[Feature] add SMOKE augmentation method (#955)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import (#839)

* [Enhance]  refactor  iou_neg_piecewise_sampler.py (#842)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import

* refactor iou_neg_piecewise_sampler.py

* add docstring

* modify docstring
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>

* [Feature] Add roipooling cuda ops (#843)

* [Refactor] Main code modification for coordinate system refactor (#677)

* [Enhance] Add script for data update (#774)

* Fixed wrong config paths and fixed a bug in test

* Fixed metafile

* Coord sys refactor (main code)

* Update test_waymo_dataset.py

* Manually resolve conflict

* Removed unused lines and fixed imports

* remove coord2box and box2coord

* update dir_limit_offset

* Some minor improvements

* Removed some \s in comments

* Revert a change

* Change Box3DMode to Coord3DMode where points are converted

* Fix points_in_bbox function

* Fix Imvoxelnet config

* Revert adding a line

* Fix rotation bug when batch size is 0

* Keep sign of dir_scores as before

* Fix several comments

* Add a comment

* Fix docstring

* Add data update scripts

* Fix comments

* fix import

* add roipooling cuda ops

* add roi extractor

* add test_roi_extractor unittest

* Modify setup.py to install roipooling ops

* modify docstring

* remove enlarge bbox in roipoint pooling

* add_roipooling_ops

* modify docstring
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>

* [Refactor] Refactor code structure and docstrings (#803)

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning

* [Feature] PointXYZWHLRBBoxCoder (#856)

* support PointBasedBoxCoder

* fix unittest bug

* support unittest in gpu

* support unittest in gpu

* modified docstring

* add args

* add args

* [Enhance] Change Groupfree3D config (#855)

* All mods

* PointSample

* PointSample

* [Doc] Add tutorials/data_pipeline Chinese version (#827)

* [Doc] Add tutorials/data_pipeline Chinese version

* refine doc

* Use the absolute link

* Use the absolute link
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

* [Doc] Add Chinese doc for `scannet_det.md` (#836)

* Part

* Complete

* Fix comments

* Fix comments

* [Doc] Add Chinese doc for `waymo_det.md` (#859)

* Add complete translation

* Refinements

* Fix comments

* Fix a minor typo
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

* Remove 2D annotations on Lyft (#867)

* Add header for files (#869)

* Add header for files

* Add header for files

* Add header for files

* Add header for files

* [fix] fix typos (#872)

* Fix 3 unworking configs (#882)

* [Fix] Fix `index.rst` for Chinese docs (#873)

* Fix index.rst for zh docs

* Change switch language

* [Fix] Centerpoint head nested list transpose  (#879)

* FIX Transpose nested lists without Numpy

* Removed unused Numpy import

* [Enhance] Update PointFusion (#791)

* update point fusion

* remove LIDAR hardcode

* move get_proj_mat_by_coord_type to utils

* fix lint

* remove todo

* fix lint

* [Doc] Add nuscenes_det.md Chinese version (#854)

* add nus chinese doc

* add nuScenes Chinese doc

* fix typo

* fix typo

* fix typo

* fix typo

* fix typo

* [Fix] Fix RegNet pretrained weight loading (#889)

* Fix regnet pretrained weight loading

* Remove unused file

* Fix centerpoint tta (#892)

* [Enhance] Add benchmark regression script (#808)

* Initial commit

* [Feature] Support DGCNN (v1.0.0.dev0) (#896)

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* support dgcnn

* fix typo

* fix typo

* fix typo

* del gf&fa registry (wo reuse pointnet module)

* fix typo

* add benchmark and add copyright header (for DGCNN only)

* fix typo

* fix typo

* fix typo

* fix typo

* fix typo

* support dgcnn

* Change cam rot_3d_in_axis (#906)

* [Doc] Add coord sys tutorial pic and change links to dev branch (#912)

* Modify link branch and add pic

* Fix pic

* [Feature] add kitti AP40 evaluation metric (v1.0.0.dev0) (#927)

* Add citation (#901)

* [Feature] Add python3.9 in CI (#900)

* Add python3.0 in CI

* Add python3.0 in CI

* Bump to v0.17.0 (#898)

* Update README.md

* Update README_zh-CN.md

* Update version.py

* Update getting_started.md

* Update getting_started.md

* Update changelog.md

* Remove "recent" in the news

* Remove "recent" in the news

* Fix comments

* [Docs] Fix the version of sphinx (#902)

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* Fix sphinx version

* add AP40

* add unitest

* add unitest

* seperate AP11 and AP40

* fix some typos
Co-authored-by: dingchang <hudingchang.vendor@sensetime.com>
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

* [Feature] add smoke backbone neck (#939)

* add smoke detecotor and it's backbone and neck

* typo fix

* fix typo

* add docstring

* fix typo

* fix comments

* fix comments

* fix comments

* fix typo

* fix typo

* fix

* fix typo

* fix docstring

* refine feature

* fix typo

* use Basemodule in Neck

* [Refactor] Refactor the transformation from image to camera coordinates (#938)

* Refactor points_img2cam

* Refine docstring

* Support array converter and add unit tests

* [Feature] FCOS3D BBox Coder (#940)

* FCOS3D BBox Coder

* Add unit tests

* Change the value from long to float/double

* Rename bbox_out as bbox

* Add comments to forward returns

* [Feature] PGD BBox Coder (#948)

* Support PGD BBox Coder

* Refine docstring

* add smoke augmentation method

* add docs

* fix docstrings

* fix typos

* change point name

* fix typos

* fix typos

* fix typos
Co-authored-by: Yezhen Cong <52420115+THU17cyz@users.noreply.github.com>
Co-authored-by: Xi Liu <75658786+xiliu8006@users.noreply.github.com>
Co-authored-by: THU17cyz <congyezhen71@hotmail.com>
Co-authored-by: Wenhao Wu <79644370+wHao-Wu@users.noreply.github.com>
Co-authored-by: Tai-Wang <tab_wang@outlook.com>
Co-authored-by: dingchang <hudingchang.vendor@sensetime.com>
Co-authored-by: 谢恩泽 <Johnny_ez@163.com>
Co-authored-by: Robin Karlsson <34254153+robin-karlsson0@users.noreply.github.com>
Co-authored-by: Danila Rukhovich <danrukh@gmail.com>

mmdet3d/datasets/__init__.py

@@ -9,14 +9,14 @@ from .lyft_dataset import LyftDataset
 from .nuscenes_dataset import NuScenesDataset
 from .nuscenes_mono_dataset import NuScenesMonoDataset
 # yapf: disable
-from .pipelines import (BackgroundPointsFilter, GlobalAlignment,
+from .pipelines import (AffineResize, BackgroundPointsFilter, GlobalAlignment,
                         GlobalRotScaleTrans, IndoorPatchPointSample,
                         IndoorPointSample, LoadAnnotations3D,
                         LoadPointsFromFile, LoadPointsFromMultiSweeps,
                         NormalizePointsColor, ObjectNameFilter, ObjectNoise,
                         ObjectRangeFilter, ObjectSample, PointSample,
                         PointShuffle, PointsRangeFilter, RandomDropPointsColor,
-                        RandomFlip3D, RandomJitterPoints,
+                        RandomFlip3D, RandomJitterPoints, RandomShiftScale,
                         VoxelBasedPointSampler)
 # yapf: enable
 from .s3dis_dataset import S3DISDataset, S3DISSegDataset
@@ -38,5 +38,5 @@ __all__ = [
     'Custom3DDataset', 'Custom3DSegDataset', 'LoadPointsFromMultiSweeps',
     'WaymoDataset', 'BackgroundPointsFilter', 'VoxelBasedPointSampler',
     'get_loading_pipeline', 'RandomDropPointsColor', 'RandomJitterPoints',
-    'ObjectNameFilter'
+    'ObjectNameFilter', 'AffineResize', 'RandomShiftScale'
 ]

mmdet3d/datasets/pipelines/__init__.py

@@ -7,13 +7,15 @@ from .loading import (LoadAnnotations3D, LoadImageFromFileMono3D,
                       LoadPointsFromMultiSweeps, NormalizePointsColor,
                       PointSegClassMapping)
 from .test_time_aug import MultiScaleFlipAug3D
-from .transforms_3d import (BackgroundPointsFilter, GlobalAlignment,
-                            GlobalRotScaleTrans, IndoorPatchPointSample,
-                            IndoorPointSample, ObjectNameFilter, ObjectNoise,
-                            ObjectRangeFilter, ObjectSample, PointSample,
-                            PointShuffle, PointsRangeFilter,
-                            RandomDropPointsColor, RandomFlip3D,
-                            RandomJitterPoints, VoxelBasedPointSampler)
+# yapf: disable
+from .transforms_3d import (AffineResize, BackgroundPointsFilter,
+                            GlobalAlignment, GlobalRotScaleTrans,
+                            IndoorPatchPointSample, IndoorPointSample,
+                            ObjectNameFilter, ObjectNoise, ObjectRangeFilter,
+                            ObjectSample, PointSample, PointShuffle,
+                            PointsRangeFilter, RandomDropPointsColor,
+                            RandomFlip3D, RandomJitterPoints, RandomShiftScale,
+                            VoxelBasedPointSampler)
 
 __all__ = [
     'ObjectSample', 'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans',
@@ -25,5 +27,5 @@ __all__ = [
     'LoadPointsFromMultiSweeps', 'BackgroundPointsFilter',
     'VoxelBasedPointSampler', 'GlobalAlignment', 'IndoorPatchPointSample',
     'LoadImageFromFileMono3D', 'ObjectNameFilter', 'RandomDropPointsColor',
-    'RandomJitterPoints'
+    'RandomJitterPoints', 'AffineResize', 'RandomShiftScale'
 ]

mmdet3d/datasets/pipelines/transforms_3d.py

 # Copyright (c) OpenMMLab. All rights reserved.
+import cv2
 import numpy as np
+import random
 import warnings
 from mmcv import is_tuple_of
 from mmcv.utils import build_from_cfg
@@ -1422,3 +1424,258 @@ class VoxelBasedPointSampler(object):
         repr_str += ' ' * indent + 'prev_voxel_generator=\n'
         repr_str += f'{_auto_indent(repr(self.prev_voxel_generator), 8)})'
         return repr_str
+
+
+@PIPELINES.register_module()
+class AffineResize(object):
+    """Get the affine transform matrices to the target size.
+
+    Different from :class:`RandomAffine` in MMDetection, this class can
+    calculate the affine transform matrices while resizing the input image
+    to a fixed size. The affine transform matrices include: 1) matrix
+    transforming original image to the network input image size. 2) matrix
+    transforming original image to the network output feature map size.
+
+    Args:
+        img_scale (tuple): Images scales for resizing.
+        down_ratio (int): The down ratio of feature map.
+            Actually the arg should be >= 1.
+        bbox_clip_border (bool, optional): Whether clip the objects
+            outside the border of the image. Defaults to True.
+    """
+
+    def __init__(self, img_scale, down_ratio, bbox_clip_border=True):
+
+        self.img_scale = img_scale
+        self.down_ratio = down_ratio
+        self.bbox_clip_border = bbox_clip_border
+
+    def __call__(self, results):
+        """Call function to do affine transform to input image and labels.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Results after affine resize, 'affine_aug', 'trans_mat'
+                keys are added in the result dict.
+        """
+        # The results have gone through RandomShiftScale before AffineResize
+        if 'center' not in results:
+            img = results['img']
+            height, width = img.shape[:2]
+            center = np.array([width / 2, height / 2], dtype=np.float32)
+            size = np.array([width, height], dtype=np.float32)
+            results['affine_aug'] = False
+        else:
+            # The results did not go through RandomShiftScale before
+            # AffineResize
+            img = results['img']
+            center = results['center']
+            size = results['size']
+
+        trans_affine = self._get_transform_matrix(center, size, self.img_scale)
+
+        img = cv2.warpAffine(img, trans_affine[:2, :], self.img_scale)
+
+        if isinstance(self.down_ratio, tuple):
+            trans_mat = [
+                self._get_transform_matrix(
+                    center, size,
+                    (self.img_scale[0] // ratio, self.img_scale[1] // ratio))
+                for ratio in self.down_ratio
+            ]  # (3, 3)
+        else:
+            trans_mat = self._get_transform_matrix(
+                center, size, (self.img_scale[0] // self.down_ratio,
+                               self.img_scale[1] // self.down_ratio))
+
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['pad_shape'] = img.shape
+        results['trans_mat'] = trans_mat
+
+        self._affine_bboxes(results, trans_affine)
+
+        if 'centers2d' in results:
+            centers2d = self._affine_transform(results['centers2d'],
+                                               trans_affine)
+            valid_index = (centers2d[:, 0] >
+                           0) & (centers2d[:, 0] <
+                                 self.img_scale[0]) & (centers2d[:, 1] > 0) & (
+                                     centers2d[:, 1] < self.img_scale[1])
+            results['centers2d'] = centers2d[valid_index]
+
+            for key in results.get('bbox_fields', []):
+                if key in ['gt_bboxes']:
+                    results[key] = results[key][valid_index]
+                    if 'gt_labels' in results:
+                        results['gt_labels'] = results['gt_labels'][
+                            valid_index]
+                    if 'gt_masks' in results:
+                        raise NotImplementedError(
+                            'AffineResize only supports bbox.')
+
+            for key in results.get('bbox3d_fields', []):
+                if key in ['gt_bboxes_3d']:
+                    results[key].tensor = results[key].tensor[valid_index]
+                    if 'gt_labels_3d' in results:
+                        results['gt_labels_3d'] = results['gt_labels_3d'][
+                            valid_index]
+
+            results['depths'] = results['depths'][valid_index]
+
+        return results
+
+    def _affine_bboxes(self, results, matrix):
+        """Affine transform bboxes to input image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            matrix (np.ndarray): Matrix transforming original
+                image to the network input image size.
+                shape: (3, 3)
+        """
+
+        for key in results.get('bbox_fields', []):
+            bboxes = results[key]
+            bboxes[:, :2] = self._affine_transform(bboxes[:, :2], matrix)
+            bboxes[:, 2:] = self._affine_transform(bboxes[:, 2:], matrix)
+            if self.bbox_clip_border:
+                bboxes[:,
+                       [0, 2]] = bboxes[:,
+                                        [0, 2]].clip(0, self.img_scale[0] - 1)
+                bboxes[:,
+                       [1, 3]] = bboxes[:,
+                                        [1, 3]].clip(0, self.img_scale[1] - 1)
+            results[key] = bboxes
+
+    def _affine_transform(self, points, matrix):
+        """Affine transform bbox points to input iamge.
+
+        Args:
+            points (np.ndarray): Points to be transformed.
+                shape: (N, 2)
+            matrix (np.ndarray): Affine transform matrix.
+                shape: (3, 3)
+
+        Returns:
+            np.ndarray: Transformed points.
+        """
+        num_points = points.shape[0]
+        hom_points_2d = np.concatenate((points, np.ones((num_points, 1))),
+                                       axis=1)
+        hom_points_2d = hom_points_2d.T
+        affined_points = np.matmul(matrix, hom_points_2d).T
+        return affined_points[:, :2]
+
+    def _get_transform_matrix(self, center, scale, output_scale):
+        """Get affine transform matrix.
+
+        Args:
+            center (tuple): Center of current image.
+            scale (tuple): Scale of current image.
+            output_scale (tuple[float]): The transform target image scales.
+
+        Returns:
+            np.ndarray: Affine transform matrix.
+        """
+        # TODO: further add rot and shift here.
+        src_w = scale[0]
+        dst_w = output_scale[0]
+        dst_h = output_scale[1]
+
+        src_dir = np.array([0, src_w * -0.5])
+        dst_dir = np.array([0, dst_w * -0.5])
+
+        src = np.zeros((3, 2), dtype=np.float32)
+        dst = np.zeros((3, 2), dtype=np.float32)
+        src[0, :] = center
+        src[1, :] = center + src_dir
+        dst[0, :] = np.array([dst_w * 0.5, dst_h * 0.5])
+        dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+
+        src[2, :] = self._get_ref_point(src[0, :], src[1, :])
+        dst[2, :] = self._get_ref_point(dst[0, :], dst[1, :])
+
+        get_matrix = cv2.getAffineTransform(src, dst)
+
+        matrix = np.concatenate((get_matrix, [[0., 0., 1.]]))
+
+        return matrix.astype(np.float32)
+
+    def _get_ref_point(self, ref_point1, ref_point2):
+        """Get reference point to calculate affine transfrom matrix.
+
+        While using opencv to calculate the affine matrix, we need at least
+        three corresponding points seperately on original image and target
+        image. Here we use two points to get the the third reference point.
+        """
+        d = ref_point1 - ref_point2
+        ref_point3 = ref_point2 + np.array([-d[1], d[0]])
+        return ref_point3
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(img_scale={self.img_scale}, '
+        repr_str += f'down_ratio={self.down_ratio}) '
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomShiftScale(object):
+    """Random shift scale.
+
+    Different from the normal shift and scale function, it doesn't
+    directly shift or scale image. It can record the shift and scale
+    infos into loading pipelines. It's desgined to be used with
+    AffineResize together.
+
+    Args:
+        shift_scale (tuple[float]): Shift and scale range.
+        aug_prob (float): The shifting and scaling probability.
+    """
+
+    def __init__(self, shift_scale, aug_prob):
+
+        self.shift_scale = shift_scale
+        self.aug_prob = aug_prob
+
+    def __call__(self, results):
+        """Call function to record random shift and scale infos.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Results after random shift and scale, 'center', 'size'
+                and 'affine_aug' keys are added in the result dict.
+        """
+        img = results['img']
+
+        height, width = img.shape[:2]
+
+        center = np.array([width / 2, height / 2], dtype=np.float32)
+        size = np.array([width, height], dtype=np.float32)
+
+        if random.random() < self.aug_prob:
+            shift, scale = self.shift_scale[0], self.shift_scale[1]
+            shift_ranges = np.arange(-shift, shift + 0.1, 0.1)
+            center[0] += size[0] * random.choice(shift_ranges)
+            center[1] += size[1] * random.choice(shift_ranges)
+            scale_ranges = np.arange(1 - scale, 1 + scale + 0.1, 0.1)
+            size *= random.choice(scale_ranges)
+            results['affine_aug'] = True
+        else:
+            results['affine_aug'] = False
+
+        results['center'] = center
+        results['size'] = size
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(shift_scale={self.shift_scale}, '
+        repr_str += f'aug_prob={self.aug_prob}) '
+        return repr_str

tests/test_data/test_pipelines/test_augmentations/test_transforms_3d.py

@@ -8,12 +8,14 @@ from mmdet3d.core import (Box3DMode, CameraInstance3DBoxes,
                           DepthInstance3DBoxes, LiDARInstance3DBoxes)
 from mmdet3d.core.bbox import Coord3DMode
 from mmdet3d.core.points import DepthPoints, LiDARPoints
-from mmdet3d.datasets import (BackgroundPointsFilter, GlobalAlignment,
-                              GlobalRotScaleTrans, ObjectNameFilter,
-                              ObjectNoise, ObjectRangeFilter, ObjectSample,
-                              PointSample, PointShuffle, PointsRangeFilter,
-                              RandomDropPointsColor, RandomFlip3D,
-                              RandomJitterPoints, VoxelBasedPointSampler)
+# yapf: disable
+from mmdet3d.datasets import (AffineResize, BackgroundPointsFilter,
+                              GlobalAlignment, GlobalRotScaleTrans,
+                              ObjectNameFilter, ObjectNoise, ObjectRangeFilter,
+                              ObjectSample, PointSample, PointShuffle,
+                              PointsRangeFilter, RandomDropPointsColor,
+                              RandomFlip3D, RandomJitterPoints,
+                              RandomShiftScale, VoxelBasedPointSampler)
 
 
 def test_remove_points_in_boxes():
@@ -755,3 +757,96 @@ def test_points_sample():
     select_idx = np.array([449, 444])
     expected_pts = points.tensor.numpy()[select_idx]
     assert np.allclose(sampled_pts.tensor.numpy(), expected_pts)
+
+
+def test_affine_resize():
+
+    def create_random_bboxes(num_bboxes, img_w, img_h):
+        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
+        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
+        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
+        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
+            np.float32)
+        return bboxes
+
+    affine_reszie = AffineResize(img_scale=(1290, 384), down_ratio=4)
+
+    # test the situation: not use Random_Scale_Shift before AffineResize
+    results = dict()
+    img = mmcv.imread('./tests/data/kitti/training/image_2/000000.png',
+                      'color')
+    results['img'] = img
+    results['bbox_fields'] = ['gt_bboxes']
+    results['bbox3d_fields'] = ['gt_bboxes_3d']
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_3d = CameraInstance3DBoxes(torch.randn((8, 7)))
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_labels3d'] = results['gt_labels']
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_3d'] = gt_bboxes_3d
+    results['depths'] = np.random.randn(gt_bboxes.shape[0])
+    centers2d_x = (gt_bboxes[:, [0]] + gt_bboxes[:, [2]]) / 2
+    centers2d_y = (gt_bboxes[:, [1]] + gt_bboxes[:, [3]]) / 2
+    centers2d = np.concatenate((centers2d_x, centers2d_y), axis=1)
+    results['centers2d'] = centers2d
+
+    results = affine_reszie(results)
+
+    assert results['gt_labels'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_labels3d'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_bboxes'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_bboxes_3d'].tensor.shape[0] == \
+           results['centers2d'].shape[0]
+    assert results['affine_aug'] is False
+
+    # test the situation: not use Random_Scale_Shift before AffineResize
+    results = dict()
+    img = mmcv.imread('./tests/data/kitti/training/image_2/000000.png',
+                      'color')
+    results['img'] = img
+    results['bbox_fields'] = ['gt_bboxes']
+    results['bbox3d_fields'] = ['gt_bboxes_3d']
+    h, w, _ = img.shape
+    center = np.array([w / 2, h / 2], dtype=np.float32)
+    size = np.array([w, h], dtype=np.float32)
+
+    results['center'] = center
+    results['size'] = size
+    results['affine_aug'] = False
+
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_3d = CameraInstance3DBoxes(torch.randn((8, 7)))
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_labels3d'] = results['gt_labels']
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_3d'] = gt_bboxes_3d
+    results['depths'] = np.random.randn(gt_bboxes.shape[0])
+    centers2d_x = (gt_bboxes[:, [0]] + gt_bboxes[:, [2]]) / 2
+    centers2d_y = (gt_bboxes[:, [1]] + gt_bboxes[:, [3]]) / 2
+    centers2d = np.concatenate((centers2d_x, centers2d_y), axis=1)
+    results['centers2d'] = centers2d
+
+    results = affine_reszie(results)
+
+    assert results['gt_labels'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_labels3d'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_bboxes'].shape[0] == results['centers2d'].shape[0]
+    assert results['gt_bboxes_3d'].tensor.shape[0] == results[
+        'centers2d'].shape[0]
+    assert 'center' in results
+    assert 'size' in results
+    assert 'affine_aug' in results
+
+
+def test_random_shift_scale():
+    random_shift_scale = RandomShiftScale(shift_scale=(0.2, 0.4), aug_prob=0.3)
+    results = dict()
+    img = mmcv.imread('./tests/data/kitti/training/image_2/000000.png',
+                      'color')
+    results['img'] = img
+    results = random_shift_scale(results)
+    assert results['center'].dtype == np.float32
+    assert results['size'].dtype == np.float32
+    assert 'affine_aug' in results