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https://github.com/PaddlePaddle/FastDeploy.git
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* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug
132 lines
4.1 KiB
C++
132 lines
4.1 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/headpose/contrib/fsanet.h"
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#include "fastdeploy/utils/perf.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 headpose {
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FSANet::FSANet(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};
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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 FSANet::Initialize() {
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// parameters for preprocess
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size = {64, 64};
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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 FSANet::Preprocess(Mat* mat, FDTensor* output,
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std::map<std::string, std::array<int, 2>>* im_info) {
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// Resize
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int resize_w = size[0];
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int resize_h = size[1];
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if (resize_h != mat->Height() || resize_w != mat->Width()) {
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Resize::Run(mat, resize_w, resize_h);
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}
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// Normalize
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std::vector<float> alpha = {1.0f / 128.0f, 1.0f / 128.0f, 1.0f / 128.0f};
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std::vector<float> beta = {-127.5f / 128.0f, -127.5f / 128.0f,
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-127.5f / 128.0f};
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Convert::Run(mat, alpha, beta);
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// Record output shape of preprocessed image
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(*im_info)["output_shape"] = {mat->Height(), mat->Width()};
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1); // reshape to n, c, h, w
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return true;
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}
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bool FSANet::Postprocess(
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FDTensor& infer_result, HeadPoseResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info) {
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FDASSERT(infer_result.shape[0] == 1, "Only support batch = 1 now.");
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if (infer_result.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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auto iter_in = im_info.find("input_shape");
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FDASSERT(iter_in != im_info.end(), "Cannot find input_shape from im_info.");
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int in_h = iter_in->second[0];
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int in_w = iter_in->second[1];
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result->Clear();
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float* data = static_cast<float*>(infer_result.Data());
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for (size_t i = 0; i < 3; ++i) {
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result->euler_angles.emplace_back(data[i]);
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}
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return true;
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}
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bool FSANet::Predict(cv::Mat* im, HeadPoseResult* result) {
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Mat mat(*im);
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std::vector<FDTensor> input_tensors(1);
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std::map<std::string, std::array<int, 2>> im_info;
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// Record the shape of image and the shape of preprocessed image
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im_info["input_shape"] = {mat.Height(), mat.Width()};
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im_info["output_shape"] = {mat.Height(), mat.Width()};
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if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
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FDERROR << "Failed to preprocess input image." << std::endl;
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return false;
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}
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input_tensors[0].name = InputInfoOfRuntime(0).name;
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std::vector<FDTensor> output_tensors;
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if (!Infer(input_tensors, &output_tensors)) {
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FDERROR << "Failed to inference." << std::endl;
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return false;
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}
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if (!Postprocess(output_tensors[0], result, im_info)) {
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FDERROR << "Failed to post process." << 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 headpose
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} // namespace vision
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} // namespace fastdeploy
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