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# mypy: allow-untyped-defs
from typing import Callable, Iterator, Tuple, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.utils.common import (
_check_unpickable_fn,
StreamWrapper,
validate_input_col,
)
__all__ = ["FilterIterDataPipe"]
_T = TypeVar("_T")
_T_co = TypeVar("_T_co", covariant=True)
@functional_datapipe("filter")
class FilterIterDataPipe(IterDataPipe[_T_co]):
r"""
Filters out elements from the source datapipe according to input ``filter_fn`` (functional name: ``filter``).
Args:
datapipe: Iterable DataPipe being filtered
filter_fn: Customized function mapping an element to a boolean.
input_col: Index or indices of data which ``filter_fn`` is applied, such as:
- ``None`` as default to apply ``filter_fn`` to the data directly.
- Integer(s) is used for list/tuple.
- Key(s) is used for dict.
Example:
>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper
>>> def is_even(n):
... return n % 2 == 0
>>> dp = IterableWrapper(range(5))
>>> filter_dp = dp.filter(filter_fn=is_even)
>>> list(filter_dp)
[0, 2, 4]
"""
datapipe: IterDataPipe[_T_co]
filter_fn: Callable
def __init__(
self,
datapipe: IterDataPipe[_T_co],
filter_fn: Callable,
input_col=None,
) -> None:
super().__init__()
self.datapipe = datapipe
_check_unpickable_fn(filter_fn)
self.filter_fn = filter_fn # type: ignore[assignment]
self.input_col = input_col
validate_input_col(filter_fn, input_col)
def _apply_filter_fn(self, data) -> bool:
if self.input_col is None:
return self.filter_fn(data)
elif isinstance(self.input_col, (list, tuple)):
args = tuple(data[col] for col in self.input_col)
return self.filter_fn(*args)
else:
return self.filter_fn(data[self.input_col])
def __iter__(self) -> Iterator[_T_co]:
for data in self.datapipe:
condition, filtered = self._returnIfTrue(data)
if condition:
yield filtered
else:
StreamWrapper.close_streams(data)
def _returnIfTrue(self, data: _T) -> Tuple[bool, _T]:
condition = self._apply_filter_fn(data)
if df_wrapper.is_column(condition):
# We are operating on DataFrames filter here
result = []
for idx, mask in enumerate(df_wrapper.iterate(condition)):
if mask:
result.append(df_wrapper.get_item(data, idx))
if len(result):
return True, df_wrapper.concat(result)
else:
return False, None # type: ignore[return-value]
if not isinstance(condition, bool):
raise ValueError(
"Boolean output is required for `filter_fn` of FilterIterDataPipe, got",
type(condition),
)
return condition, data
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