Add dynamic resolution input/interpolate position embedding to SigLIP (…

…huggingface#30719)

* Add interpolate positional encoding to siglip

* Change # of patches for siglip interpolation test

* fix formatting

* Apply nit suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/siglip/modeling_siglip.py

@@ -265,11 +265,53 @@ def __init__(self, config: SiglipVisionConfig):
         self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
         self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
 
-    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method is an adapted method for SigLIP (due to SigLIP not having class embedding unlike other ViTs)
+        that allows the model to interpolate the pre-trained position encodings such that it can be usable on
+        higher resolution images.
+
+        Source:
+        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
+        """
+        position_embeddings = self.position_embedding.weight.unsqueeze(0)
+        num_patches = embeddings.shape[1]
+        num_positions = position_embeddings.shape[1]
+        if num_patches == num_positions and height == width:
+            return position_embeddings
+
+        dim = embeddings.shape[-1]
+        height = height // self.patch_size
+        width = width // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        height, width = height + 0.1, width + 0.1
+
+        patch_pos_embed = position_embeddings.reshape(
+            1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
+        )
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            scale_factor=(height / math.sqrt(num_positions), width / math.sqrt(num_positions)),
+            mode="bicubic",
+            align_corners=False,
+        )
+        if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]:
+            raise ValueError("Width or height does not match with the interpolated position embeddings")
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
+        _, _, height, width = pixel_values.shape
         patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
         embeddings = patch_embeds.flatten(2).transpose(1, 2)
 
-        embeddings = embeddings + self.position_embedding(self.position_ids)
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embedding(self.position_ids)
         return embeddings
 
 
@@ -564,6 +606,8 @@ 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`):
+            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.
 """
@@ -601,6 +645,8 @@ 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`):
+            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.
 """
@@ -848,6 +894,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = False,
     ) -> Union[Tuple, BaseModelOutputWithPooling]:
         r"""
         Returns:
@@ -859,7 +906,7 @@ def forward(
         )
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        hidden_states = self.embeddings(pixel_values)
+        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
@@ -935,6 +982,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
     ) -> Union[Tuple, BaseModelOutputWithPooling]:
         r"""
         Returns:
@@ -965,6 +1013,7 @@ def forward(
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
 
@@ -1055,6 +1104,7 @@ def get_image_features(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
     ) -> torch.FloatTensor:
         r"""
         Returns:
@@ -1092,6 +1142,7 @@ def get_image_features(
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
         pooled_output = vision_outputs[1]
@@ -1110,6 +1161,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
     ) -> Union[Tuple, SiglipOutput]:
         r"""
         Returns:
@@ -1152,6 +1204,7 @@ def forward(
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
         text_outputs = self.text_model(
@@ -1226,6 +1279,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
     ) -> Union[tuple, ImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1271,6 +1325,7 @@ def forward(
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
         sequence_output = outputs[0]

tests/models/siglip/test_modeling_siglip.py

@@ -687,3 +687,25 @@ def test_inference(self):
         probs = torch.sigmoid(logits_per_image)  # these are the probabilities
         expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
         self.assertTrue(torch.allclose(probs, expected_probs, atol=1e-3))
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipModel.from_pretrained(model_name).to(torch_device)
+
+        # 640 x 480 image
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640})
+
+        inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the shape
+        # patch size = 16
+        # batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size
+        expected_shape = torch.Size((1, 1200, 768))
+
+        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)