[CLIPSeg] Make interpolate_pos_encoding default to True (huggingface#…

…34419)

* Remove interpolate_pos_encoding

* Make fixup

* Make interpolate_pos_encoding default to True

* Reuse existing interpolation

* Add integration test

src/transformers/models/clipseg/modeling_clipseg.py

@@ -205,7 +205,7 @@ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width:
 
         return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
 
-    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
+    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=True) -> torch.Tensor:
         batch_size, _, height, width = pixel_values.shape
         if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
             raise ValueError(
@@ -535,7 +535,7 @@ def _init_weights(self, module):
         output_hidden_states (`bool`, *optional*):
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
-        interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
+        interpolate_pos_encoding (`bool`, *optional*, defaults to `True`):
             Whether to interpolate the pre-trained position encodings.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
@@ -574,7 +574,7 @@ def _init_weights(self, module):
         output_hidden_states (`bool`, *optional*):
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
-        interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
+        interpolate_pos_encoding (`bool`, *optional*, defaults to `True`):
             Whether to interpolate the pre-trained position encodings.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
@@ -845,14 +845,13 @@ def __init__(self, config: CLIPSegVisionConfig):
 
     @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
-    # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
     def forward(
         self,
-        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor],
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-        interpolate_pos_encoding: Optional[bool] = False,
+        interpolate_pos_encoding: Optional[bool] = True,
     ) -> Union[Tuple, BaseModelOutputWithPooling]:
         r"""
         Returns:
@@ -864,9 +863,6 @@ def forward(
         )
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
         hidden_states = self.pre_layrnorm(hidden_states)
 
@@ -912,7 +908,7 @@ def forward(
         pixel_values: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        interpolate_pos_encoding: Optional[bool] = False,
+        interpolate_pos_encoding: Optional[bool] = True,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, BaseModelOutputWithPooling]:
         r"""
@@ -1035,7 +1031,7 @@ def get_image_features(
         pixel_values: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        interpolate_pos_encoding: bool = False,
+        interpolate_pos_encoding: bool = True,
         return_dict: Optional[bool] = None,
     ) -> torch.FloatTensor:
         r"""
@@ -1091,7 +1087,7 @@ def forward(
         return_loss: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        interpolate_pos_encoding: bool = False,
+        interpolate_pos_encoding: bool = True,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, CLIPSegOutput]:
         r"""
@@ -1397,7 +1393,7 @@ def forward(
         labels: Optional[torch.LongTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        interpolate_pos_encoding: bool = False,
+        interpolate_pos_encoding: bool = True,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, CLIPSegOutput]:
         r"""

tests/models/clipseg/test_modeling_clipseg.py

@@ -796,15 +796,15 @@ def test_inference_image_segmentation(self):
 
         # forward pass
         with torch.no_grad():
-            outputs = model(**inputs, interpolate_pos_encoding=True)
+            outputs = model(**inputs)
 
         # verify the predicted masks
         self.assertEqual(
             outputs.logits.shape,
             torch.Size((3, 352, 352)),
         )
         expected_masks_slice = torch.tensor(
-            [[-7.4613, -7.4785, -7.3627], [-7.3268, -7.0898, -7.1333], [-6.9838, -6.7900, -6.8913]]
+            [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
         ).to(torch_device)
 
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3))