tk
48 lines
1.6 KiB
C++
48 lines
1.6 KiB
C++
#include<iostream>
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#include<vector>
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#include "tkdnn.h"
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#include "test.h"
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#include "DarknetParser.h"
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int main() {
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std::string bin_path = "yolo4_512";
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std::vector<std::string> input_bins = {
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bin_path + "/layers/input.bin"
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};
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std::vector<std::string> output_bins = {
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bin_path + "/debug/layer139_out.bin",
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bin_path + "/debug/layer150_out.bin",
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bin_path + "/debug/layer161_out.bin"
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};
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std::string wgs_path = bin_path + "/layers";
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std::string cfg_path = "../tests/darknet/cfg/yolo4_512.cfg";
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std::string name_path = "../tests/darknet/names/coco.names";
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downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/fjFDqFmiSARKxFe/download");
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// parse darknet network
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tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
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net->print();
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// for(int i=0; i<net->num_layers; i++) {
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// if(net->layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
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// tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net->layers[i];
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// c->releaseDevice();
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// c->releaseHost(true, false);
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// }
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// if(net->layers[i]->dstData != nullptr) {
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// cudaFree(net->layers[i]->dstData);
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// net->layers[i]->dstData = nullptr;
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// }
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// }
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//convert network to tensorRT
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tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
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int ret = testInference(input_bins, output_bins, net, netRT);
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net->releaseLayers();
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delete net;
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delete netRT;
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return ret;
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}
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