diff --git a/ROS_packages/kitti_velodyne_to_ros/CMakeLists.txt b/ROS_packages/kitti_velodyne_to_ros/CMakeLists.txt new file mode 100644 index 0000000..9a15e2d --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/CMakeLists.txt @@ -0,0 +1,200 @@ +cmake_minimum_required(VERSION 2.8.3) +project(kitti_velodyne_to_ros) + +## Compile as C++11, supported in ROS Kinetic and newer +# add_compile_options(-std=c++11) + +## Find catkin macros and libraries +## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) +## is used, also find other catkin packages +find_package(catkin REQUIRED COMPONENTS + pcl_ros + roscpp + sensor_msgs + std_msgs +) + +## System dependencies are found with CMake's conventions +# find_package(Boost REQUIRED COMPONENTS system) + + +## Uncomment this if the package has a setup.py. This macro ensures +## modules and global scripts declared therein get installed +## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html +# catkin_python_setup() + +################################################ +## Declare ROS messages, services and actions ## +################################################ + +## To declare and build messages, services or actions from within this +## package, follow these steps: +## * Let MSG_DEP_SET be the set of packages whose message types you use in +## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). +## * In the file package.xml: +## * add a build_depend tag for "message_generation" +## * add a build_depend and a run_depend tag for each package in MSG_DEP_SET +## * If MSG_DEP_SET isn't empty the following dependency has been pulled in +## but can be declared for certainty nonetheless: +## * add a run_depend tag for "message_runtime" +## * In this file (CMakeLists.txt): +## * add "message_generation" and every package in MSG_DEP_SET to +## find_package(catkin REQUIRED COMPONENTS ...) +## * add "message_runtime" and every package in MSG_DEP_SET to +## catkin_package(CATKIN_DEPENDS ...) +## * uncomment the add_*_files sections below as needed +## and list every .msg/.srv/.action file to be processed +## * uncomment the generate_messages entry below +## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) + +## Generate messages in the 'msg' folder +# add_message_files( +# FILES +# Message1.msg +# Message2.msg +# ) + +## Generate services in the 'srv' folder +# add_service_files( +# FILES +# Service1.srv +# Service2.srv +# ) + +## Generate actions in the 'action' folder +# add_action_files( +# FILES +# Action1.action +# Action2.action +# ) + +## Generate added messages and services with any dependencies listed here +# generate_messages( +# DEPENDENCIES +# sensor_msgs# std_msgs +# ) + +################################################ +## Declare ROS dynamic reconfigure parameters ## +################################################ + +## To declare and build dynamic reconfigure parameters within this +## package, follow these steps: +## * In the file package.xml: +## * add a build_depend and a run_depend tag for "dynamic_reconfigure" +## * In this file (CMakeLists.txt): +## * add "dynamic_reconfigure" to +## find_package(catkin REQUIRED COMPONENTS ...) +## * uncomment the "generate_dynamic_reconfigure_options" section below +## and list every .cfg file to be processed + +## Generate dynamic reconfigure parameters in the 'cfg' folder +# generate_dynamic_reconfigure_options( +# cfg/DynReconf1.cfg +# cfg/DynReconf2.cfg +# ) + +################################### +## catkin specific configuration ## +################################### +## The catkin_package macro generates cmake config files for your package +## Declare things to be passed to dependent projects +## INCLUDE_DIRS: uncomment this if your package contains header files +## LIBRARIES: libraries you create in this project that dependent projects also need +## CATKIN_DEPENDS: catkin_packages dependent projects also need +## DEPENDS: system dependencies of this project that dependent projects also need +catkin_package( +# INCLUDE_DIRS include +# LIBRARIES kitti_velodyne_to_ros +# CATKIN_DEPENDS pcl_ros roscpp sensor_msgs std_msgs +# DEPENDS system_lib +) + +########### +## Build ## +########### + +## Specify additional locations of header files +## Your package locations should be listed before other locations +include_directories( +# include + ${catkin_INCLUDE_DIRS} +) + +## Declare a C++ library +# add_library(${PROJECT_NAME} +# src/${PROJECT_NAME}/kitti_velodyne_to_ros.cpp +# ) + +## Add cmake target dependencies of the library +## as an example, code may need to be generated before libraries +## either from message generation or dynamic reconfigure +# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Declare a C++ executable +## With catkin_make all packages are built within a single CMake context +## The recommended prefix ensures that target names across packages don't collide +add_executable(${PROJECT_NAME}_node src/kitti_velodyne_to_ros_node.cpp) + +## Rename C++ executable without prefix +## The above recommended prefix causes long target names, the following renames the +## target back to the shorter version for ease of user use +## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" +# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") + +## Add cmake target dependencies of the executable +## same as for the library above +add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Specify libraries to link a library or executable target against +target_link_libraries(${PROJECT_NAME}_node + ${catkin_LIBRARIES} +) + +############# +## Install ## +############# + +# all install targets should use catkin DESTINATION variables +# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html + +## Mark executable scripts (Python etc.) for installation +## in contrast to setup.py, you can choose the destination +# install(PROGRAMS +# scripts/my_python_script +# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark executables and/or libraries for installation +# install(TARGETS ${PROJECT_NAME} ${PROJECT_NAME}_node +# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark cpp header files for installation +# install(DIRECTORY include/${PROJECT_NAME}/ +# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} +# FILES_MATCHING PATTERN "*.h" +# PATTERN ".svn" EXCLUDE +# ) + +## Mark other files for installation (e.g. launch and bag files, etc.) +# install(FILES +# # myfile1 +# # myfile2 +# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +# ) + +############# +## Testing ## +############# + +## Add gtest based cpp test target and link libraries +# catkin_add_gtest(${PROJECT_NAME}-test test/test_kitti_velodyne_to_ros.cpp) +# if(TARGET ${PROJECT_NAME}-test) +# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) +# endif() + +## Add folders to be run by python nosetests +# catkin_add_nosetests(test) diff --git a/ROS_packages/kitti_velodyne_to_ros/README.md b/ROS_packages/kitti_velodyne_to_ros/README.md new file mode 100644 index 0000000..7a90ef1 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/README.md @@ -0,0 +1,32 @@ +### .npy file visualizer for ROS +--- +- basically It has two commands +- one is for `.bin` file from KITTI velodyne datasets, another is for `.npy` file from SqueezeSeg datasets + - commands are introduced below + + +

+![](https://github.com/tigerk0430/SqueezeSeg/blob/master/kitti_velodyne_to_ros/gif_and_pics/squeezeseg_npy.gif) + + +

+### Instrunction +--- +1. add codes in your `eval.py` like below 117-122 lines + 1.1 then you can save `pred_~~~.npy` files after evaluation. It'd be prediction data +![](https://github.com/tigerk0430/SqueezeSeg/blob/master/kitti_velodyne_to_ros/gif_and_pics/added_code.png) +2. run `eval.sh` and save `pred_~~~.npy` files into specific path +3. move this `kitty_velodyne_to_ros` folder to your `catkin_ws` folder and run `catkin_make` +4. modify `.launch` file to your data paths. +5. run commands below + + +

+### Available commands +--- +(for `.bin` file from KITTI datasets) +1. `rosrun kitti_velodyne_to_ros kitti_velodyne_to_ros_node` + +(for `.npy` file from SqueezeSeg) (after modifying path in .launch file) + +2. `roslaunch kitti_velodyne_to_ros npy_velodyne_to_ros.launch` diff --git a/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/added_code.png b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/added_code.png new file mode 100644 index 0000000..cdec4a9 Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/added_code.png differ diff --git a/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/kitti_bin.gif b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/kitti_bin.gif new file mode 100644 index 0000000..20b196b Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/kitti_bin.gif differ diff --git a/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/npy.png b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/npy.png new file mode 100644 index 0000000..ca46a08 Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/npy.png differ diff --git a/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/squeezeseg_npy.gif b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/squeezeseg_npy.gif new file mode 100644 index 0000000..96c52d9 Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/gif_and_pics/squeezeseg_npy.gif differ diff --git a/ROS_packages/kitti_velodyne_to_ros/launch/npy_tensorflow_to_ros.launch b/ROS_packages/kitti_velodyne_to_ros/launch/npy_tensorflow_to_ros.launch new file mode 100644 index 0000000..0e69f2d --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/launch/npy_tensorflow_to_ros.launch @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/ROS_packages/kitti_velodyne_to_ros/launch/npy_velodyne_to_ros.launch b/ROS_packages/kitti_velodyne_to_ros/launch/npy_velodyne_to_ros.launch new file mode 100644 index 0000000..b6fc172 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/launch/npy_velodyne_to_ros.launch @@ -0,0 +1,13 @@ + + + + + + + + + + + + + diff --git a/ROS_packages/kitti_velodyne_to_ros/move_this_folders_to_your_catkin_ws b/ROS_packages/kitti_velodyne_to_ros/move_this_folders_to_your_catkin_ws new file mode 100644 index 0000000..e69de29 diff --git a/ROS_packages/kitti_velodyne_to_ros/package.xml b/ROS_packages/kitti_velodyne_to_ros/package.xml new file mode 100644 index 0000000..9071e2e --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/package.xml @@ -0,0 +1,71 @@ + + + kitti_velodyne_to_ros + 0.0.0 + The kitti_velodyne_to_ros package + + + + + dyros-vehicle + + + + + + TODO + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + catkin + pcl_ros + roscpp + sensor_msgs + std_msgs + pcl_ros + roscpp + sensor_msgs + std_msgs + pcl_ros + roscpp + sensor_msgs + std_msgs + + + + + + + + diff --git a/ROS_packages/kitti_velodyne_to_ros/rviz/npy_velodyne_to_ros.rviz b/ROS_packages/kitti_velodyne_to_ros/rviz/npy_velodyne_to_ros.rviz new file mode 100644 index 0000000..b2002b3 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/rviz/npy_velodyne_to_ros.rviz @@ -0,0 +1,143 @@ +Panels: + - Class: rviz/Displays + Help Height: 78 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + Splitter Ratio: 0.63688761 + Tree Height: 775 + - Class: rviz/Selection + Name: Selection + - Class: rviz/Tool Properties + Expanded: + - /2D Pose Estimate1 + - /2D Nav Goal1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.588679016 + - Class: rviz/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: velodyne_points_npy +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz/Grid + Color: 160; 160; 164 + Enabled: false + Line Style: + Line Width: 0.0299999993 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 100 + Reference Frame: + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 10 + Min Value: -10 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 255; 255; 255 + Color Transformer: Intensity + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Max Intensity: 2 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: velodyne_points_npy + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.0599999987 + Style: Flat Squares + Topic: /velodyne_points_npy + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + Enabled: true + Global Options: + Background Color: 100; 100; 100 + Default Light: true + Fixed Frame: velodyne_link + Frame Rate: 30 + Name: root + Tools: + - Class: rviz/Interact + Hide Inactive Objects: true + - Class: rviz/MoveCamera + - Class: rviz/Select + - Class: rviz/FocusCamera + - Class: rviz/Measure + - Class: rviz/SetInitialPose + Topic: /initialpose + - Class: rviz/SetGoal + Topic: /move_base_simple/goal + - Class: rviz/PublishPoint + Single click: true + Topic: /clicked_point + - Class: rviz_plugin_selected_points_publisher/SelectedPointsPublisher + Value: true + Views: + Current: + Class: rviz/Orbit + Distance: 25.0534229 + Enable Stereo Rendering: + Stereo Eye Separation: 0.0599999987 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: 6.3730402 + Y: -4.70459843 + Z: 6.13416672 + Focal Shape Fixed Size: false + Focal Shape Size: 0.0500000007 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.00999999978 + Pitch: 0.600398242 + Target Frame: + Value: Orbit (rviz) + Yaw: 3.63040066 + Saved: ~ +Window Geometry: + Displays: + collapsed: false + Height: 1056 + Hide Left Dock: false + Hide Right Dock: true + QMainWindow State: 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a/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeNet/squeezenet_v1.1.pkl b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeNet/squeezenet_v1.1.pkl new file mode 100644 index 0000000..069a15c Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeNet/squeezenet_v1.1.pkl differ diff --git a/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 new file mode 100644 index 0000000..3965d02 Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 differ diff --git a/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.index b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.index new file mode 100644 index 0000000..085d310 Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.index differ diff --git a/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.meta b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.meta new file mode 100644 index 0000000..ac8fefb Binary files /dev/null and b/ROS_packages/kitti_velodyne_to_ros/script/data/SqueezeSeg/model.ckpt-23000.meta differ diff --git a/ROS_packages/kitti_velodyne_to_ros/script/npy_tensorflow_to_ros_node.py b/ROS_packages/kitti_velodyne_to_ros/script/npy_tensorflow_to_ros_node.py new file mode 100755 index 0000000..c2863a1 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/npy_tensorflow_to_ros_node.py @@ -0,0 +1,150 @@ +#!/usr/bin/python2 +# -*- coding: utf-8 -*- + +""" + online segmentation using .npy & SqueezeSeg model + + this script can + 1. read all .npy file from lidar_2d folder + 2. predict label from SqueezeSeg model using tensorflow + 3. publish to 'sqeeuze_seg/points' topic + + strongly inspried from [https://github.com/Durant35/SqueezeSeg] + original code [https://github.com/BichenWuUCB/SqueezeSeg] +""" +import sys +import os.path +import numpy as np +from PIL import Image + +import tensorflow as tf + +import rospy +from sensor_msgs.msg import PointCloud2, PointField +import sensor_msgs.point_cloud2 as pc2 +from sensor_msgs.msg import Image as ImageMsg +from std_msgs.msg import Header + +from squeezeseg.config import * +from squeezeseg.nets import SqueezeSeg +from squeezeseg.utils.util import * +from squeezeseg.utils.clock import Clock +from squeezeseg.imdb import kitti # ed: header added + + +class NPY_TENSORFLOW_TO_ROS(): + def __init__(self, pub_topic, FLAGS, npy_path="", npy_file_list=""): + + os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + self._mc = kitti_squeezeSeg_config() + self._mc.LOAD_PRETRAINED_MODEL = False + self._mc.BATCH_SIZE = 1 # TODO(bichen): fix this hard-coded batch size. + self._model = SqueezeSeg(self._mc) + self._saver = tf.train.Saver(self._model.model_params) + + self._session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) + self._saver.restore(self._session, FLAGS.checkpoint) + + # ed: Publisher + self._pub = rospy.Publisher(pub_topic, PointCloud2, queue_size=1) + + self.get_npy_from_lidar_2d(npy_path, npy_file_list) + + self.idx = 0 + while not rospy.is_shutdown(): + self.prediction_publish(self.idx) + self.idx += 1 + if self.idx > self.len_files: + self.idx = 0 + + rospy.spin() + + + # Read all .npy data from lidar_2d folder + def get_npy_from_lidar_2d(self, npy_path, npy_file_list): + self.npy_path = npy_path + self.npy_file_list = open(npy_file_list,'r').read().split('\n') + self.npy_files = [] + + for i in range(len(self.npy_file_list)): + self.npy_files.append(self.npy_path + self.npy_file_list[i] + '.npy') + + self.len_files = len(self.npy_files) + + + def prediction_publish(self, idx): + clock = Clock() + + record = np.load(os.path.join(self.npy_path,self.npy_files[idx])) + lidar = record[:,:,:5] + + # to perform prediction + lidar_mask = np.reshape( + (lidar[:, :, 4] > 0), + [self._mc.ZENITH_LEVEL, self._mc.AZIMUTH_LEVEL, 1] + ) + + norm_lidar = (lidar - self._mc.INPUT_MEAN) / self._mc.INPUT_STD + + pred_cls = self._session.run( + self._model.pred_cls, + feed_dict={ + self._model.lidar_input: [norm_lidar], + self._model.keep_prob: 1.0, + self._model.lidar_mask: [lidar_mask] + } + ) + label = pred_cls[0] + + ## point cloud for SqueezeSeg segments + x = lidar[:, :, 0].reshape(-1) + y = lidar[:, :, 1].reshape(-1) + z = lidar[:, :, 2].reshape(-1) + i = lidar[:, :, 3].reshape(-1) + label = label.reshape(-1) + cloud = np.stack((x,y,z,i, label)) + + header = Header() + header.stamp = rospy.Time().now() + header.frame_id = "velodyne_link" + + # point cloud segments + msg_segment = self.create_cloud_xyzil32(header, cloud.T) + + # publish + self._pub.publish(msg_segment) + rospy.loginfo("Point cloud processed. Took %.6f ms.", clock.takeRealTime()) + + + # create pc2_msg with 5 fields + def create_cloud_xyzil32(self, header, points): + fields = [PointField('x', 0, PointField.FLOAT32, 1), + PointField('y', 4, PointField.FLOAT32, 1), + PointField('z', 8, PointField.FLOAT32, 1), + PointField('intensity', 12, PointField.FLOAT32, 1), + PointField('label', 16, PointField.FLOAT32, 1)] + return pc2.create_cloud(header, fields, points) + + + +if __name__ == '__main__': + rospy.init_node('npy_tensorflow_ros_node') + + npy_path = rospy.get_param('npy_path') + npy_path_pred = rospy.get_param('npy_path_pred') + npy_file_list = rospy.get_param('npy_file_list') + + pub_topic = rospy.get_param('pub_topic') + checkpoint = rospy.get_param('checkpoint') + gpu = rospy.get_param('gpu') + + FLAGS = tf.app.flags.FLAGS + tf.app.flags.DEFINE_string( + 'checkpoint', checkpoint, + """Path to the model paramter file.""") + tf.app.flags.DEFINE_string('gpu', '0', """gpu id.""") + + npy_tensorflow_to_ros = NPY_TENSORFLOW_TO_ROS(pub_topic=pub_topic, + FLAGS=FLAGS, + npy_path=npy_path, + npy_file_list=npy_file_list) diff --git a/ROS_packages/kitti_velodyne_to_ros/script/npy_velodyne_to_ros_node.py b/ROS_packages/kitti_velodyne_to_ros/script/npy_velodyne_to_ros_node.py new file mode 100755 index 0000000..58de9dd --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/npy_velodyne_to_ros_node.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python +#-*- coding: utf-8 -*- + +import rospy +import std_msgs.msg +import sensor_msgs.point_cloud2 as pcl2 +from std_msgs.msg import String +from sensor_msgs.msg import PointCloud2, PointField + +import os, os.path +import numpy as np + + +class NPY_TO_ROS(): + def __init__(self, is_xyzi=True, npy_path='', npy_path_pred='', npy_file_list=''): + # ed: lidar_2d path + self.npy_path = npy_path + # ed: pred_~~~.npy path + self.npy_path_pred = npy_path_pred + self.npy_file_list = open(npy_file_list,'r').read().split('\n') + + if is_xyzi == False: + self.npy_files_list_xyz = [name for name in os.listdir(self.npy_path)] + + + self.npy_files = [] + for i in range(len(self.npy_file_list)): + self.npy_files.append(self.npy_path + self.npy_file_list[i] + '.npy') + + self.npy_files_pred = [] + for i in range(len(self.npy_file_list)): + self.npy_files_pred.append(self.npy_path_pred + 'pred_' + self.npy_file_list[i] + '.npy') + + self.len_files = len(self.npy_files) + + print("[+] There are {} .npy files".format(self.len_files)) + + self.velo_pub = rospy.Publisher('/velodyne_points_npy', PointCloud2, queue_size=1) + self.loop_rate = rospy.Rate(5) + + if is_xyzi == True: + self.processing_xyzi() + else: + self.processing_xyz() + + + def processing_xyzi(self): + for i in range(self.len_files): + print("[+] {} th file name : {} ".format(i, self.npy_files[i])) + bin_points = np.load(os.path.join(self.npy_path,self.npy_files[i])) + bin_points_pred = np.load(os.path.join(self.npy_path_pred,self.npy_files_pred[i])) + + pc2_msg = PointCloud2() + + header = std_msgs.msg.Header() + header.stamp = rospy.Time.now() + header.frame_id = 'velodyne_link' + + h,w,_ = bin_points.shape # 64, 512 ,6 + + cloud_points = [] + for j in range(h): # 64 + for k in range(w): # 512 + cloud_points.append(list(np.append(bin_points[j,:,:3][k], bin_points_pred[j][k]))) + + + pc2_msg = self.create_cloud_xyzi32(header, cloud_points) + + # ed: /velodyne_points_npy publish + self.velo_pub.publish(pc2_msg) + self.loop_rate.sleep() + + if rospy.is_shutdown(): + return + + + def processing_xyz(self): + for i in range(self.len_files): + print("[+] {} th file name : {} ".format(i, self.npy_files_list_xyz[i])) + bin_points = np.fromfile(os.path.join(self.npy_path,self.npy_files_list_xyz[i]), dtype=np.float32).reshape(-1,4) + + pc2_msg = PointCloud2() + + header = std_msgs.msg.Header() + header.stamp = rospy.Time.now() + header.frame_id = 'velodyne_link' + + cloud_points = [] + for j in range(len(bin_points)): + if(bin_points[j][0] >= 0.1 and bin_points[j][1] != 0 and bin_points[j][2] <= 5 and bin_points[j][3] < 10): + cloud_points.append(list(bin_points[j,:3])) + + pc2_msg = pcl2.create_cloud_xyz32(header, cloud_points) + + # ed: /velodyne_points_npy publish + self.velo_pub.publish(pc2_msg) + self.loop_rate.sleep() + + if rospy.is_shutdown(): + return + + + def create_cloud_xyzi32(self,header, points): + fields = [PointField('x', 0, PointField.FLOAT32, 1), + PointField('y', 4, PointField.FLOAT32, 1), + PointField('z', 8, PointField.FLOAT32, 1), + PointField('intensity', 12, PointField.FLOAT32, 1)] + return pcl2.create_cloud(header, fields, points) + + + + +if __name__ == '__main__': + rospy.init_node('npy_to_ros_node') + + # It should start from .launch file because of parameter + npy_path = rospy.get_param('npy_path') + npy_path_pred = rospy.get_param('npy_path_pred') + npy_file_list = rospy.get_param('npy_file_list') + + npy_to_ros = NPY_TO_ROS(is_xyzi=True, npy_path=npy_path, npy_path_pred=npy_path_pred, npy_file_list=npy_file_list) diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/__init__.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/__init__.py new file mode 100644 index 0000000..62da116 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/__init__.py @@ -0,0 +1 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/__init__.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/__init__.py new file mode 100644 index 0000000..d8cae02 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/__init__.py @@ -0,0 +1 @@ +from .kitti_squeezeSeg_config import kitti_squeezeSeg_config diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/config.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/config.py new file mode 100644 index 0000000..76c1799 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/config.py @@ -0,0 +1,136 @@ +#G Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#!/usr/bin/env python +#-*- coding: utf-8 -*- + +"""Base Model configurations""" + +import os +import os.path as osp +import numpy as np +from easydict import EasyDict as edict + +def base_model_config(dataset='KITTI'): + assert dataset.upper()=='KITTI', \ + 'Currently only support KITTI dataset' + + cfg = edict() + + # Dataset used to train/val/test model. Now support KITTI + cfg.DATASET = dataset.upper() + + # classes + cfg.CLASSES = [ + 'unknown', + 'car', + 'van', + 'truck', + 'pedestrian', + 'person_sitting', + 'cyclist', + 'tram', + 'misc', + ] + + # number of classes + cfg.NUM_CLASS = len(cfg.CLASSES) + + # dict from class name to id + cfg.CLS_2_ID = dict(zip(cfg.CLASSES, range(len(cfg.CLASSES)))) + + # loss weight for each class + cfg.CLS_LOSS_WEIGHT = np.array( + [1/20.0, 1.0, 2.0, 3.0, + 8.0, 10.0, 8.0, 2.0, 1.0] + ) + + # rgb color for each class + cfg.CLS_COLOR_MAP = np.array( + [[ 0.00, 0.00, 0.00], + [ 0.12, 0.56, 0.37], + [ 0.66, 0.55, 0.71], + [ 0.58, 0.72, 0.88], + [ 0.25, 0.51, 0.76], + [ 0.98, 0.47, 0.73], + [ 0.40, 0.19, 0.10], + [ 0.87, 0.19, 0.17], + [ 0.13, 0.55, 0.63]] + ) + + # Probability to keep a node in dropout + cfg.KEEP_PROB = 0.5 + + # image width + cfg.IMAGE_WIDTH = 224 + + # image height + cfg.IMAGE_HEIGHT = 224 + + # number of vertical levels + cfg.NUM_LEVEL = 10 + + # number of pie sectors of the field of view + cfg.NUM_SECTOR = 90 + + # maximum distance of a measurement + cfg.MAX_DIST = 100.0 + + # batch size + cfg.BATCH_SIZE = 20 + + # Pixel mean values (BGR order) as a (1, 1, 3) array. Below is the BGR mean + # of VGG16 + cfg.BGR_MEANS = np.array([[[103.939, 116.779, 123.68]]]) + + # Pixel mean values (BGR order) as a (1, 1, 3) array. Below is the BGR mean + # of VGG16 + cfg.RGB_MEANS = np.array([[[123.68, 116.779, 103.939]]]) + + # reduce step size after this many steps + cfg.DECAY_STEPS = 10000 + + # multiply the learning rate by this factor + cfg.LR_DECAY_FACTOR = 0.1 + + # learning rate + cfg.LEARNING_RATE = 0.005 + + # momentum + cfg.MOMENTUM = 0.9 + + # weight decay + cfg.WEIGHT_DECAY = 0.0005 + + # wether to load pre-trained model + cfg.LOAD_PRETRAINED_MODEL = True + + # path to load the pre-trained model + cfg.PRETRAINED_MODEL_PATH = '' + + # print log to console in debug mode + cfg.DEBUG_MODE = False + + # gradients with norm larger than this is going to be clipped. + cfg.MAX_GRAD_NORM = 10.0 + + # Whether to do data augmentation + cfg.DATA_AUGMENTATION = False + + # The range to randomly shift the image widht + cfg.DRIFT_X = 0 + + # The range to randomly shift the image height + cfg.DRIFT_Y = 0 + + # small value used in batch normalization to prevent dividing by 0. The + # default value here is the same with caffe's default value. + cfg.BATCH_NORM_EPSILON = 1e-5 + + # small value used in denominator to prevent division by 0 + cfg.DENOM_EPSILON = 1e-12 + + # capacity for tf.FIFOQueue + cfg.QUEUE_CAPACITY = 80 + + cfg.NUM_ENQUEUE_THREAD = 8 + + return cfg diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/kitti_squeezeSeg_config.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/kitti_squeezeSeg_config.py new file mode 100644 index 0000000..c06ec7d --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/config/kitti_squeezeSeg_config.py @@ -0,0 +1,58 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 08/25/2016 +#-*- coding: utf-8 -*- + +"""Model configuration for pascal dataset""" + +import numpy as np +from .config import base_model_config + +def kitti_squeezeSeg_config(): + """Specify the parameters to tune below.""" + mc = base_model_config('KITTI') + + mc.CLASSES = ['unknown', 'car', 'pedestrian', 'cyclist'] + mc.NUM_CLASS = len(mc.CLASSES) + mc.CLS_2_ID = dict(zip(mc.CLASSES, range(len(mc.CLASSES)))) + mc.CLS_LOSS_WEIGHT = np.array([1/15.0, 1.0, 10.0, 10.0]) + mc.CLS_COLOR_MAP = np.array([[ 0.00, 0.00, 0.00], + [ 0.12, 0.56, 0.37], + [ 0.66, 0.55, 0.71], + [ 0.58, 0.72, 0.88]]) + + mc.BATCH_SIZE = 32 + + + mc.AZIMUTH_LEVEL = 512 # for Sphrerical Projection + mc.ZENITH_LEVEL = 64 # for Sphrerical Projection + + + mc.LCN_HEIGHT = 3 # for Bilateral filter + R-CRF + mc.LCN_WIDTH = 5 # for Bilateral filter + R-CRF + mc.BILATERAL_THETA_A = np.array([.9, .9, .6, .6]) # for Bilateral filter + mc.BILATERAL_THETA_R = np.array([.015, .015, .01, .01]) # for Bilateral filter + mc.BI_FILTER_COEF = 0.1 # for Bilatreal filter + + + mc.RCRF_ITER = 3 # for R-CRF + mc.ANG_THETA_A = np.array([.9, .9, .6, .6]) # for R-CRF + mc.ANG_FILTER_COEF = 0.02 # for R-CRF + + + mc.LEARNING_RATE = 0.01 + mc.CLS_LOSS_COEF = 15.0 # for Loss funtion + mc.WEIGHT_DECAY = 0.0001 + mc.MAX_GRAD_NORM = 1.0 + mc.MOMENTUM = 0.9 + mc.DECAY_STEPS = 10000 + mc.LR_DECAY_FACTOR = 0.5 + + + mc.DATA_AUGMENTATION = True + mc.RANDOM_FLIPPING = True + + + # x, y, z, intensity, distance + mc.INPUT_MEAN = np.array([[[10.88, 0.23, -1.04, 0.21, 12.12]]]) + mc.INPUT_STD = np.array([[[11.47, 6.91, 0.86, 0.16, 12.32]]]) + + return mc diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/__init__.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/__init__.py new file mode 100644 index 0000000..2fe1000 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/__init__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +#-*- coding: utf-8 -*- + +from .kitti import kitti diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/imdb.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/imdb.py new file mode 100644 index 0000000..2217e12 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/imdb.py @@ -0,0 +1,132 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/27/2017 +#-*- coding: utf-8 -*- + +"""The data base wrapper class""" +import os +import random +import shutil + +import numpy as np + +from squeezeseg.utils.util import * + +class imdb(object): + """Image database.""" + + def __init__(self, name, mc): + self._name = name + self._image_set = [] + self._image_idx = [] + self._data_root_path = [] + self.mc = mc + + # batch reader + self._perm_idx = [] + self._cur_idx = 0 + + @property + def name(self): + return self._name + + @property + def image_idx(self): + return self._image_idx + + @property + def image_set(self): + return self._image_set + + @property + def data_root_path(self): + return self._data_root_path + + def _shuffle_image_idx(self): + self._perm_idx = [self._image_idx[i] for i in + np.random.permutation(np.arange(len(self._image_idx)))] + self._cur_idx = 0 + + def read_batch(self, shuffle=True): + """Read a batch of lidar data including labels. Data formated as numpy array + of shape: height x width x {x, y, z, intensity, range, label}. + + Args: + shuffle: whether or not to shuffle the dataset + + Returns: + lidar_per_batch: LiDAR input. Shape: batch x height x width x 5. + lidar_mask_per_batch: LiDAR mask, 0 for missing data and 1 otherwise. + Shape: batch x height x width x 1. + label_per_batch: point-wise labels. Shape: batch x height x width. + weight_per_batch: loss weights for different classes. Shape: + batch x height x width + """ + mc = self.mc + + if shuffle: + if self._cur_idx + mc.BATCH_SIZE >= len(self._image_idx): + self._shuffle_image_idx() + batch_idx = self._perm_idx[self._cur_idx:self._cur_idx+mc.BATCH_SIZE] + self._cur_idx += mc.BATCH_SIZE + + else: + if self._cur_idx + mc.BATCH_SIZE >= len(self._image_idx): + batch_idx = self._image_idx[self._cur_idx:] \ + + self._image_idx[:self._cur_idx + mc.BATCH_SIZE-len(self._image_idx)] + self._cur_idx += mc.BATCH_SIZE - len(self._image_idx) + + else: + batch_idx = self._image_idx[self._cur_idx:self._cur_idx+mc.BATCH_SIZE] + self._cur_idx += mc.BATCH_SIZE + + + lidar_per_batch = [] + lidar_mask_per_batch = [] + label_per_batch = [] + weight_per_batch = [] + + for idx in batch_idx: + + # load data + # loading from npy is 30x faster than loading from pickle + record = np.load(self._lidar_2d_path_at(idx)).astype(np.float32, copy=False) + + if mc.DATA_AUGMENTATION: + if mc.RANDOM_FLIPPING: + if np.random.rand() > 0.5: + # flip y + record = record[:, ::-1, :] + + record[:, :, 1] *= -1 + + + lidar = record[:, :, :5] # x, y, z, intensity, depth + + lidar_mask = np.reshape( + (lidar[:, :, 4] > 0), + [mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1] + ) + + + # normalize + lidar = (lidar - mc.INPUT_MEAN)/mc.INPUT_STD + + label = record[:, :, 5] + weight = np.zeros(label.shape) + + + for l in range(mc.NUM_CLASS): + weight[label==l] = mc.CLS_LOSS_WEIGHT[int(l)] + + + # Append all the data + lidar_per_batch.append(lidar) + lidar_mask_per_batch.append(lidar_mask) + label_per_batch.append(label) + weight_per_batch.append(weight) + + + return np.array(lidar_per_batch), np.array(lidar_mask_per_batch), \ + np.array(label_per_batch), np.array(weight_per_batch) + + def evaluate_detections(self): + raise NotImplementedError diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/kitti.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/kitti.py new file mode 100644 index 0000000..f6e4da3 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/imdb/kitti.py @@ -0,0 +1,53 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/27/2017 +#-*- coding: utf-8 -*- + +"""Image data base class for kitti""" + +import os +import numpy as np +import subprocess + +from .imdb import imdb + + +class kitti(imdb): + def __init__(self, image_set, data_path, mc): + imdb.__init__(self, 'kitti_'+image_set, mc) + + self._image_set = image_set + self._data_root_path = data_path + self._lidar_2d_path = os.path.join(self._data_root_path, 'lidar_2d') + self._gta_2d_path = os.path.join(self._data_root_path, 'gta') + + # a list of string indices of images in the directory + self._image_idx = self._load_image_set_idx() + # a dict of image_idx -> [[cx, cy, w, h, cls_idx]]. x,y,w,h are not divided by + # the image width and height + + ## batch reader ## + self._perm_idx = None + self._cur_idx = 0 + # TODO(bichen): add a random seed as parameter + self._shuffle_image_idx() + + + def _load_image_set_idx(self): + image_set_file = os.path.join( + self._data_root_path, 'ImageSet', self._image_set+'.txt') + assert os.path.exists(image_set_file), \ + 'File does not exist: {}'.format(image_set_file) + + with open(image_set_file) as f: + image_idx = [x.strip() for x in f.readlines()] + return image_idx + + + def _lidar_2d_path_at(self, idx): + if idx[:4] == 'gta_': + lidar_2d_path = os.path.join(self._gta_2d_path, idx+'.npy') + else: + lidar_2d_path = os.path.join(self._lidar_2d_path, idx+'.npy') + + assert os.path.exists(lidar_2d_path), \ + 'File does not exist: {}'.format(lidar_2d_path) + return lidar_2d_path diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/__init__.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/__init__.py new file mode 100644 index 0000000..1d240c5 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/__init__.py @@ -0,0 +1,3 @@ +#-*- coding: utf-8 -*- + +from .squeezeSeg import SqueezeSeg diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/squeezeSeg.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/squeezeSeg.py new file mode 100644 index 0000000..7f0fce4 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nets/squeezeSeg.py @@ -0,0 +1,184 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#-*- coding: utf-8 -*- + +"""SqueezeSeg model""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +import joblib +import numpy as np +import tensorflow as tf +from squeezeseg.nn_skeleton import ModelSkeleton + + +class SqueezeSeg(ModelSkeleton): + def __init__(self, mc, gpu_id=0): + with tf.device('/gpu:{}'.format(gpu_id)): + ModelSkeleton.__init__(self, mc) + + self._add_forward_graph() # SqueezeNet Model + self._add_output_graph() # pred_prob, pred_cls + self._add_loss_graph() # cls_loss, total_loss + self._add_train_graph() # + self._add_viz_graph() # label_to_show, depth_image_to_show, pred_image_to_show + self._add_summary_ops() # + + + def _add_forward_graph(self): + """NN architecture.""" + + mc = self.mc + + if mc.LOAD_PRETRAINED_MODEL: + assert tf.gfile.Exists(mc.PRETRAINED_MODEL_PATH), \ + 'Cannot find pretrained model at the given path:' \ + ' {}'.format(mc.PRETRAINED_MODEL_PATH) + + self.caffemodel_weight = joblib.load(mc.PRETRAINED_MODEL_PATH) + + conv1 = self._conv_layer( + 'conv1', self.lidar_input, filters=64, size=3, stride=2, + padding='SAME', freeze=False, xavier=True) + conv1_skip = self._conv_layer( + 'conv1_skip', self.lidar_input, filters=64, size=1, stride=1, + padding='SAME', freeze=False, xavier=True) + pool1 = self._pooling_layer( + 'pool1', conv1, size=3, stride=2, padding='SAME') + + fire2 = self._fire_layer( + 'fire2', pool1, s1x1=16, e1x1=64, e3x3=64, freeze=False) + fire3 = self._fire_layer( + 'fire3', fire2, s1x1=16, e1x1=64, e3x3=64, freeze=False) + pool3 = self._pooling_layer( + 'pool3', fire3, size=3, stride=2, padding='SAME') + + fire4 = self._fire_layer( + 'fire4', pool3, s1x1=32, e1x1=128, e3x3=128, freeze=False) + fire5 = self._fire_layer( + 'fire5', fire4, s1x1=32, e1x1=128, e3x3=128, freeze=False) + pool5 = self._pooling_layer( + 'pool5', fire5, size=3, stride=2, padding='SAME') + + fire6 = self._fire_layer( + 'fire6', pool5, s1x1=48, e1x1=192, e3x3=192, freeze=False) + fire7 = self._fire_layer( + 'fire7', fire6, s1x1=48, e1x1=192, e3x3=192, freeze=False) + fire8 = self._fire_layer( + 'fire8', fire7, s1x1=64, e1x1=256, e3x3=256, freeze=False) + fire9 = self._fire_layer( + 'fire9', fire8, s1x1=64, e1x1=256, e3x3=256, freeze=False) + + + # Deconvolation + fire10 = self._fire_deconv( + 'fire_deconv10', fire9, s1x1=64, e1x1=128, e3x3=128, factors=[1, 2], + stddev=0.1) + fire10_fuse = tf.add(fire10, fire5, name='fure10_fuse') + + fire11 = self._fire_deconv( + 'fire_deconv11', fire10_fuse, s1x1=32, e1x1=64, e3x3=64, factors=[1, 2], + stddev=0.1) + fire11_fuse = tf.add(fire11, fire3, name='fire11_fuse') + + fire12 = self._fire_deconv( + 'fire_deconv12', fire11_fuse, s1x1=16, e1x1=32, e3x3=32, factors=[1, 2], + stddev=0.1) + fire12_fuse = tf.add(fire12, conv1, name='fire12_fuse') + + fire13 = self._fire_deconv( + 'fire_deconv13', fire12_fuse, s1x1=16, e1x1=32, e3x3=32, factors=[1, 2], + stddev=0.1) + fire13_fuse = tf.add(fire13, conv1_skip, name='fire13_fuse') + + drop13 = tf.nn.dropout(fire13_fuse, self.keep_prob, name='drop13') + + conv14 = self._conv_layer( + 'conv14_prob', drop13, filters=mc.NUM_CLASS, size=3, stride=1, + padding='SAME', relu=False, stddev=0.1) + + + bilateral_filter_weights = self._bilateral_filter_layer( + 'bilateral_filter', self.lidar_input[:, :, :, :3], # x, y, z + thetas=[mc.BILATERAL_THETA_A, mc.BILATERAL_THETA_R], + sizes=[mc.LCN_HEIGHT, mc.LCN_WIDTH], stride=1) + + + self.output_prob = self._recurrent_crf_layer( + 'recurrent_crf', conv14, bilateral_filter_weights, + sizes=[mc.LCN_HEIGHT, mc.LCN_WIDTH], num_iterations=mc.RCRF_ITER, + padding='SAME') + + + def _fire_layer(self, layer_name, inputs, s1x1, e1x1, e3x3, stddev=0.001, + freeze=False): + """Fire layer constructor. + + Args: + layer_name: layer name + inputs: input tensor + s1x1: number of 1x1 filters in squeeze layer. + e1x1: number of 1x1 filters in expand layer. + e3x3: number of 3x3 filters in expand layer. + freeze: if true, do not train parameters in this layer. + Returns: + fire layer operation. + """ + + sq1x1 = self._conv_layer( + layer_name+'/squeeze1x1', inputs, filters=s1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + ex1x1 = self._conv_layer( + layer_name+'/expand1x1', sq1x1, filters=e1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + ex3x3 = self._conv_layer( + layer_name+'/expand3x3', sq1x1, filters=e3x3, size=3, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + return tf.concat([ex1x1, ex3x3], 3, name=layer_name+'/concat') + + + + + def _fire_deconv(self, layer_name, inputs, s1x1, e1x1, e3x3, + factors=[1, 2], freeze=False, stddev=0.001): + """Fire deconvolution layer constructor. + + Args: + layer_name: layer name + inputs: input tensor + s1x1: number of 1x1 filters in squeeze layer. + e1x1: number of 1x1 filters in expand layer. + e3x3: number of 3x3 filters in expand layer. + factors: spatial upsampling factors. + freeze: if true, do not train parameters in this layer. + Returns: + fire layer operation. + """ + + assert len(factors) == 2,'factors should be an array of size 2' + + ksize_h = factors[0] * 2 - factors[0] % 2 + ksize_w = factors[1] * 2 - factors[1] % 2 + + sq1x1 = self._conv_layer( + layer_name+'/squeeze1x1', inputs, filters=s1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + deconv = self._deconv_layer( + layer_name+'/deconv', sq1x1, filters=s1x1, size=[ksize_h, ksize_w], + stride=factors, padding='SAME', init='bilinear') + + ex1x1 = self._conv_layer( + layer_name+'/expand1x1', deconv, filters=e1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + ex3x3 = self._conv_layer( + layer_name+'/expand3x3', deconv, filters=e3x3, size=3, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + return tf.concat([ex1x1, ex3x3], 3, name=layer_name+'/concat') diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nn_skeleton.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nn_skeleton.py new file mode 100644 index 0000000..bb98130 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/nn_skeleton.py @@ -0,0 +1,987 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#-*- coding: utf-8 -*- + +"""Neural network model base class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +from squeezeseg.utils import util +import numpy as np +import tensorflow as tf + + +def _variable_on_device(name, shape, initializer, trainable=True): + """Helper to create a Variable. + + Args: + name: name of the variable + shape: list of ints + initializer: initializer for Variable + + Returns: + Variable Tensor + """ + # TODO(bichen): fix the hard-coded data type below + dtype = tf.float32 + + if not callable(initializer): + var = tf.get_variable(name, initializer=initializer, trainable=trainable) + else: + var = tf.get_variable( + name, shape, initializer=initializer, dtype=dtype, trainable=trainable) + + return var + + + + +def _variable_with_weight_decay(name, shape, wd, initializer, trainable=True): + """Helper to create an initialized Variable with weight decay. + + Note that the Variable is initialized with a truncated normal distribution. + A weight decay is added only if one is specified. + + Args: + name: name of the variable + shape: list of ints + wd: add L2Loss weight decay multiplied by this float. If None, weight + decay is not added for this Variable. + + Returns: + Variable Tensor + """ + + var = _variable_on_device(name, shape, initializer, trainable) + + if wd is not None and trainable: + weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + return var + + + +class ModelSkeleton: + """Base class of NN detection models.""" + def __init__(self, mc): + self.mc = mc + + # a scalar tensor in range (0, 1]. Usually set to 0.5 in training phase and + # 1.0 in evaluation phase + self.ph_keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + # projected lidar points on a 2D spherical surface + self.ph_lidar_input = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 5], + name='lidar_input' + ) + + # A tensor where an element is 1 if the corresponding cell contains an + # valid lidar measurement. Or if the data is missing, then mark it as 0. + self.ph_lidar_mask = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + name='lidar_mask') + + # A tensor where each element contains the class of each pixel + self.ph_label = tf.placeholder( + tf.int32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + name='label') + + # weighted loss for different classes + self.ph_loss_weight = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + name='loss_weight') + + # define a FIFOqueue for pre-fetching data + self.q = tf.FIFOQueue( + capacity=mc.QUEUE_CAPACITY, + dtypes=[tf.float32, tf.float32, tf.float32, tf.int32, tf.float32], + shapes=[[], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 5], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL]] + ) + + # train.py:129 : enqueue_op에 feed_dict가 들어간다 + self.enqueue_op = self.q.enqueue( + [self.ph_keep_prob, self.ph_lidar_input, self.ph_lidar_mask, + self.ph_label, self.ph_loss_weight] + ) + + self.keep_prob, self.lidar_input, self.lidar_mask, self.label, \ + self.loss_weight = self.q.dequeue() + + # model parameters + self.model_params = [] + + # model size counter + self.model_size_counter = [] # array of tuple of layer name, parameter size + + # flop counter + self.flop_counter = [] # array of tuple of layer name, flop number + + # activation counter + self.activation_counter = [] # array of tuple of layer name, output activations + self.activation_counter.append(('input', mc.AZIMUTH_LEVEL*mc.ZENITH_LEVEL*3)) + + + def _add_forward_graph(self): + """NN architecture specification.""" + raise NotImplementedError + + + def _add_output_graph(self): + """Define how to intepret output.""" + mc = self.mc + + with tf.variable_scope('interpret_output') as scope: + self.prob = tf.multiply( + tf.nn.softmax(self.output_prob, dim=-1), self.lidar_mask, + name='pred_prob') + + self.pred_cls = tf.argmax(self.prob, axis=3, name='pred_cls') + + # add summaries + for cls_id, cls in enumerate(mc.CLASSES): + self._activation_summary(self.prob[:, :, :, cls_id], 'prob_'+cls) + + + + def _add_loss_graph(self): + """Define the loss operation.""" + mc = self.mc + + with tf.variable_scope('cls_loss') as scope: + self.cls_loss = tf.identity( + tf.reduce_sum( + tf.nn.sparse_softmax_cross_entropy_with_logits( + labels=tf.reshape(self.label, (-1, )), + logits=tf.reshape(self.output_prob, (-1, mc.NUM_CLASS)) + ) \ + * tf.reshape(self.lidar_mask, (-1, )) \ + * tf.reshape(self.loss_weight, (-1, )) + ) / tf.reduce_sum(self.lidar_mask)*mc.CLS_LOSS_COEF, + name='cls_loss') + + tf.add_to_collection('losses', self.cls_loss) + + + # add above losses as well as weight decay losses to form the total loss + self.loss = tf.add_n(tf.get_collection('losses'), name='total_loss') + + # add loss summaries + # _add_loss_summaries(self.loss) + tf.summary.scalar(self.cls_loss.op.name, self.cls_loss) + tf.summary.scalar(self.loss.op.name, self.loss) + + + + def _add_train_graph(self): + """Define the training operation.""" + mc = self.mc + + self.global_step = tf.Variable(0, name='global_step', trainable=False) + + # 가변적으로 learning rate가 변한다 + lr = tf.train.exponential_decay(mc.LEARNING_RATE, + self.global_step, + mc.DECAY_STEPS, + mc.LR_DECAY_FACTOR, + staircase=True) + + tf.summary.scalar('learning_rate', lr) + + opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=mc.MOMENTUM) + + + grads_vars = opt.compute_gradients(self.loss, tf.trainable_variables()) + + with tf.variable_scope('clip_gradient') as scope: + for i, (grad, var) in enumerate(grads_vars): + grads_vars[i] = (tf.clip_by_norm(grad, mc.MAX_GRAD_NORM), var) + + apply_gradient_op = opt.apply_gradients(grads_vars, global_step=self.global_step) + + + with tf.control_dependencies([apply_gradient_op]): + self.train_op = tf.no_op(name='train') + + + + def _add_viz_graph(self): + """Define the visualization operation.""" + mc = self.mc + + self.label_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 3], + name='label_to_show' + ) + + self.depth_image_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + name='depth_image_to_show' + ) + + self.pred_image_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 3], + name='pred_image_to_show' + ) + + self.show_label = tf.summary.image('label_to_show', + self.label_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + self.show_depth_img = tf.summary.image('depth_image_to_show', + self.depth_image_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + self.show_pred = tf.summary.image('pred_image_to_show', + self.pred_image_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + + def _add_summary_ops(self): + """Add extra summary operations.""" + mc = self.mc + + iou_summary_placeholders = [] + iou_summary_ops = [] + + for cls in mc.CLASSES: + ph = tf.placeholder(tf.float32, name=cls+'_iou') + iou_summary_placeholders.append(ph) + iou_summary_ops.append( + tf.summary.scalar('Eval/'+cls+'_iou', ph, collections='eval_summary') + ) + + self.iou_summary_placeholders = iou_summary_placeholders + self.iou_summary_ops = iou_summary_ops + + + + def _conv_bn_layer( + self, inputs, conv_param_name, bn_param_name, scale_param_name, filters, + size, stride, padding='SAME', freeze=False, relu=True, + conv_with_bias=False, stddev=0.001): + """ Convolution + BatchNorm + [relu] layer. Batch mean and var are treated + as constant. Weights have to be initialized from a pre-trained model or + restored from a checkpoint. + + Args: + inputs: input tensor + conv_param_name: name of the convolution parameters + bn_param_name: name of the batch normalization parameters + scale_param_name: name of the scale parameters + filters: number of output filters. + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + xavier: whether to use xavier weight initializer or not. + relu: whether to use relu or not. + conv_with_bias: whether or not add bias term to the convolution output. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + mc = self.mc + + with tf.variable_scope(conv_param_name) as scope: + channels = inputs.get_shape()[3] + + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + kernel_val = np.transpose(cw[conv_param_name][0], [2,3,1,0]) + if conv_with_bias: + bias_val = cw[conv_param_name][1] + mean_val = cw[bn_param_name][0] + var_val = cw[bn_param_name][1] + gamma_val = cw[scale_param_name][0] + beta_val = cw[scale_param_name][1] + else: + kernel_val = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + if conv_with_bias: + bias_val = tf.constant_initializer(0.0) + mean_val = tf.constant_initializer(0.0) + var_val = tf.constant_initializer(1.0) + gamma_val = tf.constant_initializer(1.0) + beta_val = tf.constant_initializer(0.0) + + # re-order the caffe kernel with shape [out, in, h, w] -> tf kernel with + # shape [h, w, in, out] + kernel = _variable_with_weight_decay( + 'kernels', shape=[size, size, int(channels), filters], + wd=mc.WEIGHT_DECAY, initializer=kernel_val, trainable=(not freeze)) + + self.model_params += [kernel] + + if conv_with_bias: + biases = _variable_on_device('biases', [filters], bias_val, + trainable=(not freeze)) + self.model_params += [biases] + + gamma = _variable_on_device('gamma', [filters], gamma_val, + trainable=(not freeze)) + beta = _variable_on_device('beta', [filters], beta_val, + trainable=(not freeze)) + mean = _variable_on_device('mean', [filters], mean_val, trainable=False) + var = _variable_on_device('var', [filters], var_val, trainable=False) + + self.model_params += [gamma, beta, mean, var] + + conv = tf.nn.conv2d( + inputs, kernel, [1, 1, stride, 1], padding=padding, + name='convolution') + + if conv_with_bias: + conv = tf.nn.bias_add(conv, biases, name='bias_add') + + conv = tf.nn.batch_normalization( + conv, mean=mean, variance=var, offset=beta, scale=gamma, + variance_epsilon=mc.BATCH_NORM_EPSILON, name='batch_norm') + + self.model_size_counter.append( + (conv_param_name, (1+size*size*int(channels))*filters) + ) + + out_shape = conv.get_shape().as_list() + num_flops = \ + (1+2*int(channels)*size*size)*filters*out_shape[1]*out_shape[2] + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + self.flop_counter.append((conv_param_name, num_flops)) + + self.activation_counter.append( + (conv_param_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + if relu: + return tf.nn.relu(conv) + else: + return conv + + def _conv_layer( + self, layer_name, inputs, filters, size, stride, padding='SAME', + freeze=False, xavier=False, relu=True, stddev=0.001, bias_init_val=0.0): + """Convolutional layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + filters: number of output filters. + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + xavier: whether to use xavier weight initializer or not. + relu: whether to use relu or not. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + + mc = self.mc + use_pretrained_param = False + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + if layer_name in cw: + kernel_val = np.transpose(cw[layer_name][0], [2,3,1,0]) + bias_val = cw[layer_name][1] + # check the shape + if (kernel_val.shape == + (size, size, inputs.get_shape().as_list()[-1], filters)) \ + and (bias_val.shape == (filters, )): + use_pretrained_param = True + else: + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + else: + print ('Cannot find {} in the pretrained model. Use randomly initialized ' + 'parameters'.format(layer_name)) + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + channels = inputs.get_shape()[3] + + # re-order the caffe kernel with shape [out, in, h, w] -> tf kernel with + # shape [h, w, in, out] + if use_pretrained_param: + if mc.DEBUG_MODE: + print ('Using pretrained model for {}'.format(layer_name)) + kernel_init = tf.constant(kernel_val , dtype=tf.float32) + bias_init = tf.constant(bias_val, dtype=tf.float32) + elif xavier: + kernel_init = tf.contrib.layers.xavier_initializer_conv2d() + bias_init = tf.constant_initializer(bias_init_val) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(bias_init_val) + + kernel = _variable_with_weight_decay( + 'kernels', shape=[size, size, int(channels), filters], + wd=mc.WEIGHT_DECAY, initializer=kernel_init, trainable=(not freeze)) + + biases = _variable_on_device('biases', [filters], bias_init, + trainable=(not freeze)) + self.model_params += [kernel, biases] + + conv = tf.nn.conv2d( + inputs, kernel, [1, 1, stride, 1], padding=padding, + name='convolution') + conv_bias = tf.nn.bias_add(conv, biases, name='bias_add') + + if relu: + out = tf.nn.relu(conv_bias, 'relu') + else: + out = conv_bias + + self.model_size_counter.append( + (layer_name, (1+size*size*int(channels))*filters) + ) + + out_shape = out.get_shape().as_list() + + num_flops = \ + (1+2*int(channels)*size*size)*filters*out_shape[1]*out_shape[2] + + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append( + (layer_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + return out + + + def _deconv_layer( + self, layer_name, inputs, filters, size, stride, padding='SAME', + freeze=False, init='trunc_norm', relu=True, stddev=0.001): + """Deconvolutional layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + filters: number of output filters. + size: kernel size. An array of size 2 or 1. + stride: stride. An array of size 2 or 1. + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + init: how to initialize kernel weights. Now accept 'xavier', + 'trunc_norm', 'bilinear' + relu: whether to use relu or not. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + + assert len(size) == 1 or len(size) == 2, \ + 'size should be a scalar or an array of size 2.' + assert len(stride) == 1 or len(stride) == 2, \ + 'stride should be a scalar or an array of size 2.' + assert init == 'xavier' or init == 'bilinear' or init == 'trunc_norm', \ + 'initi mode not supported {}'.format(init) + + if len(size) == 1: + size_h, size_w = size[0], size[0] + else: + size_h, size_w = size[0], size[1] + + if len(stride) == 1: + stride_h, stride_w = stride[0], stride[0] + else: + stride_h, stride_w = stride[0], stride[1] + + mc = self.mc + # TODO(bichen): Currently do not support pretrained parameters for deconv + # layer. + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + in_height = int(inputs.get_shape()[1]) + in_width = int(inputs.get_shape()[2]) + channels = int(inputs.get_shape()[3]) + + if init == 'xavier': + kernel_init = tf.contrib.layers.xavier_initializer_conv2d() + bias_init = tf.constant_initializer(0.0) + elif init == 'bilinear': + assert size_h == 1, 'Now only support size_h=1' + assert channels == filters, \ + 'In bilinear interporlation mode, input channel size and output' \ + 'filter size should be the same' + assert stride_h == 1, \ + 'In bilinear interpolation mode, stride_h should be 1' + + kernel_init = np.zeros( + (size_h, size_w, channels, channels), + dtype=np.float32) + + factor_w = (size_w + 1)//2 + assert factor_w == stride_w, \ + 'In bilinear interpolation mode, stride_w == factor_w' + + center_w = (factor_w - 1) if (size_w % 2 == 1) else (factor_w - 0.5) + og_w = np.reshape(np.arange(size_w), (size_h, -1)) + up_kernel = (1 - np.abs(og_w - center_w)/factor_w) + for c in range(channels): + kernel_init[:, :, c, c] = up_kernel + + bias_init = tf.constant_initializer(0.0) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(0.0) + + # Kernel layout for deconv layer: [H_f, W_f, O_c, I_c] where I_c is the + # input channel size. It should be the same as the channel size of the + # input tensor. + kernel = _variable_with_weight_decay( + 'kernels', shape=[size_h, size_w, filters, channels], + wd=mc.WEIGHT_DECAY, initializer=kernel_init, trainable=(not freeze)) + biases = _variable_on_device( + 'biases', [filters], bias_init, trainable=(not freeze)) + self.model_params += [kernel, biases] + + # TODO(bichen): fix this + deconv = tf.nn.conv2d_transpose( + inputs, kernel, + [mc.BATCH_SIZE, stride_h*in_height, stride_w*in_width, filters], + [1, stride_h, stride_w, 1], padding=padding, + name='deconv') + deconv_bias = tf.nn.bias_add(deconv, biases, name='bias_add') + + if relu: + out = tf.nn.relu(deconv_bias, 'relu') + else: + out = deconv_bias + + self.model_size_counter.append( + (layer_name, (1+size_h*size_w*channels)*filters) + ) + out_shape = out.get_shape().as_list() + num_flops = \ + (1+2*channels*size_h*size_w)*filters*out_shape[1]*out_shape[2] + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append( + (layer_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + return out + + + def _pooling_layer( + self, layer_name, inputs, size, stride, padding='SAME'): + """Pooling layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + Returns: + A pooling layer operation. + """ + + with tf.variable_scope(layer_name) as scope: + out = tf.nn.max_pool(inputs, + ksize=[1, size, size, 1], + strides=[1, 1, stride, 1], + padding=padding) + activation_size = np.prod(out.get_shape().as_list()[1:]) + self.activation_counter.append((layer_name, activation_size)) + return out + + + def _fc_layer( + self, layer_name, inputs, hiddens, flatten=False, relu=True, + xavier=False, stddev=0.001, bias_init_val=0.0): + """Fully connected layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + hiddens: number of (hidden) neurons in this layer. + flatten: if true, reshape the input 4D tensor of shape + (batch, height, weight, channel) into a 2D tensor with shape + (batch, -1). This is used when the input to the fully connected layer + is output of a convolutional layer. + relu: whether to use relu or not. + xavier: whether to use xavier weight initializer or not. + stddev: standard deviation used for random weight initializer. + Returns: + A fully connected layer operation. + """ + mc = self.mc + + use_pretrained_param = False + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + if layer_name in cw: + use_pretrained_param = True + kernel_val = cw[layer_name][0] + bias_val = cw[layer_name][1] + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + input_shape = inputs.get_shape().as_list() + if flatten: + dim = input_shape[1]*input_shape[2]*input_shape[3] + inputs = tf.reshape(inputs, [-1, dim]) + if use_pretrained_param: + try: + # check the size before layout transform + assert kernel_val.shape == (hiddens, dim), \ + 'kernel shape error at {}'.format(layer_name) + kernel_val = np.reshape( + np.transpose( + np.reshape( + kernel_val, # O x (C*H*W) + (hiddens, input_shape[3], input_shape[1], input_shape[2]) + ), # O x C x H x W + (2, 3, 1, 0) + ), # H x W x C x O + (dim, -1) + ) # (H*W*C) x O + # check the size after layout transform + assert kernel_val.shape == (dim, hiddens), \ + 'kernel shape error at {}'.format(layer_name) + except: + # Do not use pretrained parameter if shape doesn't match + use_pretrained_param = False + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + else: + dim = input_shape[1] + if use_pretrained_param: + try: + kernel_val = np.transpose(kernel_val, (1,0)) + assert kernel_val.shape == (dim, hiddens), \ + 'kernel shape error at {}'.format(layer_name) + except: + use_pretrained_param = False + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + + if use_pretrained_param: + if mc.DEBUG_MODE: + print ('Using pretrained model for {}'.format(layer_name)) + kernel_init = tf.constant(kernel_val, dtype=tf.float32) + bias_init = tf.constant(bias_val, dtype=tf.float32) + elif xavier: + kernel_init = tf.contrib.layers.xavier_initializer() + bias_init = tf.constant_initializer(bias_init_val) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(bias_init_val) + + weights = _variable_with_weight_decay( + 'weights', shape=[dim, hiddens], wd=mc.WEIGHT_DECAY, + initializer=kernel_init) + biases = _variable_on_device('biases', [hiddens], bias_init) + self.model_params += [weights, biases] + + outputs = tf.nn.bias_add(tf.matmul(inputs, weights), biases) + if relu: + outputs = tf.nn.relu(outputs, 'relu') + + # count layer stats + self.model_size_counter.append((layer_name, (dim+1)*hiddens)) + + num_flops = 2 * dim * hiddens + hiddens + if relu: + num_flops += 2*hiddens + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append((layer_name, hiddens)) + + return outputs + + + def _recurrent_crf_layer( + self, layer_name, inputs, bilateral_filters, sizes=[3, 5], + num_iterations=1, padding='SAME'): + """Recurrent conditional random field layer. Iterative meanfield inference is + implemented as a reccurent neural network. + + Args: + layer_name: layer name + inputs: input tensor with shape [batch_size, zenith, azimuth, num_class]. + bilateral_filters: filter weight with shape + [batch_size, zenith, azimuth, sizes[0]*size[1]-1]. + sizes: size of the local region to be filtered. + num_iterations: number of meanfield inferences. + padding: padding strategy + Returns: + outputs: tensor with shape [batch_size, zenith, azimuth, num_class]. + """ + assert num_iterations >= 1, 'number of iterations should >= 1' + + mc = self.mc + + with tf.variable_scope(layer_name) as scope: + # initialize compatibilty matrices + compat_kernel_init = tf.constant( + np.reshape( + np.ones((mc.NUM_CLASS, mc.NUM_CLASS)) - np.identity(mc.NUM_CLASS), + [1, 1, mc.NUM_CLASS, mc.NUM_CLASS] + ), + dtype=tf.float32 + ) + + bi_compat_kernel = _variable_on_device( + name='bilateral_compatibility_matrix', + shape=[1, 1, mc.NUM_CLASS, mc.NUM_CLASS], + initializer=compat_kernel_init*mc.BI_FILTER_COEF, + trainable=True + ) + + self._activation_summary(bi_compat_kernel, 'bilateral_compat_mat') + + angular_compat_kernel = _variable_on_device( + name='angular_compatibility_matrix', + shape=[1, 1, mc.NUM_CLASS, mc.NUM_CLASS], + initializer=compat_kernel_init*mc.ANG_FILTER_COEF, + trainable=True + ) + + self._activation_summary(angular_compat_kernel, 'angular_compat_mat') + + self.model_params += [bi_compat_kernel, angular_compat_kernel] + + condensing_kernel = tf.constant( + util.condensing_matrix(sizes[0], sizes[1], mc.NUM_CLASS), + dtype=tf.float32, + name='condensing_kernel' + ) + + angular_filters = tf.constant( + util.angular_filter_kernel( + sizes[0], sizes[1], mc.NUM_CLASS, mc.ANG_THETA_A**2), + dtype=tf.float32, + name='angular_kernel' + ) + + bi_angular_filters = tf.constant( + util.angular_filter_kernel( + sizes[0], sizes[1], mc.NUM_CLASS, mc.BILATERAL_THETA_A**2), + dtype=tf.float32, + name='bi_angular_kernel' + ) + + for it in range(num_iterations): + unary = tf.nn.softmax( + inputs, dim=-1, name='unary_term_at_iter_{}'.format(it)) + + ang_output, bi_output = self._locally_connected_layer( + 'message_passing_iter_{}'.format(it), unary, + bilateral_filters, angular_filters, bi_angular_filters, + condensing_kernel, sizes=sizes, + padding=padding + ) + + # 1x1 convolution as compatibility transform + ang_output = tf.nn.conv2d( + ang_output, angular_compat_kernel, strides=[1, 1, 1, 1], + padding='SAME', name='angular_compatibility_transformation') + + self._activation_summary( + ang_output, 'ang_transfer_iter_{}'.format(it)) + + bi_output = tf.nn.conv2d( + bi_output, bi_compat_kernel, strides=[1, 1, 1, 1], padding='SAME', + name='bilateral_compatibility_transformation') + + self._activation_summary( + bi_output, 'bi_transfer_iter_{}'.format(it)) + + pairwise = tf.add(ang_output, bi_output, + name='pairwise_term_at_iter_{}'.format(it)) + + outputs = tf.add(unary, pairwise, + name='energy_at_iter_{}'.format(it)) + + inputs = outputs + + return outputs + + + + def _locally_connected_layer( + self, layer_name, inputs, bilateral_filters, + angular_filters, bi_angular_filters, condensing_kernel, sizes=[3, 5], + padding='SAME'): + """Locally connected layer with non-trainable filter parameters) + + Args: + layer_name: layer name + inputs: input tensor with shape + [batch_size, zenith, azimuth, num_class]. + bilateral_filters: bilateral filter weight with shape + [batch_size, zenith, azimuth, sizes[0]*size[1]-1]. + angular_filters: angular filter weight with shape + [sizes[0], sizes[1], in_channel, in_channel]. + condensing_kernel: tensor with shape + [size[0], size[1], num_class, (sizes[0]*size[1]-1)*num_class] + sizes: size of the local region to be filtered. + padding: padding strategy + Returns: + ang_output: output tensor filtered by anguler filter with shape + [batch_size, zenith, azimuth, num_class]. + bi_output: output tensor filtered by bilateral filter with shape + [batch_size, zenith, azimuth, num_class]. + """ + assert padding=='SAME', 'only support SAME padding strategy' + assert sizes[0] % 2 == 1 and sizes[1] % 2 == 1, \ + 'Currently only support odd filter size.' + + mc = self.mc + size_z, size_a = sizes + pad_z, pad_a = size_z//2, size_a//2 + half_filter_dim = (size_z*size_a)//2 + batch, zenith, azimuth, in_channel = inputs.shape.as_list() + + with tf.variable_scope(layer_name) as scope: + # message passing + ang_output = tf.nn.conv2d( + inputs, angular_filters, [1, 1, 1, 1], padding=padding, + name='angular_filtered_term' + ) + + bi_ang_output = tf.nn.conv2d( + inputs, bi_angular_filters, [1, 1, 1, 1], padding=padding, + name='bi_angular_filtered_term' + ) + + condensed_input = tf.reshape( + tf.nn.conv2d( + inputs*self.lidar_mask, condensing_kernel, [1, 1, 1, 1], padding=padding, + name='condensed_prob_map' + ), + [batch, zenith, azimuth, size_z*size_a-1, in_channel] + ) + + bi_output = tf.multiply( + tf.reduce_sum(condensed_input*bilateral_filters, axis=3), + self.lidar_mask, + name='bilateral_filtered_term' + ) + bi_output *= bi_ang_output + + return ang_output, bi_output + + + def _bilateral_filter_layer( + self, layer_name, inputs, thetas=[0.9, 0.01], sizes=[3, 5], stride=1, + padding='SAME'): + """Computing pairwise energy with a bilateral filter for CRF. + + Args: + layer_name: layer name + inputs: input tensor with shape [batch_size, zenith, azimuth, 2] where the + last 2 elements are intensity and range of a lidar point. + thetas: theta parameter for bilateral filter. + sizes: filter size for zenith and azimuth dimension. + strides: kernel strides. + padding: padding. + Returns: + out: bilateral filter weight output with size + [batch_size, zenith, azimuth, sizes[0]*sizes[1]-1, num_class]. Each + [b, z, a, :, cls] represents filter weights around the center position + for each class. + """ + + assert padding == 'SAME', 'currently only supports "SAME" padding stategy' + assert stride == 1, 'currently only supports striding of 1' + assert sizes[0] % 2 == 1 and sizes[1] % 2 == 1, \ + 'Currently only support odd filter size.' + + mc = self.mc + theta_a, theta_r = thetas + size_z, size_a = sizes + pad_z, pad_a = size_z//2, size_a//2 + half_filter_dim = (size_z*size_a)//2 + batch, zenith, azimuth, in_channel = inputs.shape.as_list() + + # assert in_channel == 1, 'Only support input channel == 1' + + with tf.variable_scope(layer_name) as scope: + condensing_kernel = tf.constant( + util.condensing_matrix(size_z, size_a, in_channel), + dtype=tf.float32, + name='condensing_kernel' + ) + + condensed_input = tf.nn.conv2d( + inputs, condensing_kernel, [1, 1, stride, 1], padding=padding, + name='condensed_input' + ) + + # diff_intensity = tf.reshape( + # inputs[:, :, :], [batch, zenith, azimuth, 1]) \ + # - condensed_input[:, :, :, ::in_channel] + + diff_x = tf.reshape( + inputs[:, :, :, 0], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 0::in_channel] + diff_y = tf.reshape( + inputs[:, :, :, 1], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 1::in_channel] + diff_z = tf.reshape( + inputs[:, :, :, 2], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 2::in_channel] + + bi_filters = [] + + for cls in range(mc.NUM_CLASS): + theta_a = mc.BILATERAL_THETA_A[cls] + theta_r = mc.BILATERAL_THETA_R[cls] + + bi_filter = tf.exp(-(diff_x**2+diff_y**2+diff_z**2)/2/theta_r**2) + + bi_filters.append(bi_filter) + + out = tf.transpose( + tf.stack(bi_filters), + [1, 2, 3, 4, 0], + name='bilateral_filter_weights' + ) + + return out + + + def _activation_summary(self, x, layer_name): + """Helper to create summaries for activations. + + Args: + x: layer output tensor + layer_name: name of the layer + Returns: + nothing + """ + with tf.variable_scope('activation_summary') as scope: + tf.summary.histogram(layer_name, x) + tf.summary.scalar(layer_name+'/sparsity', tf.nn.zero_fraction(x)) + tf.summary.scalar(layer_name+'/average', tf.reduce_mean(x)) + tf.summary.scalar(layer_name+'/max', tf.reduce_max(x)) + tf.summary.scalar(layer_name+'/min', tf.reduce_min(x)) diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/__init__.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/__init__.py new file mode 100644 index 0000000..62da116 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/__init__.py @@ -0,0 +1 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/clock.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/clock.py new file mode 100644 index 0000000..c20b039 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/clock.py @@ -0,0 +1,29 @@ +#! /usr/bin/python2 +# -*- coding: utf-8 -*- +""" +Clock function to take running time following Segmatch. +""" + +import datetime + +class Clock(object): + def __init__(self): + self.kSecondsToMiliseconds = 1000.0 + self.kMicrosecondsToMiliseconds = 0.001 + self.start() + + def start(self): + self.real_time_start_ = datetime.datetime.now() + + def takeTime(self): + seconds = (datetime.datetime.now() - self.real_time_start_).seconds + useconds = (datetime.datetime.now() - self.real_time_start_).microseconds + + self.real_time_ms_ = (seconds*self.kSecondsToMiliseconds + useconds*self.kMicrosecondsToMiliseconds) + 0.5 + + def getRealTime(self): + return self.real_time_ms_ + + def takeRealTime(self): + self.takeTime() + return self.getRealTime() diff --git a/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/util.py b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/util.py new file mode 100644 index 0000000..f0025b3 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/script/squeezeseg/utils/util.py @@ -0,0 +1,187 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +# -*- coding: utf-8 -*- + +"""Utility functions.""" + +import numpy as np +import time + + +def visualize_seg(label_map, mc, one_hot=False): + if one_hot: + label_map = np.argmax(label_map, axis=-1) + + out = np.zeros( + (label_map.shape[0], label_map.shape[1], label_map.shape[2], 3)) + + for l in range(1, mc.NUM_CLASS): + out[label_map==l, :] = mc.CLS_COLOR_MAP[l] + + return out + + + +def bgr_to_rgb(ims): + """Convert a list of images from BGR format to RGB format.""" + out = [] + for im in ims: + out.append(im[:,:,::-1]) + return out + + + +class Timer(object): + def __init__(self): + self.total_time = 0.0 + self.calls = 0 + self.start_time = 0.0 + self.duration = 0.0 + self.average_time = 0.0 + + def tic(self): + self.start_time = time.time() + + def toc(self, average=True): + self.duration = time.time() - self.start_time + self.total_time += self.duration + self.calls += 1 + self.average_time = self.total_time/self.calls + if average: + return self.average_time + else: + return self.duration + + + +def conf_error_rate_at_thresh_fn(mask, conf, thresh): + return np.mean((conf>thresh) != mask) + + + +def rmse_fn(diff, nnz): + return np.sqrt(np.sum(diff**2)/nnz) + + + +def abs_accuracy_at_thresh_fn(diff, thresh, mask): + return np.sum((np.abs(diff) < thresh)*mask)/float(np.sum(mask)) + + + +def rel_accuracy_at_thresh_fn(pred_ogm, gt_ogm, mask, thresh): + return np.sum( + mask * (np.maximum(pred_ogm, gt_ogm) / + np.minimum(gt_ogm, pred_ogm) < thresh) + )/float(np.sum(mask)) + + +def evaluate_iou(label, pred, n_class, epsilon=1e-12): + """Evaluation script to compute pixel level IoU. + + Args: + label: N-d array of shape [batch, W, H], where each element is a class + index. + pred: N-d array of shape [batch, W, H], the each element is the predicted + class index. + n_class: number of classes + epsilon: a small value to prevent division by 0 + + Returns: + IoU: array of lengh n_class, where each element is the average IoU for this + class. + tps: same shape as IoU, where each element is the number of TP for each + class. + fps: same shape as IoU, where each element is the number of FP for each + class. + fns: same shape as IoU, where each element is the number of FN for each + class. + """ + + assert label.shape == pred.shape, \ + 'label and pred shape mismatch: {} vs {}'.format( + label.shape, pred.shape) + + ious = np.zeros(n_class) + tps = np.zeros(n_class) + fns = np.zeros(n_class) + fps = np.zeros(n_class) + + for cls_id in range(n_class): + tp = np.sum(pred[label == cls_id] == cls_id) + fp = np.sum(label[pred == cls_id] != cls_id) + fn = np.sum(pred[label == cls_id] != cls_id) + + ious[cls_id] = tp/(tp+fn+fp+epsilon) + tps[cls_id] = tp + fps[cls_id] = fp + fns[cls_id] = fn + + return ious, tps, fps, fns + + + +def condensing_matrix(size_z, size_a, in_channel): + assert size_z % 2 == 1 and size_a % 2==1, \ + 'size_z and size_a should be odd number' + + half_filter_dim = (size_z*size_a)//2 + + # moving neigboring pixels to channel dimension + nbr2ch_mat = np.zeros( + (size_z, size_a, in_channel, size_z*size_a*in_channel), + dtype=np.float32 + ) + + for z in range(size_z): + for a in range(size_a): + for ch in range(in_channel): + nbr2ch_mat[z, a, ch, z*(size_a*in_channel) + a*in_channel + ch] = 1 + + # exclude the channel index corresponding to the center position + nbr2ch_mat = np.concatenate( + [nbr2ch_mat[:, :, :, :in_channel*half_filter_dim], + nbr2ch_mat[:, :, :, in_channel*(half_filter_dim+1):]], + axis=3 + ) + + assert nbr2ch_mat.shape == \ + (size_z, size_a, in_channel, (size_a*size_z-1)*in_channel), \ + 'error with the shape of nbr2ch_mat after removing center position' + + return nbr2ch_mat + + + +def angular_filter_kernel(size_z, size_a, in_channel, theta_sqs): + """Compute a gaussian kernel. + Args: + size_z: size on the z dimension. + size_a: size on the a dimension. + in_channel: input (and output) channel size + theta_sqs: an array with length == in_channel. Contains variance for + gaussian kernel for each channel. + Returns: + kernel: ND array of size [size_z, size_a, in_channel, in_channel], which is + just guassian kernel parameters for each channel. + """ + assert size_z % 2 == 1 and size_a % 2==1, \ + 'size_z and size_a should be odd number' + assert len(theta_sqs) == in_channel, \ + 'length of theta_sqs and in_channel does no match' + + # gaussian kernel + kernel = np.zeros((size_z, size_a, in_channel, in_channel), dtype=np.float32) + + for k in range(in_channel): + kernel_2d = np.zeros((size_z, size_a), dtype=np.float32) + for i in range(size_z): + for j in range(size_a): + diff = np.sum( + (np.array([i-size_z//2, j-size_a//2]))**2) + kernel_2d[i, j] = np.exp(-diff/2/theta_sqs[k]) + + # exclude the center position + kernel_2d[size_z//2, size_a//2] = 0 + kernel[:, :, k, k] = kernel_2d + + return kernel diff --git a/ROS_packages/kitti_velodyne_to_ros/src/kitti_velodyne_to_ros_node.cpp b/ROS_packages/kitti_velodyne_to_ros/src/kitti_velodyne_to_ros_node.cpp new file mode 100644 index 0000000..4d9df73 --- /dev/null +++ b/ROS_packages/kitti_velodyne_to_ros/src/kitti_velodyne_to_ros_node.cpp @@ -0,0 +1,89 @@ +/* + [+] reference : https://github.com/iralabdisco/kitti_player/blob/public/src/kitti_player.cpp +*/ +#include +#include +#include +#include + +#include +#include +#include +#include + + +int main(int argc, char **argv) { + ros::init(argc, argv, "lidar_annotator_node"); + + ros::NodeHandle nh; + std::string path; + std::string full_file_name; + + nh.param("kitti_bin_path", path, + std::string("/media/dyros-vehicle/edward_6/datasets/KITTI_datasets/2011_09_26_drive_0005_sync/velodyne_points/data/")); + + ros::Publisher kitti_pub = nh.advertise("/velodyne_points_kitti",1); + + // ed: 5 hz + ros::Rate loop_rate(5); + + int entries_played = 0; + int total_entries = 0; + + DIR* dir = opendir(path.c_str()); + struct dirent *ent; + unsigned int len = 0; + + // ed: get the number of files in directory + while((ent = readdir(dir))){ + len = strlen(ent->d_name); + if(len > 2) + total_entries++; + } + closedir(dir); + + /* std::cout << "total entries : " << total_entries << std::endl; // : 154 */ + + while(ros::ok() && entries_played <= total_entries-1) { + full_file_name = path + boost::str(boost::format("%010d") % entries_played) + ".bin"; + + // ed: for debugging + std::cout << "full file name : " << full_file_name << std::endl; + + // ed: open file + std::fstream input(full_file_name.c_str(), std::ios::in | std::ios::binary); + + if(!input.good()) { + ROS_ERROR_STREAM("could not read file: " << full_file_name); + return 0; + } + else { + ROS_DEBUG_STREAM("reading " << full_file_name); + // ed: go to beggining of the file + input.seekg(0, std::ios::beg); + + pcl::PointCloud::Ptr points(new pcl::PointCloud); + + // ed: read data + for(int i=0; input.good() && !input.eof(); i++) { + pcl::PointXYZI point; + input.read((char*)&point.x, 3*sizeof(float)); + input.read((char*)&point.intensity, sizeof(float)); + points->push_back(point); + } + input.close(); + + sensor_msgs::PointCloud2 pc2; + pc2.header.frame_id = "velodyne_link"; + pc2.header.stamp = ros::Time::now(); + points->header = pcl_conversions::toPCL(pc2.header); + + // ed: /velodyne_points_kitti publish + kitti_pub.publish(points); + } + loop_rate.sleep(); + entries_played++; + } + + return 0; +} diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/CMakeLists.txt b/ROS_packages/squeezeseg_cpp_preprocessing/CMakeLists.txt new file mode 100644 index 0000000..e01a4b1 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/CMakeLists.txt @@ -0,0 +1,26 @@ +cmake_minimum_required(VERSION 2.8.3) +project(squeezeseg_cpp_preprocessing) +add_compile_options(-std=c++11) + +find_package(catkin REQUIRED COMPONENTS + pcl_ros + roscpp + sensor_msgs +) + +catkin_package( +# INCLUDE_DIRS include +# LIBRARIES squeezeseg_cpp_preprocessing +# CATKIN_DEPENDS pcl_ros roscpp sensor_msgs +# DEPENDS system_lib +) + +include_directories( + ${catkin_INCLUDE_DIRS} +) + +add_executable(cpp_preprocessing src/cpp_preprocessing.cpp) +add_dependencies(cpp_preprocessing ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) +target_link_libraries(cpp_preprocessing + ${catkin_LIBRARIES} +) diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 new file mode 100644 index 0000000..3965d02 Binary files /dev/null and b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.data-00000-of-00001 differ diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.index b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.index new file mode 100644 index 0000000..085d310 Binary files /dev/null and b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.index differ diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.meta b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.meta new file mode 100644 index 0000000..ac8fefb Binary files /dev/null and b/ROS_packages/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000.meta differ diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/launch/ss_cpp_preprocessing.launch b/ROS_packages/squeezeseg_cpp_preprocessing/launch/ss_cpp_preprocessing.launch new file mode 100644 index 0000000..7212c52 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/launch/ss_cpp_preprocessing.launch @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/package.xml b/ROS_packages/squeezeseg_cpp_preprocessing/package.xml new file mode 100644 index 0000000..5ce4468 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/package.xml @@ -0,0 +1,68 @@ + + + squeezeseg_cpp_preprocessing + 0.0.0 + The squeezeseg_cpp_preprocessing package + + + + + dyros-vehicle + + + + + + TODO + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + catkin + pcl_ros + roscpp + sensor_msgs + pcl_ros + roscpp + sensor_msgs + pcl_ros + roscpp + sensor_msgs + + + + + + + + diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/online.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/online.py new file mode 100755 index 0000000..3ec69c4 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/online.py @@ -0,0 +1,38 @@ +#!/usr/bin/python2 +# -*- coding: utf-8 -*- +''' + #+DESCRITION: online segmentation + #+FROM: github.com/durant35/SqueezeSeg + #+DATE: 2018-07-23-Mon + #+AUTHOR: Edward Im (edwardim@snu.ac.kr) +''' +import argparse +import tensorflow as tf +import rospy + +from segment_node import SegmentNode + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string( + 'checkpoint', '/home/dyros-vehicle/gitrepo/ims_ros/catkin_ws_kinetic/src/squeezeseg_cpp_preprocessing/checkpoint/SqueezeSeg/model.ckpt-23000', + """Path to the model parameter file.""") + +if __name__ == '__main__': + # parse arguments from command line + parser = argparse.ArgumentParser(description='LiDAR point cloud semantic segmentation') + parser.add_argument('--sub_topic', type=str, + help='the pointcloud message topic to be subscribed, default `/ss_filtered`', + default='/ss_filtered') + parser.add_argument('--pub_topic', type=str, + help='the pointcloud message topic to be published, default `/squeeze_seg/points`', + default='/squeeze_seg/points') + + args = parser.parse_args(rospy.myargv()[1:]) + + rospy.init_node('segment_node') + + node = SegmentNode(sub_topic=args.sub_topic, + pub_topic=args.pub_topic, + FLAGS=FLAGS) + diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/segment_node.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/segment_node.py new file mode 100755 index 0000000..6739139 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/segment_node.py @@ -0,0 +1,150 @@ +#!/usr/bin/python2 +# -*- coding: utf-8 -*- +''' + #+DESCRITION: online segmentation + #+FROM: github.com/durant35/SqueezeSeg + #+DATE: 2018-08-08-Wed + #+AUTHOR: Edward Im (edwardim@snu.ac.kr) +''' +import sys +import os.path +import numpy as np +from PIL import Image + +import tensorflow as tf + +import rospy +from sensor_msgs.msg import PointCloud2 +import sensor_msgs.point_cloud2 as pc2 +from sensor_msgs.msg import Image as ImageMsg +from std_msgs.msg import Header +from std_msgs.msg import Int8 + +sys.path.append("/home/dyros-vehicle/gitrepo/ims_ros/catkin_ws_kinetic/src/squeezeseg_cpp_preprocessing/script/squeezeseg") +sys.path.append("./squeezeseg") +from config import * +from nets import SqueezeSeg +from utils.util import * +from utils.clock import Clock + +from imdb import kitti # ed: header added + +def _make_point_field(num_field): + msg_pf1 = pc2.PointField() + msg_pf1.name = np.str('x') + msg_pf1.offset = np.uint32(0) + msg_pf1.datatype = np.uint8(7) + msg_pf1.count = np.uint32(1) + + msg_pf2 = pc2.PointField() + msg_pf2.name = np.str('y') + msg_pf2.offset = np.uint32(4) + msg_pf2.datatype = np.uint8(7) + msg_pf2.count = np.uint32(1) + + msg_pf3 = pc2.PointField() + msg_pf3.name = np.str('z') + msg_pf3.offset = np.uint32(8) + msg_pf3.datatype = np.uint8(7) + msg_pf3.count = np.uint32(1) + + msg_pf4 = pc2.PointField() + msg_pf4.name = np.str('intensity') + msg_pf4.offset = np.uint32(16) + msg_pf4.datatype = np.uint8(7) + msg_pf4.count = np.uint32(1) + + if num_field == 4: + return [msg_pf1, msg_pf2, msg_pf3, msg_pf4] + + msg_pf5 = pc2.PointField() + msg_pf5.name = np.str('label') + msg_pf5.offset = np.uint32(20) + msg_pf5.datatype = np.uint8(4) + msg_pf5.count = np.uint32(1) + + return [msg_pf1, msg_pf2, msg_pf3, msg_pf4, msg_pf5] + + +class SegmentNode(): + """LiDAR point cloud segment ros node""" + + def __init__(self, + sub_topic, pub_topic, FLAGS): + # os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + + self._mc = kitti_squeezeSeg_config() + self._mc.LOAD_PRETRAINED_MODEL = False + self._mc.BATCH_SIZE = 1 # TODO(bichen): fix this hard-coded batch size. + self._model = SqueezeSeg(self._mc) + self._saver = tf.train.Saver(self._model.model_params) + + self._session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) + self._saver.restore(self._session, FLAGS.checkpoint) + + self._sub = rospy.Subscriber("/ss_filtered", PointCloud2, self.point_cloud_callback, queue_size=1) + self._pub = rospy.Publisher(pub_topic, PointCloud2, queue_size=1) + + rospy.spin() + + def point_cloud_callback(self, cloud_msg): + """ + + :param cloud_msg: + :return: + """ + clock = Clock() + # rospy.logwarn("subscribed. width: %d, height: %u, point_step: %d, row_step: %d", + # cloud_msg.width, cloud_msg.height, cloud_msg.point_step, cloud_msg.row_step) + + pc = pc2.read_points(cloud_msg, skip_nans=False, field_names=("x", "y", "z","intensity","d")) + # to conver pc into numpy.ndarray format + np_p = np.array(list(pc)) + + # print("shape : {}".format(np_p.shape)) + + # get depth map + lidar = np_p.reshape(64,512,5) + + # print("{}".format(lidar.shape)) + + lidar_f = lidar.astype(np.float32) + + # to perform prediction + lidar_mask = np.reshape( + (lidar[:, :, 4] > 0), + [self._mc.ZENITH_LEVEL, self._mc.AZIMUTH_LEVEL, 1] + ) + lidar_f = (lidar_f - self._mc.INPUT_MEAN) / self._mc.INPUT_STD + pred_cls = self._session.run( + self._model.pred_cls, + feed_dict={ + self._model.lidar_input: [lidar_f], + self._model.keep_prob: 1.0, + self._model.lidar_mask: [lidar_mask] + } + ) + label = pred_cls[0] + + ## point cloud for SqueezeSeg segments + x = lidar[:, :, 0].reshape(-1) + y = lidar[:, :, 1].reshape(-1) + z = lidar[:, :, 2].reshape(-1) + i = lidar[:, :, 3].reshape(-1) + label = label.reshape(-1) + cloud = np.stack((x, y, z, i, label)) + + + header = Header() + header.stamp = rospy.Time() + header.frame_id = "velodyne_link" + + # point cloud segments + # 4 PointFields as channel description + msg_segment = pc2.create_cloud(header=header, + fields=_make_point_field(cloud.shape[0]), + points=cloud.T) + + # ed: /squeeze_seg/points publish + self._pub.publish(msg_segment) + rospy.loginfo("Point cloud processed. Took %.6f ms.", clock.takeRealTime()) diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/__init__.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/__init__.py new file mode 100644 index 0000000..62da116 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/__init__.py @@ -0,0 +1 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/__init__.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/__init__.py new file mode 100644 index 0000000..d8cae02 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/__init__.py @@ -0,0 +1 @@ +from .kitti_squeezeSeg_config import kitti_squeezeSeg_config diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/config.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/config.py new file mode 100644 index 0000000..76c1799 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/config.py @@ -0,0 +1,136 @@ +#G Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#!/usr/bin/env python +#-*- coding: utf-8 -*- + +"""Base Model configurations""" + +import os +import os.path as osp +import numpy as np +from easydict import EasyDict as edict + +def base_model_config(dataset='KITTI'): + assert dataset.upper()=='KITTI', \ + 'Currently only support KITTI dataset' + + cfg = edict() + + # Dataset used to train/val/test model. Now support KITTI + cfg.DATASET = dataset.upper() + + # classes + cfg.CLASSES = [ + 'unknown', + 'car', + 'van', + 'truck', + 'pedestrian', + 'person_sitting', + 'cyclist', + 'tram', + 'misc', + ] + + # number of classes + cfg.NUM_CLASS = len(cfg.CLASSES) + + # dict from class name to id + cfg.CLS_2_ID = dict(zip(cfg.CLASSES, range(len(cfg.CLASSES)))) + + # loss weight for each class + cfg.CLS_LOSS_WEIGHT = np.array( + [1/20.0, 1.0, 2.0, 3.0, + 8.0, 10.0, 8.0, 2.0, 1.0] + ) + + # rgb color for each class + cfg.CLS_COLOR_MAP = np.array( + [[ 0.00, 0.00, 0.00], + [ 0.12, 0.56, 0.37], + [ 0.66, 0.55, 0.71], + [ 0.58, 0.72, 0.88], + [ 0.25, 0.51, 0.76], + [ 0.98, 0.47, 0.73], + [ 0.40, 0.19, 0.10], + [ 0.87, 0.19, 0.17], + [ 0.13, 0.55, 0.63]] + ) + + # Probability to keep a node in dropout + cfg.KEEP_PROB = 0.5 + + # image width + cfg.IMAGE_WIDTH = 224 + + # image height + cfg.IMAGE_HEIGHT = 224 + + # number of vertical levels + cfg.NUM_LEVEL = 10 + + # number of pie sectors of the field of view + cfg.NUM_SECTOR = 90 + + # maximum distance of a measurement + cfg.MAX_DIST = 100.0 + + # batch size + cfg.BATCH_SIZE = 20 + + # Pixel mean values (BGR order) as a (1, 1, 3) array. Below is the BGR mean + # of VGG16 + cfg.BGR_MEANS = np.array([[[103.939, 116.779, 123.68]]]) + + # Pixel mean values (BGR order) as a (1, 1, 3) array. Below is the BGR mean + # of VGG16 + cfg.RGB_MEANS = np.array([[[123.68, 116.779, 103.939]]]) + + # reduce step size after this many steps + cfg.DECAY_STEPS = 10000 + + # multiply the learning rate by this factor + cfg.LR_DECAY_FACTOR = 0.1 + + # learning rate + cfg.LEARNING_RATE = 0.005 + + # momentum + cfg.MOMENTUM = 0.9 + + # weight decay + cfg.WEIGHT_DECAY = 0.0005 + + # wether to load pre-trained model + cfg.LOAD_PRETRAINED_MODEL = True + + # path to load the pre-trained model + cfg.PRETRAINED_MODEL_PATH = '' + + # print log to console in debug mode + cfg.DEBUG_MODE = False + + # gradients with norm larger than this is going to be clipped. + cfg.MAX_GRAD_NORM = 10.0 + + # Whether to do data augmentation + cfg.DATA_AUGMENTATION = False + + # The range to randomly shift the image widht + cfg.DRIFT_X = 0 + + # The range to randomly shift the image height + cfg.DRIFT_Y = 0 + + # small value used in batch normalization to prevent dividing by 0. The + # default value here is the same with caffe's default value. + cfg.BATCH_NORM_EPSILON = 1e-5 + + # small value used in denominator to prevent division by 0 + cfg.DENOM_EPSILON = 1e-12 + + # capacity for tf.FIFOQueue + cfg.QUEUE_CAPACITY = 80 + + cfg.NUM_ENQUEUE_THREAD = 8 + + return cfg diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/kitti_squeezeSeg_config.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/kitti_squeezeSeg_config.py new file mode 100644 index 0000000..6ebc48f --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/config/kitti_squeezeSeg_config.py @@ -0,0 +1,58 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 08/25/2016 +#-*- coding: utf-8 -*- + +"""Model configuration for pascal dataset""" + +import numpy as np +from .config import base_model_config + +def kitti_squeezeSeg_config(): + """Specify the parameters to tune below.""" + mc = base_model_config('KITTI') + + mc.CLASSES = ['unknown', 'car', 'pedestrian', 'cyclist'] + mc.NUM_CLASS = len(mc.CLASSES) + mc.CLS_2_ID = dict(zip(mc.CLASSES, range(len(mc.CLASSES)))) + mc.CLS_LOSS_WEIGHT = np.array([1/15.0, 1.0, 10.0, 10.0]) + mc.CLS_COLOR_MAP = np.array([[ 0.00, 0.00, 0.00], + [ 0.12, 0.56, 0.37], + [ 0.66, 0.55, 0.71], + [ 0.58, 0.72, 0.88]]) + + mc.BATCH_SIZE = 32 + + + mc.AZIMUTH_LEVEL = 512 # for Sphrerical Projection + mc.ZENITH_LEVEL = 64 # for Sphrerical Projection + + + mc.LCN_HEIGHT = 3 # for Bilateral filter + R-CRF (필터 사이즈) + mc.LCN_WIDTH = 5 # for Bilateral filter + R-CRF (필터 사이즈) + mc.BILATERAL_THETA_A = np.array([.9, .9, .6, .6]) # for Bilateral filter (클래스의 갯수만큼 array) + mc.BILATERAL_THETA_R = np.array([.015, .015, .01, .01]) # for Bilateral filter (클래스의 갯수만큼 array) + mc.BI_FILTER_COEF = 0.1 # for Bilatreal filter + + + mc.RCRF_ITER = 3 # for R-CRF (meanfield inference 반복 횟수) + mc.ANG_THETA_A = np.array([.9, .9, .6, .6]) # for R-CRF + mc.ANG_FILTER_COEF = 0.02 # for R-CRF + + + mc.LEARNING_RATE = 0.01 + mc.CLS_LOSS_COEF = 15.0 # for Loss funtion + mc.WEIGHT_DECAY = 0.0001 # for lr을 주기적으로 변경하는 파라미터 + mc.MAX_GRAD_NORM = 1.0 # for gradient 수동 업데이트를 위한 파라미터 + mc.MOMENTUM = 0.9 # for momentum Optimizer에 사용하는 파라미터 + mc.DECAY_STEPS = 10000 # for learning_rate.exponential_decay() 함수에 사용하는 파라미터 (10000번 학습마다 lr을 낮춘다) + mc.LR_DECAY_FACTOR = 0.5 # for learning_rate.exponential_decay() 함수에 사용하는 파라미터 + + + mc.DATA_AUGMENTATION = True # for imdb.py data y flipping (학습데이터 양을 늘리기 위한 기법 중 하나) + mc.RANDOM_FLIPPING = True # for imdb.py data y flipping + + + # x, y, z, intensity, distance + mc.INPUT_MEAN = np.array([[[10.88, 0.23, -1.04, 0.21, 12.12]]]) + mc.INPUT_STD = np.array([[[11.47, 6.91, 0.86, 0.16, 12.32]]]) + + return mc diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/demo.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/demo.py new file mode 100644 index 0000000..44430c8 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/demo.py @@ -0,0 +1,160 @@ + +#-*- coding: utf-8 -*- +"""Evaluation""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from datetime import datetime +import os.path +import sys +import time +import glob + +import numpy as np +from six.moves import xrange +import tensorflow as tf +from PIL import Image + +from config import * +from imdb import kitti +from utils.util import * +from nets import * + + +# ed: Windows version +IS_WINDOWS=False + + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('gpu', '0', """gpu id.""") + + +if IS_WINDOWS: + # ed: for Windows + tf.app.flags.DEFINE_string( + 'checkpoint', '.\\data\\SqueezeSeg\\model.ckpt-23000', + """Path to the model parameter file.""") + tf.app.flags.DEFINE_string( + 'input_path', '.\\data\\samples\\*', + """Input lidar scan to be detected. Can process glob input such as """ + """./data/samples/*.npy or single input.""") + tf.app.flags.DEFINE_string( + 'out_dir', '.\\data\\samples_out\\', """Directory to dump output.""") +else: + # ed: for Linux + tf.app.flags.DEFINE_string( + 'checkpoint', './data/SqueezeSeg/model.ckpt-23000', + """Path to the model parameter file.""") + tf.app.flags.DEFINE_string( + 'input_path', './data/samples/*', + """Input lidar scan to be detected. Can process glob input such as """ + """./data/samples/*.npy or single input.""") + tf.app.flags.DEFINE_string( + 'out_dir', './data/samples_out/', """Directory to dump output.""") + + + +def _normalize(x): + return (x - x.min())/(x.max() - x.min()) + + + +def detect(): + """Detect LiDAR data.""" + + os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + + with tf.Graph().as_default(): + mc = kitti_squeezeSeg_config() + + # ed: SqueezeNet의 pretrained model weight를 불러올 필요가 없고 + # 바로 SqueezeNet Model Checkpoint를 불러오면 되므로 False로 선언 + mc.LOAD_PRETRAINED_MODEL = False + + + mc.BATCH_SIZE = 1 # TODO(bichen): fix this hard-coded batch size. + + model = SqueezeSeg(mc) + + # ed: 모델 가중치를 불러오는 saver 변수 선언 + saver = tf.train.Saver(model.model_params) + + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: + # ed: 저장된 모델 가중치를 불러옵니다 + saver.restore(sess, FLAGS.checkpoint) + + for f in glob.iglob(FLAGS.input_path): + lidar = np.load(f).astype(np.float32, copy=False)[:, :, :5] + + lidar_mask = np.reshape( + (lidar[:, :, 4] > 0), + [mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1] + ) + + lidar = (lidar - mc.INPUT_MEAN)/mc.INPUT_STD + + pred_cls = sess.run( + model.pred_cls, + feed_dict={ + model.lidar_input:[lidar], + model.keep_prob: 1.0, + model.lidar_mask:[lidar_mask] + } + ) + + # save the data + file_name = f.strip('.npy').split('/')[-1] + + if IS_WINDOWS: + np.save( + os.path.join(FLAGS.out_dir, 'pred_windows.npy'), + pred_cls[0] + ) + else: + np.save( + os.path.join(FLAGS.out_dir, 'pred_'+file_name+'.npy'), + pred_cls[0] + ) + + + # save the plot + # ed: intensity 값을 통해 depth_map을 변수에 저장한다 + depth_map = Image.fromarray( + (255 * _normalize(lidar[:, :, 3])).astype(np.uint8)) + + # ed: 예측된 레이블 포인트들의 이미지 데이터를 label_map 데이터에 저장한다 + label_map = Image.fromarray( + (255 * visualize_seg(pred_cls, mc)[0]).astype(np.uint8)) + + # ed: 위 두 이미지를 합성한다 + blend_map = Image.blend( + depth_map.convert('RGBA'), + label_map.convert('RGBA'), + alpha=0.4 + ) + + # ed: 최종적으로 합성한 이미지를 png 파일로 저장한다 + if IS_WINDOWS: + blend_map.save( + os.path.join(FLAGS.out_dir, 'plot_windows.png')) + else: + blend_map.save( + os.path.join(FLAGS.out_dir, 'plot_'+file_name+'.png')) + + + +def main(argv=None): + if not tf.gfile.Exists(FLAGS.out_dir): + tf.gfile.MakeDirs(FLAGS.out_dir) + + detect() + + print('Detection output written to {}'.format(FLAGS.out_dir)) + + + +if __name__ == '__main__': + tf.app.run() diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval.py new file mode 100644 index 0000000..14d9758 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval.py @@ -0,0 +1,284 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 03/07/2017 +#-*- coding: utf-8 -*- + +"""Evaluation""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from datetime import datetime +import os.path +import sys +import time + +import numpy as np +from six.moves import xrange +import tensorflow as tf + +from config import * +from imdb import kitti +from utils.util import * +from nets import * + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('dataset', 'KITTI', + """Currently support KITTI dataset.""") + +tf.app.flags.DEFINE_string('data_path', '../data', """Root directory of data""") + +tf.app.flags.DEFINE_string('image_set', 'val', + """Can be train, trainval, val, or test""") + +tf.app.flags.DEFINE_string('eval_dir', '../log/eval/', + """Directory where to write event logs """) + +tf.app.flags.DEFINE_string('checkpoint_path', '../log/train/', + """Path to the training checkpoint.""") + +tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 1, + """How often to check if new cpt is saved.""") + +tf.app.flags.DEFINE_boolean('run_once', False, + """Whether to run eval only once.""") + +tf.app.flags.DEFINE_string('net', 'squeezeSeg', + """Neural net architecture.""") + +tf.app.flags.DEFINE_string('gpu', '0', """gpu id.""") + + + +def eval_once(saver, ckpt_path, summary_writer, eval_summary_ops, eval_summary_phs, imdb, model): + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: + # Restores from checkpoint + saver.restore(sess, ckpt_path) + # saver.restore(sess, '/media/dyros-data/gitrepo/ims_ml/SqueezeSeg/log/train/model.ckpt-9999') + + # Assuming model_checkpoint_path looks something like: + # /ckpt_dir/model.ckpt-0, + # extract global_step from it. + global_step = ckpt_path.split('/')[-1].split('-')[-1] + + mc = model.mc + mc.DATA_AUGMENTATION = False + + num_images = len(imdb.image_idx) + + _t = { + 'detect': Timer(), + 'read': Timer(), + 'eval': Timer() + } + + tot_error_rate, tot_rmse, tot_th_correct = 0.0, 0.0, 0.0 + + # class-level metrics + tp_sum = np.zeros(mc.NUM_CLASS) + fn_sum = np.zeros(mc.NUM_CLASS) + fp_sum = np.zeros(mc.NUM_CLASS) + # instance-level metrics + itp_sum = np.zeros(mc.NUM_CLASS) + ifn_sum = np.zeros(mc.NUM_CLASS) + ifp_sum = np.zeros(mc.NUM_CLASS) + # instance-level object matching metrics + otp_sum = np.zeros(mc.NUM_CLASS) + ofn_sum = np.zeros(mc.NUM_CLASS) + ofp_sum = np.zeros(mc.NUM_CLASS) + + # ed: 이 부분이 데이터를 읽어서 evaluation을 하는 코드인듯 + for i in xrange(int(num_images/mc.BATCH_SIZE)): + offset = max((i+1)*mc.BATCH_SIZE - num_images, 0) + + _t['read'].tic() + + # ed: lidar_2d (.npy) 데이터를 불러온다 + lidar_per_batch, lidar_mask_per_batch, label_per_batch, _ \ + = imdb.read_batch(shuffle=False) + + _t['read'].toc() + + _t['detect'].tic() + + # ed: prediction을 하는 코드 + pred_cls = sess.run( + model.pred_cls, + feed_dict={ + model.lidar_input:lidar_per_batch, + model.keep_prob: 1.0, + model.lidar_mask:lidar_mask_per_batch + } + ) + + _t['detect'].toc() + + _t['eval'].tic() + # ed: IOU evalution을 하는 코드 + # Evaluation + iou, tps, fps, fns = evaluate_iou( + label_per_batch[:mc.BATCH_SIZE-offset], # ed: 정답데이터 + pred_cls[:mc.BATCH_SIZE-offset]*np.squeeze(lidar_mask_per_batch[:mc.BATCH_SIZE-offset]), # ed: 예측데이터 + mc.NUM_CLASS + ) + + tp_sum += tps + fn_sum += fns + fp_sum += fps + _t['eval'].toc() + + print ('detect: {:d}/{:d} im_read: {:.3f}s ' + 'detect: {:.3f}s evaluation: {:.3f}s'.format( + (i+1)*mc.BATCH_SIZE-offset, num_images, + _t['read'].average_time/mc.BATCH_SIZE, # im_read : 0.040s + _t['detect'].average_time/mc.BATCH_SIZE, # detect : 0.012s + _t['eval'].average_time/mc.BATCH_SIZE)) # evaluation : 0.002s + + + # ed: Precision, Recall, IOU를 구하는 코드 + ious = tp_sum.astype(np.float)/(tp_sum + fn_sum + fp_sum + mc.DENOM_EPSILON) + pr = tp_sum.astype(np.float)/(tp_sum + fp_sum + mc.DENOM_EPSILON) + re = tp_sum.astype(np.float)/(tp_sum + fn_sum + mc.DENOM_EPSILON) + + + print ('Evaluation summary:') + print (' Timing:') + print (' read: {:.3f}s detect: {:.3f}s'.format( + _t['read'].average_time/mc.BATCH_SIZE, + _t['detect'].average_time/mc.BATCH_SIZE + )) + + + eval_sum_feed_dict = { + eval_summary_phs['Timing/detect']:_t['detect'].average_time/mc.BATCH_SIZE, + eval_summary_phs['Timing/read']:_t['read'].average_time/mc.BATCH_SIZE, + } + + + print (' Accuracy:') + + # ed: for tensorboard + for i in range(1, mc.NUM_CLASS): + print (' {}:'.format(mc.CLASSES[i])) + print ('\tPixel-seg: P: {:.3f}, R: {:.3f}, IoU: {:.3f}'.format( + pr[i], re[i], ious[i])) + + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_iou']] = ious[i] + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_precision']] = pr[i] + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_recall']] = re[i] + + # ed: for tensorboard + eval_summary_str = sess.run(eval_summary_ops, feed_dict=eval_sum_feed_dict) + + # ed: for tensorboard + for sum_str in eval_summary_str: + summary_writer.add_summary(sum_str, global_step) + + summary_writer.flush() + + + + +def evaluate(): + """Evaluate.""" + assert FLAGS.dataset == 'KITTI', \ + 'Currently only supports KITTI dataset' + + os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + + with tf.Graph().as_default() as g: + assert FLAGS.net == 'squeezeSeg', \ + 'Selected neural net architecture not supported: {}'.format(FLAGS.net) + + if FLAGS.net == 'squeezeSeg': + mc = kitti_squeezeSeg_config() + mc.LOAD_PRETRAINED_MODEL = False + mc.BATCH_SIZE = 1 # TODO(bichen): fix this hard-coded batch size. + model = SqueezeSeg(mc) + + imdb = kitti(FLAGS.image_set, FLAGS.data_path, mc) + + eval_summary_ops = [] + eval_summary_phs = {} + + eval_summary_names = [ + 'Timing/read', + 'Timing/detect', + ] + + # ed: 아래 코드들은 tensorboard에서 확인할 수 있다 + for i in range(1, mc.NUM_CLASS): + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_iou') + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_precision') + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_recall') + + + # ed: for tensorboard + for sm in eval_summary_names: + ph = tf.placeholder(tf.float32) + eval_summary_phs[sm] = ph + eval_summary_ops.append(tf.summary.scalar(sm, ph)) + + saver = tf.train.Saver(model.model_params) + + summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g) + + ckpts = set() + + while True: + if FLAGS.run_once: + # When run_once is true, checkpoint_path should point to the exact + # checkpoint file. + eval_once( + saver, FLAGS.checkpoint_path, summary_writer, eval_summary_ops, + eval_summary_phs, imdb, model) + return + + else: + # When run_once is false, checkpoint_path should point to the directory + # that stores checkpoint files. + ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_path) + + if ckpt and ckpt.model_checkpoint_path: + + # ed: ckpt 파일들을 순차적으로 돌면서 eval_once() 함수를 실행시키고 ckpt 파일은 ckpts() 배열에 넣는다 + if ckpt.model_checkpoint_path in ckpts: + # Do not evaluate on the same checkpoint + print ('Wait {:d}s for new checkpoints to be saved ... ' + .format(FLAGS.eval_interval_secs)) + time.sleep(FLAGS.eval_interval_secs) + + else: + ckpts.add(ckpt.model_checkpoint_path) + print ('Evaluating {}...'.format(ckpt.model_checkpoint_path)) + + eval_once( + saver, ckpt.model_checkpoint_path, summary_writer, + eval_summary_ops, eval_summary_phs, imdb, model) + + + else: + print('No checkpoint file found') + + if not FLAGS.run_once: + print ('Wait {:d}s for new checkpoints to be saved ... ' + .format(FLAGS.eval_interval_secs)) + time.sleep(FLAGS.eval_interval_secs) + + + +def main(argv=None): # pylint: disable=unused-argument + if tf.gfile.Exists(FLAGS.eval_dir): + tf.gfile.DeleteRecursively(FLAGS.eval_dir) + + tf.gfile.MakeDirs(FLAGS.eval_dir) + + evaluate() + + + +if __name__ == '__main__': + tf.app.run() diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval_ed.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval_ed.py new file mode 100644 index 0000000..48184d7 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/eval_ed.py @@ -0,0 +1,293 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 03/07/2017 +#-*- coding: utf-8 -*- + +"""Evaluation""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from datetime import datetime +import os.path +import sys +import time + +import numpy as np +from six.moves import xrange +import tensorflow as tf + +from config import * +from imdb import kitti +from utils.util import * +from nets import * + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('dataset', 'KITTI', + """Currently support KITTI dataset.""") + +tf.app.flags.DEFINE_string('data_path', '../data', """Root directory of data""") + +tf.app.flags.DEFINE_string('image_set', 'val', + """Can be train, trainval, val, or test""") + +tf.app.flags.DEFINE_string('eval_dir', '../log/eval/', + """Directory where to write event logs """) + +tf.app.flags.DEFINE_string('checkpoint_path', '../log/train/', + """Path to the training checkpoint.""") + +tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 1, + """How often to check if new cpt is saved.""") + +tf.app.flags.DEFINE_boolean('run_once', False, + """Whether to run eval only once.""") + +tf.app.flags.DEFINE_string('net', 'squeezeSeg', + """Neural net architecture.""") + +tf.app.flags.DEFINE_string('gpu', '0', """gpu id.""") + + + +def eval_once(saver, ckpt_path, summary_writer, eval_summary_ops, eval_summary_phs, imdb, model): + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: + # Restores from checkpoint + saver.restore(sess, ckpt_path) + # saver.restore(sess, '/media/dyros-data/gitrepo/ims_ml/SqueezeSeg/log/train/model.ckpt-9999') + + # Assuming model_checkpoint_path looks something like: + # /ckpt_dir/model.ckpt-0, + # extract global_step from it. + global_step = ckpt_path.split('/')[-1].split('-')[-1] + + mc = model.mc + mc.DATA_AUGMENTATION = False + + num_images = len(imdb.image_idx) + + _t = { + 'detect': Timer(), + 'read': Timer(), + 'eval': Timer() + } + + tot_error_rate, tot_rmse, tot_th_correct = 0.0, 0.0, 0.0 + + # class-level metrics + tp_sum = np.zeros(mc.NUM_CLASS) + fn_sum = np.zeros(mc.NUM_CLASS) + fp_sum = np.zeros(mc.NUM_CLASS) + # instance-level metrics + itp_sum = np.zeros(mc.NUM_CLASS) + ifn_sum = np.zeros(mc.NUM_CLASS) + ifp_sum = np.zeros(mc.NUM_CLASS) + # instance-level object matching metrics + otp_sum = np.zeros(mc.NUM_CLASS) + ofn_sum = np.zeros(mc.NUM_CLASS) + ofp_sum = np.zeros(mc.NUM_CLASS) + + # ed: 이 부분이 데이터를 읽어서 evaluation을 하는 코드인듯 + for i in xrange(int(num_images/mc.BATCH_SIZE)): + offset = max((i+1)*mc.BATCH_SIZE - num_images, 0) + + _t['read'].tic() + + # ed: lidar_2d (.npy) 데이터를 불러온다 + lidar_per_batch, lidar_mask_per_batch, label_per_batch, _ \ + = imdb.read_batch(shuffle=False) + + _t['read'].toc() + + _t['detect'].tic() + + # ed: prediction을 하는 코드 + pred_cls = sess.run( + model.pred_cls, + feed_dict={ + model.lidar_input:lidar_per_batch, + model.keep_prob: 1.0, + model.lidar_mask:lidar_mask_per_batch + } + ) + _t['detect'].toc() + + + # ed: code added + out_dir = "./data/sample_out_ed" + if not tf.gfile.Exists(out_dir): + tf.gfile.MakeDirs(out_dir) + + np.save(os.path.join(out_dir, 'pred_'+ imdb.image_idx[i] +'.npy'), + pred_cls[0]) + + + _t['eval'].tic() + # ed: IOU evalution을 하는 코드 + # Evaluation + iou, tps, fps, fns = evaluate_iou( + label_per_batch[:mc.BATCH_SIZE-offset], # ed: 정답데이터 + pred_cls[:mc.BATCH_SIZE-offset]*np.squeeze(lidar_mask_per_batch[:mc.BATCH_SIZE-offset]), # ed: 예측데이터 + mc.NUM_CLASS + ) + + tp_sum += tps + fn_sum += fns + fp_sum += fps + _t['eval'].toc() + + print ('detect: {:d}/{:d} im_read: {:.3f}s ' + 'detect: {:.3f}s evaluation: {:.3f}s'.format( + (i+1)*mc.BATCH_SIZE-offset, num_images, + _t['read'].average_time/mc.BATCH_SIZE, # im_read : 0.040s + _t['detect'].average_time/mc.BATCH_SIZE, # detect : 0.012s + _t['eval'].average_time/mc.BATCH_SIZE)) # evaluation : 0.002s + + + # ed: Precision, Recall, IOU를 구하는 코드 + ious = tp_sum.astype(np.float)/(tp_sum + fn_sum + fp_sum + mc.DENOM_EPSILON) + pr = tp_sum.astype(np.float)/(tp_sum + fp_sum + mc.DENOM_EPSILON) + re = tp_sum.astype(np.float)/(tp_sum + fn_sum + mc.DENOM_EPSILON) + + + print ('Evaluation summary:') + print (' Timing:') + print (' read: {:.3f}s detect: {:.3f}s'.format( + _t['read'].average_time/mc.BATCH_SIZE, + _t['detect'].average_time/mc.BATCH_SIZE + )) + + + eval_sum_feed_dict = { + eval_summary_phs['Timing/detect']:_t['detect'].average_time/mc.BATCH_SIZE, + eval_summary_phs['Timing/read']:_t['read'].average_time/mc.BATCH_SIZE, + } + + + print (' Accuracy:') + + # ed: for tensorboard + for i in range(1, mc.NUM_CLASS): + print (' {}:'.format(mc.CLASSES[i])) + print ('\tPixel-seg: P: {:.3f}, R: {:.3f}, IoU: {:.3f}'.format( + pr[i], re[i], ious[i])) + + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_iou']] = ious[i] + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_precision']] = pr[i] + eval_sum_feed_dict[ + eval_summary_phs['Pixel_seg_accuracy/'+mc.CLASSES[i]+'_recall']] = re[i] + + # ed: for tensorboard + eval_summary_str = sess.run(eval_summary_ops, feed_dict=eval_sum_feed_dict) + + # ed: for tensorboard + for sum_str in eval_summary_str: + summary_writer.add_summary(sum_str, global_step) + + summary_writer.flush() + + + + +def evaluate(): + """Evaluate.""" + assert FLAGS.dataset == 'KITTI', \ + 'Currently only supports KITTI dataset' + + os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + + with tf.Graph().as_default() as g: + assert FLAGS.net == 'squeezeSeg', \ + 'Selected neural net architecture not supported: {}'.format(FLAGS.net) + + if FLAGS.net == 'squeezeSeg': + mc = kitti_squeezeSeg_config() + mc.LOAD_PRETRAINED_MODEL = False + mc.BATCH_SIZE = 1 # TODO(bichen): fix this hard-coded batch size. + model = SqueezeSeg(mc) + + imdb = kitti(FLAGS.image_set, FLAGS.data_path, mc) + + eval_summary_ops = [] + eval_summary_phs = {} + + eval_summary_names = [ + 'Timing/read', + 'Timing/detect', + ] + + # ed: 아래 코드들은 tensorboard에서 확인할 수 있다 + for i in range(1, mc.NUM_CLASS): + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_iou') + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_precision') + eval_summary_names.append('Pixel_seg_accuracy/'+mc.CLASSES[i]+'_recall') + + + # ed: for tensorboard + for sm in eval_summary_names: + ph = tf.placeholder(tf.float32) + eval_summary_phs[sm] = ph + eval_summary_ops.append(tf.summary.scalar(sm, ph)) + + saver = tf.train.Saver(model.model_params) + + summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g) + + ckpts = set() + + while True: + if FLAGS.run_once: + # When run_once is true, checkpoint_path should point to the exact + # checkpoint file. + eval_once( + saver, FLAGS.checkpoint_path, summary_writer, eval_summary_ops, + eval_summary_phs, imdb, model) + return + + else: + # When run_once is false, checkpoint_path should point to the directory + # that stores checkpoint files. + ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_path) + + if ckpt and ckpt.model_checkpoint_path: + + # ed: ckpt 파일들을 순차적으로 돌면서 eval_once() 함수를 실행시키고 ckpt 파일은 ckpts() 배열에 넣는다 + if ckpt.model_checkpoint_path in ckpts: + # Do not evaluate on the same checkpoint + print ('Wait {:d}s for new checkpoints to be saved ... ' + .format(FLAGS.eval_interval_secs)) + time.sleep(FLAGS.eval_interval_secs) + + else: + ckpts.add(ckpt.model_checkpoint_path) + print ('Evaluating {}...'.format(ckpt.model_checkpoint_path)) + + eval_once( + saver, ckpt.model_checkpoint_path, summary_writer, + eval_summary_ops, eval_summary_phs, imdb, model) + + + else: + print('No checkpoint file found') + + if not FLAGS.run_once: + print ('Wait {:d}s for new checkpoints to be saved ... ' + .format(FLAGS.eval_interval_secs)) + time.sleep(FLAGS.eval_interval_secs) + + + +def main(argv=None): # pylint: disable=unused-argument + if tf.gfile.Exists(FLAGS.eval_dir): + tf.gfile.DeleteRecursively(FLAGS.eval_dir) + + tf.gfile.MakeDirs(FLAGS.eval_dir) + + evaluate() + + + +if __name__ == '__main__': + tf.app.run() diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/__init__.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/__init__.py new file mode 100644 index 0000000..2fe1000 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/__init__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python +#-*- coding: utf-8 -*- + +from .kitti import kitti diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/imdb.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/imdb.py new file mode 100644 index 0000000..937a103 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/imdb.py @@ -0,0 +1,144 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/27/2017 +#-*- coding: utf-8 -*- + +"""The data base wrapper class""" +import os +import random +import shutil + +import numpy as np + +from utils.util import * + +class imdb(object): + """Image database.""" + + def __init__(self, name, mc): + self._name = name + self._image_set = [] + self._image_idx = [] + self._data_root_path = [] + self.mc = mc + + # batch reader + self._perm_idx = [] + self._cur_idx = 0 + + @property + def name(self): + return self._name + + @property + def image_idx(self): + return self._image_idx + + @property + def image_set(self): + return self._image_set + + @property + def data_root_path(self): + return self._data_root_path + + def _shuffle_image_idx(self): + self._perm_idx = [self._image_idx[i] for i in + np.random.permutation(np.arange(len(self._image_idx)))] + self._cur_idx = 0 + + # ed: Input (.npy) 파일들을 SqueezeSeg에 맞게 불러오는 함수인듯 + def read_batch(self, shuffle=True): + """Read a batch of lidar data including labels. Data formated as numpy array + of shape: height x width x {x, y, z, intensity, range, label}. + + Args: + shuffle: whether or not to shuffle the dataset + + Returns: + lidar_per_batch: LiDAR input. Shape: batch x height x width x 5. + lidar_mask_per_batch: LiDAR mask, 0 for missing data and 1 otherwise. + Shape: batch x height x width x 1. + label_per_batch: point-wise labels. Shape: batch x height x width. + weight_per_batch: loss weights for different classes. Shape: + batch x height x width + """ + mc = self.mc + + if shuffle: + if self._cur_idx + mc.BATCH_SIZE >= len(self._image_idx): + self._shuffle_image_idx() + batch_idx = self._perm_idx[self._cur_idx:self._cur_idx+mc.BATCH_SIZE] + self._cur_idx += mc.BATCH_SIZE + + else: + if self._cur_idx + mc.BATCH_SIZE >= len(self._image_idx): + batch_idx = self._image_idx[self._cur_idx:] \ + + self._image_idx[:self._cur_idx + mc.BATCH_SIZE-len(self._image_idx)] + self._cur_idx += mc.BATCH_SIZE - len(self._image_idx) + + else: + batch_idx = self._image_idx[self._cur_idx:self._cur_idx+mc.BATCH_SIZE] + self._cur_idx += mc.BATCH_SIZE + + + lidar_per_batch = [] + lidar_mask_per_batch = [] + label_per_batch = [] + weight_per_batch = [] + + for idx in batch_idx: + + # ed: .npy 데이터형식이 pickle보다 30배이상 빠르기 때문에 사용한다고 한다 + # load data + # loading from npy is 30x faster than loading from pickle + record = np.load(self._lidar_2d_path_at(idx)).astype(np.float32, copy=False) + + # ed: data augmentation을 위한 코드 (데이터의 양을 늘리기 위한 기법 중 하나) + if mc.DATA_AUGMENTATION: + if mc.RANDOM_FLIPPING: + if np.random.rand() > 0.5: + # ed: y축 순서를 좌표를 반전시키는 코드 + # flip y + record = record[:, ::-1, :] + + # ed: y축 부호 또한 (-)로 반전시킨다 + record[:, :, 1] *= -1 + + + lidar = record[:, :, :5] # x, y, z, intensity, depth + + lidar_mask = np.reshape( + (lidar[:, :, 4] > 0), + [mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1] + ) + + + # ed: INPUT_MEAN, INPUT_STD 모두 하드코딩된 값이다. 이 값들로 lidar 데이터를 정규화시킨다 + # mc.INPUT_MEAN = np.array([[[10.88, 0.23, -1.04, 0.21, 12.12]]]) + # mc.INPUT_STD = np.array([[[11.47, 6.91, 0.86, 0.16, 12.32]]]) + # normalize + lidar = (lidar - mc.INPUT_MEAN)/mc.INPUT_STD + + label = record[:, :, 5] + weight = np.zeros(label.shape) + + + # ed: weight 변수에 pedestrian, cyclist의 가중치를 더 크게 설정하는 코드 + # mc.CLASSES = ['unknown', 'car', 'pedestrian', 'cyclist'] + # mc.NUM_CLASS = 4 + # mc.CLC_LOSS_WEIGHT = np.array([1/15.0, 1.0, 10.0, 10.0]) + for l in range(mc.NUM_CLASS): + weight[label==l] = mc.CLS_LOSS_WEIGHT[int(l)] + + + # Append all the data + lidar_per_batch.append(lidar) + lidar_mask_per_batch.append(lidar_mask) + label_per_batch.append(label) + weight_per_batch.append(weight) + + + return np.array(lidar_per_batch), np.array(lidar_mask_per_batch), \ + np.array(label_per_batch), np.array(weight_per_batch) + + def evaluate_detections(self): + raise NotImplementedError diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/kitti.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/kitti.py new file mode 100644 index 0000000..f6e4da3 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/imdb/kitti.py @@ -0,0 +1,53 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/27/2017 +#-*- coding: utf-8 -*- + +"""Image data base class for kitti""" + +import os +import numpy as np +import subprocess + +from .imdb import imdb + + +class kitti(imdb): + def __init__(self, image_set, data_path, mc): + imdb.__init__(self, 'kitti_'+image_set, mc) + + self._image_set = image_set + self._data_root_path = data_path + self._lidar_2d_path = os.path.join(self._data_root_path, 'lidar_2d') + self._gta_2d_path = os.path.join(self._data_root_path, 'gta') + + # a list of string indices of images in the directory + self._image_idx = self._load_image_set_idx() + # a dict of image_idx -> [[cx, cy, w, h, cls_idx]]. x,y,w,h are not divided by + # the image width and height + + ## batch reader ## + self._perm_idx = None + self._cur_idx = 0 + # TODO(bichen): add a random seed as parameter + self._shuffle_image_idx() + + + def _load_image_set_idx(self): + image_set_file = os.path.join( + self._data_root_path, 'ImageSet', self._image_set+'.txt') + assert os.path.exists(image_set_file), \ + 'File does not exist: {}'.format(image_set_file) + + with open(image_set_file) as f: + image_idx = [x.strip() for x in f.readlines()] + return image_idx + + + def _lidar_2d_path_at(self, idx): + if idx[:4] == 'gta_': + lidar_2d_path = os.path.join(self._gta_2d_path, idx+'.npy') + else: + lidar_2d_path = os.path.join(self._lidar_2d_path, idx+'.npy') + + assert os.path.exists(lidar_2d_path), \ + 'File does not exist: {}'.format(lidar_2d_path) + return lidar_2d_path diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/__init__.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/__init__.py new file mode 100644 index 0000000..1d240c5 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/__init__.py @@ -0,0 +1,3 @@ +#-*- coding: utf-8 -*- + +from .squeezeSeg import SqueezeSeg diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/squeezeSeg.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/squeezeSeg.py new file mode 100644 index 0000000..d9b1da2 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nets/squeezeSeg.py @@ -0,0 +1,189 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#-*- coding: utf-8 -*- + +"""SqueezeSeg model""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +import joblib +from utils import util +import numpy as np +import tensorflow as tf +from nn_skeleton import ModelSkeleton + + +class SqueezeSeg(ModelSkeleton): + def __init__(self, mc, gpu_id=0): + with tf.device('/gpu:{}'.format(gpu_id)): + ModelSkeleton.__init__(self, mc) + + self._add_forward_graph() # ed: SqueezeNet Model + self._add_output_graph() # pred_prob, pred_cls + self._add_loss_graph() # cls_loss, total_loss + self._add_train_graph() # + self._add_viz_graph() # label_to_show, depth_image_to_show, pred_image_to_show + self._add_summary_ops() # + + + def _add_forward_graph(self): + """NN architecture.""" + + mc = self.mc + + if mc.LOAD_PRETRAINED_MODEL: + assert tf.gfile.Exists(mc.PRETRAINED_MODEL_PATH), \ + 'Cannot find pretrained model at the given path:' \ + ' {}'.format(mc.PRETRAINED_MODEL_PATH) + + self.caffemodel_weight = joblib.load(mc.PRETRAINED_MODEL_PATH) + + conv1 = self._conv_layer( + 'conv1', self.lidar_input, filters=64, size=3, stride=2, + padding='SAME', freeze=False, xavier=True) + conv1_skip = self._conv_layer( + 'conv1_skip', self.lidar_input, filters=64, size=1, stride=1, + padding='SAME', freeze=False, xavier=True) + pool1 = self._pooling_layer( + 'pool1', conv1, size=3, stride=2, padding='SAME') + + fire2 = self._fire_layer( + 'fire2', pool1, s1x1=16, e1x1=64, e3x3=64, freeze=False) + fire3 = self._fire_layer( + 'fire3', fire2, s1x1=16, e1x1=64, e3x3=64, freeze=False) + pool3 = self._pooling_layer( + 'pool3', fire3, size=3, stride=2, padding='SAME') + + fire4 = self._fire_layer( + 'fire4', pool3, s1x1=32, e1x1=128, e3x3=128, freeze=False) + fire5 = self._fire_layer( + 'fire5', fire4, s1x1=32, e1x1=128, e3x3=128, freeze=False) + pool5 = self._pooling_layer( + 'pool5', fire5, size=3, stride=2, padding='SAME') + + fire6 = self._fire_layer( + 'fire6', pool5, s1x1=48, e1x1=192, e3x3=192, freeze=False) + fire7 = self._fire_layer( + 'fire7', fire6, s1x1=48, e1x1=192, e3x3=192, freeze=False) + fire8 = self._fire_layer( + 'fire8', fire7, s1x1=64, e1x1=256, e3x3=256, freeze=False) + fire9 = self._fire_layer( + 'fire9', fire8, s1x1=64, e1x1=256, e3x3=256, freeze=False) + + + # Deconvolation + # ed: deconv를 하면서 _fuse 변수에서 기존에 fire5, fire3, conv1, conv1_skip 같은 이전 layer들을 더해준다 + fire10 = self._fire_deconv( + 'fire_deconv10', fire9, s1x1=64, e1x1=128, e3x3=128, factors=[1, 2], + stddev=0.1) + fire10_fuse = tf.add(fire10, fire5, name='fure10_fuse') + + fire11 = self._fire_deconv( + 'fire_deconv11', fire10_fuse, s1x1=32, e1x1=64, e3x3=64, factors=[1, 2], + stddev=0.1) + fire11_fuse = tf.add(fire11, fire3, name='fire11_fuse') + + fire12 = self._fire_deconv( + 'fire_deconv12', fire11_fuse, s1x1=16, e1x1=32, e3x3=32, factors=[1, 2], + stddev=0.1) + fire12_fuse = tf.add(fire12, conv1, name='fire12_fuse') + + fire13 = self._fire_deconv( + 'fire_deconv13', fire12_fuse, s1x1=16, e1x1=32, e3x3=32, factors=[1, 2], + stddev=0.1) + fire13_fuse = tf.add(fire13, conv1_skip, name='fire13_fuse') + + drop13 = tf.nn.dropout(fire13_fuse, self.keep_prob, name='drop13') + + conv14 = self._conv_layer( + 'conv14_prob', drop13, filters=mc.NUM_CLASS, size=3, stride=1, + padding='SAME', relu=False, stddev=0.1) + + + # ed: bilateral Filter를 적용한 weight 생성 + bilateral_filter_weights = self._bilateral_filter_layer( + 'bilateral_filter', self.lidar_input[:, :, :, :3], # x, y, z + thetas=[mc.BILATERAL_THETA_A, mc.BILATERAL_THETA_R], + sizes=[mc.LCN_HEIGHT, mc.LCN_WIDTH], stride=1) + + + # ed: Recurrent CRF을 적용한 output_prob 변수를 생성한다 + self.output_prob = self._recurrent_crf_layer( + 'recurrent_crf', conv14, bilateral_filter_weights, + sizes=[mc.LCN_HEIGHT, mc.LCN_WIDTH], num_iterations=mc.RCRF_ITER, + padding='SAME') + + + # ed: fire module layer 1층을 만드는 함수 + def _fire_layer(self, layer_name, inputs, s1x1, e1x1, e3x3, stddev=0.001, + freeze=False): + """Fire layer constructor. + + Args: + layer_name: layer name + inputs: input tensor + s1x1: number of 1x1 filters in squeeze layer. + e1x1: number of 1x1 filters in expand layer. + e3x3: number of 3x3 filters in expand layer. + freeze: if true, do not train parameters in this layer. + Returns: + fire layer operation. + """ + + sq1x1 = self._conv_layer( + layer_name+'/squeeze1x1', inputs, filters=s1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + ex1x1 = self._conv_layer( + layer_name+'/expand1x1', sq1x1, filters=e1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + ex3x3 = self._conv_layer( + layer_name+'/expand3x3', sq1x1, filters=e3x3, size=3, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + return tf.concat([ex1x1, ex3x3], 3, name=layer_name+'/concat') + + + + + def _fire_deconv(self, layer_name, inputs, s1x1, e1x1, e3x3, + factors=[1, 2], freeze=False, stddev=0.001): + """Fire deconvolution layer constructor. + + Args: + layer_name: layer name + inputs: input tensor + s1x1: number of 1x1 filters in squeeze layer. + e1x1: number of 1x1 filters in expand layer. + e3x3: number of 3x3 filters in expand layer. + factors: spatial upsampling factors. + freeze: if true, do not train parameters in this layer. + Returns: + fire layer operation. + """ + + assert len(factors) == 2,'factors should be an array of size 2' + + ksize_h = factors[0] * 2 - factors[0] % 2 + ksize_w = factors[1] * 2 - factors[1] % 2 + + sq1x1 = self._conv_layer( + layer_name+'/squeeze1x1', inputs, filters=s1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + deconv = self._deconv_layer( + layer_name+'/deconv', sq1x1, filters=s1x1, size=[ksize_h, ksize_w], + stride=factors, padding='SAME', init='bilinear') + + ex1x1 = self._conv_layer( + layer_name+'/expand1x1', deconv, filters=e1x1, size=1, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + ex3x3 = self._conv_layer( + layer_name+'/expand3x3', deconv, filters=e3x3, size=3, stride=1, + padding='SAME', freeze=freeze, stddev=stddev) + + return tf.concat([ex1x1, ex3x3], 3, name=layer_name+'/concat') diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nn_skeleton.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nn_skeleton.py new file mode 100644 index 0000000..699ad09 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/nn_skeleton.py @@ -0,0 +1,1009 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +#-*- coding: utf-8 -*- + +"""Neural network model base class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +from utils import util +import numpy as np +import tensorflow as tf + + +def _variable_on_device(name, shape, initializer, trainable=True): + """Helper to create a Variable. + + Args: + name: name of the variable + shape: list of ints + initializer: initializer for Variable + + Returns: + Variable Tensor + """ + # TODO(bichen): fix the hard-coded data type below + dtype = tf.float32 + + if not callable(initializer): + var = tf.get_variable(name, initializer=initializer, trainable=trainable) + else: + var = tf.get_variable( + name, shape, initializer=initializer, dtype=dtype, trainable=trainable) + + return var + + + + +def _variable_with_weight_decay(name, shape, wd, initializer, trainable=True): + """Helper to create an initialized Variable with weight decay. + + Note that the Variable is initialized with a truncated normal distribution. + A weight decay is added only if one is specified. + + Args: + name: name of the variable + shape: list of ints + wd: add L2Loss weight decay multiplied by this float. If None, weight + decay is not added for this Variable. + + Returns: + Variable Tensor + """ + + var = _variable_on_device(name, shape, initializer, trainable) + + if wd is not None and trainable: + weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + return var + + + +class ModelSkeleton: + """Base class of NN detection models.""" + def __init__(self, mc): + self.mc = mc + + # a scalar tensor in range (0, 1]. Usually set to 0.5 in training phase and + # 1.0 in evaluation phase + self.ph_keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + # projected lidar points on a 2D spherical surface + self.ph_lidar_input = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 5], + name='lidar_input' + ) + + # A tensor where an element is 1 if the corresponding cell contains an + # valid lidar measurement. Or if the data is missing, then mark it as 0. + self.ph_lidar_mask = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + name='lidar_mask') + + # A tensor where each element contains the class of each pixel + self.ph_label = tf.placeholder( + tf.int32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + name='label') + + # weighted loss for different classes + self.ph_loss_weight = tf.placeholder( + tf.float32, [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + name='loss_weight') + + # define a FIFOqueue for pre-fetching data + self.q = tf.FIFOQueue( + capacity=mc.QUEUE_CAPACITY, + dtypes=[tf.float32, tf.float32, tf.float32, tf.int32, tf.float32], + shapes=[[], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 5], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL], + [mc.BATCH_SIZE, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL]] + ) + + # ed: tensorflow의 FIFOQueue라는 함수를 사용해서 여러 placeholder들을 묶어서 처리하는듯하다 + # train.py:129 : enqueue_op에 feed_dict가 들어간다 + self.enqueue_op = self.q.enqueue( + [self.ph_keep_prob, self.ph_lidar_input, self.ph_lidar_mask, + self.ph_label, self.ph_loss_weight] + ) + + self.keep_prob, self.lidar_input, self.lidar_mask, self.label, \ + self.loss_weight = self.q.dequeue() + + # model parameters + self.model_params = [] + + # model size counter + self.model_size_counter = [] # array of tuple of layer name, parameter size + + # flop counter + self.flop_counter = [] # array of tuple of layer name, flop number + + # activation counter + self.activation_counter = [] # array of tuple of layer name, output activations + self.activation_counter.append(('input', mc.AZIMUTH_LEVEL*mc.ZENITH_LEVEL*3)) + + + def _add_forward_graph(self): + """NN architecture specification.""" + raise NotImplementedError + + + def _add_output_graph(self): + """Define how to intepret output.""" + mc = self.mc + + with tf.variable_scope('interpret_output') as scope: + # ed: self.output_prob : SqueezeSeg Model에서 마지막 변수 + self.prob = tf.multiply( + tf.nn.softmax(self.output_prob, dim=-1), self.lidar_mask, + name='pred_prob') + + # ed: axis=3 이므로 단순한 scalar 값이 나오는게 아니라 행렬이 출력된다 + self.pred_cls = tf.argmax(self.prob, axis=3, name='pred_cls') + + # add summaries + for cls_id, cls in enumerate(mc.CLASSES): + self._activation_summary(self.prob[:, :, :, cls_id], 'prob_'+cls) + + + + def _add_loss_graph(self): + """Define the loss operation.""" + mc = self.mc + + with tf.variable_scope('cls_loss') as scope: + self.cls_loss = tf.identity( + tf.reduce_sum( + tf.nn.sparse_softmax_cross_entropy_with_logits( + labels=tf.reshape(self.label, (-1, )), + logits=tf.reshape(self.output_prob, (-1, mc.NUM_CLASS)) + ) \ + * tf.reshape(self.lidar_mask, (-1, )) \ + * tf.reshape(self.loss_weight, (-1, )) + ) / tf.reduce_sum(self.lidar_mask)*mc.CLS_LOSS_COEF, + name='cls_loss') + + tf.add_to_collection('losses', self.cls_loss) + + + # ed: self.loss = cls_loss + weight_decay loss를 합친 변수 + # add above losses as well as weight decay losses to form the total loss + self.loss = tf.add_n(tf.get_collection('losses'), name='total_loss') + + # ed: for tensorboard + # add loss summaries + # _add_loss_summaries(self.loss) + tf.summary.scalar(self.cls_loss.op.name, self.cls_loss) + tf.summary.scalar(self.loss.op.name, self.loss) + + + + def _add_train_graph(self): + """Define the training operation.""" + mc = self.mc + + self.global_step = tf.Variable(0, name='global_step', trainable=False) + + # ed: learning rate를 설정하는 코드인듯 + # 가변적으로 learning rate가 변한다 + lr = tf.train.exponential_decay(mc.LEARNING_RATE, + self.global_step, + mc.DECAY_STEPS, + mc.LR_DECAY_FACTOR, + staircase=True) + + tf.summary.scalar('learning_rate', lr) + + # ed: momentum optimizer를 사용하는구만, 이게뭐지? + opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=mc.MOMENTUM) + + + # ed: Tensorflow가 알아서 계산해주는 Gradient 위에 조작된 값을 덮어쓰기 위해 사용하는 코드 + # self.loss의 weights들을 minimize() 함수를 사용하는 대신 임의로 덮어쓰기 위한 코드인듯 + grads_vars = opt.compute_gradients(self.loss, tf.trainable_variables()) + + # ed: gradient clipping : exploding gradient를 방지하기 위해 최대 가중치값을 limit으로 정해놓는다 + with tf.variable_scope('clip_gradient') as scope: + for i, (grad, var) in enumerate(grads_vars): + grads_vars[i] = (tf.clip_by_norm(grad, mc.MAX_GRAD_NORM), var) + + apply_gradient_op = opt.apply_gradients(grads_vars, global_step=self.global_step) + + + with tf.control_dependencies([apply_gradient_op]): + # ed: no operation은 뭐징 + # 위에 모든것들을 한꺼번에 실행시키기 위한 empty Variable을 선언하는듯하다 + self.train_op = tf.no_op(name='train') + + + + def _add_viz_graph(self): + """Define the visualization operation.""" + mc = self.mc + + self.label_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 3], + name='label_to_show' + ) + + self.depth_image_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 1], + name='depth_image_to_show' + ) + + self.pred_image_to_show = tf.placeholder( + tf.float32, [None, mc.ZENITH_LEVEL, mc.AZIMUTH_LEVEL, 3], + name='pred_image_to_show' + ) + + # ed: tensorboard에서 이미지를 표시해주는 코드인듯 + self.show_label = tf.summary.image('label_to_show', + self.label_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + self.show_depth_img = tf.summary.image('depth_image_to_show', + self.depth_image_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + self.show_pred = tf.summary.image('pred_image_to_show', + self.pred_image_to_show, collections='image_summary', + max_outputs=mc.BATCH_SIZE) + + + # ed: tensorboard에서 요약창에 띄울 데이터를 만들기 위한 함수인듯 + def _add_summary_ops(self): + """Add extra summary operations.""" + mc = self.mc + + iou_summary_placeholders = [] + iou_summary_ops = [] + + for cls in mc.CLASSES: + ph = tf.placeholder(tf.float32, name=cls+'_iou') + iou_summary_placeholders.append(ph) + iou_summary_ops.append( + tf.summary.scalar('Eval/'+cls+'_iou', ph, collections='eval_summary') + ) + + self.iou_summary_placeholders = iou_summary_placeholders + self.iou_summary_ops = iou_summary_ops + + + + # ed: conv + batch nomarlization layer를 만들어주는 함수 (SqueezeSeg에서는 안쓰인다) (not used) + def _conv_bn_layer( + self, inputs, conv_param_name, bn_param_name, scale_param_name, filters, + size, stride, padding='SAME', freeze=False, relu=True, + conv_with_bias=False, stddev=0.001): + """ Convolution + BatchNorm + [relu] layer. Batch mean and var are treated + as constant. Weights have to be initialized from a pre-trained model or + restored from a checkpoint. + + Args: + inputs: input tensor + conv_param_name: name of the convolution parameters + bn_param_name: name of the batch normalization parameters + scale_param_name: name of the scale parameters + filters: number of output filters. + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + xavier: whether to use xavier weight initializer or not. + relu: whether to use relu or not. + conv_with_bias: whether or not add bias term to the convolution output. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + mc = self.mc + + with tf.variable_scope(conv_param_name) as scope: + channels = inputs.get_shape()[3] + + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + kernel_val = np.transpose(cw[conv_param_name][0], [2,3,1,0]) + if conv_with_bias: + bias_val = cw[conv_param_name][1] + mean_val = cw[bn_param_name][0] + var_val = cw[bn_param_name][1] + gamma_val = cw[scale_param_name][0] + beta_val = cw[scale_param_name][1] + else: + kernel_val = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + if conv_with_bias: + bias_val = tf.constant_initializer(0.0) + mean_val = tf.constant_initializer(0.0) + var_val = tf.constant_initializer(1.0) + gamma_val = tf.constant_initializer(1.0) + beta_val = tf.constant_initializer(0.0) + + # re-order the caffe kernel with shape [out, in, h, w] -> tf kernel with + # shape [h, w, in, out] + kernel = _variable_with_weight_decay( + 'kernels', shape=[size, size, int(channels), filters], + wd=mc.WEIGHT_DECAY, initializer=kernel_val, trainable=(not freeze)) + + self.model_params += [kernel] + + if conv_with_bias: + biases = _variable_on_device('biases', [filters], bias_val, + trainable=(not freeze)) + self.model_params += [biases] + + gamma = _variable_on_device('gamma', [filters], gamma_val, + trainable=(not freeze)) + beta = _variable_on_device('beta', [filters], beta_val, + trainable=(not freeze)) + mean = _variable_on_device('mean', [filters], mean_val, trainable=False) + var = _variable_on_device('var', [filters], var_val, trainable=False) + + self.model_params += [gamma, beta, mean, var] + + conv = tf.nn.conv2d( + inputs, kernel, [1, 1, stride, 1], padding=padding, + name='convolution') + + if conv_with_bias: + conv = tf.nn.bias_add(conv, biases, name='bias_add') + + conv = tf.nn.batch_normalization( + conv, mean=mean, variance=var, offset=beta, scale=gamma, + variance_epsilon=mc.BATCH_NORM_EPSILON, name='batch_norm') + + self.model_size_counter.append( + (conv_param_name, (1+size*size*int(channels))*filters) + ) + + out_shape = conv.get_shape().as_list() + num_flops = \ + (1+2*int(channels)*size*size)*filters*out_shape[1]*out_shape[2] + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + self.flop_counter.append((conv_param_name, num_flops)) + + self.activation_counter.append( + (conv_param_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + if relu: + return tf.nn.relu(conv) + else: + return conv + + # ed: Convolution Net 1층을 만들어주는 함수 + def _conv_layer( + self, layer_name, inputs, filters, size, stride, padding='SAME', + freeze=False, xavier=False, relu=True, stddev=0.001, bias_init_val=0.0): + """Convolutional layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + filters: number of output filters. + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + xavier: whether to use xavier weight initializer or not. + relu: whether to use relu or not. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + + mc = self.mc + use_pretrained_param = False + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + if layer_name in cw: + kernel_val = np.transpose(cw[layer_name][0], [2,3,1,0]) + bias_val = cw[layer_name][1] + # check the shape + if (kernel_val.shape == + (size, size, inputs.get_shape().as_list()[-1], filters)) \ + and (bias_val.shape == (filters, )): + use_pretrained_param = True + else: + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + else: + print ('Cannot find {} in the pretrained model. Use randomly initialized ' + 'parameters'.format(layer_name)) + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + channels = inputs.get_shape()[3] + + # re-order the caffe kernel with shape [out, in, h, w] -> tf kernel with + # shape [h, w, in, out] + if use_pretrained_param: + if mc.DEBUG_MODE: + print ('Using pretrained model for {}'.format(layer_name)) + kernel_init = tf.constant(kernel_val , dtype=tf.float32) + bias_init = tf.constant(bias_val, dtype=tf.float32) + elif xavier: + kernel_init = tf.contrib.layers.xavier_initializer_conv2d() + bias_init = tf.constant_initializer(bias_init_val) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(bias_init_val) + + kernel = _variable_with_weight_decay( + 'kernels', shape=[size, size, int(channels), filters], + wd=mc.WEIGHT_DECAY, initializer=kernel_init, trainable=(not freeze)) + + biases = _variable_on_device('biases', [filters], bias_init, + trainable=(not freeze)) + self.model_params += [kernel, biases] + + conv = tf.nn.conv2d( + inputs, kernel, [1, 1, stride, 1], padding=padding, + name='convolution') + conv_bias = tf.nn.bias_add(conv, biases, name='bias_add') + + if relu: + out = tf.nn.relu(conv_bias, 'relu') + else: + out = conv_bias + + self.model_size_counter.append( + (layer_name, (1+size*size*int(channels))*filters) + ) + + out_shape = out.get_shape().as_list() + + num_flops = \ + (1+2*int(channels)*size*size)*filters*out_shape[1]*out_shape[2] + + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append( + (layer_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + return out + + + # ed: Deconvolution Net 1층을 만들어주는 함수 + def _deconv_layer( + self, layer_name, inputs, filters, size, stride, padding='SAME', + freeze=False, init='trunc_norm', relu=True, stddev=0.001): + """Deconvolutional layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + filters: number of output filters. + size: kernel size. An array of size 2 or 1. + stride: stride. An array of size 2 or 1. + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + freeze: if true, then do not train the parameters in this layer. + init: how to initialize kernel weights. Now accept 'xavier', + 'trunc_norm', 'bilinear' + relu: whether to use relu or not. + stddev: standard deviation used for random weight initializer. + Returns: + A convolutional layer operation. + """ + + assert len(size) == 1 or len(size) == 2, \ + 'size should be a scalar or an array of size 2.' + assert len(stride) == 1 or len(stride) == 2, \ + 'stride should be a scalar or an array of size 2.' + assert init == 'xavier' or init == 'bilinear' or init == 'trunc_norm', \ + 'initi mode not supported {}'.format(init) + + if len(size) == 1: + size_h, size_w = size[0], size[0] + else: + size_h, size_w = size[0], size[1] + + if len(stride) == 1: + stride_h, stride_w = stride[0], stride[0] + else: + stride_h, stride_w = stride[0], stride[1] + + mc = self.mc + # TODO(bichen): Currently do not support pretrained parameters for deconv + # layer. + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + in_height = int(inputs.get_shape()[1]) + in_width = int(inputs.get_shape()[2]) + channels = int(inputs.get_shape()[3]) + + if init == 'xavier': + kernel_init = tf.contrib.layers.xavier_initializer_conv2d() + bias_init = tf.constant_initializer(0.0) + elif init == 'bilinear': + assert size_h == 1, 'Now only support size_h=1' + assert channels == filters, \ + 'In bilinear interporlation mode, input channel size and output' \ + 'filter size should be the same' + assert stride_h == 1, \ + 'In bilinear interpolation mode, stride_h should be 1' + + kernel_init = np.zeros( + (size_h, size_w, channels, channels), + dtype=np.float32) + + factor_w = (size_w + 1)//2 + assert factor_w == stride_w, \ + 'In bilinear interpolation mode, stride_w == factor_w' + + center_w = (factor_w - 1) if (size_w % 2 == 1) else (factor_w - 0.5) + og_w = np.reshape(np.arange(size_w), (size_h, -1)) + up_kernel = (1 - np.abs(og_w - center_w)/factor_w) + for c in range(channels): + kernel_init[:, :, c, c] = up_kernel + + bias_init = tf.constant_initializer(0.0) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(0.0) + + # Kernel layout for deconv layer: [H_f, W_f, O_c, I_c] where I_c is the + # input channel size. It should be the same as the channel size of the + # input tensor. + kernel = _variable_with_weight_decay( + 'kernels', shape=[size_h, size_w, filters, channels], + wd=mc.WEIGHT_DECAY, initializer=kernel_init, trainable=(not freeze)) + biases = _variable_on_device( + 'biases', [filters], bias_init, trainable=(not freeze)) + self.model_params += [kernel, biases] + + # TODO(bichen): fix this + deconv = tf.nn.conv2d_transpose( + inputs, kernel, + [mc.BATCH_SIZE, stride_h*in_height, stride_w*in_width, filters], + [1, stride_h, stride_w, 1], padding=padding, + name='deconv') + deconv_bias = tf.nn.bias_add(deconv, biases, name='bias_add') + + if relu: + out = tf.nn.relu(deconv_bias, 'relu') + else: + out = deconv_bias + + self.model_size_counter.append( + (layer_name, (1+size_h*size_w*channels)*filters) + ) + out_shape = out.get_shape().as_list() + num_flops = \ + (1+2*channels*size_h*size_w)*filters*out_shape[1]*out_shape[2] + if relu: + num_flops += 2*filters*out_shape[1]*out_shape[2] + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append( + (layer_name, out_shape[1]*out_shape[2]*out_shape[3]) + ) + + return out + + + # ed: Pooling Layer 1층을 만들어주는 함수 + def _pooling_layer( + self, layer_name, inputs, size, stride, padding='SAME'): + """Pooling layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + size: kernel size. + stride: stride + padding: 'SAME' or 'VALID'. See tensorflow doc for detailed description. + Returns: + A pooling layer operation. + """ + + with tf.variable_scope(layer_name) as scope: + out = tf.nn.max_pool(inputs, + ksize=[1, size, size, 1], + strides=[1, 1, stride, 1], + padding=padding) + activation_size = np.prod(out.get_shape().as_list()[1:]) + self.activation_counter.append((layer_name, activation_size)) + return out + + + # ed: Fully Connected Layer 1층을 만들어주는 함수 + def _fc_layer( + self, layer_name, inputs, hiddens, flatten=False, relu=True, + xavier=False, stddev=0.001, bias_init_val=0.0): + """Fully connected layer operation constructor. + + Args: + layer_name: layer name. + inputs: input tensor + hiddens: number of (hidden) neurons in this layer. + flatten: if true, reshape the input 4D tensor of shape + (batch, height, weight, channel) into a 2D tensor with shape + (batch, -1). This is used when the input to the fully connected layer + is output of a convolutional layer. + relu: whether to use relu or not. + xavier: whether to use xavier weight initializer or not. + stddev: standard deviation used for random weight initializer. + Returns: + A fully connected layer operation. + """ + mc = self.mc + + use_pretrained_param = False + if mc.LOAD_PRETRAINED_MODEL: + cw = self.caffemodel_weight + if layer_name in cw: + use_pretrained_param = True + kernel_val = cw[layer_name][0] + bias_val = cw[layer_name][1] + + if mc.DEBUG_MODE: + print('Input tensor shape to {}: {}'.format(layer_name, inputs.get_shape())) + + with tf.variable_scope(layer_name) as scope: + input_shape = inputs.get_shape().as_list() + if flatten: + dim = input_shape[1]*input_shape[2]*input_shape[3] + inputs = tf.reshape(inputs, [-1, dim]) + if use_pretrained_param: + try: + # check the size before layout transform + assert kernel_val.shape == (hiddens, dim), \ + 'kernel shape error at {}'.format(layer_name) + kernel_val = np.reshape( + np.transpose( + np.reshape( + kernel_val, # O x (C*H*W) + (hiddens, input_shape[3], input_shape[1], input_shape[2]) + ), # O x C x H x W + (2, 3, 1, 0) + ), # H x W x C x O + (dim, -1) + ) # (H*W*C) x O + # check the size after layout transform + assert kernel_val.shape == (dim, hiddens), \ + 'kernel shape error at {}'.format(layer_name) + except: + # Do not use pretrained parameter if shape doesn't match + use_pretrained_param = False + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + else: + dim = input_shape[1] + if use_pretrained_param: + try: + kernel_val = np.transpose(kernel_val, (1,0)) + assert kernel_val.shape == (dim, hiddens), \ + 'kernel shape error at {}'.format(layer_name) + except: + use_pretrained_param = False + print ('Shape of the pretrained parameter of {} does not match, ' + 'use randomly initialized parameter'.format(layer_name)) + + if use_pretrained_param: + if mc.DEBUG_MODE: + print ('Using pretrained model for {}'.format(layer_name)) + kernel_init = tf.constant(kernel_val, dtype=tf.float32) + bias_init = tf.constant(bias_val, dtype=tf.float32) + elif xavier: + kernel_init = tf.contrib.layers.xavier_initializer() + bias_init = tf.constant_initializer(bias_init_val) + else: + kernel_init = tf.truncated_normal_initializer( + stddev=stddev, dtype=tf.float32) + bias_init = tf.constant_initializer(bias_init_val) + + weights = _variable_with_weight_decay( + 'weights', shape=[dim, hiddens], wd=mc.WEIGHT_DECAY, + initializer=kernel_init) + biases = _variable_on_device('biases', [hiddens], bias_init) + self.model_params += [weights, biases] + + outputs = tf.nn.bias_add(tf.matmul(inputs, weights), biases) + if relu: + outputs = tf.nn.relu(outputs, 'relu') + + # count layer stats + self.model_size_counter.append((layer_name, (dim+1)*hiddens)) + + num_flops = 2 * dim * hiddens + hiddens + if relu: + num_flops += 2*hiddens + self.flop_counter.append((layer_name, num_flops)) + + self.activation_counter.append((layer_name, hiddens)) + + return outputs + + + # ed: Recurrent CRF layer 1층을 만들어주는 함수 + def _recurrent_crf_layer( + self, layer_name, inputs, bilateral_filters, sizes=[3, 5], + num_iterations=1, padding='SAME'): + """Recurrent conditional random field layer. Iterative meanfield inference is + implemented as a reccurent neural network. + + Args: + layer_name: layer name + inputs: input tensor with shape [batch_size, zenith, azimuth, num_class]. + bilateral_filters: filter weight with shape + [batch_size, zenith, azimuth, sizes[0]*size[1]-1]. + sizes: size of the local region to be filtered. + num_iterations: number of meanfield inferences. + padding: padding strategy + Returns: + outputs: tensor with shape [batch_size, zenith, azimuth, num_class]. + """ + assert num_iterations >= 1, 'number of iterations should >= 1' + + mc = self.mc + + # ed: layer_name : recurrent_crf + with tf.variable_scope(layer_name) as scope: + # initialize compatibilty matrices + compat_kernel_init = tf.constant( + np.reshape( + np.ones((mc.NUM_CLASS, mc.NUM_CLASS)) - np.identity(mc.NUM_CLASS), + [1, 1, mc.NUM_CLASS, mc.NUM_CLASS] + ), + dtype=tf.float32 + ) + + bi_compat_kernel = _variable_on_device( + name='bilateral_compatibility_matrix', + shape=[1, 1, mc.NUM_CLASS, mc.NUM_CLASS], + initializer=compat_kernel_init*mc.BI_FILTER_COEF, + trainable=True + ) + + self._activation_summary(bi_compat_kernel, 'bilateral_compat_mat') + + angular_compat_kernel = _variable_on_device( + name='angular_compatibility_matrix', + shape=[1, 1, mc.NUM_CLASS, mc.NUM_CLASS], + initializer=compat_kernel_init*mc.ANG_FILTER_COEF, + trainable=True + ) + + self._activation_summary(angular_compat_kernel, 'angular_compat_mat') + + self.model_params += [bi_compat_kernel, angular_compat_kernel] + + condensing_kernel = tf.constant( + util.condensing_matrix(sizes[0], sizes[1], mc.NUM_CLASS), + dtype=tf.float32, + name='condensing_kernel' + ) + + angular_filters = tf.constant( + util.angular_filter_kernel( + sizes[0], sizes[1], mc.NUM_CLASS, mc.ANG_THETA_A**2), + dtype=tf.float32, + name='angular_kernel' + ) + + bi_angular_filters = tf.constant( + util.angular_filter_kernel( + sizes[0], sizes[1], mc.NUM_CLASS, mc.BILATERAL_THETA_A**2), + dtype=tf.float32, + name='bi_angular_kernel' + ) + + for it in range(num_iterations): + unary = tf.nn.softmax( + inputs, dim=-1, name='unary_term_at_iter_{}'.format(it)) + + ang_output, bi_output = self._locally_connected_layer( + 'message_passing_iter_{}'.format(it), unary, + bilateral_filters, angular_filters, bi_angular_filters, + condensing_kernel, sizes=sizes, + padding=padding + ) + + # 1x1 convolution as compatibility transform + ang_output = tf.nn.conv2d( + ang_output, angular_compat_kernel, strides=[1, 1, 1, 1], + padding='SAME', name='angular_compatibility_transformation') + + self._activation_summary( + ang_output, 'ang_transfer_iter_{}'.format(it)) + + bi_output = tf.nn.conv2d( + bi_output, bi_compat_kernel, strides=[1, 1, 1, 1], padding='SAME', + name='bilateral_compatibility_transformation') + + self._activation_summary( + bi_output, 'bi_transfer_iter_{}'.format(it)) + + pairwise = tf.add(ang_output, bi_output, + name='pairwise_term_at_iter_{}'.format(it)) + + outputs = tf.add(unary, pairwise, + name='energy_at_iter_{}'.format(it)) + + inputs = outputs + + return outputs + + + + def _locally_connected_layer( + self, layer_name, inputs, bilateral_filters, + angular_filters, bi_angular_filters, condensing_kernel, sizes=[3, 5], + padding='SAME'): + """Locally connected layer with non-trainable filter parameters) + + Args: + layer_name: layer name + inputs: input tensor with shape + [batch_size, zenith, azimuth, num_class]. + bilateral_filters: bilateral filter weight with shape + [batch_size, zenith, azimuth, sizes[0]*size[1]-1]. + angular_filters: angular filter weight with shape + [sizes[0], sizes[1], in_channel, in_channel]. + condensing_kernel: tensor with shape + [size[0], size[1], num_class, (sizes[0]*size[1]-1)*num_class] + sizes: size of the local region to be filtered. + padding: padding strategy + Returns: + ang_output: output tensor filtered by anguler filter with shape + [batch_size, zenith, azimuth, num_class]. + bi_output: output tensor filtered by bilateral filter with shape + [batch_size, zenith, azimuth, num_class]. + """ + assert padding=='SAME', 'only support SAME padding strategy' + assert sizes[0] % 2 == 1 and sizes[1] % 2 == 1, \ + 'Currently only support odd filter size.' + + mc = self.mc + size_z, size_a = sizes + pad_z, pad_a = size_z//2, size_a//2 # ed: not used + half_filter_dim = (size_z*size_a)//2 # not used + batch, zenith, azimuth, in_channel = inputs.shape.as_list() + + with tf.variable_scope(layer_name) as scope: + # message passing + ang_output = tf.nn.conv2d( + inputs, angular_filters, [1, 1, 1, 1], padding=padding, + name='angular_filtered_term' + ) + + bi_ang_output = tf.nn.conv2d( + inputs, bi_angular_filters, [1, 1, 1, 1], padding=padding, + name='bi_angular_filtered_term' + ) + + condensed_input = tf.reshape( + tf.nn.conv2d( + inputs*self.lidar_mask, condensing_kernel, [1, 1, 1, 1], padding=padding, + name='condensed_prob_map' + ), + [batch, zenith, azimuth, size_z*size_a-1, in_channel] + ) + + bi_output = tf.multiply( + tf.reduce_sum(condensed_input*bilateral_filters, axis=3), + self.lidar_mask, + name='bilateral_filtered_term' + ) + bi_output *= bi_ang_output + + return ang_output, bi_output + + + # ed: bilateral filter가 적용된 layer 1층을 만드는 함수 + def _bilateral_filter_layer( + self, layer_name, inputs, thetas=[0.9, 0.01], sizes=[3, 5], stride=1, + padding='SAME'): + """Computing pairwise energy with a bilateral filter for CRF. + + Args: + layer_name: layer name + inputs: input tensor with shape [batch_size, zenith, azimuth, 2] where the + last 2 elements are intensity and range of a lidar point. + thetas: theta parameter for bilateral filter. + sizes: filter size for zenith and azimuth dimension. + strides: kernel strides. + padding: padding. + Returns: + out: bilateral filter weight output with size + [batch_size, zenith, azimuth, sizes[0]*sizes[1]-1, num_class]. Each + [b, z, a, :, cls] represents filter weights around the center position + for each class. + """ + + assert padding == 'SAME', 'currently only supports "SAME" padding stategy' + assert stride == 1, 'currently only supports striding of 1' + assert sizes[0] % 2 == 1 and sizes[1] % 2 == 1, \ + 'Currently only support odd filter size.' + + mc = self.mc + theta_a, theta_r = thetas + size_z, size_a = sizes + pad_z, pad_a = size_z//2, size_a//2 # ed: not used + half_filter_dim = (size_z*size_a)//2 # not used + batch, zenith, azimuth, in_channel = inputs.shape.as_list() + + # assert in_channel == 1, 'Only support input channel == 1' + + with tf.variable_scope(layer_name) as scope: + condensing_kernel = tf.constant( + util.condensing_matrix(size_z, size_a, in_channel), + dtype=tf.float32, + name='condensing_kernel' + ) + + condensed_input = tf.nn.conv2d( + inputs, condensing_kernel, [1, 1, stride, 1], padding=padding, + name='condensed_input' + ) + + # diff_intensity = tf.reshape( + # inputs[:, :, :], [batch, zenith, azimuth, 1]) \ + # - condensed_input[:, :, :, ::in_channel] + + diff_x = tf.reshape( + inputs[:, :, :, 0], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 0::in_channel] + diff_y = tf.reshape( + inputs[:, :, :, 1], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 1::in_channel] + diff_z = tf.reshape( + inputs[:, :, :, 2], [batch, zenith, azimuth, 1]) \ + - condensed_input[:, :, :, 2::in_channel] + + bi_filters = [] + + for cls in range(mc.NUM_CLASS): + theta_a = mc.BILATERAL_THETA_A[cls] + theta_r = mc.BILATERAL_THETA_R[cls] + + bi_filter = tf.exp(-(diff_x**2+diff_y**2+diff_z**2)/2/theta_r**2) + + bi_filters.append(bi_filter) + + out = tf.transpose( + tf.stack(bi_filters), + [1, 2, 3, 4, 0], + name='bilateral_filter_weights' + ) + + return out + + + def _activation_summary(self, x, layer_name): + """Helper to create summaries for activations. + + Args: + x: layer output tensor + layer_name: name of the layer + Returns: + nothing + """ + with tf.variable_scope('activation_summary') as scope: + tf.summary.histogram(layer_name, x) + tf.summary.scalar(layer_name+'/sparsity', tf.nn.zero_fraction(x)) + tf.summary.scalar(layer_name+'/average', tf.reduce_mean(x)) + tf.summary.scalar(layer_name+'/max', tf.reduce_max(x)) + tf.summary.scalar(layer_name+'/min', tf.reduce_min(x)) diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/train.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/train.py new file mode 100644 index 0000000..c0a4fbc --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/train.py @@ -0,0 +1,278 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 08/25/2016 +#-*- coding: utf-8 -*- + +"""Train""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from datetime import datetime +import os.path +import sys +import time + +import math +import numpy as np +from six.moves import xrange +import tensorflow as tf +import threading + +from config import * +from imdb import kitti +from utils.util import * +from nets import * + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('dataset', 'KITTI', + """Currently only support KITTI dataset.""") + +tf.app.flags.DEFINE_string('data_path', '', """Root directory of data""") + +tf.app.flags.DEFINE_string('image_set', 'train', + """ Can be train, trainval, val, or test""") + +tf.app.flags.DEFINE_string('train_dir', '/tmp/bichen/logs/squeezeseg/train', + """Directory where to write event logs """ + """and checkpoint.""") + +tf.app.flags.DEFINE_integer('max_steps', 1000000, + """Maximum number of batches to run.""") + +tf.app.flags.DEFINE_string('net', 'squeezeSeg', + """Neural net architecture. """) + +tf.app.flags.DEFINE_string('pretrained_model_path', '', + """Path to the pretrained model.""") + +tf.app.flags.DEFINE_integer('summary_step', 50, + """Number of steps to save summary.""") + +tf.app.flags.DEFINE_integer('checkpoint_step', 1000, + """Number of steps to save summary.""") + +tf.app.flags.DEFINE_string('gpu', '0', """gpu id.""") + + +def train(): + """Train SqueezeSeg model""" + assert FLAGS.dataset == 'KITTI', \ + 'Currently only support KITTI dataset' + + os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu + + with tf.Graph().as_default(): + assert FLAGS.net == 'squeezeSeg', \ + 'Selected neural net architecture not supported: {}'.format(FLAGS.net) + + if FLAGS.net == 'squeezeSeg': + mc = kitti_squeezeSeg_config() + # ed: SqueezeSeg을 본격적으로 training 하기 전에 전이학습을 위해 SqueezeNet의 pretrained_model을 불러오는듯 + mc.PRETRAINED_MODEL_PATH = FLAGS.pretrained_model_path + model = SqueezeSeg(mc) + + imdb = kitti(FLAGS.image_set, FLAGS.data_path, mc) + + # ed: Model의 여러 정보를 저장하기 위한 코드 + # save model size, flops, activations by layers + with open(os.path.join(FLAGS.train_dir, 'model_metrics.txt'), 'w') as f: + f.write('Number of parameter by layer:\n') + + # ed: parameter size를 기록하는 코드 + count = 0 + for c in model.model_size_counter: + f.write('\t{}: {}\n'.format(c[0], c[1])) + count += c[1] + f.write('\ttotal: {}\n'.format(count)) + + # ed: output activation이 정확히 뭔지 모르겠지만 그걸 기록해주는 코드 + count = 0 + f.write('\nActivation size by layer:\n') + for c in model.activation_counter: + f.write('\t{}: {}\n'.format(c[0], c[1])) + count += c[1] + f.write('\ttotal: {}\n'.format(count)) + + + # ed: Flop Count를 기록해주는 코드 + count = 0 + f.write('\nNumber of flops by layer:\n') + for c in model.flop_counter: + f.write('\t{}: {}\n'.format(c[0], c[1])) + count += c[1] + f.write('\ttotal: {}\n'.format(count)) + + f.close() + + + print ('Model statistics saved to {}.'.format( + os.path.join(FLAGS.train_dir, 'model_metrics.txt'))) + + + + def enqueue(sess, coord): + with coord.stop_on_exception(): + while not coord.should_stop(): + # ed: 여기가 Input (.npy) 파일들을 처리하는 코드인듯 + # read batch input + lidar_per_batch, lidar_mask_per_batch, label_per_batch,\ + weight_per_batch = imdb.read_batch() + + feed_dict = { + model.ph_keep_prob: mc.KEEP_PROB, + model.ph_lidar_input: lidar_per_batch, + model.ph_lidar_mask: lidar_mask_per_batch, + model.ph_label: label_per_batch, + model.ph_loss_weight: weight_per_batch, + } + + # ed: placeholder에 데이터를 넣어주는 코드 + # FIFOQueue라는 함수를 사용해서 여러 input들을 병렬적으로 처리하는듯 + sess.run(model.enqueue_op, feed_dict=feed_dict) + + + saver = tf.train.Saver(tf.all_variables()) + summary_op = tf.summary.merge_all() + init = tf.initialize_all_variables() + + # ed: sess 초기화 + sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) + sess.run(init) + + summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph) + + coord = tf.train.Coordinator() + enq_threads = [] + + for _ in range(mc.NUM_ENQUEUE_THREAD): + eqth = threading.Thread(target=enqueue, args=[sess, coord]) + eqth.start() + enq_threads.append(eqth) + + # ed: 특정 시간이상 연산이 초과되면 assertion을 내주기 위한 코드인듯 + run_options = tf.RunOptions(timeout_in_ms=60000) + + try: + # ed: 학습하는 코드 + for step in xrange(FLAGS.max_steps): + start_time = time.time() + + # ed: 50번 마다 실행되고 마지막 step에서 실행되는 제어문 + if step % FLAGS.summary_step == 0 or step == FLAGS.max_steps-1: + op_list = [ + model.lidar_input, model.lidar_mask, model.label, model.train_op, + model.loss, model.pred_cls, summary_op + ] + + # ed: 50번 step과 마지막 step에만 실행되는 코드, 학습이 끝나고 성능을 알아보기 위해 실행하는 듯하다 + # 이런식으로 Queue를 사용해서 일괄적으로 placeholder들에 feeding을 할 수 있는듯하다 + lidar_per_batch, lidar_mask_per_batch, label_per_batch, \ + _, loss_value, pred_cls, summary_str = sess.run(op_list, + options=run_options) + + # ed: label, pred_cls에 의해서 class가 정해진 곳에 colorize를 해주는 코드 + label_image = visualize_seg(label_per_batch[:6, :, :], mc) + pred_image = visualize_seg(pred_cls[:6, :, :], mc) + + # ed: IOU를 계산하는 코드 + # Run evaluation on the batch + ious, _, _, _ = evaluate_iou( + label_per_batch, pred_cls * np.squeeze(lidar_mask_per_batch), + mc.NUM_CLASS) + + + feed_dict = {} + + # Assume that class-0 is the background class + for i in range(1, mc.NUM_CLASS): + feed_dict[model.iou_summary_placeholders[i]] = ious[i] + + iou_summary_list = sess.run(model.iou_summary_ops[1:], feed_dict) + + + # ed: 여기서 summary 형식으로 visualize 해주는건 뭘까? ==> tensorboard를 위한 코드 + # Run visualization + viz_op_list = [model.show_label, model.show_depth_img, model.show_pred] + + viz_summary_list = sess.run( + viz_op_list, + feed_dict={ + model.depth_image_to_show: lidar_per_batch[:6, :, :, [4]], + model.label_to_show: label_image, + model.pred_image_to_show: pred_image, + } + ) + + # Add summaries + summary_writer.add_summary(summary_str, step) + + for sum_str in iou_summary_list: + summary_writer.add_summary(sum_str, step) + + for viz_sum in viz_summary_list: + summary_writer.add_summary(viz_sum, step) + + # force tensorflow to synchronise summaries + summary_writer.flush() + + else: + # ed: 실제 학습을 하는 코드 + _, loss_value = sess.run( + [model.train_op, model.loss], options=run_options) + + + # ed: 알고리즘 수행시간 체크 + duration = time.time() - start_time + + # ed: 여러 loss value 중 invalid한 값이 없어야 한다 + assert not np.isnan(loss_value), \ + 'Model diverged. Total loss: {}, conf_loss: {}, bbox_loss: {}, ' \ + 'class_loss: {}'.format(loss_value, conf_loss, bbox_loss, + class_loss) + + + # ed: 10번에 한번씩 print 해주는 코드 + if step % 10 == 0: + num_images_per_step = mc.BATCH_SIZE + images_per_sec = num_images_per_step / duration + sec_per_batch = float(duration) + + format_str = ('%s: step %d, loss = %.2f (%.1f images/sec; %.3f ' + 'sec/batch)') + + print (format_str % (datetime.now(), step, loss_value, + images_per_sec, sec_per_batch)) + sys.stdout.flush() + + + # ed: default=1000 번에 한번씩 model의 가중치를 저장한다 + # Save the model checkpoint periodically. + if step % FLAGS.checkpoint_step == 0 or step == FLAGS.max_steps-1: + checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt') + saver.save(sess, checkpoint_path, global_step=step) + + + except Exception, e: + coord.request_stop(e) + + finally: + coord.request_stop() + sess.run(model.q.close(cancel_pending_enqueues=True)) + # Wait for all the threads to terminate. + coord.join(enq_threads) + + + +def main(argv=None): # pylint: disable=unused-argument + if tf.gfile.Exists(FLAGS.train_dir): + tf.gfile.DeleteRecursively(FLAGS.train_dir) + + tf.gfile.MakeDirs(FLAGS.train_dir) + + train() + + +if __name__ == '__main__': + # ed: run main() code + tf.app.run() diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/__init__.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/__init__.py new file mode 100644 index 0000000..62da116 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/__init__.py @@ -0,0 +1 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/clock.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/clock.py new file mode 100644 index 0000000..c20b039 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/clock.py @@ -0,0 +1,29 @@ +#! /usr/bin/python2 +# -*- coding: utf-8 -*- +""" +Clock function to take running time following Segmatch. +""" + +import datetime + +class Clock(object): + def __init__(self): + self.kSecondsToMiliseconds = 1000.0 + self.kMicrosecondsToMiliseconds = 0.001 + self.start() + + def start(self): + self.real_time_start_ = datetime.datetime.now() + + def takeTime(self): + seconds = (datetime.datetime.now() - self.real_time_start_).seconds + useconds = (datetime.datetime.now() - self.real_time_start_).microseconds + + self.real_time_ms_ = (seconds*self.kSecondsToMiliseconds + useconds*self.kMicrosecondsToMiliseconds) + 0.5 + + def getRealTime(self): + return self.real_time_ms_ + + def takeRealTime(self): + self.takeTime() + return self.getRealTime() diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/util.py b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/util.py new file mode 100644 index 0000000..07a9b83 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/util.py @@ -0,0 +1,191 @@ +# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017 +# -*- coding: utf-8 -*- + +"""Utility functions.""" + +import numpy as np +import time + + +# ed: label, pred_cls에서 class가 정해진 좌표에 colorize(=visualize)를 해서 리턴하는 함수 +def visualize_seg(label_map, mc, one_hot=False): + if one_hot: + label_map = np.argmax(label_map, axis=-1) + + out = np.zeros( + (label_map.shape[0], label_map.shape[1], label_map.shape[2], 3)) + + for l in range(1, mc.NUM_CLASS): + out[label_map==l, :] = mc.CLS_COLOR_MAP[l] + + return out + + + +def bgr_to_rgb(ims): + """Convert a list of images from BGR format to RGB format.""" + out = [] + for im in ims: + out.append(im[:,:,::-1]) + return out + + + +class Timer(object): + def __init__(self): + self.total_time = 0.0 + self.calls = 0 + self.start_time = 0.0 + self.duration = 0.0 + self.average_time = 0.0 + + def tic(self): + self.start_time = time.time() + + def toc(self, average=True): + self.duration = time.time() - self.start_time + self.total_time += self.duration + self.calls += 1 + self.average_time = self.total_time/self.calls + if average: + return self.average_time + else: + return self.duration + + + +def conf_error_rate_at_thresh_fn(mask, conf, thresh): + return np.mean((conf>thresh) != mask) + + + +def rmse_fn(diff, nnz): + return np.sqrt(np.sum(diff**2)/nnz) + + + +def abs_accuracy_at_thresh_fn(diff, thresh, mask): + return np.sum((np.abs(diff) < thresh)*mask)/float(np.sum(mask)) + + + +def rel_accuracy_at_thresh_fn(pred_ogm, gt_ogm, mask, thresh): + return np.sum( + mask * (np.maximum(pred_ogm, gt_ogm) / + np.minimum(gt_ogm, pred_ogm) < thresh) + )/float(np.sum(mask)) + + +# ed: IOU를 계산하는 함수 +def evaluate_iou(label, pred, n_class, epsilon=1e-12): + """Evaluation script to compute pixel level IoU. + + Args: + label: N-d array of shape [batch, W, H], where each element is a class + index. + pred: N-d array of shape [batch, W, H], the each element is the predicted + class index. + n_class: number of classes + epsilon: a small value to prevent division by 0 + + Returns: + IoU: array of lengh n_class, where each element is the average IoU for this + class. + tps: same shape as IoU, where each element is the number of TP for each + class. + fps: same shape as IoU, where each element is the number of FP for each + class. + fns: same shape as IoU, where each element is the number of FN for each + class. + """ + + assert label.shape == pred.shape, \ + 'label and pred shape mismatch: {} vs {}'.format( + label.shape, pred.shape) + + ious = np.zeros(n_class) + tps = np.zeros(n_class) + fns = np.zeros(n_class) + fps = np.zeros(n_class) + + # ed: 실제 IOU가 아니라 point-wise labeling 이므로 + # 아래처럼 true positive, false negative...등을 클래스별로 구하는듯하다 + for cls_id in range(n_class): + tp = np.sum(pred[label == cls_id] == cls_id) + fp = np.sum(label[pred == cls_id] != cls_id) + fn = np.sum(pred[label == cls_id] != cls_id) + + ious[cls_id] = tp/(tp+fn+fp+epsilon) + tps[cls_id] = tp + fps[cls_id] = fp + fns[cls_id] = fn + + return ious, tps, fps, fns + + + +def condensing_matrix(size_z, size_a, in_channel): + assert size_z % 2 == 1 and size_a % 2==1, \ + 'size_z and size_a should be odd number' + + half_filter_dim = (size_z*size_a)//2 + + # moving neigboring pixels to channel dimension + nbr2ch_mat = np.zeros( + (size_z, size_a, in_channel, size_z*size_a*in_channel), + dtype=np.float32 + ) + + for z in range(size_z): + for a in range(size_a): + for ch in range(in_channel): + nbr2ch_mat[z, a, ch, z*(size_a*in_channel) + a*in_channel + ch] = 1 + + # exclude the channel index corresponding to the center position + nbr2ch_mat = np.concatenate( + [nbr2ch_mat[:, :, :, :in_channel*half_filter_dim], + nbr2ch_mat[:, :, :, in_channel*(half_filter_dim+1):]], + axis=3 + ) + + assert nbr2ch_mat.shape == \ + (size_z, size_a, in_channel, (size_a*size_z-1)*in_channel), \ + 'error with the shape of nbr2ch_mat after removing center position' + + return nbr2ch_mat + + + +def angular_filter_kernel(size_z, size_a, in_channel, theta_sqs): + """Compute a gaussian kernel. + Args: + size_z: size on the z dimension. + size_a: size on the a dimension. + in_channel: input (and output) channel size + theta_sqs: an array with length == in_channel. Contains variance for + gaussian kernel for each channel. + Returns: + kernel: ND array of size [size_z, size_a, in_channel, in_channel], which is + just guassian kernel parameters for each channel. + """ + assert size_z % 2 == 1 and size_a % 2==1, \ + 'size_z and size_a should be odd number' + assert len(theta_sqs) == in_channel, \ + 'length of theta_sqs and in_channel does no match' + + # gaussian kernel + kernel = np.zeros((size_z, size_a, in_channel, in_channel), dtype=np.float32) + + for k in range(in_channel): + kernel_2d = np.zeros((size_z, size_a), dtype=np.float32) + for i in range(size_z): + for j in range(size_a): + diff = np.sum( + (np.array([i-size_z//2, j-size_a//2]))**2) + kernel_2d[i, j] = np.exp(-diff/2/theta_sqs[k]) + + # exclude the center position + kernel_2d[size_z//2, size_a//2] = 0 + kernel[:, :, k, k] = kernel_2d + + return kernel diff --git a/ROS_packages/squeezeseg_cpp_preprocessing/src/cpp_preprocessing.cpp b/ROS_packages/squeezeseg_cpp_preprocessing/src/cpp_preprocessing.cpp new file mode 100644 index 0000000..63a2265 --- /dev/null +++ b/ROS_packages/squeezeseg_cpp_preprocessing/src/cpp_preprocessing.cpp @@ -0,0 +1,134 @@ +/* + * #+DESCRIPTION: c++ conversion of preprocessor from velodyne points to [512,64] dataset + * strongly inspried from github.com/durant35/SqueezeSeg + * + * #+DATE: 2018-08-28 Tue + * #+AUTHOR: Edward Im (edwardim@snu.ac.kr) + */ +#include +#include +#include + +#define PCL_NO_PRECOMPILE +#include +#include +#include +#include + +#include + +#include +#include + +#define D2R M_PI/180.0 +#define R2D 180.0/M_PI + +using namespace std; + +namespace pcl { +struct PointXYZID { + PCL_ADD_POINT4D // Macro quad-word XYZ + float intensity; // Laser intensity + float d; // Distance for SqueezeSeg + EIGEN_MAKE_ALIGNED_OPERATOR_NEW // Ensure proper alignment +} EIGEN_ALIGN16; +} //end of namespace pcl + +POINT_CLOUD_REGISTER_POINT_STRUCT(PointXYZID, + (float, x, x) + (float, y, y) + (float, z, z) + (float, intensity, intensity) + (float, d, d) + ) +PCL_INSTANTIATE_PointCloud(pcl::PointXYZID); + +typedef pcl::PointXYZI VPoint; +typedef pcl::PointCloud VPointCloud; +typedef pcl::PointXYZID VPointXYZID; +typedef pcl::PointCloud VPointCloudXYZID; + +ros::Publisher ss_pub; + +// /velodyne_points topic's subcriber callback function +void velo_callback(const sensor_msgs::PointCloud2::ConstPtr &msg){ + VPointCloud::Ptr pcl_msg(new VPointCloud); + pcl::fromROSMsg(*msg, *pcl_msg); + + if(pcl_msg->points.empty()) + return; + + VPointCloud::Ptr out_msg(new VPointCloud); + out_msg->points.clear(); + + // hv_in_range substitute loop + for(int i=0; i <= pcl_msg->points.size()-1; i++) { + double x = pcl_msg->points[i].x; + double y = pcl_msg->points[i].y; + double z = pcl_msg->points[i].z; + + double a = atan2(y,x) * R2D; + if(a > -45 && a < 45) + out_msg->points.push_back(pcl_msg->points[i]); + } + + VPointCloudXYZID filtered_cloud; + sensor_msgs::PointCloud2 ss_msg; + filtered_cloud.points.resize(32768); // 64 * 512 + + double dphi = 180/512.0 * D2R; + double dtheta = 0.4 * D2R; + + // pto_depth_map substitute loop + for(int i=0; i <= out_msg->points.size(); i++) { + double x = out_msg->points[i].x; + double y = out_msg->points[i].y; + double z = out_msg->points[i].z; + double d = sqrt(x*x + y*y + z*z); + double r = sqrt(x*x + y*y); + + double phi = 90 * D2R - asin(y/r); + double phi2 = (int)(phi/dphi); + double theta = 2 * D2R - asin(z/d); + double theta2 = (int)(theta/dtheta); + + if(phi2 < 0) + phi2 = 0; + if(phi2 >= 512) + phi2 = 511; + + if(theta2 < 0) + theta2 = 0; + if(theta2 >= 64) + theta2 = 63; + + filtered_cloud.points[theta2*512 + phi2].x = out_msg->points[i].x; + filtered_cloud.points[theta2*512 + phi2].y = out_msg->points[i].y; + filtered_cloud.points[theta2*512 + phi2].z = out_msg->points[i].z; + // TODO(edward): SqueezeSeg got error if there are intensity data from here. need to fix + /* filtered_cloud.points[theta2*512 + phi2].intensity = out_msg->points[i].intensity; */ + filtered_cloud.points[theta2*512 + phi2].d = d; + } + // cout << "[+] " << filtered_cloud.points.size() << endl; // DEBUG + + pcl::toROSMsg(filtered_cloud, ss_msg); + ss_msg.header.frame_id = "velodyne_link"; + ss_msg.header.stamp = ros::Time::now(); + + // publish to /ss_filtered + ss_pub.publish(ss_msg); + filtered_cloud.points.clear(); +} + +int main(int argc, char **argv){ + ros::init(argc, argv, "cpp_preprocessing"); + + ros::NodeHandle nh; + + ros::Subscriber velodyne_sub = nh.subscribe("/velodyne_points", 1, velo_callback); + + ss_pub = nh.advertise("/ss_filtered",1); + + ros::spin(); + return 0; +} diff --git a/src/eval.py b/src/eval.py index 54c6b28..58f5d75 100644 --- a/src/eval.py +++ b/src/eval.py @@ -98,6 +98,14 @@ def eval_once( ) _t['detect'].toc() + # Uncomment this lines if you want to use visualize with Rviz. + # see kitti_velodyne_to_ros in my gitrepo + + # out_dir = "./data/sample_out_ed" + # if not tf.gfile.Exists(out_dir): + # tf.gfile.MakeDirs(out_dir) + # np.save(os.path.join(out_dir, 'pred_' + imdb._image_idx[i] + '.npy'), pred_cls[0]) + _t['eval'].tic() # Evaluation iou, tps, fps, fns = evaluate_iou(