def test_concat_ade(separate_eval):
test_dataset = ADE20KDataset(
pipeline=[],
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
assert len(test_dataset) == 5
concat_dataset = ConcatDataset([test_dataset, test_dataset],
separate_eval=separate_eval)
assert len(concat_dataset) == 10
# Test format_results
pseudo_results = []
for _ in range(len(concat_dataset)):
h, w = (2, 2)
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
# test format per image
file_paths = []
for i in range(len(pseudo_results)):
file_paths.extend(
concat_dataset.format_results([pseudo_results[i]],
'.format_ade',
indices=[i]))
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
shutil.rmtree('.format_ade')
# test default argument
file_paths = concat_dataset.format_results(pseudo_results, '.format_ade')
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
shutil.rmtree('.format_ade')
def test_concat_cityscapes(separate_eval):
cityscape_dataset = CityscapesDataset(
pipeline=[],
img_dir=osp.join(osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/leftImg8bit'),
ann_dir=osp.join(osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/gtFine'))
assert len(cityscape_dataset) == 1
with pytest.raises(NotImplementedError):
_ = ConcatDataset([cityscape_dataset, cityscape_dataset],
separate_eval=separate_eval)
ade_dataset = ADE20KDataset(pipeline=[],
img_dir=osp.join(
osp.dirname(__file__),
'../data/pseudo_dataset/imgs'))
assert len(ade_dataset) == 5
with pytest.raises(NotImplementedError):
_ = ConcatDataset([cityscape_dataset, ade_dataset],
separate_eval=separate_eval)
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)
