因此,让我们创建一个“DataLoaderOptions”对象并设置适当的属性: //define the data_loaderautodata_loader = torch::data::make_data_loader( std::move(dataset), torch::data::DataLoaderOptions().batch_size(kBatchSize).workers(2)); 数据检查的输出结果 数据加载器返回的数据类型是torch::data::Example,...
}introws =1000;intcols =1000;autodataset =CustomDataset(sourceList, targetList, rows, cols).map(torch::data::transforms::Stack<>());intbatchSize =10;autodataLoader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(std::move(dataset), batchSize);for(auto& batch :...
size().value(); auto train_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>( std::move(train_dataset), kTrainBatchSize); auto test_dataset = torch::data::datasets::MNIST( kDataRoot, torch::data::datasets::MNIST::Mode::kTest) .map(torch::data::...
使用LibTorch的`torch::data::make_data_loader`函数,我们可以创建一个数据加载器对象。以下是一个示例代码: cpp auto data_loader = torch::data::make_data_loader<torch::data::datasets::MNIST>(std::move(dataset), batch_size); 在这个例子中,我们使用`make_data_loader`函数创建了一个名为`data_loade...
autonet=std::make_shared<Net>(); // Create a multi-threaded data loader for the MNIST dataset. autodata_loader=torch::data::make_data_loader( torch::data::datasets::MNIST("./data").map( torch::data::transforms::Stack<>()),
auto test_loader = torch::data::make_data_loader(std::move(test_dataset), kTestBatchSize); torch::optim::SGD optimizer( model.parameters(), torch::optim::SGDOptions(0.01).momentum(0.5)); for (size_t epoch = 1; epoch <= kNumberOfEpochs; ++epoch) { ...
auto dataset_train = MyDataset("D:\\dataset\\hymenoptera_data\\train", dict_label).map(torch::data::transforms::Stack<>()); // batchszie int batchSize = 1; // 设置dataloader auto dataLoader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(std::move(dataset_...
实例:在ImgLoader类的基础上,我们使用torch::data::make_data_loader创建一个DataLoader实例,指定RandomSampler进行随机采样,并设置批量大小为64。这样,在训练过程中,数据将以随机顺序批量加载,提高训练效率和模型的泛化能力。 三、模型定义 在LibTorch中,定义神经网络模型需继承torch::nn::Module类,并实现forward函数。
map( torch::data::transforms::Stack<>()); // dataloader auto data_loader = torch::data::make_data_loader(train_dataset,/*batch_size=*/64); const size_t test_dataset_size = train_dataset.size().value(); // 优化器 torch::optim::SGD optimizer(net->parameters(), /*lr=*/0.01); ...
;torch::nn::Linear fc1{nullptr};torch::nn::Linear fc2{nullptr};};intmain(){// 构建模型auto net=std::make_shared<Net>();// 构建datasetauto train_dataset=torch::data::datasets::MNIST("E:\\data").map(torch::data::transforms::Stack<>())...