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98 lines
3.4 KiB
98 lines
3.4 KiB
2 years ago
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# Authors
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# * Mirco Ravanelli 2020
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# * Guillermo Cámbara 2021
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# * Sarthak Yadav 2022
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# Copyright (c) 2022 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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# Modified from speechbrain(https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/nnet/normalization.py)
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import paddle.nn as nn
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from paddlespeech.s2t.modules.align import BatchNorm1D
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class BatchNorm1d(nn.Layer):
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"""Applies 1d batch normalization to the input tensor.
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Arguments
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---------
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input_shape : tuple
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The expected shape of the input. Alternatively, use ``input_size``.
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input_size : int
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The expected size of the input. Alternatively, use ``input_shape``.
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eps : float
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This value is added to std deviation estimation to improve the numerical
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stability.
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momentum : float
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It is a value used for the running_mean and running_var computation.
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affine : bool
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When set to True, the affine parameters are learned.
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track_running_stats : bool
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When set to True, this module tracks the running mean and variance,
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and when set to False, this module does not track such statistics.
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combine_batch_time : bool
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When true, it combines batch an time axis.
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Example
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-------
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>>> input = paddle.randn([100, 10])
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>>> norm = BatchNorm1d(input_shape=input.shape)
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>>> output = norm(input)
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>>> output.shape
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Paddle.Shape([100, 10])
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"""
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def __init__(
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self,
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input_shape=None,
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input_size=None,
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eps=1e-05,
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momentum=0.9,
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combine_batch_time=False,
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skip_transpose=False, ):
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super().__init__()
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self.combine_batch_time = combine_batch_time
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self.skip_transpose = skip_transpose
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if input_size is None and skip_transpose:
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input_size = input_shape[1]
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elif input_size is None:
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input_size = input_shape[-1]
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self.norm = BatchNorm1D(input_size, momentum=momentum, epsilon=eps)
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def forward(self, x):
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"""Returns the normalized input tensor.
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Arguments
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---------
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x : paddle.Tensor (batch, time, [channels])
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input to normalize. 2d or 3d tensors are expected in input
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4d tensors can be used when combine_dims=True.
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"""
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shape_or = x.shape
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if self.combine_batch_time:
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if x.ndim == 3:
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x = x.reshape(shape_or[0] * shape_or[1], shape_or[2])
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else:
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x = x.reshape(shape_or[0] * shape_or[1], shape_or[3],
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shape_or[2])
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elif not self.skip_transpose:
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x = x.transpose([0, 2, 1])
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x_n = self.norm(x)
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if self.combine_batch_time:
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x_n = x_n.reshape(shape_or)
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elif not self.skip_transpose:
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x_n = x_n.transpose([0, 2, 1])
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return x_n
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