ssn_opts.py

import argparse
parser = argparse.ArgumentParser(description="PyTorch code to train Structured Segment Networks (SSN)")
parser.add_argument('dataset', type=str, choices=['activitynet1.2', 'thumos14'])
parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff'])

# ========================= Model Configs ==========================
parser.add_argument('--arch', type=str, default="BNInception")
parser.add_argument('--num_aug_segments', type=int, default=2)
parser.add_argument('--num_body_segments', type=int, default=5)

parser.add_argument('--dropout', '--do', default=0.8, type=float,
                    metavar='DO', help='dropout ratio (default: 0.8)')

# ========================= Learning Configs ==========================
parser.add_argument('--epochs', default=7, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--training_epoch_multiplier', '--tem', default=10, type=int,
                    help='replicate the training set by N times in one epoch')
parser.add_argument('-b', '--batch-size', default=16, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('-i', '--iter-size', default=1, type=int,
                    metavar='N', help='number of iterations before on update')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--lr_steps', default=[3, 6], 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('--bn_mode', '--bn', default='frozen', type=str,
                    help="the mode of bn layers")
parser.add_argument('--comp_loss_weight', '--lw', default=0.1, type=float,
                    metavar='LW', help='the weight for the completeness loss')
parser.add_argument('--reg_loss_weight', '--rw', default=0.1, type=float,
                    metavar='LW', help='the weight for the location regression loss')

# ========================= 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=1, 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('--kinetics_pretrain', '--kin', default=False, action='store_true',
                    help='whether to use kinetics pretrained models')
parser.add_argument('--init_weights', default='', type=str, metavar='PATH',
                    help='path to pretrained weights')
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)