KM_12_Real_Time_Object_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KM-12-Real Time Object Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/KM_12_Real_Time_Object_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGFzqi6rB_Ki"
      },
      "source": [
        "# Real Time Object Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A578sDm_-BJ9"
      },
      "source": [
        "# import dependencies\n",
        "from IPython.display import display, Javascript, Image\n",
        "from google.colab.output import eval_js\n",
        "from google.colab.patches import cv2_imshow\n",
        "from base64 import b64decode, b64encode\n",
        "import cv2\n",
        "import numpy as np\n",
        "import PIL\n",
        "import io\n",
        "import html\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEEcnuT50tKK"
      },
      "source": [
        "## Cloning and Setting Up Darknet for YOLOv4\n",
        "Disini akan menggunakan AlexeyAB (https://github.com/AlexeyAB/darknet) darknet untuk melakukan deteksi."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UlRIVhaYPd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35b2e0a5-77c2-4791-b8ef-20517cbce3d6"
      },
      "source": [
        "# clone darknet repo\n",
        "!git clone https://github.com/AlexeyAB/darknet"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 15339, done.\u001b[K\n",
            "remote: Counting objects: 100% (11/11), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
            "remote: Total 15339 (delta 10), reused 9 (delta 9), pack-reused 15328\u001b[K\n",
            "Receiving objects: 100% (15339/15339), 13.99 MiB | 13.97 MiB/s, done.\n",
            "Resolving deltas: 100% (10315/10315), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGNvNdcX9rGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "864de254-8cf6-40f5-83fc-96cba0729dc8"
      },
      "source": [
        "# change makefile to have GPU, OPENCV and LIBSO enabled\n",
        "%cd darknet\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",
        "!sed -i 's/LIBSO=0/LIBSO=1/' Makefile"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5ogzikW9wk6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d62b2d5e-9cb7-4bcc-a812-54b0fa0336a5"
      },
      "source": [
        "# make darknet (builds darknet so that you can then use the darknet.py file and have its dependencies)\n",
        "!make"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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:386:72:\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[Kprint_cocos\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:486:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n",
            "                 fprintf(fp, \u001b[01;35m\u001b[Kbuff\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:579:21:\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:700:13:\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:698:13:\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:1315:24:\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:1336:15:\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:1335:15:\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:1871:19:\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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",
            "./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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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",
            "./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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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",
            "./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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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",
            "./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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC 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",
            "g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC ./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 src/yolo_v2_class.cpp -o libdarknet.so -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",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:439:40:\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[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:454:34:\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[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:478:40:\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[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -o uselib src/yolo_console_dll.cpp -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++ -L ./ -l:libdarknet.so\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\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",
            "             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n",
            "                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\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",
            "                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n",
            "                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n",
            "               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AORz9dHs7_kc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afb56a88-b14e-4548-b58e-800ef8b5e5c5"
      },
      "source": [
        "# get bthe scaled yolov4 weights file that is pre-trained to detect 80 classes (objects) from shared google drive\n",
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq\" -O yolov4-csp.weights && rm -rf /tmp/cookies.txt"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-11 11:20:25--  https://docs.google.com/uc?export=download&confirm=pT38&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq\n",
            "Resolving docs.google.com (docs.google.com)... 173.194.197.101, 173.194.197.113, 173.194.197.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|173.194.197.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download [following]\n",
            "--2021-11-11 11:20:25--  https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download\n",
            "Resolving doc-0s-80-docs.googleusercontent.com (doc-0s-80-docs.googleusercontent.com)... 142.250.152.132, 2607:f8b0:4001:c56::84\n",
            "Connecting to doc-0s-80-docs.googleusercontent.com (doc-0s-80-docs.googleusercontent.com)|142.250.152.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=t8hf2pr1g1f4i&continue=https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e%3Ddownload&hash=3at0etj7i1aehrjrl7u7p8n4qq74g87h [following]\n",
            "--2021-11-11 11:20:25--  https://docs.google.com/nonceSigner?nonce=t8hf2pr1g1f4i&continue=https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e%3Ddownload&hash=3at0etj7i1aehrjrl7u7p8n4qq74g87h\n",
            "Connecting to docs.google.com (docs.google.com)|173.194.197.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download&nonce=t8hf2pr1g1f4i&user=12985794598184685748Z&hash=in081fflikvv8dtq6j4ufb8lhahebd8h [following]\n",
            "--2021-11-11 11:20:25--  https://doc-0s-80-docs.googleusercontent.com/docs/securesc/bje5rog1spat9k8oo3lcifdiaeeml0b8/9kke1hjan1fonrgm1m19cjdnhvupngah/1636629600000/17800843676226924807/12985794598184685748Z/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download&nonce=t8hf2pr1g1f4i&user=12985794598184685748Z&hash=in081fflikvv8dtq6j4ufb8lhahebd8h\n",
            "Connecting to doc-0s-80-docs.googleusercontent.com (doc-0s-80-docs.googleusercontent.com)|142.250.152.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 211944840 (202M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-csp.weights’\n",
            "\n",
            "yolov4-csp.weights  100%[===================>] 202.13M   180MB/s    in 1.1s    \n",
            "\n",
            "2021-11-11 11:20:26 (180 MB/s) - ‘yolov4-csp.weights’ saved [211944840/211944840]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5o5Gs75ZuMv"
      },
      "source": [
        "## Darknet for Python\n",
        "In order to utilize YOLOv4 with Python code we will use some of the pre-built functions found within darknet.py by importing the functions into our workstation. Feel free to checkout the darknet.py file to see the function definitions in detail! "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhrQqPc2HR27"
      },
      "source": [
        "# import darknet functions to perform object detections\n",
        "from darknet import *\n",
        "# load in our YOLOv4 architecture network\n",
        "network, class_names, class_colors = load_network(\"cfg/yolov4-csp.cfg\", \"cfg/coco.data\", \"yolov4-csp.weights\")\n",
        "width = network_width(network)\n",
        "height = network_height(network)\n",
        "\n",
        "# darknet helper function to run detection on image\n",
        "def darknet_helper(img, width, height):\n",
        "  darknet_image = make_image(width, height, 3)\n",
        "  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  img_resized = cv2.resize(img_rgb, (width, height),\n",
        "                              interpolation=cv2.INTER_LINEAR)\n",
        "\n",
        "  # get image ratios to convert bounding boxes to proper size\n",
        "  img_height, img_width, _ = img.shape\n",
        "  width_ratio = img_width/width\n",
        "  height_ratio = img_height/height\n",
        "\n",
        "  # run model on darknet style image to get detections\n",
        "  copy_image_from_bytes(darknet_image, img_resized.tobytes())\n",
        "  detections = detect_image(network, class_names, darknet_image)\n",
        "  free_image(darknet_image)\n",
        "  return detections, width_ratio, height_ratio"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymeEhEnFbsdm"
      },
      "source": [
        "## YOLOv4 Example on Test Image\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwfFDhs5TMhi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "c6417399-1065-44c7-c774-efd26d90bdfd"
      },
      "source": [
        "# run test on person.jpg image that comes with repository\n",
        "image = cv2.imread(\"data/person.jpg\")\n",
        "detections, width_ratio, height_ratio = darknet_helper(image, width, height)\n",
        "\n",
        "for label, confidence, bbox in detections:\n",
        "  left, top, right, bottom = bbox2points(bbox)\n",
        "  left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)\n",
        "  cv2.rectangle(image, (left, top), (right, bottom), class_colors[label], 2)\n",
        "  cv2.putText(image, \"{} [{:.2f}]\".format(label, float(confidence)),\n",
        "                    (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                    class_colors[label], 2)\n",
        "cv2_imshow(image)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=640x424 at 0x7F9A6E65BF10>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMxnV9kt1Yti"
      },
      "source": [
        "## Helper Functions\n",
        "Here are a few helper functions defined that will be used to easily convert between different image types within our later steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Zg82Yi292ku"
      },
      "source": [
        "# function to convert the JavaScript object into an OpenCV image\n",
        "def js_to_image(js_reply):\n",
        "  \"\"\"\n",
        "  Params:\n",
        "          js_reply: JavaScript object containing image from webcam\n",
        "  Returns:\n",
        "          img: OpenCV BGR image\n",
        "  \"\"\"\n",
        "  # decode base64 image\n",
        "  image_bytes = b64decode(js_reply.split(',')[1])\n",
        "  # convert bytes to numpy array\n",
        "  jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n",
        "  # decode numpy array into OpenCV BGR image\n",
        "  img = cv2.imdecode(jpg_as_np, flags=1)\n",
        "\n",
        "  return img\n",
        "\n",
        "# function to convert OpenCV Rectangle bounding box image into base64 byte string to be overlayed on video stream\n",
        "def bbox_to_bytes(bbox_array):\n",
        "  \"\"\"\n",
        "  Params:\n",
        "          bbox_array: Numpy array (pixels) containing rectangle to overlay on video stream.\n",
        "  Returns:\n",
        "        bytes: Base64 image byte string\n",
        "  \"\"\"\n",
        "  # convert array into PIL image\n",
        "  bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')\n",
        "  iobuf = io.BytesIO()\n",
        "  # format bbox into png for return\n",
        "  bbox_PIL.save(iobuf, format='png')\n",
        "  # format return string\n",
        "  bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))\n",
        "\n",
        "  return bbox_bytes"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4j-Wl_95e0Z"
      },
      "source": [
        "## YOLOv4 on Webcam Videos\n",
        "Running YOLOv4 on webcam video is a little more complex than images. We need to start a video stream using our webcam as input. Then we run each frame through our YOLOv4 model and create an overlay image that contains bounding box of detection(s). We then overlay the bounding box image back onto the next frame of our video stream. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZk68RUQW3Xq"
      },
      "source": [
        "# JavaScript to properly create our live video stream using our webcam as input\n",
        "def video_stream():\n",
        "  js = Javascript('''\n",
        "    var video;\n",
        "    var div = null;\n",
        "    var stream;\n",
        "    var captureCanvas;\n",
        "    var imgElement;\n",
        "    var labelElement;\n",
        "    \n",
        "    var pendingResolve = null;\n",
        "    var shutdown = false;\n",
        "    \n",
        "    function removeDom() {\n",
        "       stream.getVideoTracks()[0].stop();\n",
        "       video.remove();\n",
        "       div.remove();\n",
        "       video = null;\n",
        "       div = null;\n",
        "       stream = null;\n",
        "       imgElement = null;\n",
        "       captureCanvas = null;\n",
        "       labelElement = null;\n",
        "    }\n",
        "    \n",
        "    function onAnimationFrame() {\n",
        "      if (!shutdown) {\n",
        "        window.requestAnimationFrame(onAnimationFrame);\n",
        "      }\n",
        "      if (pendingResolve) {\n",
        "        var result = \"\";\n",
        "        if (!shutdown) {\n",
        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
        "        }\n",
        "        var lp = pendingResolve;\n",
        "        pendingResolve = null;\n",
        "        lp(result);\n",
        "      }\n",
        "    }\n",
        "    \n",
        "    async function createDom() {\n",
        "      if (div !== null) {\n",
        "        return stream;\n",
        "      }\n",
        "\n",
        "      div = document.createElement('div');\n",
        "      div.style.border = '2px solid black';\n",
        "      div.style.padding = '3px';\n",
        "      div.style.width = '100%';\n",
        "      div.style.maxWidth = '600px';\n",
        "      document.body.appendChild(div);\n",
        "      \n",
        "      const modelOut = document.createElement('div');\n",
        "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
        "      labelElement = document.createElement('span');\n",
        "      labelElement.innerText = 'No data';\n",
        "      labelElement.style.fontWeight = 'bold';\n",
        "      modelOut.appendChild(labelElement);\n",
        "      div.appendChild(modelOut);\n",
        "           \n",
        "      video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      video.width = div.clientWidth - 6;\n",
        "      video.setAttribute('playsinline', '');\n",
        "      video.onclick = () => { shutdown = true; };\n",
        "      stream = await navigator.mediaDevices.getUserMedia(\n",
        "          {video: { facingMode: \"environment\"}});\n",
        "      div.appendChild(video);\n",
        "\n",
        "      imgElement = document.createElement('img');\n",
        "      imgElement.style.position = 'absolute';\n",
        "      imgElement.style.zIndex = 1;\n",
        "      imgElement.onclick = () => { shutdown = true; };\n",
        "      div.appendChild(imgElement);\n",
        "      \n",
        "      const instruction = document.createElement('div');\n",
        "      instruction.innerHTML = \n",
        "          '<span style=\"color: red; font-weight: bold;\">' +\n",
        "          'When finished, click here or on the video to stop this demo</span>';\n",
        "      div.appendChild(instruction);\n",
        "      instruction.onclick = () => { shutdown = true; };\n",
        "      \n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      captureCanvas = document.createElement('canvas');\n",
        "      captureCanvas.width = 640; //video.videoWidth;\n",
        "      captureCanvas.height = 480; //video.videoHeight;\n",
        "      window.requestAnimationFrame(onAnimationFrame);\n",
        "      \n",
        "      return stream;\n",
        "    }\n",
        "    async function stream_frame(label, imgData) {\n",
        "      if (shutdown) {\n",
        "        removeDom();\n",
        "        shutdown = false;\n",
        "        return '';\n",
        "      }\n",
        "\n",
        "      var preCreate = Date.now();\n",
        "      stream = await createDom();\n",
        "      \n",
        "      var preShow = Date.now();\n",
        "      if (label != \"\") {\n",
        "        labelElement.innerHTML = label;\n",
        "      }\n",
        "            \n",
        "      if (imgData != \"\") {\n",
        "        var videoRect = video.getClientRects()[0];\n",
        "        imgElement.style.top = videoRect.top + \"px\";\n",
        "        imgElement.style.left = videoRect.left + \"px\";\n",
        "        imgElement.style.width = videoRect.width + \"px\";\n",
        "        imgElement.style.height = videoRect.height + \"px\";\n",
        "        imgElement.src = imgData;\n",
        "      }\n",
        "      \n",
        "      var preCapture = Date.now();\n",
        "      var result = await new Promise(function(resolve, reject) {\n",
        "        pendingResolve = resolve;\n",
        "      });\n",
        "      shutdown = false;\n",
        "      \n",
        "      return {'create': preShow - preCreate, \n",
        "              'show': preCapture - preShow, \n",
        "              'capture': Date.now() - preCapture,\n",
        "              'img': result};\n",
        "    }\n",
        "    ''')\n",
        "\n",
        "  display(js)\n",
        "  \n",
        "def video_frame(label, bbox):\n",
        "  data = eval_js('stream_frame(\"{}\", \"{}\")'.format(label, bbox))\n",
        "  return data"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfaHiGn461oG"
      },
      "source": [
        "### Running on Webcam Video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "RPDr23YFW_7c",
        "outputId": "07119193-ac46-4f22-c037-ddf9df6327f6"
      },
      "source": [
        "# start streaming video from webcam\n",
        "video_stream()\n",
        "# label for video\n",
        "label_html = 'Capturing...'\n",
        "# initialze bounding box to empty\n",
        "bbox = ''\n",
        "count = 0 \n",
        "while True:\n",
        "    js_reply = video_frame(label_html, bbox)\n",
        "    if not js_reply:\n",
        "        break\n",
        "\n",
        "    # convert JS response to OpenCV Image\n",
        "    frame = js_to_image(js_reply[\"img\"])\n",
        "\n",
        "    # create transparent overlay for bounding box\n",
        "    bbox_array = np.zeros([480,640,4], dtype=np.uint8)\n",
        "\n",
        "    # call our darknet helper on video frame\n",
        "    detections, width_ratio, height_ratio = darknet_helper(frame, width, height)\n",
        "\n",
        "    # loop through detections and draw them on transparent overlay image\n",
        "    for label, confidence, bbox in detections:\n",
        "      left, top, right, bottom = bbox2points(bbox)\n",
        "      left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)\n",
        "      bbox_array = cv2.rectangle(bbox_array, (left, top), (right, bottom), class_colors[label], 2)\n",
        "      bbox_array = cv2.putText(bbox_array, \"{} [{:.2f}]\".format(label, float(confidence)),\n",
        "                        (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                        class_colors[label], 2)\n",
        "\n",
        "    bbox_array[:,:,3] = (bbox_array.max(axis = 2) > 0 ).astype(int) * 255\n",
        "    # convert overlay of bbox into bytes\n",
        "    bbox_bytes = bbox_to_bytes(bbox_array)\n",
        "    # update bbox so next frame gets new overlay\n",
        "    bbox = bbox_bytes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "    \n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "    \n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "    \n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "    \n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "      \n",
              "      const modelOut = document.createElement('div');\n",
              "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
              "      labelElement = document.createElement('span');\n",
              "      labelElement.innerText = 'No data';\n",
              "      labelElement.style.fontWeight = 'bold';\n",
              "      modelOut.appendChild(labelElement);\n",
              "      div.appendChild(modelOut);\n",
              "           \n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "      \n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML = \n",
              "          '<span style=\"color: red; font-weight: bold;\">' +\n",
              "          'When finished, click here or on the video to stop this demo</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "      \n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640; //video.videoWidth;\n",
              "      captureCanvas.height = 480; //video.videoHeight;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "      \n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame(label, imgData) {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "      \n",
              "      var preShow = Date.now();\n",
              "      if (label != \"\") {\n",
              "        labelElement.innerHTML = label;\n",
              "      }\n",
              "            \n",
              "      if (imgData != \"\") {\n",
              "        var videoRect = video.getClientRects()[0];\n",
              "        imgElement.style.top = videoRect.top + \"px\";\n",
              "        imgElement.style.left = videoRect.left + \"px\";\n",
              "        imgElement.style.width = videoRect.width + \"px\";\n",
              "        imgElement.style.height = videoRect.height + \"px\";\n",
              "        imgElement.src = imgData;\n",
              "      }\n",
              "      \n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "      \n",
              "      return {'create': preShow - preCreate, \n",
              "              'show': preCapture - preShow, \n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}