| import argparse
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| import template
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| parser = argparse.ArgumentParser(description='EDSR and MDSR')
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| parser.add_argument('--debug', action='store_true',
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| help='Enables debug mode')
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| parser.add_argument('--template', default='.',
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| help='You can set various templates in option.py')
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| parser.add_argument('--n_threads', type=int, default=18,
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| help='number of threads for data loading')
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| parser.add_argument('--cpu', action='store_true',
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| help='use cpu only')
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| parser.add_argument('--n_GPUs', type=int, default=1,
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| help='number of GPUs')
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| parser.add_argument('--seed', type=int, default=1,
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| help='random seed')
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| parser.add_argument('--dir_data', type=str, default='/data/ssd/public/liuhy/DNDM/dataset',help='dataset directory')
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| parser.add_argument('--data_train', type=str, default='DIV2K',
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| help='train dataset name')
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| parser.add_argument('--data_test', type=str, default='DIV2K',
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| help='test dataset name')
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| parser.add_argument('--data_range', type=str, default='1-800/943-1000',
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| help='train/test data range')
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| parser.add_argument('--ext', type=str, default='sep',
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| help='dataset file extension')
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| parser.add_argument('--scale', type=str, default='4',
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| help='super resolution scale')
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| parser.add_argument('--patch_size', type=int, default=192,
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| help='output patch size')
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| parser.add_argument('--rgb_range', type=int, default=1,
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| help='maximum value of RGB')
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| parser.add_argument('--n_colors', type=int, default=3,
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| help='number of color channels to use')
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| parser.add_argument('--chop', action='store_true',
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| help='enable memory-efficient forward')
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| parser.add_argument('--no_augment', action='store_true',
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| help='do not use data augmentation')
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| parser.add_argument('--model', default='LAMBDANET',
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| help='model name')
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| parser.add_argument('--act', type=str, default='relu',
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| help='activation function')
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| parser.add_argument('--pre_train', type=str, default='.',
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| help='pre-trained model directory')
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| parser.add_argument('--extend', type=str, default='.',
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| help='pre-trained model directory')
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| parser.add_argument('--n_resblocks', type=int, default=16,
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| help='number of residual blocks')
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| parser.add_argument('--recurrence', type=int, default=5,
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| help='number of recurrence')
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| parser.add_argument('--n_feats', type=int, default=64,
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| help='number of feature maps')
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| parser.add_argument('--res_scale', type=float, default=1,
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| help='residual scaling')
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| parser.add_argument('--shift_mean', default=True,
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| help='subtract pixel mean from the input')
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| parser.add_argument('--amp', action='store_true',
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| help='subtract pixel mean from the input')
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| parser.add_argument('--detach', action='store_true',
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| help='subtract pixel mean from the input')
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| parser.add_argument('--spectral', action='store_true',
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| help='subtract pixel mean from the input')
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| parser.add_argument('--dilation', action='store_true',
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| help='use dilated convolution')
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| parser.add_argument('--precision', type=str, default='single',
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| choices=('single', 'half'),
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| help='FP precision for test (single | half)')
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| parser.add_argument('--normalization', type=str, default='batch')
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| parser.add_argument('--lamradius', type=int, default=23)
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| parser.add_argument('--decay_gamma', type=float, default=0.8)
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| parser.add_argument('--G0', type=int, default=64,
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| help='default number of filters. (Use in RDN)')
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| parser.add_argument('--RDNkSize', type=int, default=3,
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| help='default kernel size. (Use in RDN)')
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| parser.add_argument('--RDNconfig', type=str, default='B',
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| help='parameters config of RDN. (Use in RDN)')
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| parser.add_argument('--depth', type=int, default=12,
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| help='number of residual groups')
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| parser.add_argument('--n_resgroups', type=int, default=10,
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| help='number of residual groups')
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| parser.add_argument('--reduction', type=int, default=16,
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| help='number of feature maps reduction')
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| parser.add_argument('--reset', action='store_true',
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| help='reset the training')
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| parser.add_argument('--test_every', type=int, default=1000,
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| help='do test per every N batches')
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| parser.add_argument('--epochs', type=int, default=1000,
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| help='number of epochs to train')
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| parser.add_argument('--batch_size', type=int, default=16,
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| help='input batch size for training')
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| parser.add_argument('--split_batch', type=int, default=1,
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| help='split the batch into smaller chunks')
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| parser.add_argument('--self_ensemble', action='store_true',
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| help='use self-ensemble method for test')
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| parser.add_argument('--test_only', action='store_true',
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| help='set this option to test the model')
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| parser.add_argument('--gan_k', type=int, default=1,
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| help='k value for adversarial loss')
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| parser.add_argument('--lr', type=float, default=1e-4,
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| help='learning rate')
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| parser.add_argument('--decay', type=str, default='200-400-600-800',
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| help='learning rate decay type')
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| parser.add_argument('--gamma', type=float, default=0.5,
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| help='learning rate decay factor for step decay')
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| parser.add_argument('--optimizer', default='ADAM',
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| choices=('SGD', 'ADAM', 'RMSprop', "ADAMW"),
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| help='optimizer to use (SGD | ADAM | RMSprop)')
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| parser.add_argument('--momentum', type=float, default=0.9,
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| help='SGD momentum')
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| parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
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| help='ADAM beta')
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| parser.add_argument('--epsilon', type=float, default=1e-8,
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| help='ADAM epsilon for numerical stability')
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| parser.add_argument('--weight_decay', type=float, default=0,
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| help='weight decay')
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| parser.add_argument('--gclip', type=float, default=0,
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| help='gradient clipping threshold (0 = no clipping)')
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| parser.add_argument('--loss', type=str, default='1*L1',
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| help='loss function configuration')
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| parser.add_argument('--skip_threshold', type=float, default='1e8',
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| help='skipping batch that has large error')
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| parser.add_argument('--save', type=str, default='test',
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| help='file name to save')
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| parser.add_argument('--load', type=str, default='',
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| help='file name to load')
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| parser.add_argument('--resume', type=int, default=0,
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| help='resume from specific checkpoint')
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| parser.add_argument('--save_models', action='store_true',
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| help='save all intermediate models')
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| parser.add_argument('--print_every', type=int, default=100,
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| help='how many batches to wait before logging training status')
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| parser.add_argument('--save_results', action='store_true',
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| help='save output results')
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| parser.add_argument('--save_gt', action='store_true',
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| help='save low-resolution and high-resolution images together')
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| args = parser.parse_args()
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| template.set_template(args)
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| args.scale = list(map(lambda x: int(x), args.scale.split('+')))
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| args.data_train = args.data_train.split('+')
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| args.data_test = args.data_test.split('+')
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| if args.epochs == 0:
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| args.epochs = 1e8
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| for arg in vars(args):
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| if vars(args)[arg] == 'True':
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| vars(args)[arg] = True
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| elif vars(args)[arg] == 'False':
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| vars(args)[arg] = False
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