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https://github.com/PaddlePaddle/FastDeploy.git
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117 lines
5.0 KiB
C++
117 lines
5.0 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 YOLOv5 : public FastDeployModel {
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public:
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// 当model_format为ONNX时，无需指定params_file
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// 当model_format为Paddle时，则需同时指定model_file & params_file
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YOLOv5(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const Frontend& model_format = Frontend::ONNX);
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// 定义模型的名称
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std::string ModelName() const { return "yolov5"; }
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// 模型预测接口，即用户调用的接口
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// im 为用户的输入数据，目前对于CV均定义为cv::Mat
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// result 为模型预测的输出结构体
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// conf_threshold 为后处理的参数
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// nms_iou_threshold 为后处理的参数
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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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// 输入图像预处理操作
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// Mat为FastDeploy定义的数据结构
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// FDTensor为预处理后的Tensor数据，传给后端进行推理
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// im_info为预处理过程保存的数据，在后处理中需要用到
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static bool Preprocess(Mat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info,
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const std::vector<int>& size = {640, 640},
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const std::vector<float> padding_value = {114.0, 114.0,
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114.0},
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bool is_mini_pad = false, bool is_no_pad = false,
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bool is_scale_up = false, int stride = 32,
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float max_wh = 7680.0, bool multi_label = true);
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// 后端推理结果后处理，输出给用户
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// infer_result 为后端推理后的输出Tensor
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// result 为模型预测的结果
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// im_info 为预处理记录的信息，后处理用于还原box
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// conf_threshold 后处理时过滤box的置信度阈值
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// nms_iou_threshold 后处理时NMS设定的iou阈值
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// multi_label 后处理时box选取是否采用多标签方式
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static bool Postprocess(
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std::vector<FDTensor>& infer_results, 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, bool multi_label,
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float max_wh = 7680.0);
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// 以下为模型在预测时的一些参数，基本是前后处理所需
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// 用户在创建模型后，可根据模型的要求，以及自己的需求
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// 对参数进行修改
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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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// for different strategies to get boxes when postprocessing
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bool multi_label_;
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private:
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// 初始化函数，包括初始化后端，以及其它模型推理需要涉及的操作
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bool Initialize();
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// 查看输入是否为动态维度的 不建议直接使用 不同模型的逻辑可能不一致
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bool IsDynamicInput() const { return is_dynamic_input_; }
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static void LetterBox(Mat* mat, std::vector<int> size,
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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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// YOLOv5 official 'export_onnx.py' script will export dynamic ONNX by
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// default.
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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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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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