model.py

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
from utils import tf_util
from utils.pointnet_util import pointnet_sa_module, pointnet_fp_module
from utils.loss import *


def placeholder_inputs(batch_size, num_point):
    pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 9))
    labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
    sem_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point))
    return pointclouds_pl, labels_pl, sem_pl


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[:, :, :3]
    l0_points = point_cloud[:, :, 3:]
    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_sem = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256, 256], is_training, bn_decay,
                                       scope='sem_fa_layer1')
    l2_points_sem = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points_sem, [256, 256], is_training, bn_decay,
                                       scope='sem_fa_layer2')
    l1_points_sem = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points_sem, [256, 128], is_training, bn_decay,
                                       scope='sem_fa_layer3')
    l0_points_sem = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points_sem, [128, 128, 128], is_training, bn_decay,
                                       scope='sem_fa_layer4')

    # FC layers
    net_sem = tf_util.conv1d(l0_points_sem, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='sem_fc1',
                             bn_decay=bn_decay)
    net_sem_cache = tf_util.conv1d(net_sem, 128, 1, padding='VALID', bn=True, is_training=is_training,
                                   scope='sem_cache', bn_decay=bn_decay)

    # ins
    l3_points_ins = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256, 256], is_training, bn_decay,
                                       scope='ins_fa_layer1')
    l2_points_ins = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points_ins, [256, 256], is_training, bn_decay,
                                       scope='ins_fa_layer2')
    l1_points_ins = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points_ins, [256, 128], is_training, bn_decay,
                                       scope='ins_fa_layer3')
    l0_points_ins = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points_ins, [128, 128, 128], is_training, bn_decay,
                                       scope='ins_fa_layer4')

    net_ins = tf_util.conv1d(l0_points_ins, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='ins_fc1',
                             bn_decay=bn_decay)

    net_ins = tf_util.non_local_block(net_sem, net_ins, scope='itos')

    net_ins = tf_util.dropout(net_ins, keep_prob=0.5, is_training=is_training, scope='ins_dp1')
    net_ins = tf_util.conv1d(net_ins, 5, 1, padding='VALID', activation_fn=None, scope='ins_fc4')

    net_sem = tf_util.non_local_block(net_ins, net_sem, scope='stoi')

    net_sem = tf_util.dropout(net_sem, keep_prob=0.5, is_training=is_training, scope='sem_dp1')
    net_sem = tf_util.conv1d(net_sem, num_class, 1, padding='VALID', activation_fn=None, scope='sem_fc4')

    return net_sem, net_ins


def get_loss(pred, ins_label, pred_sem_label, pred_sem, sem_label):
    """ pred:   BxNxE,
        ins_label:  BxN
        pred_sem_label: BxN
        pred_sem: BxNx13
        sem_label: BxN
    """
    classify_loss = tf.losses.sparse_softmax_cross_entropy(labels=sem_label, logits=pred_sem)
    tf.summary.scalar('classify loss', classify_loss)

    feature_dim = pred.get_shape()[-1]
    delta_v = 0.5
    delta_d = 1.5
    param_var = 1.
    param_dist = 1.
    param_reg = 0.001

    disc_loss, l_var, l_dist, l_reg = discriminative_loss(pred, ins_label, feature_dim,
                                                          delta_v, delta_d, param_var, param_dist, param_reg)

    loss = classify_loss + disc_loss

    tf.add_to_collection('losses', loss)
    return loss, classify_loss, disc_loss, l_var, l_dist, l_reg


if __name__ == '__main__':
    with tf.Graph().as_default():
        inputs = tf.zeros((32, 2048, 3))
        net, _ = get_model(inputs, tf.constant(True), 10)
        print(net)