[Fix] Fix mmdeploy rewriter (#366)

* fix mmdeploy rewriter

* fix unit test

mmyolo/deploy/models/dense_heads/yolov5_head.py

@@ -48,8 +48,7 @@ def yolov5_bbox_decoder(priors: Tensor, bbox_preds: Tensor,
 @FUNCTION_REWRITER.register_rewriter(
     func_name='mmyolo.models.dense_heads.yolov5_head.'
     'YOLOv5Head.predict_by_feat')
-def yolov5_head__predict_by_feat(ctx,
-                                 self,
+def yolov5_head__predict_by_feat(self,
                                  cls_scores: List[Tensor],
                                  bbox_preds: List[Tensor],
                                  objectnesses: Optional[List[Tensor]] = None,
@@ -85,6 +84,7 @@ def yolov5_head__predict_by_feat(ctx,
             tensor in the tuple is (N, num_box), and each element
             represents the class label of the corresponding box.
     """
+    ctx = FUNCTION_REWRITER.get_context()
     detector_type = type(self)
     deploy_cfg = ctx.cfg
     use_efficientnms = deploy_cfg.get('use_efficientnms', False)
@@ -163,7 +163,7 @@ def yolov5_head__predict_by_feat(ctx,
     func_name='mmyolo.models.dense_heads.yolov5_head.'
     'YOLOv5Head.predict',
     backend='rknn')
-def yolov5_head__predict__rknn(ctx, self, x: Tuple[Tensor], *args,
+def yolov5_head__predict__rknn(self, x: Tuple[Tensor], *args,
                                **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
     """Perform forward propagation of the detection head and predict detection
     results on the features of the upstream network.
@@ -181,8 +181,7 @@ def yolov5_head__predict__rknn(ctx, self, x: Tuple[Tensor], *args,
     'YOLOv5HeadModule.forward',
     backend='rknn')
 def yolov5_head_module__forward__rknn(
-        ctx, self, x: Tensor, *args,
-        **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
+        self, x: Tensor, *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
     """Forward feature of a single scale level."""
     out = []
     for i, feat in enumerate(x):