import argparse import logging import os import random import sys import numpy as np import torch import torch.backends.cudnn as cudnn import torch.nn as nn from torch.utils.data import DataLoader from tqdm import tqdm from datasets.dataset_synapse import Synapse_dataset from utils import test_single_volume from networks.vision_transformer import SwinUnet as ViT_seg from trainer import trainer_synapse from config import get_config parser = argparse.ArgumentParser() parser.add_argument('--volume_path', type=str, default='../data/Synapse/test_vol_h5', help='root dir for validation volume data') # for acdc volume_path=root_dir parser.add_argument('--dataset', type=str, default='Synapse', help='experiment_name') parser.add_argument('--num_classes', type=int, default=9, help='output channel of network') parser.add_argument('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') parser.add_argument('--output_dir', 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=150, help='maximum epoch number to train') parser.add_argument('--batch_size', type=int, default=24, help='batch_size per gpu') parser.add_argument('--img_size', type=int, default=224, help='input patch size of network input') parser.add_argument('--is_savenii', action="store_true", help='whether to save results during inference') parser.add_argument('--test_save_dir', type=str, default='../predictions', help='saving prediction as nii!') 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('--seed', type=int, default=1234, help='random seed') parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', ) 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.volume_path = os.path.join(args.volume_path, "test_vol_h5") config = get_config(args) def inference(args, model, test_save_path=None): db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", list_dir=args.list_dir) testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1) logging.info("{} test iterations per epoch".format(len(testloader))) model.eval() metric_list = 0.0 for i_batch, sampled_batch in tqdm(enumerate(testloader)): h, w = sampled_batch["image"].size()[2:] image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0] metric_i = test_single_volume(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size], test_save_path=test_save_path, case=case_name, z_spacing=args.z_spacing) metric_list += np.array(metric_i) logging.info('idx %d case %s mean_dice %f mean_hd95 %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1])) metric_list = metric_list / len(db_test) for i in range(1, args.num_classes): logging.info('Mean class %d mean_dice %f mean_hd95 %f' % (i, metric_list[i-1][0], metric_list[i-1][1])) performance = np.mean(metric_list, axis=0)[0] mean_hd95 = np.mean(metric_list, axis=0)[1] logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f' % (performance, mean_hd95)) return "Testing Finished!" if __name__ == "__main__": 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_config = { 'Synapse': { 'Dataset': Synapse_dataset, 'volume_path': args.volume_path, 'list_dir': './lists/lists_Synapse', 'num_classes': 9, 'z_spacing': 1, }, } dataset_name = args.dataset args.num_classes = dataset_config[dataset_name]['num_classes'] args.volume_path = dataset_config[dataset_name]['volume_path'] args.Dataset = dataset_config[dataset_name]['Dataset'] args.list_dir = dataset_config[dataset_name]['list_dir'] args.z_spacing = dataset_config[dataset_name]['z_spacing'] args.is_pretrain = True net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes).cuda() snapshot = os.path.join(args.output_dir, 'best_model.pth') if not os.path.exists(snapshot): snapshot = snapshot.replace('best_model', 'epoch_'+str(args.max_epochs-1)) msg = net.load_state_dict(torch.load(snapshot)) print("self trained swin unet",msg) snapshot_name = snapshot.split('/')[-1] log_folder = './test_log/test_log_' os.makedirs(log_folder, exist_ok=True) logging.basicConfig(filename=log_folder + '/'+snapshot_name+".txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) logging.info(str(args)) logging.info(snapshot_name) if args.is_savenii: args.test_save_dir = os.path.join(args.output_dir, "predictions") test_save_path = args.test_save_dir os.makedirs(test_save_path, exist_ok=True) else: test_save_path = None inference(args, net, test_save_path)