def test_deepfashion_dataset():
dataset = 'DeepFashionDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/deepfashion_full.py').dataset_info
# test JHMDB datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(num_output_channels=8,
dataset_joints=8,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7])
data_cfg = dict(image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
image_thr=0.0,
bbox_file='')
# Test gt bbox
custom_dataset = dataset_class(ann_file='tests/data/fld/test_fld.json',
img_prefix='tests/data/fld/',
subset='full',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'deepfashion_full'
image_id = 128
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
assert_almost_equal(infos['PCK'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric='mAP')
def test_Panoptic2D_dataset():
dataset = 'PanopticDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/panoptic_hand2d.py').dataset_info
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/panoptic/test_panoptic.json',
img_prefix='tests/data/panoptic/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/panoptic/test_panoptic.json',
img_prefix='tests/data/panoptic/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.dataset_name == 'panoptic_hand2d'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric=['PCKh', 'EPE', 'AUC'])
assert_almost_equal(infos['PCKh'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric='mAP')
def test_animal_fly_dataset():
dataset = 'AnimalFlyDataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/fly.py').dataset_info
channel_cfg = dict(num_output_channels=32,
dataset_joints=32,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 31
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31
])
data_cfg = dict(image_size=[192, 192],
heatmap_size=[48, 48],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/fly/test_fly.json',
img_prefix='tests/data/fly/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(ann_file='tests/data/fly/test_fly.json',
img_prefix='tests/data/fly/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'fly'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
assert_almost_equal(infos['PCK'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_top_down_Panoptic_dataset():
dataset = 'PanopticDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(
ann_file='tests/data/panoptic/test_panoptic.json',
img_prefix='tests/data/panoptic/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/panoptic/test_panoptic.json',
img_prefix='tests/data/panoptic/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
outputs = load_json_to_output('tests/data/panoptic/test_panoptic.json',
'tests/data/panoptic/', 1, False)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir,
['PCKh', 'EPE', 'AUC'])
assert_almost_equal(infos['PCKh'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_animal_Macaque_dataset():
dataset = 'AnimalMacaqueDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/macaque/test_macaque.json',
img_prefix='tests/data/macaque/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/macaque/test_macaque.json',
img_prefix='tests/data/macaque/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
def test_bottom_up_AIC_dataset():
dataset = 'BottomUpAicDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/aic.py').dataset_info
# test MHP datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=14,
dataset_joints=14,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=1,
scale_aware_sigma=False,
)
_ = dataset_class(ann_file='tests/data/aic/test_aic.json',
img_prefix='tests/data/aic/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
custom_dataset = dataset_class(ann_file='tests/data/aic/test_aic.json',
img_prefix='tests/data/aic/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.dataset_name == 'aic'
image_id = 1
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 3
_ = custom_dataset[0]
outputs = convert_coco_to_output(custom_dataset.coco)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_deepfashion_dataset_compatibility():
dataset = 'DeepFashionDataset'
# test JHMDB datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(num_output_channels=8,
dataset_joints=8,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7])
data_cfg = dict(image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
image_thr=0.0,
bbox_file='')
# Test gt bbox
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(ann_file='tests/data/fld/test_fld.json',
img_prefix='tests/data/fld/',
subset='full',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'deepfashion_full'
image_id = 128
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK', 'EPE', 'AUC'])
assert_almost_equal(infos['PCK'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_bottom_up_COCO_wholebody_dataset():
dataset = 'BottomUpCocoWholeBodyDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/coco_wholebody.py').dataset_info
# test COCO-wholebody datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=133,
dataset_joints=133,
dataset_channel=[
list(range(133)),
],
inference_channel=list(range(133)))
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128, 256],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=2,
scale_aware_sigma=False,
)
_ = dataset_class(ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
custom_dataset = dataset_class(
ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'coco_wholebody'
image_id = 785
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 4
_ = custom_dataset[0]
results = convert_coco_to_output(custom_dataset.coco, is_wholebody=True)
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
def test_bottom_up_MHP_dataset():
dataset = 'BottomUpMhpDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/mhp.py').dataset_info
# test MHP datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
dataset_joints=16,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
])
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=1,
scale_aware_sigma=False,
)
_ = dataset_class(ann_file='tests/data/mhp/test_mhp.json',
img_prefix='tests/data/mhp/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
custom_dataset = dataset_class(ann_file='tests/data/mhp/test_mhp.json',
img_prefix='tests/data/mhp/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.dataset_name == 'mhp'
image_id = 2889
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
results = convert_coco_to_output(custom_dataset.coco)
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
def test_animal_locust_dataset_compatibility():
dataset = 'AnimalLocustDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=35,
dataset_joints=35,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 31, 32, 33, 34
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34
])
data_cfg = dict(image_size=[160, 160],
heatmap_size=[40, 40],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/locust/test_locust.json',
img_prefix='tests/data/locust/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/locust/test_locust.json',
img_prefix='tests/data/locust/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
assert_almost_equal(infos['PCK'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_bottom_up_COCO_wholebody_dataset_compatibility():
dataset = 'BottomUpCocoWholeBodyDataset'
# test COCO-wholebody datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=133,
dataset_joints=133,
dataset_channel=[
list(range(133)),
],
inference_channel=list(range(133)))
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128, 256],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=2,
scale_aware_sigma=False,
)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
test_mode=False)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'coco_wholebody'
image_id = 785
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 4
_ = custom_dataset[0]
outputs = convert_coco_to_output(custom_dataset.coco, is_wholebody=True)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_top_down_OneHand10K_dataset_compatibility():
dataset = 'OneHand10KDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
with pytest.warns(DeprecationWarning):
_ = dataset_class(
ann_file='tests/data/onehand10k/test_onehand10k.json',
img_prefix='tests/data/onehand10k/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/onehand10k/test_onehand10k.json',
img_prefix='tests/data/onehand10k/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK', 'EPE', 'AUC'])
assert_almost_equal(infos['PCK'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_bottom_up_MHP_dataset_compatibility():
dataset = 'BottomUpMhpDataset'
# test MHP datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
dataset_joints=16,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
])
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=1,
scale_aware_sigma=False,
)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/mhp/test_mhp.json',
img_prefix='tests/data/mhp/',
data_cfg=data_cfg,
pipeline=[],
test_mode=False)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(ann_file='tests/data/mhp/test_mhp.json',
img_prefix='tests/data/mhp/',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
image_id = 2889
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
assert custom_dataset.dataset_name == 'mhp'
outputs = convert_coco_to_output(custom_dataset.coco)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_animal_horse10_dataset():
dataset = 'AnimalHorse10Dataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/horse10.py').dataset_info
channel_cfg = dict(num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 21
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/horse10/test_horse10.json',
img_prefix='tests/data/horse10/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/horse10/test_horse10.json',
img_prefix='tests/data/horse10/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'horse10'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 3
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric=['PCK'])
assert_almost_equal(infos['PCK'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric='mAP')
def test_face_WFLW_dataset_compatibility():
dataset = 'FaceWFLWDataset'
# test Face WFLW datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=98,
dataset_joints=98,
dataset_channel=[
list(range(98)),
],
inference_channel=list(range(98)))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
with pytest.warns(DeprecationWarning):
_ = dataset_class(
ann_file='tests/data/wflw/test_wflw.json',
img_prefix='tests/data/wflw/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/wflw/test_wflw.json',
img_prefix='tests/data/wflw/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['NME'])
assert_almost_equal(infos['NME'], 0.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_animal_horse10_dataset():
dataset = 'AnimalHorse10Dataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(
ann_file='tests/data/horse10/test_horse10.json',
img_prefix='tests/data/horse10/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/horse10/test_horse10.json',
img_prefix='tests/data/horse10/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 3
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
assert_almost_equal(infos['PCK'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_body3d_semi_supervision_dataset_compatibility():
# Test Body3d Semi-supervision Dataset
# load labeled dataset
labeled_data_cfg = dict(num_joints=17,
seq_len=27,
seq_frame_interval=1,
causall=False,
temporal_padding=True,
joint_2d_src='gt',
subset=1,
subjects=['S1'],
need_camera_param=True,
camera_param_file='tests/data/h36m/cameras.pkl')
labeled_dataset = dict(type='Body3DH36MDataset',
ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=labeled_data_cfg,
pipeline=[])
# load unlabled data
unlabeled_data_cfg = dict(num_joints=17,
seq_len=27,
seq_frame_interval=1,
causal=False,
temporal_padding=True,
joint_2d_src='gt',
subjects=['S5', 'S7', 'S8'],
need_camera_param=True,
camera_param_file='tests/data/h36m/cameras.pkl',
need_2d_label=True)
unlabeled_dataset = dict(type='Body3DH36MDataset',
ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=unlabeled_data_cfg,
pipeline=[
dict(type='Collect',
keys=[('input_2d', 'unlabeled_input')],
meta_name='metas',
meta_keys=[])
])
# combine labeled and unlabeled dataset to form a new dataset
dataset = 'Body3DSemiSupervisionDataset'
dataset_class = DATASETS.get(dataset)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(labeled_dataset, unlabeled_dataset)
item = custom_dataset[0]
assert 'unlabeled_input' in item.keys()
unlabeled_dataset = build_dataset(unlabeled_dataset)
assert len(unlabeled_dataset) == len(custom_dataset)
def test_bottom_up_COCO_dataset():
dataset = 'BottomUpCocoDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/coco.py').dataset_info
# test COCO datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
])
data_cfg = dict(image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128, 256],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=2,
scale_aware_sigma=False,
use_nms=True)
_ = dataset_class(ann_file='tests/data/coco/test_coco.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
custom_dataset = dataset_class(ann_file='tests/data/coco/test_coco.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.dataset_name == 'coco'
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
outputs = convert_coco_to_output(custom_dataset.coco)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_mesh_Adversarial_dataset():
# test mesh Adversarial dataset
# load train dataset
dataset = 'MeshMixDataset'
dataset_class = DATASETS.get(dataset)
data_cfg = dict(
image_size=[256, 256],
iuv_size=[64, 64],
num_joints=24,
use_IUV=True,
uv_type='BF')
train_dataset = dataset_class(
configs=[
dict(
ann_file='tests/data/h36m/test_h36m.npz',
img_prefix='tests/data/h36m',
data_cfg=data_cfg,
pipeline=[]),
dict(
ann_file='tests/data/h36m/test_h36m.npz',
img_prefix='tests/data/h36m',
data_cfg=data_cfg,
pipeline=[]),
],
partition=[0.6, 0.4])
# load adversarial dataset
dataset = 'MoshDataset'
dataset_class = DATASETS.get(dataset)
adversarial_dataset = dataset_class(
ann_file='tests/data/mosh/test_mosh.npz', pipeline=[])
# combine train and adversarial dataset to form a new dataset
dataset = 'MeshAdversarialDataset'
dataset_class = DATASETS.get(dataset)
custom_dataset = dataset_class(train_dataset, adversarial_dataset)
item = custom_dataset[0]
assert 'mosh_theta' in item.keys()
def test_bottom_up_CrowdPose_dataset():
dataset = 'BottomUpCrowdPoseDataset'
# test CrowdPose datasets
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=14,
dataset_joints=14,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
data_cfg = dict(
image_size=512,
base_size=256,
base_sigma=2,
heatmap_size=[128, 256],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
num_scales=2,
scale_aware_sigma=False)
_ = dataset_class(
ann_file='tests/data/crowdpose/test_crowdpose.json',
img_prefix='tests/data/crowdpose/',
data_cfg=data_cfg,
pipeline=[],
test_mode=False)
custom_dataset = dataset_class(
ann_file='tests/data/crowdpose/test_crowdpose.json',
img_prefix='tests/data/crowdpose/',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
image_id = 103319
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
assert custom_dataset.dataset_name == 'crowdpose'
outputs = convert_coco_to_output(custom_dataset.coco)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_face_COFW_dataset():
dataset = 'FaceCOFWDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/cofw.py').dataset_info
# test Face COFW datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(num_output_channels=29,
dataset_joints=29,
dataset_channel=[
list(range(29)),
],
inference_channel=list(range(29)))
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/cofw/test_cofw.json',
img_prefix='tests/data/cofw/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(ann_file='tests/data/cofw/test_cofw.json',
img_prefix='tests/data/cofw/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.dataset_name == 'cofw'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['NME'])
assert_almost_equal(infos['NME'], 0.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_NVGesture_dataset():
dataset = 'NVGestureDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/nvgesture.py').dataset_info
dataset_class = DATASETS.get(dataset)
data_cfg = dict(
video_size=[320, 240],
modality=['rgb', 'depth'],
bbox_file='tests/data/nvgesture/bboxes.json',
)
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/nvgesture/test_nvgesture.lst',
vid_prefix='tests/data/nvgesture/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/nvgesture/test_nvgesture.lst',
vid_prefix='tests/data/nvgesture/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.dataset_name == 'nvgesture'
assert custom_dataset.test_mode is False
assert len(custom_dataset) == 1
sample = custom_dataset[0]
# make pseudo prediction for evaluation
sample['logits'] = {
modal: torch.zeros(1, 25, 1)
for modal in sample['modality']
}
sample['logits']['rgb'][:, sample['label']] = 1
sample['logits']['depth'][:, (sample['label'] + 1) % 25] = 1
sample['label'] = torch.tensor([sample['label']]).long()
infos = custom_dataset.evaluate([sample], metric=['AP'])
assert_almost_equal(infos['AP_rgb'], 1.0)
assert_almost_equal(infos['AP_depth'], 0.0)
assert_almost_equal(infos['AP_mean'], 0.5)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate([sample], metric='mAP')
def test_top_down_InterHand2D_dataset():
dataset = 'InterHand2DDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(
ann_file='tests/data/interhand2d/test_interhand2d_data.json',
camera_file='tests/data/interhand2d/test_interhand2d_camera.json',
joint_file='tests/data/interhand2d/test_interhand2d_joint_3d.json',
img_prefix='tests/data/interhand2d/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/interhand2d/test_interhand2d_data.json',
camera_file='tests/data/interhand2d/test_interhand2d_camera.json',
joint_file='tests/data/interhand2d/test_interhand2d_joint_3d.json',
img_prefix='tests/data/interhand2d/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
assert len(custom_dataset.db) == 6
_ = custom_dataset[0]
def test_top_down_h36m_dataset():
dataset = 'TopDownH36MDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/h36m.py').dataset_info
# test AIC datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test gt bbox
custom_dataset = dataset_class(
ann_file='tests/data/h36m/h36m_coco.json',
img_prefix='tests/data/h36m/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'h36m'
image_id = 1
assert image_id in custom_dataset.img_ids
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='EPE')
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='AUC')
def test_face_coco_wholebody_dataset():
dataset = 'FaceCocoWholeBodyDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/coco_wholebody_face.py').dataset_info
# test Face wholebody datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(num_output_channels=68,
dataset_joints=68,
dataset_channel=[
list(range(68)),
],
inference_channel=list(range(68)))
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric=['NME'])
assert_almost_equal(infos['NME'], 0.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='mAP')
def test_animal_zebra_dataset():
dataset = 'AnimalZebraDataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/zebra.py').dataset_info
channel_cfg = dict(num_output_channels=9,
dataset_joints=9,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8])
data_cfg = dict(image_size=[160, 160],
heatmap_size=[40, 40],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(ann_file='tests/data/zebra/test_zebra.json',
img_prefix='tests/data/zebra/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(ann_file='tests/data/zebra/test_zebra.json',
img_prefix='tests/data/zebra/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'zebra'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
assert_almost_equal(infos['PCK'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_top_down_h36m_dataset_compatibility():
dataset = 'TopDownH36MDataset'
# test AIC datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test gt bbox
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/h36m/h36m_coco.json',
img_prefix='tests/data/h36m/',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'h36m'
image_id = 1
assert image_id in custom_dataset.img_ids
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir, 'EPE')
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'AUC')
def test_top_down_OneHand10K_dataset():
dataset = 'TopDownOneHand10KDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(
num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(
ann_file='tests/data/OneHand10K/test_onehand10k.json',
img_prefix='tests/data/OneHand10K/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/OneHand10K/test_onehand10k.json',
img_prefix='tests/data/OneHand10K/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
_ = custom_dataset[0]
def test_body3d_h36m_dataset():
# Test Human3.6M dataset
dataset = 'Body3DH36MDataset'
dataset_class = DATASETS.get(dataset)
data_cfg = dict(num_joints=17,
seq_len=1,
seq_frame_interval=1,
joint_2d_src='pipeline',
joint_2d_det_file=None,
causal=False,
need_camera_param=True,
camera_param_file='tests/data/h36m/cameras.pkl')
_ = dataset_class(ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=data_cfg,
pipeline=[],
test_mode=False)
custom_dataset = dataset_class(
ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
assert custom_dataset.test_mode is True
_ = custom_dataset[0]
with tempfile.TemporaryDirectory() as tmpdir:
outputs = []
for result in custom_dataset:
outputs.append({
'preds': result['target'][None, ...],
'target_image_paths': [result['target_image_path']],
})
metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe']
infos = custom_dataset.evaluate(outputs, tmpdir, metrics)
np.testing.assert_almost_equal(infos['MPJPE'], 0.0)
np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0)
np.testing.assert_almost_equal(infos['N-MPJPE'], 0.0)
def test_top_down_MPII_dataset():
dataset = 'TopDownMpiiDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/mpii.py').dataset_info
# test COCO datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=16,
dataset_joints=16,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
])
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
)
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
custom_dataset = dataset_class(
ann_file='tests/data/mpii/test_mpii.json',
img_prefix='tests/data/mpii/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
)
assert len(custom_dataset) == 5
assert custom_dataset.dataset_name == 'mpii'
_ = custom_dataset[0]
