Ros2 v0.8.0 traffic light ssd fine detector (autowarefoundation#260)

* fix typos in perception (autowarefoundation#862) * update README.md in perception (autowarefoundation#1007) * update traffic light recognition model (autowarefoundation#1086) * update traffic light recognition model * download model when hash has changed * fix CMakeLists * udpate tl model to scale ai dataset one (autowarefoundation#1118) Co-authored-by: Kazuki Miyahara <[email protected]> Co-authored-by: Satoshi Tanaka <[email protected]> Co-authored-by: Taichi Higashide <[email protected]>
kosuke55 · Jan 30, 2021 · a95e615 · a95e615
1 parent 3f99fec
commit a95e615
Show file tree

Hide file tree

Showing 3 changed files with 46 additions and 28 deletions.
diff --git a/perception/traffic_light_recognition/traffic_light_ssd_fine_detector/CMakeLists.txt b/perception/traffic_light_recognition/traffic_light_ssd_fine_detector/CMakeLists.txt
@@ -27,13 +27,13 @@ if (CUDA_FOUND)
     ${CUDA_TOOLKIT_ROOT_DIR}/lib
   )
   if (CUDA_VERBOSE)
-    message("CUDA is available!")
-    message("CUDA Libs: ${CUDA_LIBRARIES}")
-    message("CUDA Headers: ${CUDA_INCLUDE_DIRS}")
+    message(STATUS "CUDA is available!")
+    message(STATUS "CUDA Libs: ${CUDA_LIBRARIES}")
+    message(STATUS "CUDA Headers: ${CUDA_INCLUDE_DIRS}")
   endif ()
   set(CUDA_AVAIL ON)
 else()
-  message("CUDA NOT FOUND")
+  message(STATUS "CUDA NOT FOUND")
   set(CUDA_AVAIL OFF)
 endif (CUDA_FOUND)
 
@@ -45,15 +45,15 @@ find_library(NVONNXPARSER nvonnxparser)
 find_library(NVINFER_PLUGIN NAMES nvinfer_plugin)
 if(NVINFER AND NVONNXPARSER AND NVINFER_PLUGIN)
   if (CUDA_VERBOSE)
-    message("TensorRT is available!")
-    message("NVINFER: ${NVINFER}")
-    message("NVPARSERS: ${NVPARSERS}")
-    message("NVINFER_PLUGIN: ${NVINFER_PLUGIN}")
-    message("NVONNXPARSER: ${NVONNXPARSER}")
+    message(STATUS "TensorRT is available!")
+    message(STATUS "NVINFER: ${NVINFER}")
+    message(STATUS "NVPARSERS: ${NVPARSERS}")
+    message(STATUS "NVINFER_PLUGIN: ${NVINFER_PLUGIN}")
+    message(STATUS "NVONNXPARSER: ${NVONNXPARSER}")
   endif ()
   set(TRT_AVAIL ON)
 else()
-  message("TensorRT is NOT Available")
+  message(STATUS "TensorRT is NOT Available")
   set(TRT_AVAIL OFF)
 endif()
 
@@ -67,38 +67,56 @@ PATH_SUFFIXES lib lib64 bin
 DOC "CUDNN library." )
 if(CUDNN_LIBRARY)
   if (CUDA_VERBOSE)
-    message("CUDNN is available!")
-    message("CUDNN_LIBRARY: ${CUDNN_LIBRARY}")
+    message(STATUS "CUDNN is available!")
+    message(STATUS "CUDNN_LIBRARY: ${CUDNN_LIBRARY}")
   endif ()
   set(CUDNN_AVAIL ON)
 else()
-  message("CUDNN is NOT Available")
+  message(STATUS "CUDNN is NOT Available")
   set(CUDNN_AVAIL OFF)
 endif()
 
 # Download caffemodel and prototxt
+set(PRETRAINED_MODEL_LINK "https://drive.google.com/uc?id=1USFDPRH9JrVdGoqt27qHjRgittwc0kcO")
+set(PRETRAINED_MODEL_HASH 34ce7f2cbacbf6da8bc35769f027b73f)
+set(LAMP_LABEL_LINK "https://drive.google.com/uc?id=1hPcKvKgKz0fqEo0cNAXH7roEletqZErL")
+set(LAMP_LABEL_HASH e9f45efb02f2a9aa8ac27b3d5c164905)
+
 find_program(GDOWN_AVAIL "gdown")
 if (NOT GDOWN_AVAIL)
-  message("gdown: command not found. External files could not be downloaded.")
+  message(STATUS "gdown: command not found. External files could not be downloaded.")
 endif()
 set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/data")
 if (NOT EXISTS "${PATH}")
   execute_process(COMMAND mkdir -p ${PATH})
 endif()
+
 set(FILE "${PATH}/mb2-ssd-lite-tlr.onnx")
 message(STATUS "Checking and downloading mb2-ssd-lite-tlr.onnx")
-if (NOT EXISTS "${FILE}")
+if (EXISTS "${FILE}")
+  file(MD5 "${FILE}" EXISTING_FILE_HASH)
+  if (NOT "${PRETRAINED_MODEL_HASH}" EQUAL "${EXISTING_FILE_HASH}")
+    message(STATUS "... file hash changed. Downloading now ...")
+    execute_process(COMMAND gdown --quiet "${PRETRAINED_MODEL_LINK}" -O ${PATH}/mb2-ssd-lite-tlr.onnx)
+  endif()
+else()
   message(STATUS "... file does not exist. Downloading now ...")
-  execute_process(COMMAND gdown "https://drive.google.com/uc?id=1PGxVstVV_sVOLEEyl47m5lNXdLy_Iyob" -O ${PATH}/mb2-ssd-lite-tlr.onnx)
+  execute_process(COMMAND gdown --quiet "${PRETRAINED_MODEL_LINK}" -O ${PATH}/mb2-ssd-lite-tlr.onnx)
 endif()
+
 set(FILE "${PATH}/voc_labels_tl.txt")
 message(STATUS "Checking and downloading voc_labels_tl.txt")
-if (NOT EXISTS "${FILE}")
+if (EXISTS "${FILE}")
+  file(MD5 "${FILE}" EXISTING_FILE_HASH)
+  if (NOT "${LAMP_LABEL_HASH}" EQUAL "${EXISTING_FILE_HASH}")
+    message(STATUS "... file does not exist. Downloading now ...")
+    execute_process(COMMAND gdown --quiet "${LAMP_LABEL_LINK}" -O ${PATH}/voc_labels_tl.txt)
+  endif()
+else()
   message(STATUS "... file does not exist. Downloading now ...")
-  execute_process(COMMAND gdown "https://drive.google.com/uc?id=1hPcKvKgKz0fqEo0cNAXH7roEletqZErL" -O ${PATH}/voc_labels_tl.txt)
+  execute_process(COMMAND gdown --quiet "${LAMP_LABEL_LINK}" -O ${PATH}/voc_labels_tl.txt)
 endif()
 
-
 if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL)
   include_directories(
     ${OpenCV_INCLUDE_DIRS}
@@ -146,6 +164,6 @@ if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL)
   )
 
 else()
-  message("TrafficLightSSDFineDetector won't be built, CUDA and/or TensorRT were not found.")
+  message(STATUS "TrafficLightSSDFineDetector won't be built, CUDA and/or TensorRT were not found.")
 endif()
 
diff --git a/perception/traffic_light_recognition/traffic_light_ssd_fine_detector/README.md b/perception/traffic_light_recognition/traffic_light_ssd_fine_detector/README.md
@@ -1,10 +1,10 @@
-### Note
-There's a fine detector implementation of Mobilenet SSD.
+# traffic\_light\_ssd\_fine\_detector
 
-The model of Mobilenet SSD is based on [pytorch-ssd] (https://github.com/qfgaohao/pytorch-ssd).
+This is a traffic light fine detector implementation of MobileNetV2 + SSDLite.
 
-## Training
-The model of Mobilenet SSD used in this package was trained by [AutowareMLPlatform/detection_2d](https://github.com/tier4/AutowareMLPlatform/tree/master/tasks/detection_2d)
+The trained model is based on [pytorch-ssd](https://github.com/qfgaohao/pytorch-ssd).
+
+## Reference
+
+M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 4510-4520, doi: 10.1109/CVPR.2018.00474.
 
-## model detail
-TODO: atach AWS URL here for quick reference when, where and how the model was trained.
diff --git a/...ition/traffic_light_ssd_fine_detector/include/traffic_light_ssd_fine_detector/nodelet.hpp b/...ition/traffic_light_ssd_fine_detector/include/traffic_light_ssd_fine_detector/nodelet.hpp
@@ -62,7 +62,7 @@ class TrafficLightSSDFineDetectorNodelet : public rclcpp::Node
     const std::vector<cv::Mat> & in_imgs, const int num_rois, std::vector<float> & data);
   bool cnnOutput2BoxDetection(
     const float * scores, const float * boxes, const int tlr_id,
-    const std::vector<cv::Mat> & in_imgs, const int num_rois, std::vector<Detection> & detctions);
+    const std::vector<cv::Mat> & in_imgs, const int num_rois, std::vector<Detection> & detections);
   bool rosMsg2CvMat(const sensor_msgs::msg::Image::ConstSharedPtr image_msg, cv::Mat & image);
   bool fitInFrame(cv::Point & lt, cv::Point & rb, const cv::Size & size);
   void cvRect2TlRoiMsg(