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option.py
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option.py
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import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true',
help='Enables debug mode')
# Hardware specifications
parser.add_argument('--n_threads', type=int, default=2,
help='number of threads for data loading')
parser.add_argument('--cpu', type=bool, default=False,
help='use cpu only')
parser.add_argument('--n_GPUs', type=int, default=2,
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='F:/LongguangWang/Data',
help='dataset directory')
parser.add_argument('--data_train', type=str, default='DIV2K',
help='train dataset name')
parser.add_argument('--data_test', type=str, default='Set5',
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('--asymm', type=bool, default=True,
help='use asymmetric scale factors (only used during training phase)')
parser.add_argument('--scale', type=str, default='',
help='super resolution scale')
parser.add_argument('--scale2', type=str, default='',
help='super resolution scale2')
parser.add_argument('--patch_size', type=int, default=50,
help='input 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', default=False,
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='ArbRCAN',
help='model name')
parser.add_argument('--act', type=str, default='relu',
help='activation function')
parser.add_argument('--pre_train', type=str, default= 'model/RCAN_BIX4.pt',
help='pre-trained model directory')
parser.add_argument('--extend', type=str, default='.',
help='pre-trained model directory')
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)')
# 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=150,
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', type=bool, default=False,
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('--lr_decay', type=int, default=20,
help='learning rate decay per N epochs')
parser.add_argument('--decay_type', type=str, default='step',
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('--beta1', type=float, default=0.9,
help='ADAM beta1')
parser.add_argument('--beta2', type=float, default=0.999,
help='ADAM beta2')
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('--start_epoch', type=int, default=0,
help='resume from the snapshot, and the start_epoch')
# Loss specifications
parser.add_argument('--loss', type=str, default='1*L1',
help='loss function configuration')
parser.add_argument('--skip_threshold', type=float, default='1e6',
help='skipping batch that has large error')
# Log specifications
parser.add_argument('--save', type=str, default='ArbRCAN',
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=200,
help='how many batches to wait before logging training status')
parser.add_argument('--save_results', default=False,
help='save output results')
# Quick test specifications
parser.add_argument('--dir_img', type=str, default='experiment/quick_test/img_004.png',
help='image directory for quick test')
parser.add_argument('--sr_size', default='512+512',
help='size of SR images for quick test')
args = parser.parse_args()
if args.scale=='' or args.scale2=='':
# asymmetric mode: non-integer scale factors + asymmetric scale factors
if args.asymm:
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,
1.5, 1.5, 1.5, 1.5, 1.5,
2.0, 2.0, 2.0, 2.0, 2.0,
2.5, 2.5, 2.5, 2.5, 2.5,
3.0, 3.0, 3.0, 3.0, 3.0,
3.5, 3.5, 3.5, 3.5, 3.5,
4.0, 4.0, 4.0, 4.0, 4.0,
]
args.scale2 = [
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,
2.0, 2.5, 3.0, 3.5, 4.0,
1.5, 2.5, 3.0, 3.5, 4.0,
1.5, 2.0, 3.0, 3.5, 4.0,
1.5, 2.0, 2.5, 3.5, 4.0,
1.5, 2.0, 2.5, 3.0, 4.0,
1.5, 2.0, 2.5, 3.0, 3.5,
]
# symmetric mode: only non-integer scale factors
else:
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
]
args.scale2 = args.scale
else:
args.scale = list(map(lambda x: float(x), args.scale.split('+')))
args.scale2 = list(map(lambda x: float(x), args.scale2.split('+')))
args.sr_size = list(map(lambda x: float(x), args.sr_size.split('+')))
assert len(args.scale) == len(args.scale2)
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