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- import argparse
- parser = argparse.ArgumentParser(description="PyTorch implementation of Temporal Segment Networks")
- parser.add_argument('dataset', type=str, choices=['ucf101', 'hmdb51', 'kinetics'])
- parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff'])
- parser.add_argument('train_list', type=str)
- parser.add_argument('val_list', type=str)
-
- # ========================= Model Configs ==========================
- parser.add_argument('--arch', type=str, default="resnet101")
- parser.add_argument('--num_segments', type=int, default=3)
- parser.add_argument('--consensus_type', type=str, default='avg',
- choices=['avg', 'max', 'topk', 'identity', 'rnn', 'cnn'])
- parser.add_argument('--k', type=int, default=3)
-
- parser.add_argument('--dropout', '--do', default=0.5, type=float,
- metavar='DO', help='dropout ratio (default: 0.5)')
- parser.add_argument('--loss_type', type=str, default="nll",
- choices=['nll'])
-
- # ========================= Learning Configs ==========================
- parser.add_argument('--epochs', default=45, type=int, metavar='N',
- help='number of total epochs to run')
- parser.add_argument('-b', '--batch-size', default=256, type=int,
- metavar='N', help='mini-batch size (default: 256)')
- parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
- metavar='LR', help='initial learning rate')
- parser.add_argument('--lr_steps', default=[20, 40], type=float, nargs="+",
- metavar='LRSteps', help='epochs to decay learning rate by 10')
- parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
- help='momentum')
- parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
- metavar='W', help='weight decay (default: 5e-4)')
- parser.add_argument('--clip-gradient', '--gd', default=None, type=float,
- metavar='W', help='gradient norm clipping (default: disabled)')
- parser.add_argument('--no_partialbn', '--npb', default=False, action="store_true")
-
- # ========================= Monitor Configs ==========================
- parser.add_argument('--print-freq', '-p', default=20, type=int,
- metavar='N', help='print frequency (default: 10)')
- parser.add_argument('--eval-freq', '-ef', default=5, type=int,
- metavar='N', help='evaluation frequency (default: 5)')
-
-
- # ========================= Runtime Configs ==========================
- parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
- help='number of data loading workers (default: 4)')
- parser.add_argument('--resume', default='', type=str, metavar='PATH',
- help='path to latest checkpoint (default: none)')
- parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
- help='evaluate model on validation set')
- parser.add_argument('--snapshot_pref', type=str, default="")
- parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
- help='manual epoch number (useful on restarts)')
- parser.add_argument('--gpus', nargs='+', type=int, default=None)
- parser.add_argument('--flow_prefix', default="", type=str)
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