def get_model(imgs, is_training, weight_decay=0.0, bn_decay=None):
batch_size = imgs.get_shape()[0].value
im_dim = imgs.get_shape()[1].value
########
with tf.variable_scope('Encoding'):
# (batch_size, 64, 64, 64)
net = tf_utils.conv2d(imgs, 64, [7,7],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv1', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 32, 32, 64)
net = tf_utils.conv2d(net, 64, [5,5],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv2', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 16, 16, 128)
net = tf_utils.conv2d(net, 128, [5,5],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv3', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 8, 8, 128)
net = tf_utils.conv2d(net, 128, [3,3],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv4', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 4, 4, 256)
net = tf_utils.conv2d(net, 256, [3,3],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv5', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 1, 1, 512)
net = tf_utils.conv2d(net, 512, [4,4],
padding='VALID', stride=[1,1],
bn=True, is_training=is_training,
scope='conv6', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
########
with tf.variable_scope('Latent_variable'):
net = tf.reshape(net, [batch_size, 512])
net = tf_utils.fully_connected(net, 512, scope="fc1",
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
net = tf_utils.fully_connected(net, 128*4*4*4, scope="fc2",
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
net = tf.reshape(net, [batch_size, 4, 4, 4, 128])
########
with tf.variable_scope('Decoding'):
# (batch_size, 8, 8, 8, 64)
net = tf_utils.conv3d_transpose(net, 64, [3, 3, 3], scope="deconv1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 16, 16, 16, 32)
net = tf_utils.conv3d_transpose(net, 32, [3, 3, 3], scope="deconv2",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 32, 32, 32, 32)
net = tf_utils.conv3d_transpose(net, 32, [3, 3, 3], scope="deconv3",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
##################
## regressed color
##################
# (batch_size, 64, 64, 64, 24)
net_reg_clr = tf_utils.conv3d_transpose(net, 16, [3, 3, 3], scope="deconv_reg_clr1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 3)
net_reg_clr = tf_utils.conv3d(net_reg_clr, 3, [3, 3, 3], scope="deconv_reg_clr2",
stride=[1, 1, 1], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
##############
### confidence
##############
net_conf = tf_utils.conv3d_transpose(net, 16, [3, 3, 3], scope="deconv_conf1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 1)
net_conf = tf_utils.conv3d(net_conf, 1, [3, 3, 3], scope="conv_conf2",
stride=[1, 1, 1], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
##############
### flow color
##############
net_flow = tf_utils.conv3d_transpose(net, 2, [3, 3, 3], scope="deconv_flow",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 3)
net_flow_clr = tf_utils.Sampler(net_flow, imgs)
#################
### blended color
#################
net_blended_clr = net_reg_clr * net_conf + net_flow_clr * (1.0 - net_conf)
return net_reg_clr, net_conf, net_flow, net_blended_clr
def get_model(imgs, is_training, weight_decay=0.0, bn_decay=None):
"""
Args:
imgs: (batch_size, im_dim, im_dim, 3)
is_training: a boolean placeholder.
Return:
shape: (batch_size, vol_dim, vol_dim, vol_dim, 1)
"""
batch_size = imgs.get_shape()[0].value
im_dim = imgs.get_shape()[1].value
########
with tf.variable_scope('Encoding'):
# (batch_size, 64, 64, 64)
net = tf_utils.conv2d(imgs,
64, [7, 7],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 32, 32, 64)
net = tf_utils.conv2d(net,
64, [5, 5],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 16, 16, 128)
net = tf_utils.conv2d(net,
128, [5, 5],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 8, 8, 128)
net = tf_utils.conv2d(net,
128, [3, 3],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 4, 4, 256)
net = tf_utils.conv2d(net,
256, [3, 3],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 1, 1, 512)
net = tf_utils.conv2d(net,
512, [4, 4],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv6',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
########
with tf.variable_scope('Latent_variable'):
net = tf.reshape(net, [batch_size, 512])
net = tf_utils.fully_connected(net,
512,
scope="fc1",
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
net = tf_utils.fully_connected(net,
128 * 4 * 4 * 4,
scope="fc2",
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
net = tf.reshape(net, [batch_size, 4, 4, 4, 128])
########
with tf.variable_scope('Decoding'):
# (batch_size, 8, 8, 8, 64)
net = tf_utils.conv3d_transpose(net,
64, [3, 3, 3],
scope="deconv1",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 16, 16, 16, 32)
net = tf_utils.conv3d_transpose(net,
32, [3, 3, 3],
scope="deconv2",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 32, 32, 32, 32)
net = tf_utils.conv3d_transpose(net,
32, [3, 3, 3],
scope="deconv3",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 64, 64, 64, 16)
net = tf_utils.conv3d_transpose(net,
24, [3, 3, 3],
scope="deconv4",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 64, 64, 64, 1)
net = tf_utils.conv3d(net,
1, [3, 3, 3],
scope="deconv5",
stride=[1, 1, 1],
padding='SAME',
weight_decay=weight_decay,
activation_fn=None,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
return net