高通 QNN¶
Paddle Lite 已支持高通 QNN 在 x86 （模拟器）和 ARM 设备（例如SA8295P）上进行预测部署。 目前支持子图接入方式，其接入原理是在线分析 Paddle 模型，将 Paddle 算子先转为统一的 NNAdapter 标准算子，再通过 Qualcomm QNN 组网 API 进行网络构建，在线生成并执行模型。
支持现状¶
已支持的芯片¶
高通 8295 芯片
已支持的设备¶
SA8295P
已支持的 Paddle 模型¶
图像分类
目标检测
自然语言处理 & 语义理解
已支持（或部分支持）的 Paddle 算子（ Kernel 接入方式）¶
您可以查阅 NNAdapter 算子支持列表获得各算子在不同新硬件上的最新支持信息。
参考示例演示¶
测试设备( 高通 SA8295P)¶
准备设备环境¶
设备安装 QNX 和 Android 双系统，网口和串口都已配置，配置详情请咨询高通。
运行图像分类示例程序¶
下载示例程序 PaddleLite-generic-demo.tar.gz ，解压后清单如下：
- PaddleLite-generic-demo - image_classification_demo - assets - configs - imagenet_224.txt # config 文件 - synset_words.txt # 1000 分类 label 文件 - datasets - test # dataset - inputs - tabby_cat.jpg # 输入图片 - outputs - tabby_cat.jpg # 输出图片 - list.txt # 图片清单 - models - resnet50_fp32_224 # Paddle non-combined 格式的 resnet50 float32 模型 - __model__ # Paddle fluid 模型组网文件，可拖入 https://lutzroeder.github.io/netron/ 进行可视化显示网络结构 - bn2a_branch1_mean # Paddle fluid 模型参数文件 - bn2a_branch1_scale ... - shell - CMakeLists.txt # 示例程序 CMake 脚本 - build.linux.amd64 # 已编译好的，适用于 linux amd64 - demo # 已编译好的，适用于 linux amd64 的示例程序 - build.qnx.arm64 # 已编译好的，适用于 qnx arm64 - demo # 已编译好的，适用于 qnx arm64 的示例程序 ... ... - demo.cc # 示例程序源码 - build.sh # 示例程序编译脚本 - run.sh # 示例程序本地运行脚本 - run_with_ssh.sh # 示例程序 ssh 运行脚本 - run_with_adb.sh # 示例程序 adb 运行脚本 - libs - PaddleLite - qnx - amd64 - include # Paddle Lite 头文件 - lib # Paddle Lite 库文件 - qualcomm_qnn # 高通 QNN 运行时库、NNAdapter 运行时库、device HAL 库 - libnnadapter.so # NNAdapter 运行时库 - libqualcomm_qnn.so # NNAdapter device HAL 库 - libqualcomm_qnn_cpu_custom_op_package.so # 高通 QNN CPU 自定义算子库 - libqualcomm_qnn_htp_custom_op_package.so # 高通 QNN HTP 自定义算子库 - libQnnHtp.so # 下列为高通 QNN 在真机的 QNX 系统上运行时所需库 - libQnnCpu.so - libQnn*.so - hexagon-v68/lib/unsigned/ # 高通在 QNX 运行的 dsp 库 - android - arm64-v8a - armeabi-v7a - linux - amd64 - include # Paddle Lite 头文件 - lib # Paddle Lite 库文件 - qualcomm_qnn # 高通 QNN 运行时库、NNAdapter 运行时库、device HAL 库 - libnnadapter.so # NNAdapter 运行时库 - libqualcomm_qnn.so # NNAdapter device HAL 库 - libqualcomm_qnn_cpu_custom_op_package.so # 高通 QNN CPU 自定义算子库 - libqualcomm_qnn_htp_custom_op_package.so # 高通 QNN HTP 自定义算子库 - libQnnHtp.so # 下列为高通 QNN 在 x86 模拟器上运行时所需库 - libQnnCpu.so - libQnn*.so - cpu - libiomp5.so # Intel OpenMP 库 - libmklml_intel.so # Intel MKL 库 - libmklml_gnu.so # GNU MKL 库 - libpaddle_full_api_shared.so # 预编译 Paddle Lite full api 库 - libpaddle_light_api_shared.so # 预编译 Paddle Lite light api 库 - arm64 - include - lib - armhf ... - object_detection_demo # 目标检测示例程序 - model_test # 基于简单模型测试的示例程序 - tools # 工具库，包含模型处理工具、编译脚本和代码风格检查工具进入
PaddleLite-generic-demo/image_classification_demo/shell/；执行以下命令分别比较 mobilenet_v1_int8_224_per_layer 和 mobilenet_v1_fp32_224 模型的性能和结果；
运行 mobilenet_v1_int8_224_per_layer 模型
Intel CPU ( QNN x86 Simulator )
$ unset FILE_TRANSFER_COMMAND $ ./run.sh mobilenet_v1_int8_224_per_layer imagenet_224.txt test linux amd64 qualcomm_qnn Top1 Egyptian cat - 0.491380 Top2 tabby, tabby cat - 0.397784 Top3 tiger cat - 0.093596 Top4 lynx, catamount - 0.011700 Top5 tiger shark, Galeocerdo cuvieri - 0.000000 Preprocess time: 4.351000 ms, avg 4.351000 ms, max 4.351000 ms, min 4.351000 ms Prediction time: 1570.060000 ms, avg 1570.060000 ms, max 1570.060000 ms, min 1570.060000 ms Postprocess time: 5.100000 ms, avg 5.100000 ms, max 5.100000 ms, min 5.100000 ms
Qualcomm 8295P EVB ( Android )
(android arm64 cpu only) $ adb -s 858e5789 root $ ./run_with_adb.sh mobilenet_v1_int8_224_per_layer imagenet_224.txt test android arm64-v8a cpu 858e5789 Top1 Egyptian cat - 0.503239 Top2 tabby, tabby cat - 0.419854 Top3 tiger cat - 0.065506 Top4 lynx, catamount - 0.007992 Top5 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor - 0.000494 Preprocess time: 9.461000 ms, avg 9.461000 ms, max 9.461000 ms, min 9.461000 ms Prediction time: 39.883000 ms, avg 39.883000 ms, max 39.883000 ms, min 39.883000 ms Postprocess time: 7.039000 ms, avg 7.039000 ms, max 7.039000 ms, min 7.039000 ms (android arm64 cpu + qualcomm qnn) $ adb -s 858e5789 root $ ./run_with_adb.sh mobilenet_v1_int8_224_per_layer imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 Top1 Egyptian cat - 0.491380 Top2 tabby, tabby cat - 0.397784 Top3 tiger cat - 0.093596 Top4 lynx, catamount - 0.011700 Top5 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias - 0.000000 Preprocess time: 5.510000 ms, avg 5.510000 ms, max 5.510000 ms, min 5.510000 ms Prediction time: 1.029000 ms, avg 1.029000 ms, max 1.029000 ms, min 1.029000 ms Postprocess time: 4.644000 ms, avg 4.644000 ms, max 4.644000 ms, min 4.644000 ms
Qualcomm 8295P EVB ( QNX )
$ export FILE_TRANSFER_COMMAND=lftp $ adb -s 858e5789 root $ rm -rf ../assets/models/cache 推送到 EVK 开发板上在 android 执行生成 cache 和 nb 模型 $ ./run_with_adb.sh mobilenet_v1_int8_224_per_layer imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 null cache 推送到 EVK 开发板上在 QNX 上推理 $ ./run_with_ssh.sh mobilenet_v1_int8_224_per_layer imagenet_224.txt test qnx arm64 qualcomm_qnn 192.168.1.1 22 root root null cache Top1 Egyptian cat - 0.491380 Top2 tabby, tabby cat - 0.397784 Top3 tiger cat - 0.093596 Top4 lynx, catamount - 0.011700 Top5 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias - 0.000000 Preprocess time: 9.265000 ms, avg 9.265000 ms, max 9.265000 ms, min 9.265000 ms Prediction time: 1.009000 ms, avg 1.009000 ms, max 1.009000 ms, min 1.009000 ms Postprocess time: 11.492000 ms, avg 11.492000 ms, max 11.492000 ms, min 11.492000 ms
将测试模型改为 mobilenet_v1_fp32_224，执行命令：
Intel CPU ( QNN x86 Simulator )
$ unset FILE_TRANSFER_COMMAND $ ./run.sh mobilenet_v1_fp32_224 imagenet_224.txt test linux amd64 qualcomm_qnn Top1 Egyptian cat - 0.482870 Top2 tabby, tabby cat - 0.471596 Top3 tiger cat - 0.039779 Top4 lynx, catamount - 0.002430 Top5 ping-pong ball - 0.000508 Preprocess time: 3.691000 ms, avg 3.691000 ms, max 3.691000 ms, min 3.691000 ms Prediction time: 554.162000 ms, avg 554.162000 ms, max 554.162000 ms, min 554.162000 ms Postprocess time: 5.210000 ms, avg 5.210000 ms, max 5.210000 ms, min 5.210000 ms
Qualcomm 8295P EVB ( Android )
(android arm64 cpu only) $ adb -s 858e5789 root $ ./run_with_adb.sh mobilenet_v1_fp32_224 imagenet_224.txt test android arm64-v8a cpu 858e5789 Top1 Egyptian cat - 0.482871 Top2 tabby, tabby cat - 0.471594 Top3 tiger cat - 0.039779 Top4 lynx, catamount - 0.002430 Top5 ping-pong ball - 0.000508 Preprocess time: 8.733000 ms, avg 8.733000 ms, max 8.733000 ms, min 8.733000 ms Prediction time: 32.636000 ms, avg 32.636000 ms, max 32.636000 ms, min 32.636000 ms Postprocess time: 6.327000 ms, avg 6.327000 ms, max 6.327000 ms, min 6.327000 ms (android arm64 cpu + qualcomm qnn) $ adb -s 858e5789 root $ ./run_with_adb.sh mobilenet_v1_fp32_224 imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 Top1 Egyptian cat - 0.482869 Top2 tabby, tabby cat - 0.471597 Top3 tiger cat - 0.039779 Top4 lynx, catamount - 0.002430 Top5 ping-pong ball - 0.000508 Preprocess time: 11.663000 ms, avg 11.663000 ms, max 11.663000 ms, min 11.663000 ms Prediction time: 8529.121000 ms, avg 8529.121000 ms, max 8529.121000 ms, min 8529.121000 ms Postprocess time: 10.063000 ms, avg 10.063000 ms, max 10.063000 ms, min 10.063000 ms 以 FP16 方式运行 $ ./run_with_adb.sh mobilenet_v1_fp32_224 imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 "QUALCOMM_QNN_ENABLE_FP16=true" Top1 Egyptian cat - 0.482910 Top2 tabby, tabby cat - 0.471924 Top3 tiger cat - 0.039612 Top4 lynx, catamount - 0.002340 Top5 ping-pong ball - 0.000490 Preprocess time: 5.511000 ms, avg 5.511000 ms, max 5.511000 ms, min 5.511000 ms Prediction time: 1.805000 ms, avg 1.805000 ms, max 1.805000 ms, min 1.805000 ms Postprocess time: 4.213000 ms, avg 4.213000 ms, max 4.213000 ms, min 4.213000 ms
Qualcomm 8295P EVB ( QNX )
$ export FILE_TRANSFER_COMMAND=lftp $ adb -s 858e5789 root $ rm -rf ../assets/models/cache 推送到 EVK 开发板上在 android 执行生成 cache 和 nb 模型 $ ./run_with_adb.sh mobilenet_v1_fp32_224 imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 null cache 推送到 EVK 开发板上在 QNX 上推理 $ ./run_with_ssh.sh mobilenet_v1_fp32_224 imagenet_224.txt test qnx arm64 qualcomm_qnn 192.168.1.1 22 root root null cache Top1 Egyptian cat - 0.482871 Top2 tabby, tabby cat - 0.471595 Top3 tiger cat - 0.039779 Top4 lynx, catamount - 0.002430 Top5 ping-pong ball - 0.000508 Preprocess time: 38.618000 ms, avg 38.618000 ms, max 38.618000 ms, min 38.618000 ms Prediction time: 8472.844000 ms, avg 8472.844000 ms, max 8472.844000 ms, min 8472.844000 ms Postprocess time: 13.605000 ms, avg 13.605000 ms, max 13.605000 ms, min 13.605000 ms 以 FP16 方式运行 $ rm -rf ../assets/models/cache $ ./run_with_adb.sh mobilenet_v1_fp32_224 imagenet_224.txt test android arm64-v8a qualcomm_qnn 858e5789 "QUALCOMM_QNN_ENABLE_FP16=true" cache $ ./run_with_ssh.sh mobilenet_v1_fp32_224 imagenet_224.txt test qnx arm64 qualcomm_qnn 192.168.1.1 22 root root "QUALCOMM_QNN_ENABLE_FP16=true" cache Top1 Egyptian cat - 0.482910 Top2 tabby, tabby cat - 0.471924 Top3 tiger cat - 0.039612 Top4 lynx, catamount - 0.002340 Top5 ping-pong ball - 0.000490 Preprocess time: 8.393000 ms, avg 8.393000 ms, max 8.393000 ms, min 8.393000 ms Prediction time: 1.689000 ms, avg 1.689000 ms, max 1.689000 ms, min 1.689000 ms Postprocess time: 10.612000 ms, avg 10.612000 ms, max 10.612000 ms, min 10.612000 ms
如果需要更改测试图片，可将图片拷贝到
PaddleLite-generic-demo/image_classification_demo/assets/datasets/test/inputs目录下，同时将图片文件名添加到PaddleLite-generic-demo/image_classification_demo/assets/datasets/test/list.txt中；如果需要重新编译示例程序，直接运行
For linux amd64 $ ./build.sh linux amd64 For android arm64-v8a 需设置 Android ndk 交叉编译环境 $ ./build.sh android arm64-v8a For qnx arm64 需设置 qnx 交叉编译环境 $ ./build.sh qnx arm64
准备编译环境¶
为了保证编译环境一致，建议根据下述约束进行环境配置。
cmake版本：3.16 # 下载参考链接：https://cmake.org/files/v3.16/cmake-3.16.0-rc1-Linux-x86_64.tar.gz gcc版本：7.1以上(8.4.0、9.3.0已验证) # 需要支持c++17 clang版本：6.0以上（9.0已验证） # 需要支持c++17
更新支持 Qualcomm Qnn 的 Paddle Lite 库¶
下载 Paddle Lite 源码；
$ git clone https://github.com/PaddlePaddle/Paddle-Lite.git $ cd Paddle-Lite获取 qualcomm_qnn 适配源码（暂未开源）；
在 Paddle-Lite/lite/backends/nnadapter/nnadapter/src/driver 目录里下载 qualcomm_qnn 代码
请向高通索取 QNN SDK ，解压后目录为 qnn-v1.15.0.220706112757_38277;
请向高通索取 Hexagon SDK，解压后目录为：Hexagon_SDK；
下述为各个平台下的预测库编译命令，根据自身所需进行编译。
编译 Linux x86 simulator 预测库
注：编译时 nnadapter_qualcomm_qnn_sdk_root 和 nnadapter_qualcomm_hexagon_sdk_root 两个变量需要使用绝对路径，采用相对路径可能会产生编译问题 注：请使用 clang 编译 $ export CC=<path/to/clang> $ export CXX=<path/to/clang++> $ cd Paddle-Lite $ ./lite/tools/build_linux.sh \ --arch=x86 \ --with_extra=ON \ --with_log=ON \ --toolchain=clang \ --with_exception=ON \ --with_nnadapter=ON \ --nnadapter_with_qualcomm_qnn=ON \ --nnadapter_qualcomm_qnn_sdk_root=<path/to/qnn-v1.15.0.220706112757_38277> \ --nnadapter_qualcomm_hexagon_sdk_root=<path/to/Hexagon_SDK/4.3.0.0> \ full_publish 替换 x86 simulator预测库 $ rm -rf PaddleLite-generic-demo/libs/PaddleLite/linux/amd64/include $ cp -rf build.lite.linux.x86.clang/inference_lite_lib/cxx/include PaddleLite-generic-demo/libs/PaddleLite/linux/amd64 $ cp build.lite.linux.x86.clang/inference_lite_lib/cxx/lib/libpaddle*.so PaddleLite-generic-demo/libs/PaddleLite/linux/amd64/lib/ $ cp build.lite.linux.x86.clang/inference_lite_lib/cxx/lib/libnnadapter.so PaddleLite-generic-demo/libs/PaddleLite/linux/amd64/lib/qualcomm_qnn/ $ cp build.lite.linux.x86.clang/inference_lite_lib/cxx/lib/libqualcomm_qnn* PaddleLite-generic-demo/libs/PaddleLite/linux/amd64/lib/qualcomm_qnn/ 将高通 QNN SDK 中的依赖的库拷贝到 demo 程序中 $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/x86_64-linux-clang/lib/* PaddleLite-generic-demo/libs/PaddleLite/linux/amd64/lib/qualcomm_qnn
编译 Android arm64-v8a/ameabi-v7a 预测库
For Android arm64-v8a $ cd Paddle-Lite $ ./lite/tools/build_android.sh \ --arch=armv8 \ --with_extra=ON \ --with_log=ON \ --toolchain=clang \ --with_exception=ON \ --with_nnadapter=ON \ --android_stl=c++_shared \ --nnadapter_with_qualcomm_qnn=ON \ --nnadapter_qualcomm_qnn_sdk_root=<path/to/qnn-v1.15.0.220706112757_38277> \ --nnadapter_qualcomm_hexagon_sdk_root=<path/to/Hexagon_SDK/4.3.0.0> \ full_publish 替换 Android arm64-v8a 预测库 $ rm -rf PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/include $ cp -rf build.lite.android.armv8.clang/inference_lite_lib.android.armv8.nnadapter/cxx/include/ PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/include/ $ cp -rf build.lite.android.armv8.clang/inference_lite_lib.android.armv8.nnadapter/cxx/lib/libnnadapter.so PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/lib/qualcomm_qnn/ $ cp -rf build.lite.android.armv8.clang/inference_lite_lib.android.armv8.nnadapter/cxx/lib/libqualcomm_qnn* PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/lib/qualcomm_qnn/ $ cp -rf build.lite.android.armv8.clang/inference_lite_lib.android.armv8.nnadapter/cxx/lib/libpaddle*.so PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/lib/ 将高通 QNN SDK 中的依赖的库拷贝到 demo 程序中 $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/aarch64-android/lib/* PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/lib/qualcomm_qnn $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/hexagon-v68/lib/unsigned/* PaddleLite-generic-demo/libs/PaddleLite/android/arm64-v8a/lib/qualcomm_qnn/hexagon-v68/lib/unsigned For Android ameabi-v7a $ cd Paddle-Lite $ ./lite/tools/build_android.sh \ --arch=armv7 \ --with_extra=ON \ --with_log=ON \ --android_stl=c++_shared \ --toolchain=clang \ --with_exception=ON \ --with_nnadapter=ON \ --nnadapter_with_qualcomm_qnn=ON \ --nnadapter_qualcomm_qnn_sdk_root=<path/to/qnn-v1.15.0.220706112757_38277> \ --nnadapter_qualcomm_hexagon_sdk_root=<path/to/Hexagon_SDK/4.3.0.0> \ full_publish 替换 Android ameabi-v7a 预测库 $ rm -rf PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/include $ cp -rf build.lite.android.armv7.clang/inference_lite_lib.android.armv7.nnadapter/cxx/include/ PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/include/ $ cp -rf build.lite.android.armv7.clang/inference_lite_lib.android.armv7.nnadapter/cxx/lib/libnnadapter.so PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/lib/qualcomm_qnn/ $ cp -rf build.lite.android.armv7.clang/inference_lite_lib.android.armv7.nnadapter/cxx/lib/libqualcomm_qnn* PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/lib/qualcomm_qnn/ $ cp -rf build.lite.android.armv7.clang/inference_lite_lib.android.armv7.nnadapter/cxx/lib/libpaddle*.so PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/lib/ 将高通 QNN SDK 中的依赖的库拷贝到 demo 程序中 $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/arm-android/lib/* PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/lib/qualcomm_qnn $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/hexagon-v68/lib/unsigned/* PaddleLite-generic-demo/libs/PaddleLite/android/armeabi-v7a/lib/qualcomm_qnn/hexagon-v68/lib/unsigned
编译 QNX arm64 预测库
请向高通索取 QNX 工具链，解压后目录为：SDP;
请向高通索取 License，解压后目录为：qnx-key；
设置 QNN 交叉编译环境
$ source SDP/qnx710/qnxsdp-env.sh $ cd qnx-key && tar xvf qnx_license_700_710.tar.gz && cd .. $ export QNX_CONFIGURATION=$(pwd)/qnx-key/home/jone/.qnx $ cp -r $(pwd)/qnx-key/home/jone/.qnx ~/
编译 QNX 预测库
$ cd Paddle-Lite $ ./lite/tools/build_qnx.sh \ --with_extra=ON \ --with_log=ON \ --with_nnadapter=ON \ --nnadapter_with_qualcomm_qnn=ON \ --nnadapter_qualcomm_qnn_sdk_root=<path/to/qnn-v1.15.0.220706112757_38277> \ --nnadapter_qualcomm_hexagon_sdk_root=<path/to/Hexagon_SDK/4.3.0.0>\ full_publish 替换 QNX 预测库 $ rm -rf PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/include $ cp -rf build.lite.qnx.armv8.gcc/inference_lite_lib.qnx.armv8.nnadapter/cxx/include PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64 $ cp build.lite.qnx.armv8.gcc/inference_lite_lib.qnx.armv8.nnadapter/cxx/lib/libpaddle*.so PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/lib/ $ cp build.lite.qnx.armv8.gcc/inference_lite_lib.qnx.armv8.nnadapter/cxx/lib/libnnadapter.so PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/lib/qualcomm_qnn/ $ cp build.lite.qnx.armv8.gcc/inference_lite_lib.qnx.armv8.nnadapter/cxx/lib/libqualcomm_qnn* PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/lib/qualcomm_qnn/
将高通 QNN SDK 中的依赖的库拷贝到 demo 程序中
$ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/aarch64-qnx/lib/* PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/lib/qualcomm_qnn $ cp <path/to/qnn-v1.15.0.220706112757_38277>/target/hexagon-v68/lib/unsigned/* PaddleLite-generic-demo/libs/PaddleLite/qnx/arm64/lib/qualcomm_qnn/hexagon-v68/lib/unsigned
高级特性¶
高级参数
QUALCOMM_QNN_DEVICE_TYPE:
指定使用的高通设备，Options: “CPU”, “GPU”, “DSP”, “HTP”。
QUALCOMM_QNN_LOG_LEVEL:
指定日志等级，Options: “error”, “warn”, “info”, “verbose”, “debug”
QUALCOMM_QNN_SKIP_SYMM2ASYMM
指定是否跳过将输入输出从对称量化转非对称量化的步骤，Options: “true”, “false”, “1”, “0”。
QUALCOMM_QNN_ENABLE_FP16
指定是否开启 FP16 功能，Options: “true”, “false”, “1”, “0”。
FAQ¶
1. 在 QNX 系统上面使用 HTP 推理, 设置 DSP 运行库路径变量¶
指定 DSP 运行库设置如下变量
ADSP_LIBRARY_PATH 或者 CDSP0_LIBRARY_PATH CDSP1_LIBRARY_PATH
如果设置上述变量后，还是会有环境问题导致推理异常（非 Demo 本身问题），该问题一般发生在 QNN SDK 版本切换时(例如: QNN SDK v1.12 <-> QNN SDK v1.15 版本之间的切换)，可以将 <path/to/qnn-v1.15.0.220706112757_38277>/target/hexagon-v68/lib/unsigned/libQnnHtp* 拷贝到板子上 /mnt/etc/images/cdsp0 路径下。如果遇到权限问题拷贝失败，可以通过在 QNX 中执行
mount -uw /mnt命令解决。
其它说明¶
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