sampler

(s3prl.dataio.sampler)

Control how torch DataLoader group instances into a batch

s3prl.dataio.sampler.balanced_weighted_sampler	For datasets with highly unbalanced class
s3prl.dataio.sampler.distributed_sampler	Wrap any batch sampler for distributed training
s3prl.dataio.sampler.fixed_batch_size_batch_sampler	The most commonly used batch sampler, recover the default batch sampler used in torch DataLoader
s3prl.dataio.sampler.group_same_item_sampler	Group the data points with the same key into the same batch
s3prl.dataio.sampler.max_timestamp_batch_sampler	Limit the maximum timestamps in a batch to realize dynamic batching.
s3prl.dataio.sampler.sorted_sampler	The most commonly used batch sampler in S3PRL legacy codebase, which sorts the lengths of all the data points and group the instances with the similar lengths together.

BalancedWeightedSampler

class s3prl.dataio.sampler.BalancedWeightedSampler(labels: List[str], batch_size: int, duplicate: int = 1, seed: int = 12345678)

Bases: object

This batch sampler is always randomized, hence cannot be used for testing

set_epoch(epoch: int)

DistributedBatchSamplerWrapper

class s3prl.dataio.sampler.DistributedBatchSamplerWrapper(batch_sampler: BatchSampler, num_replicas: Optional[int] = None, rank: Optional[int] = None, allow_duplicates: bool = False, allow_uneven: bool = False)

Bases: object

set_epoch(epoch: int) → None

FixedBatchSizeBatchSampler

class s3prl.dataio.sampler.FixedBatchSizeBatchSampler(data_source, batch_size: int, shuffle: bool = False, seed: int = 12345678)

Bases: object

The reduced timestamps for a batch should not exceed the max_timestamp. If shuffled, each indices are first shuffled before aggregated into batches

Parameters:

data_source – __len__ is implemented

set_epoch(epoch: int) → None

GroupSameItemSampler

class s3prl.dataio.sampler.GroupSameItemSampler(items: List[str])

Bases: object

set_epoch(epoch: int)

MaxTimestampBatchSampler

class s3prl.dataio.sampler.MaxTimestampBatchSampler(lengths: List[int], max_length: int, shuffle: bool = False, seed: int = 12345678, reduce_func: Optional[callable] = None)

Bases: object

The reduced timestamps for a batch should not exceed the max_timestamp. If shuffled, each indices are first shuffled before aggregated into batches

set_epoch(epoch: int)

SortedBucketingSampler

class s3prl.dataio.sampler.SortedBucketingSampler(lengths: List[int], batch_size: int, max_length: int = 300000, shuffle: bool = False, in_batch_shuffle: bool = False, seed: int = 12345678)

Bases: object

Parameters:

• lengths (List[int]) –

• batch_size (int) – the default batch size

• max_length (int) – if a batch contains at least on utt longer than max_length, half the batch

• get_length_func (callable) – get the length of each item in the dataset, if None, a default function will be used

• shuffle (bool) – Whether to shuffle the batches

• in_batch_shuffle (bool) – if False, batches are sorted by length from long to short

set_epoch(epoch: int)

SortedSliceSampler

class s3prl.dataio.sampler.SortedSliceSampler(lengths: List[int], batch_size: int, max_length: int = 300000, seed: int = 12345678, in_batch_shuffle: bool = False)

Bases: object

This sampler should only be used for training hence is always in random shuffle mode

Parameters:

• lengths (List[int]) –

• batch_size (int) – the default batch size

• max_length (int) – if a batch contains at least on utt longer than max_length, half the batch

• get_length_func (callable) – get the length of each item in the dataset, if None, a default function will be used

• in_batch_shuffle (bool) – if False, batches are sorted by length from long to short

set_epoch(epoch: int)