Files
FastDeploy/fastdeploy/fastdeploy_model.h
Zheng_Bicheng 4ffcfbe726 [Backend] Add RKNPU2 backend support (#456)
* 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>
2022-11-01 11:14:05 +08:00

124 lines
4.5 KiB
C++

// Copyright (c) 2022 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.
#pragma once
#include "fastdeploy/runtime.h"
namespace fastdeploy {
/*! @brief Base model object for all the vision models
*/
class FASTDEPLOY_DECL FastDeployModel {
public:
/// Get model's name
virtual std::string ModelName() const { return "NameUndefined"; }
/** \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
*/
virtual bool Infer(std::vector<FDTensor>& input_tensors,
std::vector<FDTensor>* output_tensors);
RuntimeOption runtime_option;
/** \brief Model's valid cpu backends. This member defined all the cpu backends have successfully tested for the model
*/
std::vector<Backend> valid_cpu_backends = {Backend::ORT};
/** Model's valid gpu backends. This member defined all the gpu backends have successfully tested for the model
*/
std::vector<Backend> valid_gpu_backends = {Backend::ORT};
/** Model's valid ipu backends. This member defined all the ipu backends have successfully tested for the model
*/
std::vector<Backend> valid_ipu_backends = {Backend::PDINFER};
/** Model's valid hardware backends. This member defined all the gpu backends have successfully tested for the model
*/
std::vector<Backend> valid_rknpu_backends = {};
/// Get number of inputs for this model
virtual int NumInputsOfRuntime() { return runtime_->NumInputs(); }
/// Get number of outputs for this model
virtual int NumOutputsOfRuntime() { return runtime_->NumOutputs(); }
/// Get input information for this model
virtual TensorInfo InputInfoOfRuntime(int index) {
return runtime_->GetInputInfo(index);
}
/// Get output information for this model
virtual TensorInfo OutputInfoOfRuntime(int index) {
return runtime_->GetOutputInfo(index);
}
/// Check if the model is initialized successfully
virtual bool Initialized() const {
return runtime_initialized_ && initialized;
}
/** \brief This is a debug interface, used to record the time of backend runtime
*
* example code @code
* auto model = fastdeploy::vision::PPYOLOE("model.pdmodel", "model.pdiparams", "infer_cfg.yml");
* if (!model.Initialized()) {
* std::cerr << "Failed to initialize." << std::endl;
* return -1;
* }
* model.EnableRecordTimeOfRuntime();
* cv::Mat im = cv::imread("test.jpg");
* for (auto i = 0; i < 1000; ++i) {
* fastdeploy::vision::DetectionResult result;
* model.Predict(&im, &result);
* }
* model.PrintStatisInfoOfRuntime();
* @endcode After called the `PrintStatisInfoOfRuntime()`, the statistical information of runtime will be printed in the console
*/
virtual void EnableRecordTimeOfRuntime() {
time_of_runtime_.clear();
std::vector<double>().swap(time_of_runtime_);
enable_record_time_of_runtime_ = true;
}
/** \brief Disable to record the time of backend runtime, see `EnableRecordTimeOfRuntime()` for more detail
*/
virtual void DisableRecordTimeOfRuntime() {
enable_record_time_of_runtime_ = false;
}
/** \brief Print the statistic information of runtime in the console, see function `EnableRecordTimeOfRuntime()` for more detail
*/
virtual std::map<std::string, float> PrintStatisInfoOfRuntime();
/** \brief Check if the `EnableRecordTimeOfRuntime()` method is enabled.
*/
virtual bool EnabledRecordTimeOfRuntime() {
return enable_record_time_of_runtime_;
}
protected:
virtual bool InitRuntime();
virtual bool CreateCpuBackend();
virtual bool CreateGpuBackend();
virtual bool CreateIpuBackend();
virtual bool CreateRKNPUBackend();
bool initialized = false;
std::vector<Backend> valid_external_backends;
private:
std::shared_ptr<Runtime> runtime_;
bool runtime_initialized_ = false;
// whether to record inference time
bool enable_record_time_of_runtime_ = false;
// record inference time for backend
std::vector<double> time_of_runtime_;
};
} // namespace fastdeploy