YOLOv4_Blood_Cell_Count_and_Detection_(BCCD)_Darknet_Roboflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOv4 Blood Cell Count and Detection (BCCD)「YOLOv4-Darknet-Roboflow.ipynb」的副本",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JosephBless/DL/blob/main/YOLOv4_Blood_Cell_Count_and_Detection_(BCCD)_Darknet_Roboflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNVU7eu9CQj3"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "\n",
        "In this notebook, we implement [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training on your own dataset.\n",
        "\n",
        "We also recommend reading our blog post on [Training YOLOv4 on custom data](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) side by side.\n",
        "\n",
        "We will take the following steps to implement YOLOv4 on our custom data:\n",
        "* Configure our GPU environment on Google Colab\n",
        "* Install the Darknet YOLOv4 training environment\n",
        "* Download our custom dataset for YOLOv4 and set up directories\n",
        "* Configure a custom YOLOv4 training config file for Darknet\n",
        "* Train our custom YOLOv4 object detector\n",
        "* Reload YOLOv4 trained weights and make inference on test images\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/L0n564N.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai) \n",
        "\n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDTvGt2zt7cm"
      },
      "source": [
        "# Configuring cuDNN on Colab for YOLOv4\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-bguKWgtxSx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfaf746c-83b2-4680-f72d-be5d8e74efd4"
      },
      "source": [
        "# CUDA: Let's check that Nvidia CUDA drivers are already pre-installed and which version is it.\n",
        "!/usr/local/cuda/bin/nvcc --version\n",
        "# We need to install the correct cuDNN according to this output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJYM7-_Had0Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42ff538b-47d0-43da-f12d-7970ab8364a8"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Oct 11 17:41:24 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.74       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_qFPIBgvlkn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35a6f0bc-94a0-4f90-914b-94ca2957db02"
      },
      "source": [
        "# This cell ensures you have the correct architecture for your respective GPU\n",
        "# If you command is not found, look through these GPUs, find the respective\n",
        "# GPU and add them to the archTypes dictionary\n",
        "\n",
        "# Tesla V100\n",
        "# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "\n",
        "# Tesla K80 \n",
        "# ARCH= -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores\n",
        "# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]\n",
        "\n",
        "# Jetson XAVIER\n",
        "# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]\n",
        "\n",
        "# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4\n",
        "# ARCH= -gencode arch=compute_61,code=sm_61\n",
        "\n",
        "# GP100/Tesla P100 - DGX-1\n",
        "# ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:\n",
        "# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]\n",
        "\n",
        "# For Jetson Tx2 or Drive-PX2 uncomment:\n",
        "# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]\n",
        "import os\n",
        "os.environ['GPU_TYPE'] = str(os.popen('nvidia-smi --query-gpu=name --format=csv,noheader').read())\n",
        "\n",
        "def getGPUArch(argument):\n",
        "  try:\n",
        "    argument = argument.strip()\n",
        "    # All Colab GPUs\n",
        "    archTypes = {\n",
        "        \"Tesla V100-SXM2-16GB\": \"-gencode arch=compute_70,code=[sm_70,compute_70]\",\n",
        "        \"Tesla K80\": \"-gencode arch=compute_37,code=sm_37\",\n",
        "        \"Tesla T4\": \"-gencode arch=compute_75,code=[sm_75,compute_75]\",\n",
        "        \"Tesla P40\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P4\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P100-PCIE-16GB\": \"-gencode arch=compute_60,code=sm_60\"\n",
        "\n",
        "      }\n",
        "    return archTypes[argument]\n",
        "  except KeyError:\n",
        "    return \"GPU must be added to GPU Commands\"\n",
        "os.environ['ARCH_VALUE'] = getGPUArch(os.environ['GPU_TYPE'])\n",
        "\n",
        "print(\"GPU Type: \" + os.environ['GPU_TYPE'])\n",
        "print(\"ARCH Value: \" + os.environ['ARCH_VALUE'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU Type: Tesla P100-PCIE-16GB\n",
            "\n",
            "ARCH Value: -gencode arch=compute_60,code=sm_60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3nkYzWwMuBk"
      },
      "source": [
        "## STEP 1. Install cuDNN according to the current CUDA version\n",
        "Colab added cuDNN as an inherent install - so you don't have to do a thing - major win\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16pvdFMa1FEe"
      },
      "source": [
        "# Step 2: Installing Darknet for YOLOv4 on Colab\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9uY-38P93oz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b09643c-61ae-4f12-c2d4-fca2acb53e44"
      },
      "source": [
        "%cd /content/\n",
        "%rm -rf darknet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQEktcfj9y9O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8af0068-0d65-4f9a-d34e-f281ded53d13"
      },
      "source": [
        "#we clone the fork of darknet maintained by roboflow\n",
        "#small changes have been made to configure darknet for training\n",
        "!git clone https://github.com/roboflow-ai/darknet.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 13289, done.\u001b[K\n",
            "remote: Total 13289 (delta 0), reused 0 (delta 0), pack-reused 13289\u001b[K\n",
            "Receiving objects: 100% (13289/13289), 12.13 MiB | 9.35 MiB/s, done.\n",
            "Resolving deltas: 100% (9105/9105), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9O6dTiq5ga0I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "173c969f-f053-45bc-d26e-1fb3a18d97e9"
      },
      "source": [
        "%cd /content/darknet/\n",
        "%rm Makefile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OyTAyEhOgd__",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2662bbb-2e94-48ae-ee81-2e89e166b9b0"
      },
      "source": [
        "#colab occasionally shifts dependencies around, at the time of authorship, this Makefile works for building Darknet on Colab\n",
        "\n",
        "%%writefile Makefile\n",
        "GPU=1\n",
        "CUDNN=1\n",
        "CUDNN_HALF=0\n",
        "OPENCV=1\n",
        "AVX=0\n",
        "OPENMP=0\n",
        "LIBSO=1\n",
        "ZED_CAMERA=0\n",
        "ZED_CAMERA_v2_8=0\n",
        "\n",
        "# set GPU=1 and CUDNN=1 to speedup on GPU\n",
        "# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher\n",
        "# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)\n",
        "# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above\n",
        "# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X\n",
        "\n",
        "USE_CPP=0\n",
        "DEBUG=0\n",
        "\n",
        "ARCH= -gencode arch=compute_35,code=sm_35 \\\n",
        "      -gencode arch=compute_50,code=[sm_50,compute_50] \\\n",
        "      -gencode arch=compute_52,code=[sm_52,compute_52] \\\n",
        "\t    -gencode arch=compute_61,code=[sm_61,compute_61] \\\n",
        "      -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "OS := $(shell uname)\n",
        "\n",
        "VPATH=./src/\n",
        "EXEC=darknet\n",
        "OBJDIR=./obj/\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "LIBNAMESO=libdarknet.so\n",
        "APPNAMESO=uselib\n",
        "endif\n",
        "\n",
        "ifeq ($(USE_CPP), 1)\n",
        "CC=g++\n",
        "else\n",
        "CC=gcc\n",
        "endif\n",
        "\n",
        "CPP=g++ -std=c++11\n",
        "NVCC=nvcc\n",
        "OPTS=-Ofast\n",
        "LDFLAGS= -lm -pthread\n",
        "COMMON= -Iinclude/ -I3rdparty/stb/include\n",
        "CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC\n",
        "\n",
        "ifeq ($(DEBUG), 1)\n",
        "#OPTS= -O0 -g\n",
        "#OPTS= -Og -g\n",
        "COMMON+= -DDEBUG\n",
        "CFLAGS+= -DDEBUG\n",
        "else\n",
        "ifeq ($(AVX), 1)\n",
        "CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a\n",
        "endif\n",
        "endif\n",
        "\n",
        "CFLAGS+=$(OPTS)\n",
        "\n",
        "ifneq (,$(findstring MSYS_NT,$(OS)))\n",
        "LDFLAGS+=-lws2_32\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENCV), 1)\n",
        "COMMON+= -DOPENCV\n",
        "CFLAGS+= -DOPENCV\n",
        "LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`\n",
        "COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENMP), 1)\n",
        "CFLAGS+= -fopenmp\n",
        "LDFLAGS+= -lgomp\n",
        "endif\n",
        "\n",
        "ifeq ($(GPU), 1)\n",
        "COMMON+= -DGPU -I/usr/local/cuda/include/\n",
        "CFLAGS+= -DGPU\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN), 1)\n",
        "COMMON+= -DCUDNN\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cuda/include\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcudnn\n",
        "else\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cudnn/include\n",
        "LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN_HALF), 1)\n",
        "COMMON+= -DCUDNN_HALF\n",
        "CFLAGS+= -DCUDNN_HALF\n",
        "ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "endif\n",
        "\n",
        "ifeq ($(ZED_CAMERA), 1)\n",
        "CFLAGS+= -DZED_STEREO -I/usr/local/zed/include\n",
        "ifeq ($(ZED_CAMERA_v2_8), 1)\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "endif\n",
        "endif\n",
        "\n",
        "OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o\n",
        "ifeq ($(GPU), 1)\n",
        "LDFLAGS+= -lstdc++\n",
        "OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o\n",
        "endif\n",
        "\n",
        "OBJS = $(addprefix $(OBJDIR), $(OBJ))\n",
        "DEPS = $(wildcard src/*.h) Makefile include/darknet.h\n",
        "\n",
        "all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "CFLAGS+= -fPIC\n",
        "\n",
        "$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp\n",
        "\t$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)\n",
        "\n",
        "$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)\n",
        "endif\n",
        "\n",
        "$(EXEC): $(OBJS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)\n",
        "\n",
        "$(OBJDIR)%.o: %.c $(DEPS)\n",
        "\t$(CC) $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cpp $(DEPS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cu $(DEPS)\n",
        "\t$(NVCC) $(ARCH) $(COMMON) --compiler-options \"$(CFLAGS)\" -c $< -o $@\n",
        "\n",
        "$(OBJDIR):\n",
        "\tmkdir -p $(OBJDIR)\n",
        "backup:\n",
        "\tmkdir -p backup\n",
        "results:\n",
        "\tmkdir -p results\n",
        "setchmod:\n",
        "\tchmod +x *.sh\n",
        "\n",
        ".PHONY: clean\n",
        "\n",
        "clean:\n",
        "\trm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing Makefile\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyMBDkaL-Aep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de677f6e-d017-44ee-f706-88483cec925a"
      },
      "source": [
        "#install environment from the Makefile\n",
        "#note if you are on Colab Pro this works on a P100 GPU\n",
        "#if you are on Colab free, you may need to change the Makefile for the K80 GPU\n",
        "#this goes for any GPU, you need to change the Makefile to inform darknet which GPU you are running on.\n",
        "#note the Makefile above should work for you, if you need to tweak, try the below\n",
        "%cd /content/darknet/\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/g' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile\n",
        "!sed -i \"s/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= ${ARCH_VALUE}/g\" Makefile\n",
        "!make"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n",
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:910:23:\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 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:1391:14:\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:1367:9:\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:1371:9:\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:1375:9:\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:1378:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n",
            " #define INVALID_SOCKET (-1)\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n",
            " #define INVALID_SOCKET -1\n",
            " \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:249:21:\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:507:113:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2039:15:\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:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/utils.c -o obj/utils.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\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[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\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:276:64:\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:276:82:\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:286:37:\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:307:43:\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:322:64:\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:328:63:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1204:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\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:77:5:\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:77:5:\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:77:5:\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:77:5:\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:77:5:\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:287:5:\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:287:5:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1134:24:\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:984:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:610:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1573:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:451:29:\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:544:21:\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:657:13:\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:655:13:\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:1276:15:\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:1275:15:\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:1792:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:191:9:\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 (l.x_gpu)                   cuda_free(l.x_gpu);  l.x_gpu = NULL;\n",
            "         \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/layer.c:191:61:\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",
            "         if (l.x_gpu)                   cuda_free(l.x_gpu);  \u001b[01;36m\u001b[Kl\u001b[m\u001b[K.x_gpu = NULL;\n",
            "                                                             \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:138:9:\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:831:13:\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:1261:49:\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:1261:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/demo.c -o obj/demo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:58:9:\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:57:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:61:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c: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.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:95:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:104:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:371:25:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:460:25:\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",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1686): 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:1082:5:\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_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\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:359:7:\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",
            "\u001b[01m\u001b[K./src/network_kernels.cu:368:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kim\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[Kimage im;\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K \n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC ./obj/image_opencv.o ./obj/http_stream.o ./obj/gemm.o ./obj/utils.o ./obj/dark_cuda.o ./obj/convolutional_layer.o ./obj/list.o ./obj/image.o ./obj/activations.o ./obj/im2col.o ./obj/col2im.o ./obj/blas.o ./obj/crop_layer.o ./obj/dropout_layer.o ./obj/maxpool_layer.o ./obj/softmax_layer.o ./obj/data.o ./obj/matrix.o ./obj/network.o ./obj/connected_layer.o ./obj/cost_layer.o ./obj/parser.o ./obj/option_list.o ./obj/darknet.o ./obj/detection_layer.o ./obj/captcha.o ./obj/route_layer.o ./obj/writing.o ./obj/box.o ./obj/nightmare.o ./obj/normalization_layer.o ./obj/avgpool_layer.o ./obj/coco.o ./obj/dice.o ./obj/yolo.o ./obj/detector.o ./obj/layer.o ./obj/compare.o ./obj/classifier.o ./obj/local_layer.o ./obj/swag.o ./obj/shortcut_layer.o ./obj/activation_layer.o ./obj/rnn_layer.o ./obj/gru_layer.o ./obj/rnn.o ./obj/rnn_vid.o ./obj/crnn_layer.o ./obj/demo.o ./obj/tag.o ./obj/cifar.o ./obj/go.o ./obj/batchnorm_layer.o ./obj/art.o ./obj/region_layer.o ./obj/reorg_layer.o ./obj/reorg_old_layer.o ./obj/super.o ./obj/voxel.o ./obj/tree.o ./obj/yolo_layer.o ./obj/gaussian_yolo_layer.o ./obj/upsample_layer.o ./obj/lstm_layer.o ./obj/conv_lstm_layer.o ./obj/scale_channels_layer.o ./obj/sam_layer.o ./obj/convolutional_kernels.o ./obj/activation_kernels.o ./obj/im2col_kernels.o ./obj/col2im_kernels.o ./obj/blas_kernels.o ./obj/crop_layer_kernels.o ./obj/dropout_layer_kernels.o ./obj/maxpool_layer_kernels.o ./obj/network_kernels.o ./obj/avgpool_layer_kernels.o src/yolo_v2_class.cpp -o libdarknet.so -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:370:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:385:34:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     if (\u001b[01;35m\u001b[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:409:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -o uselib src/yolo_console_dll.cpp -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++ -L ./ -l:libdarknet.so\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n",
            "              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n",
            "     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n",
            "                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n",
            "                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n",
            "               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::detection_data_t()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:398:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::exit_flag\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Kexit_flag\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:396:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kbool main(int, char**)::detection_data_t::new_detection\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     bool \u001b[01;35m\u001b[Knew_detection\u001b[m\u001b[K;\n",
            "                          \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:401:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kdetection_data_t\u001b[m\u001b[K() : exit_flag(false), new_detection(false) {}\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGPDEjfAALrQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78268389-c08e-4bb0-a2d1-42c0c300d726"
      },
      "source": [
        "#download the newly released yolov4 ConvNet weights\n",
        "%cd /content/darknet\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n",
            "--2021-10-11 17:42:34--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/75388965/48bfe500-889d-11ea-819e-c4d182fcf0db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211011T174234Z&X-Amz-Expires=300&X-Amz-Signature=200b74f87451386efc887976d8b9c1a1026ed8e58c289000d837216ffe12b381&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.conv.137&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-10-11 17:42:34--  https://github-releases.githubusercontent.com/75388965/48bfe500-889d-11ea-819e-c4d182fcf0db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20211011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20211011T174234Z&X-Amz-Expires=300&X-Amz-Signature=200b74f87451386efc887976d8b9c1a1026ed8e58c289000d837216ffe12b381&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.conv.137&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 170038676 (162M) [application/octet-stream]\n",
            "Saving to: ‘yolov4.conv.137’\n",
            "\n",
            "yolov4.conv.137     100%[===================>] 162.16M  58.6MB/s    in 2.8s    \n",
            "\n",
            "2021-10-11 17:42:37 (58.6 MB/s) - ‘yolov4.conv.137’ saved [170038676/170038676]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWOiKj37l4wW"
      },
      "source": [
        "# Set up Custom Dataset for YOLOv4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbniFj-eSimL"
      },
      "source": [
        "We'll use Roboflow to convert our dataset from any format to the YOLO Darknet format. \n",
        "\n",
        "1. To do so, create a free [Roboflow account](https://app.roboflow.ai).\n",
        "2. Upload your images and their annotations (in any format: VOC XML, COCO JSON, TensorFlow CSV, etc).\n",
        "3. Apply preprocessing and augmentation steps you may like. We recommend at least `auto-orient` and a `resize` to 416x416. Generate your dataset.\n",
        "4. Export your dataset in the **YOLO Darknet format**.\n",
        "5. Copy your download link, and paste it below.\n",
        "\n",
        "See our [blog post](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) for greater detail.\n",
        "\n",
        "In this example, I used the open source [BCCD Dataset](https://public.roboflow.ai/object-detection/bccd). (You can `fork` it to your Roboflow account to follow along.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiQvussOcXdJ",
        "outputId": "68eb9ba2-ae96-43d5-a944-2aee56ea6a2a"
      },
      "source": [
        "#follow the link below to get your download code from from Roboflow\n",
        "!pip install -q roboflow\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(model_format=\"darknet\", notebook=\"roboflow-yolov4\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▉                              | 10 kB 36.0 MB/s eta 0:00:01\r\u001b[K     |███▋                            | 20 kB 39.2 MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 30 kB 32.6 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 40 kB 21.2 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 51 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 61 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 71 kB 9.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 81 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 92 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 102 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▏           | 112 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 122 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 133 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 143 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 153 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 163 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 174 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 178 kB 8.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 51.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 138 kB 60.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 636 kB 44.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.0 MB/s \n",
            "\u001b[?25h  Building wheel for roboflow (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "upload and label your dataset, and get an API KEY here: https://app.roboflow.com/?model=yolov5&ref=roboflow-yolov5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cdj4tmT5Cmdl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b66a52d4-880c-41cb-bdac-5ea46dcbe193"
      },
      "source": [
        "# from roboflow import Roboflow\n",
        "# rf = Roboflow(api_key=\"YOUR_API_KEY\")\n",
        "# project = rf.workspace().project(\"YOUR_PROJECT\")\n",
        "# dataset = project.version(\"YOUR_VERSION\").download(\"darknet\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in BCCD-9 to darknet: 100% [8010974 / 8010974] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to BCCD-9 in darknet:: 100%|██████████| 735/735 [00:00<00:00, 1719.02it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "cwW1B9kFfnmk",
        "outputId": "77dc877a-f496-4ede-be17-acd91785b29d"
      },
      "source": [
        "dataset.location"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/darknet/BCCD-9'"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiCILEbs1NII",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b24929b4-b48b-4221-8c49-f7df01e8404d"
      },
      "source": [
        "#Set up training file directories for custom dataset\n",
        "%cd /content/darknet/\n",
        "%cp {dataset.location}/train/_darknet.labels data/obj.names\n",
        "%mkdir data/obj\n",
        "#copy image and labels\n",
        "%cp {dataset.location}/train/*.jpg data/obj/\n",
        "%cp {dataset.location}/valid/*.jpg data/obj/\n",
        "\n",
        "%cp {dataset.location}/train/*.txt data/obj/\n",
        "%cp {dataset.location}/valid/*.txt data/obj/\n",
        "\n",
        "with open('data/obj.data', 'w') as out:\n",
        "  out.write('classes = 3\\n')\n",
        "  out.write('train = data/train.txt\\n')\n",
        "  out.write('valid = data/valid.txt\\n')\n",
        "  out.write('names = data/obj.names\\n')\n",
        "  out.write('backup = backup/')\n",
        "\n",
        "#write train file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/train.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir(dataset.location + '/train') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')\n",
        "\n",
        "#write the valid file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/valid.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir(dataset.location + '/valid') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HtRqO3QvjkP"
      },
      "source": [
        "# Write Custom Training Config for YOLOv4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_WJcqHhpeVr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a9d7188-0a16-433e-d451-59afd1b1ef6c"
      },
      "source": [
        "#we build config dynamically based on number of classes\n",
        "#we build iteratively from base config files. This is the same file shape as cfg/yolo-obj.cfg\n",
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len(dataset.location + '/train/_darknet.labels')\n",
        "print(\"writing config for a custom YOLOv4 detector detecting number of classes: \" + str(num_classes))\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line max_batches to (classes*2000 but not less than number of training images, and not less than 6000), f.e. max_batches=6000 if you train for 3 classes\n",
        "#change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400\n",
        "if os.path.exists('./cfg/custom-yolov4-detector.cfg'): os.remove('./cfg/custom-yolov4-detector.cfg')\n",
        "\n",
        "\n",
        "with open('./cfg/custom-yolov4-detector.cfg', 'a') as f:\n",
        "  f.write('[net]' + '\\n')\n",
        "  f.write('batch=64' + '\\n')\n",
        "  #####smaller subdivisions help the GPU run faster. 12 is optimal, but you might need to change to 24,36,64####\n",
        "  f.write('subdivisions=24' + '\\n')\n",
        "  f.write('width=416' + '\\n')\n",
        "  f.write('height=416' + '\\n')\n",
        "  f.write('channels=3' + '\\n')\n",
        "  f.write('momentum=0.949' + '\\n')\n",
        "  f.write('decay=0.0005' + '\\n')\n",
        "  f.write('angle=0' + '\\n')\n",
        "  f.write('saturation = 1.5' + '\\n')\n",
        "  f.write('exposure = 1.5' + '\\n')\n",
        "  f.write('hue = .1' + '\\n')\n",
        "  f.write('\\n')\n",
        "  f.write('learning_rate=0.001' + '\\n')\n",
        "  f.write('burn_in=1000' + '\\n')\n",
        "  ######you can adjust up and down to change training time#####\n",
        "  ##Darknet does iterations with batches, not epochs####\n",
        "  # max_batches = num_classes*2000\n",
        "  max_batches = 2000\n",
        "  f.write('max_batches=' + str(max_batches) + '\\n')\n",
        "  f.write('policy=steps' + '\\n')\n",
        "  steps1 = .8 * max_batches\n",
        "  steps2 = .9 * max_batches\n",
        "  f.write('steps='+str(steps1)+','+str(steps2) + '\\n')\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line classes=80 to your number of objects in each of 3 [yolo]-layers:\n",
        "#change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.\n",
        "\n",
        "  with open('cfg/yolov4-custom2.cfg', 'r') as f2:\n",
        "    content = f2.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 0,1,2' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom3.cfg', 'r') as f3:\n",
        "    content = f3.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 3,4,5' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom4.cfg', 'r') as f4:\n",
        "    content = f4.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 6,7,8' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "    \n",
        "  with open('cfg/yolov4-custom5.cfg', 'r') as f5:\n",
        "    content = f5.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)\n",
        "\n",
        "print(\"file is written!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "writing config for a custom YOLOv4 detector detecting number of classes: 3\n",
            "file is written!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2LAciMh4Cut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b7b3930-4b9b-496f-fc8d-d103ffedefbd"
      },
      "source": [
        "#here is the file that was just written. \n",
        "#you may consider adjusting certain things\n",
        "\n",
        "#like the number of subdivisions 64 runs faster but Colab GPU may not be big enough\n",
        "#if Colab GPU memory is too small, you will need to adjust subdivisions to 16\n",
        "%cat cfg/custom-yolov4-detector.cfg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[net]\n",
            "batch=64\n",
            "subdivisions=24\n",
            "width=416\n",
            "height=416\n",
            "channels=3\n",
            "momentum=0.949\n",
            "decay=0.0005\n",
            "angle=0\n",
            "saturation = 1.5\n",
            "exposure = 1.5\n",
            "hue = .1\n",
            "\n",
            "learning_rate=0.001\n",
            "burn_in=1000\n",
            "max_batches=2000\n",
            "policy=steps\n",
            "steps=1600.0,1800.0\n",
            "scales=.1,.1\n",
            "\n",
            "#cutmix=1\n",
            "mosaic=1\n",
            "\n",
            "#:104x104 54:52x52 85:26x26 104:13x13 for 416\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "# Downsample\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -1,-7\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "# Downsample\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -1,-10\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "# Downsample\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -1,-28\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "# Downsample\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -1,-28\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "# Downsample\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=1024\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[shortcut]\n",
            "from=-3\n",
            "activation=linear\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "[route]\n",
            "layers = -1,-16\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=1024\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=mish\n",
            "\n",
            "##########################\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=1024\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "### SPP ###\n",
            "[maxpool]\n",
            "stride=1\n",
            "size=5\n",
            "\n",
            "[route]\n",
            "layers=-2\n",
            "\n",
            "[maxpool]\n",
            "stride=1\n",
            "size=9\n",
            "\n",
            "[route]\n",
            "layers=-4\n",
            "\n",
            "[maxpool]\n",
            "stride=1\n",
            "size=13\n",
            "\n",
            "[route]\n",
            "layers=-1,-3,-5,-6\n",
            "### End SPP ###\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=1024\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[upsample]\n",
            "stride=2\n",
            "\n",
            "[route]\n",
            "layers = 85\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1, -3\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[upsample]\n",
            "stride=2\n",
            "\n",
            "[route]\n",
            "layers = 54\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1, -3\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=256\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=256\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "##########################\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=256\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=24\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 0,1,2\n",
            "anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401\n",
            "classes=3\n",
            "num=9\n",
            "jitter=.3\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "scale_x_y = 1.2\n",
            "iou_thresh=0.213\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "max_delta=5\n",
            "\n",
            "\n",
            "[route]\n",
            "layers = -4\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "filters=256\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1, -16\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=24\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 3,4,5\n",
            "anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401\n",
            "classes=3\n",
            "num=9\n",
            "jitter=.3\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "scale_x_y = 1.1\n",
            "iou_thresh=0.213\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "max_delta=5\n",
            "\n",
            "\n",
            "[route]\n",
            "layers = -4\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "filters=512\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1, -37\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=1024\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=1024\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "filters=1024\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=24\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 6,7,8\n",
            "anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401\n",
            "classes=3\n",
            "num=9\n",
            "jitter=.3\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=1\n",
            "scale_x_y = 1.05\n",
            "iou_thresh=0.213\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "max_delta=5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWrG9EGamSpH"
      },
      "source": [
        "# Train Custom YOLOv4 Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6miYFbvExqMd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c68ef852-9ef5-4489-bc93-098b8c37950c"
      },
      "source": [
        "!./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg yolov4.conv.137 -dont_show -map\n",
        "#If you get CUDA out of memory adjust subdivisions above!\n",
        "#adjust max batches down for shorter training above"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " Prepare additional network for mAP calculation...\n",
            " compute_capability = 600, cudnn_half = 0 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 24, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n",
            "   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   3 route  1 \t\t                           ->  208 x 208 x  64 \n",
            "   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n",
            "   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n",
            "   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   9 route  8 2 \t                           ->  208 x 208 x 128 \n",
            "  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n",
            "  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n",
            "  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  13 route  11 \t\t                           ->  104 x 104 x 128 \n",
            "  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  22 route  21 12 \t                           ->  104 x 104 x 128 \n",
            "  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n",
            "  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n",
            "  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  26 route  24 \t\t                           ->   52 x  52 x 256 \n",
            "  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  53 route  52 25 \t                           ->   52 x  52 x 256 \n",
            "  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n",
            "  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n",
            "  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  57 route  55 \t\t                           ->   26 x  26 x 512 \n",
            "  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  84 route  83 56 \t                           ->   26 x  26 x 512 \n",
            "  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n",
            "  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  88 route  86 \t\t                           ->   13 x  13 x1024 \n",
            "  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 103 route  102 87 \t                           ->   13 x  13 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n",
            " 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n",
            " 109 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n",
            " 111 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n",
            " 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n",
            " 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n",
            " 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            " 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n",
            " 119 route  85 \t\t                           ->   26 x  26 x 512 \n",
            " 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 121 route  120 118 \t                           ->   26 x  26 x 512 \n",
            " 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n",
            " 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n",
            " 129 route  54 \t\t                           ->   52 x  52 x 256 \n",
            " 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 131 route  130 128 \t                           ->   52 x  52 x 256 \n",
            " 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 138 conv     24       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  24 0.033 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   52 x  52 x 128 \n",
            " 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n",
            " 142 route  141 126 \t                           ->   26 x  26 x 512 \n",
            " 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 149 conv     24       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  24 0.017 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   26 x  26 x 256 \n",
            " 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n",
            " 153 route  152 116 \t                           ->   13 x  13 x1024 \n",
            " 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 160 conv     24       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  24 0.008 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 59.578 \n",
            "avg_outputs = 490041 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "custom-yolov4-detector\n",
            " compute_capability = 600, cudnn_half = 0 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 2, batch = 48, time_steps = 1, train = 1 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n",
            "   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   3 route  1 \t\t                           ->  208 x 208 x  64 \n",
            "   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n",
            "   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n",
            "   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   9 route  8 2 \t                           ->  208 x 208 x 128 \n",
            "  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n",
            "  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n",
            "  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  13 route  11 \t\t                           ->  104 x 104 x 128 \n",
            "  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  22 route  21 12 \t                           ->  104 x 104 x 128 \n",
            "  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n",
            "  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n",
            "  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  26 route  24 \t\t                           ->   52 x  52 x 256 \n",
            "  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  53 route  52 25 \t                           ->   52 x  52 x 256 \n",
            "  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n",
            "  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n",
            "  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  57 route  55 \t\t                           ->   26 x  26 x 512 \n",
            "  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  84 route  83 56 \t                           ->   26 x  26 x 512 \n",
            "  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n",
            "  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  88 route  86 \t\t                           ->   13 x  13 x1024 \n",
            "  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 103 route  102 87 \t                           ->   13 x  13 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n",
            " 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n",
            " 109 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n",
            " 111 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n",
            " 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n",
            " 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n",
            " 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            " 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n",
            " 119 route  85 \t\t                           ->   26 x  26 x 512 \n",
            " 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 121 route  120 118 \t                           ->   26 x  26 x 512 \n",
            " 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n",
            " 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n",
            " 129 route  54 \t\t                           ->   52 x  52 x 256 \n",
            " 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 131 route  130 128 \t                           ->   52 x  52 x 256 \n",
            " 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 138 conv     24       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  24 0.033 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   52 x  52 x 128 \n",
            " 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n",
            " 142 route  141 126 \t                           ->   26 x  26 x 512 \n",
            " 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 149 conv     24       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  24 0.017 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   26 x  26 x 256 \n",
            " 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n",
            " 153 route  152 116 \t                           ->   13 x  13 x1024 \n",
            " 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 160 conv     24       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  24 0.008 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 59.578 \n",
            "avg_outputs = 490041 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from yolov4.conv.137...\n",
            " seen 64, trained: 0 K-images (0 Kilo-batches_64) \n",
            "Done! Loaded 137 layers from weights-file \n",
            "Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005\n",
            "Resizing, random_coef = 1.40 \n",
            "\n",
            " 608 x 608 \n",
            " Create 6 permanent cpu-threads \n",
            " try to allocate additional workspace_size = 53.23 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 1: 1859.324829, 1859.324829 avg loss, 0.000000 rate, 6.064052 seconds, 48 images, -1.000000 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 2: 1853.530640, 1858.745361 avg loss, 0.000000 rate, 5.811369 seconds, 96 images, 3.367266 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 3: 1856.115234, 1858.482300 avg loss, 0.000000 rate, 6.011846 seconds, 144 images, 3.365847 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBnwpBV5ZXxQ"
      },
      "source": [
        "# Infer Custom Objects with Saved YOLOv4 Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzoJQQw8Zdco"
      },
      "source": [
        "#define utility function\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.rcParams['figure.figsize'] = [10, 5]\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3dJB6NZv4kh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e44f0cd-1f92-4ff7-a1a9-822280aa9919"
      },
      "source": [
        "#check if weigths have saved yet\n",
        "#backup houses the last weights for our detector\n",
        "#(file yolo-obj_last.weights will be saved to the build\\darknet\\x64\\backup\\ for each 100 iterations)\n",
        "#(file yolo-obj_xxxx.weights will be saved to the build\\darknet\\x64\\backup\\ for each 1000 iterations)\n",
        "#After training is complete - get result yolo-obj_final.weights from path build\\darknet\\x64\\bac\n",
        "!ls backup\n",
        "#if it is empty you haven't trained for long enough yet, you need to train for at least 100 iterations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "custom-yolov4-detector_1000.weights  custom-yolov4-detector_final.weights\n",
            "custom-yolov4-detector_2000.weights  custom-yolov4-detector_last.weights\n",
            "custom-yolov4-detector_best.weights\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-_E3O5Mf4Mf"
      },
      "source": [
        "#coco.names is hardcoded somewhere in the detector\n",
        "%cp data/obj.names data/coco.names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjKzw2TvZrOQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b72f6161-7602-4818-e128-9705d8f307b9"
      },
      "source": [
        "\n",
        "#/test has images that we can test our detector on\n",
        "test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "import random\n",
        "img_path = \"test/\" + random.choice(test_images);\n",
        "\n",
        "#test out our detector!\n",
        "!./darknet detect cfg/custom-yolov4-detector.cfg backup/custom-yolov4-detector_final.weights {img_path} -dont-show\n",
        "imShow('/content/darknet/predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  \n",
            " OpenCV version: 3.2.0\n",
            " compute_capability = 700, cudnn_half = 0 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 24, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n",
            "   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   3 route  1 \t\t                           ->  208 x 208 x  64 \n",
            "   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n",
            "   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n",
            "   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n",
            "   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n",
            "   9 route  8 2 \t                           ->  208 x 208 x 128 \n",
            "  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n",
            "  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n",
            "  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  13 route  11 \t\t                           ->  104 x 104 x 128 \n",
            "  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n",
            "  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n",
            "  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "  22 route  21 12 \t                           ->  104 x 104 x 128 \n",
            "  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n",
            "  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n",
            "  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  26 route  24 \t\t                           ->   52 x  52 x 256 \n",
            "  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            "  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n",
            "  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  53 route  52 25 \t                           ->   52 x  52 x 256 \n",
            "  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n",
            "  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n",
            "  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  57 route  55 \t\t                           ->   26 x  26 x 512 \n",
            "  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            "  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n",
            "  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  84 route  83 56 \t                           ->   26 x  26 x 512 \n",
            "  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n",
            "  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n",
            "  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  88 route  86 \t\t                           ->   13 x  13 x1024 \n",
            "  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            "  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            "  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            "  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            " 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n",
            " 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n",
            " 103 route  102 87 \t                           ->   13 x  13 x1024 \n",
            " 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n",
            " 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n",
            " 109 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n",
            " 111 route  107 \t\t                           ->   13 x  13 x 512 \n",
            " 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n",
            " 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n",
            " 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n",
            " 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            " 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n",
            " 119 route  85 \t\t                           ->   26 x  26 x 512 \n",
            " 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 121 route  120 118 \t                           ->   26 x  26 x 512 \n",
            " 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n",
            " 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n",
            " 129 route  54 \t\t                           ->   52 x  52 x 256 \n",
            " 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 131 route  130 128 \t                           ->   52 x  52 x 256 \n",
            " 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n",
            " 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n",
            " 138 conv     24       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  24 0.033 BF\n",
            " 139 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 140 route  136 \t\t                           ->   52 x  52 x 128 \n",
            " 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n",
            " 142 route  141 126 \t                           ->   26 x  26 x 512 \n",
            " 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n",
            " 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n",
            " 149 conv     24       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  24 0.017 BF\n",
            " 150 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            " 151 route  147 \t\t                           ->   26 x  26 x 256 \n",
            " 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n",
            " 153 route  152 116 \t                           ->   13 x  13 x1024 \n",
            " 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n",
            " 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n",
            " 160 conv     24       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  24 0.008 BF\n",
            " 161 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 59.578 \n",
            "avg_outputs = 490041 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "Loading weights from backup/custom-yolov4-detector_final.weights...\n",
            " seen 64, trained: 96 K-images (1 Kilo-batches_64) \n",
            "Done! Loaded 162 layers from weights-file \n",
            "test/BloodImage_00284_jpg.rf.fb0089e655be201ea27173598ef30825.jpg: Predicted in 11.755000 milli-seconds.\n",
            "RBC: 95%\n",
            "RBC: 100%\n",
            "RBC: 93%\n",
            "RBC: 100%\n",
            "RBC: 67%\n",
            "RBC: 100%\n",
            "RBC: 72%\n",
            "RBC: 100%\n",
            "RBC: 97%\n",
            "WBC: 99%\n",
            "RBC: 100%\n",
            "RBC: 100%\n",
            "RBC: 100%\n",
            "RBC: 100%\n",
            "RBC: 100%\n",
            "RBC: 99%\n",
            "RBC: 30%\n",
            "RBC: 90%\n",
            "Unable to init server: Could not connect: Connection refused\n",
            "\n",
            "(predictions:4089): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m23:24:49.356\u001b[0m: cannot open display: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}