text: import os
  import sys
  BASE_DIR = os.path.dirname(__file__)
  sys.path.append(BASE_DIR)
  sys.path.append(os.path.join(BASE_DIR, '../utils'))
  import tensorflow as tf
  import numpy as np
  import tf_util
  from pointnet_util import pointnet_sa_module, pointnet_fp_module
  
  def placeholder_inputs(batch_size, num_point):
      pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 5))
      labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
      smpws_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point))
      return pointclouds_pl, labels_pl, smpws_pl
  
  
  def get_model(point_cloud, is_training, num_class, bn_decay=None):
      """ Semantic segmentation PointNet, input is BxNx5, output Bxnum_class """
      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,-1,2])
      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, tf.concat([l0_xyz,l0_points],axis=-1), 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_loss(pred, label, smpw):
      """ pred: BxNxC,
          label: BxN, 
  	smpw: BxN """
      classify_loss = tf.losses.sparse_softmax_cross_entropy(labels=label, logits=pred, weights=smpw)
      tf.summary.scalar('classify loss', classify_loss)
      tf.add_to_collection('losses', classify_loss)
      return classify_loss
  
  if __name__=='__main__':
      with tf.Graph().as_default():
          inputs = tf.zeros((32,2048,5))
          net, _ = get_model(inputs, tf.constant(True), 10)
          print(net)
  