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FastDeploy/fastdeploy/vision/headpose/contrib/fsanet.cc
WJJ1995 d3845eb4e1 [Benchmark]Compare diff for OCR (#1415)
* 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
2023-02-23 18:57:39 +08:00

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, c, h, w
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