argparser.py

import argparse


def modify_command_options(opts):
    if opts.dataset == 'voc':
        opts.num_classes = 21
    elif opts.dataset == 'cts':
        opts.num_classes = 19

    if not opts.visualize:
        opts.sample_num = 0

    opts.no_cross_val = not opts.cross_val
    opts.pooling = round(opts.crop_size / opts.output_stride)
    opts.test_batch_size = 1

    return opts


def get_argparser():
    parser = argparse.ArgumentParser()

    # Performance Options
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument("--random_seed", type=int, default=42,
                        help="random seed (default: 42)")
    parser.add_argument("--num_workers", type=int, default=8,
                        help='number of workers (default: 8)')
    # parser.add_argument('--opt_level', type=str, choices=['O0', 'O1', 'O2', 'O3'], default='O0')
    parser.add_argument("--device", type=int, default=None,
                        help='Specify the device you want to use.')

    # Datset Options
    parser.add_argument("--data_root", type=str, default='data/',
                        help="path to Dataset")
    parser.add_argument("--dataset", type=str, default='voc',
                        choices=['voc', 'cts'], help='Name of dataset')

    # Train Options
    parser.add_argument("--epochs", type=int, default=30,
                        help="epoch number (default: 30)")

    parser.add_argument("--batch_size", type=int, default=4,
                        help='batch size (default: 4)')
    parser.add_argument("--batch_size_test", type=int, default=4,
                        help='batch size (default: 4)')
    parser.add_argument("--crop_size", type=int, default=512,
                        help="crop size (default: 513)")

    parser.add_argument("--lr", type=float, default=0.007,
                        help="learning rate (default: 0.007)")
    parser.add_argument("--momentum", type=float, default=0.9,
                        help='momentum for SGD (default: 0.9)')
    parser.add_argument("--weight_decay", type=float, default=1e-4,
                        help='weight decay (default: 1e-4)')

    parser.add_argument("--lr_policy", type=str, default='poly',
                        choices=['poly', 'step'], help="lr schedule policy (default: poly)")
    parser.add_argument("--lr_decay_step", type=int, default=5000,
                        help="decay step for stepLR (default: 5000)")
    parser.add_argument("--lr_decay_factor", type=float, default=0.1,
                        help="decay factor for stepLR (default: 0.1)")
    parser.add_argument("--lr_power", type=float, default=0.9,
                        help="power for polyLR (default: 0.9)")

    # Logging Options
    parser.add_argument("--logdir", type=str, default='./logs',
                        help="path to Log directory (default: ./logs)")
    parser.add_argument("--name", type=str, default='Experiment',
                        help="name of the experiment - to append to log directory (default: Experiment)")
    parser.add_argument("--sample_num", type=int, default=16,
                        help='number of samples for visualization (default: 0)')
    parser.add_argument("--debug",  action='store_true', default=False,
                        help="verbose option")
    parser.add_argument("--visualize",  action='store_false', default=True,
                        help="visualization on tensorboard (def: Yes)")
    parser.add_argument("--print_interval", type=int, default=10,
                        help="print interval of loss (default: 10)")
    parser.add_argument("--val_interval", type=int, default=1,
                        help="epoch interval for eval (default: 1)")

    # Model Options
    parser.add_argument("--backbone", type=str, default='resnext101',
                        choices=['resnet50', 'resnet101', 'resnext101'], help='backbone for the body (def: resnext101)')
    parser.add_argument("--deeplab", type=str, default="v3",
                        choices=['v3', 'v2', 'none'], help='network head')
    parser.add_argument("--output_stride", type=int, default=8,
                        choices=[8, 16], help='stride for the backbone (def: 8)')
    parser.add_argument("--no_pretrained", action='store_true', default=False,
                        help='Wheather to use pretrained or not (def: True)')
    parser.add_argument("--norm_act", type=str, default="iabn_sync",
                        choices=['iabn_sync', 'iabn', 'abn'], help='Which BN to use (def: abn_sync')
    parser.add_argument("--pooling", type=int, default=32,
                        help='pooling in ASPP for the validation phase (def: 32)')
    parser.add_argument("--hnm", action='store_true', default=False,
                        help='Use hnm or not (def: False)')

    # Test and Checkpoint options
    parser.add_argument("--test",  action='store_true', default=False,
                        help="Whether to train or test only (def: train and test)")
    parser.add_argument("--ckpt", default=None, type=str,
                        help="path to trained model. Leave it None if you want to retrain your model")
    parser.add_argument("--continue_ckpt", default=False, action='store_true',
                        help="Restart from the ckpt. Named taken automatically from method name.")
    parser.add_argument("--ckpt_interval", type=int, default=1,
                        help="epoch interval for saving model (default: 1)")
    parser.add_argument("--cross_val", action='store_true', default=False,
                        help="If validate on training or on validation (default: Val)")

    return parser