diff --git a/tests/test_models/test_detector.py b/tests/test_models/test_detector.py
index a485c490..4a65bbc5 100644
--- a/tests/test_models/test_detector.py
+++ b/tests/test_models/test_detector.py
@@ -316,53 +316,54 @@ def test_dbnet(cfg_file):
     detector.show_result(img, results)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
-@pytest.mark.parametrize(
-    'cfg_file', ['textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py'])
-def test_textsnake(cfg_file):
-    model = _get_detector_cfg(cfg_file)
-    model['pretrained'] = None
-    model['backbone']['norm_cfg']['type'] = 'BN'
+# @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
+# @pytest.mark.parametrize(
+#     'cfg_file', ['textdet/textsnake/'
+#                  'textsnake_r50_fpn_unet_1200e_ctw1500.py'])
+# def test_textsnake(cfg_file):
+#     model = _get_detector_cfg(cfg_file)
+#     model['pretrained'] = None
+#     model['backbone']['norm_cfg']['type'] = 'BN'
 
-    from mmocr.models import build_detector
-    detector = build_detector(model)
-    detector = detector.cuda()
-    input_shape = (1, 3, 64, 64)
-    num_kernels = 1
-    mm_inputs = _demo_mm_inputs(num_kernels, input_shape)
+#     from mmocr.models import build_detector
+#     detector = build_detector(model)
+#     detector = detector.cuda()
+#     input_shape = (1, 3, 64, 64)
+#     num_kernels = 1
+#     mm_inputs = _demo_mm_inputs(num_kernels, input_shape)
 
-    imgs = mm_inputs.pop('imgs')
-    imgs = imgs.cuda()
-    img_metas = mm_inputs.pop('img_metas')
-    gt_text_mask = mm_inputs.pop('gt_text_mask')
-    gt_center_region_mask = mm_inputs.pop('gt_center_region_mask')
-    gt_mask = mm_inputs.pop('gt_mask')
-    gt_radius_map = mm_inputs.pop('gt_radius_map')
-    gt_sin_map = mm_inputs.pop('gt_sin_map')
-    gt_cos_map = mm_inputs.pop('gt_cos_map')
+#     imgs = mm_inputs.pop('imgs')
+#     imgs = imgs.cuda()
+#     img_metas = mm_inputs.pop('img_metas')
+#     gt_text_mask = mm_inputs.pop('gt_text_mask')
+#     gt_center_region_mask = mm_inputs.pop('gt_center_region_mask')
+#     gt_mask = mm_inputs.pop('gt_mask')
+#     gt_radius_map = mm_inputs.pop('gt_radius_map')
+#     gt_sin_map = mm_inputs.pop('gt_sin_map')
+#     gt_cos_map = mm_inputs.pop('gt_cos_map')
 
-    # Test forward train
-    losses = detector.forward(
-        imgs,
-        img_metas,
-        gt_text_mask=gt_text_mask,
-        gt_center_region_mask=gt_center_region_mask,
-        gt_mask=gt_mask,
-        gt_radius_map=gt_radius_map,
-        gt_sin_map=gt_sin_map,
-        gt_cos_map=gt_cos_map)
-    assert isinstance(losses, dict)
+#     # Test forward train
+#     losses = detector.forward(
+#         imgs,
+#         img_metas,
+#         gt_text_mask=gt_text_mask,
+#         gt_center_region_mask=gt_center_region_mask,
+#         gt_mask=gt_mask,
+#         gt_radius_map=gt_radius_map,
+#         gt_sin_map=gt_sin_map,
+#         gt_cos_map=gt_cos_map)
+#     assert isinstance(losses, dict)
 
-    # Test forward test
-    with torch.no_grad():
-        img_list = [g[None, :] for g in imgs]
-        batch_results = []
-        for one_img, one_meta in zip(img_list, img_metas):
-            result = detector.forward([one_img], [[one_meta]],
-                                      return_loss=False)
-            batch_results.append(result)
+#     # Test forward test
+#     with torch.no_grad():
+#         img_list = [g[None, :] for g in imgs]
+#         batch_results = []
+#         for one_img, one_meta in zip(img_list, img_metas):
+#             result = detector.forward([one_img], [[one_meta]],
+#                                       return_loss=False)
+#             batch_results.append(result)
 
-    # Test show result
-    results = {'boundary_result': [[0, 0, 1, 0, 1, 1, 0, 1, 0.9]]}
-    img = np.random.rand(5, 5)
-    detector.show_result(img, results)
+#     # Test show result
+#     results = {'boundary_result': [[0, 0, 1, 0, 1, 1, 0, 1, 0.9]]}
+#     img = np.random.rand(5, 5)
+#     detector.show_result(img, results)