Francesco Gatti
152 lines
6.8 KiB
C++
152 lines
6.8 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/yolo_relu/layers/input.bin";
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const char *c0_bin = "../tests/yolo_relu/layers/c0.bin";
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const char *c2_bin = "../tests/yolo_relu/layers/c2.bin";
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const char *c4_bin = "../tests/yolo_relu/layers/c4.bin";
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const char *c5_bin = "../tests/yolo_relu/layers/c5.bin";
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const char *c6_bin = "../tests/yolo_relu/layers/c6.bin";
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const char *c8_bin = "../tests/yolo_relu/layers/c8.bin";
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const char *c9_bin = "../tests/yolo_relu/layers/c9.bin";
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const char *c10_bin = "../tests/yolo_relu/layers/c10.bin";
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const char *c12_bin = "../tests/yolo_relu/layers/c12.bin";
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const char *c13_bin = "../tests/yolo_relu/layers/c13.bin";
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const char *c14_bin = "../tests/yolo_relu/layers/c14.bin";
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const char *c15_bin = "../tests/yolo_relu/layers/c15.bin";
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const char *c16_bin = "../tests/yolo_relu/layers/c16.bin";
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const char *c18_bin = "../tests/yolo_relu/layers/c18.bin";
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const char *c19_bin = "../tests/yolo_relu/layers/c19.bin";
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const char *c20_bin = "../tests/yolo_relu/layers/c20.bin";
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const char *c21_bin = "../tests/yolo_relu/layers/c21.bin";
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const char *c22_bin = "../tests/yolo_relu/layers/c22.bin";
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const char *c23_bin = "../tests/yolo_relu/layers/c23.bin";
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const char *c24_bin = "../tests/yolo_relu/layers/c24.bin";
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const char *c26_bin = "../tests/yolo_relu/layers/c26.bin";
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const char *c29_bin = "../tests/yolo_relu/layers/c29.bin";
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const char *c30_bin = "../tests/yolo_relu/layers/c30.bin";
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const char *g31_bin = "../tests/yolo_relu/layers/g31.bin";
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const char *output_bin = "../tests/yolo_relu/layers/output.bin";
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 608, 608, 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, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c2 (&net, 64, 3, 3, 1, 1, 1, 1, c2_bin, true);
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tk::dnn::Activation a2 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c4 (&net, 128, 3, 3, 1, 1, 1, 1, c4_bin, true);
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tk::dnn::Activation a4 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c5 (&net, 64, 1, 1, 1, 1, 0, 0, c5_bin, true);
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tk::dnn::Activation a5 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c6 (&net, 128, 3, 3, 1, 1, 1, 1, c6_bin, true);
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tk::dnn::Activation a6 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling p7 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c8 (&net, 256, 3, 3, 1, 1, 1, 1, c8_bin, true);
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tk::dnn::Activation a8 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c9 (&net, 128, 1, 1, 1, 1, 0, 0, c9_bin, true);
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tk::dnn::Activation a9 (&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c10(&net, 256, 3, 3, 1, 1, 1, 1, c10_bin, true);
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tk::dnn::Activation a10(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling p11(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
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tk::dnn::Activation a12(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c13(&net, 256, 1, 1, 1, 1, 0, 0, c13_bin, true);
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tk::dnn::Activation a13(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c14(&net, 512, 3, 3, 1, 1, 1, 1, c14_bin, true);
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tk::dnn::Activation a14(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c15(&net, 256, 1, 1, 1, 1, 0, 0, c15_bin, true);
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tk::dnn::Activation a15(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c16(&net, 512, 3, 3, 1, 1, 1, 1, c16_bin, true);
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tk::dnn::Activation a16(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling p17(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
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tk::dnn::Activation a18(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c19(&net, 512, 1, 1, 1, 1, 0, 0, c19_bin, true);
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tk::dnn::Activation a19(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
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tk::dnn::Activation a20(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c21(&net, 512, 1, 1, 1, 1, 0, 0, c21_bin, true);
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tk::dnn::Activation a21(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
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tk::dnn::Activation a22(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
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tk::dnn::Activation a23(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
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tk::dnn::Activation a24(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Layer *m25_layers[1] = { &a16 };
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tk::dnn::Route m25(&net, m25_layers, 1);
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tk::dnn::Conv2d c26(&net, 64, 1, 1, 1, 1, 0, 0, c26_bin, true);
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tk::dnn::Activation a26(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Reorg r27(&net, 2);
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tk::dnn::Layer *m28_layers[2] = { &r27, &a24 };
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tk::dnn::Route m28(&net, m28_layers, 2);
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tk::dnn::Conv2d c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
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tk::dnn::Activation a29(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d c30(&net, 425, 1, 1, 1, 1, 0, 0, c30_bin, false);
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tk::dnn::Region g31(&net, 80, 4, 5);
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tk::dnn::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.3f, g31_bin);
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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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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(&net, "yolo_relu.rt");
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dnnType *out_data, *out_data2; // cudnn output, tensorRT 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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tk::dnn::dataDim_t dim2 = dim;
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printCenteredTitle(" TENSORRT inference ", '=', 30); {
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dim2.print();
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TIMER_START
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out_data2 = netRT.infer(dim2, data);
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TIMER_STOP
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dim2.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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std::cout<<"TRT vs correct"; checkResult(out_dim, out_data2, out);
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std::cout<<"CUDNN vs TRT "; checkResult(out_dim, out_data, out_data2);
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std::cout<<"\n\nDetected objects: \n";
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dnnType *output_h = new dnnType[rI.output_dim.tot()];
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checkCuda(cudaMemcpy(output_h, out_data2,
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rI.output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost));
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rI.interpretData(output_h, 608, 608);
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rI.showImageResult(input_h);
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return 0;
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}
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