def test_custom_dataset_custom_palette():
dataset = CustomDataset(pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=('bus', 'car'),
palette=[[100, 100, 100], [200, 200, 200]],
test_mode=True)
assert tuple(dataset.PALETTE) == tuple([[100, 100, 100], [200, 200, 200]])
def test_custom_dataset_random_palette_is_generated():
dataset = CustomDataset(pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=('bus', 'car'),
test_mode=True)
assert len(dataset.PALETTE) == 2
for class_color in dataset.PALETTE:
assert len(class_color) == 3
assert all(x >= 0 and x <= 255 for x in class_color)
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)
def test_custom_dataset():
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(128, 256), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# with img_dir and ann_dir
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with img_dir, ann_dir, split
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png',
split='splits/train.txt')
assert len(train_dataset) == 4
# no data_root
train_dataset = CustomDataset(
train_pipeline,
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
ann_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts'),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with data_root but img_dir/ann_dir are abs path
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir=osp.abspath(
osp.join(osp.dirname(__file__),
'../data/pseudo_dataset/imgs')),
ann_dir=osp.abspath(
osp.join(osp.dirname(__file__),
'../data/pseudo_dataset/gts')),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# test_mode=True
test_dataset = CustomDataset(test_pipeline,
img_dir=osp.join(
osp.dirname(__file__),
'../data/pseudo_dataset/imgs'),
img_suffix='img.jpg',
test_mode=True,
classes=('pseudo_class', ))
assert len(test_dataset) == 5
# training data get
train_data = train_dataset[0]
assert isinstance(train_data, dict)
# test data get
test_data = test_dataset[0]
assert isinstance(test_data, dict)
# get gt seg map
gt_seg_maps = train_dataset.get_gt_seg_maps(efficient_test=True)
assert isinstance(gt_seg_maps, Generator)
gt_seg_maps = list(gt_seg_maps)
assert len(gt_seg_maps) == 5
# format_results not implemented
with pytest.raises(NotImplementedError):
test_dataset.format_results([], '')
pseudo_results = []
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
# test past evaluation without CLASSES
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(pseudo_results,
metric=['mDice', 'mIoU'])
# test past evaluation with CLASSES
train_dataset.CLASSES = tuple(['a'] * 7)
eval_results = train_dataset.evaluate(pseudo_results, metric='mIoU')
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
assert isinstance(eval_results, dict)
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
assert isinstance(eval_results, dict)
assert 'mRecall' in eval_results
assert 'mPrecision' in eval_results
assert 'mFscore' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results,
metric=['mIoU', 'mDice', 'mFscore'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
assert 'mFscore' in eval_results
assert 'mPrecision' in eval_results
assert 'mRecall' in eval_results
assert not np.isnan(eval_results['mIoU'])
assert not np.isnan(eval_results['mDice'])
assert not np.isnan(eval_results['mAcc'])
assert not np.isnan(eval_results['aAcc'])
assert not np.isnan(eval_results['mFscore'])
assert not np.isnan(eval_results['mPrecision'])
assert not np.isnan(eval_results['mRecall'])
# test evaluation with pre-eval and the dataset.CLASSES is necessary
train_dataset.CLASSES = tuple(['a'] * 7)
pseudo_results = []
for idx in range(len(train_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
pseudo_results.extend(train_dataset.pre_eval(pseudo_result, idx))
eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
assert isinstance(eval_results, dict)
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
assert isinstance(eval_results, dict)
assert 'mRecall' in eval_results
assert 'mPrecision' in eval_results
assert 'mFscore' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results,
metric=['mIoU', 'mDice', 'mFscore'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
assert 'mFscore' in eval_results
assert 'mPrecision' in eval_results
assert 'mRecall' in eval_results
assert not np.isnan(eval_results['mIoU'])
assert not np.isnan(eval_results['mDice'])
assert not np.isnan(eval_results['mAcc'])
assert not np.isnan(eval_results['aAcc'])
assert not np.isnan(eval_results['mFscore'])
assert not np.isnan(eval_results['mPrecision'])
assert not np.isnan(eval_results['mRecall'])
def test_custom_dataset():
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(128, 256), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# with img_dir and ann_dir
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with img_dir, ann_dir, split
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png',
split='splits/train.txt')
assert len(train_dataset) == 4
# no data_root
train_dataset = CustomDataset(
train_pipeline,
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
ann_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts'),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with data_root but img_dir/ann_dir are abs path
train_dataset = CustomDataset(train_pipeline,
data_root=osp.join(osp.dirname(__file__),
'../data/pseudo_dataset'),
img_dir=osp.abspath(
osp.join(osp.dirname(__file__),
'../data/pseudo_dataset/imgs')),
ann_dir=osp.abspath(
osp.join(osp.dirname(__file__),
'../data/pseudo_dataset/gts')),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# test_mode=True
test_dataset = CustomDataset(test_pipeline,
img_dir=osp.join(
osp.dirname(__file__),
'../data/pseudo_dataset/imgs'),
img_suffix='img.jpg',
test_mode=True)
assert len(test_dataset) == 5
# training data get
train_data = train_dataset[0]
assert isinstance(train_data, dict)
# test data get
test_data = test_dataset[0]
assert isinstance(test_data, dict)
# get gt seg map
gt_seg_maps = train_dataset.get_gt_seg_maps()
assert len(gt_seg_maps) == 5
# evaluation
pseudo_results = []
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
eval_results = train_dataset.evaluate(pseudo_results)
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
# evaluation with CLASSES
train_dataset.CLASSES = tuple(['a'] * 7)
eval_results = train_dataset.evaluate(pseudo_results)
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results