net = slim.conv2d(net, 64, [5, 5], stride=1, scope='conv6')
net = slim.conv2d(net, 3, [5, 5], stride=1, activation_fn=tf.tanh,
normalizer_fn=None, scope='conv7')
+ net_copy = net
flow = net[:, :, :, 0:2]
mask = tf.expand_dims(net[:, :, :, 2], 3)
mask = tf.tile(mask, [1, 1, 1, 3])
net = tf.multiply(mask, output_1) + tf.multiply(1.0 - mask, output_2)
- return net
+ return [net, net_copy]