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