def test_UnsortedBatchSampler_repr(shape_files, drop_last):
sampler = UnsortedBatchSampler(2,
key_file=shape_files[0],
drop_last=drop_last)
print(sampler)
def test_UnsortedBatchSampler_len(shape_files, drop_last):
sampler = UnsortedBatchSampler(2,
key_file=shape_files[0],
drop_last=drop_last)
len(sampler)
def build_batch_sampler(
type: str,
batch_size: int,
batch_bins: int,
shape_files: Union[Tuple[str, ...], List[str]],
sort_in_batch: str = "descending",
sort_batch: str = "ascending",
drop_last: bool = False,
min_batch_size: int = 1,
fold_lengths: Sequence[int] = (),
padding: bool = True,
utt2category_file: str = None,
) -> AbsSampler:
"""Helper function to instantiate BatchSampler.
Args:
type: mini-batch type. "unsorted", "sorted", "folded", "numel", or, "length"
batch_size: The mini-batch size. Used for "unsorted", "sorted", "folded" mode
batch_bins: Used for "numel" model
shape_files: Text files describing the length and dimension
of each features. e.g. uttA 1330,80
sort_in_batch:
sort_batch:
drop_last:
min_batch_size: Used for "numel" or "folded" mode
fold_lengths: Used for "folded" mode
padding: Whether sequences are input as a padded tensor or not.
used for "numel" mode
"""
assert check_argument_types()
if len(shape_files) == 0:
raise ValueError("No shape file are given")
if type == "unsorted":
retval = UnsortedBatchSampler(batch_size=batch_size,
key_file=shape_files[0],
drop_last=drop_last)
elif type == "sorted":
retval = SortedBatchSampler(
batch_size=batch_size,
shape_file=shape_files[0],
sort_in_batch=sort_in_batch,
sort_batch=sort_batch,
drop_last=drop_last,
)
elif type == "folded":
if len(fold_lengths) != len(shape_files):
raise ValueError(f"The number of fold_lengths must be equal to "
f"the number of shape_files: "
f"{len(fold_lengths)} != {len(shape_files)}")
retval = FoldedBatchSampler(
batch_size=batch_size,
shape_files=shape_files,
fold_lengths=fold_lengths,
sort_in_batch=sort_in_batch,
sort_batch=sort_batch,
drop_last=drop_last,
min_batch_size=min_batch_size,
utt2category_file=utt2category_file,
)
elif type == "numel":
retval = NumElementsBatchSampler(
batch_bins=batch_bins,
shape_files=shape_files,
sort_in_batch=sort_in_batch,
sort_batch=sort_batch,
drop_last=drop_last,
padding=padding,
min_batch_size=min_batch_size,
)
elif type == "length":
retval = LengthBatchSampler(
batch_bins=batch_bins,
shape_files=shape_files,
sort_in_batch=sort_in_batch,
sort_batch=sort_batch,
drop_last=drop_last,
padding=padding,
min_batch_size=min_batch_size,
)
else:
raise ValueError(f"Not supported: {type}")
assert check_return_type(retval)
return retval