https://github.com/liuxinhai/Point2Sequence
Tip revision: 3ee6a8c42b210a1175180dfb29ce7f2dc82ba2d7 authored by Xinhai Liu on 25 November 2020, 05:51:20 UTC
Update README.md
Update README.md
Tip revision: 3ee6a8c
point2sequence_cls.py
'''
Basic Point2Sequence classification model
'''
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, point2sequence_module
def placeholder_inputs(batch_size, num_point):
pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
return pointclouds_pl, labels_pl
def get_model(point_cloud, is_training, bn_decay=None):
""" Classification Point2Sequence, input is BxNx3, output BxCLASSES """
batch_size = point_cloud.get_shape()[0].value
end_points = {}
l0_xyz = point_cloud
l0_points = None
# Set abstraction layers
l1_xyz, l1_points = point2sequence_module(l0_xyz, l0_points, 384, [16,32,64,128], [[32,64,128], [64,64,128], [64,64,128], [128,128,128]], 128, 128, is_training, bn_decay, scope='layer1')
l2_xyz, l2_points, _ = pointnet_sa_module(l1_xyz, l1_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer2')
# Fully connected layers
net = tf.reshape(l2_points, [batch_size, -1])
net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp1')
net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
return net, end_points
def get_loss(pred, label, end_points):
""" pred: B*NUM_CLASSES,
label: B, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
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((32,1024,3))
net, _ = get_model(inputs, tf.constant(True))
print(net)