
+                 contrast_range: Sequence[Number] = (0.5, 1.5),
+                 saturation_range: Sequence[Number] = (0.5, 1.5),
+                 hue_delta: int = 18) -> None:
+        self.brightness_delta = brightness_delta
+        self.contrast_lower, self.contrast_upper = contrast_range
+        self.saturation_lower, self.saturation_upper = saturation_range
+        self.hue_delta = hue_delta
+
+    @cache_randomness
+    def _random_flags(self) -> Sequence[Number]:
+        mode = random.randint(2)
+        brightness_flag = random.randint(2)
+        contrast_flag = random.randint(2)
+        saturation_flag = random.randint(2)
+        hue_flag = random.randint(2)
+        swap_flag = random.randint(2)
+        delta_value = random.uniform(-self.brightness_delta,
+                                     self.brightness_delta)
+        alpha_value = random.uniform(self.contrast_lower, self.contrast_upper)
+        saturation_value = random.uniform(self.saturation_lower,
+                                          self.saturation_upper)
+        hue_value = random.uniform(-self.hue_delta, self.hue_delta)
+        swap_value = random.permutation(3)
+
+        return (mode, brightness_flag, contrast_flag, saturation_flag,
+                hue_flag, swap_flag, delta_value, alpha_value,
+                saturation_value, hue_value, swap_value)
+
+    def transform(self, results: dict) -> dict:
+        """Transform function to perform photometric distortion on images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images distorted.
+        """
+        assert 'img' in results, '`img` is not found in results'
+        img = results['img']
+        img = img.astype(np.float32)
+
+        (mode, brightness_flag, contrast_flag, saturation_flag, hue_flag,
+         swap_flag, delta_value, alpha_value, saturation_value, hue_value,
+         swap_value) = self._random_flags()
+
+        # random brightness
+        if brightness_flag:
+            img += delta_value
+
+        # mode == 0 --> do random contrast first
+        # mode == 1 --> do random contrast last
+        if mode == 1:
+            if contrast_flag:
+                img *= alpha_value
+
+        # convert color from BGR to HSV
+        img = mmcv.bgr2hsv(img)
+
+        # random saturation
+        if saturation_flag:
+            img[..., 1] *= saturation_value
+            # For image(type=float32), after convert bgr to hsv by opencv,
+            # valid saturation value range is [0, 1]
+            if saturation_value > 1:
+                img[..., 1] = img[..., 1].clip(0, 1)
+
+        # random hue
+        if hue_flag:
+            img[..., 0] += hue_value
+            img[..., 0][img[..., 0] > 360] -= 360
+            img[..., 0][img[..., 0] < 0] += 360
+
+        # convert color from HSV to BGR
+        img = mmcv.hsv2bgr(img)
+
+        # random contrast
+        if mode == 0:
+            if contrast_flag:
+                img *= alpha_value
+
+        # randomly swap channels
+        if swap_flag:
+            img = img[..., swap_value]
+
+        results['img'] = img
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(brightness_delta={self.brightness_delta}, '
+        repr_str += 'contrast_range='
+        repr_str += f'{(self.contrast_lower, self.contrast_upper)}, '
+        repr_str += 'saturation_range='
+        repr_str += f'{(self.saturation_lower, self.saturation_upper)}, '
+        repr_str += f'hue_delta={self.hue_delta})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Expand(BaseTransform):
+    """Random expand the image & bboxes & masks & segmentation map.
+
+    Randomly place the original image on a canvas of ``ratio`` x original image
+    size filled with mean values. The ratio is in the range of ratio_range.
+
+    Required Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes
+    - gt_masks
+    - gt_seg_map
+
+
+    Args:
+        mean (sequence): mean value of dataset.
+        to_rgb (bool): if need to convert the order of mean to align with RGB.
+        ratio_range (sequence)): range of expand ratio.
+        seg_ignore_label (int): label of ignore segmentation map.
+        prob (float): probability of applying this transformation
+    """
+
+    def __init__(self,
+                 mean: Sequence[Number] = (0, 0, 0),
+                 to_rgb: bool = True,
+                 ratio_range: Sequence[Number] = (1, 4),
+                 seg_ignore_label: int = None,
+                 prob: float = 0.5) -> None:
+        self.to_rgb = to_rgb
+        self.ratio_range = ratio_range
+        if to_rgb:
+            self.mean = mean[::-1]
+        else:
+            self.mean = mean
+        self.min_ratio, self.max_ratio = ratio_range
+        self.seg_ignore_label = seg_ignore_label
+        self.prob = prob
+
+    @cache_randomness
+    def _random_prob(self) -> float:
+        return random.uniform(0, 1)
+
+    @cache_randomness
+    def _random_ratio(self) -> float:
+        return random.uniform(self.min_ratio, self.max_ratio)
+
+    @cache_randomness
+    def _random_left_top(self, ratio: float, h: int,
+                         w: int) -> Tuple[int, int]:
+        left = int(random.uniform(0, w * ratio - w))
+        top = int(random.uniform(0, h * ratio - h))
+        return left, top
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to expand images, bounding boxes, masks,
+        segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images, bounding boxes, masks, segmentation
+                map expanded.
+        """
+        if self._random_prob() > self.prob:
+            return results
+        assert 'img' in results, '`img` is not found in results'
+        img = results['img']
+        h, w, c = img.shape
+        ratio = self._random_ratio()
+        # speedup expand when meets large image
+        if np.all(self.mean == self.mean[0]):
+            expand_img = np.empty((int(h * ratio), int(w * ratio), c),
+                                  img.dtype)
+            expand_img.fill(self.mean[0])
+        else:
+            expand_img = np.full((int(h * ratio), int(w * ratio), c),
+                                 self.mean,
+                                 dtype=img.dtype)
+        left, top = self._random_left_top(ratio, h, w)
+        expand_img[top:top + h, left:left + w] = img
+        results['img'] = expand_img
+        results['img_shape'] = expand_img.shape[:2]
+
+        # expand bboxes
+        if results.get('gt_bboxes', None) is not None:
+            results['gt_bboxes'].translate_([left, top])
+
+        # expand masks
+        if results.get('gt_masks', None) is not None:
+            results['gt_masks'] = results['gt_masks'].expand(
+                int(h * ratio), int(w * ratio), top, left)
+
+        # expand segmentation map
+        if results.get('gt_seg_map', None) is not None:
+            gt_seg = results['gt_seg_map']
+            expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
+                                    self.seg_ignore_label,
+                                    dtype=gt_seg.dtype)
+            expand_gt_seg[top:top + h, left:left + w] = gt_seg
+            results['gt_seg_map'] = expand_gt_seg
+
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(mean={self.mean}, to_rgb={self.to_rgb}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class MinIoURandomCrop(BaseTransform):
+    """Random crop the image & bboxes & masks & segmentation map, the cropped
+    patches have minimum IoU requirement with original image & bboxes & masks.
+
+    & segmentation map, the IoU threshold is randomly selected from min_ious.
+
+
+    Required Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - gt_seg_map (np.uint8) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes
+    - gt_bboxes_labels
+    - gt_masks
+    - gt_ignore_flags
+    - gt_seg_map
+
+
+    Args:
+        min_ious (Sequence[float]): minimum IoU threshold for all intersections
+            with bounding boxes.
+        min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w,
+        where a >= min_crop_size).
+        bbox_clip_border (bool, optional): Whether clip the objects outside
+            the border of the image. Defaults to True.
+    """
+
+    def __init__(self,
+                 min_ious: Sequence[float] = (0.1, 0.3, 0.5, 0.7, 0.9),
+                 min_crop_size: float = 0.3,
+                 bbox_clip_border: bool = True) -> None:
+
+        self.min_ious = min_ious
+        self.sample_mode = (1, *min_ious, 0)
+        self.min_crop_size = min_crop_size
+        self.bbox_clip_border = bbox_clip_border
+
+    @cache_randomness
+    def _random_mode(self) -> Number:
+        return random.choice(self.sample_mode)
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to crop images and bounding boxes with minimum
+        IoU constraint.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images and bounding boxes cropped, \
+                'img_shape' key is updated.
+        """
+        assert 'img' in results, '`img` is not found in results'
+        assert 'gt_bboxes' in results, '`gt_bboxes` is not found in results'
+        img = results['img']
+        boxes = results['gt_bboxes']
+        h, w, c = img.shape
+        while True:
+            mode = self._random_mode()
+            self.mode = mode
+            if mode == 1:
+                return results
+
+            min_iou = self.mode
+            for i in range(50):
+                new_w = random.uniform(self.min_crop_size * w, w)
+                new_h = random.uniform(self.min_crop_size * h, h)
+
+                # h / w in [0.5, 2]
+                if new_h / new_w < 0.5 or new_h / new_w > 2:
+                    continue
+
+                left = random.uniform(w - new_w)
+                top = random.uniform(h - new_h)
+
+                patch = np.array(
+                    (int(left), int(top), int(left + new_w), int(top + new_h)))
+                # Line or point crop is not allowed
+                if patch[2] == patch[0] or patch[3] == patch[1]:
+                    continue
+                overlaps = boxes.overlaps(
+                    HorizontalBoxes(patch.reshape(-1, 4).astype(np.float32)),
+                    boxes).numpy().reshape(-1)
+                if len(overlaps) > 0 and overlaps.min() < min_iou:
+                    continue
+
+                # center of boxes should inside the crop img
+                # only adjust boxes and instance masks when the gt is not empty
+                if len(overlaps) > 0:
+                    # adjust boxes
+                    def is_center_of_bboxes_in_patch(boxes, patch):
+                        centers = boxes.centers.numpy()
+                        mask = ((centers[:, 0] > patch[0]) *
+                                (centers[:, 1] > patch[1]) *
+                                (centers[:, 0] < patch[2]) *
+                                (centers[:, 1] < patch[3]))
+                        return mask
+
+                    mask = is_center_of_bboxes_in_patch(boxes, patch)
+                    if not mask.any():
+                        continue
+                    if results.get('gt_bboxes', None) is not None:
+                        boxes = results['gt_bboxes']
+                        mask = is_center_of_bboxes_in_patch(boxes, patch)
+                        boxes = boxes[mask]
+                        boxes.translate_([-patch[0], -patch[1]])
+                        if self.bbox_clip_border:
+                            boxes.clip_(
+                                [patch[3] - patch[1], patch[2] - patch[0]])
+                        results['gt_bboxes'] = boxes
+
+                        # ignore_flags
+                        if results.get('gt_ignore_flags', None) is not None:
+                            results['gt_ignore_flags'] = \
+                                results['gt_ignore_flags'][mask]
+
+                        # labels
+                        if results.get('gt_bboxes_labels', None) is not None:
+                            results['gt_bboxes_labels'] = results[
+                                'gt_bboxes_labels'][mask]
+
+                        # mask fields
+                        if results.get('gt_masks', None) is not None:
+                            results['gt_masks'] = results['gt_masks'][
+                                mask.nonzero()[0]].crop(patch)
+                # adjust the img no matter whether the gt is empty before crop
+                img = img[patch[1]:patch[3], patch[0]:patch[2]]
+                results['img'] = img
+                results['img_shape'] = img.shape[:2]
+
+                # seg fields
+                if results.get('gt_seg_map', None) is not None:
+                    results['gt_seg_map'] = results['gt_seg_map'][
+                        patch[1]:patch[3], patch[0]:patch[2]]
+                return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(min_ious={self.min_ious}, '
+        repr_str += f'min_crop_size={self.min_crop_size}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Corrupt(BaseTransform):
+    """Corruption augmentation.
+
+    Corruption transforms implemented based on
+    `imagecorruptions <https://github.com/bethgelab/imagecorruptions>`_.
+
+    Required Keys:
+
+    - img (np.uint8)
+
+
+    Modified Keys:
+
+    - img (np.uint8)
+
+
+    Args:
+        corruption (str): Corruption name.
+        severity (int): The severity of corruption. Defaults to 1.
+    """
+
+    def __init__(self, corruption: str, severity: int = 1) -> None:
+        self.corruption = corruption
+        self.severity = severity
+
+    def transform(self, results: dict) -> dict:
+        """Call function to corrupt image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images corrupted.
+        """
+
+        if corrupt is None:
+            raise RuntimeError('imagecorruptions is not installed')
+        results['img'] = corrupt(
+            results['img'].astype(np.uint8),
+            corruption_name=self.corruption,
+            severity=self.severity)
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(corruption={self.corruption}, '
+        repr_str += f'severity={self.severity})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+@avoid_cache_randomness
+class Albu(BaseTransform):
+    """Albumentation augmentation.
+
+    Adds custom transformations from Albumentations library.
+    Please, visit `https://albumentations.readthedocs.io`
+    to get more information.
+
+    Required Keys:
+
+    - img (np.uint8)
+    - gt_bboxes (HorizontalBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+
+    Modified Keys:
+
+    - img (np.uint8)
+    - gt_bboxes (HorizontalBoxes[torch.float32]) (optional)
+    - gt_masks (BitmapMasks | PolygonMasks) (optional)
+    - img_shape (tuple)
+
+    An example of ``transforms`` is as followed:
+
+    .. code-block::
+
+        [
+            dict(
+                type='ShiftScaleRotate',
+                shift_limit=0.0625,
+                scale_limit=0.0,
+                rotate_limit=0,
+                interpolation=1,
+                p=0.5),
+            dict(
+                type='RandomBrightnessContrast',
+                brightness_limit=[0.1, 0.3],
+                contrast_limit=[0.1, 0.3],
+                p=0.2),
+            dict(type='ChannelShuffle', p=0.1),
+            dict(
+                type='OneOf',
+                transforms=[
+                    dict(type='Blur', blur_limit=3, p=1.0),
+                    dict(type='MedianBlur', blur_limit=3, p=1.0)
+                ],
+                p=0.1),
+        ]
+
+    Args:
+        transforms (list[dict]): A list of albu transformations
+        bbox_params (dict, optional): Bbox_params for albumentation `Compose`
+        keymap (dict, optional): Contains
+            {'input key':'albumentation-style key'}
+        skip_img_without_anno (bool): Whether to skip the image if no ann left
+            after aug. Defaults to False.
+    """
+
+    def __init__(self,
+                 transforms: List[dict],
+                 bbox_params: Optional[dict] = None,
+                 keymap: Optional[dict] = None,
+                 skip_img_without_anno: bool = False) -> None:
+        if Compose is None:
+            raise RuntimeError('albumentations is not installed')
+
+        # Args will be modified later, copying it will be safer
+        transforms = copy.deepcopy(transforms)
+        if bbox_params is not None:
+            bbox_params = copy.deepcopy(bbox_params)
+        if keymap is not None:
+            keymap = copy.deepcopy(keymap)
+        self.transforms = transforms
+        self.filter_lost_elements = False
+        self.skip_img_without_anno = skip_img_without_anno
+
+        # A simple workaround to remove masks without boxes
+        if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
+                and 'filter_lost_elements' in bbox_params):
+            self.filter_lost_elements = True
+            self.origin_label_fields = bbox_params['label_fields']
+            bbox_params['label_fields'] = ['idx_mapper']
+            del bbox_params['filter_lost_elements']
+
+        self.bbox_params = (
+            self.albu_builder(bbox_params) if bbox_params else None)
+        self.aug = Compose([self.albu_builder(t) for t in self.transforms],
+                           bbox_params=self.bbox_params)
+
+        if not keymap:
+            self.keymap_to_albu = {
+                'img': 'image',
+                'gt_masks': 'masks',
+                'gt_bboxes': 'bboxes'
+            }
+        else:
+            self.keymap_to_albu = keymap
+        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
+
+    def albu_builder(self, cfg: dict) -> albumentations:
+        """Import a module from albumentations.
+
+        It inherits some of :func:`build_from_cfg` logic.
+
+        Args:
+            cfg (dict): Config dict. It should at least contain the key "type".
+
+        Returns:
+            obj: The constructed object.
+        """
+
+        assert isinstance(cfg, dict) and 'type' in cfg
+        args = cfg.copy()
+        obj_type = args.pop('type')
+        if is_str(obj_type):
+            if albumentations is None:
+                raise RuntimeError('albumentations is not installed')
+            obj_cls = getattr(albumentations, obj_type)
+        elif inspect.isclass(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        if 'transforms' in args:
+            args['transforms'] = [
+                self.albu_builder(transform)
+                for transform in args['transforms']
+            ]
+
+        return obj_cls(**args)
+
+    @staticmethod
+    def mapper(d: dict, keymap: dict) -> dict:
+        """Dictionary mapper. Renames keys according to keymap provided.
+
+        Args:
+            d (dict): old dict
+            keymap (dict): {'old_key':'new_key'}
+        Returns:
+            dict: new dict.
+        """
+        updated_dict = {}
+        for k, v in zip(d.keys(), d.values()):
+            new_k = keymap.get(k, k)
+            updated_dict[new_k] = d[k]
+        return updated_dict
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> Union[dict, None]:
+        """Transform function of Albu."""
+        # TODO: gt_seg_map is not currently supported
+        # dict to albumentations format
+        results = self.mapper(results, self.keymap_to_albu)
+        results, ori_masks = self._preprocess_results(results)
+        results = self.aug(**results)
+        results = self._postprocess_results(results, ori_masks)
+        if results is None:
+            return None
+        # back to the original format
+        results = self.mapper(results, self.keymap_back)
+        results['img_shape'] = results['img'].shape[:2]
+        return results
+
+    def _preprocess_results(self, results: dict) -> tuple:
+        """Pre-processing results to facilitate the use of Albu."""
+        if 'bboxes' in results:
+            # to list of boxes
+            if not isinstance(results['bboxes'], HorizontalBoxes):
+                raise NotImplementedError(
+                    'Albu only supports horizontal boxes now')
+            bboxes = results['bboxes'].numpy()
+            results['bboxes'] = [x for x in bboxes]
+            # add pseudo-field for filtration
+            if self.filter_lost_elements:
+                results['idx_mapper'] = np.arange(len(results['bboxes']))
+
+        # TODO: Support mask structure in albu
+        ori_masks = None
+        if 'masks' in results:
+            if isinstance(results['masks'], PolygonMasks):
+                raise NotImplementedError(
+                    'Albu only supports BitMap masks now')
+            ori_masks = results['masks']
+            if albumentations.__version__ < '0.5':
+                results['masks'] = results['masks'].masks
+            else:
+                results['masks'] = [mask for mask in results['masks'].masks]
+
+        return results, ori_masks
+
+    def _postprocess_results(
+            self,
+            results: dict,
+            ori_masks: Optional[Union[BitmapMasks,
+                                      PolygonMasks]] = None) -> dict:
+        """Post-processing Albu output."""
+        # albumentations may return np.array or list on different versions
+        if 'gt_bboxes_labels' in results and isinstance(
+                results['gt_bboxes_labels'], list):
+            results['gt_bboxes_labels'] = np.array(
+                results['gt_bboxes_labels'], dtype=np.int64)
+        if 'gt_ignore_flags' in results and isinstance(
+                results['gt_ignore_flags'], list):
+            results['gt_ignore_flags'] = np.array(
+                results['gt_ignore_flags'], dtype=bool)
+
+        if 'bboxes' in results:
+            if isinstance(results['bboxes'], list):
+                results['bboxes'] = np.array(
+                    results['bboxes'], dtype=np.float32)
+            results['bboxes'] = results['bboxes'].reshape(-1, 4)
+            results['bboxes'] = HorizontalBoxes(results['bboxes'])
+
+            # filter label_fields
+            if self.filter_lost_elements:
+
+                for label in self.origin_label_fields:
+                    results[label] = np.array(
+                        [results[label][i] for i in results['idx_mapper']])
+                if 'masks' in results:
+                    assert ori_masks is not None
+                    results['masks'] = np.array(
+                        [results['masks'][i] for i in results['idx_mapper']])
+                    results['masks'] = ori_masks.__class__(
+                        results['masks'],
+                        results['masks'][0].shape[0],
+                        results['masks'][0].shape[1],
+                    )
+                if (not len(results['idx_mapper'])
+                        and self.skip_img_without_anno):
+                    return None
+            elif 'masks' in results:
+                results['masks'] = ori_masks.__class__(results['masks'],
+                                                       ori_masks.height,
+                                                       ori_masks.width)
+
+        return results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+@avoid_cache_randomness
+class RandomCenterCropPad(BaseTransform):
+    """Random center crop and random around padding for CornerNet.
+
+    This operation generates randomly cropped image from the original image and
+    pads it simultaneously. Different from :class:`RandomCrop`, the output
+    shape may not equal to ``crop_size`` strictly. We choose a random value
+    from ``ratios`` and the output shape could be larger or smaller than
+    ``crop_size``. The padding operation is also different from :class:`Pad`,
+    here we use around padding instead of right-bottom padding.
+
+    The relation between output image (padding image) and original image:
+
+    .. code:: text
+
+                        output image
+
+               +----------------------------+
+               |          padded area       |
+        +------|----------------------------|----------+
+        |      |         cropped area       |          |
+        |      |         +---------------+  |          |
+        |      |         |    .   center |  |          | original image
+        |      |         |        range  |  |          |
+        |      |         +---------------+  |          |
+        +------|----------------------------|----------+
+               |          padded area       |
+               +----------------------------+
+
+    There are 5 main areas in the figure:
+
+    - output image: output image of this operation, also called padding
+      image in following instruction.
+    - original image: input image of this operation.
+    - padded area: non-intersect area of output image and original image.
+    - cropped area: the overlap of output image and original image.
+    - center range: a smaller area where random center chosen from.
+      center range is computed by ``border`` and original image's shape
+      to avoid our random center is too close to original image's border.
+
+    Also this operation act differently in train and test mode, the summary
+    pipeline is listed below.
+
+    Train pipeline:
+
+    1. Choose a ``random_ratio`` from ``ratios``, the shape of padding image
+       will be ``random_ratio * crop_size``.
+    2. Choose a ``random_center`` in center range.
+    3. Generate padding image with center matches the ``random_center``.
+    4. Initialize the padding image with pixel value equals to ``mean``.
+    5. Copy the cropped area to padding image.
+    6. Refine annotations.
+
+    Test pipeline:
+
+    1. Compute output shape according to ``test_pad_mode``.
+    2. Generate padding image with center matches the original image
+       center.
+    3. Initialize the padding image with pixel value equals to ``mean``.
+    4. Copy the ``cropped area`` to padding image.
+
+    Required Keys:
+
+    - img (np.float32)
+    - img_shape (tuple)
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+
+    Modified Keys:
+
+    - img (np.float32)
+    - img_shape (tuple)
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+
+    Args:
+        crop_size (tuple, optional): expected size after crop, final size will
+            computed according to ratio. Requires  (width, height)
+            in train mode, and None in test mode.
+        ratios (tuple, optional): random select a ratio from tuple and crop
+            image to (crop_size[0] * ratio) * (crop_size[1] * ratio).
+            Only available in train mode. Defaults to (0.9, 1.0, 1.1).
+        border (int, optional): max distance from center select area to image
+            border. Only available in train mode. Defaults to 128.
+        mean (sequence, optional): Mean values of 3 channels.
+        std (sequence, optional): Std values of 3 channels.
+        to_rgb (bool, optional): Whether to convert the image from BGR to RGB.
+        test_mode (bool): whether involve random variables in transform.
+            In train mode, crop_size is fixed, center coords and ratio is
+            random selected from predefined lists. In test mode, crop_size
+            is image's original shape, center coords and ratio is fixed.
+            Defaults to False.
+        test_pad_mode (tuple, optional): padding method and padding shape
+            value, only available in test mode. Default is using
+            'logical_or' with 127 as padding shape value.
+
+            - 'logical_or': final_shape = input_shape | padding_shape_value
+            - 'size_divisor': final_shape = int(
+              ceil(input_shape / padding_shape_value) * padding_shape_value)
+
+            Defaults to ('logical_or', 127).
+        test_pad_add_pix (int): Extra padding pixel in test mode.
+            Defaults to 0.
+        bbox_clip_border (bool): Whether clip the objects outside
+            the border of the image. Defaults to True.
+    """
+
+    def __init__(self,
+                 crop_size: Optional[tuple] = None,
+                 ratios: Optional[tuple] = (0.9, 1.0, 1.1),
+                 border: Optional[int] = 128,
+                 mean: Optional[Sequence] = None,
+                 std: Optional[Sequence] = None,
+                 to_rgb: Optional[bool] = None,
+                 test_mode: bool = False,
+                 test_pad_mode: Optional[tuple] = ('logical_or', 127),
+                 test_pad_add_pix: int = 0,
+                 bbox_clip_border: bool = True) -> None:
+        if test_mode:
+            assert crop_size is None, 'crop_size must be None in test mode'
+            assert ratios is None, 'ratios must be None in test mode'
+            assert border is None, 'border must be None in test mode'
+            assert isinstance(test_pad_mode, (list, tuple))
+            assert test_pad_mode[0] in ['logical_or', 'size_divisor']
+        else:
+            assert isinstance(crop_size, (list, tuple))
+            assert crop_size[0] > 0 and crop_size[1] > 0, (
+                'crop_size must > 0 in train mode')
+            assert isinstance(ratios, (list, tuple))
+            assert test_pad_mode is None, (
+                'test_pad_mode must be None in train mode')
+
+        self.crop_size = crop_size
+        self.ratios = ratios
+        self.border = border
+        # We do not set default value to mean, std and to_rgb because these
+        # hyper-parameters are easy to forget but could affect the performance.
+        # Please use the same setting as Normalize for performance assurance.
+        assert mean is not None and std is not None and to_rgb is not None
+        self.to_rgb = to_rgb
+        self.input_mean = mean
+        self.input_std = std
+        if to_rgb:
+            self.mean = mean[::-1]
+            self.std = std[::-1]
+        else:
+            self.mean = mean
+            self.std = std
+        self.test_mode = test_mode
+        self.test_pad_mode = test_pad_mode
+        self.test_pad_add_pix = test_pad_add_pix
+        self.bbox_clip_border = bbox_clip_border
+
+    def _get_border(self, border, size):
+        """Get final border for the target size.
+
+        This function generates a ``final_border`` according to image's shape.
+        The area between ``final_border`` and ``size - final_border`` is the
+        ``center range``. We randomly choose center from the ``center range``
+        to avoid our random center is too close to original image's border.
+        Also ``center range`` should be larger than 0.
+
+        Args:
+            border (int): The initial border, default is 128.
+            size (int): The width or height of original image.
+        Returns:
+            int: The final border.
+        """
+        k = 2 * border / size
+        i = pow(2, np.ceil(np.log2(np.ceil(k))) + (k == int(k)))
+        return border // i
+
+    def _filter_boxes(self, patch, boxes):
+        """Check whether the center of each box is in the patch.
+
+        Args:
+            patch (list[int]): The cropped area, [left, top, right, bottom].
+            boxes (numpy array, (N x 4)): Ground truth boxes.
+
+        Returns:
+            mask (numpy array, (N,)): Each box is inside or outside the patch.
+        """
+        center = boxes.centers.numpy()
+        mask = (center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * (
+            center[:, 0] < patch[2]) * (
+                center[:, 1] < patch[3])
+        return mask
+
+    def _crop_image_and_paste(self, image, center, size):
+        """Crop image with a given center and size, then paste the cropped
+        image to a blank image with two centers align.
+
+        This function is equivalent to generating a blank image with ``size``
+        as its shape. Then cover it on the original image with two centers (
+        the center of blank image and the random center of original image)
+        aligned. The overlap area is paste from the original image and the
+        outside area is filled with ``mean pixel``.
+
+        Args:
+            image (np array, H x W x C): Original image.
+            center (list[int]): Target crop center coord.
+            size (list[int]): Target crop size. [target_h, target_w]
+
+        Returns:
+            cropped_img (np array, target_h x target_w x C): Cropped image.
+            border (np array, 4): The distance of four border of
+                ``cropped_img`` to the original image area, [top, bottom,
+                left, right]
+            patch (list[int]): The cropped area, [left, top, right, bottom].
+        """
+        center_y, center_x = center
+        target_h, target_w = size
+        img_h, img_w, img_c = image.shape
+
+        x0 = max(0, center_x - target_w // 2)
+        x1 = min(center_x + target_w // 2, img_w)
+        y0 = max(0, center_y - target_h // 2)
+        y1 = min(center_y + target_h // 2, img_h)
+        patch = np.array((int(x0), int(y0), int(x1), int(y1)))
+
+        left, right = center_x - x0, x1 - center_x
+        top, bottom = center_y - y0, y1 - center_y
+
+        cropped_center_y, cropped_center_x = target_h // 2, target_w // 2
+        cropped_img = np.zeros((target_h, target_w, img_c), dtype=image.dtype)
+        for i in range(img_c):
+            cropped_img[:, :, i] += self.mean[i]
+        y_slice = slice(cropped_center_y - top, cropped_center_y + bottom)
+        x_slice = slice(cropped_center_x - left, cropped_center_x + right)
+        cropped_img[y_slice, x_slice, :] = image[y0:y1, x0:x1, :]
+
+        border = np.array([
+            cropped_center_y - top, cropped_center_y + bottom,
+            cropped_center_x - left, cropped_center_x + right
+        ],
+                          dtype=np.float32)
+
+        return cropped_img, border, patch
+
+    def _train_aug(self, results):
+        """Random crop and around padding the original image.
+
+        Args:
+            results (dict): Image infomations in the augment pipeline.
+
+        Returns:
+            results (dict): The updated dict.
+        """
+        img = results['img']
+        h, w, c = img.shape
+        gt_bboxes = results['gt_bboxes']
+        while True:
+            scale = random.choice(self.ratios)
+            new_h = int(self.crop_size[1] * scale)
+            new_w = int(self.crop_size[0] * scale)
+            h_border = self._get_border(self.border, h)
+            w_border = self._get_border(self.border, w)
+
+            for i in range(50):
+                center_x = random.randint(low=w_border, high=w - w_border)
+                center_y = random.randint(low=h_border, high=h - h_border)
+
+                cropped_img, border, patch = self._crop_image_and_paste(
+                    img, [center_y, center_x], [new_h, new_w])
+
+                if len(gt_bboxes) == 0:
+                    results['img'] = cropped_img
+                    results['img_shape'] = cropped_img.shape[:2]
+                    return results
+
+                # if image do not have valid bbox, any crop patch is valid.
+                mask = self._filter_boxes(patch, gt_bboxes)
+                if not mask.any():
+                    continue
+
+                results['img'] = cropped_img
+                results['img_shape'] = cropped_img.shape[:2]
+
+                x0, y0, x1, y1 = patch
+
+                left_w, top_h = center_x - x0, center_y - y0
+                cropped_center_x, cropped_center_y = new_w // 2, new_h // 2
+
+                # crop bboxes accordingly and clip to the image boundary
+                gt_bboxes = gt_bboxes[mask]
+                gt_bboxes.translate_([
+                    cropped_center_x - left_w - x0,
+                    cropped_center_y - top_h - y0
+                ])
+                if self.bbox_clip_border:
+                    gt_bboxes.clip_([new_h, new_w])
+                keep = gt_bboxes.is_inside([new_h, new_w]).numpy()
+                gt_bboxes = gt_bboxes[keep]
+
+                results['gt_bboxes'] = gt_bboxes
+
+                # ignore_flags
+                if results.get('gt_ignore_flags', None) is not None:
+                    gt_ignore_flags = results['gt_ignore_flags'][mask]
+                    results['gt_ignore_flags'] = \
+                        gt_ignore_flags[keep]
+
+                # labels
+                if results.get('gt_bboxes_labels', None) is not None:
+                    gt_labels = results['gt_bboxes_labels'][mask]
+                    results['gt_bboxes_labels'] = gt_labels[keep]
+
+                if 'gt_masks' in results or 'gt_seg_map' in results:
+                    raise NotImplementedError(
+                        'RandomCenterCropPad only supports bbox.')
+
+                return results
+
+    def _test_aug(self, results):
+        """Around padding the original image without cropping.
+
+        The padding mode and value are from ``test_pad_mode``.
+
+        Args:
+            results (dict): Image infomations in the augment pipeline.
+
+        Returns:
+            results (dict): The updated dict.
+        """
+        img = results['img']
+        h, w, c = img.shape
+        if self.test_pad_mode[0] in ['logical_or']:
+            # self.test_pad_add_pix is only used for centernet
+            target_h = (h | self.test_pad_mode[1]) + self.test_pad_add_pix
+            target_w = (w | self.test_pad_mode[1]) + self.test_pad_add_pix
+        elif self.test_pad_mode[0] in ['size_divisor']:
+            divisor = self.test_pad_mode[1]
+            target_h = int(np.ceil(h / divisor)) * divisor
+            target_w = int(np.ceil(w / divisor)) * divisor
+        else:
+            raise NotImplementedError(
+                'RandomCenterCropPad only support two testing pad mode:'
+                'logical-or and size_divisor.')
+
+        cropped_img, border, _ = self._crop_image_and_paste(
+            img, [h // 2, w // 2], [target_h, target_w])
+        results['img'] = cropped_img
+        results['img_shape'] = cropped_img.shape[:2]
+        results['border'] = border
+        return results
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        img = results['img']
+        assert img.dtype == np.float32, (
+            'RandomCenterCropPad needs the input image of dtype np.float32,'
+            ' please set "to_float32=True" in "LoadImageFromFile" pipeline')
+        h, w, c = img.shape
+        assert c == len(self.mean)
+        if self.test_mode:
+            return self._test_aug(results)
+        else:
+            return self._train_aug(results)
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(crop_size={self.crop_size}, '
+        repr_str += f'ratios={self.ratios}, '
+        repr_str += f'border={self.border}, '
+        repr_str += f'mean={self.input_mean}, '
+        repr_str += f'std={self.input_std}, '
+        repr_str += f'to_rgb={self.to_rgb}, '
+        repr_str += f'test_mode={self.test_mode}, '
+        repr_str += f'test_pad_mode={self.test_pad_mode}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class CutOut(BaseTransform):
+    """CutOut operation.
+
+    Randomly drop some regions of image used in
+    `Cutout <https://arxiv.org/abs/1708.04552>`_.
+
+    Required Keys:
+
+    - img
+
+    Modified Keys:
+
+    - img
+
+    Args:
+        n_holes (int or tuple[int, int]): Number of regions to be dropped.
+            If it is given as a list, number of holes will be randomly
+            selected from the closed interval [``n_holes[0]``, ``n_holes[1]``].
+        cutout_shape (tuple[int, int] or list[tuple[int, int]], optional):
+            The candidate shape of dropped regions. It can be
+            ``tuple[int, int]`` to use a fixed cutout shape, or
+            ``list[tuple[int, int]]`` to randomly choose shape
+            from the list. Defaults to None.
+        cutout_ratio (tuple[float, float] or list[tuple[float, float]],
+            optional): The candidate ratio of dropped regions. It can be
+            ``tuple[float, float]`` to use a fixed ratio or
+            ``list[tuple[float, float]]`` to randomly choose ratio
+            from the list. Please note that ``cutout_shape`` and
+            ``cutout_ratio`` cannot be both given at the same time.
+            Defaults to None.
+        fill_in (tuple[float, float, float] or tuple[int, int, int]): The value
+            of pixel to fill in the dropped regions. Defaults to (0, 0, 0).
+    """
+
+    def __init__(
+        self,
+        n_holes: Union[int, Tuple[int, int]],
+        cutout_shape: Optional[Union[Tuple[int, int],
+                                     List[Tuple[int, int]]]] = None,
+        cutout_ratio: Optional[Union[Tuple[float, float],
+                                     List[Tuple[float, float]]]] = None,
+        fill_in: Union[Tuple[float, float, float], Tuple[int, int,
+                                                         int]] = (0, 0, 0)
+    ) -> None:
+
+        assert (cutout_shape is None) ^ (cutout_ratio is None), \
+            'Either cutout_shape or cutout_ratio should be specified.'
+        assert (isinstance(cutout_shape, (list, tuple))
+                or isinstance(cutout_ratio, (list, tuple)))
+        if isinstance(n_holes, tuple):
+            assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
+        else:
+            n_holes = (n_holes, n_holes)
+        self.n_holes = n_holes
+        self.fill_in = fill_in
+        self.with_ratio = cutout_ratio is not None
+        self.candidates = cutout_ratio if self.with_ratio else cutout_shape
+        if not isinstance(self.candidates, list):
+            self.candidates = [self.candidates]
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Call function to drop some regions of image."""
+        h, w, c = results['img'].shape
+        n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
+        for _ in range(n_holes):
+            x1 = np.random.randint(0, w)
+            y1 = np.random.randint(0, h)
+            index = np.random.randint(0, len(self.candidates))
+            if not self.with_ratio:
+                cutout_w, cutout_h = self.candidates[index]
+            else:
+                cutout_w = int(self.candidates[index][0] * w)
+                cutout_h = int(self.candidates[index][1] * h)
+
+            x2 = np.clip(x1 + cutout_w, 0, w)
+            y2 = np.clip(y1 + cutout_h, 0, h)
+            results['img'][y1:y2, x1:x2, :] = self.fill_in
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(n_holes={self.n_holes}, '
+        repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
+                     else f'cutout_shape={self.candidates}, ')
+        repr_str += f'fill_in={self.fill_in})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class Mosaic(BaseTransform):
+    """Mosaic augmentation.
+
+    Given 4 images, mosaic transform combines them into
+    one output image. The output image is composed of the parts from each sub-
+    image.
+
+    .. code:: text
+
+                        mosaic transform
+                           center_x
+                +------------------------------+
+                |       pad        |  pad      |
+                |      +-----------+           |
+                |      |           |           |
+                |      |  image1   |--------+  |
+                |      |           |        |  |
+                |      |           | image2 |  |
+     center_y   |----+-------------+-----------|
+                |    |   cropped   |           |
+                |pad |   image3    |  image4   |
+                |    |             |           |
+                +----|-------------+-----------+
+                     |             |
+                     +-------------+
+
+     The mosaic transform steps are as follows:
+
+         1. Choose the mosaic center as the intersections of 4 images
+         2. Get the left top image according to the index, and randomly
+            sample another 3 images from the custom dataset.
+         3. Sub image will be cropped if image is larger than mosaic patch
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - mix_results (List[dict])
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+
+    Args:
+        img_scale (Sequence[int]): Image size before mosaic pipeline of single
+            image. The shape order should be (width, height).
+            Defaults to (640, 640).
+        center_ratio_range (Sequence[float]): Center ratio range of mosaic
+            output. Defaults to (0.5, 1.5).
+        bbox_clip_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.
+        pad_val (int): Pad value. Defaults to 114.
+        prob (float): Probability of applying this transformation.
+            Defaults to 1.0.
+    """
+
+    def __init__(self,
+                 img_scale: Tuple[int, int] = (640, 640),
+                 center_ratio_range: Tuple[float, float] = (0.5, 1.5),
+                 bbox_clip_border: bool = True,
+                 pad_val: float = 114.0,
+                 prob: float = 1.0) -> None:
+        assert isinstance(img_scale, tuple)
+        assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \
+                                 f'got {prob}.'
+
+        log_img_scale(img_scale, skip_square=True, shape_order='wh')
+        self.img_scale = img_scale
+        self.center_ratio_range = center_ratio_range
+        self.bbox_clip_border = bbox_clip_border
+        self.pad_val = pad_val
+        self.prob = prob
+
+    @cache_randomness
+    def get_indexes(self, dataset: BaseDataset) -> int:
+        """Call function to collect indexes.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+
+        Returns:
+            list: indexes.
+        """
+
+        indexes = [random.randint(0, len(dataset)) for _ in range(3)]
+        return indexes
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Mosaic transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        if random.uniform(0, 1) > self.prob:
+            return results
+
+        assert 'mix_results' in results
+        mosaic_bboxes = []
+        mosaic_bboxes_labels = []
+        mosaic_ignore_flags = []
+        if len(results['img'].shape) == 3:
+            mosaic_img = np.full(
+                (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2), 3),
+                self.pad_val,
+                dtype=results['img'].dtype)
+        else:
+            mosaic_img = np.full(
+                (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2)),
+                self.pad_val,
+                dtype=results['img'].dtype)
+
+        # mosaic center x, y
+        center_x = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[0])
+        center_y = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[1])
+        center_position = (center_x, center_y)
+
+        loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        for i, loc in enumerate(loc_strs):
+            if loc == 'top_left':
+                results_patch = copy.deepcopy(results)
+            else:
+                results_patch = copy.deepcopy(results['mix_results'][i - 1])
+
+            img_i = results_patch['img']
+            h_i, w_i = img_i.shape[:2]
+            # keep_ratio resize
+            scale_ratio_i = min(self.img_scale[1] / h_i,
+                                self.img_scale[0] / w_i)
+            img_i = mmcv.imresize(
+                img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
+
+            # compute the combine parameters
+            paste_coord, crop_coord = self._mosaic_combine(
+                loc, center_position, img_i.shape[:2][::-1])
+            x1_p, y1_p, x2_p, y2_p = paste_coord
+            x1_c, y1_c, x2_c, y2_c = crop_coord
+
+            # crop and paste image
+            mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
+
+            # adjust coordinate
+            gt_bboxes_i = results_patch['gt_bboxes']
+            gt_bboxes_labels_i = results_patch['gt_bboxes_labels']
+            gt_ignore_flags_i = results_patch['gt_ignore_flags']
+
+            padw = x1_p - x1_c
+            padh = y1_p - y1_c
+            gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])
+            gt_bboxes_i.translate_([padw, padh])
+            mosaic_bboxes.append(gt_bboxes_i)
+            mosaic_bboxes_labels.append(gt_bboxes_labels_i)
+            mosaic_ignore_flags.append(gt_ignore_flags_i)
+
+        mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)
+        mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)
+        mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)
+
+        if self.bbox_clip_border:
+            mosaic_bboxes.clip_([2 * self.img_scale[1], 2 * self.img_scale[0]])
+        # remove outside bboxes
+        inside_inds = mosaic_bboxes.is_inside(
+            [2 * self.img_scale[1], 2 * self.img_scale[0]]).numpy()
+        mosaic_bboxes = mosaic_bboxes[inside_inds]
+        mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]
+        mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]
+
+        results['img'] = mosaic_img
+        results['img_shape'] = mosaic_img.shape[:2]
+        results['gt_bboxes'] = mosaic_bboxes
+        results['gt_bboxes_labels'] = mosaic_bboxes_labels
+        results['gt_ignore_flags'] = mosaic_ignore_flags
+        return results
+
+    def _mosaic_combine(
+            self, loc: str, center_position_xy: Sequence[float],
+            img_shape_wh: Sequence[int]) -> Tuple[Tuple[int], Tuple[int]]:
+        """Calculate global coordinate of mosaic image and local coordinate of
+        cropped sub-image.
+
+        Args:
+            loc (str): Index for the sub-image, loc in ('top_left',
+              'top_right', 'bottom_left', 'bottom_right').
+            center_position_xy (Sequence[float]): Mixing center for 4 images,
+                (x, y).
+            img_shape_wh (Sequence[int]): Width and height of sub-image
+
+        Returns:
+            tuple[tuple[float]]: Corresponding coordinate of pasting and
+                cropping
+                - paste_coord (tuple): paste corner coordinate in mosaic image.
+                - crop_coord (tuple): crop corner coordinate in mosaic image.
+        """
+        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        if loc == 'top_left':
+            # index0 to top left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             center_position_xy[0], \
+                             center_position_xy[1]
+            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
+                y2 - y1), img_shape_wh[0], img_shape_wh[1]
+
+        elif loc == 'top_right':
+            # index1 to top right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[0] * 2), \
+                             center_position_xy[1]
+            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
+                img_shape_wh[0], x2 - x1), img_shape_wh[1]
+
+        elif loc == 'bottom_left':
+            # index2 to bottom left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             center_position_xy[1], \
+                             center_position_xy[0], \
+                             min(self.img_scale[1] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
+                y2 - y1, img_shape_wh[1])
+
+        else:
+            # index3 to bottom right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             center_position_xy[1], \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[0] * 2), \
+                             min(self.img_scale[1] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = 0, 0, min(img_shape_wh[0],
+                                   x2 - x1), min(y2 - y1, img_shape_wh[1])
+
+        paste_coord = x1, y1, x2, y2
+        return paste_coord, crop_coord
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(img_scale={self.img_scale}, '
+        repr_str += f'center_ratio_range={self.center_ratio_range}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class MixUp(BaseTransform):
+    """MixUp data augmentation.
+
+    .. code:: text
+
+                         mixup transform
+                +------------------------------+
+                | mixup image   |              |
+                |      +--------|--------+     |
+                |      |        |        |     |
+                |---------------+        |     |
+                |      |                 |     |
+                |      |      image      |     |
+                |      |                 |     |
+                |      |                 |     |
+                |      |-----------------+     |
+                |             pad              |
+                +------------------------------+
+
+     The mixup transform steps are as follows:
+
+        1. Another random image is picked by dataset and embedded in
+           the top left patch(after padding and resizing)
+        2. The target of mixup transform is the weighted average of mixup
+           image and origin image.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - mix_results (List[dict])
+
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+
+
+    Args:
+        img_scale (Sequence[int]): Image output size after mixup pipeline.
+            The shape order should be (width, height). Defaults to (640, 640).
+        ratio_range (Sequence[float]): Scale ratio of mixup image.
+            Defaults to (0.5, 1.5).
+        flip_ratio (float): Horizontal flip ratio of mixup image.
+            Defaults to 0.5.
+        pad_val (int): Pad value. Defaults to 114.
+        max_iters (int): The maximum number of iterations. If the number of
+            iterations is greater than `max_iters`, but gt_bbox is still
+            empty, then the iteration is terminated. Defaults to 15.
+        bbox_clip_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.
+    """
+
+    def __init__(self,
+                 img_scale: Tuple[int, int] = (640, 640),
+                 ratio_range: Tuple[float, float] = (0.5, 1.5),
+                 flip_ratio: float = 0.5,
+                 pad_val: float = 114.0,
+                 max_iters: int = 15,
+                 bbox_clip_border: bool = True) -> None:
+        assert isinstance(img_scale, tuple)
+        log_img_scale(img_scale, skip_square=True, shape_order='wh')
+        self.dynamic_scale = img_scale
+        self.ratio_range = ratio_range
+        self.flip_ratio = flip_ratio
+        self.pad_val = pad_val
+        self.max_iters = max_iters
+        self.bbox_clip_border = bbox_clip_border
+
+    @cache_randomness
+    def get_indexes(self, dataset: BaseDataset) -> int:
+        """Call function to collect indexes.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+
+        Returns:
+            list: indexes.
+        """
+
+        for i in range(self.max_iters):
+            index = random.randint(0, len(dataset))
+            gt_bboxes_i = dataset[index]['gt_bboxes']
+            if len(gt_bboxes_i) != 0:
+                break
+
+        return index
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """MixUp transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+
+        assert 'mix_results' in results
+        assert len(
+            results['mix_results']) == 1, 'MixUp only support 2 images now !'
+
+        if results['mix_results'][0]['gt_bboxes'].shape[0] == 0:
+            # empty bbox
+            return results
+
+        retrieve_results = results['mix_results'][0]
+        retrieve_img = retrieve_results['img']
+
+        jit_factor = random.uniform(*self.ratio_range)
+        is_flip = random.uniform(0, 1) > self.flip_ratio
+
+        if len(retrieve_img.shape) == 3:
+            out_img = np.ones(
+                (self.dynamic_scale[1], self.dynamic_scale[0], 3),
+                dtype=retrieve_img.dtype) * self.pad_val
+        else:
+            out_img = np.ones(
+                self.dynamic_scale[::-1],
+                dtype=retrieve_img.dtype) * self.pad_val
+
+        # 1. keep_ratio resize
+        scale_ratio = min(self.dynamic_scale[1] / retrieve_img.shape[0],
+                          self.dynamic_scale[0] / retrieve_img.shape[1])
+        retrieve_img = mmcv.imresize(
+            retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),
+                           int(retrieve_img.shape[0] * scale_ratio)))
+
+        # 2. paste
+        out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img
+
+        # 3. scale jit
+        scale_ratio *= jit_factor
+        out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),
+                                          int(out_img.shape[0] * jit_factor)))
+
+        # 4. flip
+        if is_flip:
+            out_img = out_img[:, ::-1, :]
+
+        # 5. random crop
+        ori_img = results['img']
+        origin_h, origin_w = out_img.shape[:2]
+        target_h, target_w = ori_img.shape[:2]
+        padded_img = np.ones((max(origin_h, target_h), max(
+            origin_w, target_w), 3)) * self.pad_val
+        padded_img = padded_img.astype(np.uint8)
+        padded_img[:origin_h, :origin_w] = out_img
+
+        x_offset, y_offset = 0, 0
+        if padded_img.shape[0] > target_h:
+            y_offset = random.randint(0, padded_img.shape[0] - target_h)
+        if padded_img.shape[1] > target_w:
+            x_offset = random.randint(0, padded_img.shape[1] - target_w)
+        padded_cropped_img = padded_img[y_offset:y_offset + target_h,
+                                        x_offset:x_offset + target_w]
+
+        # 6. adjust bbox
+        retrieve_gt_bboxes = retrieve_results['gt_bboxes']
+        retrieve_gt_bboxes.rescale_([scale_ratio, scale_ratio])
+        if self.bbox_clip_border:
+            retrieve_gt_bboxes.clip_([origin_h, origin_w])
+
+        if is_flip:
+            retrieve_gt_bboxes.flip_([origin_h, origin_w],
+                                     direction='horizontal')
+
+        # 7. filter
+        cp_retrieve_gt_bboxes = retrieve_gt_bboxes.clone()
+        cp_retrieve_gt_bboxes.translate_([-x_offset, -y_offset])
+        if self.bbox_clip_border:
+            cp_retrieve_gt_bboxes.clip_([target_h, target_w])
+
+        # 8. mix up
+        ori_img = ori_img.astype(np.float32)
+        mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img.astype(np.float32)
+
+        retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']
+        retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']
+
+        mixup_gt_bboxes = cp_retrieve_gt_bboxes.cat(
+            (results['gt_bboxes'], cp_retrieve_gt_bboxes), dim=0)
+        mixup_gt_bboxes_labels = np.concatenate(
+            (results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)
+        mixup_gt_ignore_flags = np.concatenate(
+            (results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)
+
+        # remove outside bbox
+        inside_inds = mixup_gt_bboxes.is_inside([target_h, target_w]).numpy()
+        mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]
+        mixup_gt_bboxes_labels = mixup_gt_bboxes_labels[inside_inds]
+        mixup_gt_ignore_flags = mixup_gt_ignore_flags[inside_inds]
+
+        results['img'] = mixup_img.astype(np.uint8)
+        results['img_shape'] = mixup_img.shape[:2]
+        results['gt_bboxes'] = mixup_gt_bboxes
+        results['gt_bboxes_labels'] = mixup_gt_bboxes_labels
+        results['gt_ignore_flags'] = mixup_gt_ignore_flags
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(dynamic_scale={self.dynamic_scale}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'flip_ratio={self.flip_ratio}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'max_iters={self.max_iters}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class RandomAffine(BaseTransform):
+    """Random affine transform data augmentation.
+
+    This operation randomly generates affine transform matrix which including
+    rotation, translation, shear and scaling transforms.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+
+    Args:
+        max_rotate_degree (float): Maximum degrees of rotation transform.
+            Defaults to 10.
+        max_translate_ratio (float): Maximum ratio of translation.
+            Defaults to 0.1.
+        scaling_ratio_range (tuple[float]): Min and max ratio of
+            scaling transform. Defaults to (0.5, 1.5).
+        max_shear_degree (float): Maximum degrees of shear
+            transform. Defaults to 2.
+        border (tuple[int]): Distance from width and height sides of input
+            image to adjust output shape. Only used in mosaic dataset.
+            Defaults to (0, 0).
+        border_val (tuple[int]): Border padding values of 3 channels.
+            Defaults to (114, 114, 114).
+        bbox_clip_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.
+    """
+
+    def __init__(self,
+                 max_rotate_degree: float = 10.0,
+                 max_translate_ratio: float = 0.1,
+                 scaling_ratio_range: Tuple[float, float] = (0.5, 1.5),
+                 max_shear_degree: float = 2.0,
+                 border: Tuple[int, int] = (0, 0),
+                 border_val: Tuple[int, int, int] = (114, 114, 114),
+                 bbox_clip_border: bool = True) -> None:
+        assert 0 <= max_translate_ratio <= 1
+        assert scaling_ratio_range[0] <= scaling_ratio_range[1]
+        assert scaling_ratio_range[0] > 0
+        self.max_rotate_degree = max_rotate_degree
+        self.max_translate_ratio = max_translate_ratio
+        self.scaling_ratio_range = scaling_ratio_range
+        self.max_shear_degree = max_shear_degree
+        self.border = border
+        self.border_val = border_val
+        self.bbox_clip_border = bbox_clip_border
+
+    @cache_randomness
+    def _get_random_homography_matrix(self, height, width):
+        # Rotation
+        rotation_degree = random.uniform(-self.max_rotate_degree,
+                                         self.max_rotate_degree)
+        rotation_matrix = self._get_rotation_matrix(rotation_degree)
+
+        # Scaling
+        scaling_ratio = random.uniform(self.scaling_ratio_range[0],
+                                       self.scaling_ratio_range[1])
+        scaling_matrix = self._get_scaling_matrix(scaling_ratio)
+
+        # Shear
+        x_degree = random.uniform(-self.max_shear_degree,
+                                  self.max_shear_degree)
+        y_degree = random.uniform(-self.max_shear_degree,
+                                  self.max_shear_degree)
+        shear_matrix = self._get_shear_matrix(x_degree, y_degree)
+
+        # Translation
+        trans_x = random.uniform(-self.max_translate_ratio,
+                                 self.max_translate_ratio) * width
+        trans_y = random.uniform(-self.max_translate_ratio,
+                                 self.max_translate_ratio) * height
+        translate_matrix = self._get_translation_matrix(trans_x, trans_y)
+
+        warp_matrix = (
+            translate_matrix @ shear_matrix @ rotation_matrix @ scaling_matrix)
+        return warp_matrix
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        img = results['img']
+        height = img.shape[0] + self.border[1] * 2
+        width = img.shape[1] + self.border[0] * 2
+
+        warp_matrix = self._get_random_homography_matrix(height, width)
+
+        img = cv2.warpPerspective(
+            img,
+            warp_matrix,
+            dsize=(width, height),
+            borderValue=self.border_val)
+        results['img'] = img
+        results['img_shape'] = img.shape[:2]
+
+        bboxes = results['gt_bboxes']
+        num_bboxes = len(bboxes)
+        if num_bboxes:
+            bboxes.project_(warp_matrix)
+            if self.bbox_clip_border:
+                bboxes.clip_([height, width])
+            # remove outside bbox
+            valid_index = bboxes.is_inside([height, width]).numpy()
+            results['gt_bboxes'] = bboxes[valid_index]
+            results['gt_bboxes_labels'] = results['gt_bboxes_labels'][
+                valid_index]
+            results['gt_ignore_flags'] = results['gt_ignore_flags'][
+                valid_index]
+
+            if 'gt_masks' in results:
+                raise NotImplementedError('RandomAffine only supports bbox.')
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(max_rotate_degree={self.max_rotate_degree}, '
+        repr_str += f'max_translate_ratio={self.max_translate_ratio}, '
+        repr_str += f'scaling_ratio_range={self.scaling_ratio_range}, '
+        repr_str += f'max_shear_degree={self.max_shear_degree}, '
+        repr_str += f'border={self.border}, '
+        repr_str += f'border_val={self.border_val}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+    @staticmethod
+    def _get_rotation_matrix(rotate_degrees: float) -> np.ndarray:
+        radian = math.radians(rotate_degrees)
+        rotation_matrix = np.array(
+            [[np.cos(radian), -np.sin(radian), 0.],
+             [np.sin(radian), np.cos(radian), 0.], [0., 0., 1.]],
+            dtype=np.float32)
+        return rotation_matrix
+
+    @staticmethod
+    def _get_scaling_matrix(scale_ratio: float) -> np.ndarray:
+        scaling_matrix = np.array(
+            [[scale_ratio, 0., 0.], [0., scale_ratio, 0.], [0., 0., 1.]],
+            dtype=np.float32)
+        return scaling_matrix
+
+    @staticmethod
+    def _get_shear_matrix(x_shear_degrees: float,
+                          y_shear_degrees: float) -> np.ndarray:
+        x_radian = math.radians(x_shear_degrees)
+        y_radian = math.radians(y_shear_degrees)
+        shear_matrix = np.array([[1, np.tan(x_radian), 0.],
+                                 [np.tan(y_radian), 1, 0.], [0., 0., 1.]],
+                                dtype=np.float32)
+        return shear_matrix
+
+    @staticmethod
+    def _get_translation_matrix(x: float, y: float) -> np.ndarray:
+        translation_matrix = np.array([[1, 0., x], [0., 1, y], [0., 0., 1.]],
+                                      dtype=np.float32)
+        return translation_matrix
+
+
+@TRANSFORMS.register_module()
+class YOLOXHSVRandomAug(BaseTransform):
+    """Apply HSV augmentation to image sequentially. It is referenced from
+    https://github.com/Megvii-
+    BaseDetection/YOLOX/blob/main/yolox/data/data_augment.py#L21.
+
+    Required Keys:
+
+    - img
+
+    Modified Keys:
+
+    - img
+
+    Args:
+        hue_delta (int): delta of hue. Defaults to 5.
+        saturation_delta (int): delta of saturation. Defaults to 30.
+        value_delta (int): delat of value. Defaults to 30.
+    """
+
+    def __init__(self,
+                 hue_delta: int = 5,
+                 saturation_delta: int = 30,
+                 value_delta: int = 30) -> None:
+        self.hue_delta = hue_delta
+        self.saturation_delta = saturation_delta
+        self.value_delta = value_delta
+
+    @cache_randomness
+    def _get_hsv_gains(self):
+        hsv_gains = np.random.uniform(-1, 1, 3) * [
+            self.hue_delta, self.saturation_delta, self.value_delta
+        ]
+        # random selection of h, s, v
+        hsv_gains *= np.random.randint(0, 2, 3)
+        # prevent overflow
+        hsv_gains = hsv_gains.astype(np.int16)
+        return hsv_gains
+
+    def transform(self, results: dict) -> dict:
+        img = results['img']
+        hsv_gains = self._get_hsv_gains()
+        img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.int16)
+
+        img_hsv[..., 0] = (img_hsv[..., 0] + hsv_gains[0]) % 180
+        img_hsv[..., 1] = np.clip(img_hsv[..., 1] + hsv_gains[1], 0, 255)
+        img_hsv[..., 2] = np.clip(img_hsv[..., 2] + hsv_gains[2], 0, 255)
+        cv2.cvtColor(img_hsv.astype(img.dtype), cv2.COLOR_HSV2BGR, dst=img)
+
+        results['img'] = img
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(hue_delta={self.hue_delta}, '
+        repr_str += f'saturation_delta={self.saturation_delta}, '
+        repr_str += f'value_delta={self.value_delta})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class CopyPaste(BaseTransform):
+    """Simple Copy-Paste is a Strong Data Augmentation Method for Instance
+    Segmentation The simple copy-paste transform steps are as follows:
+
+    1. The destination image is already resized with aspect ratio kept,
+       cropped and padded.
+    2. Randomly select a source image, which is also already resized
+       with aspect ratio kept, cropped and padded in a similar way
+       as the destination image.
+    3. Randomly select some objects from the source image.
+    4. Paste these source objects to the destination image directly,
+       due to the source and destination image have the same size.
+    5. Update object masks of the destination image, for some origin objects
+       may be occluded.
+    6. Generate bboxes from the updated destination masks and
+       filter some objects which are totally occluded, and adjust bboxes
+       which are partly occluded.
+    7. Append selected source bboxes, masks, and labels.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (BaseBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - gt_masks (BitmapMasks) (optional)
+
+    Modified Keys:
+
+    - img
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+    - gt_masks (optional)
+
+    Args:
+        max_num_pasted (int): The maximum number of pasted objects.
+            Defaults to 100.
+        bbox_occluded_thr (int): The threshold of occluded bbox.
+            Defaults to 10.
+        mask_occluded_thr (int): The threshold of occluded mask.
+            Defaults to 300.
+        selected (bool): Whether select objects or not. If select is False,
+            all objects of the source image will be pasted to the
+            destination image.
+            Defaults to True.
+        paste_by_box (bool): Whether use boxes as masks when masks are not
+            available.
+            Defaults to False.
+    """
+
+    def __init__(
+        self,
+        max_num_pasted: int = 100,
+        bbox_occluded_thr: int = 10,
+        mask_occluded_thr: int = 300,
+        selected: bool = True,
+        paste_by_box: bool = False,
+    ) -> None:
+        self.max_num_pasted = max_num_pasted
+        self.bbox_occluded_thr = bbox_occluded_thr
+        self.mask_occluded_thr = mask_occluded_thr
+        self.selected = selected
+        self.paste_by_box = paste_by_box
+
+    @cache_randomness
+    def get_indexes(self, dataset: BaseDataset) -> int:
+        """Call function to collect indexes.s.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+        Returns:
+            list: Indexes.
+        """
+        return random.randint(0, len(dataset))
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to make a copy-paste of image.
+
+        Args:
+            results (dict): Result dict.
+        Returns:
+            dict: Result dict with copy-paste transformed.
+        """
+
+        assert 'mix_results' in results
+        num_images = len(results['mix_results'])
+        assert num_images == 1, \
+            f'CopyPaste only supports processing 2 images, got {num_images}'
+        if self.selected:
+            selected_results = self._select_object(results['mix_results'][0])
+        else:
+            selected_results = results['mix_results'][0]
+        return self._copy_paste(results, selected_results)
+
+    @cache_randomness
+    def _get_selected_inds(self, num_bboxes: int) -> np.ndarray:
+        max_num_pasted = min(num_bboxes + 1, self.max_num_pasted)
+        num_pasted = np.random.randint(0, max_num_pasted)
+        return np.random.choice(num_bboxes, size=num_pasted, replace=False)
+
+    def get_gt_masks(self, results: dict) -> BitmapMasks:
+        """Get gt_masks originally or generated based on bboxes.
+
+        If gt_masks is not contained in results,
+        it will be generated based on gt_bboxes.
+        Args:
+            results (dict): Result dict.
+        Returns:
+            BitmapMasks: gt_masks, originally or generated based on bboxes.
+        """
+        if results.get('gt_masks', None) is not None:
+            if self.paste_by_box:
+                warnings.warn('gt_masks is already contained in results, '
+                              'so paste_by_box is disabled.')
+            return results['gt_masks']
+        else:
+            if not self.paste_by_box:
+                raise RuntimeError('results does not contain masks.')
+            return results['gt_bboxes'].create_masks(results['img'].shape[:2])
+
+    def _select_object(self, results: dict) -> dict:
+        """Select some objects from the source results."""
+        bboxes = results['gt_bboxes']
+        labels = results['gt_bboxes_labels']
+        masks = self.get_gt_masks(results)
+        ignore_flags = results['gt_ignore_flags']
+
+        selected_inds = self._get_selected_inds(bboxes.shape[0])
+
+        selected_bboxes = bboxes[selected_inds]
+        selected_labels = labels[selected_inds]
+        selected_masks = masks[selected_inds]
+        selected_ignore_flags = ignore_flags[selected_inds]
+
+        results['gt_bboxes'] = selected_bboxes
+        results['gt_bboxes_labels'] = selected_labels
+        results['gt_masks'] = selected_masks
+        results['gt_ignore_flags'] = selected_ignore_flags
+        return results
+
+    def _copy_paste(self, dst_results: dict, src_results: dict) -> dict:
+        """CopyPaste transform function.
+
+        Args:
+            dst_results (dict): Result dict of the destination image.
+            src_results (dict): Result dict of the source image.
+        Returns:
+            dict: Updated result dict.
+        """
+        dst_img = dst_results['img']
+        dst_bboxes = dst_results['gt_bboxes']
+        dst_labels = dst_results['gt_bboxes_labels']
+        dst_masks = self.get_gt_masks(dst_results)
+        dst_ignore_flags = dst_results['gt_ignore_flags']
+
+        src_img = src_results['img']
+        src_bboxes = src_results['gt_bboxes']
+        src_labels = src_results['gt_bboxes_labels']
+        src_masks = src_results['gt_masks']
+        src_ignore_flags = src_results['gt_ignore_flags']
+
+        if len(src_bboxes) == 0:
+            return dst_results
+
+        # update masks and generate bboxes from updated masks
+        composed_mask = np.where(np.any(src_masks.masks, axis=0), 1, 0)
+        updated_dst_masks = self._get_updated_masks(dst_masks, composed_mask)
+        updated_dst_bboxes = updated_dst_masks.get_bboxes(type(dst_bboxes))
+        assert len(updated_dst_bboxes) == len(updated_dst_masks)
+
+        # filter totally occluded objects
+        l1_distance = (updated_dst_bboxes.tensor - dst_bboxes.tensor).abs()
+        bboxes_inds = (l1_distance <= self.bbox_occluded_thr).all(
+            dim=-1).numpy()
+        masks_inds = updated_dst_masks.masks.sum(
+            axis=(1, 2)) > self.mask_occluded_thr
+        valid_inds = bboxes_inds | masks_inds
+
+        # Paste source objects to destination image directly
+        img = dst_img * (1 - composed_mask[..., np.newaxis]
+                         ) + src_img * composed_mask[..., np.newaxis]
+        bboxes = src_bboxes.cat([updated_dst_bboxes[valid_inds], src_bboxes])
+        labels = np.concatenate([dst_labels[valid_inds], src_labels])
+        masks = np.concatenate(
+            [updated_dst_masks.masks[valid_inds], src_masks.masks])
+        ignore_flags = np.concatenate(
+            [dst_ignore_flags[valid_inds], src_ignore_flags])
+
+        dst_results['img'] = img
+        dst_results['gt_bboxes'] = bboxes
+        dst_results['gt_bboxes_labels'] = labels
+        dst_results['gt_masks'] = BitmapMasks(masks, masks.shape[1],
+                                              masks.shape[2])
+        dst_results['gt_ignore_flags'] = ignore_flags
+
+        return dst_results
+
+    def _get_updated_masks(self, masks: BitmapMasks,
+                           composed_mask: np.ndarray) -> BitmapMasks:
+        """Update masks with composed mask."""
+        assert masks.masks.shape[-2:] == composed_mask.shape[-2:], \
+            'Cannot compare two arrays of different size'
+        masks.masks = np.where(composed_mask, 0, masks.masks)
+        return masks
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(max_num_pasted={self.max_num_pasted}, '
+        repr_str += f'bbox_occluded_thr={self.bbox_occluded_thr}, '
+        repr_str += f'mask_occluded_thr={self.mask_occluded_thr}, '
+        repr_str += f'selected={self.selected}), '
+        repr_str += f'paste_by_box={self.paste_by_box})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class RandomErasing(BaseTransform):
+    """RandomErasing operation.
+
+    Random Erasing randomly selects a rectangle region
+    in an image and erases its pixels with random values.
+    `RandomErasing <https://arxiv.org/abs/1708.04896>`_.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (HorizontalBoxes[torch.float32]) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - gt_masks (BitmapMasks) (optional)
+
+    Modified Keys:
+    - img
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+    - gt_masks (optional)
+
+    Args:
+        n_patches (int or tuple[int, int]): Number of regions to be dropped.
+            If it is given as a tuple, number of patches will be randomly
+            selected from the closed interval [``n_patches[0]``,
+            ``n_patches[1]``].
+        ratio (float or tuple[float, float]): The ratio of erased regions.
+            It can be ``float`` to use a fixed ratio or ``tuple[float, float]``
+            to randomly choose ratio from the interval.
+        squared (bool): Whether to erase square region. Defaults to True.
+        bbox_erased_thr (float): The threshold for the maximum area proportion
+            of the bbox to be erased. When the proportion of the area where the
+            bbox is erased is greater than the threshold, the bbox will be
+            removed. Defaults to 0.9.
+        img_border_value (int or float or tuple): The filled values for
+            image border. If float, the same fill value will be used for
+            all the three channels of image. If tuple, it should be 3 elements.
+            Defaults to 128.
+        mask_border_value (int): The fill value used for masks. Defaults to 0.
+        seg_ignore_label (int): The fill value used for segmentation map.
+            Note this value must equals ``ignore_label`` in ``semantic_head``
+            of the corresponding config. Defaults to 255.
+    """
+
+    def __init__(
+        self,
+        n_patches: Union[int, Tuple[int, int]],
+        ratio: Union[float, Tuple[float, float]],
+        squared: bool = True,
+        bbox_erased_thr: float = 0.9,
+        img_border_value: Union[int, float, tuple] = 128,
+        mask_border_value: int = 0,
+        seg_ignore_label: int = 255,
+    ) -> None:
+        if isinstance(n_patches, tuple):
+            assert len(n_patches) == 2 and 0 <= n_patches[0] < n_patches[1]
+        else:
+            n_patches = (n_patches, n_patches)
+        if isinstance(ratio, tuple):
+            assert len(ratio) == 2 and 0 <= ratio[0] < ratio[1] <= 1
+        else:
+            ratio = (ratio, ratio)
+
+        self.n_patches = n_patches
+        self.ratio = ratio
+        self.squared = squared
+        self.bbox_erased_thr = bbox_erased_thr
+        self.img_border_value = img_border_value
+        self.mask_border_value = mask_border_value
+        self.seg_ignore_label = seg_ignore_label
+
+    @cache_randomness
+    def _get_patches(self, img_shape: Tuple[int, int]) -> List[list]:
+        """Get patches for random erasing."""
+        patches = []
+        n_patches = np.random.randint(self.n_patches[0], self.n_patches[1] + 1)
+        for _ in range(n_patches):
+            if self.squared:
+                ratio = np.random.random() * (self.ratio[1] -
+                                              self.ratio[0]) + self.ratio[0]
+                ratio = (ratio, ratio)
+            else:
+                ratio = (np.random.random() * (self.ratio[1] - self.ratio[0]) +
+                         self.ratio[0], np.random.random() *
+                         (self.ratio[1] - self.ratio[0]) + self.ratio[0])
+            ph, pw = int(img_shape[0] * ratio[0]), int(img_shape[1] * ratio[1])
+            px1, py1 = np.random.randint(0,
+                                         img_shape[1] - pw), np.random.randint(
+                                             0, img_shape[0] - ph)
+            px2, py2 = px1 + pw, py1 + ph
+            patches.append([px1, py1, px2, py2])
+        return np.array(patches)
+
+    def _transform_img(self, results: dict, patches: List[list]) -> None:
+        """Random erasing the image."""
+        for patch in patches:
+            px1, py1, px2, py2 = patch
+            results['img'][py1:py2, px1:px2, :] = self.img_border_value
+
+    def _transform_bboxes(self, results: dict, patches: List[list]) -> None:
+        """Random erasing the bboxes."""
+        bboxes = results['gt_bboxes']
+        # TODO: unify the logic by using operators in BaseBoxes.
+        assert isinstance(bboxes, HorizontalBoxes)
+        bboxes = bboxes.numpy()
+        left_top = np.maximum(bboxes[:, None, :2], patches[:, :2])
+        right_bottom = np.minimum(bboxes[:, None, 2:], patches[:, 2:])
+        wh = np.maximum(right_bottom - left_top, 0)
+        inter_areas = wh[:, :, 0] * wh[:, :, 1]
+        bbox_areas = (bboxes[:, 2] - bboxes[:, 0]) * (
+            bboxes[:, 3] - bboxes[:, 1])
+        bboxes_erased_ratio = inter_areas.sum(-1) / (bbox_areas + 1e-7)
+        valid_inds = bboxes_erased_ratio < self.bbox_erased_thr
+        results['gt_bboxes'] = HorizontalBoxes(bboxes[valid_inds])
+        results['gt_bboxes_labels'] = results['gt_bboxes_labels'][valid_inds]
+        results['gt_ignore_flags'] = results['gt_ignore_flags'][valid_inds]
+        if results.get('gt_masks', None) is not None:
+            results['gt_masks'] = results['gt_masks'][valid_inds]
+
+    def _transform_masks(self, results: dict, patches: List[list]) -> None:
+        """Random erasing the masks."""
+        for patch in patches:
+            px1, py1, px2, py2 = patch
+            results['gt_masks'].masks[:, py1:py2,
+                                      px1:px2] = self.mask_border_value
+
+    def _transform_seg(self, results: dict, patches: List[list]) -> None:
+        """Random erasing the segmentation map."""
+        for patch in patches:
+            px1, py1, px2, py2 = patch
+            results['gt_seg_map'][py1:py2, px1:px2] = self.seg_ignore_label
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Transform function to erase some regions of image."""
+        patches = self._get_patches(results['img_shape'])
+        self._transform_img(results, patches)
+        if results.get('gt_bboxes', None) is not None:
+            self._transform_bboxes(results, patches)
+        if results.get('gt_masks', None) is not None:
+            self._transform_masks(results, patches)
+        if results.get('gt_seg_map', None) is not None:
+            self._transform_seg(results, patches)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(n_patches={self.n_patches}, '
+        repr_str += f'ratio={self.ratio}, '
+        repr_str += f'squared={self.squared}, '
+        repr_str += f'bbox_erased_thr={self.bbox_erased_thr}, '
+        repr_str += f'img_border_value={self.img_border_value}, '
+        repr_str += f'mask_border_value={self.mask_border_value}, '
+        repr_str += f'seg_ignore_label={self.seg_ignore_label})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class CachedMosaic(Mosaic):
+    """Cached mosaic augmentation.
+
+    Cached mosaic transform will random select images from the cache
+    and combine them into one output image.
+
+    .. code:: text
+
+                        mosaic transform
+                           center_x
+                +------------------------------+
+                |       pad        |  pad      |
+                |      +-----------+           |
+                |      |           |           |
+                |      |  image1   |--------+  |
+                |      |           |        |  |
+                |      |           | image2 |  |
+     center_y   |----+-------------+-----------|
+                |    |   cropped   |           |
+                |pad |   image3    |  image4   |
+                |    |             |           |
+                +----|-------------+-----------+
+                     |             |
+                     +-------------+
+
+     The cached mosaic transform steps are as follows:
+
+         1. Append the results from the last transform into the cache.
+         2. Choose the mosaic center as the intersections of 4 images
+         3. Get the left top image according to the index, and randomly
+            sample another 3 images from the result cache.
+         4. Sub image will be cropped if image is larger than mosaic patch
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (np.float32) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+
+    Args:
+        img_scale (Sequence[int]): Image size before mosaic pipeline of single
+            image. The shape order should be (width, height).
+            Defaults to (640, 640).
+        center_ratio_range (Sequence[float]): Center ratio range of mosaic
+            output. Defaults to (0.5, 1.5).
+        bbox_clip_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.
+        pad_val (int): Pad value. Defaults to 114.
+        prob (float): Probability of applying this transformation.
+            Defaults to 1.0.
+        max_cached_images (int): The maximum length of the cache. The larger
+            the cache, the stronger the randomness of this transform. As a
+            rule of thumb, providing 10 caches for each image suffices for
+            randomness. Defaults to 40.
+        random_pop (bool): Whether to randomly pop a result from the cache
+            when the cache is full. If set to False, use FIFO popping method.
+            Defaults to True.
+    """
+
+    def __init__(self,
+                 *args,
+                 max_cached_images: int = 40,
+                 random_pop: bool = True,
+                 **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.results_cache = []
+        self.random_pop = random_pop
+        assert max_cached_images >= 4, 'The length of cache must >= 4, ' \
+                                       f'but got {max_cached_images}.'
+        self.max_cached_images = max_cached_images
+
+    @cache_randomness
+    def get_indexes(self, cache: list) -> list:
+        """Call function to collect indexes.
+
+        Args:
+            cache (list): The results cache.
+
+        Returns:
+            list: indexes.
+        """
+
+        indexes = [random.randint(0, len(cache) - 1) for _ in range(3)]
+        return indexes
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """Mosaic transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        # cache and pop images
+        self.results_cache.append(copy.deepcopy(results))
+        if len(self.results_cache) > self.max_cached_images:
+            if self.random_pop:
+                index = random.randint(0, len(self.results_cache) - 1)
+            else:
+                index = 0
+            self.results_cache.pop(index)
+
+        if len(self.results_cache) <= 4:
+            return results
+
+        if random.uniform(0, 1) > self.prob:
+            return results
+        indices = self.get_indexes(self.results_cache)
+        mix_results = [copy.deepcopy(self.results_cache[i]) for i in indices]
+
+        # TODO: refactor mosaic to reuse these code.
+        mosaic_bboxes = []
+        mosaic_bboxes_labels = []
+        mosaic_ignore_flags = []
+        mosaic_masks = []
+        with_mask = True if 'gt_masks' in results else False
+
+        if len(results['img'].shape) == 3:
+            mosaic_img = np.full(
+                (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2), 3),
+                self.pad_val,
+                dtype=results['img'].dtype)
+        else:
+            mosaic_img = np.full(
+                (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2)),
+                self.pad_val,
+                dtype=results['img'].dtype)
+
+        # mosaic center x, y
+        center_x = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[0])
+        center_y = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[1])
+        center_position = (center_x, center_y)
+
+        loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        for i, loc in enumerate(loc_strs):
+            if loc == 'top_left':
+                results_patch = copy.deepcopy(results)
+            else:
+                results_patch = copy.deepcopy(mix_results[i - 1])
+
+            img_i = results_patch['img']
+            h_i, w_i = img_i.shape[:2]
+            # keep_ratio resize
+            scale_ratio_i = min(self.img_scale[1] / h_i,
+                                self.img_scale[0] / w_i)
+            img_i = mmcv.imresize(
+                img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
+
+            # compute the combine parameters
+            paste_coord, crop_coord = self._mosaic_combine(
+                loc, center_position, img_i.shape[:2][::-1])
+            x1_p, y1_p, x2_p, y2_p = paste_coord
+            x1_c, y1_c, x2_c, y2_c = crop_coord
+
+            # crop and paste image
+            mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
+
+            # adjust coordinate
+            gt_bboxes_i = results_patch['gt_bboxes']
+            gt_bboxes_labels_i = results_patch['gt_bboxes_labels']
+            gt_ignore_flags_i = results_patch['gt_ignore_flags']
+
+            padw = x1_p - x1_c
+            padh = y1_p - y1_c
+            gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])
+            gt_bboxes_i.translate_([padw, padh])
+            mosaic_bboxes.append(gt_bboxes_i)
+            mosaic_bboxes_labels.append(gt_bboxes_labels_i)
+            mosaic_ignore_flags.append(gt_ignore_flags_i)
+            if with_mask and results_patch.get('gt_masks', None) is not None:
+                gt_masks_i = results_patch['gt_masks']
+                gt_masks_i = gt_masks_i.rescale(float(scale_ratio_i))
+                gt_masks_i = gt_masks_i.translate(
+                    out_shape=(int(self.img_scale[0] * 2),
+                               int(self.img_scale[1] * 2)),
+                    offset=padw,
+                    direction='horizontal')
+                gt_masks_i = gt_masks_i.translate(
+                    out_shape=(int(self.img_scale[0] * 2),
+                               int(self.img_scale[1] * 2)),
+                    offset=padh,
+                    direction='vertical')
+                mosaic_masks.append(gt_masks_i)
+
+        mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)
+        mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)
+        mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)
+
+        if self.bbox_clip_border:
+            mosaic_bboxes.clip_([2 * self.img_scale[1], 2 * self.img_scale[0]])
+        # remove outside bboxes
+        inside_inds = mosaic_bboxes.is_inside(
+            [2 * self.img_scale[1], 2 * self.img_scale[0]]).numpy()
+        mosaic_bboxes = mosaic_bboxes[inside_inds]
+        mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]
+        mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]
+
+        results['img'] = mosaic_img
+        results['img_shape'] = mosaic_img.shape[:2]
+        results['gt_bboxes'] = mosaic_bboxes
+        results['gt_bboxes_labels'] = mosaic_bboxes_labels
+        results['gt_ignore_flags'] = mosaic_ignore_flags
+
+        if with_mask:
+            mosaic_masks = mosaic_masks[0].cat(mosaic_masks)
+            results['gt_masks'] = mosaic_masks[inside_inds]
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(img_scale={self.img_scale}, '
+        repr_str += f'center_ratio_range={self.center_ratio_range}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'prob={self.prob}, '
+        repr_str += f'max_cached_images={self.max_cached_images}, '
+        repr_str += f'random_pop={self.random_pop})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class CachedMixUp(BaseTransform):
+    """Cached mixup data augmentation.
+
+    .. code:: text
+
+                         mixup transform
+                +------------------------------+
+                | mixup image   |              |
+                |      +--------|--------+     |
+                |      |        |        |     |
+                |---------------+        |     |
+                |      |                 |     |
+                |      |      image      |     |
+                |      |                 |     |
+                |      |                 |     |
+                |      |-----------------+     |
+                |             pad              |
+                +------------------------------+
+
+     The cached mixup transform steps are as follows:
+
+        1. Append the results from the last transform into the cache.
+        2. Another random image is picked from the cache and embedded in
+           the top left patch(after padding and resizing)
+        3. The target of mixup transform is the weighted average of mixup
+           image and origin image.
+
+    Required Keys:
+
+    - img
+    - gt_bboxes (np.float32) (optional)
+    - gt_bboxes_labels (np.int64) (optional)
+    - gt_ignore_flags (bool) (optional)
+    - mix_results (List[dict])
+
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_bboxes (optional)
+    - gt_bboxes_labels (optional)
+    - gt_ignore_flags (optional)
+
+
+    Args:
+        img_scale (Sequence[int]): Image output size after mixup pipeline.
+            The shape order should be (width, height). Defaults to (640, 640).
+        ratio_range (Sequence[float]): Scale ratio of mixup image.
+            Defaults to (0.5, 1.5).
+        flip_ratio (float): Horizontal flip ratio of mixup image.
+            Defaults to 0.5.
+        pad_val (int): Pad value. Defaults to 114.
+        max_iters (int): The maximum number of iterations. If the number of
+            iterations is greater than `max_iters`, but gt_bbox is still
+            empty, then the iteration is terminated. Defaults to 15.
+        bbox_clip_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.
+        max_cached_images (int): The maximum length of the cache. The larger
+            the cache, the stronger the randomness of this transform. As a
+            rule of thumb, providing 10 caches for each image suffices for
+            randomness. Defaults to 20.
+        random_pop (bool): Whether to randomly pop a result from the cache
+            when the cache is full. If set to False, use FIFO popping method.
+            Defaults to True.
+        prob (float): Probability of applying this transformation.
+            Defaults to 1.0.
+    """
+
+    def __init__(self,
+                 img_scale: Tuple[int, int] = (640, 640),
+                 ratio_range: Tuple[float, float] = (0.5, 1.5),
+                 flip_ratio: float = 0.5,
+                 pad_val: float = 114.0,
+                 max_iters: int = 15,
+                 bbox_clip_border: bool = True,
+                 max_cached_images: int = 20,
+                 random_pop: bool = True,
+                 prob: float = 1.0) -> None:
+        assert isinstance(img_scale, tuple)
+        assert max_cached_images >= 2, 'The length of cache must >= 2, ' \
+                                       f'but got {max_cached_images}.'
+        assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \
+                                 f'got {prob}.'
+        self.dynamic_scale = img_scale
+        self.ratio_range = ratio_range
+        self.flip_ratio = flip_ratio
+        self.pad_val = pad_val
+        self.max_iters = max_iters
+        self.bbox_clip_border = bbox_clip_border
+        self.results_cache = []
+
+        self.max_cached_images = max_cached_images
+        self.random_pop = random_pop
+        self.prob = prob
+
+    @cache_randomness
+    def get_indexes(self, cache: list) -> int:
+        """Call function to collect indexes.
+
+        Args:
+            cache (list): The result cache.
+
+        Returns:
+            int: index.
+        """
+
+        for i in range(self.max_iters):
+            index = random.randint(0, len(cache) - 1)
+            gt_bboxes_i = cache[index]['gt_bboxes']
+            if len(gt_bboxes_i) != 0:
+                break
+        return index
+
+    @autocast_box_type()
+    def transform(self, results: dict) -> dict:
+        """MixUp transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        # cache and pop images
+        self.results_cache.append(copy.deepcopy(results))
+        if len(self.results_cache) > self.max_cached_images:
+            if self.random_pop:
+                index = random.randint(0, len(self.results_cache) - 1)
+            else:
+                index = 0
+            self.results_cache.pop(index)
+
+        if len(self.results_cache) <= 1:
+            return results
+
+        if random.uniform(0, 1) > self.prob:
+            return results
+
+        index = self.get_indexes(self.results_cache)
+        retrieve_results = copy.deepcopy(self.results_cache[index])
+
+        # TODO: refactor mixup to reuse these code.
+        if retrieve_results['gt_bboxes'].shape[0] == 0:
+            # empty bbox
+            return results
+
+        retrieve_img = retrieve_results['img']
+        with_mask = True if 'gt_masks' in results else False
+
+        jit_factor = random.uniform(*self.ratio_range)
+        is_flip = random.uniform(0, 1) > self.flip_ratio
+
+        if len(retrieve_img.shape) == 3:
+            out_img = np.ones(
+                (self.dynamic_scale[1], self.dynamic_scale[0], 3),
+                dtype=retrieve_img.dtype) * self.pad_val
+        else:
+            out_img = np.ones(
+                self.dynamic_scale[::-1],
+                dtype=retrieve_img.dtype) * self.pad_val
+
+        # 1. keep_ratio resize
+        scale_ratio = min(self.dynamic_scale[1] / retrieve_img.shape[0],
+                          self.dynamic_scale[0] / retrieve_img.shape[1])
+        retrieve_img = mmcv.imresize(
+            retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),
+                           int(retrieve_img.shape[0] * scale_ratio)))
+
+        # 2. paste
+        out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img
+
+        # 3. scale jit
+        scale_ratio *= jit_factor
+        out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),
+                                          int(out_img.shape[0] * jit_factor)))
+
+        # 4. flip
+        if is_flip:
+            out_img = out_img[:, ::-1, :]
+
+        # 5. random crop
+        ori_img = results['img']
+        origin_h, origin_w = out_img.shape[:2]
+        target_h, target_w = ori_img.shape[:2]
+        padded_img = np.ones((max(origin_h, target_h), max(
+            origin_w, target_w), 3)) * self.pad_val
+        padded_img = padded_img.astype(np.uint8)
+        padded_img[:origin_h, :origin_w] = out_img
+
+        x_offset, y_offset = 0, 0
+        if padded_img.shape[0] > target_h:
+            y_offset = random.randint(0, padded_img.shape[0] - target_h)
+        if padded_img.shape[1] > target_w:
+            x_offset = random.randint(0, padded_img.shape[1] - target_w)
+        padded_cropped_img = padded_img[y_offset:y_offset + target_h,
+                                        x_offset:x_offset + target_w]
+
+        # 6. adjust bbox
+        retrieve_gt_bboxes = retrieve_results['gt_bboxes']
+        retrieve_gt_bboxes.rescale_([scale_ratio, scale_ratio])
+        if with_mask:
+            retrieve_gt_masks = retrieve_results['gt_masks'].rescale(
+                scale_ratio)
+
+        if self.bbox_clip_border:
+            retrieve_gt_bboxes.clip_([origin_h, origin_w])
+
+        if is_flip:
+            retrieve_gt_bboxes.flip_([origin_h, origin_w],
+                                     direction='horizontal')
+            if with_mask:
+                retrieve_gt_masks = retrieve_gt_masks.flip()
+
+        # 7. filter
+        cp_retrieve_gt_bboxes = retrieve_gt_bboxes.clone()
+        cp_retrieve_gt_bboxes.translate_([-x_offset, -y_offset])
+        if with_mask:
+            retrieve_gt_masks = retrieve_gt_masks.translate(
+                out_shape=(target_h, target_w),
+                offset=-x_offset,
+                direction='horizontal')
+            retrieve_gt_masks = retrieve_gt_masks.translate(
+                out_shape=(target_h, target_w),
+                offset=-y_offset,
+                direction='vertical')
+
+        if self.bbox_clip_border:
+            cp_retrieve_gt_bboxes.clip_([target_h, target_w])
+
+        # 8. mix up
+        ori_img = ori_img.astype(np.float32)
+        mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img.astype(np.float32)
+
+        retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']
+        retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']
+
+        mixup_gt_bboxes = cp_retrieve_gt_bboxes.cat(
+            (results['gt_bboxes'], cp_retrieve_gt_bboxes), dim=0)
+        mixup_gt_bboxes_labels = np.concatenate(
+            (results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)
+        mixup_gt_ignore_flags = np.concatenate(
+            (results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)
+        if with_mask:
+            mixup_gt_masks = retrieve_gt_masks.cat(
+                [results['gt_masks'], retrieve_gt_masks])
+
+        # remove outside bbox
+        inside_inds = mixup_gt_bboxes.is_inside([target_h, target_w]).numpy()
+        mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]
+        mixup_gt_bboxes_labels = mixup_gt_bboxes_labels[inside_inds]
+        mixup_gt_ignore_flags = mixup_gt_ignore_flags[inside_inds]
+        if with_mask:
+            mixup_gt_masks = mixup_gt_masks[inside_inds]
+
+        results['img'] = mixup_img.astype(np.uint8)
+        results['img_shape'] = mixup_img.shape[:2]
+        results['gt_bboxes'] = mixup_gt_bboxes
+        results['gt_bboxes_labels'] = mixup_gt_bboxes_labels
+        results['gt_ignore_flags'] = mixup_gt_ignore_flags
+        if with_mask:
+            results['gt_masks'] = mixup_gt_masks
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(dynamic_scale={self.dynamic_scale}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'flip_ratio={self.flip_ratio}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'max_iters={self.max_iters}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border}, '
+        repr_str += f'max_cached_images={self.max_cached_images}, '
+        repr_str += f'random_pop={self.random_pop}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str

mmdet/datasets/transforms/wrappers.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+from typing import Callable, Dict, List, Optional, Union
+
+import numpy as np
+from mmcv.transforms import BaseTransform, Compose
+from mmcv.transforms.utils import cache_random_params, cache_randomness
+
+from mmdet.registry import TRANSFORMS
+
+
+@TRANSFORMS.register_module()
+class MultiBranch(BaseTransform):
+    r"""Multiple branch pipeline wrapper.
+
+    Generate multiple data-augmented versions of the same image.
+    `MultiBranch` needs to specify the branch names of all
+    pipelines of the dataset, perform corresponding data augmentation
+    for the current branch, and return None for other branches,
+    which ensures the consistency of return format across
+    different samples.
+
+    Args:
+        branch_field (list): List of branch names.
+        branch_pipelines (dict): Dict of different pipeline configs
+            to be composed.
+
+    Examples:
+        >>> branch_field = ['sup', 'unsup_teacher', 'unsup_student']
+        >>> sup_pipeline = [
+        >>>     dict(type='LoadImageFromFile'),
+        >>>     dict(type='LoadAnnotations', with_bbox=True),
+        >>>     dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+        >>>     dict(type='RandomFlip', prob=0.5),
+        >>>     dict(
+        >>>         type='MultiBranch',
+        >>>         branch_field=branch_field,
+        >>>         sup=dict(type='PackDetInputs'))
+        >>>     ]
+        >>> weak_pipeline = [
+        >>>     dict(type='LoadImageFromFile'),
+        >>>     dict(type='LoadAnnotations', with_bbox=True),
+        >>>     dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+        >>>     dict(type='RandomFlip', prob=0.0),
+        >>>     dict(
+        >>>         type='MultiBranch',
+        >>>         branch_field=branch_field,
+        >>>         sup=dict(type='PackDetInputs'))
+        >>>     ]
+        >>> strong_pipeline = [
+        >>>     dict(type='LoadImageFromFile'),
+        >>>     dict(type='LoadAnnotations', with_bbox=True),
+        >>>     dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+        >>>     dict(type='RandomFlip', prob=1.0),
+        >>>     dict(
+        >>>         type='MultiBranch',
+        >>>         branch_field=branch_field,
+        >>>         sup=dict(type='PackDetInputs'))
+        >>>     ]
+        >>> unsup_pipeline = [
+        >>>     dict(type='LoadImageFromFile'),
+        >>>     dict(type='LoadEmptyAnnotations'),
+        >>>     dict(
+        >>>         type='MultiBranch',
+        >>>         branch_field=branch_field,
+        >>>         unsup_teacher=weak_pipeline,
+        >>>         unsup_student=strong_pipeline)
+        >>>     ]
+        >>> from mmcv.transforms import Compose
+        >>> sup_branch = Compose(sup_pipeline)
+        >>> unsup_branch = Compose(unsup_pipeline)
+        >>> print(sup_branch)
+        >>> Compose(
+        >>>     LoadImageFromFile(ignore_empty=False, to_float32=False, color_type='color', imdecode_backend='cv2') # noqa
+        >>>     LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, imdecode_backend='cv2') # noqa
+        >>>     Resize(scale=(1333, 800), scale_factor=None, keep_ratio=True, clip_object_border=True), backend=cv2), interpolation=bilinear) # noqa
+        >>>     RandomFlip(prob=0.5, direction=horizontal)
+        >>>     MultiBranch(branch_pipelines=['sup'])
+        >>> )
+        >>> print(unsup_branch)
+        >>> Compose(
+        >>>     LoadImageFromFile(ignore_empty=False, to_float32=False, color_type='color', imdecode_backend='cv2') # noqa
+        >>>     LoadEmptyAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, seg_ignore_label=255) # noqa
+        >>>     MultiBranch(branch_pipelines=['unsup_teacher', 'unsup_student'])
+        >>> )
+    """
+
+    def __init__(self, branch_field: List[str],
+                 **branch_pipelines: dict) -> None:
+        self.branch_field = branch_field
+        self.branch_pipelines = {
+            branch: Compose(pipeline)
+            for branch, pipeline in branch_pipelines.items()
+        }
+
+    def transform(self, results: dict) -> dict:
+        """Transform function to apply transforms sequentially.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict:
+
+            - 'inputs' (Dict[str, obj:`torch.Tensor`]): The forward data of
+                models from different branches.
+            - 'data_sample' (Dict[str,obj:`DetDataSample`]): The annotation
+                info of the sample from different branches.
+        """
+
+        multi_results = {}
+        for branch in self.branch_field:
+            multi_results[branch] = {'inputs': None, 'data_samples': None}
+        for branch, pipeline in self.branch_pipelines.items():
+            branch_results = pipeline(copy.deepcopy(results))
+            # If one branch pipeline returns None,
+            # it will sample another data from dataset.
+            if branch_results is None:
+                return None
+            multi_results[branch] = branch_results
+
+        format_results = {}
+        for branch, results in multi_results.items():
+            for key in results.keys():
+                if format_results.get(key, None) is None:
+                    format_results[key] = {branch: results[key]}
+                else:
+                    format_results[key][branch] = results[key]
+        return format_results
+
+    def __repr__(self) -> str:
+        repr_str = self.__class__.__name__
+        repr_str += f'(branch_pipelines={list(self.branch_pipelines.keys())})'
+        return repr_str
+
+
+@TRANSFORMS.register_module()
+class RandomOrder(Compose):
+    """Shuffle the transform Sequence."""
+
+    @cache_randomness
+    def _random_permutation(self):
+        return np.random.permutation(len(self.transforms))
+
+    def transform(self, results: Dict) -> Optional[Dict]:
+        """Transform function to apply transforms in random order.
+
+        Args:
+            results (dict): A result dict contains the results to transform.
+
+        Returns:
+            dict or None: Transformed results.
+        """
+        inds = self._random_permutation()
+        for idx in inds:
+            t = self.transforms[idx]
+            results = t(results)
+            if results is None:
+                return None
+        return results
+
+    def __repr__(self):
+        """Compute the string representation."""
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += f'{t.__class__.__name__}, '
+        format_string += ')'
+        return format_string
+
+
+@TRANSFORMS.register_module()
+class ProposalBroadcaster(BaseTransform):
+    """A transform wrapper to apply the wrapped transforms to process both
+    `gt_bboxes` and `proposals` without adding any codes. It will do the
+    following steps:
+
+        1. Scatter the broadcasting targets to a list of inputs of the wrapped
+           transforms. The type of the list should be list[dict, dict], which
+           the first is the original inputs, the second is the processing
+           results that `gt_bboxes` being rewritten by the `proposals`.
+        2. Apply ``self.transforms``, with same random parameters, which is
+           sharing with a context manager. The type of the outputs is a
+           list[dict, dict].
+        3. Gather the outputs, update the `proposals` in the first item of
+           the outputs with the `gt_bboxes` in the second .
+
+    Args:
+         transforms (list, optional): Sequence of transform
+            object or config dict to be wrapped. Defaults to [].
+
+    Note: The `TransformBroadcaster` in MMCV can achieve the same operation as
+          `ProposalBroadcaster`, but need to set more complex parameters.
+
+    Examples:
+        >>> pipeline = [
+        >>>     dict(type='LoadImageFromFile'),
+        >>>     dict(type='LoadProposals', num_max_proposals=2000),
+        >>>     dict(type='LoadAnnotations', with_bbox=True),
+        >>>     dict(
+        >>>         type='ProposalBroadcaster',
+        >>>         transforms=[
+        >>>             dict(type='Resize', scale=(1333, 800),
+        >>>                  keep_ratio=True),
+        >>>             dict(type='RandomFlip', prob=0.5),
+        >>>         ]),
+        >>>     dict(type='PackDetInputs')]
+    """
+
+    def __init__(self, transforms: List[Union[dict, Callable]] = []) -> None:
+        self.transforms = Compose(transforms)
+
+    def transform(self, results: dict) -> dict:
+        """Apply wrapped transform functions to process both `gt_bboxes` and
+        `proposals`.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        assert results.get('proposals', None) is not None, \
+            '`proposals` should be in the results, please delete ' \
+            '`ProposalBroadcaster` in your configs, or check whether ' \
+            'you have load proposals successfully.'
+
+        inputs = self._process_input(results)
+        outputs = self._apply_transforms(inputs)
+        outputs = self._process_output(outputs)
+        return outputs
+
+    def _process_input(self, data: dict) -> list:
+        """Scatter the broadcasting targets to a list of inputs of the wrapped
+        transforms.
+
+        Args:
+            data (dict): The original input data.
+
+        Returns:
+            list[dict]: A list of input data.
+        """
+        cp_data = copy.deepcopy(data)
+        cp_data['gt_bboxes'] = cp_data['proposals']
+        scatters = [data, cp_data]
+        return scatters
+
+    def _apply_transforms(self, inputs: list) -> list:
+        """Apply ``self.transforms``.
+
+        Args:
+            inputs (list[dict, dict]): list of input data.
+
+        Returns:
+            list[dict]: The output of the wrapped pipeline.
+        """
+        assert len(inputs) == 2
+        ctx = cache_random_params
+        with ctx(self.transforms):
+            output_scatters = [self.transforms(_input) for _input in inputs]
+        return output_scatters
+
+    def _process_output(self, output_scatters: list) -> dict:
+        """Gathering and renaming data items.
+
+        Args:
+            output_scatters (list[dict, dict]): The output of the wrapped
+                pipeline.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        assert isinstance(output_scatters, list) and \
+               isinstance(output_scatters[0], dict) and \
+               len(output_scatters) == 2
+        outputs = output_scatters[0]
+        outputs['proposals'] = output_scatters[1]['gt_bboxes']
+        return outputs

mmdet/datasets/utils.py

+# Copyright (c) OpenMMLab. All rights reserved.
+
+from mmcv.transforms import LoadImageFromFile
+
+from mmdet.datasets.transforms import LoadAnnotations, LoadPanopticAnnotations
+from mmdet.registry import TRANSFORMS
+
+
+def get_loading_pipeline(pipeline):
+    """Only keep loading image and annotations related configuration.
+
+    Args:
+        pipeline (list[dict]): Data pipeline configs.
+
+    Returns:
+        list[dict]: The new pipeline list with only keep
+            loading image and annotations related configuration.
+
+    Examples:
+        >>> pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(type='LoadAnnotations', with_bbox=True),
+        ...    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+        ...    dict(type='RandomFlip', flip_ratio=0.5),
+        ...    dict(type='Normalize', **img_norm_cfg),
+        ...    dict(type='Pad', size_divisor=32),
+        ...    dict(type='DefaultFormatBundle'),
+        ...    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ...    ]
+        >>> expected_pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(type='LoadAnnotations', with_bbox=True)
+        ...    ]
+        >>> assert expected_pipelines ==\
+        ...        get_loading_pipeline(pipelines)
+    """
+    loading_pipeline_cfg = []
+    for cfg in pipeline:
+        obj_cls = TRANSFORMS.get(cfg['type'])
+        # TODO:use more elegant way to distinguish loading modules
+        if obj_cls is not None and obj_cls in (LoadImageFromFile,
+                                               LoadAnnotations,
+                                               LoadPanopticAnnotations):
+            loading_pipeline_cfg.append(cfg)
+    assert len(loading_pipeline_cfg) == 2, \
+        'The data pipeline in your config file must include ' \
+        'loading image and annotations related pipeline.'
+    return loading_pipeline_cfg

mmdet/datasets/v3det.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path
+from typing import Optional
+
+import mmengine
+
+from mmdet.registry import DATASETS
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class V3DetDataset(CocoDataset):
+    """Dataset for V3Det."""
+
+    METAINFO = {
+        'classes': None,
+        'palette': None,
+    }
+
+    def __init__(
+            self,
+            *args,
+            metainfo: Optional[dict] = None,
+            data_root: str = '',
+            label_file='annotations/category_name_13204_v3det_2023_v1.txt',  # noqa
+            **kwargs) -> None:
+        class_names = tuple(
+            mmengine.list_from_file(os.path.join(data_root, label_file)))
+        if metainfo is None:
+            metainfo = {'classes': class_names}
+        super().__init__(
+            *args, data_root=data_root, metainfo=metainfo, **kwargs)

mmdet/datasets/voc.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.registry import DATASETS
+from .xml_style import XMLDataset
+
+
+@DATASETS.register_module()
+class VOCDataset(XMLDataset):
+    """Dataset for PASCAL VOC."""
+
+    METAINFO = {
+        'classes':
+        ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
+         'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
+         'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'),
+        # palette is a list of color tuples, which is used for visualization.
+        'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
+                    (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
+                    (153, 69, 1), (120, 166, 157), (0, 182, 199),
+                    (0, 226, 252), (182, 182, 255), (0, 0, 230), (220, 20, 60),
+                    (163, 255, 0), (0, 82, 0), (3, 95, 161), (0, 80, 100),
+                    (183, 130, 88)]
+    }
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if 'VOC2007' in self.sub_data_root:
+            self._metainfo['dataset_type'] = 'VOC2007'
+        elif 'VOC2012' in self.sub_data_root:
+            self._metainfo['dataset_type'] = 'VOC2012'
+        else:
+            self._metainfo['dataset_type'] = None

mmdet/datasets/wider_face.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import xml.etree.ElementTree as ET
+
+from mmengine.dist import is_main_process
+from mmengine.fileio import get_local_path, list_from_file
+from mmengine.utils import ProgressBar
+
+from mmdet.registry import DATASETS
+from mmdet.utils.typing_utils import List, Union
+from .xml_style import XMLDataset
+
+
+@DATASETS.register_module()
+class WIDERFaceDataset(XMLDataset):
+    """Reader for the WIDER Face dataset in PASCAL VOC format.
+
+    Conversion scripts can be found in
+    https://github.com/sovrasov/wider-face-pascal-voc-annotations
+    """
+    METAINFO = {'classes': ('face', ), 'palette': [(0, 255, 0)]}
+
+    def load_data_list(self) -> List[dict]:
+        """Load annotation from XML style ann_file.
+
+        Returns:
+            list[dict]: Annotation info from XML file.
+        """
+        assert self._metainfo.get('classes', None) is not None, \
+            'classes in `XMLDataset` can not be None.'
+        self.cat2label = {
+            cat: i
+            for i, cat in enumerate(self._metainfo['classes'])
+        }
+
+        data_list = []
+        img_ids = list_from_file(self.ann_file, backend_args=self.backend_args)
+
+        # loading process takes around 10 mins
+        if is_main_process():
+            prog_bar = ProgressBar(len(img_ids))
+
+        for img_id in img_ids:
+            raw_img_info = {}
+            raw_img_info['img_id'] = img_id
+            raw_img_info['file_name'] = f'{img_id}.jpg'
+            parsed_data_info = self.parse_data_info(raw_img_info)
+            data_list.append(parsed_data_info)
+
+            if is_main_process():
+                prog_bar.update()
+        return data_list
+
+    def parse_data_info(self, img_info: dict) -> Union[dict, List[dict]]:
+        """Parse raw annotation to target format.
+
+        Args:
+            img_info (dict): Raw image information, usually it includes
+                `img_id`, `file_name`, and `xml_path`.
+
+        Returns:
+            Union[dict, List[dict]]: Parsed annotation.
+        """
+        data_info = {}
+        img_id = img_info['img_id']
+        xml_path = osp.join(self.data_prefix['img'], 'Annotations',
+                            f'{img_id}.xml')
+        data_info['img_id'] = img_id
+        data_info['xml_path'] = xml_path
+
+        # deal with xml file
+        with get_local_path(
+                xml_path, backend_args=self.backend_args) as local_path:
+            raw_ann_info = ET.parse(local_path)
+        root = raw_ann_info.getroot()
+        size = root.find('size')
+        width = int(size.find('width').text)
+        height = int(size.find('height').text)
+        folder = root.find('folder').text
+        img_path = osp.join(self.data_prefix['img'], folder,
+                            img_info['file_name'])
+        data_info['img_path'] = img_path
+
+        data_info['height'] = height
+        data_info['width'] = width
+
+        # Coordinates are in range [0, width - 1 or height - 1]
+        data_info['instances'] = self._parse_instance_info(
+            raw_ann_info, minus_one=False)
+        return data_info

mmdet/datasets/xml_style.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import xml.etree.ElementTree as ET
+from typing import List, Optional, Union
+
+import mmcv
+from mmengine.fileio import get, get_local_path, list_from_file
+
+from mmdet.registry import DATASETS
+from .base_det_dataset import BaseDetDataset
+
+
+@DATASETS.register_module()
+class XMLDataset(BaseDetDataset):
+    """XML dataset for detection.
+
+    Args:
+        img_subdir (str): Subdir where images are stored. Default: JPEGImages.
+        ann_subdir (str): Subdir where annotations are. Default: Annotations.
+        backend_args (dict, optional): Arguments to instantiate the
+            corresponding backend. Defaults to None.
+    """
+
+    def __init__(self,
+                 img_subdir: str = 'JPEGImages',
+                 ann_subdir: str = 'Annotations',
+                 **kwargs) -> None:
+        self.img_subdir = img_subdir
+        self.ann_subdir = ann_subdir
+        super().__init__(**kwargs)
+
+    @property
+    def sub_data_root(self) -> str:
+        """Return the sub data root."""
+        return self.data_prefix.get('sub_data_root', '')
+
+    def load_data_list(self) -> List[dict]:
+        """Load annotation from XML style ann_file.
+
+        Returns:
+            list[dict]: Annotation info from XML file.
+        """
+        assert self._metainfo.get('classes', None) is not None, \
+            '`classes` in `XMLDataset` can not be None.'
+        self.cat2label = {
+            cat: i
+            for i, cat in enumerate(self._metainfo['classes'])
+        }
+
+        data_list = []
+        img_ids = list_from_file(self.ann_file, backend_args=self.backend_args)
+        for img_id in img_ids:
+            file_name = osp.join(self.img_subdir, f'{img_id}.jpg')
+            xml_path = osp.join(self.sub_data_root, self.ann_subdir,
+                                f'{img_id}.xml')
+
+            raw_img_info = {}
+            raw_img_info['img_id'] = img_id
+            raw_img_info['file_name'] = file_name
+            raw_img_info['xml_path'] = xml_path
+
+            parsed_data_info = self.parse_data_info(raw_img_info)
+            data_list.append(parsed_data_info)
+        return data_list
+
+    @property
+    def bbox_min_size(self) -> Optional[int]:
+        """Return the minimum size of bounding boxes in the images."""
+        if self.filter_cfg is not None:
+            return self.filter_cfg.get('bbox_min_size', None)
+        else:
+            return None
+
+    def parse_data_info(self, img_info: dict) -> Union[dict, List[dict]]:
+        """Parse raw annotation to target format.
+
+        Args:
+            img_info (dict): Raw image information, usually it includes
+                `img_id`, `file_name`, and `xml_path`.
+
+        Returns:
+            Union[dict, List[dict]]: Parsed annotation.
+        """
+        data_info = {}
+        img_path = osp.join(self.sub_data_root, img_info['file_name'])
+        data_info['img_path'] = img_path
+        data_info['img_id'] = img_info['img_id']
+        data_info['xml_path'] = img_info['xml_path']
+
+        # deal with xml file
+        with get_local_path(
+                img_info['xml_path'],
+                backend_args=self.backend_args) as local_path:
+            raw_ann_info = ET.parse(local_path)
+        root = raw_ann_info.getroot()
+        size = root.find('size')
+        if size is not None:
+            width = int(size.find('width').text)
+            height = int(size.find('height').text)
+        else:
+            img_bytes = get(img_path, backend_args=self.backend_args)
+            img = mmcv.imfrombytes(img_bytes, backend='cv2')
+            height, width = img.shape[:2]
+            del img, img_bytes
+
+        data_info['height'] = height
+        data_info['width'] = width
+
+        data_info['instances'] = self._parse_instance_info(
+            raw_ann_info, minus_one=True)
+
+        return data_info
+
+    def _parse_instance_info(self,
+                             raw_ann_info: ET,
+                             minus_one: bool = True) -> List[dict]:
+        """parse instance information.
+
+        Args:
+            raw_ann_info (ElementTree): ElementTree object.
+            minus_one (bool): Whether to subtract 1 from the coordinates.
+                Defaults to True.
+
+        Returns:
+            List[dict]: List of instances.
+        """
+        instances = []
+        for obj in raw_ann_info.findall('object'):
+            instance = {}
+            name = obj.find('name').text
+            if name not in self._metainfo['classes']:
+                continue
+            difficult = obj.find('difficult')
+            difficult = 0 if difficult is None else int(difficult.text)
+            bnd_box = obj.find('bndbox')
+            bbox = [
+                int(float(bnd_box.find('xmin').text)),
+                int(float(bnd_box.find('ymin').text)),
+                int(float(bnd_box.find('xmax').text)),
+                int(float(bnd_box.find('ymax').text))
+            ]
+
+            # VOC needs to subtract 1 from the coordinates
+            if minus_one:
+                bbox = [x - 1 for x in bbox]
+
+            ignore = False
+            if self.bbox_min_size is not None:
+                assert not self.test_mode
+                w = bbox[2] - bbox[0]
+                h = bbox[3] - bbox[1]
+                if w < self.bbox_min_size or h < self.bbox_min_size:
+                    ignore = True
+            if difficult or ignore:
+                instance['ignore_flag'] = 1
+            else:
+                instance['ignore_flag'] = 0
+            instance['bbox'] = bbox
+            instance['bbox_label'] = self.cat2label[name]
+            instances.append(instance)
+        return instances
+
+    def filter_data(self) -> List[dict]:
+        """Filter annotations according to filter_cfg.
+
+        Returns:
+            List[dict]: Filtered results.
+        """
+        if self.test_mode:
+            return self.data_list
+
+        filter_empty_gt = self.filter_cfg.get('filter_empty_gt', False) \
+            if self.filter_cfg is not None else False
+        min_size = self.filter_cfg.get('min_size', 0) \
+            if self.filter_cfg is not None else 0
+
+        valid_data_infos = []
+        for i, data_info in enumerate(self.data_list):
+            width = data_info['width']
+            height = data_info['height']
+            if filter_empty_gt and len(data_info['instances']) == 0:
+                continue
+            if min(width, height) >= min_size:
+                valid_data_infos.append(data_info)
+
+        return valid_data_infos

mmdet/datasets/youtube_vis_dataset.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.registry import DATASETS
+from .base_video_dataset import BaseVideoDataset
+
+
+@DATASETS.register_module()
+class YouTubeVISDataset(BaseVideoDataset):
+    """YouTube VIS dataset for video instance segmentation.
+
+    Args:
+        dataset_version (str): Select dataset year version.
+    """
+
+    def __init__(self, dataset_version: str, *args, **kwargs):
+        self.set_dataset_classes(dataset_version)
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def set_dataset_classes(cls, dataset_version: str) -> None:
+        """Pass the category of the corresponding year to metainfo.
+
+        Args:
+            dataset_version (str): Select dataset year version.
+        """
+        classes_2019_version = ('person', 'giant_panda', 'lizard', 'parrot',
+                                'skateboard', 'sedan', 'ape', 'dog', 'snake',
+                                'monkey', 'hand', 'rabbit', 'duck', 'cat',
+                                'cow', 'fish', 'train', 'horse', 'turtle',
+                                'bear', 'motorbike', 'giraffe', 'leopard',
+                                'fox', 'deer', 'owl', 'surfboard', 'airplane',
+                                'truck', 'zebra', 'tiger', 'elephant',
+                                'snowboard', 'boat', 'shark', 'mouse', 'frog',
+                                'eagle', 'earless_seal', 'tennis_racket')
+
+        classes_2021_version = ('airplane', 'bear', 'bird', 'boat', 'car',
+                                'cat', 'cow', 'deer', 'dog', 'duck',
+                                'earless_seal', 'elephant', 'fish',
+                                'flying_disc', 'fox', 'frog', 'giant_panda',
+                                'giraffe', 'horse', 'leopard', 'lizard',
+                                'monkey', 'motorbike', 'mouse', 'parrot',
+                                'person', 'rabbit', 'shark', 'skateboard',
+                                'snake', 'snowboard', 'squirrel', 'surfboard',
+                                'tennis_racket', 'tiger', 'train', 'truck',
+                                'turtle', 'whale', 'zebra')
+
+        if dataset_version == '2019':
+            cls.METAINFO = dict(classes=classes_2019_version)
+        elif dataset_version == '2021':
+            cls.METAINFO = dict(classes=classes_2021_version)
+        else:
+            raise NotImplementedError('Not supported YouTubeVIS dataset'
+                                      f'version: {dataset_version}')

mmdet/engine/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .hooks import *  # noqa: F401, F403
+from .optimizers import *  # noqa: F401, F403
+from .runner import *  # noqa: F401, F403
+from .schedulers import *  # noqa: F401, F403

mmdet/engine/hooks/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .checkloss_hook import CheckInvalidLossHook
+from .mean_teacher_hook import MeanTeacherHook
+from .memory_profiler_hook import MemoryProfilerHook
+from .num_class_check_hook import NumClassCheckHook
+from .pipeline_switch_hook import PipelineSwitchHook
+from .set_epoch_info_hook import SetEpochInfoHook
+from .sync_norm_hook import SyncNormHook
+from .utils import trigger_visualization_hook
+from .visualization_hook import (DetVisualizationHook,
+                                 GroundingVisualizationHook,
+                                 TrackVisualizationHook)
+from .yolox_mode_switch_hook import YOLOXModeSwitchHook
+
+__all__ = [
+    'YOLOXModeSwitchHook', 'SyncNormHook', 'CheckInvalidLossHook',
+    'SetEpochInfoHook', 'MemoryProfilerHook', 'DetVisualizationHook',
+    'NumClassCheckHook', 'MeanTeacherHook', 'trigger_visualization_hook',
+    'PipelineSwitchHook', 'TrackVisualizationHook',
+    'GroundingVisualizationHook'
+]

mmdet/engine/hooks/checkloss_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+import torch
+from mmengine.hooks import Hook
+from mmengine.runner import Runner
+
+from mmdet.registry import HOOKS
+
+
+@HOOKS.register_module()
+class CheckInvalidLossHook(Hook):
+    """Check invalid loss hook.
+
+    This hook will regularly check whether the loss is valid
+    during training.
+
+    Args:
+        interval (int): Checking interval (every k iterations).
+            Default: 50.
+    """
+
+    def __init__(self, interval: int = 50) -> None:
+        self.interval = interval
+
+    def after_train_iter(self,
+                         runner: Runner,
+                         batch_idx: int,
+                         data_batch: Optional[dict] = None,
+                         outputs: Optional[dict] = None) -> None:
+        """Regularly check whether the loss is valid every n iterations.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training process.
+            batch_idx (int): The index of the current batch in the train loop.
+            data_batch (dict, Optional): Data from dataloader.
+                Defaults to None.
+            outputs (dict, Optional): Outputs from model. Defaults to None.
+        """
+        if self.every_n_train_iters(runner, self.interval):
+            assert torch.isfinite(outputs['loss']), \
+                runner.logger.info('loss become infinite or NaN!')

mmdet/engine/hooks/mean_teacher_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+import torch.nn as nn
+from mmengine.hooks import Hook
+from mmengine.model import is_model_wrapper
+from mmengine.runner import Runner
+
+from mmdet.registry import HOOKS
+
+
+@HOOKS.register_module()
+class MeanTeacherHook(Hook):
+    """Mean Teacher Hook.
+
+    Mean Teacher is an efficient semi-supervised learning method in
+    `Mean Teacher <https://arxiv.org/abs/1703.01780>`_.
+    This method requires two models with exactly the same structure,
+    as the student model and the teacher model, respectively.
+    The student model updates the parameters through gradient descent,
+    and the teacher model updates the parameters through
+    exponential moving average of the student model.
+    Compared with the student model, the teacher model
+    is smoother and accumulates more knowledge.
+
+    Args:
+        momentum (float): The momentum used for updating teacher's parameter.
+            Teacher's parameter are updated with the formula:
+           `teacher = (1-momentum) * teacher + momentum * student`.
+            Defaults to 0.001.
+        interval (int): Update teacher's parameter every interval iteration.
+            Defaults to 1.
+        skip_buffers (bool): Whether to skip the model buffers, such as
+            batchnorm running stats (running_mean, running_var), it does not
+            perform the ema operation. Default to True.
+    """
+
+    def __init__(self,
+                 momentum: float = 0.001,
+                 interval: int = 1,
+                 skip_buffer=True) -> None:
+        assert 0 < momentum < 1
+        self.momentum = momentum
+        self.interval = interval
+        self.skip_buffers = skip_buffer
+
+    def before_train(self, runner: Runner) -> None:
+        """To check that teacher model and student model exist."""
+        model = runner.model
+        if is_model_wrapper(model):
+            model = model.module
+        assert hasattr(model, 'teacher')
+        assert hasattr(model, 'student')
+        # only do it at initial stage
+        if runner.iter == 0:
+            self.momentum_update(model, 1)
+
+    def after_train_iter(self,
+                         runner: Runner,
+                         batch_idx: int,
+                         data_batch: Optional[dict] = None,
+                         outputs: Optional[dict] = None) -> None:
+        """Update teacher's parameter every self.interval iterations."""
+        if (runner.iter + 1) % self.interval != 0:
+            return
+        model = runner.model
+        if is_model_wrapper(model):
+            model = model.module
+        self.momentum_update(model, self.momentum)
+
+    def momentum_update(self, model: nn.Module, momentum: float) -> None:
+        """Compute the moving average of the parameters using exponential
+        moving average."""
+        if self.skip_buffers:
+            for (src_name, src_parm), (dst_name, dst_parm) in zip(
+                    model.student.named_parameters(),
+                    model.teacher.named_parameters()):
+                dst_parm.data.mul_(1 - momentum).add_(
+                    src_parm.data, alpha=momentum)
+        else:
+            for (src_parm,
+                 dst_parm) in zip(model.student.state_dict().values(),
+                                  model.teacher.state_dict().values()):
+                # exclude num_tracking
+                if dst_parm.dtype.is_floating_point:
+                    dst_parm.data.mul_(1 - momentum).add_(
+                        src_parm.data, alpha=momentum)

mmdet/engine/hooks/memory_profiler_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional, Sequence
+
+from mmengine.hooks import Hook
+from mmengine.runner import Runner
+
+from mmdet.registry import HOOKS
+from mmdet.structures import DetDataSample
+
+
+@HOOKS.register_module()
+class MemoryProfilerHook(Hook):
+    """Memory profiler hook recording memory information including virtual
+    memory, swap memory, and the memory of the current process.
+
+    Args:
+        interval (int): Checking interval (every k iterations).
+            Default: 50.
+    """
+
+    def __init__(self, interval: int = 50) -> None:
+        try:
+            from psutil import swap_memory, virtual_memory
+            self._swap_memory = swap_memory
+            self._virtual_memory = virtual_memory
+        except ImportError:
+            raise ImportError('psutil is not installed, please install it by: '
+                              'pip install psutil')
+
+        try:
+            from memory_profiler import memory_usage
+            self._memory_usage = memory_usage
+        except ImportError:
+            raise ImportError(
+                'memory_profiler is not installed, please install it by: '
+                'pip install memory_profiler')
+
+        self.interval = interval
+
+    def _record_memory_information(self, runner: Runner) -> None:
+        """Regularly record memory information.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training or evaluation
+                process.
+        """
+        # in Byte
+        virtual_memory = self._virtual_memory()
+        swap_memory = self._swap_memory()
+        # in MB
+        process_memory = self._memory_usage()[0]
+        factor = 1024 * 1024
+        runner.logger.info(
+            'Memory information '
+            'available_memory: '
+            f'{round(virtual_memory.available / factor)} MB, '
+            'used_memory: '
+            f'{round(virtual_memory.used / factor)} MB, '
+            f'memory_utilization: {virtual_memory.percent} %, '
+            'available_swap_memory: '
+            f'{round((swap_memory.total - swap_memory.used) / factor)}'
+            ' MB, '
+            f'used_swap_memory: {round(swap_memory.used / factor)} MB, '
+            f'swap_memory_utilization: {swap_memory.percent} %, '
+            'current_process_memory: '
+            f'{round(process_memory)} MB')
+
+    def after_train_iter(self,
+                         runner: Runner,
+                         batch_idx: int,
+                         data_batch: Optional[dict] = None,
+                         outputs: Optional[dict] = None) -> None:
+        """Regularly record memory information.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training process.
+            batch_idx (int): The index of the current batch in the train loop.
+            data_batch (dict, optional): Data from dataloader.
+                Defaults to None.
+            outputs (dict, optional): Outputs from model. Defaults to None.
+        """
+        if self.every_n_inner_iters(batch_idx, self.interval):
+            self._record_memory_information(runner)
+
+    def after_val_iter(
+            self,
+            runner: Runner,
+            batch_idx: int,
+            data_batch: Optional[dict] = None,
+            outputs: Optional[Sequence[DetDataSample]] = None) -> None:
+        """Regularly record memory information.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the validation process.
+            batch_idx (int): The index of the current batch in the val loop.
+            data_batch (dict, optional): Data from dataloader.
+                Defaults to None.
+            outputs (Sequence[:obj:`DetDataSample`], optional):
+                Outputs from model. Defaults to None.
+        """
+        if self.every_n_inner_iters(batch_idx, self.interval):
+            self._record_memory_information(runner)
+
+    def after_test_iter(
+            self,
+            runner: Runner,
+            batch_idx: int,
+            data_batch: Optional[dict] = None,
+            outputs: Optional[Sequence[DetDataSample]] = None) -> None:
+        """Regularly record memory information.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the testing process.
+            batch_idx (int): The index of the current batch in the test loop.
+            data_batch (dict, optional): Data from dataloader.
+                Defaults to None.
+            outputs (Sequence[:obj:`DetDataSample`], optional):
+                Outputs from model. Defaults to None.
+        """
+        if self.every_n_inner_iters(batch_idx, self.interval):
+            self._record_memory_information(runner)

mmdet/engine/hooks/num_class_check_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn import VGG
+from mmengine.hooks import Hook
+from mmengine.runner import Runner
+
+from mmdet.registry import HOOKS
+
+
+@HOOKS.register_module()
+class NumClassCheckHook(Hook):
+    """Check whether the `num_classes` in head matches the length of `classes`
+    in `dataset.metainfo`."""
+
+    def _check_head(self, runner: Runner, mode: str) -> None:
+        """Check whether the `num_classes` in head matches the length of
+        `classes` in `dataset.metainfo`.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training or evaluation
+                process.
+        """
+        assert mode in ['train', 'val']
+        model = runner.model
+        dataset = runner.train_dataloader.dataset if mode == 'train' else \
+            runner.val_dataloader.dataset
+        if dataset.metainfo.get('classes', None) is None:
+            runner.logger.warning(
+                f'Please set `classes` '
+                f'in the {dataset.__class__.__name__} `metainfo` and'
+                f'check if it is consistent with the `num_classes` '
+                f'of head')
+        else:
+            classes = dataset.metainfo['classes']
+            assert type(classes) is not str, \
+                (f'`classes` in {dataset.__class__.__name__}'
+                 f'should be a tuple of str.'
+                 f'Add comma if number of classes is 1 as '
+                 f'classes = ({classes},)')
+            from mmdet.models.roi_heads.mask_heads import FusedSemanticHead
+            for name, module in model.named_modules():
+                if hasattr(module, 'num_classes') and not name.endswith(
+                        'rpn_head') and not isinstance(
+                            module, (VGG, FusedSemanticHead)):
+                    assert module.num_classes == len(classes), \
+                        (f'The `num_classes` ({module.num_classes}) in '
+                         f'{module.__class__.__name__} of '
+                         f'{model.__class__.__name__} does not matches '
+                         f'the length of `classes` '
+                         f'{len(classes)}) in '
+                         f'{dataset.__class__.__name__}')
+
+    def before_train_epoch(self, runner: Runner) -> None:
+        """Check whether the training dataset is compatible with head.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training or evaluation
+                process.
+        """
+        self._check_head(runner, 'train')
+
+    def before_val_epoch(self, runner: Runner) -> None:
+        """Check whether the dataset in val epoch is compatible with head.
+
+        Args:
+            runner (:obj:`Runner`): The runner of the training or evaluation
+                process.
+        """
+        self._check_head(runner, 'val')

mmdet/engine/hooks/pipeline_switch_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms import Compose
+from mmengine.hooks import Hook
+
+from mmdet.registry import HOOKS
+
+
+@HOOKS.register_module()
+class PipelineSwitchHook(Hook):
+    """Switch data pipeline at switch_epoch.
+
+    Args:
+        switch_epoch (int): switch pipeline at this epoch.
+        switch_pipeline (list[dict]): the pipeline to switch to.
+    """
+
+    def __init__(self, switch_epoch, switch_pipeline):
+        self.switch_epoch = switch_epoch
+        self.switch_pipeline = switch_pipeline
+        self._restart_dataloader = False
+        self._has_switched = False
+
+    def before_train_epoch(self, runner):
+        """switch pipeline."""
+        epoch = runner.epoch
+        train_loader = runner.train_dataloader
+        if epoch >= self.switch_epoch and not self._has_switched:
+            runner.logger.info('Switch pipeline now!')
+            # The dataset pipeline cannot be updated when persistent_workers
+            # is True, so we need to force the dataloader's multi-process
+            # restart. This is a very hacky approach.
+            train_loader.dataset.pipeline = Compose(self.switch_pipeline)
+            if hasattr(train_loader, 'persistent_workers'
+                       ) and train_loader.persistent_workers is True:
+                train_loader._DataLoader__initialized = False
+                train_loader._iterator = None
+                self._restart_dataloader = True
+            self._has_switched = True
+        else:
+            # Once the restart is complete, we need to restore
+            # the initialization flag.
+            if self._restart_dataloader:
+                train_loader._DataLoader__initialized = True

mmdet/engine/hooks/set_epoch_info_hook.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import Hook
+from mmengine.model.wrappers import is_model_wrapper
+
+from mmdet.registry import HOOKS
+
+
+@HOOKS.register_module()
+class SetEpochInfoHook(Hook):
+    """Set runner's epoch information to the model."""
+
+    def before_train_epoch(self, runner):
+        epoch = runner.epoch
+        model = runner.model
+        if is_model_wrapper(model):
+            model = model.module
+        model.set_epoch(epoch)