def METHOD_NAME(self, mock_extract_feat):
    model_cfg = self.model_cfg.copy()
    model_cfg['det_head']['in_channels'] = 6
    model_cfg['det_head']['text_region_thr'] = 0.8
    model_cfg['det_head']['center_region_thr'] = 0.8
    model = MODELS.build(model_cfg)
    imgs, data_samples, _ = self._get_drrg_inputs()
    maps = torch.zeros((1, 6, 224, 224), dtype=torch.float)
    maps[:, 0:2, :, :] = -10.
    maps[:, 0, 60:100, 50:170] = 10.
    maps[:, 1, 75:85, 60:160] = 10.
    maps[:, 2, 75:85, 60:160] = 0.
    maps[:, 3, 75:85, 60:160] = 1.
    maps[:, 4, 75:85, 60:160] = 10.
    maps[:, 5, 75:85, 60:160] = 10.
    mock_extract_feat.return_value = maps
    with torch.no_grad():
        full_pass_weight = torch.zeros((6, 6, 1, 1))
        for i in range(6):
            full_pass_weight[i, i, 0, 0] = 1
        model.det_head.out_conv.weight.data = full_pass_weight
        model.det_head.out_conv.bias.data.fill_(0.)
        results = model(imgs, data_samples, mode='predict')
    self.assertIn('polygons', results[0].pred_instances)
    self.assertIn('scores', results[0].pred_instances)
    self.assertTrue(
        isinstance(results[0].pred_instances['scores'], torch.FloatTensor))