mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-06-03 21:53:39 +08:00
Merge remote-tracking branch 'upstream/dygraph' into dy1
This commit is contained in:
Leif
commit
941d1501c1
@ -173,7 +173,7 @@ This project is released under <a href="https://github.com/PaddlePaddle/PaddleOC
|
||||
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
|
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- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
|
||||
- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
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- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitignore and discard set PYTHONPATH manually.
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- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
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- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
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- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
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@ -149,7 +149,7 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测[2]、检测
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- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
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- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
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- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitignore、处理手动设置PYTHONPATH环境变量的问题
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- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
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- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
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- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
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@ -138,12 +138,22 @@ endif()
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# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
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if(WITH_STATIC_LIB)
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
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if(WIN32)
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set(DEPS
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${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
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else()
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
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endif()
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else()
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
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endif()
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if(WIN32)
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set(DEPS
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${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
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else()
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
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endif()
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endif(WITH_STATIC_LIB)
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if (NOT WIN32)
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set(DEPS ${DEPS}
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@ -62,6 +62,10 @@ public:
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this->cls_thresh = stod(config_map_["cls_thresh"]);
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this->visualize = bool(stoi(config_map_["visualize"]));
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this->use_tensorrt = bool(stoi(config_map_["use_tensorrt"]));
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this->use_fp16 = bool(stod(config_map_["use_fp16"]));
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}
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bool use_gpu = false;
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@ -96,6 +100,10 @@ public:
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bool visualize = true;
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bool use_tensorrt = false;
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bool use_fp16 = false;
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void PrintConfigInfo();
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private:
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@ -39,7 +39,8 @@ public:
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explicit Classifier(const std::string &model_dir, const bool &use_gpu,
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const int &gpu_id, const int &gpu_mem,
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const int &cpu_math_library_num_threads,
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const bool &use_mkldnn, const double &cls_thresh) {
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const bool &use_mkldnn, const double &cls_thresh,
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const bool &use_tensorrt, const bool &use_fp16) {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->gpu_mem_ = gpu_mem;
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@ -47,6 +48,8 @@ public:
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this->use_mkldnn_ = use_mkldnn;
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this->cls_thresh = cls_thresh;
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this->use_tensorrt_ = use_tensorrt;
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this->use_fp16_ = use_fp16;
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LoadModel(model_dir);
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}
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@ -69,7 +72,8 @@ private:
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std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
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std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
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bool is_scale_ = true;
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bool use_tensorrt_ = false;
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bool use_fp16_ = false;
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// pre-process
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ClsResizeImg resize_op_;
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Normalize normalize_op_;
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@ -44,8 +44,8 @@ public:
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const bool &use_mkldnn, const int &max_side_len,
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const double &det_db_thresh,
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const double &det_db_box_thresh,
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const double &det_db_unclip_ratio,
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const bool &visualize) {
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const double &det_db_unclip_ratio, const bool &visualize,
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const bool &use_tensorrt, const bool &use_fp16) {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->gpu_mem_ = gpu_mem;
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@ -59,6 +59,8 @@ public:
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this->det_db_unclip_ratio_ = det_db_unclip_ratio;
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this->visualize_ = visualize;
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this->use_tensorrt_ = use_tensorrt;
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this->use_fp16_ = use_fp16;
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LoadModel(model_dir);
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}
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@ -85,6 +87,8 @@ private:
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double det_db_unclip_ratio_ = 2.0;
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bool visualize_ = true;
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bool use_tensorrt_ = false;
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bool use_fp16_ = false;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
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@ -41,12 +41,15 @@ public:
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explicit CRNNRecognizer(const std::string &model_dir, const bool &use_gpu,
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const int &gpu_id, const int &gpu_mem,
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const int &cpu_math_library_num_threads,
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const bool &use_mkldnn, const string &label_path) {
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const bool &use_mkldnn, const string &label_path,
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const bool &use_tensorrt, const bool &use_fp16) {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->gpu_mem_ = gpu_mem;
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this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
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this->use_mkldnn_ = use_mkldnn;
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this->use_tensorrt_ = use_tensorrt;
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this->use_fp16_ = use_fp16;
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this->label_list_ = Utility::ReadDict(label_path);
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this->label_list_.insert(this->label_list_.begin(),
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@ -76,7 +79,8 @@ private:
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std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
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std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
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||||
bool is_scale_ = true;
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||||
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||||
bool use_tensorrt_ = false;
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bool use_fp16_ = false;
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// pre-process
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CrnnResizeImg resize_op_;
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Normalize normalize_op_;
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@ -54,18 +54,20 @@ int main(int argc, char **argv) {
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.max_side_len, config.det_db_thresh,
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config.det_db_box_thresh, config.det_db_unclip_ratio,
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config.visualize);
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config.visualize, config.use_tensorrt, config.use_fp16);
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Classifier *cls = nullptr;
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if (config.use_angle_cls == true) {
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cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.cls_thresh);
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config.use_mkldnn, config.cls_thresh,
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config.use_tensorrt, config.use_fp16);
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}
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CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.char_list_file);
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config.use_mkldnn, config.char_list_file,
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config.use_tensorrt, config.use_fp16);
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auto start = std::chrono::system_clock::now();
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std::vector<std::vector<std::vector<int>>> boxes;
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@ -75,11 +77,11 @@ int main(int argc, char **argv) {
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auto end = std::chrono::system_clock::now();
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auto duration =
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std::chrono::duration_cast<std::chrono::microseconds>(end - start);
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std::cout << "花费了"
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std::cout << "Cost"
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<< double(duration.count()) *
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std::chrono::microseconds::period::num /
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std::chrono::microseconds::period::den
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<< "秒" << std::endl;
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<< "s" << std::endl;
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return 0;
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}
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@ -76,6 +76,13 @@ void Classifier::LoadModel(const std::string &model_dir) {
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 10, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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@ -24,10 +24,13 @@ void DBDetector::LoadModel(const std::string &model_dir) {
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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// config.EnableTensorRtEngine(
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// 1 << 20, 1, 3,
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// AnalysisConfig::Precision::kFloat32,
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// false, false);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 10, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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@ -76,7 +76,7 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
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float(*std::max_element(&predict_batch[n * predict_shape[2]],
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&predict_batch[(n + 1) * predict_shape[2]]));
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||||
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||||
if (argmax_idx > 0 && (not(i > 0 && argmax_idx == last_index))) {
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if (argmax_idx > 0 && (!(i > 0 && argmax_idx == last_index))) {
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score += max_value;
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count += 1;
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str_res.push_back(label_list_[argmax_idx]);
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@ -99,6 +99,13 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 10, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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@ -176,4 +183,4 @@ cv::Mat CRNNRecognizer::GetRotateCropImage(const cv::Mat &srcimage,
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}
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}
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} // namespace PaddleOCR
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} // namespace PaddleOCR
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@ -24,3 +24,7 @@ char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
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# show the detection results
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visualize 1
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# use_tensorrt
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use_tensorrt 0
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use_fp16 0
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|
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61
deploy/slim/quantization/README.md
Normal file
61
deploy/slim/quantization/README.md
Normal file
@ -0,0 +1,61 @@
|
||||
|
||||
## 介绍
|
||||
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
|
||||
模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。
|
||||
|
||||
本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
|
||||
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
|
||||
|
||||
在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
|
||||
|
||||
|
||||
## 快速开始
|
||||
量化多适用于轻量模型在移动端的部署，当训练出一个模型后，如果希望进一步的压缩模型大小并加速预测，可使用量化的方法压缩模型。
|
||||
|
||||
模型量化主要包括五个步骤：
|
||||
1. 安装 PaddleSlim
|
||||
2. 准备训练好的模型
|
||||
3. 量化训练
|
||||
4. 导出量化推理模型
|
||||
5. 量化模型预测部署
|
||||
|
||||
### 1. 安装PaddleSlim
|
||||
|
||||
```bash
|
||||
git clone https://github.com/PaddlePaddle/PaddleSlim.git
|
||||
cd Paddleslim
|
||||
python setup.py install
|
||||
```
|
||||
|
||||
### 2. 准备训练好的模型
|
||||
|
||||
PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.md)，如果待量化的模型不在列表中，需要按照[常规训练](../../../doc/doc_ch/quickstart.md)方法得到训练好的模型。
|
||||
|
||||
### 3. 量化训练
|
||||
量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。
|
||||
|
||||
|
||||
量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
|
||||
```bash
|
||||
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
|
||||
|
||||
# 比如下载提供的训练模型
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
|
||||
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
|
||||
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
|
||||
|
||||
```
|
||||
如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
|
||||
|
||||
### 4. 导出模型
|
||||
|
||||
在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
|
||||
|
||||
```bash
|
||||
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
|
||||
```
|
||||
|
||||
### 5. 量化模型部署
|
||||
|
||||
上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
|
||||
量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)
|
||||
68
deploy/slim/quantization/README_en.md
Normal file
68
deploy/slim/quantization/README_en.md
Normal file
@ -0,0 +1,68 @@
|
||||
|
||||
## Introduction
|
||||
|
||||
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
|
||||
Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
|
||||
so as to reduce model calculation complexity and improve model inference performance.
|
||||
|
||||
This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
|
||||
|
||||
It is recommended that you could understand following pages before reading this example：
|
||||
- [The training strategy of OCR model](../../../doc/doc_en/quickstart_en.md)
|
||||
- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
|
||||
|
||||
## Quick Start
|
||||
Quantization is mostly suitable for the deployment of lightweight models on mobile terminals.
|
||||
After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps.
|
||||
|
||||
1. Install PaddleSlim
|
||||
2. Prepare trained model
|
||||
3. Quantization-Aware Training
|
||||
4. Export inference model
|
||||
5. Deploy quantization inference model
|
||||
|
||||
|
||||
### 1. Install PaddleSlim
|
||||
|
||||
```bash
|
||||
git clone https://github.com/PaddlePaddle/PaddleSlim.git
|
||||
cd Paddleslim
|
||||
python setup.py install
|
||||
```
|
||||
|
||||
|
||||
### 2. Download Pretrain Model
|
||||
PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
|
||||
If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
|
||||
|
||||
|
||||
### 3. Quant-Aware Training
|
||||
Quantization training includes offline quantization training and online quantization training.
|
||||
Online quantization training is more effective. It is necessary to load the pre-training model.
|
||||
After the quantization strategy is defined, the model can be quantified.
|
||||
|
||||
The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
|
||||
```bash
|
||||
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
|
||||
|
||||
# download provided model
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
|
||||
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
|
||||
python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
|
||||
|
||||
```
|
||||
|
||||
|
||||
### 4. Export inference model
|
||||
|
||||
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
|
||||
|
||||
```bash
|
||||
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
|
||||
```
|
||||
|
||||
### 5. Deploy
|
||||
The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
|
||||
The derived model can be converted through the `opt tool` of PaddleLite.
|
||||
|
||||
For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
|
||||
118
deploy/slim/quantization/export_model.py
Executable file
118
deploy/slim/quantization/export_model.py
Executable file
@ -0,0 +1,118 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
|
||||
sys.path.append(
|
||||
os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
|
||||
|
||||
import argparse
|
||||
|
||||
import paddle
|
||||
from paddle.jit import to_static
|
||||
|
||||
from ppocr.modeling.architectures import build_model
|
||||
from ppocr.postprocess import build_post_process
|
||||
from ppocr.utils.save_load import init_model
|
||||
from ppocr.utils.logging import get_logger
|
||||
from tools.program import load_config, merge_config, ArgsParser
|
||||
from ppocr.metrics import build_metric
|
||||
import tools.program as program
|
||||
from paddleslim.dygraph.quant import QAT
|
||||
from ppocr.data import build_dataloader
|
||||
|
||||
|
||||
def main():
|
||||
############################################################################################################
|
||||
# 1. quantization configs
|
||||
############################################################################################################
|
||||
quant_config = {
|
||||
# weight preprocess type, default is None and no preprocessing is performed.
|
||||
'weight_preprocess_type': None,
|
||||
# activation preprocess type, default is None and no preprocessing is performed.
|
||||
'activation_preprocess_type': None,
|
||||
# weight quantize type, default is 'channel_wise_abs_max'
|
||||
'weight_quantize_type': 'channel_wise_abs_max',
|
||||
# activation quantize type, default is 'moving_average_abs_max'
|
||||
'activation_quantize_type': 'moving_average_abs_max',
|
||||
# weight quantize bit num, default is 8
|
||||
'weight_bits': 8,
|
||||
# activation quantize bit num, default is 8
|
||||
'activation_bits': 8,
|
||||
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
|
||||
'dtype': 'int8',
|
||||
# window size for 'range_abs_max' quantization. default is 10000
|
||||
'window_size': 10000,
|
||||
# The decay coefficient of moving average, default is 0.9
|
||||
'moving_rate': 0.9,
|
||||
# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
|
||||
'quantizable_layer_type': ['Conv2D', 'Linear'],
|
||||
}
|
||||
FLAGS = ArgsParser().parse_args()
|
||||
config = load_config(FLAGS.config)
|
||||
merge_config(FLAGS.opt)
|
||||
logger = get_logger()
|
||||
# build post process
|
||||
|
||||
post_process_class = build_post_process(config['PostProcess'],
|
||||
config['Global'])
|
||||
|
||||
# build model
|
||||
# for rec algorithm
|
||||
if hasattr(post_process_class, 'character'):
|
||||
char_num = len(getattr(post_process_class, 'character'))
|
||||
config['Architecture']["Head"]['out_channels'] = char_num
|
||||
model = build_model(config['Architecture'])
|
||||
|
||||
# get QAT model
|
||||
quanter = QAT(config=quant_config)
|
||||
quanter.quantize(model)
|
||||
|
||||
init_model(config, model, logger)
|
||||
model.eval()
|
||||
|
||||
# build metric
|
||||
eval_class = build_metric(config['Metric'])
|
||||
|
||||
# build dataloader
|
||||
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
|
||||
|
||||
# start eval
|
||||
metirc = program.eval(model, valid_dataloader, post_process_class,
|
||||
eval_class)
|
||||
logger.info('metric eval ***************')
|
||||
for k, v in metirc.items():
|
||||
logger.info('{}:{}'.format(k, v))
|
||||
|
||||
save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
|
||||
infer_shape = [3, 32, 100] if config['Architecture'][
|
||||
'model_type'] != "det" else [3, 640, 640]
|
||||
|
||||
quanter.save_quantized_model(
|
||||
model,
|
||||
save_path,
|
||||
input_spec=[
|
||||
paddle.static.InputSpec(
|
||||
shape=[None] + infer_shape, dtype='float32')
|
||||
])
|
||||
logger.info('inference QAT model is saved to {}'.format(save_path))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
config, device, logger, vdl_writer = program.preprocess()
|
||||
main()
|
||||
166
deploy/slim/quantization/quant.py
Executable file
166
deploy/slim/quantization/quant.py
Executable file
@ -0,0 +1,166 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
|
||||
sys.path.append(
|
||||
os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
|
||||
|
||||
import yaml
|
||||
import paddle
|
||||
import paddle.distributed as dist
|
||||
|
||||
paddle.seed(2)
|
||||
|
||||
from ppocr.data import build_dataloader
|
||||
from ppocr.modeling.architectures import build_model
|
||||
from ppocr.losses import build_loss
|
||||
from ppocr.optimizer import build_optimizer
|
||||
from ppocr.postprocess import build_post_process
|
||||
from ppocr.metrics import build_metric
|
||||
from ppocr.utils.save_load import init_model
|
||||
import tools.program as program
|
||||
from paddleslim.dygraph.quant import QAT
|
||||
|
||||
dist.get_world_size()
|
||||
|
||||
|
||||
class PACT(paddle.nn.Layer):
|
||||
def __init__(self):
|
||||
super(PACT, self).__init__()
|
||||
alpha_attr = paddle.ParamAttr(
|
||||
name=self.full_name() + ".pact",
|
||||
initializer=paddle.nn.initializer.Constant(value=20),
|
||||
learning_rate=1.0,
|
||||
regularizer=paddle.regularizer.L2Decay(2e-5))
|
||||
|
||||
self.alpha = self.create_parameter(
|
||||
shape=[1], attr=alpha_attr, dtype='float32')
|
||||
|
||||
def forward(self, x):
|
||||
out_left = paddle.nn.functional.relu(x - self.alpha)
|
||||
out_right = paddle.nn.functional.relu(-self.alpha - x)
|
||||
x = x - out_left + out_right
|
||||
return x
|
||||
|
||||
|
||||
quant_config = {
|
||||
# weight preprocess type, default is None and no preprocessing is performed.
|
||||
'weight_preprocess_type': None,
|
||||
# activation preprocess type, default is None and no preprocessing is performed.
|
||||
'activation_preprocess_type': None,
|
||||
# weight quantize type, default is 'channel_wise_abs_max'
|
||||
'weight_quantize_type': 'channel_wise_abs_max',
|
||||
# activation quantize type, default is 'moving_average_abs_max'
|
||||
'activation_quantize_type': 'moving_average_abs_max',
|
||||
# weight quantize bit num, default is 8
|
||||
'weight_bits': 8,
|
||||
# activation quantize bit num, default is 8
|
||||
'activation_bits': 8,
|
||||
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
|
||||
'dtype': 'int8',
|
||||
# window size for 'range_abs_max' quantization. default is 10000
|
||||
'window_size': 10000,
|
||||
# The decay coefficient of moving average, default is 0.9
|
||||
'moving_rate': 0.9,
|
||||
# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
|
||||
'quantizable_layer_type': ['Conv2D', 'Linear'],
|
||||
}
|
||||
|
||||
|
||||
def main(config, device, logger, vdl_writer):
|
||||
# init dist environment
|
||||
if config['Global']['distributed']:
|
||||
dist.init_parallel_env()
|
||||
|
||||
global_config = config['Global']
|
||||
|
||||
# build dataloader
|
||||
train_dataloader = build_dataloader(config, 'Train', device, logger)
|
||||
if config['Eval']:
|
||||
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
|
||||
else:
|
||||
valid_dataloader = None
|
||||
|
||||
# build post process
|
||||
post_process_class = build_post_process(config['PostProcess'],
|
||||
global_config)
|
||||
|
||||
# build model
|
||||
# for rec algorithm
|
||||
if hasattr(post_process_class, 'character'):
|
||||
char_num = len(getattr(post_process_class, 'character'))
|
||||
config['Architecture']["Head"]['out_channels'] = char_num
|
||||
model = build_model(config['Architecture'])
|
||||
|
||||
# prepare to quant
|
||||
quanter = QAT(config=quant_config, act_preprocess=PACT)
|
||||
quanter.quantize(model)
|
||||
|
||||
if config['Global']['distributed']:
|
||||
model = paddle.DataParallel(model)
|
||||
|
||||
# build loss
|
||||
loss_class = build_loss(config['Loss'])
|
||||
|
||||
# build optim
|
||||
optimizer, lr_scheduler = build_optimizer(
|
||||
config['Optimizer'],
|
||||
epochs=config['Global']['epoch_num'],
|
||||
step_each_epoch=len(train_dataloader),
|
||||
parameters=model.parameters())
|
||||
|
||||
# build metric
|
||||
eval_class = build_metric(config['Metric'])
|
||||
# load pretrain model
|
||||
pre_best_model_dict = init_model(config, model, logger, optimizer)
|
||||
|
||||
logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
|
||||
format(len(train_dataloader), len(valid_dataloader)))
|
||||
# start train
|
||||
program.train(config, train_dataloader, valid_dataloader, device, model,
|
||||
loss_class, optimizer, lr_scheduler, post_process_class,
|
||||
eval_class, pre_best_model_dict, logger, vdl_writer)
|
||||
|
||||
|
||||
def test_reader(config, device, logger):
|
||||
loader = build_dataloader(config, 'Train', device, logger)
|
||||
import time
|
||||
starttime = time.time()
|
||||
count = 0
|
||||
try:
|
||||
for data in loader():
|
||||
count += 1
|
||||
if count % 1 == 0:
|
||||
batch_time = time.time() - starttime
|
||||
starttime = time.time()
|
||||
logger.info("reader: {}, {}, {}".format(
|
||||
count, len(data[0]), batch_time))
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
logger.info("finish reader: {}, Success!".format(count))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
config, device, logger, vdl_writer = program.preprocess(is_train=True)
|
||||
main(config, device, logger, vdl_writer)
|
||||
# test_reader(config, device, logger)
|
||||
@ -21,9 +21,8 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
|
||||
|
||||
```
|
||||
" 图像文件名 图像标注信息 "
|
||||
|
||||
train_data/cls/word_001.jpg 0
|
||||
train_data/cls/word_002.jpg 180
|
||||
train/word_001.jpg 0
|
||||
train/word_002.jpg 180
|
||||
```
|
||||
|
||||
最终训练集应有如下文件结构：
|
||||
@ -55,6 +54,8 @@ train_data/cls/word_002.jpg 180
|
||||
|
||||
### 启动训练
|
||||
|
||||
将准备好的txt文件和图片文件夹路径分别写入配置文件的 `Train/Eval.dataset.label_file_list` 和 `Train/Eval.dataset.data_dir` 字段下，`Train/Eval.dataset.data_dir`字段下的路径和文件里记载的图片名构成了图片的绝对路径。
|
||||
|
||||
PaddleOCR提供了训练脚本、评估脚本和预测脚本。
|
||||
|
||||
开始训练:
|
||||
|
||||
@ -211,6 +211,6 @@ PaddleOCR
|
||||
├── README_ch.md // 中文说明文档
|
||||
├── README_en.md // 英文说明文档
|
||||
├── README.md // 主页说明文档
|
||||
├── requirements.txt // 安装依赖
|
||||
├── requirements.txt // 安装依赖
|
||||
├── setup.py // whl包打包脚本
|
||||
├── train.sh // 启动训练脚本
|
||||
|
||||
@ -23,8 +23,8 @@ First put the training images in the same folder (train_images), and use a txt f
|
||||
```
|
||||
" Image file name Image annotation "
|
||||
|
||||
train_data/word_001.jpg 0
|
||||
train_data/word_002.jpg 180
|
||||
train/word_001.jpg 0
|
||||
train/word_002.jpg 180
|
||||
```
|
||||
|
||||
The final training set should have the following file structure:
|
||||
@ -57,6 +57,7 @@ containing all images (test) and a cls_gt_test.txt. The structure of the test se
|
||||
```
|
||||
|
||||
### TRAINING
|
||||
Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file.
|
||||
|
||||
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
|
||||
|
||||
|
||||
@ -26,6 +26,8 @@ class RecMetric(object):
|
||||
all_num = 0
|
||||
norm_edit_dis = 0.0
|
||||
for (pred, pred_conf), (target, _) in zip(preds, labels):
|
||||
pred = pred.replace(" ", "")
|
||||
target = target.replace(" ", "")
|
||||
norm_edit_dis += Levenshtein.distance(pred, target) / max(
|
||||
len(pred), len(target))
|
||||
if pred == target:
|
||||
|
||||
@ -57,7 +57,7 @@ def get_image_file_list(img_file):
|
||||
elif os.path.isdir(img_file):
|
||||
for single_file in os.listdir(img_file):
|
||||
file_path = os.path.join(img_file, single_file)
|
||||
if imghdr.what(file_path) in img_end:
|
||||
if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
|
||||
imgs_lists.append(file_path)
|
||||
if len(imgs_lists) == 0:
|
||||
raise Exception("not found any img file in {}".format(img_file))
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user