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36825f5d
编写于
10月 30, 2019
作者:
Y
Yibing Liu
提交者:
GitHub
10月 30, 2019
浏览文件
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差异文件
Merge pull request #375 from lfchener/fix
Change StaticRNN to fluid.layers.rnn
上级
da50f63e
0d5ed1b4
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
91 addition
and
78 deletion
+91
-78
model_utils/network.py
model_utils/network.py
+91
-78
未找到文件。
model_utils/network.py
浏览文件 @
36825f5d
...
@@ -59,55 +59,62 @@ def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
...
@@ -59,55 +59,62 @@ def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
return
padding_reset
return
padding_reset
def
simple_rnn
(
input
,
size
,
param_attr
=
None
,
bias_attr
=
None
,
is_reverse
=
False
):
class
RNNCell
(
fluid
.
layers
.
RNNCell
):
'''A simple rnn layer.
"""A simple rnn cell."""
:param input: input layer.
:type input: Variable
def
__init__
(
self
,
:param size: Dimension of RNN cells.
hidden_size
,
:type size: int
param_attr
=
None
,
:param param_attr: Parameter properties of hidden layer weights that
bias_attr
=
None
,
hidden_activation
=
None
,
activation
=
None
,
dtype
=
"float32"
,
name
=
"RNNCell"
):
"""Initialize simple rnn cell.
:param hidden_size: Dimension of RNN cells.
:type hidden_size: int
:param param_attr: Parameter properties of hidden layer weights that
can be learned
can be learned
:type param_attr: ParamAttr
:type param_attr: ParamAttr
:param bias_attr: Bias properties of hidden layer weights that can be learned
:param bias_attr: Bias properties of hidden layer weights that can be learned
:type bias_attr: ParamAttr
:type bias_attr: ParamAttr
:param is_reverse: Whether to calculate the inverse RNN
:param hidden_activation: Activation for hidden cell
:type is_reverse: bool
:type hidden_activation: Activation
:return: A simple RNN layer.
:param activation: Activation for output
:rtype: Variable
:type activation: Activation
'''
:param name: Name of cell
if
is_reverse
:
:type name: string
input
=
fluid
.
layers
.
sequence_reverse
(
x
=
input
)
"""
pad_value
=
fluid
.
layers
.
assign
(
input
=
np
.
array
([
0.0
],
dtype
=
np
.
float32
))
self
.
hidden_size
=
hidden_size
input
,
length
=
fluid
.
layers
.
sequence_pad
(
input
,
pad_value
)
self
.
param_attr
=
param_attr
rnn
=
fluid
.
layers
.
StaticRNN
()
self
.
bias_attr
=
bias_attr
input
=
fluid
.
layers
.
transpose
(
input
,
[
1
,
0
,
2
])
self
.
hidden_activation
=
hidden_activation
with
rnn
.
step
():
self
.
activation
=
activation
or
fluid
.
layers
.
brelu
in_
=
rnn
.
step_input
(
input
)
self
.
name
=
name
mem
=
rnn
.
memory
(
shape
=
[
-
1
,
size
],
batch_ref
=
in_
)
out
=
fluid
.
layers
.
fc
(
def
call
(
self
,
inputs
,
states
):
input
=
mem
,
new_hidden
=
fluid
.
layers
.
fc
(
size
=
size
,
input
=
states
,
act
=
None
,
size
=
self
.
hidden_size
,
param_attr
=
param_attr
,
act
=
self
.
hidden_activation
,
bias_attr
=
bias_attr
)
param_attr
=
self
.
param_attr
,
out
=
fluid
.
layers
.
elementwise_add
(
out
,
in_
)
bias_attr
=
self
.
bias_attr
)
out
=
fluid
.
layers
.
brelu
(
out
)
new_hidden
=
fluid
.
layers
.
elementwise_add
(
new_hidden
,
inputs
)
rnn
.
update_memory
(
mem
,
out
)
new_hidden
=
self
.
activation
(
new_hidden
)
rnn
.
output
(
out
)
return
new_hidden
,
new_hidden
out
=
rnn
()
out
=
fluid
.
layers
.
transpose
(
out
,
[
1
,
0
,
2
])
@
property
out
=
fluid
.
layers
.
sequence_unpad
(
x
=
out
,
length
=
length
)
def
state_shape
(
self
):
return
[
self
.
hidden_size
]
if
is_reverse
:
out
=
fluid
.
layers
.
sequence_reverse
(
x
=
out
)
return
out
def
bidirectional_simple_rnn_bn_layer
(
name
,
input
,
size
,
share_weights
):
def
bidirectional_simple_rnn_bn_layer
(
name
,
input
,
size
,
share_weights
):
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
The batch normalization is only performed on input-state weights.
:param name: Name of the layer parameters.
:param name: Name of the layer parameters.
:type name: string
:type name: string
:param input: Input layer.
:param input: Input layer.
...
@@ -120,6 +127,20 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
...
@@ -120,6 +127,20 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
:return: Bidirectional simple rnn layer.
:return: Bidirectional simple rnn layer.
:rtype: Variable
:rtype: Variable
"""
"""
forward_cell
=
RNNCell
(
hidden_size
=
size
,
activation
=
fluid
.
layers
.
brelu
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_bias'
))
reverse_cell
=
RNNCell
(
hidden_size
=
size
,
activation
=
fluid
.
layers
.
brelu
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_bias'
))
pad_value
=
fluid
.
layers
.
assign
(
input
=
np
.
array
([
0.0
],
dtype
=
np
.
float32
))
if
share_weights
:
if
share_weights
:
#input-hidden weights shared between bi-directional rnn.
#input-hidden weights shared between bi-directional rnn.
input_proj
=
fluid
.
layers
.
fc
(
input_proj
=
fluid
.
layers
.
fc
(
...
@@ -130,28 +151,14 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
...
@@ -130,28 +151,14 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
bias_attr
=
False
)
bias_attr
=
False
)
# batch norm is only performed on input-state projection
# batch norm is only performed on input-state projection
input_proj_bn
=
fluid
.
layers
.
batch_norm
(
input_proj_bn
_forward
=
fluid
.
layers
.
batch_norm
(
input
=
input_proj
,
input
=
input_proj
,
act
=
None
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_batch_norm_weight'
),
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_batch_norm_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_batch_norm_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_batch_norm_bias'
),
moving_mean_name
=
name
+
'_batch_norm_moving_mean'
,
moving_mean_name
=
name
+
'_batch_norm_moving_mean'
,
moving_variance_name
=
name
+
'_batch_norm_moving_variance'
)
moving_variance_name
=
name
+
'_batch_norm_moving_variance'
)
#forward and backword in time
input_proj_bn_reverse
=
input_proj_bn_forward
forward_rnn
=
simple_rnn
(
input
=
input_proj_bn
,
size
=
size
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_bias'
),
is_reverse
=
False
)
reverse_rnn
=
simple_rnn
(
input
=
input_proj_bn
,
size
=
size
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_bias'
),
is_reverse
=
True
)
else
:
else
:
input_proj_forward
=
fluid
.
layers
.
fc
(
input_proj_forward
=
fluid
.
layers
.
fc
(
input
=
input
,
input
=
input
,
...
@@ -159,7 +166,7 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
...
@@ -159,7 +166,7 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
act
=
None
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_fc_weight'
),
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_fc_weight'
),
bias_attr
=
False
)
bias_attr
=
False
)
input_proj_
backward
=
fluid
.
layers
.
fc
(
input_proj_
reverse
=
fluid
.
layers
.
fc
(
input
=
input
,
input
=
input
,
size
=
size
,
size
=
size
,
act
=
None
,
act
=
None
,
...
@@ -174,27 +181,29 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
...
@@ -174,27 +181,29 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_batch_norm_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_batch_norm_bias'
),
moving_mean_name
=
name
+
'_forward_batch_norm_moving_mean'
,
moving_mean_name
=
name
+
'_forward_batch_norm_moving_mean'
,
moving_variance_name
=
name
+
'_forward_batch_norm_moving_variance'
)
moving_variance_name
=
name
+
'_forward_batch_norm_moving_variance'
)
input_proj_bn_
backward
=
fluid
.
layers
.
batch_norm
(
input_proj_bn_
reverse
=
fluid
.
layers
.
batch_norm
(
input
=
input_proj_
backward
,
input
=
input_proj_
reverse
,
act
=
None
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_weight'
),
name
=
name
+
'_reverse_batch_norm_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_bias'
),
moving_mean_name
=
name
+
'_reverse_batch_norm_moving_mean'
,
moving_mean_name
=
name
+
'_reverse_batch_norm_moving_mean'
,
moving_variance_name
=
name
+
'_reverse_batch_norm_moving_variance'
)
moving_variance_name
=
name
+
'_reverse_batch_norm_moving_variance'
)
# forward and backward in time
# forward and backward in time
forward_rnn
=
simple_rnn
(
input
,
length
=
fluid
.
layers
.
sequence_pad
(
input_proj_bn_forward
,
pad_value
)
input
=
input_proj_bn_forward
,
forward_rnn
,
_
=
fluid
.
layers
.
rnn
(
size
=
size
,
cell
=
forward_cell
,
inputs
=
input
,
time_major
=
False
,
is_reverse
=
False
)
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_weight'
),
forward_rnn
=
fluid
.
layers
.
sequence_unpad
(
x
=
forward_rnn
,
length
=
length
)
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_rnn_bias'
),
is_reverse
=
False
)
input
,
length
=
fluid
.
layers
.
sequence_pad
(
input_proj_bn_reverse
,
pad_value
)
reverse_rnn
=
simple_rnn
(
reverse_rnn
,
_
=
fluid
.
layers
.
rnn
(
input
=
input_proj_bn_backward
,
cell
=
reverse_cell
,
size
=
size
,
inputs
=
input
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_weight'
),
sequence_length
=
length
,
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_rnn_bias'
),
time_major
=
False
,
is_reverse
=
True
)
is_reverse
=
True
)
reverse_rnn
=
fluid
.
layers
.
sequence_unpad
(
x
=
reverse_rnn
,
length
=
length
)
out
=
fluid
.
layers
.
concat
(
input
=
[
forward_rnn
,
reverse_rnn
],
axis
=
1
)
out
=
fluid
.
layers
.
concat
(
input
=
[
forward_rnn
,
reverse_rnn
],
axis
=
1
)
return
out
return
out
...
@@ -202,6 +211,7 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
...
@@ -202,6 +211,7 @@ def bidirectional_simple_rnn_bn_layer(name, input, size, share_weights):
def
bidirectional_gru_bn_layer
(
name
,
input
,
size
,
act
):
def
bidirectional_gru_bn_layer
(
name
,
input
,
size
,
act
):
"""Bidirectonal gru layer with sequence-wise batch normalization.
"""Bidirectonal gru layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
The batch normalization is only performed on input-state weights.
:param name: Name of the layer.
:param name: Name of the layer.
:type name: string
:type name: string
:param input: Input layer.
:param input: Input layer.
...
@@ -219,7 +229,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
...
@@ -219,7 +229,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
act
=
None
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_fc_weight'
),
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_fc_weight'
),
bias_attr
=
False
)
bias_attr
=
False
)
input_proj_
backward
=
fluid
.
layers
.
fc
(
input_proj_
reverse
=
fluid
.
layers
.
fc
(
input
=
input
,
input
=
input
,
size
=
size
*
3
,
size
=
size
*
3
,
act
=
None
,
act
=
None
,
...
@@ -233,8 +243,8 @@ def bidirectional_gru_bn_layer(name, input, size, act):
...
@@ -233,8 +243,8 @@ def bidirectional_gru_bn_layer(name, input, size, act):
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_batch_norm_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_batch_norm_bias'
),
moving_mean_name
=
name
+
'_forward_batch_norm_moving_mean'
,
moving_mean_name
=
name
+
'_forward_batch_norm_moving_mean'
,
moving_variance_name
=
name
+
'_forward_batch_norm_moving_variance'
)
moving_variance_name
=
name
+
'_forward_batch_norm_moving_variance'
)
input_proj_bn_
backward
=
fluid
.
layers
.
batch_norm
(
input_proj_bn_
reverse
=
fluid
.
layers
.
batch_norm
(
input
=
input_proj_
backward
,
input
=
input_proj_
reverse
,
act
=
None
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_weight'
),
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_weight'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_reverse_batch_norm_bias'
),
...
@@ -250,7 +260,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
...
@@ -250,7 +260,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_gru_bias'
),
bias_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'_forward_gru_bias'
),
is_reverse
=
False
)
is_reverse
=
False
)
reverse_gru
=
fluid
.
layers
.
dynamic_gru
(
reverse_gru
=
fluid
.
layers
.
dynamic_gru
(
input
=
input_proj_bn_
backward
,
input
=
input_proj_bn_
reverse
,
size
=
size
,
size
=
size
,
gate_activation
=
'sigmoid'
,
gate_activation
=
'sigmoid'
,
candidate_activation
=
act
,
candidate_activation
=
act
,
...
@@ -262,6 +272,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
...
@@ -262,6 +272,7 @@ def bidirectional_gru_bn_layer(name, input, size, act):
def
conv_group
(
input
,
num_stacks
,
seq_len_data
,
masks
):
def
conv_group
(
input
,
num_stacks
,
seq_len_data
,
masks
):
"""Convolution group with stacked convolution layers.
"""Convolution group with stacked convolution layers.
:param input: Input layer.
:param input: Input layer.
:type input: Variable
:type input: Variable
:param num_stacks: Number of stacked convolution layers.
:param num_stacks: Number of stacked convolution layers.
...
@@ -315,6 +326,7 @@ def conv_group(input, num_stacks, seq_len_data, masks):
...
@@ -315,6 +326,7 @@ def conv_group(input, num_stacks, seq_len_data, masks):
def
rnn_group
(
input
,
size
,
num_stacks
,
num_conv_layers
,
use_gru
,
def
rnn_group
(
input
,
size
,
num_stacks
,
num_conv_layers
,
use_gru
,
share_rnn_weights
):
share_rnn_weights
):
"""RNN group with stacked bidirectional simple RNN or GRU layers.
"""RNN group with stacked bidirectional simple RNN or GRU layers.
:param input: Input layer.
:param input: Input layer.
:type input: Variable
:type input: Variable
:param size: Dimension of RNN cells in each layer.
:param size: Dimension of RNN cells in each layer.
...
@@ -359,6 +371,7 @@ def deep_speech_v2_network(audio_data,
...
@@ -359,6 +371,7 @@ def deep_speech_v2_network(audio_data,
use_gru
=
False
,
use_gru
=
False
,
share_rnn_weights
=
True
):
share_rnn_weights
=
True
):
"""The DeepSpeech2 network structure.
"""The DeepSpeech2 network structure.
:param audio_data: Audio spectrogram data layer.
:param audio_data: Audio spectrogram data layer.
:type audio_data: Variable
:type audio_data: Variable
:param text_data: Transcription text data layer.
:param text_data: Transcription text data layer.
...
...
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