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')