mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
e5c955dd3e
* yolov5 use external stream * yolov5lite/v6/v7/v7e2etrt: optimize output tensor and cuda stream * avoid reallocating output tensors * add input output tensors to FastDeployModel * add cuda.cmake * rename to reused_input/output_tensors * eliminate cmake cuda arch error * use swap to release input and output tensors Co-authored-by: Jason <jiangjiajun@baidu.com>
107 lines
4.3 KiB
C++
107 lines
4.3 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.
|
|
|
|
#pragma once
|
|
#include "fastdeploy/fastdeploy_model.h"
|
|
#include "fastdeploy/vision/common/processors/transform.h"
|
|
#include "fastdeploy/vision/common/result.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace detection {
|
|
/*! @brief YOLOv7End2EndTRT model object used when to load a YOLOv7End2EndTRT model exported by YOLOv7.
|
|
*/
|
|
class FASTDEPLOY_DECL YOLOv7End2EndTRT : public FastDeployModel {
|
|
public:
|
|
/** \brief Set path of model file and the configuration of runtime.
|
|
*
|
|
* \param[in] model_file Path of model file, e.g ./yolov7end2end_trt.onnx
|
|
* \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
|
|
* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
|
|
* \param[in] model_format Model format of the loaded model, default is ONNX format
|
|
*/
|
|
YOLOv7End2EndTRT(const std::string& model_file,
|
|
const std::string& params_file = "",
|
|
const RuntimeOption& custom_option = RuntimeOption(),
|
|
const ModelFormat& model_format = ModelFormat::ONNX);
|
|
|
|
~YOLOv7End2EndTRT();
|
|
|
|
virtual std::string ModelName() const { return "yolov7end2end_trt"; }
|
|
/** \brief Predict the detection result for an input image
|
|
*
|
|
* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
|
|
* \param[in] result The output detection result will be writen to this structure
|
|
* \param[in] conf_threshold confidence threashold for postprocessing, default is 0.25
|
|
* \return true if the prediction successed, otherwise false
|
|
*/
|
|
virtual bool Predict(cv::Mat* im, DetectionResult* result,
|
|
float conf_threshold = 0.25);
|
|
|
|
|
|
void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
|
|
|
|
/*! @brief
|
|
Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
|
|
*/
|
|
std::vector<int> size;
|
|
// padding value, size should be the same as channels
|
|
|
|
std::vector<float> padding_value;
|
|
// only pad to the minimum rectange which height and width is times of stride
|
|
bool is_mini_pad;
|
|
// while is_mini_pad = false and is_no_pad = true,
|
|
// will resize the image to the set size
|
|
bool is_no_pad;
|
|
// if is_scale_up is false, the input image only can be zoom out,
|
|
// the maximum resize scale cannot exceed 1.0
|
|
bool is_scale_up;
|
|
// padding stride, for is_mini_pad
|
|
int stride;
|
|
|
|
private:
|
|
bool Initialize();
|
|
|
|
bool Preprocess(Mat* mat, FDTensor* output,
|
|
std::map<std::string, std::array<float, 2>>* im_info);
|
|
|
|
bool CudaPreprocess(Mat* mat, FDTensor* output,
|
|
std::map<std::string, std::array<float, 2>>* im_info);
|
|
|
|
bool Postprocess(std::vector<FDTensor>& infer_results,
|
|
DetectionResult* result,
|
|
const std::map<std::string, std::array<float, 2>>& im_info,
|
|
float conf_threshold);
|
|
|
|
void LetterBox(Mat* mat, const std::vector<int>& size,
|
|
const std::vector<float>& color, bool _auto,
|
|
bool scale_fill = false, bool scale_up = true,
|
|
int stride = 32);
|
|
|
|
bool is_dynamic_input_;
|
|
// CUDA host buffer for input image
|
|
uint8_t* input_img_cuda_buffer_host_ = nullptr;
|
|
// CUDA device buffer for input image
|
|
uint8_t* input_img_cuda_buffer_device_ = nullptr;
|
|
// CUDA device buffer for TRT input tensor
|
|
float* input_tensor_cuda_buffer_device_ = nullptr;
|
|
// Whether to use CUDA preprocessing
|
|
bool use_cuda_preprocessing_ = false;
|
|
// CUDA stream
|
|
void* cuda_stream_ = nullptr;
|
|
};
|
|
} // namespace detection
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|