LibTorch之张量操作与线性回归-白红宇

LibTorch之张量操作与线性回归

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发布时间：2023-01-31

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LibTorch 张量操作与线性回归

在LibTorch中，张量操作与PyTorch相比，主要体现在基础操作的写法上。值得注意的首要事务是理解LibTorch与PyTorch之间的语法差异，尤其是变量命名方式的变化。

一张量初始化

1. torch::zeros

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::zeros({2, 3});
    cout << b << endl;
    system("pause");
    return 0;
}

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2. torch::ones

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::ones({2, 3});
    cout << b << endl;
    system("pause");
    return 0;
}

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3. torch::eye

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::eye(3);
    cout << b << endl;
    system("pause");
    return 0;
}

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4. torch::full

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::full({2, 3}, 999);
    cout << b << endl;
    system("pause");
    return 0;
}

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5. torch::tensor

#include 
   
    
using namespace std;
int main() {
    torch::Tensor  b = torch::tensor({
        {1,2,3},
        {4,5,6}
    });
    cout << b << endl;
    system("pause");
    return 0;
}

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6. torch::rand

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::rand({2, 3});
    cout << b << endl;
    system("pause");
    return 0;
}

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7. torch::randn

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::randn({2, 3});
    cout << b << endl;
    system("pause");
    return 0;
}

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8. torch::randint

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b = torch::randint(1, 10, {2, 3});
    cout << b << endl;
    system("pause");
    return 0;
}

二深浅拷贝

1. 浅拷贝

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b1 = torch::zeros({2, 3});
    torch::Tensor b2 = b1;  // 浅拷贝
    auto b3 = torch::Tensor(b1);
}

2. 深拷贝

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b1 = torch::zeros({2, 3});
    torch::Tensor b2 = b1.clone();
    // 测试深拷贝后的独立性
    b2[0][0] = 888;
    cout << "b2: " << b2 << endl;
    cout << "b1: " << b1 << endl;
}

3. 张量大小一致的深拷贝

#include 
   
    
using namespace std;
int main() {
    torch::Tensor b1 = torch::zeros({2, 3});
    torch::Tensor b2 = torch::zeros_like(b1);
    // 测试形状一致性
    cout << "b2（ones-like）: " << b2 << endl;
    b2 = torch::ones_like(b1);
    cout << "b2（ones-like）：" << b2 << endl;
    b2 = torch::rand_like(b1, torch::kFloat);
    cout << "b2（random-like）：" << b2 << endl;
}

三 C++ 常用数据类型转换为Tensor变量

1. 使用from_blob方法从数组转换Tensor

#include 
   
    
using namespace std;
int main() {
    int a[] = {1, 2, 3, 4, 5, 6};
    torch::Tensor b = torch::from_blob(a, {6}, torch::kInt);
    cout << b << endl;
}

2. 使用from_blob方法从浮点数数组转换Tensor

#include 
   
    
using namespace std;
int main() {
    float data[] = {1, 2, 3, 4, 5, 6};
    torch::Tensor f = torch::from_blob(data, {2, 3});
}

3. 从std::vector

转换Tensor

#include 
   
    
using namespace std;
int main() {
    vector
    
      a = {1, 2, 3, 4, 5, 6};
    // 方法一：直接使用 tensorSYSTEMrepr
    torch::Tensor b = torch::tensor(a);
    // 方法二：使用from_blob
    torch::Tensor b = torch::from_blob(a.data(), {2}, torch::kFloat);
}

四张量维度变换

1. 拼接操作：torch::cat

#include 
   
    
using namespace std;
int main() {
    auto a = torch::ones({2, 3});
    auto a1 = torch::cat({a, a}, 0);
    auto a2 = torch::cat({a, a}, 1);
    cout << "a1: " << a1 << endl;
    cout << "a2: " << a2 << endl;
}

2. 堆叠操作：torch::stack

#include 
   
    
using namespace std;
int main() {
    auto a = torch::ones({2, 3});
    auto a1 = torch::stack({a, a}, 2);
    cout << a1.shape() << endl;
}

3. 切分操作：torch::chunk

#include 
   
    
using namespace std;
int main() {
    auto a = torch::ones({2, 5});
    auto chunks = torch::chunk(a, 2, 1);
    for (int idx, t : chunks) {
        cout << "idx: " << idx << " tensor: " << t << endl;
    }
}

4. unsqueeze操作（无维度展开）：torch::unsqueeze

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3});
    // 查看原始形状
    cout << a << endl;
    // 增加一个维度（默认在第一个维度）
    auto b = a.unsqueeze(1);
    cout << b << endl;
    // 测试形状变化
    cout << b.shape() << endl;
}

五截取张量

1. 截取张量的特定部分

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3, 5, 4});
    // 截取最后两个元素
    auto b = a.slice(2, -2, -1);
    cout << b << endl;
}

六张量的四则运算

1. 加法运算

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3});
    auto b = torch::rand({3, 2});
    auto c = a + b;
    cout << c << endl;
}

2. 乘法运算

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3});
    auto b = torch::rand({3, 2});
    auto c = torch::matmul(a, b);
    cout << c << endl;
}

3. 绝对值

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3});
    auto b = torch::abs(a);
    cout << b << endl;
}

七获取张量的大小

#include 
   
    
using namespace std;
int main() {
    auto a = torch::rand({2, 3, 6, 6});
    cout << a << endl;
    cout << "Tensor size: " << a.sizes() << endl;
}