# MIT License
#
# Copyright (c) 2017 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""Variational autoencoder based on the paper
'Deep Feature Consistent Variational Autoencoder'
(https://arxiv.org/pdf/1610.00291.pdf)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
import generative.models.vae_base # @UnresolvedImport
class Vae(generative.models.vae_base.Vae):
def __init__(self, latent_variable_dim):
super(Vae, self).__init__(latent_variable_dim, 64)
def encoder(self, images, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('encoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = images
net = slim.conv2d(net, 32, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = slim.conv2d(net, 64, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = slim.conv2d(net, 128, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = slim.conv2d(net, 256, [4, 4], 2, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 256, [4, 4], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.flatten(net)
fc1 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_1')
fc2 = slim.fully_connected(net, self.latent_variable_dim, activation_fn=None, normalizer_fn=None, scope='Fc_2')
return fc1, fc2
def decoder(self, latent_var, is_training):
activation_fn = leaky_relu # tf.nn.relu
weight_decay = 0.0
with tf.variable_scope('decoder'):
with slim.arg_scope([slim.batch_norm],
is_training=is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=self.batch_norm_params):
net = slim.fully_connected(latent_var, 4096, activation_fn=None, normalizer_fn=None, scope='Fc_1')
net = tf.reshape(net, [-1,4,4,256], name='Reshape')
net = tf.image.resize_nearest_neighbor(net, size=(8,8), name='Upsample_1')
net = slim.conv2d(net, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 128, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_1b')
net = tf.image.resize_nearest_neighbor(net, size=(16,16), name='Upsample_2')
net = slim.conv2d(net, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 64, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_2b')
net = tf.image.resize_nearest_neighbor(net, size=(32,32), name='Upsample_3')
net = slim.conv2d(net, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 32, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_3b')
net = tf.image.resize_nearest_neighbor(net, size=(64,64), name='Upsample_4')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4a')
net = slim.repeat(net, 3, conv2d_block, 0.1, 3, [3, 3], 1, activation_fn=activation_fn, scope='Conv2d_4b')
net = slim.conv2d(net, 3, [3, 3], 1, activation_fn=None, scope='Conv2d_4c')
return net
def conv2d_block(inp, scale, *args, **kwargs):
return inp + slim.conv2d(inp, *args, **kwargs) * scale
def leaky_relu(x):
return tf.maximum(0.1*x,x)
