def parse_option():
parser = argparse.ArgumentParser('argument for training')
# exp name
parser.add_argument('--exp-name', type=str, default='exp',
help='experiment name, used to determine checkpoint/tensorboard dir')
# optimization
parser.add_argument('--learning-rate', '--lr', type=float, default=0.003, help='learning rate')
parser.add_argument('--weight-decay', type=float, default=5e-4, help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum for SGD')
# root folders
parser.add_argument('--output-root', type=str, default='./output', help='root directory for output')
# dataset
parser.add_argument('--dataset', type=str, default='cifar10')
# resume
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
# model definition
parser.add_argument('--model-width', type=int, default=1, help='width of resnet, eg, 1, 2, 4')
parser.add_argument('--alpha', type=float, default=0.999, help='exponential moving average weight')
# loss function
parser.add_argument('--nce-k', type=int, default=4096)
parser.add_argument('--nce-t', type=float, default=0.3)
# misc
parser.add_argument('--print-freq', type=int, default=100, help='print frequency')
parser.add_argument('--tb-freq', type=int, default=500, help='tb frequency')
parser.add_argument('--save-freq', type=int, default=10, help='save frequency')
parser.add_argument('--batch-size', type=int, default=32, help='batch_size')
parser.add_argument('--num-workers', type=int, default=8, help='num of workers to use')
parser.add_argument('--epochs', type=int, default=200, help='number of training epochs')
parser.add_argument('--start-epoch', type=int, default=1, help='used for resume')
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()
# set the path according to the environment
output_dir = check_dir(os.path.join(args.output_root, args.dataset, args.exp_name))
args.model_folder = check_dir(os.path.join(output_dir, 'models'))
args.tb_folder = check_dir(os.path.join(output_dir, 'tensorboard'))
return args
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--print-freq',
type=int,
default=10,
help='print frequency')
parser.add_argument('--tb-freq',
type=int,
default=500,
help='tb frequency')
parser.add_argument('--save-freq',
type=int,
default=5,
help='save frequency')
parser.add_argument('--batch-size',
type=int,
default=256,
help='train batch size')
parser.add_argument('--val-batch-size',
type=int,
default=256,
help='valiate batch size')
parser.add_argument('--num-workers',
type=int,
default=8,
help='num of workers to use')
parser.add_argument('--epochs',
type=int,
default=100,
help='number of training epochs')
# optimization
parser.add_argument('--learning-rate',
type=float,
default=3,
help='learning rate')
parser.add_argument('--lr-decay-epochs',
type=int,
default=[30, 60, 90],
nargs='+',
help='where to decay lr, can be a list')
parser.add_argument('--lr-decay-rate',
type=float,
default=0.1,
help='decay rate for learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight-decay',
type=float,
default=0,
help='weight decay')
parser.add_argument('--adam',
action='store_true',
help='use adam optimizer')
parser.add_argument('--beta1',
type=float,
default=0.5,
help='beta1 for Adam')
parser.add_argument('--beta2',
type=float,
default=0.999,
help='beta2 for Adam')
# model definition
parser.add_argument('--model-width',
type=int,
default=1,
help='width of resnet, eg, 1, 2, 4')
parser.add_argument('--layer',
type=int,
default=6,
help='which layer to evaluate')
# crop
parser.add_argument('--crop',
type=float,
default=0.08,
help='minimum crop')
# dataset
parser.add_argument('--dataset',
type=str,
default='cifar10',
choices=['imagenet100', 'imagenet', 'cifar10'])
# specify folder
parser.add_argument('--model-path', type=str, help="model path")
parser.add_argument('--output-root',
type=str,
default='./output',
help='root directory for output')
parser.add_argument('--data-root',
type=str,
default='./data',
help='root directory of dataset')
# experiment name
parser.add_argument('--exp-name',
type=str,
default='exp',
help='experiment name')
# resume
parser.add_argument('--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
# others
parser.add_argument('--aug',
type=str,
default='CJ',
choices=['NULL', 'CJ'])
parser.add_argument('--bn',
action='store_true',
help='use parameter-free BN')
parser.add_argument('--cosine',
action='store_true',
help='use cosine annealing')
parser.add_argument('-e',
'--eval',
action='store_true',
help='only evaluate')
# for DistributedDataParallel
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()
output_dir = check_dir(
os.path.join(args.output_root, args.dataset, args.exp_name))
args.model_folder = check_dir(os.path.join(output_dir, 'linear_models'))
args.tb_folder = check_dir(os.path.join(output_dir, 'linear_tensorboard'))
if args.dataset == 'imagenet100':
args.n_label = 100
if args.dataset == 'imagenet':
args.n_label = 1000
if args.dataset == 'cifar10':
args.n_label = 10
return args
def parse_option():
parser = argparse.ArgumentParser('argument for training')
# exp name
parser.add_argument(
'--exp-name',
type=str,
default='exp',
help='experiment name, used to determine checkpoint/tensorboard dir')
# optimization
parser.add_argument('--learning-rate',
'--lr',
type=float,
default=0.03,
help='learning rate')
parser.add_argument('--lr-decay-epochs',
type=int,
default=[120, 160, 200],
nargs='+',
help='where to decay lr, can be a list')
parser.add_argument('--lr-decay-rate',
type=float,
default=0.1,
help='decay rate for learning rate')
parser.add_argument('--weight-decay',
type=float,
default=1e-4,
help='weight decay')
parser.add_argument('--momentum',
type=float,
default=0.9,
help='momentum for SGD')
# root folders
parser.add_argument(
'--data-root',
type=str,
default='/misc/lmbssd/marrakch/ILSVRC2015/Data/CLS-LOC/',
help='root directory of dataset')
parser.add_argument('--output-root',
type=str,
default='./output',
help='root directory for output')
# dataset
parser.add_argument('--dataset',
type=str,
default='imagenet',
choices=['imagenet100', 'imagenet'])
parser.add_argument('--crop', type=float, default=0.2, help='minimum crop')
parser.add_argument('--aug',
type=str,
default='CJ',
choices=['NULL', 'CJ'])
# resume
parser.add_argument('--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
# model definition
parser.add_argument('--model',
type=str,
default='resnet50',
choices=['resnet50'])
parser.add_argument('--model-width',
type=int,
default=1,
help='width of resnet, eg, 1, 2, 4')
parser.add_argument('--alpha',
type=float,
default=0.999,
help='exponential moving average weight')
# loss function
parser.add_argument('--nce-k', type=int, default=16384)
parser.add_argument('--nce-t', type=float, default=0.07)
# misc
parser.add_argument('--print-freq',
type=int,
default=10,
help='print frequency')
parser.add_argument('--tb-freq',
type=int,
default=500,
help='tb frequency')
parser.add_argument('--save-freq',
type=int,
default=10,
help='save frequency')
parser.add_argument('--batch-size',
type=int,
default=32,
help='batch_size')
parser.add_argument('--num-workers',
type=int,
default=4,
help='num of workers to use')
parser.add_argument('--epochs',
type=int,
default=200,
help='number of training epochs')
parser.add_argument('--start-epoch',
type=int,
default=1,
help='used for resume')
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()
# set the path according to the environment
output_dir = check_dir(
os.path.join(args.output_root, args.dataset, args.exp_name))
args.model_folder = check_dir(os.path.join(output_dir, 'models'))
args.tb_folder = check_dir(os.path.join(output_dir, 'tensorboard'))
return args