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