Merge with master, add yolov4tiny_512

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

tests/centernet/dla34_cnet/dla34_cnet.cpp

@@ -479,6 +479,18 @@ int main()
     //print network model
     net.print();
 
+//     for(int i=0; i<net.num_layers; i++) {
+//         if(net.layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net.layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net.layers[i]->dstData != nullptr) {
+//             cudaFree(net.layers[i]->dstData);
+//             net.layers[i]->dstData = nullptr;
+//         }
+//    }
+
     //convert network to tensorRT
     tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet"));
 

tests/centernet/resnet101_cnet/resnet101_cnet.cpp

@@ -353,6 +353,18 @@ int main()
     //print network model
     net.print();
 
+//     for(int i=0; i<net.num_layers; i++) {
+//         if(net.layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net.layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net.layers[i]->dstData != nullptr) {
+//             cudaFree(net.layers[i]->dstData);
+//             net.layers[i]->dstData = nullptr;
+//         }
+//    }
+
     //convert network to tensorRT
     tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101_cnet"));
 

tests/darknet/cfg/yolo4tiny.cfg

@@ -0,0 +1,281 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6

tests/darknet/cfg/yolo4tiny_512.cfg

@@ -0,0 +1,281 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=1
+width=512
+height=512
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6

tests/darknet/csresnext50-panet-spp.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer137_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/csresnext50-panet-spp.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/csresnext50-panet-spp.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/Kcs4xBozwY4wFx8/download");
 
     // parse darknet network

tests/darknet/csresnext50-panet-spp_berkeley.cpp

@@ -15,10 +15,9 @@ int main() {
         bin_path + "/debug/layer137_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/csresnext50-panet-spp_berkeley.cfg";
-    std::string name_path = "../tests/darknet/names/berkeley.names";
-    // FIXME: wrong weights
-    // downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/csresnext50-panet-spp_berkeley.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/berkeley.names";
+    downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/q82qHAtqpoaFYo5/download");
 
     // parse darknet network
     tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);

tests/darknet/viz_yolo3.cpp

@@ -0,0 +1,70 @@
+#include<iostream>
+#include<vector>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "tkdnn.h"
+#include "test.h"
+#include "DarknetParser.h"
+#include "NetworkViz.h"
+
+int main(int argc, char *argv[]) {
+    if(argc <2)
+        FatalError("you must provide an input image");
+    std::string input_image = argv[1];
+    std::string bin_path  = "yolo3";
+    std::string wgs_path  = bin_path + "/layers";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
+    downloadWeightsifDoNotExist(wgs_path, bin_path, "https://cloud.hipert.unimore.it/s/jPXmHyptpLoNdNR/download");
+
+    // parse darknet network
+    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
+    net->print();
+
+    // input data
+    dnnType *input_d;
+    checkCuda( cudaMalloc(&input_d, sizeof(dnnType)*net->input_dim.tot()));
+
+    // load image
+    cv::Mat frame, frameFloat;
+    frame = cv::imread(input_image);
+    cv::resize(frame, frame, cv::Size(net->input_dim.w, net->input_dim.h));
+    frame.convertTo(frameFloat, CV_32FC3, 1/255.0); 
+
+    //split channels
+    cv::Mat bgr[3];
+    cv::split(frameFloat,bgr);//split source
+
+    //write channels
+    for(int i=0; i<net->input_dim.c; i++) {
+        int idx = i*frameFloat.rows*frameFloat.cols;
+        int ch = net->input_dim.c-1 -i;
+        checkCuda( cudaMemcpy(input_d + idx, (void*)bgr[ch].data, frameFloat.rows*frameFloat.cols*sizeof(dnnType), cudaMemcpyHostToDevice));     
+    }
+    
+    tk::dnn::dataDim_t dim =  net->input_dim;     
+    dim.print();
+    std::cout<<"infer\n";
+    net->infer(dim, input_d);    
+
+    // output directory
+    std::string output_viz = "viz/";
+    system( (std::string("mkdir -p ") + output_viz).c_str() );
+
+    for(int i=0; i<net->num_layers; i++) {
+        std::string output_png = output_viz + "/layer" + std::to_string(i) + ".png";
+        std::cout<<"saving "<<output_png<<"\n";
+        cv::Mat viz = vizLayer2Mat(net, i);
+        cv::imwrite(output_png, viz);
+        //cv::imshow("layer", viz);
+        //cv::waitKey(0);
+    }
+
+    checkCuda(cudaFree(input_d));
+    net->releaseLayers();
+    delete net;
+    return 0;
+}
+
+ 

tests/darknet/yolo2.cpp

@@ -13,8 +13,8 @@ int main() {
         bin_path + "/layers/output.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo2.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo2.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/nf4PJ3k8bxBETwL/download");
 
     // parse darknet network

tests/darknet/yolo2_voc.cpp

@@ -13,8 +13,8 @@ int main() {
         bin_path + "/layers/output.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo2_voc.cfg";
-    std::string name_path = "../tests/darknet/names/voc.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo2_voc.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/voc.names";
     downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/DJC5Fi2pEjfNDP9/download");
 
     // parse darknet network

tests/darknet/yolo2tiny.cpp

@@ -13,8 +13,8 @@ int main() {
         bin_path + "/layers/output.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo2tiny.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo2tiny.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
     // FIXME: wrong weights
     //downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
 

tests/darknet/yolo3.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer106_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3.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/jPXmHyptpLoNdNR/download");
 
     // parse darknet network
@@ -31,4 +31,4 @@ int main() {
     delete net;
     delete netRT;
     return ret;
-}
+}

tests/darknet/yolo3_512.cpp

@@ -15,14 +15,26 @@ int main() {
         bin_path + "/debug/layer106_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3_512.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3_512.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/RGecMeGLD4cXEWL/download");
 
     // parse darknet network
     tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
     net->print();
 
+//     for(int i=0; i<net->num_layers; i++) {
+//         if(net->layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net->layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net->layers[i]->dstData != nullptr) {
+//             cudaFree(net->layers[i]->dstData);
+//             net->layers[i]->dstData = nullptr;
+//         }
+//    }
+
     //convert network to tensorRT
     tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
     

tests/darknet/yolo3_berkeley.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer106_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3_berkeley.cfg";
-    std::string name_path = "../tests/darknet/names/berkeley.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3_berkeley.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/berkeley.names";
     downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/o5cHa4AjTKS64oD/download");
 
     // parse darknet network

tests/darknet/yolo3_coco4.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer106_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3_coco4.cfg";
-    std::string name_path = "../tests/darknet/names/coco4.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3_coco4.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco4.names";
     downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/o27NDzSAartbyc4/download");
 
     // parse darknet network

tests/darknet/yolo3_flir.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer106_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3_flir.cfg";
-    std::string name_path = "../tests/darknet/names/flir.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3_flir.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/flir.names";
     downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/62DECncmF6bMMiH/download");
 
     // parse darknet network

tests/darknet/yolo3tiny.cpp

@@ -14,8 +14,8 @@ int main() {
         bin_path + "/debug/layer23_out.bin",
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3tiny.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3tiny.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/LMcSHtWaLeps8yN/download");
 
     // parse darknet network

tests/darknet/yolo3tiny_512.cpp

@@ -14,14 +14,27 @@ int main() {
         bin_path + "/debug/layer23_out.bin",
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo3tiny_512.cfg";
-    std::string name_path = "../tests/darknet/names/coco.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo3tiny_512.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/8Zt6bHwHADqP4JC/download");
 
     // parse darknet network
     tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
     net->print();
 
+//     for(int i=0; i<net->num_layers; i++) {
+//         if(net->layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net->layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net->layers[i]->dstData != nullptr) {
+//             cudaFree(net->layers[i]->dstData);
+//             net->layers[i]->dstData = nullptr;
+//         }
+//    }
+
+
     //convert network to tensorRT
     tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
     

tests/darknet/yolo4_512.cpp

@@ -23,6 +23,19 @@ int main() {
     tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
     net->print();
 
+//     for(int i=0; i<net->num_layers; i++) {
+//         if(net->layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net->layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net->layers[i]->dstData != nullptr) {
+//             cudaFree(net->layers[i]->dstData);
+//             net->layers[i]->dstData = nullptr;
+//         }
+//    }
+
+
     //convert network to tensorRT
     tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
     

tests/darknet/yolo4_berkeley.cpp

@@ -15,8 +15,8 @@ int main() {
         bin_path + "/debug/layer161_out.bin"
     };
     std::string wgs_path  = bin_path + "/layers";
-    std::string cfg_path  = "../tests/darknet/cfg/yolo4_berkeley.cfg";
-    std::string name_path = "../tests/darknet/names/berkeley.names";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4_berkeley.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/berkeley.names";
     downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download");
 
     // parse darknet network

tests/darknet/yolo4tiny.cpp

@@ -0,0 +1,33 @@
+#include<iostream>
+#include<vector>
+#include "tkdnn.h"
+#include "test.h"
+#include "DarknetParser.h"
+
+int main() {
+    std::string bin_path  = "yolo4tiny";
+    std::vector<std::string> input_bins = { 
+        bin_path + "/layers/input.bin"
+    };
+    std::vector<std::string> output_bins = {
+        bin_path + "/debug/layer30_out.bin",
+        bin_path + "/debug/layer37_out.bin"
+    };
+    std::string wgs_path  = bin_path + "/layers";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4tiny.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/iRnc4pSqmx78gJs/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/yolo4tiny_512.cpp

@@ -0,0 +1,45 @@
+#include<iostream>
+#include<vector>
+#include "tkdnn.h"
+#include "test.h"
+#include "DarknetParser.h"
+
+int main() {
+    std::string bin_path  = "yolo4tiny_512";
+    std::vector<std::string> input_bins = { 
+        bin_path + "/layers/input.bin"
+    };
+    std::vector<std::string> output_bins = {
+        bin_path + "/debug/layer30_out.bin",
+        bin_path + "/debug/layer37_out.bin"
+    };
+    std::string wgs_path  = bin_path + "/layers";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4tiny_512.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/qa2ws4GXg7mS5nN/download");
+
+    // parse darknet network
+    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
+    net->print();
+
+//         for(int i=0; i<net->num_layers; i++) {
+//         if(net->layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net->layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net->layers[i]->dstData != nullptr) {
+//             cudaFree(net->layers[i]->dstData);
+//             net->layers[i]->dstData = nullptr;
+//         }
+//    }
+
+    //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/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp

@@ -469,6 +469,19 @@ int main()
     //print network model
     net.print();
 
+//     for(int i=0; i<net.num_layers; i++) {
+//         if(net.layers[i]->getLayerType() == tk::dnn::LAYER_CONV2D) {
+//             tk::dnn::Conv2d *c = (tk::dnn::Conv2d*) net.layers[i];
+//             c->releaseDevice();
+//             c->releaseHost(true, false);
+//         }
+//         if(net.layers[i]->dstData != nullptr) {
+//             cudaFree(net.layers[i]->dstData);
+//             net.layers[i]->dstData = nullptr;
+//         }
+//    }
+
+
     // convert network to tensorRT
     tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("mobilenetv2ssd512"));