def __call__(self, x, reuse=False, output_name=None):
with tf.variable_scope(self.name) as scope:
if reuse:
scope.reuse_variables()
# Initial dense multiplication
x = layers.linear(x, "G_FC1", 512 * 8 * 8)
batch_size = tf.shape(x)[0]
if FLAGS.data_format == "NHWC":
target_shape = (batch_size, 8, 8, 512)
elif FLAGS.data_format == "NCHW":
target_shape = (batch_size, 512, 8, 8)
x = layers.reshape(x, target_shape)
x = tf.contrib.layers.batch_norm(x, fused=True, data_format=FLAGS.data_format)
x = layers.lrelu(x)
x = layers.G_conv2d_block(x, "G_conv2D1", 256, 3, data_format=FLAGS.data_format, bn=True)
x = layers.upsampleNN(x, "G_up1", 2, data_format=FLAGS.data_format)
x = layers.G_conv2d_block(x, "G_conv2D2", 128, 3, data_format=FLAGS.data_format, bn=True)
x = layers.upsampleNN(x, "G_up2", 2, data_format=FLAGS.data_format)
x = layers.G_conv2d_block(x, "G_conv2D3", 64, 3, data_format=FLAGS.data_format, bn=True)
x = layers.upsampleNN(x, "G_up3", 2, data_format=FLAGS.data_format)
# Last conv
x = layers.conv2d(x, "G_conv2D4", 64, FLAGS.channels, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.tanh(x, name=output_name)
return x
def __call__(self, x, reuse=False, output_name=None):
with tf.variable_scope(self.name) as scope:
if reuse:
scope.reuse_variables()
# Initial dense multiplication
x = layers.linear(x, "G_FC1", self.nb_filters * 8 * 8)
batch_size = tf.shape(x)[0]
if FLAGS.data_format == "NHWC":
target_shape = (batch_size, 8, 8, self.nb_filters)
elif FLAGS.data_format == "NCHW":
target_shape = (batch_size, self.nb_filters, 8, 8)
x = layers.reshape(x, target_shape)
# x = tf.contrib.layers.batch_norm(x, fused=True, data_format=FLAGS.data_format)
x = tf.nn.elu(x)
x = layers.dec_conv2d_block(x, "G_conv2D1", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "G_up1", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "G_conv2D2", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "G_up2", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "G_conv2D3", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "G_up3", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "G_conv2D4", self.nb_filters, 3, data_format=FLAGS.data_format)
# Last conv
x = layers.conv2d(x, "G_conv2D5", self.nb_filters, FLAGS.channels, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.tanh(x, name=output_name)
return x
def __call__(self, x, reuse=False, output_name=None):
with tf.variable_scope(self.name) as scope:
if reuse:
scope.reuse_variables()
##################
# Encoding part
##################
# First conv
x = layers.conv2d(x, "D_conv2D1", FLAGS.channels, self.nb_filters, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.elu(x)
# Conv blocks
x = layers.enc_conv2d_block(x, "D_enc_conv2D2", self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D3", 2 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D4", 3 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D5", 4 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format, downsampling=False)
# Flatten
batch_size = tf.shape(x)[0]
other_dims = x.get_shape().as_list()[1:]
prod_dim = 1
for d in other_dims:
prod_dim *= d
x = layers.reshape(x, (batch_size, prod_dim))
# Linear
x = layers.linear(x, "D_FC1", self.h_dim, activation_fn=None)
##################
# Decoding part
##################
x = layers.linear(x, "D_FC2", self.nb_filters * 8 * 8)
batch_size = tf.shape(x)[0]
if FLAGS.data_format == "NHWC":
target_shape = (batch_size, 8, 8, self.nb_filters)
elif FLAGS.data_format == "NCHW":
target_shape = (batch_size, self.nb_filters, 8, 8)
x = layers.reshape(x, target_shape)
x = tf.contrib.layers.batch_norm(x, fused=True, data_format=FLAGS.data_format)
x = tf.nn.elu(x)
x = layers.dec_conv2d_block(x, "D_dec_conv2D1", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up1", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D2", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up2", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D3", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up3", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D4", self.nb_filters, 3, data_format=FLAGS.data_format)
# Last conv
x = layers.conv2d(x, "D_dec_conv2D5", self.nb_filters, FLAGS.channels, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.tanh(x, name=output_name)
return x
def __call__(self, x, reuse=False, output_name=None):
with tf.variable_scope(self.name) as scope:
if reuse:
scope.reuse_variables()
##################
# Encoding part
##################
# First conv
x = layers.conv2d(x, "D_conv2D1", FLAGS.channels, self.nb_filters, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.elu(x)
# Conv blocks
x = layers.enc_conv2d_block(x, "D_enc_conv2D2", self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D3", 2 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D4", 3 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format)
x = layers.enc_conv2d_block(x, "D_enc_conv2D5", 4 * self.nb_filters, 3, activation_fn=tf.nn.elu, data_format=FLAGS.data_format, downsampling=False)
# Flatten
batch_size = tf.shape(x)[0]
other_dims = x.get_shape().as_list()[1:]
prod_dim = 1
for d in other_dims:
prod_dim *= d
x = layers.reshape(x, (batch_size, prod_dim))
# Linear
x = layers.linear(x, "D_FC1", self.h_dim, activation_fn=None)
##################
# Decoding part
##################
x = layers.linear(x, "D_FC2", self.nb_filters * 8 * 8)
batch_size = tf.shape(x)[0]
if FLAGS.data_format == "NHWC":
target_shape = (batch_size, 8, 8, self.nb_filters)
elif FLAGS.data_format == "NCHW":
target_shape = (batch_size, self.nb_filters, 8, 8)
x = layers.reshape(x, target_shape)
# x = tf.contrib.layers.batch_norm(x, fused=True, data_format=FLAGS.data_format)
x = tf.nn.elu(x)
x = layers.dec_conv2d_block(x, "D_dec_conv2D1", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up1", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D2", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up2", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D3", self.nb_filters, 3, data_format=FLAGS.data_format)
x = layers.upsampleNN(x, "D_up3", 2, data_format=FLAGS.data_format)
x = layers.dec_conv2d_block(x, "D_dec_conv2D4", self.nb_filters, 3, data_format=FLAGS.data_format)
# Last conv
x = layers.conv2d(x, "D_dec_conv2D5", self.nb_filters, FLAGS.channels, 3, 1, "SAME", data_format=FLAGS.data_format)
x = tf.nn.tanh(x, name=output_name)
return x