Qt配置Libtorch并简单测试-Toy模板网

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纯小白初次学习深度学习，根据目前所学、所查资料总结该配置文章，如有更好的方法将深度学习模型通过C++部署到实际工程中，希望学习交流。

软件版本

Python3.10.9
Pytorch2.0.0
Qt Creator 5.0.1 (Community)
Qt 1.15.2(MSVC 2019, 64bit)

一、下载Libtorch

下载位置（可以按照参考的方法来下载）：Pytorch

二、配置Qt Creator

Qt Creator配置：
- 创建.pri文件；
- 内部写入如下内容：

INCLUDEPATH += C:\soft_install\libtorch-win-shared-with-deps-2.0.0+cpu\libtorch\include
INCLUDEPATH += C:\soft_install\libtorch-win-shared-with-deps-2.0.0+cpu\libtorch\include\torch\csrc\api\include


LIBS += -LC:\soft_install\libtorch-win-shared-with-deps-2.0.0+cpu\libtorch\lib\
        -lasmjit\
        -lc10\
        -lclog\
        -lcpuinfo\
        -ldnnl\
        -lfbgemm\
        -lfbjni\
        -lkineto\
        -llibprotobuf\
        -llibprotobuf-lite\
        -llibprotoc\
        -lpthreadpool\
        -lpytorch_jni\
        -ltorch\
        -ltorch_cpu\
        -lXNNPACK

三、测试项目

模型来源
- B站：我是土堆；课程：PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】
创建项目
- .pro文件中引入Libtorch和Opencv的.pri

DISTFILES += \
    pri/opencv.pri \
    pri/libtorch.pri

include(./pri/opencv.pri)
include(./pri/libtorch.pri)

c++调用Libtorch接口时头文件

Libtorch的头文件应该放在cpp或.h中最头的位置（忘记在哪里看到的了）；否则需要添加

#undef slots
#include <torch/script.h>
#include <ATen/ATen.h>
#include <torch/torch.h>
#define slots Q_SLOTS

.pth 转 .pt

import torch
import torchvision.transforms
from PIL import Image
from torch import nn


test = TestSeq()
model = torch.load("../StuPytorch/testSeq_9.pth", map_location=torch.device("cpu"))

# 设置一个示例输入
example = torch.rand(1, 3, 32, 32)
# 使用 torch.jit.trace 生成 torch.jit.ScriptModule
model.eval()
traced_script_module = torch.jit.trace(model, example)
# 保存转换后的模型
traced_script_module.save("../StuPytorch/testSeq_9.pt")

简单实现代码（见参考）
- Pytorch实现

import torch
import torchvision.transforms
from PIL import Image
from torch import nn
from model import *

img_path = "../imgs/dog.jpg"
img = Image.open(img_path)
img = img.convert("RGB")
print(img)
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
                                            torchvision.transforms.ToTensor()])
image = transform(img)
print(image.shape)

model = torch.load("../StuPytorch/testSeq_29.pth", map_location=torch.device("cpu"))
print(model)

image = torch.reshape(image, (1, 3, 32, 32))
print(image)
model.eval()
with torch.no_grad():
    output = model(image)

class_id = {0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer',
            5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck'}
print(output)
print(output.argmax(1))
print(class_id[output.argmax(1).item()])


---输出
TestSeq(
  (model1): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)
tensor([[-1.0712, -2.4485,  1.6878,  0.9057,  1.1824,  1.8202,  0.0850,  0.5854,
         -2.2806, -2.1651]])
tensor([5])
dog

- C++实现

cv::Mat img = cv::imread("D:\\projects\\PyTorchStud\\imgs\\dog.png");
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
cv::Mat resizedImg;
cv::resize(img, resizedImg, cv::Size(32, 32));

//#停止autograd模块的工作，以起到加速和节省显存的作用
torch::NoGradGuard no_grad;
torch::jit::script::Module module;
module = torch::jit::load("D:\\projects\\PyTorchStud\\StuPytorch\\testSeq_29.pt");
std::cout << "Succeed in loading model" << std::endl;

//标签名
std::vector<std::string> labels;
labels.push_back("airplane");
labels.push_back("automobile");
labels.push_back("bird");
labels.push_back("cat");
labels.push_back("deer");
labels.push_back("dog");
labels.push_back("frog");
labels.push_back("horse");
labels.push_back("ship");
labels.push_back("truck");

torch::Tensor inputTensor = torch::from_blob(resizedImg.data, {1, 32, 32, 3}, torch::kByte);
//将张量的参数顺序转化为torch输入的格式[B,C,H,W]
inputTensor = inputTensor.permute({0, 3, 1, 2});
inputTensor = inputTensor.toType(torch::kFloat);
clock_t start_time = clock();

module.eval();
torch::Tensor outTensor = module.forward({inputTensor}).toTensor();
auto prediction = outTensor.argmax(1);
std::cout << "prediction:" << prediction << std::endl;

auto results = outTensor.sort(-1, true);
auto softmaxs = std::get<0>(results)[0].softmax(0);
auto indexs = std::get<1>(results)[0];

//输出运行时间
clock_t end_time = clock();
std::cout << "Running time is: "
          << static_cast<double>(end_time - start_time) / CLOCKS_PER_SEC * 1000 << "ms"
          << std::endl;

for (int i = 0; i < 1; ++i) {
    auto idx = indexs[i].item<int>();
    std::cout << "    ============= Top-" << i + 1 << " =============" << std::endl;
    std::cout << "    idx:  " << idx << std::endl;
    std::cout << "    Label:  " << labels[idx] << std::endl;
    std::cout << "    With Probability:  " << softmaxs[i].item<float>() * 100.0f << "%"
              << std::endl;
}
  

---输出结果
Succeed in loading model
prediction: 5
[ CPULongType{1} ]
Running time is: 118ms
    ============= Top-1 =============
    idx:  5
    Label:  dog
    With Probability:  100%