espnetez.dataloader.Dataloader

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class espnetez.dataloader.Dataloader(**kwargs)

Bases: AbsIterFactory

DataLoader class for building data iterators.

This class extends the AbsIterFactory and provides a method to create a PyTorch DataLoader. It accepts keyword arguments that can be passed directly to the DataLoader constructor.

kwargs

A dictionary of keyword arguments for the DataLoader.

• Type: dict

• Parameters:**kwargs –

  Arbitrary keyword arguments that are passed to the torch.utils.data.DataLoader constructor. Common arguments include:

  • dataset: The dataset from which to load the data.
  • batch_size: Number of samples per batch.
  • shuffle: Whether to shuffle the data at every epoch.
  • num_workers: Number of subprocesses to use for data loading.
  • collate_fn: A function to merge a list of samples to form a

  mini-batch.

####### Examples

>>> from torchvision import datasets, transforms
>>> transform = transforms.Compose([transforms.ToTensor()])
>>> dataset = datasets.MNIST(root='./data', train=True,
...                            transform=transform, download=True)
>>> dataloader = Dataloader(dataset=dataset, batch_size=32,
...                          shuffle=True)
>>> train_loader = dataloader.build_iter(epoch=1)

NOTE

The build_iter method does not utilize the epoch and shuffle parameters in its current implementation. They are included for potential future use, such as enabling shuffling based on epoch number.

build_iter(epoch: int, shuffle: bool | None = None) → DataLoader

Constructs a PyTorch DataLoader instance.

This method initializes and returns a DataLoader using the parameters provided during the instantiation of the Dataloader class. It can be customized with various options to suit the needs of the data loading process.

• Parameters:
  • epoch (int) – The current epoch number. This can be used to implement epoch-based behaviors, such as shuffling the dataset.
  • shuffle (bool , optional) – A flag indicating whether to shuffle the dataset. If set to True, the data will be shuffled before each epoch. Defaults to None, which means the value set in kwargs will be used.
• Returns: A PyTorch DataLoader instance configured with the provided arguments.
• Return type: DataLoader

####### Examples

>>> dataloader = Dataloader(batch_size=32, dataset=my_dataset)
>>> train_loader = dataloader.build_iter(epoch=1, shuffle=True)
>>> for data in train_loader:
...     # Process data
...     pass

NOTE

Ensure that the dataset provided in kwargs is compatible with the DataLoader’s expected format.