opencv viz

include/Layer.h

@@ -30,13 +30,13 @@ public:
     virtual ~Layer();
     virtual layerType_t getLayerType() = 0;
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData) {
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData) {
         std::cout<<"No infer action for this layer\n";
         return NULL;
     }
 
     dataDim_t input_dim, output_dim;
-    value_type *dstData;  //where results will be putted
+    dnnType *dstData;  //where results will be putted
 
     std::string getLayerName() {
         layerType_t type = getLayerType();
@@ -75,14 +75,14 @@ public:
     int inputs, outputs;
     std::string weights_path;
 
-    value_type *data_h, *data_d;
-    value_type *bias_h, *bias_d;
+    dnnType *data_h, *data_d;
+    dnnType *bias_h, *bias_d;
 
     //batchnorm
     bool batchnorm;
-    value_type *scales_h,   *scales_d;
-    value_type *mean_h,     *mean_d;
-    value_type *variance_h, *variance_d;
+    dnnType *scales_h,   *scales_d;
+    dnnType *mean_h,     *mean_d;
+    dnnType *variance_h, *variance_d;
 };
 
 
@@ -96,7 +96,7 @@ public:
     virtual ~Dense();
     virtual layerType_t getLayerType() { return LAYER_DENSE; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 };
 
 
@@ -120,7 +120,7 @@ public:
     virtual ~Activation();
     virtual layerType_t getLayerType() { return LAYER_ACTIVATION; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
 protected:
     cudnnActivationDescriptor_t activDesc;
@@ -139,7 +139,7 @@ public:
     virtual ~Conv2d();
     virtual layerType_t getLayerType() { return LAYER_CONV2D; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
     int kernelH, kernelW, strideH, strideW, paddingH, paddingW;
 
@@ -165,7 +165,7 @@ public:
     virtual ~Flatten();
     virtual layerType_t getLayerType() { return LAYER_FLATTEN; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 };
 
 
@@ -176,15 +176,15 @@ public:
 class MulAdd : public Layer {
 
 public:
-    MulAdd(Network *net, value_type mul, value_type add); 
+    MulAdd(Network *net, dnnType mul, dnnType add); 
     virtual ~MulAdd();
     virtual layerType_t getLayerType() { return LAYER_MULADD; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
 protected:
-    value_type mul, add;
-    value_type *add_vector;
+    dnnType mul, add;
+    dnnType *add_vector;
 };
 
 
@@ -214,13 +214,13 @@ public:
     virtual ~Pooling();
     virtual layerType_t getLayerType() { return LAYER_POOLING; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
 protected:
 
     cudnnPoolingDescriptor_t poolingDesc;
     tkdnnPoolingMode_t pool_mode;
-    value_type *tmpInputData, *tmpOutputData;
+    dnnType *tmpInputData, *tmpOutputData;
     bool poolOn3d;
 };
 
@@ -234,7 +234,7 @@ public:
     virtual ~Softmax();
     virtual layerType_t getLayerType() { return LAYER_SOFTMAX; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 };
 
 /**
@@ -248,7 +248,7 @@ public:
     virtual ~Route();
     virtual layerType_t getLayerType() { return LAYER_ROUTE; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
 public:
     Layer **layers;  //ids of layers to be merged
@@ -267,7 +267,7 @@ public:
     virtual ~Reorg();
     virtual layerType_t getLayerType() { return LAYER_REORG; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
     int stride;
 };
@@ -288,11 +288,13 @@ public:
     virtual ~Region();
     virtual layerType_t getLayerType() { return LAYER_REGION; };
 
-    virtual value_type* infer(dataDim_t &dim, value_type* srcData);
+    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
 
-    value_type *bias_h, *bias_d;
+    dnnType *bias_h, *bias_d;
     int classes, coords, num;
     float thresh;
+    box res_boxes[256];
+    int res_boxes_n;
 
     int entry_index(int batch, int location, int entry);
     box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride);
@@ -301,6 +303,7 @@ public:
                             int *map, float tree_thresh, int relative); 
     void correct_region_boxes(box *boxes, int n, int w, int h, int netw, int neth, int relative);
     void interpretData();
+    void showImageResult(dnnType *input_h);
 };
 
 

include/Network.h

@@ -43,7 +43,7 @@ public:
     /**
         Do inferece for every added layer
     */
-    value_type* infer(dataDim_t &dim, value_type* data);
+    dnnType* infer(dataDim_t &dim, dnnType* data);
 
     bool addLayer(Layer *l);
     void print();

include/NetworkRT.h

@@ -21,7 +21,7 @@ public:
     int buf_input_idx, buf_output_idx;
 
     dataDim_t output_dim;
-    value_type *output;
+    dnnType *output;
     cudaStream_t stream;
 
     NetworkRT(Network *net);
@@ -30,7 +30,7 @@ public:
     /**
         Do inferece
     */
-    value_type* infer(dataDim_t &dim, value_type* data);
+    dnnType* infer(dataDim_t &dim, dnnType* data);
 
     nvinfer1::ITensor* convert_layer(nvinfer1::ITensor *input, Layer *l);
     nvinfer1::ITensor* convert_layer(nvinfer1::ITensor *input, Conv2d *l);

include/kernels.h

@@ -1,10 +1,10 @@
 #include "utils.h"
 
-void activationELUForward(value_type* srcData, value_type* dstData, int size);
-void activationLEAKYForward(value_type* srcData, value_type* dstData, int size);
-void activationLOGISTICForward(value_type* srcData, value_type* dstData, int size);
+void activationELUForward(dnnType* srcData, dnnType* dstData, int size);
+void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size);
+void activationLOGISTICForward(dnnType* srcData, dnnType* dstData, int size);
 
-void reorgForward(  value_type* srcData, value_type* dstData, 
+void reorgForward(  dnnType* srcData, dnnType* dstData, 
                     int n, int c, int h, int w, int stride);
 void softmaxForward(float *input, int n, int batch, int batch_offset, 
                     int groups, int group_offset, int stride, float temp, float *output);

include/utils.h

@@ -12,7 +12,7 @@
 #include <cublas_v2.h>
 #include <cudnn.h>
 
-#define value_type float
+#define dnnType float
 
 // Colored output
 #define COL_END "\033[0m"
@@ -88,13 +88,13 @@
 }
 
 void printCenteredTitle(const char *title, char fill, int dim);
-void readBinaryFile(const char* fname, int size, value_type** data_h, value_type** data_d, int seek = 0);
-int checkResult(int size, value_type *data_d, value_type *correct_d, bool device = true);
-void printDeviceVector(int size, value_type* vec_d, bool device = true);
-void resize(int size, value_type **data);
+void readBinaryFile(const char* fname, int size, dnnType** data_h, dnnType** data_d, int seek = 0);
+int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true);
+void printDeviceVector(int size, dnnType* vec_d, bool device = true);
+void resize(int size, dnnType **data);
 
-void matrixTranspose(cublasHandle_t handle, value_type* srcData, value_type* dstData, int rows, int cols);
+void matrixTranspose(cublasHandle_t handle, dnnType* srcData, dnnType* dstData, int rows, int cols);
 
-void matrixMulAdd(  cublasHandle_t handle, value_type* srcData, value_type* dstData, 
-                    value_type* add_vector, int dim, value_type mul);
+void matrixMulAdd(  cublasHandle_t handle, dnnType* srcData, dnnType* dstData, 
+                    dnnType* add_vector, int dim, dnnType mul);
 #endif //UTILS_H