mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
aa6931bee9
* add onnx_ort_runtime demo * rm in requirements * support batch eval * fixed MattingResults bug * move assignment for DetectionResult * integrated x2paddle * add model convert readme * update readme * re-lint * add processor api * Add MattingResult Free * change valid_cpu_backends order * add ppocr benchmark * mv bs from 64 to 32 * fixed quantize.md * fixed quantize bugs * Add Monitor for benchmark * update mem monitor * Set trt_max_batch_size default 1 * fixed ocr benchmark bug * support yolov5 in serving * Fixed yolov5 serving * Fixed postprocess * update yolov5 to 7.0 * add poros runtime demos * update readme * Support poros abi=1 * rm useless note * deal with comments * support pp_trt for ppseg * fixed symlink problem * Add is_mini_pad and stride for yolov5 * Add yolo series for paddle format * fixed bugs * fixed bug * support yolov5seg * fixed bug * refactor yolov5seg * fixed bug * mv Mask int32 to uint8 * add yolov5seg example * rm log info * fixed code style * add yolov5seg example in python * fixed dtype bug * update note * deal with comments * get sorted index * add yolov5seg test case * Add GPL-3.0 License * add round func * deal with comments * deal with commens Co-authored-by: Jason <jiangjiajun@baidu.com>
81 lines
2.7 KiB
C++
81 lines
2.7 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/detection/contrib/yolov5seg/yolov5seg.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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YOLOv5Seg::YOLOv5Seg(const std::string& model_file, const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::OPENVINO, Backend::ORT};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {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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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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bool YOLOv5Seg::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 YOLOv5Seg::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 YOLOv5Seg::BatchPredict(const std::vector<cv::Mat>& images, std::vector<DetectionResult>* results) {
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std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
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std::vector<FDMat> fd_images = WrapMat(images);
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, &ims_info)) {
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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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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, ims_info)) {
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FDERROR << "Failed to postprocess the inference results by runtime." << 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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