config.py

import configargparse


def get_opts():
    parser = configargparse.ArgumentParser()

    # configure file
    parser.add_argument('--config', is_config_file=True, help='config file path')

    # dataset
    parser.add_argument('--dataset_dir', type=str)
    parser.add_argument('--dataset_name', type=str, default='robocar', choices=['kitti', 'nyu', 'ddad', 'robocar'])
    parser.add_argument('--sequence_length', type=int, default=3, help='number of images for training')
    parser.add_argument('--skip_frames', type=int, default=1, help='jump sampling from video')

    # model
    parser.add_argument('--model_version', type=str, default='v1', choices=['v1', 'v2'])
    parser.add_argument('--resnet_layers', type=int, default=18)
    parser.add_argument('--ckpt_path', type=str, default=None, help='pretrained checkpoint path to load')

    # loss
    parser.add_argument('--photo_weight', type=float, default=1.0, help='photometric loss weight')
    parser.add_argument('--geometry_weight', type=float, default=0.5, help='geometry loss weight')
    parser.add_argument('--smooth_weight', type=float, default=0.1, help='smoothness loss weight')
    parser.add_argument('--rot_t_weight', type=float, default=0.5, help='rotation triplet loss weight')
    parser.add_argument('--rot_c_weight', type=float, default=0.1, help='rotation consistency loss weight')
    parser.add_argument('--val_mode', type=str, default='photo', choices=['photo', 'depth'], help='how to run validation')

    # for ablation study
    parser.add_argument('--no_ssim', action='store_true', help='use ssim in photometric loss')
    parser.add_argument('--no_auto_mask', action='store_true', help='masking invalid static points')
    parser.add_argument('--no_dynamic_mask', action='store_false', help='masking dynamic regions')
    parser.add_argument('--no_min_optimize', action='store_false', help='optimize the minimum loss')

    # training options
    parser.add_argument('--exp_name', type=str, default='omni', help='experiment name')
    parser.add_argument('--batch_size', type=int, default=1, help='batch size')
    parser.add_argument('--epoch_size', type=int, default=1000, help='number of training epochs')
    parser.add_argument('--num_epochs', type=int, default=100, help='number of training epochs')
    parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
    parser.add_argument('--num_gpus', type=int, default=1, help='number of gpus')

    # inference options
    parser.add_argument('--input_dir', type=str, help='input image path')
    parser.add_argument('--output_dir', type=str, help='output depth path')
    parser.add_argument('--save-vis', action='store_true', help='save depth visualization')
    parser.add_argument('--save-depth', action='store_true', help='save depth with factor 1000')

    return parser.parse_args()