mirror of
				https://github.com/PaddlePaddle/FastDeploy.git
				synced 2025-10-31 03:46:40 +08:00 
			
		
		
		
	 7150e6405c
			
		
	
	7150e6405c
	
	
	
		
			
			* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test * add rvm support * support rvm model * add rvm demo * fixed bugs * add rvm readme * add TRT support * add trt support * add rvm test * add EXPORT.md * rename export.md * rm poros doxyen * deal with comments * deal with comments * add rvm video_mode note * add fsanet * fixed bug * update readme * fixed for ci * deal with comments * deal with comments * deal with comments Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
		
			
				
	
	
		
			132 lines
		
	
	
		
			4.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			132 lines
		
	
	
		
			4.1 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
 | |
| //
 | |
| // Licensed under the Apache License, Version 2.0 (the "License");
 | |
| // you may not use this file except in compliance with the License.
 | |
| // You may obtain a copy of the License at
 | |
| //
 | |
| //     http://www.apache.org/licenses/LICENSE-2.0
 | |
| //
 | |
| // Unless required by applicable law or agreed to in writing, software
 | |
| // distributed under the License is distributed on an "AS IS" BASIS,
 | |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 | |
| // See the License for the specific language governing permissions and
 | |
| // limitations under the License.
 | |
| 
 | |
| #include "fastdeploy/vision/headpose/contrib/fsanet.h"
 | |
| #include "fastdeploy/utils/perf.h"
 | |
| #include "fastdeploy/vision/utils/utils.h"
 | |
| 
 | |
| namespace fastdeploy {
 | |
| 
 | |
| namespace vision {
 | |
| 
 | |
| namespace headpose {
 | |
| 
 | |
| FSANet::FSANet(const std::string& model_file,
 | |
|                const std::string& params_file,
 | |
|                const RuntimeOption& custom_option,
 | |
|                const ModelFormat& model_format) {
 | |
|   if (model_format == ModelFormat::ONNX) {
 | |
|     valid_cpu_backends = {Backend::OPENVINO, Backend::ORT}; 
 | |
|     valid_gpu_backends = {Backend::ORT, Backend::TRT}; 
 | |
|   } else {
 | |
|     valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
 | |
|     valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
 | |
|   }
 | |
|   runtime_option = custom_option;
 | |
|   runtime_option.model_format = model_format;
 | |
|   runtime_option.model_file = model_file;
 | |
|   runtime_option.params_file = params_file;
 | |
|   initialized = Initialize();
 | |
| }
 | |
| 
 | |
| bool FSANet::Initialize() {
 | |
|   // parameters for preprocess
 | |
|   size = {64, 64};
 | |
| 
 | |
|   if (!InitRuntime()) {
 | |
|     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
 | |
|     return false;
 | |
|   }
 | |
|   return true;
 | |
| }
 | |
| 
 | |
| bool FSANet::Preprocess(Mat* mat, FDTensor* output,
 | |
|                       std::map<std::string, std::array<int, 2>>* im_info) {
 | |
|   // Resize
 | |
|   int resize_w = size[0];
 | |
|   int resize_h = size[1];
 | |
|   if (resize_h != mat->Height() || resize_w != mat->Width()) {
 | |
|     Resize::Run(mat, resize_w, resize_h);
 | |
|   }
 | |
| 
 | |
|   // Normalize
 | |
|   std::vector<float> alpha = {1.0f / 128.0f, 1.0f / 128.0f, 1.0f / 128.0f};
 | |
|   std::vector<float> beta = {-127.5f / 128.0f, -127.5f / 128.0f, -127.5f / 128.0f};
 | |
|   Convert::Run(mat, alpha, beta);
 | |
| 
 | |
|   // Record output shape of preprocessed image
 | |
|   (*im_info)["output_shape"] = {mat->Height(), mat->Width()};
 | |
| 
 | |
|   HWC2CHW::Run(mat);
 | |
|   Cast::Run(mat, "float");
 | |
| 
 | |
|   mat->ShareWithTensor(output);
 | |
|   output->shape.insert(output->shape.begin(), 1);  // reshape to n, h, w, c
 | |
|   return true;
 | |
| }
 | |
| 
 | |
| bool FSANet::Postprocess(FDTensor& infer_result, HeadPoseResult* result,
 | |
|                        const std::map<std::string, std::array<int, 2>>& im_info) {
 | |
|   FDASSERT(infer_result.shape[0] == 1, "Only support batch = 1 now.");
 | |
|   if (infer_result.dtype != FDDataType::FP32) {
 | |
|     FDERROR << "Only support post process with float32 data." << std::endl;
 | |
|     return false;
 | |
|   }
 | |
| 
 | |
|   auto iter_in = im_info.find("input_shape");
 | |
|   FDASSERT(iter_in != im_info.end(),
 | |
|            "Cannot find input_shape from im_info.");
 | |
|   int in_h = iter_in->second[0];
 | |
|   int in_w = iter_in->second[1];
 | |
| 
 | |
|   result->Clear();
 | |
|   float* data = static_cast<float*>(infer_result.Data());
 | |
|   for (size_t i = 0; i < 3; ++i) {
 | |
|     result->euler_angles.emplace_back(data[i]);
 | |
|   }
 | |
| 
 | |
|   return true;
 | |
| }
 | |
| 
 | |
| bool FSANet::Predict(cv::Mat* im, HeadPoseResult* result) {
 | |
|   Mat mat(*im);
 | |
|   std::vector<FDTensor> input_tensors(1);
 | |
| 
 | |
|   std::map<std::string, std::array<int, 2>> im_info;
 | |
| 
 | |
|   // Record the shape of image and the shape of preprocessed image
 | |
|   im_info["input_shape"] = {mat.Height(), mat.Width()};
 | |
|   im_info["output_shape"] = {mat.Height(), mat.Width()};
 | |
| 
 | |
|   if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
 | |
|     FDERROR << "Failed to preprocess input image." << std::endl;
 | |
|     return false;
 | |
|   }
 | |
|   input_tensors[0].name = InputInfoOfRuntime(0).name;
 | |
|   std::vector<FDTensor> output_tensors;
 | |
|   if (!Infer(input_tensors, &output_tensors)) {
 | |
|     FDERROR << "Failed to inference." << std::endl;
 | |
|     return false;
 | |
|   }
 | |
| 
 | |
|   if (!Postprocess(output_tensors[0], result, im_info)) {
 | |
|     FDERROR << "Failed to post process." << std::endl;
 | |
|     return false;
 | |
|   }
 | |
|   return true;
 | |
| }
 | |
| 
 | |
| }  // namespace headpose
 | |
| }  // namespace vision
 | |
| }  // namespace fastdeploy
 |