import tensorflow as tf
import math
import time
import numpy as np
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import tf_util
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))
return pointclouds_pl, labels_pl
def get_model(point_cloud, is_training, bn_decay=None):
""" ConvNet baseline, input is BxNx3 gray image """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
input_image = tf.expand_dims(point_cloud, -1)
# CONV
net = tf_util.conv2d(input_image, 64, [1,9], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay)
net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay)
net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay)
points_feat1 = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv5', bn_decay=bn_decay)
# MAX
pc_feat1 = tf_util.max_pool2d(points_feat1, [num_point,1], padding='VALID', scope='maxpool1')
# FC
pc_feat1 = tf.reshape(pc_feat1, [batch_size, -1])
pc_feat1 = tf_util.fully_connected(pc_feat1, 256, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
pc_feat1 = tf_util.fully_connected(pc_feat1, 128, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
print(pc_feat1)
# CONCAT
pc_feat1_expand = tf.tile(tf.reshape(pc_feat1, [batch_size, 1, 1, -1]), [1, num_point, 1, 1])
points_feat1_concat = tf.concat(axis=3, values=[points_feat1, pc_feat1_expand])
# CONV
net = tf_util.conv2d(points_feat1_concat, 512, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv6')
net = tf_util.conv2d(net, 256, [1,1], padding='VALID', stride=[1,1],
bn=True, is_training=is_training, scope='conv7')
net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training, scope='dp1')
net = tf_util.conv2d(net, 13, [1,1], padding='VALID', stride=[1,1],
activation_fn=None, scope='conv8')
net = tf.squeeze(net, [2])
return net
def get_loss(pred, label):
""" pred: B,N,13
label: B,N """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
return tf.reduce_mean(loss)
if __name__ == "__main__":
with tf.Graph().as_default():
a = tf.placeholder(tf.float32, shape=(32,4096,9))
net = get_model(a, tf.constant(True))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
start = time.time()
for i in range(100):
print(i)
sess.run(net, feed_dict={a:np.random.rand(32,4096,9)})
print(time.time() - start)
