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parser.add_argument('--save_results', action='store_true',
help='save output results')
# options for residual group and feature channel reduction
parser.add_argument('--n_resgroups', type=int, default=20,
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()
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
parser.add_argument('--save_results', action='store_true',
help='save output results')
# options for residual group and feature channel reduction
parser.add_argument('--n_resgroups', type=int, 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()
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
help='resume from specific checkpoint')
parser.add_argument('--print_model', action='store_true',
help='print model')
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')
parser.add_argument('--save_branches', action='store_true',
help='save outputs of each branches in IRL setup')
parser.add_argument('--save_residuals', action='store_true',
help='save residuals of output results')
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='test',
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')
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='meta',
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')
args = parser.parse_args()
template.set_template(args)
#args.scale = list(map(lambda x: int(x), args.scale.split('+')))
###here we redefine the scale
if args.scale=='':
import numpy as np
#args.scale = np.linspace(1.1,4,30)
args.scale = [1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3.0,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4.0]
#print(args.scale)
else:
args.scale = list(map(lambda x: float(x), args.scale.split('+')))
print(args.scale)
if args.epochs == 0:
args.epochs = 1e8
for arg in vars(args):
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')
parser.add_argument('--save_gt', action='store_true',
help='save low-resolution and high-resolution images together')
args = parser.parse_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
# New options
parser.add_argument('--n_resgroups', type=int, default=10,
help='number of residual groups')
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='save_path',
help='file name to save')
parser.add_argument('--load', type=str, default='.',
help='file name to load')
parser.add_argument('--resume', action='store_true',
help='resume from the latest if true')
parser.add_argument('--print_every', type=int, default=100,
help='how many batches to wait before logging training status')
parser.add_argument('--save_images', default=True, action='store_false',
help='save images')
args = parser.parse_args()
template.set_template(args)
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