mmr/tkDNN
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better print

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This commit is contained in:
Francesco Gatti
2017-08-09 16:04:49 +00:00
parent 1c6888f312
commit b75fa637cb
6 changed files with 33 additions and 20 deletions
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CMakeLists.txt
+4
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@@ -13,6 +13,10 @@ if( ${BUILD_DEPS} )
message("Finished dowloading test weights") message("Finished dowloading test weights")
endif() endif()
if(DEBUG)
add_definitions(-DDEBUG)
endif()
find_package(CUDA QUIET REQUIRED) find_package(CUDA QUIET REQUIRED)
cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS}) cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS})
src/Network.cpp
-1
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@@ -58,7 +58,6 @@ dataDim_t Network::getOutputDim() {
void Network::print() { void Network::print() {
std::cout<<"\n";
printCenteredTitle(" NETWORK MODEL ", '=', 60); printCenteredTitle(" NETWORK MODEL ", '=', 60);
std::cout.width(3); std::cout<<std::left<<"N."; std::cout.width(3); std::cout<<std::left<<"N.";
std::cout<<" "; std::cout<<" ";
src/NetworkRT.cpp
+14 -12
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@@ -12,7 +12,9 @@ using namespace nvinfer1;
// Logger for info/warning/errors // Logger for info/warning/errors
class Logger : public ILogger { class Logger : public ILogger {
void log(Severity severity, const char* msg) override { void log(Severity severity, const char* msg) override {
#ifdef DEBUG
std::cout <<"TENSORRT LOG: "<< msg << std::endl; std::cout <<"TENSORRT LOG: "<< msg << std::endl;
#endif
} }
} loggerRT; } loggerRT;
@@ -59,7 +61,7 @@ NetworkRT::NetworkRT(Network *net) {
builderRT->setMaxBatchSize(1); builderRT->setMaxBatchSize(1);
builderRT->setMaxWorkspaceSize(1 << 20); builderRT->setMaxWorkspaceSize(1 << 20);
std::cout<<"BUILD cuda engine\n"; std::cout<<"Building tensorRT cuda engine...\n";
engineRT = builderRT->buildCudaEngine(*networkRT); engineRT = builderRT->buildCudaEngine(*networkRT);
// we don't need the network any more // we don't need the network any more
//networkRT->destroy(); //networkRT->destroy();
@@ -127,7 +129,7 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Layer *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Dense *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Dense *l) {
std::cout<<"convert Dense\n"; //std::cout<<"convert Dense\n";
Weights w { dtRT, l->data_h, l->inputs*l->outputs}; Weights w { dtRT, l->data_h, l->inputs*l->outputs};
Weights b = { dtRT, l->bias_h, l->outputs}; Weights b = { dtRT, l->bias_h, l->outputs};
@@ -138,7 +140,7 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Dense *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Conv2d *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
std::cout<<"convert conv2D\n"; //std::cout<<"convert conv2D\n";
Weights w { dtRT, l->data_h, l->inputs*l->outputs*l->kernelH*l->kernelW}; Weights w { dtRT, l->data_h, l->inputs*l->outputs*l->kernelH*l->kernelW};
Weights b; Weights b;
@@ -190,7 +192,7 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Pooling *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Pooling *l) {
std::cout<<"convert Pooling\n"; //std::cout<<"convert Pooling\n";
IPoolingLayer *lRT = networkRT->addPooling(*input, IPoolingLayer *lRT = networkRT->addPooling(*input,
PoolingType::kMAX, DimsHW{l->winH, l->winW}); PoolingType::kMAX, DimsHW{l->winH, l->winW});
@@ -201,10 +203,10 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Pooling *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Activation *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Activation *l) {
std::cout<<"convert Activation\n"; //std::cout<<"convert Activation\n";
if(l->act_mode == ACTIVATION_LEAKY) { if(l->act_mode == ACTIVATION_LEAKY) {
std::cout<<"New plugin LEAKY\n"; //std::cout<<"New plugin LEAKY\n";
IPlugin *plugin = new ActivationLeakyRT(); IPlugin *plugin = new ActivationLeakyRT();
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin); IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT); checkNULL(lRT);
@@ -217,7 +219,7 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Activation *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Softmax *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Softmax *l) {
std::cout<<"convert softmax\n"; //std::cout<<"convert softmax\n";
ISoftMaxLayer *lRT = networkRT->addSoftMax(*input); ISoftMaxLayer *lRT = networkRT->addSoftMax(*input);
checkNULL(lRT); checkNULL(lRT);
@@ -226,7 +228,7 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Softmax *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Route *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Route *l) {
std::cout<<"convert route\n"; //std::cout<<"convert route\n";
ITensor *tens[256]; ITensor *tens[256];
for(int i=0; i<l->layers_n; i++) for(int i=0; i<l->layers_n; i++)
@@ -238,9 +240,9 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Route *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Reorg *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Reorg *l) {
std::cout<<"convert Reorg\n"; //std::cout<<"convert Reorg\n";
std::cout<<"New plugin REORG\n"; //std::cout<<"New plugin REORG\n";
IPlugin *plugin = new ReorgRT(l->stride); IPlugin *plugin = new ReorgRT(l->stride);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin); IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT); checkNULL(lRT);
@@ -248,9 +250,9 @@ ITensor* NetworkRT::convert_layer(ITensor *input, Reorg *l) {
} }
ITensor* NetworkRT::convert_layer(ITensor *input, Region *l) { ITensor* NetworkRT::convert_layer(ITensor *input, Region *l) {
std::cout<<"convert Region\n"; //std::cout<<"convert Region\n";
std::cout<<"New plugin REGION\n"; //std::cout<<"New plugin REGION\n";
IPlugin *plugin = new RegionRT(l->classes, l->coords, l->num, l->thresh); IPlugin *plugin = new RegionRT(l->classes, l->coords, l->num, l->thresh);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin); IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT); checkNULL(lRT);
src/utils.cpp
+3 -1
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@@ -5,7 +5,9 @@ void printCenteredTitle(const char *title, char fill, int dim) {
int len = strlen(title); int len = strlen(title);
int first = dim/2 + len/2; int first = dim/2 + len/2;
if(len >0)
std::cout<<"\n";
std::cout.width(first); std::cout.fill(fill); std::cout<<std::right<<title; std::cout.width(first); std::cout.fill(fill); std::cout<<std::right<<title;
std::cout.width(dim - first); std::cout<<"\n"; std::cout.width(dim - first); std::cout<<"\n";
std::cout.fill(' '); std::cout.fill(' ');
tests/yolo-tiny/yolo-tiny.cpp
+6 -3
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@@ -56,13 +56,16 @@ int main() {
value_type *input_h; value_type *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data); readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
net.print();
//convert network to tensorRT //convert network to tensorRT
tkDNN::NetworkRT netRT(&net); tkDNN::NetworkRT netRT(&net);
value_type *out_data, *out_data2; // cudnn output, tensorRT output value_type *out_data, *out_data2; // cudnn output, tensorRT output
tkDNN::dataDim_t dim1 = dim; //input dim tkDNN::dataDim_t dim1 = dim; //input dim
std::cout<<"\n==== CUDNN inference =======\n"; { printCenteredTitle(" CUDNN inference ", '=', 30); {
dim1.print(); dim1.print();
TIMER_START TIMER_START
out_data = net.infer(dim1, data); out_data = net.infer(dim1, data);
@@ -71,7 +74,7 @@ int main() {
} }
tkDNN::dataDim_t dim2 = dim; tkDNN::dataDim_t dim2 = dim;
std::cout<<"\n==== TENSORRT inference ====\n"; { printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print(); dim2.print();
TIMER_START TIMER_START
out_data2 = netRT.infer(dim2, data); out_data2 = netRT.infer(dim2, data);
@@ -79,7 +82,7 @@ int main() {
dim2.print(); dim2.print();
} }
std::cout<<"\n======= CHECK RESULT =======\n"; printCenteredTitle(" CHECK RESULTS ", '=', 30);
value_type *out, *out_h; value_type *out, *out_h;
int out_dim = net.getOutputDim().tot(); int out_dim = net.getOutputDim().tot();
readBinaryFile(output_bin, out_dim, &out_h, &out); readBinaryFile(output_bin, out_dim, &out_h, &out);
tests/yolo/yolo.cpp
+6 -3
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@@ -103,6 +103,9 @@ int main() {
value_type *data; value_type *data;
value_type *input_h; value_type *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data); readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
net.print();
//convert network to tensorRT //convert network to tensorRT
tkDNN::NetworkRT netRT(&net); tkDNN::NetworkRT netRT(&net);
@@ -110,7 +113,7 @@ int main() {
value_type *out_data, *out_data2; // cudnn output, tensorRT output value_type *out_data, *out_data2; // cudnn output, tensorRT output
tkDNN::dataDim_t dim1 = dim; //input dim tkDNN::dataDim_t dim1 = dim; //input dim
std::cout<<"\n==== CUDNN inference =======\n"; { printCenteredTitle(" CUDNN inference ", '=', 30); {
dim1.print(); dim1.print();
TIMER_START TIMER_START
out_data = net.infer(dim1, data); out_data = net.infer(dim1, data);
@@ -119,7 +122,7 @@ int main() {
} }
tkDNN::dataDim_t dim2 = dim; tkDNN::dataDim_t dim2 = dim;
std::cout<<"\n==== TENSORRT inference ====\n"; { printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print(); dim2.print();
TIMER_START TIMER_START
out_data2 = netRT.infer(dim2, data); out_data2 = netRT.infer(dim2, data);
@@ -127,7 +130,7 @@ int main() {
dim2.print(); dim2.print();
} }
std::cout<<"\n======= CHECK RESULT =======\n"; printCenteredTitle(" CHECK RESULTS ", '=', 30);
value_type *out, *out_h; value_type *out, *out_h;
int out_dim = net.getOutputDim().tot(); int out_dim = net.getOutputDim().tot();
readBinaryFile(output_bin, out_dim, &out_h, &out); readBinaryFile(output_bin, out_dim, &out_h, &out);