Fix DeiT Masked Image Modeling output

src/transformers/modeling_tf_outputs.py

@@ -949,3 +949,30 @@ class TFImageClassifierOutputWithNoAttention(ModelOutput):
     loss: Optional[tf.Tensor] = None
     logits: tf.Tensor = None
     hidden_states: Optional[Tuple[tf.Tensor, ...]] = None
+
+
+@dataclass
+class TFMaskedImageCompletionOutput(ModelOutput):
+    """
+    Base class for outputs of masked image completion / in-painting models.
+
+    Args:
+        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Reconstruction loss.
+        reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
+           Reconstructed / completed images.
+        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `tf.Tensor` (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[tf.Tensor] = None
+    reconstruction: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+    attentions: Optional[Tuple[tf.Tensor]] = None

src/transformers/models/deit/modeling_deit.py

@@ -26,7 +26,12 @@
 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 (
@@ -588,7 +593,7 @@ def __init__(self, config: DeiTConfig) -> None:
         self.post_init()
 
     @add_start_docstrings_to_model_forward(DEIT_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,
@@ -597,7 +602,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: 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).
@@ -666,9 +671,9 @@ def forward(
             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,
         )

src/transformers/models/deit/modeling_tf_deit.py

@@ -27,7 +27,7 @@
     TFBaseModelOutput,
     TFBaseModelOutputWithPooling,
     TFImageClassifierOutput,
-    TFMaskedLMOutput,
+    TFMaskedImageCompletionOutput,
 )
 from ...modeling_tf_utils import (
     TFPreTrainedModel,
@@ -765,7 +765,7 @@ def __init__(self, config: DeiTConfig) -> None:
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=TFMaskedImageCompletionOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
         pixel_values: Optional[tf.Tensor] = None,
@@ -775,7 +775,7 @@ def call(
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         training: bool = False,
-    ) -> Union[tuple, TFMaskedLMOutput]:
+    ) -> Union[tuple, TFMaskedImageCompletionOutput]:
         r"""
         bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`):
             Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
@@ -856,18 +856,20 @@ def call(
             output = (reconstructed_pixel_values,) + outputs[1:]
             return ((masked_im_loss,) + output) if masked_im_loss is not None else output
 
-        return TFMaskedLMOutput(
+        return TFMaskedImageCompletionOutput(
             loss=masked_im_loss,
-            logits=reconstructed_pixel_values,
+            reconstruction=reconstructed_pixel_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
 
-    def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
+    def serving_output(self, output: TFMaskedImageCompletionOutput) -> TFMaskedImageCompletionOutput:
         hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFMaskedLMOutput(logits=output.logits, hidden_states=hidden_states, attentions=attentions)
+        return TFMaskedImageCompletionOutput(
+            reconstruction=output.reconstruction, hidden_states=hidden_states, attentions=attentions
+        )
 
 
 @add_start_docstrings(

tests/models/deit/test_modeling_deit.py

@@ -145,7 +145,7 @@ def create_and_check_for_masked_image_modeling(self, config, pixel_values, label
         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
@@ -156,7 +156,7 @@ def create_and_check_for_masked_image_modeling(self, config, pixel_values, label
 
         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

tests/models/deit/test_modeling_tf_deit.py

@@ -130,7 +130,7 @@ def create_and_check_for_masked_image_modeling(self, config, pixel_values, label
         model = TFDeiTForMaskedImageModeling(config=config)
         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
@@ -139,7 +139,7 @@ def create_and_check_for_masked_image_modeling(self, config, pixel_values, label
 
         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