"""
* 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