modeling_mitsua_japanese_clip.py

# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. + Abstract Engine. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionConfig, CLIPVisionModel, SiglipTextConfig, SiglipTextModel
from transformers.models.clip.modeling_clip import CLIPOutput,clip_loss
from .configuration_mitsua_japanese_clip import MitsuaJapaneseCLIPConfig

class MitsuaJapaneseCLIPModel(CLIPPreTrainedModel):
    config_class = MitsuaJapaneseCLIPConfig
    def __init__(self, config: MitsuaJapaneseCLIPConfig):
        CLIPPreTrainedModel.__init__(self, config)

        if not isinstance(config.text_config, SiglipTextConfig):
            raise TypeError(
                "config.text_config is expected to be of type SiglipTextConfig but is of type"
                f" {type(config.text_config)}."
            )

        if not isinstance(config.vision_config, CLIPVisionConfig):
            raise TypeError(
                "config.vision_config is expected to be of type CLIPVisionConfig but is of type"
                f" {type(config.vision_config)}."
            )

        text_config = config.text_config
        vision_config = config.vision_config

        self.projection_dim = config.projection_dim
        self.text_embed_dim = text_config.hidden_size
        self.vision_embed_dim = vision_config.hidden_size

        text_model = SiglipTextModel._from_config(text_config, attn_implementation=config._attn_implementation)
        self.text_model = text_model.text_model

        vision_model = CLIPVisionModel._from_config(vision_config, attn_implementation=config._attn_implementation)
        self.vision_model = vision_model.vision_model

        self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))

        # Initialize weights and apply final processing
        self.post_init()

    def get_text_features(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        text_outputs = self.text_model(
            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,
        )

        pooled_output = text_outputs[1]
        return pooled_output

    def get_image_features(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
            applying the projection layer to the pooled output of [`CLIPVisionModel`].
        Examples:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, CLIPModel
        >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs = processor(images=image, return_tensors="pt")
        >>> image_features = model.get_image_features(**inputs)
        ```"""
        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = vision_outputs[1] 
        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,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CLIPOutput]:
        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        text_outputs = self.text_model(
            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,
        )

        image_embeds = vision_outputs[1]
        image_embeds = self.visual_projection(image_embeds)

        text_embeds = text_outputs[1]

        # normalized features
        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) * logit_scale.to(
            text_embeds.device
        )
        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_outputs,
            vision_model_output=vision_outputs,
        )