def id_loss(images, renders, vggpath):
model = VGGFace(vggpath, False)
inputs = tf.concat([images, renders], axis=0)
layers, _, _ = model.encoder(inputs, False)
z = layers['fc7']
z_images, z_renders = tf.split(z, 2, axis=0)
loss = tf.reduce_mean(tf.square(z_images - z_renders), name='id_loss')
return loss
class CoarseModel(object):
def __init__(self, vggpath='', basis3dmm=None, trainable=True):
self.basis3dmm = basis3dmm
self.model = VGGFace(vggpath, trainable)
def encoder3DMM(self, imgs, reuse=False):
with tf.variable_scope('CoarseModel', reuse=tf.AUTO_REUSE):
layers, _, _ = self.model.encoder(imgs, reuse)
z = tf.reshape(layers['fc7'], [-1, 4096])
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE) as scope:
para_shape = tf.layers.dense(z,
self.basis3dmm['bases_shape'].shape[0],
use_bias=False,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.002),
name='para_shape')
para_exp = tf.layers.dense(z,
self.basis3dmm['bases_exp'].shape[0],
use_bias=False,
kernel_initializer=tf.zeros_initializer(),
name='para_exp')
para_tex = tf.layers.dense(z,
self.basis3dmm['bases_tex'].shape[0],
use_bias=False,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.002),
name='para_tex')
para_pose = tf.layers.dense(z,
6,
use_bias=False,
kernel_initializer=tf.zeros_initializer(),
name='para_pose')
para_illum = tf.layers.dense(z,
27,
use_bias=False,
kernel_initializer=tf.zeros_initializer(),
name='para_illum')
return para_shape, para_exp, para_tex, para_pose, para_illum