mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
CoolCola
e3b285c762
* Support PETR v2 * make petrv2 precision equal with the origin repo * delete extra func * modify review problem * delete visualize * Update README_CN.md * Update README.md * Update README_CN.md * fix build problem * delete external variable and function --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
84 lines
2.6 KiB
C++
Executable File
84 lines
2.6 KiB
C++
Executable File
// 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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#include "fastdeploy/vision/perception/paddle3d/petr/petr.h"
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namespace fastdeploy {
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namespace vision {
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namespace perception {
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Petr::Petr(const std::string& model_file, const std::string& params_file,
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const std::string& config_file, const RuntimeOption& custom_option,
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const ModelFormat& model_format)
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: preprocessor_(config_file) {
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valid_cpu_backends = {Backend::PDINFER};
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valid_gpu_backends = {Backend::PDINFER};
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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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runtime_option.params_file = params_file;
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runtime_option.paddle_infer_option.enable_mkldnn = false;
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initialized = Initialize();
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}
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bool Petr::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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return true;
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}
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bool Petr::Predict(const cv::Mat& im, PerceptionResult* result) {
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std::vector<PerceptionResult> results;
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if (!BatchPredict({im}, &results)) {
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return false;
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}
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if (results.size()) {
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*result = std::move(results[0]);
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}
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return true;
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}
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bool Petr::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<PerceptionResult>* 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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reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
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reused_input_tensors_[1].name = InputInfoOfRuntime(1).name;
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reused_input_tensors_[2].name = InputInfoOfRuntime(2).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 perception
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} // namespace vision
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} // namespace fastdeploy
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