text: """
      FlowNet3D model with up convolution
  """
  
  import tensorflow as tf
  import numpy as np
  import math
  import sys
  import os
  BASE_DIR = os.path.dirname(os.path.abspath(__file__))
  sys.path.append(os.path.join(BASE_DIR, 'utils'))
  import tf_util
  from pointnet_util import *
  
  def placeholder_inputs(batch_size, num_point):
      pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point * 2, 6))
      labels_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
      masks_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point))
      return pointclouds_pl, labels_pl, masks_pl
  
  
  def get_model(point_cloud, is_training, bn_decay=None, reuse=False):
      """ FlowNet3D, for evaluating on KITTI
          input: Bx(N1+N2)x3,
          output: BxN1x3 """
      end_points = {}
      batch_size = point_cloud.get_shape()[0].value
      num_point = point_cloud.get_shape()[1].value // 2
  
      l0_xyz_f1 = point_cloud[:, :num_point, 0:3]
      l0_points_f1 = point_cloud[:, :num_point, 3:]
      l0_xyz_f2 = point_cloud[:, num_point:, 0:3]
      l0_points_f2 = point_cloud[:, num_point:, 3:]
  
      RADIUS1 = 0.5
      RADIUS2 = 1.0
      RADIUS3 = 2.0
      RADIUS4 = 4.0
      with tf.variable_scope('sa1') as scope:
          if reuse:
              scope.reuse_variables()
  
          # Frame 1, Layer 1
          l1_xyz_f1, l1_points_f1, l1_indices_f1 = pointnet_sa_module(l0_xyz_f1, l0_points_f1, npoint=8192, radius=RADIUS1, nsample=256, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
          end_points['l1_indices_f1'] = l1_indices_f1
  
          # Frame 1, Layer 2
          l2_xyz_f1, l2_points_f1, l2_indices_f1 = pointnet_sa_module(l1_xyz_f1, l1_points_f1, npoint=2048, radius=RADIUS2, nsample=256, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
          end_points['l2_indices_f1'] = l2_indices_f1
  
          if not reuse:
              scope.reuse_variables()
  
          # Frame 2, Layer 1
          l1_xyz_f2, l1_points_f2, l1_indices_f2 = pointnet_sa_module(l0_xyz_f2, l0_points_f2, npoint=8192, radius=RADIUS1, nsample=256, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
          # Frame 2, Layer 2
          l2_xyz_f2, l2_points_f2, l2_indices_f2 = pointnet_sa_module(l1_xyz_f2, l1_points_f2, npoint=2048, radius=RADIUS2, nsample=256, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
  
      _, l2_points_f1_new = flow_embedding_module(l2_xyz_f1, l2_xyz_f2, l2_points_f1, l2_points_f2, radius=3.0, nsample=256, mlp=[128,128,128], is_training=is_training, bn_decay=bn_decay, scope='flow_embedding', bn=True, pooling='max', knn=True, corr_func='concat')
  
      # Layer 3
      l3_xyz_f1, l3_points_f1, l3_indices_f1 = pointnet_sa_module(l2_xyz_f1, l2_points_f1_new, npoint=512, radius=RADIUS3, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3')
      end_points['l3_indices_f1'] = l3_indices_f1
  
      # Layer 4
      l4_xyz_f1, l4_points_f1, l4_indices_f1 = pointnet_sa_module(l3_xyz_f1, l3_points_f1, npoint=256, radius=RADIUS4, nsample=64, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4')
      end_points['l4_indices_f1'] = l4_indices_f1
  
      # Feature Propagation
      l3_feat_f1 = set_upconv_module(l3_xyz_f1, l4_xyz_f1, l3_points_f1, l4_points_f1, nsample=4, radius=2.4, mlp=[], mlp2=[256,256], scope='up_sa_layer1', is_training=is_training, bn_decay=bn_decay, knn=True)
      l2_feat_f1 = set_upconv_module(l2_xyz_f1, l3_xyz_f1, tf.concat(axis=-1, values=[l2_points_f1, l2_points_f1_new]), l3_feat_f1, nsample=4, radius=1.2, mlp=[128,128,256], mlp2=[256], scope='up_sa_layer2', is_training=is_training, bn_decay=bn_decay, knn=True)
      l1_feat_f1 = set_upconv_module(l1_xyz_f1, l2_xyz_f1, l1_points_f1, l2_feat_f1, nsample=4, radius=0.6, mlp=[128,128,256], mlp2=[256], scope='up_sa_layer3', is_training=is_training, bn_decay=bn_decay, knn=True)
      l0_feat_f1 = pointnet_fp_module(l0_xyz_f1, l1_xyz_f1, l0_points_f1, l1_feat_f1, [256,256], is_training, bn_decay, scope='fa_layer4')
  
      # FC layers
      net = tf_util.conv1d(l0_feat_f1, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
      net = tf_util.conv1d(net, 3, 1, padding='VALID', activation_fn=None, scope='fc2')
  
      return net, end_points
  
  
  def huber_loss(error, delta):
      abs_error = tf.abs(error)
      quadratic = tf.minimum(abs_error, delta)
      linear = (abs_error - quadratic)
      losses = 0.5 * quadratic**2 + delta * linear
      return tf.reduce_mean(losses)
  
  
  def get_loss(pred, label, mask, end_points):
      """ pred: BxNx3,
          label: BxNx3,
          mask: BxN
      """
      batch_size = pred.get_shape()[0].value
      num_point = pred.get_shape()[1].value
      l2_loss = tf.reduce_mean(mask * tf.reduce_sum((pred-label) * (pred-label), axis=2) / 2.0)
      tf.summary.scalar('l2 loss', l2_loss)
      tf.add_to_collection('losses', l2_loss)
      return l2_loss
  
  if __name__=='__main__':
      with tf.Graph().as_default():
          inputs = tf.zeros((32,1024*2,6))
          outputs = get_model(inputs, tf.constant(True))
          print(outputs)
  