/
model_vae.py
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model_vae.py
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import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
flags = tf.app.flags
FLAGS = flags.FLAGS
def encoder(input_imgs, is_train = True, reuse = False):
'''
input_imgs: the input images to be encoded into a vector as latent representation. size here is [b_size,64,64,3]
'''
z_dim = FLAGS.z_dim # 512
ef_dim = 64 # encoder filter number
w_init = tf.random_normal_initializer(stddev=0.02)
gamma_init = tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("encoder", reuse = reuse):
tl.layers.set_name_reuse(reuse)
net_in = InputLayer(input_imgs, name='en/in') # (b_size,64,64,3)
net_h0 = Conv2d(net_in, ef_dim, (5, 5), (2, 2), act=None,
padding='SAME', W_init=w_init, name='en/h0/conv2d')
net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu,
is_train=is_train, gamma_init=gamma_init, name='en/h0/batch_norm')
# net_h0.outputs._shape = (b_size,32,32,64)
net_h1 = Conv2d(net_h0, ef_dim*2, (5, 5), (2, 2), act=None,
padding='SAME', W_init=w_init, name='en/h1/conv2d')
net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu,
is_train=is_train, gamma_init=gamma_init, name='en/h1/batch_norm')
# net_h1.outputs._shape = (b_size,16,16,64*2)
net_h2 = Conv2d(net_h1, ef_dim*4, (5, 5), (2, 2), act=None,
padding='SAME', W_init=w_init, name='en/h2/conv2d')
net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu,
is_train=is_train, gamma_init=gamma_init, name='en/h2/batch_norm')
# net_h2.outputs._shape = (b_size,8,8,64*4)
net_h3 = Conv2d(net_h2, ef_dim*8, (5, 5), (2, 2), act=None,
padding='SAME', W_init=w_init, name='en/h3/conv2d')
net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu,
is_train=is_train, gamma_init=gamma_init, name='en/h3/batch_norm')
# net_h2.outputs._shape = (b_size,4,4,64*8)
# mean of z
net_h4 = FlattenLayer(net_h3, name='en/h4/flatten')
# net_h4.outputs._shape = (b_size,8*8*64*4)
net_out1 = DenseLayer(net_h4, n_units=z_dim, act=tf.identity,
W_init = w_init, name='en/h3/lin_sigmoid')
net_out1 = BatchNormLayer(net_out1, act=tf.identity,
is_train=is_train, gamma_init=gamma_init, name='en/out1/batch_norm')
# net_out1 = DenseLayer(net_h4, n_units=z_dim, act=tf.nn.relu,
# W_init = w_init, name='en/h4/lin_sigmoid')
z_mean = net_out1.outputs # (b_size,512)
# log of variance of z(covariance matrix is diagonal)
net_h5 = FlattenLayer(net_h3, name='en/h5/flatten')
net_out2 = DenseLayer(net_h5, n_units=z_dim, act=tf.identity,
W_init = w_init, name='en/h4/lin_sigmoid')
net_out2 = BatchNormLayer(net_out2, act=tf.nn.softplus,
is_train=is_train, gamma_init=gamma_init, name='en/out2/batch_norm')
# net_out2 = DenseLayer(net_h5, n_units=z_dim, act=tf.nn.relu,
# W_init = w_init, name='en/h5/lin_sigmoid')
z_log_sigma_sq = net_out2.outputs + 1e-6# (b_size,512)
return net_out1, net_out2, z_mean, z_log_sigma_sq
def generator(inputs, is_train = True, reuse = False):
'''
generator of GAN, which can also be seen as a decoder of VAE
inputs: latent representation from encoder. [b_size,z_dim]
'''
image_size = FLAGS.output_size # 64 the output size of generator
s2, s4, s8, s16 = int(image_size/2), int(image_size/4), int(image_size/8), int(image_size/16) # 32,16,8,4
gf_dim = 64
c_dim = FLAGS.c_dim # n_color 3
batch_size = FLAGS.batch_size # 64
w_init = tf.random_normal_initializer(stddev=0.02)
gamma_init = tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("generator", reuse = reuse):
tl.layers.set_name_reuse(reuse)
net_in = InputLayer(inputs, name='g/in')
net_h0 = DenseLayer(net_in, n_units=gf_dim*4*s8*s8, W_init=w_init,
act = tf.identity, name='g/h0/lin')
# net_h0.outputs._shape = (b_size,256*8*8)
net_h0 = ReshapeLayer(net_h0, shape=[-1, s8, s8, gf_dim*4], name='g/h0/reshape')
# net_h0.outputs._shape = (b_size,8,8,256)
net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train,
gamma_init=gamma_init, name='g/h0/batch_norm')
# upsampling
net_h1 = DeConv2d(net_h0, gf_dim*4, (5, 5), out_size=(s4, s4), strides=(2, 2),
padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h1/decon2d')
net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,
gamma_init=gamma_init, name='g/h1/batch_norm')
# net_h1.outputs._shape = (b_size,16,16,256)
net_h2 = DeConv2d(net_h1, gf_dim*2, (5, 5), out_size=(s2, s2), strides=(2, 2),
padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h2/decon2d')
net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,
gamma_init=gamma_init, name='g/h2/batch_norm')
# net_h2.outputs._shape = (b_size,32,32,128)
net_h3 = DeConv2d(net_h2, gf_dim//2, (5, 5), out_size=(image_size, image_size), strides=(2, 2),
padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h3/decon2d')
net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,
gamma_init=gamma_init, name='g/h3/batch_norm')
# net_h3.outputs._shape = (b_size,64,64,32)
# no BN on last deconv
net_h4 = DeConv2d(net_h3, c_dim, (5, 5), out_size=(image_size, image_size), strides=(1, 1),
padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h4/decon2d')
# net_h4.outputs._shape = (b_size,64,64,3)
# net_h4 = Conv2d(net_h3, c_dim, (5,5),(1,1), padding='SAME', W_init=w_init, name='g/h4/conv2d')
logits = net_h4.outputs
net_h4.outputs = tf.nn.tanh(net_h4.outputs)
return net_h4, logits
# net_in = InputLayer(inputs, name='g/in')
# net_h0 = DenseLayer(net_in, n_units=gf_dim*8*s16*s16, W_init=w_init,
# act = tf.identity, name='g/h0/lin')
# # net_h0.outputs._shape = (b_size,512*4*4)
# net_h0 = ReshapeLayer(net_h0, shape=[-1, s16, s16, gf_dim*8], name='g/h0/reshape')
# # net_h0.outputs._shape = (b_size,4,4,512)
# net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train,
# gamma_init=gamma_init, name='g/h0/batch_norm')
# # upsampling
# net_h1 = DeConv2d(net_h0, gf_dim*4, (5, 5), out_size=(s8, s8), strides=(2, 2),
# padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h1/decon2d')
# net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,
# gamma_init=gamma_init, name='g/h1/batch_norm')
# # net_h1.outputs._shape = (b_size,8,8,256)
# net_h2 = DeConv2d(net_h1, gf_dim*2, (5, 5), out_size=(s4, s4), strides=(2, 2),
# padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h2/decon2d')
# net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,
# gamma_init=gamma_init, name='g/h2/batch_norm')
# # net_h2.outputs._shape = (b_size,16,16,128)
# net_h3 = DeConv2d(net_h2, gf_dim, (5, 5), out_size=(s2, s2), strides=(2, 2),
# padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h3/decon2d')
# net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,
# gamma_init=gamma_init, name='g/h3/batch_norm')
# # net_h3.outputs._shape = (b_size,32,32,64)
# net_h4 = DeConv2d(net_h3, gf_dim//2, (5, 5), out_size=(image_size, image_size), strides=(2, 2),
# padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h4/decon2d')
# net_h4 = BatchNormLayer(net_h4, act=tf.nn.relu, is_train=is_train,
# gamma_init=gamma_init, name='g/h4/batch_norm')
# # net_h4.outputs._shape = (b_size,64,64,32)
# # no BN on last deconv
# net_h5 = DeConv2d(net_h4, c_dim, (5, 5), out_size=(image_size, image_size), strides=(1, 1),
# padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h5/decon2d')
# # net_h4.outputs._shape = (b_size,64,64,3)
# # net_h4 = Conv2d(net_h3, c_dim, (5,5),(1,1), padding='SAME', W_init=w_init, name='g/h4/conv2d')
# logits = net_h5.outputs
# net_h5.outputs = tf.nn.tanh(net_h5.outputs)
# return net_h5, logits