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https://github.com/PaddlePaddle/FastDeploy.git
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34bea7649d
* Add Sophgo Device add sophgo backend in fastdeploy add resnet50, yolov5s, liteseg examples. * replace sophgo lib with download links; fix model.cc bug * modify CodeStyle * remove unuseful files;change the names of sophgo device and sophgo backend * sophgo support python and add python examples * remove unuseful rows in cmake according pr Co-authored-by: Zilong Xing <zilong.xing@sophgo.com>
100 lines
3.6 KiB
C++
Executable File
100 lines
3.6 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/segmentation/ppseg/model.h"
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#include "fastdeploy/utils/unique_ptr.h"
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namespace fastdeploy {
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namespace vision {
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namespace segmentation {
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PaddleSegModel::PaddleSegModel(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) : preprocessor_(config_file),
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postprocessor_(config_file) {
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if(model_format == ModelFormat::SOPHGO) {
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valid_sophgonpu_backends = {Backend::SOPHGOTPU};
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}
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else{
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valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::ORT, Backend::LITE};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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}
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valid_rknpu_backends = {Backend::RKNPU2};
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valid_timvx_backends = {Backend::LITE};
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valid_kunlunxin_backends = {Backend::LITE};
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valid_ascend_backends = {Backend::LITE};
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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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initialized = Initialize();
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}
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std::unique_ptr<PaddleSegModel> PaddleSegModel::Clone() const {
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std::unique_ptr<PaddleSegModel> clone_model = fastdeploy::utils::make_unique<PaddleSegModel>(PaddleSegModel(*this));
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clone_model->SetRuntime(clone_model->CloneRuntime());
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return clone_model;
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}
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bool PaddleSegModel::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 PaddleSegModel::Predict(cv::Mat* im, SegmentationResult* result) {
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return Predict(*im, result);
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}
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bool PaddleSegModel::Predict(const cv::Mat& im, SegmentationResult* result) {
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std::vector<SegmentationResult> 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 PaddleSegModel::BatchPredict(const std::vector<cv::Mat>& imgs,
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std::vector<SegmentationResult>* results) {
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std::vector<FDMat> fd_images = WrapMat(imgs);
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// Record the shape of input images
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std::map<std::string, std::vector<std::array<int, 2>>> imgs_info;
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, &imgs_info)) {
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FDERROR << "Failed to preprocess input data while using model:"
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<< ModelName() << "." << 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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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference while using model:" << ModelName() << "."
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<< std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, results, imgs_info)) {
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FDERROR << "Failed to postprocess while using model:" << ModelName() << "."
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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 segmentation
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} // namespace vision
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} // namespace fastdeploy
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