option.py

import argparse

parser = argparse.ArgumentParser(description='HyperSR')

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=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=1,
                    help='number of GPUs')
parser.add_argument('--seed', type=int, default=1,
                    help='random seed')

# Data specifications
parser.add_argument('--dataset', type=str, default='CAVE',
                    help='dataset')
parser.add_argument('--dir_data', type=str, default='D:/LongguangWang/Data',
                    help='dataset directory')
parser.add_argument('--dir_demo', type=str, default='../test',
                    help='demo image directory')

# Model specifications
parser.add_argument('--model', default='FTnet',
                    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('--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('--n_iters', type=int, default=100001,
                    help='number of iterations to train')
parser.add_argument('--batch_size', type=int, default=32,
                    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('--test_only', type=bool, default=False,
                    help='set this option to test the model')

# Optimization specifications
parser.add_argument('--lr', type=float, default=5e-4,
                    help='learning rate')
parser.add_argument('--lr_decay', type=int, default=200,
                    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('--n_steps', type=float, default=20000,
                    help='number of steps for lr 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')

# Log specifications
parser.add_argument('--save', type=str, default='SMSR_LR',
                    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')

args = parser.parse_args()


for arg in vars(args):
    if vars(args)[arg] == 'True':
        vars(args)[arg] = True
    elif vars(args)[arg] == 'False':
        vars(args)[arg] = False