Revert changes in logit size for semantic segmentation models (#15722)

* Revert changes in logit size for semantic segmentation models

* Address review comments

src/transformers/modeling_outputs.py

@@ -822,8 +822,17 @@ class SemanticSegmentationModelOutput(ModelOutput):
     Args:
         loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
             Classification (or regression if config.num_labels==1) loss.
-        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, height, width)`):
+        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
             Classification scores for each pixel.
+
+            <Tip warning={true}>
+
+            The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
+            to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
+            original image size as post-processing. You should always check your logits shape and resize as needed.
+
+            </Tip>
+
         hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
             shape `(batch_size, patch_size, hidden_size)`.

src/transformers/models/beit/configuration_beit.py

@@ -93,10 +93,6 @@ class BeitConfig(PretrainedConfig):
             Whether to concatenate the output of the auxiliary head with the input before the classification layer.
         semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
             The index that is ignored by the loss function of the semantic segmentation model.
-        legacy_output (`bool`, *optional*, defaults to `False`):
-            Whether to return the legacy outputs or not (with logits of shape `height / 4 , width / 4`)
-
-            This argument is only present for backward compatibility reasons and will be removed in v5 of Transformers.
 
     Example:
 
@@ -145,7 +141,6 @@ class BeitConfig(PretrainedConfig):
         auxiliary_num_convs=1,
         auxiliary_concat_input=False,
         semantic_loss_ignore_index=255,
-        legacy_output=False,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -181,4 +176,3 @@ class BeitConfig(PretrainedConfig):
         self.auxiliary_num_convs = auxiliary_num_convs
         self.auxiliary_concat_input = auxiliary_concat_input
         self.semantic_loss_ignore_index = semantic_loss_ignore_index
-        self.legacy_output = legacy_output

src/transformers/models/beit/modeling_beit.py

@@ -17,7 +17,6 @@
 
 import collections.abc
 import math
-import warnings
 from dataclasses import dataclass
 
 import torch
@@ -1121,8 +1120,11 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def compute_loss(self, upsampled_logits, auxiliary_logits, labels):
+    def compute_loss(self, logits, auxiliary_logits, labels):
         # upsample logits to the images' original size
+        upsampled_logits = nn.functional.interpolate(
+            logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+        )
         if auxiliary_logits is not None:
             upsampled_auxiliary_logits = nn.functional.interpolate(
                 auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
@@ -1145,17 +1147,11 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        legacy_output=None,
     ):
         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, ...,
             config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
-        legacy_output (`bool`, *optional*):
-            Whether to return the legacy outputs or not (with logits of shape `height / 4 , width / 4`). Will default
-            to `self.config.legacy_output`.
-
-            This argument is only present for backward compatibility reasons and will be removed in v5 of Transformers.
 
         Returns:
 
@@ -1181,14 +1177,6 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
-        legacy_output = legacy_output if legacy_output is not None else self.config.legacy_output
-        if not legacy_output:
-            warnings.warn(
-                "The output of this model has changed in v4.17.0 and the logits now have the same size as the inputs. "
-                "You can activate the previous behavior by passing `legacy_output=True` to this call or the "
-                "configuration of this model (only until v5, then that argument will be removed).",
-                FutureWarning,
-            )
 
         outputs = self.beit(
             pixel_values,
@@ -1216,10 +1204,6 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
 
         logits = self.decode_head(features)
 
-        upsampled_logits = nn.functional.interpolate(
-            logits, size=pixel_values.shape[-2:], mode="bilinear", align_corners=False
-        )
-
         auxiliary_logits = None
         if self.auxiliary_head is not None:
             auxiliary_logits = self.auxiliary_head(features)
@@ -1229,26 +1213,18 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
             if self.config.num_labels == 1:
                 raise ValueError("The number of labels should be greater than one")
             else:
-                loss = self.compute_loss(upsampled_logits, auxiliary_logits, labels)
+                loss = self.compute_loss(logits, auxiliary_logits, labels)
 
         if not return_dict:
             if output_hidden_states:
-                output = (logits if legacy_output else upsampled_logits,) + outputs[2:]
+                output = (logits,) + outputs[2:]
             else:
-                output = (logits if legacy_output else upsampled_logits,) + outputs[3:]
+                output = (logits,) + outputs[3:]
             return ((loss,) + output) if loss is not None else output
 
-        if legacy_output:
-            return SequenceClassifierOutput(
-                loss=loss,
-                logits=logits,
-                hidden_states=outputs.hidden_states if output_hidden_states else None,
-                attentions=outputs.attentions,
-            )
-        else:
         return SemanticSegmentationModelOutput(
             loss=loss,
-                logits=upsampled_logits,
+            logits=logits,
             hidden_states=outputs.hidden_states if output_hidden_states else None,
             attentions=outputs.attentions,
         )

src/transformers/models/segformer/configuration_segformer.py

@@ -83,10 +83,6 @@ class SegformerConfig(PretrainedConfig):
             required for the semantic segmentation model.
         semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
             The index that is ignored by the loss function of the semantic segmentation model.
-        legacy_output (`bool`, *optional*, defaults to `False`):
-            Whether to return the legacy outputs or not (with logits of shape `height / 4 , width / 4`)
-
-            This argument is only present for backward compatibility reasons and will be removed in v5 of Transformers.
 
     Example:
 
@@ -128,7 +124,6 @@ class SegformerConfig(PretrainedConfig):
         is_encoder_decoder=False,
         reshape_last_stage=True,
         semantic_loss_ignore_index=255,
-        legacy_output=False,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -154,4 +149,3 @@ class SegformerConfig(PretrainedConfig):
         self.decoder_hidden_size = decoder_hidden_size
         self.reshape_last_stage = reshape_last_stage
         self.semantic_loss_ignore_index = semantic_loss_ignore_index
-        self.legacy_output = legacy_output

src/transformers/models/segformer/modeling_segformer.py

@@ -17,7 +17,6 @@
 
 import collections
 import math
-import warnings
 
 import torch
 import torch.utils.checkpoint
@@ -697,17 +696,11 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        legacy_output=None,
     ):
         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, ...,
             config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
-        legacy_output (`bool`, *optional*):
-            Whether to return the legacy outputs or not (with logits of shape `height / 4 , width / 4`). Will default
-            to `self.config.legacy_output`.
-
-            This argument is only present for backward compatibility reasons and will be removed in v5 of Transformers.
 
         Returns:
 
@@ -732,14 +725,6 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
-        legacy_output = legacy_output if legacy_output is not None else self.config.legacy_output
-        if not legacy_output:
-            warnings.warn(
-                "The output of this model has changed in v4.17.0 and the logits now have the same size as the inputs. "
-                "You can activate the previous behavior by passing `legacy_output=True` to this call or the "
-                "configuration of this model (only until v5, then that argument will be removed).",
-                FutureWarning,
-            )
 
         outputs = self.segformer(
             pixel_values,
@@ -752,37 +737,28 @@ class SegformerForSemanticSegmentation(SegformerPreTrainedModel):
 
         logits = self.decode_head(encoder_hidden_states)
 
-        upsampled_logits = nn.functional.interpolate(
-            logits, size=pixel_values.shape[-2:], mode="bilinear", align_corners=False
-        )
-
         loss = None
         if labels is not None:
             if self.config.num_labels == 1:
                 raise ValueError("The number of labels should be greater than one")
             else:
                 # upsample logits to the images' original size
+                upsampled_logits = nn.functional.interpolate(
+                    logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+                )
                 loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
                 loss = loss_fct(upsampled_logits, labels)
 
         if not return_dict:
             if output_hidden_states:
-                output = (logits if legacy_output else upsampled_logits,) + outputs[1:]
+                output = (logits,) + outputs[1:]
             else:
-                output = (logits if legacy_output else upsampled_logits,) + outputs[2:]
+                output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        if legacy_output:
-            return SequenceClassifierOutput(
-                loss=loss,
-                logits=logits,
-                hidden_states=outputs.hidden_states if output_hidden_states else None,
-                attentions=outputs.attentions,
-            )
-        else:
         return SemanticSegmentationModelOutput(
             loss=loss,
-                logits=upsampled_logits,
+            logits=logits,
             hidden_states=outputs.hidden_states if output_hidden_states else None,
             attentions=outputs.attentions,
         )

tests/beit/test_modeling_beit.py

@@ -162,11 +162,11 @@ class BeitModelTester:
         model.eval()
         result = model(pixel_values)
         self.parent.assertEqual(
-            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
         )
         result = model(pixel_values, labels=pixel_labels)
         self.parent.assertEqual(
-            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
         )
 
     def prepare_config_and_inputs_for_common(self):
@@ -533,14 +533,14 @@ class BeitModelIntegrationTest(unittest.TestCase):
         logits = outputs.logits
 
         # verify the logits
-        expected_shape = torch.Size((1, 150, 640, 640))
+        expected_shape = torch.Size((1, 150, 160, 160))
         self.assertEqual(logits.shape, expected_shape)
 
         expected_slice = torch.tensor(
             [
-                [[-4.9225, -4.9225, -4.6066], [-4.9225, -4.9225, -4.6066], [-4.6675, -4.6675, -4.3617]],
-                [[-5.8168, -5.8168, -5.5163], [-5.8168, -5.8168, -5.5163], [-5.5728, -5.5728, -5.2842]],
-                [[-0.0078, -0.0078, 0.4926], [-0.0078, -0.0078, 0.4926], [0.3664, 0.3664, 0.8309]],
+                [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
+                [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
+                [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
             ]
         ).to(torch_device)
 

tests/segformer/test_modeling_segformer.py

@@ -135,11 +135,11 @@ class SegformerModelTester:
         model.eval()
         result = model(pixel_values)
         self.parent.assertEqual(
-            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
         )
         result = model(pixel_values, labels=labels)
         self.parent.assertEqual(
-            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
         )
 
     def prepare_config_and_inputs_for_common(self):
@@ -363,14 +363,14 @@ class SegformerModelIntegrationTest(unittest.TestCase):
         with torch.no_grad():
             outputs = model(pixel_values)
 
-        expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
+        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
         self.assertEqual(outputs.logits.shape, expected_shape)
 
         expected_slice = torch.tensor(
             [
-                [[-4.6309, -4.6309, -4.7425], [-4.6309, -4.6309, -4.7425], [-4.7011, -4.7011, -4.8136]],
-                [[-12.1391, -12.1391, -12.2858], [-12.1391, -12.1391, -12.2858], [-12.2309, -12.2309, -12.3758]],
-                [[-12.5134, -12.5134, -12.6328], [-12.5134, -12.5134, -12.6328], [-12.5576, -12.5576, -12.6865]],
+                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
+                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
+                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
             ]
         ).to(torch_device)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4))
@@ -392,14 +392,14 @@ class SegformerModelIntegrationTest(unittest.TestCase):
         with torch.no_grad():
             outputs = model(pixel_values)
 
-        expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
+        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
         self.assertEqual(outputs.logits.shape, expected_shape)
 
         expected_slice = torch.tensor(
             [
-                [[-13.5729, -13.5729, -13.6149], [-13.5729, -13.5729, -13.6149], [-13.6697, -13.6697, -13.7224]],
-                [[-17.1638, -17.1638, -17.0022], [-17.1638, -17.1638, -17.0022], [-17.1754, -17.1754, -17.0358]],
-                [[-3.6452, -3.6452, -3.5670], [-3.6452, -3.6452, -3.5670], [-3.5744, -3.5744, -3.5079]],
+                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
+                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
+                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
             ]
         ).to(torch_device)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1))