test.py

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.LaplacianFormer import LaplacianFormer


parser = argparse.ArgumentParser()
parser.add_argument('--test_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,
                    default='./results/', help='output dir')   
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('--seed', type=int, default=1234, help='random seed')
parser.add_argument('--pretrained_path', type=str,
                    default='./results/LaplacianFormer.pth', help='Pretrained model path')
parser.add_argument('--z_spacing', type=int,
                    default=1, help='z_spacing')
                    
args = parser.parse_args()


def inference(args, testloader, model, test_save_path=None):
    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(testloader.dataset)
    
    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)


    os.makedirs(args.output_dir , exist_ok=True)
    logging.basicConfig(filename=args.output_dir + 'test_log' + ".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))

    # Loading dataset
    db_test = Synapse_dataset(base_dir=args.test_path, split="test_vol", list_dir=args.list_dir, img_size=args.img_size)
    testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1)

    # Loading model
    model = LaplacianFormer(num_classes=9).cuda()
    msg = model.load_state_dict(torch.load(args.pretrained_path)['model'])
    print("Laplacian-Former Model: ", msg)

    inference(args, testloader, model, test_save_path=(args.output_dir if args.is_savenii else None))