[doc] TX2 doc TensorRT 5-->TensorRT 6 (#1700)

* update cpp build script * rename document and update script * set WITH_MKL=ON for pass CI * set WITH_GPU=OFF and WITH_TENSORRT=OFF for pass CI

[doc] TX2 doc TensorRT 5-->TensorRT 6 (#1700)
* update cpp build script * rename document and update script * set WITH_MKL=ON for pass CI * set WITH_GPU=OFF and WITH_TENSORRT=OFF for pass CI
5edb9c6c · cnn · GitHub · 53da7ffd · 5edb9c6c · 5edb9c6c
5 changed file
--- a/deploy/README.md
+++ b/deploy/README.md
@@ -25,3 +25,4 @@ yolov3_darknet # 模型目录
 - [2. C++预测(支持 Linux 和 Windows)](https://github.com/PaddlePaddle/PaddleDetection/blob/master/deploy/cpp)
 - [3. 在线服务化部署](./serving/README.md)
 - [4. 移动端部署](https://github.com/PaddlePaddle/Paddle-Lite-Demo)
+- [5. Jetson设备部署](./cpp/docs/Jetson_build.md)
--- a/deploy/cpp/CMakeLists.txt
+++ b/deploy/cpp/CMakeLists.txt
@@ -10,7 +10,8 @@ SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
 SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
 SET(CUDA_LIB "" CACHE PATH "Location of libraries")
 SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
-SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
+SET(TENSORRT_INC_DIR "" CACHE PATH "Compile demo with TensorRT")
+SET(TENSORRT_LIB_DIR "" CACHE PATH "Compile demo with TensorRT")

 include(cmake/yaml-cpp.cmake)

@@ -112,8 +113,9 @@ endif()

 if (NOT WIN32)
  if (WITH_TENSORRT AND WITH_GPU)
-     include_directories("${TENSORRT_DIR}/include")
-     link_directories("${TENSORRT_DIR}/lib")
+	  include_directories("${TENSORRT_INC_DIR}/include")
+	  #link_directories("${TENSORRT_LIB_DIR}/lib")
+	  link_directories("${TENSORRT_LIB_DIR}/")
  endif()
 endif(NOT WIN32)

@@ -170,7 +172,7 @@ endif()

 if (NOT WIN32)
    set(DEPS ${DEPS}
-        ${MATH_LIB} ${MKLDNN_LIB} 
+        ${MATH_LIB} ${MKLDNN_LIB}
        glog gflags protobuf z xxhash yaml-cpp
        )
    if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
@@ -195,8 +197,8 @@ endif(NOT WIN32)
 if(WITH_GPU)
  if(NOT WIN32)
    if (WITH_TENSORRT)
-      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
-      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
    endif()
    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
@@ -212,7 +214,7 @@ if (NOT WIN32)
    set(DEPS ${DEPS} ${EXTERNAL_LIB})
 endif()

-set(DEPS ${DEPS} ${OpenCV_LIBS}) 
+set(DEPS ${DEPS} ${OpenCV_LIBS})
 add_executable(main src/main.cc src/preprocess_op.cc src/object_detector.cc)
 ADD_DEPENDENCIES(main ext-yaml-cpp)
 target_link_libraries(main ${DEPS})

--- a/deploy/cpp/docs/TX2_build.md
+++ b/deploy/cpp/docs/TX2_build.md
-# TX2平台编译指南
+# Jetson平台编译指南

 ## 说明
-本文档在`TX2`平台上使用`jetpack 4.3`进行测试。`TX2`平台的开发指南请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html).
+`NVIDIA Jetson`设备是具有`NVIDIA GPU`的嵌入式设备，可以将目标检测算法部署到该设备上。本文档是在`Jetson`硬件上部署`PaddleDetection`模型的教程。

-## TX2环境搭建
-`TX2`系统软件安装，请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html).
+本文档以`Jetson TX2`硬件、`JetPack 4.3`版本为例进行说明。
+
+`Jetson`平台的开发指南请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html).
+
+## Jetson环境搭建
+`Jetson`系统软件安装，请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html).

 * (1) 查看硬件系统的l4t的版本号
 ```
@@ -12,12 +16,14 @@ cat /etc/nv_tegra_release
 ```
 * (2) 根据硬件，选择硬件可安装的`JetPack`版本，硬件和`JetPack`版本对应关系请参考[jetpack-archive](https://developer.nvidia.com/embedded/jetpack-archive).

-* (3) 下载`JetPack`，请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html)中的`Preparing a Jetson Developer Kit for Use`章节内容进行刷写系统镜像。
+* (3) 下载`JetPack`，请参考[NVIDIA Jetson Linux Developer Guide](https://docs.nvidia.com/jetson/l4t/index.html) 中的`Preparing a Jetson Developer Kit for Use`章节内容进行刷写系统镜像。

-## `Paddle`预测库
-本文档使用`Paddle`在`TX2`平台上预先编译好的预测库，下载地址[fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.8.4-nv-jetson-cuda10-cudnn7.5-trt5/fluid_inference.tgz), `Paddle`版本`1.8.4`,`CUDA`版本`10.0`,`CUDNN`版本`7.5`，`TensorRT`版本`5`。
+## 下载或编译`Paddle`预测库
+本文档使用`Paddle`在`JetPack4.3`上预先编译好的预测库，请根据硬件在[安装与编译 Linux 预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc/guides/05_inference_deployment/inference/build_and_install_lib_cn.html) 中选择对应版本的`Paddle`预测库。

-若需要自己在`TX2`平台上编译`Paddle`，请参考文档[安装与编译 Linux 预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html) 的`NVIDIA Jetson嵌入式硬件预测库源码编译`部分内容。
+这里选择[nv_jetson_cuda10_cudnn7.6_trt6(jetpack4.3)](https://paddle-inference-lib.bj.bcebos.com/2.0.0-rc0-nv-jetson-cuda10-cudnn7.6-trt6/paddle_inference.tgz), `Paddle`版本`2.0.0-rc0`,`CUDA`版本`10.0`,`CUDNN`版本`7.6`，`TensorRT`版本`6`。
+
+若需要自己在`Jetson`平台上自定义编译`Paddle`库，请参考文档[安装与编译 Linux 预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html) 的`NVIDIA Jetson嵌入式硬件预测库源码编译`部分内容。

 ### Step1: 下载代码

@@ -28,7 +34,7 @@ cat /etc/nv_tegra_release

 ### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference

-PaddlePaddle C++ 预测库针对不同的硬件平台，针对不同`CUDA`版本提供了不同的预编译版本，请根据实际情况下载:  [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)
+解压下载的[nv_jetson_cuda10_cudnn7.6_trt6(jetpack4.3)](https://paddle-inference-lib.bj.bcebos.com/2.0.0-rc0-nv-jetson-cuda10-cudnn7.6-trt6/paddle_inference.tgz) 。

 下载并解压后`/root/projects/fluid_inference`目录包含内容为：
 ```
@@ -40,7 +46,7 @@ fluid_inference
 └── version.txt # 版本和编译信息
 ```

-**注意:** 预编译库`nv-jetson-cuda10-cudnn7.5-trt5`使用的`GCC`版本是`7.5.0`，其他都是使用`GCC 4.8.5`编译的。使用高版本的GCC可能存在`ABI`兼容性问题，建议降级或[自行编译预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。
+**注意:** 预编译库`nv-jetson-cuda10-cudnn7.6-trt6`使用的`GCC`版本是`7.5.0`，其他都是使用`GCC 4.8.5`编译的。使用高版本的GCC可能存在`ABI`兼容性问题，建议降级或[自行编译预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。


 ### Step4: 编译
@@ -59,8 +65,11 @@ WITH_MKL=OFF
 # 是否集成 TensorRT(仅WITH_GPU=ON 有效)
 WITH_TENSORRT=ON

+# TensorRT 的include路径
+TENSORRT_INC_DIR=/usr/include/aarch64-linux-gnu
+
 # TensorRT 的lib路径
-TENSORRT_DIR=/path/to/TensorRT/
+TENSORRT_LIB_DIR=/usr/lib/aarch64-linux-gnu

 # Paddle 预测库路径
 PADDLE_DIR=/path/to/fluid_inference/
@@ -70,10 +79,10 @@ PADDLE_DIR=/path/to/fluid_inference/
 WITH_STATIC_LIB=OFF

 # CUDA 的 lib 路径
-CUDA_LIB=/path/to/cuda/lib/
+CUDA_LIB=/usr/local/cuda-10.0/lib64

 # CUDNN 的 lib 路径
-CUDNN_LIB=/path/to/cudnn/lib/
+CUDNN_LIB=/usr/lib/aarch64-linux-gnu

 # OPENCV_DIR 的路径
 # linux平台请下载：https://bj.bcebos.com/paddleseg/deploy/opencv3.4.6gcc4.8ffmpeg.tar.gz2，并解压到deps文件夹下
@@ -107,11 +116,14 @@ WITH_MKL=OFF
 # 是否集成 TensorRT(仅WITH_GPU=ON 有效)
 WITH_TENSORRT=OFF

-# TensorRT 的路径
-TENSORRT_DIR=/home/nvidia/PaddleDetection_infer/tensorrt/
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/usr/include/aarch64-linux-gnu
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/usr/lib/aarch64-linux-gnu

 # Paddle 预测库路径
-PADDLE_DIR=/home/nvidia/PaddleDetection_infer/fluid_inference_1.8.4-_cuda10_cudnnv7.5_trt5_jetson_sm53_62_72/
+PADDLE_DIR=/home/nvidia/PaddleDetection_infer/fluid_inference/

 # Paddle 的预测库是否使用静态库来编译
 # 使用TensorRT时，Paddle的预测库通常为动态库

--- a/deploy/cpp/docs/linux_build.md
+++ b/deploy/cpp/docs/linux_build.md
@@ -49,21 +49,24 @@ WITH_MKL=ON
 # 是否集成 TensorRT(仅WITH_GPU=ON 有效)
 WITH_TENSORRT=OFF

+# TensorRT 的include路径
+TENSORRT_LIB_DIR=/path/to/TensorRT/include
+
 # TensorRT 的lib路径
-TENSORRT_DIR=/path/to/TensorRT/
+TENSORRT_LIB_DIR=/path/to/TensorRT/lib

 # Paddle 预测库路径
-PADDLE_DIR=/path/to/fluid_inference/
+PADDLE_DIR=/path/to/fluid_inference

 # Paddle 的预测库是否使用静态库来编译
 # 使用TensorRT时，Paddle的预测库通常为动态库
 WITH_STATIC_LIB=OFF

 # CUDA 的 lib 路径
-CUDA_LIB=/path/to/cuda/lib/
+CUDA_LIB=/path/to/cuda/lib

 # CUDNN 的 lib 路径
-CUDNN_LIB=/path/to/cudnn/lib/
+CUDNN_LIB=/path/to/cudnn/lib

 修改脚本设置好主要参数后，执行`build`脚本：
 ```shell
@@ -77,7 +80,8 @@ cmake .. \
    -DWITH_GPU=${WITH_GPU} \
    -DWITH_MKL=${WITH_MKL} \
    -DWITH_TENSORRT=${WITH_TENSORRT} \
-    -DTENSORRT_DIR=${TENSORRT_DIR} \
+    -DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
+    -DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
    -DPADDLE_DIR=${PADDLE_DIR} \
    -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
    -DCUDA_LIB=${CUDA_LIB} \

--- a/deploy/cpp/scripts/build.sh
+++ b/deploy/cpp/scripts/build.sh
 # 是否使用GPU(即是否使用 CUDA)
 WITH_GPU=OFF

-# 是否使用MKL or openblas
+# 是否使用MKL or openblas，TX2需要设置为OFF
 WITH_MKL=ON

 # 是否集成 TensorRT(仅WITH_GPU=ON 有效)
 WITH_TENSORRT=OFF

-# TensorRT 的路径
-TENSORRT_DIR=/path/to/TensorRT/
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/path/to/tensorrt/lib
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/path/to/tensorrt/include

 # Paddle 预测库路径
 PADDLE_DIR=/path/to/fluid_inference/
@@ -18,13 +21,19 @@ PADDLE_DIR=/path/to/fluid_inference/
 WITH_STATIC_LIB=OFF

 # CUDA 的 lib 路径
-CUDA_LIB=/path/to/cuda/lib/
+CUDA_LIB=/path/to/cuda/lib

 # CUDNN 的 lib 路径
-CUDNN_LIB=/path/to/cudnn/lib/
+CUDNN_LIB=/path/to/cudnn/lib
+
+
+MACHINE_TYPE=`uname -m`
+echo "MACHINE_TYPE: "${MACHINE_TYPE}
+

-# OPENCV 路径, 如果使用自带预编译版本可不修改
-if [ "$(uname -m)"=="x86_64" ]; then
+if [ "$MACHINE_TYPE" = "x86_64" ]
+then
+  echo "set OPENCV_DIR for x86_64"
  # linux系统通过以下命令下载预编译的opencv
  mkdir -p $(pwd)/deps && cd $(pwd)/deps
  wget -c https://bj.bcebos.com/paddleseg/deploy/opencv3.4.6gcc4.8ffmpeg.tar.gz2
@@ -33,7 +42,9 @@ if [ "$(uname -m)"=="x86_64" ]; then
  # set OPENCV_DIR
  OPENCV_DIR=$(pwd)/deps/opencv3.4.6gcc4.8ffmpeg/

-elif [ "$(uname -m)"=="aarch64" ];then
+elif [ "$MACHINE_TYPE" = "aarch64" ]
+then
+  echo "set OPENCV_DIR for aarch64"
  # TX2平台通过以下命令下载预编译的opencv
  mkdir -p $(pwd)/deps && cd $(pwd)/deps
  wget -c https://paddlemodels.bj.bcebos.com/TX2_JetPack4.3_opencv_3.4.10_gcc7.5.0.zip
@@ -42,9 +53,11 @@ elif [ "$(uname -m)"=="aarch64" ];then
  # set OPENCV_DIR
  OPENCV_DIR=$(pwd)/deps/TX2_JetPack4.3_opencv_3.4.10_gcc7.5.0/

+else
+  echo "Please set OPENCV_DIR manually"
 fi

-# 请检查以上各个路径是否正确
+echo "OPENCV_DIR: "$OPENCV_DIR

 # 以下无需改动
 rm -rf build
@@ -54,7 +67,8 @@ cmake .. \
    -DWITH_GPU=${WITH_GPU} \
    -DWITH_MKL=${WITH_MKL} \
    -DWITH_TENSORRT=${WITH_TENSORRT} \
-    -DTENSORRT_DIR=${TENSORRT_DIR} \
+    -DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
+    -DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
    -DPADDLE_DIR=${PADDLE_DIR} \
    -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
    -DCUDA_LIB=${CUDA_LIB} \