Fruit_Detection_YOLOv4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sZ8FpXQ4Dc_J"
   },
   "source": [
    "# Problem Statement & Data Understanding\n",
    "\n",
    "The particular project discusses about building a robust model for fruit detections. There can be many advanced usecase for this. Some of them are:\n",
    "\n",
    "1.   You are working in a warehouse where lakhs of fruits comes in daily and if you try to seperate and package each of the fruit boxes manually, then it is going to require alot of manpower. So, you can build an automated system which can detect fruits and seperate them for packaging.\n",
    "2.   You are owner of a very big orchid. If you want to harvest fruits from it manually, it will require alot of manpower too. In such scenarios you can build a robot or a self driving truck which can detect fruits on specific trees and harvest them for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrOA7OSwKeXo"
   },
   "source": [
    "# Building a YOLOv4 Object Detector with Darknet in the Cloud! (GPU ENABLED)\n",
    "This tutorial will help you build YOLOv4 easily in the cloud with GPU enabled so that you can build & train object detections 100x faster!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tlKYQSSnKmU3"
   },
   "source": [
    "## Step 1: Enabling GPU within your notebook\n",
    "I am enabling GPU acceleration within my Colab notebook, therefore YOLOv4 system will be able to process detections over 100 times faster than CPU.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OdOgjVTSK3eB"
   },
   "source": [
    "## Step 2: Cloning and Building Darknet\n",
    "The following cells will clone darknet from AlexeyAB's famous repository, adjust the Makefile to enable OPENCV and GPU for darknet and then build darknet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2684,
     "status": "ok",
     "timestamp": 1625914402384,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "68eMertgIxaB",
    "outputId": "5125ed10-97d3-4734-8fd1-e138ad99dbbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'darknet'...\n",
      "remote: Enumerating objects: 15218, done.\u001b[K\n",
      "remote: Counting objects: 100% (33/33), done.\u001b[K\n",
      "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
      "remote: Total 15218 (delta 16), reused 27 (delta 16), pack-reused 15185\u001b[K\n",
      "Receiving objects: 100% (15218/15218), 13.62 MiB | 22.98 MiB/s, done.\n",
      "Resolving deltas: 100% (10324/10324), done.\n"
     ]
    }
   ],
   "source": [
    "# clone darknet repo\n",
    "!git clone https://github.com/AlexeyAB/darknet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 822,
     "status": "ok",
     "timestamp": 1625914403193,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "xym8_m8CIyXK",
    "outputId": "3ee54efc-96b2-44d3-8a74-c532563d77ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/darknet\n"
     ]
    }
   ],
   "source": [
    "# change makefile to have GPU and OPENCV 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1625914403194,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "5uloUwmUKF05",
    "outputId": "48e21ac6-1e19-43dd-b755-001ce91ba1da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvcc: NVIDIA (R) Cuda compiler driver\n",
      "Copyright (c) 2005-2020 NVIDIA Corporation\n",
      "Built on Wed_Jul_22_19:09:09_PDT_2020\n",
      "Cuda compilation tools, release 11.0, V11.0.221\n",
      "Build cuda_11.0_bu.TC445_37.28845127_0\n"
     ]
    }
   ],
   "source": [
    "# verify CUDA\n",
    "!/usr/local/cuda/bin/nvcc --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 73082,
     "status": "ok",
     "timestamp": 1625914476270,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "q2Jjv0yRKLPe",
    "outputId": "c502c3db-7c7d-40d7-d63d-e215361ab21d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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 -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 -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 -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 -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:1045:12:\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 -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:231:20:\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:265:18:\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:396:40:\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:396:64:\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:396:82:\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:406:37:\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:427:43:\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:442:64:\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:448:63:\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:288:23:\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:287:12:\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 -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:1341:32:\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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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:4707:39:\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 -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 -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 -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:4707:39:\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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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:1332:15:\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:1331:15:\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:1867:19:\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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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 -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:4707:39:\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:4707:39:\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:4707:39:\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:4707:39:\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 -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:4707:39:\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:4707:39:\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:4707:39:\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:4707:39:\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 -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 -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 -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 -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 -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\" -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\" -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\" -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",
      "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\" -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\" -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\" -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\" -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\" -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\" -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\" -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 obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n"
     ]
    }
   ],
   "source": [
    "# make darknet (builds darknet so that you can then use the darknet executable file to run or train object detectors)\n",
    "!make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "error",
     "timestamp": 1625914476286,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "32MX4xIkUyKM",
    "outputId": "a2947fa8-f770-4b44-b9c1-b9387d00bb78"
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-90e9831ca4f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This stops 'Run all' at this cell by causing an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# This stops 'Run all' at this cell by causing an error\n",
    "assert False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SZq0sNDeeOwX"
   },
   "source": [
    "# Demo with a pretrained model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PLhXV4qVLBpe"
   },
   "source": [
    "## Download pre-trained YOLOv4 weights\n",
    "YOLOv4 has been trained already on the coco dataset which has 80 classes that it can predict. I am downloading these pretrained weights so that we can run YOLOv4 on these pretrained classes and get detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V1NsPOqCLElK"
   },
   "outputs": [],
   "source": [
    "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YP9PHVIae2Kw"
   },
   "source": [
    "## Run Your Detections with Darknet and YOLOv4!\n",
    "\n",
    "Darknet is now built and ready to run detections using YOLOv4 in the cloud! You can find out which sorts of classes the pre-trained YOLOv4 weights can detect by clicking here. [COCO CLASSES](http://cocodataset.org/#explore)\n",
    "\n",
    "The object detector can be run using the following command\n",
    "```bash\n",
    "!./darknet detector test <path to .data file> <path to config> <path to weights> <path to image>\n",
    "```\n",
    "Darknet comes with a few images already installed in the darknet/data/ folder.\n",
    "\n",
    "**Note:** After running detections OpenCV can't open the image instantly in the cloud so we must run: \n",
    "```bash\n",
    "imShow('predictions.jpg')\n",
    "```\n",
    "This will output the image with the detections shown. The most recent detections are always saved to 'predictions.jpg'\n",
    "\n",
    "Try out the examples below for yourself!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Sou43BU_e9hZ"
   },
   "outputs": [],
   "source": [
    "# run darknet detection on test images\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/person.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dszjRbWcf5fj"
   },
   "outputs": [],
   "source": [
    "# define helper functions that will allow you to show the image in your Colab Notebook after running your detections\n",
    "def imShow(path):\n",
    "  import cv2\n",
    "  import matplotlib.pyplot as plt\n",
    "  %matplotlib inline\n",
    "\n",
    "  image = cv2.imread(path)\n",
    "  height, width = image.shape[:2]\n",
    "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
    "\n",
    "  fig = plt.gcf()\n",
    "  fig.set_size_inches(18, 10)\n",
    "  plt.axis(\"off\")\n",
    "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
    "  plt.show()\n",
    "\n",
    "# show image using our helper function\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j6BBUBb8hJvf"
   },
   "outputs": [],
   "source": [
    "# run darknet with YOLOv4 on your personal image! \n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights horses.jpg\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Pt7imdQihdoz"
   },
   "outputs": [],
   "source": [
    "%cd ..\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Bn3mnJWihjjr"
   },
   "outputs": [],
   "source": [
    "# this creates a symbolic link so that now the path /content/drive/My\\ Drive/ is equal to /mydrive\n",
    "!ln -s /content/drive/My\\ Drive/ /mydrive\n",
    "!ls /mydrive/Object_Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NdCkkDr3iLrn"
   },
   "outputs": [],
   "source": [
    "# cd back into the darknet folder to run detections\n",
    "%cd darknet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z9pSG6TQho0m"
   },
   "outputs": [],
   "source": [
    "# run detections on image within your Google Drive!\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights /mydrive/Object_Detection/street.jpg\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VCCCBOAPiWm6"
   },
   "source": [
    "## Customize YOLOv4 with the different command line flags.\n",
    "Darknet and YOLOv4 have a lot of command line flags you can add to your '!./darknet detector ...' to allow it to be customizeable and flexible.\n",
    "\n",
    "I will show a few examples of these flags that you can take advantage of! Feel free to mix and match them together to customize your detections in any way you want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u1P7-xo4iaf-"
   },
   "source": [
    "### Threshold Flag\n",
    "There is a flag '-thresh' you can use to add a threshold for confidences on the detections.  Only detections with a confidence level above the threshold you set will be returned.\n",
    "\n",
    "In the example below we run darknet with YOLOv4 without a threshold on the test image dog.jpg. The image returns four detections, the lowest confidence being on the pottedplant with 33%.\n",
    "\n",
    "If we add the '-thresh 0.5' flag this will only output three detections as now pottedplant falls below the threshold and is ignored. \n",
    "\n",
    "Check it out below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sZ4ubWkRifAf"
   },
   "outputs": [],
   "source": [
    "# this is ran without the threshold flag set\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T2q_TV7Bij-u"
   },
   "outputs": [],
   "source": [
    "# same detections but ran with the threshold flag set to 0.5 (pottedplant is no longer detected!)\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg -thresh 0.5\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19j3jI9Oiope"
   },
   "source": [
    "### Output Bounding Box Coordinates\n",
    "You can output bounding box coordinates for each detection with the flag '-ext_output'. This external outputs flag will give you a few extra details about each detection within an image.\n",
    "\n",
    "Check it out below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6Tz0HUzZiqRT"
   },
   "outputs": [],
   "source": [
    "# darknet run with external output flag to print bounding box coordinates\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/person.jpg -ext_output\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_ARls-oivMM"
   },
   "source": [
    "### Don't Show Image\n",
    "You can add the flag '-dont_show' to not have the image outputted after running darknet. This doesn't really affect anything when running in Colab as the image is unable to output properly straight from darknet anyways. However, by adding the -dont_show flag you will get rid of the following warning from showing.\n",
    "```\n",
    "Unable to init server: Could not connect: Connection refused\n",
    "\n",
    "(predictions:1850): Gtk-WARNING **: 17:01:00.687: cannot open display:\n",
    "```\n",
    "\n",
    "This is an important flag to have when running darknet with YOLOv4 on video as it will suppress having the video shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B5sKNhmKizhz"
   },
   "outputs": [],
   "source": [
    "# running darknet with dont show flag set (no longer get warnings)\n",
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/person.jpg -dont_show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TbyuASzhi4ga"
   },
   "source": [
    "### Multiple Images at Once\n",
    "YOLOv4 object detections can be run on multiple images at once. This is done through having a text file which has the paths to several images that you want to have the detector run on.\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "The .txt file should be in this format. One path to an image per line.\n",
    "\n",
    "This file is stored to my Google Drive root directory and holds the path to three images within my Google Drive images folder.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hwKgzhs9i8AB"
   },
   "source": [
    "#### Save Results to .JSON File\n",
    "Here is an example of saving the multiple image detections to a .JSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XbS795ISi9bu"
   },
   "outputs": [],
   "source": [
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < /mydrive/Object_Detection/images.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aw5FdcHFjBC6"
   },
   "source": [
    "#### Saving Results to a .txt File\n",
    "You can also save the results of running multiple images to a text file.\n",
    "The result.txt file will be saved in your route directory in Google drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O1tp2KxCjCbM"
   },
   "outputs": [],
   "source": [
    "!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < /mydrive/Object_Detection/images.txt > /mydrive/Object_Detection/result.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hF3NC24nnVzu"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vafjWk8NLnqh"
   },
   "source": [
    "# Preparing dataset for training Yolo\n",
    "\n",
    "In order to create a custom YOLOv4 detector we will need the following:\n",
    "\n",
    "*   Labeled Custom Dataset\n",
    "*   Custom .cfg file\n",
    "*   obj.data and obj.names files\n",
    "*   train.txt file (test.txt is optional here as well)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cELdqBQXL3Zb"
   },
   "source": [
    "We need to divide out dataset intro Train set and Validation set.  \n",
    "Once your datasets are properly formatted to be used for training and validation, we need to move them into this cloud VM so that when it comes the time we can actually train and validate our model.\n",
    "\n",
    "I recommend renaming the trainin dataset folder with your images and text files on your local machine to be called '**obj**' and then creating a .zip folder of the 'obj' folder. Then I recommend uploading the zip to your Google Drive. So you should now have obj.zip someplace in your Google drive. \n",
    "\n",
    "Do the same with your validation dataset but name it '**test**'. So you should now have **test.zip** also uploaded to your Google Drive.\n",
    "\n",
    "This will **greatly reduce** the time it takes to transfer our dataset into our cloud VM.\n",
    "\n",
    "Now we can copy in the zips and unzip them in your cloud VM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9VVlLwqBuYsv"
   },
   "outputs": [],
   "source": [
    "# this is where my datasets are stored within my Google Drive  \n",
    "!ls /mydrive/Object_Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RFJq31FTueBB"
   },
   "outputs": [],
   "source": [
    "# copy over both datasets into the root directory of the Colab VM (comment out test.zip if you are not using a validation dataset)\n",
    "!cp /mydrive/Object_Detection/obj.zip ../\n",
    "!cp /mydrive/Object_Detection/test.zip ../"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SWjNcjfcXKgx"
   },
   "source": [
    "# Configuring Files for Training\n",
    "This step involves properly configuring your custom .cfg, obj.data, obj.names, train.txt and test.txt files.\n",
    "\n",
    "It is important to configure all these files with extreme caution as typos or small errors can cause major problems with your custom training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nWZqyTvxdZW1"
   },
   "source": [
    "## i) Cfg File\n",
    "Edit the yolov4.cfg for your custom model training using the colab editor and then copy it to your Google Drive by running the cell below. This will allow you to use the edited cfg file directly from google drive as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YQUDXrhJekxl"
   },
   "source": [
    "Now you need to edit the .cfg to fit your needs based on your object detector. Open it up in a code editor to do so.\n",
    "\n",
    "I recommend having **batch = 64** and **subdivisions = 16** for ultimate results. If you run into any issues then up subdivisions to 32.\n",
    "\n",
    "Make the rest of the changes to the cfg based on how many classes you are training your detector on.\n",
    "\n",
    "**Note:** \n",
    "I set my **max_batches = 6000**, **steps = 4800, 5400**, I changed the **classes = 1** in the three YOLO layers and **filters = 18** in the three convolutional layers before the YOLO layers.\n",
    "\n",
    "How to Configure Your Variables:\n",
    "\n",
    "width = 416\n",
    "\n",
    "height = 416\n",
    "**(these can be any multiple of 32, 416 is standard, you can sometimes improve results by making value larger like 608 but will slow down training)**\n",
    "\n",
    "max_batches = (# of classes) * 2000\n",
    "**(but no less than 6000 so if you are training for 1, 2, or 3 classes it will be 6000, however detector for 5 classes would have max_batches=10000)**\n",
    "\n",
    "steps = (80% of max_batches), (90% of max_batches)\n",
    "**(so if your max_batches = 10000, then steps = 8000, 9000)**\n",
    "\n",
    "filters = (# of classes + 5) * 3\n",
    "**(so if you are training for one class then your filters = 18, but if you are training for 4 classes then your filters = 27)**\n",
    "\n",
    "\n",
    "**Optional:** If you run into memory issues or find the training taking a super long time. In each of the three yolo layers in the cfg, change one line from random = 1 to **random = 0** to speed up training but slightly reduce accuracy of model. Will also help save memory if you run into any memory issues.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 514,
     "status": "ok",
     "timestamp": 1625915156267,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "AB3LljIxvfpN",
    "outputId": "454b96f4-b931-45f0-f9e9-f329f5621138"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp: cannot create regular file '/mydrive/Object_Detection/yolov4-obj.cfg': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "# download cfg to google drive and change its name\n",
    "# !cp cfg/yolov4-custom.cfg /mydrive/Object_Detection/yolov4-obj.cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5KQPk-mMvpWY"
   },
   "outputs": [],
   "source": [
    "# upload the custom .cfg back to cloud VM from Google Drive\n",
    "!cp /mydrive/yolov4/yolov4-obj.cfg ./cfg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a4b33o9EiHUD"
   },
   "source": [
    "## ii) obj.names and obj.data \n",
    "Create a new file within a code or text editor called **obj.names** where you will have one class name per line in the same order as your classes.txt from the dataset generation step.\n",
    "\n",
    "**NOTE:** You do not want to have spaces in your class name. For this reason I changed \"Vehicle Registration Plate\" to **license_plate**.\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "Example for multiclass obj.names file:\n",
    "\n",
    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moy0ZPjbjl86pGBTpTjm3nECSQJJAsnZrKQuSfjhzzd4/t0FaHHkkJst2JPnzjxkCSQJJAkkZxGS7mtz8fTNWzy+vknplyoEZjMPs9myKYEkgSSB5IxCUhuu3ovg5eoE94BQlHU8wduXv2H3Sv8pj6nNVmUnz/00MBNIEkgSSM4oJG1R3vIIv//yM3794znef3iH4YvbwNMhXe35CmkCSQJJAskZhSQP8duO4MKly7h8oQHFOxLIWOSM2vfTvMG/AmgCSQJJAsl5Xon/CgDIvR+HLoEkgSSBJIGkwhKej0ND08BKIEkgSSBJIEkgqUYDBJIEkgSSaiqIpnlNJL8TPWkCSQJJAkkCSeJJqtEAgSSBJIGkmgpCPKuJnpWm2YRAkkCSQJJAkniSajRAIEkgSSCppoJomtdE8jvRcyaQJJAkkCSQJJ6kGg0QSBJIEkiqqSDEs5roWWmaTQgkCSQJJAkkiSepRgMEkgSSBJJqKoimeU0kvxM9ZwJJAkkCSQJJ4kmq0QCBJIEkgaSaCkI8q4melabZhECSQHJKkLS0tIKFhQXxOAhQNVADTDCZLBKIDdRq4G+2trawtrbWwApCPClN8xxJfonmP0UDBJLEgyQNJNEA0YAaDRBIqjHOp7Q65B7irRANLCwNEEgSSBIvgmiAaECNBggk1RiHeAQLyyMg5UnK81M0QCBJIEm8CKIBogE1GiCQVGOcT2l1yD3EWyEaWFgaIJAkkCReBNEA0YAaDRBIqjHORI+ACQf3JQg1NwVrWvctrJZ1ol1I/ohNFq4G5hgkdbEkZj12bEtHkJHB3GvdjENwse8Bvulvwhov+88GSjMzMywJDp5W8PDwAIvFmiUbasHdfzliV4TDyEBnluJYuJWOAHV+le0cg6Q5dlXdx5OHAhQ52cy9ysfSwvKcKnz98DG+GbiNeAcLsD+DR5m4ZjVGBPxpheraGujp6c2SDQ2RtOMYGs6dgpONycfjYLLgFb4ZZ86UYGOwx2drXAiM5heM5mp5LThImtj7Iz0nH2uNDT9eeT8FcDrGWLP7PL55/ATi3kas9rAF81OeM417PL28UFBYiIKiqYfklGRwudzZsQFjupBkY9GaQtTW1SAnxh9sJqm8cxUIJF0TtbngIJlRcg2Pf/wap+ysZgkQDLC0dLGu+CK+e/Qjvh1sQoKT5ax7lEwm9TKG6YXZE/w0IclggMlig8PhgMVkzlq5zF5+J1YcEpfm2GTBQXJb6Q08efINTtnPHiSpCsLWN8P6k7fx6MkTiHqakOBoqUGVf/qQJFDRHKgstLL+YpBka+vBwz8Y4RGRiIyIwPLgQFib2E46Jskzd8SikFDJ9ZGRCA9dCndbeZfa1MEDEREROH2tA0+efIfL65MRGRmJsNDFMNGTCVQHjh4BWBYWQZ+jrg9Z5ANTnU/zbti6Rth+5gYe/vgjvhtuQqKr9ax4lGuT1kL0zdcqQ1dvD6ysZqpBYMHQzBYeXj7w8/Ojg5e7M4x1OeMaAGVIcnV4cPXwpq/39fGGi505OEpdaiZ4Jnbw9fWBrTFPzfAED67evvDzcASTyYaxlQO8qP/9fOHt4QxDrYkTUUwmBxa2TvCmr/ODr68v3B2toMOSlTn1yYKZlSt8fbxgqK8DbQOLsTx6e7qCpysdlmDrwdbZA76+fvDz9YG7gyW0lJ4jfSaTBUMLO2na/ODr7QU7CxX5YjLp6zy9Jfak0ubh4gB9rmLayPe5DtUvAkkW2wxFVbcgfPADHj9+hEePHuLh99/i/o161N/umjBxE5CwCZ19o/j2h4d49OgRHj56hMePHoLfdwdxQZLXvC3PPIofHj7E4x9/xJMnT/CYvu4hvh6+jWXODDB41siruIlR0QM8fCiJ89Hjx3j43de4VZYHq08EJUfPCLnnWvHwxycQ991GnI2pGgh8WoUIjwjH1RvXcU1FqKqtgYnJFCZPPjYGyuLAb0UaTpw5i5raOtTV1qKurg61NVUoObYXy9xMFUApg+RpuLgsQ+aBU6iqrkEtdV9dHaoqypCTHAZ9tqzxYcF/+VbU1FVj01IfNRM3PjhwrhYNp3JgH5yCY6UVqKmh0lGPutoqHC7cABs92TMZYOhZYmPufpSdPUfHXStNc825cuSlhEF3DHA6iErej/rqcoTEJ2PPsTOokj63vrYah/M3wNjAGCvTd+HsuWpQz6mvr0dNZSm2rFikDHwmC67L1tFpo/IribMWFWeOIinEUZ43JgfesVtwsqyCvmYsbVWliHb6NB3MdZgs1PR9fkjqGiHn3B08+fEhuq6WIX1NFPwDA7AmrQCNPSJ8/8PjCZCMz9iFtuu12Lk9HVGhAfBeFIrsIxX47tFjDFzcCV0uC3pm1vDy8cbuunt48uQBKleEw9vHG56eTtDXYoBh7ozyOy2oObkbackJWOzjjfCEdFxsGcWPPwhRGOH5yXDTNrLAzrp7ePzkCR70X8FS8/lWCbhwi8xARX09yo7sxKqoYDhaWcHZIwCrN+5AeXUdqkp2w91QSwpKKSRrzuFo+TkczE9HaKAXrO0cERK1BvtPVqC+5iySg2QrFKYJyYrTOFZyAmmJkXBxsILHogjk7DuN+rpq7FjhBZbMSzV2ws5DB7B1/WqEBHrBxtICnotWYM/xCtRVncYyG2NpeqWQrKtF+blS5K2Phqu9Nf3c/CNlaGioxqHs3ThbdhSJ4YGwtXHAkpWpOH2uBnUVh+DN05Y+hwO7oHUoq6tDaXEBVoYGwMbGBkHhCdh76hwaqk8izFayJMrIOQRnqmpRdqgQ0UskaXP3D0F8UhJ8reabPjQ7vZ8dknY+qzH44EeMtlbByUwmPqoQWLDxjkOH4OEESGrr6MJAV0sJYiyeHy4PPMCPolYE6cmfM+mYJIsNA0MetNiKBc6E34Zd+OHxY3SULAdTVvk+5nWNO89kceC6/gi+e/IED7/pRZKvYhx//buWlhaMjY2nFQwMDOiJnqm07lxLVxSXVqHm9G7YGesp2ZnKm19yEeoa6rFvY6DUU5JCsqEe5bvWwoCr0A1mMmHgHYuy2npUH9oEPdpW04RkQx22RjvIYchgwNAnEhU1dajYvx5sltSbZLGhq6czbracCcf4bahvqMOeNTJISyHZUI+j20OhraABA6s4lNQ2oKG2EuuC3BQ8QTYidhxCQ0Mttofr05BkG1kh99hZVJ3eB0cLyTGZfa3c16CspgH7t8RAh8GAa0AqqGVYG6P95c+kbUFNvv11TcjiJZ+zb8vPDsmA3Bt4+OQJzubHTBy/41qj+FL/BEhKhMCGuY0TwmPXYvOWDBTsPIku4fd48n0P1unJFzRPCkkZ2LT14OK3FGuSN2B7diH21l3Hox9/xMDFcKVKOR3xuSfmo1f8PR5+3Y+CxCXQksU1Q5+x8XG439M9rXCmrBS6urpSD0i9kBy9V+NcbT2K1oWDq6ICM7meKD7XgMoTObCj8ySFZO1ZrPVQNR7Kw47j1WioO4EQejx4mpCs2A9XrkK3mopTxwcHK2vQcDoPbLYClKlzHG1Y2LlhybIwrIxbjZSCg2hoaMDhzbbKnmRtBeK8HZVsoqVjin1lDagq3Q93c+U4dVdmo76hAfmrJT9sMLeLQmlNA/ZtiYH2ODvpmVrjYGkdSvdvg7k2A3beUaisqUPJnkz42c7WelX15TodDZNrJ7flZ4dkVEMfnjx5jKJV44ROVz4Vi8mZLJi4R6LsWie++e57fP/993ggFmF0ZAQPqK75FCHJ0dJHcOoedA1/TT/ju2+/gUggwCBfhB9/fPJJkKS8LLfVheB//wgPHwxhd/IScGdhiUtkVKRkPPLmDVybYjh4qBg6OvLGQ10lcA/ORU19LZIj3ZUAIruH2gdmx/FaVJfth5cOJSb5mKTzJIvJV+06i4aGc4hzpa6fJiRP5ci9RVoX1DM8sK9CGZJsrg7cIlJxorQC1dXVqDpXibLSUnocUCUkq8sR6CHzLiWVgqujhz2lDTh7LAf248CntXgTqhUgaeO7A1UNDairrUFVVZVyqK5GfX0Dyg9nwUKfASbHBEmZB1BZU4vaqkoU56bD28kK+lpslTaW2Zp8SsplLtnhs0Oy6KaAHjPMjlBljImQ1DF0RnW7EI++E6JqfxrCl4dicYAvXJ2WobZTPGVIuqQch/CHxxB03cDWVSuxLCQIvp6esNu4k17v+CmepEtiEXpE3+PhN4PITQiacQ/ycwnFK3YXautrkRTmorICU5DMOFKL6vKD8KY9QxkkJ//FTfLeSjQ0lGOFA1XO04ckS9alVgNJp4hNqKytw8l9O7A00A8ebi6wtbaCYXQa7QFO8CSryxEwGSSPZsN+LC6JNrUWpStB0nZZDmoaGnAoPw3xq+JVhuiwAOhJZ69ZHAM4+S5Dxs5jqKlrQF1NJYpzUmDDUaV9cuxz6X268Xx2SAZVdOLHJ09wfIuL0tgXnXAtB5TcGFHqbpstP4mvnzxBW0UWDBRbeq4H6rq+njIkD98R03DOC1L2YI3X5kwfkiwW3BOyMPrdIzz8egC7k4LAHVfBplsQ6q6nFmEbGhp+NFBjl+qeM9k5F99NqKqvQ9aapePG9yQVl8l2wv6KBlQey4Q1nU8pJKtL4G9rNjFOjjUKz9SioeoAPOkymx1IbiiuRl3NKSyzVu4mG0TODiQtHdajor4BRRsjwJlGeVONjIGtD3bsL6XhvWONbGyXgHEyTc6l458dkt6RZ/Dg8RN0NeyEsbaySKzdktD7jfLstkVGLX548gRXD69SqoyWftEYevBoIiRLqMXkD1AZqbwDZFXXD3jypB8bFLvDLD1sPEj9Qmd63W2buEL0fv0DHn03ivxEqoutnI+ZLuDoFdG43Xz3oyFl3TolG001Hfr2vjhVWYvKIzkw1hm/JpIB5xWZqG2ox+71soZNCsmGWhSnLh4XJxN2IetQVd+A0sJo6bjz7EAy+2QDaqsOwlcRWEx9rMqkJlxUjEn+RU9S38IFh8prUXE0H+YG6n/yyWZzJ0zQWLguQ2l1A/ZvjqMnd6ZaPuS62a1fH7PvZ4ekgbU7rnaL8eTR16jZmYJF3q6ws3dGUFgKLnaKIP7+ByVP0nTxTow+fAJh1yWsDvGBrb0DApZGoebuAL59+OMESPoVVNHrJLvPb4e3qxO8A/1hqs/A9otDtAd74Xg6vFzs4eTqi4Rtp9FPTf48mTokXWK2YuDBD7QHuTcl5LN0sWWQvNN8F5MFCqKfCkkGSx8Rmw/QIDyxcxMCPJxgamgIcys7LAqPw8nKGlQcL4SLnsxTlUKyvg7VdZXYHB8Ie2tTGJtZwDsoCsVnqlBbcRzRTrK1laogqYtVeSdQV12OuABzKWjl6ySn0t1O3FlOLwvaujoYVqZGMLN2QGjcVpwqr5oVSDLYeojYtBv1DbU4npcKHw9nmBgZwtTCmv5hRHrBXqzylPRUvAIysD0lGp6uDjCmbGntgMjVu1BdX4/MVb7T8kQ/VonJ+dmF6GeHJIPJhuGyzbg39ACPHj/Ct9+IIRAI8c2DB2g+dxoVl7uVIMnSMUNWRTO+f/gIP3z7DQRC6tpv0VZfhet9E7vbDL0luNr3Pb1I/Rtqgqe/kV5MbuEdh9ahb+iF61+LBBCKvsY3wiFkVlTj+0dTnN22X4Pb/O/w8NtR7FwbPCuTNKoET72ogsfjfTR8anebipOlzUNU2i5UVtWgpvocyktLUX62EtU11ag4uRdLHBR/USKFZFUJojfmofRcFSrPlqOs/Cyqa2pRXVmC1FAXsMcWc6uCpDXyT9eiob4Om2L9pEMv04OkmVsITpRV0RMpFeVlKD9bgYqSIwhKz52VMUnKTmxtI6zNOUovRq+prkJ5GWWnClRVV6OyZB+CpDPkHoHb6dnt6qpzKCsrQ3lFJaqrq3AkOxGGWsrDA6rKnBybXfBNx76fH5LSrpGhgy/Wp29DdnY2snZsR0psCPRZ+giJ3YDszC0IVnyfpL4VVialITMrG9lZmUhLXgkb6hcSqRnI3rERXlzlLqKF23Js3pZFP++JxXwAACAASURBVHvLV/Gw4lEGZ8PGaynStmynj2du24zoRa7gugZie1YW0lc5TOgeqTKkX0I6tsct/iwepKr4Z/UYiwt7j0WIXhmHhIQEJMTFIWyJL3hayvZlMLThERiOhJVR9PskzZz8EBMbT98TuyICHlaG48abVUGSAVufZYhbGQU7Y9mMrzlCqbgjF6tY42mGZTFxSIgKUnhJBhMmDj6Iiokdi9vN2ghMOy/EJyQgzI8n9VA5cPQIRXzsCliaKr+nlM3hYml0AmLCF8NQsdtOAdHKF7EJCQhyl3nQkorLZOvA1S8EK+MkeY6Pi0XY0kCYjnnaVKOjD69FoYgdu2Ylgn1doM0hgJxVDY8rw5mI64tBciYS/yWeQb+JZxYK4kvk5fPFyUZgVC5qayoREeg0bgxz7ngMn88eJM/zydYEkgR4sw4taj1pbNYJ1J47hcUuU1u7OZ8qEUnrwoY+gSSB5KxAkqOlDWtLcxjyjODgFY+T1fU4tSsVZrO8EoAAa2ED60uUL4EkgeSsQNLU1hlHT5/B8aMncK6mDjUl++FlLRsjJBX5S1R2Euen6Y5AkkByViCpa2SG5PRtyMzcgS0b18JtwmTOpwmWVHRit8+tAQJJAslZgeTnFjKJj8BztjRAIEkgSSBJNEA0oEYDBJJqjDNbLRN5LvF6iAbmjwb+ZmPjACsrO9KSEFgSDRANEA2o0MDfbGycYGVlT4yjwjiktZ8/rT0pK1JWs6UBAkkCR9JAEg0QDajRAIGkGuPMVstEnku8HqKB+aMBAkkCSeJFEA0QDajRAIGkGuOQ1n7+tPakrEhZzZYGCCQJJIkXQTRANKBGAwSSaowzWy3TF32uiQvSt2chJydHIWxDuPEX+l01kwnPVenIydoBf3flLTe+qJ00TRckv5M2FBoJSSabC56RCUxNTaCnPf6FsvOr26JjbAEnZ2c4TxIcbC3BZSvkySEc19vuo7enF729/eALRBCL+7HP8QsBislC4rk2iAVDSIsPnFSoXwaYTFC/Qbezs4eBnvYcS5tCmRLAzWrZaBwkDW08sHJVCnbk5qOoIBehnrbj3qI9v8QXsPUERGIxxJOEnqYyOBgp5IlrAEdXN7i7ucN9USTqmvsIJCeDDEcLiScuY6CvG2sifWa1In6ZRkBBF5PZgByHxkDSyNoZK5I3oaCwANkZ6dieXbCgIDnSeh779+2fEAoyVsNEZ5LKYOSIsqYeAsnJQMDVxld1HRDz+7GWQFJjGwmNgaRr9CYUFRUheUUgLIz0sHxd9oKCZH/tpumLmEBSvc0IJNXbZ7LGZYEd1xhI6hk6wMXakC50JouNUAJJMKYMSQ48lq7GmQv3MCoQQiwSQTDUg9pjOVjiKLGprLuoZ2aL+jY+BttqsWiRG1KzT6Gzf4QeDhCO9qFi9ya4mivsWa04Jpm0AmFJBbjeRg0BiCEWjOBaxR4sctQbV1l1EBCTgcrLtzAwJHm2WCyCcKgX50t2YrmnbCtbiQfNM0nAvWEx7l85jiUuQcg/dRGDo0I6jtH+ThRvS4ARW7IVLMPYCeW3eicdvpANa4iEAmyNmsRDX2CQkJWtpn5qDCQVC5hAUlq5pwJJJgtL0g+hZ1gAwcgArlWX4vChE2hs6YZAJMJQ53lE2sknv2hItvMxMtiL5o5OCAV8DHTewfVrTegf4kMsEqKxLBNmHGkaZJAU8nGruRlDfAGGOptx5cp1dNNwFaHr4l7YjG1PS91nhb1VzRga6Ed35z1cvXgJly/fRGfvIIQiEUa6LyPGSrYDIwM0JEfEGBnqRkfnIASjg2huuo7rjc0YpiauBEM4usFVMjZtYIWsY+dw6eIlXLp0BW29IxCLBGi5fUNyjDpOhQsXsCqAQFKxXi3U7wSSC2TiZqjxBFYlrJoQwgLdwJ3Ms5kCJI2cluJmzwiEg23YFOUKtuxZ2nbIOHYTfJEYl49tgZ50Bl0GSdrjGrmP4i2R0JPuM22xJAm3+gQQDLQgxtNC4h3KIEl5jsIRNBxOg52RZLMwbbdY3B0SQzDShbWL9RW8SV14BQbAzlhPadKNa+6K/TckXuCJzJCx62WQpNI0ev86UsM9waX22mHpImzvBXriq+PCfljI8ib7JN3tMRsuVABOJV8EkgsEkpSHJhAIJoQbp7Im7Cc9JowpQDJwfQVGxWLcOp0Dg3F7Rlt6BqN1UIih1lpYGEuWyIxBUjCAw8l+4Ch6gFpGyCy9A5F4GCURfhLAjUFSiKvHNoKnLe320qBiYc/1YYj4Q9gU46yywnK4WjDgGcHISBKW5JyHQCzGlSOboSeFnQySwqFOpIe4giWDIIMBfdMs3BeL0XurFO4Kx2kbEUiqtPmYfsbba4H+TyC5QCA5cvs0EhMTJ4SIxR7Qmky8U4DkuqpOUON9l0/vRFzMSqxUCPGpGbjXx4e47zaszSWL0WWQFPTehr+z8bhKpot1+XUQioW4nL8ETMqbk0FyknWSu65KILl5PCR1HZCUkY8TpeW4cr0JTU2ScK9jgPYMrx3fDgNpvmWQHGg7D19L5S6yPm8HOilI3i6Fx3g7EUiOKz9l22kKLAkkFwgkZ2t2O/fiID2JIRIJIRRODPQazSlDUgfJuVUQiv4aJLWM3HH8cjs9/jjc044bVy/j4sWLdLjRRiCpKfD6XPkkkCSQVLtOMrFC4klWHslEUFCQ6hDoC22uZPJGrSfJNMCmvVchEo+iKjFQubs9DU/SI+U0RoRi9N2pRaiPHXS15JM07ltq6O428SQ10+ubDXASSBJIqoVkYNo5enLm2rEd0Bs3JqlKkOogyTV0xqlbfRAO38daPwdJV+4Tutt+x5po7/byoS1j445UWlg6JkgovgGhWIwZgSRHC6nnWunZ7+y4xUqTRKryTo4tTDBrDiSZLLBYksDmcCWLyQtzEeZtB7b0OIvFnHdjMLKfJQ7UbQGHw5kQ2GyWUuVmMlnya8xcpGsC+3HAxW7sOFvBDmbuy+lxR2qNY0luAix1FOPQhpVPOFZHLh6bQZdD8g4WuZuDw2aDzWaDo2OBtCNXMCoUo/3CIdjpSr2/T4Ck785LNAjvXzsNa2NdOt086yU4XHMTw0Lqt+gzBEkmF3Hb6yEQi9B3vQSeRtpSG2nDOmgVFrssTCgQ2CuXq8ZA0twjBtm5ecijQz4KCovoX+AU5OdLj+UhOcobnPGD93P8fxkkqcXdba1tE8KNqr2w4ckL3XfFOty9J72urRNDowKIxUL0dXZK772Ho3HL5Et9GLoISdmPjkHqOjGEo4Nob2tHW6vkXmpM8sK+jdCR2kkGSXoJ0Gg/mm9eQ33dBbR2D4Aa1+y8cRaRbrryxugTIGnutApNPZJF5PxBau1jD0Qiar3jAJrq74E/U5BkMGDpGY2bXbLF8MPobG9DV+8wyGJyuaYWOlQ1BpKmblHYun07tqsJieFe8w6S3ql70N7ePmm4WVsMWwVI+sWsR3Pr5Ne3t7fgRHyoAiSpGWguzJz9UHiiBndbWiVxtbXg1vWLOLBjLaz0dca81TFICvno7OwYS1fzzUsoWB8Js/Fv02GysOLYJbS33kFS1MSXSGRW3EV7yx2khEm75xSMWWyY+UbjZP0NtFF5b2vFleoTiPZ3gbtnIW61t+Pc7g3Ql4Jb3ygKF5rbcfvyGXiaKVduXYONuNbejsa6YriobBDZMLXxwd6S87gntVtbcxNK92+Bk77ysxY6LDQ1fxoDSU0t4M+dbxkkVS8BIlD53OVB4vvrmiOQVOk9/HXDaqo4CSSJdhaa9gkkCSTl44MzYAsCSQJJAskZqEgLzYgkP3IwEEjKbUF0sTBsQTxJAv0Z9SRZHC4cXD3h6e4CXW35Im8CjIUBDE0sRwJJAskZhaQmViKS54XdABBIEkgSSBINEA2o0QCBpBrjEA9hYXsIpHxJ+U5FAwSSBJLEiyAaIBpQowECSTXGmUorQ64h3gjRwMLWAIEkgSTxIogGiAbUaIBAUo1xiIewsD0EUr6kfKeigb/Z2DjAysqOtCQElkQDRANEAyo08DdbW1tYW1sT46gwzlRamfl9DRNsLhdcDnvsLT7zOz/EMyLlN/MaIJDUSDhKhGTiGoWSC024WX8cwRaKW7bOvNCmVXnZhvANjUFSynqkpqbSIWn1SjhJt4iY+CwWrN0XY9WalLHr1yetxXJvyeZkE6//wvnTYM3Nx7LQOEiyudrQNzAAj8cDz8AAOlqcBeJFsaBrYARzCwtYWlpKgoU5TAwNwFF407iiSH32X6N3FqReuns+R+F9jV+wEnNNHLHnXBP9MmChUAD+KB98vhAiYQe2GOhN6PEwmVoIX38YXf3D9EZlfD6fvofatOzGQdcJ1yvmn3wnjcVUNKA5kGRrwdknBKvXpyM3v5B+K3lRQR62bkjCEi/befeyXcXCZfPMsHbLTtReuyt907hY8hZx/hA67lxBdqStSljYhqWjpZ+P0d47WONrqvIaxXg+x/eYwgsYFYnR13IBW5LjERUVheiYeCStjYOnCk9SyzcVLYMiCEZ6cThvI2KiohEVtQKr1qzFisWGcyJPn8NuJI7ZA77GQNIqIAG5BQXIzNiEdYlxiIiIRlJqGnLyC1GYux0BjibzskJpGdnh4Pl2CEQUKAZxr+k6Ljacx/n6C7h05QaaOzpQvdVDZd6o/W60tLShrcWV7IH9BT1ISSX3RlUHtVXCEAoieFPy8OOKr9ANQtPpFGhN4jETgMweQDTBthoDSY6eKfwD3GHK0x6rfEyWNvyiklBYVITstYvnCCimIWgdE2w+fYXeFEvUfxubVvjATJ8rByJLC6a2DnC2UthT5ouDUE3+bFaiZVgA8fBNRLJZ8nxMmmZD7KxrhVgswMl1RlO4Xk3ck8ZB7tEEEKrLo8ZAcjIjaDsGIa+gEEWZa2DEnF+7JVr7paB9WATBUBu2LbWaGiSco9EyMEqP31HjdpLQi30Ok61wcMCxq/ch7L4BKytbLErciistPRAIhRDwR9B8/jjCHS3BUoCMgb0/Co+cxe2Ofno8kY6DP4rWG3VIiw2E/mR2dl+FzhEBxINXEcSaCiQtcOBiB8TiURyN/xgkWbByD0bB8QvoH5bkXzDcj2uVexHhYQ0Wk8Bwsjqi6cc1HpJs2wAJJLclTF55FQAwlwSz7GAjPfHSXLETplPYE5tOu00wzl6+gVuNt3Cr6S76h/lq991mMBxw/FoXxP1tON5wB0N8EQTDA2i+dRddQ5LtW7tvlMKMpzUGaWpzMr5QBNHwKLpa7tJxdXQPQiQSQzQ6gOINAWPXBidn4/jR45JQdh6D1Jawo104d0x6THruYO56aHPZYHhG4siRY9J7SnCra5j2JO/UlcifQ91TvBch5nJw2gcn4lbXEERiEbpuX0bJidO4ea8LQpEIw903sNZ9nnjbc1SLc6lezHRaNBuSTBacgmJRUFiIrTE+Y93wmTby7DyPh+O3JFudntyxeAw6XDtvhIaGKoVFrgZj55XSYuSIsqaeqUGS3k62H+V7N8Jeume2gd0iXOkchni0E+us5GO6pm7+CPV1gBZbwTtj8xCTUYlhkRhd107BUVtybsOJ6/SYIr0FrVgy4aTq++idEhjocsCI2UEDWNU1Ssf4XUh3knjHHD0rHLh8HyL+EE7nxEBL5jVyTLCmqI5OU+elg7DWUUgvgZFqzWigXTQakrqGDkjdXoCC7C1wt5oEJHNWFB6o7hylAbN7DWdM0CaZlaCXwVBLYaSh8Yj32PlPh+QITq/1gbaix8q0xq6Km+jquoMsSxWz49TEkLYOdHV16WBp54nGPjGGOi/B114CJF1DE1hYWEhCyEZ0jQohHmpEjJWV/LiFBcxNDSVjxtr69DInyT1eOH7tPsRiPs585ap0vYW5GfQ4kjejG/kXoFcoRu/tSnjwlN+WrsWzRE27APz+FoT5yEGvZKc5qwEC9c9RThoLSS1DKySmZ6OoMA8JQY7gyryLeVMhPFAjheSuNfLxO53AFdi0aRMdTtbfpLvjd45P3M+aFte0PMle5Jsbq4btBJtx4BEci20Fe1FaWY+rV67S4fqNRgzyxRi+fxl+Dioq+CyNSfrtuUg3Jh0Xj2JF6HIsVwhhkStw5tYAxCO9iFumehXA56iIJA4Vepigqy9zjQZCkgktA0us/CoThQW5SAx2A5f1ZYz/1yqGEY7dosbjxDi9I0glvOLyTtLjgJ8VkiwulqUfxNAIHyL+CDpb76KpsRGNjY24dfsORoSfH5IJx2/RdhJRi9MVPGzZd6FIDPEogeRf0+N8rENTS7PGQVLb0AaJaZkoKshBXJALdBXHzeZIyzVVsa44fov2FBvLc2HImVjgXwKS+mZOaGgfhWi0D/u/ioedqd7YzLGBqRWu93x+SPrsuUxDsvXScURHR6sOUeGwMiM/Y5yq9jTpOg2CJBM6htZYtSkPhQXZiF3iBs689CDlMLTxyUDniAiCvmbEB058k9OXgKSZTQraR8UY6jgPT1t5WhkMFhz9Q3G3//ND0jhsPwZFYnReOT426aRJlZzkVVGH0/+uMZDUMbJD0uYsFOVnIcLbDlrzHJCU8Nk6ZthZcRdisQhD9xuRmbBYKV9fApIm1mFoHhRDONiKdX6StZtMFhuhG/bgLr0E5/NDUpvniHP3hiASDKH20CbYayuuh2VC39INqxLCwJtnPQkCv+kD71NspjGQXJyUI/m9dmEh8nPzkKcifLVqCbTnW0UxW4RDNU0YpdYXisUYHe5Ha3ML2jqpBd/UkhoRmg7LZ7d9olNw604LWppb0HKvDYOjAlAvuOhpa5Mca76Dw7FLwR6zg3SdpHhqEzdsfVPsrLoreXGGkBqTbEUv9SsasQj9HbfQPST87BM3DAYHnmHb0NwrWQ0gEoyio5WyQSt6h0ZAjUl2XDwAq7E8f57K9ykVltzz+ctGYyC5JCkTOTk5akPqqpD5B0kGAxwdQ7hHrEfZ+Vvo6u3HwMAA+nu7cOfmRRRtSICtqXyhd2B8Grr6BuhrqOsmhh6UrQ5XeOGHHYobmjEw0IJMM/ni7MkrKxO6PDvsOFyLrh5JWrrbbuPojiS4uDij9GY37t+thbd0CZDSc1xjcaerFwMd9Qic0i9uzLCr+jYGBrpxIOYjL7NgcmHm4I5tByvQ1tUjyXd/H7o6mlFxKA/eNqYKDcPnr4hKdiCwVjkR+aVspDGQ/FIGJvES4BANzG8NEEiSVntOtdoEKPMbKAux/AgkCSQJJIkGiAbUaIBAUo1xFmKrSPJEPDWigelpgECSQJJ4EUQDRANqNEAgqcY4pMWdXotL7EXstRA1QCBJIEm8CKIBogE1GiCQVGOchdgqkjwRb49oYHoaIJAkkCReBNEA0YAaDRBIqjEOaXGn1+ISexF7LUQNEEgSSBIvgmiAaECNBggk1RhnIbaKJE/E2yMamJ4GCCQJJIkXQTRANKBGAwSSaoxDWtzptbjEXsReC1EDBJIaDEkmRwuOXgHw97AHh634IlpS2RdiZSd5+jRdE0hqMCSdonajvX8YQ333sN3LgnS5NFgLBKCTA1RzIMlkw8DMBp6+AQgODqZDUGAAHCwNwZynlcPELw5bMzKwPiYEeiry4BIUiYyMDKREmKsEoM/ea5I3iIsFqNtmr/Iaja48TBZsfJchOWU93B1U21Cj7aNCcwvRHn+zkm4CvxAzp5gnnpk7NmfloaAgH3l5ecjLy0chtZVDXi5S44Kgz5HvXa1431z+7ra5BgKxGPcqdsFMhWA/tseNsb0/zjRcx7W6E3Az1yeQHG9DjhbWV7dAONKPtZGT7F0+/h7y/4LT0d+0tXWgpaW94DI2Hm56hrZYGrIYHm6usLe3g529I7x8g5G2PQ9FBbmI8pZsWjX+vrn8/1+F5FzO25xIG1cbX9V1QMwnkJwT5fGFGiCNgSRVyEzm+HEHFhwWr0RBYRF2rA5QcX789XPr/0+CpI4RPH184efnpxC8Yast3wdHsULo6lvB29cPrjbmYDNZMLV1ho+P5F5vdyfoabGVGlgWVwvOnr7w8XSGDpcBbZ4lPLx86Lh8fbxgbaw3yfAGE3rGlnD38oEvlTZfX3h7uMLUYGIDrqNnDx8/P7jYmIHFZMPCzhk+vpI0eblNTJMsP0w2B5b2LmPp9/X1gauDNXQUd87kaMPR3Vtim8DF2HW9G2LBEPK3rlWwlyR9FoaKemBCS98IDs5u8PaW29fHywNWJgbKeebqwdXLBx5OltDSMZbk2ccbVgY6YHK04eDqCT8/X7jamynsNaQHF/q5PjDXU4yX+m4Ed18qTg8YqwAJR8cITm5e8JXaiLItlS5bo3HpUnGvzHaa/KlRkFRV0OY+EcgvLML2BB/NgKTzCrQN8emdFandFSWhH/scrZVgJ7OVR1AuBgRi3DhQgNScE7jT2QchvQujGILhPlwqL4QbV15p9cxsUd/Ox2B7PSJT1qH+eguG+UI6HpFwFO2NtVjtP7Fr774iE+cbW+lrRdJ0CUYGcOtSBWK9eEppc/M7ikGxGJcOF2FTwSncu99P73hI5YU/1IsLJ7fDkjlutp5jja2HzqGlq38s/SKREIPdbajctwmGXCnsjZ1Qfqt3nH1kdpJ/ikQCbI2S55thaIHc8svo6huEQCDZuZJKj5A/jNbGC9i42FkOSgtfXOkcxL2Kvcg4egFDfCFEQgHulWbCdWUB2geoXR1F6G+9inAP2QZnfrjYR9lxEBv9FOKlwRaDJtrGdxE/DnSmHpEoO9+I3iE+RCJp+kUiCPmDqEiJVoDw+GeS/2V1QKMhyWLrIjjuKxTkZyPUbf4NzH+SJ2nsgrSsXOTn5SN/VzHudA9DLP44JAcH+yDgC9HReBFH9u7GrgMnx7ZoLc9bAS2ply6DJH90BEMjIxjpbUXFyQPYVbQXVTc6IBSL0XnlGOwNZB4oE46RaegeEUA40osLZcexu7AQu/YeQn1TBz2xNNJxEcts5aCUQXJooAd8gQhtNxpQvLsIew+fQdsAtX2tAKe2BoMrBQaby8P2I1chFInQ39aIU4f2oyAvH8WnKtHRTzUYQpw/uBo8CvY6xojduF1in4Ii1LUMQCwcRXXpEckxym5UyM1DkJMCSEyssa/qIqorSnHy0F76msKd+1B5vhF8kQiCrgsINJLmmYbkEPr7ejE03IuzR4txs4cP8dA9NHUOo7WhBGer70IoHMHRpDDpLo7ThyTHwBTFV+6Dapwa6ytxcHcR8vMKsGvPAZw4fRp5UUvIDpHjGhUZGBU/NQ6S5o6e8PTygv+iZUjeuAW5+QVYG+YO7Xm4TlAGycH7d3D2dAlKxoULtySQuXN8kkkHI0eUNfVMCZJikQh3zuXAjacr9YhY8AvPQR9fjP47FTA3lHTXZZCkvaie60gMMANb2p3Vt/VATeswRCNd+MrfkfYOmSwDHL85CJFgGMUbo6HPkXmATOgYmqPofDfEIgHqilZDS/ocGSSp402nt8KapyNJE5MD/+ijGBCK0X3zJGyk3WGT5UXoFYgx2HEVYR7GYEkrBpPNhb3PNrSOiCAYaEOU/zhvejpjkiw2eIY8cMfSLwEoV9cGp24P0iDet0Y6pCGFJGWjuxU7YMBlYmttDygbD7TWwdtGF76RqRgS8FG3IVYK++lD0tgyFveGxRhqv4BA23HDKUwm2GxZQ6UA+ylAQxEgmvBd4yAZsbEIRUXykL1lPZb4e8BQh6PUpZsPhS+DJFXZ1IWZgOTAnUq4GyhXNG17L1y5Pwzx4D0sspDsyS2DpGjkPjYvH7esiM3DlqM3IRKPompNIA02lnsmesRiDHXUI0BpjE9Scb2WZWBQIEbf9QPQ15FUahkku6+Vwk5fOU1G9m642c2HoKcRzjZUV5WL9LMttH1qdy4bA6SsfJlcPewovweRaBjH44KVz08HkjK4MNkwNLWDT0AgAqWhoL6Ljr88O0iiMRkkR9oQ7ywZethcTTVWYpRnSTxg74g1GOT/NUjqm/rjUicfopFeFGelwtPWFKwJ4/IEkDItTPapcZDk6uhCV1cXuvo82LkFICVtGwoLC7BjXRj02PNLMDJItlUfhKuxMYzHhZTdZXR3dSYg2V63C4ba4+xj6oqq5j6IRzuRbGVCA0AGSUHvbfg7G49reHSQnFcNoViIy/lL6DFgnbSTNBzuNxSo7PoZ+YSic1AAced5GOhJJnFkkLxbkj9hooJp6YRKqos8cI+e+GAwbHGyiRpjHMW+aN1x6aHyo4VVW6shEAtxbVc02IoQmQYkmSw2bJauRd31FvT09mNoeBjD0jAqHaOsyA1WhmT3FRgb6NDHZJDcuVqyFG0mIMlgaiFiw0F0D4yCHn/t68G9mxeQvz4KVqY8cBQnrGSQJ58TNKJxkBzfWrD0TJGwOR9FRfmIcDOYYKDx18+l/2WQ/NR1koxpdLdVQtLEFVV3pwnJ3CoIRXJIWm4rpSHZWZen0va6XkvRNsiHuPPilCDJsHBChRIknVB6tx9i8Qj2RqqGZPymCghEQlzdE/XJkDQP2Yjm/lF6/PJm9SlkpKUgJUUSTt3so/OoCpJGEyApGW6YEUjSwGPDJTAK6TsPo6m1DwLp5M1Axw0UJAeODYXMJV3PtbRoPCSpAvGIy6C74KuDbVVW1LlWaLL0fGlIajv44VrXCIS9TXAxl0ysqPUk2YbYcfQW3bU9EeEn6W6vOUwviO+/XQJnhVlyWR5dlq5G/6gI/TePTOhuq/IkTez90dQrwOj9K3CyorqyZth/pZuGVGmGtXQ8Ve4RMzm6SD9BpWkEJ2Mn624PIDlqknFdqee16dx9Oo7rp7bCRIuttFJiXVn7DEDSB+d7qNntIaQFyNNP2YnLW4XmSWa3ZXakPF2ulhbMnJdiz9krGBGKIOpvRJiR8nCF7HryKbcxgSTHBHFb8lBUlItgWy6B5LjulmwJkCpP0n31Pnp5UOeVYpjoSbqJ6iCpb7IEFzpH4IPtaAAAF0ZJREFUwe9vQbin5LfiLMNINA2KIRzpxtZQhWUydDq0sO30HdrzrM9PmjBxowqSXmlnMCIUo6V2D8y0JEIPKKqnZ9XvXz8JJ2PlyQod13hc7+VDMNiOlQHjVjhwtLDuzF2IhMM4vGap8njlODvtv0ZNzohxJnOxgoaYMHZdiob2oRmApB7O3KMgKUJBonyCiatnjKySK9IlUBOXAKmCHVPHHxfvU2kawq5QSXdf1XXkmEQ/GgNJrp4RLMwtxoKllTUcnNwRt24rCgvzkRa/aGwZy3wRx6d4knrG5vD28YUvFZbGoOEe1RUdRElMpOSYjw8cTeS/Z5dBsvtmGcKX+MHNxQnOrp5YmpiJe0MiiPm9yI71GvPQZJAUDrZj45pweLu7wcnRGT6Lo3Hiehc9U335yAaYyMZ/mSys2X+R7gaOdl3DxqgQ+Pv6wj9gCTYdaqC9zMGOKwh3l6+tlI1Jdpw/jQh/P7g5O8LZzQuhqYXoHBZCNHwfW8Msx2Cla7gU9e3DEIv4uFWejeDFgXRegyJXo57qmov4uHzkK5iMAx+DwUZ46kmMisQQdN9AakQwvcjdPyAYCdv2YWWg3NtYd6aVHv+9f/0MooP94ecfiBXp+9HaNwqxtIv717rbDCQeaaJh33bpOJYv9oN/4DLsrm6GUDSC4VFq8k4ZkjzDGGzevAYhwUEI8POTlK9fIBI2FqFvVAh+31WE6cwvx+BL1E2NgaTlkiTk5OSMhdy8PBQWFqGoMB/JEb4wGPfLkS9RGNON81MgGZiQjt4B2aTCCARCauGzCPyREelEQz/OrokYW2QsgyTlJQlHR9DX240easG0UATB0H0UrQyErsJkhwyS9Gy7kI+h/j50d/dgeFQAkZCPq8e2wIo3zpvTd8LeunbJyzZEQoyODGNklE//P9x5AxFetkqTOjJIUnEIqDT1SNIkFIowOtCGrEg/aCkCj8mG1fINaOmn1lCKIRTw6bzyqbyLBLh6MhPW49IkKwttY1+U3ZaMKYqEQowMD2OUXvzNV1pMbuIejqYuahG4JE0jI5LF26OdV5FTeXcGPEkGTNyXo6lnZCyOUb6AXjpVUbgKZXepvClD0sy6kF45IBYJ5eUrTZdgpAeZiYvIbLeiTib5rjGQNHJdgpUrV46FmBUrsDTIBxaG87e7YRmajoOHDyN/QywMVBSwb3QyDh06jJwk+VirU8ByHCg+jMOHJwsHkbbYa6xrKYPkUEcTThw9RN936FAxCrZ/BS+biRMhMkiKRvtRfuaEJJ5Dh7B/Zx5WBrmOwVcGIdkntWbRLzIZO/cdkMRx8AByNiXCwWCipyODZH9LE04ckuTjUHEx8retg4fJxJ8xyuLQMXFAypY8HCiW5OPA7kKsWuo5tuhcdt34T66OGRLTcsbuK96/G5vWLIcpR+5JMhhMmLssQWbRPtrmh4sPIGvjKtgac2Eal0HnaUOUs8S75dliW9E+HN61DXpSTy40fRd9zcoAycSNjUcg9h08iE3BvkoNhI5zCLJ37qfjKN63C6tDPWibrs0+iMOHc+GroANtPQ8kb87G/n0HUDxmpwPI354KH1MyFjm+nCf7X2MgOZkByHHFij7xuwySqsYkVdlOBknVS4AmPl/VMz52TAZJVWOSH7uXnJ+ZMtAkOxJIKrS8mlTwU80rgSSBylS1slCvI5AkkByb4FAlcgJJAklVutCkYwSSBJIEkkQDajWgSUBUlVcCSVJB1FYQJpNFvwiBzZ76m9upFydQYeL7O2fGKxtLE2vqaVIlfnJsZspjoduRQJJAUi0kF3oFIPkjoPyYBggkCSQJJIkGiAbUaIBAUo1xPtbCkPPECyEaWPgaIJAkkCReBNEA0YAaDRBIqjEO8RIWvpdAypiU8cc0QCBJIEm8CKIBogE1GiCQVGOcj7Uw5DzxQogGFr4GCCQJJIkXQTRANKBGAwSSaoyz0L0E6s07ti5e8HC2Bocl26Vw4XsGC71cSf5mVsMEkhoMSYfQPDT3DGCg+xbSXcyIN6HBWiBgnRysmgtJrj5cPb3h4+MDL3cX6KrYX2WuC8d1aRzWrFnz8ZCwXMVbtxnw2X9d8qJbsRANmeO2fyXAII0G0QCtAQ2FJBN2vpHIpd5MXlSE3G0bYGUweUsyV2GZVtEBPp8/FgRCag8UMag3aAsUjvO76+GjQvAmzktQfaMZd6+ehbfV/Nopcq6WCUnX/KtHHyszjYQky8gJqTvykZ+dhh058xeSjovCEbsyVhriULD/GgRiEdovHEFinOx4LGIjl8BQBSQ/Jg5yfuFVeFKm0y9TjYMki2OA8PUZKMzbjlA3S6Rsm7+QVBY8E8sTKsEXi3D7RCq0lbYWUBCGlgEcnV3g6uqqEJxgzuWo7V6ytXRhbecIFxfF++Tfbc3Gb7PAhJ6hGRycXOh7XFycYWdlBi3OxAkirq4BnJxdYG9pCOoNP7pG5nB0kqTR2ckBpiq2cFDOu0L+SGOgthyJ3aavFY2DpKVXBHIKCrAuOhC6+jwkaxoknVegbYhPd8uprrkk9GOfo3yb0vEViantjcJTdbjfPywdw5TdJ/+8uEv5foewNFRfbcYwn9poTAyRSIi+zrs4VbAWumxlUNoExaJ3RIhbx9fDLWYrqq+1YITeR1oMIX8YTeePYbGjHqn8pAH4IhrQKEhyeI5IzSpEbkYSbPUYoDwYjYOkqTu2Fe7G3j17sffgcdzrGYZYPDkkmdpmyKm8A5FYiPu3GrArcyvSMwpokAkpyA61Ys/W9YjwkW35yoZFWIZ0a9duVB/fh7SvUpG95zhudw5CJOLjbH4kdFjyFl0Gya62Dgzz+ehpvYHyk4ex78Bx3LzXSwP24q4kaCvcMx7k5H+5PYktZtYWmgNJjj5C125CUd4OLLIzplskjYSkojdi5Iiyph61kLTyWoL2QSFGOy/Cx14+uaNt5IGaDmqiSIi8VXIvks0zx+FLnRALBnBk7VIFsLFg7x2HO/1CjNy/Dn8H+bMkkJR4nC01++BjzRvzGDzCU9DPF2Gw8Qj0dZS3oiUwmFkYEHuqtqfGQNLKMxzZBUVIjQmENltiDALJj0NybI+bhl0w1FEWUXp1J92VPrx12RjUzGwS0DoixmBrPTysxo1T6hojr6YNYkE/8oI8wJQCWwZJfstZOGgrj40aOXhh76FjOFzwFXTm4d7oBDzKmpmP9tAISLKNXJGelY+CjNWw0Ja/8p9A8uOQdPCLQdewGEP3zsFGYU9rFtcBpS3U2KYQRYmWY5C0STiJYbEY/R03sScnBznZiqEQddc7IBIPo2Sp3wRIjjQdgy4B4Zgt5yNQFmKaFzwkmRwdhMSnobAgD0mRQXB3cx8LXj7+SM8qQl5WBpb4UcddYWUwXzdtn+Ls9jS72xwjOxy70gGxWIDG0gIkREdgeWQ88k+dh0AkxkjrGbjpyb0F2/Xl4I9NCMknduSTRNQxAsmFCJOFmqcFD0m2Dg+Jm3LpReMFBQUYHwqLJAvKJcdzEOduMk9b8tmBJNfYHSVNPRDwRzE8yqcXqY/yBRCMDqPtRg2CnZR/zmjlsQd9IjHuXzkJDyMeeDxVwQC6HPn4oqy7TTxJeWOzUIEzH/O14CFJvcTBztkDnp6eE4KPfyA2ZRchPysDSwOp8x6wMdQmkFTwNn0SD2NIKEL14WwsXR6JuLh4xMfFITo8BDZG48YcGQwYWQbgZq8AI91NiHScWoNDIEngOJfhueAhqc74mjgmqcszkS8IDwxHXXMfxOJBnApfKl1c7gIbQ/2x8cLFGfUQiMW4WbYbi3x94OPtAy8Pd/paZ0c7GOuNG57QNkT6icsQikQYabuEr1YugoebG329u6cPFi2LR/6uLNgbycFAICm3hTq9knP/v70zb2vqaMP4NyELIQQImyHiBkEJi4jYUpcWxVcpSqsWqyK0WLEhiSjRy15UC2Jp66uIW1WsbQ2LWlRIINtZvs/9XichC9leoSwnyfPHXJHj5JyZZ+75zTMzT86sjZ0IkikWTF7bdBrvbXOYm53D3JwDDCuE3nBwOxy+a7MzGDr6KeTz3uS68kaM292+gHCWA8uy8Lic3rz26bf4+9ljnG+uRbokKODM/C24PDwJluPBC7Ccv7fTw4LjeNjG72BbQTA/QTJoCwKh+GyR0pCUKZSoP3AEhw/sRV5YeEviiVUC/c5vMHBrED1f70F6jMBr3a796L95C7duxUoD6KyvhmwekgW1TXj21gHXzCR+HhhA/41+b7o5OISHLybgZjiwc6/wuc4XexqwmzwT1ftP4ErfAAYHfc/66cfrMJxtQYk6GGEg5M8v24G+/kH0XzwJhXzh/wXuF7IEQNfEB5JkbpOUhmQyN+zy1E2Jq88d4DgOV8/UQBriLaalSaDMKccNIXCcZzFyIRhQvjzPJhCQHcWhAYIkeShxNqpq8MjGgedYXDq2KQySacjZtBePX82CZ+dg2a2Jcx9xiJ2gQ+2wFA0QJAmSceAmx9khq3dt0T03jdFHI+iz9KDnkgVDd55gatoBjmXw8m4PijKDIT1LESJ9hwAmVg0QJAmScSCZBlWeDl+2X8Ho5Du4PcHgcJZxYuKPhzCcasGWgshQILEKnspFMF6sBgiSBMm4kPQKSiJFukKBjIyMBUmRLodkwToldcDFdkDKL37NECQJkv8fkmQjslEKa4AgmcKNT16M+L0YaqO1byOCJEGSvCTSAGkgjgYIknGMI+ZRXKFQQKVSpUQqLCzEk6e/B5Lw0oxUqTvVc+01TpAkSIoeOATJtQdFKsOaIEmQJEimiEeeyqD7N3UnSCYBJIXQHIlEkrQpKysrMNUWpt1yuRB6lLz1pbqJq20JkkkCSTGvn/7bsgleQOiapCzkhb3/9t70/bXfPRZ7GxAkCZKi39kkSBLI1hKkBEmCJEEyQTWwluBIpWcTJBO0g4SGAAlrkisl2vJdFzDL8Hg90gN1xtp4NORJro3dV0pTiXbflIGkUr0OLSfb0d4ePR39eEPgyIJEaEQBkiX6r/Dk7zGMjY3BarXGSH/hpxO6JUM0cO42QXLJNkwEPVEZYw9EKQPJzHyt79AvQxe+6ehAR1g6+vHGhINkWVUHphwsWA8LRjjBcEFiwXmPdnXjTmv5kjs4QTJ25yGwpIZtUg6SZ1r2IFuhgOCJhaZ0WWIdGyCUPTs7D0Xa9dBoNFCr1SGpELuNI3DzPOzj96BfryJIJuiyCoF47UGccpA8daQeGUnQYQRI+gNkw9ck11UewpidAc9Ow3RQFzjUK7TDKQpL0HL2Ai73WmDp7YW5qwN1uqIIbzrUk8xVpaPysy9g6umFxWKBubMVG9WZEQDW721Cb+9FHN1UhDR5NuoPn4b5kgUWSy+MnSeh14QPSHJoqz9Ga9t36Lko5POVyXi+DTtLNciiEKAIG4e2Jf17ZUFKkExQYMaCpEy1HsZ7E+A4D55cOw65VBLWwWTYsv04nr6xg2EY2KffY3raDoZlwdjf4MKhMihC3hHph+Q/TwbQ88soXAwLj9sFl4f1Huvg+OcBGsI81cZzP4DlHBg63IyLt61wC99xOeFyM97vTFtvY8f6kM0m/R5M2JzekxgZtwsOhwMut8d7to579hUuNdZQnGSC6jQZAE6QTFDxRYekDHu//QVOlod9Yhh1mkgvT1W2B6NvneB5B26ea0C5rhSlZeX49OQVvPXwcE9b0VClDYDVD0mPxwOWcePB1U58tKMGNbX7cOtvu/eo2Sc/nkGWLDia+yDJYtbmAue2YaDrKGprqlFb34BfXkyD55wYPLY96LWW1qDvsgH/adiNutrtqKqqwvaaj9D2w0Pvmd8e+yhBMkF1SpBMoIbzb9yYzWaYTCZvMnR1obW5AbpNWihk4R5XsNOLsaGjQTK/ognjsx5wrvfoaoxyeqFUgSMX7oHhGPx5rXWhlylNR5P5AVjOg/vfNUI+7036Icl55nDr/EGoQ7zMjZUtmHKycL15hKJ8ZQCsPkjy8MxY0XagPHA8bVqaHIe+7QfD8Rgf/GTBW82jvuFcko+hSdbr8dIvbsStRzH2keUqU8p4khnZBTjYfBzHj/lS69dn8L3BCJPJDFP3BbTUlwQ9mwSAfzgkJVIVzHdfg+MYPDCegCpimp0GmTIL157bwblncHyfHlKpdEGqPHAKDg8H29OLgfOv/ZB8+/sP0GQuXEuUFuvx8I0DvOsNDhXlR0ByYnAPpGG2zMpbB51Oh83asM0kiRQyZR6qdjXiVFs72ufTyKSHIBlmw+Xq/HSfDxt4UgaS0QQhkSpQWtuILqMJZmMn9OrE8SYXQFKZi7qOIXh4Hrax26gujn5yYYYqGw+neHCMG389e4h7w/cWpEejVjAsD9eLa1Ck+4Doh+SbaHGS+WW4M2YD757CMW0kJMcGagPgjGZ//zVFnhatvb9ixsF4p++8N3QpeOiYsHZKnuSHdWi/Telz+eyV0pD0CkmSicY2A4RpeGOV+oM6tRgEGArJzXWfY3zGA9b5Gm3lmpgesUKVjftTvnO0bTPvMDU1FTVNDJsiPMmokCwow91xO3j3PzgSxZP8IEhKpNhvGoGH4+B6Z0X3V/uxubgQwjskhXTthYs8SfIk17RfEiQlWTjY3g2z2YR9upAdV5ELMwBJdSWuPHsHjucwbPkCSnlsb1imVKF31AbO+R7N9SUfJLx4nmSurg4vZzxwv32KjQXB6bN/TfJDICmRSjFgZSCseX7fvD1iet7zzEmQFLkWxeA0rGQZUhqSEmk61ld/CoPRhO7OFmgSaPPGC8mcPLRcug2W4zBnHUBF2JphhHCkCjR3D4PhWPzZ3w5trirM65RAmZOLTKUiANBYkJQostDQfd8bsP7H4Dlky4PTm8VC8rdXHDj3LDoObQuUJyOnCJ913MAcyxMkCZIBPUZoehVskzKQVBWUYGfdLuyaT5/s/QzNLSdw3mBEd1cHarYE19TWoiEW+0wBkptrTmNs1gOeZzE1+l9c77seNXUf2xgQmXrDPoy+d4PnGEyO3sf1K5dh+N4Ag7EH12/cxL3Hj3F4jz6Q3w9J5/tx3LCY8PXxFnzZ+g36fn4AO8ODn/0Tn5cVBPIL9VgMJNMkUpz+9bXXE3738hGumo24aOnD8LMJeFhhaYAguVhtUP7ggL0ctkgZSK6raILBbPauPXrDgIzd6Dr3LU5+cRjlUeIJl8O4K3kPAZLbO0e8cYThGx3hf1tvVIVATIK86iYM3n8B25zLG7DNcRw4joXHYcfY82Hs2xGcim/YegR3Rl9iZtYFlhXy+fK6ZqfxfOQmGksLA96fv76LgmRaGjTbduPBi9dwMZxv44ZlYJsax51rZ3D29iR5kqvgLfnbjj4jAZsykFRkr8OG4mJotVpvKirSIF+dszBWMIHEKEAyf5MeO+vqUFtbi8rKypipbENkULlclYstum0h36lAeVkJCnMzwqAngSInH5tLt0JfEXzGttJNUGdG30VXa4q99y3bGFynjN/5JMjK12KrvsJXngo9thQXQi6TQL15K3bW7aTd7QTSZvy2joSQ2POnDCTF3hCLLZ93TXL+AKvw324v9l5iz0/vk0w8sIhdU4spH0EyQUdogiSBYzEdnfIuXS8ESYJkyHrl0oW0kp2QPElxtstKtrmY7k2QTAJIKpXKpD5iNSt74ZGy6enpSV1fOlKWjpQVveciplEsVllCp9v+90om66fwy5vQnyXm5OQE3qWZrHWmeqlE08bkSSaBJ5nsHYogKR5gJLvWotWPIEmQFM2IHU2gwjWCJEEyljZW4zpBMkEhGWsanqzXQ9fpkrWOVC9xblARJAmStEZMGiANxNEAQTKOcWhkF+fITu1C7bKaGiBIEiTJiyANkAbiaIAgGcc4qzla0bPIOyINiFMDBEmCJHkRpAHSQBwNECTjGIdGdnGO7NQu1C6rqQGCJEGSvAjSAGkgjgYIknGMs5qjFT2LvCPSgDg18D8zKXgEy/fu4QAAAABJRU5ErkJggg==)\n",
    "\n",
    "You will also create a **obj.data** file and fill it in like this (change your number of classes accordingly, as well as your backup location)\n",
    "\n",
    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)\n",
    "\n",
    "This backup path is where we will save the weights to of our model throughout training. Create a backup folder in your google drive and put its correct path in this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1269,
     "status": "ok",
     "timestamp": 1624993304405,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "4IgyVjb9vwTq",
    "outputId": "755e9418-e70c-4251-8e79-468dcbc63adb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp: cannot stat '/mydrive/Object_Detection/obj.names': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "# upload the obj.names and obj.data files to cloud VM from Google Drive\n",
    "!cp /mydrive/Object_Detection/obj.names ./data\n",
    "!cp /mydrive/Object_Detection/obj.data  ./data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1411,
     "status": "ok",
     "timestamp": 1624993339605,
     "user": {
      "displayName": "Murtuza Dahodwala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiKZLaxDvS1_H82rK00RkB207Vvfm1_-1OB57JEurE=s64",
      "userId": "15494468085790008302"
     },
     "user_tz": -330
    },
    "id": "JxLx-p6fJin6",
    "outputId": "0ba2af05-78fd-42b2-a7c8-acc13a6eb8d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classes = 6\n",
      "train = data/train.txt\n",
      "test = data/test.txt\n",
      "names = data/obj.names\n",
      "backup = /content/gdrive/MyDrive/Object_Detection/backup"
     ]
    }
   ],
   "source": [
    "!cat ./data/obj.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zIA38VpCU1y9"
   },
   "source": [
    "# Generating Train and Test files\n",
    "The last configuration files needed before we can begin to train our custom detector are the train.txt and test.txt files which hold the relative paths to all our training images and valdidation images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rrKzIx19n94g"
   },
   "outputs": [],
   "source": [
    "%cd /content/drive/MyDrive/Object_Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4mgiK3C2ZF0k"
   },
   "outputs": [],
   "source": [
    "# upload the generate_train.py and generate_test.py script to cloud VM from Google Drive\n",
    "!cp /mydrive/Object_Detection/generate_train.py generate_train.py\n",
    "!cp /mydrive/Object_Detection/generate_test.py generate_test.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eq9gDuAaJWM6"
   },
   "source": [
    "Now simply run both scripts to do the work for you of generating the two txt files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DAKjJSJwJb5R"
   },
   "outputs": [],
   "source": [
    "!python generate_train.py\n",
    "!python generate_test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3-9DvAePoCsk"
   },
   "outputs": [],
   "source": [
    "%cd /content/darknet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G4c0_6_XoDit"
   },
   "outputs": [],
   "source": [
    "!cp /mydrive/yolov4/data/test.txt ./data\n",
    "!cp /mydrive/yolov4/data/train.txt  ./data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nv5yDQptaJRE"
   },
   "outputs": [],
   "source": [
    "# verify that the newly generated train.txt and test.txt can be seen in our darknet/data folder\n",
    "!ls data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5mFOT8pC6Qbg"
   },
   "source": [
    "If you are uncertain whether generating the files worked and want to double check that everything went as planned, double click on **train.txt** on the left side File Explorer and it should look like this.\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "It will contain one line for each training image path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nrkC4l-PbmNS"
   },
   "source": [
    "# Training Yolo model.\n",
    "We will be using the YoloV4 weights, it helps your custom object detector to be way more accurate and not have to train as long. You don't have to use these weights but trust me it will help your model converge and be accurate way faster. USE IT!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "POozxsvFdXTu"
   },
   "source": [
    "##  Training Custom Object Detector!\n",
    "The time has finally come! You have made it to the moment of truth! You are now ready to train your custom YOLOv4 object detector on whatever crazy classes you have decided on. So run the following command. (-dont_show flag stops chart from popping up since Colab Notebook can't open images on the spot, -map flag overlays mean average precision on chart to see how accuracy of your model is, only add map flag if you have a validation dataset)\n",
    "```\n",
    "!./darknet detector train <path to obj.data> <path to custom config> yolov4.conv.137 -dont_show -map\n",
    "```\n",
    "**TIP:** This training could take several hours depending on how many iterations you chose in the .cfg file. You will want to let this run as you sleep or go to work for the day, etc. However, Colab Cloud Service kicks you off it's VMs if you are idle for too long (30-90 mins).\n",
    "\n",
    "To avoid this hold (CTRL + SHIFT + i) at the same time to open up the inspector view on your browser.\n",
    "\n",
    "Paste the following code into your console window and hit **Enter**\n",
    "```\n",
    "function ClickConnect(){\n",
    "console.log(\"Working\"); \n",
    "document\n",
    "  .querySelector('#top-toolbar > colab-connect-button')\n",
    "  .shadowRoot.querySelector('#connect')\n",
    "  .click() \n",
    "}\n",
    "setInterval(ClickConnect,60000)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "imc0NP19hLuq"
   },
   "outputs": [],
   "source": [
    "# train your custom detector! (uncomment %%capture below if you run into memory issues or your Colab is crashing)\n",
    "# %%capture\n",
    "!./darknet detector train data/obj.data cfg/yolov4-obj.cfg yolov4.conv.137 -dont_show -map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5GjIkYFjhnYL"
   },
   "source": [
    "After training, you can observe a chart of how your model did throughout the training process by running the below command. It shows a chart of your average loss vs. iterations. For your model to be 'accurate' you should aim for a loss under 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uXPGYKmchv7Z"
   },
   "outputs": [],
   "source": [
    "# show chart.png of how custom object detector did with training\n",
    "imShow('chart.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "28HHdjQOG1QL"
   },
   "source": [
    "Here is what the chart.png should look like after an uninterrupted training! If you stop training or it crashes during training your chart will look like the above one but don't worry you can still check accuracy of your model in the next steps.\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VghTZN6yyyq0"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8q2gyKSujCxr"
   },
   "source": [
    "# Training Yolo model from a checkpoint\n",
    "If for some reason you get an error or your Colab goes idle during training, you have not lost your partially trained model and weights! Every 100 iterations a weights file called **yolov4-obj_last.weights** is saved to **mydrive/yolov4/backup/** folder (wherever your backup folder is). This is why we created this folder in our Google drive and not on the cloud VM. If your runtime crashes and your backup folder was in your cloud VM you would lose your weights and your training progress.\n",
    "\n",
    "We can kick off training from our last saved weights file so that we don't have to restart! WOOHOO! Just run the following command but with your backup location.\n",
    "```\n",
    "!./darknet detector train data/obj.data cfg/yolov4-obj.cfg /mydrive/yolov4/backup/yolov4-obj_last.weights -dont_show -map\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Fl7PsmikjCBW"
   },
   "outputs": [],
   "source": [
    "# kick off training from where it last saved\n",
    "!./darknet detector train data/obj.data cfg/yolov4-obj.cfg /mydrive/Object_Detection/backup/yolov4-obj_last.weights -dont_show -map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "voia4UtxlPMa"
   },
   "source": [
    "# Model Evaluation\n",
    "### Checking the Mean Average Precision (mAP) of Your Model\n",
    "If you didn't run the training with the '-map- flag added then you can still find out the mAP of your model after training. Run the following command on any of the saved weights from the training to see the mAP value for that specific weight's file. I would suggest to run it on multiple of the saved weights to compare and find the weights with the highest mAP as that is the most accurate one!\n",
    "\n",
    "**NOTE:** If you think your final weights file has overfitted then it is important to run these mAP commands to see if one of the previously saved weights is a more accurate model for your classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2UoSBmTFlNep"
   },
   "outputs": [],
   "source": [
    "!./darknet detector map data/obj.data cfg/yolov4-obj.cfg /mydrive/Object_Detection/backup/yolov4-obj_last.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8dfPY2h39m-T"
   },
   "source": [
    "# Running our Custom Object Detector\n",
    "You have done it! You now have a custom object detector to make your very own detections. Time to test it out and have some fun!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zGvxCZX7z4f0"
   },
   "outputs": [],
   "source": [
    "# need to set our custom cfg to test mode \n",
    "%cd cfg\n",
    "!sed -i 's/batch=64/batch=1/' yolov4-obj.cfg\n",
    "!sed -i 's/subdivisions=16/subdivisions=1/' yolov4-obj.cfg\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o7IIzEal90dA"
   },
   "outputs": [],
   "source": [
    "# run your custom detector with this command (upload an image to your google drive to test, thresh flag sets accuracy that detection must be in order to show it)\n",
    "!./darknet detector test data/obj.data cfg/yolov4-obj.cfg /content/drive/MyDrive/Object_Detection/backup/yolov4-obj_best.weights /content/drive/MyDrive/Object_Detection/mangoes.jpg -thresh 0.3\n",
    "imShow('predictions.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K7OhwmjYvkCv"
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "count = 0\n",
    "for i in glob.glob(\"data/test/*.jpg\"):\n",
    "  !./darknet detector test data/obj.data cfg/yolov4-obj.cfg /content/drive/MyDrive/Object_Detection/backup/yolov4-obj_last.weights {i} -thresh 0.3\n",
    "  filename = f'{count}.jpg'\n",
    "  !mv predictions.jpg /content/drive/MyDrive/Object_Detection/predictions/{filename}\n",
    "  print(count,i)\n",
    "  count += 1"
   ]
  }
 ],
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