Create pipeline.py

pipeline.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright @2023 RhapsodyAI, ModelBest Inc. (modelbest.cn)
+#
+# @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=100):
+    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 = 100):
+        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=1)
+    # 2
+    topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the training loss curve of this paper?', topk=1)
+    # 3
+    topk_doc_ids_np, topk_values_np, images_topk = retriever.retrieve(knowledge_base='test', query='the experiment table?', topk=1)
+    # 2
+