'-a',
metavar='MODEL',
default='alexnet',
choices=model_names,
help='model architecture: ' + ' | '.join(model_names) +
' (default: alexnet)')
parser.add_argument('--input_size',
type=int,
default=None,
help='image input size')
parser.add_argument('--model_config',
default='',
help='additional architecture configuration')
parser.add_argument('--dtype',
default='float',
help='type of tensor: ' + ' | '.join(torch_dtypes.keys()) +
' (default: half)')
parser.add_argument('--device',
default='cuda',
help='device assignment ("cpu" or "cuda")')
parser.add_argument('--device_ids',
default=[0],
type=int,
nargs='+',
help='device ids assignment (e.g 0 1 2 3')
parser.add_argument('-j',
'--workers',
default=8,
type=int,
metavar='N',
help='number of data loading workers (default: 8)')
parser.add_argument('--datasets-dir', metavar='DATASETS_DIR', default='~/Datasets',
help='datasets dir')
parser.add_argument('--dataset', metavar='DATASET', default='imagenet',
help='dataset name or folder')
parser.add_argument('--model', '-a', metavar='MODEL', default='alexnet',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: alexnet)')
parser.add_argument('--input-size', type=int, default=None,
help='image input size')
parser.add_argument('--model-config', default='',
help='additional architecture configuration')
parser.add_argument('--dtype', default='float',
help='type of tensor: ' +
' | '.join(torch_dtypes.keys()) +
' (default: float)')
parser.add_argument('--device', default='cuda',
help='device assignment ("cpu" or "cuda")')
parser.add_argument('--device-ids', default=[0], type=int, nargs='+',
help='device ids assignment (e.g 0 1 2 3')
parser.add_argument('--world-size', default=-1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int,
help='rank of distributed processes')
parser.add_argument('--dist-init', default='env://', type=str,
help='init used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 8)')
