install vscode
c/c++ plugin
launch.json
{
// 使用 IntelliSense 了解相关属性。
// 悬停以查看现有属性的描述。
// 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "(gdb) 启动",
"type": "cppdbg",
"request": "launch",
"program": "${workspaceFolder}/build/${fileBasenameNoExtension}.o",
"args": [],
"stopAtEntry": false,
"cwd": "${workspaceFolder}",
"environment": [],
"externalConsole": false,
"MIMode": "gdb",
"setupCommands": [
{
"description": "为 gdb 启用整齐打印",
"text": "-enable-pretty-printing",
"ignoreFailures": true
}
],
"preLaunchTask": "cmake build",
"miDebuggerPath": "/usr/bin/gdb",
}
]
}tasks.json
{
"version": "2.0.0",
"tasks": [
{
"type": "shell",
"label": "cmake build", //lauch.json preLaunchTask
"command": "cmake",
"args": [
"-DCMAKE_PREFIX_PATH=/home/hlx/libtorch",
"..",
"&&",
"make",
"-j",
],
"options": {
"cwd": "${workspaceFolder}/build"
},
"problemMatcher": [
"$gcc"
],
"group": {
"kind": "build",
"isDefault": true
}
}
]
}
c_cpp_properties.json
{
"configurations": [
{
"name": "Linux",
"includePath": [
"${workspaceFolder}/**",
"/home/hlx/libtorch/**"
],
"defines": [],
"compilerPath": "/usr/bin/gcc",
"cStandard": "gnu11",
"cppStandard": "c++17",
"intelliSenseMode": "gcc-x64"
}
],
"version": 4
}
autograd.cpp
#include <torch/torch.h>
#include <iostream>
using namespace torch::autograd;
void basic_autograd_operations_example() {
std::cout << "====== Running: \"Basic autograd operations\" ======" << std::endl;
// Create a tensor and set ``torch::requires_grad()`` to track computation with it
auto x = torch::ones({2, 2}, torch::requires_grad());
std::cout << x << std::endl;
// Do a tensor operation:
auto y = x + 2;
std::cout << y << std::endl;
// ``y`` was created as a result of an operation, so it has a ``grad_fn``.
std::cout << y.grad_fn()->name() << std::endl;
// Do more operations on ``y``
auto z = y * y * 3;
auto out = z.mean();
std::cout << z << std::endl;
std::cout << z.grad_fn()->name() << std::endl;
std::cout << out << std::endl;
std::cout << out.grad_fn()->name() << std::endl;
// ``.requires_grad_( ... )`` changes an existing tensor's ``requires_grad`` flag in-place.
auto a = torch::randn({2, 2});
a = ((a * 3) / (a - 1));
std::cout << a.requires_grad() << std::endl;
a.requires_grad_(true);
std::cout << a.requires_grad() << std::endl;
auto b = (a * a).sum();
std::cout << b.grad_fn()->name() << std::endl;
// Let's backprop now. Because ``out`` contains a single scalar, ``out.backward()``
// is equivalent to ``out.backward(torch::tensor(1.))``.
out.backward();
// Print gradients d(out)/dx
std::cout << x.grad() << std::endl;
// Now let's take a look at an example of vector-Jacobian product:
x = torch::randn(3, torch::requires_grad());
y = x * 2;
while (y.norm().item<double>() < 1000) {
y = y * 2;
}
std::cout << y << std::endl;
std::cout << y.grad_fn()->name() << std::endl;
// If we want the vector-Jacobian product, pass the vector to ``backward`` as argument:
auto v = torch::tensor({0.1, 1.0, 0.0001}, torch::kFloat);
y.backward(v);
std::cout << x.grad() << std::endl;
// You can also stop autograd from tracking history on tensors that require gradients
// either by putting ``torch::NoGradGuard`` in a code block
std::cout << x.requires_grad() << std::endl;
std::cout << x.pow(2).requires_grad() << std::endl;
{
torch::NoGradGuard no_grad;
std::cout << x.pow(2).requires_grad() << std::endl;
}
// Or by using ``.detach()`` to get a new tensor with the same content but that does
// not require gradients:
std::cout << x.requires_grad() << std::endl;
y = x.detach();
std::cout << y.requires_grad() << std::endl;
std::cout << x.eq(y).all().item<bool>() << std::endl;
}
void compute_higher_order_gradients_example() {
std::cout << "====== Running \"Computing higher-order gradients in C++\" ======" << std::endl;
// One of the applications of higher-order gradients is calculating gradient penalty.
// Let's see an example of it using ``torch::autograd::grad``:
auto model = torch::nn::Linear(4, 3);
auto input = torch::randn({3, 4}).requires_grad_(true);
auto output = model(input);
// Calculate loss
auto target = torch::randn({3, 3});
auto loss = torch::nn::MSELoss()(output, target);
// Use norm of gradients as penalty
auto grad_output = torch::ones_like(output);
auto gradient = torch::autograd::grad({output}, {input}, /*grad_outputs=*/{grad_output}, /*create_graph=*/true)[0];
auto gradient_penalty = torch::pow((gradient.norm(2, /*dim=*/1) - 1), 2).mean();
// Add gradient penalty to loss
auto combined_loss = loss + gradient_penalty;
combined_loss.backward();
std::cout << input.grad() << std::endl;
}
// Inherit from Function
class LinearFunction : public Function<LinearFunction> {
public:
// Note that both forward and backward are static functions
// bias is an optional argument
static torch::Tensor forward(
AutogradContext *ctx, torch::Tensor input, torch::Tensor weight, torch::Tensor bias = torch::Tensor()) {
ctx->save_for_backward({input, weight, bias});
auto output = input.mm(weight.t());
if (bias.defined()) {
output += bias.unsqueeze(0).expand_as(output);
}
return output;
}
static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
auto saved = ctx->get_saved_variables();
auto input = saved[0];
auto weight = saved[1];
auto bias = saved[2];
auto grad_output = grad_outputs[0];
auto grad_input = grad_output.mm(weight);
auto grad_weight = grad_output.t().mm(input);
auto grad_bias = torch::Tensor();
if (bias.defined()) {
grad_bias = grad_output.sum(0);
}
return {grad_input, grad_weight, grad_bias};
}
};
class MulConstant : public Function<MulConstant> {
public:
static torch::Tensor forward(AutogradContext *ctx, torch::Tensor tensor, double constant) {
// ctx is a context object that can be used to stash information
// for backward computation
ctx->saved_data["constant"] = constant;
return tensor * constant;
}
static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
// We return as many input gradients as there were arguments.
// Gradients of non-tensor arguments to forward must be `torch::Tensor()`.
return {grad_outputs[0] * ctx->saved_data["constant"].toDouble(), torch::Tensor()};
}
};
void custom_autograd_function_example() {
std::cout << "====== Running \"Using custom autograd function in C++\" ======" << std::endl;
{
auto x = torch::randn({2, 3}).requires_grad_();
auto weight = torch::randn({4, 3}).requires_grad_();
auto y = LinearFunction::apply(x, weight);
y.sum().backward();
std::cout << x.grad() << std::endl;
std::cout << weight.grad() << std::endl;
}
{
auto x = torch::randn({2}).requires_grad_();
auto y = MulConstant::apply(x, 5.5);
y.sum().backward();
std::cout << x.grad() << std::endl;
}
}
int main() {
std::cout << std::boolalpha;
basic_autograd_operations_example();
std::cout << "\n";
compute_higher_order_gradients_example();
std::cout << "\n";
custom_autograd_function_example();
}
CMakeList.txt
cmake_minimum_required(VERSION 2.8)
project(autograd)
include_directories( /usr/local/cuda/include )
include_directories( /usr/local/cuda/lib64 )
#set(CMAKE_PREFIX_PATH ${CMAKE_PREFIX_PATH} "/home/hlx/libtorch")
#set(TORCH_LIBRARIES ${Torch_DIR}/lib/)
#include_directories( ${Torch_DIR} )
#include_directories( ${Torch_DIR}/include )
#include_directories( ${Torch_DIR}/include/torch/csrc/api/include/ )
#include_directories( ${Torch_DIR}/include/torch)
find_package(Torch REQUIRED)
add_executable(autograd "autograd.cpp")
target_link_libraries(autograd "${TORCH_LIBRARIES}")
set_property(TARGET autograd PROPERTY CXX_STANDARD 14)The following error will appear, but an executable file will be generated. Refer to the explanation on the Internet, it is still not resolved. . . . . . . .
reference：
VSCODE+CMAKE+Libtorch environment configuration to realize one-key compilation and one-key DEBUG
https://blog.csdn.net/qq_27009517/article/details/112180982