[Types] Fix misleading List[Tensor] types

mmseg/models/decode_heads/decode_head.py

@@ -263,7 +263,7 @@ def loss(self, inputs: Tuple[Tensor], batch_data_samples: SampleList,
         return losses
 
     def predict(self, inputs: Tuple[Tensor], batch_img_metas: List[dict],
-                test_cfg: ConfigType) -> List[Tensor]:
+                test_cfg: ConfigType) -> Tensor:
         """Forward function for prediction.
 
         Args:
@@ -276,7 +276,7 @@ def predict(self, inputs: Tuple[Tensor], batch_img_metas: List[dict],
             test_cfg (dict): The testing config.
 
         Returns:
-            List[Tensor]: Outputs segmentation logits map.
+            Tensor: Outputs segmentation logits map.
         """
         seg_logits = self.forward(inputs)
 

mmseg/models/segmentors/base.py

@@ -126,7 +126,7 @@ def _forward(self,
 
     def postprocess_result(self,
                            seg_logits: Tensor,
-                           data_samples: OptSampleList = None) -> list:
+                           data_samples: OptSampleList = None) -> SampleList:
         """ Convert results list to `SegDataSample`.
         Args:
             seg_logits (Tensor): The segmentation results, seg_logits from

mmseg/models/segmentors/cascade_encoder_decoder.py

@@ -70,7 +70,7 @@ def _init_decode_head(self, decode_head: ConfigType) -> None:
         self.num_classes = self.decode_head[-1].num_classes
 
     def encode_decode(self, inputs: Tensor,
-                      batch_img_metas: List[dict]) -> List[Tensor]:
+                      batch_img_metas: List[dict]) -> Tensor:
         """Encode images with backbone and decode into a semantic segmentation
         map of the same size as input."""
         x = self.extract_feat(inputs)

mmseg/models/segmentors/encoder_decoder.py

@@ -120,7 +120,7 @@ def extract_feat(self, inputs: Tensor) -> List[Tensor]:
         return x
 
     def encode_decode(self, inputs: Tensor,
-                      batch_img_metas: List[dict]) -> List[Tensor]:
+                      batch_img_metas: List[dict]) -> Tensor:
         """Encode images with backbone and decode into a semantic segmentation
         map of the same size as input."""
         x = self.extract_feat(inputs)