RandomSplitter¶
- class torchdata.datapipes.iter.RandomSplitter(source_datapipe: IterDataPipe, weights: Dict[T, Union[int, float]], seed, total_length: Optional[int] = None, target: Optional[T] = None)¶
Randomly split samples from a source DataPipe into groups (functional name:
random_split
). Since there is no buffer, only ONE group of samples (i.e. one child DataPipe) can be iterated through at any time. Attempts to iterate through multiple of them simultaneously will fail.Note that by default, multiple iterations of this DataPipe will yield the same split for consistency across epochs. You can invoke
override_seed
on the output(s) to update the seed whenever needed (such as per epoch to get a different split per epoch).- Parameters:
source_datapipe – Iterable DataPipe being split
weights – Dict of weights; the length of this list determines how many output DataPipes there will be. It is recommended to provide integer weights that sum up to
total_length
, which allows resulting DataPipes’ length values to be known in advance.seed – random _seed used to determine the randomness of the split
total_length – Length of the
source_datapipe
, optional but providing an integer is highly encouraged, because not allIterDataPipe
haslen
, espeically ones that can be easily known in advance.target – Optional key (that must exist in
weights
) to indicate the specific group to return. If set to the defaultNone
, returnsList[IterDataPipe]
. If target is specified, returnsIterDataPipe
.
Example
>>> from torchdata.datapipes.iter import IterableWrapper >>> dp = IterableWrapper(range(10)) >>> train, valid = dp.random_split(total_length=10, weights={"train": 0.5, "valid": 0.5}, seed=0) >>> list(train) [2, 3, 5, 7, 8] >>> list(valid) [0, 1, 4, 6, 9] >>> # You can also specify a target key if you only need a specific group of samples >>> train = dp.random_split(total_length=10, weights={"train": 0.5, "valid": 0.5}, seed=0, target='train') >>> list(train) [2, 3, 5, 7, 8] >>> # Be careful to use the same seed as before when specifying `target` to get the correct split. >>> valid = dp.random_split(total_length=10, weights={"train": 0.5, "valid": 0.5}, seed=0, target='valid') >>> list(valid) [0, 1, 4, 6, 9]