Function inference

test_synapse.py:69–90

(args, model, test_save_path=None)

    classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'pancreas', 'liver', 'stomach', 'aorta']

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, nclass=args.num_classes)
    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=1, class_names=classes)
        metric_list += np.array(metric_i)
        logging.info('idx %d case %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1], np.mean(metric_i, axis=0)[2], np.mean(metric_i, axis=0)[3]))
    metric_list = metric_list / len(db_test)
    for i in range(1, args.num_classes):
        logging.info('Mean class (%d) %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i, classes[i-1], metric_list[i-1][0], metric_list[i-1][1], metric_list[i-1][2], metric_list[i-1][3]))
    performance = np.mean(metric_list, axis=0)[0]
    mean_hd95 = np.mean(metric_list, axis=0)[1]
    mean_jacard = np.mean(metric_list, axis=0)[2]
    mean_asd = np.mean(metric_list, axis=0)[3]
    logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f, mean_jacard : %f mean_asd : %f' % (performance, mean_hd95, mean_jacard, mean_asd))
    return "Testing Finished!"

if __name__ == "__main__":