if __name__ == '__main__':
# Disable traceback on Ctrl+c
import sys
import signal
signal.signal(signal.SIGINT, lambda x, y: sys.exit(0))
import configargparse
import numpy as np
np.set_printoptions(linewidth=np.inf)
parser = configargparse.ArgParser()
aae_training.add_arguments(parser)
# Dataset
parser.add_argument('--dataset', default=['w300'], type=str, choices=cfg.get_registered_dataset_names(),
nargs='+', help='dataset for training and testing')
parser.add_argument('--test-split', default='full', type=str, help='test set split for 300W/AFLW/WFLW',
choices=['challenging', 'common', '300w', 'full', 'frontal']+wflw.SUBSETS)
parser.add_argument('--benchmark', default=False, action='store_true', help='evaluate performance on testset')
# Landmarks
parser.add_argument('--sigma', default=7, type=float, help='size of landmarks in heatmap')
parser.add_argument('--ocular-norm', default=lmconfig.LANDMARK_OCULAR_NORM, type=str,
help='how to normalize landmark errors', choices=['pupil', 'outer', 'none'])
args = parser.parse_args()
if args.resume is None:
raise ValueError("Please specify the model to be evaluated: '-r MODELNAME'")
default=2,
type=int,
help='update the discriminator every N steps')
parser.add_argument('--update-E-freq',
default=1,
type=int,
help='update the encoder every N steps')
# Datasets
parser.add_argument(
'--dataset-train',
default=['vggface2', 'affectnet'],
# default=['affectnet'],
# default=['vggface2'],
type=str,
choices=cfg.get_registered_dataset_names(),
nargs='+',
help='dataset(s) for training.')
parser.add_argument('--dataset-val',
default=['vggface2'],
type=str,
help='dataset for training.',
choices=cfg.get_registered_dataset_names(),
nargs='+')
parser.add_argument(
'--test-split',
default='train',
type=str,
choices=['train', 'challenging', 'common', '300w', 'full', 'frontal'] +
wflw.SUBSETS,
help='test set split for 300W/AFLW/WFLW')