本文主要是介绍LibTorch实战二：MNIST的libtorch代码，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
目录
一、前言
二、另一种下载数据集方式
三、MNIST的Pytorch源码
四、MNIST的Libtorch源码
一、前言
前面介绍过了MNIST的python的训练代码、和基于torchscript的模型序列化（导出模型）。今天看看，如何使用libtorch C++来实现手写数字训练。
二、另一种下载数据集方式
同时，我已经说过了，对你MNIST数据集该如何下载。有关数据集的下载，这种不重要的问题卡了很久，简直浪费时间，差评。这里再介绍一种下载方式，在官方仓库中，有个脚本可以直接下载https://github.com/pytorch/examples/blob/main/cpp/tools/download_mnist.py，直接在命令行窗口执行就可以下载，如下，可能网络会很卡，不过下载好了。
这里直接把download_mnist.py源码贴出来吧：
from __future__ import division
from __future__ import print_functionimport argparse
import gzip
import os
import sys
import urllibtry:from urllib.error import URLErrorfrom urllib.request import urlretrieve
except ImportError:from urllib2 import URLErrorfrom urllib import urlretrieveRESOURCES = ['train-images-idx3-ubyte.gz','train-labels-idx1-ubyte.gz','t10k-images-idx3-ubyte.gz','t10k-labels-idx1-ubyte.gz',
]def report_download_progress(chunk_number, chunk_size, file_size):if file_size != -1:percent = min(1, (chunk_number * chunk_size) / file_size)bar = '#' * int(64 * percent)sys.stdout.write('\r0% |{:<64}| {}%'.format(bar, int(percent * 100)))def download(destination_path, url, quiet):if os.path.exists(destination_path):if not quiet:print('{} already exists, skipping ...'.format(destination_path))else:print('Downloading {} ...'.format(url))try:hook = None if quiet else report_download_progressurlretrieve(url, destination_path, reporthook=hook)except URLError:raise RuntimeError('Error downloading resource!')finally:if not quiet:# Just a newline.print()def unzip(zipped_path, quiet):unzipped_path = os.path.splitext(zipped_path)[0]if os.path.exists(unzipped_path):if not quiet:print('{} already exists, skipping ... '.format(unzipped_path))returnwith gzip.open(zipped_path, 'rb') as zipped_file:with open(unzipped_path, 'wb') as unzipped_file:unzipped_file.write(zipped_file.read())if not quiet:print('Unzipped {} ...'.format(zipped_path))def main():parser = argparse.ArgumentParser(description='Download the MNIST dataset from the internet')parser.add_argument('-d', '--destination', default='.', help='Destination directory')parser.add_argument('-q','--quiet',action='store_true',help="Don't report about progress")options = parser.parse_args()if not os.path.exists(options.destination):os.makedirs(options.destination)try:for resource in RESOURCES:path = os.path.join(options.destination, resource)url = 'http://yann.lecun.com/exdb/mnist/{}'.format(resource)download(path, url, options.quiet)unzip(path, options.quiet)except KeyboardInterrupt:print('Interrupted')if __name__ == '__main__':main()执行下载过程中，可能会很卡，下载信息如下：
(base) C:\Users\Administrator\Desktop\examples-master_2\examples-master\cpp\tools>python download_mnist.py
.\train-images-idx3-ubyte.gz already exists, skipping ...
.\train-images-idx3-ubyte already exists, skipping ...
.\train-labels-idx1-ubyte.gz already exists, skipping ...
.\train-labels-idx1-ubyte already exists, skipping ...
.\t10k-images-idx3-ubyte.gz already exists, skipping ...
.\t10k-images-idx3-ubyte already exists, skipping ...
.\t10k-labels-idx1-ubyte.gz already exists, skipping ...
.\t10k-labels-idx1-ubyte already exists, skipping ... python代码训练5个epoch结果。
Test set: Average loss: 0.0287, Accuracy: 9907/10000 (99%)三、MNIST的Pytorch源码
MNIST 的python源码：
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLRclass Net(nn.Module):def __init__(self): # self指的是类实例对象本身(注意：不是类本身)。# self不是关键词# super 用于继承，https://www.runoob.com/python/python-func-super.htmlsuper(Net, self).__init__()self.conv1 = nn.Conv2d(1, 32, 3, 1)self.conv2 = nn.Conv2d(32, 64, 3, 1)self.dropout1 = nn.Dropout(0.25)self.dropout2 = nn.Dropout(0.5)self.fc1 = nn.Linear(9216, 128)self.fc2 = nn.Linear(128, 10)def forward(self, x):# input:28*28x = self.conv1(x) # -> (28 - 3 + 1 = 26),26*26*32x = F.relu(x)# input:26*26*32x = self.conv2(x) # -> (26 - 3 + 1 = 24),24*24*64# input:24*24*64x = F.relu(x)x = F.max_pool2d(x, 2)# -> 12*12*64 = 9216x = self.dropout1(x) #不改变维度x = torch.flatten(x, 1) # 9216*1# w = 128*9216x = self.fc1(x) # -> 128*1x = F.relu(x)x = self.dropout2(x)# w = 10*128x = self.fc2(x) # -> 10*1output = F.log_softmax(x, dim=1) # softmax归一化return outputdef train(args, model, device, train_loader, optimizer, epoch):# 在使用pytorch构建神经网络的时候，训练过程中会在程序上方添加一句model.train()，# 作用是启用batch normalization和drop out。# 测试过程中会使用model.eval()，这时神经网络会沿用batch normalization的值，并不使用drop out。model.train()# 可以查看下卷积核的参数尺寸#model.conv1.weight.shape torch.Size([32, 1, 3, 3]#model.conv2.weight.shape torch.Size([64, 32, 3, 3])for batch_idx, (data, target) in enumerate(train_loader):# train_loader.dataset.data.shape# Out[9]: torch.Size([60000, 28, 28])# batch_size:64# data:64个样本输入,torch.Size([64, 1, 28, 28])# target: 64个label,torch.Size([64])data, target = data.to(device), target.to(device)optimizer.zero_grad()# output:torch.Size([64, 10])output = model(data)# 类似于交叉熵# reference: https://blog.csdn.net/qq_22210253/article/details/85229988loss = F.nll_loss(output, target)loss.backward()optimizer.step()# 我们打印一个卷积核参数看看# print(model.conv2._parameters)if batch_idx % args.log_interval == 0:print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))if args.dry_run:breakdef test(model, device, test_loader):model.eval()test_loss = 0correct = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch losspred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probabilitycorrect += pred.eq(target.view_as(pred)).sum().item()test_loss /= len(test_loader.dataset)print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(test_loader.dataset),100. * correct / len(test_loader.dataset)))def main():# Training settingsparser = argparse.ArgumentParser(description='PyTorch MNIST Example')parser.add_argument('--batch-size', type=int, default=64, metavar='N',help='input batch size for training (default: 64)')parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',help='input batch size for testing (default: 1000)')parser.add_argument('--epochs', type=int, default=5, metavar='N',help='number of epochs to train (default: 14)')parser.add_argument('--lr', type=float, default=1.0, metavar='LR',help='learning rate (default: 1.0)')parser.add_argument('--gamma', type=float, default=0.7, metavar='M',help='Learning rate step gamma (default: 0.7)')parser.add_argument('--no-cuda', action='store_true', default=False,help='disables CUDA training')parser.add_argument('--dry-run', action='store_true', default=False,help='quickly check a single pass')parser.add_argument('--seed', type=int, default=1, metavar='S',help='random seed (default: 1)')parser.add_argument('--log-interval', type=int, default=10, metavar='N',help='how many batches to wait before logging training status')parser.add_argument('--save-model', action='store_true', default=True,help='For Saving the current Model')args = parser.parse_args()use_cuda = not args.no_cuda and torch.cuda.is_available()torch.manual_seed(args.seed)device = torch.device("cuda" if use_cuda else "cpu")train_kwargs = {'batch_size': args.batch_size}test_kwargs = {'batch_size': args.test_batch_size}if use_cuda:cuda_kwargs = {'num_workers': 1,'pin_memory': True, # 锁页内存，可以加快内存到显存的速度'shuffle': True}train_kwargs.update(cuda_kwargs)test_kwargs.update(cuda_kwargs)# torchvision.transforms是pytorch中的图像预处理包。一般用Compose把多个步骤整合到一起#transform = transforms.Compose([transforms.ToTensor(), # (H x W x C)、[0, 255] -> (C x H x W)、[0.0, 1.0]transforms.Normalize((0.1307,), (0.3081,)) # 数据的归一化])dataset1 = datasets.MNIST('../data', train=True, download=True,transform=transform)dataset2 = datasets.MNIST('../data', train=False,transform=transform)train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)model = Net().to(device)optimizer = optim.Adadelta(model.parameters(), lr=args.lr)# 固定步长衰减# reference: https://zhuanlan.zhihu.com/p/93624972scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)for epoch in range(1, args.epochs + 1):train(args, model, device, train_loader, optimizer, epoch)test(model, device, test_loader)scheduler.step()if args.save_model:#torch.save(model.state_dict(), "pytorch_mnist.pt")torch.save(model, "pytorch_mnist.pth")if __name__ == '__main__':main()四、MNIST的Libtorch源码
以下是C++代码(官方的C++代码的网络结果似乎和python代码不能完全对应上，所以我作了修改，其实就是改了网络模型，请看struct Net : torch::nn::Module)：可以对一下struct Net : torch::nn::Module和上述python代码中的 class Net(nn.Module):
#include<torch/torch.h>
#include<cstddef>
#include<iostream>
#include<vector>
#include<string>
// 继承自Module模块
struct Net : torch::nn::Module
{// 构造函数Net() :conv1(torch::nn::Conv2dOptions(1, 32, 3)), // kernel_size = 5conv2(torch::nn::Conv2dOptions(32, 64, 3)),fc1(9216, 128),fc2(128, 10){register_module("conv1", conv1);register_module("conv2", conv2);register_module("conv2_drop", conv2_drop);register_module("fc1", fc1);register_module("fc2", fc2);}// 成员函数：前向传播torch::Tensor forward(torch::Tensor x){// input:1*28*28x = torch::relu(conv1->forward(x)); //conv1:(28 - 3 + 1 = 26), 26*26*32// input:26*26*32x = torch::max_pool2d(torch::relu(conv2->forward(x)), 2);//conv2:(26 - 3 + 1 = 24),24*24*64; max_poolded:12*12*64 = 9216x = torch::dropout(x, 0.25, is_training());x = x.view({ -1, 9216 });// 9216*1// w:128*9216x = torch::relu(fc1->forward(x)); //fc1:w = 128*9216,w * x ->128*1x = torch::dropout(x, 0.5, is_training());// w:10*128x = fc2->forward(x);//fc2:w = 10*128,w * x -> 10*1x = torch::log_softmax(x, 1);return x;}// 模块成员torch::nn::Conv2d conv1;torch::nn::Conv2d conv2;torch::nn::Dropout2d conv2_drop;torch::nn::Linear fc1;torch::nn::Linear fc2;
};//train
template<typename DataLoader>
void train(size_t epoch, Net& model, torch::Device device, DataLoader& data_loader, torch::optim::Optimizer& optimizer, size_t dataset_size)
{//set "train" modemodel.train();size_t batch_idx = 0;for (auto& batch: data_loader){auto data = batch.data.to(device);auto targets = batch.target.to(device);optimizer.zero_grad();auto output = model.forward(data);auto loss = torch::nll_loss(output, targets);AT_ASSERT(!std::isnan(loss.template item<float>()));loss.backward();optimizer.step();// 每10个batch_size打印一次lossif (batch_idx++ % 10 == 0){std::printf("\rTrain Epoch: %ld [%5ld/%5ld] Loss: %.4f",epoch,batch_idx * batch.data.size(0),dataset_size,loss.template item<float>());}}
}template<typename DataLoader>
void test(Net& model, torch::Device device, DataLoader& data_loader, size_t dataset_size)
{torch::NoGradGuard no_grad;// set "test" modemodel.eval();double test_loss = 0;int32_t correct = 0;for (const auto& batch: data_loader){auto data = batch.data.to(device);auto targets = batch.target.to(device);auto output = model.forward(data);test_loss += torch::nll_loss(output, targets, /*weight=*/{}, torch::Reduction::Sum).template item<float>();auto pred = output.argmax(1);// eq = equal 判断prediction 是否等于labelcorrect += pred.eq(targets).sum().template item<int64_t>();}test_loss /= dataset_size;std::printf("\nTest set: Average loss: %.4f | Accuracy: %.3f\n",test_loss,static_cast<double>(correct) / dataset_size);
}int main()
{torch::manual_seed(1);torch::DeviceType device_type;if (torch::cuda::is_available()){std::cout << "CUDA available! Training on GPU." << std::endl;device_type = torch::kCUDA;}else{std::cout << "Training on CPU." << std::endl;device_type = torch::kCPU;}torch::Device device(device_type);Net model;model.to(device);// load train dataauto train_dataset = torch::data::datasets::MNIST("D://MNIST//").map(torch::data::transforms::Normalize<>(0.1307, 0.3081)).map(torch::data::transforms::Stack<>());const size_t train_dataset_size = train_dataset.size().value();std::cout << train_dataset_size << std::endl;auto train_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(std::move(train_dataset), 64);// load test dataauto test_dataset = torch::data::datasets::MNIST("D://MNIST//", torch::data::datasets::MNIST::Mode::kTest).map(torch::data::transforms::Normalize<>(0.1307, 0.3081)).map(torch::data::transforms::Stack<>());const size_t test_dataset_size = test_dataset.size().value();auto test_loader =torch::data::make_data_loader(std::move(test_dataset), 1000);// optimizertorch::optim::SGD optimizer(model.parameters(), torch::optim::SGDOptions(0.01).momentum(0.5));//trainfor (size_t epoch = 0; epoch < 5; epoch++){train(epoch, model, device, *train_loader, optimizer, train_dataset_size);test(model, device, *test_loader, test_dataset_size);}// savereturn 1;
}C++代码训练结果如图：
可以看到C++版本的 MNIST代码能够正常训练模型
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