def generator(self, x_data, name='g_'):
with tf.variable_scope(name):
x_data = flatten(x_data)
g0 = tf.nn.relu(tf_utils.linear(x_data,
self.num_hiddens,
name='fc1'),
name='relu1')
g1 = tf_utils.linear(g0,
self.image_size[0] * self.image_size[1],
name='fc2')
return self.out_func(g1)
def discriminator(self, data, name='d_', is_reuse=False):
with tf.variable_scope(name, reuse=is_reuse):
# 256 -> 128
h0_conv2d = tf_utils.conv2d(data, self.dis_c[0], name='h0_conv2d')
h0_lrelu = tf_utils.lrelu(h0_conv2d, name='h0_lrelu')
# 128 -> 64
h1_conv2d = tf_utils.conv2d(h0_lrelu, self.dis_c[1], name='h1_conv2d')
h1_batchnorm = tf_utils.batch_norm(h1_conv2d, name='h1_batchnorm', _ops=self._dis_train_ops)
h1_lrelu = tf_utils.lrelu(h1_batchnorm, name='h1_lrelu')
# 64 -> 32
h2_conv2d = tf_utils.conv2d(h1_lrelu, self.dis_c[2], name='h2_conv2d')
h2_batchnorm = tf_utils.batch_norm(h2_conv2d, name='h2_batchnorm', _ops=self._dis_train_ops)
h2_lrelu = tf_utils.lrelu(h2_batchnorm, name='h2_lrelu')
# 32 -> 32
h3_conv2d = tf_utils.conv2d(h2_lrelu, self.dis_c[3], d_h=1, d_w=1, name='h3_conv2d')
h3_batchnorm = tf_utils.batch_norm(h3_conv2d, name='h3_batchnorm', _ops=self._dis_train_ops)
h3_lrelu = tf_utils.lrelu(h3_batchnorm, name='h3_lrelu')
# linear
h3_flatten = flatten(h3_lrelu)
h4_linear = tf_utils.linear(h3_flatten, self.dis_c[4], name='h4_linear')
return tf.nn.sigmoid(h4_linear), h4_linear
def discriminator_image(self, data, name='d_', is_reuse=False):
with tf.variable_scope(name) as scope:
if is_reuse is True:
scope.reuse_variables()
# conv1: (N, 640, 640, 4) -> (N,, 160, 160, 32)
conv1 = tf_utils.conv2d(data, self.dis_c, k_h=3, k_w=3, d_h=2, d_w=2, name='conv1_conv1')
conv1 = tf_utils.batch_norm(conv1, name='conv1_batch1', _ops=self._dis_train_ops)
conv1 = tf.nn.relu(conv1, name='conv1_relu1')
conv1 = tf_utils.conv2d(conv1, self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv1_conv2')
conv1 = tf_utils.batch_norm(conv1, name='conv1_batch2', _ops=self._dis_train_ops)
conv1 = tf.nn.relu(conv1, name='conv1_relu2')
pool1 = tf_utils.max_pool_2x2(conv1, name='maxpool1')
# conv2: (N, 160, 160, 32) -> (N, 40, 40, 64)
conv2 = tf_utils.conv2d(pool1, 2*self.dis_c, k_h=3, k_w=3, d_h=2, d_w=2, name='conv2_conv1')
conv2 = tf_utils.batch_norm(conv2, name='conv2_batch1', _ops=self._dis_train_ops)
conv2 = tf.nn.relu(conv2, name='conv2_relu1')
conv2 = tf_utils.conv2d(conv2, 2*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv2_conv2')
conv2 = tf_utils.batch_norm(conv2, name='conv2_batch2', _ops=self._dis_train_ops)
conv2 = tf.nn.relu(conv2, name='conv2_relu2')
pool2 = tf_utils.max_pool_2x2(conv2, name='maxpool2')
# conv3: (N, 40, 40, 64) -> (N, 20, 20, 128)
conv3 = tf_utils.conv2d(pool2, 4*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv3_conv1')
conv3 = tf_utils.batch_norm(conv3, name='conv3_batch1', _ops=self._dis_train_ops)
conv3 = tf.nn.relu(conv3, name='conv3_relu1')
conv3 = tf_utils.conv2d(conv3, 4*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv3_conv2')
conv3 = tf_utils.batch_norm(conv3, name='conv3_batch2', _ops=self._dis_train_ops)
conv3 = tf.nn.relu(conv3, name='conv3_relu2')
pool3 = tf_utils.max_pool_2x2(conv3, name='maxpool3')
# conv4: (N, 20, 20, 128) -> (N, 10, 10, 256)
conv4 = tf_utils.conv2d(pool3, 8*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv4_conv1')
conv4 = tf_utils.batch_norm(conv4, name='conv4_batch1', _ops=self._dis_train_ops)
conv4 = tf.nn.relu(conv4, name='conv4_relu1')
conv4 = tf_utils.conv2d(conv4, 8*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv4_conv2')
conv4 = tf_utils.batch_norm(conv4, name='conv4_batch2', _ops=self._dis_train_ops)
conv4 = tf.nn.relu(conv4, name='conv4_relu2')
pool4 = tf_utils.max_pool_2x2(conv4, name='maxpool4')
# conv5: (N, 10, 10, 256) -> (N, 10, 10, 512)
conv5 = tf_utils.conv2d(pool4, 16*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv5_conv1')
conv5 = tf_utils.batch_norm(conv5, name='conv5_batch1', _ops=self._dis_train_ops)
conv5 = tf.nn.relu(conv5, name='conv5_relu1')
conv5 = tf_utils.conv2d(conv5, 16*self.dis_c, k_h=3, k_w=3, d_h=1, d_w=1, name='conv5_conv2')
conv5 = tf_utils.batch_norm(conv5, name='conv5_batch2', _ops=self._dis_train_ops)
conv5 = tf.nn.relu(conv5, name='conv5_relu2')
# output layer: (N, 10, 10, 512) -> (N, 1, 1, 512) -> (N, 1)
shape = conv5.get_shape().as_list()
gap = tf.layers.average_pooling2d(inputs=conv5, pool_size=shape[1], strides=1, padding='VALID',
name='global_vaerage_pool')
gap_flatten = tf.reshape(gap, [-1, 16*self.dis_c])
output = tf_utils.linear(gap_flatten, 1, name='linear_output')
return tf.nn.sigmoid(output), output
def generator(self, data, name='g_'):
with tf.variable_scope(name):
data_flatten = flatten(data)
# 4 x 4
h0_linear = tf_utils.linear(data_flatten, 4*4*self.gen_c[0], name='h0_linear')
h0_reshape = tf.reshape(h0_linear, [tf.shape(h0_linear)[0], 4, 4, self.gen_c[0]])
h0_batchnorm = tf_utils.batch_norm(h0_reshape, name='h0_batchnorm', _ops=self._gen_train_ops)
h0_relu = tf.nn.relu(h0_batchnorm, name='h0_relu')
# 8 x 8
h1_deconv = tf_utils.deconv2d(h0_relu, self.gen_c[1], name='h1_deconv2d')
h1_batchnorm = tf_utils.batch_norm(h1_deconv, name='h1_batchnorm', _ops=self._gen_train_ops)
h1_relu = tf.nn.relu(h1_batchnorm, name='h1_relu')
# 16 x 16
h2_deconv = tf_utils.deconv2d(h1_relu, self.gen_c[2], name='h2_deconv2d')
h2_batchnorm = tf_utils.batch_norm(h2_deconv, name='h2_batchnorm', _ops=self._gen_train_ops)
h2_relu = tf.nn.relu(h2_batchnorm, name='h2_relu')
# 32 x 32
h3_deconv = tf_utils.deconv2d(h2_relu, self.gen_c[3], name='h3_deconv2d')
h3_batchnorm = tf_utils.batch_norm(h3_deconv, name='h3_batchnorm', _ops=self._gen_train_ops)
h3_relu = tf.nn.relu(h3_batchnorm, name='h3_relu')
# 64 x 64
h4_deconv = tf_utils.deconv2d(h3_relu, self.gen_c[4], name='h4_deconv2d')
h4_batchnorm = tf_utils.batch_norm(h4_deconv, name='h4_batchnorm', _ops=self._gen_train_ops)
h4_relu = tf.nn.relu(h4_batchnorm, name='h4_relu')
# 128 x 128
h5_deconv = tf_utils.deconv2d(h4_relu, self.gen_c[5], name='h5_deconv2d')
h5_batchnorm = tf_utils.batch_norm(h5_deconv, name='h5_batchnorm', _ops=self._gen_train_ops)
h5_relu = tf.nn.relu(h5_batchnorm, name='h5_relu')
# 256 x 256
h6_deconv = tf_utils.deconv2d(h5_relu, self.gen_c[6], name='h6_deconv2d')
return tf.nn.tanh(h6_deconv)
def discriminator(self, data, name='d_', is_reuse=False):
with tf.variable_scope(name) as scope:
if is_reuse is True:
scope.reuse_variables()
# 256 -> 128
h0_conv = tf_utils.conv2d(data, self.dis_c[0], name='h0_conv2d')
h0_lrelu = tf_utils.lrelu(h0_conv, name='h0_lrelu')
# 128 -> 64
h1_conv = tf_utils.conv2d(h0_lrelu, self.dis_c[1], name='h1_conv2d')
h1_batchnorm = tf_utils.batch_norm(h1_conv, name='h1_batchnorm', _ops=self._dis_train_ops)
h1_lrelu = tf_utils.lrelu(h1_batchnorm, name='h1_lrelu')
# 64 -> 32
h2_conv = tf_utils.conv2d(h1_lrelu, self.dis_c[2], name='h2_conv2d')
h2_batchnorm = tf_utils.batch_norm(h2_conv, name='h2_batchnorm', _ops=self._dis_train_ops)
h2_lrelu = tf_utils.lrelu(h2_batchnorm, name='h2_lrelu')
# 32 -> 16
h3_conv = tf_utils.conv2d(h2_lrelu, self.dis_c[3], name='h3_conv2d')
h3_batchnorm = tf_utils.batch_norm(h3_conv, name='h3_batchnorm', _ops=self._dis_train_ops)
h3_lrelu = tf_utils.lrelu(h3_batchnorm, name='h3_lrelu')
# 16 -> 8
h4_conv = tf_utils.conv2d(h3_lrelu, self.dis_c[4], name='h4_conv2d')
h4_batchnorm = tf_utils.batch_norm(h4_conv, name='h4_batchnorm', _ops=self._dis_train_ops)
h4_lrelu = tf_utils.lrelu(h4_batchnorm, name='h4_lrelu')
# 8 -> 4
h5_conv = tf_utils.conv2d(h4_lrelu, self.dis_c[5], name='h5_conv2d')
h5_batchnorm = tf_utils.batch_norm(h5_conv, name='h5_batchnorm', _ops=self._dis_train_ops)
h5_lrelu = tf_utils.lrelu(h5_batchnorm, name='h5_lrelu')
h5_flatten = flatten(h5_lrelu)
h6_linear = tf_utils.linear(h5_flatten, self.dis_c[6], name='h6_linear')
return tf.nn.sigmoid(h6_linear), h6_linear