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- # Copyright (c) OpenMMLab. All rights reserved.
- import copy
- import os.path as osp
- import unittest
- import mmcv
- import numpy as np
- import torch
- from mmcv.transforms import LoadImageFromFile
- # yapf:disable
- from mmdet.datasets.transforms import (CopyPaste, CutOut, Expand,
- FixShapeResize, MinIoURandomCrop, MixUp,
- Mosaic, Pad, PhotoMetricDistortion,
- RandomAffine, RandomCenterCropPad,
- RandomCrop, RandomErasing, RandomFlip,
- RandomShift, Resize, SegRescale,
- YOLOXHSVRandomAug)
- # yapf:enable
- from mmdet.evaluation import bbox_overlaps
- from mmdet.registry import TRANSFORMS
- from mmdet.structures.bbox import HorizontalBoxes, bbox_project
- from mmdet.structures.mask import BitmapMasks
- from .utils import construct_toy_data, create_full_masks, create_random_bboxes
- try:
- import albumentations
- from albumentations import Compose
- except ImportError:
- albumentations = None
- Compose = None
- # yapf:enable
- class TestResize(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod()
- -> tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.data_info1 = dict(
- img=np.random.random((1333, 800, 3)),
- gt_seg_map=np.random.random((1333, 800, 3)),
- gt_bboxes=np.array([[0, 0, 112, 112]], dtype=np.float32),
- gt_masks=BitmapMasks(
- rng.rand(1, 1333, 800), height=1333, width=800))
- self.data_info2 = dict(
- img=np.random.random((300, 400, 3)),
- gt_bboxes=np.array([[200, 150, 600, 450]], dtype=np.float32),
- dtype=np.float32)
- self.data_info3 = dict(img=np.random.random((300, 400, 3)))
- def test_resize(self):
- # test keep_ratio is True
- transform = Resize(scale=(2000, 2000), keep_ratio=True)
- results = transform(copy.deepcopy(self.data_info1))
- self.assertEqual(results['img_shape'], (2000, 1200))
- self.assertEqual(results['scale_factor'], (1200 / 800, 2000 / 1333))
- # test resize_bboxes/seg/masks
- transform = Resize(scale_factor=(1.5, 2))
- results = transform(copy.deepcopy(self.data_info1))
- self.assertTrue((results['gt_bboxes'] == np.array([[0, 0, 168,
- 224]])).all())
- self.assertEqual(results['gt_masks'].height, 2666)
- self.assertEqual(results['gt_masks'].width, 1200)
- self.assertEqual(results['gt_seg_map'].shape[:2], (2666, 1200))
- # test clip_object_border = False
- transform = Resize(scale=(200, 150), clip_object_border=False)
- results = transform(self.data_info2)
- self.assertTrue((results['gt_bboxes'] == np.array([100, 75, 300,
- 225])).all())
- # test only with image
- transform = Resize(scale=(200, 150), clip_object_border=False)
- results = transform(self.data_info3)
- self.assertTupleEqual(results['img'].shape[:2], (150, 200))
- # test geometric transformation with homography matrix
- transform = Resize(scale_factor=(1.5, 2))
- results = transform(copy.deepcopy(self.data_info1))
- self.assertTrue((bbox_project(
- copy.deepcopy(self.data_info1['gt_bboxes']),
- results['homography_matrix']) == results['gt_bboxes']).all())
- def test_resize_use_box_type(self):
- data_info1 = copy.deepcopy(self.data_info1)
- data_info1['gt_bboxes'] = HorizontalBoxes(data_info1['gt_bboxes'])
- data_info2 = copy.deepcopy(self.data_info2)
- data_info2['gt_bboxes'] = HorizontalBoxes(data_info2['gt_bboxes'])
- # test keep_ratio is True
- transform = Resize(scale=(2000, 2000), keep_ratio=True)
- results = transform(copy.deepcopy(data_info1))
- self.assertEqual(results['img_shape'], (2000, 1200))
- self.assertEqual(results['scale_factor'], (1200 / 800, 2000 / 1333))
- # test resize_bboxes/seg/masks
- transform = Resize(scale_factor=(1.5, 2))
- results = transform(copy.deepcopy(data_info1))
- self.assertTrue(
- (results['gt_bboxes'].numpy() == np.array([[0, 0, 168,
- 224]])).all())
- self.assertEqual(results['gt_masks'].height, 2666)
- self.assertEqual(results['gt_masks'].width, 1200)
- self.assertEqual(results['gt_seg_map'].shape[:2], (2666, 1200))
- # test clip_object_border = False
- transform = Resize(scale=(200, 150), clip_object_border=False)
- results = transform(data_info2)
- self.assertTrue(
- (results['gt_bboxes'].numpy() == np.array([100, 75, 300,
- 225])).all())
- # test geometric transformation with homography matrix
- transform = Resize(scale_factor=(1.5, 2))
- results = transform(copy.deepcopy(data_info1))
- self.assertTrue((bbox_project(
- copy.deepcopy(data_info1['gt_bboxes'].numpy()),
- results['homography_matrix']) == results['gt_bboxes'].numpy()
- ).all())
- def test_repr(self):
- transform = Resize(scale=(2000, 2000), keep_ratio=True)
- self.assertEqual(
- repr(transform), ('Resize(scale=(2000, 2000), '
- 'scale_factor=None, keep_ratio=True, '
- 'clip_object_border=True), backend=cv2), '
- 'interpolation=bilinear)'))
- class TestFIXShapeResize(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.data_info1 = dict(
- img=np.random.random((1333, 800, 3)),
- gt_seg_map=np.random.random((1333, 800, 3)),
- gt_bboxes=np.array([[0, 0, 112, 1333]], dtype=np.float32),
- gt_masks=BitmapMasks(
- rng.rand(1, 1333, 800), height=1333, width=800))
- self.data_info2 = dict(
- img=np.random.random((300, 400, 3)),
- gt_bboxes=np.array([[200, 150, 600, 450]], dtype=np.float32),
- dtype=np.float32)
- self.data_info3 = dict(img=np.random.random((300, 400, 3)))
- self.data_info4 = dict(
- img=np.random.random((600, 800, 3)),
- gt_bboxes=np.array([[200, 150, 300, 400]], dtype=np.float32),
- dtype=np.float32)
- def test_resize(self):
- # test keep_ratio is True
- transform = FixShapeResize(width=2000, height=800, keep_ratio=True)
- results = transform(copy.deepcopy(self.data_info1))
- self.assertEqual(results['img_shape'], (800, 2000))
- self.assertEqual(results['scale_factor'], (800 / 1333, 800 / 1333))
- # test resize_bboxes/seg/masks
- transform = FixShapeResize(width=2000, height=800, keep_ratio=False)
- results = transform(copy.deepcopy(self.data_info1))
- self.assertTrue((results['gt_bboxes'] == np.array([[0, 0, 280,
- 800]])).all())
- self.assertEqual(results['gt_masks'].height, 800)
- self.assertEqual(results['gt_masks'].width, 2000)
- self.assertEqual(results['gt_seg_map'].shape[:2], (800, 2000))
- # test clip_object_border = False
- transform = FixShapeResize(
- width=200, height=150, clip_object_border=False)
- results = transform(copy.deepcopy(self.data_info2))
- self.assertTrue((results['gt_bboxes'] == np.array([100, 75, 300,
- 225])).all())
- # test only with image
- transform = FixShapeResize(
- width=200, height=150, clip_object_border=False)
- results = transform(self.data_info3)
- self.assertTupleEqual(results['img'].shape[:2], (150, 200))
- # test geometric transformation with homography matrix
- transform = FixShapeResize(width=400, height=300)
- results = transform(copy.deepcopy(self.data_info4))
- self.assertTrue((bbox_project(
- copy.deepcopy(self.data_info4['gt_bboxes']),
- results['homography_matrix']) == results['gt_bboxes']).all())
- def test_resize_with_boxlist(self):
- data_info1 = copy.deepcopy(self.data_info1)
- data_info1['gt_bboxes'] = HorizontalBoxes(data_info1['gt_bboxes'])
- data_info2 = copy.deepcopy(self.data_info2)
- data_info2['gt_bboxes'] = HorizontalBoxes(data_info2['gt_bboxes'])
- data_info4 = copy.deepcopy(self.data_info4)
- data_info4['gt_bboxes'] = HorizontalBoxes(data_info4['gt_bboxes'])
- # test keep_ratio is True
- transform = FixShapeResize(width=2000, height=800, keep_ratio=True)
- results = transform(copy.deepcopy(data_info1))
- self.assertEqual(results['img_shape'], (800, 2000))
- self.assertEqual(results['scale_factor'], (800 / 1333, 800 / 1333))
- # test resize_bboxes/seg/masks
- transform = FixShapeResize(width=2000, height=800, keep_ratio=False)
- results = transform(copy.deepcopy(data_info1))
- self.assertTrue(
- (results['gt_bboxes'].numpy() == np.array([[0, 0, 280,
- 800]])).all())
- self.assertEqual(results['gt_masks'].height, 800)
- self.assertEqual(results['gt_masks'].width, 2000)
- self.assertEqual(results['gt_seg_map'].shape[:2], (800, 2000))
- # test clip_object_border = False
- transform = FixShapeResize(
- width=200, height=150, clip_object_border=False)
- results = transform(copy.deepcopy(data_info2))
- self.assertTrue(
- (results['gt_bboxes'].numpy() == np.array([100, 75, 300,
- 225])).all())
- # test only with image
- transform = FixShapeResize(
- width=200, height=150, clip_object_border=False)
- results = transform(self.data_info3)
- self.assertTupleEqual(results['img'].shape[:2], (150, 200))
- # test geometric transformation with homography matrix
- transform = FixShapeResize(width=400, height=300)
- results = transform(copy.deepcopy(data_info4))
- self.assertTrue((bbox_project(
- copy.deepcopy(self.data_info4['gt_bboxes']),
- results['homography_matrix']) == results['gt_bboxes'].numpy()
- ).all())
- def test_repr(self):
- transform = FixShapeResize(width=2000, height=2000, keep_ratio=True)
- self.assertEqual(
- repr(transform), ('FixShapeResize(width=2000, height=2000, '
- 'keep_ratio=True, '
- 'clip_object_border=True), backend=cv2), '
- 'interpolation=bilinear)'))
- class TestRandomFlip(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.results1 = {
- 'img': np.random.random((224, 224, 3)),
- 'gt_bboxes': np.array([[0, 1, 100, 101]], dtype=np.float32),
- 'gt_masks':
- BitmapMasks(rng.rand(1, 224, 224), height=224, width=224),
- 'gt_seg_map': np.random.random((224, 224))
- }
- self.results2 = {'img': self.results1['img']}
- def test_transform(self):
- # test with image, gt_bboxes, gt_masks, gt_seg_map
- transform = RandomFlip(1.0)
- results_update = transform.transform(copy.deepcopy(self.results1))
- self.assertTrue(
- (results_update['gt_bboxes'] == np.array([[124, 1, 224,
- 101]])).all())
- # test only with image
- transform = RandomFlip(1.0)
- results_update = transform.transform(copy.deepcopy(self.results2))
- self.assertTrue(
- (results_update['img'] == self.results2['img'][:, ::-1]).all())
- # test geometric transformation with homography matrix
- # (1) Horizontal Flip
- transform = RandomFlip(1.0)
- results_update = transform.transform(copy.deepcopy(self.results1))
- bboxes = copy.deepcopy(self.results1['gt_bboxes'])
- self.assertTrue((bbox_project(
- bboxes,
- results_update['homography_matrix']) == results_update['gt_bboxes']
- ).all())
- # (2) Vertical Flip
- transform = RandomFlip(1.0, direction='vertical')
- results_update = transform.transform(copy.deepcopy(self.results1))
- bboxes = copy.deepcopy(self.results1['gt_bboxes'])
- self.assertTrue((bbox_project(
- bboxes,
- results_update['homography_matrix']) == results_update['gt_bboxes']
- ).all())
- # (3) Diagonal Flip
- transform = RandomFlip(1.0, direction='diagonal')
- results_update = transform.transform(copy.deepcopy(self.results1))
- bboxes = copy.deepcopy(self.results1['gt_bboxes'])
- self.assertTrue((bbox_project(
- bboxes,
- results_update['homography_matrix']) == results_update['gt_bboxes']
- ).all())
- def test_transform_use_box_type(self):
- results1 = copy.deepcopy(self.results1)
- results1['gt_bboxes'] = HorizontalBoxes(results1['gt_bboxes'])
- # test with image, gt_bboxes, gt_masks, gt_seg_map
- transform = RandomFlip(1.0)
- results_update = transform.transform(copy.deepcopy(results1))
- self.assertTrue((results_update['gt_bboxes'].numpy() == np.array(
- [[124, 1, 224, 101]])).all())
- # test geometric transformation with homography matrix
- # (1) Horizontal Flip
- transform = RandomFlip(1.0)
- results_update = transform.transform(copy.deepcopy(results1))
- bboxes = copy.deepcopy(results1['gt_bboxes'].numpy())
- self.assertTrue((bbox_project(bboxes,
- results_update['homography_matrix']) ==
- results_update['gt_bboxes'].numpy()).all())
- # (2) Vertical Flip
- transform = RandomFlip(1.0, direction='vertical')
- results_update = transform.transform(copy.deepcopy(results1))
- bboxes = copy.deepcopy(results1['gt_bboxes'].numpy())
- self.assertTrue((bbox_project(bboxes,
- results_update['homography_matrix']) ==
- results_update['gt_bboxes'].numpy()).all())
- # (3) Diagonal Flip
- transform = RandomFlip(1.0, direction='diagonal')
- results_update = transform.transform(copy.deepcopy(results1))
- bboxes = copy.deepcopy(results1['gt_bboxes'].numpy())
- self.assertTrue((bbox_project(bboxes,
- results_update['homography_matrix']) ==
- results_update['gt_bboxes'].numpy()).all())
- def test_repr(self):
- transform = RandomFlip(0.1)
- transform_str = str(transform)
- self.assertIsInstance(transform_str, str)
- class TestPad(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.results = {
- 'img': np.random.random((1333, 800, 3)),
- 'gt_masks':
- BitmapMasks(rng.rand(4, 1333, 800), height=1333, width=800)
- }
- def test_transform(self):
- # test pad img/gt_masks with size
- transform = Pad(size=(1200, 2000))
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['img'].shape[:2], (2000, 1200))
- self.assertEqual(results['gt_masks'].masks.shape[1:], (2000, 1200))
- # test pad img/gt_masks with size_divisor
- transform = Pad(size_divisor=11)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['img'].shape[:2], (1342, 803))
- self.assertEqual(results['gt_masks'].masks.shape[1:], (1342, 803))
- # test pad img/gt_masks with pad_to_square
- transform = Pad(pad_to_square=True)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['img'].shape[:2], (1333, 1333))
- self.assertEqual(results['gt_masks'].masks.shape[1:], (1333, 1333))
- # test pad img/gt_masks with pad_to_square and size_divisor
- transform = Pad(pad_to_square=True, size_divisor=11)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['img'].shape[:2], (1342, 1342))
- self.assertEqual(results['gt_masks'].masks.shape[1:], (1342, 1342))
- # test pad img/gt_masks with pad_to_square and size_divisor
- transform = Pad(pad_to_square=True, size_divisor=11)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['img'].shape[:2], (1342, 1342))
- self.assertEqual(results['gt_masks'].masks.shape[1:], (1342, 1342))
- def test_repr(self):
- transform = Pad(
- pad_to_square=True, size_divisor=11, padding_mode='edge')
- self.assertEqual(
- repr(transform),
- ('Pad(size=None, size_divisor=11, pad_to_square=True, '
- "pad_val={'img': 0, 'seg': 255}), padding_mode=edge)"))
- class TestMinIoURandomCrop(unittest.TestCase):
- def test_transform(self):
- results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- results['img'] = img
- results['img_shape'] = img.shape[:2]
- gt_bboxes = create_random_bboxes(1, results['img_shape'][1],
- results['img_shape'][0])
- results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
- results['gt_bboxes'] = gt_bboxes
- transform = MinIoURandomCrop()
- results = transform.transform(copy.deepcopy(results))
- self.assertEqual(results['gt_labels'].shape[0],
- results['gt_bboxes'].shape[0])
- self.assertEqual(results['gt_labels'].dtype, np.int64)
- self.assertEqual(results['gt_bboxes'].dtype, np.float32)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- patch = np.array(
- [0, 0, results['img_shape'][1], results['img_shape'][0]])
- ious = bbox_overlaps(patch.reshape(-1, 4),
- results['gt_bboxes']).reshape(-1)
- mode = transform.mode
- if mode == 1:
- self.assertTrue(np.equal(results['gt_bboxes'], gt_bboxes).all())
- else:
- self.assertTrue((ious >= mode).all())
- def test_transform_use_box_type(self):
- results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- results['img'] = img
- results['img_shape'] = img.shape[:2]
- gt_bboxes = create_random_bboxes(1, results['img_shape'][1],
- results['img_shape'][0])
- results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
- results['gt_bboxes'] = HorizontalBoxes(gt_bboxes)
- transform = MinIoURandomCrop()
- results = transform.transform(copy.deepcopy(results))
- self.assertEqual(results['gt_labels'].shape[0],
- results['gt_bboxes'].shape[0])
- self.assertEqual(results['gt_labels'].dtype, np.int64)
- self.assertEqual(results['gt_bboxes'].dtype, torch.float32)
- patch = np.array(
- [0, 0, results['img_shape'][1], results['img_shape'][0]])
- ious = bbox_overlaps(
- patch.reshape(-1, 4), results['gt_bboxes'].numpy()).reshape(-1)
- mode = transform.mode
- if mode == 1:
- self.assertTrue((results['gt_bboxes'].numpy() == gt_bboxes).all())
- else:
- self.assertTrue((ious >= mode).all())
- def test_repr(self):
- transform = MinIoURandomCrop()
- self.assertEqual(
- repr(transform), ('MinIoURandomCrop'
- '(min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), '
- 'min_crop_size=0.3, '
- 'bbox_clip_border=True)'))
- class TestPhotoMetricDistortion(unittest.TestCase):
- def test_transform(self):
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- transform = PhotoMetricDistortion()
- # test uint8 input
- results = dict()
- results['img'] = img
- results = transform.transform(copy.deepcopy(results))
- self.assertEqual(results['img'].dtype, np.float32)
- # test float32 input
- results = dict()
- results['img'] = img.astype(np.float32)
- results = transform.transform(copy.deepcopy(results))
- self.assertEqual(results['img'].dtype, np.float32)
- def test_repr(self):
- transform = PhotoMetricDistortion()
- self.assertEqual(
- repr(transform), ('PhotoMetricDistortion'
- '(brightness_delta=32, '
- 'contrast_range=(0.5, 1.5), '
- 'saturation_range=(0.5, 1.5), '
- 'hue_delta=18)'))
- class TestExpand(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.results = {
- 'img': np.random.random((224, 224, 3)),
- 'img_shape': (224, 224),
- 'gt_bboxes': np.array([[0, 1, 100, 101]]),
- 'gt_masks':
- BitmapMasks(rng.rand(1, 224, 224), height=224, width=224),
- 'gt_seg_map': np.random.random((224, 224))
- }
- def test_transform(self):
- transform = Expand()
- results = transform.transform(copy.deepcopy(self.results))
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- self.assertEqual(
- results['img_shape'],
- (results['gt_masks'].height, results['gt_masks'].width))
- self.assertEqual(results['img_shape'], results['gt_seg_map'].shape)
- def test_transform_use_box_type(self):
- results = copy.deepcopy(self.results)
- results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
- transform = Expand()
- results = transform.transform(results)
- self.assertEqual(
- results['img_shape'],
- (results['gt_masks'].height, results['gt_masks'].width))
- self.assertEqual(results['img_shape'], results['gt_seg_map'].shape)
- def test_repr(self):
- transform = Expand()
- self.assertEqual(
- repr(transform), ('Expand'
- '(mean=(0, 0, 0), to_rgb=True, '
- 'ratio_range=(1, 4), '
- 'seg_ignore_label=None, '
- 'prob=0.5)'))
- class TestSegRescale(unittest.TestCase):
- def setUp(self) -> None:
- seg_map = np.random.randint(0, 255, size=(32, 32), dtype=np.int32)
- self.results = {'gt_seg_map': seg_map}
- def test_transform(self):
- # test scale_factor != 1
- transform = SegRescale(scale_factor=2)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['gt_seg_map'].shape[:2], (64, 64))
- # test scale_factor = 1
- transform = SegRescale(scale_factor=1)
- results = transform(copy.deepcopy(self.results))
- self.assertEqual(results['gt_seg_map'].shape[:2], (32, 32))
- def test_repr(self):
- transform = SegRescale(scale_factor=2)
- self.assertEqual(
- repr(transform), ('SegRescale(scale_factor=2, backend=cv2)'))
- class TestRandomCrop(unittest.TestCase):
- def test_init(self):
- # test invalid crop_type
- with self.assertRaisesRegex(ValueError, 'Invalid crop_type'):
- RandomCrop(crop_size=(10, 10), crop_type='unknown')
- crop_type_list = ['absolute', 'absolute_range']
- for crop_type in crop_type_list:
- # test h > 0 and w > 0
- for crop_size in [(0, 0), (0, 1), (1, 0)]:
- with self.assertRaises(AssertionError):
- RandomCrop(crop_size=crop_size, crop_type=crop_type)
- # test type(h) = int and type(w) = int
- for crop_size in [(1.0, 1), (1, 1.0), (1.0, 1.0)]:
- with self.assertRaises(AssertionError):
- RandomCrop(crop_size=crop_size, crop_type=crop_type)
- # test crop_size[0] <= crop_size[1]
- with self.assertRaises(AssertionError):
- RandomCrop(crop_size=(10, 5), crop_type='absolute_range')
- # test h in (0, 1] and w in (0, 1]
- crop_type_list = ['relative_range', 'relative']
- for crop_type in crop_type_list:
- for crop_size in [(0, 1), (1, 0), (1.1, 0.5), (0.5, 1.1)]:
- with self.assertRaises(AssertionError):
- RandomCrop(crop_size=crop_size, crop_type=crop_type)
- def test_transform(self):
- # test relative and absolute crop
- src_results = {
- 'img': np.random.randint(0, 255, size=(24, 32), dtype=np.int32)
- }
- target_shape = (12, 16)
- for crop_type, crop_size in zip(['relative', 'absolute'], [(0.5, 0.5),
- (16, 12)]):
- transform = RandomCrop(crop_size=crop_size, crop_type=crop_type)
- results = transform(copy.deepcopy(src_results))
- print(results['img'].shape[:2])
- self.assertEqual(results['img'].shape[:2], target_shape)
- # test absolute_range crop
- transform = RandomCrop(crop_size=(10, 20), crop_type='absolute_range')
- results = transform(copy.deepcopy(src_results))
- h, w = results['img'].shape
- self.assertTrue(10 <= w <= 20)
- self.assertTrue(10 <= h <= 20)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- # test relative_range crop
- transform = RandomCrop(
- crop_size=(0.5, 0.5), crop_type='relative_range')
- results = transform(copy.deepcopy(src_results))
- h, w = results['img'].shape
- self.assertTrue(16 <= w <= 32)
- self.assertTrue(12 <= h <= 24)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- # test with gt_bboxes, gt_bboxes_labels, gt_ignore_flags,
- # gt_masks, gt_seg_map
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = np.array([[0, 0, 7, 7], [2, 3, 9, 9]], dtype=np.float32)
- gt_bboxes_labels = np.array([0, 1], dtype=np.int64)
- gt_ignore_flags = np.array([0, 1], dtype=bool)
- gt_masks_ = np.zeros((2, 10, 10), np.uint8)
- gt_masks_[0, 0:7, 0:7] = 1
- gt_masks_[1, 2:7, 3:8] = 1
- gt_masks = BitmapMasks(gt_masks_.copy(), height=10, width=10)
- gt_seg_map = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- src_results = {
- 'img': img,
- 'gt_bboxes': gt_bboxes,
- 'gt_bboxes_labels': gt_bboxes_labels,
- 'gt_ignore_flags': gt_ignore_flags,
- 'gt_masks': gt_masks,
- 'gt_seg_map': gt_seg_map
- }
- transform = RandomCrop(
- crop_size=(7, 5),
- allow_negative_crop=False,
- recompute_bbox=False,
- bbox_clip_border=True)
- results = transform(copy.deepcopy(src_results))
- h, w = results['img'].shape
- self.assertEqual(h, 5)
- self.assertEqual(w, 7)
- self.assertEqual(results['gt_bboxes'].shape[0], 2)
- self.assertEqual(results['gt_bboxes_labels'].shape[0], 2)
- self.assertEqual(results['gt_ignore_flags'].shape[0], 2)
- self.assertTupleEqual(results['gt_seg_map'].shape[:2], (5, 7))
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- # test geometric transformation with homography matrix
- bboxes = copy.deepcopy(src_results['gt_bboxes'])
- self.assertTrue((bbox_project(bboxes, results['homography_matrix'],
- (5, 7)) == results['gt_bboxes']).all())
- # test recompute_bbox = True
- gt_masks_ = np.zeros((2, 10, 10), np.uint8)
- gt_masks = BitmapMasks(gt_masks_.copy(), height=10, width=10)
- gt_bboxes = np.array([[0.1, 0.1, 0.2, 0.2]])
- src_results = {
- 'img': img,
- 'gt_bboxes': gt_bboxes,
- 'gt_masks': gt_masks
- }
- target_gt_bboxes = np.zeros((1, 4), dtype=np.float32)
- transform = RandomCrop(
- crop_size=(10, 11),
- allow_negative_crop=False,
- recompute_bbox=True,
- bbox_clip_border=True)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue((results['gt_bboxes'] == target_gt_bboxes).all())
- # test bbox_clip_border = False
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(
- crop_size=(10, 11),
- allow_negative_crop=False,
- recompute_bbox=True,
- bbox_clip_border=False)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(
- (results['gt_bboxes'] == src_results['gt_bboxes']).all())
- # test the crop does not contain any gt-bbox
- # allow_negative_crop = False
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = np.zeros((0, 4), dtype=np.float32)
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(crop_size=(5, 3), allow_negative_crop=False)
- results = transform(copy.deepcopy(src_results))
- self.assertIsNone(results)
- # allow_negative_crop = True
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = np.zeros((0, 4), dtype=np.float32)
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(crop_size=(5, 3), allow_negative_crop=True)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(isinstance(results, dict))
- def test_transform_use_box_type(self):
- # test with gt_bboxes, gt_bboxes_labels, gt_ignore_flags,
- # gt_masks, gt_seg_map
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = np.array([[0, 0, 7, 7], [2, 3, 9, 9]], dtype=np.float32)
- gt_bboxes_labels = np.array([0, 1], dtype=np.int64)
- gt_ignore_flags = np.array([0, 1], dtype=bool)
- gt_masks_ = np.zeros((2, 10, 10), np.uint8)
- gt_masks_[0, 0:7, 0:7] = 1
- gt_masks_[1, 2:7, 3:8] = 1
- gt_masks = BitmapMasks(gt_masks_.copy(), height=10, width=10)
- gt_seg_map = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- src_results = {
- 'img': img,
- 'gt_bboxes': HorizontalBoxes(gt_bboxes),
- 'gt_bboxes_labels': gt_bboxes_labels,
- 'gt_ignore_flags': gt_ignore_flags,
- 'gt_masks': gt_masks,
- 'gt_seg_map': gt_seg_map
- }
- transform = RandomCrop(
- crop_size=(7, 5),
- allow_negative_crop=False,
- recompute_bbox=False,
- bbox_clip_border=True)
- results = transform(copy.deepcopy(src_results))
- h, w = results['img'].shape
- self.assertEqual(h, 5)
- self.assertEqual(w, 7)
- self.assertEqual(results['gt_bboxes'].shape[0], 2)
- self.assertEqual(results['gt_bboxes_labels'].shape[0], 2)
- self.assertEqual(results['gt_ignore_flags'].shape[0], 2)
- self.assertTupleEqual(results['gt_seg_map'].shape[:2], (5, 7))
- # test geometric transformation with homography matrix
- bboxes = copy.deepcopy(src_results['gt_bboxes'].numpy())
- print(bboxes, results['gt_bboxes'])
- self.assertTrue(
- (bbox_project(bboxes, results['homography_matrix'],
- (5, 7)) == results['gt_bboxes'].numpy()).all())
- # test recompute_bbox = True
- gt_masks_ = np.zeros((2, 10, 10), np.uint8)
- gt_masks = BitmapMasks(gt_masks_.copy(), height=10, width=10)
- gt_bboxes = HorizontalBoxes(np.array([[0.1, 0.1, 0.2, 0.2]]))
- src_results = {
- 'img': img,
- 'gt_bboxes': gt_bboxes,
- 'gt_masks': gt_masks
- }
- target_gt_bboxes = np.zeros((1, 4), dtype=np.float32)
- transform = RandomCrop(
- crop_size=(10, 11),
- allow_negative_crop=False,
- recompute_bbox=True,
- bbox_clip_border=True)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(
- (results['gt_bboxes'].numpy() == target_gt_bboxes).all())
- # test bbox_clip_border = False
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(
- crop_size=(10, 10),
- allow_negative_crop=False,
- recompute_bbox=True,
- bbox_clip_border=False)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(
- (results['gt_bboxes'].numpy() == src_results['gt_bboxes'].numpy()
- ).all())
- # test the crop does not contain any gt-bbox
- # allow_negative_crop = False
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = HorizontalBoxes(np.zeros((0, 4), dtype=np.float32))
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(crop_size=(5, 2), allow_negative_crop=False)
- results = transform(copy.deepcopy(src_results))
- self.assertIsNone(results)
- # allow_negative_crop = True
- img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
- gt_bboxes = HorizontalBoxes(np.zeros((0, 4), dtype=np.float32))
- src_results = {'img': img, 'gt_bboxes': gt_bboxes}
- transform = RandomCrop(crop_size=(5, 2), allow_negative_crop=True)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(isinstance(results, dict))
- def test_repr(self):
- crop_type = 'absolute'
- crop_size = (10, 5)
- allow_negative_crop = False
- recompute_bbox = True
- bbox_clip_border = False
- transform = RandomCrop(
- crop_size=crop_size,
- crop_type=crop_type,
- allow_negative_crop=allow_negative_crop,
- recompute_bbox=recompute_bbox,
- bbox_clip_border=bbox_clip_border)
- self.assertEqual(
- repr(transform),
- f'RandomCrop(crop_size={crop_size}, crop_type={crop_type}, '
- f'allow_negative_crop={allow_negative_crop}, '
- f'recompute_bbox={recompute_bbox}, '
- f'bbox_clip_border={bbox_clip_border})')
- class TestCutOut(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- self.results = {'img': img}
- def test_transform(self):
- # test n_holes
- with self.assertRaises(AssertionError):
- transform = CutOut(n_holes=(5, 3), cutout_shape=(8, 8))
- with self.assertRaises(AssertionError):
- transform = CutOut(n_holes=(3, 4, 5), cutout_shape=(8, 8))
- # test cutout_shape and cutout_ratio
- with self.assertRaises(AssertionError):
- transform = CutOut(n_holes=1, cutout_shape=8)
- with self.assertRaises(AssertionError):
- transform = CutOut(n_holes=1, cutout_ratio=0.2)
- # either of cutout_shape and cutout_ratio should be given
- with self.assertRaises(AssertionError):
- transform = CutOut(n_holes=1)
- with self.assertRaises(AssertionError):
- transform = CutOut(
- n_holes=1, cutout_shape=(2, 2), cutout_ratio=(0.4, 0.4))
- transform = CutOut(n_holes=1, cutout_shape=(10, 10))
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].sum() < self.results['img'].sum())
- transform = CutOut(
- n_holes=(2, 4),
- cutout_shape=[(10, 10), (15, 15)],
- fill_in=(255, 255, 255))
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].sum() > self.results['img'].sum())
- transform = CutOut(
- n_holes=1, cutout_ratio=(0.8, 0.8), fill_in=(255, 255, 255))
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].sum() > self.results['img'].sum())
- def test_repr(self):
- transform = CutOut(n_holes=1, cutout_shape=(10, 10))
- self.assertEqual(
- repr(transform), ('CutOut(n_holes=(1, 1), '
- 'cutout_shape=[(10, 10)], '
- 'fill_in=(0, 0, 0))'))
- transform = CutOut(
- n_holes=1, cutout_ratio=(0.8, 0.8), fill_in=(255, 255, 255))
- self.assertEqual(
- repr(transform), ('CutOut(n_holes=(1, 1), '
- 'cutout_ratio=[(0.8, 0.8)], '
- 'fill_in=(255, 255, 255))'))
- class TestMosaic(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.results = {
- 'img':
- np.random.random((224, 224, 3)),
- 'img_shape': (224, 224),
- 'gt_bboxes_labels':
- np.array([1, 2, 3], dtype=np.int64),
- 'gt_bboxes':
- np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
- dtype=np.float32),
- 'gt_ignore_flags':
- np.array([0, 0, 1], dtype=bool),
- 'gt_masks':
- BitmapMasks(rng.rand(3, 224, 224), height=224, width=224),
- }
- def test_transform(self):
- # test assertion for invalid img_scale
- with self.assertRaises(AssertionError):
- transform = Mosaic(img_scale=640)
- # test assertion for invalid probability
- with self.assertRaises(AssertionError):
- transform = Mosaic(prob=1.5)
- transform = Mosaic(img_scale=(12, 10))
- # test assertion for invalid mix_results
- with self.assertRaises(AssertionError):
- results = transform(copy.deepcopy(self.results))
- self.results['mix_results'] = [copy.deepcopy(self.results)] * 3
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].shape[:2] == (20, 24))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == np.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- def test_transform_with_no_gt(self):
- self.results['gt_bboxes'] = np.empty((0, 4), dtype=np.float32)
- self.results['gt_bboxes_labels'] = np.empty((0, ), dtype=np.int64)
- self.results['gt_ignore_flags'] = np.empty((0, ), dtype=bool)
- transform = Mosaic(img_scale=(12, 10))
- self.results['mix_results'] = [copy.deepcopy(self.results)] * 3
- results = transform(copy.deepcopy(self.results))
- self.assertIsInstance(results, dict)
- self.assertTrue(results['img'].shape[:2] == (20, 24))
- self.assertTrue(
- results['gt_bboxes_labels'].shape[0] == results['gt_bboxes'].
- shape[0] == results['gt_ignore_flags'].shape[0] == 0)
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == np.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- def test_transform_use_box_type(self):
- transform = Mosaic(img_scale=(12, 10))
- results = copy.deepcopy(self.results)
- results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
- results['mix_results'] = [results] * 3
- results = transform(results)
- self.assertTrue(results['img'].shape[:2] == (20, 24))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- def test_repr(self):
- transform = Mosaic(img_scale=(640, 640), )
- self.assertEqual(
- repr(transform), ('Mosaic(img_scale=(640, 640), '
- 'center_ratio_range=(0.5, 1.5), '
- 'pad_val=114.0, '
- 'prob=1.0)'))
- class TestMixUp(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- rng = np.random.RandomState(0)
- self.results = {
- 'img':
- np.random.random((224, 224, 3)),
- 'img_shape': (224, 224),
- 'gt_bboxes_labels':
- np.array([1, 2, 3], dtype=np.int64),
- 'gt_bboxes':
- np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
- dtype=np.float32),
- 'gt_ignore_flags':
- np.array([0, 0, 1], dtype=bool),
- 'gt_masks':
- BitmapMasks(rng.rand(3, 224, 224), height=224, width=224),
- }
- def test_transform(self):
- # test assertion for invalid img_scale
- with self.assertRaises(AssertionError):
- transform = MixUp(img_scale=640)
- transform = MixUp(img_scale=(12, 10))
- # test assertion for invalid mix_results
- with self.assertRaises(AssertionError):
- results = transform(copy.deepcopy(self.results))
- with self.assertRaises(AssertionError):
- self.results['mix_results'] = [copy.deepcopy(self.results)] * 2
- results = transform(copy.deepcopy(self.results))
- self.results['mix_results'] = [copy.deepcopy(self.results)]
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].shape[:2] == (224, 224))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == np.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- def test_transform_use_box_type(self):
- results = copy.deepcopy(self.results)
- results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
- transform = MixUp(img_scale=(12, 10))
- results['mix_results'] = [results]
- results = transform(results)
- self.assertTrue(results['img'].shape[:2] == (224, 224))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- def test_repr(self):
- transform = MixUp(
- img_scale=(640, 640),
- ratio_range=(0.8, 1.6),
- pad_val=114.0,
- )
- self.assertEqual(
- repr(transform), ('MixUp(dynamic_scale=(640, 640), '
- 'ratio_range=(0.8, 1.6), '
- 'flip_ratio=0.5, '
- 'pad_val=114.0, '
- 'max_iters=15, '
- 'bbox_clip_border=True)'))
- class TestRandomAffine(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- self.results = {
- 'img':
- np.random.random((224, 224, 3)),
- 'img_shape': (224, 224),
- 'gt_bboxes_labels':
- np.array([1, 2, 3], dtype=np.int64),
- 'gt_bboxes':
- np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
- dtype=np.float32),
- 'gt_ignore_flags':
- np.array([0, 0, 1], dtype=bool),
- }
- def test_transform(self):
- # test assertion for invalid translate_ratio
- with self.assertRaises(AssertionError):
- transform = RandomAffine(max_translate_ratio=1.5)
- # test assertion for invalid scaling_ratio_range
- with self.assertRaises(AssertionError):
- transform = RandomAffine(scaling_ratio_range=(1.5, 0.5))
- with self.assertRaises(AssertionError):
- transform = RandomAffine(scaling_ratio_range=(0, 0.5))
- transform = RandomAffine()
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].shape[:2] == (224, 224))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == np.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- def test_transform_use_box_type(self):
- results = copy.deepcopy(self.results)
- results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
- transform = RandomAffine()
- results = transform(copy.deepcopy(results))
- self.assertTrue(results['img'].shape[:2] == (224, 224))
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- def test_repr(self):
- transform = RandomAffine(
- scaling_ratio_range=(0.1, 2),
- border=(-320, -320),
- )
- self.assertEqual(
- repr(transform), ('RandomAffine(max_rotate_degree=10.0, '
- 'max_translate_ratio=0.1, '
- 'scaling_ratio_range=(0.1, 2), '
- 'max_shear_degree=2.0, '
- 'border=(-320, -320), '
- 'border_val=(114, 114, 114), '
- 'bbox_clip_border=True)'))
- class TestYOLOXHSVRandomAug(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- self.results = {
- 'img':
- img,
- 'img_shape': (224, 224),
- 'gt_bboxes_labels':
- np.array([1, 2, 3], dtype=np.int64),
- 'gt_bboxes':
- np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
- dtype=np.float32),
- 'gt_ignore_flags':
- np.array([0, 0, 1], dtype=bool),
- }
- def test_transform(self):
- transform = YOLOXHSVRandomAug()
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(
- results['img'].shape[:2] == self.results['img'].shape[:2])
- self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
- results['gt_bboxes'].shape[0])
- self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
- self.assertTrue(results['gt_bboxes'].dtype == np.float32)
- self.assertTrue(results['gt_ignore_flags'].dtype == bool)
- def test_repr(self):
- transform = YOLOXHSVRandomAug()
- self.assertEqual(
- repr(transform), ('YOLOXHSVRandomAug(hue_delta=5, '
- 'saturation_delta=30, '
- 'value_delta=30)'))
- class TestRandomCenterCropPad(unittest.TestCase):
- def test_init(self):
- # test assertion for invalid crop_size while test_mode=False
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=(-1, 0), test_mode=False, test_pad_mode=None)
- # test assertion for invalid ratios while test_mode=False
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=(511, 511),
- ratios=(1.0, 1.0),
- test_mode=False,
- test_pad_mode=None)
- # test assertion for invalid mean, std and to_rgb
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=(511, 511),
- mean=None,
- std=None,
- to_rgb=None,
- test_mode=False,
- test_pad_mode=None)
- # test assertion for invalid crop_size while test_mode=True
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=(511, 511),
- ratios=None,
- border=None,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('logical_or', 127))
- # test assertion for invalid ratios while test_mode=True
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=None,
- ratios=(0.9, 1.0, 1.1),
- border=None,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('logical_or', 127))
- # test assertion for invalid border while test_mode=True
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=None,
- ratios=None,
- border=128,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('logical_or', 127))
- # test assertion for invalid test_pad_mode while test_mode=True
- with self.assertRaises(AssertionError):
- RandomCenterCropPad(
- crop_size=None,
- ratios=None,
- border=None,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('do_nothing', 100))
- def test_transform(self):
- results = dict(
- img_path=osp.join(osp.dirname(__file__), '../../data/color.jpg'))
- load = LoadImageFromFile(to_float32=True)
- results = load(results)
- test_results = copy.deepcopy(results)
- h, w = results['img_shape']
- gt_bboxes = create_random_bboxes(4, w, h)
- gt_bboxes_labels = np.array([1, 2, 3, 1], dtype=np.int64)
- gt_ignore_flags = np.array([0, 0, 1, 1], dtype=bool)
- results['gt_bboxes'] = gt_bboxes
- results['gt_bboxes_labels'] = gt_bboxes_labels
- results['gt_ignore_flags'] = gt_ignore_flags
- crop_module = RandomCenterCropPad(
- crop_size=(w - 20, h - 20),
- ratios=(1.0, ),
- border=128,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=False,
- test_pad_mode=None)
- train_results = crop_module(results)
- assert train_results['img'].shape[:2] == (h - 20, w - 20)
- # All bboxes should be reserved after crop
- assert train_results['img_shape'][:2] == (h - 20, w - 20)
- assert train_results['gt_bboxes'].shape[0] == 4
- assert train_results['gt_bboxes'].dtype == np.float32
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- crop_module = RandomCenterCropPad(
- crop_size=None,
- ratios=None,
- border=None,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('logical_or', 127))
- test_results = crop_module(test_results)
- assert test_results['img'].shape[:2] == (h | 127, w | 127)
- assert test_results['img_shape'][:2] == (h | 127, w | 127)
- assert 'border' in test_results
- def test_transform_use_box_type(self):
- results = dict(
- img_path=osp.join(osp.dirname(__file__), '../../data/color.jpg'))
- load = LoadImageFromFile(to_float32=True)
- results = load(results)
- test_results = copy.deepcopy(results)
- h, w = results['img_shape']
- gt_bboxes = create_random_bboxes(4, w, h)
- gt_bboxes_labels = np.array([1, 2, 3, 1], dtype=np.int64)
- gt_ignore_flags = np.array([0, 0, 1, 1], dtype=bool)
- results['gt_bboxes'] = HorizontalBoxes(gt_bboxes)
- results['gt_bboxes_labels'] = gt_bboxes_labels
- results['gt_ignore_flags'] = gt_ignore_flags
- crop_module = RandomCenterCropPad(
- crop_size=(w - 20, h - 20),
- ratios=(1.0, ),
- border=128,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=False,
- test_pad_mode=None)
- train_results = crop_module(results)
- assert train_results['img'].shape[:2] == (h - 20, w - 20)
- # All bboxes should be reserved after crop
- assert train_results['img_shape'][:2] == (h - 20, w - 20)
- assert train_results['gt_bboxes'].shape[0] == 4
- assert train_results['gt_bboxes'].dtype == torch.float32
- crop_module = RandomCenterCropPad(
- crop_size=None,
- ratios=None,
- border=None,
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=('logical_or', 127))
- test_results = crop_module(test_results)
- assert test_results['img'].shape[:2] == (h | 127, w | 127)
- assert test_results['img_shape'][:2] == (h | 127, w | 127)
- assert 'border' in test_results
- class TestCopyPaste(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- h, w, _ = img.shape
- dst_bboxes = np.array([[0.2 * w, 0.2 * h, 0.4 * w, 0.4 * h],
- [0.5 * w, 0.5 * h, 0.6 * w, 0.6 * h]],
- dtype=np.float32)
- src_bboxes = np.array([[0.1 * w, 0.1 * h, 0.3 * w, 0.5 * h],
- [0.4 * w, 0.4 * h, 0.7 * w, 0.7 * h],
- [0.8 * w, 0.8 * h, 0.9 * w, 0.9 * h]],
- dtype=np.float32)
- self.dst_results = {
- 'img': img.copy(),
- 'gt_bboxes': dst_bboxes,
- 'gt_bboxes_labels': np.ones(dst_bboxes.shape[0], dtype=np.int64),
- 'gt_masks': create_full_masks(dst_bboxes, w, h),
- 'gt_ignore_flags': np.array([0, 1], dtype=bool),
- }
- self.src_results = {
- 'img': img.copy(),
- 'gt_bboxes': src_bboxes,
- 'gt_bboxes_labels':
- np.ones(src_bboxes.shape[0], dtype=np.int64) * 2,
- 'gt_masks': create_full_masks(src_bboxes, w, h),
- 'gt_ignore_flags': np.array([0, 0, 1], dtype=bool),
- }
- def test_transform(self):
- transform = CopyPaste(selected=False)
- # test assertion for invalid mix_results
- with self.assertRaises(AssertionError):
- results = transform(copy.deepcopy(self.dst_results))
- results = copy.deepcopy(self.dst_results)
- results['mix_results'] = [copy.deepcopy(self.src_results)]
- results = transform(results)
- self.assertEqual(results['img'].shape[:2],
- self.dst_results['img'].shape[:2])
- # one object of destination image is totally occluded
- self.assertEqual(
- results['gt_bboxes'].shape[0],
- self.dst_results['gt_bboxes'].shape[0] +
- self.src_results['gt_bboxes'].shape[0] - 1)
- self.assertEqual(
- results['gt_bboxes_labels'].shape[0],
- self.dst_results['gt_bboxes_labels'].shape[0] +
- self.src_results['gt_bboxes_labels'].shape[0] - 1)
- self.assertEqual(
- results['gt_masks'].masks.shape[0],
- self.dst_results['gt_masks'].masks.shape[0] +
- self.src_results['gt_masks'].masks.shape[0] - 1)
- self.assertEqual(
- results['gt_ignore_flags'].shape[0],
- self.dst_results['gt_ignore_flags'].shape[0] +
- self.src_results['gt_ignore_flags'].shape[0] - 1)
- # the object of destination image is partially occluded
- ori_bbox = self.dst_results['gt_bboxes'][0]
- occ_bbox = results['gt_bboxes'][0]
- ori_mask = self.dst_results['gt_masks'].masks[0]
- occ_mask = results['gt_masks'].masks[0]
- self.assertTrue(ori_mask.sum() > occ_mask.sum())
- self.assertTrue(
- np.all(np.abs(occ_bbox - ori_bbox) <= transform.bbox_occluded_thr)
- or occ_mask.sum() > transform.mask_occluded_thr)
- # test copypaste with selected objects
- transform = CopyPaste()
- results = copy.deepcopy(self.dst_results)
- results['mix_results'] = [copy.deepcopy(self.src_results)]
- results = transform(results)
- # test copypaste with an empty source image
- results = copy.deepcopy(self.dst_results)
- valid_inds = [False] * self.src_results['gt_bboxes'].shape[0]
- results['mix_results'] = [{
- 'img':
- self.src_results['img'].copy(),
- 'gt_bboxes':
- self.src_results['gt_bboxes'][valid_inds],
- 'gt_bboxes_labels':
- self.src_results['gt_bboxes_labels'][valid_inds],
- 'gt_masks':
- self.src_results['gt_masks'][valid_inds],
- 'gt_ignore_flags':
- self.src_results['gt_ignore_flags'][valid_inds],
- }]
- results = transform(results)
- def test_transform_use_box_type(self):
- src_results = copy.deepcopy(self.src_results)
- src_results['gt_bboxes'] = HorizontalBoxes(src_results['gt_bboxes'])
- dst_results = copy.deepcopy(self.dst_results)
- dst_results['gt_bboxes'] = HorizontalBoxes(dst_results['gt_bboxes'])
- transform = CopyPaste(selected=False)
- results = copy.deepcopy(dst_results)
- results['mix_results'] = [copy.deepcopy(src_results)]
- results = transform(results)
- self.assertEqual(results['img'].shape[:2],
- self.dst_results['img'].shape[:2])
- # one object of destination image is totally occluded
- self.assertEqual(
- results['gt_bboxes'].shape[0],
- self.dst_results['gt_bboxes'].shape[0] +
- self.src_results['gt_bboxes'].shape[0] - 1)
- self.assertEqual(
- results['gt_bboxes_labels'].shape[0],
- self.dst_results['gt_bboxes_labels'].shape[0] +
- self.src_results['gt_bboxes_labels'].shape[0] - 1)
- self.assertEqual(
- results['gt_masks'].masks.shape[0],
- self.dst_results['gt_masks'].masks.shape[0] +
- self.src_results['gt_masks'].masks.shape[0] - 1)
- self.assertEqual(
- results['gt_ignore_flags'].shape[0],
- self.dst_results['gt_ignore_flags'].shape[0] +
- self.src_results['gt_ignore_flags'].shape[0] - 1)
- # the object of destination image is partially occluded
- ori_bbox = dst_results['gt_bboxes'][0].numpy()
- occ_bbox = results['gt_bboxes'][0].numpy()
- ori_mask = dst_results['gt_masks'].masks[0]
- occ_mask = results['gt_masks'].masks[0]
- self.assertTrue(ori_mask.sum() > occ_mask.sum())
- self.assertTrue(
- np.all(np.abs(occ_bbox - ori_bbox) <= transform.bbox_occluded_thr)
- or occ_mask.sum() > transform.mask_occluded_thr)
- # test copypaste with selected objects
- transform = CopyPaste()
- results = copy.deepcopy(dst_results)
- results['mix_results'] = [copy.deepcopy(src_results)]
- results = transform(results)
- # test copypaste with an empty source image
- results = copy.deepcopy(dst_results)
- valid_inds = [False] * self.src_results['gt_bboxes'].shape[0]
- results['mix_results'] = [{
- 'img':
- src_results['img'].copy(),
- 'gt_bboxes':
- src_results['gt_bboxes'][valid_inds],
- 'gt_bboxes_labels':
- src_results['gt_bboxes_labels'][valid_inds],
- 'gt_masks':
- src_results['gt_masks'][valid_inds],
- 'gt_ignore_flags':
- src_results['gt_ignore_flags'][valid_inds],
- }]
- results = transform(results)
- def test_repr(self):
- transform = CopyPaste()
- self.assertEqual(
- repr(transform), ('CopyPaste(max_num_pasted=100, '
- 'bbox_occluded_thr=10, '
- 'mask_occluded_thr=300, '
- 'selected=True)'))
- class TestAlbu(unittest.TestCase):
- @unittest.skipIf(albumentations is None, 'albumentations is not installed')
- def test_transform(self):
- results = dict(
- img_path=osp.join(osp.dirname(__file__), '../../data/color.jpg'))
- # Define simple pipeline
- load = dict(type='LoadImageFromFile')
- load = TRANSFORMS.build(load)
- albu_transform = dict(
- type='Albu', transforms=[dict(type='ChannelShuffle', p=1)])
- albu_transform = TRANSFORMS.build(albu_transform)
- # Execute transforms
- results = load(results)
- results = albu_transform(results)
- self.assertEqual(results['img'].dtype, np.uint8)
- # test bbox
- albu_transform = dict(
- type='Albu',
- transforms=[dict(type='ChannelShuffle', p=1)],
- bbox_params=dict(
- type='BboxParams',
- format='pascal_voc',
- label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
- keymap={
- 'img': 'image',
- 'gt_bboxes': 'bboxes'
- })
- albu_transform = TRANSFORMS.build(albu_transform)
- results = {
- 'img':
- np.random.random((224, 224, 3)),
- 'img_shape': (224, 224),
- 'gt_bboxes_labels':
- np.array([1, 2, 3], dtype=np.int64),
- 'gt_bboxes':
- np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
- dtype=np.float32),
- 'gt_ignore_flags':
- np.array([0, 0, 1], dtype=bool),
- }
- results = albu_transform(results)
- self.assertEqual(results['img'].dtype, np.float64)
- self.assertEqual(results['gt_bboxes'].dtype, np.float32)
- self.assertEqual(results['gt_ignore_flags'].dtype, bool)
- self.assertEqual(results['gt_bboxes_labels'].dtype, np.int64)
- self.assertEqual(results['img_shape'], results['img'].shape[:2])
- @unittest.skipIf(albumentations is None, 'albumentations is not installed')
- def test_repr(self):
- albu_transform = dict(
- type='Albu', transforms=[dict(type='ChannelShuffle', p=1)])
- albu_transform = TRANSFORMS.build(albu_transform)
- self.assertEqual(
- repr(albu_transform), 'Albu(transforms=['
- '{\'type\': \'ChannelShuffle\', '
- '\'p\': 1}])')
- class TestCorrupt(unittest.TestCase):
- def test_transform(self):
- results = dict(
- img_path=osp.join(osp.dirname(__file__), '../../data/color.jpg'))
- # Define simple pipeline
- load = dict(type='LoadImageFromFile')
- load = TRANSFORMS.build(load)
- corrupt_transform = dict(type='Corrupt', corruption='gaussian_blur')
- corrupt_transform = TRANSFORMS.build(corrupt_transform)
- # Execute transforms
- results = load(results)
- results = corrupt_transform(results)
- self.assertEqual(results['img'].dtype, np.uint8)
- def test_repr(self):
- corrupt_transform = dict(type='Corrupt', corruption='gaussian_blur')
- corrupt_transform = TRANSFORMS.build(corrupt_transform)
- self.assertEqual(
- repr(corrupt_transform), 'Corrupt(corruption=gaussian_blur, '
- 'severity=1)')
- class TestRandomShift(unittest.TestCase):
- def test_init(self):
- # test assertion for invalid shift_ratio
- with self.assertRaises(AssertionError):
- RandomShift(prob=1.5)
- # test assertion for invalid max_shift_px
- with self.assertRaises(AssertionError):
- RandomShift(max_shift_px=-1)
- def test_transform(self):
- results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- results['img'] = img
- h, w, _ = img.shape
- gt_bboxes = create_random_bboxes(8, w, h)
- results['gt_bboxes_labels'] = np.ones(
- gt_bboxes.shape[0], dtype=np.int64)
- results['gt_bboxes'] = gt_bboxes
- transform = RandomShift(prob=1.0)
- results = transform(results)
- self.assertEqual(results['img'].shape[:2], (h, w))
- self.assertEqual(results['gt_bboxes_labels'].shape[0],
- results['gt_bboxes'].shape[0])
- self.assertEqual(results['gt_bboxes_labels'].dtype, np.int64)
- self.assertEqual(results['gt_bboxes'].dtype, np.float32)
- def test_transform_use_box_type(self):
- results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../../data/color.jpg'), 'color')
- results['img'] = img
- h, w, _ = img.shape
- gt_bboxes = create_random_bboxes(8, w, h)
- results['gt_bboxes_labels'] = np.ones(
- gt_bboxes.shape[0], dtype=np.int64)
- results['gt_bboxes'] = HorizontalBoxes(gt_bboxes)
- transform = RandomShift(prob=1.0)
- results = transform(results)
- self.assertEqual(results['img'].shape[:2], (h, w))
- self.assertEqual(results['gt_bboxes_labels'].shape[0],
- results['gt_bboxes'].shape[0])
- self.assertEqual(results['gt_bboxes_labels'].dtype, np.int64)
- self.assertEqual(results['gt_bboxes'].dtype, torch.float32)
- def test_repr(self):
- transform = RandomShift()
- self.assertEqual(
- repr(transform), ('RandomShift(prob=0.5, '
- 'max_shift_px=32, '
- 'filter_thr_px=1)'))
- class TestRandomErasing(unittest.TestCase):
- def setUp(self):
- """Setup the model and optimizer which are used in every test method.
- TestCase calls functions in this order: setUp() -> testMethod() ->
- tearDown() -> cleanUp()
- """
- self.results = construct_toy_data(poly2mask=True)
- def test_transform(self):
- transform = RandomErasing(
- n_patches=(1, 5), ratio=(0.4, 0.8), img_border_value=0)
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].sum() < self.results['img'].sum())
- transform = RandomErasing(
- n_patches=1, ratio=0.999, img_border_value=255)
- results = transform(copy.deepcopy(self.results))
- self.assertTrue(results['img'].sum() > self.results['img'].sum())
- # test empty results
- empty_results = copy.deepcopy(self.results)
- empty_results['gt_bboxes'] = np.zeros((0, 4), dtype=np.float32)
- empty_results['gt_bboxes_labels'] = np.zeros((0, ), dtype=np.int64)
- empty_results['gt_masks'] = empty_results['gt_masks'][False]
- empty_results['gt_ignore_flags'] = np.zeros((0, ), dtype=bool)
- empty_results['gt_seg_map'] = np.ones_like(
- empty_results['gt_seg_map']) * 255
- results = transform(copy.deepcopy(empty_results))
- self.assertTrue(results['img'].sum() > self.results['img'].sum())
- def test_transform_use_box_type(self):
- src_results = copy.deepcopy(self.results)
- src_results['gt_bboxes'] = HorizontalBoxes(src_results['gt_bboxes'])
- transform = RandomErasing(
- n_patches=(1, 5), ratio=(0.4, 0.8), img_border_value=0)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(results['img'].sum() < src_results['img'].sum())
- transform = RandomErasing(
- n_patches=1, ratio=0.999, img_border_value=255)
- results = transform(copy.deepcopy(src_results))
- self.assertTrue(results['img'].sum() > src_results['img'].sum())
- # test empty results
- empty_results = copy.deepcopy(src_results)
- empty_results['gt_bboxes'] = HorizontalBoxes([], dtype=torch.float32)
- empty_results['gt_bboxes_labels'] = np.zeros((0, ), dtype=np.int64)
- empty_results['gt_masks'] = empty_results['gt_masks'][False]
- empty_results['gt_ignore_flags'] = np.zeros((0, ), dtype=bool)
- empty_results['gt_seg_map'] = np.ones_like(
- empty_results['gt_seg_map']) * 255
- results = transform(copy.deepcopy(empty_results))
- self.assertTrue(results['img'].sum() > src_results['img'].sum())
- def test_repr(self):
- transform = RandomErasing(n_patches=(1, 5), ratio=(0, 0.2))
- self.assertEqual(
- repr(transform), ('RandomErasing(n_patches=(1, 5), '
- 'ratio=(0, 0.2), '
- 'squared=True, '
- 'bbox_erased_thr=0.9, '
- 'img_border_value=128, '
- 'mask_border_value=0, '
- 'seg_ignore_label=255)'))
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