Added TensorRT8 support

- Migrated to TRT8 API, but I didn't really try maintaining old compatiblity. Lowest support now could be TRT6.
- Refractored some stuff that are bad C++ practices and made coding really hard, like including headers in namespaces or not using an include guard.
- Needed to move the yolo container outside tkdnn object, which means we now only have one and global. Deserialization in TRT8 doesn't happen in your object, but in the plugin itself so it couldn't access the yolo objects. I think the need to hold onto yolo layers itself is flawed and shouldn't be nessesarry.

tests/mnist/test_mnistRT.cpp

@@ -13,9 +13,9 @@ const char *output_bin   = "mnist/output.bin";
 using namespace nvinfer1;
 
 // 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) noexcept override
 	{
 		// suppress info-level messages
 		if (severity != Severity::kINFO)
@@ -39,20 +39,20 @@ int main() {
     tk::dnn::Activation l5(&net, CUDNN_ACTIVATION_RELU);
     tk::dnn::Dense      l6(&net, 10, d3_bin);
     tk::dnn::Softmax    l7(&net);
- 
+
     // Load input
     dnnType *data;
     dnnType *input_h;
     readBinaryFile(input_bin, dim.tot(), &input_h, &data);
 
     dim.print(); //print initial dimension
-    
+
     // Inference
     {
         TKDNN_TSTART
         data = net.infer(dim, data);
         TKDNN_TSTOP
-        dim.print();   
+        dim.print();
     }
 
     // Print real test
@@ -61,19 +61,19 @@ int main() {
     dnnType *out_h;
     readBinaryFile(output_bin, dim.tot(), &out_h, &out);
     std::cout<<"Diff: "<<checkResult(dim.tot(), out, data)<<"\n";
- 
+
 
     std::cout<<"\n==== TensorRT ====\n";
 	// create the builder
 	IBuilder* builder = nvinfer1::createInferBuilder(gLogger);
-	INetworkDefinition* network = builder->createNetwork();
+	INetworkDefinition* network = builder->createNetworkV2(0u);
 
     DataType dt = DataType::kFLOAT;
 	//  Create input of shape { 1, 1, 28, 28 } with name referenced by "data"
-	auto input = network->addInput("data", dt, DimsCHW{ 1, 28, 28});
+	auto input = network->addInput("data", dt, Dims3{ 1, 28, 28});
 	assert(input != nullptr);
 
-    tk::dnn::Conv2d *c0 = &l0; 
+    tk::dnn::Conv2d *c0 = &l0;
     Weights w { dt, c0->data_h, c0->inputs*c0->outputs*c0->kernelH*c0->kernelW};
     Weights b { dt, c0->bias_h, c0->outputs};
 	// Add a convolution layer with 20 outputs and a 5x5 filter.
@@ -86,7 +86,7 @@ int main() {
 	assert(pool1 != nullptr);
 	pool1->setStride(DimsHW{2, 2});
 
-    tk::dnn::Conv2d *c1 = &l2; 
+    tk::dnn::Conv2d *c1 = &l2;
     Weights w1 { dt, c1->data_h, c1->inputs*c1->outputs*c1->kernelH*c1->kernelW};
     Weights b1 { dt, c1->bias_h, c1->outputs};
 	// Add a second convolution layer with 50 outputs and a 5x5 filter.
@@ -99,7 +99,7 @@ int main() {
 	assert(pool2 != nullptr);
 	pool2->setStride(DimsHW{2, 2});
 
-    tk::dnn::Dense *d2 = &l4; 
+    tk::dnn::Dense *d2 = &l4;
     Weights w2 { dt, d2->data_h, d2->inputs*d2->outputs};
     Weights b2 { dt, d2->bias_h, d2->outputs};
 	// Add a fully connected layer with 500 outputs.
@@ -110,7 +110,7 @@ int main() {
 	auto relu1 = network->addActivation(*ip1->getOutput(0), ActivationType::kRELU);
 	assert(relu1 != nullptr);
 
-    tk::dnn::Dense *d3 = &l6; 
+    tk::dnn::Dense *d3 = &l6;
     Weights w3 { dt, d3->data_h, d3->inputs*d3->outputs};
     Weights b3 { dt, d3->bias_h, d3->outputs};
 	// Add a second fully connected layer with 20 outputs.
@@ -125,10 +125,21 @@ int main() {
 	network->markOutput(*prob->getOutput(0));
 
 	// Build the engine
-	builder->setMaxBatchSize(1);
+#if NV_TENSORRT_MAJOR >= 6
+  auto config = builder->createBuilderConfig();
+	config->setMaxWorkspaceSize(1 << 20);
+#else
 	builder->setMaxWorkspaceSize(1 << 20);
+#endif
+
+	builder->setMaxBatchSize(1);
+
+#if NV_TENSORRT_MAJOR >= 6
+        auto engine = builder->buildEngineWithConfig(*network, *config);
+#else
+        auto engine = builder->buildCudaEngine(*network);
+#endif
 
-	auto engine = builder->buildCudaEngine(*network);
 	// we don't need the network any more
 	network->destroy();
 
@@ -142,10 +153,10 @@ int main() {
 
 	// In order to bind the buffers, we need to know the names of the input and output tensors.
 	// note that indices are guaranteed to be less than IEngine::getNbBindings()
-	int inputIndex = engine->getBindingIndex("data"); 
-    int outputIndex = engine->getBindingIndex("out");
+	int inputIndex = engine->getBindingIndex("data");
+  int outputIndex = engine->getBindingIndex("out");
 
-    float output[10];
+  float output[10];
 	// create GPU buffers and a stream
 	checkCuda(cudaMalloc(&buffers[inputIndex], 28*28*sizeof(float)));
 	checkCuda(cudaMalloc(&buffers[outputIndex], 10*sizeof(float)));