args.py

import argparse
import torch

def Configs():
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--datapath', dest='datapath', type=str, 
                        default='provid the path to the dataset', 
                        help='path to dataset')
    parser.add_argument('--dataset', dest='dataset', type=str, default='csiq',
                        help='Support datasets: clive|koniq|fblive|live|csiq|tid2013')
    parser.add_argument('--svpath', dest='svpath', type=str,
                        default='path to save the results',
                        help='the path to save the info')
    parser.add_argument('--train_patch_num', dest='train_patch_num', type=int, default=50, 
                        help='Number of sample patches from training image')
    parser.add_argument('--test_patch_num', dest='test_patch_num', type=int, default=50, 
                        help='Number of sample patches from testing image')
    parser.add_argument('--lr', dest='lr', type=float, default=2e-5, 
                        help='Learning rate')
    parser.add_argument('--weight_decay', dest='weight_decay', type=float, default=5e-4, 
                        help='Weight decay')
    parser.add_argument('--batch_size', dest='batch_size', type=int, default=8, 
                        help='Batch size')
    parser.add_argument('--epochs', dest='epochs', type=int, default=3, 
                        help='Epochs for training')
    parser.add_argument('--seed', dest='seed', type=int, default=2021, 
                        help='for reproducing the results')
    parser.add_argument('--vesion', dest='vesion', type=int, default=1,
                        help='vesion number for saving')
    parser.add_argument('--patch_size', dest='patch_size', type=int, default=224, 
                        help='Crop size for training & testing image patches')
    parser.add_argument('--droplr', dest='droplr', type=int, default=5, 
                        help='drop lr by every x iteration')   
    parser.add_argument('--gpunum', dest='gpunum', type=str, default='0',
                        help='the id for the gpu that will be used')
    parser.add_argument('--network', dest='network', type=str, default='resnet50',
                        help='the resnet backbone to use')
    parser.add_argument('--nheadt', dest='nheadt', type=int, default=16,
                        help='nheadt in the transformer')
    parser.add_argument('--num_encoder_layerst', dest='num_encoder_layerst', type=int, default=2,
                        help='num encoder layers in the transformer')
    parser.add_argument('--dim_feedforwardt', dest='dim_feedforwardt', type=int, default=64,
                        help='dim feedforward in the transformer')
    return parser.parse_args()
    
    
if __name__ == '__main__':
    config = Configs()
    for arg in vars(config):
        print(arg, getattr(config, arg))