Create MaskedImageCompletionOutput and fix ViT docs (#22152)

* create MaskedImageCompletionOutput

* fix bugs

* fix bugs

src/transformers/modeling_outputs.py

@@ -1281,6 +1281,34 @@ class ImageSuperResolutionOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class MaskedImageCompletionOutput(ModelOutput):
+    """
+    Base class for outputs of masked image completion / in-painting models.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Reconstruction loss.
+        reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+           Reconstructed / completed images.
+        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, if the model has an embedding layer, +
+            one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
+            (also called feature maps) of the model at the output of each stage.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    reconstruction: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class Wav2Vec2BaseModelOutput(ModelOutput):
     """

src/transformers/models/vit/modeling_vit.py

@@ -25,7 +25,12 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput
+from ...modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    ImageClassifierOutput,
+    MaskedImageCompletionOutput,
+)
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
@@ -643,7 +648,7 @@ class ViTForMaskedImageModeling(ViTPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=MaskedImageCompletionOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         pixel_values: Optional[torch.Tensor] = None,
@@ -653,7 +658,7 @@ class ViTForMaskedImageModeling(ViTPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         interpolate_pos_encoding: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ) -> Union[tuple, MaskedLMOutput]:
+    ) -> Union[tuple, MaskedImageCompletionOutput]:
         r"""
         bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
             Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
@@ -723,9 +728,9 @@ class ViTForMaskedImageModeling(ViTPreTrainedModel):
             output = (reconstructed_pixel_values,) + outputs[1:]
             return ((masked_im_loss,) + output) if masked_im_loss is not None else output
 
-        return MaskedLMOutput(
+        return MaskedImageCompletionOutput(
             loss=masked_im_loss,
-            logits=reconstructed_pixel_values,
+            reconstruction=reconstructed_pixel_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )

tests/models/vit/test_modeling_vit.py

@@ -134,7 +134,7 @@ class ViTModelTester:
         model.eval()
         result = model(pixel_values)
         self.parent.assertEqual(
-            result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
+            result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
         )
 
         # test greyscale images
@@ -145,7 +145,7 @@ class ViTModelTester:
 
         pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         result = model(pixel_values)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size))
+        self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size))
 
     def create_and_check_for_image_classification(self, config, pixel_values, labels):
         config.num_labels = self.type_sequence_label_size