mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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113 lines
3.8 KiB
C++
113 lines
3.8 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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#include "fastdeploy/vision/perception/paddle3d/caddn/preprocessor.h"
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#include "fastdeploy/function/concat.h"
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#include "yaml-cpp/yaml.h"
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namespace fastdeploy {
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namespace vision {
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namespace perception {
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CaddnPreprocessor::CaddnPreprocessor(const std::string& config_file) {
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config_file_ = config_file;
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FDASSERT(BuildPreprocessPipeline(),
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"Failed to create Paddle3DDetPreprocessor.");
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initialized_ = true;
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}
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bool CaddnPreprocessor::BuildPreprocessPipeline() {
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processors_.clear();
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// preprocess
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processors_.push_back(std::make_shared<BGR2RGB>());
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std::vector<float> alpha = {1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0};
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std::vector<float> beta = {0.0, 0.0, 0.0};
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processors_.push_back(std::make_shared<Convert>(alpha, beta));
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processors_.push_back(std::make_shared<Cast>("float"));
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processors_.push_back(std::make_shared<HWC2CHW>());
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// Fusion will improve performance
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FuseTransforms(&processors_);
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return true;
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}
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bool CaddnPreprocessor::Apply(FDMatBatch* image_batch,
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std::vector<float>& input_cam_data,
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std::vector<float>& input_lidar_data,
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std::vector<FDTensor>* outputs) {
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if (image_batch->mats->empty()) {
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FDERROR << "The size of input images should be greater than 0."
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<< std::endl;
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return false;
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}
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if (!initialized_) {
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FDERROR << "The preprocessor is not initialized." << std::endl;
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return false;
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}
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// There are 3 outputs, image, cam_data, lidar_data
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outputs->resize(3);
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int batch = static_cast<int>(image_batch->mats->size());
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// Allocate memory for cam_data
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(*outputs)[1].Resize({batch, 3, 4}, FDDataType::FP32);
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// Allocate memory for lidar_data
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(*outputs)[2].Resize({batch, 4, 4}, FDDataType::FP32);
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auto* cam_data_ptr = reinterpret_cast<float*>((*outputs)[1].MutableData());
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auto* lidar_data_ptr = reinterpret_cast<float*>((*outputs)[2].MutableData());
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for (size_t i = 0; i < image_batch->mats->size(); ++i) {
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FDMat* mat = &(image_batch->mats->at(i));
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for (size_t j = 0; j < processors_.size(); ++j) {
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if (!(*(processors_[j].get()))(mat)) {
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FDERROR << "Failed to processs image:" << i << " in "
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<< processors_[j]->Name() << "." << std::endl;
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return false;
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}
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}
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memcpy(cam_data_ptr + i * 12, input_cam_data.data(), 12 * sizeof(float));
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memcpy(lidar_data_ptr + i * 16, input_lidar_data.data(),
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16 * sizeof(float));
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}
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FDTensor* tensor = image_batch->Tensor();
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(*outputs)[0].SetExternalData(tensor->Shape(), tensor->Dtype(),
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tensor->Data(), tensor->device,
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tensor->device_id);
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return true;
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}
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bool CaddnPreprocessor::Run(std::vector<FDMat>* images,
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std::vector<float>& input_cam_data,
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std::vector<float>& input_lidar_data,
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std::vector<FDTensor>* outputs) {
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FDMatBatch image_batch(images);
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PreApply(&image_batch);
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bool ret = Apply(&image_batch, input_cam_data, input_lidar_data, outputs);
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PostApply();
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return ret;
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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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