def __init__(
self,
iterable: Iterable,
batch_size: Optional[int] = None,
is_shuffle: bool = True,
transforms_dict: Dict[str, List[Transform]] = None,
batcher=None,
collate_fn=None,
chunk_size: int = 1000,
is_cycle: bool = False,
length: Optional[int] = None,
rank: int = 0,
num_workers: int = 1,
):
self.iterable = itertools.cycle(iterable) if is_cycle else iterable
if num_workers > 1:
self.iterable = shard(self.iterable, rank, num_workers)
self.batch_size = batch_size or batcher.batch_size
self.batcher = batcher or Batcher(self.batch_size)
self.is_shuffle = is_shuffle
self.transforms_dict = transforms_dict or {}
self.collate_fun = collate_fn or default_collate_fn
self.chunk_size = chunk_size # num of batches per chunk
self.is_cycle = is_cycle
self.length = length
self.iterable = ChunkIterator(
self.iterable, self.chunk_size * self.batch_size, self.length
)
def __init__(
self,
iterable: Iterable,
batch_size: int = 1,
is_shuffle: bool = True,
transform: Optional[Union[nn.Module, Callable]] = None,
custom_batcher: Optional[Batcher] = None,
collate_fn: Optional[Callable] = None,
chunk_size: Optional[int] = 1000,
is_cycle: bool = False,
length: Optional[int] = None,
rank: int = 0,
world_size: int = 1,
):
self.iterable = itertools.cycle(iterable) if is_cycle else iterable
if world_size > 1:
logger.error(
f"data sharding for rank: {rank}, world_size: {world_size}")
self.iterable = shard(self.iterable, rank, world_size)
self.batch(batch_size, custom_batcher)
self.is_shuffle = is_shuffle
self.transform = RowsToColumnarTransform(transform
or IdentityTransform())
self.collate_fn = collate_fn
self.chunk_size = chunk_size # num of batches per chunk
self.is_cycle = is_cycle
self.length = length
if self.chunk_size and self.batch_size:
self.iterable = ChunkIterator(self.iterable,
self.chunk_size * self.batch_size,
self.length)
def __init__(
self,
iterable: Iterable,
batch_size: Optional[int] = None,
is_shuffle: bool = True,
transform: Optional[Transform] = None,
custom_batcher: Optional[Batcher] = None,
collate_fn=None,
chunk_size: Optional[int] = 1000,
is_cycle: bool = False,
length: Optional[int] = None,
rank: int = 0,
world_size: int = 1,
):
self.iterable = itertools.cycle(iterable) if is_cycle else iterable
if world_size > 1:
self.iterable = shard(self.iterable, rank, world_size)
self.batch(batch_size, custom_batcher)
self.is_shuffle = is_shuffle
self.transform = RowsToColumnarTransform(
transform) or IdentityTransform()
self.collate_fn = collate_fn
self.chunk_size = chunk_size # num of batches per chunk
self.is_cycle = is_cycle
self.length = length
if self.chunk_size and self.batch_size:
self.iterable = ChunkIterator(self.iterable,
self.chunk_size * self.batch_size,
self.length)