-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodels.py
165 lines (138 loc) · 7.97 KB
/
models.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
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import tensorflow as tf
import numpy as np
import cv2
from ops import *
class UNET(object):
"""AFGAN Model"""
def __init__(self, model_path):
self.graph_path = model_path+"/tf_graph/"
self.save_path = model_path + "/saved_model/"
self.output_path = model_path + "/results/"
def encoder(self, img, code_length):
with tf.variable_scope("Encoder") as scope:
E_conv1 = Conv_2D(img, output_chan=32, use_bn=True,name="E_Conv1")
self.E_conv1 = max_pool(E_conv1)
E_conv2 = Conv_2D(self.E_conv1, output_chan=64, use_bn=True ,name="E_Conv2")
E_conv2 = max_pool(E_conv2)
E_conv3 = Conv_2D(E_conv2, output_chan=128, use_bn=True ,name="E_Conv3")
E_conv3 = max_pool(E_conv3)
E_conv4 = Conv_2D(E_conv3, output_chan=512, use_bn=False ,name="E_Conv4")
self.E_conv4 = max_pool(E_conv4)
E_conv4_r = tf.reshape(self.E_conv4, shape=[-1, int(np.prod(E_conv4.get_shape()[1:]))])
# TODO: assert the shape of sigma and mean
E_mean = Dense(E_conv4_r, output_dim=code_length, activation=None,use_bn=False, name="E_mean")
# E_sigma = Dense(E_conv4_r, output_dim=1, activation=None, use_bn=False, name ="E_sigma")
# Get the variance in the log space
E_log_var = Dense(E_conv4_r, output_dim=code_length, activation=None, use_bn=False, name ="E_sigma")
E_sigma = tf.exp(0.5 * E_log_var)
return E_mean, E_sigma
# return E_mean, E_log_var
def generator(self,z,batch_size):
with tf.variable_scope("Generator") as scope:
G_linear1 = Dense(z, output_dim=1024, name="G_hidden1")
G_linear2 = Dense(G_linear1, output_dim=4*4*512, name="G_hidden2")
G_linear2_r = tf.reshape(G_linear2, shape=[-1,4, 4,512])
G_Dconv3 = Dconv_2D(G_linear2_r, output_chan=256,batch_size=batch_size ,name="G_hidden3")
G_Dconv4 = Dconv_2D(G_Dconv3, output_chan=128,batch_size=batch_size , name="G_hidden4")
G_Dconv5 = Dconv_2D(G_Dconv4, output_chan=64,batch_size=batch_size , name="G_hidden5")
G_Dconv6 = Dconv_2D(G_Dconv5, output_chan=32,batch_size=batch_size , name="G_hidden6")
G_Dconv7 = Dconv_2D(G_Dconv6, output_chan=16,batch_size=batch_size , name="G_hidden7")
G_Dconv8 = Dconv_2D(G_Dconv7, output_chan=8,batch_size=batch_size , name="G_hidden8")
G_Dconv9 = Dconv_2D(G_Dconv8, output_chan=3,batch_size=batch_size , name="G_output", use_bn=False)
return tf.nn.tanh(G_Dconv9)
def discriminator(self, img, reuse=False):
with tf.variable_scope("Discriminator", reuse=reuse) as scope:
D_conv1 = Conv_2D(img, output_chan=32, use_bn=True, name="D_conv1")
D_conv1 = max_pool(D_conv1)
D_conv2 = Conv_2D(D_conv1, output_chan=64, use_bn=True, name="D_conv2")
D_conv2 = max_pool(D_conv2)
D_conv3 = Conv_2D(D_conv2, output_chan=128, use_bn=True, name="D_conv3")
D_conv3 = max_pool(D_conv3)
D_conv4 = Conv_2D(D_conv3, output_chan=512, use_bn=False, name="D_conv4")
D_conv4 = max_pool(D_conv4)
# D_conv4_r = tf.reshape(D_conv2, shape=[-1, int(np.prod(D_conv2.get_shape().as_list()[1:]))])
D_conv4_r = tf.reshape(D_conv4, shape=[-1, int(np.prod(D_conv4.get_shape()[1:]))])
D_linear5 = Dense(D_conv4_r,output_dim=1028, name="D_dense5")
D_linear6 = Dense(D_linear5,output_dim=1, name="D_dense6")
return tf.nn.sigmoid(D_linear6)
def build_model(self, batch_size=4):
with tf.name_scope("Inputs") as scope:
self.x_norm = tf.placeholder(tf.float32, shape=[None,512,512,3], name="Input_Normal")
self.x_blur = tf.placeholder(tf.float32, shape=[None,512,512,3], name="Input_Blurred")
self.z = tf.placeholder(tf.float32, shape=[None, 100], name="Noise")
self.train_phase = tf.placeholder(tf.bool, name="is_train")
self.x_norm_summ = tf.summary.image("Input Images", self.x_norm)
self.x_blur_summ = tf.summary.image("Input Images", self.x_blur)
self.z_summ = tf.summary.histogram("Input Noise", self.z)
with tf.name_scope("Model") as scope:
self.encod_mean, self.encod_sigma = self.encoder(self.x_norm, 100)
self.gen_in = self.encod_mean + self.encod_sigma*self.z
self.gen_out = self.generator(self.gen_in, batch_size)
self.dis_real = self.discriminator(self.x_blur, reuse=False)
self.dis_fake = self.discriminator(self.gen_out, reuse=True)
self.gen_summ = tf.summary.image("Generator images", self.gen_out)
with tf.name_scope("Loss") as scope:
# self.marg_likeli = tf.reduce_sum(self.x_blur * tf.log(self.gen_out) + (1 - self.x_blur) * tf.log(1 - self.gen_out), 1)
self.KL_diver = 0.5 * tf.reduce_sum(tf.square(self.encod_mean) + tf.square(self.encod_sigma) - tf.log(1e-8 + tf.square(self.encod_sigma)) - 1, 1)
# self.marg_likeli = tf.reduce_mean(self.marg_likeli)
self.KL_diver = tf.reduce_mean(self.KL_diver, axis=0)
self.encod_loss = self.KL_diver# - self.marg_likeli
self.dis_real_loss = tf.reduce_mean(-tf.log(self.dis_real))
self.dis_fake_loss = tf.reduce_mean(-tf.log(1-self.dis_fake))
self.dis_loss = self.dis_real_loss + self.dis_fake_loss
self.gen_loss = tf.reduce_mean(-tf.log(self.dis_fake))
self.dis_loss_summ = tf.summary.scalar("Discriminator Loss", self.dis_loss)
self.gen_loss_summ = tf.summary.scalar("Generator Loss", self.gen_loss)
train_vars = tf.trainable_variables()
self.d_vars = [var for var in train_vars if "D_" in var.name]
self.g_vars = [var for var in train_vars if "G_" in var.name]
self.enc_vars = [var for var in train_vars if "E_" in var.name]
with tf.name_scope("Optimizers") as scope:
self.D_solver = tf.train.AdamOptimizer(learning_rate=1e-5, beta1=0.1).minimize(self.dis_loss, var_list=self.d_vars)
self.G_solver = tf.train.AdamOptimizer(learning_rate=1e-5, beta1=0.3).minimize(self.gen_loss, var_list=self.g_vars)
self.E_solver = tf.train.AdamOptimizer(learning_rate=1e-5, beta1=0.1).minimize(self.encod_loss, var_list=self.enc_vars)
self.sess = tf.Session()
self.writer = tf.summary.FileWriter(self.graph_path)
self.writer.add_graph(self.sess.graph)
self.saver = tf.train.Saver()
self.sess.run(tf.global_variables_initializer())
def train_model(self,inputs,learning_rate=1e-5, batch_size=4, epoch_size=1000000000):
with tf.name_scope("Training") as scope:
sample_z = np.random.normal(size=(batch_size,100))
for epoch in range(epoch_size):
for itr in xrange(0, len(inputs[0])-batch_size, batch_size):
norm_images = inputs[0][itr:itr+batch_size]
blur_images = inputs[1][itr:itr+batch_size]
batch_z = np.random.normal(size=(batch_size,100))
D_inputs = [self.D_solver ,self.dis_real_loss, self.dis_fake_loss, self.dis_loss]
D_outputs = self.sess.run(D_inputs, {self.x_norm:norm_images,self.x_blur:blur_images,
self.z:batch_z, self.train_phase:True})
G_inputs = [self.G_solver, self.E_solver, self.gen_loss, self.encod_loss, self.encod_mean, self.encod_sigma, self.E_conv1, self.E_conv4]
G_outputs = self.sess.run(G_inputs, {self.x_norm:norm_images,self.x_blur:blur_images,
self.z:batch_z, self.train_phase:True})
if itr%5==0:
print "Epoch: ", epoch, "Iteration: ", itr
print "Dis Fake Loss: ", D_outputs[2], "Dis Real Loss: ", D_outputs[1], "Dis Total Loss", D_outputs[3]
print "Generator Loss: ", G_outputs[2]
print "Encoder Loss: ", G_outputs[3]
# print "Enc conv1: ", G_outputs[-2]
# print "Enc conv4: ", G_outputs[-1]
# print 'Mean: {}\tSigma:{}'.format(G_outputs[-4], G_outputs[-3])
if epoch%10==0:
self.saver.save(self.sess, self.save_path)
print "Checkpoint saved"
input_img = inputs[0][5:9]
generated_images = self.sess.run([self.gen_out], {self.x_norm: input_img, self.z : sample_z, self.train_phase:False})
all_images = np.array(generated_images[0])
for i in range(2):
image_grid_horizontal = 255.0*all_images[i*2]
for j in range(1):
image = 255.0*all_images[i*2+j+1]
image_grid_horizontal = np.hstack((image_grid_horizontal, image))
if i==0:
image_grid_vertical = image_grid_horizontal
else:
image_grid_vertical = np.vstack((image_grid_vertical, image_grid_horizontal))
cv2.imwrite(self.output_path +"/img_"+str(epoch)+".jpg", image_grid_vertical)