Road_Analyzer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jabranLmhani/Potholes-Detection-Deep-Learning-Yolov3/blob/main/Road_Analyzer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36Bbw5KG9NJN"
      },
      "source": [
        "#YOLO You Only Look Once : Training Potholes Detector\n",
        "\n",
        "> Bienvenu a Road-Analyzer \n",
        "\n",
        "\n",
        "\n",
        "La détection automatique des nids de poules par traitement d'images est un aspect important de l'entretien des routes. C'est également un problème difficile en raison de l'inhomogénéité des dommages de la route et de l'arrière-plan compliqué dans les images de la route. Ces dernières années, des méthodes basées sur des réseaux de neurones à convolution profonde ont été utilisées pour relever les défis de la détection et de la classification des dommages de la routes. tout au long de ce projet, nous proposons l'approche YOLO - Real-Time Object Detection pour relever ces défis. \n",
        "\n",
        "Dans cette approche, nous appliquons un seul réseau de neurones à l'image complète. Ce réseau divise l'image en régions et prédit les boîtes englobantes et les probabilités pour chaque région. Ces boîtes englobantes sont pondérées par les probabilités prédites."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9x9BFQOfNowN"
      },
      "source": [
        "## Installation de Darknet\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pt0Y06QTNyZG",
        "outputId": "f004d572-999f-4689-98ef-199042deca40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "rm: cannot remove 'darknet/': No such file or directory\n",
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 15424, done.\u001b[K\n",
            "remote: Counting objects: 100% (1/1), done.\u001b[K\n",
            "remote: Total 15424 (delta 0), reused 1 (delta 0), pack-reused 15423\u001b[K\n",
            "Receiving objects: 100% (15424/15424), 14.05 MiB | 7.92 MiB/s, done.\n",
            "Resolving deltas: 100% (10364/10364), done.\n",
            "/content/darknet\n",
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:946:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
            "                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1147:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "             \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (iteration_old == 0)\n",
            "             \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1150:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "          \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (iteration_old != 0){\n",
            "          \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1444:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
            "              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1420:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1424:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1428:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1431:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:253:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
            "                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:511:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
            "                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:867:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n",
            "                         \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:875:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n",
            "                          \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:894:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:930:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2044:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gemm.c:2043:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/utils.c -o obj/utils.o\n",
            "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/utils.c:1061:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n",
            "            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:230:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
            "                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcublas_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:264:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum cudaError_enum\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "       if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDA_SUCCESS)\n",
            "                  \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
            "                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
            "             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
            "                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
            "                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:405:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
            "                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:426:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
            "                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:441:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:447:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "At top level:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:287:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[KswitchBlasHandle\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            " static cublasHandle_t \u001b[01;35m\u001b[KswitchBlasHandle\u001b[m\u001b[K[16];\n",
            "                       \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:286:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[KswitchBlasInit\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            " static int \u001b[01;35m\u001b[KswitchBlasInit\u001b[m\u001b[K[16] = { 0 };\n",
            "            \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1342:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                  \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                  \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                           \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                           \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "     fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d  classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, step);\n",
            "                                                                                                   \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                                   \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:423:71:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\" Error: too large number of bboxes: contr_size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K > max_contr_size  = %d \\n\", contr_size, max_contr_size);\n",
            "                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1297:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
            "                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:1090:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_network_waitkey\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:435:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kema_period\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kema_period\u001b[m\u001b[K = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);\n",
            "             \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:660:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1777:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksave_implicit_weights\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1909:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:438:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n",
            "         \u001b[01;35m\u001b[Kclasses_multipliers = (float *)calloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K./src/parser.c:3:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:541:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            " extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n",
            "              \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:395:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "             \u001b[01;35m\u001b[K(iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000\u001b[m\u001b[K)\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:588:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
            "                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:709:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:707:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1326:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kcur_prob\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 double \u001b[01;35m\u001b[Kcur_prob\u001b[m\u001b[K = 0;\n",
            "                        \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1347:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1346:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1890:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:208:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     if (l.delta_gpu && (l.optimized_memory < 1 || \u001b[01;35m\u001b[Kl.keep_delta_gpu && l.optimized_memory < 3\u001b[m\u001b[K)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;\n",
            "                                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
            "                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/representation_layer.c -o obj/representation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/demo.c -o obj/demo.o\n",
            "\u001b[01m\u001b[K./src/demo.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdetect_in_thread\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/demo.c:101:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         layer \u001b[01;35m\u001b[Kl\u001b[m\u001b[K = net.layers[net.n - 1];\n",
            "               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:63:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/region_layer.c:62:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:75:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:106:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:115:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:426:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:707:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:706:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:668:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int b, \u001b[01;35m\u001b[Kn\u001b[m\u001b[K;\n",
            "            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:72:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:79:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:111:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:120:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:1130:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
            "     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:379:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!rm -r darknet/\n",
        "!git clone https://github.com/AlexeyAB/darknet.git\n",
        "%cd darknet\n",
        "import re\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n",
        "# !sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile\n",
        "!make\n",
        "!chmod +x ./darknet\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m88U5A5Kl21f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dc55192-8757-4235-920c-5a702beff9a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow 1.x selected.\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 1.x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "TufrIIQAlIt4",
        "outputId": "d55d48e5-3c1e-4c4a-842c-88f3a4d860ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.15.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WD5FBWmjrsks"
      },
      "outputs": [],
      "source": [
        "\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  \n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()\n",
        "  \n",
        "  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7_yikWWhqCi"
      },
      "source": [
        "## Cloner un référentiel d'assistance\n",
        "Cela facilite l'entrainement du model.\n",
        "Afin de s'entraîner sur nos propres données, ce dépôt peut être utilisé comme modèle.\n",
        "Des modifications de certains fichiers doivent être apportées en fonction de nos propres données."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qs8vZ5uK7Ry9",
        "outputId": "17648182-4cf8-4004-fa18-8eddf1be3d8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "rm: cannot remove '/content/yolotinyv3_medmask_demo': No such file or directory\n",
            "Cloning into 'yolotinyv3_medmask_demo'...\n",
            "remote: Enumerating objects: 1733, done.\u001b[K\n",
            "remote: Total 1733 (delta 0), reused 0 (delta 0), pack-reused 1733\u001b[K\n",
            "Receiving objects: 100% (1733/1733), 208.88 MiB | 14.10 MiB/s, done.\n",
            "Resolving deltas: 100% (120/120), done.\n",
            "Checking out files: 100% (1537/1537), done.\n",
            "/content/yolotinyv3_medmask_demo\n"
          ]
        }
      ],
      "source": [
        "repo_url = 'https://github.com/GotG/yolotinyv3_medmask_demo'\n",
        "import os\n",
        "%cd /content\n",
        "repo_dir_path = os.path.abspath(os.path.join('.', os.path.basename(repo_url)))\n",
        "%rm -r {repo_dir_path}\n",
        "!git clone {repo_url}\n",
        "%cd {repo_dir_path}\n",
        "os.rename(repo_dir_path, repo_dir_path.replace(\"medmask\",\"potholes\"))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvHPNTpyiXiz"
      },
      "source": [
        "### Configurations\n",
        "\n",
        "Le dossier `obj` contient à la fois les images ET les annotations associées au format yolo.\n",
        "\n",
        "\n",
        "* le fichier 'obj.data' contient les chemins des fichiers utilisés par Darknet et le nombre de classes pour former le modèle yolo.\n",
        "\n",
        "* le fichier 'obj.names' contient les étiquettes des objets à détecter. le nombre d'objets donne le nombre de classes dans le fichier 'obj.data'.\n",
        "\n",
        "* le train.txt contient le chemin vers chaque image à utiliser dans la formation. de même, valid.txt et test.txt contiennent le chemin d'accès à chaque image pour validation et test. lorsqu'il n'y a pas beaucoup d'images disponibles pour la formation (quelques centaines), utilisez toutes les images pour la formation pour de meilleures performances du modèle.\n",
        "\n",
        "* les yolov4-tiny.conv.29 sont les poids COCO pré-entraînés\n",
        "\n",
        "* le yolov4-tiny_obj.cfg est le petit fichier de configuration yolov4 qui indique à Darknet les paramètres et la structure du modèle\n",
        "\n",
        "Nous pouvons donc voir que tous ces fichiers sont modifiés en fonction de nos propres données.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgoBpxrIo7nU"
      },
      "source": [
        "## Commençons par les images et les annotations\n",
        "Nous copions les images sur notre lecteur Google dans un dossier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E-rIHLrdqMNP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b4a3014-31e6-49f7-940b-4566fa03d859"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "## REMOVE THIS TO USE YOUR OWN IMAGES\n",
        "if (True):\n",
        "  #remove the obj folder\n",
        "  !rm -rf /content/yolotinyv3_potholes_demo/obj\n",
        "  #copy images to obj folder\n",
        "  #this can take a few minutes depending on dataset size. \n",
        "  !cp -rf '/content/drive/My Drive/datasets/potholes_dataset/' '/content/yolotinyv3_potholes_demo/obj'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94rKAaMmrHvm"
      },
      "source": [
        "## Nous Remplaçons les étiquettes dans obj.names par nos étiquettes actuelles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbtL6iM0rRtf",
        "outputId": "774a2aff-a258-4be1-9242-d89851889e53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fissure | Trace des roues\n",
            "Fissure | Jointure de construction\n",
            "Fissure | Interval égal\n",
            "Fissure | Jointure de construction\n",
            "Fissure | Alligator crack\n",
            "Affaissement | Nid de poule\n",
            "Passage pour piétons\n",
            "Ligne blanche"
          ]
        }
      ],
      "source": [
        "labels_path = '/content/yolotinyv3_potholes_demo/obj.names'\n",
        "#make a list of your labels\n",
        "labels = ['Fissure | Trace des roues','Fissure | Jointure de construction','Fissure | Interval égal','Fissure | Jointure de construction','Fissure | Alligator crack','Affaissement | Nid de poule','Passage pour piétons','Ligne blanche']\n",
        "\n",
        "\n",
        "with open(labels_path, 'w') as f:\n",
        "\n",
        "    f.write('\\n'.join(labels))\n",
        "\n",
        "#check that the labels file is correct\n",
        "!cat /content/yolotinyv3_potholes_demo/obj.names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmsQt5L5uETj"
      },
      "source": [
        "## On modifie le nombre de classes dans obj.data.\n",
        "\n",
        "> Bloc en retrait\n",
        "\n",
        "\n",
        "Les chemins sont relatifs, donc aucun changement tant que la structure de dossier/fichier/les noms ne sont pas modifiés."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JufYpzjtuSAC",
        "outputId": "d346b338-1a7a-405d-8857-8b4c657f3369"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classes= 8\n",
            "train  = /content/yolotinyv3_potholes_demo/train.txt\n",
            "valid  = /content/yolotinyv3_potholes_demo/valid.txt\n",
            "names = /content/yolotinyv3_potholes_demo/obj.names\n",
            "backup = backup/"
          ]
        }
      ],
      "source": [
        "import re\n",
        "objdata = '/content/yolotinyv3_potholes_demo/obj.data'\n",
        "with open(objdata) as f:\n",
        "    s = f.read()\n",
        "\n",
        "#the number of classes is equal to the number of labels\n",
        "num_classes = len(labels)   \n",
        "s = re.sub('classes = \\d*','classes = ' + str(num_classes),s)\n",
        "\n",
        "with open(objdata, 'w') as f:\n",
        "  f.write(s)\n",
        "!cat /content/yolotinyv3_potholes_demo/obj.data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6sn7OICvAxh"
      },
      "source": [
        "## Nous devons maintenant créer un fichier train.txt, valid.txt et test.txt pour nos images à partir du dossier `obj`.\n",
        "Nous allons utiliser un script qui divise les images en fonction d'un pourcentage spécifié et les écrit dans les fichiers train.txt, valid.txt et test.txt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZM0V1011qF7",
        "outputId": "ee9bf405-ed81-4e12-f280-786a24d1f02c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your image file extension is: .jpg\n",
            "Number of images: 585\n",
            "Number of images used for training 498\n",
            "Number of images used for validation 58\n",
            "Number of images used for testing 29\n"
          ]
        }
      ],
      "source": [
        "#in this case we use 85 percent of the images for training\n",
        "#10 percent for validation, 5 for testing.\n",
        "#as the dataset is small, one can use 90 percent for training and split the rest\n",
        "#for validation/testing as desired.\n",
        "#double clicking on the train or test text files will open the file in a new\n",
        "#tab in colab and display the content.\n",
        "!python3 folder2textYolo.py 85 10 /content/yolotinyv3_potholes_demo/obj"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukAx4g40KZrh"
      },
      "source": [
        "## La dernière chose que nous devons changer est le fichier yolov3-tiny_obj.cfg.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhilmLuwKyLN",
        "outputId": "eb99b986-306a-4525-e792-836f5ef0e465"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[net]\n",
            "# Testing\n",
            "#batch=1\n",
            "#subdivisions=1\n",
            "# Training\n",
            "batch=64\n",
            "subdivisions=16\n",
            "width=416\n",
            "height=416\n",
            "channels=3\n",
            "momentum=0.9\n",
            "decay=0.0005\n",
            "angle=0\n",
            "saturation = 1.5\n",
            "exposure = 1.5\n",
            "hue=.1\n",
            "\n",
            "learning_rate=0.001\n",
            "burn_in=1000\n",
            "max_batches = 4000\n",
            "policy=steps\n",
            "steps=3200,3600\n",
            "scales=.1,.1\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!head -n 24 /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9s5IKRC_LlCI"
      },
      "source": [
        "### Configuration justifiee:\n",
        "* Le paramètre de Batch détermine la taille du lot. Celui-ci reste généralement à 64.\n",
        "* Les subdivisions déterminent le nombre d'images chargées en mémoire. Un nombre plus petit se traduit par une formation plus rapide. Nous utiliserons 12. Si une erreur de manque de mémoire CUDA est déclenchée, les subdivisions doivent être augmentées à 16, 24, 32 ou un autre nombre. (64 est le maximum) À moins de s'entraîner avec une résolution supérieure à 416, il ne devrait y avoir aucun problème avec 12.\n",
        "* La largeur et la hauteur sont par défaut à 416. Une autre résolution que l'on peut essayer est 320 ou 608.\n",
        "* Le paramètre suivant qui nous intéresse est max_batches. Cela détermine la durée du processus de formation. Sa valeur doit être au moins d'environ 2000 pour chaque classe utilisée. Donc pour 3 classes, au moins 6000.\n",
        "* Les pas sont calculés en fonction de max_batches. La première valeur est 0,8 * max_batches et la seconde valeur 0,9 * max_batches. Dans ce cas, il s'agit de 8000 * 0,8 et 8000 * 0,9.\n",
        "\n",
        "References :\n",
        "https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zq6MLtxbOkML"
      },
      "source": [
        "### Modifions les paramètres"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NP5BpLDdOq7Y"
      },
      "outputs": [],
      "source": [
        "# set the number of max_batches - min 2000 per class:\n",
        "max_batch=16000\n",
        "# calculate the 2 steps values:\n",
        "step1 = 0.8 * max_batch\n",
        "step2 = 0.9 * max_batch\n",
        "\n",
        "# we also need to adjust the number of classes and a parameter called filter size \n",
        "# that are both is inside the model structure\n",
        "\n",
        "# num_classes = len(labels)\n",
        "num_filters = (num_classes + 5) * 3\n",
        "\n",
        "\n",
        "cfg_file = '/content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg'\n",
        "# cfg_file = '/content/yolotinyv3_potholes_demo/yolov4-tiny.cfg'\n",
        "\n",
        "with open(cfg_file) as f:\n",
        "    s = f.read()\n",
        "# (re.sub('[a-z]*@', 'ABC@', s))\n",
        "s = re.sub('max_batches = \\d*','max_batches = '+str(max_batch),s)\n",
        "s = re.sub('steps=\\d*,\\d*','steps='+\"{:.0f}\".format(step1)+','+\"{:.0f}\".format(step2),s)\n",
        "s = re.sub('classes=\\d*','classes='+str(num_classes),s)\n",
        "s = re.sub('pad=1\\nfilters=\\d*','pad=1\\nfilters='+\"{:.0f}\".format(num_filters),s)\n",
        "# pad=1\\nfilters=\\d\\d\n",
        "# s = re.sub('CUDNN=0','CUDNN=1',s)\n",
        "# s = re.sub('OPENCV=0','OPENCV=1',s)\n",
        "\n",
        "with open(cfg_file, 'w') as f:\n",
        "  # s = re.sub('GPU=0','GPU=1',s)\n",
        "  f.write(s)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V97ckmh1OYMn"
      },
      "source": [
        "Visualison la Config. Pour verifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQYhB3N9dHgm",
        "outputId": "372c0669-5494-401b-b551-d8d52149a97b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[net]\n",
            "# Testing\n",
            "#batch=1\n",
            "#subdivisions=1\n",
            "# Training\n",
            "batch=64\n",
            "subdivisions=16\n",
            "width=416\n",
            "height=416\n",
            "channels=3\n",
            "momentum=0.9\n",
            "decay=0.0005\n",
            "angle=0\n",
            "saturation = 1.5\n",
            "exposure = 1.5\n",
            "hue=.1\n",
            "\n",
            "learning_rate=0.001\n",
            "burn_in=1000\n",
            "max_batches = 16000\n",
            "policy=steps\n",
            "steps=12800,14400\n",
            "scales=.1,.1\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!head -n 24 /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gorENDyZOhus"
      },
      "source": [
        "On check le nombre de classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vNWG-Tfcmn3",
        "outputId": "6e6a3950-57a7-4edd-bac0-ab968b276cb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=39\n",
            "activation=linear\n",
            "\n",
            "\n",
            "\n",
            "[yolo]\n",
            "mask = 3,4,5\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=8\n",
            "num=6\n",
            "jitter=.3\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=1\n",
            "\n",
            "[route]\n",
            "layers = -4\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[upsample]\n",
            "stride=2\n",
            "\n",
            "[route]\n",
            "layers = -1, 8\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=39\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 1,2,3\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=8\n",
            "num=6\n",
            "jitter=.3\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=1\n"
          ]
        }
      ],
      "source": [
        "!tail -n 64 /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_4747QHIGZ7"
      },
      "source": [
        "# Entraînement du modèle Tiny Yolo\n",
        "\n",
        "\n",
        "* les fichiers qui se terminent par 1 000, 2 000, etc. sont des poids enregistrés tous les 1 000 lots\n",
        "* le fichier des meilleurs poids sont les poids qui ont donné les meilleurs résultats pendant l'entraînement\n",
        "* le fichier des poids finaux contient les poids finaux à la fin de la formation\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUaW_i1UL0Pp"
      },
      "source": [
        "## Début Entraînement\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdt4WtP9kpm8",
        "outputId": "c4eca98c-54f6-40ee-a5d0-7b62807139ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ],
      "source": [
        "%cd /content/darknet/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_RVNMRPisVX",
        "outputId": "12cb87bd-c198-47b2-9001-38c954759f54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " Prepare additional network for mAP calculation...\n",
            " 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 Create CUDA-stream - 0 \n",
            " Create cudnn-handle 0 \n",
            "conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF\n",
            "   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF\n",
            "   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF\n",
            "   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF\n",
            "   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF\n",
            "   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF\n",
            "   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF\n",
            "   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF\n",
            "   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF\n",
            "  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF\n",
            "  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  15 conv     39       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  39 0.007 BF\n",
            "  16 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "  17 route  13 \t\t                           ->   13 x  13 x 256 \n",
            "  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF\n",
            "  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128\n",
            "  20 route  19 8 \t                           ->   26 x  26 x 384 \n",
            "  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF\n",
            "  22 conv     39       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  39 0.013 BF\n",
            "  23 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 5.459 \n",
            "avg_outputs = 326324 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "yolov3-tiny_obj\n",
            " 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 4, batch = 64, time_steps = 1, train = 1 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF\n",
            "   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF\n",
            "   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF\n",
            "   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF\n",
            "   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF\n",
            "   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF\n",
            "   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF\n",
            "   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF\n",
            "   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF\n",
            "  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF\n",
            "  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  15 conv     39       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  39 0.007 BF\n",
            "  16 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "  17 route  13 \t\t                           ->   13 x  13 x 256 \n",
            "  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF\n",
            "  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128\n",
            "  20 route  19 8 \t                           ->   26 x  26 x 384 \n",
            "  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF\n",
            "  22 conv     39       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  39 0.013 BF\n",
            "  23 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 5.459 \n",
            "avg_outputs = 326324 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from /content/yolotinyv3_potholes_demo/yolov3-tiny.conv.15...\n",
            " seen 64, trained: 0 K-images (0 Kilo-batches_64) \n",
            "Done! Loaded 15 layers from weights-file \n",
            "Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005\n",
            " Detection layer: 16 - type = 28 \n",
            " Detection layer: 23 - type = 28 \n",
            "Resizing, random_coef = 1.40 \n",
            "\n",
            " 608 x 608 \n",
            " Create 6 permanent cpu-threads \n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "!./darknet detector train /content/yolotinyv3_potholes_demo/obj.data /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg /content/yolotinyv3_potholes_demo/yolov3-tiny.conv.15 -dont_show -ext_output -map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JTBMYlIn9Jw"
      },
      "source": [
        "# On verifie la performence du model\n",
        "Les métriques s'exécutent sur les images de validation, elles peuvent donc ne pas être entièrement représentatives, uniquement indicatives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MwAVsyrxrwSI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df4e1089-83f7-480c-d0ff-3bf33926eed1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 Create CUDA-stream - 0 \n",
            " Create cudnn-handle 0 \n",
            "conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF\n",
            "   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF\n",
            "   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF\n",
            "   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF\n",
            "   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF\n",
            "   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF\n",
            "   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF\n",
            "   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF\n",
            "   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF\n",
            "  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF\n",
            "  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  15 conv     39       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  39 0.007 BF\n",
            "  16 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "  17 route  13 \t\t                           ->   13 x  13 x 256 \n",
            "  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF\n",
            "  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128\n",
            "  20 route  19 8 \t                           ->   26 x  26 x 384 \n",
            "  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF\n",
            "  22 conv     39       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  39 0.013 BF\n",
            "  23 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 5.459 \n",
            "avg_outputs = 326324 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from /content/darknet/backup/yolov3-tiny_obj_best.weights...\n",
            " seen 64, trained: 742 K-images (11 Kilo-batches_64) \n",
            "Done! Loaded 24 layers from weights-file \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            " Detection layer: 16 - type = 28 \n",
            " Detection layer: 23 - type = 28 \n",
            "60\n",
            " detections_count = 210, unique_truth_count = 91  \n",
            "class_id = 0, name = Fissure | Trace des roues, ap = 93.48%   \t (TP = 45, FP = 4) \n",
            "class_id = 1, name = Fissure | Jointure de construction, ap = 0.00%   \t (TP = 0, FP = 0) \n",
            "class_id = 2, name = Fissure | Interval égal, ap = 93.77%   \t (TP = 17, FP = 4) \n",
            "class_id = 3, name = Fissure | Jointure de construction, ap = 0.00%   \t (TP = 0, FP = 0) \n",
            "class_id = 4, name = Fissure | Alligator crack, ap = 85.71%   \t (TP = 6, FP = 0) \n",
            "class_id = 5, name = Affaissement | Nid de poule, ap = 82.27%   \t (TP = 10, FP = 1) \n",
            "class_id = 6, name = Passage pour piétons, ap = 0.00%   \t (TP = 0, FP = 0) \n",
            "class_id = 7, name = Ligne blanche, ap = 0.00%   \t (TP = 0, FP = 0) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.90, recall = 0.86, F1-score = 0.88 \n",
            " for conf_thresh = 0.25, TP = 78, FP = 9, FN = 13, average IoU = 63.89 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.444044, or 44.40 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n"
          ]
        }
      ],
      "source": [
        "!./darknet detector map /content/yolotinyv3_potholes_demo/obj.data /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg \"/content/darknet/backup/yolov3-tiny_obj_best.weights\" -points 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxIm51pFoUQO"
      },
      "source": [
        "## Run detection on an image. I choose one of the test images from test.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RRI2S26nsNzM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a38dd1dd-5d8f-46ee-e376-f6db2660e9ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 Create CUDA-stream - 0 \n",
            " Create cudnn-handle 0 \n",
            "conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF\n",
            "   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF\n",
            "   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF\n",
            "   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF\n",
            "   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF\n",
            "   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF\n",
            "   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF\n",
            "   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF\n",
            "   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF\n",
            "  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF\n",
            "  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  15 conv     39       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  39 0.007 BF\n",
            "  16 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "  17 route  13 \t\t                           ->   13 x  13 x 256 \n",
            "  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF\n",
            "  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128\n",
            "  20 route  19 8 \t                           ->   26 x  26 x 384 \n",
            "  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF\n",
            "  22 conv     39       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  39 0.013 BF\n",
            "  23 yolo\n",
            "[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00\n",
            "Total BFLOPS 5.459 \n",
            "avg_outputs = 326324 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from /content/darknet/backup/yolov3-tiny_obj_best.weights...\n",
            " seen 64, trained: 742 K-images (11 Kilo-batches_64) \n",
            "Done! Loaded 24 layers from weights-file \n",
            " Detection layer: 16 - type = 28 \n",
            " Detection layer: 23 - type = 28 \n",
            "/content/yolotinyv3_potholes_demo/obj/898.jpg: Predicted in 2.999000 milli-seconds.\n",
            "Fissure | Trace des roues: 80%\t(left_x:  316   top_y:  445   width:   42   height:   87)\n",
            "Unable to init server: Could not connect: Connection refused\n",
            "\n",
            "(predictions:4743): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m18:22:31.604\u001b[0m: cannot open display: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "!./darknet detector test /content/yolotinyv3_potholes_demo/obj.data  /content/yolotinyv3_potholes_demo/yolov3-tiny_obj.cfg  \"/content/darknet/backup/yolov3-tiny_obj_best.weights\" /content/yolotinyv3_potholes_demo/obj/898.jpg -ext_output\n",
        "imShow('predictions.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RfWHgEyy8dz1"
      },
      "source": [
        "On enregistre les meilleurs poids sur le drive : \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j9sTCNRt1338"
      },
      "outputs": [],
      "source": [
        "!cp /content/darknet/backup/yolov3-tiny_obj_best.weights  '/content/drive/My Drive/Weights/Train1'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktnynoVEdn4W"
      },
      "source": [
        "###On passe pour l'application android : Road-Analyzer\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Road-Analyzer.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}