How to make use of the
torch.utils.data.DataLoader on your own data (not just the
Is there a way to use the inbuilt
DataLoaders which they use on
TorchVisionDatasets to be used on any dataset?
Yes, that is possible. Just create the objects by yourself, e.g.
import torch.utils.data as data_utils train = data_utils.TensorDataset(features, targets) train_loader = data_utils.DataLoader(train, batch_size=50, shuffle=True)
targets are tensors.
features has to be 2-D, i.e. a matrix where each line represents one training sample, and
targets may be 1-D or 2-D, depending on whether you are trying to predict a scalar or a vector.
Hope that helps!
EDIT: response to @sarthak’s question
Basically yes. If you create an object of type
TensorData, then the constructor investigates whether the first dimensions of the feature tensor (which is actually called
data_tensor) and the target tensor (called
target_tensor) have the same length:
assert data_tensor.size(0) == target_tensor.size(0)
However, if you want to feed these data into a neural network subsequently, then you need to be careful. While convolution layers work on data like yours, (I think) all of the other types of layers expect the data to be given in matrix form. So, if you run into an issue like this, then an easy solution would be to convert your 4D-dataset (given as some kind of tensor, e.g.
FloatTensor) into a matrix by using the method
view. For your 5000xnxnx3 dataset, this would look like this:
2d_dataset = 4d_dataset.view(5000, -1)
-1 tells PyTorch to figure out the length of the second dimension automatically.)