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model.py
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import tensorflow as tf
EPS = 1e-12
# Class for batch normalization node
class batch_norm(object):
def __init__(self, epsilon=1e-5, momentum=0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x, train=True):
return tf.contrib.layers.batch_norm(x,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=train,
scope=self.name,
reuse=tf.AUTO_REUSE # if tensorflow vesrion < 1.4, delete this line
)
# leaky relu function
def lrelu(X, leak=0.2):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * X + f2 * tf.abs(X)
class Pix2Pix:
# Network Parameters
def __init__(self, sess, batch_size):
self.learning_rate = 0.0002
self.sess = sess
self.batch_size = batch_size
self.keep_prob = 0.5
self.image_shape = [256, 256, 3]
self.l1_weight = 100.0
'''channels'''
# Gen_Encoding
self.ch_G0 = 3
self.ch_G1 = 64
self.ch_G2 = 128
self.ch_G3 = 256
self.ch_G4 = 512
self.ch_G5 = 512
self.ch_G6 = 512
self.ch_G7 = 512
self.ch_G8 = 512
# Gen_Decoding
self.ch_G9 = 512
self.ch_G10 = 512
self.ch_G11 = 512
self.ch_G12 = 512
self.ch_G13 = 256
self.ch_G14 = 128
self.ch_G15 = 64
self.ch_G16 = 3
# Discrim
self.ch_D0 = 6
self.ch_D1 = 64
self.ch_D2 = 128
self.ch_D3 = 256
self.ch_D4 = 512
self.ch_D5 = 1
'''parameters'''
# Gen_encoding
self.G_W1 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G0, self.ch_G1], stddev=0.02), name="G_W1")
self.G_W2 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G1, self.ch_G2], stddev=0.02), name='G_W2')
self.G_bn2 = batch_norm(name="G_bn2")
self.G_W3 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G2, self.ch_G3], stddev=0.02), name='G_W3')
self.G_bn3 = batch_norm(name="G_bn3")
self.G_W4 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G3, self.ch_G4], stddev=0.02), name='G_W4')
self.G_bn4 = batch_norm(name="G_bn4")
self.G_W5 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G4, self.ch_G5], stddev=0.02), name='G_W5')
self.G_bn5 = batch_norm(name="G_bn5")
self.G_W6 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G5, self.ch_G6], stddev=0.02), name='G_W6')
self.G_bn6 = batch_norm(name="G_bn6")
self.G_W7 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G6, self.ch_G7], stddev=0.02), name='G_W7')
self.G_bn7 = batch_norm(name="G_bn7")
self.G_W8 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G7, self.ch_G8], stddev=0.02), name='G_W8')
self.G_bn8 = batch_norm(name="G_bn8")
# Gen_Decoding
self.G_W9 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G9, self.ch_G8], stddev=0.02), name='G_W9')
self.G_bn9 = batch_norm(name="G_bn9")
self.G_W10 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G10, self.ch_G9 + self.ch_G7], stddev=0.02), name='G_W10')
self.G_bn10 = batch_norm(name="G_bn10")
self.G_W11 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G11, self.ch_G10 + self.ch_G6], stddev=0.02), name='G_W11')
self.G_bn11 = batch_norm(name="G_bn11")
self.G_W12 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G12, self.ch_G11 + self.ch_G5], stddev=0.02), name='G_W12')
self.G_bn12 = batch_norm(name="G_bn12")
self.G_W13 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G13, self.ch_G12 + self.ch_G4], stddev=0.02), name='G_W13')
self.G_bn13 = batch_norm(name="G_bn13")
self.G_W14 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G14, self.ch_G13 + self.ch_G3], stddev=0.02), name='G_W14')
self.G_bn14 = batch_norm(name="G_bn14")
self.G_W15 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G15, self.ch_G14 + self.ch_G2], stddev=0.02), name='G_W15')
self.G_bn15 = batch_norm(name="G_bn15")
self.G_W16 = tf.Variable(tf.truncated_normal([4, 4, self.ch_G16, self.ch_G15 + self.ch_G1], stddev=0.02), name='G_W16')
# Discrim
self.D_W1 = tf.Variable(tf.truncated_normal([4, 4, self.ch_D0, self.ch_D1], stddev=0.02), name='D_W1')
self.D_bn1 = batch_norm(name="D_bn1")
self.D_W2 = tf.Variable(tf.truncated_normal([4, 4, self.ch_D1, self.ch_D2], stddev=0.02), name='D_W2')
self.D_bn2 = batch_norm(name="D_bn2")
self.D_W3 = tf.Variable(tf.truncated_normal([4, 4, self.ch_D2, self.ch_D3], stddev=0.02), name='D_W3')
self.D_bn3 = batch_norm(name="D_bn3")
self.D_W4 = tf.Variable(tf.truncated_normal([4, 4, self.ch_D3, self.ch_D4], stddev=0.02), name='D_W4')
self.D_bn4 = batch_norm(name="D_bn4")
self.D_W5 = tf.Variable(tf.truncated_normal([4, 4, self.ch_D4, self.ch_D5], stddev=0.02), name='D_W5')
self.gen_params = [
self.G_W1,
self.G_W2,
self.G_W3,
self.G_W4,
self.G_W5,
self.G_W6,
self.G_W7,
self.G_W8,
self.G_W9,
self.G_W10,
self.G_W11,
self.G_W12,
self.G_W13,
self.G_W14,
self.G_W15,
self.G_W16
]
self.discrim_params = [
self.D_W1,
self.D_W2,
self.D_W3,
self.D_W4,
self.D_W5
]
self._build_model()
# Build the Network
def _build_model(self):
self.input_img = tf.placeholder(tf.float32, [self.batch_size] + self.image_shape)
self.target_img = tf.placeholder(tf.float32, [self.batch_size] + self.image_shape)
gen_img = self.generate(self.input_img)
d_real = self.discriminate(self.input_img, self.target_img)
d_fake = self.discriminate(self.input_img, gen_img)
self.D_loss = tf.reduce_mean(-(tf.log(d_real + EPS) + tf.log(1 - d_fake + EPS)))
# G_loss_GAN = tf.reduce_mean(-tf.log(d_fake + EPS))
self.G_loss_GAN = tf.reduce_mean(-tf.log(d_fake + EPS))
# G_loss_L1 = tf.reduce_mean(tf.abs(self.target_img - gen_img))
self.G_loss_L1 = tf.reduce_mean(tf.abs(self.target_img - gen_img))
# self.G_loss = G_loss_GAN + G_loss_L1 * self.l1_weight
self.G_loss = self.G_loss_GAN + self.G_loss_L1 * self.l1_weight
self.train_op_discrim = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.D_loss, var_list=self.discrim_params)
self.train_op_gen = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.G_loss, var_list=self.gen_params)
def generate(self, input_img):
h1 = h1_ = tf.nn.conv2d(input_img, self.G_W1, strides=[1, 2, 2, 1], padding='SAME') # [?,256,256,3] -> [?,128,128,64]
h1 = lrelu(h1)
h2 = tf.nn.conv2d(h1, self.G_W2, strides=[1, 2, 2, 1], padding='SAME') # [?,128,128,64] -> [?,64,64,128]
h2 = h2_ = self.G_bn2(h2)
h2 = lrelu(h2)
h3 = tf.nn.conv2d(h2, self.G_W3, strides=[1, 2, 2, 1], padding='SAME') # [?,64,64,128] -> [?,32,32,256]
h3 = h3_ = self.G_bn3(h3)
h3 = lrelu(h3)
h4 = tf.nn.conv2d(h3, self.G_W4, strides=[1, 2, 2, 1], padding='SAME') # [?,32,32,256] -> [?,16,16,512]
h4 = h4_ = self.G_bn4(h4)
h4 = lrelu(h4)
h5 = tf.nn.conv2d(h4, self.G_W5, strides=[1, 2, 2, 1], padding='SAME') # [?,16,16,512] -> [?,8,8,512]
h5 = h5_ = self.G_bn5(h5)
h5 = lrelu(h5)
h6 = tf.nn.conv2d(h5, self.G_W6, strides=[1, 2, 2, 1], padding='SAME') # [?,8,8,512] -> [?,4,4,512]
h6 = h6_ = self.G_bn6(h6)
h6 = lrelu(h6)
h7 = tf.nn.conv2d(h6, self.G_W7, strides=[1, 2, 2, 1], padding='SAME') # [?,4,4,512] -> [?,2,2,512]
h7 = h7_ = self.G_bn7(h7)
h7 = lrelu(h7)
h8 = tf.nn.conv2d(h7, self.G_W8, strides=[1, 2, 2, 1], padding='SAME') # [?,2,2,512] -> [?,1,1,512]
h8 = self.G_bn8(h8)
h8 = tf.nn.relu(h8)
h9 = tf.nn.conv2d_transpose(h8, self.G_W9, output_shape=[self.batch_size, 2, 2, self.ch_G9], strides=[1, 2, 2, 1]) # [?,1,1,512] -> [?,2,2,512]
h9 = tf.nn.dropout(self.G_bn9(h9), keep_prob=self.keep_prob)
h9 = tf.nn.relu(h9)
h9 = tf.concat([h9, h7_], axis=3)
h10 = tf.nn.conv2d_transpose(h9, self.G_W10, output_shape=[self.batch_size, 4, 4, self.ch_G10], strides=[1, 2, 2, 1]) # [?,2,2,512+512] -> [?,4,4,512]
h10 = tf.nn.dropout(self.G_bn10(h10), keep_prob=self.keep_prob)
h10 = tf.nn.relu(h10)
h10 = tf.concat([h10, h6_], axis=3)
h11 = tf.nn.conv2d_transpose(h10, self.G_W11, output_shape=[self.batch_size, 8, 8, self.ch_G11], strides=[1, 2, 2, 1]) # [?,4,4,512+512] -> [?,8,8,512]
h11 = tf.nn.dropout(self.G_bn11(h11), keep_prob=self.keep_prob)
h11 = tf.nn.relu(h11)
h11 = tf.concat([h11, h5_], axis=3)
h12 = tf.nn.conv2d_transpose(h11, self.G_W12, output_shape=[self.batch_size, 16, 16, self.ch_G12], strides=[1, 2, 2, 1]) # [?,8,8,512+512] -> [?,16,16,512]
h12 = self.G_bn12(h12)
h12 = tf.nn.relu(h12)
h12 = tf.concat([h12, h4_], axis=3)
h13 = tf.nn.conv2d_transpose(h12, self.G_W13, output_shape=[self.batch_size, 32, 32, self.ch_G13], strides=[1, 2, 2, 1]) # [?,16,16,512+512] -> [?,32,32,256]
h13 = self.G_bn13(h13)
h13 = tf.nn.relu(h13)
h13 = tf.concat([h13, h3_], axis=3)
h14 = tf.nn.conv2d_transpose(h13, self.G_W14, output_shape=[self.batch_size, 64, 64, self.ch_G14], strides=[1, 2, 2, 1]) # [?,32,32,256+256] -> [?,64,64,128]
h14 = self.G_bn14(h14)
h14 = tf.nn.relu(h14)
h14 = tf.concat([h14, h2_], axis=3)
h15 = tf.nn.conv2d_transpose(h14, self.G_W15, output_shape=[self.batch_size, 128, 128, self.ch_G15], strides=[1, 2, 2, 1]) # [?,64,64,128+128] -> [?,128,128,64]
h15 = self.G_bn15(h15)
h15 = tf.nn.relu(h15)
h15 = tf.concat([h15, h1_], axis=3)
h16 = tf.nn.conv2d_transpose(h15, self.G_W16, output_shape=[self.batch_size, 256, 256, self.ch_G16], strides=[1, 2, 2, 1]) # [?,128,128,64+64] -> [?,256,256,3]
h16 = tf.nn.tanh(h16)
return h16
def discriminate(self, input_img, target):
img_concat = tf.concat([input_img, target], axis=3)
h1 = tf.nn.conv2d(img_concat, self.D_W1, strides=[1, 2, 2, 1], padding='SAME') # [?,256,256,6] -> [?,128,128,64]
h1 = self.D_bn1(h1)
h1 = lrelu(h1)
h2 = tf.nn.conv2d(h1, self.D_W2, strides=[1, 2, 2, 1], padding='SAME') # [?,128,128,64] -> [?,64,64,128]
h2 = self.D_bn2(h2)
h2 = lrelu(h2)
h3 = tf.nn.conv2d(h2, self.D_W3, strides=[1, 2, 2, 1], padding='SAME') # [?,64,64,128] -> [?,32,32,256]
h3 = self.D_bn3(h3)
h3 = lrelu(h3)
h4 = tf.pad(h3, [[0, 0], [1, 1], [1, 1], [0, 0]], mode='CONSTANT') # [?,32,32,256] -> [?,31,31,512]
h4 = tf.nn.conv2d(h4, self.D_W4, strides=[1, 1, 1, 1], padding='VALID')
h4 = self.D_bn4(h4)
h4 = lrelu(h4)
h5 = tf.pad(h4, [[0, 0], [1, 1], [1, 1], [0, 0]], mode='CONSTANT') # [?,31,31,256] -> [?,30,30,1]
h5 = tf.nn.conv2d(h5, self.D_W5, strides=[1, 1, 1, 1], padding='VALID')
h5 = tf.nn.sigmoid(h5)
return h5
# Method for generating the fake images
def sample_generator(self, input_image, batch_size=1):
input_img = tf.placeholder(tf.float32, [batch_size] + self.image_shape)
h1 = h1_ = tf.nn.conv2d(input_img, self.G_W1, strides=[1, 2, 2, 1], padding='SAME') # [?,256,256,3] -> [?,128,128,64]
h1 = lrelu(h1)
h2 = tf.nn.conv2d(h1, self.G_W2, strides=[1, 2, 2, 1], padding='SAME') # [?,128,128,64] -> [?,64,64,128]
h2 = h2_ = self.G_bn2(h2)
h2 = lrelu(h2)
h3 = tf.nn.conv2d(h2, self.G_W3, strides=[1, 2, 2, 1], padding='SAME') # [?,64,64,128] -> [?,32,32,256]
h3 = h3_ = self.G_bn3(h3)
h3 = lrelu(h3)
h4 = tf.nn.conv2d(h3, self.G_W4, strides=[1, 2, 2, 1], padding='SAME') # [?,32,32,256] -> [?,16,16,512]
h4 = h4_ = self.G_bn4(h4)
h4 = lrelu(h4)
h5 = tf.nn.conv2d(h4, self.G_W5, strides=[1, 2, 2, 1], padding='SAME') # [?,16,16,512] -> [?,8,8,512]
h5 = h5_ = self.G_bn5(h5)
h5 = lrelu(h5)
h6 = tf.nn.conv2d(h5, self.G_W6, strides=[1, 2, 2, 1], padding='SAME') # [?,8,8,512] -> [?,4,4,512]
h6 = h6_ = self.G_bn6(h6)
h6 = lrelu(h6)
h7 = tf.nn.conv2d(h6, self.G_W7, strides=[1, 2, 2, 1], padding='SAME') # [?,4,4,512] -> [?,2,2,512]
h7 = h7_ = self.G_bn7(h7)
h7 = lrelu(h7)
h8 = tf.nn.conv2d(h7, self.G_W8, strides=[1, 2, 2, 1], padding='SAME') # [?,2,2,512] -> [?,1,1,512]
h8 = self.G_bn8(h8)
h8 = tf.nn.relu(h8)
h9 = tf.nn.conv2d_transpose(h8, self.G_W9, output_shape=[batch_size, 2, 2, self.ch_G9], strides=[1, 2, 2, 1]) # [?,1,1,512] -> [?,2,2,512]
h9 = tf.nn.dropout(self.G_bn9(h9), keep_prob=self.keep_prob)
h9 = tf.nn.relu(h9)
h9 = tf.concat([h9, h7_], axis=3)
h10 = tf.nn.conv2d_transpose(h9, self.G_W10, output_shape=[batch_size, 4, 4, self.ch_G10], strides=[1, 2, 2, 1]) # [?,2,2,512+512] -> [?,4,4,512]
h10 = tf.nn.dropout(self.G_bn10(h10), keep_prob=self.keep_prob)
h10 = tf.nn.relu(h10)
h10 = tf.concat([h10, h6_], axis=3)
h11 = tf.nn.conv2d_transpose(h10, self.G_W11, output_shape=[batch_size, 8, 8, self.ch_G11], strides=[1, 2, 2, 1]) # [?,4,4,512+512] -> [?,8,8,512]
h11 = tf.nn.dropout(self.G_bn11(h11), keep_prob=self.keep_prob)
h11 = tf.nn.relu(h11)
h11 = tf.concat([h11, h5_], axis=3)
h12 = tf.nn.conv2d_transpose(h11, self.G_W12, output_shape=[batch_size, 16, 16, self.ch_G12], strides=[1, 2, 2, 1]) # [?,8,8,512+512] -> [?,16,16,512]
h12 = self.G_bn12(h12)
h12 = tf.nn.relu(h12)
h12 = tf.concat([h12, h4_], axis=3)
h13 = tf.nn.conv2d_transpose(h12, self.G_W13, output_shape=[batch_size, 32, 32, self.ch_G13], strides=[1, 2, 2, 1]) # [?,16,16,512+512] -> [?,32,32,256]
h13 = self.G_bn13(h13)
h13 = tf.nn.relu(h13)
h13 = tf.concat([h13, h3_], axis=3)
h14 = tf.nn.conv2d_transpose(h13, self.G_W14, output_shape=[batch_size, 64, 64, self.ch_G14], strides=[1, 2, 2, 1]) # [?,32,32,256+256] -> [?,64,64,128]
h14 = self.G_bn14(h14)
h14 = tf.nn.relu(h14)
h14 = tf.concat([h14, h2_], axis=3)
h15 = tf.nn.conv2d_transpose(h14, self.G_W15, output_shape=[batch_size, 128, 128, self.ch_G15], strides=[1, 2, 2, 1]) # [?,64,64,128+128] -> [?,128,128,64]
h15 = self.G_bn15(h15)
h15 = tf.nn.relu(h15)
h15 = tf.concat([h15, h1_], axis=3)
h16 = tf.nn.conv2d_transpose(h15, self.G_W16, output_shape=[batch_size, 256, 256, self.ch_G16], strides=[1, 2, 2, 1]) # [?,128,128,64+64] -> [?,256,256,3]
h16 = tf.nn.tanh(h16)
generated_samples = self.sess.run(h16, feed_dict={input_img: input_image})
return generated_samples
# Train Generator and return the loss
def train_gen(self, input_img, target_img):
# _, loss_val_G = self.sess.run([self.train_op_gen, self.G_loss], feed_dict={self.input_img: input_img, self.target_img: target_img})
_, loss_val_GAN, loss_val_L1 = self.sess.run([self.train_op_gen, self.G_loss_GAN, self.G_loss_L1], feed_dict={self.input_img: input_img, self.target_img: target_img})
# return loss_val_G
return loss_val_GAN, loss_val_L1
# Train Discriminator and return the loss
def train_discrim(self, input_img, target_img):
_, loss_val_D = self.sess.run([self.train_op_discrim, self.D_loss], feed_dict={self.input_img: input_img, self.target_img: target_img})
return loss_val_D