4：Youtu-Parsing 文档解析模型：2B参数的文档智能解析技术深度解析

# NaViT动态视觉编码器核心代码示例
class NaViTDynamicEncoder:
    def __init__(self, hidden_dim, num_heads, num_layers):
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.layers = nn.ModuleList()
        for _ in range(num_layers):
            self.layers.append(NaViTLayer(hidden_dim, num_heads))
    
    def forward(self, images, attention_mask=None):
        """动态视觉编码"""
        # 自适应分辨率处理
        images = self.adaptive_resolution(images)
        # 初始特征提取
        features = self.initial_features(images)
        # 多层编码
        for layer in self.layers:
            features = layer(features, attention_mask)
        return features
    
    def adaptive_resolution(self, images):
        """根据内容复杂度调整分辨率"""
        # 分析图像复杂度
        complexity_map = self.estimate_complexity(images)
        # 根据复杂度调整分辨率
        processed_images = []
        for img, complexity in zip(images, complexity_map):
            if complexity > 0.7:
                # 高复杂度区域使用高分辨率
                processed_images.append(self.high_resolution_process(img))
            else:
                # 低复杂度区域使用低分辨率
                processed_images.append(self.low_resolution_process(img))
        return processed_images

# prompt-guided框架核心代码示例
class PromptGuidedFramework:
    def __init__(self, instruction_parser, task_planner, context_manager):
        self.instruction_parser = instruction_parser
        self.task_planner = task_planner
        self.context_manager = context_manager
    
    def process_instruction(self, instruction, document_features):
        """处理解析指令"""
        # 解析指令
        parsed_instruction = self.instruction_parser.parse(instruction)
        # 分析文档特征
        document_analysis = self.analyze_document(document_features)
        # 生成任务计划
        task_plan = self.task_planner.plan(
            parsed_instruction, document_analysis
        )
        # 执行任务计划
        results = self.execute_plan(task_plan, document_features)
        return results
    
    def execute_plan(self, task_plan, document_features):
        """执行解析任务计划"""
        results = {}
        for task in task_plan:
            # 执行单个任务
            task_result = self.execute_task(
                task, document_features,
                self.context_manager
            )
            results[task['type']] = task_result
            # 更新上下文
            self.context_manager.update(task_result)
        return results

# 双并行推理核心代码示例
class DualParallelInference:
    def __init__(self, model, max_parallel_tokens=128, max_parallel_queries=8):
        self.model = model
        self.max_parallel_tokens = max_parallel_tokens
        self.max_parallel_queries = max_parallel_queries
    
    def infer(self, document_features, queries):
        """双并行推理"""
        # 批量处理查询
        query_batches = self.batch_queries(queries)
        results = []
        
        for query_batch in query_batches:
            # 并行处理多个查询
            batch_results = self.parallel_process_queries(
                document_features, query_batch
            )
            results.extend(batch_results)
        
        return results
    
    def parallel_process_queries(self, document_features, queries):
        """并行处理多个查询"""
        # 准备查询输入
        query_inputs = []
        for query in queries:
            query_inputs.append(self.prepare_query_input(document_features, query))
        
        # 执行并行推理
        with torch.no_grad():
            # 使用torch.nn.parallel进行并行计算
            outputs = torch.nn.parallel.parallel_apply(
                [self.model] * len(query_inputs),
                query_inputs
            )
        
        return outputs

# 结构化识别核心代码示例
class StructuredRecognizer:
    def __init__(self, type_detector, structure_parser, content_extractor):
        self.type_detector = type_detector
        self.structure_parser = structure_parser
        self.content_extractor = content_extractor
    
    def recognize(self, document_features):
        """识别文档中的结构化元素"""
        # 检测元素类型
        elements = self.type_detector.detect(document_features)
        structured_results = {}
        
        for element in elements:
            element_type = element['type']
            element_region = element['region']
            
            # 解析元素结构
            structure = self.structure_parser.parse(
                document_features, element_region, element_type
            )
            
            # 提取元素内容
            content = self.content_extractor.extract(
                document_features, element_region, element_type, structure
            )
            
            # 存储结果
            if element_type not in structured_results:
                structured_results[element_type] = []
            structured_results[element_type].append({
                'region': element_region,
                'structure': structure,
                'content': content
            })
        
        return structured_results

import gradio as gr
import numpy as np
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

# 加载模型
youtu_parsing_pipeline = pipeline(
    Tasks.document_parsing,
    model='Tencent-YouTu-Research/Youtu-Parsing'
)

# 处理函数
def parse_document(image, instruction="解析文档中的所有内容"):
    """解析文档图像"""
    # 执行文档解析
    result = youtu_parsing_pipeline({
        'image': image,
        'instruction': instruction
    })
    
    # 格式化输出
    output = f"解析指令: {instruction}\n\n"
    
    # 处理文本内容
    if 'texts' in result:
        output += "识别到的文本:\n"
        for i, text in enumerate(result['texts']):
            output += f"{i+1}. {text}\n"
        output += "\n"
    
    # 处理表格内容
    if 'tables' in result:
        output += "识别到的表格:\n"
        for i, table in enumerate(result['tables']):
            output += f"表格 {i+1}:\n"
            for row in table:
                output += " | ".join(row) + "\n"
            output += "\n"
    
    # 处理公式内容
    if 'formulas' in result:
        output += "识别到的公式:\n"
        for i, formula in enumerate(result['formulas']):
            output += f"公式 {i+1}: {formula}\n"
        output += "\n"
    
    # 处理版面分析
    if 'layout' in result:
        output += "版面分析结果:\n"
        for item in result['layout']:
            output += f"{item['type']}: {item['bbox']}\n"
    
    # 生成统计信息
    stats = f"解析时间: {result.get('processing_time', 'N/A')}秒\n"
    stats += f"文本段落数: {len(result.get('texts', []))}\n"
    stats += f"表格数: {len(result.get('tables', []))}\n"
    stats += f"公式数: {len(result.get('formulas', []))}\n"
    stats += f"版面元素数: {len(result.get('layout', []))}\n"
    
    return output, stats

# 创建Gradio界面
with gr.Blocks(title="Youtu-Parsing 文档解析") as demo:
    gr.Markdown("# Youtu-Parsing 文档解析演示")
    gr.Markdown("上传文档图像并输入解析指令进行处理")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="文档图像")
            instruction_input = gr.Textbox(
                label="解析指令", 
                value="解析文档中的所有内容",
                placeholder="例如：'解析文档中的表格'、'识别所有公式'等"
            )
            parse_btn = gr.Button("解析")
        
        with gr.Column(scale=2):
            output_text = gr.Textbox(label="解析结果", lines=20)
            stats_output = gr.Textbox(label="解析统计", lines=6)
    
    # 绑定事件
    parse_btn.click(
        fn=parse_document,
        inputs=[image_input, instruction_input],
        outputs=[output_text, stats_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)

pytorch==2.0.1
modelscope==1.9.1
gradio==4.14.0
Pillow==10.1.0
numpy==1.24.4
opencv-python==4.8.1.78

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

EXPOSE 7860

CMD ["python", "app.py"]