pointnet2_sem_seg.py

import os
import sys
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
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, name=""):
    pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3), name=name+"pc")
    labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point), name=name+"label")
    smpws_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point), name=name+"smpw")
    return pointclouds_pl, labels_pl, smpws_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
    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_loss(pred, label, smpw):
    """ pred: BxNxC,
        label: BxN, 
	smpw: BxN """
    bsize = pred.get_shape()[0]
    classify_loss1 = tf.losses.sparse_softmax_cross_entropy(labels=label[0:bsize//2,...], logits=pred[0:bsize//2,...], weights=smpw[0:bsize//2,...])
    classify_loss2 = tf.losses.sparse_softmax_cross_entropy(labels=label[bsize//2:bsize,...], logits=pred[bsize//2:bsize,...], weights=smpw[bsize//2:bsize,...])
    classify_loss = classify_loss1 + 0.75*classify_loss2
    tf.summary.scalar('classify loss', classify_loss)
    tf.add_to_collection('losses', classify_loss)
    return classify_loss, classify_loss1, classify_loss2

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