def __init__(self, num_points=2500, global_feat=True, routing=None):
super(PointNetfeat, self).__init__()
self.stn1 = input_transform_net(num_points=num_points)
self.stn2 = feature_transform_net(num_points=num_points, K=64)
self.routing = routing
self.conv1 = nn.Conv1D(64, 1)
self.conv1_feat_trans = nn.Conv1D(64, 1)
self.conv2 = nn.Conv1D(128, 1)
self.conv3 = nn.Conv1D(1024, 1)
self.bn1 = nn.BatchNorm(in_channels=64)
self.bn1_feat_trans = nn.BatchNorm(in_channels=64)
self.bn2 = nn.BatchNorm(in_channels=128)
self.bn3 = nn.BatchNorm(in_channels=1024)
self.mp1 = nn.MaxPool1D(num_points)
self.num_points = num_points
self.global_feat = global_feat
def get_model(point_cloud, is_training, bn_decay=None):
"""
Classification PointNetwork
:param point_cloud: input pointcloud BxNx3
:param is_training: training flag
:param bn_decay: decay flag
:return: output model BxNx50
"""
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(point_cloud, is_training, bn_decay, K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
net = tf_util.conv2d(input_image, 64, [1, 3], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv1', bn_decay=bn_decay)
net = tf_util.conv2d(net, 64, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv2', bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
point_feat = tf.expand_dims(net_transformed, [2])
print(point_feat)
net = tf_util.conv2d(point_feat, 64, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv3', bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv4', bn_decay=bn_decay)
net = tf_util.conv2d(net, 1024, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv5', bn_decay=bn_decay)
global_feat = tf_util.max_pool2d(net, [num_point, 1], padding='VALID', scope='maxpool')
print(global_feat)
global_feat_expand = tf.tile(global_feat, [1, num_point, 1, 1])
concat_feat = tf.concat(3, [point_feat, global_feat_expand])
print(concat_feat)
net = tf_util.conv2d(concat_feat, 512, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv6', bn_decay=bn_decay)
net = tf_util.conv2d(net, 256, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv7', bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv8', bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1, 1], padding='VALID', stride=[1, 1], bn=True, is_training=is_training,
scope='conv9', bn_decay=bn_decay)
net = tf_util.conv2d(net, 50, [1, 1], padding='VALID', stride=[1, 1], activation_fn=None, scope='conv10')
net = tf.squeeze(net, [2]) # BxNxC
return net, end_points
def get_model(point_cloud, is_training, bn_decay=None):
"""
Classification PointNet,
input is BxNx3,
output Bx40
"""
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(point_cloud,
is_training,
bn_decay,
K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
net = utils.tf_util.conv2d(input_image,
64, [1, 3],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
net = utils.tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
net_transformed = tf.expand_dims(net_transformed, [2])
net = utils.tf_util.conv2d(net_transformed,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = utils.tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
net = utils.tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay)
# Symmetric function: max pooling
net = utils.tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
net = tf.reshape(net, [batch_size, -1])
net = utils.tf_util.fully_connected(net,
512,
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
net = utils.tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp1')
net = utils.tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
net = utils.tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp2')
net = utils.tf_util.fully_connected(net,
40,
activation_fn=None,
scope='fc3')
return net, end_points
def get_model(point_cloud,
is_training,
classes,
dtype=tf.float32,
bn=True,
bn_decay=None):
""" Classification PointNet, input is BxNxF, output BxNxC
B = number of images per batch
N = number of points
F = number of features
C = number of classes """
# batch_size = point_cloud.get_shape()[0].value
global num_classes
num_classes = classes
num_point = point_cloud.get_shape()[1].value
features = point_cloud.get_shape()[2].value
end_points = {}
with tf.compat.v1.variable_scope('transform_net1'):
transform = input_transform_net(point_cloud,
is_training,
bn_decay,
K=features,
dtype=dtype)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
net = tf_util.conv2d(input_image,
64, [1, features],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay,
dtype=dtype)
with tf.compat.v1.variable_scope('transform_net2'):
transform = feature_transform_net(net,
is_training,
bn_decay,
K=64,
dtype=dtype)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
point_feat = tf.expand_dims(net_transformed, [2])
logger.info('point_feat = %s', point_feat)
net = tf_util.conv2d(point_feat,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay,
dtype=dtype)
global_feat = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
logger.info('global_feat = %s', global_feat)
global_feat_expand = tf.tile(global_feat, [1, num_point, 1, 1])
concat_feat = tf.concat([point_feat, global_feat_expand], 3)
logger.info('concat_feat = %s', concat_feat)
net = tf_util.conv2d(concat_feat,
512, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv6',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv7',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv8',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv9',
bn_decay=bn_decay,
dtype=dtype)
net = tf_util.conv2d(net,
classes, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
scope='conv10',
dtype=dtype)
net = tf.squeeze(net, [2]) # BxNxC
logger.info('net = %s', net)
return net, end_points
def get_model(point_cloud, is_training, bn_decay=None):
""" Classification PointNet, input is BxNx3, output BxNx50 """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
with tf.variable_scope('transform_net1') as sc:
transform = input_transform_net(point_cloud,
is_training,
bn_decay,
K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
net = tf_util.conv2d(input_image,
64, [1, 3],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
with tf.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
point_feat = tf.expand_dims(net_transformed, [2])
net = tf_util.conv2d(point_feat,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay)
global_feat = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
global_feat_expand = tf.tile(global_feat, [1, num_point, 1, 1])
concat_feat = tf.concat(axis=3,
values=[point_feat,
global_feat_expand]) # (32,1024,1,1088)
Fsem = tf_util.conv2d(concat_feat,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=False,
is_training=is_training,
scope='Fsem')
ptssemseg_logits = tf_util.conv2d(Fsem,
50, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
scope='ptssemseg_logits')
ptssemseg_logits = tf.squeeze(ptssemseg_logits, [2])
# ptssemseg = tf.nn.softmax(ptssemseg_logits, name="ptssemseg")
# Similarity matrix
fts = tf.reshape(ptssemseg_logits, [batch_size, -1])
H = HGNN.construct_H_with_KNN(fts)
G = HGNN.generate_G_from_H(H)
# G = tf.convert_to_tensor(G)
net = tf_util.fully_connected(fts,
128,
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
net = tf.matmul(G, net)
net = tf_util.fully_connected(net,
40,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
net = tf.matmul(G, net)
return net, end_points
def get_model_other(point_cloud, is_training, bn_decay=None):
""" Classification PointNet, input is BxNx3 and BxNx1, output Bx4 """
batch_size = point_cloud.get_shape()[0] # .value
num_point = point_cloud.get_shape()[1] # .value
end_points = {}
# transform net for input x,y,z
with tf.compat.v1.variable_scope('transform_net1') as sc:
transform = input_transform_net(point_cloud[:, :, 1:4],
is_training,
bn_decay,
K=3)
point_cloud_transformed = tf.matmul(point_cloud[:, :, 1:4], transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)
# First MLP layers
net = tf_util.conv2d(input_image,
64, [1, 3],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
# transform net for the features
with tf.compat.v1.variable_scope('transform_net2') as sc:
transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
# point_feat = tf.expand_dims(net_transformed, [2])
# add the additional features to the second MLP layers
# point_cloud_SF = tf.expand_dims(point_cloud[:, :, 0:1], [2])
concat_other = tf.concat(axis=2,
values=[
point_cloud[:, :, 0:1], net_transformed,
point_cloud[:, :, 4:para.dim]
])
concat_other = tf.expand_dims(concat_other, [2])
# second MLP layers
net = tf_util.conv2d(
concat_other,
64,
[1, 1], # 64 is the output #neuron
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay)
# 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')
net = tf_util.fully_connected(net, 4, activation_fn=None, scope='fc3')
return net, end_points