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)

IterToMapConverter

class torchdata.datapipes.map.IterToMapConverter(datapipe: IterDataPipe, key_value_fn: Optional[Callable] = None)

Lazily load data from IterDataPipe to construct a MapDataPipe with the key-value pair generated by key_value_fn (functional name: to_map_datapipe). If key_value_fn is not given, each data from the source IterDataPipe must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value.

For the opposite converter, use MapToIterConverter.

Parameters:

• datapipe – Source IterDataPipe

• key_value_fn – Function being applied over each data to generate key-value pair

Note

If a key being added is already present, the corresponding value will be replaced by the new value.

Example

>>> from torchdata.datapipes.iter import IterableWrapper
>>> source_dp = IterableWrapper([(i, i) for i in range(10)])
>>> map_dp = source_dp.to_map_datapipe()
>>> list(map_dp)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> source_dp2 = IterableWrapper([('a', 1), ('b', 2), ('c', 1)])
>>> map_dp2 = source_dp2.to_map_datapipe()
>>> map_dp2['a']
1
>>> def row_to_tuple(row):
>>>     label = row[0]
>>>     data = row[1:]
>>>     return label, data
>>> source_dp3 = IterableWrapper([('a', 1, 1, 1, 1, 1, 1), ('b', 2, 2, 2, 2, 2, 2), ('c', 3, 3, 3, 3, 3, 3)])
>>> map_dp3 = source_dp3.to_map_datapipe(key_value_fn=row_to_tuple)
>>> map_dp3['a']
(1, 1, 1, 1, 1, 1)