torch_data

Torch DataLoader over batches produced by another loader.

Machine-readable model

• to_torch_loaderfunctionWrap a re-iterable of NumPy batches, such as a Grain dataset, as a Torch DataLoader.

function

to_torch_loader

Python

Wrap a re-iterable of NumPy batches, such as a Grain dataset, as a Torch DataLoader.

helia_edge/data/torch_data.py:29

to_torch_loader(dataset: Iterable[Any]) -> torch.utils.data.DataLoader

Wrap a re-iterable of NumPy batches, such as a Grain dataset, as a Torch DataLoader.

The loader adds no batching, shuffling or workers of its own: each iter() iterates dataset once more in this process and converts its NumPy leaves to tensors, keeping the structure (dicts, lists, tuples) and the order. Arrays in worker shared memory are copied first, so tensors stay valid after the next element arrives. Do batching and parallel reads in dataset.

Parameters

Parameters of to_torch_loader

Name	Type	Default	Description
dataset	Iterable[Any]	Required	Re-iterable whose ``iter()`` starts from the first element.

Returns

Returns of to_torch_loader

Type	Description
torch.utils.data.DataLoader	torch.utils.data.DataLoader: With ``batch_size=None``, so elements pass through as they are.

Raises

Errors raised by to_torch_loader

Type	Description
ImportError	If Torch is not installed (``helia-edge[torch]``).