推理加速对比：PyTorch vs TensorFlow Lite vs ONNX Runtime

推理加速对比：PyTorch vs TensorFlow Lite vs ONNX Runtime

实验环境

• Python 3.9
• PyTorch 2.0
• TensorFlow Lite 2.13
• ONNX Runtime 1.15
• 测试模型：ResNet50 (224x224输入)

模型转换与部署

PyTorch原生推理

import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
model.eval()
example = torch.rand(1, 3, 224, 224)
traced_model = torch.jit.trace(model, example)
torch.jit.save(traced_model, "resnet50_traced.pt")

ONNX导出

import torch.onnx
torch.onnx.export(model, example, "resnet50.onnx", 
                  export_params=True, opset_version=11)

TensorFlow Lite转换

import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('resnet50_tf')
tflite_model = converter.convert()
with open("resnet50.tflite", "wb") as f:
    f.write(tflite_model)

性能测试

使用torchbench进行基准测试，100次推理平均耗时：

• PyTorch JIT Trace: 12.3ms
• ONNX Runtime (CPU): 15.7ms
• TensorFlow Lite: 18.2ms
• PyTorch Eager Mode: 25.1ms

结论

PyTorch JIT Trace在保持原生性能的同时，显著优于其他框架。ONNX Runtime虽有优化，但受限于CPU调度机制，延迟较高。TensorFlow Lite在移动端表现优异，但在服务器端推理场景中性能略逊。

建议：生产环境优先选用PyTorch JIT Trace方案，若需跨平台兼容性可考虑ONNX Runtime。