config.py

import argparse
import math
import os

def parse_option():
    parser = argparse.ArgumentParser('argument for training')

    parser.add_argument('--print_freq', type=int, default=10,
                        help='print frequency')
    parser.add_argument('--save_freq', type=int, default=50,
                        help='save frequency')
    parser.add_argument('--batch_size', type=int, default=128,
                        help='batch_size')
    parser.add_argument('--num_workers', type=int, default=8,
                        help='num of workers to use')
    parser.add_argument('--epochs', type=int, default=100,
                        help='number of training epochs')
    parser.add_argument('--device', type=str, default='cuda:0')
    # optimization
    parser.add_argument('--learning_rate', type=float, default=0.05,
                        help='learning rate')
    parser.add_argument('--patient_lambda', type=float, default=1,
                        help='learning rate')
    parser.add_argument('--cluster_lambda', type=float, default=1,
                        help='learning rate')
    parser.add_argument('--lr_decay_epochs', type=str, default='100',
                        help='where to decay lr, can be a list')
    parser.add_argument('--lr_decay_rate', type=float, default=0.1,
                        help='decay rate for learning rate')
    parser.add_argument('--weight_decay', type=float, default=1e-4,
                        help='weight decay')
    parser.add_argument('--momentum', type=float, default=0.9,
                        help='momentum')
    parser.add_argument('--train_csv_path', type=str, default='train data csv')
    parser.add_argument('--test_csv_path', type=str, default='test data csv')
    parser.add_argument('--train_image_path', type=str, default='train data csv')
    parser.add_argument('--test_image_path', type=str, default='test data csv')

    parser.add_argument('--parallel', type=int, default=1, help='data parallel')
    parser.add_argument('--ncls', type=int, default=6, help='Number of Classes')
    # model dataset
    parser.add_argument('--model', type=str, default='resnet50')
    parser.add_argument('--dataset', type=str, default='TREX_DME',
                        choices=[ 'OLIVES'], help='dataset')
    parser.add_argument('--mean', type=str, help='mean of dataset in path in form of str tuple')
    parser.add_argument('--std', type=str, help='std of dataset in path in form of str tuple')
    parser.add_argument('--data_folder', type=str, default=None, help='path to custom dataset')
    parser.add_argument('--size', type=int, default=128, help='parameter for RandomResizedCrop')

    # temperature
    parser.add_argument('--temp', type=float, default=0.07,
                        help='temperature for loss function')



    opt = parser.parse_args()

    # check if dataset is path that passed required arguments
    if opt.dataset == 'path':
        assert opt.data_folder is not None \
               and opt.mean is not None \
               and opt.std is not None

    # set the path according to the environment
    if opt.data_folder is None:
        opt.data_folder = './datasets/'
    opt.model_path = './save/{}_models'.format(opt.dataset)

    iterations = opt.lr_decay_epochs.split(',')
    opt.lr_decay_epochs = list([])
    for it in iterations:
        opt.lr_decay_epochs.append(int(it))

    opt.model_name = '{}_lr_{}_decay_{}_bsz_{}_temp_{}'. \
        format(opt.model, opt.learning_rate,
               opt.weight_decay, opt.batch_size, opt.temp)


    opt.save_folder = os.path.join(opt.model_path, opt.model_name)
    if not os.path.isdir(opt.save_folder):
        os.makedirs(opt.save_folder)

    return opt