-
Notifications
You must be signed in to change notification settings - Fork 15
/
keras_contrib_backend.py
185 lines (160 loc) · 6.11 KB
/
keras_contrib_backend.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
import tensorflow as tf
import numpy as np
try:
from tensorflow.python.ops import ctc_ops as ctc
except ImportError:
import tensorflow.contrib.ctc as ctc
from keras.backend import tensorflow_backend as KTF
from keras.backend import dtype
from keras.backend.common import floatx
from keras.backend.common import image_data_format
from keras.backend.tensorflow_backend import _to_tensor
from keras.backend.tensorflow_backend import logsumexp
py_all = all
def _preprocess_conv2d_input(x, data_format):
"""Transpose and cast the input before the conv2d.
# Arguments
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
"""
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
if data_format == 'channels_first':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, rows, cols)
# TF input shape: (samples, rows, cols, input_depth)
x = tf.transpose(x, (0, 2, 3, 1))
return x
def _postprocess_conv2d_output(x, data_format):
"""Transpose and cast the output from conv2d if needed.
# Arguments
x: A tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
"""
if data_format == 'channels_first':
x = tf.transpose(x, (0, 3, 1, 2))
if floatx() == 'float64':
x = tf.cast(x, 'float64')
return x
def _preprocess_padding(padding):
"""Convert keras' padding to tensorflow's padding.
# Arguments
padding: string, `"same"` or `"valid"`.
# Returns
a string, `"SAME"` or `"VALID"`.
# Raises
ValueError: if `padding` is invalid.
"""
if padding == 'same':
padding = 'SAME'
elif padding == 'valid':
padding = 'VALID'
else:
raise ValueError('Invalid padding:', padding)
return padding
def conv2d(x, kernel, strides=(1, 1), padding='valid', data_format='channels_first',
image_shape=None, filter_shape=None):
'''2D convolution.
# Arguments
kernel: kernel tensor.
strides: strides tuple.
padding: string, "same" or "valid".
data_format: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering
in inputs/kernels/ouputs.
'''
if padding == 'same':
padding = 'SAME'
elif padding == 'valid':
padding = 'VALID'
else:
raise Exception('Invalid border mode: ' + str(padding))
strides = (1,) + strides + (1,)
if floatx() == 'float64':
# tf conv2d only supports float32
x = tf.cast(x, 'float32')
kernel = tf.cast(kernel, 'float32')
if data_format == 'channels_first':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, rows, cols)
# TF input shape: (samples, rows, cols, input_depth)
# TH kernel shape: (depth, input_depth, rows, cols)
# TF kernel shape: (rows, cols, input_depth, depth)
x = tf.transpose(x, (0, 2, 3, 1))
kernel = tf.transpose(kernel, (2, 3, 1, 0))
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
x = tf.transpose(x, (0, 3, 1, 2))
elif data_format == 'channels_last':
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
else:
raise Exception('Unknown data_format: ' + str(data_format))
if floatx() == 'float64':
x = tf.cast(x, 'float64')
return x
def extract_image_patches(x, ksizes, ssizes, padding='same',
data_format='channels_last'):
'''
Extract the patches from an image
# Parameters
x : The input image
ksizes : 2-d tuple with the kernel size
ssizes : 2-d tuple with the strides size
padding : 'same' or 'valid'
data_format : 'channels_last' or 'channels_first'
# Returns
The (k_w,k_h) patches extracted
TF ==> (batch_size,w,h,k_w,k_h,c)
TH ==> (batch_size,w,h,c,k_w,k_h)
'''
kernel = [1, ksizes[0], ksizes[1], 1]
strides = [1, ssizes[0], ssizes[1], 1]
padding = _preprocess_padding(padding)
if data_format == 'channels_first':
x = KTF.permute_dimensions(x, (0, 2, 3, 1))
bs_i, w_i, h_i, ch_i = KTF.int_shape(x)
patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1],
padding)
# Reshaping to fit Theano
bs, w, h, ch = KTF.int_shape(patches)
patches = tf.reshape(tf.transpose(tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i]), [0, 1, 2, 4, 3]),
[-1, w, h, ch_i, ksizes[0], ksizes[1]])
if data_format == 'channels_last':
patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
return patches
def depth_to_space(input, scale, data_format=None):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
if data_format is None:
data_format = image_data_format()
data_format = data_format.lower()
input = _preprocess_conv2d_input(input, data_format)
out = tf.depth_to_space(input, scale)
out = _postprocess_conv2d_output(out, data_format)
return out
def moments(x, axes, shift=None, keep_dims=False):
''' Wrapper over tensorflow backend call '''
return tf.nn.moments(x, axes, shift=shift, keep_dims=keep_dims)
def clip(x, min_value, max_value):
"""Element-wise value clipping.
If min_value > max_value, clipping range is [min_value,min_value].
# Arguments
x: Tensor or variable.
min_value: Tensor, float, int, or None.
If min_value is None, defaults to -infinity.
max_value: Tensor, float, int, or None.
If max_value is None, defaults to infinity.
# Returns
A tensor.
"""
if max_value is None:
max_value = np.inf
if min_value is None:
min_value = -np.inf
min_value = _to_tensor(min_value, x.dtype.base_dtype)
max_value = _to_tensor(max_value, x.dtype.base_dtype)
max_value = tf.maximum(min_value, max_value)
return tf.clip_by_value(x, min_value, max_value)