import argparse def parse_args(): parser = argparse.ArgumentParser(description='Training script') # General arguments parser.add_argument('-d', '--dataset', default='h36m', type=str, metavar='NAME', help='target dataset') # h36m or humaneva parser.add_argument('-k', '--keypoints', default='cpn_ft_h36m_dbb', type=str, metavar='NAME', help='2D detections to use') parser.add_argument('-str', '--subjects-train', default='S1,S5,S6,S7,S8', type=str, metavar='LIST', help='training subjects separated by comma') parser.add_argument('-ste', '--subjects-test', default='S9,S11', type=str, metavar='LIST', help='test subjects separated by comma') parser.add_argument('-a', '--actions', default='*', type=str, metavar='LIST', help='actions to train/test on, separated by comma, or * for all') parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH', help='checkpoint directory') parser.add_argument('--checkpoint-frequency', default=10, type=int, metavar='N', help='create a checkpoint every N epochs') parser.add_argument('-r', '--resume', default='', type=str, metavar='FILENAME', help='checkpoint to resume (file name)') parser.add_argument('--evaluate', default='', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)') parser.add_argument('--render', action='store_true', help='visualize a particular video') parser.add_argument('--by-subject', action='store_true', help='break down error by subject (on evaluation)') parser.add_argument('--export-training-curves', action='store_true', help='save training curves as .png images') # Model arguments parser.add_argument('-s', '--stride', default=1, type=int, metavar='N', help='chunk size to use during training') parser.add_argument('-arc', '--architecture', default='3,3,3', type=str, metavar='LAYERS', help='filter widths separated by comma') parser.add_argument('--causal', action='store_true', help='use causal convolutions for real-time processing') parser.add_argument('-ch', '--channels', default=128, type=int, metavar='N', help='number of channels in convolution layers') # Experimental setting parser.add_argument('-e', '--epochs', default=60, type=int, metavar='N', help='number of training epochs') parser.add_argument('-b', '--batch-size', default=128, type=int, metavar='N', help='batch size in terms of predicted frames') parser.add_argument('-drop', '--dropout', default=0.05, type=float, metavar='P', help='dropout probability') parser.add_argument('-lr', '--learning-rate', default=0.001, type=float, metavar='LR', help='initial learning rate') parser.add_argument('-lrd', '--lr-decay', default=0.95, type=float, metavar='LR', help='learning rate decay per epoch') parser.add_argument('-no-da', '--no-data-augmentation', dest='data_augmentation', action='store_false', help='disable train-time flipping') parser.add_argument('-no-tta', '--no-test-time-augmentation', dest='test_time_augmentation', action='store_false', help='disable test-time flipping') parser.add_argument('--subset', default=1, type=float, metavar='FRACTION', help='reduce dataset size by fraction') parser.add_argument('--downsample', default=5, type=int, metavar='FACTOR', help='downsample frame rate by factor (semi-supervised)') parser.add_argument('--no-eval', action='store_true', help='disable epoch evaluation while training (small speed-up)') parser.add_argument('--disable-optimizations', action='store_true', help='disable optimized model for single-frame predictions') # Visualization parser.add_argument('--viz-subject', type=str, metavar='STR', help='subject to render') parser.add_argument('--viz-action', type=str, metavar='STR', help='action to render') parser.add_argument('--viz-camera', type=int, default=0, metavar='N', help='camera to render') parser.add_argument('--viz-video', type=str, metavar='PATH', help='path to input video') parser.add_argument('--viz-skip', type=int, default=0, metavar='N', help='skip first N frames of input video') parser.add_argument('--viz-output', type=str, metavar='PATH', help='output file name (.gif or .mp4)') parser.add_argument('--viz-export', type=str, metavar='PATH', help='output file name for coordinates') parser.add_argument('--viz-bitrate', type=int, default=3000, metavar='N', help='bitrate for mp4 videos') parser.add_argument('--viz-no-ground-truth', action='store_true', help='do not show ground-truth poses') parser.add_argument('--viz-limit', type=int, default=-1, metavar='N', help='only render first N frames') parser.add_argument('--viz-downsample', type=int, default=1, metavar='N', help='downsample FPS by a factor N') parser.add_argument('--viz-size', type=int, default=5, metavar='N', help='image size') parser.set_defaults(bone_length_term=True) parser.set_defaults(data_augmentation=True) parser.set_defaults(test_time_augmentation=True) args = parser.parse_args() # Check invalid configuration if args.resume and args.evaluate: print('Invalid flags: --resume and --evaluate cannot be set at the same time') exit() if args.export_training_curves and args.no_eval: print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time') exit() return args