# 测试步骤和方法 使用https://zhuanlan.zhihu.com/p/363319763 中的： - "PyTorch resnet50 benchmark步骤" - "LibTorch resnet50 benchmark步骤" 注意：MLab HomePod 1.0中的PyTorch版本为1.8.1 # 测试模型：resnet50 ### 环境 宿主机OS：Ubuntu 20.04 软件环境：MLab HomePod 1.0 CPU：Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz GPU：NVIDIA GTX 2080ti GPU驱动：NVIDIA-SMI 450.102.04 Driver Version: 450.102.04 CUDA Version: 11.0 |测试场景 |CPU利用率| 内存(GB) | GPU利用率 | 显存(GB) | 线程数 | |---------------|----------------|-----------------|--------------|--------------|---------------------| |C++ libtorch |~105%| ~4.1 | 100% | ~3.7 | 25 | |PyTorch |~132%| ~3.9 | ~95% | ~9| 32 | ### input shape = 224x224 |测试场景 |forward time(ms)| |---------------|----------------| |C++ libtorch | 3.950| |PyTorch | 6.4478| ### input shape = 640x640 |测试场景 |forward time(ms)| |---------------|----------------| |C++ libtorch | 11.242| |PyTorch | 10.4656| ### input shape = 1280x720 |测试场景 |forward time(ms)| |---------------|----------------| |C++ libtorch | 24.678| |PyTorch |20.8056 | ### input shape = 1280x1280 |测试场景 |forward time(ms)| |---------------|----------------| |C++ libtorch | 38.094| |PyTorch |37.6037 |
