92 lines
4.3 KiB
C++
Executable File
92 lines
4.3 KiB
C++
Executable File
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#pragma once
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#include <sstream>
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#include <opencv2/dnn.hpp>
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#include "vp_node.h"
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namespace vp_nodes {
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// infer type
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// infer on the whole frame or small cropped image?
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enum vp_infer_type {
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PRIMARY, // infer on the whole frame, like detector, pose estimatation
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SECONDARY // infer on small cropped image, like classifier, feature extractor and secondary detector which need detect on small cropped images.
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};
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// base class for infer node, can't be instanstiated directly.
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// note:
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// the class is based on opencv::dnn module which is the default way for all deep learning inference in code,
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// we can implement it using other backends such as tensorrt with cuda acceleration, see vp_ppocr_text_detector_node which is based on PaddlePaddle dl framework from BaiDu corporation.
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class vp_infer_node: public vp_node {
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private:
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// load labels if need
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void load_labels();
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protected:
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vp_infer_type infer_type;
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std::string model_path;
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std::string model_config_path;
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std::string labels_path;
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int input_width;
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int input_height;
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int batch_size;
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cv::Scalar mean;
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cv::Scalar std;
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float scale;
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bool swap_rb;
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// transpose channel or not, NCHW -> NHWC
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bool swap_chn;
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// protected as it can't be instanstiated directly.
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vp_infer_node(std::string node_name,
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vp_infer_type infer_type,
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std::string model_path,
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std::string model_config_path = "",
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std::string labels_path = "",
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int input_width = 128,
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int input_height = 128,
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int batch_size = 1,
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float scale = 1.0,
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cv::Scalar mean = cv::Scalar(123.675, 116.28, 103.53), // imagenet dataset
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cv::Scalar std = cv::Scalar(1),
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bool swap_rb = true,
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bool swap_chn = false);
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// the 1st step, MUST implement in specific derived class.
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// prepare data for infer, fetch frames from frame meta.
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virtual void prepare(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch, std::vector<cv::Mat>& mats_to_infer) = 0;
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// the 2nd step, has a default implementation.
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// preprocess data, such as normalization, mean substract.
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virtual void preprocess(const std::vector<cv::Mat>& mats_to_infer, cv::Mat& blob_to_infer);
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// the 3rd step, has a default implementation.
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// infer and retrive raw outputs.
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virtual void infer(const cv::Mat& blob_to_infer, std::vector<cv::Mat>& raw_outputs);
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// the 4th step, MUST implement in specific derived class.
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// postprocess on raw outputs and create/update something back to frame meta again.
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virtual void postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) = 0;
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// debug purpose(ms)
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virtual void infer_combinations_time_cost(int data_size, int prepare_time, int preprocess_time, int infer_time, int postprocess_time);
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// infer operations(call prepare/preprocess/infer/postprocess by default)
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// we can define new logic for infer operations by overriding it.
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virtual void run_infer_combinations(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch);
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// labels as text format
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std::vector<std::string> labels;
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// opencv::dnn as backend
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cv::dnn::Net net;
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// re-implementation for one by one mode, marked as 'final' as we need not override any more in specific derived classes.
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virtual std::shared_ptr<vp_objects::vp_meta> handle_frame_meta(std::shared_ptr<vp_objects::vp_frame_meta> meta) override final;
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// re-implementation for batch by batch mode, marked as 'final' as we need not override any more in specific derived classes.
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virtual void handle_frame_meta(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& meta_with_batch) override final;
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public:
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~vp_infer_node();
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};
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} |