def test_body3d_semi_supervision_dataset():
# Test Body3d Semi-supervision Dataset
dataset_info = Config.fromfile(
'configs/_base_/datasets/h36m.py').dataset_info
# 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_cfg = dict(type='Body3DH36MDataset',
ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=labeled_data_cfg,
dataset_info=dataset_info,
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_cfg = dict(
type='Body3DH36MDataset',
ann_file='tests/data/h36m/test_h36m_body3d.npz',
img_prefix='tests/data/h36m',
data_cfg=unlabeled_data_cfg,
dataset_info=dataset_info,
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)
custom_dataset = dataset_class(labeled_dataset_cfg, unlabeled_dataset_cfg)
item = custom_dataset[0]
assert custom_dataset.labeled_dataset.dataset_name == 'h36m'
assert 'unlabeled_input' in item.keys()
unlabeled_dataset = build_dataset(unlabeled_dataset_cfg)
assert len(unlabeled_dataset) == len(custom_dataset)
def test_concat_dataset():
# build COCO-like dataset config
dataset_info = Config.fromfile(
'configs/_base_/datasets/coco.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=[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',
)
dataset_cfg = dict(
type='TopDownCocoDataset',
ann_file='tests/data/coco/test_coco.json',
img_prefix='tests/data/coco/',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info)
dataset = build_dataset(dataset_cfg)
# Case 1: build ConcatDataset explicitly
concat_dataset_cfg = dict(
type='ConcatDataset', datasets=[dataset_cfg, dataset_cfg])
concat_dataset = build_dataset(concat_dataset_cfg)
assert len(concat_dataset) == 2 * len(dataset)
# Case 2: build ConcatDataset from cfg sequence
concat_dataset = build_dataset([dataset_cfg, dataset_cfg])
assert len(concat_dataset) == 2 * len(dataset)
# Case 3: build ConcatDataset from ann_file sequence
concat_dataset_cfg = dataset_cfg.copy()
for key in ['ann_file', 'type', 'img_prefix', 'dataset_info']:
val = concat_dataset_cfg[key]
concat_dataset_cfg[key] = [val] * 2
for key in ['num_joints', 'dataset_channel']:
val = concat_dataset_cfg['data_cfg'][key]
concat_dataset_cfg['data_cfg'][key] = [val] * 2
concat_dataset = build_dataset(concat_dataset_cfg)
assert len(concat_dataset) == 2 * len(dataset)
def __init__(self, labeled_dataset, unlabeled_dataset):
super().__init__()
self.labeled_dataset = build_dataset(labeled_dataset)
self.unlabeled_dataset = build_dataset(unlabeled_dataset)
self.length = len(self.unlabeled_dataset)
def __init__(self, train_dataset, adversarial_dataset):
super().__init__()
self.train_dataset = build_dataset(train_dataset)
self.adversarial_dataset = build_dataset(adversarial_dataset)
self.length = len(self.train_dataset)