import logging
from typing import Iterator, Tuple
from typeguard import typechecked
from espnet2.fileio.read_text import load_num_sequence_text
from espnet2.samplers.abs_sampler import AbsSampler
[docs]class SortedBatchSampler(AbsSampler):
"""BatchSampler with sorted samples by length.
Args:
batch_size:
shape_file:
sort_in_batch: 'descending', 'ascending' or None.
sort_batch:
"""
@typechecked
def __init__(
self,
batch_size: int,
shape_file: str,
sort_in_batch: str = "descending",
sort_batch: str = "ascending",
drop_last: bool = False,
):
assert batch_size > 0
self.batch_size = batch_size
self.shape_file = shape_file
self.sort_in_batch = sort_in_batch
self.sort_batch = sort_batch
self.drop_last = drop_last
# utt2shape: (Length, ...)
# uttA 100,...
# uttB 201,...
utt2shape = load_num_sequence_text(shape_file, loader_type="csv_int")
if sort_in_batch == "descending":
# Sort samples in descending order (required by RNN)
keys = sorted(utt2shape, key=lambda k: -utt2shape[k][0])
elif sort_in_batch == "ascending":
# Sort samples in ascending order
keys = sorted(utt2shape, key=lambda k: utt2shape[k][0])
else:
raise ValueError(
f"sort_in_batch must be either one of "
f"ascending, descending, or None: {sort_in_batch}"
)
if len(keys) == 0:
raise RuntimeError(f"0 lines found: {shape_file}")
# Apply max(, 1) to avoid 0-batches
N = max(len(keys) // batch_size, 1)
if not self.drop_last:
# Split keys evenly as possible as. Note that If N != 1,
# the these batches always have size of batch_size at minimum.
self.batch_list = [
keys[i * len(keys) // N : (i + 1) * len(keys) // N] for i in range(N)
]
else:
self.batch_list = [
tuple(keys[i * batch_size : (i + 1) * batch_size]) for i in range(N)
]
if len(self.batch_list) == 0:
logging.warning(f"{shape_file} is empty")
if sort_in_batch != sort_batch:
if sort_batch not in ("ascending", "descending"):
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}"
)
self.batch_list.reverse()
if len(self.batch_list) == 0:
raise RuntimeError("0 batches")
def __repr__(self):
return (
f"{self.__class__.__name__}("
f"N-batch={len(self)}, "
f"batch_size={self.batch_size}, "
f"shape_file={self.shape_file}, "
f"sort_in_batch={self.sort_in_batch}, "
f"sort_batch={self.sort_batch})"
)
def __len__(self):
return len(self.batch_list)
def __iter__(self) -> Iterator[Tuple[str, ...]]:
return iter(self.batch_list)