Update yolo4-mbzirc-objs with better training

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

tests/darknet/names/mbzirc.names

@@ -0,0 +1,20 @@
+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

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

tests/darknet/names/vessels.names

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

tests/darknet/yolo4-mbzirc-objs.cpp

@@ -0,0 +1,35 @@
+#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

@@ -0,0 +1,35 @@
+#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

@@ -0,0 +1,35 @@
+#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

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