diff --git a/deploy/lite/readme.md b/deploy/lite/readme.md index 112ea6a747ce2aaef6ca1123811d39608b74f765..473984dd3315fbc547fd83a1df57c68953b2fbbb 100644 --- a/deploy/lite/readme.md +++ b/deploy/lite/readme.md @@ -158,7 +158,7 @@ git clone https://github.com/LDOUBLEV/AutoLog.git ``` ```shell -# 克隆 Autolog 代码库，以便获取自动化日志 +# 编译 make -j ``` diff --git a/docs/en/inference_deployment/paddle_lite_deploy_en.md b/docs/en/inference_deployment/paddle_lite_deploy_en.md index 12fa23fb90208adaa2f8c9d7f13bb19f7b7f82b0..8fe92ee18fa80c30522cf80a38403ffd6078c0d5 100644 --- a/docs/en/inference_deployment/paddle_lite_deploy_en.md +++ b/docs/en/inference_deployment/paddle_lite_deploy_en.md @@ -18,6 +18,7 @@ If you only want to test speed, please refer to [The tutorial of Paddle-Lite mob - [2.1.1 [RECOMMEND] Use pip to install Paddle-Lite and optimize model](#2.1.1) - [2.1.2 Compile Paddle-Lite to generate opt tool](#2.1.2) - [2.1.3 Demo of get the optimized model](#2.1.3) + - [2.1.4 Compile to get the executable file clas_system](#2.1.4) - [2.2 Run optimized model on Phone](#2.2) - [3. FAQ](#3) @@ -40,8 +41,8 @@ For the detailed compilation directions of different development environments, p |Platform|Inference Library Download Link| |-|-| -|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)| -|iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)| +|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz) | +|iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)| **NOTE**: @@ -53,7 +54,7 @@ For the detailed compilation directions of different development environments, p The structure of the inference library is as follows: ``` -inference_lite_lib.android.armv8/ +inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/ |-- cxx C++ inference library and header files | |-- include C++ header files | | |-- paddle_api.h @@ -148,6 +149,23 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel -- ``` When the above code command is completed, there will be ``MobileNetV3_large_x1_0.nb` in the current directory, which is the converted model file. + + +#### 2.1.4 Compile to get the executable file clas_system + +```shell +# Clone the Autolog repository to get automation logs +cd PaddleClas_root_path +cd deploy/lite/ +git clone https://github.com/LDOUBLEV/AutoLog.git +``` + +```shell +# Compile +make -j +``` + +After executing the `make` command, the `clas_system` executable file is generated in the current directory, which is used for Lite prediction. ## 2.2 Run optimized model on Phone @@ -172,7 +190,7 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0 * Install ADB for windows If install ADB fo Windows, you need to download from Google's Android platform: [Download Link](https://developer.android.com/studio). - First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows: +3. First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows: ```shell $ adb devices @@ -183,42 +201,22 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0 If there is `device` output like the above, it means the installation was successful. -4. Prepare optimized model, inference library files, test image and dictionary file used. +4. Push the optimized model, prediction library file, test image and class map file to the phone. ```shell -cd PaddleClas_root_path -cd deploy/lite/ - -# prepare.sh will put the inference library files, the test image and the dictionary files in demo/cxx/clas -sh prepare.sh /{lite inference library path}/inference_lite_lib.android.armv8 - -# enter the working directory of lite demo -cd /{lite inference library path}/inference_lite_lib.android.armv8/ -cd demo/cxx/clas/ - -# copy the C++ inference dynamic library file （ie. .so) to the debug folder -cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/ +```shell +adb shell mkdir -p /data/local/tmp/arm_cpu/ +adb push clas_system /data/local/tmp/arm_cpu/ +adb shell chmod +x /data/local/tmp/arm_cpu//clas_system +adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/ +adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/ +adb push config.txt /data/local/tmp/arm_cpu/ +adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/ +adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/ ``` -The `prepare.sh` take `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` as the test image, and copy it to the `demo/cxx/clas/debug/` directory. - You should put the model that optimized by `paddle_lite_opt` under the `demo/cxx/clas/debug/` directory. In this example, use `MobileNetV3_large_x1_0.nb` model file generated in [2.1.3](#2.1.3). -The structure of the clas demo is as follows after the above command is completed: - -``` -demo/cxx/clas/ -|-- debug/ -| |--MobileNetV3_large_x1_0.nb class model -| |--tabby_cat.jpg test image -| |--imagenet1k_label_list.txt dictionary file -| |--libpaddle_light_api_shared.so C++ .so file -| |--config.txt config file -|-- config.txt config file -|-- image_classfication.cpp source code -|-- Makefile compile file -``` - **NOTE**: * `Imagenet1k_label_list.txt` is the category mapping file of the `ImageNet1k` dataset. If use a custom category, you need to replace the category mapping file. @@ -229,33 +227,22 @@ clas_model_file ./MobileNetV3_large_x1_0.nb # path of model file label_path ./imagenet1k_label_list.txt # path of category mapping file resize_short_size 256 # the short side length after resize crop_size 224 # side length used for inference after cropping - visualize 0 # whether to visualize. If you set it to 1, an image file named 'clas_result.png' will be generated in the current directory. +num_threads 1 # The number of threads, the default is 1 +precision FP32 # Precision type, you can choose FP32 or INT8, the default is FP32 +runtime_device arm_cpu # Device type, the default is arm_cpu +enable_benchmark 0 # Whether to enable benchmark, the default is 0 +tipc_benchmark 0 # Whether to enable tipc_benchmark, the default is 0 ``` 5. Run Model on Phone -```shell -# run compile to get the executable file 'clas_system' -make -j - -# move the compiled executable file to the debug folder -mv clas_system ./debug/ - -# push the debug folder to Phone -adb push debug /data/local/tmp/ +Execute the following command to complete the prediction on the mobile phone. -adb shell -cd /data/local/tmp/debug -export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH - -# the usage of clas_system is as follows: -# ./clas_system "path of config file" "path of test image" -./clas_system ./config.txt ./tabby_cat.jpg +```shell +adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg' ``` -**NOTE**: If you make changes to the code, you need to recompile and repush the `debug ` folder to the phone. - The result is as follows: ![](../../images/inference_deployment/lite_demo_result.png) diff --git a/docs/zh_CN/inference_deployment/paddle_lite_deploy.md b/docs/zh_CN/inference_deployment/paddle_lite_deploy.md index 5bfdd7c44925b1e69640487789de1d53fddbcb44..68480f769a67aae33ca614b0eede2581fcf57392 100644 --- a/docs/zh_CN/inference_deployment/paddle_lite_deploy.md +++ b/docs/zh_CN/inference_deployment/paddle_lite_deploy.md @@ -172,7 +172,7 @@ git clone https://github.com/LDOUBLEV/AutoLog.git ``` ```shell -# 克隆 Autolog 代码库，以便获取自动化日志 +# 编译 make -j ```