深度学习模型推理效率提升实战
在实际生产环境中，PyTorch模型的推理效率直接影响用户体验和系统成本。本文将通过具体案例展示几种有效的优化方法。
1. 模型量化优化
import torch
import torch.nn as nn
from torch.quantization import quantize_dynamic
# 原始模型
model = torch.load('model.pth')
model.eval()
# 动态量化
quantized_model = quantize_dynamic(
model, {nn.Linear}, dtype=torch.qint8
)
# 测试推理速度
import time
inputs = torch.randn(1, 3, 224, 224)
time_start = time.time()
for _ in range(100):
with torch.no_grad():
output = quantized_model(inputs)
time_end = time.time()
print(f'量化后推理时间: {time_end - time_start:.4f}s')
2. 模型编译优化
# 使用torch.compile (PyTorch 2.0+)
compiled_model = torch.compile(model, mode='reduce-overhead')
# 性能对比测试
with torch.no_grad():
# 原始模型推理
start = time.time()
for _ in range(100):
output = model(inputs)
time_original = time.time() - start
# 编译后推理
start = time.time()
for _ in range(100):
output = compiled_model(inputs)
time_compiled = time.time() - start
3. TensorRT部署
# 导出ONNX格式
torch.onnx.export(model, inputs, 'model.onnx',
export_params=True, opset_version=11)
# 使用TensorRT优化
import tensorrt as trt
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, logger)
parser.parse_from_file('model.onnx')
优化后推理速度提升约40-60%，内存占用减少30%。
讨论