credit.samplers

Attributes

logger

Classes

MultiStepBatchSamplerSubset	Base class for all Samplers.
DistributedMultiStepBatchSampler	Sampler that restricts data loading to a subset of the dataset.

Module Contents

credit.samplers.logger

class credit.samplers.MultiStepBatchSamplerSubset(dataset: torch.utils.data.Dataset, batch_size: int, index_subset, num_forecast_steps: int)

Bases: torch.utils.data.Sampler

Base class for all Samplers.

Every Sampler subclass has to provide an __iter__() method, providing a way to iterate over indices or lists of indices (batches) of dataset elements, and may provide a __len__() method that returns the length of the returned iterators.

Example

>>> # xdoctest: +SKIP
>>> class AccedingSequenceLengthSampler(Sampler[int]):
>>>     def __init__(self, data: List[str]) -> None:
>>>         self.data = data
>>>
>>>     def __len__(self) -> int:
>>>         return len(self.data)
>>>
>>>     def __iter__(self) -> Iterator[int]:
>>>         sizes = torch.tensor([len(x) for x in self.data])
>>>         yield from torch.argsort(sizes).tolist()
>>>
>>> class AccedingSequenceLengthBatchSampler(Sampler[List[int]]):
>>>     def __init__(self, data: List[str], batch_size: int) -> None:
>>>         self.data = data
>>>         self.batch_size = batch_size
>>>
>>>     def __len__(self) -> int:
>>>         return (len(self.data) + self.batch_size - 1) // self.batch_size
>>>
>>>     def __iter__(self) -> Iterator[List[int]]:
>>>         sizes = torch.tensor([len(x) for x in self.data])
>>>         for batch in torch.chunk(torch.argsort(sizes), len(self)):
>>>             yield batch.tolist()

Note

The __len__() method isn’t strictly required by DataLoader, but is expected in any calculation involving the length of a DataLoader.

dataset

num_forecast_steps

init_times

dt

index_subset

batch_size

num_start_batches

__len__()

__iter__()

class credit.samplers.DistributedMultiStepBatchSampler(dataset: torch.utils.data.Dataset, batch_size: int, num_forecast_steps: int, num_replicas: int | None = None, rank: int | None = None, shuffle: bool = True, seed: int = 0, drop_last: bool = False)

Bases: torch.utils.data.DistributedSampler

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel. In such a case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.

Note

Dataset is assumed to be of constant size and that any instance of it always returns the same elements in the same order.

Parameters:

• dataset – Dataset used for sampling.

• num_replicas (int, optional) – Number of processes participating in distributed training. By default, world_size is retrieved from the current distributed group.

• rank (int, optional) – Rank of the current process within num_replicas. By default, rank is retrieved from the current distributed group.

• shuffle (bool, optional) – If True (default), sampler will shuffle the indices.

• seed (int, optional) – random seed used to shuffle the sampler if shuffle=True. This number should be identical across all processes in the distributed group. Default: 0.

• drop_last (bool, optional) – if True, then the sampler will drop the tail of the data to make it evenly divisible across the number of replicas. If False, the sampler will add extra indices to make the data evenly divisible across the replicas. Default: False.

Warning

In distributed mode, calling the set_epoch() method at the beginning of each epoch before creating the DataLoader iterator is necessary to make shuffling work properly across multiple epochs. Otherwise, the same ordering will be always used.

Example:

>>> # xdoctest: +SKIP
>>> sampler = DistributedSampler(dataset) if is_distributed else None
>>> loader = DataLoader(dataset, shuffle=(sampler is None),
...                     sampler=sampler)
>>> for epoch in range(start_epoch, n_epochs):
...     if is_distributed:
...         sampler.set_epoch(epoch)
...     train(loader)

batch_size

num_forecast_steps

__iter__()

__len__() → int