def test_animal_ATRW_dataset():
dataset = 'AnimalATRWDataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/atrw.py').dataset_info
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
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'],
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/atrw/test_atrw.json',
img_prefix='tests/data/atrw/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(ann_file='tests/data/atrw/test_atrw.json',
img_prefix='tests/data/atrw/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'atrw'
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_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]
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_animal_Macaque_dataset_compatibility():
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)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/macaque/test_macaque.json',
img_prefix='tests/data/macaque/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
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_animal_locust_dataset():
dataset = 'AnimalLocustDataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/locust.py').dataset_info
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)
_ = dataset_class(ann_file='tests/data/locust/test_locust.json',
img_prefix='tests/data/locust/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/locust/test_locust.json',
img_prefix='tests/data/locust/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'locust'
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_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]
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_top_down_Panoptic_dataset_compatibility():
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)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/panoptic/test_panoptic.json',
img_prefix='tests/data/panoptic/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
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 = convert_db_to_output(custom_dataset.db)
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_fly_dataset_compatibility():
dataset = 'AnimalFlyDataset'
dataset_class = DATASETS.get(dataset)
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)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/fly/test_fly.json',
img_prefix='tests/data/fly/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(ann_file='tests/data/fly/test_fly.json',
img_prefix='tests/data/fly/',
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_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_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_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_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_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_MHP_dataset():
dataset = 'TopDownMhpDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/mhp.py').dataset_info
# test MHP 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=[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,
bbox_thr=1.0,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
# Test det bbox
with pytest.raises(AssertionError):
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
_ = dataset_class(ann_file='tests/data/mhp/test_mhp.json',
img_prefix='tests/data/mhp/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
# Test gt bbox
_ = 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.test_mode is 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]
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):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_top_down_halpe_dataset():
dataset = 'TopDownHalpeDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/halpe.py').dataset_info
# test Halpe datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(num_output_channels=136,
dataset_joints=136,
dataset_channel=[
list(range(136)),
],
inference_channel=list(range(136)))
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='tests/data/coco/test_coco_det_AP_H_56.json',
)
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
_ = dataset_class(ann_file='tests/data/halpe/test_halpe.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
_ = dataset_class(ann_file='tests/data/halpe/test_halpe.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
# Test gt bbox
custom_dataset = dataset_class(ann_file='tests/data/halpe/test_halpe.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 == 'halpe'
image_id = 785
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 4
_ = 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):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_top_down_JHMDB_dataset_compatibility():
dataset = 'TopDownJhmdbDataset'
# test JHMDB datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
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='')
with pytest.raises(AssertionError):
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
img_prefix='tests/data/jhmdb/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
img_prefix='tests/data/jhmdb/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
# Test gt bbox
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
img_prefix='tests/data/jhmdb/',
data_cfg=data_cfg,
pipeline=[],
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'jhmdb'
image_id = 2290001
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 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['Mean PCK'], 1.0)
infos = custom_dataset.evaluate(outputs, tmpdir, ['tPCK'])
assert_almost_equal(infos['Mean tPCK'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_top_down_COCO_wholebody_dataset_compatibility():
dataset = 'TopDownCocoWholeBodyDataset'
# test COCO datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
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=[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='tests/data/coco/test_coco_det_AP_H_56.json',
)
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = 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_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
_ = dataset_class(ann_file='tests/data/coco/test_coco_wholebody.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=False)
# Test gt bbox
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_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):
_ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
def test_InterHand3D_dataset():
dataset = 'InterHand3DDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/interhand3d.py').dataset_info
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=42,
dataset_joints=42,
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, 35, 36, 37,
38, 39, 40, 41
],
],
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, 35, 36, 37, 38, 39, 40,
41
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64, 64],
heatmap3d_depth_bound=400.0,
heatmap_size_root=64,
root_depth_bound=400.0,
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/interhand2.6m/test_interhand2.6m_data.json',
camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
img_prefix='tests/data/interhand2.6m/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
img_prefix='tests/data/interhand2.6m/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.dataset_name == 'interhand3d'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 4
assert len(custom_dataset.db) == 4
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db,
keys=['rel_root_depth', 'hand_type'],
is_3d=True)
infos = custom_dataset.evaluate(
results, metric=['MRRPE', 'MPJPE', 'Handedness_acc'])
assert_almost_equal(infos['MRRPE'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5)
assert_almost_equal(infos['Handedness_acc'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric='mAP')
def test_top_down_COCO_dataset():
dataset = 'TopDownCocoDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/coco.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=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='tests/data/coco/test_coco_det_AP_H_56.json',
)
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
_ = dataset_class(
ann_file='tests/data/coco/test_coco.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
_ = dataset_class(
ann_file='tests/data/coco/test_coco.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
# Test gt bbox
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.test_mode is True
assert custom_dataset.dataset_name == 'coco'
image_id = 785
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 4
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['AP'], 1.0)
infos = custom_dataset.evaluate(results, metric='mAP', rle_score=True)
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
# Test when gt annotations are absent
del custom_dataset.coco.dataset['annotations']
with pytest.warns(UserWarning):
_ = custom_dataset.evaluate(results, metric='mAP')
def test_top_down_PoseTrack18_dataset():
dataset = 'TopDownPoseTrack18Dataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/posetrack18.py').dataset_info
# test PoseTrack 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=[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='tests/data/posetrack18/annotations/'
'test_posetrack18_human_detections.json',
)
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
_ = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
_ = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
# Test gt bbox
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'posetrack18'
image_id = 10128340000
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 3
assert len(custom_dataset) == 14
_ = custom_dataset[0]
# Test evaluate function, use gt bbox
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['Total AP'], 100)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
# Test evaluate function, use det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert len(custom_dataset) == 278
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='mAP')
# since the det box input assume each keypoint position to be (0,0)
# the Total AP will be zero.
assert_almost_equal(infos['Total AP'], 0.)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
def test_top_down_PoseTrack18Video_dataset():
dataset = 'TopDownPoseTrack18VideoDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/posetrack18.py').dataset_info
# test PoseTrack18Video dataset
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=[288, 384],
heatmap_size=[72, 96],
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'],
use_nms=True,
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='tests/data/posetrack18/annotations/'
'test_posetrack18_human_detections.json',
# frame-related arguments
frame_index_rand=True,
frame_index_range=[-2, 2],
num_adj_frames=1,
frame_indices_test=[-2, 2, -1, 1, 0],
frame_weight_train=(0.0, 1.0),
frame_weight_test=(0.3, 0.1, 0.25, 0.25, 0.1),
)
# Test value of dataset_info
with pytest.raises(ValueError):
_ = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=None,
test_mode=False)
# Test train mode (must use gt bbox)
_ = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
# # Test gt bbox + test mode
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'posetrack18'
assert custom_dataset.ph_fill_len == 6
image_id = 10128340000
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 3
assert len(custom_dataset) == 14
_ = custom_dataset[0]
# Test det bbox + test mode
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.frame_indices_test == [-2, -1, 0, 1, 2]
assert len(custom_dataset) == 278
# Test non-random index
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['frame_index_rand'] = False
data_cfg_copy['frame_indices_train'] = [0, -1]
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.frame_indices_train == [-1, 0]
# Test evaluate function, use gt bbox
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['Total AP'], 100)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
# Test evaluate function, use det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
custom_dataset = dataset_class(
ann_file='tests/data/posetrack18/annotations/'
'test_posetrack18_val.json',
img_prefix='tests/data/posetrack18/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric='mAP')
# since the det box input assume each keypoint position to be (0,0),
# the Total AP will be zero.
assert_almost_equal(infos['Total AP'], 0)
with pytest.raises(KeyError):
_ = custom_dataset.evaluate(results, metric='PCK')
def test_top_down_AIC_dataset():
dataset = 'TopDownAicDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/aic.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=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=[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='')
with pytest.raises(AssertionError):
# Test det bbox
data_cfg_copy = copy.deepcopy(data_cfg)
data_cfg_copy['use_gt_bbox'] = False
_ = dataset_class(
ann_file='tests/data/aic/test_aic.json',
img_prefix='tests/data/aic/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
_ = dataset_class(
ann_file='tests/data/aic/test_aic.json',
img_prefix='tests/data/aic/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
# Test gt bbox
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.test_mode is 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]
results = convert_db_to_output(custom_dataset.db)
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_top_down_InterHand3D_dataset_compatibility():
dataset = 'InterHand3DDataset'
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=42,
dataset_joints=42,
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, 35, 36, 37,
38, 39, 40, 41
],
],
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, 35, 36, 37, 38, 39, 40,
41
])
data_cfg = dict(image_size=[256, 256],
heatmap_size=[64, 64, 64],
heatmap3d_depth_bound=400.0,
heatmap_size_root=64,
root_depth_bound=400.0,
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/interhand2.6m/test_interhand2.6m_data.json',
camera_file='tests/data/interhand2.6m/'
'test_interhand2.6m_camera.json',
joint_file='tests/data/interhand2.6m/'
'test_interhand2.6m_joint_3d.json',
img_prefix='tests/data/interhand2.6m/',
data_cfg=data_cfg_copy,
pipeline=[],
test_mode=True)
with pytest.warns(DeprecationWarning):
custom_dataset = dataset_class(
ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
camera_file='tests/data/interhand2.6m/'
'test_interhand2.6m_camera.json',
joint_file='tests/data/interhand2.6m/'
'test_interhand2.6m_joint_3d.json',
img_prefix='tests/data/interhand2.6m/',
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) == 4
_ = custom_dataset[0]
outputs = convert_db_to_output(custom_dataset.db,
keys=['rel_root_depth', 'hand_type'],
is_3d=True)
with tempfile.TemporaryDirectory() as tmpdir:
infos = custom_dataset.evaluate(outputs, tmpdir,
['MRRPE', 'MPJPE', 'Handedness_acc'])
assert_almost_equal(infos['MRRPE'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5)
assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5)
assert_almost_equal(infos['Handedness_acc'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
def test_ap10k_dataset():
dataset = 'AnimalAP10KDataset'
dataset_class = DATASETS.get(dataset)
dataset_info = Config.fromfile(
'configs/_base_/datasets/ap10k.py').dataset_info
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'],
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/ap10k/test_ap10k.json',
img_prefix='tests/data/ap10k/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=True)
custom_dataset = dataset_class(ann_file='tests/data/ap10k/test_ap10k.json',
img_prefix='tests/data/ap10k/',
data_cfg=data_cfg_copy,
dataset_info=dataset_info,
pipeline=[],
test_mode=False)
assert custom_dataset.dataset_name == 'ap10k'
assert custom_dataset.test_mode is False
assert custom_dataset.num_images == 2
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
for output in results:
# as there is only one box in each image for test
output['bbox_ids'] = [0 for _ in range(len(output['bbox_ids']))]
infos = custom_dataset.evaluate(results, metric='mAP')
assert_almost_equal(infos['AP'], 1.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric=['PCK'])
# Test when gt annotations are absent
del custom_dataset.coco.dataset['annotations']
with pytest.warns(UserWarning):
_ = custom_dataset.evaluate(results, metric='mAP')
