How do I make custom pytorch datasets structured like the torchvision datasets?
I'm new to pytorch and I'm trying to reuse a Fashion MNIST CNN (from deeplizard) to categorize my timeseries data. I'm finding it hard to understand the structure of datasets, because following this official tutorial and this SO question as best I can, I'm getting something too simple. I think this is because I don't understand OOP very well. The dataset I've made works fine in my CNN for training but then trying to analyse the results with their code I get stuck.
So I create a dataset from two pytorch tensors called features [4050, 1, 150, 6] and targets[4050]:
train_dataset = TensorDataset(features,targets) # create your datset
train_dataloader = DataLoader(train_dataset, batch_size=50, shuffle=False) # create your dataloader
print(train_dataset.__dict__.keys()) # list the attributes

I get this printed output from inspecting the attributes

dict_keys(['tensors'])

But in the Fashion MNIST tutorial they access the data like this:
train_set = torchvision.datasets.FashionMNIST(
    root='./data'
    ,train=True
    ,download=True
    ,transform=transforms.Compose([
        transforms.ToTensor()
    ])
)

train_loader = torch.utils.data.DataLoader(train_set, batch_size=1000, shuffle=True)
print(train_set.__dict__.keys()) # list the attributes

And you get this printed output from inspecting the attributes

dict_keys(['root', 'transform', 'target_transform', 'transforms',
'train', 'data', 'targets'])

My dataset works fine for training but when I get to later analysis parts of the tutorial, they want me to access parts of the dataset and I get an error:
# Analytics
prediction_loader = torch.utils.data.DataLoader(train_dataset, batch_size=50)
train_preds = get_all_preds(network, prediction_loader)
preds_correct = train_preds.argmax(dim=1).eq(train_dataset.targets).sum().item()

print('total correct:', preds_correct)
print('accuracy:', preds_correct / len(train_set))


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
&lt;ipython-input-73-daa87335a92a&gt; in &lt;module&gt;
      4 prediction_loader = torch.utils.data.DataLoader(train_dataset, batch_size=50)
      5 train_preds = get_all_preds(network, prediction_loader)
----&gt; 6 preds_correct = train_preds.argmax(dim=1).eq(train_dataset.targets).sum().item()
      7 
      8 print('total correct:', preds_correct)

AttributeError: 'TensorDataset' object has no attribute 'targets'


Can anyone tell me what's going on here? Is this something I need to change in how I make the datasets, or can I rewrite the analysis code somehow to access the right part of the dataset?

The equivalent of .targets for TensorDatasets would be train_dataset.tensors[1].
The implementation of TensorDataset is very simple:
class TensorDataset(Dataset[Tuple[Tensor, ...]]):
    r&quot;&quot;&quot;Dataset wrapping tensors.
    Each sample will be retrieved by indexing tensors along the first dimension.
    Arguments:
        *tensors (Tensor): tensors that have the same size of the first dimension.
    &quot;&quot;&quot;
    tensors: Tuple[Tensor, ...]

    def __init__(self, *tensors: Tensor) -&gt; None:
        assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
        self.tensors = tensors

    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)

    def __len__(self):
        return self.tensors[0].size(0)

