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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>
90 lines
3.5 KiB
C++
90 lines
3.5 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/fastdeploy_model.h"
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All segmentation model APIs are defined inside this namespace
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*
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*/
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namespace segmentation {
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/*! @brief PaddleSeg serials model object used when to load a PaddleSeg model exported by PaddleSeg repository
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*/
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class FASTDEPLOY_DECL PaddleSegModel : public FastDeployModel {
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public:
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/** \brief Set path of model file and configuration file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g unet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g unet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] config_file Path of configuration file for deployment, e.g unet/deploy.yml
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
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* \param[in] model_format Model format of the loaded model, default is Paddle format
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*/
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PaddleSegModel(const std::string& model_file, const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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/// Get model's name
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std::string ModelName() const { return "PaddleSeg"; }
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/** \brief Predict the segmentation result for an input image
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*
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result The output segmentation result will be writen to this structure
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* \return true if the segmentation prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, SegmentationResult* result);
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/** \brief Whether applying softmax operator in the postprocess, default value is false
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*/
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bool apply_softmax = false;
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/** \brief For PP-HumanSeg model, set true if the input image is vertical image(height > width), default value is false
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*/
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bool is_vertical_screen = false;
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// This function will disable normalize and hwc2chw in preprocessing step.
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void DisableNormalizeAndPermute();
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private:
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bool Initialize();
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bool BuildPreprocessPipelineFromConfig();
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bool Preprocess(Mat* mat, FDTensor* outputs);
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bool Postprocess(FDTensor* infer_result, SegmentationResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info);
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bool is_with_softmax = false;
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bool is_with_argmax = true;
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std::vector<std::shared_ptr<Processor>> processors_;
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std::string config_file_;
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// for recording the switch of normalize and hwc2chw
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bool disable_normalize_and_permute = false;
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};
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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