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 == 'pointcloud':
transform = transforms.Compose([
data.SubsamplePointcloud(cfg['data']['pointcloud_n']),
data.PointcloudNoise(cfg['data']['pointcloud_noise'])
])
inputs_field = data.PointCloudField(
cfg['data']['pointcloud_file'],
transform=transform,
multi_files=cfg['data']['multi_files'])
elif input_type == 'idx':
inputs_field = data.IndexField()
else:
raise ValueError('Invalid input type (%s)' % input_type)
return inputs_field
# Shorthands
out_dir = cfg['training']['out_dir']
generation_dir = os.path.join(out_dir, cfg['generation']['generation_dir'])
if not args.eval_input:
out_file = os.path.join(generation_dir, 'eval_meshes_full.pkl')
out_file_class = os.path.join(generation_dir, 'eval_meshes.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
points_field = data.PointsField(cfg['data']['points_iou_file'],
unpackbits=cfg['data']['points_unpackbits'],
multi_files=cfg['data']['multi_files'])
pointcloud_field = data.PointCloudField(cfg['data']['pointcloud_chamfer_file'],
multi_files=cfg['data']['multi_files'])
fields = {
'points_iou': points_field,
'pointcloud_chamfer': pointcloud_field,
'idx': data.IndexField(),
}
print('Test split: ', cfg['data']['test_split'])
dataset_folder = cfg['data']['path']
dataset = data.Shapes3dDataset(dataset_folder,
fields,
cfg['data']['test_split'],
categories=cfg['data']['classes'],
cfg=cfg)