项目

深度学习图像识别系统

0

基于PyTorch的图像分类系统，支持多种模型切换、实时推理和结果可视化。

项目简介

深度学习图像识别系统是一个基于PyTorch开发的Web应用，支持ResNet、VGG、EfficientNet等多种模型，提供在线推理和模型管理功能。

系统架构

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┌─────────────────────────────────────────────────┐
│                  Web Frontend                   │
│              (Vue.js + Element UI)              │
└────────────────────┬────────────────────────────┘
                     │ REST API
┌────────────────────▼────────────────────────────┐
│                  Flask Backend                   │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────┐  │
│  │  Inference  │  │   Model     │  │  Auth   │  │
│  │   Engine    │  │  Manager    │  │ Service │  │
│  └─────────────┘  └─────────────┘  └─────────┘  │
└────────────────────┬────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────┐
│                PyTorch Models                    │
│   ResNet50 / VGG16 / EfficientNet-B0             │
└─────────────────────────────────────────────────┘

功能特性

  • 模型在线切换
  • 实时推理
  • 结果可视化
  • API接口
  • 批量预测
  • 模型对比

核心代码

1. 模型定义

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import torch
import torch.nn as nn
from torchvision import models

class ImageClassifier(nn.Module):
    def __init__(self, model_name='resnet50', num_classes=1000, pretrained=True):
        super().__init__()
        
        if model_name == 'resnet50':
            self.backbone = models.resnet50(pretrained=pretrained)
            self.backbone.fc = nn.Linear(2048, num_classes)
        elif model_name == 'vgg16':
            self.backbone = models.vgg16(pretrained=pretrained)
            self.backbone.classifier[-1] = nn.Linear(4096, num_classes)
        elif model_name == 'efficientnet_b0':
            self.backbone = models.efficientnet_b0(pretrained=pretrained)
            self.backbone.classifier[-1] = nn.Linear(1280, num_classes)
            
    def forward(self, x):
        return self.backbone(x)

2. 数据预处理

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from torchvision import transforms

def get_transforms():
    return transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]
        )
    ])

def preprocess_image(image_path, transform):
    from PIL import Image
    image = Image.open(image_path).convert('RGB')
    return transform(image).unsqueeze(0)

3. 推理引擎

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import torch.nn.functional as F

class InferenceEngine:
    def __init__(self, model_path, device='cuda'):
        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
        self.model = torch.jit.load(model_path)
        self.model.to(self.device)
        self.model.eval()
        
    @torch.no_grad()
    def predict(self, image_tensor, top_k=5):
        image_tensor = image_tensor.to(self.device)
        outputs = self.model(image_tensor)
        probs = F.softmax(outputs, dim=1)
        
        top_probs, top_indices = torch.topk(probs, top_k, dim=1)
        return top_probs.cpu().numpy(), top_indices.cpu().numpy()

4. Flask API

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from flask import Flask, request, jsonify
from werkzeug.utils import secure_filename

app = Flask(__name__)
engine = InferenceEngine('models/resnet50.pth')

@app.route('/predict', methods=['POST'])
def predict():
    if 'image' not in request.files:
        return jsonify({'error': 'No image provided'}), 400
    
    file = request.files['image']
    filename = secure_filename(file.filename)
    
    # 保存并预处理
    file.save(f'/tmp/{filename}')
    image = preprocess_image(f'/tmp/{filename}', get_transforms())
    
    # 推理
    probs, indices = engine.predict(image)
    
    return jsonify({
        'predictions': [
            {'class': idx, 'probability': float(prob)}
            for prob, idx in zip(probs[0], indices[0])
        ]
    })

@app.route('/switch_model', methods=['POST'])
def switch_model():
    data = request.json
    model_name = data.get('model_name')
    engine.load_model(f'models/{model_name}.pth')
    return jsonify({'status': 'success', 'model': model_name})

5. 前端展示

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// Vue.js 组件
export default {
  data() {
    return {
      image: null,
      predictions: [],
      loading: false,
      currentModel: 'resnet50'
    }
  },
  methods: {
    async uploadImage(event) {
      const file = event.target.files[0];
      this.image = URL.createObjectURL(file);
      
      const formData = new FormData();
      formData.append('image', file);
      
      this.loading = true;
      const response = await fetch('/predict', {
        method: 'POST',
        body: formData
      });
      this.predictions = (await response.json()).predictions;
      this.loading = false;
    },
    async switchModel(model) {
      await fetch('/switch_model', {
        method: 'POST',
        headers: {'Content-Type': 'application/json'},
        body: JSON.stringify({model_name: model})
      });
      this.currentModel = model;
    }
  }
}

部署配置

Docker

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FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 5000

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

requirements.txt

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torch==2.0.0
torchvision==0.15.0
flask==2.3.0
Pillow==9.5.0
numpy==1.24.0

项目成果

  • 支持3种主流模型切换
  • 推理速度 < 100ms (GPU)
  • 准确率: ImageNet Top-1 76.5%

总结

深度学习图像识别系统展示了PyTorch在实际应用中的潜力，通过模块化设计实现了灵活的模型管理和高效推理能力。