量化后模型推理精度验证：与原始FP32模型精度差异分析
在实际部署场景中，量化后的模型精度损失是工程师最关心的问题。本文以ResNet50为例，展示完整的精度验证流程。
1. 环境准备
pip install torch torchvision onnxruntime onnx
2. 原始FP32模型评估
import torch
import torchvision.models as models
from torch.utils.data import DataLoader
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_fp32 = models.resnet50(pretrained=True).to(device)
model_fp32.eval()
# 验证集准备
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
# 计算FP32精度
correct = 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model_fp32(images)
_, predicted = torch.max(outputs.data, 1)
correct += (predicted == labels).sum().item()
accuracy_fp32 = correct / len(val_dataset)
print(f'FP32 Accuracy: {accuracy_fp32:.4f}')
3. 量化模型构建与部署
import torch.quantization as quantization
# 模型准备
model_quant = models.resnet50(pretrained=True)
model_quant.eval()
# 配置量化
quantization.prepare(model_quant, inplace=True)
# 这里需要使用真实数据进行校准
with torch.no_grad():
for images, labels in val_loader:
images = images.to(device)
model_quant(images)
quantization.convert(model_quant, inplace=True)
# 保存量化模型
torch.save(model_quant.state_dict(), 'resnet50_quant.pth')
4. 精度对比验证
# 加载并测试量化模型
model_quant.load_state_dict(torch.load('resnet50_quant.pth'))
model_quant.eval()
# 验证量化精度
correct_quant = 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model_quant(images)
_, predicted = torch.max(outputs.data, 1)
correct_quant += (predicted == labels).sum().item()
accuracy_quant = correct_quant / len(val_dataset)
print(f'Quantized Accuracy: {accuracy_quant:.4f}')
print(f'精度损失: {accuracy_fp32 - accuracy_quant:.4f}')
5. 实际部署效果
在实际部署中，量化后模型通常能保持98%以上的FP32精度，同时推理速度提升2-3倍。建议通过TensorRT或ONNX Runtime进行最终验证。
讨论