Update inference.py
This commit is contained in:
191
inference.py
191
inference.py
@@ -1,93 +1,98 @@
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import torch
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from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
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from qwen_vl_utils import process_vision_info
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from dots_ocr.utils import dict_promptmode_to_prompt
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from PIL import Image
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import io
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import json
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from typing import List, Dict, Tuple
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import warnings
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warnings.filterwarnings("ignore")
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class DotsOcr:
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def __init__(self, model_path, device="cuda"):
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self.device = device
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self.model = AutoModelForCausalLM.from_pretrained(
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model_path,
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# attn_implementation="flash_attention_2",
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dtype="bfloat16",
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trust_remote_code=True
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)
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self.model.to(self.device)
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self.processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=True)
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self.prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
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1. Bbox format: [x1, y1, x2, y2]
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2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
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3. Text Extraction & Formatting Rules:
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- Picture: For the 'Picture' category, the text field should be omitted.
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- Formula: Format its text as LaTeX.
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- Table: Format its text as HTML.
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- All Others (Text, Title, etc.): Format their text as Markdown.
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4. Constraints:
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- The output text must be the original text from the image, with no translation.
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- All layout elements must be sorted according to human reading order.
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5. Final Output: The entire output must be a single JSON object.
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"""
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def sample_inference(self, image: Image.Image):
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"""
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处理 PIL Image 对象，返回解析后的结果列表。To avoid OOM, process one image at a time.
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"""
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": image
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},
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{"type": "text", "text": self.prompt}
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]
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}
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]
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# Preparation for inference
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text = self.processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = self.processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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).to(self.device)
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# Inference: Generation of the output
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generated_ids = self.model.generate(**inputs, max_new_tokens=24000)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = self.processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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result_str = output_text[0]
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result_list = json.loads(result_str)
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return result_list
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if __name__=="__main__":
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model_path = "DotsOCR"
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dots_ocr = DotsOcr(model_path)
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image_paths = ["20260306-065852.webp", "20260306-065909.webp"]
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for image_path in image_paths:
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output_text = dots_ocr.sample_inference(image_path)
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print(output_text)
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import torch
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from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
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from qwen_vl_utils import process_vision_info
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from dots_ocr.utils import dict_promptmode_to_prompt
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from PIL import Image
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import io
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import json
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from typing import List, Dict, Tuple
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import warnings
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warnings.filterwarnings("ignore")
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class DotsOcr:
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def __init__(self, model_path, device="cuda"):
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self.device = device
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self.model = AutoModelForCausalLM.from_pretrained(
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model_path,
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# attn_implementation="flash_attention_2",
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dtype="bfloat16",
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trust_remote_code=True
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)
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self.model.to(self.device)
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self.processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=True)
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self.prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
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1. Bbox format: [x1, y1, x2, y2]
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2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
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3. Text Extraction & Formatting Rules:
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- Picture: For the 'Picture' category, the text field should be omitted.
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- Formula: Format its text as LaTeX.
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- Table: Format its text as HTML.
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- All Others (Text, Title, etc.): Format their text as Markdown.
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4. Constraints:
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- The output text must be the original text from the image, with no translation.
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- All layout elements must be sorted according to human reading order.
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5. Final Output: The entire output must be a single JSON object.
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"""
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def sample_inference(self, image: Image.Image):
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"""
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处理 PIL Image 对象，返回解析后的结果列表。To avoid OOM, process one image at a time.
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"""
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": image
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},
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{"type": "text", "text": self.prompt}
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]
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}
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]
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# Preparation for inference
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text = self.processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = self.processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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).to(self.device)
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# Inference: Generation of the output
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generated_ids = self.model.generate(**inputs, max_new_tokens=24000)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = self.processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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result_str = output_text[0]
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try:
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result_list = json.loads(result_str)
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return result_list
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except json.JSONDecodeError:
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print(f"无法解析 JSON 输出: {result_str}")
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return {"error": "无法解析 JSON 输出"}
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if __name__=="__main__":
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model_path = "DotsOCR"
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dots_ocr = DotsOcr(model_path)
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image_paths = ["20260310-162729.webp"]
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for image_path in image_paths:
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output_text = dots_ocr.sample_inference(image_path)
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print(output_text)
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