From 565c89de87ffe9cb4af6cee130a578550931fbfc Mon Sep 17 00:00:00 2001 From: Chunwei Date: Thu, 12 Sep 2019 20:05:32 +0800 Subject: [PATCH] update cpp_demo.md --- cpp_demo.md | 170 +++++++++++++++++++++++++++------------------------- 1 file changed, 87 insertions(+), 83 deletions(-) diff --git a/cpp_demo.md b/cpp_demo.md index 2dd07083fb..887e1acefb 100644 --- a/cpp_demo.md +++ b/cpp_demo.md @@ -1,12 +1,13 @@ -* [C++ Demo](#c-demo) - * [编译](#编译-1) - * [准备执行环境](#准备执行环境) - * [使用安卓手机](#使用安卓手机) - * [使用安卓模拟器](#使用安卓模拟器) - * [下载模型并运行示例](#下载模型并运行示例) - * [Demo 程序运行结果](#demo-程序运行结果) - * [如何在代码中使用 API](#如何在代码中使用-api) + +- [C++ Demo](#c-demo) + - [编译](#编译-1) + - [准备执行环境](#准备执行环境) + - [使用安卓手机](#使用安卓手机) + - [使用安卓模拟器](#使用安卓模拟器) + - [下载模型并运行示例](#下载模型并运行示例) + - [Demo 程序运行结果](#demo-程序运行结果) + - [如何在代码中使用 API](#如何在代码中使用-api) @@ -84,6 +85,7 @@ adb -s emulator-5554 push mobilenetv1_full_api /data/local/tmp/ adb -s emulator-5554 shell chmod +x /data/local/tmp/mobilenetv1_full_api adb -s emulator-5554 shell "/data/local/tmp/mobilenetv1_full_api --model_dir=/data/local/tmp/mobilenet_v1 --optimized_model_dir=/data/local/tmp/mobilenet_v1.opt" ``` + 注：我们也提供了轻量级 API 的 demo，可以执行以下代码运行轻量级 API 示例。 ```bash @@ -93,7 +95,9 @@ adb -s emulator-5554 push mobilenetv1_light_api /data/local/tmp/ adb -s emulator-5554 shell chmod +x /data/local/tmp/mobilenetv1_light_api adb -s emulator-5554 shell "/data/local/tmp/mobilenetv1_light_api --model_dir=/data/local/tmp/mobilenet_v1.opt --threads=1 " ``` + ## Demo 程序运行结果 + Demo 运行成功后 ，将在控制台输出预测结果的前10个类别的预测概率： ```bash @@ -128,76 +132,76 @@ Output[900]: 0.000586 - 通过MobileConfig设置：模型文件位置（model_dir）、线程数（thread）和能耗模式( power mode )。输入数据（input），从 MobileConfig 创建 PaddlePredictor 并执行预测。 （注：Lite还支持从memory直接加载模型，可以通过MobileConfig::set_model_buffer方法实现） 代码示例： -```cpp - // 1. Create MobileConfig - MobileConfig config; - - // 2. Load model - config.set_model_dir("path to your model directory"); //model dir - /*load model: Lite supports loading model from file or from memory (naive buffer from optimized model) - //Method One: Load model from memory: - void set_model_buffer(const char* model_buffer, - size_t model_buffer_size, - const char* param_buffer, - size_t param_buffer_size) - //Method Two: Load model from file: - void set_model_dir(const std::string& model_dir) */ - - // 3. Set MobileConfig (or you can skip this step to use default value): - config.set_power_mode(LITE_POWER_HIGH); //power mode - /*power modes: Lite supports the following power modes - LITE_POWER_HIGH - LITE_POWER_LOW - LITE_POWER_FULL - LITE_POWER_NO_BIND - LITE_POWER_RAND_HIGH - LITE_POWER_RAND_LOW */ - config.set_threads("num of threads"); //threads - - // 4. Create PaddlePredictor by MobileConfig - std::shared_ptr predictor = - CreatePaddlePredictor(config); - // 5. Prepare input data - std::unique_ptr input_tensor(std::move(predictor->GetInput(0))); - input_tensor->Resize({1, 3, 224, 224}); - auto* data = input_tensor->mutable_data(); - for (int i = 0; i < ShapeProduction(input_tensor->shape()); ++i) { - data[i] = 1; - } +```cpp +// 1. Create MobileConfig +MobileConfig config; + +// 2. Load model +config.set_model_dir("path to your model directory"); // model dir +//load model: Lite supports loading model from file or from memory (naive buffer from optimized model) +//Method One: Load model from memory: +void set_model_buffer(const char* model_buffer, + size_t model_buffer_size, + const char* param_buffer, + size_t param_buffer_size) +//Method Two: Load model from file: +void set_model_dir(const std::string& model_dir) */ + +// 3. Set MobileConfig (or you can skip this step to use default value): +config.set_power_mode(LITE_POWER_HIGH); // power mode +/*power modes: Lite supports the following power modes + LITE_POWER_HIGH + LITE_POWER_LOW + LITE_POWER_FULL + LITE_POWER_NO_BIND + LITE_POWER_RAND_HIGH + LITE_POWER_RAND_LOW */ +config.set_threads("num of threads"); // threads + +// 4. Create PaddlePredictor by MobileConfig +std::shared_ptr predictor = + CreatePaddlePredictor(config); + +// 5. Prepare input data +std::unique_ptr input_tensor(std::move(predictor->GetInput(0))); +input_tensor->Resize({1, 3, 224, 224}); +auto *data = input_tensor -> mutable_data(); +for (int i = 0; i < ShapeProduction(input_tensor->shape()); ++i) { + data[i] = 1; +} - // 6. Run predictor - predictor->Run(); +// 6. Run predictor +predictor->Run(); - // 7. Get output - std::unique_ptr output_tensor( - std::move(predictor->GetOutput(0))); +// 7. Get output +std::unique_ptr output_tensor(std::move(predictor->GetOutput(0))); ``` ## CxxConfig案例: OCR_model的运行 1. OCR 模型文件： - - 我们提供Pb格式的[ocr_attention_mode](https://paddle-inference-dist.cdn.bcebos.com/ocr_attention.tar.gz)l下载 - - 也可以从[Paddle/model项目](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/ocr_recognition)中训练出模型 - + - 我们提供Pb格式的[ocr_attention_mode](https://paddle-inference-dist.cdn.bcebos.com/ocr_attention.tar.gz)l下载 + - 也可以从[Paddle/model项目](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/ocr_recognition)中训练出模型 2. 示例代码： ```c++ +#include "paddle_api.h" // NOLINT +#include "paddle_use_kernels.h" // NOLINT +#include "paddle_use_ops.h" // NOLINT +#include "paddle_use_passes.h" // NOLINT #include #include #include -#include "paddle_api.h" // NOLINT -#include "paddle_use_kernels.h" // NOLINT -#include "paddle_use_ops.h" // NOLINT -#include "paddle_use_passes.h" // NOLINT -using namespace paddle::lite_api; // NOLINT +using namespace paddle::lite_api; // NOLINT DEFINE_string(model_dir, "", "Model dir path."); DEFINE_bool(prefer_int8_kernel, false, "Prefer to run model with int8 kernels"); -int64_t ShapeProduction(const shape_t& shape) { +int64_t ShapeProduction(const shape_t &shape) { int64_t res = 1; - for (auto i : shape) res *= i; + for (auto i : shape) + res *= i; return res; } @@ -205,7 +209,8 @@ void RunModel() { // 1. Set CxxConfig CxxConfig config; config.set_model_dir(FLAGS_model_dir); - std::vector valid_places{Place{TARGET(kARM), PRECISION(kFloat)},Place{TARGET(kHost), PRECISION(kFloat)}}; + std::vector valid_places{Place{TARGET(kARM), PRECISION(kFloat)}, + Place{TARGET(kHost), PRECISION(kFloat)}}; config.set_preferred_place(Place{TARGET(kARM), PRECISION(kFloat)}); config.set_valid_places(valid_places); @@ -214,45 +219,44 @@ void RunModel() { CreatePaddlePredictor(config); // 3. Prepare input data - //input 0 + // input 0 std::unique_ptr input_tensor(std::move(predictor->GetInput(0))); - input_tensor->Resize(shape_t({1,1,48,512})); - auto* data = input_tensor->mutable_data(); - for(int i = 0; i < ShapeProduction(input_tensor->shape()); ++i){ - data[i] = 1; + input_tensor->Resize(shape_t({1, 1, 48, 512})); + auto *data = input_tensor->mutable_data(); + for (int i = 0; i < ShapeProduction(input_tensor->shape()); ++i) { + data[i] = 1; } - //input1 + // input1 std::unique_ptr init_ids(std::move(predictor->GetInput(1))); - init_ids->Resize(shape_t({1,1})); - auto* data_ids = init_ids->mutable_data(); - for(int i = 0; i < ShapeProduction(init_ids->shape()); ++i){ - data_ids[i] = 0; + init_ids->Resize(shape_t({1, 1})); + auto *data_ids = init_ids->mutable_data(); + for (int i = 0; i < ShapeProduction(init_ids->shape()); ++i) { + data_ids[i] = 0; } - lod_t lod_i{{0,1},{0,1}}; + lod_t lod_i({{0, 1}, {0, 1}}); init_ids->SetLoD(lod_i); - //input2 + // input2 std::unique_ptr init_scores(std::move(predictor->GetInput(2))); - init_scores->Resize(shape_t({1,1})); - auto* data_scores = init_scores->mutable_data(); - for(int i = 0; i < ShapeProduction(init_scores->shape()); ++i){ - data_scores[i] = 0; + init_scores->Resize(shape_t({1, 1})); + auto *data_scores = init_scores->mutable_data(); + for (int i = 0; i < ShapeProduction(init_scores->shape()); ++i) { + data_scores[i] = 0; } - lod_t lod_s{{0,1},{0,1}}; + lod_t lod_s{{0, 1}, {0, 1}}; init_scores->SetLoD(lod_s); - // 4. Run predictor predictor->Run(); - // 5. Get output + // 5. Get output std::unique_ptr output_tensor( std::move(predictor->GetOutput(0))); - for (int i = 0; i < ShapeProduction(output_tensor->shape()); i ++) { + for (int i = 0; i < ShapeProduction(output_tensor->shape()); i++) { printf("Output[%d]: %f\n", i, output_tensor->data()[i]); } } -int main(int argc, char** argv) { +int main(int argc, char **argv) { google::ParseCommandLineFlags(&argc, &argv, true); RunModel(); return 0; @@ -260,8 +264,8 @@ int main(int argc, char** argv) { ``` 3. 运行方法： -参考以上代码编译出可执行文件`OCR_DEMO`，模型文件夹为`ocr_attention`。手机以USB调试、文件传输模式连接电脑 -在终端中输入以下命令执行OCR model测试： + 参考以上代码编译出可执行文件`OCR_DEMO`，模型文件夹为`ocr_attention`。手机以USB调试、文件传输模式连接电脑 + 在终端中输入以下命令执行OCR model测试： ``` #OCR_DEMO为编译出的可执行文件名称，ocr_attention为ocr_attention模型的文件夹名称 -- GitLab