Fix SDK backend (#844)

mmdeploy/codebase/mmcls/deploy/classification_model.py

@@ -139,8 +139,7 @@ def forward(self, img: List[torch.Tensor], *args, **kwargs) -> list:
             list: A list contains predictions.
         """
 
-        pred = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])[0]
+        pred = self.wrapper.invoke(img[0].contiguous().detach().cpu().numpy())
         pred = np.array(pred, dtype=np.float32)
         return pred[np.argsort(pred[:, 0])][np.newaxis, :, 1]
 

mmdeploy/codebase/mmdet/deploy/object_detection_model.py

@@ -637,7 +637,7 @@ def forward(self, img: Sequence[torch.Tensor], img_metas: Sequence[dict],
             list: A list contains predictions.
         """
         dets, labels, masks = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])[0]
+            img[0].contiguous().detach().cpu().numpy())
         det_results = bbox2result(dets[np.newaxis, ...], labels[np.newaxis,
                                                                 ...],
                                   len(self.CLASSES))

mmdeploy/codebase/mmedit/deploy/super_resolution_model.py

@@ -223,7 +223,7 @@ def forward(self,
             list | dict: High resolution image or a evaluation results.
         """
         img = tensor2img(lq)
-        output = self.wrapper.invoke([img])[0]
+        output = self.wrapper.invoke(img)
         if test_mode:
             output = torch.from_numpy(output)
             output = output.permute(2, 0, 1)

mmdeploy/codebase/mmocr/deploy/text_detection_model.py

@@ -169,7 +169,7 @@ def forward(self, img: Sequence[torch.Tensor],
             list: A list contains predictions.
         """
         boundaries = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])[0]
+            img[0].contiguous().detach().cpu().numpy())
         boundaries = [list(x) for x in boundaries]
         return [
             dict(

mmdeploy/codebase/mmocr/deploy/text_recognition_model.py

@@ -181,9 +181,9 @@ def forward(self, img: Sequence[torch.Tensor],
         Returns:
             list[str]: Text label result of each image.
         """
-        results = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])
-        results = [dict(text=text, score=score) for text, score in results]
+        text, score = self.wrapper.invoke(
+            img[0].contiguous().detach().cpu().numpy())
+        results = [dict(text=text, score=score)]
         return results
 
 

mmdeploy/codebase/mmpose/deploy/pose_detection_model.py

@@ -218,8 +218,8 @@ def forward(self, img: List[torch.Tensor], *args, **kwargs) -> list:
             image_paths.append(img_meta['image_file'])
             bbox_ids.append(img_meta['bbox_id'])
 
-        pred = self.wrapper.handle(
-            [img[0].contiguous().detach().cpu().numpy()], [sdk_boxes])[0]
+        pred = self.wrapper.handle(img[0].contiguous().detach().cpu().numpy(),
+                                   sdk_boxes)
 
         result = dict(
             preds=pred,

mmdeploy/codebase/mmrotate/deploy/rotated_detection_model.py

@@ -212,7 +212,7 @@ def forward(self, img: List[torch.Tensor], *args, **kwargs) -> list:
         """
         results = []
         dets, labels = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])[0]
+            img[0].contiguous().detach().cpu().numpy())
         dets_results = [dets[labels == i, :] for i in range(len(self.CLASSES))]
         results.append(dets_results)
 

mmdeploy/codebase/mmseg/deploy/segmentation_model.py

@@ -166,8 +166,7 @@ def forward(self, img: Sequence[torch.Tensor],
         Returns:
             list: A list contains predictions.
         """
-        masks = self.wrapper.invoke(
-            [img[0].contiguous().detach().cpu().numpy()])[0]
+        masks = self.wrapper.invoke(img[0].contiguous().detach().cpu().numpy())
         return masks