Feature/point pillars

ros/src/computing/perception/detection/lidar_detector/packages/lidar_point_pillars/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8.3)
+project(lidar_point_pillars)
+
+# set flags for CUDA availability
+option(CUDA_AVAIL "CUDA available" OFF)
+find_package(CUDA)
+if (CUDA_FOUND)
+  message("CUDA is available!")
+  message("CUDA Libs: ${CUDA_LIBRARIES}")
+  message("CUDA Headers: ${CUDA_INCLUDE_DIRS}")
+  set(CUDA_AVAIL ON)
+else()
+  message("CUDA NOT FOUND")
+  set(CUDA_AVAIL OFF)
+endif (CUDA_FOUND)
+
+# set flags for TensorRT availability
+option(TRT_AVAIL "TensorRT available" OFF)
+# try to find the tensorRT modules
+find_library(NVINFER NAMES nvinfer)
+find_library(NVPARSERS NAMES nvparsers)
+if(NVINFER AND NVPARSERS)
+  message("TensorRT is available!")
+  message("NVINFER: ${NVINFER}")
+  message("NVPARSERS: ${NVPARSERS}")
+  set(TRT_AVAIL ON)
+  add_definitions(-DTRT_AVAIL)
+else()
+  message("TensorRT is NOT Available")
+  set(TRT_AVAIL OFF)
+endif()
+
+if(TRT_AVAIL AND CUDA_AVAIL)
+
+  find_package(autoware_build_flags REQUIRED)
+  find_package(catkin REQUIRED COMPONENTS
+   roscpp
+   pcl_ros
+   autoware_msgs
+   )
+  set(CMAKE_CXX_STANDARD 11)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+
+  catkin_package(
+   CATKIN_DEPENDS
+   roscpp
+   pcl_ros
+   autoware_msgs
+   )
+
+  include_directories(
+   include
+   ${catkin_INCLUDE_DIRS}
+  )
+  set(SOURCE_FILES
+   nodes/lidar_point_pillars_node.cpp
+   nodes/point_pillars_ros.cpp
+   )
+
+  add_executable(lidar_point_pillars
+         ${SOURCE_FILES})
+
+  add_dependencies(lidar_point_pillars
+         ${catkin_EXPORTED_TARGETS}
+         )
+
+
+  cuda_add_library(gpu_point_pillars_lib
+         nodes/preprocess_points_cuda.cu
+         nodes/anchor_mask_cuda.cu
+         nodes/scatter_cuda.cu
+         nodes/postprocess_cuda.cu
+         nodes/nms_cuda.cu
+         )
+
+  target_link_libraries(gpu_point_pillars_lib
+   ${CUDA_LIBRARIES}
+  )
+
+  add_library(point_pillars_lib
+         nodes/point_pillars.cpp
+         nodes/preprocess_points.cpp
+         )
+
+  target_link_libraries(point_pillars_lib
+         nvinfer
+         nvonnxparser
+         gpu_point_pillars_lib
+         )
+
+  target_link_libraries(lidar_point_pillars
+         ${catkin_LIBRARIES}
+         point_pillars_lib
+         )
+
+
+  install(TARGETS
+         lidar_point_pillars
+         ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+         LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+         RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
+  )
+
+  install(DIRECTORY launch/
+         DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}/launch
+         PATTERN ".svn" EXCLUDE)
+
+  install(DIRECTORY include/
+         DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
+         PATTERN ".svn" EXCLUDE
+         )
+
+  if (CATKIN_ENABLE_TESTING)
+    find_package(rostest REQUIRED)
+    catkin_add_gtest(test-point_pillars test/src/test_point_pillars.cpp)
+    target_link_libraries(test-point_pillars ${catkin_LIBRARIES} point_pillars_lib)
+  endif()
+ELSE()
+  message("PointPillars won't be built, CUDA and/or TensorRT were not found.")
+ENDIF ()

ros/src/computing/perception/detection/lidar_detector/packages/lidar_point_pillars/README.md

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+# Point Pillars for 3D Object Detection: ver. 1.0
+
+Autoware package for Point Pillars.  [Referenced paper](https://arxiv.org/abs/1812.05784).
+
+## Requirements
+
+CUDA Toolkit v9.0 or v10.0
+
+TensorRT: Tested with 5.0.2 -> [How to install](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing)
+
+
+
+## How to launch
+
+* Launch file:
+`roslaunch lidar_point_pillars lidar_point_pillars.launch pfe_onnx_file:=/PATH/TO/FILE.onnx rpn_onnx_file:=/PATH/TO/FILE.onnx input_topic:=/points_raw`
+
+* You can launch it through the runtime manager in Computing tab, as well.
+
+## API
+```
+/**
+* @brief Call PointPillars for the inference.
+* @param[in] in_points_array pointcloud array
+* @param[in] in_num_points Number of points
+* @param[out] out_detections Output bounding box from the network
+* @details This is an interface for the algorithm.
+*/
+void doInference(float* in_points_array, int in_num_points, std::vector<float> out_detection);
+```
+
+## Parameters
+
+|Parameter| Type| Description|Default|
+----------|-----|--------|----|
+|`input_topic`|*String*|Input topic Pointcloud. Default.|`/points_raw`|
+|`baselink_support`|*Bool*|Whether to use baselink to adjust parameters. Default.|`True`|
+|`reproduce_result_mode`|*Bool*|Whether to enable reproducible result mode at the cost of the runtime. Default.|`False`|
+|`score_threshold`|*Float*|Minimum score required to include the result (0.1)|0.5|
+|`nms_overlap_threshold`|*Float*|Minimum IOU required to have when applying NMS (0.1)|0.5|
+|`pfe_onnx_file`|*String* |Path to the PFE onnx file||
+|`rpn_onnx_file`|*String* |Path to the RPN onnx file||
+
+## Outputs
+
+|Topic|Type|Description|
+|---|---|---|
+|`/detection/lidar_detector/objects`|`autoware_msgs/DetectedObjetArray`|Array of Detected Objects in Autoware format|
+
+## Notes
+
+* To display the results in Rviz `objects_visualizer` is required.
+(Launch file launches automatically this node).
+
+* Pre trained models can be downloaded from the [github repository](https://github.com/cirpue49/kitti_pretrained_pp). Notice that this model is under `BY-NC-SA 3.0` license.
+
+* If trained model comes from KITTI data, users might not be allowed to use the model for commercial purposes.

ros/src/computing/perception/detection/lidar_detector/packages/lidar_point_pillars/include/anchor_mask_cuda.h

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+/*
+ * Copyright 2018-2019 Autoware Foundation. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+* @file anchor_mask_cuda.h
+* @brief Make anchor mask for filtering output
+* @author Kosuke Murakami
+* @date 2019/02/26
+*/
+
+#ifndef ANCHOR_MASK_CUDA_H
+#define ANCHOR_MASK_CUDA_H
+
+class AnchorMaskCuda
+{
+private:
+  const int NUM_INDS_FOR_SCAN_;
+  const int NUM_ANCHOR_X_INDS_;
+  const int NUM_ANCHOR_Y_INDS_;
+  const int NUM_ANCHOR_R_INDS_;
+  const float MIN_X_RANGE_;
+  const float MIN_Y_RANGE_;
+  const float PILLAR_X_SIZE_;
+  const float PILLAR_Y_SIZE_;
+  const int GRID_X_SIZE_;
+  const int GRID_Y_SIZE_;
+
+public:
+  /**
+  * @brief Constructor
+  * @param[in] NUM_INDS_FOR_SCAN Number of indexes for scan(cumsum)
+  * @param[in] NUM_ANCHOR_X_INDS Number of x-indexes for anchors
+  * @param[in] NUM_ANCHOR_Y_INDS Number of y-indexes for anchors
+  * @param[in] NUM_ANCHOR_R_INDS Number of rotation-indexes for anchors
+  * @param[in] MIN_X_RANGE Minimum x value for pointcloud
+  * @param[in] MIN_Y_RANGE Minimum y value for pointcloud
+  * @param[in] PILLAR_X_SIZE Size of x-dimension for a pillar
+  * @param[in] PILLAR_Y_SIZE Size of y-dimension for a pillar
+  * @param[in] GRID_X_SIZE Number of pillars in x-coordinate
+  * @param[in] GRID_Y_SIZE Number of pillars in y-coordinate
+  * @details Captital variables never change after the compile
+  */
+  AnchorMaskCuda(const int NUM_INDS_FOR_SCAN, const int NUM_ANCHOR_X_INDS, const int NUM_ANCHOR_Y_INDS,
+                 const int NUM_ANCHOR_R_INDS, const float MIN_X_RANGE, const float MIN_Y_RANGE,
+                 const float PILLAR_X_SIZE, const float PILLAR_Y_SIZE, const int GRID_X_SIZE, const int GRID_Y_SIZE);
+
+  /**
+  * @brief call cuda code for making anchor mask
+  * @param[in] dev_sparse_pillar_map Grid map representation for pillar occupancy
+  * @param[in] dev_cumsum_along_x Cumsum dev_sparse_pillar_map along x axis
+  * @param[in] dev_cumsum_along_y Cumsum dev_cumsum_along_x along y axis
+  * @param[in] dev_box_anchors_min_x Array for storng min x value for each anchor
+  * @param[in] dev_box_anchors_min_y Array for storng min y value for each anchor
+  * @param[in] dev_box_anchors_max_x Array for storng max x value for each anchor
+  * @param[in] dev_box_anchors_max_y Array for storng max y value for each anchor
+  * @param[in] dev_box_anchors_max_y Array for storng max y value for each anchor
+  * @param[out] dev_anchor_mask Anchor mask for filtering output
+  * @details dev_* means device memory. Make a mask for activating pillar occupancy area
+  */
+  void doAnchorMaskCuda(int* dev_sparse_pillar_map, int* dev_cumsum_along_x, int* dev_cumsum_along_y,
+                        const float* dev_box_anchors_min_x, const float* dev_box_anchors_min_y,
+                        const float* dev_box_anchors_max_x, const float* dev_box_anchors_max_y, int* dev_anchor_mask);
+};
+
+#endif  // ANCHOR_MASK_CUDA_H

ros/src/computing/perception/detection/lidar_detector/packages/lidar_point_pillars/include/common.h

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+/*
+ * Copyright 2018-2019 Autoware Foundation. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+* @file common.h
+* @brief MACRO for CUDA codes
+* @author Kosuke Murakami
+* @date 2019/02/26
+*/
+
+#ifndef COMMON_H
+#define COMMON_H
+
+// headers in STL
+#include <stdio.h>
+
+// headers in CUDA
+#include <cuda_runtime_api.h>
+
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+#define GPU_CHECK(ans)                                                                                                 \
+  {                                                                                                                    \
+    GPUAssert((ans), __FILE__, __LINE__);                                                                              \
+  }
+inline void GPUAssert(cudaError_t code, const char* file, int line, bool abort = true)
+{
+  if (code != cudaSuccess)
+  {
+    fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
+    if (abort)
+      exit(code);
+  }
+}
+
+#endif  // COMMON_H

ros/src/computing/perception/detection/lidar_detector/packages/lidar_point_pillars/include/nms_cuda.h

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+/*
+ * Copyright 2018-2019 Autoware Foundation. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+* @file nms_cuda.h
+* @brief Non-maximum suppresion for network output
+* @author Modified by Kosuke Murakami
+* @date 2019/02/26
+*/
+
+#ifndef NMS_CUDA_H
+#define NMS_CUDA_H
+
+// heders in STL
+#include <vector>
+#include <iostream>
+
+// headers in local files
+#include "common.h"
+
+class NMSCuda
+{
+private:
+  const int NUM_THREADS_;
+  const float nms_overlap_threshold_;
+
+public:
+  /**
+  * @brief Constructor
+  * @param[in] NUM_THRESD Number of threads for launching cuda kernel
+  * @param[in] nms_overlap_threshold IOU threshold for NMS
+  * @details Captital variables never change after the compile, Non-captital variables could be chaned through rosparam
+  */
+  NMSCuda(const int NUM_THREADS, const float nms_overlap_threshold);
+
+  /**
+  * @brief GPU Non-Maximum Suppresion for network output
+  * @param[in] host_filter_count Number of filtered output
+  * @param[in] dev_sorted_box_for_nms Bounding box output sorted by score
+  * @param[out] out_keep_inds Indexes for selected bounding box
+  * @param[out] out_num_to_keep Number of keep bounding box
+  * @details Includes CUDA NMS and postprocessing for selecting box in CPU
+  */
+  void doNMSCuda(const int host_filter_count, float* dev_sorted_box_for_nms, int* out_keep_inds, int& out_num_to_keep);
+};
+
+#endif  // NMS_CUDA_H