diff --git a/tests/test_codebase/test_mmdet3d/test_mmdet3d_models.py b/tests/test_codebase/test_mmdet3d/test_mmdet3d_models.py
index 48b41ebb3..5f55d3d05 100644
--- a/tests/test_codebase/test_mmdet3d/test_mmdet3d_models.py
+++ b/tests/test_codebase/test_mmdet3d/test_mmdet3d_models.py
@@ -9,7 +9,6 @@ from mmdeploy.codebase import import_codebase
 from mmdeploy.utils import Backend, Codebase, Task, load_config
 from mmdeploy.utils.test import WrapModel, check_backend, get_rewrite_outputs
 
-import_codebase(Codebase.MMDET3D)
 try:
     import_codebase(Codebase.MMDET3D)
 except ImportError:
@@ -61,7 +60,6 @@ def test_pillar_encoder(backend_type: Backend):
     num_points = torch.randint(0, 32, (3945, ), dtype=torch.int32)
     coors = torch.randint(0, 10, (3945, 4), dtype=torch.int32)
     model_outputs = model.forward(features, num_points, coors)
-    model_outputs = [model_outputs]
     wrapped_model = WrapModel(model, 'forward')
     rewrite_inputs = {
         'features': features,
@@ -74,11 +72,11 @@ def test_pillar_encoder(backend_type: Backend):
         deploy_cfg=deploy_cfg)
     if isinstance(rewrite_outputs, dict):
         rewrite_outputs = rewrite_outputs['output']
-    for model_output, rewrite_output in zip(model_outputs, rewrite_outputs):
-        if isinstance(rewrite_output, torch.Tensor):
-            rewrite_output = rewrite_output.cpu().numpy()
-        assert np.allclose(
-            model_output.shape, rewrite_output.shape, rtol=1e-03, atol=1e-03)
+    if isinstance(rewrite_outputs, list):
+        rewrite_outputs = rewrite_outputs[0]
+
+    assert np.allclose(
+        model_outputs.shape, rewrite_outputs.shape, rtol=1e-03, atol=1e-03)
 
 
 @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
@@ -99,20 +97,16 @@ def test_pointpillars_scatter(backend_type: Backend):
     voxel_features = torch.rand(16 * 16, 64) * 100
     coors = torch.randint(0, 10, (16 * 16, 4), dtype=torch.int32)
     model_outputs = model.forward_batch(voxel_features, coors, 1)
-    model_outputs = [model_outputs]
     wrapped_model = WrapModel(model, 'forward_batch')
     rewrite_inputs = {'voxel_features': voxel_features, 'coors': coors}
     rewrite_outputs, is_backend_output = get_rewrite_outputs(
         wrapped_model=wrapped_model,
         model_inputs=rewrite_inputs,
         deploy_cfg=deploy_cfg)
-    if isinstance(rewrite_outputs, dict):
-        rewrite_outputs = rewrite_outputs['output']
-    for model_output, rewrite_output in zip(model_outputs, rewrite_outputs):
-        if isinstance(rewrite_output, torch.Tensor):
-            rewrite_output = rewrite_output.cpu().numpy()
-        assert np.allclose(
-            model_output.shape, rewrite_output.shape, rtol=1e-03, atol=1e-03)
+    if isinstance(rewrite_outputs, list):
+        rewrite_outputs = rewrite_outputs[0]
+    assert np.allclose(
+        model_outputs.shape, rewrite_outputs.shape, rtol=1e-03, atol=1e-03)
 
 
 def get_centerpoint():