add csrc code

csrc/fastdeploy/vision/detection/contrib/yolov5lite.h

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+// 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 {
+
+class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
+ public:
+  // 当model_format为ONNX时，无需指定params_file
+  // 当model_format为Paddle时，则需同时指定model_file & params_file
+  YOLOv5Lite(const std::string& model_file, const std::string& params_file = "",
+             const RuntimeOption& custom_option = RuntimeOption(),
+             const Frontend& model_format = Frontend::ONNX);
+
+  // 定义模型的名称
+  virtual std::string ModelName() const { return "YOLOv5-Lite"; }
+  // 模型预测接口，即用户调用的接口
+  // im 为用户的输入数据，目前对于CV均定义为cv::Mat
+  // result 为模型预测的输出结构体
+  // conf_threshold 为后处理的参数
+  // nms_iou_threshold 为后处理的参数
+  virtual bool Predict(cv::Mat* im, DetectionResult* result,
+                       float conf_threshold = 0.45,
+                       float nms_iou_threshold = 0.25);
+
+  // 以下为模型在预测时的一些参数，基本是前后处理所需
+  // 用户在创建模型后，可根据模型的要求，以及自己的需求
+  // 对参数进行修改
+  // tuple of (width, height)
+  std::vector<int> size;
+  // padding value, size should be same with 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;
+  // for offseting the boxes by classes when using NMS
+  float max_wh;
+  // downsample strides for YOLOv5Lite to generate anchors, will take
+  // (8,16,32) as default values, might have stride=64.
+  std::vector<int> downsample_strides;
+  // anchors parameters, downsample_strides will take
+  // (8,16,32), each stride has three anchors with width and hight.
+  std::vector<std::vector<float>> anchor_config;
+  // whether the model_file was exported with decode module. The official
+  // YOLOv5Lite/export.py script will export ONNX file without
+  // decode module. Please set it 'true' manually if the model file
+  // was exported with decode module.
+  // false : ONNX files without decode module.
+  // true : ONNX file with decode module.
+  bool is_decode_exported;
+
+ private:
+  // necessary parameters for GenerateAnchors to generate anchors when ONNX file
+  // without decode module.
+  struct Anchor {
+    int grid0;
+    int grid1;
+    int stride;
+    float anchor_w;
+    float anchor_h;
+  };
+
+  // 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
+  bool Initialize();
+
+  // 输入图像预处理操作
+  // Mat为FastDeploy定义的数据结构
+  // FDTensor为预处理后的Tensor数据，传给后端进行推理
+  // im_info为预处理过程保存的数据，在后处理中需要用到
+  bool Preprocess(Mat* mat, FDTensor* output,
+                  std::map<std::string, std::array<float, 2>>* im_info);
+
+  // 后端推理结果后处理，输出给用户
+  // infer_result 为后端推理后的输出Tensor
+  // result 为模型预测的结果
+  // im_info 为预处理记录的信息，后处理用于还原box
+  // conf_threshold 后处理时过滤box的置信度阈值
+  // nms_iou_threshold 后处理时NMS设定的iou阈值
+  bool Postprocess(FDTensor& infer_result, DetectionResult* result,
+                   const std::map<std::string, std::array<float, 2>>& im_info,
+                   float conf_threshold, float nms_iou_threshold);
+
+  // YOLOv5Lite的官方脚本默认导出不带decode模块的模型文件 需要在后处理进行decode
+  // the official YOLOv5Lite/export.py will export ONNX file without decode
+  // module.
+  // this fuction support the postporocess for ONNX file without decode module.
+  // set the `is_decode_exported = false`, this function will work.
+  bool PostprocessWithDecode(
+      FDTensor& infer_result, DetectionResult* result,
+      const std::map<std::string, std::array<float, 2>>& im_info,
+      float conf_threshold, float nms_iou_threshold);
+
+  // 对图片进行LetterBox处理
+  // mat 为读取到的原图
+  // size 为输入模型的图像尺寸
+  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);
+  // generate anchors for decodeing when ONNX file without decode module.
+  void GenerateAnchors(const std::vector<int>& size,
+                       const std::vector<int>& downsample_strides,
+                       std::vector<Anchor>* anchors, const int num_anchors = 3);
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
+  // value will
+  // auto check by fastdeploy after the internal Runtime already initialized.
+  bool is_dynamic_input_;
+};
+}  // namespace detection
+}  // namespace vision
+}  // namespace fastdeploy