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import argparse
import template
parser = argparse.ArgumentParser(description='EDSR and MDSR')
parser.add_argument('--debug', action='store_true',
help='Enables debug mode')
parser.add_argument('--template', default='.',
help='You can set various templates in option.py')
# Hardware specifications
parser.add_argument('--n_threads', type=int, default=6,
help='number of threads for data loading')
parser.add_argument('--cpu', action='store_true',
help='use cpu only')
parser.add_argument('--n_GPUs', type=int, default=1,
help='number of GPUs')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
# Data specifications
parser.add_argument('--dir_data', type=str, default='/home/iyj0121/EDSR-PyTorch/dataset',
help='dataset directory')
parser.add_argument('--dir_demo', type=str, default='../test',
help='demo image directory')
parser.add_argument('--data_train', type=str, default='DIV2K',
help='train dataset name')
parser.add_argument('--data_test', type=str, default='DIV2K',
help='test dataset name')
parser.add_argument('--data_range', type=str, default='1-800/801-810',
help='train/test data range')
parser.add_argument('--ext', type=str, default='sep',
help='dataset file extension')
parser.add_argument('--scale', type=str, default='4',
help='super resolution scale')
parser.add_argument('--patch_size', type=int, default=192,
help='output patch size')
parser.add_argument('--rgb_range', type=int, default=255,
help='maximum value of RGB')
parser.add_argument('--n_colors', type=int, default=3,
help='number of color channels to use')
parser.add_argument('--chop', action='store_true',
help='enable memory-efficient forward')
parser.add_argument('--no_augment', action='store_true',
help='do not use data augmentation')
# Model specifications
parser.add_argument('--model', default='EDSR',
help='model name')
parser.add_argument('--act', type=str, default='relu',
help='activation function')
parser.add_argument('--pre_train', type=str, default='',
help='pre-trained model directory')
parser.add_argument('--extend', type=str, default='.',
help='pre-trained model directory')
parser.add_argument('--n_resblocks', type=int, default=16,
help='number of residual blocks')
parser.add_argument('--n_feats', type=int, default=64,
help='number of feature maps')
parser.add_argument('--res_scale', type=float, default=1,
help='residual scaling')
parser.add_argument('--shift_mean', default=True,
help='subtract pixel mean from the input')
parser.add_argument('--dilation', action='store_true',
help='use dilated convolution')
parser.add_argument('--precision', type=str, default='single',
choices=('single', 'half'),
help='FP precision for test (single | half)')
# Option for Residual dense network (RDN)
parser.add_argument('--G0', type=int, default=64,
help='default number of filters. (Use in RDN)')
parser.add_argument('--RDNkSize', type=int, default=3,
help='default kernel size. (Use in RDN)')
parser.add_argument('--RDNconfig', type=str, default='B',
help='parameters config of RDN. (Use in RDN)')
# Option for Residual channel attention network (RCAN)
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')
# Training specifications
parser.add_argument('--reset', action='store_true',
help='reset the training')
parser.add_argument('--test_every', type=int, default=1000,
help='do test per every N batches')
parser.add_argument('--epochs', type=int, default=300,
help='number of epochs to train')
parser.add_argument('--batch_size', type=int, default=16,
help='input batch size for training')
parser.add_argument('--split_batch', type=int, default=1,
help='split the batch into smaller chunks')
parser.add_argument('--self_ensemble', action='store_true',
help='use self-ensemble method for test')
parser.add_argument('--test_only', action='store_true',
help='set this option to test the model')
parser.add_argument('--gan_k', type=int, default=1,
help='k value for adversarial loss')
# Optimization specifications
parser.add_argument('--lr', type=float, default=1e-4,
help='learning rate')
parser.add_argument('--decay', type=str, default='200',
help='learning rate decay type')
parser.add_argument('--gamma', type=float, default=0.5,
help='learning rate decay factor for step decay')
parser.add_argument('--optimizer', default='ADAM',
choices=('SGD', 'ADAM', 'RMSprop'),
help='optimizer to use (SGD | ADAM | RMSprop)')
parser.add_argument('--momentum', type=float, default=0.9,
help='SGD momentum')
parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
help='ADAM beta')
parser.add_argument('--epsilon', type=float, default=1e-8,
help='ADAM epsilon for numerical stability')
parser.add_argument('--weight_decay', type=float, default=0,
help='weight decay')
parser.add_argument('--gclip', type=float, default=0,
help='gradient clipping threshold (0 = no clipping)')
# Loss specifications
parser.add_argument('--loss', type=str, default='1*L1',
help='loss function configuration')
parser.add_argument('--skip_threshold', type=float, default='1e8',
help='skipping batch that has large error')
# 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')
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