rtinference nstep

tests/darknet/names/mbzirc.names

@@ -1,20 +0,0 @@
-vesselA
-vesselB
-vesselC
-vesselD
-vesselE
-vesselF
-vesselG
-vesselH
-obj1
-obj2
-obj3
-obj4
-obj5
-obj6
-obj7
-obj8
-obj9
-obj10
-obj11
-obj12

tests/darknet/names/objs.names

@@ -1,10 +0,0 @@
-sdbh
-sdb
-sc
-ldb
-lc
-lgb
-sbb
-lac
-none
-none

tests/darknet/names/vessels.names

@@ -1,10 +0,0 @@
-vessel_a
-vessel_b
-vessel_c
-vessel_d
-vessel_e
-vessel_f
-vessel_g
-USV
-none
-none

tests/darknet/yolo4-mbzirc-objs.cpp

@@ -1,35 +0,0 @@
-#include<iostream>
-#include<vector>
-#include "tkdnn.h"
-#include "test.h"
-#include "DarknetParser.h"
-
-int main() {
-    std::string bin_path  = "yolo4-mbzirc-objs";
-    std::vector<std::string> input_bins = { 
-        bin_path + "/layers/input.bin"
-    };
-    std::vector<std::string> output_bins = {
-        bin_path + "/debug/layer139_out.bin",
-        bin_path + "/debug/layer150_out.bin",
-        bin_path + "/debug/layer161_out.bin"
-    };
-    std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-mbzirc-10.cfg";
-    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/objs.names";
-    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/BByqxdGNzp38kHx/download");
-
-    // parse darknet network
-    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
-    net->print();
-
-    //convert network to tensorRT
-    tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
-    
-    int ret = testInference(input_bins, output_bins, net, netRT);
-    net->releaseLayers();
-    delete net;
-    netRT->destroy();
-    delete netRT;
-    return ret;
-}

tests/darknet/yolo4-mbzirc-vessels.cpp

@@ -1,35 +0,0 @@
-#include<iostream>
-#include<vector>
-#include "tkdnn.h"
-#include "test.h"
-#include "DarknetParser.h"
-
-int main() {
-    std::string bin_path  = "yolo4-mbzirc-vessel";
-    std::vector<std::string> input_bins = { 
-        bin_path + "/layers/input.bin"
-    };
-    std::vector<std::string> output_bins = {
-        bin_path + "/debug/layer139_out.bin",
-        bin_path + "/debug/layer150_out.bin",
-        bin_path + "/debug/layer161_out.bin"
-    };
-    std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-mbzirc-10.cfg";
-    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/vessels.names";
-    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/76FRPXYqbGarTJi/download");
-
-    // parse darknet network
-    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
-    net->print();
-
-    //convert network to tensorRT
-    tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
-    
-    int ret = testInference(input_bins, output_bins, net, netRT);
-    net->releaseLayers();
-    delete net;
-    netRT->destroy();
-    delete netRT;
-    return ret;
-}

tests/darknet/yolo4-mbzirc.cpp

@@ -1,35 +0,0 @@
-#include<iostream>
-#include<vector>
-#include "tkdnn.h"
-#include "test.h"
-#include "DarknetParser.h"
-
-int main() {
-    std::string bin_path  = "yolo4-mbzirc";
-    std::vector<std::string> input_bins = { 
-        bin_path + "/layers/input.bin"
-    };
-    std::vector<std::string> output_bins = {
-        bin_path + "/debug/layer139_out.bin",
-        bin_path + "/debug/layer150_out.bin",
-        bin_path + "/debug/layer161_out.bin"
-    };
-    std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-mbzirc.cfg";
-    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/mbzirc.names";
-    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/pNsZxzogfMcKTK4/download");
-
-    // parse darknet network
-    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
-    net->print();
-
-    //convert network to tensorRT
-    tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
-    
-    int ret = testInference(input_bins, output_bins, net, netRT);
-    net->releaseLayers();
-    delete net;
-    netRT->destroy();
-    delete netRT;
-    return ret;
-}

tests/test_rtinference/rtinference.cpp

@@ -13,6 +13,10 @@ int main(int argc, char *argv[]) {
     if(argc >2)
         BATCH_SIZE = atoi(argv[2]);
 
+    int NTEST = 100;
+    if(argc >3)
+        NTEST = atoi(argv[3]);
+
     //always same test 
     srand (0); 
 
@@ -35,7 +39,7 @@ int main(int argc, char *argv[]) {
     std::vector<double> stats;
     printCenteredTitle(" TENSORRT inference ", '=', 30); 
     float total_time = 0;
-    for(int i=0; i<64; i++) {
+    for(int i=0; (NTEST > 0 ? i<NTEST : true) ; i++) {
 
         // generate input
         for(int j=0; j<netRT.input_dim.tot(); j++) {