Micaela Verucchi
101 lines
4.0 KiB
C++
101 lines
4.0 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/yolo_tiny/layers/input.bin";
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const char *c0_bin = "../tests/yolo_tiny/layers/c0.bin";
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const char *c2_bin = "../tests/yolo_tiny/layers/c2.bin";
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const char *c4_bin = "../tests/yolo_tiny/layers/c4.bin";
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const char *c5_bin = "../tests/yolo_tiny/layers/c5.bin";
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const char *c6_bin = "../tests/yolo_tiny/layers/c6.bin";
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const char *c8_bin = "../tests/yolo_tiny/layers/c8.bin";
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const char *c10_bin = "../tests/yolo_tiny/layers/c10.bin";
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const char *c11_bin = "../tests/yolo_tiny/layers/c11.bin";
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const char *c12_bin = "../tests/yolo_tiny/layers/c12.bin";
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const char *c13_bin = "../tests/yolo_tiny/layers/c13.bin";
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const char *g14_bin = "../tests/yolo_tiny/layers/g14.bin";
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const char *output_bin = "../tests/yolo_tiny/layers/output.bin";
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int main() {
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downloadWeightsifDoNotExist(input_bin, "../tests/yolo_tiny", "https://cloud.hipert.unimore.it/s/m3orfJr8pGrN5mQ/download");
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d c0 (&net, 16, 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::Pooling p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c2 (&net, 32, 3, 3, 1, 1, 1, 1, c2_bin, true);
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tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
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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, 64, 3, 3, 1, 1, 1, 1, c4_bin, true);
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tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Pooling p5 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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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, tk::dnn::ACTIVATION_LEAKY);
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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, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Pooling p9(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
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tk::dnn::Conv2d c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
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tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
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tk::dnn::Activation a11(&net, tk::dnn::ACTIVATION_LEAKY);
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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, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
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tk::dnn::Region g14(&net, 80, 4, 5);
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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, net.getNetworkRTName("yolo_tiny"));
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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";
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int ret_cudnn = checkResult(out_dim, out_data, out) == 0 ? 0: ERROR_CUDNN;
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std::cout<<"TRT vs correct";
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int ret_tensorrt = checkResult(out_dim, out_data2, out) == 0 ? 0 : ERROR_TENSORRT;
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std::cout<<"CUDNN vs TRT ";
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int ret_cudnn_tensorrt = checkResult(out_dim, out_data, out_data2) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
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return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
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}
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