src/transformers/models/owlvit/modeling_owlvit.py/OwlViTClassPredictionHead/forward
class OwlViTClassPredictionHead:    def forward(
        self,
        image_embeds: torch.FloatTensor,
        query_embeds: Optional[torch.FloatTensor],
        query_mask: Optional[torch.Tensor],
    ) -> Tuple[torch.FloatTensor]:
        image_class_embeds = self.dense0(image_embeds)
        if query_embeds is None:
            device = image_class_embeds.device
            batch_size, num_patches = image_class_embeds.shape[:2]
            pred_logits = torch.zeros((batch_size, num_patches, self.query_dim)).to(device)
            return (pred_logits, image_class_embeds)

        # Normalize image and text features
        image_class_embeds /= torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6
        query_embeds /= torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6

        # Get class predictions
        pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds)

        # Apply a learnable shift and scale to logits
        logit_shift = self.logit_shift(image_embeds)
        logit_scale = self.logit_scale(image_embeds)
        logit_scale = self.elu(logit_scale) + 1
        pred_logits = (pred_logits + logit_shift) * logit_scale

        if query_mask is not None:
            if query_mask.ndim > 1:
                query_mask = torch.unsqueeze(query_mask, dim=-2)

            pred_logits = pred_logits.to(torch.float64)
            pred_logits = torch.where(query_mask == 0, -1e6, pred_logits)
            pred_logits = pred_logits.to(torch.float32)

        return (pred_logits, image_class_embeds)