def get_inputs_field(mode, cfg):
''' Returns the inputs fields.
Args:
mode (str): the mode which is used
cfg (dict): config dictionary
'''
input_type = cfg['data']['input_type']
if input_type is None:
inputs_field = None
elif input_type == 'img_seq':
if mode == 'train' and cfg['data']['img_augment']:
resize_op = transforms.RandomResizedCrop(cfg['data']['img_size'],
(0.75, 1.), (1., 1.))
else:
resize_op = transforms.Resize((cfg['data']['img_size']))
transform = transforms.Compose([
resize_op,
transforms.ToTensor(),
])
if mode == 'train':
random_view = True
else:
random_view = False
inputs_field = data.ImageSubseqField(cfg['data']['img_seq_folder'],
transform,
random_view=random_view)
elif input_type == 'pcl_seq':
connected_samples = cfg['data']['input_pointcloud_corresponding']
transform = transforms.Compose([
data.SubsamplePointcloudSeq(cfg['data']['input_pointcloud_n'],
connected_samples=connected_samples),
data.PointcloudNoise(cfg['data']['input_pointcloud_noise'])
])
inputs_field = data.PointCloudSubseqField(
cfg['data']['pointcloud_seq_folder'],
transform,
seq_len=cfg['data']['length_sequence'])
elif input_type == 'end_pointclouds':
transform = data.SubsamplePointcloudSeq(
cfg['data']['input_pointcloud_n'],
connected_samples=cfg['data']['input_pointcloud_corresponding'])
inputs_field = data.PointCloudSubseqField(
cfg['data']['pointcloud_seq_folder'],
only_end_points=True,
seq_len=cfg['data']['length_sequence'],
transform=transform)
elif input_type == 'idx':
inputs_field = data.IndexField()
else:
raise ValueError('Invalid input type (%s)' % input_type)
return inputs_field
def get_data_fields(mode, cfg):
''' Returns the data fields.
Args:
mode (str): mode (train|val|test)
cfg (yaml file): yaml config file
'''
fields = {}
# Define transform operation
if mode == 'train':
transform = data.SubsamplePointcloudSeq(
cfg['data']['n_training_pcl_points'], connected_samples=True)
else:
transform = data.SubsamplePointcloudSeq(
cfg['training']['n_eval_points'], random=False)
# Define points field
fields['points_mesh'] = data.PointCloudSubseqField(
cfg['data']['pointcloud_seq_folder'],
transform=transform,
)
return fields
out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
project_to_final_mesh = cfg['test']['project_to_final_mesh']
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_tmp = os.path.join(generation_dir, 'eval_meshes_full_tmp.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.csv')
out_file_class_tmp = os.path.join(generation_dir, 'eval_meshes_tmp.csv')
else:
out_file = os.path.join(generation_dir, 'eval_input_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_input.csv')
# Dataset
fields = {
'pointcloud_chamfer':
data.PointCloudSubseqField(cfg['data']['pointcloud_seq_folder'],
seq_len=cfg['data']['length_sequence']),
'idx':
data.IndexField(),
}
if cfg['test']['eval_mesh_iou']:
fields['points'] = data.PointsSubseqField(
cfg['data']['points_iou_seq_folder'],
all_steps=True,
seq_len=cfg['data']['length_sequence'])
print('Test split: ', cfg['data']['test_split'])
dataset = data.HumansDataset(
dataset_folder,
fields,
split=cfg['data']['test_split'],
out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
project_to_final_mesh = cfg['test']['project_to_final_mesh']
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_tmp = os.path.join(generation_dir, 'eval_meshes_full_tmp.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.csv')
out_file_class_tmp = os.path.join(generation_dir, 'eval_meshes_tmp.csv')
else:
out_file = os.path.join(generation_dir, 'eval_input_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_input.csv')
# Dataset
fields = {
'pointcloud_chamfer': data.PointCloudSubseqField(
cfg['data']['pointcloud_seq_folder'],
seq_len=cfg['data']['length_sequence'],
select_steps=cfg['data']['select_steps']),
'idx': data.IndexField(),
}
if cfg['test']['eval_mesh_iou']:
fields['points'] = data.PointsSubseqField(
cfg['data']['points_iou_seq_folder'], all_steps=True,
seq_len=cfg['data']['length_sequence'], select_steps=cfg['data']['select_steps'],
unpackbits=cfg['data']['points_unpackbits'])
print('Test split: ', cfg['data']['test_split'])
dataset = data.HumansDataset(
dataset_folder, fields, split=cfg['data']['test_split'],