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configuration_vtde.py

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+from transformers import VisionTextDualEncoderConfig
+
+class VTDEConfig(VisionTextDualEncoderConfig):
+    def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, 
+    text_pooling_mode='mean',
+    vision_pooling_mode='max',
+    **kwargs):
+        """
+        pooling_mode in ['mean', 'max', 'cls']
+        https://arxiv.org/pdf/2210.09996.pdf
+        https://github.com/kahnchana/clippy/blob/3c102c29c32f7c66c6e52e09b795fe9c061bbb03/src/open_clip/hf_model.py#L56
+        """
+        self.text_pooling_mode = text_pooling_mode
+        self.vision_pooling_mode = vision_pooling_mode
+        super().__init__(projection_dim, logit_scale_init_value, **kwargs)

modelling_vtde.py

@@ -0,0 +1,160 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/12_modelling.ipynb.
+
+# %% auto 0
+__all__ = ['VTDEConfig', 'VTDE']
+
+# %% ../notebooks/12_modelling.ipynb 1
+from transformers.models.clip.modeling_clip import CLIPOutput, clip_loss
+from typing import Optional, Tuple, Union
+from transformers import VisionTextDualEncoderConfig, AutoModel, PreTrainedModel, VisionTextDualEncoderModel
+from .configuration_vtde import VTDEConfig
+import torch
+
+
+class VTDE(VisionTextDualEncoderModel):
+    config_class = VTDEConfig
+
+    def __init__(
+        self,
+        config: Optional[VTDEConfig] = None,
+        vision_model: Optional[PreTrainedModel] = None,
+        text_model: Optional[PreTrainedModel] = None,
+    ):
+        # You can customize the constructor if needed
+        super().__init__(config, vision_model, text_model)
+        self.text_pooling_mode = config.text_pooling_mode
+        self.vision_pooling_mode = config.vision_pooling_mode
+
+    def get_text_features(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        token_type_ids=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        if self.text_pooling_mode == 'cls':
+            pooled_output = text_outputs[1]
+        elif self.text_pooling_mode == 'mean':
+            pooled_output = torch.mean(text_outputs[0], dim=1)
+        elif self.text_pooling_mode == 'max':
+            pooled_output = torch.max(text_outputs[0], dim=1)[0]
+        elif self.text_pooling_mode == 'norm':
+            """we select the patch with the largest norm"""
+            last_hidden_states = text_outputs[0]
+            patch_norms = torch.norm(last_hidden_states[:, :-1, :], dim=-1)
+            max_norm_idx = torch.argmax(patch_norms, dim=1)
+            pooled_output = last_hidden_states[:, max_norm_idx, :][:, 0, :]
+        else:
+            "We want to raise the name of the pooling mode"
+            raise NotImplementedError
+
+        text_features = self.text_projection(pooled_output)
+
+        return text_features
+
+    def get_image_features(
+        self,
+        pixel_values=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.vision_pooling_mode == 'cls':
+            pooled_output = vision_outputs[1]
+        elif self.vision_pooling_mode == 'mean':
+            pooled_output = torch.mean(vision_outputs[0], dim=1)
+        elif self.vision_pooling_mode == 'max':
+            pooled_output = torch.max(vision_outputs[0], dim=1)[0]
+        elif self.vision_pooling_mode == 'norm':
+            """we select the patch with the largest norm"""
+            last_hidden_states = vision_outputs[0]
+            patch_norms = torch.norm(last_hidden_states[:, :-1, :], dim=-1)
+            max_norm_idx = torch.argmax(patch_norms, dim=1)
+            pooled_output = last_hidden_states[:, max_norm_idx, :][:, 0, :]
+        else:
+            raise NotImplementedError
+
+        image_features = self.visual_projection(pooled_output)
+
+        return image_features
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        return_loss: Optional[bool] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], CLIPOutput]:
+
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        image_embeds = self.get_image_features(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        text_embeds = self.get_text_features(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # normalized features
+        image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
+
+        # cosine similarity as logits
+        logit_scale = self.logit_scale.exp()
+        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
+        logits_per_image = logits_per_text.T
+
+        loss = None
+        if return_loss:
+            loss = clip_loss(logits_per_text)
+
+        if not return_dict:
+            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
+            return ((loss,) + output) if loss is not None else output
+
+        return CLIPOutput(
+            loss=loss,
+            logits_per_image=logits_per_image,
+            logits_per_text=logits_per_text,
+            text_embeds=text_embeds,
+            image_embeds=image_embeds,
+            text_model_output=text_embeds,
+            vision_model_output=image_embeds,
+        )
+    
+VTDEConfig.register_for_auto_class()
+VTDE.register_for_auto_class("AutoModel")
+