Jackwaterveg
4 years ago
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GitHub
commit
ad6d09d622
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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 paddle import nn
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from paddle.nn import functional as F
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from deepspeech.modules.activation import brelu
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from deepspeech.modules.mask import make_non_pad_mask
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ['ConvStack', "conv_output_size"]
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def conv_output_size(I, F, P, S):
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# https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks#hyperparameters
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# Output size after Conv:
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# By noting I the length of the input volume size,
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# F the length of the filter,
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# P the amount of zero padding,
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# S the stride,
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# then the output size O of the feature map along that dimension is given by:
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# O = (I - F + Pstart + Pend) // S + 1
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# When Pstart == Pend == P, we can replace Pstart + Pend by 2P.
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# When Pstart == Pend == 0
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# O = (I - F - S) // S
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# https://iq.opengenus.org/output-size-of-convolution/
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# Output height = (Input height + padding height top + padding height bottom - kernel height) / (stride height) + 1
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# Output width = (Output width + padding width right + padding width left - kernel width) / (stride width) + 1
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return (I - F + 2 * P - S) // S
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# receptive field calculator
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# https://fomoro.com/research/article/receptive-field-calculator
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# https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks#hyperparameters
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# https://distill.pub/2019/computing-receptive-fields/
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# Rl-1 = Sl * Rl + (Kl - Sl)
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class ConvBn(nn.Layer):
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"""Convolution layer with batch normalization.
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:param kernel_size: The x dimension of a filter kernel. Or input a tuple for
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two image dimension.
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:type kernel_size: int|tuple|list
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:param num_channels_in: Number of input channels.
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:type num_channels_in: int
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:param num_channels_out: Number of output channels.
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:type num_channels_out: int
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:param stride: The x dimension of the stride. Or input a tuple for two
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image dimension.
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:type stride: int|tuple|list
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:param padding: The x dimension of the padding. Or input a tuple for two
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image dimension.
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:type padding: int|tuple|list
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:param act: Activation type, relu|brelu
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:type act: string
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:return: Batch norm layer after convolution layer.
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:rtype: Variable
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"""
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def __init__(self, num_channels_in, num_channels_out, kernel_size, stride,
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padding, act):
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super().__init__()
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assert len(kernel_size) == 2
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assert len(stride) == 2
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assert len(padding) == 2
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.conv = nn.Conv2D(
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num_channels_in,
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num_channels_out,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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weight_attr=None,
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bias_attr=False,
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data_format='NCHW')
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self.bn = nn.BatchNorm2D(
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num_channels_out,
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weight_attr=None,
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bias_attr=None,
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data_format='NCHW')
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self.act = F.relu if act == 'relu' else brelu
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def forward(self, x, x_len):
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"""
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x(Tensor): audio, shape [B, C, D, T]
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"""
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x = self.conv(x)
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x = self.bn(x)
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x = self.act(x)
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x_len = (x_len - self.kernel_size[1] + 2 * self.padding[1]
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) // self.stride[1] + 1
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# reset padding part to 0
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masks = make_non_pad_mask(x_len) #[B, T]
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masks = masks.unsqueeze(1).unsqueeze(1) # [B, 1, 1, T]
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# https://github.com/PaddlePaddle/Paddle/pull/29265
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# rhs will type promote to lhs
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x = x * masks
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return x, x_len
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class ConvStack(nn.Layer):
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"""Convolution group with stacked convolution layers.
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:param feat_size: audio feature dim.
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:type feat_size: int
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:param num_stacks: Number of stacked convolution layers.
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:type num_stacks: int
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"""
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def __init__(self, feat_size, num_stacks):
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super().__init__()
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self.feat_size = feat_size # D
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self.num_stacks = num_stacks
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self.conv_in = ConvBn(
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num_channels_in=1,
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num_channels_out=32,
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kernel_size=(41, 11), #[D, T]
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stride=(2, 3),
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padding=(20, 5),
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act='brelu')
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out_channel = 32
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convs = [
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ConvBn(
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num_channels_in=32,
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num_channels_out=out_channel,
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kernel_size=(21, 11),
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stride=(2, 1),
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padding=(10, 5),
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act='brelu') for i in range(num_stacks - 1)
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]
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self.conv_stack = nn.LayerList(convs)
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# conv output feat_dim
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output_height = (feat_size - 1) // 2 + 1
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for i in range(self.num_stacks - 1):
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output_height = (output_height - 1) // 2 + 1
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self.output_height = out_channel * output_height
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def forward(self, x, x_len):
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"""
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x: shape [B, C, D, T]
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x_len : shape [B]
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"""
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x, x_len = self.conv_in(x, x_len)
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for i, conv in enumerate(self.conv_stack):
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x, x_len = conv(x, x_len)
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return x, x_len
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@ -1,314 +0,0 @@
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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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import math
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle.nn import initializer as I
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from deepspeech.modules.activation import brelu
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from deepspeech.modules.mask import make_non_pad_mask
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ['RNNStack']
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class RNNCell(nn.RNNCellBase):
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r"""
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Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
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computes the outputs and updates states.
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The formula used is as follows:
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.. math::
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h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
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y_{t} & = h_{t}
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where :math:`act` is for :attr:`activation`.
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"""
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def __init__(self,
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hidden_size: int,
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activation="tanh",
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weight_ih_attr=None,
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weight_hh_attr=None,
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bias_ih_attr=None,
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bias_hh_attr=None,
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name=None):
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super().__init__()
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std = 1.0 / math.sqrt(hidden_size)
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self.weight_hh = self.create_parameter(
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(hidden_size, hidden_size),
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weight_hh_attr,
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default_initializer=I.Uniform(-std, std))
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self.bias_ih = None
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self.bias_hh = self.create_parameter(
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(hidden_size, ),
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bias_hh_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.hidden_size = hidden_size
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if activation not in ["tanh", "relu", "brelu"]:
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raise ValueError(
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"activation for SimpleRNNCell should be tanh or relu, "
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"but get {}".format(activation))
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self.activation = activation
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self._activation_fn = paddle.tanh \
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if activation == "tanh" \
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else F.relu
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if activation == 'brelu':
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self._activation_fn = brelu
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def forward(self, inputs, states=None):
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if states is None:
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states = self.get_initial_states(inputs, self.state_shape)
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pre_h = states
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i2h = inputs
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if self.bias_ih is not None:
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i2h += self.bias_ih
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h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
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if self.bias_hh is not None:
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h2h += self.bias_hh
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h = self._activation_fn(i2h + h2h)
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return h, h
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@property
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def state_shape(self):
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return (self.hidden_size, )
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class GRUCell(nn.RNNCellBase):
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r"""
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Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
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it computes the outputs and updates states.
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The formula for GRU used is as follows:
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.. math::
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r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
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z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
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\widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
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h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
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y_{t} & = h_{t}
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where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
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multiplication operator.
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"""
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def __init__(self,
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input_size: int,
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hidden_size: int,
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weight_ih_attr=None,
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weight_hh_attr=None,
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bias_ih_attr=None,
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bias_hh_attr=None,
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name=None):
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super().__init__()
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std = 1.0 / math.sqrt(hidden_size)
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self.weight_hh = self.create_parameter(
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(3 * hidden_size, hidden_size),
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weight_hh_attr,
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default_initializer=I.Uniform(-std, std))
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self.bias_ih = None
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self.bias_hh = self.create_parameter(
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(3 * hidden_size, ),
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bias_hh_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.hidden_size = hidden_size
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self.input_size = input_size
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self._gate_activation = F.sigmoid
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self._activation = paddle.tanh
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def forward(self, inputs, states=None):
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if states is None:
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states = self.get_initial_states(inputs, self.state_shape)
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pre_hidden = states
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x_gates = inputs
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if self.bias_ih is not None:
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x_gates = x_gates + self.bias_ih
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h_gates = paddle.matmul(pre_hidden, self.weight_hh, transpose_y=True)
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if self.bias_hh is not None:
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h_gates = h_gates + self.bias_hh
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x_r, x_z, x_c = paddle.split(x_gates, num_or_sections=3, axis=1)
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h_r, h_z, h_c = paddle.split(h_gates, num_or_sections=3, axis=1)
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r = self._gate_activation(x_r + h_r)
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z = self._gate_activation(x_z + h_z)
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c = self._activation(x_c + r * h_c) # apply reset gate after mm
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h = (pre_hidden - c) * z + c
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# https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/fluid/layers/dynamic_gru_cn.html#dynamic-gru
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return h, h
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@property
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def state_shape(self):
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r"""
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The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
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size would be automatically inserted into shape). The shape corresponds
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to the shape of :math:`h_{t-1}`.
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"""
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return (self.hidden_size, )
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class BiRNNWithBN(nn.Layer):
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"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
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The batch normalization is only performed on input-state weights.
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:param size: Dimension of RNN cells.
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:type size: int
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:param share_weights: Whether to share input-hidden weights between
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forward and backward directional RNNs.
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:type share_weights: bool
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:return: Bidirectional simple rnn layer.
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:rtype: Variable
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"""
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def __init__(self, i_size: int, h_size: int, share_weights: bool):
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super().__init__()
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self.share_weights = share_weights
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if self.share_weights:
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#input-hidden weights shared between bi-directional rnn.
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self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
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# batch norm is only performed on input-state projection
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self.fw_bn = nn.BatchNorm1D(
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h_size, bias_attr=None, data_format='NLC')
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self.bw_fc = self.fw_fc
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self.bw_bn = self.fw_bn
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else:
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self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
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self.fw_bn = nn.BatchNorm1D(
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h_size, bias_attr=None, data_format='NLC')
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self.bw_fc = nn.Linear(i_size, h_size, bias_attr=False)
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self.bw_bn = nn.BatchNorm1D(
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h_size, bias_attr=None, data_format='NLC')
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self.fw_cell = RNNCell(hidden_size=h_size, activation='brelu')
|
|
||||||
self.bw_cell = RNNCell(hidden_size=h_size, activation='brelu')
|
|
||||||
self.fw_rnn = nn.RNN(
|
|
||||||
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
|
|
||||||
self.bw_rnn = nn.RNN(
|
|
||||||
self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
|
|
||||||
|
|
||||||
def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
|
|
||||||
# x, shape [B, T, D]
|
|
||||||
fw_x = self.fw_bn(self.fw_fc(x))
|
|
||||||
bw_x = self.bw_bn(self.bw_fc(x))
|
|
||||||
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
|
|
||||||
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
|
|
||||||
x = paddle.concat([fw_x, bw_x], axis=-1)
|
|
||||||
return x, x_len
|
|
||||||
|
|
||||||
|
|
||||||
class BiGRUWithBN(nn.Layer):
|
|
||||||
"""Bidirectonal gru layer with sequence-wise batch normalization.
|
|
||||||
The batch normalization is only performed on input-state weights.
|
|
||||||
|
|
||||||
:param name: Name of the layer.
|
|
||||||
:type name: string
|
|
||||||
:param input: Input layer.
|
|
||||||
:type input: Variable
|
|
||||||
:param size: Dimension of GRU cells.
|
|
||||||
:type size: int
|
|
||||||
:param act: Activation type.
|
|
||||||
:type act: string
|
|
||||||
:return: Bidirectional GRU layer.
|
|
||||||
:rtype: Variable
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, i_size: int, h_size: int):
|
|
||||||
super().__init__()
|
|
||||||
hidden_size = h_size * 3
|
|
||||||
|
|
||||||
self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
|
|
||||||
self.fw_bn = nn.BatchNorm1D(
|
|
||||||
hidden_size, bias_attr=None, data_format='NLC')
|
|
||||||
self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
|
|
||||||
self.bw_bn = nn.BatchNorm1D(
|
|
||||||
hidden_size, bias_attr=None, data_format='NLC')
|
|
||||||
|
|
||||||
self.fw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
|
|
||||||
self.bw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
|
|
||||||
self.fw_rnn = nn.RNN(
|
|
||||||
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
|
|
||||||
self.bw_rnn = nn.RNN(
|
|
||||||
self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
|
|
||||||
|
|
||||||
def forward(self, x, x_len):
|
|
||||||
# x, shape [B, T, D]
|
|
||||||
fw_x = self.fw_bn(self.fw_fc(x))
|
|
||||||
bw_x = self.bw_bn(self.bw_fc(x))
|
|
||||||
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
|
|
||||||
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
|
|
||||||
x = paddle.concat([fw_x, bw_x], axis=-1)
|
|
||||||
return x, x_len
|
|
||||||
|
|
||||||
|
|
||||||
class RNNStack(nn.Layer):
|
|
||||||
"""RNN group with stacked bidirectional simple RNN or GRU layers.
|
|
||||||
|
|
||||||
:param input: Input layer.
|
|
||||||
:type input: Variable
|
|
||||||
:param size: Dimension of RNN cells in each layer.
|
|
||||||
:type size: int
|
|
||||||
:param num_stacks: Number of stacked rnn layers.
|
|
||||||
:type num_stacks: int
|
|
||||||
:param use_gru: Use gru if set True. Use simple rnn if set False.
|
|
||||||
:type use_gru: bool
|
|
||||||
:param share_rnn_weights: Whether to share input-hidden weights between
|
|
||||||
forward and backward directional RNNs.
|
|
||||||
It is only available when use_gru=False.
|
|
||||||
:type share_weights: bool
|
|
||||||
:return: Output layer of the RNN group.
|
|
||||||
:rtype: Variable
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self,
|
|
||||||
i_size: int,
|
|
||||||
h_size: int,
|
|
||||||
num_stacks: int,
|
|
||||||
use_gru: bool,
|
|
||||||
share_rnn_weights: bool):
|
|
||||||
super().__init__()
|
|
||||||
rnn_stacks = []
|
|
||||||
for i in range(num_stacks):
|
|
||||||
if use_gru:
|
|
||||||
#default:GRU using tanh
|
|
||||||
rnn_stacks.append(BiGRUWithBN(i_size=i_size, h_size=h_size))
|
|
||||||
else:
|
|
||||||
rnn_stacks.append(
|
|
||||||
BiRNNWithBN(
|
|
||||||
i_size=i_size,
|
|
||||||
h_size=h_size,
|
|
||||||
share_weights=share_rnn_weights))
|
|
||||||
i_size = h_size * 2
|
|
||||||
|
|
||||||
self.rnn_stacks = nn.LayerList(rnn_stacks)
|
|
||||||
|
|
||||||
def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
|
|
||||||
"""
|
|
||||||
x: shape [B, T, D]
|
|
||||||
x_len: shpae [B]
|
|
||||||
"""
|
|
||||||
for i, rnn in enumerate(self.rnn_stacks):
|
|
||||||
x, x_len = rnn(x, x_len)
|
|
||||||
masks = make_non_pad_mask(x_len) #[B, T]
|
|
||||||
masks = masks.unsqueeze(-1) # [B, T, 1]
|
|
||||||
# https://github.com/PaddlePaddle/Paddle/pull/29265
|
|
||||||
# rhs will type promote to lhs
|
|
||||||
x = x * masks
|
|
||||||
return x, x_len
|
|
||||||
Loading…
Reference in new issue