def setup_args(args):
template.set_template(args)
args.scale = list(map(lambda x: int(x), args.scale.split('+')))
args.data_train = args.data_train.split('+')
args.data_test = args.data_test.split('+')
if args.epochs == 0:
args.epochs = 1e8
for arg in vars(args):
if vars(args)[arg] == 'True':
vars(args)[arg] = True
elif vars(args)[arg] == 'False':
vars(args)[arg] = False
parser.add_argument('--reduction',
type=int,
default=16,
help='number of feature maps reduction')
parser.add_argument('--testpath',
type=str,
default='../test/DIV2K_val_LR_our',
help='dataset directory for testing')
parser.add_argument('--testset',
type=str,
default='Set5',
help='dataset name for testing')
parser.add_argument('--degradation',
type=str,
default='BI',
help='degradation model: BI, BD')
args = parser.parse_args()
template.set_template(args)
args.scale = list(map(lambda x: int(x), args.scale.split('+')))
if args.epochs == 0:
args.epochs = 1e8
for arg in vars(args):
if vars(args)[arg] == 'True':
vars(args)[arg] = True
elif vars(args)[arg] == 'False':
vars(args)[arg] = False
# Log specifications
parser.add_argument('--save', type=str, default='metardn',
help='file name to save')
parser.add_argument('--load', type=str, default='.',
help='file name to load')
parser.add_argument('--resume', type=int, default=0,
help='resume from specific checkpoint')
parser.add_argument('--save_models', action='store_true',
help='save all intermediate models')
parser.add_argument('--print_every', type=int, default=100,
help='how many batches to wait before logging training status')
parser.add_argument('--save_results', action='store_true',
help='save output results')
args1 = parser.parse_args()
template.set_template(args1)
#args.scale = list(map(lambda x: int(x), args.scale.split('+')))
###here we redefine the scale
if args1.scale=='':
import numpy as np
#args.scale = np.linspace(1.1,4,30)
args1.scale = [4.0]
#print(args.scale)
else:
args1.scale = list(map(lambda x: float(x), args1.scale.split('+')))
print(args1.scale)
if args1.epochs == 0:
args1.epochs = 1e8
default='baseline',
help='Name of model')
parser.add_argument('--print_model', action='store_true', help='print model')
parser.add_argument(
'--pre_train',
type=str,
default='.',
help='path of pre_trained data if load from other directory')
parser.add_argument('--dictionary',
type=list,
default=list(),
help='Dictionary of elements')
args = parser.parse_args()
set_template(args)
specials = [
'{', '}', '(', ')', '[', ']', '<', '>', r'\{', r'\}', '+', '-', r'\pm',
r'\times', r'\div', '=', r'\neq', r'\leq', r'\geq', '.', '_', '^', '&',
'|', '/', "'", ",", '!', r'\prime', r'\frac', r'\cos', r'\sin', r'\tan',
r'\log', r'\lim', r'\sqrt', r'\sum', r'\int', r'\cdot', r'\ldots',
r'\forall', r'\in', r'\infty', r'\arrow', r'\to', r'\exists'
]
greeks = [
r'\alpha', r'\beta', r'\gamma', r'\Delta', r'\lambda', r'\theta', r'\pi',
r'\mu', r'\sigma', r'\phi'
]
numbers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0']
letters = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o',
default=10,
help='number of residual groups')
parser.add_argument('--reduction',
type=int,
default=16,
help='number of feature maps reduction')
# options for test
parser.add_argument('--testpath',
type=str,
default='../test/DIV2K_val_LR_our',
help='dataset directory for testing')
parser.add_argument('--testset',
type=str,
default='Set5',
help='dataset name for testing')
args = parser.parse_args()
# 要求对之前相关属性进行赋值 在运行脚本中已写明 args为实体，拥有一切上述属性
template.set_template(args) # 赋值完之后传入样本
args.scale = list(map(lambda x: int(x), args.scale.split('+'))) # 以加号为分隔符分割所有
if args.epochs == 0:
args.epochs = 1e8
for arg in vars(args):
if vars(args)[arg] == 'True':
vars(args)[arg] = True
elif vars(args)[arg] == 'False':
vars(args)[arg] = False
