https://github.com/yangyanli/PointCNN
Revision 58bba3ec8b1be1b53e1b70a4b85279aa0b20e0c3 authored by Garrett Smith on 18 December 2018, 15:56:12 UTC, committed by Garrett Smith on 23 December 2018, 21:23:07 UTC
This commit also includes misc changes: - Fix default path to S3DIS data (was '../../../data') - Handle dataset .DS_Store (non directory) files - Use canonical Python name case convention and whitespace - Support restarts - i.e. don't repeat processing work
1 parent 1d8ae27
Tip revision: 58bba3ec8b1be1b53e1b70a4b85279aa0b20e0c3 authored by Garrett Smith on 18 December 2018, 15:56:12 UTC
Configurable data and output dirs for prepare_s3dis_label
Configurable data and output dirs for prepare_s3dis_label
Tip revision: 58bba3e
pointnetpp_cls.py
import os
import sys
import tensorflow as tf
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'pointnetpp_cls', 'utils'))
import tf_util
from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg
l3_input_shape = (16, 128, 3)
FC1_inputs_shape = (16, 1024)
FC2_inputs_shape = (16, 512)
FC3_inputs_shape = (16, 256)
class Net:
def __init__(self, points, features, is_training, setting):
bn_decay = setting.get_bn_decay(tf.train.get_global_step())
l0_xyz = points
l0_points = None
num_class = setting.num_class
# Set abstraction layers
l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1, 0.2, 0.4], [32, 64, 128],
[[32, 32, 64], [64, 64, 128], [64, 96, 128]], is_training, bn_decay,
scope='layer1')
l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.2, 0.4, 0.8], [64, 64, 128],
[[64, 64, 128], [128, 128, 256], [128, 128, 256]], is_training,
bn_decay, scope='layer2')
l3_xyz, l3_points, _ = pointnet_sa_module(l3_input_shape, l2_xyz, l2_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='layer3')
# Fully connected layers
net = tf.reshape(l3_points, [l3_input_shape[0], -1])
net = tf_util.fully_connected(FC1_inputs_shape, 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(FC2_inputs_shape, 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(FC3_inputs_shape, net, num_class, activation_fn=None, scope='fc3')
self.logits = tf.expand_dims(net,axis = 1)
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