diff --git a/tests/test_models/test_detector.py b/tests/test_models/test_detector.py
index 4a65bbc5..a5a255c1 100644
--- a/tests/test_models/test_detector.py
+++ b/tests/test_models/test_detector.py
@@ -316,54 +316,55 @@ 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)