train.py

import argparse
import logging
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from networks.FocalNet.main import FUnet
from trainer import trainer_synapse


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument('--root_path', type=str,
                        default='./data/Synapse', help='root dir for data')
    parser.add_argument('--dataset', type=str,
                        default='Synapse', help='experiment_name')
    parser.add_argument('--list_dir', type=str,
                        default='./lists/lists_Synapse', help='list dir')
    parser.add_argument('--num_classes', type=int,
                        default=9, help='output channel of network')
    parser.add_argument('--output_dir', default='./checkpoint', type=str, help='output dir')
    parser.add_argument('--max_iterations', type=int,
                        default=30000, help='maximum epoch number to train')
    parser.add_argument('--max_epochs', type=int,
                        default=127, help='maximum epoch number to train')
    parser.add_argument('--batch_size', type=int,
                        default=24, help='batch_size per gpu')
    parser.add_argument('--n_gpu', type=int, default=1, help='total gpu')
    parser.add_argument('--deterministic', type=int, default=1,
                        help='whether use deterministic training')
    parser.add_argument('--base_lr', type=float, default=0.01,
                        help='segmentation network learning rate')
    parser.add_argument('--img_size', type=int,
                        default=224, help='input patch size of network input')
    parser.add_argument('--seed', type=int, default=1234, help='random seed')
    parser.add_argument(
        "--opts",
        help="Modify config options by adding 'KEY VALUE' pairs. ",
        default=None,
        nargs='+',
    )
    parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
    parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
                        help='no: no cache, '
                             'full: cache all data, '
                             'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
    parser.add_argument('--resume', help='resume from checkpoint')
    parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps")
    parser.add_argument('--use-checkpoint', action='store_true',
                        help="whether to use gradient checkpointing to save memory")
    parser.add_argument('--amp-opt-level', type=str, default='O1', choices=['O0', 'O1', 'O2'],
                        help='mixed precision opt level, if O0, no amp is used')
    parser.add_argument('--tag', help='tag of experiment')
    parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
    parser.add_argument('--throughput', action='store_true', help='Test throughput only')
    args = parser.parse_args()
    if args.dataset == "Synapse":
        args.root_path = os.path.join(args.root_path, "train_npz")
    if not args.deterministic:
        cudnn.benchmark = True
        cudnn.deterministic = False
    else:
        cudnn.benchmark = False
        cudnn.deterministic = True

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    dataset_name = args.dataset
    dataset_config = {
        'Synapse': {
            'root_path': args.root_path,
            'list_dir': './lists/lists_Synapse',
            'num_classes': 9,
        },
    }

    if args.batch_size != 24 and args.batch_size % 6 == 0:
        args.base_lr *= args.batch_size / 24
    args.num_classes = dataset_config[dataset_name]['num_classes']
    args.root_path = dataset_config[dataset_name]['root_path']
    args.list_dir = dataset_config[dataset_name]['list_dir']

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    net = FUnet()
    print("number of parameters:", sum(p.numel() for p in net.parameters() if p.requires_grad) // 10 ** 6)

    trainer = {'Synapse': trainer_synapse,}
    trainer[dataset_name](args, net, args.output_dir)