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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>
86 lines
2.8 KiB
C++
86 lines
2.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. //NOLINT
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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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#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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RKYOLO::RKYOLO(const std::string& model_file,
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const fastdeploy::RuntimeOption& custom_option,
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const fastdeploy::ModelFormat& model_format) {
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if (model_format == ModelFormat::RKNN) {
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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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} else {
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FDERROR << "RKYOLO Only Support run in RKNPU2" << std::endl;
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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initialized = Initialize();
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}
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bool RKYOLO::Initialize() {
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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auto size = GetPreprocessor().GetSize();
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GetPostprocessor().SetHeightAndWeight(size[0], size[1]);
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return true;
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}
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bool RKYOLO::Predict(const cv::Mat& im, DetectionResult* result) {
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std::vector<DetectionResult> results;
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if (!BatchPredict({im}, &results)) {
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return false;
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}
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*result = std::move(results[0]);
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return true;
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}
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bool RKYOLO::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<DetectionResult>* results) {
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std::vector<FDMat> fd_images = WrapMat(images);
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
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FDERROR << "Failed to preprocess the input image." << std::endl;
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return false;
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}
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auto pad_hw_values_ = preprocessor_.GetPadHWValues();
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postprocessor_.SetPadHWValues(preprocessor_.GetPadHWValues());
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std::cout << "preprocessor_ scale_ = " << preprocessor_.GetScale()[0]
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<< std::endl;
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postprocessor_.SetScale(preprocessor_.GetScale());
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reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, results)) {
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FDERROR << "Failed to postprocess the inference results by runtime."
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<< std::endl;
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return false;
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}
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return true;
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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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