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83 lines
3.0 KiB
83 lines
3.0 KiB
4 years ago
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# 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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from typing import List
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import numpy as np
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from deepspeech.utils.log import Log
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__all__ = ["pad_sequence"]
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logger = Log(__name__).getlog()
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def pad_sequence(sequences: List[np.ndarray],
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batch_first: bool=True,
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padding_value: float=0.0) -> np.ndarray:
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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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>>> a = np.ones([25, 300])
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>>> b = np.ones([22, 300])
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>>> c = np.ones([15, 300])
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>>> pad_sequence([a, b, c]).shape
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[25, 3, 300]
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Note:
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This function returns a np.ndarray 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[np.ndarray]): 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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np.ndarray of size ``T x B x *`` if :attr:`batch_first` is ``False``.
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np.ndarray 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 = sequences[0].shape
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trailing_dims = max_size[1:]
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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 = np.full(out_dims, padding_value, dtype=sequences[0].dtype)
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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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if batch_first:
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out_tensor[i, :length, ...] = 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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