def METHOD_NAME(self):
    # single img
    img_path = 'tests/data/det_toy_dataset/imgs/test/img_1.jpg'
    res_path = self.inferencer(img_path, return_vis=True)
    # ndarray
    img = mmcv.imread(img_path)
    res_ndarray = self.inferencer(img, return_vis=True)
    self.assert_predictions_equal(res_path['predictions'],
                                  res_ndarray['predictions'])
    self.assertTrue(
        np.allclose(res_path['visualization'],
                    res_ndarray['visualization']))
    # multiple images
    img_paths = [
        'tests/data/det_toy_dataset/imgs/test/img_1.jpg',
        'tests/data/det_toy_dataset/imgs/test/img_2.jpg'
    ]
    res_path = self.inferencer(img_paths, return_vis=True)
    # list of ndarray
    imgs = [mmcv.imread(p) for p in img_paths]
    res_ndarray = self.inferencer(imgs, return_vis=True)
    self.assert_predictions_equal(res_path['predictions'],
                                  res_ndarray['predictions'])
    for i in range(len(img_paths)):
        self.assertTrue(
            np.allclose(res_path['visualization'][i],
                        res_ndarray['visualization'][i]))
    # img dir, test different batch sizes
    img_dir = 'tests/data/det_toy_dataset/imgs/test/'
    res_bs1 = self.inferencer(img_dir, batch_size=1, return_vis=True)
    res_bs3 = self.inferencer(img_dir, batch_size=3, return_vis=True)
    self.assert_predictions_equal(res_bs1['predictions'],
                                  res_bs3['predictions'])
    self.assertTrue(
        np.array_equal(res_bs1['visualization'], res_bs3['visualization']))