-
Notifications
You must be signed in to change notification settings - Fork 36
/
ops.py
207 lines (164 loc) · 8.26 KB
/
ops.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
import tensorflow as tf
import math
import numpy as np
#config e.g. dilations: [1,4,16,] In most cases[1,4,] is enough
def nextitnet_residual_block(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
relu1 = tf.nn.relu(input_ln)
return input_ + relu1
#Aggregated Residual Transformations for Deep Neural Networks block1 =resnet if cardinality==1
def get_mp(input_,cardinality=32, name="mp"):
with tf.variable_scope(name):
residual_channels = input_.get_shape()[-1]
hidden_size = residual_channels / (cardinality * 4)
blocksets = list()
for i in range(cardinality):
conv_down_i = conv1d(input_, hidden_size,
name="mp_conv1_down_{}".format(i)
)
conv_down_i = gelu(conv_down_i)
conv_up_i = conv1d(conv_down_i, residual_channels,
name="mp_conv1_up_{}".format(i)
)
blocksets.append(conv_up_i)
output = tf.add_n(blocksets)
return input_+output
# peter_2mp_parallel
def peter_2mp_parallel(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True,mp=True,cardinality=32):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
if mp:
after_adapter = get_mp(input_, cardinality,name="mp_1")
dilated_conv = tf.add(dilated_conv, after_adapter)
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
#input_ln=tf.contrib.layers.layer_norm(dilated_conv,reuse=not train, trainable=train) #performance is not good, paramter wrong?
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
if mp:
after_adapter=get_mp(relu1,cardinality,name="mp_2")
dilated_conv = tf.add(dilated_conv, after_adapter)
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
relu1 = tf.nn.relu(input_ln)
return relu1+input_
# peter_2mp_parallel peter_2mp_serial
def peter_2mp_serial(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True,mp=True,cardinality=32):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
if mp:
after_adapter = get_mp(dilated_conv, cardinality,name="mp_1")
dilated_conv = after_adapter
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
if mp:
after_adapter=get_mp(dilated_conv,cardinality,name="mp_2")
dilated_conv = after_adapter
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
relu1 = tf.nn.relu(input_ln)
return input_ + relu1
def peter_mp_serial(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True,mp=True,cardinality=32):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
if mp:
after_adapter=get_mp(dilated_conv,cardinality)
dilated_conv = after_adapter
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
relu1 = tf.nn.relu(input_ln)
return input_ + relu1
def conv1d(input_, output_channels,
dilation=1, kernel_size=1, causal=False,
name="dilated_conv"):
with tf.variable_scope(name):
weight = tf.get_variable('weight', [1, kernel_size, input_.get_shape()[-1], output_channels],
initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1))
bias = tf.get_variable('bias', [output_channels],
initializer=tf.constant_initializer(0.0))
if causal:
padding = [[0, 0], [(kernel_size - 1) * dilation, 0], [0, 0]]
padded = tf.pad(input_, padding)
input_expanded = tf.expand_dims(padded, dim=1)
out = tf.nn.atrous_conv2d(input_expanded, weight, rate=dilation, padding='VALID') + bias
else:
input_expanded = tf.expand_dims(input_, dim=1)
# out = tf.nn.conv2d(input_expanded, weight, strides=[1, 1, 1, 1], padding="SAME") + bias
out = tf.nn.atrous_conv2d(input_expanded, weight, rate=dilation, padding='SAME') + bias
return tf.squeeze(out, [1])
# tf.contrib.layers.layer_norm
def layer_norm(x, name, epsilon=1e-8, trainable=True):
with tf.variable_scope(name):
shape = x.get_shape()
beta = tf.get_variable('beta', [int(shape[-1])],
initializer=tf.constant_initializer(0), trainable=trainable)
gamma = tf.get_variable('gamma', [int(shape[-1])],
initializer=tf.constant_initializer(1), trainable=trainable)
mean, variance = tf.nn.moments(x, axes=[len(shape) - 1], keep_dims=True)
x = (x - mean) / tf.sqrt(variance + epsilon)
return gamma * x + beta
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf