《jetson系列编译部署libtorch》

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jetson系列编译部署libtorch

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安装

• 查看设备cuda版本

Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_28_22:34:44_PST_2021
Cuda compilation tools, release 10.2, V10.2.300
Build cuda_10.2_r440.TC440_70.29663091_0

• 官网下载对应编译库Start Locally | PyTorch，jetson系列无法使用，会提示版本不符合，需要自己编译

• 根据Jetpack版本，去论坛找相应源码[PyTorch for Jetson - version 1.9.0 now available]

• 安装依赖环境

sudo apt-get update 
sudo apt-get upgrade
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev
pip3 install Cython

• 编译pytorch

sudo pip3 install torch-1.9.0-cp36-cp36m-linux_aarch64.whl

• 查看安装版本

pip3 list | grep torch

模型转换

• pytorch模型转化为libtorch的torchscript模型(.pth—>.pt)

def convert_model(model, path_out, img_tensor, running_mode='gpu'):if running_mode == 'gpu' and torch.cuda.is_available():print("gpu")device = torch.device("cuda:0")model = model.cuda(device)      # mdoel.to(device)model.load_state_dict(torch.load(path_model), strict=False)img_tensor = img_tensor.to(device)else:print("cpu")# device = torch.device('cpu')model.load_state_dict(torch.load(path_model, map_location='cpu'), strict=False)model.eval()traced_script_module = torch.jit.trace(model, img_tensor)traced_script_module.save(path_out)

测试

测试环境

• jetson nano 4G
• torch 1.10.0
• torchvision 0.11
• JetPack 4.6
• ubuntu 18.06.
• cuda 10.2

Makefile

TARGET  := demo
SOURCE := main.cpp
OBJS := main.o
CXX      := g++
LIBS    := -lc10 -lc10_cuda -ltorch_cuda -ltorch -lshm -ltorch_cpu
LDFLAGS := -L/usr/local/lib/python3.6/dist-packages/torch/lib
DEFINES := 
INCLUDE := -I./ -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include
CFLAGS  := -g -Wall -O3 $(DEFINES) $(INCLUDE) -fPIC
PKGS	:= opencv4
LIBS	+= `pkg-config --libs $(PKGS)`
CFLAGS	+= `pkg-config --cflags $(PKGS)`
CXXFLAGS:= $(CFLAGS) -DHAVE_CONFIG_H -std=c++14 -Wunused-function -Wunused-variable -Wfatal-errors
CXXFLAGS += -Wl,--no-as-needed -ltorch_cuda.PHONY : everything objs clean veryclean rebuild
everything : $(TARGET)
all : $(TARGET) 
objs : $(OBJS) 
rebuild: veryclean everythingclean :rm -rf $(OBJS)rm -rf *.o
veryclean : cleanrm -fr $(TARGET)
%.o : %.cpp$(CXX) $(CXXFLAGS) -c $< -o $@
$(TARGET) : $(OBJS)$(CXX) $(CXXFLAGS) -o $@ $(OBJS) $(LDFLAGS) $(LIBS)

main.cpp

#include <iostream>
#include <opencv2/opencv.hpp>
#include <torch/torch.h>
#include <torch/script.h>int main(){cv::Mat img, img_tmp, img_float;char *model_path = "test.pt";torch::jit::script::Module module = torch::jit::load(model_path);module.to(at::kCUDA);char *img_path = (char *)"1.jpg";img = cv::imread(img_path, -1);if(img.empty()){printf("error open img file:[%s]\n", img_path);return -1;}cv::cvtColor(img, img_tmp, cv::COLOR_BGR2RGB);cv::resize(img_tmp, img_tmp, cv::Size(120, 70));img_tmp.convertTo(img_float, CV_32F, 1.0 / 255); // ->(0,1)// batchsize rows, cols, channels,   B,H,W,Ctorch::Tensor img_tensor = torch::from_blob(img_float.data, {1, img_float.rows, img_float.cols, img_float.channels()}, torch::kFloat32);img_tensor = img_tensor.permute({0, 3, 1, 2});  // ->B, C, H, Wfloat mean_[] = {0.5, 0.5, 0.5};float std_[] = {0.5, 0.5, 0.5};for(int i = 0; i < 3; i++){ // normalize->(-1,1)img_tensor[0][i] = img_tensor[0][i].sub_(mean_[i]).div_(std_[i]);}torch::Tensor img_tensor_cuda = img_tensor.cuda();torch::Tensor result = module.forward({img_tensor_cuda}).toTensor();auto max_result = result.max(1, true);auto max_ind = std::get<1>(max_result).item<float>();std::cout << max_ind << std::endl;std::cout << "CUDA:   " << torch::cuda::is_available() << std::endl;std::cout << "CUDNN:  " << torch::cuda::cudnn_is_available() << std::endl;std::cout << "GPU(s): " << torch::cuda::device_count() << std::endl;return 0;
}

测试

• 使用100张128 * 72图片

	CPU	内存占比	GPU	耗时(s)
libtorch	50%	62.6%	46%	24.021
pytorch	390%	55.2%	50%	32.384

what(): PyTorch is not linked with support for cuda devices

• 参考Error: “PyTorch is not linked with support for cuda devices” - C++ - PyTorch Forums

• 不忽略链接时没有用到的动态库torch_cuda，加入以下

CXXFLAGS += -Wl,--no-as-needed -ltorch_cuda

conversion to non-scalar type torch::jit::load(“model.pt”)

• 参考conversion to non-scalar type torch::jit::load(“model.pt”) · Issue #22382 · pytorch/pytorch · GitHub

• 输出类型改变，不再是指针类型

std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(model_path);

• 需要改为以下

torch::jit::script::Module module = torch::jit::load(model_path);

cannot open shared object file: No such file or directory

• 编译可以通过，但是无法使用
• 需要将libtorch的lib库加入到环境变量

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/python3.6/dist-packages/torch/lib

参考链接

Start Locally | PyTorch

C++ — PyTorch 1.10.1 documentation

Library API — PyTorch master documentation

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