def backbone_pointnet2(self, X_pc, is_train=None): import helper_pointnet2 as pnet2 points_num = tf.shape(X_pc)[1] l0_xyz = X_pc[:,:,0:3] l0_points = X_pc[:,:,3:9] l1_xyz, l1_points, l1_indices = pnet2.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=None, bn_decay=None, scope='layer1') l2_xyz, l2_points, l2_indices = pnet2.pointnet_sa_module(l1_xyz, l1_points, npoint=256, radius=0.2, nsample=64, mlp=[64, 64, 128], mlp2=None, group_all=False, is_training=None, bn_decay=None, scope='layer2') l3_xyz, l3_points, l3_indices = pnet2.pointnet_sa_module(l2_xyz, l2_points, npoint=64, radius=0.4, nsample=128, mlp=[128, 128, 256], mlp2=None, group_all=False, is_training=None, bn_decay=None, scope='layer3') l4_xyz, l4_points, l4_indices = pnet2.pointnet_sa_module(l3_xyz, l3_points, npoint=None, radius=None, nsample=None, mlp=[256, 256, 512], mlp2=None, group_all=True, is_training=None, bn_decay=None, scope='layer4') # Feature Propagation layers l3_points = pnet2.pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256, 256], is_training=None, bn_decay=None, scope='fa_layer1') l2_points = pnet2.pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256, 256], is_training=None, bn_decay=None,scope='fa_layer2') l1_points = pnet2.pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256, 128], is_training=None, bn_decay=None,scope='fa_layer3') l0_points = pnet2.pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([l0_xyz, l0_points], axis=-1), l1_points,[128, 128, 128, 128], is_training=None, bn_decay=None, scope='fa_layer4') global_features = tf.reshape(l4_points, [-1, 512]) point_features = l0_points # sem l0_points = l0_points[:,:,None,:] sem1 = Ops.xxlu(Ops.conv2d(l0_points, k=(1, 1), out_c=128, str=1, pad='VALID', name='sem1'), label='lrelu') sem2 = Ops.xxlu(Ops.conv2d(sem1, k=(1, 1), out_c=64, str=1, pad='VALID', name='sem2'), label='lrelu') sem2 = Ops.dropout(sem2, keep_prob=0.5, is_train=is_train, name='sem2_dropout') sem3 = Ops.conv2d(sem2, k=(1, 1), out_c=self.sem_num, str=1, pad='VALID', name='sem3') sem3 = tf.reshape(sem3, [-1, points_num, self.sem_num]) self.y_psem_logits = sem3 y_sem_pred = tf.nn.softmax(self.y_psem_logits, name='y_sem_pred') return point_features, global_features, y_sem_pred
def backbone_pointnet(self, X_pc, is_train): [_, _, points_cc] = X_pc.get_shape() points_num = tf.shape(X_pc)[1] X_pc = tf.reshape(X_pc, [-1, points_num, int(points_cc), 1]) l1 = Ops.xxlu(Ops.conv2d(X_pc, k=(1, points_cc), out_c=64, str=1, pad='VALID', name='l1'), label='lrelu') l2 = Ops.xxlu(Ops.conv2d(l1, k=(1, 1), out_c=64, str=1, pad='VALID', name='l2'), label='lrelu') l3 = Ops.xxlu(Ops.conv2d(l2, k=(1, 1), out_c=64, str=1, pad='VALID', name='l3'), label='lrelu') l4 = Ops.xxlu(Ops.conv2d(l3, k=(1, 1), out_c=128, str=1, pad='VALID', name='l4'), label='lrelu') l5 = Ops.xxlu(Ops.conv2d(l4, k=(1, 1), out_c=1024, str=1, pad='VALID', name='l5'), label='lrelu') global_features = tf.reduce_max(l5, axis=1, name='maxpool') global_features = tf.reshape(global_features, [-1, int(l5.shape[-1])]) point_features = tf.reshape(l5, [-1, points_num, int(l5.shape[-1])]) #### sem g1 = Ops.xxlu(Ops.fc(global_features, out_d=256, name='semg1'), label='lrelu') g2 = Ops.xxlu(Ops.fc(g1, out_d=128, name='semg2'), label='lrelu') sem1 = tf.tile(g2[:,None,None,:], [1, points_num, 1, 1]) sem1 = tf.concat([l5, sem1], axis=-1) sem1 = Ops.xxlu(Ops.conv2d(sem1, k=(1,1), out_c=512, str=1, pad='VALID', name='sem1'), label='lrelu') sem2 = Ops.xxlu(Ops.conv2d(sem1, k=(1, 1), out_c=256, str=1, pad='VALID', name='sem2'), label='lrelu') sem3 = Ops.xxlu(Ops.conv2d(sem2, k=(1, 1), out_c=128, str=1, pad='VALID', name='sem3'), label='lrelu') sem3 = Ops.dropout(sem3, keep_prob=0.5, is_train=is_train, name='sem3_dropout') sem4 = Ops.conv2d(sem3, k=(1, 1), out_c=self.sem_num, str=1, pad='VALID', name='sem4') sem4 = tf.reshape(sem4, [-1, points_num, self.sem_num]) self.y_psem_logits = sem4 y_sem_pred = tf.nn.softmax(self.y_psem_logits, name='y_sem_pred') return point_features, global_features, y_sem_pred
def pmask_net(self, point_features, global_features, bbox, bboxscore): p_f_num = int(point_features.shape[-1]) p_num = tf.shape(point_features)[1] bb_num = int(bbox.shape[1]) global_features = tf.tile(Ops.xxlu(Ops.fc(global_features, out_d=256, name='down_g1'), label='lrelu')[:,None,None,:], [1, p_num, 1, 1]) point_features = Ops.xxlu(Ops.conv2d(point_features[:,:,:,None],k=(1, p_f_num), out_c=256, str=1,name='down_p1',pad='VALID'), label='lrelu') point_features = tf.concat([point_features, global_features], axis=-1) point_features = Ops.xxlu(Ops.conv2d(point_features, k=(1,int(point_features.shape[-2])), out_c=128, str=1, pad='VALID', name='down_p2'), label='lrelu') point_features = Ops.xxlu(Ops.conv2d(point_features, k=(1, int(point_features.shape[-2])), out_c=128, str=1, pad='VALID',name='down_p3'), label='lrelu') point_features = tf.squeeze(point_features, axis=-2) bbox_info = tf.tile(tf.concat([tf.reshape(bbox, [-1, bb_num, 6]), bboxscore[:,:,None]],axis=-1)[:,:,None,:], [1,1,p_num,1]) pmask0 = tf.tile(point_features[:,None,:,:], [1, bb_num, 1, 1]) pmask0 = tf.concat([pmask0, bbox_info], axis=-1) pmask0 = tf.reshape(pmask0, [-1, p_num, int(pmask0.shape[-1]), 1]) pmask1 = Ops.xxlu(Ops.conv2d(pmask0, k=(1,int(pmask0.shape[-2])), out_c=64, str=1, pad='VALID', name='pmask1'), label='lrelu') pmask2 = Ops.xxlu(Ops.conv2d(pmask1, k=(1, 1), out_c=32, str=1, pad='VALID', name='pmask2'),label='lrelu') pmask3 = Ops.conv2d(pmask2, k=(1,1), out_c=1, str=1, pad='VALID', name='pmask3') pmask3 = tf.reshape(pmask3, [-1, bb_num, p_num]) y_pmask_logits = pmask3 y_pmask_pred = tf.nn.sigmoid(y_pmask_logits, name='y_pmask_pred') return y_pmask_pred
def pointnet_fp_module(xyz1,
xyz2,
points1,
points2,
mlp,
is_training,
bn_decay,
scope,
bn=True):
''' PointNet Feature Propogation (FP) Module
Input:
xyz1: (batch_size, ndataset1, 3) TF tensor
xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1
points1: (batch_size, ndataset1, nchannel1) TF tensor
points2: (batch_size, ndataset2, nchannel2) TF tensor
mlp: list of int32 -- output size for MLP on each point
Return:
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
dist = tf.maximum(dist, 1e-10)
norm = tf.reduce_sum((1.0 / dist), axis=2, keep_dims=True)
norm = tf.tile(norm, [1, 1, 3])
weight = (1.0 / dist) / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points1 = tf.concat(
axis=2, values=[interpolated_points,
points1]) # B,ndataset1,nchannel1+nchannel2
else:
new_points1 = interpolated_points
new_points1 = tf.expand_dims(new_points1, 2)
for i, num_out_channel in enumerate(mlp):
####################################
new_points1 = Ops.xxlu(Ops.conv2d(new_points1,
k=(1, 1),
out_c=num_out_channel,
str=1,
pad='VALID',
name='llll' + str(i)),
label='lrelu')
#new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1],padding='VALID', stride=[1,1],
# bn=bn, is_training=is_training,scope='conv_%d'%(i), bn_decay=bn_decay)
new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1]
return new_points1
def pointnet_sa_module_msg(xyz,
points,
npoint,
radius_list,
nsample_list,
mlp_list,
is_training,
bn_decay,
scope,
bn=True,
use_xyz=True,
use_nchw=False):
''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
Input:
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor
npoint: int32 -- #points sampled in farthest point sampling
radius: list of float32 -- search radius in local region
nsample: list of int32 -- how many points in each local region
mlp: list of list of int32 -- output size for MLP on each point
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
Return:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor
'''
data_format = 'NCHW' if use_nchw else 'NHWC'
with tf.variable_scope(scope) as sc:
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
new_points_list = []
for i in range(len(radius_list)):
radius = radius_list[i]
nsample = nsample_list[i]
idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
grouped_xyz = group_point(xyz, idx)
grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2),
[1, 1, nsample, 1])
if points is not None:
grouped_points = group_point(points, idx)
if use_xyz:
grouped_points = tf.concat([grouped_points, grouped_xyz],
axis=-1)
else:
grouped_points = grouped_xyz
if use_nchw:
grouped_points = tf.transpose(grouped_points, [0, 3, 1, 2])
for j, num_out_channel in enumerate(mlp_list[i]):
####################################
grouped_points = Ops.xxlu(Ops.conv2d(grouped_points,
k=(1, 1),
out_c=num_out_channel,
str=1,
pad='VALID',
name='lll' + str(i)),
label='lrelu')
#grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1],
#padding='VALID', stride=[1,1], bn=bn, is_training=is_training,scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
if use_nchw:
grouped_points = tf.transpose(grouped_points, [0, 2, 3, 1])
new_points = tf.reduce_max(grouped_points, axis=[2])
new_points_list.append(new_points)
new_points_concat = tf.concat(new_points_list, axis=-1)
return new_xyz, new_points_concat
def pointnet_sa_module(xyz,
points,
npoint,
radius,
nsample,
mlp,
mlp2,
group_all,
is_training,
bn_decay,
scope,
bn=True,
pooling='max',
knn=False,
use_xyz=True,
use_nchw=False):
''' PointNet Set Abstraction (SA) Module
Input:
xyz: (batch_size, ndataset, 3) TF tensor
points: (batch_size, ndataset, channel) TF tensor
npoint: int32 -- #points sampled in farthest point sampling
radius: float32 -- search radius in local region
nsample: int32 -- how many points in each local region
mlp: list of int32 -- output size for MLP on each point
mlp2: list of int32 -- output size for MLP on each region
group_all: bool -- group all points into one PC if set true, OVERRIDE
npoint, radius and nsample settings
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
Return:
new_xyz: (batch_size, npoint, 3) TF tensor
new_points: (batch_size, npoint, mlp[-1] or mlp2[-1]) TF tensor
idx: (batch_size, npoint, nsample) int32 -- indices for local regions
'''
data_format = 'NCHW' if use_nchw else 'NHWC'
with tf.variable_scope(scope) as sc:
# Sample and Grouping
if group_all:
nsample = xyz.get_shape()[1].value
new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(
xyz, points, use_xyz)
else:
new_xyz, new_points, idx, grouped_xyz = sample_and_group(
npoint, radius, nsample, xyz, points, knn, use_xyz)
# Point Feature Embedding
if use_nchw: new_points = tf.transpose(new_points, [0, 3, 1, 2])
for i, num_out_channel in enumerate(mlp):
####################################
new_points = Ops.xxlu(Ops.conv2d(new_points,
k=(1, 1),
out_c=num_out_channel,
str=1,
pad='VALID',
name='l' + str(i)),
label='lrelu')
#new_points = tf_util.conv2d(new_points, num_out_channel, [1,1],padding='VALID', stride=[1,1],
# bn=bn, is_training=is_training, scope='conv%d'%(i), bn_decay=bn_decay, data_format=data_format)
if use_nchw: new_points = tf.transpose(new_points, [0, 2, 3, 1])
# Pooling in Local Regions
if pooling == 'max':
new_points = tf.reduce_max(new_points,
axis=[2],
keep_dims=True,
name='maxpool')
elif pooling == 'avg':
new_points = tf.reduce_mean(new_points,
axis=[2],
keep_dims=True,
name='avgpool')
elif pooling == 'weighted_avg':
with tf.variable_scope('weighted_avg'):
dists = tf.norm(grouped_xyz, axis=-1, ord=2, keep_dims=True)
exp_dists = tf.exp(-dists * 5)
weights = exp_dists / tf.reduce_sum(
exp_dists, axis=2,
keep_dims=True) # (batch_size, npoint, nsample, 1)
new_points *= weights # (batch_size, npoint, nsample, mlp[-1])
new_points = tf.reduce_sum(new_points, axis=2, keep_dims=True)
elif pooling == 'max_and_avg':
max_points = tf.reduce_max(new_points,
axis=[2],
keep_dims=True,
name='maxpool')
avg_points = tf.reduce_mean(new_points,
axis=[2],
keep_dims=True,
name='avgpool')
new_points = tf.concat([avg_points, max_points], axis=-1)
# [Optional] Further Processing
if mlp2 is not None:
if use_nchw: new_points = tf.transpose(new_points, [0, 3, 1, 2])
for i, num_out_channel in enumerate(mlp2):
####################################
new_points = Ops.xxlu(Ops.conv2d(new_points,
k=(1, 1),
out_c=num_out_channel,
str=1,
pad='VALID',
name='ll' + str(i)),
label='lrelu')
#new_points = tf_util.conv2d(new_points, num_out_channel, [1,1], padding='VALID', stride=[1,1],
#bn=bn, is_training=is_training, scope='conv_post_%d'%(i), bn_decay=bn_decay,data_format=data_format)
if use_nchw: new_points = tf.transpose(new_points, [0, 2, 3, 1])
new_points = tf.squeeze(new_points,
[2]) # (batch_size, npoints, mlp2[-1])
return new_xyz, new_points, idx