Rename to SemanticSegmenterOutput (#15849)

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

src/transformers/modeling_outputs.py

@@ -815,7 +815,7 @@ class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
 
 
 @dataclass
-class SemanticSegmentationModelOutput(ModelOutput):
+class SemanticSegmenterOutput(ModelOutput):
     """
     Base class for outputs of semantic segmentation models.
 

src/transformers/models/beit/modeling_beit.py

@@ -30,7 +30,7 @@ from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
     MaskedLMOutput,
-    SemanticSegmentationModelOutput,
+    SemanticSegmenterOutput,
     SequenceClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
@@ -1188,7 +1188,7 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         return loss
 
     @add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=SemanticSegmentationModelOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         pixel_values: Optional[torch.Tensor] = None,
@@ -1197,7 +1197,7 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[tuple, SemanticSegmentationModelOutput]:
+    ) -> Union[tuple, SemanticSegmenterOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
             Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
@@ -1272,7 +1272,7 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
                 output = (logits,) + outputs[3:]
             return ((loss,) + output) if loss is not None else output
 
-        return SemanticSegmentationModelOutput(
+        return SemanticSegmenterOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states if output_hidden_states else None,

src/transformers/models/segformer/modeling_segformer.py

@@ -24,7 +24,7 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, SemanticSegmentationModelOutput, SequenceClassifierOutput
+from ...modeling_outputs import BaseModelOutput, SemanticSegmenterOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     add_code_sample_docstrings,
@@ -720,7 +720,7 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=SemanticSegmentationModelOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         pixel_values: torch.FloatTensor,
@@ -728,7 +728,7 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SemanticSegmentationModelOutput]:
+    ) -> Union[Tuple, SemanticSegmenterOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
             Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
@@ -788,7 +788,7 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
                 output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return SemanticSegmentationModelOutput(
+        return SemanticSegmenterOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states if output_hidden_states else None,