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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright @2023 RhapsodyAI
#
# @author: bokai xu <[email protected]>
# @date: 2024/07/13
#
import tqdm
from PIL import Image
import hashlib
import torch
import fitz
def get_image_md5(img: Image.Image):
img_byte_array = img.tobytes()
hash_md5 = hashlib.md5()
hash_md5.update(img_byte_array)
hex_digest = hash_md5.hexdigest()
return hex_digest
def pdf_to_images(pdf_path, dpi=200):
doc = fitz.open(pdf_path)
images = []
for page in tqdm.tqdm(doc):
pix = page.get_pixmap(dpi=dpi)
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
images.append(img)
return images
class PDFVisualRetrieval:
def __init__(self, model, tokenizer):
self.tokenizer = tokenizer
self.model = model
self.reps = {}
self.images = {}
def add_visual_documents(self, knowledge_base_name: str, images: Image.Image):
if knowledge_base_name not in self.reps:
self.reps[knowledge_base_name] = {}
if knowledge_base_name not in self.images:
self.images[knowledge_base_name] = {}
for image in tqdm.tqdm(images):
image_md5 = get_image_md5(image)
with torch.no_grad():
reps = self.model(text=[''], image=[image], tokenizer=self.tokenizer).reps
self.reps[knowledge_base_name][image_md5] = reps.squeeze(0)
self.images[knowledge_base_name][image_md5] = image
return
def retrieve(self, knowledge_base: str, query: str, topk: int):
doc_reps = list(self.reps[knowledge_base].values())
query_with_instruction = "Represent this query for retrieving relavant document: " + query
with torch.no_grad():
query_rep = self.model(text=[query_with_instruction], image=[None], tokenizer=self.tokenizer).reps.squeeze(0)
doc_reps_cat = torch.stack(doc_reps, dim=0)
similarities = torch.matmul(query_rep, doc_reps_cat.T)
topk_values, topk_doc_ids = torch.topk(similarities, k=topk)
topk_values_np = topk_values.cpu().numpy()
topk_doc_ids_np = topk_doc_ids.cpu().numpy()
similarities_np = similarities.cpu().numpy()
all_images_doc_list = list(self.images[knowledge_base].values())
images_topk = [all_images_doc_list[idx] for idx in topk_doc_ids_np]
return topk_doc_ids_np, topk_values_np, images_topk
def add_pdf(self, knowledge_base_name: str, pdf_file_path: str, dpi: int = 200):
print("[1/2] rendering pdf to images..")
images = pdf_to_images(pdf_file_path, dpi=dpi)
print("[2/2] model encoding images..")
self.add_visual_documents(knowledge_base_name=knowledge_base_name, images=images)
print("add pdf ok.")
return
if __name__ == "__main__":
from transformers import AutoModel
from transformers import AutoTokenizer
from PIL import Image
import torch
device = 'cuda:0'
# Load model, be sure to substitute `model_path` by your model path
model_path = '/home/jeeves/xubokai/minicpm-visual-embedding-v0'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model.to(device)
pdf_path = "/home/jeeves/xubokai/minicpm-visual-embedding-v0/2406.07422v1.pdf"
retriever = PDFVisualRetrieval(model=model, tokenizer=tokenizer)
retriever.add_pdf('test', pdf_path)
topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='what is the number of VQ of this kind of codec method?', topk=5)
# 2
topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the training loss curve of this paper?', topk=5)
# 3
topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the experiment table?', topk=5)
# 2
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