Mapper

class torchdata.datapipes.iter.Mapper(datapipe: IterDataPipe, fn: Callable, input_col=None, output_col=None)

Applies a function over each item from the source DataPipe (functional name: map).

The function can be any regular Python function or partial object. Lambda function is not recommended as it is not supported by pickle.

Parameters:

• datapipe – Source Iterable DataPipe

• fn – Function being applied over each item

• input_col –

  Index or indices of data which fn is applied, such as:

  • None as default to apply fn to the data directly.

  • Integer(s) is used for list/tuple.

  • Key(s) is used for dict.

• output_col –

  Index of data where result of fn is placed. output_col can be specified only when input_col is not None

  • None as default to replace the index that input_col specified; For input_col with multiple indices, the left-most one is used, and other indices will be removed.

  • Integer is used for list/tuple. -1 represents to append result at the end.

  • Key is used for dict. New key is acceptable.

Example

>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper, Mapper
>>> def add_one(x):
...     return x + 1
>>> dp = IterableWrapper(range(10))
>>> map_dp_1 = dp.map(add_one)  # Invocation via functional form is preferred
>>> list(map_dp_1)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> # We discourage the usage of `lambda` functions as they are not serializable with `pickle`
>>> # Use `functools.partial` or explicitly define the function instead
>>> map_dp_2 = Mapper(dp, lambda x: x + 1)
>>> list(map_dp_2)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]