[SegFormer] Add support for segmentation masks with one label (#20279)

* Add support for binary segmentation

* Fix loss calculation and add test

* Remove space

* use fstring

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

src/transformers/models/segformer/modeling_segformer.py

@@ -806,15 +806,20 @@ def forward(
 
         loss = None
         if labels is not None:
-            if not 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
-                )
+            # upsample logits to the images' original size
+            upsampled_logits = nn.functional.interpolate(
+                logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+            )
+            if self.config.num_labels > 1:
                 loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
                 loss = loss_fct(upsampled_logits, labels)
+            elif self.config.num_labels == 1:
+                valid_mask = ((labels >= 0) & (labels != self.config.semantic_loss_ignore_index)).float()
+                loss_fct = BCEWithLogitsLoss(reduction="none")
+                loss = loss_fct(upsampled_logits.squeeze(1), labels.float())
+                loss = (loss * valid_mask).mean()
+            else:
+                raise ValueError(f"Number of labels should be >=0: {self.config.num_labels}")
 
         if not return_dict:
             if output_hidden_states:

tests/models/segformer/test_modeling_segformer.py

@@ -140,6 +140,16 @@ def create_and_check_for_image_segmentation(self, config, pixel_values, labels):
         self.parent.assertEqual(
             result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
         )
+        self.parent.assertGreater(result.loss, 0.0)
+
+    def create_and_check_for_binary_image_segmentation(self, config, pixel_values, labels):
+        config.num_labels = 1
+        model = SegformerForSemanticSegmentation(config=config)
+        model.to(torch_device)
+        model.eval()
+        labels = torch.randint(0, 1, (self.batch_size, self.image_size, self.image_size)).to(torch_device)
+        result = model(pixel_values, labels=labels)
+        self.parent.assertGreater(result.loss, 0.0)
 
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
@@ -177,6 +187,10 @@ def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_for_binary_image_segmentation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_binary_image_segmentation(*config_and_inputs)
+
     def test_for_image_segmentation(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)