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Fix typos (#107)

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Signed-off-by: micaela <micaelaverucchi@gmail.com>
This commit is contained in:
micaela
2020-09-11 09:13:59 +02:00
parent 38106a9495
commit fa2b3d26cb
19 changed files with 50 additions and 50 deletions
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include/tkDNN/DetectionNN.h
+4 -4
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@@ -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;
include/tkDNN/ImuOdom.h
+2 -2
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@@ -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 ) {
include/tkDNN/Layer.h
+10 -10
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@@ -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 {
include/tkDNN/Network.h
+4 -4
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@@ -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);
include/tkDNN/NetworkRT.h
+1 -1
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@@ -91,7 +91,7 @@ public:
}
/**
Do inferece
Do inference
*/
dnnType* infer(dataDim_t &dim, dnnType* data);
void enqueue(int batchSize = 1);
include/tkDNN/evaluation.h
+4 -4
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@@ -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,
include/tkDNN/pluginsRT/DeformableConvRT.h
+1 -1
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@@ -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,
include/tkDNN/test.h
+1 -1
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@@ -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