def test_dataset_wrapper():
# CustomDataset.load_annotations = MagicMock()
# CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
dataset_a = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_a = 10
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
dataset_b = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_b = 20
dataset_b.img_infos = MagicMock()
dataset_b.img_infos.__len__.return_value = len_b
concat_dataset = ConcatDataset([dataset_a, dataset_b])
assert concat_dataset[5] == 5
assert concat_dataset[25] == 15
assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
repeat_dataset = RepeatDataset(dataset_a, 10)
assert repeat_dataset[5] == 5
assert repeat_dataset[15] == 5
assert repeat_dataset[27] == 7
assert len(repeat_dataset) == 10 * len(dataset_a)
def test_dataset_wrapper():
# CustomDataset.load_annotations = MagicMock()
# CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
dataset_a = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_a = 10
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
dataset_b = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_b = 20
dataset_b.img_infos = MagicMock()
dataset_b.img_infos.__len__.return_value = len_b
concat_dataset = ConcatDataset([dataset_a, dataset_b])
assert concat_dataset[5] == 5
assert concat_dataset[25] == 15
assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
repeat_dataset = RepeatDataset(dataset_a, 10)
assert repeat_dataset[5] == 5
assert repeat_dataset[15] == 5
assert repeat_dataset[27] == 7
assert len(repeat_dataset) == 10 * len(dataset_a)
img_scale = (60, 60)
pipeline = [
dict(type='RandomMosaic', prob=1, img_scale=img_scale),
dict(type='RandomFlip', prob=0.5),
dict(type='Resize', img_scale=img_scale, keep_ratio=False),
]
CustomDataset.load_annotations = MagicMock()
results = []
for _ in range(2):
height = np.random.randint(10, 30)
weight = np.random.randint(10, 30)
img = np.ones((height, weight, 3))
gt_semantic_seg = np.random.randint(5, size=(height, weight))
results.append(dict(gt_semantic_seg=gt_semantic_seg, img=img))
classes = ['0', '1', '2', '3', '4']
palette = [(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]
CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx])
dataset_a = CustomDataset(img_dir=MagicMock(),
pipeline=[],
test_mode=True,
classes=classes,
palette=palette)
len_a = 2
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
assert len(multi_image_mix_dataset) == len(dataset_a)
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
# test skip_type_keys
multi_image_mix_dataset = MultiImageMixDataset(
dataset_a, pipeline, skip_type_keys=('RandomFlip'))
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
assert results_['img'].shape == (img_scale[0], img_scale[1], 3)
skip_type_keys = ('RandomFlip', 'Resize')
multi_image_mix_dataset.update_skip_type_keys(skip_type_keys)
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
assert results_['img'].shape[:2] != img_scale
# test pipeline
with pytest.raises(TypeError):
pipeline = [['Resize']]
multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)