added yolo4x and yolo4-csp from the github repo and download file corrections

tests/darknet/cfg/yolo4x.cfg

@@ -5,8 +5,8 @@
 # Training
 batch=64
 subdivisions=8
-width=672
-height=672
+width=640
+height=640
 channels=3
 momentum=0.949
 decay=0.0005
@@ -15,7 +15,7 @@ saturation = 1.5
 exposure = 1.5
 hue=.1
 
-learning_rate=0.00261
+learning_rate=0.001
 burn_in=1000
 max_batches = 500500
 policy=steps
@@ -26,6 +26,8 @@ mosaic=1
 
 letter_box=1
 
+#optimized_memory=1
+
 [convolutional]
 batch_normalize=1
 filters=32
@@ -1131,6 +1133,7 @@ size=1
 stride=1
 pad=1
 activation=mish
+stopbackward=800
 
 ##########################
 
@@ -1147,7 +1150,7 @@ size=1
 stride=1
 pad=1
 filters=255
-activation=linear
+activation=logistic
 
 
 [yolo]
@@ -1156,6 +1159,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
 classes=80
 num=9
 jitter=.1
+scale_x_y = 2.0
 objectness_smooth=0
 ignore_thresh = .7
 truth_thresh = 1
@@ -1169,6 +1173,7 @@ iou_loss=ciou
 nms_kind=diounms
 beta_nms=0.6
 new_coords=1
+max_delta=5
 
 [route]
 layers = -4
@@ -1275,7 +1280,7 @@ size=1
 stride=1
 pad=1
 filters=255
-activation=linear
+activation=logistic
 
 
 [yolo]
@@ -1284,6 +1289,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
 classes=80
 num=9
 jitter=.1
+scale_x_y = 2.0
 objectness_smooth=1
 ignore_thresh = .7
 truth_thresh = 1
@@ -1297,6 +1303,7 @@ iou_loss=ciou
 nms_kind=diounms
 beta_nms=0.6
 new_coords=1
+max_delta=5
 
 [route]
 layers = -4
@@ -1403,7 +1410,7 @@ size=1
 stride=1
 pad=1
 filters=255
-activation=linear
+activation=logistic
 
 
 [yolo]
@@ -1412,6 +1419,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
 classes=80
 num=9
 jitter=.1
+scale_x_y = 2.0
 objectness_smooth=1
 ignore_thresh = .7
 truth_thresh = 1
@@ -1425,3 +1433,4 @@ iou_loss=ciou
 nms_kind=diounms
 beta_nms=0.6
 new_coords=1
+max_delta=2

tests/darknet/yolo4-csp.cpp

@@ -0,0 +1,36 @@
+#include<iostream>
+#include<vector>
+#include "tkdnn.h"
+#include "test.h"
+#include "DarknetParser.h"
+
+int main() {
+    std::string bin_path  = "yolo4-csp";
+    std::vector<std::string> input_bins = {
+            bin_path + "/layers/input.bin"
+    };
+    std::vector<std::string> output_bins = {
+            bin_path + "/debug/layer144_out.bin",
+            bin_path + "/debug/layer159_out.bin",
+            bin_path + "/debug/layer174_out.bin"
+    };
+    std::string wgs_path  = bin_path + "/layers";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-csp.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
+    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/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;
+    delete netRT;
+    return ret;
+}

tests/darknet/yolo4x.cpp

@@ -17,7 +17,7 @@ int main() {
     std::string wgs_path  = bin_path + "/layers";
     std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4x.cfg";
     std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
-    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download");
+    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download");