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from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
from typing import Callable, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
__all__ = ["MapperMapDataPipe", "default_fn"]
T_co = TypeVar('T_co', covariant=True)
# Default function to return each item directly
# In order to keep datapipe picklable, eliminates the usage
# of python lambda function
def default_fn(data):
return data
@functional_datapipe('map')
class MapperMapDataPipe(MapDataPipe[T_co]):
r"""
Apply the input 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.
Args:
datapipe: Source MapDataPipe
fn: Function being applied to each item
Example:
>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.map import SequenceWrapper, Mapper
>>> def add_one(x):
... return x + 1
>>> dp = SequenceWrapper(range(10))
>>> map_dp_1 = dp.map(add_one)
>>> list(map_dp_1)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> map_dp_2 = Mapper(dp, lambda x: x + 1)
>>> list(map_dp_2)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
"""
datapipe: MapDataPipe
fn: Callable
def __init__(
self,
datapipe: MapDataPipe,
fn: Callable = default_fn,
) -> None:
super().__init__()
self.datapipe = datapipe
_check_unpickable_fn(fn)
self.fn = fn # type: ignore[assignment]
def __len__(self) -> int:
return len(self.datapipe)
def __getitem__(self, index) -> T_co:
return self.fn(self.datapipe[index])
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