Remove the final parameter from the layers

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

src/Conv2d.cpp

@@ -132,10 +132,10 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
 
 Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
                 int strideH, int strideW, int paddingH, int paddingW,
-                std::string fname_weights, bool batchnorm, bool deConv, bool final, int groups, bool additional_bias) :
+                std::string fname_weights, bool batchnorm, bool deConv, int groups, bool additional_bias) :
     
     LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1, 
-             fname_weights, batchnorm, additional_bias, final, deConv, groups) {
+             fname_weights, batchnorm, additional_bias, deConv, groups) {
     this->kernelH = kernelH;
     this->kernelW = kernelW;
     this->strideH = strideH;

src/Layer.cpp

@@ -4,10 +4,10 @@
 
 namespace tk { namespace dnn {
 
-Layer::Layer(Network *net, bool final) {
+Layer::Layer(Network *net) {
 
     this->net = net;
-    this->final = final;
+    this->final = false;
     if(net != nullptr) {
         this->input_dim = net->getOutputDim();
         this->output_dim = input_dim;

src/LayerWgs.cpp

@@ -8,7 +8,7 @@ namespace tk { namespace dnn {
 
 LayerWgs::LayerWgs(Network *net, int inputs, int outputs, 
                    int kh, int kw, int kl, 
-                   std::string fname_weights, bool batchnorm, bool additional_bias, bool final, bool deConv, int groups) : Layer(net, final) {
+                   std::string fname_weights, bool batchnorm, bool additional_bias, bool deConv, int groups) : Layer(net) {
     inputs = inputs/groups;
         
     this->inputs  = inputs;

src/Pooling.cpp

@@ -7,8 +7,8 @@ namespace tk { namespace dnn {
 
 Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
                   int paddingH, int paddingW,
-                  tkdnnPoolingMode_t pool_mode, bool final) : 
-    Layer(net, final) {
+                  tkdnnPoolingMode_t pool_mode) : 
+    Layer(net) {
 
     this->winH = winH;
     this->winW = winW;

src/Reshape.cpp

@@ -5,7 +5,7 @@
 
 namespace tk { namespace dnn {
 
-Reshape::Reshape(Network *net, dataDim_t new_dim, bool final) : Layer(net, final) {
+Reshape::Reshape(Network *net, dataDim_t new_dim) : Layer(net) {
 
     checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );
 

src/Route.cpp

@@ -5,7 +5,7 @@
 
 namespace tk { namespace dnn {
 
-Route::Route(Network *net, Layer **layers, int layers_n, bool final) : Layer(net, final) {
+Route::Route(Network *net, Layer **layers, int layers_n) : Layer(net) {
 
     // copy input layers
     if(layers_n > MAX_LAYERS) {

src/Softmax.cpp

@@ -5,7 +5,7 @@
 
 namespace tk { namespace dnn {
 
-Softmax::Softmax(Network *net, const tk::dnn::dataDim_t* dim, bool final, const cudnnSoftmaxMode_t mode) : Layer(net, final) {
+Softmax::Softmax(Network *net, const tk::dnn::dataDim_t* dim, const cudnnSoftmaxMode_t mode) : Layer(net) {
 
     checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );