Francesco Gatti
67 lines
2.1 KiB
C++
67 lines
2.1 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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#include "NvInfer.h"
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const char *input_bin = "../tests/mnist/input.bin";
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const char *c0_bin = "../tests/mnist/layers/c0.bin";
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const char *c1_bin = "../tests/mnist/layers/c1.bin";
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const char *d2_bin = "../tests/mnist/layers/d2.bin";
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const char *d3_bin = "../tests/mnist/layers/d3.bin";
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const char *output_bin = "../tests/mnist/output.bin";
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// Logger for info/warning/errors
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class Logger : public nvinfer1::ILogger
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{
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void log(Severity severity, const char* msg) override
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{
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// suppress info-level messages
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if (severity != Severity::kINFO)
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std::cout << msg << std::endl;
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}
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} gLogger;
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int main() {
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std::cout<<"\n==== CUDNN ====\n";
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// Network layout
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tkDNN::Network net;
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tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
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tkDNN::Layer *l;
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l = new tkDNN::Conv2d (&net, dim, 20, 5, 5, 1, 1, 0, 0, c0_bin);
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l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
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l = new tkDNN::Conv2d (&net, l->output_dim, 50, 5, 5, 1, 1, 0, 0, c1_bin);
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l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
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l = new tkDNN::Dense (&net, l->output_dim, 500, d2_bin);
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l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
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l = new tkDNN::Dense (&net, l->output_dim, 10, d3_bin);
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l = new tkDNN::Softmax (&net, l->output_dim);
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// Load input
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value_type *data;
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value_type *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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dim.print(); //print initial dimension
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TIMER_START
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// Inference
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data = net.infer(dim, data);
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TIMER_STOP
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dim.print();
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// Print real test
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std::cout<<"\n==== CHECK CUDNN RESULT ====\n";
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value_type *out;
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value_type *out_h;
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readBinaryFile(output_bin, dim.tot(), &out_h, &out);
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std::cout<<"Diff: "<<checkResult(dim.tot(), out, data)<<"\n";
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std::cout<<"\n==== TensorRT ====\n";
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// create the builder
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nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(gLogger);
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nvinfer1::INetworkDefinition* network = builder->createNetwork();
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return 0;
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}
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