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https://github.com/PaddlePaddle/FastDeploy.git
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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>
64 lines
2.4 KiB
C++
64 lines
2.4 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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#include "fastdeploy/vision/perception/paddle3d/petr/postprocessor.h"
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace perception {
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PetrPostprocessor::PetrPostprocessor() {}
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bool PetrPostprocessor::Run(const std::vector<FDTensor>& tensors,
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std::vector<PerceptionResult>* results) {
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results->resize(1);
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(*results)[0].Clear();
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(*results)[0].Reserve(tensors[0].shape[0]);
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if (tensors[0].dtype != FDDataType::FP32) {
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FDERROR << "Only support post process with float32 data." << std::endl;
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return false;
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}
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const float* data_0 = reinterpret_cast<const float*>(tensors[0].Data());
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auto result = &(*results)[0];
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for (int i = 0; i < tensors[0].shape[0] * tensors[0].shape[1]; i += 9) {
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// item 1 ~ 3 : box3d w, h, l
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// item 4 ~ 6 : box3d bottom center x, y, z
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// item 7 : box3d yaw angle
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// item 8 ~ 9 : speed x,y
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std::vector<float> vec(data_0 + i, data_0 + i + 9);
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result->boxes.emplace_back(std::array<float, 7>{
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0, 0, 0, 0, vec[0], vec[1], vec[2]});
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result->center.emplace_back(std::array<float, 3>{vec[3], vec[4], vec[5]});
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result->yaw_angle.push_back(vec[6]);
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result->velocity.push_back(std::array<float, 3>{vec[7], vec[8]});
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}
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const float* data_1 = reinterpret_cast<const float*>(tensors[1].Data());
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for (int i = 0; i < tensors[1].shape[0]; i += 1) {
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std::vector<float> vec(data_1 + i, data_1 + i + 1);
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result->scores.push_back(vec[0]);
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}
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const long long* data_2 = reinterpret_cast<const long long*>(tensors[2].Data());
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for (int i = 0; i < tensors[2].shape[0]; i++) {
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std::vector<long long> vec(data_2 + i, data_2 + i + 1);
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result->label_ids.push_back(vec[0]);
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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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