3：Youtu-VL 视觉-语言模型：40亿参数的轻量级多任务视觉-语言统一方案

# VLUAS核心代码示例
class VLUASystem:
    def __init__(self, visual_encoder, language_encoder, decoder):
        self.visual_encoder = visual_encoder
        self.language_encoder = language_encoder
        self.decoder = decoder
    
    def forward(self, images, texts, tasks):
        """前向传播"""
        # 编码视觉输入
        visual_features = self.visual_encoder(images)
        # 编码语言输入
        language_features = self.language_encoder(texts)
        # 融合多模态特征
        fused_features = self.fuse_features(visual_features, language_features)
        # 生成任务特定的自回归目标
        targets = self.generate_targets(fused_features, tasks)
        # 自回归解码
        outputs = self.decoder(fused_features, targets)
        return outputs
    
    def generate_targets(self, features, tasks):
        """为不同任务生成统一的自回归目标"""
        targets = []
        for feature, task in zip(features, tasks):
            if task == 'detection':
                # 生成目标检测的自回归目标
                target = self.generate_detection_target(feature)
            elif task == 'segmentation':
                # 生成语义分割的自回归目标
                target = self.generate_segmentation_target(feature)
            elif task == 'vqa':
                # 生成视觉问答的自回归目标
                target = self.generate_vqa_target(feature)
            # 其他任务...
            targets.append(target)
        return targets

# 多任务视觉能力核心代码示例
class MultiTaskVisionSystem:
    def __init__(self, shared_encoder, task_heads):
        self.shared_encoder = shared_encoder
        self.task_heads = task_heads  # 包含不同任务的输出头
    
    def forward(self, images, task_type):
        """处理不同类型的视觉任务"""
        # 提取共享视觉特征
        features = self.shared_encoder(images)
        # 根据任务类型选择对应的输出头
        if task_type in self.task_heads:
            output = self.task_heads[task_type](features)
        else:
            # 对于未知任务，使用通用处理
            output = self.task_heads['general'](features)
        return output
    
    def detect_objects(self, images):
        """目标检测任务"""
        return self.forward(images, 'detection')
    
    def segment_image(self, images):
        """语义分割任务"""
        return self.forward(images, 'segmentation')
    
    def estimate_pose(self, images):
        """姿态估计任务"""
        return self.forward(images, 'pose')

# 多模态融合核心代码示例
class MultimodalFusion:
    def __init__(self, visual_dim, language_dim, hidden_dim):
        self.visual_proj = nn.Linear(visual_dim, hidden_dim)
        self.language_proj = nn.Linear(language_dim, hidden_dim)
        self.attention = MultiheadAttention(hidden_dim, num_heads=8)
        self.output_proj = nn.Linear(hidden_dim, hidden_dim)
    
    def forward(self, visual_features, language_features):
        """融合视觉和语言特征"""
        # 特征投影到同一维度
        visual_proj = self.visual_proj(visual_features)
        language_proj = self.language_proj(language_features)
        # 多模态注意力
        attended_features, _ = self.attention(
            visual_proj, language_proj, language_proj
        )
        # 输出投影
        fused_features = self.output_proj(attended_features)
        return fused_features

# 推理优化核心代码示例
class OptimizedInferenceEngine:
    def __init__(self, model):
        self.model = model
        self.quantize_model()
        self.setup_cache()
    
    def quantize_model(self):
        """模型量化"""
        self.model = torch.quantization.quantize_dynamic(
            self.model,
            {torch.nn.Linear},
            dtype=torch.qint8
        )
    
    def setup_cache(self):
        """设置缓存机制"""
        self.cache = {}
        self.cache_size = 1000
    
    def infer(self, inputs):
        """高效推理"""
        # 生成输入哈希作为缓存键
        input_hash = self.generate_input_hash(inputs)
        # 检查缓存
        if input_hash in self.cache:
            return self.cache[input_hash]
        # 执行推理
        with torch.no_grad():
            output = self.model(inputs)
        # 更新缓存
        if len(self.cache) >= self.cache_size:
            # 移除最早的缓存项
            oldest_key = next(iter(self.cache))
            del self.cache[oldest_key]
        self.cache[input_hash] = output
        return output

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

# 加载模型
youtu_vl_pipeline = pipeline(
    Tasks.multimodal_understanding,
    model='tencent-YouTu/Youtu-VL'
)

# 处理函数
def process_multimodal(image, text, task_type):
    """处理多模态任务"""
    result = youtu_vl_pipeline({
        'image': image,
        'text': text,
        'task_type': task_type
    })
    
    # 格式化输出
    if task_type == 'vqa':
        output = f"问题: {text}\n回答: {result['answer']}"
    elif task_type == 'ocr':
        output = "识别到的文本:\n"
        for text_item in result['texts']:
            output += f"- {text_item}\n"
    elif task_type == 'detection':
        output = "检测到的目标:\n"
        for obj in result['objects']:
            output += f"- {obj['label']}: {obj['bbox']}\n"
    elif task_type == 'caption':
        output = f"图像描述: {result['caption']}"
    elif task_type == 'segmentation':
        output = "语义分割结果:\n"
        output += f"类别数: {len(result['segments'])}\n"
        for seg in result['segments']:
            output += f"- {seg['label']}\n"
    else:
        output = str(result)
    
    # 生成统计信息
    stats = f"任务类型: {task_type}\n"
    stats += f"处理时间: {result.get('processing_time', 'N/A')}ms\n"
    stats += f"置信度: {result.get('confidence', 'N/A')}\n"
    
    return output, stats

# 创建Gradio界面
with gr.Blocks(title="Youtu-VL 多模态理解") as demo:
    gr.Markdown("# Youtu-VL 多模态理解演示")
    gr.Markdown("上传图像并输入文本，选择任务类型进行处理")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="图像输入")
            text_input = gr.Textbox(label="文本输入", placeholder="输入问题、指令或查询")
            task_type = gr.Dropdown(
                choices=['vqa', 'ocr', 'detection', 'caption', 'segmentation'],
                value='vqa',
                label="任务类型"
            )
            process_btn = gr.Button("处理")
        
        with gr.Column(scale=2):
            output_text = gr.Textbox(label="处理结果", lines=10)
            stats_output = gr.Textbox(label="处理统计", lines=5)
    
    # 绑定事件
    process_btn.click(
        fn=process_multimodal,
        inputs=[image_input, text_input, task_type],
        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"]