parser.add_argument(
'--freeze-bn',
action='store_true',
help=
"freeze running statistics in BatchNorm layers during training (default: False)"
)
parser.add_argument(
'--label-smooth',
action='store_true',
help="use label smoothing regularizer in cross entropy loss")
# Architecture
parser.add_argument('-a',
'--arch',
type=str,
default='resnet50',
choices=models.get_names())
# Miscs
parser.add_argument('--print-freq',
type=int,
default=10,
help="print frequency")
parser.add_argument('--seed', type=int, default=1, help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument(
'--load-weights',
type=str,
default='',
help="load pretrained weights but ignores layers that don't match in size")
parser.add_argument('--evaluate', action='store_true', help="evaluation only")
parser.add_argument(
'--eval-step',
parser.add_argument('--stepsize', default=[20, 40], nargs='+', type=int,
help="stepsize to decay learning rate")
parser.add_argument('--gamma', default=0.1, type=float,
help="learning rate decay")
parser.add_argument('--weight-decay', default=5e-04, type=float,
help="weight decay (default: 5e-04)")
parser.add_argument('--fixbase-epoch', default=0, type=int,
help="epochs to fix base network (only train classifier, default: 0)")
parser.add_argument('--fixbase-lr', default=0.0003, type=float,
help="learning rate (when base network is frozen)")
parser.add_argument('--freeze-bn', action='store_true',
help="freeze running statistics in BatchNorm layers during training (default: False)")
parser.add_argument('--label-smooth', action='store_true',
help="use label smoothing regularizer in cross entropy loss")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
parser.add_argument('--pool', type=str, default='avg', choices=['avg', 'max'])
# Miscs
parser.add_argument('--print-freq', type=int, default=10,
help="print frequency")
parser.add_argument('--seed', type=int, default=1,
help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--load-weights', type=str, default='',
help="load pretrained weights but ignores layers that don't match in size")
parser.add_argument('--evaluate', action='store_true',
help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0,
help="start to evaluate after specific epoch")