def input_transform_net_edge_net(point_cloud, mask,is_training, bn_decay=None, K=4):
""" Input (XYZ) Transform Net, input is BxNx3 gray image
Return:
Transformation matrix of size 3xK """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
net, kernel,_,_ = edge_net.edge_unit(point_cloud, mask, 'max', scope='tconv1', bn=True, is_training=is_training,
bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1,1],
padding='VALID', stride=[1,1],
bn=True, is_training=is_training,
scope='tconv2', bn_decay=bn_decay)
net = tf_util.conv2d(net, 1024, [1,1],
padding='VALID', stride=[1,1],
bn=True, is_training=is_training,
scope='tconv3', bn_decay=bn_decay)
net = tf_util.max_pool2d(net, [num_point,1],
padding='VALID', scope='tmaxpool')
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
scope='tfc1', bn_decay=bn_decay)
net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
scope='tfc2', bn_decay=bn_decay)
with tf.variable_scope('transform_XYZ') as sc:
if (K == 4):
weights = tf.get_variable('weights', [256, 4 * 4],
initializer=tf.constant_initializer(0.0),
dtype=tf.float32)
biases = tf.get_variable('biases', [4 * 4],
initializer=tf.constant_initializer(0.0),
dtype=tf.float32)
biases += tf.constant(np.eye(K).flatten(), dtype=tf.float32)
transform = tf.matmul(net, weights)
transform = tf.nn.bias_add(transform, biases)
transform = tf.reshape(transform, [batch_size, 4, 4])
return transform
def get_model_ec(group_data, mask, is_training, bn_decay=None):
# groupdata B N K*C
batch_size = group_data.get_shape()[0].value
num_point = group_data.get_shape()[1].value
ec = econ.create_ec(group_data, mask) # B N K ec_leghth
ec_length = ec.get_shape()[3].value
ec = tf.reshape(ec, [batch_size, num_point, -1]) # B N 9
with tf.variable_scope('transform_net1_ec') as sc:
transform = input_transform_net_edge_net(ec,
mask,
is_training,
bn_decay,
K=ec_length)
ec_transformed = tf.matmul(tf.reshape(ec, [batch_size, -1, ec_length]),
transform)
ec_transformed = tf.reshape(ec_transformed, [batch_size, num_point, -1])
input_image = ec_transformed
with tf.variable_scope('ec_net1') as sc:
net, kernel, max_index_local_ec, masked_result = edge_net.edge_unit(
input_image,
mask,
'max',
config.neighbor_num,
32,
scope='conv1',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
net_transformed = tf.matmul(tf.squeeze(net), transform)
net_transformed = tf.expand_dims(net_transformed, [2])
net = tf_util.conv2d(net_transformed,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
max_index_ec = tf.squeeze(tf.argmax(net,
1)) # Symmetric function: max pooling
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net,
512,
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp1')
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp2')
return net, transform, max_index_ec, max_index_local_ec, masked_result
def get_model_groupdata(group_data, mask, is_training, bn_decay=None):
""" Classification PointNet, input is BxNx4, output Bx40 """
batch_size = group_data.get_shape()[0].value #32
num_point = group_data.get_shape()[1].value #1024
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net_edge_net(group_data,
mask,
is_training,
bn_decay,
K=4)
group_data_transformed = tf.matmul(
tf.reshape(group_data, [batch_size, -1, 4]), transform)
group_data_transformed = tf.reshape(group_data_transformed,
[batch_size, num_point, -1]) # B N K C
#input_image = tf.expand_dims(group_data_transformed, -1)
input_image = group_data_transformed
with tf.variable_scope('edge_net1') as sc:
net, kernel, max_index_local_neighbor, masked_result = edge_net.edge_unit(
input_image,
mask,
'max',
config.neighbor_num,
32,
scope='conv1',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
net_transformed = tf.matmul(tf.squeeze(net), transform)
net_transformed = tf.expand_dims(net_transformed, [2])
net = tf_util.conv2d(net_transformed,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
# Symmetric function: max pooling
max_index_neighbor = tf.squeeze(tf.argmax(net, 1))
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net,
512,
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp1')
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp2')
return net, transform, max_index_neighbor, max_index_local_neighbor