tag2text/models/tag2text.py

"""
 * Tag2Text
 * Written by Xinyu Huang
"""
import json
import warnings

import numpy as np
import torch
from models.bert import BertConfig
from models.bert import BertLMHeadModel
from models.bert import BertModel
from models.swin_transformer import SwinTransformer
from models.utils import *
from models.vit import VisionTransformer
from torch import nn

warnings.filterwarnings("ignore")


class Tag2Text_Caption(nn.Module):
    def __init__(
        self,
        med_config=f"{CONFIG_PATH}/configs/med_config.json",
        image_size=384,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        prompt="a picture of ",
        threshold=0.68,
        delete_tag_index=[],
        tag_list=f"{CONFIG_PATH}/data/tag_list.txt",
    ):
        r"""Tag2Text inference module, both captioning and tagging are included.
        Tag2Text is an efficient and controllable vision-language pre-training framework.
        Described in the paper "Tag2Text: Guiding Vision-Language Model via Image Tagging" https://arxiv.org/abs/2303.05657

        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
            threshold (int): tagging threshold
            delete_tag_index (list): delete some tags that may disturb captioning
        """
        super().__init__()

        # create image encoder
        if vit == "swin_b":
            if image_size == 224:
                vision_config_path = f"{CONFIG_PATH}/configs/swin/config_swinB_224.json"
            elif image_size == 384:
                vision_config_path = f"{CONFIG_PATH}/configs/swin/config_swinB_384.json"
            vision_config = read_json(vision_config_path)
            assert image_size == vision_config["image_res"]
            # assert config['patch_size'] == 32
            vision_width = vision_config["vision_width"]

            self.visual_encoder = SwinTransformer(
                img_size=vision_config["image_res"],
                patch_size=4,
                in_chans=3,
                embed_dim=vision_config["embed_dim"],
                depths=vision_config["depths"],
                num_heads=vision_config["num_heads"],
                window_size=vision_config["window_size"],
                mlp_ratio=4.0,
                qkv_bias=True,
                drop_rate=0.0,
                drop_path_rate=0.1,
                ape=False,
                patch_norm=True,
                use_checkpoint=False,
            )

        else:
            self.visual_encoder, vision_width = create_vit(
                vit, image_size, vit_grad_ckpt, vit_ckpt_layer
            )

        # create tokenzier
        self.tokenizer = init_tokenizer()

        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder
        # create image-tag interaction encoder
        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.tag_encoder = BertModel(config=encoder_config, add_pooling_layer=False)

        # create image-tag-text decoder
        decoder_config = BertConfig.from_json_file(med_config)
        self.text_decoder = BertLMHeadModel(config=decoder_config)

        self.delete_tag_index = delete_tag_index
        self.prompt = prompt
        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1

        # load tag list
        self.tag_list = self.load_tag_list(tag_list)

        # create image-tag recognition decoder
        self.threshold = threshold
        self.num_class = len(self.tag_list)
        q2l_config = BertConfig.from_json_file(f"{CONFIG_PATH}/configs/q2l_config.json")
        q2l_config.encoder_width = vision_width
        self.tagging_head = BertModel(config=q2l_config, add_pooling_layer=False)
        self.tagging_head.resize_token_embeddings(len(self.tokenizer))
        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
        self.fc = GroupWiseLinear(self.num_class, q2l_config.hidden_size, bias=True)
        self.del_selfattention()

        # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder"
        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, "", " ")

    def load_tag_list(self, tag_list_file):
        with open(tag_list_file) as f:
            tag_list = f.read().splitlines()
        tag_list = np.array(tag_list)
        return tag_list

    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label
    def del_selfattention(self):
        del self.tagging_head.embeddings
        for layer in self.tagging_head.encoder.layer:
            del layer.attention

    def generate(
        self,
        image,
        sample=False,
        num_beams=3,
        max_length=30,
        min_length=10,
        top_p=0.9,
        repetition_penalty=1.0,
        tag_input=None,
        return_tag_predict=False,
    ):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )

        # if not user specified tags, recognized image tags using image-tag recogntiion decoder
        if tag_input == None:
            image_cls_embeds = image_embeds[:, 0, :]
            image_spatial_embeds = image_embeds[:, 1:, :]

            bs = image_spatial_embeds.shape[0]
            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
            tagging_embed = self.tagging_head(
                encoder_embeds=label_embed,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=False,
                mode="tagging",
            )

            logits = self.fc(tagging_embed[0])

            targets = torch.where(
                torch.sigmoid(logits) > self.threshold,
                torch.tensor(1.0).to(image.device),
                torch.zeros(self.num_class).to(image.device),
            )

            tag = targets.cpu().numpy()

            # delete some tags that may disturb captioning
            tag[:, self.delete_tag_index] = 0

            tag_input = []
            for b in range(bs):
                index = np.argwhere(tag[b] == 1)
                token = self.tag_list[index].squeeze(axis=1)
                tag_input.append(" | ".join(token))

        tag_output = tag_input

        # beam search for text generation(default)
        if not sample:
            image_embeds = image_embeds.repeat_interleave(num_beams, dim=0)
            tag_input_temp = []
            for tag in tag_input:
                for i in range(num_beams):
                    tag_input_temp.append(tag)
            tag_input = tag_input_temp

        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )

        # tokenizer input tags
        tag_input_tokenzier = self.tokenizer(
            tag_input,
            padding="max_length",
            truncation=True,
            max_length=40,
            return_tensors="pt",
        ).to(image.device)
        encoder_input_ids = tag_input_tokenzier.input_ids
        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id

        # put input tag into image-tag interaction encoder to interact with image embeddings
        output_tagembedding = self.tag_encoder(
            encoder_input_ids,
            attention_mask=tag_input_tokenzier.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        # prompt trick for better captioning, followed BLIP
        prompt = [self.prompt] * image.size(0)
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(
            image.device
        )
        input_ids[:, 0] = self.tokenizer.bos_token_id
        input_ids = input_ids[:, :-1]

        if sample:
            # nucleus sampling
            model_kwargs = {
                "encoder_hidden_states": output_tagembedding.last_hidden_state,
                "encoder_attention_mask": None,
            }
            outputs = self.text_decoder.generate(
                input_ids=input_ids,
                max_length=max_length,
                min_length=min_length,
                do_sample=True,
                top_p=top_p,
                num_return_sequences=1,
                eos_token_id=self.tokenizer.sep_token_id,
                pad_token_id=self.tokenizer.pad_token_id,
                repetition_penalty=1.1,
                **model_kwargs,
            )
        else:
            # beam search (default)
            model_kwargs = {
                "encoder_hidden_states": output_tagembedding.last_hidden_state,
                "encoder_attention_mask": None,
            }
            outputs = self.text_decoder.generate(
                input_ids=input_ids,
                max_length=max_length,
                min_length=min_length,
                num_beams=num_beams,
                eos_token_id=self.tokenizer.sep_token_id,
                pad_token_id=self.tokenizer.pad_token_id,
                repetition_penalty=repetition_penalty,
                **model_kwargs,
            )

        captions = []
        for output in outputs:
            caption = self.tokenizer.decode(output, skip_special_tokens=True)
            captions.append(caption[len(self.prompt) :])
        if return_tag_predict == True:
            return captions, tag_output
        return captions


# load pretrained model parameters
def tag2text_caption(pretrained="", **kwargs):
    model = Tag2Text_Caption(**kwargs)
    if pretrained:
        if kwargs["vit"] == "swin_b":
            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)
        else:
            model, msg = load_checkpoint(model, pretrained)
        print("vit:", kwargs["vit"])
        print("msg", msg)
    return model