# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unility functions for Transformer."""
from typing import List
from typing import Tuple
import paddle
from paddlespeech.s2t.utils.log import Log
__all__ = ["pad_sequence", "add_sos_eos", "th_accuracy", "has_tensor"]
logger = Log(__name__).getlog()
[docs]def has_tensor(val):
if isinstance(val, (list, tuple)):
for item in val:
if has_tensor(item):
return True
elif isinstance(val, dict):
for k, v in val.items():
print(k)
if has_tensor(v):
return True
else:
return paddle.is_tensor(val)
[docs]def pad_sequence(sequences: List[paddle.Tensor],
batch_first: bool=False,
padding_value: float=0.0) -> paddle.Tensor:
r"""Pad a list of variable length Tensors with ``padding_value``
``pad_sequence`` stacks a list of Tensors along a new dimension,
and pads them to equal length. For example, if the input is list of
sequences with size ``L x *`` and if batch_first is False, and ``T x B x *``
otherwise.
`B` is batch size. It is equal to the number of elements in ``sequences``.
`T` is length of the longest sequence.
`L` is length of the sequence.
`*` is any number of trailing dimensions, including none.
Example:
>>> from paddle.nn.utils.rnn import pad_sequence
>>> a = paddle.ones(25, 300)
>>> b = paddle.ones(22, 300)
>>> c = paddle.ones(15, 300)
>>> pad_sequence([a, b, c]).shape
paddle.Tensor([25, 3, 300])
Note:
This function returns a Tensor of size ``T x B x *`` or ``B x T x *``
where `T` is the length of the longest sequence. This function assumes
trailing dimensions and type of all the Tensors in sequences are same.
Args:
sequences (list[Tensor]): list of variable length sequences.
batch_first (bool, optional): output will be in ``B x T x *`` if True, or in
``T x B x *`` otherwise
padding_value (float, optional): value for padded elements. Default: 0.
Returns:
Tensor of size ``T x B x *`` if :attr:`batch_first` is ``False``.
Tensor of size ``B x T x *`` otherwise
"""
# assuming trailing dimensions and type of all the Tensors
# in sequences are same and fetching those from sequences[0]
max_size = paddle.shape(sequences[0])
# (TODO Hui Zhang): slice not supprot `end==start`
# trailing_dims = max_size[1:]
trailing_dims = tuple(
max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()
max_len = max([s.shape[0] for s in sequences])
if batch_first:
out_dims = (len(sequences), max_len) + trailing_dims
else:
out_dims = (max_len, len(sequences)) + trailing_dims
out_tensor = sequences[0].new_full(out_dims, padding_value)
for i, tensor in enumerate(sequences):
length = tensor.shape[0]
# use index notation to prevent duplicate references to the tensor
logger.debug(
f"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}"
)
if batch_first:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# TODO (Hui Zhang): set_value op not support int16
# TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]
# out_tensor[i, :length, ...] = tensor
if length != 0:
out_tensor[i, :length] = tensor
else:
out_tensor[i, length] = tensor
else:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# out_tensor[:length, i, ...] = tensor
if length != 0:
out_tensor[:length, i] = tensor
else:
out_tensor[length, i] = tensor
return out_tensor
[docs]def add_sos_eos(ys_pad: paddle.Tensor, sos: int, eos: int,
ignore_id: int) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Add <sos> and <eos> labels.
Args:
ys_pad (paddle.Tensor): batch of padded target sequences (B, Lmax)
sos (int): index of <sos>
eos (int): index of <eeos>
ignore_id (int): index of padding
Returns:
ys_in (paddle.Tensor) : (B, Lmax + 1)
ys_out (paddle.Tensor) : (B, Lmax + 1)
Examples:
>>> sos_id = 10
>>> eos_id = 11
>>> ignore_id = -1
>>> ys_pad
tensor([[ 1, 2, 3, 4, 5],
[ 4, 5, 6, -1, -1],
[ 7, 8, 9, -1, -1]], dtype=paddle.int32)
>>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id)
>>> ys_in
tensor([[10, 1, 2, 3, 4, 5],
[10, 4, 5, 6, 11, 11],
[10, 7, 8, 9, 11, 11]])
>>> ys_out
tensor([[ 1, 2, 3, 4, 5, 11],
[ 4, 5, 6, 11, -1, -1],
[ 7, 8, 9, 11, -1, -1]])
"""
# TODO(Hui Zhang): using comment code,
#_sos = paddle.to_tensor(
# [sos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
#_eos = paddle.to_tensor(
# [eos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
#ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys
#ys_in = [paddle.cat([_sos, y], dim=0) for y in ys]
#ys_out = [paddle.cat([y, _eos], dim=0) for y in ys]
#return pad_sequence(ys_in, padding_value=eos), pad_sequence(ys_out, padding_value=ignore_id)
B = ys_pad.shape[0]
_sos = paddle.ones([B, 1], dtype=ys_pad.dtype) * sos
_eos = paddle.ones([B, 1], dtype=ys_pad.dtype) * eos
ys_in = paddle.cat([_sos, ys_pad], dim=1)
mask_pad = (ys_in == ignore_id)
ys_in = ys_in.masked_fill(mask_pad, eos)
ys_out = paddle.cat([ys_pad, _eos], dim=1)
ys_out = ys_out.masked_fill(mask_pad, eos)
mask_eos = (ys_out == ignore_id)
ys_out = ys_out.masked_fill(mask_eos, eos)
ys_out = ys_out.masked_fill(mask_pad, ignore_id)
return ys_in, ys_out
[docs]def th_accuracy(pad_outputs: paddle.Tensor,
pad_targets: paddle.Tensor,
ignore_label: int) -> float:
"""Calculate accuracy.
Args:
pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
pad_targets (LongTensor): Target label tensors (B, Lmax, D).
ignore_label (int): Ignore label id.
Returns:
float: Accuracy value (0.0 - 1.0).
"""
pad_pred = pad_outputs.view(pad_targets.shape[0], pad_targets.shape[1],
pad_outputs.shape[1]).argmax(2)
mask = pad_targets != ignore_label
numerator = paddle.sum(
pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
denominator = paddle.sum(mask)
return float(numerator) / float(denominator)