[Enhancement] refine mmagic SDKEnd2EndModel forward (#2092)

* update SDKEnd2EndModel of super_resolution_model.py

* recover

* docstring

mmdeploy/codebase/mmagic/deploy/super_resolution_model.py

@@ -144,6 +144,7 @@ class SDKEnd2EndModel(End2EndModel):
     """SDK inference class, converts SDK output to mmagic format."""
 
     def __init__(self, *args, **kwargs):
+        kwargs.update(dict(data_preprocessor=None))
         super(SDKEnd2EndModel, self).__init__(*args, **kwargs)
 
     def forward(self,
@@ -159,7 +160,8 @@ class SDKEnd2EndModel(End2EndModel):
         it is an image.
 
         Args:
-            inputs (torch.Tensor): The input tensors
+            inputs (torch.Tensor): A list contains input image(s)
+                in [C x H x W] format.
             data_samples (List[BaseDataElement], optional): The data samples.
                 Defaults to None.
             mode (str, optional): forward mode, only support `predict`.
@@ -169,28 +171,18 @@ class SDKEnd2EndModel(End2EndModel):
         Returns:
             list | dict: High resolution image or a evaluation results.
         """
-
-        if hasattr(self.data_preprocessor, 'destructor'):
-            inputs = self.data_preprocessor.destructor(
-                inputs.to(self.data_preprocessor.std.device))
-
         outputs = []
-        for i in range(inputs.shape[0]):
-            output = self.wrapper.invoke(inputs[i].permute(
-                1, 2, 0).contiguous().detach().cpu().numpy() * 255.)
+        for input in inputs:
+            output = self.wrapper.invoke(
+                input.permute(1, 2, 0).contiguous().detach().cpu().numpy())
             outputs.append(
                 torch.from_numpy(output).permute(2, 0, 1).contiguous())
-        outputs = torch.stack(outputs, 0) / 255.
-        assert hasattr(self.data_preprocessor, 'destruct')
-        outputs = self.data_preprocessor.destruct(
-            outputs.to(self.data_preprocessor.std.device), data_samples)
+        outputs = torch.stack(outputs, 0)
+        outputs = DataSample(pred_img=outputs.cpu()).split()
 
-        # create a stacked data sample here
-        predictions = DataSample(pred_img=outputs.cpu())
-
-        predictions = self.convert_to_datasample_list(predictions,
-                                                      data_samples, inputs)
-        return predictions
+        for data_sample, pred in zip(data_samples, outputs):
+            data_sample.output = pred
+        return data_samples
 
 
 def build_super_resolution_model(

mmdeploy/codebase/mmdet/deploy/object_detection_model.py

@@ -685,8 +685,8 @@ class SDKEnd2EndModel(End2EndModel):
         """Run forward inference.
 
         Args:
-            img (Sequence[Tensor]): A list contains input image(s)
-                in [N x C x H x W] format.
+            inputs (Sequence[Tensor]): A list contains input image(s)
+                in [C x H x W] format.
             data_samples (List[BaseDataElement]): A list of meta info
                 for image(s).
             *args: Other arguments.