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c8d6c8244e
* add yolo cuda preprocessing * cmake build cuda src * yolov5 support cuda preprocessing * yolov5 cuda preprocessing configurable * yolov5 update get mat data api * yolov5 check cuda preprocess args * refactor cuda function name * yolo cuda preprocess padding value configurable * yolov5 release cuda memory * cuda preprocess pybind api update * move use_cuda_preprocessing option to yolov5 model * yolov5lite cuda preprocessing * yolov6 cuda preprocessing * yolov7 cuda preprocessing * yolov7_e2e cuda preprocessing * remove cuda preprocessing in runtime option * refine log and cmake variable name * fix model runtime ptr type Co-authored-by: Jason <jiangjiajun@baidu.com>
93 lines
3.4 KiB
C++
93 lines
3.4 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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class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
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public:
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YOLOv7(const std::string& model_file, 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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~YOLOv7();
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virtual std::string ModelName() const { return "yolov7"; }
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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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float nms_iou_threshold = 0.5);
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void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
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// tuple of (width, height)
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std::vector<int> size;
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// padding value, size should be same with 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, will resize the image to
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// 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, the maximum
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// 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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// for offseting the boxes by classes when using NMS
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float max_wh;
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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(FDTensor& infer_result, DetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_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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// whether to inference with dynamic shape (e.g ONNX export with dynamic shape
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// or not.)
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// while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
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// value will
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// auto check by fastdeploy after the internal Runtime already initialized.
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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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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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