Pre-process, Process and Post-process work

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>

include/sorting.h

@@ -0,0 +1,19 @@
+#include <thrust/sort.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/transform.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/gather.h>
+#include <thrust/copy.h>
+
+
+#include "tkdnn.h"
+
+void sort(dnnType *src_begin, dnnType *src_end, int *idsrc);
+void topk(dnnType *src_begin, int *idsrc, int K, float *topk_scores,
+            int *topk_inds, float *topk_ys, float *topk_xs);
+void sortAndTopKonDevice(dnnType *src_begin, int *idsrc, float *topk_scores, int *topk_inds, float *topk_ys, float *topk_xs, const int size, const int K, const int n_classes);            
+void subtractWithThreshold(dnnType *src_begin, dnnType *src_end, dnnType *src2_begin, dnnType *src_out);
+void topKxyclasses(int *ids_begin, int *ids_end, const int K, const int size, const int wh, int *clses, int *xs, int *ys);
+void topKxyAddOffset(int * ids_begin, const int K, const int size, int *intxs_begin, int *intys_begin, float *xs_begin, float *ys_begin, dnnType *src_begin);
+void bboxes(int * ids_begin, const int K, const int size, float *xs_begin, float *ys_begin, dnnType *src_begin, float *bbx0, float *bbx1, float *bby0, float *bby1);

include/tkDNN/CenternetDetection.h

@@ -0,0 +1,112 @@
+#include <iostream>
+#include <cstring>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+#include <unistd.h>
+#include <mutex>
+#include "utils.h"
+#include <time.h>
+#include "kernels.h"
+#include <vector>
+#include <numeric>      // std::iota
+#include <algorithm>    // std::sort
+
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "tkdnn.h"
+#include "sorting.h"
+
+namespace tk { namespace dnn {
+
+/**
+ * 
+ * @author Francesco Gatti
+ */
+class CenternetDetection {
+
+    private:
+        tk::dnn::NetworkRT *netRT = nullptr;
+        dnnType *input_h, *input, *input_d;
+
+        int ndets = 0;
+        // tk::dnn::Yolo::detection *dets = nullptr;
+
+        cv::Mat imageF;
+        cv::Mat bgr[3]; 
+
+        // variable to test cnet on dog pictures
+        tk::dnn::dataDim_t dim;
+        tk::dnn::dataDim_t dim2;
+        cv::Size sz;
+        const char *input_bin = "../tests/resnet101_cnet/debug/input.bin";
+
+        // pre-process
+        tk::dnn::dataDim_t dim_hm; 
+        tk::dnn::dataDim_t dim_wh; 
+        tk::dnn::dataDim_t dim_reg;
+        float *topk_scores;
+        int *topk_inds_;
+        float *topk_ys_;
+        float *topk_xs_;
+        int *ids_d, *ids_, *ids_2, *ids_2d;
+
+        float *scores, *scores_d;
+        int *clses, *clses_d; 
+        int *topk_inds_d;
+        float *topk_ys_d; 
+        float *topk_xs_d;
+        int *inttopk_xs_d, *inttopk_ys_d;
+    
+
+        float *bbx0, *bby0, *bbx1, *bby1;
+        float *bbx0_d, *bby0_d, *bbx1_d, *bby1_d;
+        
+        float *target_coords;
+        
+        cv::Vec<float, 3> mean;
+        cv::Vec<float, 3> stddev;
+        cv::Mat src;
+        cv::Mat dst;  
+        //processing
+        float toll = 0.000001;
+        int K = 100;
+        int width = 56;        // TODO
+    
+    
+    public:
+        dnnType *rt_out[4];
+
+        float inp_height = 224;//512;
+        float inp_width = 224;//512;
+    
+        int classes = 80;
+        int num = 0;
+        int n_masks = 0;
+        float thresh = 0.3;
+        cv::Scalar colors[256];
+
+        // this is filled with results
+        std::vector<tk::dnn::box> detected;
+        // draw
+        std::vector<std::string> coco_class_name;
+
+        CenternetDetection() {}
+
+        virtual ~CenternetDetection() {}
+
+        /**
+         * Method used for inizialize the class
+         * 
+         * @return Success of the initialization
+         */
+        bool init(std::string tensor_path);
+        void testdog();
+        cv::Mat draw(cv::Mat &frame);
+        void update(cv::Mat &frame);
+
+};
+
+}}

include/tkDNN/Layer.h

@@ -292,7 +292,7 @@ public:
     Pooling(Network *net, int winH, int winW, 
             int strideH, int strideW, 
             int paddingH = 0, int paddingW = 0,
-            tkdnnPoolingMode_t pool_mode = POOLING_MAX); 
+            tkdnnPoolingMode_t pool_mode = POOLING_MAX, bool final = false); 
     virtual ~Pooling();
     virtual layerType_t getLayerType() { return LAYER_POOLING; };