Attention

June 2024 Status Update: Removing DataPipes and DataLoader V2

We are re-focusing the torchdata repo to be an iterative enhancement of torch.utils.data.DataLoader. We do not plan on continuing development or maintaining the [DataPipes] and [DataLoaderV2] solutions, and they will be removed from the torchdata repo. We’ll also be revisiting the DataPipes references in pytorch/pytorch. In release torchdata==0.8.0 (July 2024) they will be marked as deprecated, and in 0.9.0 (Oct 2024) they will be deleted. Existing users are advised to pin to torchdata==0.8.0 or an older version until they are able to migrate away. Subsequent releases will not include DataPipes or DataLoaderV2. Please reach out if you suggestions or comments (please use this issue for feedback)

Collator

class torchdata.datapipes.iter.Collator(datapipe: ~IterDataPipe, conversion: ~Optional[~Union[~Callable[[...], ~Any], ~Dict[~Union[str, ~Any], ~Union[~Callable, ~Any]]]] = <function default_collate>, collate_fn: ~Optional[~Callable] = None)

Collates samples from DataPipe to Tensor(s) by a custom collate function (functional name: collate).

By default, it uses torch.utils.data.default_collate().

Note

While writing a custom collate function, you can import torch.utils.data.default_collate() for the default behavior and functools.partial to specify any additional arguments.

Parameters:

• datapipe – Iterable DataPipe being collated

• collate_fn – Customized collate function to collect and combine data or a batch of data. Default function collates to Tensor(s) based on data type.

Example

>>> # xdoctest: +SKIP
>>> # Convert integer data to float Tensor
>>> class MyIterDataPipe(torch.utils.data.IterDataPipe):
...     def __init__(self, start, end):
...         super(MyIterDataPipe).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         return iter(range(self.start, self.end))
...
...     def __len__(self):
...         return self.end - self.start
...
>>> ds = MyIterDataPipe(start=3, end=7)
>>> print(list(ds))
[3, 4, 5, 6]
>>> def collate_fn(batch):
...     return torch.tensor(batch, dtype=torch.float)
...
>>> collated_ds = CollateIterDataPipe(ds, collate_fn=collate_fn)
>>> print(list(collated_ds))
[tensor(3.), tensor(4.), tensor(5.), tensor(6.)]