def discriminator(self, image, is_training, reuse=False):
with tf.variable_scope("discriminator"):
if reuse:
tf.get_variable_scope().reuse_variables()
# [batch,256,256,1] -> [batch,128,128,64]
h0 = lrelu(conv2d(image, self.discriminator_dim,
scope="d_h0_conv"))
# [batch,128,128,64] -> [batch,64,64,64*2]
h1 = lrelu(
batch_norm(conv2d(h0,
self.discriminator_dim * 2,
scope="d_h1_conv"),
is_training,
scope="d_bn_1"))
# [batch,64,64,64*2] -> [batch,32,32,64*4]
h2 = lrelu(
batch_norm(conv2d(h1,
self.discriminator_dim * 4,
scope="d_h2_conv"),
is_training,
scope="d_bn_2"))
# [batch,32,32,64*4] -> [batch,31,31,64*8]
h3 = lrelu(
batch_norm(conv2d(h2,
self.discriminator_dim * 8,
sh=1,
sw=1,
scope="d_h3_conv"),
is_training,
scope="d_bn_3"))
# real or fake binary loss
fc1 = fc(tf.reshape(h3, [self.batch_size, -1]), 1, scope="d_fc1")
return tf.sigmoid(fc1), fc1
def _make_descriminator(self, input, phase_train):
conv1 = ops.batch_norm(ops.conv2d(input, self.df_dim,
name='d_h0_conv'),
name='d_bn0',
phase_train=phase_train)
h0 = ops.lrelu(conv1)
h1 = ops.lrelu(
ops.batch_norm(ops.conv2d(h0, self.df_dim * 2, name='d_h1_conv'),
name='d_bn1',
phase_train=phase_train))
#h2 = ops.lrelu(ops.batch_norm(ops.conv2d(h1, self.df_dim*4, name='d_h2_conv'), name='d_bn2'))
#h3 = ops.lrelu(ops.batch_norm(ops.conv2d(h2, self.df_dim*8, name='d_h3_conv'), name='d_bn3'))
h2 = ops.lrelu(tf.reshape(h1, [self.batch_size, -1]), 1, 'd_h1_lin')
return h2
def _make_generator(self, input, phase_train):
s_h, s_w = self.img_size, self.img_size
s_h2, s_w2 = self._conv_out_size_same(s_h,
2), self._conv_out_size_same(
s_w, 2)
s_h4, s_w4 = self._conv_out_size_same(s_h2,
2), self._conv_out_size_same(
s_w2, 2)
#s_h8, s_w8 = self._conv_out_size_same(s_h4, 2), self._conv_out_size_same(s_w4, 2)
#s_h16, s_w16 = self._conv_out_size_same(s_h8, 2), self._conv_out_size_same(s_w8, 2)
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = ops.linear(input,
self.gf_dim * 8 * s_h4 *
s_w4,
'g_h0_lin',
with_w=True)
normalized_value = ops.batch_norm(self.z_,
name='g_bn0',
axes=[0],
phase_train=phase_train)
self.h0 = tf.reshape(normalized_value,
[-1, s_h4, s_w4, self.gf_dim * 8])
h0 = ops.lrelu(self.h0)
self.h1, self.h1_w, self.h1_b = ops.deconv2d(
h0, [self.batch_size, s_h2, s_w2, self.gf_dim * 4],
name='g_h1',
with_w=True)
h1 = ops.lrelu(
ops.batch_norm(self.h1, name='g_bn1', phase_train=phase_train))
# h2, self.h2_w, self.h2_b = ops.deconv2d(
# h1, [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='g_h2', with_w=True)
# h2 = tf.nn.relu(ops.batch_norm(h2, name='g_bn2'))
#
# h3, self.h3_w, self.h3_b = ops.deconv2d(
# h2, [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='g_h3', with_w=True)
# h3 = tf.nn.relu(ops.batch_norm(h3, name='g_bn3'))
h2, self.h2_w, self.h2_b = ops.deconv2d(
h1, [self.batch_size, s_h, s_w, self.c_dim],
name='g_h4',
with_w=True)
h2_non_linear = ops.lrelu(h2, leak=0)
return h2_non_linear
def encode_layer(x, output_filters, layer):
act = lrelu(x)
conv = conv2d(act,
output_filters=output_filters,
scope="g_e%d_conv" % layer)
enc = batch_norm(conv, is_training, scope="g_e%d_bn" % layer)
encode_layers["e%d" % layer] = enc
return enc
def decode_layer(x,
output_width,
output_filters,
layer,
enc_layer,
dropout=False,
do_concat=True):
dec = deconv2d(lrelu(x), [
self.batch_size, output_width, output_width, output_filters
],
scope="g_d%d_deconv" % layer)
if layer != 8:
# IMPORTANT: normalization for last layer
# Very important, otherwise GAN is unstable
dec = batch_norm(dec,
is_training,
scope="g_d%d_bn" % layer)
if dropout:
dec = tf.nn.dropout(dec, 0.5)
if do_concat:
dec = tf.concat([dec, enc_layer], 3)
return dec
def cnn(self, input_images, output_dim, phase=True, bn=False, reuse=None):
h1 = tf.layers.conv2d(
input_images,
filters=16,
kernel_size=[5, 5],
strides=2,
padding="same",
activation=None if bn else ops.lrelu,
name="rec_conv_h1",
kernel_initializer=tf.contrib.layers.xavier_initializer(),
reuse=reuse) # 112x112x1 -> 56x56x16
if bn:
h1bn = tf.layers.batch_normalization(h1,
center=True,
scale=True,
training=phase,
name='rec_conv_h1_bn',
reuse=reuse)
h1bnact = ops.lrelu(h1bn)
else:
h1bnact = h1
h2 = tf.layers.conv2d(
h1bnact,
filters=32,
kernel_size=[5, 5],
strides=2,
padding="same",
activation=None if bn else ops.lrelu,
name="rec_conv_h2",
kernel_initializer=tf.contrib.layers.xavier_initializer(),
reuse=reuse) # 56x56x16 -> 28x28x32
if bn:
h2bn = tf.layers.batch_normalization(h2,
center=True,
scale=True,
training=phase,
name='rec_conv_h2_bn',
reuse=reuse)
h2bnact = ops.lrelu(h2bn)
else:
h2bnact = h2
h3 = tf.layers.conv2d(
h2bnact,
filters=64,
kernel_size=[5, 5],
strides=2,
padding="same",
activation=None if bn else ops.lrelu,
name="rec_conv_h3",
kernel_initializer=tf.contrib.layers.xavier_initializer(),
reuse=reuse) # 28x28x32 -> 14x14x64
if bn:
h3bn = tf.layers.batch_normalization(h3,
center=True,
scale=True,
training=phase,
name='rec_conv_h3_bn',
reuse=reuse)
h3bnact = ops.lrelu(h3bn)
else:
h3bnact = h3
h4 = tf.layers.conv2d(
h3bnact,
filters=128,
kernel_size=[5, 5],
strides=2,
padding="same",
activation=None if bn else ops.lrelu,
name="rec_conv_h4",
kernel_initializer=tf.contrib.layers.xavier_initializer(),
reuse=reuse) # 14x14x64 -> 7x7x128
if bn:
h4bn = tf.layers.batch_normalization(h4,
center=True,
scale=True,
training=phase,
name='rec_conv_h4_bn',
reuse=reuse)
h4bnact = ops.lrelu(h4bn)
else:
h4bnact = h4
h4_flat = tf.reshape(h4bnact, [
-1, self.args.img_size_w // 16 * self.args.img_size_h // 16 * 128
])
z = tf.layers.dense(h4_flat,
units=output_dim,
name="z_rec",
reuse=reuse)
return z
def forward_pass(self,
state_in,
reshape=True,
sigmoid_out=False,
reuse=None):
self.state_in = state_in
shape_in = self.state_in.get_shape().as_list()
# Get number of input channels for weight/bias init
channels_in = shape_in[-1]
with tf.variable_scope(self.scope, reuse=reuse):
if reshape:
# Reshape [batch_size, traj_len, H, W, C] into [batch_size*traj_len, H, W, C]
self.state_in = tf.reshape(
self.state_in, [-1, shape_in[2], shape_in[3], shape_in[4]])
self.conv1 = conv2d(
self.state_in,
self.num_filters,
self.kernels[0],
self.strides[0],
kernel_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(channels_in * self.kernels[0] * self.kernels[0]))), (
1.0 / tf.sqrt(
float(channels_in * self.kernels[0] *
self.kernels[0])))),
bias_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(channels_in * self.kernels[0] * self.kernels[0]))), (
1.0 / tf.sqrt(
float(channels_in * self.kernels[0] *
self.kernels[0])))),
scope='conv1')
self.conv1 = lrelu(self.conv1, self.lrelu_alpha, scope='conv1')
self.conv2 = conv2d(
self.conv1,
self.num_filters,
self.kernels[1],
self.strides[1],
kernel_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[1] *
self.kernels[1]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[1] *
self.kernels[1])))),
bias_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[1] *
self.kernels[1]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[1] *
self.kernels[1])))),
scope='conv2')
self.conv2 = lrelu(self.conv2, self.lrelu_alpha, scope='conv2')
self.conv3 = conv2d(
self.conv2,
self.num_filters,
self.kernels[2],
self.strides[2],
kernel_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[2] *
self.kernels[2]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[2] *
self.kernels[2])))),
bias_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[2] *
self.kernels[2]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[2] *
self.kernels[2])))),
scope='conv3')
self.conv3 = lrelu(self.conv3, self.lrelu_alpha, scope='conv3')
self.conv4 = conv2d(
self.conv3,
self.num_filters,
self.kernels[3],
self.strides[3],
kernel_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[3] *
self.kernels[3]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[3] *
self.kernels[3])))),
bias_init=tf.random_uniform_initializer((-1.0 / tf.sqrt(
float(self.num_filters * self.kernels[3] *
self.kernels[3]))), (1.0 / tf.sqrt(
float(self.num_filters * self.kernels[3] *
self.kernels[3])))),
scope='conv4')
self.conv4 = lrelu(self.conv4, self.lrelu_alpha, scope='conv4')
self.flatten = flatten(self.conv4)
self.dense = dense(self.flatten,
self.dense_size,
kernel_init=tf.random_uniform_initializer(
(-1.0 / tf.sqrt(float(self.num_filters))),
(1.0 / tf.sqrt(float(self.num_filters)))),
bias_init=tf.random_uniform_initializer(
(-1.0 / tf.sqrt(float(self.num_filters))),
(1.0 / tf.sqrt(float(self.num_filters)))))
self.output = dense(self.dense,
1,
kernel_init=tf.random_uniform_initializer(
(-1.0 / tf.sqrt(float(self.dense_size))),
(1.0 / tf.sqrt(float(self.dense_size)))),
bias_init=tf.random_uniform_initializer(
(-1.0 / tf.sqrt(float(self.dense_size))),
(1.0 / tf.sqrt(float(self.dense_size)))),
scope='output')
if sigmoid_out:
self.output = tf.nn.sigmoid(self.output)
if reshape:
# Reshape 1d reward output [batch_size*traj_len] into batches [batch_size, traj_len]
self.output = tf.reshape(self.output, [-1, shape_in[1]])
self.network_params = tf.trainable_variables(scope=self.scope)
return self.output
def discriminator(params, x_init, reuse=False):
""" Discriminator.
Parameters
----------
params: dict.
x_init: input tensor.
reuse: bool, reuse the net if True.
Returns
-------
x_gan: tensor, outputs for adversarial training.
x_reg: tensor, outputs for gaze estimation.
"""
layers = 5
channel = 64
image_size = params.image_size
with tf.compat.v1.variable_scope('discriminator', reuse=reuse):
# 64 3 -> 32 64 -> 16 128 -> 8 256 -> 4 512 -> 2 1024
x = conv2d(x_init,
channel,
conv_filters_dim=4,
d_h=2,
d_w=2,
scope='conv_0',
pad=1,
use_bias=True)
x = lrelu(x)
for i in range(1, layers):
x = conv2d(x,
channel * 2,
conv_filters_dim=4,
d_h=2,
d_w=2,
scope='conv_%d' % i,
pad=1,
use_bias=True)
x = lrelu(x)
channel = channel * 2
filter_size = int(image_size / 2**layers)
x_gan = conv2d(x,
1,
conv_filters_dim=filter_size,
d_h=1,
d_w=1,
pad=1,
scope='conv_logit_gan',
use_bias=False)
x_reg = conv2d(x,
2,
conv_filters_dim=filter_size,
d_h=1,
d_w=1,
pad=0,
scope='conv_logit_reg',
use_bias=False)
x_reg = tf.reshape(x_reg, [-1, 2])
return x_gan, x_reg
