def g_block(x, out_channels, training, name):
"""Builds the residual blocks used in the generator.
Compared with block, optimized_block always downsamples the spatial
resolution of the input vector by a factor of 4.
Args:
x: The 4D input vector.
out_channels: Number of features in the output layer.
name: The variable scope name for the block.
Returns:
A `Tensor` representing the output of the operation.
"""
with tf.variable_scope(name):
bn0 = ops.batch_norm(name='bn0')
bn1 = ops.batch_norm(name='bn1')
x_0 = x
x = tf.nn.relu(bn0(x, train=training))
x = usample(x)
x = ops.conv2d(x, out_channels, 3, 3, 1, 1, name='conv1')
x = tf.nn.relu(bn1(x, train=training))
x = ops.conv2d(x, out_channels, 3, 3, 1, 1, name='conv2')
x_0 = usample(x_0)
x_0 = ops.conv2d(x_0, out_channels, 1, 1, 1, 1, name='conv3')
return x_0 + x
def e_block(x, out_channels, training, name, downsample=True, \
act=tf.nn.relu):
"""Builds the residual blocks used in the discriminator in SNGAN.
Args:
x: The 4D input vector.
out_channels: Number of features in the output layer.
name: The variable scope name for the block.
update_collection: The update collections used in the
spectral_normed_weight.
downsample: If True, downsample the spatial size the input tensor by
a factor of 4. If False, the spatial size of the input
tensor is unchanged.
act: The activation function used in the block.
Returns:
A `Tensor` representing the output of the operation.
"""
with tf.variable_scope(name):
bn0 = ops.batch_norm(name='bn0')
bn1 = ops.batch_norm(name='bn1')
input_channels = x.get_shape().as_list()[-1]
x_0 = x
x = act(bn0(x, train=training))
x = ops.conv2d(x, out_channels, 3, 3, 1, 1, name='conv1')
x = act(bn1(x, train=training))
x = ops.conv2d(x, out_channels, 3, 3, 1, 1, name='conv2')
if downsample:
x = dsample_pool(x, "e_dsample_1")
if downsample or input_channels != out_channels:
x_0 = ops.conv2d(x_0, out_channels, 1, 1, 1, 1, name='conv3')
if downsample:
x_0 = dsample_pool(x_0, "e_dsample_2")
return x_0 + x
def generator_resnet_cifar(z, x_shape, dim=128, name = 'generator', \
reuse=False, training=True):
dim = dim * 2 # 256 like sn-gan paper
x_dim = x_shape[0] * x_shape[1] * x_shape[2]
with tf.variable_scope(name, reuse=reuse):
act0 = ops.linear(z, dim * 4 * 4, scope='g_linear0')
act0 = tf.reshape(act0, [-1, 4, 4, dim])
act1 = g_block(act0, dim, training, 'g_block1') # 8 * 8
act2 = g_block(act1, dim, training, 'g_block2') # 16 * 16
act3 = g_block(act2, dim, training, 'g_block3') # 32 * 32
bn = ops.batch_norm(name='g_bn')
act3 = tf.nn.relu(bn(act3, training))
act4 = ops.conv2d(act3, 3, 3, 3, 1, 1, name='g_conv_last')
out = tf.nn.sigmoid(act4)
return tf.reshape(out, [-1, x_dim])
def generator_resnet_stl10(z, x_shape, dim=64, name = 'generator', \
reuse=False, training=True):
x_dim = x_shape[0] * x_shape[1] * x_shape[2]
with tf.variable_scope(name, reuse=reuse):
act0 = ops.linear(z, dim * 8 * 6 * 6, scope='g_linear0')
act0 = tf.reshape(act0, [-1, 6, 6, dim * 8]) # 6 * 6 * dim * 8
act1 = g_block(act0, dim * 4, training,
'g_block1') # 12 * 12 * dim * 4
act2 = g_block(act1, dim * 2, training,
'g_block2') # 24 * 24 * dim * 2
act3 = g_block(act2, dim * 1, training,
'g_block3') # 48 * 48 * dim * 1
bn = ops.batch_norm(name='g_bn')
act3 = tf.nn.relu(bn(act3, training))
act4 = ops.conv2d(act3, 3, 3, 3, 1, 1, name='g_conv_last')
out = tf.nn.sigmoid(act4)
return tf.reshape(out, [-1, x_dim])
def encoder_resnet_stl10(x, x_shape, z_dim=128, dim=64, \
name = 'encoder', reuse=False, training=True):
act = lrelu
with tf.variable_scope(name, reuse=reuse):
image = tf.reshape(x, [-1, x_shape[0], x_shape[1], x_shape[2]])
image = ops.conv2d(image, dim, 3, 3, 1, 1, \
name='e_conv0') # 48 * 48 * dim
act0 = e_block(image, dim * 2, training = training,\
name = 'e_block1', act=act) # 24 * 24 * dim * 2
act1 = e_block(act0, dim * 4, training, \
name = 'e_block2', act=act) # 12 * 12 * dim * 4
act2 = e_block(act1, dim * 8, training, \
name = 'e_block3', act=act)# 6 * 6 * dim * 8
bn = ops.batch_norm(name='e_bn')
act2 = act(bn(act2, training))
act2 = tf.reshape(act2, [-1, 6 * 6 * dim * 8])
out = ops.linear(act2, z_dim)
return out
def encoder_resnet_cifar(x, x_shape, z_dim=128, dim=128, \
name = 'encoder', reuse=False, training=True):
dim = dim * 2 # 256 like sn-gan paper
act = lrelu
with tf.variable_scope(name, reuse=reuse):
image = tf.reshape(x, [-1, x_shape[0], x_shape[1], x_shape[2]])
image = ops.conv2d(image, dim, 3, 3, 1, 1, \
name='e_conv0') # 32 * 32
act0 = e_block(image, dim, training = training,\
name = 'e_block1', act=act) # 16 * 16
act1 = e_block(act0, dim, training, \
name = 'e_block2', act=act) # 8 * 8
act2 = e_block(act1, dim, training, \
name = 'e_block3', act=act)# 4 * 4
bn = ops.batch_norm(name='e_bn')
act2 = act(bn(act2, training))
act2 = tf.reshape(act2, [-1, 4 * 4 * dim])
out = ops.linear(act2, z_dim)
return out