Hui Zhang
3 years ago
committed by
GitHub
commit
a8fa07db73
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from .deepspeech2 import DeepSpeech2Model
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from .deepspeech2 import DeepSpeech2InferModel
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__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']
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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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# TODO(Hui Zhang): not support bool multiply
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# masks = masks.type_as(x)
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masks = masks.astype(x.dtype)
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x = x.multiply(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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@ -0,0 +1,262 @@
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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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"""Deepspeech2 ASR Model"""
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from typing import Optional
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import paddle
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from paddle import nn
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from yacs.config import CfgNode
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from deepspeech.models.ds2.conv import ConvStack
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from deepspeech.modules.ctc import CTCDecoder
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from deepspeech.models.ds2.rnn import RNNStack
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from deepspeech.utils import layer_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferMode']
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class CRNNEncoder(nn.Layer):
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__()
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self.rnn_size = rnn_size
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self.feat_size = feat_size # 161 for linear
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self.dict_size = dict_size
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self.conv = ConvStack(feat_size, num_conv_layers)
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i_size = self.conv.output_height # H after conv stack
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self.rnn = RNNStack(
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i_size=i_size,
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h_size=rnn_size,
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num_stacks=num_rnn_layers,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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@property
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def output_size(self):
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return self.rnn_size * 2
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def forward(self, audio, audio_len):
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"""Compute Encoder outputs
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Args:
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audio (Tensor): [B, Tmax, D]
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text (Tensor): [B, Umax]
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audio_len (Tensor): [B]
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text_len (Tensor): [B]
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Returns:
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x (Tensor): encoder outputs, [B, T, D]
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x_lens (Tensor): encoder length, [B]
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"""
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# [B, T, D] -> [B, D, T]
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audio = audio.transpose([0, 2, 1])
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# [B, D, T] -> [B, C=1, D, T]
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x = audio.unsqueeze(1)
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x_lens = audio_len
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# convolution group
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x, x_lens = self.conv(x, x_lens)
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# convert data from convolution feature map to sequence of vectors
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#B, C, D, T = paddle.shape(x) # not work under jit
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x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
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#x = x.reshape([B, T, C * D]) #[B, T, C*D] # not work under jit
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x = x.reshape([0, 0, -1]) #[B, T, C*D]
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# remove padding part
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x, x_lens = self.rnn(x, x_lens) #[B, T, D]
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return x, x_lens
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class DeepSpeech2Model(nn.Layer):
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"""The DeepSpeech2 network structure.
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:param audio_data: Audio spectrogram data layer.
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:type audio_data: Variable
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:param text_data: Transcription text data layer.
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:type text_data: Variable
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:param audio_len: Valid sequence length data layer.
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:type audio_len: Variable
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:param masks: Masks data layer to reset padding.
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:type masks: Variable
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:param dict_size: Dictionary size for tokenized transcription.
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:type dict_size: int
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:param num_conv_layers: Number of stacking convolution layers.
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:type num_conv_layers: int
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:param num_rnn_layers: Number of stacking RNN layers.
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:type num_rnn_layers: int
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:param rnn_size: RNN layer size (dimension of RNN cells).
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:type rnn_size: int
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:param use_gru: Use gru if set True. Use simple rnn if set False.
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:type use_gru: bool
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:param share_rnn_weights: Whether to share input-hidden weights between
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forward and backward direction RNNs.
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It is only available when use_gru=False.
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:type share_weights: bool
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:return: A tuple of an output unnormalized log probability layer (
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before softmax) and a ctc cost layer.
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:rtype: tuple of LayerOutput
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"""
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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default = CfgNode(
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dict(
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num_conv_layers=2, #Number of stacking convolution layers.
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num_rnn_layers=3, #Number of stacking RNN layers.
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rnn_layer_size=1024, #RNN layer size (number of RNN cells).
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use_gru=True, #Use gru if set True. Use simple rnn if set False.
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share_rnn_weights=True #Whether to share input-hidden weights between forward and backward directional RNNs.Notice that for GRU, weight sharing is not supported.
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))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__()
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self.encoder = CRNNEncoder(
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feat_size=feat_size,
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dict_size=dict_size,
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num_conv_layers=num_conv_layers,
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num_rnn_layers=num_rnn_layers,
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rnn_size=rnn_size,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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assert (self.encoder.output_size == rnn_size * 2)
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self.decoder = CTCDecoder(
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odim=dict_size, # <blank> is in vocab
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enc_n_units=self.encoder.output_size,
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blank_id=0, # first token is <blank>
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dropout_rate=0.0,
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reduction=True, # sum
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batch_average=True) # sum / batch_size
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def forward(self, audio, audio_len, text, text_len):
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"""Compute Model loss
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Args:
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audio (Tenosr): [B, T, D]
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audio_len (Tensor): [B]
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text (Tensor): [B, U]
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text_len (Tensor): [B]
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Returns:
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loss (Tenosr): [1]
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"""
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eouts, eouts_len = self.encoder(audio, audio_len)
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loss = self.decoder(eouts, eouts_len, text, text_len)
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return loss
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@paddle.no_grad()
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def decode(self, audio, audio_len, vocab_list, decoding_method,
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lang_model_path, beam_alpha, beam_beta, beam_size, cutoff_prob,
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cutoff_top_n, num_processes):
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# init once
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# decoders only accept string encoded in utf-8
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self.decoder.init_decode(
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beam_alpha=beam_alpha,
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beam_beta=beam_beta,
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lang_model_path=lang_model_path,
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vocab_list=vocab_list,
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decoding_method=decoding_method)
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eouts, eouts_len = self.encoder(audio, audio_len)
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probs = self.decoder.softmax(eouts)
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return self.decoder.decode_probs(
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probs.numpy(), eouts_len, vocab_list, decoding_method,
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lang_model_path, beam_alpha, beam_beta, beam_size, cutoff_prob,
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cutoff_top_n, num_processes)
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@classmethod
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def from_pretrained(cls, dataloader, config, checkpoint_path):
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"""Build a DeepSpeech2Model model from a pretrained model.
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Parameters
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----------
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dataloader: paddle.io.DataLoader
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config: yacs.config.CfgNode
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model configs
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checkpoint_path: Path or str
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the path of pretrained model checkpoint, without extension name
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Returns
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-------
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DeepSpeech2Model
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The model built from pretrained result.
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"""
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model = cls(feat_size=dataloader.collate_fn.feature_size,
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dict_size=dataloader.collate_fn.vocab_size,
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num_conv_layers=config.model.num_conv_layers,
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num_rnn_layers=config.model.num_rnn_layers,
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rnn_size=config.model.rnn_layer_size,
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use_gru=config.model.use_gru,
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share_rnn_weights=config.model.share_rnn_weights)
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infos = Checkpoint().load_parameters(
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model, checkpoint_path=checkpoint_path)
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logger.info(f"checkpoint info: {infos}")
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layer_tools.summary(model)
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return model
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class DeepSpeech2InferModel(DeepSpeech2Model):
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__(
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feat_size=feat_size,
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dict_size=dict_size,
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num_conv_layers=num_conv_layers,
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num_rnn_layers=num_rnn_layers,
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rnn_size=rnn_size,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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def forward(self, audio, audio_len):
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"""export model function
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Args:
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audio (Tensor): [B, T, D]
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audio_len (Tensor): [B]
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Returns:
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probs: probs after softmax
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"""
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eouts, eouts_len = self.encoder(audio, audio_len)
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probs = self.decoder.softmax(eouts)
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return probs
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@ -0,0 +1,314 @@
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# 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.
|
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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
|
||||
from deepspeech.modules.mask import make_non_pad_mask
|
||||
from deepspeech.utils.log import Log
|
||||
|
||||
logger = Log(__name__).getlog()
|
||||
|
||||
__all__ = ['RNNStack']
|
||||
|
||||
|
||||
class RNNCell(nn.RNNCellBase):
|
||||
r"""
|
||||
Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
|
||||
computes the outputs and updates states.
|
||||
The formula used is as follows:
|
||||
.. math::
|
||||
h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
|
||||
y_{t} & = h_{t}
|
||||
|
||||
where :math:`act` is for :attr:`activation`.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
hidden_size: int,
|
||||
activation="tanh",
|
||||
weight_ih_attr=None,
|
||||
weight_hh_attr=None,
|
||||
bias_ih_attr=None,
|
||||
bias_hh_attr=None,
|
||||
name=None):
|
||||
super().__init__()
|
||||
std = 1.0 / math.sqrt(hidden_size)
|
||||
self.weight_hh = self.create_parameter(
|
||||
(hidden_size, hidden_size),
|
||||
weight_hh_attr,
|
||||
default_initializer=I.Uniform(-std, std))
|
||||
self.bias_ih = None
|
||||
self.bias_hh = self.create_parameter(
|
||||
(hidden_size, ),
|
||||
bias_hh_attr,
|
||||
is_bias=True,
|
||||
default_initializer=I.Uniform(-std, std))
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
if activation not in ["tanh", "relu", "brelu"]:
|
||||
raise ValueError(
|
||||
"activation for SimpleRNNCell should be tanh or relu, "
|
||||
"but get {}".format(activation))
|
||||
self.activation = activation
|
||||
self._activation_fn = paddle.tanh \
|
||||
if activation == "tanh" \
|
||||
else F.relu
|
||||
if activation == 'brelu':
|
||||
self._activation_fn = brelu
|
||||
|
||||
def forward(self, inputs, states=None):
|
||||
if states is None:
|
||||
states = self.get_initial_states(inputs, self.state_shape)
|
||||
pre_h = states
|
||||
i2h = inputs
|
||||
if self.bias_ih is not None:
|
||||
i2h += self.bias_ih
|
||||
h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
|
||||
if self.bias_hh is not None:
|
||||
h2h += self.bias_hh
|
||||
h = self._activation_fn(i2h + h2h)
|
||||
return h, h
|
||||
|
||||
@property
|
||||
def state_shape(self):
|
||||
return (self.hidden_size, )
|
||||
|
||||
|
||||
class GRUCell(nn.RNNCellBase):
|
||||
r"""
|
||||
Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
|
||||
it computes the outputs and updates states.
|
||||
The formula for GRU used is as follows:
|
||||
.. math::
|
||||
r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
|
||||
z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
|
||||
\widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
|
||||
h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
|
||||
y_{t} & = h_{t}
|
||||
|
||||
where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
|
||||
multiplication operator.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
input_size: int,
|
||||
hidden_size: int,
|
||||
weight_ih_attr=None,
|
||||
weight_hh_attr=None,
|
||||
bias_ih_attr=None,
|
||||
bias_hh_attr=None,
|
||||
name=None):
|
||||
super().__init__()
|
||||
std = 1.0 / math.sqrt(hidden_size)
|
||||
self.weight_hh = self.create_parameter(
|
||||
(3 * hidden_size, hidden_size),
|
||||
weight_hh_attr,
|
||||
default_initializer=I.Uniform(-std, std))
|
||||
self.bias_ih = None
|
||||
self.bias_hh = self.create_parameter(
|
||||
(3 * hidden_size, ),
|
||||
bias_hh_attr,
|
||||
is_bias=True,
|
||||
default_initializer=I.Uniform(-std, std))
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
self.input_size = input_size
|
||||
self._gate_activation = F.sigmoid
|
||||
self._activation = paddle.tanh
|
||||
|
||||
def forward(self, inputs, states=None):
|
||||
if states is None:
|
||||
states = self.get_initial_states(inputs, self.state_shape)
|
||||
|
||||
pre_hidden = states
|
||||
x_gates = inputs
|
||||
if self.bias_ih is not None:
|
||||
x_gates = x_gates + self.bias_ih
|
||||
h_gates = paddle.matmul(pre_hidden, self.weight_hh, transpose_y=True)
|
||||
if self.bias_hh is not None:
|
||||
h_gates = h_gates + self.bias_hh
|
||||
|
||||
x_r, x_z, x_c = paddle.split(x_gates, num_or_sections=3, axis=1)
|
||||
h_r, h_z, h_c = paddle.split(h_gates, num_or_sections=3, axis=1)
|
||||
|
||||
r = self._gate_activation(x_r + h_r)
|
||||
z = self._gate_activation(x_z + h_z)
|
||||
c = self._activation(x_c + r * h_c) # apply reset gate after mm
|
||||
h = (pre_hidden - c) * z + c
|
||||
# https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/fluid/layers/dynamic_gru_cn.html#dynamic-gru
|
||||
|
||||
return h, h
|
||||
|
||||
@property
|
||||
def state_shape(self):
|
||||
r"""
|
||||
The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
|
||||
size would be automatically inserted into shape). The shape corresponds
|
||||
to the shape of :math:`h_{t-1}`.
|
||||
"""
|
||||
return (self.hidden_size, )
|
||||
|
||||
|
||||
class BiRNNWithBN(nn.Layer):
|
||||
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
|
||||
The batch normalization is only performed on input-state weights.
|
||||
|
||||
:param size: Dimension of RNN cells.
|
||||
:type size: int
|
||||
:param share_weights: Whether to share input-hidden weights between
|
||||
forward and backward directional RNNs.
|
||||
:type share_weights: bool
|
||||
:return: Bidirectional simple rnn layer.
|
||||
:rtype: Variable
|
||||
"""
|
||||
|
||||
def __init__(self, i_size: int, h_size: int, share_weights: bool):
|
||||
super().__init__()
|
||||
self.share_weights = share_weights
|
||||
if self.share_weights:
|
||||
#input-hidden weights shared between bi-directional rnn.
|
||||
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
|
||||
# batch norm is only performed on input-state projection
|
||||
self.fw_bn = nn.BatchNorm1D(
|
||||
h_size, bias_attr=None, data_format='NLC')
|
||||
self.bw_fc = self.fw_fc
|
||||
self.bw_bn = self.fw_bn
|
||||
else:
|
||||
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
|
||||
self.fw_bn = nn.BatchNorm1D(
|
||||
h_size, bias_attr=None, data_format='NLC')
|
||||
self.bw_fc = nn.Linear(i_size, h_size, bias_attr=False)
|
||||
self.bw_bn = nn.BatchNorm1D(
|
||||
h_size, bias_attr=None, data_format='NLC')
|
||||
|
||||
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.ModuleList(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]
|
||||
# TODO(Hui Zhang): not support bool multiply
|
||||
masks = masks.astype(x.dtype)
|
||||
x = x.multiply(masks)
|
||||
return x, x_len
|
||||
Loading…
Reference in new issue