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188dcedc02
* 修复picodet格式 * * 修正错误文档 * 修复rknpu2 backend后端的部分错误 * 更新pphumanseg example格式 * * 更新pphumanseg example格式 * * 更新picodet example格式 * * 更新scrfd example格式 * * 更新ppseg rknpu2 python example中的错误 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 Co-authored-by: Jason <jiangjiajun@baidu.com>
102 lines
4.8 KiB
C++
102 lines
4.8 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/vision/detection/contrib/rknpu2/rkyolo.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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class FASTDEPLOY_DECL RKYOLOV5 : public RKYOLO {
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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 picodet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g picodet/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 picodet/infer_cfg.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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RKYOLOV5(const std::string& model_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::RKNN)
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: RKYOLO(model_file, custom_option, model_format) {
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valid_cpu_backends = {};
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valid_gpu_backends = {};
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valid_rknpu_backends = {Backend::RKNPU2};
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std::vector<int> anchors = {10, 13, 16, 30, 33, 23, 30, 61, 62,
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45, 59, 119, 116, 90, 156, 198, 373, 326};
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int anchor_per_branch_ = 3;
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GetPostprocessor().SetAnchor(anchors, anchor_per_branch_);
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}
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virtual std::string ModelName() const { return "RKYOLOV5"; }
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};
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class FASTDEPLOY_DECL RKYOLOV7 : public RKYOLO {
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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 picodet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g picodet/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 picodet/infer_cfg.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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RKYOLOV7(const std::string& model_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::RKNN)
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: RKYOLO(model_file, custom_option, model_format) {
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valid_cpu_backends = {};
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valid_gpu_backends = {};
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valid_rknpu_backends = {Backend::RKNPU2};
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std::vector<int> anchors = {12, 16, 19, 36, 40, 28, 36, 75, 76,
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55, 72, 146, 142, 110, 192, 243, 459, 401};
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int anchor_per_branch_ = 3;
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GetPostprocessor().SetAnchor(anchors, anchor_per_branch_);
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}
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virtual std::string ModelName() const { return "RKYOLOV7"; }
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};
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class FASTDEPLOY_DECL RKYOLOX : public RKYOLO {
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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 picodet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g picodet/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 picodet/infer_cfg.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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RKYOLOX(const std::string& model_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::RKNN)
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: RKYOLO(model_file, custom_option, model_format) {
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valid_cpu_backends = {};
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valid_gpu_backends = {};
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valid_rknpu_backends = {Backend::RKNPU2};
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std::vector<int> anchors = {10, 13, 16, 30, 33, 23, 30, 61, 62,
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45, 59, 119, 116, 90, 156, 198, 373, 326};
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int anchor_per_branch_ = 1;
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GetPostprocessor().SetAnchor(anchors, anchor_per_branch_);
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}
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virtual std::string ModelName() const { return "RKYOLOV7"; }
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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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