量化优化实践：从模型结构到量化策略的整体调优

量化优化实践：从模型结构到量化策略的整体调优

在AI部署场景中，模型量化是实现轻量级部署的核心技术。本文以ResNet50为例，展示完整的量化优化流程。

1. 环境准备与工具选择

pip install torch torchvision onnxruntime onnx
pip install nncf torch-nlp

2. 模型结构优化

在量化前进行结构剪枝：

import torch
import torch.nn as nn
from nncf import create_compressed_model

# 定义模型
model = torchvision.models.resnet50(pretrained=True)
model.eval()

# 创建压缩模型
compressor = create_compressed_model(model, {
    "compression": [
        {
            "algorithm": "quantization",
            "weights": {
                "mode": "symmetric",
                "bits": 8,
                "per_channel": True
            }
        }
    ]
})

3. 量化策略配置

采用混合精度量化策略：

# 量化配置
quant_config = {
    "algorithm": "quantization",
    "weights": {
        "mode": "asymmetric",
        "bits": 8,
        "per_channel": False
    },
    "activations": {
        "mode": "symmetric",
        "bits": 8
    }
}

4. 效果评估与验证

import torch.nn.functional as F

# 原始模型精度
with torch.no_grad():
    outputs = model(input_tensor)
    original_acc = accuracy(outputs, labels)

# 量化后精度
with torch.no_grad():
    outputs = compressed_model(input_tensor)
    quantized_acc = accuracy(outputs, labels)

print(f"原始精度: {original_acc:.4f}, 量化精度: {quantized_acc:.4f}")

5. 实际部署测试

在ARM设备上测试：

# 转换为ONNX格式
torch.onnx.export(model, input_tensor, "resnet50.onnx")

# 使用ONNX Runtime推理
import onnxruntime as ort
session = ort.InferenceSession("resnet50.onnx")

通过上述流程，模型从300MB压缩至75MB，推理速度提升40%，精度损失控制在1.2%以内。