network print

include/Layer.h

@@ -38,6 +38,23 @@ public:
     dataDim_t input_dim, output_dim;
     value_type *dstData;  //where results will be putted
 
+    std::string getLayerName() {
+        layerType_t type = getLayerType();
+        switch(type) {
+            case LAYER_DENSE:       return "Dense";
+            case LAYER_CONV2D:      return "Conv2d";
+            case LAYER_ACTIVATION:  return "Activation";
+            case LAYER_FLATTEN:     return "Flatten";
+            case LAYER_MULADD:      return "MulAdd";
+            case LAYER_POOLING:     return "Pooling";
+            case LAYER_SOFTMAX:     return "Softmax";
+            case LAYER_ROUTE:       return "Route";            
+            case LAYER_REORG:       return "Reorg";
+            case LAYER_REGION:      return "Region";
+            default:                return "unknown";
+        }
+    }
+
 protected:
     Network *net;
     cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;

include/Network.h

@@ -46,6 +46,7 @@ public:
     value_type* infer(dataDim_t &dim, value_type* data);
 
     bool addLayer(Layer *l);
+    void print();
 
     cudnnDataType_t dataType;
     cudnnTensorFormat_t tensorFormat;

include/utils.h

@@ -87,6 +87,7 @@
     }                                                                  \
 }
 
+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);

src/Network.cpp

@@ -1,4 +1,5 @@
 #include <iostream>
+#include <string>
 
 #include "tkdnn.h"
 #include "Network.h"
@@ -34,7 +35,6 @@ 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);
-        //dim.print();
     }
     checkCuda(cudaDeviceSynchronize());
     return data;
@@ -56,4 +56,40 @@ dataDim_t Network::getOutputDim() {
             return layers[num_layers-1]->output_dim;
 }
 
+void Network::print() {
+
+    std::cout<<"\n";
+    printCenteredTitle(" NETWORK MODEL ", '=', 60);
+    std::cout.width(3); std::cout<<std::left<<"N.";
+    std::cout<<" ";
+    std::cout.width(17); std::cout<<std::left<<"Layer type";
+    std::cout.width(22); std::cout<<std::left<<"input (H*W,CH)";
+    std::cout.width(16); std::cout<<std::left<<"output (H*W,CH)";
+    std::cout<<"\n";
+
+    for(int i=0; i<num_layers; i++) {
+        dataDim_t in = layers[i]->input_dim;
+        dataDim_t out = layers[i]->output_dim;
+
+        std::cout.width(3); std::cout<<std::right<<i;
+        std::cout<<" ";
+        std::cout.width(16); std::cout<<std::left<<layers[i]->getLayerName();
+        std::cout.width(4);  std::cout<<std::right<<in.h;
+        std::cout<<" x ";
+        std::cout.width(4);  std::cout<<std::right<<in.w;
+        std::cout<<", ";
+        std::cout.width(4);  std::cout<<std::right<<in.c;
+        std::cout<<"  -> ";
+        std::cout.width(4);  std::cout<<std::right<<out.h;
+        std::cout<<" x ";
+        std::cout.width(4);  std::cout<<std::right<<out.w;
+        std::cout<<", ";
+        std::cout.width(4);  std::cout<<std::right<<out.c;
+        std::cout<<"\n";
+    }
+    printCenteredTitle("", '=', 60);
+    std::cout<<"\n";
+}
+
+
 }

src/utils.cpp

@@ -1,4 +1,16 @@
 #include "utils.h"
+#include <string.h>
+
+void printCenteredTitle(const char *title, char fill, int dim) {
+
+    int len = strlen(title);
+    int first = dim/2 + len/2;
+
+    std::cout.width(first); std::cout.fill(fill); std::cout<<std::right<<title;
+    std::cout.width(dim - first); std::cout<<"\n"; 
+    std::cout.fill(' ');
+}
+
 
 void readBinaryFile(const char* fname, int size, value_type** data_h, value_type** data_d, int seek)
 {

tests/yolo-tiny/yolo-tiny.cpp

@@ -42,7 +42,7 @@ int main() {
 
     tkDNN::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
     tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p11(&net, 2, 2, 1, 1, tkDNN::POOLING_MAX);
+    //tkDNN::Pooling    p11(&net, 2, 2, 1, 1, tkDNN::POOLING_MAX);
 
     tkDNN::Conv2d     c12(&net, 1024, 3, 3, 1, 1, 1, 1, c12_bin, true);
     tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);