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* 10-29/14:05 * 新增cmake * 新增rknpu2 backend * 10-29/14:43 * Runtime fd_type新增RKNPU代码 * 10-29/15:02 * 新增ppseg RKNPU2推理代码 * 10-29/15:46 * 新增ppseg RKNPU2 cpp example代码 * 10-29/15:51 * 新增README文档 * 10-29/15:51 * 按照要求修改部分注释以及变量名称 * 10-29/15:51 * 修复重命名之后,cc文件中的部分代码还用旧函数名的bug * 10-29/22:32 * str(Device::NPU)将输出NPU而不是UNKOWN * 修改runtime文件中的注释格式 * 新增Building Summary ENABLE_RKNPU2_BACKEND输出 * pybind新增支持rknpu2 * 新增python编译选项 * 新增PPSeg Python代码 * 新增以及更新各种文档 * 10-30/14:11 * 尝试修复编译cuda时产生的错误 * 10-30/19:27 * 修改CpuName和CoreMask层级 * 修改ppseg rknn推理层级 * 图片将移动到网络进行下载 * 10-30/19:39 * 更新文档 * 10-30/19:39 * 更新文档 * 更新ppseg rknpu2 example中的函数命名方式 * 更新ppseg rknpu2 example为一个cc文件 * 修复disable_normalize_and_permute部分的逻辑错误 * 移除rknpu2初始化时的无用参数 * 10-30/19:39 * 尝试重置python代码 * 10-30/10:16 * rknpu2_config.h文件不再包含rknn_api头文件防止出现导入错误的问题 * 10-31/14:31 * 修改pybind,支持最新的rknpu2 backends * 再次支持ppseg python推理 * 移动cpuname 和 coremask的层级 * 10-31/15:35 * 尝试修复rknpu2导入错误 * 10-31/19:00 * 新增RKNPU2模型导出代码以及其对应的文档 * 更新大量文档错误 * 10-31/19:00 * 现在编译完fastdeploy仓库后无需重新设置RKNN2_TARGET_SOC * 10-31/19:26 * 修改部分错误文档 * 10-31/19:26 * 修复错误删除的部分 * 修复各种错误文档 * 修复FastDeploy.cmake在设置RKNN2_TARGET_SOC错误时,提示错误的信息 * 修复rknpu2_backend.cc中存在的中文注释 * 10-31/20:45 * 删除无用的注释 * 10-31/20:45 * 按照要求修改Device::NPU为Device::RKNPU,硬件将共用valid_hardware_backends * 删除无用注释以及debug代码 * 11-01/09:45 * 更新变量命名方式 * 11-01/10:16 * 修改部分文档,修改函数命名方式 Co-authored-by: Jason <jiangjiajun@baidu.com>
124 lines
4.5 KiB
C++
124 lines
4.5 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "fastdeploy/runtime.h"
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namespace fastdeploy {
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/*! @brief Base model object for all the vision models
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*/
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class FASTDEPLOY_DECL FastDeployModel {
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public:
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/// Get model's name
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virtual std::string ModelName() const { return "NameUndefined"; }
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/** \brief Inference the model by the runtime. This interface is included in the `Predict()` function, so we don't call `Infer()` directly in most common situation
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*/
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virtual bool Infer(std::vector<FDTensor>& input_tensors,
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std::vector<FDTensor>* output_tensors);
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RuntimeOption runtime_option;
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/** \brief Model's valid cpu backends. This member defined all the cpu backends have successfully tested for the model
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*/
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std::vector<Backend> valid_cpu_backends = {Backend::ORT};
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/** Model's valid gpu backends. This member defined all the gpu backends have successfully tested for the model
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*/
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std::vector<Backend> valid_gpu_backends = {Backend::ORT};
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/** Model's valid ipu backends. This member defined all the ipu backends have successfully tested for the model
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*/
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std::vector<Backend> valid_ipu_backends = {Backend::PDINFER};
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/** Model's valid hardware backends. This member defined all the gpu backends have successfully tested for the model
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*/
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std::vector<Backend> valid_rknpu_backends = {};
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/// Get number of inputs for this model
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virtual int NumInputsOfRuntime() { return runtime_->NumInputs(); }
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/// Get number of outputs for this model
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virtual int NumOutputsOfRuntime() { return runtime_->NumOutputs(); }
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/// Get input information for this model
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virtual TensorInfo InputInfoOfRuntime(int index) {
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return runtime_->GetInputInfo(index);
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}
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/// Get output information for this model
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virtual TensorInfo OutputInfoOfRuntime(int index) {
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return runtime_->GetOutputInfo(index);
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}
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/// Check if the model is initialized successfully
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virtual bool Initialized() const {
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return runtime_initialized_ && initialized;
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}
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/** \brief This is a debug interface, used to record the time of backend runtime
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*
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* example code @code
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* auto model = fastdeploy::vision::PPYOLOE("model.pdmodel", "model.pdiparams", "infer_cfg.yml");
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* if (!model.Initialized()) {
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* std::cerr << "Failed to initialize." << std::endl;
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* return -1;
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* }
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* model.EnableRecordTimeOfRuntime();
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* cv::Mat im = cv::imread("test.jpg");
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* for (auto i = 0; i < 1000; ++i) {
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* fastdeploy::vision::DetectionResult result;
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* model.Predict(&im, &result);
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* }
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* model.PrintStatisInfoOfRuntime();
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* @endcode After called the `PrintStatisInfoOfRuntime()`, the statistical information of runtime will be printed in the console
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*/
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virtual void EnableRecordTimeOfRuntime() {
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time_of_runtime_.clear();
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std::vector<double>().swap(time_of_runtime_);
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enable_record_time_of_runtime_ = true;
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}
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/** \brief Disable to record the time of backend runtime, see `EnableRecordTimeOfRuntime()` for more detail
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*/
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virtual void DisableRecordTimeOfRuntime() {
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enable_record_time_of_runtime_ = false;
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}
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/** \brief Print the statistic information of runtime in the console, see function `EnableRecordTimeOfRuntime()` for more detail
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*/
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virtual std::map<std::string, float> PrintStatisInfoOfRuntime();
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/** \brief Check if the `EnableRecordTimeOfRuntime()` method is enabled.
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*/
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virtual bool EnabledRecordTimeOfRuntime() {
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return enable_record_time_of_runtime_;
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}
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protected:
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virtual bool InitRuntime();
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virtual bool CreateCpuBackend();
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virtual bool CreateGpuBackend();
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virtual bool CreateIpuBackend();
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virtual bool CreateRKNPUBackend();
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bool initialized = false;
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std::vector<Backend> valid_external_backends;
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private:
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std::shared_ptr<Runtime> runtime_;
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bool runtime_initialized_ = false;
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// whether to record inference time
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bool enable_record_time_of_runtime_ = false;
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// record inference time for backend
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std::vector<double> time_of_runtime_;
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};
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} // namespace fastdeploy
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