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parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float)
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parser.add_argument('--gamma',
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default=0.1,
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type=float,
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help='learing rate multiplier')
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parser.add_argument('--input-size',
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default=112,
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type=int,
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help='input size (default: 112x112)')
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parser.add_argument('--feature-dim',
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default=256,
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type=int,
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metavar='D',
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help='feature dimension (default: 256)')
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parser.add_argument('--num-classes',
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default=1000,
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type=int,
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metavar='N',
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help='number of classes (default: 1000)')
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parser.add_argument('--sample-num',
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default=1000,
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type=int,
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help='sampling number of classes out of all classes')
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parser.add_argument('--print-freq',
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default=100,
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type=int,
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help='logger.info frequency (default: 10)')
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parser.add_argument('--resume',
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default='',
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type=str,
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metavar='PATH',
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help='path to latest checkpoint (default: none)')
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parser.add_argument('--save-path',
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default='checkpoints/ckpt',
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type=str,
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help='path to store checkpoint (default: checkpoints)')
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parser.add_argument('-e',
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'--evaluate',
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dest='evaluate',
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action='store_true',
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help='evaluate model on validation set')
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parser.add_argument('--sampled',
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dest='sampled',
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action='store_true',
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help='sampling from full softmax')
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parser.add_argument('--classifier-type',
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default='linear',
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choices=classifier_types,
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help='choose different type of classifier')
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parser.add_argument('--distributed',
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dest='distributed',
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action='store_true',
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help='distributed training')
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parser.add_argument('--dist-addr',
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default='127.0.0.1',
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type=str,
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help='distributed address')
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parser.add_argument('--dist-port',
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default='23456',
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type=str,
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help='distributed port')
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parser.add_argument('--dist-backend',
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default='nccl',
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type=str,
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help='distributed backend')
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parser.add_argument('--tmp-client-id',
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default=9999,
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type=int,
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help='tmp client used to communicate with paramserver')
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best_prec1 = 0
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def main():
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global args, best_prec1
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args = parser.parse_args()
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# init dist
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gpu_num = torch.cuda.device_count()
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if args.distributed:
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args.rank, args.world_size = init_processes(args.dist_addr,
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args.dist_port, gpu_num,
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args.dist_backend)
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print("=> using {} GPUS for distributed training".format(
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args.world_size))
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else:
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args.rank = 0
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print("=> using {} GPUS for training".format(gpu_num))
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# create logger
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if args.rank == 0:
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mkdir_if_no_exist(args.save_path,
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subdirs=['events/', 'logs/', 'checkpoints/'])
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tb_logger = SummaryWriter('{}/events'.format(args.save_path))
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logger = create_logger('global_logger',
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'{}/logs/log.txt'.format(args.save_path))
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logger.debug(args) # log args only to file
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else:
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tb_logger = None
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logger = None
|
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