Function inference

test_ACDC.py:16–37

(args, model, testloader, test_save_path=None)

from lib.networks import MaxViT, MaxViT4Out, MaxViT_CASCADE, MERIT_Parallel, MERIT_Cascaded
        
def inference(args, model, testloader, test_save_path=None):
    logging.info("{} test iterations per epoch".format(len(testloader)))
    model.eval()
    metric_list = 0.0
    with torch.no_grad():
        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, 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(testloader)
        for i in range(1, args.num_classes):
            logging.info('Mean class (%d) mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i, 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))
        logging.info("Testing Finished!")
        return performance, mean_hd95, mean_jacard, mean_asd

if __name__ == "__main__":