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from typing import Collection
from typing import Dict
from typing import List
from typing import Tuple
from typing import Union
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from espnet.nets.pytorch_backend.nets_utils import pad_list
class CommonCollateFn:
"""Functor class of common_collate_fn()"""
def __init__(
self,
float_pad_value: Union[float, int] = 0.0,
int_pad_value: int = -32768,
not_sequence: Collection[str] = (),
):
assert check_argument_types()
self.float_pad_value = float_pad_value
self.int_pad_value = int_pad_value
self.not_sequence = set(not_sequence)
def __repr__(self):
return (
f"{self.__class__}(float_pad_value={self.float_pad_value}, "
f"int_pad_value={self.float_pad_value})"
)
def __call__(
self, data: Collection[Tuple[str, Dict[str, np.ndarray]]]
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
return common_collate_fn(
data,
float_pad_value=self.float_pad_value,
int_pad_value=self.int_pad_value,
not_sequence=self.not_sequence,
)
def common_collate_fn(
data: Collection[Tuple[str, Dict[str, np.ndarray]]],
float_pad_value: Union[float, int] = 0.0,
int_pad_value: int = -32768,
not_sequence: Collection[str] = (),
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
"""Concatenate ndarray-list to an array and convert to torch.Tensor.
Examples:
>>> from espnet2.samplers.constant_batch_sampler import ConstantBatchSampler,
>>> import espnet2.tasks.abs_task
>>> from espnet2.train.dataset import ESPnetDataset
>>> sampler = ConstantBatchSampler(...)
>>> dataset = ESPnetDataset(...)
>>> keys = next(iter(sampler)
>>> batch = [dataset[key] for key in keys]
>>> batch = common_collate_fn(batch)
>>> model(**batch)
Note that the dict-keys of batch are propagated from
that of the dataset as they are.
"""
assert check_argument_types()
uttids = [u for u, _ in data]
data = [d for _, d in data]
assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
assert all(
not k.endswith("_lengths") for k in data[0]
), f"*_lengths is reserved: {list(data[0])}"
output = {}
for key in data[0]:
# NOTE(kamo):
# Each models, which accepts these values finally, are responsible
# to repaint the pad_value to the desired value for each tasks.
if data[0][key].dtype.kind == "i":
pad_value = int_pad_value
else:
pad_value = float_pad_value
array_list = [d[key] for d in data]
# Assume the first axis is length:
# tensor_list: Batch x (Length, ...)
tensor_list = [torch.from_numpy(a) for a in array_list]
# tensor: (Batch, Length, ...)
tensor = pad_list(tensor_list, pad_value)
output[key] = tensor
# lens: (Batch,)
if key not in not_sequence:
lens = torch.tensor([d[key].shape[0] for d in data], dtype=torch.long)
output[key + "_lengths"] = lens
output = (uttids, output)
assert check_return_type(output)
return output
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