from typing import Iterator, List, Optional, Sequence, Tuple, Union
from typeguard import typechecked
from espnet2.fileio.read_text import load_num_sequence_text, read_2columns_text
from espnet2.samplers.abs_sampler import AbsSampler
[docs]class FoldedBatchSampler(AbsSampler):
@typechecked
def __init__(
self,
batch_size: int,
shape_files: Union[Tuple[str, ...], List[str]],
fold_lengths: Sequence[int],
min_batch_size: int = 1,
sort_in_batch: str = "descending",
sort_batch: str = "ascending",
drop_last: bool = False,
utt2category_file: Optional[str] = None,
):
assert batch_size > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}"
)
if sort_in_batch != "descending" and sort_in_batch != "ascending":
raise ValueError(
f"sort_in_batch must be ascending or descending: {sort_in_batch}"
)
self.batch_size = batch_size
self.shape_files = shape_files
self.sort_in_batch = sort_in_batch
self.sort_batch = sort_batch
self.drop_last = drop_last
# utt2shape: (Length, ...)
# uttA 100,...
# uttB 201,...
utt2shapes = [
load_num_sequence_text(s, loader_type="csv_int") for s in shape_files
]
first_utt2shape = utt2shapes[0]
for s, d in zip(shape_files, utt2shapes):
if set(d) != set(first_utt2shape):
raise RuntimeError(
f"keys are mismatched between {s} != {shape_files[0]}"
)
# Sort samples in ascending order
# (shape order should be like (Length, Dim))
keys = sorted(first_utt2shape, key=lambda k: first_utt2shape[k][0])
if len(keys) == 0:
raise RuntimeError(f"0 lines found: {shape_files[0]}")
category2utt = {}
if utt2category_file is not None:
utt2category = read_2columns_text(utt2category_file)
if set(utt2category) != set(first_utt2shape):
raise RuntimeError(
"keys are mismatched between "
f"{utt2category_file} != {shape_files[0]}"
)
for k in keys:
category2utt.setdefault(utt2category[k], []).append(k)
else:
category2utt["default_category"] = keys
self.batch_list = []
for d, v in category2utt.items():
category_keys = v
# Decide batch-sizes
start = 0
batch_sizes = []
while True:
k = category_keys[start]
factor = max(int(d[k][0] / m) for d, m in zip(utt2shapes, fold_lengths))
bs = max(min_batch_size, int(batch_size / (1 + factor)))
if self.drop_last and start + bs > len(category_keys):
# This if-block avoids 0-batches
if len(self.batch_list) > 0:
break
bs = min(len(category_keys) - start, bs)
batch_sizes.append(bs)
start += bs
if start >= len(category_keys):
break
if len(batch_sizes) == 0:
# Maybe we can't reach here
raise RuntimeError("0 batches")
# If the last batch-size is smaller than minimum batch_size,
# the samples are redistributed to the other mini-batches
if len(batch_sizes) > 1 and batch_sizes[-1] < min_batch_size:
for i in range(batch_sizes.pop(-1)):
batch_sizes[-(i % len(batch_sizes)) - 2] += 1
if not self.drop_last:
# Bug check
assert sum(batch_sizes) == len(
category_keys
), f"{sum(batch_sizes)} != {len(category_keys)}"
# Set mini-batch
cur_batch_list = []
start = 0
for bs in batch_sizes:
assert len(category_keys) >= start + bs, "Bug"
minibatch_keys = category_keys[start : start + bs]
start += bs
if sort_in_batch == "descending":
minibatch_keys.reverse()
elif sort_in_batch == "ascending":
# Key are already sorted in ascending
pass
else:
raise ValueError(
"sort_in_batch must be ascending or "
f"descending: {sort_in_batch}"
)
cur_batch_list.append(tuple(minibatch_keys))
if sort_batch == "ascending":
pass
elif sort_batch == "descending":
cur_batch_list.reverse()
else:
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}"
)
self.batch_list.extend(cur_batch_list)
def __repr__(self):
return (
f"{self.__class__.__name__}("
f"N-batch={len(self)}, "
f"batch_size={self.batch_size}, "
f"shape_files={self.shape_files}, "
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)