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option.py
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option.py
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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('--num_workers','-nw', type=int, default=4,
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='/share/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='Set5',
# help='test dataset name')
# parser.add_argument('--img_ext', type=str, default='.bmp',
# help='test dataset type')
# parser.add_argument('--benchmark_noise', action='store_true',
# help='use noisy benchmark sets')
# parser.add_argument('--n_train', type=int, default=800,
# help='number of training set')
# parser.add_argument('--n_val', type=int, default=5,
# help='number of validation set')
# parser.add_argument('--offset_val', type=int, default=800,
# help='validation index offest')
# parser.add_argument('--ext', type=str, default='sep_reset',
# help='dataset file extension')
parser.add_argument('--scale', default='2',
help='super resolution scale')
parser.add_argument('--patch_size', type=int, default=384,
help='output patch size')
parser.add_argument('--rgb_range', type=int, default=1,
help='maximum value of RGB')
parser.add_argument('--n_colors', type=int, default=3,
help='number of color channels to use')
parser.add_argument('--noise', type=str, default='.',
help='Gaussian noise std.')
parser.add_argument('--chop', action='store_true',
help='enable memory-efficient forward')
# parser.add_argument('--task_type', type=str, default='denoising',
# help='image restoration tasks')
# Model specifications
parser.add_argument('--model', default='MWCNN',
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=20,
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('--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=12,
# help='do test per every N batches')
parser.add_argument('--epochs','-e', type=int, default=1000,
help='number of epochs to train')
parser.add_argument('--batch_size', type=int, default=1,
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=0.5e-3,
help='learning rate')
parser.add_argument('--lr_decay', type=int, default=50,
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')
# Loss specifications
parser.add_argument('--loss', type=str, default='1*MSE',
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='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('--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')
# 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')
parser.add_argument('--noise_dir', '-n', default='/home/dell/Downloads/noise', help='path to noise folder image')
parser.add_argument('--gt_dir', '-g', default='/home/dell/Downloads/gt', help='path to gt folder image')
parser.add_argument('--image_size', '-sz', default=256, type=int, help='size of image')
parser.add_argument('--save_every', '-se', default=200, type=int, help='save_every')
parser.add_argument('--loss_every', '-le', default=10, type=int, help='loss_every')
parser.add_argument('--burst_length', '-b', default=4, type=int, help='batch size')
parser.add_argument('--model_type', '-m', default="noise", help='type of model : MWCNN, noise,DGF')
parser.add_argument('--data_type', '-d', default="rgb", help='type of model : rgb, raw, filter')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='checkpoints',
help='the checkpoint to eval')
parser.add_argument('--restart', '-r', action='store_true',
help='Whether to remove all old files and restart the training process')
parser.add_argument('--save_img', "-s", default="", type=str, help='save image in eval_img folder ')
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