Fix typos (#107)

Signed-off-by: micaela <micaelaverucchi@gmail.com>

src/DarknetParser.cpp

@@ -201,7 +201,7 @@ namespace tk { namespace dnn {
 
         tk::dnn::Network *net = nullptr;
         
-        // layers without activations to retrive correct id number
+        // layers without activations to retrieve correct id number
         std::vector<tk::dnn::Layer*> netLayers;
 
         std::ifstream if_cfg(cfg_file);

src/DeformConv2d.cpp

@@ -95,7 +95,7 @@ dnnType* DeformConv2d::infer(dataDim_t &dim, dnnType* srcData) {
     // split conv2d outputs into offset and mask
     checkCuda(cudaMemcpy(offset, output_conv, 2*chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
     checkCuda(cudaMemcpy(mask, output_conv + 2*chunk_dim, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
-    // kernel sigmoide
+    // kernel sigmoid
     activationSIGMOIDForward(mask, mask, chunk_dim);
 
     // deformable convolution

src/Dense.cpp

@@ -37,7 +37,7 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
     // place bias into dstData
     checkCuda( cudaMemcpy(dstData, bias_d, dim_y*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
     
-    //do matrix moltiplication
+    //do matrix multiplication
     checkERROR( cublasSgemv(net->cublasHandle, CUBLAS_OP_T,
                             dim_x, dim_y,
                             &alpha,

src/LSTM.cpp

@@ -133,7 +133,7 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
     output_dim = input_dim;
     output_dim.c = stateSize*(bidirectional ? 2 : 1);
 
-    // if retunseq is disabled only the last timestep is returned
+    // if retunseq is disabled only the last timestamp is returned
     if(!returnSeq) {
         output_dim.h = 1;
         output_dim.w = 1;
@@ -254,7 +254,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
             rnnDesc,
             seqLen,                     // number of time steps (nT)
             x_desc_vec_.data(),         // input array of desc (nT*nC_in)
-            srcF,                    // input pointer
+            srcF,                       // input pointer
             hx_desc_,                   // initial hidden state desc     
             hx_ptr,                     // initial hidden state pointer 
             cx_desc_,                   // initial cell state desc      
@@ -281,7 +281,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
             rnnDesc,
             seqLen,                     // number of time steps (nT)
             x_desc_vec_.data(),         // input array of desc (nT*nC_in)
-            srcB,                    // input pointer
+            srcB,                       // input pointer
             hx_desc_,                   // initial hidden state desc     
             hx_ptr,                     // initial hidden state pointer 
             cx_desc_,                   // initial cell state desc      
@@ -289,7 +289,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
             w_desc_,                    // weights desc
             wb_ptr,                     // weights pointer
             y_desc_vec_.data(),         // output desc     (nT*nC_out)
-            dstB_NR,                       // output pointer
+            dstB_NR,                    // output pointer
             hy_desc_,                   // final hidden state desc        
             hy_ptr,                     // final hidden state pointer 
             cy_desc_,                   // final cell state desc          
@@ -307,7 +307,7 @@ dnnType* LSTM::infer(dataDim_t &dim, dnnType* srcData) {
             one_output_dim.c*sizeof(dnnType), cudaMemcpyDeviceToDevice));
     }    
 
-    // if retunseq is disabled only the last timestep is returned
+    // if retunseq is disabled only the last timestamp is returned
     if(returnSeq) {
         // forward transpose
         matrixTranspose(net->cublasHandle, dstF, dstData, 

src/LayerWgs.cpp

@@ -105,7 +105,7 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
         float2half(tmp_d, variance16_d, b_size);
         cudaMemcpy(variance16_h, variance16_d, b_size*sizeof(__half), cudaMemcpyDeviceToHost);
 
-        //conver scales
+        //convert scales
         float2half(scales_d, scales16_d, b_size);
         cudaMemcpy(scales16_h, scales16_d, b_size*sizeof(__half), cudaMemcpyDeviceToHost);
     

src/MulAdd.cpp

@@ -12,7 +12,7 @@ MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net) {
 
     int size = input_dim.tot();
 
-    // create a vector with all value setted to add 
+    // create a vector with all value set to add 
     dnnType *add_vector_h = new dnnType[size];
     for(int i=0; i<size; i++)
         add_vector_h[i] = add;

src/NetworkRT.cpp

@@ -163,7 +163,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
 	// note that indices are guaranteed to be less than IEngine::getNbBindings()
 	buf_input_idx = engineRT->getBindingIndex("data"); 
     buf_output_idx = engineRT->getBindingIndex("out");
-    std::cout<<"input idex = "<<buf_input_idx<<" -> output index = "<<buf_output_idx<<"\n";
+    std::cout<<"input index = "<<buf_input_idx<<" -> output index = "<<buf_output_idx<<"\n";
 
 
     Dims iDim = engineRT->getBindingDimensions(buf_input_idx);

src/Region.cpp

@@ -63,7 +63,7 @@ dnnType* Region::infer(dataDim_t &dim, dnnType* srcData) {
 }
 
 
-/* Intepret class */
+/* Interpret class */
 RegionInterpret::RegionInterpret(dataDim_t input_dim, dataDim_t output_dim, 
     int classes, int coords, int num, float thresh, std::string fname_weights) {
 

src/Shortcut.cpp

@@ -13,7 +13,7 @@ Shortcut::Shortcut(Network *net, Layer *backLayer) : Layer(net) {
     if( /*backLayer->output_dim.c != input_dim.c ||*/
         backLayer->output_dim.w != input_dim.w ||
         backLayer->output_dim.h != input_dim.h   )
-        FatalError("Shortcut dim missmatch");
+        FatalError("Shortcut dim mismatch");
 }
 
 Shortcut::~Shortcut() {

src/evaluation.cpp

@@ -63,7 +63,7 @@ double computeMap(  std::vector<Frame> &images,const int classes,
     
     int gt_checked = 0;
 
-    // for each detection comput IoU with groundtruth and match detetcion and 
+    // for each detection compute IoU with groundtruth and match detetcion and 
     // groundtruth with IoU greater than IoU_thresh
     for(auto &img:images){
         for(size_t i=0; i<img.det.size(); i++){
@@ -153,7 +153,7 @@ double computeMap(  std::vector<Frame> &images,const int classes,
         }
     }
 
-    //compute average precision for each class. Two methods are avaible, 
+    //compute average precision for each class. Two methods are available, 
     //based on map_points required
     double mean_average_precision = 0;
     double last_recall, last_precision, delta_recall;
@@ -287,7 +287,7 @@ void computeTPFPFN( std::vector<Frame> &images,const int classes,
         }
     }
 
-    //count all TP, FP, FN and compute precsion, recall and f1-score
+    //count all TP, FP, FN and compute precision, recall and f1-score
     double avg_precision = 0, avg_recall = 0, f1_score = 0;
     int TP = 0, FP = 0, FN = 0;
     for(size_t i=0; i<classes; i++){