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,...
数据集定义之后还要定义数据加载器data_loader,方法是torch::data::make_data_loader,它是个模板函数,可以建立不同类型的数据加载器,比如序列式torch::data::samplers::SequentialSampler的采样器,随机化的采样器torch::data::samplers::RandomSampler,以及两者分布式的版本,然后的参数是是否移动数据,以及每个批次样本的...
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...
autodata_loader=torch::data::make_data_loader( torch::data::datasets::MNIST("./data").map( torch::data::transforms::Stack<>()), /*batch_size=*/64); // Instantiate an SGD optimization algorithm to update our Net's parameters.
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) { train(epoch, model, device, *train_loader, optimizer, train_...
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_...
This kept bugging me, so I also had a look and I think I found the problem: make_data_loader ( pytorch/torch/csrc/api/include/torch/data/dataloader.h Line 28 in 4cb534f std::move(dataset), std::move(sampler), std::move(options)); ) and the StatelessDataLoader constructor ( ...
DataLoader&data_loader,torch::optim::Optimizer&optimizer,size_t dataset_size){model.train();size_t batch_idx=0;for(auto&batch:data_loader){auto data=batch.data.to(device),targets=batch.target.to(device);optimizer.zero_grad();auto output=model.forward(data);auto loss=torch::nll_loss(...
() { // Create a new Net. auto net = std::make_shared(); // Create a multi-threaded data loader for the MNIST dataset. auto data_loader = torch::data::make_data_loader( torch::data::datasets::MNIST("./data").map( torch::data::transforms::Stack<>()), /*batch_size=*/64)...