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.vit_seg_modeling import VisionTransformer as ViT_seg from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg 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=4, help='output channel of network') parser.add_argument('--list_dir', type=str, default='./lists/lists_Synapse', help='list dir') parser.add_argument('--max_iterations', type=int,default=20000, help='maximum epoch number to train') parser.add_argument('--max_epochs', type=int, default=30, 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('--n_skip', type=int, default=3, help='using number of skip-connect, default is num') parser.add_argument('--vit_name', type=str, default='ViT-B_16', help='select one vit model') 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('--vit_patches_size', type=int, default=16, help='vit_patches_size, default is 16') args = parser.parse_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': '../data/Synapse/test_vol_h5', '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 # name the same snapshot defined in train script! args.exp = 'TU_' + dataset_name + str(args.img_size) snapshot_path = "../model/{}/{}".format(args.exp, 'TU') snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path snapshot_path += '_' + args.vit_name snapshot_path = snapshot_path + '_skip' + str(args.n_skip) snapshot_path = snapshot_path + '_vitpatch' + str(args.vit_patches_size) if args.vit_patches_size!=16 else snapshot_path snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 30 else snapshot_path if dataset_name == 'ACDC': # using max_epoch instead of iteration to control training duration snapshot_path = snapshot_path + '_' + str(args.max_iterations)[0:2] + 'k' if args.max_iterations != 30000 else snapshot_path snapshot_path = snapshot_path+'_bs'+str(args.batch_size) snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.01 else snapshot_path snapshot_path = snapshot_path + '_'+str(args.img_size) snapshot_path = snapshot_path + '_s'+str(args.seed) if args.seed!=1234 else snapshot_path config_vit = CONFIGS_ViT_seg[args.vit_name] config_vit.n_classes = args.num_classes config_vit.n_skip = args.n_skip config_vit.patches.size = (args.vit_patches_size, args.vit_patches_size) if args.vit_name.find('R50') !=-1: config_vit.patches.grid = (int(args.img_size/args.vit_patches_size), int(args.img_size/args.vit_patches_size)) net = ViT_seg(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda() snapshot = os.path.join(snapshot_path, 'best_model.pth') if not os.path.exists(snapshot): snapshot = snapshot.replace('best_model', 'epoch_'+str(args.max_epochs-1)) net.load_state_dict(torch.load(snapshot)) snapshot_name = snapshot_path.split('/')[-1] log_folder = './test_log/test_log_' + args.exp 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 = '../predictions' test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name) os.makedirs(test_save_path, exist_ok=True) else: test_save_path = None inference(args, net, test_save_path)