def require_args():
cfg.add_argument('--net-heads', nargs='*', type=int, help='net heads')
cfg.add_argument('--net-avgpool-size',
default=3,
type=int,
choices=[3, 5, 7],
help='Avgpool kernel size determined by inputs size')
def require_args():
# args for training
cfg.add_argument('--max-epochs',
default=200,
type=int,
help='maximal training epoch')
cfg.add_argument('--display-freq',
default=80,
type=int,
help='log display frequency')
cfg.add_argument('--batch-size',
default=256,
type=int,
help='size of mini-batch')
cfg.add_argument('--num-workers',
default=4,
type=int,
help='number of workers used for loading data')
cfg.add_argument('--data-nrepeat',
default=1,
type=int,
help='how many times each image in a ' +
'mini-batch should be repeated')
cfg.add_argument('--pica-lamda',
default=2.0,
type=float,
help='weight of negative entropy regularisation')
def require_args():
# args for training
cfg.add_argument('--max-epochs',
default=200,
type=int,
help='maximal training epoch')
cfg.add_argument('--display-freq',
default=80,
type=int,
help='log display frequency')
cfg.add_argument('--embedding-freq',
default=80,
type=int,
help='Embedding log frequency')
cfg.add_argument('--batch-size',
default=256,
type=int,
help='size of mini-batch')
cfg.add_argument('--local_rank',
default=0,
type=int,
help='The local rank in case of multiprocessing')
cfg.add_argument('--num-workers',
default=4,
type=int,
help='number of workers used for loading data')
cfg.add_argument('--data-nrepeat',
default=1,
type=int,
help='how many times each image in a ' +
'mini-batch should be repeated')
cfg.add_argument('--pica-lamda',
default=2.0,
type=float,
help='weight of negative entropy regularisation')
cfg.add_argument('--pica-target',
default=None,
type=eval,
help='the target class distribution')
cfg.add_argument('--pica-iic',
default=False,
type=eval,
help='whether to use additional iic loss')
def require_args():
cfg.add_argument('--net-heads', nargs='*', type=int, help='net heads')
def require_args():
# args for training
cfg.add_argument('--max-epochs',
default=200,
type=int,
help='maximal training epoch')
cfg.add_argument('--display-freq',
default=80,
type=int,
help='log display frequency')
cfg.add_argument('--batch-size',
default=256,
type=int,
help='size of mini-batch')
cfg.add_argument('--num-workers',
default=4,
type=int,
help='number of workers used for loading data')
cfg.add_argument('--data-nrepeat',
default=1,
type=int,
help='how many times each image in a ' +
'mini-batch should be repeated')
cfg.add_argument('--dc-lamda',
default=0.5,
type=float,
help='temperature of contrastive learning')
