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generator.py
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generator.py
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import tensorflow as tf
def convLayer1(input, k, reuse=False, activation='relu', is_training=True, name=None):
with tf.variable_scope(name, reuse=reuse):
weights_shape = [7, 7, input.get_shape()[3], k]
W_var = tf.get_variable("W_var", weights_shape,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02, dtype=tf.float32))
padInput = tf.pad(input, [[0,0],[3,3],[3,3],[0,0]], 'REFLECT')
conv = tf.nn.conv2d(padInput, W_var,
strides=[1, 1, 1, 1], padding='VALID')
normalized = instance_norm(conv)
if activation == 'relu':
output = tf.nn.relu(normalized)
if activation == 'tanh':
output = tf.nn.tanh(normalized)
return output
def convLayer2(input, k, reuse=False, is_training=True, name=None):
with tf.variable_scope(name, reuse=reuse):
weights_shape = [3, 3, input.get_shape()[3], k]
W_var = tf.get_variable("W_var", weights_shape,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02, dtype=tf.float32))
conv = tf.nn.conv2d(input, W_var,
strides=[1, 2, 2, 1], padding='SAME')
normalized = instance_norm(conv)
output = tf.nn.relu(normalized)
return output
def residualBlock(input, k, reuse=False, is_training=True, name=None):
with tf.variable_scope(name, reuse=reuse):
with tf.variable_scope('layer1', reuse=reuse):
weights_shape = [3, 3, input.get_shape()[3], k]
W_var1 = tf.get_variable("W_var1", weights_shape,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02, dtype=tf.float32))
padded_input = tf.pad(input, [[0,0],[1,1],[1,1],[0,0]], 'REFLECT')
conv1 = tf.nn.conv2d(padded_input, W_var1,
strides=[1, 1, 1, 1], padding='VALID')
normalized1 = instance_norm(conv1)
relu1 = tf.nn.relu(normalized1)
with tf.variable_scope('layer2', reuse=reuse):
weights_shape = [3, 3, relu1.get_shape()[3], k]
W_var2 = tf.get_variable("W_var2", weights_shape,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02, dtype=tf.float32))
padded_relu = tf.pad(relu1, [[0,0],[1,1],[1,1],[0,0]], 'REFLECT')
conv2 = tf.nn.conv2d(padded_relu, W_var2,
strides=[1, 1, 1, 1], padding='VALID')
normalized2 = instance_norm(conv2)
output = input+normalized2
return output
def resNet(input, reuse, is_training=True, n=6):
depth = input.get_shape()[3]
for i in range(1,n+1):
output = residualBlock(input, depth, reuse, is_training, 'R{}_{}'.format(depth, i))
input = output
return output
def deConvLayer(input, k, reuse=False, is_training=True, name=None, output_size=None):
with tf.variable_scope(name, reuse=reuse):
input_shape = input.get_shape().as_list()
weights_shape = [3, 3, k, input_shape[3]]
W_var = tf.get_variable("W_var", weights_shape,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02, dtype=tf.float32))
if not output_size:
output_size = input_shape[1]*2
output_shape = [input_shape[0], output_size, output_size, k]
fsconv = tf.nn.conv2d_transpose(input, W_var,
output_shape=output_shape,
strides=[1, 2, 2, 1], padding='SAME')
normalized = instance_norm(fsconv)
output = tf.nn.relu(normalized)
return output
class Generator:
def __init__(self, name, is_training, ngf=64, image_size=128):
self.name = name
self.reuse = False
self.ngf = ngf
self.is_training = is_training
self.image_size = image_size
def __call__(self, input):
with tf.variable_scope(self.name):
C64 = convLayer1(input, self.ngf, is_training=self.is_training,
reuse=self.reuse, name='C64')
C128 = convLayer2(C64, 2*self.ngf, is_training=self.is_training,
reuse=self.reuse, name='C128')
C256 = convLayer2(C128, 4*self.ngf, is_training=self.is_training,
reuse=self.reuse, name='C256')
res_output = resNet(C256, reuse=self.reuse, n=6)
D128 = deConvLayer(res_output, 2*self.ngf, is_training=self.is_training,
reuse=self.reuse, name='D128')
D64 = deConvLayer(D128, self.ngf, is_training=self.is_training,
reuse=self.reuse, name='D64', output_size=self.image_size)
output = convLayer1(D64, 3,
activation='tanh', reuse=self.reuse, name='output')
self.reuse = True
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output
def instance_norm(input):
with tf.variable_scope("instance_norm"):
depth = input.get_shape()[3]
scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(mean=1.0, stddev=0.02, dtype=tf.float32))
offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0))
mean, variance = tf.nn.moments(input, axes=[1,2], keep_dims=True)
epsilon = 1e-5
inv = tf.rsqrt(variance + epsilon)
normalized = (input-mean)*inv
return scale*normalized + offset