[Fix] Fix VFNet test (open-mmlab#281)

* [Fix] fix bugs for mmcls performance test (open-mmlab#269) * fix bugs for mmcls performance test * fix yapf * add comments of CLASSES attribute * Fix test_get_bboxes_of_vfnet_head * Fix Co-authored-by: hanrui1sensetime <83800577+hanrui1sensetime@users.noreply.github.com>
humu789 · Dec 13, 2021 · a96e5f9 · a96e5f9
1 parent 0f90a0a
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diff --git a/docs/src/pytorch-sphinx-theme b/docs/src/pytorch-sphinx-theme
diff --git a/mmdeploy/codebase/mmdet/models/roi_heads/cascade_roi_head.py b/mmdeploy/codebase/mmdet/models/roi_heads/cascade_roi_head.py
@@ -76,13 +76,9 @@ def cascade_roi_head__simple_test(ctx, self, x, proposals, img_metas,
     cls_score = sum(ms_scores) / float(len(ms_scores))
     bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, 4)
     rois = rois.reshape(batch_size, num_proposals_per_img, -1)
+    scale_factor = img_metas[0].get('scale_factor', None)
     det_bboxes, det_labels = self.bbox_head[-1].get_bboxes(
-        rois,
-        cls_score,
-        bbox_pred,
-        max_shape,
-        img_metas[0]['scale_factor'],
-        cfg=rcnn_test_cfg)
+        rois, cls_score, bbox_pred, max_shape, scale_factor, cfg=rcnn_test_cfg)
 
     if not self.with_mask:
         return det_bboxes, det_labels

diff --git a/tests/test_codebase/test_mmdet/test_mmdet_models.py b/tests/test_codebase/test_mmdet/test_mmdet_models.py
@@ -1053,44 +1053,13 @@ def get_vfnet_head_model():
     return model
 
 
-@pytest.mark.parametrize('backend_type', [Backend.OPENVINO])
+@pytest.mark.parametrize('backend_type',
+                         [Backend.OPENVINO, Backend.ONNXRUNTIME])
 def test_get_bboxes_of_vfnet_head(backend_type: Backend):
     """Test get_bboxes rewrite of VFNet head."""
     check_backend(backend_type)
-
-    class TestModel(torch.nn.Module):
-        """Stub for VFNetHead with fake bbox_preds operations.
-
-        Then bbox_preds will be one of the inputs to the ONNX graph.
-        """
-
-        def __init__(self, vfnet_head):
-            super().__init__()
-            self.vfnet_head = vfnet_head
-
-        def get_bboxes(self,
-                       cls_scores,
-                       bbox_preds,
-                       bbox_preds_refine,
-                       img_metas,
-                       cfg=None,
-                       rescale=None,
-                       with_nms=True):
-            tmp_bbox_pred_refine = []
-            for bbox_pred, bbox_pred_refine in zip(bbox_preds,
-                                                   bbox_preds_refine):
-                tmp = bbox_pred_refine + bbox_pred
-                tmp = tmp - bbox_pred
-                tmp_bbox_pred_refine.append(tmp)
-            bbox_preds_refine = tmp_bbox_pred_refine
-            return self.vfnet_head.get_bboxes(cls_scores, bbox_preds,
-                                              bbox_preds_refine, img_metas,
-                                              cfg, rescale, with_nms)
-
-    test_model = TestModel(get_vfnet_head_model())
-    test_model.requires_grad_(False)
-    test_model.cpu().eval()
-
+    vfnet_head = get_vfnet_head_model()
+    vfnet_head.cpu().eval()
     s = 16
     img_metas = [{
         'scale_factor': np.ones(4),
@@ -1116,32 +1085,24 @@ def get_bboxes(self,
 
     seed_everything(1234)
     cls_score = [
-        torch.rand(1, test_model.vfnet_head.num_classes, pow(2, i), pow(2, i))
+        torch.rand(1, vfnet_head.num_classes, pow(2, i), pow(2, i))
         for i in range(5, 0, -1)
     ]
     seed_everything(5678)
     bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]
     seed_everything(9101)
-    bbox_preds_refine = [
-        torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
-    ]
 
     model_inputs = {
         'cls_scores': cls_score,
         'bbox_preds': bboxes,
-        'bbox_preds_refine': bbox_preds_refine,
         'img_metas': img_metas
     }
-    model_outputs = get_model_outputs(test_model, 'get_bboxes', model_inputs)
+    model_outputs = get_model_outputs(vfnet_head, 'get_bboxes', model_inputs)
 
     img_metas[0]['img_shape'] = torch.Tensor([s, s])
     wrapped_model = WrapModel(
-        test_model, 'get_bboxes', img_metas=img_metas, with_nms=True)
-    rewrite_inputs = {
-        'cls_scores': cls_score,
-        'bbox_preds': bboxes,
-        'bbox_preds_refine': bbox_preds_refine
-    }
+        vfnet_head, 'get_bboxes', img_metas=img_metas, with_nms=True)
+    rewrite_inputs = {'cls_scores': cls_score, 'bbox_preds': bboxes}
     rewrite_outputs, is_backend_output = get_rewrite_outputs(
         wrapped_model=wrapped_model,
         model_inputs=rewrite_inputs,