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from typing import Iterator
from typing import List
from typing import Tuple
from typing import Union
import numpy as np
from typeguard import check_argument_types
from espnet2.fileio.read_text import load_num_sequence_text
from espnet2.samplers.abs_sampler import AbsSampler
class NumElementsBatchSampler(AbsSampler):
def __init__(
self,
batch_bins: int,
shape_files: Union[Tuple[str, ...], List[str]],
min_batch_size: int = 1,
sort_in_batch: str = "descending",
sort_batch: str = "ascending",
drop_last: bool = False,
padding: bool = True,
):
assert check_argument_types()
assert batch_bins > 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_bins = batch_bins
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]}")
if padding:
# If padding case, the feat-dim must be same over whole corpus,
# therefore the first sample is referred
feat_dims = [np.prod(d[keys[0]][1:]) for d in utt2shapes]
else:
feat_dims = None
# Decide batch-sizes
batch_sizes = []
current_batch_keys = []
for key in keys:
current_batch_keys.append(key)
# shape: (Length, dim1, dim2, ...)
if padding:
for d, s in zip(utt2shapes, shape_files):
if tuple(d[key][1:]) != tuple(d[keys[0]][1:]):
raise RuntimeError(
"If padding=True, the "
f"feature dimension must be unified: {s}",
)
bins = sum(
len(current_batch_keys) * sh[key][0] * d
for sh, d in zip(utt2shapes, feat_dims)
)
else:
bins = sum(
np.prod(d[k]) for k in current_batch_keys for d in utt2shapes
)
if bins > batch_bins and len(current_batch_keys) >= min_batch_size:
batch_sizes.append(len(current_batch_keys))
current_batch_keys = []
else:
if len(current_batch_keys) != 0 and (
not self.drop_last or len(batch_sizes) == 0
):
batch_sizes.append(len(current_batch_keys))
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)) - 1] += 1
if not self.drop_last:
# Bug check
assert sum(batch_sizes) == len(keys), f"{sum(batch_sizes)} != {len(keys)}"
# Set mini-batch
self.batch_list = []
iter_bs = iter(batch_sizes)
bs = next(iter_bs)
minibatch_keys = []
for key in keys:
minibatch_keys.append(key)
if len(minibatch_keys) == 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"
f" or descending: {sort_in_batch}"
)
self.batch_list.append(tuple(minibatch_keys))
minibatch_keys = []
try:
bs = next(iter_bs)
except StopIteration:
break
if sort_batch == "ascending":
pass
elif sort_batch == "descending":
self.batch_list.reverse()
else:
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}"
)
def __repr__(self):
return (
f"{self.__class__.__name__}("
f"N-batch={len(self)}, "
f"batch_bins={self.batch_bins}, "
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)
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