Fix typos (#107)

Signed-off-by: micaela <micaelaverucchi@gmail.com>
2020-09-11 09:13:59 +02:00
parent 38106a9495
commit fa2b3d26cb
19 changed files with 50 additions and 50 deletions
@@ -15,7 +15,7 @@ M. Verucchi, G. Brilli, D. Sapienza, M. Verasani, M. Arena, F. Gatti, A. Capoton
 ```

 ## FPS Results
-Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimesion as the input size, on 
+Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on 
  * RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
  * Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
  * Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
@@ -169,7 +169,7 @@ tkDNN implement and easy parser for darknet cfg files, a network can be converte
 tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
 net->print();
 ```
-All models from darknet are now parsed directly from cfg, you still need to export the weights with the descripted tools in the previus section.
+All models from darknet are now parsed directly from cfg, you still need to export the weights with the described tools in the previous section.
 <details>
  <summary>Supported layers</summary>
  convolutional
@@ -203,7 +203,7 @@ cmake .. -DDEBUG=True
 make
 ```

-Once you have succesfully created your rt file, run the demo: 
+Once you have successfully created your rt file, run the demo: 
 ```
 ./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
 ```
@@ -247,7 +247,7 @@ You should provide image_list.txt and label_list.txt, using training images. How
 ```
 bash scripts/download_validation.sh COCO
 ```
-to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD insted of COCO to download BDD validation. 
+to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD instead of COCO to download BDD validation. 

 Then a complete example using yolo3 and COCO dataset would be:
 ```
@@ -269,8 +269,8 @@ N.B.
 export TKDNN_BATCHSIZE=2
 # build tensorRT files
 ```
-This will create a TensorRT file with the desidered **max** batch size.
-The test will still run with a batch of 1, but the created tensorRT can manage the desidered batch size.
+This will create a TensorRT file with the desired **max** batch size.
+The test will still run with a batch of 1, but the created tensorRT can manage the desired batch size.

 ### Test batch Inference
 This will test the network with random input and check if the output of each batch is the same.
@@ -316,7 +316,7 @@ cd build
 ./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
 ```

-This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to subit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
+This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to submit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).

 ## Existing tests and supported networks

@@ -76,10 +76,10 @@ class DetectionNN {
        ~DetectionNN(){};

        /**
-         * Method used to inialize the class, allocate memory and compute 
+         * Method used to initialize the class, allocate memory and compute 
         * needed data.
         * 
-         * @param tensor_path path to the rt file og the NN.
+         * @param tensor_path path to the rt file of the NN.
         * @param n_classes number of classes for the given dataset.
         * @param n_batches maximum number of batches to use in inference
         * @return true if everything is correct, false otherwise.
@@ -141,9 +141,9 @@ class DetectionNN {
        }      

        /**
-         * Method to draw boundixg boxes and labels on a frame.
+         * Method to draw bounding boxes and labels on a frame.
         * 
-         * @param frames orginal frame to draw bounding box on.
+         * @param frames original frame to draw bounding box on.
         */
        void draw(std::vector<cv::Mat>& frames) {
            tk::dnn::box b;
@@ -44,7 +44,7 @@ class ImuOdom {
        virtual ~ImuOdom() {}

        /**
-         * Method used for inizialize the class
+         * Method used for initialize the class
         * 
         * @return Success of the initialization
         */
@@ -141,7 +141,7 @@ class ImuOdom {
            //odomPOS = odomPOS + deltaP.cast<double>(); // V2
            odomROT = odomROT * q.normalized().toRotationMatrix();
            
-            // compute euler
+            // compute Euler
            auto newEULER = odomROT.eulerAngles(0, 1, 2);
            for(int i=0; i<3; i++) {
                while( fabs(newEULER(i) - odomEULER(i)) > M_PI_2 )  {
@@ -171,7 +171,7 @@ public:


 /**
-    Input layer (it doesnt need weigths)
+    Input layer (it doesn't need weights)
 */
 class Input : public Layer {

@@ -207,7 +207,7 @@ public:


 /**
-    Avaible activation functions
+    Available activation functions
 */
 typedef enum {
    ACTIVATION_ELU     = 100,
@@ -216,7 +216,7 @@ typedef enum {
 } tkdnnActivationMode_t;

 /**
-    Activation layer (it doesnt need weigths)
+    Activation layer (it doesn't need weights)
 */
 class Activation : public Layer {

@@ -318,9 +318,9 @@ public:
    virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);

    const bool bidirectional = true; /**> is the net bidir */
-    bool returnSeq = false;       /**> if false return only the result of last timestep */
+    bool returnSeq = false;       /**> if false return only the result of last timestamp */
    int stateSize = 0; /**> number of hidden states */
-    int seqLen = 0;    /**> number of timesteps */
+    int seqLen = 0;    /**> number of timestamp */
    int numLayers = 1; /**> number of internal layers */

 protected:
@@ -367,7 +367,7 @@ public:


 /**
-    Deformable Convolutionl 2d layer
+    Deformable Convolutional 2d layer
 */  
 class DeformConv2d : public LayerWgs {

@@ -449,7 +449,7 @@ protected:


 /**
-    Avaible pooling functions (padding on tkDNN is not supported)
+    Available pooling functions (padding on tkDNN is not supported)
 */
 typedef enum {
    POOLING_MAX     = 0,
@@ -460,7 +460,7 @@ typedef enum {

 /**
    Pooling layer
-    currenty supported only 2d pooing (also on 3d input)
+    currently supported only 2d pooing (also on 3d input)
 */
 class Pooling : public Layer {

@@ -526,7 +526,7 @@ public:

 /**
    Reorg layer
-    Mantain same dimension but change C*H*W distribution
+    Maintains same dimension but change C*H*W distribution
 */
 class Reorg : public Layer {

@@ -559,7 +559,7 @@ public:

 /**
    Upsample layer
-    Mantain same dimension but change C*H*W distribution
+    Maintains same dimension but change C*H*W distribution
 */
 class Upsample : public Layer {

@@ -7,12 +7,12 @@
 namespace tk { namespace dnn {

 /**
-    Data rapresentation beetween layers
+    Data representation between layers
    n = batch size
    c = channels
-    h = heigth (lines)
+    h = height (lines)
    w = width  (rows)
-    l = lenght (3rd dimension)
+    l = length (3rd dimension)
 */
 struct dataDim_t {

@@ -43,7 +43,7 @@ public:
    void releaseLayers();

    /**
-        Do inferece for every added layer
+        Do inference for every added layer
    */
    dnnType* infer(dataDim_t &dim, dnnType* data);

@@ -91,7 +91,7 @@ public:
    }

    /**
-        Do inferece
+        Do inference
    */
    dnnType* infer(dataDim_t &dim, dnnType* data);
    void enqueue(int batchSize = 1);    
@@ -73,12 +73,12 @@ double computeMap(  std::vector<Frame> &images,const int classes,
 *                  all the recall levels are evaluated, otherwise only 
 *                  map_point recall levels are used. For COCO evaluation 
 *                  101 points are used.
- * @param map_step step used to increment IoU theshold
+ * @param map_step step used to increment IoU threshold
 * @param map_levels number of IoU step to perform
 * @param verbose is set to true, prints on screen additional info
 * @param write_on_file if set to true, the results produced by this function 
 *                      are written on file
- * @param net name of the considerd neural network 
+ * @param net name of the considered neural network 
 *
 * @return  mAP IoU_tresh:IoU_tresh+map_step*map_levels (e.g. mAP 0.5:0.95 when 
 *          map_step=0.05 and map_levels=10)
@@ -89,7 +89,7 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
                const int map_levels=10, const bool verbose=false, 
                const bool write_on_file = false, std::string net = "");
 /**
- * This method computes the numper of True Positive (TP), False Positive (FP),
+ * This method computes the number of True Positive (TP), False Positive (FP),
 * False Negative (FN), precision, recall and f1-score.
 * Those values are computer over all the detections, over all the classes.
 *
@@ -101,7 +101,7 @@ double computeMapNIoULevels(std::vector<Frame> &images,const int classes,
 * @param verbose is set to true, prints on screen additional info
 * @param write_on_file if set to true, the results produced by this function 
 *                      are written on file
- * @param net name of the considerd neural network 
+ * @param net name of the considered neural network 
 */
 void computeTPFPFN( std::vector<Frame> &images,const int classes,
                    const float IoU_thresh=0.5, const float conf_thresh=0.3, 
@@ -89,7 +89,7 @@ public:
 		for(int b=0; b<batchSize; b++) {
 			checkCuda(cudaMemcpy(offset, output_conv + b * 3 * chunk_dim, 2*chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
 			checkCuda(cudaMemcpy(mask, output_conv + b * 3 * chunk_dim + 2*chunk_dim, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); 
-			// kernel sigmoide
+			// kernel sigmoid
 			activationSIGMOIDForward(mask, mask, chunk_dim);
 			// deformable convolution
 			dcnV2CudaForward(stat, handle, 
@@ -20,7 +20,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
    }
    if(output_bins.size() != outputs.size()) {
        std::cout<<output_bins.size()<<" "<<outputs.size()<<"\n";
-        FatalError("outputs size missmatch");
+        FatalError("outputs size mismatch");
    }

    // Load input
@@ -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);
@@ -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
@@ -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,
@@ -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, 
@@ -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);
    
@@ -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;
@@ -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);
@@ -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) {

@@ -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() {
@@ -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++){