def discriminator(self, video, reuse=False):
with tf.variable_scope('d_', reuse=reuse) as vs:
initial_dim = 64
""" CONV BLOCK 1 """
d_h0 = dis_block(video, 3, initial_dim, 'block1', reuse=reuse)
""" CONV BLOCK 2 """
d_h1 = dis_block(d_h0,
initial_dim,
initial_dim * 2,
'block2',
reuse=reuse)
""" CONV BLOCK 3 """
d_h2 = dis_block(d_h1,
initial_dim * 2,
initial_dim * 4,
'block3',
reuse=reuse)
""" CONV BLOCK 4 """
d_h3 = dis_block(d_h2,
initial_dim * 4,
initial_dim * 8,
'block4',
reuse=reuse)
""" CONV BLOCK 5 """
d_h4 = dis_block(d_h3,
initial_dim * 8,
1,
'block5',
reuse=reuse,
normalize=False)
""" LINEAR BLOCK """
d_h5 = linear(tf.reshape(d_h4, [self.batch_size, -1]), 1)
variables = tf.contrib.framework.get_variables(vs)
return d_h5, variables
def discriminatorVid(self, video, reuse=False):
with tf.variable_scope('disc_v', reuse=reuse) as vs:
initial_dim = 64
d_h0 = dis_block(video, 3, initial_dim, 'block1', reuse=reuse)
d_h1 = dis_block(d_h0,
initial_dim,
initial_dim * 2,
'block2',
reuse=reuse)
d_h2 = dis_block(d_h1,
initial_dim * 2,
initial_dim * 4,
'block3',
reuse=reuse)
d_h3 = dis_block(d_h2,
initial_dim * 4,
initial_dim * 8,
'block4',
reuse=reuse)
d_h4 = dis_block(d_h3,
initial_dim * 8,
1,
'block5',
reuse=reuse,
normalize=False)
d_h5 = linear(tf.reshape(d_h4, [self.batch_size, -1]), 1)
# variables = tf.contrib.framework.get_variables(vs)
# return d_h5, variables
return d_h5
def discriminator(self, video, reuse=False):
with tf.variable_scope('d_', reuse=reuse) as vs:
initial_dim = self.crop_size
video = tf.reshape(video, [self.batch_size, self.frame_size, self.crop_size, self.crop_size, self.channels])
d_h0 = dis_block(video, self.channels, initial_dim, 'block1', reuse=reuse, ddd=True)
d_h1 = dis_block(d_h0, initial_dim, initial_dim * 2, 'block2', reuse=reuse, ddd=True)
d_h2 = dis_block(d_h1, initial_dim * 2, initial_dim * 4, 'block3', reuse=reuse, ddd=True)
d_h3 = dis_block(d_h2, initial_dim * 4, initial_dim * 8, 'block4', reuse=reuse, ddd=True)
d_h4 = dis_block(d_h3, initial_dim * 8, 1, 'block5', reuse=reuse, normalize=False, ddd=True)
d_h5 = linear(tf.reshape(d_h4, [self.batch_size, -1]), 1)
variables = tf.contrib.framework.get_variables(vs)
return d_h5, variables