-
Notifications
You must be signed in to change notification settings - Fork 91
/
hallucination_net.py
313 lines (251 loc) · 13.5 KB
/
hallucination_net.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
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
"""
" License:
" -----------------------------------------------------------------------------
" Copyright (c) 2017, Gabriel Eilertsen.
" All rights reserved.
"
" Redistribution and use in source and binary forms, with or without
" modification, are permitted provided that the following conditions are met:
"
" 1. Redistributions of source code must retain the above copyright notice,
" this list of conditions and the following disclaimer.
"
" 2. Redistributions in binary form must reproduce the above copyright notice,
" this list of conditions and the following disclaimer in the documentation
" and/or other materials provided with the distribution.
"
" 3. Neither the name of the copyright holder nor the names of its contributors
" may be used to endorse or promote products derived from this software
" without specific prior written permission.
"
" THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
" IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
" ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
" LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
" CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
" SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
" INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
" CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
" ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
" POSSIBILITY OF SUCH DAMAGE.
" -----------------------------------------------------------------------------
"
" Description: TensorFlow autoencoder CNN for HDR image reconstruction.
" Author: Gabriel Eilertsen, [email protected]
" Date: Aug 2017
"""
import tensorflow as tf
import tensorlayer as tl
import numpy as np
# The HDR reconstruction autoencoder fully convolutional neural network
def model(x, batch_size=1, is_training=False):
# Encoder network (VGG16, until pool5)
x_in = tf.scalar_mul(255.0, x)
net_in = tl.layers.InputLayer(x_in, name='input_layer')
conv_layers, skip_layers = encoder(net_in)
# Fully convolutional layers on top of VGG16 conv layers
network = tl.layers.Conv2dLayer(conv_layers,
act=tf.identity,
shape=[3, 3, 512, 512],
strides=[1, 1, 1, 1],
padding='SAME',
name='encoder/h6/conv')
#network = tf.layers.batch_normalization(network, training=is_training, name='encoder/h6/batch_norm')
network = tl.layers.BatchNormLayer(network, is_train=is_training, name='encoder/h6/batch_norm')
network.outputs = tf.nn.relu(network.outputs, name='encoder/h6/relu')
# Decoder network
network = decoder(network, skip_layers, batch_size, is_training)
"""if is_training:
return network, conv_layers"""
return network, conv_layers
# Final prediction of the model, including blending with input
def get_final(network, x_in):
sb, sy, sx, sf = x_in.get_shape().as_list()
y_predict = network.outputs
# Highlight mask
thr = 0.05
alpha = tf.reduce_max(x_in, reduction_indices=[3])
alpha = tf.minimum(1.0, tf.maximum(0.0, alpha - 1.0 + thr) / thr)
alpha = tf.reshape(alpha, [-1, sy, sx, 1])
alpha = tf.tile(alpha, [1, 1, 1, 3])
# Linearied input and prediction
x_lin = tf.pow(x_in, 2.0)
y_predict = tf.exp(y_predict) - 1.0 / 255.0
# Alpha blending
y_final = (1 - alpha) * x_lin + alpha * y_predict
return y_final
# Convolutional layers of the VGG16 model used as encoder network
def encoder(input_layer):
VGG_MEAN = [103.939, 116.779, 123.68]
# Convert RGB to BGR
red, green, blue = tf.split(input_layer.outputs, 3, 3)
bgr = tf.concat([blue - VGG_MEAN[0], green - VGG_MEAN[1], red - VGG_MEAN[2]], axis=3)
network = tl.layers.InputLayer(bgr, name='encoder/input_layer_bgr')
# Convolutional layers size 1
network = conv_layer(network, [3, 64], 'encoder/h1/conv_1')
beforepool1 = conv_layer(network, [64, 64], 'encoder/h1/conv_2')
network = pool_layer(beforepool1, 'encoder/h1/pool')
# Convolutional layers size 2
network = conv_layer(network, [64, 128], 'encoder/h2/conv_1')
beforepool2 = conv_layer(network, [128, 128], 'encoder/h2/conv_2')
network = pool_layer(beforepool2, 'encoder/h2/pool')
# Convolutional layers size 3
network = conv_layer(network, [128, 256], 'encoder/h3/conv_1')
network = conv_layer(network, [256, 256], 'encoder/h3/conv_2')
beforepool3 = conv_layer(network, [256, 256], 'encoder/h3/conv_3')
network = pool_layer(beforepool3, 'encoder/h3/pool')
# Convolutional layers size 4
network = conv_layer(network, [256, 512], 'encoder/h4/conv_1')
network = conv_layer(network, [512, 512], 'encoder/h4/conv_2')
beforepool4 = conv_layer(network, [512, 512], 'encoder/h4/conv_3')
network = pool_layer(beforepool4, 'encoder/h4/pool')
# Convolutional layers size 5
network = conv_layer(network, [512, 512], 'encoder/h5/conv_1')
network = conv_layer(network, [512, 512], 'encoder/h5/conv_2')
beforepool5 = conv_layer(network, [512, 512], 'encoder/h5/conv_3')
network = pool_layer(beforepool5, 'encoder/h5/pool')
return network, (input_layer, beforepool1, beforepool2, beforepool3, beforepool4, beforepool5)
# Decoder network
def decoder(input_layer, skip_layers, batch_size=1, is_training=False):
sb, sx, sy, sf = input_layer.outputs.get_shape().as_list()
alpha = 0.0
# Upsampling 1
network = deconv_layer(input_layer, (batch_size, sx, sy, sf, sf), 'decoder/h1/decon2d', alpha, is_training)
# Upsampling 2
network = skip_connection_layer(network, skip_layers[5], 'decoder/h2/fuse_skip_connection', is_training)
network = deconv_layer(network, (batch_size, sx, sy, sf, sf), 'decoder/h2/decon2d', alpha, is_training)
# Upsampling 3
network = skip_connection_layer(network, skip_layers[4], 'decoder/h3/fuse_skip_connection', is_training)
network = deconv_layer(network, (batch_size, sx, sy, sf, sf / 2), 'decoder/h3/decon2d', alpha, is_training)
# Upsampling 4
network = skip_connection_layer(network, skip_layers[3], 'decoder/h4/fuse_skip_connection', is_training)
network = deconv_layer(network, (batch_size, sx, sy, sf / 2, sf / 4), 'decoder/h4/decon2d', alpha,
is_training)
# Upsampling 5
network = skip_connection_layer(network, skip_layers[2], 'decoder/h5/fuse_skip_connection', is_training)
network = deconv_layer(network, (batch_size, sx, sy, sf / 4, sf / 8), 'decoder/h5/decon2d', alpha,
is_training)
# Skip-connection at full size
network = skip_connection_layer(network, skip_layers[1], 'decoder/h6/fuse_skip_connection', is_training)
# Final convolution
network = tl.layers.Conv2dLayer(network,
act=tf.identity,
shape=[1, 1, int(sf / 8), 3],
strides=[1, 1, 1, 1],
padding='SAME',
W_init=tf.contrib.layers.xavier_initializer(uniform=False),
b_init=tf.constant_initializer(value=0.0),
name='decoder/h7/conv2d')
# Final skip-connection
network = tl.layers.BatchNormLayer(network, is_train=is_training, name='decoder/h7/batch_norm')
network.outputs = tf.maximum(alpha * network.outputs, network.outputs, name='decoder/h7/leaky_relu')
network = skip_connection_layer(network, skip_layers[0], 'decoder/h7/fuse_skip_connection')
return network
# Load weights for VGG16 encoder convolutional layers
# Weights are from a .npy file generated with the caffe-tensorflow tool
def load_vgg_weights(network, weight_file, session):
params = []
if weight_file.lower().endswith('.npy'):
npy = np.load(weight_file, encoding='latin1')
for key, val in sorted(npy.item().items()):
if (key[:4] == "conv"):
print(" Loading %s" % (key))
print(" weights with size %s " % str(val['weights'].shape))
print(" and biases with size %s " % str(val['biases'].shape))
params.append(val['weights'])
params.append(val['biases'])
else:
print('No weights in suitable .npy format found for path ', weight_file)
print('Assigning loaded weights..')
tl.files.assign_params(session, params, network)
return network
# === Layers ==================================================================
# Convolutional layer
def conv_layer(input_layer, sz, str):
network = tl.layers.Conv2dLayer(input_layer,
act=tf.nn.relu,
shape=[3, 3, sz[0], sz[1]],
strides=[1, 1, 1, 1],
padding='SAME',
name=str)
return network
# Max-pooling layer
def pool_layer(input_layer, str):
network = tl.layers.PoolLayer(input_layer,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool=tf.nn.max_pool,
name=str)
return network
# Concatenating fusion of skip-connections
def skip_connection_layer(input_layer, skip_layer, str, is_training=False):
_, sx, sy, sf = input_layer.outputs.get_shape().as_list()
_, sx_, sy_, sf_ = skip_layer.outputs.get_shape().as_list()
#assert (sx_, sy_, sf_) == (sx, sy, sf)
# skip-connection domain transformation, from LDR encoder to log HDR decoder
# skip_layer.outputs = tf.log(tf.pow(tf.scalar_mul(1.0 / 255, skip_layer.outputs), 2.0) + 1.0 / 255.0)
skip_layer.outputs = tf.scalar_mul(1.0 / 255, skip_layer.outputs)
# specify weights for fusion of concatenation, so that it performs an element-wise addition
weights = np.zeros((1, 1, sf + sf_, sf))
for i in range(sf):
weights[0, 0, i, i] = 1
weights[:, :, i + sf_, i] = 1
add_init = tf.constant_initializer(value=weights, dtype=tf.float32)
# concatenate layers
network = tl.layers.ConcatLayer([input_layer, skip_layer], concat_dim=3, name='%s/skip_connection' % str)
# fuse concatenated layers using the specified weights for initialization
network = tl.layers.Conv2dLayer(network,
act=tf.identity,
shape=[1, 1, sf + sf_, sf],
strides=[1, 1, 1, 1],
padding='SAME',
W_init=add_init,
b_init=tf.constant_initializer(value=0.0),
name=str)
return network
# Deconvolution layer
def deconv_layer(input_layer, sz, str, alpha, is_training=False):
scale = 2
filter_size = (2 * scale - scale % 2)
num_in_channels = int(sz[3])
num_out_channels = int(sz[4])
network = tl.layers.UpSampling2dLayer(input_layer, (scale, scale), True, 1, False, '%s/NN_dc' % str)
network = tl.layers.PadLayer(network, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT", name='inpad')
# network = conv_layer(network, [num_in_channels, num_out_channels], str)
network = tl.layers.Conv2dLayer(network,
act=tf.nn.relu,
shape=[3, 3, num_in_channels, num_out_channels],
strides=[1, 1, 1, 1],
padding='VALID',
name=str)
# # create bilinear weights in numpy array
# bilinear_kernel = np.zeros([filter_size, filter_size], dtype=np.float32)
# scale_factor = (filter_size + 1) // 2
# if filter_size % 2 == 1:
# center = scale_factor - 1
# else:
# center = scale_factor - 0.5
# for x in range(filter_size):
# for y in range(filter_size):
# bilinear_kernel[x, y] = (1 - abs(x - center) / scale_factor) * \
# (1 - abs(y - center) / scale_factor)
# weights = np.zeros((filter_size, filter_size, num_out_channels, num_in_channels))
# for i in range(num_out_channels):
# weights[:, :, i, i] = bilinear_kernel
#
# init_matrix = tf.constant_initializer(value=weights, dtype=tf.float32)
#
# """network = tl.layers.DeConv2d(input_layer,
# shape=[filter_size, filter_size, num_out_channels, num_in_channels],
# output_shape=[sz[0], sz[1] * scale, sz[2] * scale, num_out_channels],
# strides=[1, scale, scale, 1],
# W_init=init_matrix,
# padding='SAME',
# act=tf.identity,
# name=str)"""
# network = tl.layers.DeConv2d(input_layer, num_out_channels, (filter_size, filter_size), (scale, scale), 'SAME', tf.identity, W_init=init_matrix, name=str)
network = tl.layers.BatchNormLayer(network, is_train=is_training, name='%s/batch_norm_dc' % str)
network.outputs = tf.maximum(alpha * network.outputs, network.outputs, name='%s/leaky_relu_dc' % str)
return network