Refactoring and modularization

Signed-off-by: Micaela Verucchi <micaela.verucchi@unimore.it>

src/Dense.cpp

@@ -2,10 +2,13 @@
 
 #include "Layer.h"
 
-namespace tk { namespace dnn {
+namespace tk
+{
+namespace dnn
+{
 
-Dense::Dense(Network *net, int out_ch, const char* fname_weights) : 
-    LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights) {
+Dense::Dense(Network *net, int out_ch, const char *fname_weights) : LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights)
+{
 
     output_dim.n = 1;
     output_dim.c = out_ch;
@@ -14,19 +17,21 @@ Dense::Dense(Network *net, int out_ch, const char* fname_weights) :
     output_dim.l = 1;
 
     //allocate data for infer result
-    checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
+    checkCuda(cudaMalloc(&dstData, output_dim.tot() * sizeof(dnnType)));
 }
 
-Dense::~Dense() {
+Dense::~Dense()
+{
 
-    checkCuda( cudaFree(dstData) );
+    checkCuda(cudaFree(dstData));
 }
 
-dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
+dnnType *Dense::infer(dataDim_t &dim, dnnType *srcData)
+{
 
     if (dim.n != 1)
-        FatalError("Not Implemented"); 
-    
+        FatalError("Not Implemented");
+
     int dim_x = dim.tot();
     int dim_y = output_dim.tot();
 
@@ -35,18 +40,18 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
 
     dnnType alpha = dnnType(1), beta = dnnType(1);
     // place bias into dstData
-    checkCuda( cudaMemcpy(dstData, bias_d, dim_y*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
-    
-    //do matrix moltiplication
-    checkERROR( cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
-                            dim_x, dim_y,
-                            &alpha,
-                            data_d, dim_x,
-                            srcData, 1,
-                            &beta,
-                            dstData, 1) );
+    checkCuda(cudaMemcpy(dstData, bias_d, dim_y * sizeof(dnnType), cudaMemcpyDeviceToDevice));
 
-    //update data dimensions    
+    //do matrix moltiplication
+    checkERROR(cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
+                           dim_x, dim_y,
+                           &alpha,
+                           data_d, dim_x,
+                           srcData, 1,
+                           &beta,
+                           dstData, 1));
+
+    //update data dimensions
     dim.h = 1;
     dim.w = 1;
     dim.l = 1;
@@ -55,4 +60,5 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}}
+} // namespace dnn
+} // namespace tk