import os
import sys
import tensorflow as tf
import scipy
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(BASE_DIR, '../utils'))
import tf_util
import pointnet
def placeholder_inputs(batch_size, img_rows=66, img_cols=200, points=16384, separately=False):
imgs_pl = tf.placeholder(tf.float32, shape=(batch_size, img_rows, img_cols, 3))
pts_pl = tf.placeholder(tf.float32, shape=(batch_size, points, 3))
if separately:
speeds_pl = tf.placeholder(tf.float32, shape=(batch_size))
angles_pl = tf.placeholder(tf.float32, shape=(batch_size))
labels_pl = [speeds_pl, angles_pl]
labels_pl = tf.placeholder(tf.float32, shape=(batch_size, 2))
return imgs_pl, pts_pl, labels_pl
def get_model(net, is_training, bn_decay=None, separately=False):
""" NVIDIA regression model, input is BxWxHx3, output Bx2"""
batch_size = net[0].get_shape()[0].value
img_net, pt_net = net[0], net[1]
for i, dim in enumerate([24, 36, 48, 64, 64]):
scope = "conv" + str(i + 1)
img_net = tf_util.conv2d(img_net, dim, [5, 5],
padding='VALID', stride=[1, 1],
bn=True, is_training=is_training,
scope=scope, bn_decay=bn_decay)
img_net = tf.reshape(img_net, [batch_size, -1])
img_net = tf_util.fully_connected(img_net, 256, bn=True,
is_training=is_training,
scope='img_fc0',
bn_decay=bn_decay)
with tf.variable_scope('pointnet'):
pt_net = pointnet.get_model(pt_net, tf.constant(True))
net = tf.reshape(tf.stack([img_net, pt_net], axis=2), [batch_size, 512])
for i, dim in enumerate([256, 128, 16]):
fc_scope = "fc" + str(i + 1)
dp_scope = "dp" + str(i + 1)
net = tf_util.fully_connected(net, dim, bn=True,
is_training=is_training,
scope=fc_scope,
bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.7,
is_training=is_training,
scope=dp_scope)
net = tf_util.fully_connected(net, 2, activation_fn=None, scope='fc5')
return net
def get_loss(pred, label, l2_weight=0.0001):
diff = tf.square(tf.subtract(pred, label))
train_vars = tf.trainable_variables()
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in train_vars[1:]]) * l2_weight
loss = tf.reduce_mean(diff + l2_loss)
tf.summary.scalar('l2 loss', l2_loss * l2_weight)
tf.summary.scalar('loss', loss)
return loss
def summary_scalar(pred, label):
threholds = [5, 4, 3, 2, 1, 0.5]
angles = [float(t) / 180 * scipy.pi for t in threholds]
speeds = [float(t) / 20 for t in threholds]
for i in range(len(threholds)):
scalar_angle = "angle(" + str(angles[i]) + ")"
scalar_speed = "speed(" + str(speeds[i]) + ")"
ac_angle = tf.abs(tf.subtract(pred[:, 1], label[:, 1])) < threholds[i]
ac_speed = tf.abs(tf.subtract(pred[:, 0], label[:, 0])) < threholds[i]
ac_angle = tf.reduce_mean(tf.cast(ac_angle, tf.float32))
ac_speed = tf.reduce_mean(tf.cast(ac_speed, tf.float32))
tf.summary.scalar(scalar_angle, ac_angle)
tf.summary.scalar(scalar_speed, ac_speed)
if __name__ == '__main__':
with tf.Graph().as_default():
inputs = [tf.zeros((32, 66, 200, 3)), tf.zeros((32, 16384, 3))]
outputs = get_model(inputs, tf.constant(True))
print(outputs)
