1、创建tensor张量

//torch::rand
torch::Tensor a = torch::rand({ 2,3 });
//torch::empty
torch::Tensor b = torch::empty({ 2,3 });
//torch::ones
torch::Tensor c = torch::ones({ 2,3 });
//torch::zeros
torch::Tensor d = torch::zeros({ 2,3 });
//torch::full
torch::Tensor e = torch::full({ 2,3 }, 5);
//torch::full_like
torch::Tensor f = torch::full_like(a, 5);

2、访问tensor中元素的值

    torch::Tensor a = torch::rand({ 3,4 });
    cout << a << endl;
    //int val = a[0][1]; 错误用法，类型不匹配
    cout << typeid(a[0][1]).name() << endl;//a[0][1]的数据类型依然为tensor
    float val = a[0][1].item().toFloat();
    cout << val << endl;

输出

 0.8186  0.3430  0.0754  0.0491
 0.7771  0.1150  0.9880  0.1210
 0.0467  0.9136  0.5798  0.7235
[ CPUFloatType{3,4} ]
class at::Tensor
0.342993

3、打印数据及类型

//.to(torch::kCUDA)指定是CPU还是GPU数据    
torch::Tensor box = torch::rand({ 2,3 }).to(torch::kCUDA);    
cout << box << endl;    //打印box的数据及类型
box.print();            //打印box的类型

输出

0.2017  0.6013  0.5738
0.1458  0.0477  0.9191
[ CUDAFloatType{2,3} ]
[CUDAFloatType [2, 3]]

4、获取tensor的维度信息

    torch::Tensor a = torch::rand({ 3,4 });
    a.print();
    cout << "维度：" << a.sizes() << endl;
    cout << "第一个维度：" << a.size(0) << endl;
    cout << "第二个维度：" << a.size(1) << endl;
    cout << "元素个数：" << a.numel() << endl;

输出

[CPUFloatType [3, 4]]
维度：[3, 4]
第一个维度：3
第二个维度：4
元素个数：12

5、拼接 tensor torch::cat

按第一个维度进行拼接（二维时为按行拼接，两个张量列数需相等）

torch::Tensor a = torch::rand({ 2,3 });
torch::Tensor b = torch::rand({ 1,3 });
torch::Tensor a_b = torch::cat({ a,b }, 0);
std::cout << a << std::endl;
std::cout << b << std::endl;
std::cout << a_b << std::endl;

输出

 0.1268  0.5906  0.4777
 0.7290  0.0518  0.3197
[ CPUFloatType{2,3} ]
 0.4441  0.3615  0.4845
[ CPUFloatType{1,3} ]
 0.1268  0.5906  0.4777
 0.7290  0.0518  0.3197
 0.4441  0.3615  0.4845
[ CPUFloatType{3,3} ]

按第二个维度进行拼接（二维时为按列拼接，两个张量行数需相等）

torch::Tensor a = torch::rand({ 2,3 });
torch::Tensor b = torch::rand({ 2,1 });
torch::Tensor a_b = torch::cat({ a,b }, 1);
std::cout << a << std::endl;
std::cout << b << std::endl;
std::cout << a_b << std::endl;

输出

 0.5937  0.5077  0.6332
 0.2676  0.3583  0.6229
[ CPUFloatType{2,3} ]
 0.2329
 0.1592
[ CPUFloatType{2,1} ]
 0.5937  0.5077  0.6332  0.2329
 0.2676  0.3583  0.6229  0.1592
[ CPUFloatType{2,4} ]

6、torch 的切片操作

index浅拷贝。取多行或多列

    torch::Tensor a = torch::rand({ 3,4 });
    cout << a << endl;
    torch::Tensor b = a.index({ "...", 2 });
    cout << b << endl;
    a[1][1] = 100;
    cout << "after changed a" << endl;
    cout << a << endl;
    cout << b << endl;

 0.2273  0.4347  0.0941  0.4216
 0.3449  0.3092  0.5139  0.6308
 0.0158  0.2166  0.6826  0.0597
[ CPUFloatType{3,4} ]
 0.0941
 0.5139
 0.6826
[ CPUFloatType{3} ]
after changed a
   0.2273    0.4347    0.0941    0.4216
   0.3449  100.0000    0.5139    0.6308
   0.0158    0.2166    0.6826    0.0597
[ CPUFloatType{3,4} ]
 0.0941
 0.5139
 0.6826
[ CPUFloatType{3} ]

7、squeeze() unsqueeze()

squeeze(arg)表示若第arg维的维度值为1，则去掉该维度。否则tensor不变

    torch::Tensor a = torch::rand({ 3,1,4 });
    a.print();
    torch::Tensor b = a.squeeze(1);
    b.print();

输出

[CPUFloatType [3, 1, 4]]
[CPUFloatType [3, 4]]

unsqueeze(arg)与squeeze(arg)作用相反，表示在第arg维增加一个维度值为1的维度。

    torch::Tensor a = torch::rand({ 3,4 });
    a.print();
    torch::Tensor b = a.unsqueeze(1);
    b.print();

输出

[CPUFloatType [3, 4]]
[CPUFloatType [3, 1, 4]]

8、cv::Mat 转tensor

void detectClass::mat2tensor(const Mat& frame, torch::Tensor& imgTensor)
{
    Mat img;
    cv::resize(frame, img, cv::Size(640, 384));    //缩放到合适的大小
    cv::cvtColor(img, img, cv::COLOR_BGR2RGB);    //BGR->RGB
    //Mat 转tensor并传递到device
    imgTensor = torch::from_blob(img.data, { img.rows, img.cols,3 }, torch::kByte).to(device);
    //调整数组维度顺序，｛height,width,channels｝to{channels,height,width}
    imgTensor = imgTensor.permute({ 2,0,1 });
    imgTensor = imgTensor.toType(torch::kFloat);//转为浮点类型
    imgTensor = imgTensor.div(255);//归一化
    imgTensor = imgTensor.unsqueeze(0);//扩展第一个维度｛1，channels，height，width｝
}

9、排序torch::sort

    torch::Tensor a = torch::rand({ 10 });
    cout << "before sort:\n" << a << endl;
    std::tuple<torch::Tensor, torch::Tensor> b = a.sort(0, 1);
    cout << "after sort:\n" << std::get<0>(b) << endl;

输出

before sort:
 0.9401
 0.6829
 0.1528
 0.3947
 0.2390
 0.5201
 0.8390
 0.4659
 0.6646
 0.9344
[ CPUFloatType{10} ]
after sort:
 0.9401
 0.9344
 0.8390
 0.6829
 0.6646
 0.5201
 0.4659
 0.3947
 0.2390
 0.1528
[ CPUFloatType{10} ]

10、clamp 把数值控制在 min max 之间

    torch::Tensor a = torch::rand({ 2,3 });
    a[0][0] = 20;
    a[0][1] = 21;
    a[0][2] = 22;
    a[1][0] = 23;
    a[1][1] = 24;
    cout << a << endl;
    torch::Tensor b = a.clamp(21, 22);
    cout << b << endl;

输出

 20.0000  21.0000  22.0000
 23.0000  24.0000   0.1455
[ CPUFloatType{2,3} ]
 21  21  22
 22  22  21
[ CPUFloatType{2,3} ]

11、判断张量中每个值是否大于指定值

    torch::Tensor a = torch::rand({ 2,3 });
    std::cout << a << std::endl;
    torch::Tensor b = a > 0.5;
    std::cout << b << std::endl;

输出

 0.3014  0.0964  0.6515
 0.4000  0.6095  0.7024
[ CPUFloatType{2,3} ]
 0  0  1
 0  1  1
[ CPUBoolType{2,3} ]

12、转置 Tensor::transpose

torch::Tensor a = torch::rand({2,3});
std::cout<<a<<std::endl;
torch::Tensor b = a.transpose(1,0);
std::cout<<b<<std::endl;

输出

 0.0411  0.1815  0.8453
 0.3202  0.9694  0.3321
[ CPUFloatType{2,3} ]
 0.0411  0.3202
 0.1815  0.9694
 0.8453  0.3321
[ CPUFloatType{3,2} ]

13、加载模型

torch::Device m_device(torch::kCUDA);
torch::jit::script::Module m_model = torch::jit::load(path_pt);
m_model.to(m_device);