Francesco Gatti
53 lines
1.8 KiB
C++
53 lines
1.8 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/yolo3_berkeley/layers/input.bin";
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const char *c0_bin = "../tests/yolo3_berkeley/layers/c0.bin";
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const char *c1_bin = "../tests/yolo3_berkeley/layers/c1.bin";
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const char *c2_bin = "../tests/yolo3_berkeley/layers/c2.bin";
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const char *c3_bin = "../tests/yolo3_berkeley/layers/c3.bin";
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const char *output_bin = "../tests/yolo3_berkeley/debug/layer3_out.bin";
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 320, 544, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
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tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
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tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
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tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
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tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
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// Load input
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dnnType *data;
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dnnType *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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//print network model
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net.print();
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dnnType *out_data; // cudnn output
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tk::dnn::dataDim_t dim1 = dim; //input dim
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printCenteredTitle(" CUDNN inference ", '=', 30); {
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dim1.print();
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TIMER_START
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out_data = net.infer(dim1, data);
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TIMER_STOP
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dim1.print();
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}
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printCenteredTitle(" CHECK RESULTS ", '=', 30);
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dnnType *out, *out_h;
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int out_dim = net.getOutputDim().tot();
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readBinaryFile(output_bin, out_dim, &out_h, &out);
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std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
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return 0;
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}
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