1 command network inference

include/Network.h

@@ -5,16 +5,31 @@
 
 namespace tkDNN {
 
+struct dataDim_t;
+class Layer;
+const int MAX_LAYERS = 256;
+
 class Network {
 
 public:
     Network();
     virtual ~Network();
 
+    /**
+        Do inferece for every added layer
+    */
+    value_type* infer(dataDim_t &dim, value_type* data);
+
+    bool addLayer(Layer *l);
+
     cudnnDataType_t dataType;
     cudnnTensorFormat_t tensorFormat;
     cudnnHandle_t cudnnHandle;
     cublasHandle_t cublasHandle;
+
+private:
+    Layer* layers[MAX_LAYERS];  //contains layers of the net
+    int num_layers; //current number of layers
 };
 
 }

src/Layer.cpp

@@ -12,6 +12,9 @@ Layer::Layer(Network *net, dataDim_t in_dim) {
     
     checkCUDNN( cudnnCreateTensorDescriptor(&srcTensorDesc) );
     checkCUDNN( cudnnCreateTensorDescriptor(&dstTensorDesc) );
+
+    if(!net->addLayer(this))
+        FatalError("Net reached max number of layers");    
 }
 
 Layer::~Layer() {

src/Network.cpp

@@ -1,6 +1,7 @@
 #include <iostream>
 
 #include "Network.h"
+#include "Layer.h"
 
 namespace tkDNN {
 
@@ -12,6 +13,8 @@ Network::Network() {
 
     checkCUDNN( cudnnCreate(&cudnnHandle) );
     checkERROR( cublasCreate(&cublasHandle) );
+
+    num_layers = 0;
 }
 
 Network::~Network() {
@@ -20,4 +23,21 @@ Network::~Network() {
     checkERROR( cublasDestroy(cublasHandle) );
 }
 
+value_type* Network::infer(dataDim_t &dim, value_type* data) {
+
+    //do infer for every layer
+    for(int i=0; i<num_layers; i++)
+        data = layers[i]->infer(dim, data);
+
+    return data;
+}
+
+bool Network::addLayer(Layer *l) {
+    if(num_layers == MAX_LAYERS)
+        return false;
+    
+    layers[num_layers++] = l;
+    return true;
+}
+
 }

tests/test.cpp

@@ -29,11 +29,15 @@ int main() {
     TIMER_START
 
     // Inference
+    data = net.infer(dim, data); dim.print();
+
+    /*
+    //old Inference method
     data = c0.infer(dim, data); dim.print();
     data = a0.infer(dim, data); dim.print();
     data = c1.infer(dim, data); dim.print();
     data = a1.infer(dim, data); dim.print();
-
+    */
     TIMER_STOP
     // Print result
     printDeviceVector(dim.tot(), data);