opts.py

import argparse


def parse_opts():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--root_path',
        default='data/',
        type=str,
        help='Root directory path of data')
    parser.add_argument(
        '--video_path',
        default='frames/',
        type=str,
        help='Directory path of Videos')
    parser.add_argument(
        '--annotation_path',
        default='ucfTrainTestlist/ucf101_01.json',
        type=str,
        help='Annotation file path')
    parser.add_argument(
        '--result_path',
        default='results/',
        type=str,
        help='Result directory path')
    parser.add_argument(
        '--n_classes',
        default=400,
        type=int,
        help=
        'Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)'
    )
    parser.add_argument(
        '--n_finetune_classes',
        default=101,
        type=int,
        help=
        'Number of classes for fine-tuning. n_classes is set to the number when pretraining.'
    )
    parser.add_argument(
        '--sample_size',
        default=112,
        type=int,
        help='Height and width of inputs')
    parser.add_argument(
        '--sample_duration',
        default=16,
        type=int,
        help='Temporal duration of inputs')
    parser.add_argument(
        '--initial_scale',
        default=1.0,
        type=float,
        help='Initial scale for multiscale cropping')
    parser.add_argument(
        '--n_scales',
        default=5,
        type=int,
        help='Number of scales for multiscale cropping')
    parser.add_argument(
        '--scale_step',
        default=0.84089641525,
        type=float,
        help='Scale step for multiscale cropping')
    parser.add_argument(
        '--train_crop',
        default='corner',
        type=str,
        help=
        'Spatial cropping method in training. random is uniform. corner is selection from 4 corners and 1 center.  (random | corner | center)'
    )
    parser.add_argument(
        '--learning_rate',
        default=0.1,
        type=float,
        help=
        'Initial learning rate (divided by 10 while training by lr scheduler)')
    parser.add_argument('--momentum', default=0.9, type=float, help='Momentum')
    parser.add_argument(
        '--dampening', default=0.9, type=float, help='dampening of SGD')
    parser.add_argument(
        '--weight_decay', default=1e-3, type=float, help='Weight Decay')
    parser.add_argument(
        '--mean_dataset',
        default='activitynet',
        type=str,
        help=
        'dataset for mean values of mean subtraction (activitynet | kinetics)')
    parser.add_argument(
        '--no_mean_norm',
        action='store_true',
        help='If true, inputs are not normalized by mean.')
    parser.set_defaults(no_mean_norm=False)
    parser.add_argument(
        '--std_norm',
        action='store_true',
        help='If true, inputs are normalized by standard deviation.')
    parser.set_defaults(std_norm=False)
    parser.add_argument(
        '--nesterov', action='store_true', help='Nesterov momentum')
    parser.set_defaults(nesterov=False)
    parser.add_argument(
        '--optimizer',
        default='sgd',
        type=str,
        help='Currently only support SGD')
    parser.add_argument(
        '--lr_patience',
        default=10,
        type=int,
        help='Patience of LR scheduler. See documentation of ReduceLROnPlateau.'
    )
    parser.add_argument(
        '--batch_size', default=24, type=int, help='Batch Size')
    parser.add_argument(
        '--n_epochs',
        default=60,
        type=int,
        help='Number of total epochs to run')
    parser.add_argument(
        '--begin_epoch',
        default=1,
        type=int,
        help=
        'Training begins at this epoch. Previous trained model indicated by resume_path is loaded.'
    )
    parser.add_argument(
        '--n_val_samples',
        default=3,
        type=int,
        help='Number of validation samples for each activity')
    parser.add_argument(
        '--resume_path',
        default='',
        type=str,
        help='Save data (.pth) of previous training')
    parser.add_argument(
        '--pretrain_path', default='pretrained/resnet-18-kinetics.pth', type=str, help='Pretrained model (.pth)')
    parser.add_argument(
        '--ft_begin_index',
        default=5,
        type=int,
        help='Begin block index of fine-tuning')
    parser.add_argument(
        '--no_train',
        action='store_true',
        help='If true, training is not performed.')
    parser.set_defaults(no_train=False)
    parser.add_argument(
        '--no_val',
        action='store_true',
        help='If true, validation is not performed.')
    parser.set_defaults(no_val=False)
    parser.add_argument(
        '--test', action='store_true', help='If true, test is performed.')
    parser.set_defaults(test=True)
    parser.add_argument(
        '--test_subset',
        default='val',
        type=str,
        help='Used subset in test (val | test)')
    parser.add_argument(
        '--scale_in_test',
        default=1.0,
        type=float,
        help='Spatial scale in test')
    parser.add_argument(
        '--crop_position_in_test',
        default='c',
        type=str,
        help='Cropping method (c | tl | tr | bl | br) in test')
    parser.add_argument(
        '--no_softmax_in_test',
        action='store_true',
        help='If true, output for each clip is not normalized using softmax.')
    parser.set_defaults(no_softmax_in_test=False)
    parser.add_argument(
        '--no_cuda', action='store_true', help='If true, cuda is not used.')
    parser.set_defaults(no_cuda=False)
    parser.add_argument(
        '--n_threads',
        default=4,
        type=int,
        help='Number of threads for multi-thread loading')
    parser.add_argument(
        '--checkpoint',
        default=10,
        type=int,
        help='Trained model is saved at every this epochs.')
    parser.add_argument(
        '--no_hflip',
        action='store_true',
        help='If true holizontal flipping is not performed.')
    parser.set_defaults(no_hflip=False)
    parser.add_argument(
        '--norm_value',
        default=1,
        type=int,
        help=
        'If 1, range of inputs is [0-255]. If 255, range of inputs is [0-1].')
    parser.add_argument(
        '--model',
        default='resnet',
        type=str,
        help='(resnet | preresnet | wideresnet | resnext | densenet | ')
    parser.add_argument(
        '--model_depth',
        default=18,
        type=int,
        help='Depth of resnet (10 | 18 | 34 | 50 | 101)')
    parser.add_argument(
        '--resnet_shortcut',
        default='A',
        type=str,
        help='Shortcut type of resnet (A | B)')
    parser.add_argument(
        '--wide_resnet_k', default=2, type=int, help='Wide resnet k')
    parser.add_argument(
        '--resnext_cardinality',
        default=32,
        type=int,
        help='ResNeXt cardinality')
    parser.add_argument(
        '--manual_seed', default=1, type=int, help='Manually set random seed')

     # acqusition args
    parser.add_argument('--init_train_size', type=int, default=101)
    # selection of the initial training dataset: random, uniform_random, same (same as active learning selection)
    parser.add_argument('--init_selection', type=str, default='uniform_random')

    parser.add_argument('--n_pool', type=int, default=101)
    parser.add_argument('--max_train_size', type=int, default=404)

    args = parser.parse_args()
    return args