args_small.py

import argparse

def get_args(description='MILNCE'):
    parser = argparse.ArgumentParser(description=description)
    parser.add_argument('--train_csv', type=str, default='../HowTo100M/csv/small_videos.csv', help='train csv')
    parser.add_argument('--video_path', type=str, default='../HowTo100M/videos', help='video_path')
    parser.add_argument('--caption_root', type=str, default='../HowTo100M/caption_json', help='video_path')
    parser.add_argument('--checkpoint_root', type=str, default='checkpoint', help='checkpoint dir root')
    parser.add_argument('--log_root', type=str, default='log', help='log dir root')
    parser.add_argument('--eval_video_root', type=str, default='../HowTo100M/downstream', help='root folder for the video at for evaluation')
    parser.add_argument('--checkpoint_dir', type=str, default='', help='checkpoint model folder')
    parser.add_argument('--optimizer', type=str, default='adam', help='opt algorithm')
    parser.add_argument('--weight_init', type=str, default='uniform', help='CNN weights inits')
    parser.add_argument('--num_thread_reader', type=int, default=20, help='')
    parser.add_argument('--num_class', type=int, default=512, help='upper epoch limit')
    parser.add_argument('--num_candidates', type=int, default=5, help='num candidates for MILNCE loss')
    parser.add_argument('--batch_size', type=int, default=12, help='batch size')
    parser.add_argument('--num_windows_test', type=int, default=4, help='number of testing windows')
    parser.add_argument('--batch_size_val', type=int, default=32, help='batch size eval')
    parser.add_argument('--momemtum', type=float, default=0.9, help='SGD momemtum')
    parser.add_argument('--n_display', type=int, default=100, help='Information display frequence')
    parser.add_argument('--num_frames', type=int, default=32, help='random seed')
    parser.add_argument('--video_size', type=int, default=224, help='random seed')
    parser.add_argument('--crop_only', type=int, default=1, help='random seed')
    parser.add_argument('--centercrop', type=int, default=0, help='random seed')
    parser.add_argument('--random_flip', type=int, default=1, help='random seed')
    parser.add_argument('--verbose', type=int, default=1, help='')
    parser.add_argument('--warmup_steps', type=int, default=1000, help='')
    parser.add_argument('--min_time', type=float, default=5.0, help='')
    parser.add_argument('--pretrain_cnn_path', type=str, default='', help='')
    parser.add_argument('--word2vec_path', type=str, default='../HowTo100M/data/word2vec.pth', help='')
    parser.add_argument('--fps', type=int, default=10, help='')
    parser.add_argument('--cudnn_benchmark', type=int, default=0, help='')
    parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run')
    parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
    parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
    parser.add_argument('--resume', dest='resume', action='store_true', help='resume training from last checkpoint')
    parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
    parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')
    parser.add_argument('--pin_memory', dest='pin_memory', action='store_true', help='use pin_memory')
    parser.add_argument('--world-size', default=-1, type=int, help='number of nodes for distributed training')
    parser.add_argument('--rank', default=-1, type=int, help='node rank for distributed training')
    parser.add_argument('--dist-file', default='dist-file', type=str, help='url used to set up distributed training')
    parser.add_argument('--dist-url', default='tcp://9.0.205.130:23456', type=str, help='url used to set up distributed training')
    parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
    parser.add_argument('--seed', default=1, type=int, help='seed for initializing training. ')
    parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
    parser.add_argument('--multiprocessing-distributed', action='store_true', help='Use multi-processing distributed training to launch N processes per node, '
                        'which has N GPUs. This is the fastest way to use PyTorch for either single node or multi node data parallel training')
    args = parser.parse_args()
    return args