def get_model(point_cloud, is_training, num_class, bn_decay=None):
""" Semantic segmentation PointNet, input is BxNx3, output Bxnum_class """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
l0_xyz = point_cloud
l0_points = None
end_points['l0_xyz'] = l0_xyz
# Layer 1
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=1024, radius=0.1, nsample=32, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=256, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=64, radius=0.4, nsample=32, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=16, radius=0.8, nsample=32, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4')
# Feature Propagation layers
l3_points = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay, scope='fa_layer1')
l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer2')
l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer3')
l0_points = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer4')
# FC layers
net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
end_points['feats'] = net
net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
net = tf_util.conv1d(net, num_class, 1, padding='VALID', activation_fn=None, scope='fc2')
return net, end_points
def get_model(point_cloud, is_training, bn_decay=None):
""" Part segmentation PointNet, input is BxNx6 (XYZ NormalX NormalY NormalZ), output Bx50 """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3])
# Set Abstraction layers
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=64, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')
# Feature Propagation layers
l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer1')
l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer2')
l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([l0_xyz,l0_points],axis=-1), l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer3')
# FC layers
net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
end_points['feats'] = net
net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2')
return net, end_points
def get_model(point_cloud, cls_label, is_training, bn_decay=None):
""" Classification PointNet, input is BxNx3, output Bx40 """
batch_size = tf.shape(point_cloud)[0]
num_point = point_cloud.get_shape()[1].value
end_points = {}
l0_xyz = tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3])
l0_points = tf.slice(point_cloud, [0, 0, 3], [-1, -1, 3])
# Set abstraction layers
l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1, 0.2, 0.4], [32, 64, 128],
[[32, 32, 64], [64, 64, 128], [64, 96, 128]], is_training, bn_decay,
scope='layer1')
l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.4, 0.8], [64, 128],
[[128, 128, 256], [128, 196, 256]], is_training, bn_decay,
scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None,
mlp=[256, 512, 1024], mlp2=None, group_all=True,
is_training=is_training, bn_decay=bn_decay, scope='layer3')
# Feature propagation layers
l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256, 256], is_training, bn_decay,
scope='fa_layer1')
l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256, 128], is_training, bn_decay,
scope='fa_layer2')
cls_label_one_hot = tf.one_hot(cls_label, depth=NUM_CATEGORIES, on_value=1.0, off_value=0.0)
cls_label_one_hot = tf.reshape(cls_label_one_hot, [batch_size, 1, NUM_CATEGORIES])
cls_label_one_hot = tf.tile(cls_label_one_hot, [1, num_point, 1])
l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([cls_label_one_hot, l0_xyz, l0_points], axis=-1),
l1_points, [128, 128], is_training, bn_decay, scope='fp_layer3')
# FC layers
net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1',
bn_decay=bn_decay)
end_points['feats'] = net
net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2')
return net, end_points
def get_model(point_cloud, normals,axis_x,axis_y,kernel, scale,interp,fit,is_training, bn_decay=None,d=1,knn=1,nsample=16,use_xyz_feature=True):
""" Classification A-CNN, input is points BxNx3 and normals BxNx3, output Bx40 """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
l0_xyz = point_cloud
l0_normals = normals
l0_axis_x=axis_x
l0_axis_y=axis_y
l0_points = None
xyz_feature=None
l1_xyz, l1_points, l1_normals,l1_axis_x,l1_axis_y, kernel_out, weight, kernel_fit,xyz_feature = lfnet_module(kernel, scale,interp,fit,l0_xyz, l0_points,
l0_normals,l0_axis_x,l0_axis_y, xyz_feature,512,
[0.23],
nsample[0],
[[32, 64, 128]],
is_training, bn_decay,mlp=[64,64],first_layer=True,
scope='layer1',d=d,knn=knn,use_xyz_feature=use_xyz_feature)
l2_xyz, l2_points, l2_normals,l2_axis_x,l2_axis_y, _, _, _ ,xyz_feature= lfnet_module(kernel, scale,interp,fit,l1_xyz, l1_points, l1_normals,l1_axis_x,l1_axis_y, xyz_feature,
128,[0.32], nsample[1],
[[128,128,256]], is_training, bn_decay,
mlp=[64,64],scope='layer2',d=d,knn=knn,use_xyz_feature=use_xyz_feature)
_, l5_points, _,_ = pointnet_sa_module(l2_xyz, l2_points, xyz_feature,npoint=None, radius=None, nsample=None, mlp=[1024], mlp2=None,
mlp3=[64,64], group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3',end=True,use_xyz_feature=use_xyz_feature)
# Fully connected layers
net = tf.reshape(l5_points, [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=0.4, 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=0.4, is_training=is_training, scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points,kernel_out,weight,kernel_fit
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 = {}
l0_xyz = point_cloud
l0_points = None
# Set abstraction layers
l1_xyz, l1_points = pointnet_sa_module_msg(
l0_xyz,
l0_points,
512, [0.1, 0.2, 0.4], [16, 32, 128],
[[32, 32, 64], [64, 64, 128], [64, 96, 128]],
is_training,
bn_decay,
scope='layer1',
use_nchw=True)
l2_xyz, l2_points = pointnet_sa_module_msg(
l1_xyz,
l1_points,
128, [0.2, 0.4, 0.8], [32, 64, 128],
[[64, 64, 128], [128, 128, 256], [128, 128, 256]],
is_training,
bn_decay,
scope='layer2')
l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz,
l2_points,
npoint=None,
radius=None,
nsample=None,
mlp=[256, 512, 1024],
mlp2=None,
group_all=True,
is_training=is_training,
bn_decay=bn_decay,
scope='layer3')
# Fully connected layers
net = tf.reshape(l3_points, [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=0.4,
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=0.4,
is_training=is_training,
scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points
def get_gen_model(point_cloud, is_training, scope, bradius = 1.0, reuse=None,use_bn = False,use_ibn = False,
use_normal=False,bn_decay=None, up_ratio = 4,num_addpoint=600,idx=None,is_crop=False):
print "Crop flag is ",is_crop
with tf.variable_scope(scope,reuse=reuse) as sc:
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
l0_xyz = point_cloud[:,:,0:3]
if use_normal:
l0_points = point_cloud[:,:,3:]
else:
l0_points = None
# Layer 1
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=num_point, radius=bradius*0.1,bn=use_bn,ibn = use_ibn,
nsample=12, mlp=[32, 32, 64], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=num_point/2, radius=bradius*0.2,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[64, 64, 128], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=num_point/4, radius=bradius*0.4,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[128, 128, 256], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=num_point/8, radius=bradius*0.6,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[256, 256, 512], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer4')
# Feature Propagation layers
if not is_training:
l0_xyz = tf.gather_nd(l0_xyz, idx[:, :int(num_point * 1/2), :])
l1_points = tf.gather_nd(l1_points, idx[:, :int(num_point * 1/2), :])
elif is_crop:
l0_xyz = tf.gather_nd(l0_xyz, idx[:, :int(num_point * 1/2), :])
l1_points = tf.gather_nd(l1_points, idx[:, :int(num_point * 1/2), :])
up_l4_points = pointnet_fp_module(l0_xyz, l4_xyz, None, l4_points, [64], is_training, bn_decay,
scope='fa_layer1',bn=use_bn,ibn = use_ibn)
up_l3_points = pointnet_fp_module(l0_xyz, l3_xyz, None, l3_points, [64], is_training, bn_decay,
scope='fa_layer2',bn=use_bn,ibn = use_ibn)
up_l2_points = pointnet_fp_module(l0_xyz, l2_xyz, None, l2_points, [64], is_training, bn_decay,
scope='fa_layer3',bn=use_bn,ibn = use_ibn)
feat = tf.concat([up_l4_points, up_l3_points, up_l2_points, l1_points,l0_xyz], axis=-1)
feat = tf.expand_dims(feat, axis=2)
#branch1: the new generate points
with tf.variable_scope('up_layer', reuse=reuse):
up_feat_list = []
for i in range(up_ratio):
up_feat = tf_util2.conv2d(feat, 256, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='conv1_%d' % (i), bn_decay=bn_decay)
up_feat = tf_util2.conv2d(up_feat, 128, [1, 1],
padding='VALID', stride=[1, 1],
bn=use_bn, is_training=is_training,
scope='conv2_%d' % (i),
bn_decay=bn_decay)
up_feat_list.append(up_feat)
up_feat = tf.concat(up_feat_list, axis=1)
dist_feat = tf_util2.conv2d(up_feat, 64, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='dist_fc1', bn_decay=bn_decay, weight_decay=0.0)
dist = tf_util2.conv2d(dist_feat, 1, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='dist_fc2', bn_decay=bn_decay,
activation_fn=None, weight_decay=0.0)
dist = tf.squeeze(dist, axis=[2, 3])
#branch2: dist to the edge
combined_feat = tf.concat((up_feat, dist_feat),axis=-1)
coord_feat = tf_util2.conv2d(combined_feat, 64, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='coord_fc1', bn_decay=bn_decay,weight_decay=0.0)
r_coord = tf_util2.conv2d(coord_feat, 3, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='coord_fc2', bn_decay=bn_decay,
activation_fn=None,weight_decay=0.0)
coord = tf.squeeze(r_coord, [2])
# prune the points according to probability(how to better prune it? as a guidance???)
# poolsize = int(num_addpoint * 1.2)
# val,idx1 = tf.nn.top_k(-dist,poolsize)
# tmp_idx0 = tf.tile(tf.reshape(tf.range(batch_size),(batch_size,1)),(1,num_addpoint))
# tmp_idx1 = tf.random_uniform((batch_size,num_addpoint),0,poolsize,tf.int32)
# idx1 = tf.gather_nd(idx1,tf.stack([tmp_idx0,tmp_idx1],axis=-1))
edge_dist, idx1 = tf.nn.top_k(-dist, num_addpoint)
idx0 = tf.tile(tf.reshape(tf.range(batch_size),(batch_size,1)),(1,num_addpoint))
idx = tf.stack([idx0,idx1],axis=-1)
return dist, coord, idx, None
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
end_points = {}
l0_xyz = point_cloud
l0_points = None
end_points['l0_xyz'] = l0_xyz
# Set abstraction layers
# Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
# So we only use NCHW for layer 1 until this issue can be resolved.
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz,
l0_points,
npoint=512,
radius=0.2,
nsample=32,
mlp=[64, 64, 128],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer1',
use_nchw=True)
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz,
l1_points,
npoint=128,
radius=0.4,
nsample=64,
mlp=[128, 128, 256],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz,
l2_points,
npoint=None,
radius=None,
nsample=None,
mlp=[256, 512, 1024],
mlp2=None,
group_all=True,
is_training=is_training,
bn_decay=bn_decay,
scope='layer3')
# Fully connected layers
net = tf.layers.flatten(l3_points)
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=0.5,
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=0.5,
is_training=is_training,
scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points
def get_model(point_cloud, is_training, num_class, hyperparams, bn_decay=None):
""" Semantic segmentation PointNet, input is BxNx3, output Bxnum_class """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
end_points = {}
if hyperparams['use_color'] or hyperparams['use_z_feature']:
feature_size = 3 * int(hyperparams['use_color']) + int(
hyperparams['use_z_feature'])
l0_xyz = tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3])
l0_points = tf.slice(point_cloud, [0, 0, 3], [-1, -1, feature_size])
else:
l0_xyz = point_cloud
l0_points = None
end_points['l0_xyz'] = l0_xyz
# Layer 1
l1_xyz, l1_points, l1_indices = pointnet_sa_module(
l0_xyz,
l0_points,
npoint=hyperparams['l1_npoint'],
radius=hyperparams['l1_radius'],
nsample=hyperparams['l1_nsample'],
mlp=[32, 32, 64],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(
l1_xyz,
l1_points,
npoint=hyperparams['l2_npoint'],
radius=hyperparams['l2_radius'],
nsample=hyperparams['l2_nsample'],
mlp=[64, 64, 128],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(
l2_xyz,
l2_points,
npoint=hyperparams['l3_npoint'],
radius=hyperparams['l3_radius'],
nsample=hyperparams['l3_nsample'],
mlp=[128, 128, 256],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(
l3_xyz,
l3_points,
npoint=hyperparams['l4_npoint'],
radius=hyperparams['l4_radius'],
nsample=hyperparams['l4_nsample'],
mlp=[256, 256, 512],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer4')
# Feature Propagation layers
l3_points = pointnet_fp_module(l3_xyz,
l4_xyz,
l3_points,
l4_points, [256, 256],
is_training,
bn_decay,
scope='fa_layer1')
l2_points = pointnet_fp_module(l2_xyz,
l3_xyz,
l2_points,
l3_points, [256, 256],
is_training,
bn_decay,
scope='fa_layer2')
l1_points = pointnet_fp_module(l1_xyz,
l2_xyz,
l1_points,
l2_points, [256, 128],
is_training,
bn_decay,
scope='fa_layer3')
l0_points = pointnet_fp_module(l0_xyz,
l1_xyz,
l0_points,
l1_points, [128, 128, 128],
is_training,
bn_decay,
scope='fa_layer4')
# FC layers
net = tf_util.conv1d(l0_points,
128,
1,
padding='VALID',
bn=True,
is_training=is_training,
scope='fc1',
bn_decay=bn_decay)
end_points['feats'] = net
net = tf_util.dropout(net,
keep_prob=0.5,
is_training=is_training,
scope='dp1')
net = tf_util.conv1d(net,
num_class,
1,
padding='VALID',
activation_fn=None,
scope='fc2')
return net, end_points
def completion(self, inputs, is_training):
num_point = inputs.get_shape()[1].value
l0_xyz = inputs[:,:,0:3]
l0_points = None
is_training = is_training
bradius = 1.0
use_bn = False
use_ibn = False
bn_decay = 0.95
up_ratio = 8
self.grid_size = 2
self.num_coarse = int(num_point * up_ratio / 4)
with tf.variable_scope('encoder_0', reuse=tf.AUTO_REUSE):
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=num_point,
radius=bradius * 0.05, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[32, 32, 64], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay,
scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=num_point / 2,
radius=bradius * 0.1, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[64, 64, 128], mlp2=None,
group_all=False,
is_training=is_training, bn_decay=bn_decay,
scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=num_point / 4,
radius=bradius * 0.2, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[128, 128, 256], mlp2=None,
group_all=False,
is_training=is_training, bn_decay=bn_decay,
scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=num_point / 8,
radius=bradius * 0.3, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[256, 256, 512], mlp2=None,
group_all=False,
is_training=is_training, bn_decay=bn_decay,
scope='layer4')
l5_xyz, l5_points, l5_indices = pointnet_sa_module(l4_xyz, l4_points, npoint=num_point / 16,
radius=bradius * 0.4, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[512, 512, 1024], mlp2=None,
group_all=False,
is_training=is_training, bn_decay=bn_decay,
scope='layer5')
gl_xyz, gl_points, gl_indices = pointnet_sa_module(l5_xyz, l5_points, npoint=1,
radius=bradius * 0.3, bn=use_bn, ibn=use_ibn,
nsample=32, mlp=[512, 512, 1024], mlp2=None,
group_all=True,
is_training=is_training, bn_decay=bn_decay,
scope='layer6')
gl_feature = tf.reduce_max(gl_points, axis=1)
print('gl_feature', gl_feature)
# Feature Propagation layers
up_gl_points = pointnet_fp_module(l0_xyz, gl_xyz, None, gl_points, [64], is_training, bn_decay,
scope='fa_layer0', bn=use_bn, ibn=use_ibn)
up_l5_points = pointnet_fp_module(l0_xyz, l5_xyz, None, l5_points, [64], is_training, bn_decay,
scope='fa_layer1', bn=use_bn, ibn=use_ibn)
up_l4_points = pointnet_fp_module(l0_xyz, l4_xyz, None, l4_points, [64], is_training, bn_decay,
scope='fa_layer2', bn=use_bn, ibn=use_ibn)
up_l3_points = pointnet_fp_module(l0_xyz, l3_xyz, None, l3_points, [64], is_training, bn_decay,
scope='fa_layer3', bn=use_bn, ibn=use_ibn)
up_l2_points = pointnet_fp_module(l0_xyz, l2_xyz, None, l2_points, [64], is_training, bn_decay,
scope='fa_layer4', bn=use_bn, ibn=use_ibn)
###concat feature
with tf.variable_scope('up_layer', reuse=tf.AUTO_REUSE):
new_points_list = []
for i in range(up_ratio):
if i>3:
transform = input_transform_net(l0_xyz, is_training, bn_decay, K=3)
xyz_transformed = tf.matmul(l0_xyz, transform)
concat_feat = tf.concat([up_gl_points, up_gl_points-up_l5_points, up_gl_points-up_l4_points, up_gl_points-up_l3_points, up_gl_points-up_l2_points, up_gl_points-l1_points, xyz_transformed],
axis=-1)
print('concat_feat1', concat_feat)
else:
concat_feat = tf.concat([up_gl_points, up_l5_points, up_l4_points, up_l3_points, up_l2_points, l1_points, l0_xyz],
axis=-1)
print('concat_feat2', concat_feat)
#concat_feat = tf.concat([up_l4_points, up_l3_points, up_l2_points, l1_points, l0_xyz], axis=-1)
concat_feat = tf.expand_dims(concat_feat, axis=2)
concat_feat = tf_util2.conv2d(concat_feat, 256, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer0_%d' % (i), bn_decay=bn_decay)
new_points = tf_util2.conv2d(concat_feat, 128, [1, 1],
padding='VALID', stride=[1, 1],
bn=use_bn, is_training=is_training,
scope='conv_%d' % (i),
bn_decay=bn_decay)
new_points_list.append(new_points)
net = tf.concat(new_points_list, axis=1)
coord_feature = tf_util2.conv2d(net, 64, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer1', bn_decay=bn_decay)
coord = tf_util2.conv2d(coord_feature, 3, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer2', bn_decay=bn_decay,
activation_fn=None, weight_decay=0.0) # B*(2N)*1*3
coarse_highres = tf.squeeze(coord, [2]) # B*(2N)*3
coord_feature = tf.squeeze(coord_feature, [2])
fps_idx = farthest_point_sample(int(self.num_fine)/2, coarse_highres)
coord_feature = gather_point(coord_feature, fps_idx)
coarse_fps = gather_point(coarse_highres, fps_idx)
coord_feature = tf.expand_dims(coord_feature, 2)
print('coord_feature', coord, coord_feature)
score = tf_util2.conv2d(coord_feature, 16, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer3', bn_decay=bn_decay)
score = tf_util2.conv2d(score, 8, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer4', bn_decay=bn_decay)
score = tf_util2.conv2d(score, 1, [1, 1],
padding='VALID', stride=[1, 1],
bn=False, is_training=is_training,
scope='fc_layer5', bn_decay=bn_decay)
score = tf.nn.softplus(score)
score = tf.squeeze(score, [2,3])
_, idx = tf.math.top_k(score, self.num_coarse)
coarse = gather_point(coarse_fps, idx)
coord_feature = tf.squeeze(coord_feature, [2])
coord_feature = gather_point(coord_feature, idx)
print('coarse', coord_feature, coarse)
with tf.variable_scope('folding', reuse=tf.AUTO_REUSE):
grid = tf.meshgrid(tf.linspace(-0.05, 0.05, self.grid_size), tf.linspace(-0.05, 0.05, self.grid_size))
print('grid:', grid)
grid = tf.expand_dims(tf.reshape(tf.stack(grid, axis=2), [-1, 2]), 0)
print('grid:', grid)
grid_feat = tf.tile(grid, [coarse.shape[0], self.num_coarse, 1])
print('grid_feat', grid_feat)
point_feat = tf.tile(tf.expand_dims(tf.concat([coarse, coord_feature], axis=-1), 2), [1, 1, self.grid_size ** 2, 1])
point_feat = tf.reshape(point_feat, [coarse.shape[0], self.num_fine, -1])
print('point_feat', point_feat)
global_feat = tf.tile(tf.expand_dims(gl_feature, 1), [1, self.num_fine, 1])
#print('global_feat', global_feat)
feat = tf.concat([grid_feat, point_feat, global_feat], axis=2)
print('feat:', feat)
center = tf.tile(tf.expand_dims(coarse, 2), [1, 1, self.grid_size ** 2, 1])
center = tf.reshape(center, [-1, self.num_fine, 3])
print('center', center)
fine = mlp_conv(feat, [512, 512, 3]) + center
print('fine:', fine)
return coarse_highres, coarse, fine
def get_gen_model(point_cloud, is_training, scope, bradius = 1.0, reuse=None, use_rv=False, use_bn = False,use_ibn = False,
use_normal=False,bn_decay=None, up_ratio = 4):
with tf.variable_scope(scope,reuse=reuse) as sc:
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
l0_xyz = point_cloud[:,:,0:3]
if use_normal:
l0_points = point_cloud[:,:,3:]
else:
l0_points = None
# Layer 1
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=num_point, radius=0.05,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[32, 32, 64], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=num_point/2, radius=0.1,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[64, 64, 128], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=num_point/4, radius=0.2,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[128, 128, 256], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=num_point/8, radius=0.3,bn=use_bn,ibn = use_ibn,
nsample=32, mlp=[256, 256, 512], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer4')
# # combine random variables into the network
# if use_rv:
# rv = tf.tile(tf.random_normal([batch_size, 1, 128], mean=0.0, stddev=1.0), [1, 16, 1])
# l4_points = tf.concat((l4_points, rv), axis=-1)
# Feature Propagation layers
l3_points = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay,
scope='fa_layer1',bn=use_bn,ibn = use_ibn)
l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay,
scope='fa_layer2',bn=use_bn,ibn = use_ibn)
l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay,
scope='fa_layer3',bn=use_bn,ibn = use_ibn)
l0_points = pointnet_fp_module(l0_xyz, l1_xyz, l0_xyz, l1_points, [128,128,128], is_training, bn_decay,
scope='fa_layer4', bn=use_bn, ibn=use_ibn)
# concat_features = tf.concat((l0_xyz, l0_points), axis=2)
feat_num = l0_points.get_shape()[2].value
###FC layer
l0_points = tf.expand_dims(l0_points, axis=2)
net = tf_util2.conv2d(l0_points, feat_num * 4, 1, padding='VALID', bn=use_bn, is_training=is_training,
scope='fc1', bn_decay=bn_decay)
net = tf.reshape(net, [batch_size, 4 * num_point, 1, -1])
net = tf_util2.conv2d(net, 64, 1, padding='VALID', bn=use_bn, is_training=is_training,
scope='fc2', bn_decay=bn_decay)
net = tf_util2.conv2d(net, 3, 1, padding='VALID', bn=use_bn, is_training=is_training,
scope='fc3', bn_decay=bn_decay, activation_fn=None)
net = tf.squeeze(net, [2])
# coord = tf.squeeze(coord, [2]) # B*(2N)*3
return net,None,None
def get_gen_model(point_cloud,
is_training,
scope,
bradius=1.0,
reuse=None,
use_rv=False,
use_bn=False,
use_ibn=False,
use_normal=False,
bn_decay=None,
up_ratio=4,
idx=None):
with tf.variable_scope(scope, reuse=reuse) as sc:
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
l0_xyz = point_cloud[:, :, 0:3]
if use_normal:
l0_points = point_cloud[:, :, 3:]
else:
l0_points = None
# Layer 1
l1_xyz, l1_points, l1_indices = pointnet_sa_module(
l0_xyz,
l0_points,
npoint=num_point,
radius=bradius * 0.05,
bn=use_bn,
ibn=use_ibn,
nsample=32,
mlp=[32, 32, 64],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer1')
l2_xyz, l2_points, l2_indices = pointnet_sa_module(
l1_xyz,
l1_points,
npoint=num_point / 2,
radius=bradius * 0.1,
bn=use_bn,
ibn=use_ibn,
nsample=32,
mlp=[64, 64, 128],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer2')
l3_xyz, l3_points, l3_indices = pointnet_sa_module(
l2_xyz,
l2_points,
npoint=num_point / 4,
radius=bradius * 0.2,
bn=use_bn,
ibn=use_ibn,
nsample=32,
mlp=[128, 128, 256],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer3')
l4_xyz, l4_points, l4_indices = pointnet_sa_module(
l3_xyz,
l3_points,
npoint=num_point / 8,
radius=bradius * 0.3,
bn=use_bn,
ibn=use_ibn,
nsample=32,
mlp=[256, 256, 512],
mlp2=None,
group_all=False,
is_training=is_training,
bn_decay=bn_decay,
scope='layer4')
# Feature Propagation layers
up_l4_points = pointnet_fp_module(l0_xyz,
l4_xyz,
None,
l4_points, [64],
is_training,
bn_decay,
scope='fa_layer1',
bn=use_bn,
ibn=use_ibn)
up_l3_points = pointnet_fp_module(l0_xyz,
l3_xyz,
None,
l3_points, [64],
is_training,
bn_decay,
scope='fa_layer2',
bn=use_bn,
ibn=use_ibn)
up_l2_points = pointnet_fp_module(l0_xyz,
l2_xyz,
None,
l2_points, [64],
is_training,
bn_decay,
scope='fa_layer3',
bn=use_bn,
ibn=use_ibn)
###concat feature
with tf.variable_scope('up_layer', reuse=reuse):
new_points_list = []
for i in range(up_ratio):
concat_feat = tf.concat([
up_l4_points, up_l3_points, up_l2_points, l1_points, l0_xyz
],
axis=-1)
concat_feat = tf.expand_dims(concat_feat, axis=2)
concat_feat = tf_util2.conv2d(concat_feat,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn=False,
is_training=is_training,
scope='fc_layer0_%d' % (i),
bn_decay=bn_decay)
new_points = tf_util2.conv2d(concat_feat,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=use_bn,
is_training=is_training,
scope='conv_%d' % (i),
bn_decay=bn_decay)
new_points_list.append(new_points)
net = tf.concat(new_points_list, axis=1)
#get the xyz
coord = tf_util2.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=False,
is_training=is_training,
scope='fc_layer1',
bn_decay=bn_decay)
coord = tf_util2.conv2d(coord,
3, [1, 1],
padding='VALID',
stride=[1, 1],
bn=False,
is_training=is_training,
scope='fc_layer2',
bn_decay=bn_decay,
activation_fn=None,
weight_decay=0.0) # B*(2N)*1*3
coord = tf.squeeze(coord, [2]) # B*(2N)*3
return coord, None
