# Network architectures.
from __future__ import division
from utils.ops import relu, conv2d, lrelu, instance_norm, deconv2d, tanh
import tensorflow as tf
import tensorflow.contrib.slim as slim
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.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
def generator(input_, angles, reuse=False):
""" Generator.
Parameters
----------
input_: tensor, input images.
angles: tensor, target gaze direction.
reuse: bool, reuse the net if True.
Returns
-------
x: tensor, generated image.
"""
channel = 64
style_dim = angles.get_shape().as_list()[-1]
angles_reshaped = tf.reshape(angles, [-1, 1, 1, style_dim])
angles_tiled = tf.tile(angles_reshaped, [1, tf.shape(input_)[1],
tf.shape(input_)[2], 1])
x = tf.concat([input_, angles_tiled], axis=3)
with tf.variable_scope('generator', reuse=reuse):
# input layer
x = conv2d(x, channel, d_h=1, d_w=1, scope='conv2d_input',
use_bias=False, pad=3, conv_filters_dim=7)
x = instance_norm(x, scope='in_input')
x = relu(x)
# encoder
for i in range(2):
x = conv2d(x, 2 * channel, d_h=2, d_w=2, scope='conv2d_%d' % i,
use_bias=False, pad=1, conv_filters_dim=4)
x = instance_norm(x, scope='in_conv_%d' % i)
x = relu(x)
channel = 2 * channel
# bottleneck
for i in range(6):
x_a = conv2d(x, channel, conv_filters_dim=3, d_h=1, d_w=1,
pad=1, use_bias=False, scope='conv_res_a_%d' % i)
x_a = instance_norm(x_a, 'in_res_a_%d' % i)
x_a = relu(x_a)
x_b = conv2d(x_a, channel, conv_filters_dim=3, d_h=1, d_w=1,
pad=1, use_bias=False, scope='conv_res_b_%d' % i)
x_b = instance_norm(x_b, 'in_res_b_%d' % i)
x = x + x_b
# decoder
for i in range(2):
x = deconv2d(x, int(channel / 2), conv_filters_dim=4, d_h=2, d_w=2,
use_bias=False, scope='deconv_%d' % i)
x = instance_norm(x, scope='in_decon_%d' % i)
x = relu(x)
channel = int(channel / 2)
x = conv2d(x, 3, conv_filters_dim=7, d_h=1, d_w=1, pad=3,
use_bias=False, scope='output')
x = tanh(x)
return x
def vgg_16(inputs, scope='vgg_16', reuse=False):
""" VGG-16.
Parameters
----------
inputs: tensor.
scope: name of scope.
reuse: reuse the net if True.
Returns
-------
net: tensor, output tensor.
end_points: dict, collection of layers.
"""
with tf.variable_scope(scope, 'vgg_16', [inputs], reuse=reuse) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope(
[slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3],
scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
return net, end_points
