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