Davide Sapienza
ce20868bad
This commit permits to obtain different .rt files for different precision optimizations of the same network. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
124 lines
5.0 KiB
C++
124 lines
5.0 KiB
C++
#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/yolo3_tiny512tp/layers/input.bin";
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const char *c0_bin = "../tests/yolo3_tiny512tp/layers/c0.bin";
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const char *c2_bin = "../tests/yolo3_tiny512tp/layers/c2.bin";
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const char *c4_bin = "../tests/yolo3_tiny512tp/layers/c4.bin";
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const char *c6_bin = "../tests/yolo3_tiny512tp/layers/c6.bin";
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const char *c8_bin = "../tests/yolo3_tiny512tp/layers/c8.bin";
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const char *c10_bin = "../tests/yolo3_tiny512tp/layers/c10.bin";
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const char *c12_bin = "../tests/yolo3_tiny512tp/layers/c12.bin";
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const char *c13_bin = "../tests/yolo3_tiny512tp/layers/c13.bin";
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const char *c14_bin = "../tests/yolo3_tiny512tp/layers/c14.bin";
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const char *c15_bin = "../tests/yolo3_tiny512tp/layers/c15.bin";
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const char *c18_bin = "../tests/yolo3_tiny512tp/layers/c18.bin";
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const char *c21_bin = "../tests/yolo3_tiny512tp/layers/c21.bin";
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const char *c22_bin = "../tests/yolo3_tiny512tp/layers/c22.bin";
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const char *g16_bin = "../tests/yolo3_tiny512tp/layers/g16.bin";
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const char *g23_bin = "../tests/yolo3_tiny512tp/layers/g23.bin";
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// const char *output_bin = "../tests/yolo3_tiny512tp/layers/output.bin";
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const char *output_bin = "../tests/yolo3_tiny512tp/debug/layer23_out.bin";
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int main() {
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int classes = 3;
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 512, 512, 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::Pooling p11(&net, 2, 2, 1, 1,0,0, tk::dnn::POOLING_MAX, false, true);
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tk::dnn::Conv2d c12(&net, 1024, 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, 256, 1, 1, 1, 1, 0, 0, c13_bin, true);
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tk::dnn::Activation a13(&net, tk::dnn::ACTIVATION_LEAKY);
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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, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c15(&net, 24, 1, 1, 1, 1, 0, 0, c15_bin, false);
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tk::dnn::Yolo yolo0 (&net, classes, 2, g16_bin);
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tk::dnn::Layer *m17_layers[1] = { &a13 };
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tk::dnn::Route m17 (&net, m17_layers, 1);
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tk::dnn::Conv2d c18(&net, 128, 1, 1, 1, 1, 0, 0, c18_bin, true);
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tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Upsample u19 (&net, 2);
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tk::dnn::Layer *m20_layers[2] = { &u19, &a8 };
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tk::dnn::Route m20 (&net, m20_layers, 2);
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tk::dnn::Conv2d c21(&net, 256, 3, 3, 1, 1, 1, 1, c21_bin, true);
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tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c22(&net, 24, 1, 1, 1, 1, 0, 0, c22_bin, false);
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tk::dnn::Yolo yolo1 (&net, classes, 2, g23_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, net.getNetworkRTName("yolo3_tiny512tp"));
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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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return 0;
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}
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