YOLOX.ipynb

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   "source": [
    "# 1. Installing YOLOX Libraries"
   ]
  },
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   "source": [
    "Credit over to Remek Kinas for the following code and his Kaggle notebook enabling YOLOX to be ran without internet: \n",
    "\n",
    "Models: https://www.kaggle.com/remekkinas/yolox-cots-models\n",
    "\n",
    "Code: https://www.kaggle.com/remekkinas/yolox-inference-on-kaggle-for-cots-lb-0-507?scriptVersionId=81625924"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import ast\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from string import Template\n",
    "import json\n",
    "import torch\n",
    "from shutil import copyfile\n",
    "import greatbarrierreef\n",
    "import importlib\n",
    "import random\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from IPython.display import display"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working/yolox-cots-models/yolox-dep\n"
     ]
    }
   ],
   "source": [
    "%cp -r /kaggle/input/yolox-cots-models /kaggle/working/\n",
    "%cd /kaggle/working/yolox-cots-models/yolox-dep"
   ]
  },
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     "text": [
      "Looking in links: ./\r\n",
      "Processing ./pip-21.3.1-py3-none-any.whl\r\n",
      "pip is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./loguru-0.5.3-py3-none-any.whl\r\n",
      "Installing collected packages: loguru\r\n",
      "Successfully installed loguru-0.5.3\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./ninja-1.10.2.3-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl\r\n",
      "Installing collected packages: ninja\r\n",
      "Successfully installed ninja-1.10.2.3\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./onnx-1.8.1-cp37-cp37m-manylinux2010_x86_64.whl\r\n",
      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /opt/conda/lib/python3.7/site-packages (from onnx==1.8.1) (3.10.0.2)\r\n",
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      "Requirement already satisfied: numpy>=1.16.6 in /opt/conda/lib/python3.7/site-packages (from onnx==1.8.1) (1.19.5)\r\n",
      "Installing collected packages: onnx\r\n",
      "Successfully installed onnx-1.8.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\n",
      "Requirement already satisfied: protobuf in /opt/conda/lib/python3.7/site-packages (from onnxruntime==1.8.0) (3.19.1)\r\n",
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      "Requirement already satisfied: numpy>=1.16.6 in /opt/conda/lib/python3.7/site-packages (from onnxruntime==1.8.0) (1.19.5)\r\n",
      "Installing collected packages: onnxruntime\r\n",
      "Successfully installed onnxruntime-1.8.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./onnxoptimizer-0.2.6-cp37-cp37m-manylinux2014_x86_64.whl\r\n",
      "Requirement already satisfied: onnx in /opt/conda/lib/python3.7/site-packages (from onnxoptimizer==0.2.6) (1.8.1)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from onnx->onnxoptimizer==0.2.6) (1.16.0)\r\n",
      "Requirement already satisfied: numpy>=1.16.6 in /opt/conda/lib/python3.7/site-packages (from onnx->onnxoptimizer==0.2.6) (1.19.5)\r\n",
      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /opt/conda/lib/python3.7/site-packages (from onnx->onnxoptimizer==0.2.6) (3.10.0.2)\r\n",
      "Requirement already satisfied: protobuf in /opt/conda/lib/python3.7/site-packages (from onnx->onnxoptimizer==0.2.6) (3.19.1)\r\n",
      "Installing collected packages: onnxoptimizer\r\n",
      "Successfully installed onnxoptimizer-0.2.6\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./thop-0.0.31.post2005241907-py3-none-any.whl\r\n",
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      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.0.0->thop==0.0.31.post2005241907) (3.10.0.2)\r\n",
      "Installing collected packages: thop\r\n",
      "Successfully installed thop-0.0.31.post2005241907\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Looking in links: ./\r\n",
      "Processing ./tabulate-0.8.9-py3-none-any.whl\r\n",
      "tabulate is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pip-21.3.1-py3-none-any.whl -f ./ --no-index\n",
    "!pip install loguru-0.5.3-py3-none-any.whl -f ./ --no-index\n",
    "!pip install ninja-1.10.2.3-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl -f ./ --no-index\n",
    "!pip install onnx-1.8.1-cp37-cp37m-manylinux2010_x86_64.whl -f ./ --no-index\n",
    "!pip install onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl -f ./ --no-index\n",
    "!pip install onnxoptimizer-0.2.6-cp37-cp37m-manylinux2014_x86_64.whl -f ./ --no-index\n",
    "!pip install thop-0.0.31.post2005241907-py3-none-any.whl -f ./ --no-index\n",
    "!pip install tabulate-0.8.9-py3-none-any.whl -f ./ --no-index"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working/yolox-cots-models/YOLOX\n",
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      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->-r requirements.txt (line 13)) (0.4.8)\r\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->-r requirements.txt (line 13)) (3.1.1)\r\n",
      "Requirement already satisfied: tomli>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from setuptools-scm>=4->matplotlib!=3.0.0,>=2.0.0->scikit-image->-r requirements.txt (line 6)) (1.2.2)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Using pip 21.3.1 from /opt/conda/lib/python3.7/site-packages/pip (python 3.7)\r\n",
      "Obtaining file:///kaggle/working/yolox-cots-models/YOLOX\r\n",
      "  Running command python setup.py egg_info\r\n",
      "  running egg_info\r\n",
      "  creating /tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info\r\n",
      "  writing /tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/PKG-INFO\r\n",
      "  writing dependency_links to /tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/dependency_links.txt\r\n",
      "  writing top-level names to /tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/top_level.txt\r\n",
      "  writing manifest file '/tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/SOURCES.txt'\r\n",
      "  reading manifest file '/tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/SOURCES.txt'\r\n",
      "  adding license file 'LICENSE'\r\n",
      "  writing manifest file '/tmp/pip-pip-egg-info-e4l8xrap/yolox.egg-info/SOURCES.txt'\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\r\n",
      "Installing collected packages: yolox\r\n",
      "  Running setup.py develop for yolox\r\n",
      "    Running command /opt/conda/bin/python3.7 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/kaggle/working/yolox-cots-models/YOLOX/setup.py'\"'\"'; __file__='\"'\"'/kaggle/working/yolox-cots-models/YOLOX/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\r\n",
      "    running develop\r\n",
      "    running egg_info\r\n",
      "    creating yolox.egg-info\r\n",
      "    writing yolox.egg-info/PKG-INFO\r\n",
      "    writing dependency_links to yolox.egg-info/dependency_links.txt\r\n",
      "    writing top-level names to yolox.egg-info/top_level.txt\r\n",
      "    writing manifest file 'yolox.egg-info/SOURCES.txt'\r\n",
      "    reading manifest file 'yolox.egg-info/SOURCES.txt'\r\n",
      "    adding license file 'LICENSE'\r\n",
      "    writing manifest file 'yolox.egg-info/SOURCES.txt'\r\n",
      "    running build_ext\r\n",
      "    building 'yolox._C' extension\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc\r\n",
      "    creating /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/cocoeval\r\n",
      "    Emitting ninja build file /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/build.ninja...\r\n",
      "    Compiling objects...\r\n",
      "    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\r\n",
      "    /opt/conda/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\r\n",
      "      EasyInstallDeprecationWarning,\r\n",
      "    /opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\r\n",
      "      setuptools.SetuptoolsDeprecationWarning,\r\n",
      "    [1/2] c++ -MMD -MF /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/cocoeval/cocoeval.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/opt/conda/include/python3.7m -c -c /kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/cocoeval/cocoeval.cpp -o /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/cocoeval/cocoeval.o -O3 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE=\"_gcc\"' '-DPYBIND11_STDLIB=\"_libstdcpp\"' '-DPYBIND11_BUILD_ABI=\"_cxxabi1013\"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++14\r\n",
      "    cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++\r\n",
      "    [2/2] c++ -MMD -MF /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/vision.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/opt/conda/include/python3.7m -c -c /kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/vision.cpp -o /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/vision.o -O3 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE=\"_gcc\"' '-DPYBIND11_STDLIB=\"_libstdcpp\"' '-DPYBIND11_BUILD_ABI=\"_cxxabi1013\"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++14\r\n",
      "    cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++\r\n",
      "    creating build/lib.linux-x86_64-3.7\r\n",
      "    creating build/lib.linux-x86_64-3.7/yolox\r\n",
      "    g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/vision.o /kaggle/working/yolox-cots-models/YOLOX/build/temp.linux-x86_64-3.7/kaggle/working/yolox-cots-models/YOLOX/yolox/layers/csrc/cocoeval/cocoeval.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-3.7/yolox/_C.cpython-37m-x86_64-linux-gnu.so\r\n",
      "    copying build/lib.linux-x86_64-3.7/yolox/_C.cpython-37m-x86_64-linux-gnu.so -> yolox\r\n",
      "    Creating /opt/conda/lib/python3.7/site-packages/yolox.egg-link (link to .)\r\n",
      "    Adding yolox 0.1.0 to easy-install.pth file\r\n",
      "\r\n",
      "    Installed /kaggle/working/yolox-cots-models/YOLOX\r\n",
      "Successfully installed yolox-0.1.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle/working/yolox-cots-models/YOLOX\n",
    "!pip install -r requirements.txt\n",
    "!pip install -v -e . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7259e3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:28:35.777922Z",
     "iopub.status.busy": "2022-01-11T22:28:35.775897Z",
     "iopub.status.idle": "2022-01-11T22:29:00.987607Z",
     "shell.execute_reply": "2022-01-11T22:29:00.987098Z",
     "shell.execute_reply.started": "2022-01-11T18:41:02.582015Z"
    },
    "papermill": {
     "duration": 25.295811,
     "end_time": "2022-01-11T22:29:00.987746",
     "exception": false,
     "start_time": "2022-01-11T22:28:35.691935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working/yolox-cots-models/yolox-dep/cocoapi/PythonAPI\n",
      "python setup.py build_ext --inplace\r\n",
      "running build_ext\r\n",
      "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\r\n",
      "/opt/conda/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /kaggle/working/yolox-cots-models/yolox-dep/cocoapi/PythonAPI/pycocotools/_mask.pyx\r\n",
      "  tree = Parsing.p_module(s, pxd, full_module_name)\r\n",
      "building 'pycocotools._mask' extension\r\n",
      "creating build\r\n",
      "creating build/common\r\n",
      "creating build/temp.linux-x86_64-3.7\r\n",
      "creating build/temp.linux-x86_64-3.7/pycocotools\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "   46 |       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\r\n",
      "      |       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:49:\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[Kfor\u001b[m\u001b[K’\r\n",
      "   46 |       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\r\n",
      "      |                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  166 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  166 |   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  167 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  167 |   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:7:\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]\r\n",
      "  212 |       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\r\n",
      "      |       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:27:\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’\r\n",
      "  212 |       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\r\n",
      "      |                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  220 |   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:22:\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[Kwhile\u001b[m\u001b[K’\r\n",
      "  220 |   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:5:\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]\r\n",
      "  228 |     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\r\n",
      "      |     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:34:\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’\r\n",
      "  228 |     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\r\n",
      "      |                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "creating build/lib.linux-x86_64-3.7\r\n",
      "creating build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "gcc -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> pycocotools\r\n",
      "rm -rf build\r\n",
      "# install pycocotools to the Python site-packages\r\n",
      "python setup.py build_ext install\r\n",
      "running build_ext\r\n",
      "skipping 'pycocotools/_mask.c' Cython extension (up-to-date)\r\n",
      "building 'pycocotools._mask' extension\r\n",
      "creating build\r\n",
      "creating build/common\r\n",
      "creating build/temp.linux-x86_64-3.7\r\n",
      "creating build/temp.linux-x86_64-3.7/pycocotools\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "   46 |       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\r\n",
      "      |       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:49:\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[Kfor\u001b[m\u001b[K’\r\n",
      "   46 |       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\r\n",
      "      |                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  166 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  166 |   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  167 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  167 |   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:7:\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]\r\n",
      "  212 |       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\r\n",
      "      |       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:27:\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’\r\n",
      "  212 |       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\r\n",
      "      |                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  220 |   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:22:\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[Kwhile\u001b[m\u001b[K’\r\n",
      "  220 |   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:5:\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]\r\n",
      "  228 |     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\r\n",
      "      |     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:34:\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’\r\n",
      "  228 |     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\r\n",
      "      |                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "creating build/lib.linux-x86_64-3.7\r\n",
      "creating build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "gcc -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\r\n",
      "running install\r\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\r\n",
      "  setuptools.SetuptoolsDeprecationWarning,\r\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\r\n",
      "  EasyInstallDeprecationWarning,\r\n",
      "running bdist_egg\r\n",
      "running egg_info\r\n",
      "creating pycocotools.egg-info\r\n",
      "writing pycocotools.egg-info/PKG-INFO\r\n",
      "writing dependency_links to pycocotools.egg-info/dependency_links.txt\r\n",
      "writing requirements to pycocotools.egg-info/requires.txt\r\n",
      "writing top-level names to pycocotools.egg-info/top_level.txt\r\n",
      "writing manifest file 'pycocotools.egg-info/SOURCES.txt'\r\n",
      "writing manifest file 'pycocotools.egg-info/SOURCES.txt'\r\n",
      "installing library code to build/bdist.linux-x86_64/egg\r\n",
      "running install_lib\r\n",
      "running build_py\r\n",
      "copying pycocotools/cocoeval.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/coco.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/mask.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/__init__.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "creating build/bdist.linux-x86_64\r\n",
      "creating build/bdist.linux-x86_64/egg\r\n",
      "creating build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/cocoeval.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/coco.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/mask.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/__init__.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/cocoeval.py to cocoeval.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/coco.py to coco.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/mask.py to mask.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/__init__.py to __init__.cpython-37.pyc\r\n",
      "creating stub loader for pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/_mask.py to _mask.cpython-37.pyc\r\n",
      "creating build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\r\n",
      "zip_safe flag not set; analyzing archive contents...\r\n",
      "pycocotools.__pycache__._mask.cpython-37: module references __file__\r\n",
      "creating dist\r\n",
      "creating 'dist/pycocotools-2.0-py3.7-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\r\n",
      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\r\n",
      "Processing pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "creating /opt/conda/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "Extracting pycocotools-2.0-py3.7-linux-x86_64.egg to /opt/conda/lib/python3.7/site-packages\r\n",
      "Adding pycocotools 2.0 to easy-install.pth file\r\n",
      "\r\n",
      "Installed /opt/conda/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "Processing dependencies for pycocotools==2.0\r\n",
      "Searching for matplotlib==3.5.0\r\n",
      "Best match: matplotlib 3.5.0\r\n",
      "Adding matplotlib 3.5.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for Cython==0.29.24\r\n",
      "Best match: Cython 0.29.24\r\n",
      "Adding Cython 0.29.24 to easy-install.pth file\r\n",
      "Installing cygdb script to /opt/conda/bin\r\n",
      "Installing cython script to /opt/conda/bin\r\n",
      "Installing cythonize script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for setuptools==59.1.1\r\n",
      "Best match: setuptools 59.1.1\r\n",
      "Adding setuptools 59.1.1 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for pyparsing==3.0.6\r\n",
      "Best match: pyparsing 3.0.6\r\n",
      "Adding pyparsing 3.0.6 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for kiwisolver==1.3.2\r\n",
      "Best match: kiwisolver 1.3.2\r\n",
      "Adding kiwisolver 1.3.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for fonttools==4.28.2\r\n",
      "Best match: fonttools 4.28.2\r\n",
      "Adding fonttools 4.28.2 to easy-install.pth file\r\n",
      "Installing fonttools script to /opt/conda/bin\r\n",
      "Installing pyftmerge script to /opt/conda/bin\r\n",
      "Installing pyftsubset script to /opt/conda/bin\r\n",
      "Installing ttx script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for python-dateutil==2.8.0\r\n",
      "Best match: python-dateutil 2.8.0\r\n",
      "Adding python-dateutil 2.8.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for packaging==21.0\r\n",
      "Best match: packaging 21.0\r\n",
      "Adding packaging 21.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for cycler==0.11.0\r\n",
      "Best match: cycler 0.11.0\r\n",
      "Adding cycler 0.11.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for numpy==1.19.5\r\n",
      "Best match: numpy 1.19.5\r\n",
      "Adding numpy 1.19.5 to easy-install.pth file\r\n",
      "Installing f2py script to /opt/conda/bin\r\n",
      "Installing f2py3 script to /opt/conda/bin\r\n",
      "Installing f2py3.7 script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for Pillow==8.2.0\r\n",
      "Best match: Pillow 8.2.0\r\n",
      "Adding Pillow 8.2.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for setuptools-scm==6.3.2\r\n",
      "Best match: setuptools-scm 6.3.2\r\n",
      "Adding setuptools-scm 6.3.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for six==1.16.0\r\n",
      "Best match: six 1.16.0\r\n",
      "Adding six 1.16.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for tomli==1.2.2\r\n",
      "Best match: tomli 1.2.2\r\n",
      "Adding tomli 1.2.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Finished processing dependencies for pycocotools==2.0\r\n",
      "rm -rf build\r\n",
      "running install\r\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\r\n",
      "  setuptools.SetuptoolsDeprecationWarning,\r\n",
      "/opt/conda/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\r\n",
      "  EasyInstallDeprecationWarning,\r\n",
      "running bdist_egg\r\n",
      "running egg_info\r\n",
      "writing pycocotools.egg-info/PKG-INFO\r\n",
      "writing dependency_links to pycocotools.egg-info/dependency_links.txt\r\n",
      "writing requirements to pycocotools.egg-info/requires.txt\r\n",
      "writing top-level names to pycocotools.egg-info/top_level.txt\r\n",
      "writing manifest file 'pycocotools.egg-info/SOURCES.txt'\r\n",
      "installing library code to build/bdist.linux-x86_64/egg\r\n",
      "running install_lib\r\n",
      "running build_py\r\n",
      "creating build\r\n",
      "creating build/lib.linux-x86_64-3.7\r\n",
      "creating build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/cocoeval.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/coco.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/mask.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "copying pycocotools/__init__.py -> build/lib.linux-x86_64-3.7/pycocotools\r\n",
      "running build_ext\r\n",
      "skipping 'pycocotools/_mask.c' Cython extension (up-to-date)\r\n",
      "building 'pycocotools._mask' extension\r\n",
      "creating build/common\r\n",
      "creating build/temp.linux-x86_64-3.7\r\n",
      "creating build/temp.linux-x86_64-3.7/pycocotools\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "   46 |       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\r\n",
      "      |       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:46:49:\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[Kfor\u001b[m\u001b[K’\r\n",
      "   46 |       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\r\n",
      "      |                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  166 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:166:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  166 |   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  167 |   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\r\n",
      "      |   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:167:54:\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[Kfor\u001b[m\u001b[K’\r\n",
      "  167 |   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\r\n",
      "      |                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:7:\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]\r\n",
      "  212 |       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\r\n",
      "      |       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:212:27:\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’\r\n",
      "  212 |       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\r\n",
      "      |                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\r\n",
      "  220 |   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:220:22:\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[Kwhile\u001b[m\u001b[K’\r\n",
      "  220 |   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\r\n",
      "      |                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:5:\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]\r\n",
      "  228 |     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\r\n",
      "      |     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\r\n",
      "\u001b[01m\u001b[K../common/maskApi.c:228:34:\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’\r\n",
      "  228 |     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\r\n",
      "      |                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\r\n",
      "gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/numpy/core/include -I../common -I/opt/conda/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\r\n",
      "gcc -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\r\n",
      "creating build/bdist.linux-x86_64\r\n",
      "creating build/bdist.linux-x86_64/egg\r\n",
      "creating build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/cocoeval.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/coco.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/mask.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "copying build/lib.linux-x86_64-3.7/pycocotools/__init__.py -> build/bdist.linux-x86_64/egg/pycocotools\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/cocoeval.py to cocoeval.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/coco.py to coco.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/mask.py to mask.cpython-37.pyc\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/__init__.py to __init__.cpython-37.pyc\r\n",
      "creating stub loader for pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\r\n",
      "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/_mask.py to _mask.cpython-37.pyc\r\n",
      "creating build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying pycocotools.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\r\n",
      "zip_safe flag not set; analyzing archive contents...\r\n",
      "pycocotools.__pycache__._mask.cpython-37: module references __file__\r\n",
      "creating 'dist/pycocotools-2.0-py3.7-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\r\n",
      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\r\n",
      "Processing pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "removing '/opt/conda/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg' (and everything under it)\r\n",
      "creating /opt/conda/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "Extracting pycocotools-2.0-py3.7-linux-x86_64.egg to /opt/conda/lib/python3.7/site-packages\r\n",
      "pycocotools 2.0 is already the active version in easy-install.pth\r\n",
      "\r\n",
      "Installed /opt/conda/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg\r\n",
      "Processing dependencies for pycocotools==2.0\r\n",
      "Searching for matplotlib==3.5.0\r\n",
      "Best match: matplotlib 3.5.0\r\n",
      "Adding matplotlib 3.5.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for Cython==0.29.24\r\n",
      "Best match: Cython 0.29.24\r\n",
      "Adding Cython 0.29.24 to easy-install.pth file\r\n",
      "Installing cygdb script to /opt/conda/bin\r\n",
      "Installing cython script to /opt/conda/bin\r\n",
      "Installing cythonize script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for setuptools==59.1.1\r\n",
      "Best match: setuptools 59.1.1\r\n",
      "Adding setuptools 59.1.1 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for kiwisolver==1.3.2\r\n",
      "Best match: kiwisolver 1.3.2\r\n",
      "Adding kiwisolver 1.3.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for setuptools-scm==6.3.2\r\n",
      "Best match: setuptools-scm 6.3.2\r\n",
      "Adding setuptools-scm 6.3.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for Pillow==8.2.0\r\n",
      "Best match: Pillow 8.2.0\r\n",
      "Adding Pillow 8.2.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for fonttools==4.28.2\r\n",
      "Best match: fonttools 4.28.2\r\n",
      "Adding fonttools 4.28.2 to easy-install.pth file\r\n",
      "Installing fonttools script to /opt/conda/bin\r\n",
      "Installing pyftmerge script to /opt/conda/bin\r\n",
      "Installing pyftsubset script to /opt/conda/bin\r\n",
      "Installing ttx script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for cycler==0.11.0\r\n",
      "Best match: cycler 0.11.0\r\n",
      "Adding cycler 0.11.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for python-dateutil==2.8.0\r\n",
      "Best match: python-dateutil 2.8.0\r\n",
      "Adding python-dateutil 2.8.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for numpy==1.19.5\r\n",
      "Best match: numpy 1.19.5\r\n",
      "Adding numpy 1.19.5 to easy-install.pth file\r\n",
      "Installing f2py script to /opt/conda/bin\r\n",
      "Installing f2py3 script to /opt/conda/bin\r\n",
      "Installing f2py3.7 script to /opt/conda/bin\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for packaging==21.0\r\n",
      "Best match: packaging 21.0\r\n",
      "Adding packaging 21.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for pyparsing==3.0.6\r\n",
      "Best match: pyparsing 3.0.6\r\n",
      "Adding pyparsing 3.0.6 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for tomli==1.2.2\r\n",
      "Best match: tomli 1.2.2\r\n",
      "Adding tomli 1.2.2 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Searching for six==1.16.0\r\n",
      "Best match: six 1.16.0\r\n",
      "Adding six 1.16.0 to easy-install.pth file\r\n",
      "\r\n",
      "Using /opt/conda/lib/python3.7/site-packages\r\n",
      "Finished processing dependencies for pycocotools==2.0\r\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle/working/yolox-cots-models/yolox-dep/cocoapi/PythonAPI\n",
    "\n",
    "!make\n",
    "!make install\n",
    "!python setup.py install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a491d44f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:29:01.090478Z",
     "iopub.status.busy": "2022-01-11T22:29:01.089298Z",
     "iopub.status.idle": "2022-01-11T22:29:01.092515Z",
     "shell.execute_reply": "2022-01-11T22:29:01.092896Z",
     "shell.execute_reply.started": "2022-01-11T18:41:27.33504Z"
    },
    "papermill": {
     "duration": 0.056613,
     "end_time": "2022-01-11T22:29:01.093037",
     "exception": false,
     "start_time": "2022-01-11T22:29:01.036424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pycocotools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e350026",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:29:01.195671Z",
     "iopub.status.busy": "2022-01-11T22:29:01.193859Z",
     "iopub.status.idle": "2022-01-11T22:29:01.197849Z",
     "shell.execute_reply": "2022-01-11T22:29:01.197290Z",
     "shell.execute_reply.started": "2022-01-11T18:41:27.343141Z"
    },
    "papermill": {
     "duration": 0.056908,
     "end_time": "2022-01-11T22:29:01.197982",
     "exception": false,
     "start_time": "2022-01-11T22:29:01.141074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "N_SPLITS = 5\n",
    "FOLD = random.randint(0, N_SPLITS - 1)\n",
    "print(FOLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0261b3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:29:01.301022Z",
     "iopub.status.busy": "2022-01-11T22:29:01.300216Z",
     "iopub.status.idle": "2022-01-11T22:29:03.320971Z",
     "shell.execute_reply": "2022-01-11T22:29:03.320290Z",
     "shell.execute_reply.started": "2022-01-11T18:41:27.36499Z"
    },
    "papermill": {
     "duration": 2.07521,
     "end_time": "2022-01-11T22:29:03.321119",
     "exception": false,
     "start_time": "2022-01-11T22:29:01.245909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working/yolox-cots-models/YOLOX\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle/working/yolox-cots-models/YOLOX\n",
    "!mkdir './annotations'\n",
    "!mkdir './train2017'\n",
    "!mkdir './val2017'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084bd35c",
   "metadata": {
    "papermill": {
     "duration": 0.048272,
     "end_time": "2022-01-11T22:29:03.417417",
     "exception": false,
     "start_time": "2022-01-11T22:29:03.369145",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2. Splitting Data Into Training/Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9eb8a86",
   "metadata": {
    "papermill": {
     "duration": 0.047804,
     "end_time": "2022-01-11T22:29:03.513169",
     "exception": false,
     "start_time": "2022-01-11T22:29:03.465365",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**EDIT Version 12**: Because we will be cutting out any entries that do not contain any starfish, we will use GroupKFolds to create our folds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8194f67a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:29:03.618573Z",
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     "shell.execute_reply": "2022-01-11T22:29:04.225539Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"/kaggle/input/tensorflow-great-barrier-reef/train.csv\")\n",
    "train['annotations'] = train['annotations'].apply(lambda x: ast.literal_eval(x))\n",
    "train['box'] = train['annotations'].apply(lambda x: [list(y.values()) for y in x])\n",
    "train['image_path'] = \"video_\" + train['video_id'].astype(str) + \"/\" + train['video_frame'].astype(str) + \".jpg\"\n",
    "train['fold'] = -1\n",
    "train = train[train['annotations'].str.len() > 0].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3585158e",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-01-11T22:29:04.361598Z",
     "shell.execute_reply": "2022-01-11T22:29:04.362018Z",
     "shell.execute_reply.started": "2022-01-11T18:41:30.130402Z"
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     "exception": false,
     "start_time": "2022-01-11T22:29:04.278178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video_id</th>\n",
       "      <th>sequence</th>\n",
       "      <th>video_frame</th>\n",
       "      <th>sequence_frame</th>\n",
       "      <th>image_id</th>\n",
       "      <th>annotations</th>\n",
       "      <th>box</th>\n",
       "      <th>image_path</th>\n",
       "      <th>fold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0-16</td>\n",
       "      <td>[{'x': 559, 'y': 213, 'width': 50, 'height': 32}]</td>\n",
       "      <td>[[559, 213, 50, 32]]</td>\n",
       "      <td>video_0/16.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>40258</td>\n",
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       "      <td>0-17</td>\n",
       "      <td>[{'x': 558, 'y': 213, 'width': 50, 'height': 32}]</td>\n",
       "      <td>[[558, 213, 50, 32]]</td>\n",
       "      <td>video_0/17.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>40258</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0-18</td>\n",
       "      <td>[{'x': 557, 'y': 213, 'width': 50, 'height': 32}]</td>\n",
       "      <td>[[557, 213, 50, 32]]</td>\n",
       "      <td>video_0/18.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>40258</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>[{'x': 556, 'y': 214, 'width': 50, 'height': 32}]</td>\n",
       "      <td>[[556, 214, 50, 32]]</td>\n",
       "      <td>video_0/19.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>40258</td>\n",
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       "      <td>20</td>\n",
       "      <td>0-20</td>\n",
       "      <td>[{'x': 555, 'y': 214, 'width': 50, 'height': 32}]</td>\n",
       "      <td>[[555, 214, 50, 32]]</td>\n",
       "      <td>video_0/20.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   video_id  sequence  video_frame  sequence_frame image_id  \\\n",
       "0         0     40258           16              16     0-16   \n",
       "1         0     40258           17              17     0-17   \n",
       "2         0     40258           18              18     0-18   \n",
       "3         0     40258           19              19     0-19   \n",
       "4         0     40258           20              20     0-20   \n",
       "\n",
       "                                         annotations                   box  \\\n",
       "0  [{'x': 559, 'y': 213, 'width': 50, 'height': 32}]  [[559, 213, 50, 32]]   \n",
       "1  [{'x': 558, 'y': 213, 'width': 50, 'height': 32}]  [[558, 213, 50, 32]]   \n",
       "2  [{'x': 557, 'y': 213, 'width': 50, 'height': 32}]  [[557, 213, 50, 32]]   \n",
       "3  [{'x': 556, 'y': 214, 'width': 50, 'height': 32}]  [[556, 214, 50, 32]]   \n",
       "4  [{'x': 555, 'y': 214, 'width': 50, 'height': 32}]  [[555, 214, 50, 32]]   \n",
       "\n",
       "       image_path  fold  \n",
       "0  video_0/16.jpg     4  \n",
       "1  video_0/17.jpg     4  \n",
       "2  video_0/18.jpg     4  \n",
       "3  video_0/19.jpg     4  \n",
       "4  video_0/20.jpg     4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = GroupKFold(n_splits=N_SPLITS)\n",
    "for fold, (train_idx, val_idx) in enumerate(kf.split(train, y = train.video_id.tolist(), groups = train.sequence)):\n",
    "    train.loc[val_idx, 'fold'] = fold\n",
    "train['fold'] = train['fold'].astype(int)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a552e334",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:29:04.472157Z",
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     "iopub.status.idle": "2022-01-11T22:29:59.887838Z",
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     "shell.execute_reply.started": "2022-01-11T18:41:30.163426Z"
    },
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     "end_time": "2022-01-11T22:29:59.888492",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in range(len(train)):\n",
    "    row = train.loc[i]\n",
    "    if(row.fold != FOLD):\n",
    "        copyfile(f'/kaggle/input/tensorflow-great-barrier-reef/train_images/{row.image_path}', f'./train2017/{row.image_id}.jpg')\n",
    "    else:\n",
    "        copyfile(f'/kaggle/input/tensorflow-great-barrier-reef/train_images/{row.image_path}', f'./val2017/{row.image_id}.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a301adba",
   "metadata": {
    "papermill": {
     "duration": 0.049096,
     "end_time": "2022-01-11T22:29:59.986785",
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     "start_time": "2022-01-11T22:29:59.937689",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Convert Dataset to YOLOX Format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3045046",
   "metadata": {
    "papermill": {
     "duration": 0.080825,
     "end_time": "2022-01-11T22:30:00.133192",
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     "start_time": "2022-01-11T22:30:00.052367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are two kinds of evaluators that can be used: the COCO evaluator and the VOC evaluator, both requiring specific formats. Only the COCO evaluator will be made for the time being. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f997cc9e",
   "metadata": {
    "execution": {
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#COCO:\n",
    "def datasetToCOCO(dataset):\n",
    "    annotation = 0\n",
    "    json = {\n",
    "        \"info\": [],\n",
    "        \"licenses\": [],\n",
    "        \"categories\": [],\n",
    "        \"images\": [],\n",
    "        \"annotations\": []\n",
    "    }\n",
    "    info = {\n",
    "        \"year\": \"2021\",\n",
    "        \"version\": \"1\",\n",
    "        \"description\": \"COTS dataset - COCO format\",\n",
    "        \"contributor\": \"\",\n",
    "        \"url\": \"https://kaggle.com\",\n",
    "        \"date_created\": \"2021-11-30T15:01:26+00:00\"\n",
    "    }\n",
    "    json['info'].append(info)\n",
    "    licenses = {\n",
    "        \"id\": 1,\n",
    "        \"url\": \"\",\n",
    "        \"name\": \"Unknown\"\n",
    "    }\n",
    "    json['licenses'].append(licenses)\n",
    "    categories = {\n",
    "        \"id\": 0, \n",
    "        \"name\": \"starfish\", \n",
    "        \"supercategory\": \"none\"\n",
    "    }\n",
    "    json[\"categories\"].append(categories)\n",
    "    for row in dataset.itertuples():\n",
    "        images = {\n",
    "            \"id\": row[0],\n",
    "            \"license\": 1,\n",
    "            \"file_name\": row.image_id + '.jpg',\n",
    "            \"height\": 720,\n",
    "            \"width\": 1280,\n",
    "            \"date_captured\": \"2021-11-30T15:01:26+00:00\"\n",
    "        }\n",
    "        json['images'].append(images)\n",
    "        boxes = row.box\n",
    "        for box in boxes:\n",
    "            width = box[2]\n",
    "            height = box[3]\n",
    "            if (box[0] + box[2] > 1280):\n",
    "                width = 1280 - box[0] \n",
    "            if (box[1] + box[3] > 720):\n",
    "                height = 720 - box[1] \n",
    "            annotations = {\n",
    "                \"id\": annotation,\n",
    "                \"image_id\": row[0],\n",
    "                \"category_id\": 0,\n",
    "                \"bbox\": [box[0], box[1], width, height],\n",
    "                \"area\": box[2] * box[3],\n",
    "                \"segmentation\": [],\n",
    "                \"iscrowd\": 0\n",
    "            }\n",
    "            annotation += 1\n",
    "            json['annotations'].append(annotations)\n",
    "    return json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de135d09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:01.668199Z",
     "iopub.status.busy": "2022-01-11T22:30:01.663208Z",
     "iopub.status.idle": "2022-01-11T22:30:01.880066Z",
     "shell.execute_reply": "2022-01-11T22:30:01.879347Z",
     "shell.execute_reply.started": "2022-01-11T18:42:41.189796Z"
    },
    "papermill": {
     "duration": 0.338728,
     "end_time": "2022-01-11T22:30:01.880213",
     "exception": false,
     "start_time": "2022-01-11T22:30:01.541485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_coco = datasetToCOCO(train[train['fold'] != FOLD])\n",
    "with open(f\"./annotations/train.json\", 'w') as f:\n",
    "    output_json = json.dumps(train_coco)\n",
    "    f.write(output_json)\n",
    "val_coco = datasetToCOCO(train[train['fold'] == FOLD])\n",
    "with open(f\"./annotations/val.json\", 'w') as f:\n",
    "    output_json = json.dumps(val_coco)\n",
    "    f.write(output_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5e0d63",
   "metadata": {
    "papermill": {
     "duration": 0.048398,
     "end_time": "2022-01-11T22:30:01.981127",
     "exception": false,
     "start_time": "2022-01-11T22:30:01.932729",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Apply Evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fee52e0",
   "metadata": {
    "papermill": {
     "duration": 0.048046,
     "end_time": "2022-01-11T22:30:02.078970",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.030924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are multiple kinds of YOLOX models, but for the time being, we will just focus on YOLOX-s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8aee492",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:02.181451Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "template = '''\n",
    "#!/usr/bin/env python3\n",
    "# -*- coding:utf-8 -*-\n",
    "# Copyright (c) Megvii, Inc. and its affiliates.\n",
    "\n",
    "import os\n",
    "\n",
    "from yolox.exp import Exp as MyExp\n",
    "\n",
    "\n",
    "class Exp(MyExp):\n",
    "    def __init__(self):\n",
    "        super(Exp, self).__init__()\n",
    "        self.num_classes = 1\n",
    "        self.depth = 0.33\n",
    "        self.width = 0.50\n",
    "        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n",
    "        self.data_dir = \"/kaggle/working/yolox-cots-models/YOLOX/\"\n",
    "        self.train_ann = 'train.json'\n",
    "        self.val_ann = 'val.json'\n",
    "        self.max_epoch = $max_epoch\n",
    "        #self.eval_interval = 1\n",
    "        #self.data_num_workers = 2\n",
    "        #self.input_size = (960, 960)\n",
    "        #self.test_size = (960, 960)\n",
    "        #self.no_aug_epochs = 2\n",
    "        #self.mosaic_scale = (0.5, 1.5)\n",
    "        #self.random_size = (10, 20)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18d62770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:02.285865Z",
     "iopub.status.busy": "2022-01-11T22:30:02.285270Z",
     "iopub.status.idle": "2022-01-11T22:30:02.288325Z",
     "shell.execute_reply": "2022-01-11T22:30:02.288781Z",
     "shell.execute_reply.started": "2022-01-11T18:42:41.455376Z"
    },
    "papermill": {
     "duration": 0.056598,
     "end_time": "2022-01-11T22:30:02.288918",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.232320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline = Template(template).substitute(max_epoch = 20)\n",
    "with open('cots_config.py', 'w') as f:\n",
    "    f.write(pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6edd8cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:02.393890Z",
     "iopub.status.busy": "2022-01-11T22:30:02.393220Z",
     "iopub.status.idle": "2022-01-11T22:30:02.396155Z",
     "shell.execute_reply": "2022-01-11T22:30:02.395742Z",
     "shell.execute_reply.started": "2022-01-11T18:42:41.508942Z"
    },
    "papermill": {
     "duration": 0.058391,
     "end_time": "2022-01-11T22:30:02.396269",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#VERSION 14: Added voc_cls to see if any differences arise.\n",
    "voc_cls = '''\n",
    "VOC_CLASSES = (\n",
    "  \"starfish\",\n",
    ")\n",
    "'''\n",
    "with open('/kaggle/working/yolox-cots-models/YOLOX/yolox/data/datasets/voc_classes.py', 'w') as f:\n",
    "    f.write(voc_cls)\n",
    "\n",
    "coco_cls = '''\n",
    "COCO_CLASSES = (\n",
    "  \"starfish\",\n",
    ")\n",
    "'''\n",
    "with open('/kaggle/working/yolox-cots-models/YOLOX/yolox/data/datasets/coco_classes.py', 'w') as f:\n",
    "    f.write(coco_cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53e51bb",
   "metadata": {
    "papermill": {
     "duration": 0.048382,
     "end_time": "2022-01-11T22:30:02.493684",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.445302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 5. Weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35710660",
   "metadata": {
    "papermill": {
     "duration": 0.048224,
     "end_time": "2022-01-11T22:30:02.590690",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.542466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And now to import the weights file for YOLOX-s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0df5d220",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:02.713814Z",
     "iopub.status.busy": "2022-01-11T22:30:02.708441Z",
     "iopub.status.idle": "2022-01-11T22:30:02.719107Z",
     "shell.execute_reply": "2022-01-11T22:30:02.718481Z",
     "shell.execute_reply.started": "2022-01-11T18:42:41.571757Z"
    },
    "papermill": {
     "duration": 0.080007,
     "end_time": "2022-01-11T22:30:02.719277",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.639270",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL = '/kaggle/working/yolox-cots-models/yolox_s.pth'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478688bd",
   "metadata": {
    "papermill": {
     "duration": 0.079975,
     "end_time": "2022-01-11T22:30:02.889785",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.809810",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 6. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6b1613e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:03.067062Z",
     "iopub.status.busy": "2022-01-11T22:30:03.065987Z",
     "iopub.status.idle": "2022-01-11T22:30:03.958375Z",
     "shell.execute_reply": "2022-01-11T22:30:03.959511Z",
     "shell.execute_reply.started": "2022-01-11T18:42:41.578982Z"
    },
    "papermill": {
     "duration": 0.989262,
     "end_time": "2022-01-11T22:30:03.959746",
     "exception": false,
     "start_time": "2022-01-11T22:30:02.970484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cp /kaggle/working/yolox-cots-models/YOLOX/tools/train.py ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "041ef868",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T22:30:04.197272Z",
     "iopub.status.busy": "2022-01-11T22:30:04.196395Z",
     "iopub.status.idle": "2022-01-11T23:47:47.066775Z",
     "shell.execute_reply": "2022-01-11T23:47:47.066210Z",
     "shell.execute_reply.started": "2022-01-11T18:42:43.081411Z"
    },
    "papermill": {
     "duration": 4662.958315,
     "end_time": "2022-01-11T23:47:47.066932",
     "exception": false,
     "start_time": "2022-01-11T22:30:04.108617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m2022-01-11 22:30:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m126\u001b[0m - \u001b[1margs: Namespace(batch_size=32, cache=False, ckpt='/kaggle/working/yolox-cots-models/yolox_s.pth', devices=1, dist_backend='nccl', dist_url=None, exp_file='cots_config.py', experiment_name='cots_config', fp16=True, machine_rank=0, name=None, num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m127\u001b[0m - \u001b[1mexp value:\r\n",
      "╒══════════════════╤════════════════════════════════════════════╕\r\n",
      "│ keys             │ values                                     │\r\n",
      "╞══════════════════╪════════════════════════════════════════════╡\r\n",
      "│ seed             │ None                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ output_dir       │ './YOLOX_outputs'                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ print_interval   │ 10                                         │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ eval_interval    │ 10                                         │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ num_classes      │ 1                                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ depth            │ 0.33                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ width            │ 0.5                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ act              │ 'silu'                                     │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ data_num_workers │ 4                                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ input_size       │ (640, 640)                                 │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ multiscale_range │ 5                                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ data_dir         │ '/kaggle/working/yolox-cots-models/YOLOX/' │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ train_ann        │ 'train.json'                               │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ val_ann          │ 'val.json'                                 │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ mosaic_prob      │ 1.0                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ mixup_prob       │ 1.0                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ hsv_prob         │ 1.0                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ flip_prob        │ 0.5                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ degrees          │ 10.0                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ translate        │ 0.1                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ mosaic_scale     │ (0.1, 2)                                   │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ mixup_scale      │ (0.5, 1.5)                                 │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ shear            │ 2.0                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ enable_mixup     │ True                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ warmup_epochs    │ 5                                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ max_epoch        │ 20                                         │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ warmup_lr        │ 0                                          │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ basic_lr_per_img │ 0.00015625                                 │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ scheduler        │ 'yoloxwarmcos'                             │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ no_aug_epochs    │ 15                                         │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ min_lr_ratio     │ 0.05                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ ema              │ True                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ weight_decay     │ 0.0005                                     │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ momentum         │ 0.9                                        │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ exp_name         │ 'cots_config'                              │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ test_size        │ (640, 640)                                 │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ test_conf        │ 0.01                                       │\r\n",
      "├──────────────────┼────────────────────────────────────────────┤\r\n",
      "│ nmsthre          │ 0.65                                       │\r\n",
      "╘══════════════════╧════════════════════════════════════════════╛\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m133\u001b[0m - \u001b[1mModel Summary: Params: 8.94M, Gflops: 26.64\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m286\u001b[0m - \u001b[1mloading checkpoint for fine tuning\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.0.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.0.weight in model is torch.Size([1, 128, 1, 1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.0.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.0.bias in model is torch.Size([1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.1.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.1.weight in model is torch.Size([1, 128, 1, 1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.1.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.1.bias in model is torch.Size([1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.2.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.2.weight in model is torch.Size([1, 128, 1, 1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36myolox.utils.checkpoint\u001b[0m:\u001b[36m26\u001b[0m - \u001b[33m\u001b[1mShape of head.cls_preds.2.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.2.bias in model is torch.Size([1]).\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.data.datasets.coco\u001b[0m:\u001b[36m45\u001b[0m - \u001b[1mloading annotations into memory...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.data.datasets.coco\u001b[0m:\u001b[36m45\u001b[0m - \u001b[1mDone (t=0.10s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m88\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m88\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\r\n",
      "  cpuset_checked))\r\n",
      "\u001b[32m2022-01-11 22:30:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m151\u001b[0m - \u001b[1minit prefetcher, this might take one minute or less...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.data.datasets.coco\u001b[0m:\u001b[36m45\u001b[0m - \u001b[1mloading annotations into memory...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.data.datasets.coco\u001b[0m:\u001b[36m45\u001b[0m - \u001b[1mDone (t=0.03s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m88\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m88\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m179\u001b[0m - \u001b[1mTraining start...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m180\u001b[0m - \u001b[1m\r\n",
      "YOLOX(\r\n",
      "  (backbone): YOLOPAFPN(\r\n",
      "    (backbone): CSPDarknet(\r\n",
      "      (stem): Focus(\r\n",
      "        (conv): BaseConv(\r\n",
      "          (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (dark2): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): CSPLayer(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv3): BaseConv(\r\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (m): Sequential(\r\n",
      "            (0): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (dark3): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): CSPLayer(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv3): BaseConv(\r\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (m): Sequential(\r\n",
      "            (0): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "            (1): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "            (2): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (dark4): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): CSPLayer(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv3): BaseConv(\r\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (m): Sequential(\r\n",
      "            (0): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "            (1): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "            (2): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (dark5): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): SPPBottleneck(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (m): ModuleList(\r\n",
      "            (0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\r\n",
      "            (1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)\r\n",
      "            (2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "        )\r\n",
      "        (2): CSPLayer(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv3): BaseConv(\r\n",
      "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (m): Sequential(\r\n",
      "            (0): Bottleneck(\r\n",
      "              (conv1): BaseConv(\r\n",
      "                (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "              (conv2): BaseConv(\r\n",
      "                (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "                (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "                (act): SiLU(inplace=True)\r\n",
      "              )\r\n",
      "            )\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (upsample): Upsample(scale_factor=2.0, mode=nearest)\r\n",
      "    (lateral_conv0): BaseConv(\r\n",
      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "      (act): SiLU(inplace=True)\r\n",
      "    )\r\n",
      "    (C3_p4): CSPLayer(\r\n",
      "      (conv1): BaseConv(\r\n",
      "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv2): BaseConv(\r\n",
      "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv3): BaseConv(\r\n",
      "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (m): Sequential(\r\n",
      "        (0): Bottleneck(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (reduce_conv1): BaseConv(\r\n",
      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "      (act): SiLU(inplace=True)\r\n",
      "    )\r\n",
      "    (C3_p3): CSPLayer(\r\n",
      "      (conv1): BaseConv(\r\n",
      "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv2): BaseConv(\r\n",
      "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv3): BaseConv(\r\n",
      "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (m): Sequential(\r\n",
      "        (0): Bottleneck(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (bu_conv2): BaseConv(\r\n",
      "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "      (act): SiLU(inplace=True)\r\n",
      "    )\r\n",
      "    (C3_n3): CSPLayer(\r\n",
      "      (conv1): BaseConv(\r\n",
      "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv2): BaseConv(\r\n",
      "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv3): BaseConv(\r\n",
      "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (m): Sequential(\r\n",
      "        (0): Bottleneck(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (bu_conv1): BaseConv(\r\n",
      "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\r\n",
      "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "      (act): SiLU(inplace=True)\r\n",
      "    )\r\n",
      "    (C3_n4): CSPLayer(\r\n",
      "      (conv1): BaseConv(\r\n",
      "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv2): BaseConv(\r\n",
      "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (conv3): BaseConv(\r\n",
      "        (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (m): Sequential(\r\n",
      "        (0): Bottleneck(\r\n",
      "          (conv1): BaseConv(\r\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "          (conv2): BaseConv(\r\n",
      "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "            (act): SiLU(inplace=True)\r\n",
      "          )\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "  )\r\n",
      "  (head): YOLOXHead(\r\n",
      "    (cls_convs): ModuleList(\r\n",
      "      (0): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (1): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (2): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (reg_convs): ModuleList(\r\n",
      "      (0): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (1): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "      (2): Sequential(\r\n",
      "        (0): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "        (1): BaseConv(\r\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\r\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "          (act): SiLU(inplace=True)\r\n",
      "        )\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (cls_preds): ModuleList(\r\n",
      "      (0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "    )\r\n",
      "    (reg_preds): ModuleList(\r\n",
      "      (0): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "    )\r\n",
      "    (obj_preds): ModuleList(\r\n",
      "      (0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "      (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\r\n",
      "    )\r\n",
      "    (stems): ModuleList(\r\n",
      "      (0): BaseConv(\r\n",
      "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (1): BaseConv(\r\n",
      "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "      (2): BaseConv(\r\n",
      "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\r\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\r\n",
      "        (act): SiLU(inplace=True)\r\n",
      "      )\r\n",
      "    )\r\n",
      "    (l1_loss): L1Loss()\r\n",
      "    (bcewithlog_loss): BCEWithLogitsLoss()\r\n",
      "    (iou_loss): IOUloss()\r\n",
      "  )\r\n",
      ")\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:30:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch1\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:31:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 10/125, mem: 13824Mb, iter_time: 2.602s, data_time: 0.782s, total_loss: 16.9, iou_loss: 3.9, l1_loss: 0.0, conf_loss: 10.8, cls_loss: 2.1, lr: 1.280e-06, size: 640, ETA: 1:47:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:31:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 20/125, mem: 13824Mb, iter_time: 3.442s, data_time: 2.101s, total_loss: 16.2, iou_loss: 4.3, l1_loss: 0.0, conf_loss: 10.4, cls_loss: 1.5, lr: 5.120e-06, size: 576, ETA: 2:04:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:32:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 30/125, mem: 13824Mb, iter_time: 4.065s, data_time: 3.103s, total_loss: 15.7, iou_loss: 4.2, l1_loss: 0.0, conf_loss: 9.8, cls_loss: 1.7, lr: 1.152e-05, size: 480, ETA: 2:18:42\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:33:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 40/125, mem: 13824Mb, iter_time: 3.582s, data_time: 2.487s, total_loss: 13.6, iou_loss: 4.0, l1_loss: 0.0, conf_loss: 7.8, cls_loss: 1.9, lr: 2.048e-05, size: 640, ETA: 2:20:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:33:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 50/125, mem: 13824Mb, iter_time: 4.126s, data_time: 2.477s, total_loss: 12.5, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 7.2, cls_loss: 1.6, lr: 3.200e-05, size: 768, ETA: 2:25:30\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:34:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 60/125, mem: 13824Mb, iter_time: 3.536s, data_time: 2.519s, total_loss: 10.7, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 6.1, cls_loss: 0.9, lr: 4.608e-05, size: 512, ETA: 2:24:43\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:35:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 70/125, mem: 13824Mb, iter_time: 4.285s, data_time: 2.686s, total_loss: 11.4, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 6.9, cls_loss: 0.8, lr: 6.272e-05, size: 736, ETA: 2:28:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:35:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 80/125, mem: 13824Mb, iter_time: 3.565s, data_time: 2.323s, total_loss: 10.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 6.4, cls_loss: 0.7, lr: 8.192e-05, size: 736, ETA: 2:27:13\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:36:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 90/125, mem: 13824Mb, iter_time: 4.112s, data_time: 2.802s, total_loss: 10.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 6.1, cls_loss: 0.7, lr: 1.037e-04, size: 608, ETA: 2:28:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:36:54\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 100/125, mem: 13824Mb, iter_time: 3.553s, data_time: 2.567s, total_loss: 10.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 6.2, cls_loss: 0.7, lr: 1.280e-04, size: 608, ETA: 2:27:28\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:37:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 110/125, mem: 13824Mb, iter_time: 4.199s, data_time: 2.703s, total_loss: 10.3, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 5.9, cls_loss: 0.7, lr: 1.549e-04, size: 672, ETA: 2:28:42\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:38:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 1/20, iter: 120/125, mem: 13824Mb, iter_time: 3.517s, data_time: 2.462s, total_loss: 9.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 5.6, cls_loss: 0.7, lr: 1.843e-04, size: 640, ETA: 2:27:22\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:38:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:38:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch2\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:39:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 10/125, mem: 13824Mb, iter_time: 4.173s, data_time: 3.249s, total_loss: 10.2, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 6.0, cls_loss: 0.7, lr: 2.333e-04, size: 608, ETA: 2:27:25\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:39:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 20/125, mem: 13824Mb, iter_time: 3.424s, data_time: 2.374s, total_loss: 9.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 5.5, cls_loss: 0.6, lr: 2.691e-04, size: 672, ETA: 2:25:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:40:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 30/125, mem: 13824Mb, iter_time: 4.333s, data_time: 2.692s, total_loss: 9.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 5.4, cls_loss: 0.7, lr: 3.075e-04, size: 800, ETA: 2:26:52\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:41:03\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 40/125, mem: 13824Mb, iter_time: 3.375s, data_time: 2.051s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.9, cls_loss: 0.7, lr: 3.485e-04, size: 736, ETA: 2:25:21\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:41:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 50/125, mem: 13824Mb, iter_time: 4.326s, data_time: 3.081s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.9, cls_loss: 0.7, lr: 3.920e-04, size: 736, ETA: 2:26:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:42:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 60/125, mem: 13824Mb, iter_time: 3.289s, data_time: 2.181s, total_loss: 8.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.9, cls_loss: 0.7, lr: 4.381e-04, size: 576, ETA: 2:24:24\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:43:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 70/125, mem: 13824Mb, iter_time: 4.333s, data_time: 3.209s, total_loss: 8.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 5.1, cls_loss: 0.7, lr: 4.867e-04, size: 800, ETA: 2:24:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:43:35\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 80/125, mem: 13824Mb, iter_time: 3.257s, data_time: 2.104s, total_loss: 8.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 0.7, lr: 5.379e-04, size: 576, ETA: 2:23:21\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:44:18\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 90/125, mem: 13824Mb, iter_time: 4.320s, data_time: 3.306s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.6, lr: 5.917e-04, size: 672, ETA: 2:23:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:44:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 100/125, mem: 13824Mb, iter_time: 3.421s, data_time: 2.188s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.6, lr: 6.480e-04, size: 736, ETA: 2:22:31\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:45:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 110/125, mem: 13824Mb, iter_time: 4.174s, data_time: 3.103s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.6, lr: 7.069e-04, size: 608, ETA: 2:22:33\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:46:09\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 2/20, iter: 120/125, mem: 13824Mb, iter_time: 3.492s, data_time: 2.477s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.6, lr: 7.683e-04, size: 608, ETA: 2:21:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:46:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:46:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch3\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:47:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 10/125, mem: 13824Mb, iter_time: 3.546s, data_time: 2.255s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.6, lr: 8.653e-04, size: 800, ETA: 2:20:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:47:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 20/125, mem: 13824Mb, iter_time: 3.981s, data_time: 2.919s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.6, cls_loss: 0.6, lr: 9.331e-04, size: 640, ETA: 2:20:27\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:48:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 30/125, mem: 13824Mb, iter_time: 3.542s, data_time: 2.440s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.6, lr: 1.004e-03, size: 672, ETA: 2:19:30\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:49:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 40/125, mem: 13824Mb, iter_time: 4.194s, data_time: 2.950s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.6, lr: 1.076e-03, size: 768, ETA: 2:19:25\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:49:41\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 50/125, mem: 13824Mb, iter_time: 3.555s, data_time: 2.359s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.6, lr: 1.152e-03, size: 544, ETA: 2:18:30\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:50:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 60/125, mem: 13824Mb, iter_time: 4.042s, data_time: 2.822s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.7, lr: 1.230e-03, size: 768, ETA: 2:18:11\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:50:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 70/125, mem: 13824Mb, iter_time: 3.622s, data_time: 2.602s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.6, lr: 1.311e-03, size: 608, ETA: 2:17:22\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:51:39\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 80/125, mem: 13824Mb, iter_time: 4.108s, data_time: 2.801s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.6, lr: 1.394e-03, size: 800, ETA: 2:17:05\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:52:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 90/125, mem: 13824Mb, iter_time: 3.546s, data_time: 2.556s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.6, lr: 1.480e-03, size: 544, ETA: 2:16:12\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:52:54\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 100/125, mem: 13824Mb, iter_time: 3.989s, data_time: 2.793s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.6, lr: 1.568e-03, size: 768, ETA: 2:15:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:53:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 110/125, mem: 13824Mb, iter_time: 3.729s, data_time: 2.366s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.6, lr: 1.659e-03, size: 800, ETA: 2:15:05\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:54:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 3/20, iter: 120/125, mem: 13824Mb, iter_time: 4.011s, data_time: 2.925s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.6, lr: 1.752e-03, size: 672, ETA: 2:14:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:54:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:54:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch4\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:55:06\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 10/125, mem: 13824Mb, iter_time: 3.363s, data_time: 2.189s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.6, lr: 1.897e-03, size: 736, ETA: 2:13:22\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:55:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 20/125, mem: 13824Mb, iter_time: 4.221s, data_time: 3.117s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.6, lr: 1.997e-03, size: 544, ETA: 2:13:08\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:56:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 30/125, mem: 13824Mb, iter_time: 3.402s, data_time: 2.450s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.6, lr: 2.100e-03, size: 640, ETA: 2:12:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:57:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 40/125, mem: 13824Mb, iter_time: 4.297s, data_time: 3.191s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 2.204e-03, size: 768, ETA: 2:11:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:57:39\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 50/125, mem: 13824Mb, iter_time: 3.458s, data_time: 2.301s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.6, lr: 2.312e-03, size: 672, ETA: 2:11:03\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:58:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 60/125, mem: 13824Mb, iter_time: 4.256s, data_time: 3.189s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 2.422e-03, size: 672, ETA: 2:10:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:58:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 70/125, mem: 13824Mb, iter_time: 3.386s, data_time: 2.217s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.6, lr: 2.535e-03, size: 768, ETA: 2:09:50\u001b[0m\r\n",
      "\u001b[32m2022-01-11 22:59:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 80/125, mem: 13824Mb, iter_time: 4.230s, data_time: 3.120s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.6, lr: 2.650e-03, size: 576, ETA: 2:09:32\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:00:13\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 90/125, mem: 13824Mb, iter_time: 3.480s, data_time: 2.462s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.6, lr: 2.768e-03, size: 672, ETA: 2:08:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:00:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 100/125, mem: 13824Mb, iter_time: 4.210s, data_time: 3.150s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.6, lr: 2.888e-03, size: 672, ETA: 2:08:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:01:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 110/125, mem: 13824Mb, iter_time: 3.377s, data_time: 2.394s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.6, lr: 3.011e-03, size: 544, ETA: 2:07:24\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 4/20, iter: 120/125, mem: 13824Mb, iter_time: 4.290s, data_time: 3.232s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.6, lr: 3.136e-03, size: 736, ETA: 2:07:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch5\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m191\u001b[0m - \u001b[1m--->No mosaic aug now!\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m193\u001b[0m - \u001b[1m--->Add additional L1 loss now!\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:02:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 10/125, mem: 13824Mb, iter_time: 3.025s, data_time: 1.919s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.6, lr: 3.329e-03, size: 608, ETA: 2:05:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 20/125, mem: 13824Mb, iter_time: 0.994s, data_time: 0.062s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 3.461e-03, size: 512, ETA: 2:03:05\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 30/125, mem: 13824Mb, iter_time: 1.154s, data_time: 0.107s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 3.596e-03, size: 576, ETA: 2:00:52\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 40/125, mem: 13824Mb, iter_time: 1.337s, data_time: 0.011s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 3.732e-03, size: 736, ETA: 1:58:50\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 50/125, mem: 13824Mb, iter_time: 1.017s, data_time: 0.008s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 3.872e-03, size: 576, ETA: 1:56:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:03:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 60/125, mem: 13824Mb, iter_time: 0.994s, data_time: 0.008s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 4.014e-03, size: 480, ETA: 1:54:35\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:04:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 70/125, mem: 13824Mb, iter_time: 1.185s, data_time: 0.007s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.6, lr: 4.159e-03, size: 640, ETA: 1:52:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:04:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 80/125, mem: 13824Mb, iter_time: 1.084s, data_time: 0.008s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 4.306e-03, size: 576, ETA: 1:50:45\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:04:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 90/125, mem: 13824Mb, iter_time: 1.188s, data_time: 0.008s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 4.456e-03, size: 640, ETA: 1:48:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:04:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 100/125, mem: 13824Mb, iter_time: 1.145s, data_time: 0.008s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.608e-03, size: 576, ETA: 1:47:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:04:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 110/125, mem: 13824Mb, iter_time: 1.116s, data_time: 0.009s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 4.763e-03, size: 608, ETA: 1:45:26\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:03\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 5/20, iter: 120/125, mem: 13824Mb, iter_time: 0.955s, data_time: 0.012s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 4.920e-03, size: 480, ETA: 1:43:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\r\n",
      "  cpuset_checked))\r\n",
      "100%|##########| 30/30 [00:27<00:00,  1.08it/s]\r\n",
      "\u001b[32m2022-01-11 23:05:37\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:37\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.15s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "COCOeval_opt.evaluate() finished in 0.20 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.04 seconds.\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 6.37 ms, Average NMS time: 3.12 ms, Average inference time: 9.50 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.043\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.125\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.017\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.054\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.063\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.105\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.125\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.153\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch6\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 10/125, mem: 13824Mb, iter_time: 0.978s, data_time: 0.012s, total_loss: 4.3, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 2.500e-04, size: 544, ETA: 1:41:09\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:05:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 20/125, mem: 13824Mb, iter_time: 1.041s, data_time: 0.011s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 2.500e-04, size: 512, ETA: 1:39:33\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:06:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 30/125, mem: 13824Mb, iter_time: 0.986s, data_time: 0.006s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 2.500e-04, size: 512, ETA: 1:37:58\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:06:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 40/125, mem: 13824Mb, iter_time: 1.095s, data_time: 0.007s, total_loss: 3.8, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 1:36:28\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:06:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 50/125, mem: 13824Mb, iter_time: 1.063s, data_time: 0.008s, total_loss: 4.0, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 576, ETA: 1:35:00\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:06:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 60/125, mem: 13824Mb, iter_time: 1.317s, data_time: 0.006s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.500e-04, size: 800, ETA: 1:33:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:06:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 70/125, mem: 13824Mb, iter_time: 1.301s, data_time: 0.011s, total_loss: 4.1, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.6, lr: 2.500e-04, size: 672, ETA: 1:32:23\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:07:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 80/125, mem: 13824Mb, iter_time: 1.055s, data_time: 0.012s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 544, ETA: 1:31:01\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:07:18\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 90/125, mem: 13824Mb, iter_time: 1.064s, data_time: 0.008s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 576, ETA: 1:29:42\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:07:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 100/125, mem: 13824Mb, iter_time: 1.293s, data_time: 0.009s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 1:28:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:07:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 110/125, mem: 13824Mb, iter_time: 1.130s, data_time: 0.012s, total_loss: 3.8, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 1:27:15\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:07:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 6/20, iter: 120/125, mem: 13824Mb, iter_time: 1.146s, data_time: 0.013s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 1:26:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:25<00:00,  1.20it/s]\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.09 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.38 ms, Average NMS time: 1.70 ms, Average inference time: 6.07 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.063\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.145\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.034\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.078\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.088\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.099\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.099\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.119\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch7\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:37\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 10/125, mem: 13824Mb, iter_time: 1.097s, data_time: 0.023s, total_loss: 3.8, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 1:24:16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:47\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 20/125, mem: 13824Mb, iter_time: 1.066s, data_time: 0.015s, total_loss: 3.8, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 1:23:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:08:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 30/125, mem: 13824Mb, iter_time: 1.086s, data_time: 0.009s, total_loss: 4.0, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 544, ETA: 1:21:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:09:10\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 40/125, mem: 13824Mb, iter_time: 1.131s, data_time: 0.008s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 1:20:51\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:09:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 50/125, mem: 13824Mb, iter_time: 1.098s, data_time: 0.011s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 1:19:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:09:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 60/125, mem: 13824Mb, iter_time: 1.138s, data_time: 0.016s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 1:18:43\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:09:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 70/125, mem: 13824Mb, iter_time: 1.297s, data_time: 0.010s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 1:17:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:09:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 80/125, mem: 13824Mb, iter_time: 1.151s, data_time: 0.013s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 1:16:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:10:09\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 90/125, mem: 13824Mb, iter_time: 1.246s, data_time: 0.013s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 1:15:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:10:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 100/125, mem: 13824Mb, iter_time: 1.133s, data_time: 0.010s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 1:14:48\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:10:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 110/125, mem: 13824Mb, iter_time: 0.931s, data_time: 0.010s, total_loss: 4.3, iou_loss: 2.4, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.6, lr: 2.500e-04, size: 480, ETA: 1:13:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:10:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 7/20, iter: 120/125, mem: 13824Mb, iter_time: 1.215s, data_time: 0.016s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 1:12:52\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:10:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.20it/s]\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "COCOeval_opt.evaluate() finished in 0.14 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.15 ms, Average NMS time: 1.78 ms, Average inference time: 5.93 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.082\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.173\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.066\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.104\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.103\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.116\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.116\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch8\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:24\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 10/125, mem: 13824Mb, iter_time: 0.995s, data_time: 0.012s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 1:11:27\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:35\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 20/125, mem: 13824Mb, iter_time: 1.067s, data_time: 0.009s, total_loss: 4.2, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 2.500e-04, size: 480, ETA: 1:10:32\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 30/125, mem: 13824Mb, iter_time: 1.074s, data_time: 0.019s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 1:09:38\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:11:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 40/125, mem: 13824Mb, iter_time: 1.159s, data_time: 0.010s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 512, ETA: 1:08:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:12:09\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 50/125, mem: 13824Mb, iter_time: 1.193s, data_time: 0.007s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 1:07:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:12:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 60/125, mem: 13824Mb, iter_time: 1.230s, data_time: 0.012s, total_loss: 3.4, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 1:07:07\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:12:33\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 70/125, mem: 13824Mb, iter_time: 1.090s, data_time: 0.010s, total_loss: 3.8, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.6, lr: 2.500e-04, size: 544, ETA: 1:06:17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:12:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 80/125, mem: 13824Mb, iter_time: 1.170s, data_time: 0.010s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 1:05:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:12:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 90/125, mem: 13824Mb, iter_time: 1.278s, data_time: 0.020s, total_loss: 3.3, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 1:04:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:13:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 100/125, mem: 13824Mb, iter_time: 1.095s, data_time: 0.009s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 1:03:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:13:20\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 110/125, mem: 13824Mb, iter_time: 1.236s, data_time: 0.029s, total_loss: 3.8, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 1:03:11\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:13:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 8/20, iter: 120/125, mem: 13824Mb, iter_time: 1.183s, data_time: 0.012s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 1:02:26\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:13:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:25<00:00,  1.19it/s]\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "COCOeval_opt.evaluate() finished in 0.26 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.20 ms, Average NMS time: 1.56 ms, Average inference time: 5.76 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.066\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.137\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.052\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.085\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.088\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.097\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.097\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.118\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch9\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 10/125, mem: 13824Mb, iter_time: 1.149s, data_time: 0.013s, total_loss: 3.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 1:01:18\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 20/125, mem: 13824Mb, iter_time: 0.887s, data_time: 0.010s, total_loss: 4.2, iou_loss: 2.4, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 480, ETA: 1:00:31\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:35\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 30/125, mem: 13824Mb, iter_time: 1.087s, data_time: 0.007s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:59:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:14:49\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 40/125, mem: 13824Mb, iter_time: 1.317s, data_time: 0.010s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:59:07\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 50/125, mem: 13824Mb, iter_time: 1.233s, data_time: 0.010s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:58:26\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 60/125, mem: 13824Mb, iter_time: 1.047s, data_time: 0.010s, total_loss: 3.5, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:57:43\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 70/125, mem: 13824Mb, iter_time: 1.070s, data_time: 0.006s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:57:01\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:33\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 80/125, mem: 13824Mb, iter_time: 1.046s, data_time: 0.007s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:56:19\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 90/125, mem: 13824Mb, iter_time: 1.174s, data_time: 0.013s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:55:40\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:15:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 100/125, mem: 13824Mb, iter_time: 1.182s, data_time: 0.009s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:55:01\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 110/125, mem: 13824Mb, iter_time: 1.149s, data_time: 0.010s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:54:22\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:20\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 9/20, iter: 120/125, mem: 13824Mb, iter_time: 1.196s, data_time: 0.023s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:53:45\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.21it/s]\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.09 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.26 ms, Average NMS time: 1.35 ms, Average inference time: 5.61 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.068\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.137\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.048\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.086\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.086\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.095\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.095\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:16:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:17:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 10/125, mem: 13824Mb, iter_time: 0.984s, data_time: 0.010s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:52:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:17:13\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 20/125, mem: 13824Mb, iter_time: 1.057s, data_time: 0.006s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:52:09\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:17:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 30/125, mem: 13824Mb, iter_time: 1.315s, data_time: 0.013s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:51:34\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:17:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 40/125, mem: 13824Mb, iter_time: 1.056s, data_time: 0.011s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:50:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:17:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 50/125, mem: 13824Mb, iter_time: 1.114s, data_time: 0.019s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:50:21\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 60/125, mem: 13824Mb, iter_time: 1.223s, data_time: 0.013s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:49:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 70/125, mem: 13824Mb, iter_time: 1.074s, data_time: 0.019s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:49:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 80/125, mem: 13824Mb, iter_time: 1.061s, data_time: 0.017s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:48:35\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 90/125, mem: 13824Mb, iter_time: 1.273s, data_time: 0.010s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:48:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 100/125, mem: 13824Mb, iter_time: 1.129s, data_time: 0.015s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:47:28\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:18:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 110/125, mem: 13824Mb, iter_time: 1.123s, data_time: 0.020s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:46:54\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 10/20, iter: 120/125, mem: 13824Mb, iter_time: 1.138s, data_time: 0.015s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:46:21\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:14\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.20it/s]\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.51 ms, Average NMS time: 1.35 ms, Average inference time: 5.86 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.060\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.121\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.048\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.077\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.079\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.088\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.088\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.108\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch11\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:19:51\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 10/125, mem: 13824Mb, iter_time: 1.095s, data_time: 0.012s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:45:31\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 20/125, mem: 13824Mb, iter_time: 1.293s, data_time: 0.011s, total_loss: 3.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:45:00\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:15\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 30/125, mem: 13824Mb, iter_time: 1.087s, data_time: 0.011s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:44:28\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 40/125, mem: 13824Mb, iter_time: 1.141s, data_time: 0.010s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:43:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:37\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 50/125, mem: 13824Mb, iter_time: 1.039s, data_time: 0.007s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:43:24\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 60/125, mem: 13824Mb, iter_time: 1.146s, data_time: 0.006s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:42:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:20:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 70/125, mem: 13824Mb, iter_time: 1.037s, data_time: 0.018s, total_loss: 3.4, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:42:21\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:21:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 80/125, mem: 13824Mb, iter_time: 1.344s, data_time: 0.009s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:41:52\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:21:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 90/125, mem: 13824Mb, iter_time: 1.271s, data_time: 0.010s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:41:23\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:21:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 100/125, mem: 13824Mb, iter_time: 1.109s, data_time: 0.012s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:40:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:21:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 110/125, mem: 13824Mb, iter_time: 1.221s, data_time: 0.011s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:40:24\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 11/20, iter: 120/125, mem: 13824Mb, iter_time: 1.146s, data_time: 0.014s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:39:55\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:25<00:00,  1.17it/s]\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.35 ms, Average NMS time: 1.60 ms, Average inference time: 5.95 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.053\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.106\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.048\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.074\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.075\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.084\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.084\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch12\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 10/125, mem: 13824Mb, iter_time: 1.052s, data_time: 0.011s, total_loss: 3.3, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:39:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:22:54\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 20/125, mem: 13824Mb, iter_time: 1.187s, data_time: 0.012s, total_loss: 3.4, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:38:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:23:06\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 30/125, mem: 13824Mb, iter_time: 1.176s, data_time: 0.012s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:38:13\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:23:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 40/125, mem: 13824Mb, iter_time: 1.105s, data_time: 0.011s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:37:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:23:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 50/125, mem: 13824Mb, iter_time: 1.229s, data_time: 0.010s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:37:17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:23:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 60/125, mem: 13824Mb, iter_time: 1.057s, data_time: 0.010s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:36:49\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:23:50\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 70/125, mem: 13824Mb, iter_time: 0.997s, data_time: 0.008s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:36:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:03\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 80/125, mem: 13824Mb, iter_time: 1.351s, data_time: 0.008s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:35:54\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:15\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 90/125, mem: 13824Mb, iter_time: 1.153s, data_time: 0.011s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:35:27\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:27\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 100/125, mem: 13824Mb, iter_time: 1.217s, data_time: 0.010s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:35:01\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:39\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 110/125, mem: 13824Mb, iter_time: 1.167s, data_time: 0.014s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:34:34\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:50\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 12/20, iter: 120/125, mem: 13824Mb, iter_time: 1.169s, data_time: 0.009s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:34:08\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:24:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.23it/s]\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.00s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.37 ms, Average NMS time: 1.49 ms, Average inference time: 5.86 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.046\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.086\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.067\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.073\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.073\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.090\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch13\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:33\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 10/125, mem: 13824Mb, iter_time: 1.163s, data_time: 0.015s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:33:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 20/125, mem: 13824Mb, iter_time: 1.068s, data_time: 0.010s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:33:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:25:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 30/125, mem: 13824Mb, iter_time: 1.314s, data_time: 0.012s, total_loss: 3.1, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:32:38\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:26:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 40/125, mem: 13824Mb, iter_time: 1.071s, data_time: 0.010s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:32:12\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:26:18\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 50/125, mem: 13824Mb, iter_time: 1.040s, data_time: 0.008s, total_loss: 3.5, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:31:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:26:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 60/125, mem: 13824Mb, iter_time: 1.130s, data_time: 0.008s, total_loss: 2.8, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:31:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:26:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 70/125, mem: 13824Mb, iter_time: 1.381s, data_time: 0.009s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:30:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:26:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 80/125, mem: 13824Mb, iter_time: 0.885s, data_time: 0.009s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:30:30\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:27:04\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 90/125, mem: 13824Mb, iter_time: 1.170s, data_time: 0.008s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:30:05\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:27:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 100/125, mem: 13824Mb, iter_time: 1.298s, data_time: 0.013s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:29:42\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:27:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 110/125, mem: 13824Mb, iter_time: 1.198s, data_time: 0.016s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:29:18\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:27:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 13/20, iter: 120/125, mem: 13824Mb, iter_time: 1.065s, data_time: 0.013s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:28:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:27:46\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.24it/s]\r\n",
      "\u001b[32m2022-01-11 23:28:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.00s)\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:28:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:28:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.45 ms, Average NMS time: 1.36 ms, Average inference time: 5.80 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.045\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.084\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.042\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.067\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.075\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.075\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.091\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch14\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 10/125, mem: 13824Mb, iter_time: 1.084s, data_time: 0.013s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:28:17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 20/125, mem: 13824Mb, iter_time: 1.158s, data_time: 0.010s, total_loss: 2.8, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:27:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 30/125, mem: 13824Mb, iter_time: 1.052s, data_time: 0.013s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:27:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:28:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 40/125, mem: 13824Mb, iter_time: 1.216s, data_time: 0.010s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:27:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:29:09\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 50/125, mem: 13824Mb, iter_time: 1.204s, data_time: 0.012s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:26:43\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:29:20\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 60/125, mem: 13824Mb, iter_time: 1.142s, data_time: 0.013s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:26:19\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:29:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 70/125, mem: 13824Mb, iter_time: 1.146s, data_time: 0.010s, total_loss: 3.4, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:25:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:29:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 80/125, mem: 13824Mb, iter_time: 1.093s, data_time: 0.013s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:25:33\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:29:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 90/125, mem: 13824Mb, iter_time: 1.210s, data_time: 0.010s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:25:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:30:06\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 100/125, mem: 13824Mb, iter_time: 1.104s, data_time: 0.010s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:24:47\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:30:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 110/125, mem: 13824Mb, iter_time: 1.132s, data_time: 0.016s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:24:25\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:30:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 14/20, iter: 120/125, mem: 13824Mb, iter_time: 1.158s, data_time: 0.010s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:24:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:30:35\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.25it/s]\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.13 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.34 ms, Average NMS time: 1.09 ms, Average inference time: 5.43 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.047\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.093\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.043\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.063\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.071\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.076\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.076\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:00\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch15\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 10/125, mem: 13824Mb, iter_time: 1.169s, data_time: 0.012s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:23:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 20/125, mem: 13824Mb, iter_time: 0.966s, data_time: 0.013s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:23:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 30/125, mem: 13824Mb, iter_time: 1.021s, data_time: 0.008s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:22:43\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 40/125, mem: 13824Mb, iter_time: 1.346s, data_time: 0.019s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:22:22\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:31:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 50/125, mem: 13824Mb, iter_time: 1.159s, data_time: 0.012s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:22:00\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:32:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 60/125, mem: 13824Mb, iter_time: 1.120s, data_time: 0.013s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:21:39\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:32:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 70/125, mem: 13824Mb, iter_time: 1.104s, data_time: 0.012s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:21:17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:32:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 80/125, mem: 13824Mb, iter_time: 1.162s, data_time: 0.009s, total_loss: 2.7, iou_loss: 1.5, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:20:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:32:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 90/125, mem: 13824Mb, iter_time: 1.129s, data_time: 0.013s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:20:34\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:32:54\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 100/125, mem: 13824Mb, iter_time: 1.203s, data_time: 0.014s, total_loss: 3.0, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:20:13\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:06\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 110/125, mem: 13824Mb, iter_time: 1.155s, data_time: 0.016s, total_loss: 3.0, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:19:52\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 15/20, iter: 120/125, mem: 13824Mb, iter_time: 1.075s, data_time: 0.010s, total_loss: 3.2, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:19:31\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.24it/s]\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.00s)\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.39 ms, Average NMS time: 1.44 ms, Average inference time: 5.83 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.052\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.097\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.047\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.066\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.070\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.077\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.077\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:48\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:33:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 10/125, mem: 13824Mb, iter_time: 1.008s, data_time: 0.011s, total_loss: 3.1, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:18:59\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:34:10\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 20/125, mem: 13824Mb, iter_time: 1.122s, data_time: 0.011s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:18:38\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:34:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 30/125, mem: 13824Mb, iter_time: 1.180s, data_time: 0.014s, total_loss: 2.9, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:18:18\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:34:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 40/125, mem: 13824Mb, iter_time: 1.198s, data_time: 0.008s, total_loss: 2.9, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:17:57\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:34:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 50/125, mem: 13824Mb, iter_time: 1.020s, data_time: 0.008s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:17:36\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:34:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 60/125, mem: 13824Mb, iter_time: 1.083s, data_time: 0.006s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:17:16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:35:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 70/125, mem: 13824Mb, iter_time: 1.235s, data_time: 0.010s, total_loss: 3.0, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:16:56\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:35:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 80/125, mem: 13824Mb, iter_time: 1.244s, data_time: 0.010s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:16:36\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:35:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 90/125, mem: 13824Mb, iter_time: 1.121s, data_time: 0.011s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.6, lr: 2.500e-04, size: 480, ETA: 0:16:16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:35:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 100/125, mem: 13824Mb, iter_time: 0.963s, data_time: 0.008s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:15:55\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:35:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 110/125, mem: 13824Mb, iter_time: 1.210s, data_time: 0.010s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:15:35\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 16/20, iter: 120/125, mem: 13824Mb, iter_time: 1.282s, data_time: 0.013s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:15:16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.22it/s]\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "COCOeval_opt.evaluate() finished in 0.09 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.60 ms, Average NMS time: 1.64 ms, Average inference time: 6.23 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.051\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.094\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.051\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.072\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.079\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.079\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:37\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 10/125, mem: 13824Mb, iter_time: 0.881s, data_time: 0.008s, total_loss: 3.5, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:14:45\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:36:56\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 20/125, mem: 13824Mb, iter_time: 1.072s, data_time: 0.015s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:14:25\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:37:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 30/125, mem: 13824Mb, iter_time: 1.116s, data_time: 0.009s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:14:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:37:18\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 40/125, mem: 13824Mb, iter_time: 1.032s, data_time: 0.007s, total_loss: 2.7, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:13:46\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:37:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 50/125, mem: 13824Mb, iter_time: 1.174s, data_time: 0.007s, total_loss: 3.0, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:13:27\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:37:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 60/125, mem: 13824Mb, iter_time: 1.422s, data_time: 0.010s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:13:08\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:37:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 70/125, mem: 13824Mb, iter_time: 1.111s, data_time: 0.012s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:12:49\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:06\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 80/125, mem: 13824Mb, iter_time: 1.080s, data_time: 0.011s, total_loss: 2.8, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:12:29\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:18\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 90/125, mem: 13824Mb, iter_time: 1.208s, data_time: 0.013s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:12:10\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 100/125, mem: 13824Mb, iter_time: 1.213s, data_time: 0.010s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:11:51\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:41\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 110/125, mem: 13824Mb, iter_time: 1.122s, data_time: 0.012s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:11:32\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 17/20, iter: 120/125, mem: 13824Mb, iter_time: 1.034s, data_time: 0.010s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:11:13\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:38:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:25<00:00,  1.17it/s]\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.00s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.74 ms, Average NMS time: 1.28 ms, Average inference time: 6.02 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.051\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.102\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.043\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.074\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.080\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.080\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:24\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch18\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:34\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 10/125, mem: 13824Mb, iter_time: 1.013s, data_time: 0.013s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:10:45\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 20/125, mem: 13824Mb, iter_time: 1.099s, data_time: 0.011s, total_loss: 2.8, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:10:26\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:39:57\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 30/125, mem: 13824Mb, iter_time: 1.242s, data_time: 0.011s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:10:08\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:40:09\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 40/125, mem: 13824Mb, iter_time: 1.221s, data_time: 0.014s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:09:49\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:40:21\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 50/125, mem: 13824Mb, iter_time: 1.123s, data_time: 0.010s, total_loss: 3.3, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:09:31\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:40:32\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 60/125, mem: 13824Mb, iter_time: 1.080s, data_time: 0.008s, total_loss: 3.2, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:09:12\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:40:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 70/125, mem: 13824Mb, iter_time: 1.035s, data_time: 0.019s, total_loss: 3.2, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:08:53\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:40:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 80/125, mem: 13824Mb, iter_time: 1.039s, data_time: 0.008s, total_loss: 3.4, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:08:35\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:41:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 90/125, mem: 13824Mb, iter_time: 1.230s, data_time: 0.011s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 736, ETA: 0:08:17\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:41:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 100/125, mem: 13824Mb, iter_time: 1.216s, data_time: 0.007s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:07:59\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:41:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 110/125, mem: 13824Mb, iter_time: 1.243s, data_time: 0.014s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:07:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:41:41\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 18/20, iter: 120/125, mem: 13824Mb, iter_time: 1.188s, data_time: 0.012s, total_loss: 2.8, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:07:23\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:41:47\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.24it/s]\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.00s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.24 ms, Average NMS time: 1.29 ms, Average inference time: 5.53 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.035\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.065\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.037\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.047\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.055\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.062\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.062\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.076\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch19\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:22\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 10/125, mem: 13824Mb, iter_time: 0.955s, data_time: 0.012s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:06:55\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:33\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 20/125, mem: 13824Mb, iter_time: 1.110s, data_time: 0.007s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:06:38\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 30/125, mem: 13824Mb, iter_time: 1.213s, data_time: 0.012s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 672, ETA: 0:06:20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:42:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 40/125, mem: 13824Mb, iter_time: 0.989s, data_time: 0.012s, total_loss: 3.4, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:06:02\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:43:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 50/125, mem: 13824Mb, iter_time: 1.014s, data_time: 0.010s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 512, ETA: 0:05:44\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:43:19\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 60/125, mem: 13824Mb, iter_time: 1.407s, data_time: 0.011s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:05:26\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:43:31\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 70/125, mem: 13824Mb, iter_time: 1.116s, data_time: 0.012s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:05:09\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:43:41\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 80/125, mem: 13824Mb, iter_time: 1.030s, data_time: 0.010s, total_loss: 3.2, iou_loss: 1.9, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:04:51\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:43:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 90/125, mem: 13824Mb, iter_time: 1.178s, data_time: 0.010s, total_loss: 2.9, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 640, ETA: 0:04:34\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 100/125, mem: 13824Mb, iter_time: 0.955s, data_time: 0.008s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:04:16\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 110/125, mem: 13824Mb, iter_time: 1.404s, data_time: 0.012s, total_loss: 3.0, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 800, ETA: 0:03:59\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:25\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 19/20, iter: 120/125, mem: 13824Mb, iter_time: 0.907s, data_time: 0.007s, total_loss: 3.1, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:03:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:33\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:24<00:00,  1.24it/s]\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.20 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.87 ms, Average NMS time: 1.42 ms, Average inference time: 6.29 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.043\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.079\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.042\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.062\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.072\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.079\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.079\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.098\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:44:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1m---> start train epoch20\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:45:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 10/125, mem: 13824Mb, iter_time: 0.998s, data_time: 0.011s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 544, ETA: 0:03:15\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:45:20\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 20/125, mem: 13824Mb, iter_time: 1.160s, data_time: 0.010s, total_loss: 2.7, iou_loss: 1.5, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 608, ETA: 0:02:58\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:45:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 30/125, mem: 13824Mb, iter_time: 1.027s, data_time: 0.007s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.3, conf_loss: 0.6, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:02:41\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:45:41\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 40/125, mem: 13824Mb, iter_time: 1.128s, data_time: 0.015s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:02:23\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:45:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 50/125, mem: 13824Mb, iter_time: 1.161s, data_time: 0.024s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:02:06\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:46:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 60/125, mem: 13824Mb, iter_time: 1.186s, data_time: 0.010s, total_loss: 2.6, iou_loss: 1.5, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.4, lr: 2.500e-04, size: 736, ETA: 0:01:49\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:46:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 70/125, mem: 13824Mb, iter_time: 1.089s, data_time: 0.012s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 576, ETA: 0:01:32\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:46:28\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 80/125, mem: 13824Mb, iter_time: 1.243s, data_time: 0.012s, total_loss: 2.8, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 768, ETA: 0:01:15\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:46:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 90/125, mem: 13824Mb, iter_time: 1.135s, data_time: 0.011s, total_loss: 2.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.4, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:00:58\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:46:51\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 100/125, mem: 13824Mb, iter_time: 1.144s, data_time: 0.008s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 704, ETA: 0:00:42\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 110/125, mem: 13824Mb, iter_time: 1.074s, data_time: 0.017s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:00:25\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:12\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m246\u001b[0m - \u001b[1mepoch: 20/20, iter: 120/125, mem: 13824Mb, iter_time: 1.028s, data_time: 0.009s, total_loss: 3.0, iou_loss: 1.7, l1_loss: 0.3, conf_loss: 0.5, cls_loss: 0.5, lr: 2.500e-04, size: 480, ETA: 0:00:08\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:17\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "100%|##########| 30/30 [00:26<00:00,  1.13it/s]\r\n",
      "\u001b[32m2022-01-11 23:47:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m171\u001b[0m - \u001b[1mEvaluate in main process...\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mLoading and preparing results...\u001b[0m\r\n",
      "Running per image evaluation...\r\n",
      "Evaluate annotation type *bbox*\r\n",
      "\u001b[32m2022-01-11 23:47:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.evaluators.coco_evaluator\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mDONE (t=0.01s)\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mcreating index...\u001b[0m\r\n",
      "COCOeval_opt.evaluate() finished in 0.08 seconds.\r\n",
      "Accumulating evaluation results...\r\n",
      "\u001b[32m2022-01-11 23:47:44\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpycocotools.coco\u001b[0m:\u001b[36m363\u001b[0m - \u001b[1mindex created!\u001b[0m\r\n",
      "COCOeval_opt.accumulate() finished in 0.01 seconds.\r\n",
      "\u001b[32m2022-01-11 23:47:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m309\u001b[0m - \u001b[1m\r\n",
      "Average forward time: 4.13 ms, Average NMS time: 1.40 ms, Average inference time: 5.53 ms\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.041\r\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.082\r\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.035\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.062\r\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.067\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.073\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.073\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.089\r\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m318\u001b[0m - \u001b[1mSave weights to ./YOLOX_outputs/cots_config\u001b[0m\r\n",
      "\u001b[32m2022-01-11 23:47:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36myolox.core.trainer\u001b[0m:\u001b[36m184\u001b[0m - \u001b[1mTraining of experiment is done and the best AP is 8.22\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!python train.py -f cots_config.py -d 1 -b 32 --fp16 -o -c {MODEL}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dedec3e",
   "metadata": {
    "papermill": {
     "duration": 0.267343,
     "end_time": "2022-01-11T23:47:47.617185",
     "exception": false,
     "start_time": "2022-01-11T23:47:47.349842",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 7. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74feea9",
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     "start_time": "2022-01-11T23:47:48.057210",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The following was taken from Remek Kina's excellent training pipeline notebook: https://www.kaggle.com/remekkinas/yolox-training-pipeline-cots-dataset-lb-0-507"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b2e311cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T23:47:48.975659Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from yolox.utils import postprocess\n",
    "from yolox.data.data_augment import ValTransform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb36d527",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_exp = importlib.import_module('cots_config')\n",
    "exp = current_exp.Exp()\n",
    "test_size = (640, 640)\n",
    "num_classes = 1\n",
    "confthre = 0.01\n",
    "nmsthre = 0.65\n",
    "model = exp.get_model()\n",
    "model.cuda()\n",
    "model.eval()\n",
    "ckpt = torch.load(\"./YOLOX_outputs/cots_config/best_ckpt.pth\", map_location=\"cpu\")\n",
    "model.load_state_dict(ckpt[\"model\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "baacf3b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T23:47:52.395813Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/working\n"
     ]
    }
   ],
   "source": [
    "%cd /kaggle/working/\n",
    "def yolox_inference(img, model, test_size): \n",
    "    bboxes = []\n",
    "    bbclasses = []\n",
    "    scores = []\n",
    "    \n",
    "    preproc = ValTransform(legacy = False)\n",
    "\n",
    "    tensor_img, _ = preproc(img, None, test_size)\n",
    "    tensor_img = torch.from_numpy(tensor_img).unsqueeze(0)\n",
    "    tensor_img = tensor_img.float()\n",
    "    tensor_img = tensor_img.cuda()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        outputs = model(tensor_img)\n",
    "        outputs = postprocess(\n",
    "                    outputs, num_classes, confthre,\n",
    "                    nmsthre, class_agnostic=True\n",
    "                )\n",
    "\n",
    "    if outputs[0] is None:\n",
    "        return [], [], []\n",
    "    \n",
    "    outputs = outputs[0].cpu()\n",
    "    bboxes = outputs[:, 0:4]\n",
    "\n",
    "    bboxes /= min(test_size[0] / img.shape[0], test_size[1] / img.shape[1])\n",
    "    bbclasses = outputs[:, 6]\n",
    "    scores = outputs[:, 4] * outputs[:, 5]\n",
    "    \n",
    "    return bboxes, bbclasses, scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c3eef4b",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-01-11T23:47:52.675634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "env = greatbarrierreef.make_env()   # initialize the environment\n",
    "iter_test = env.iter_test()    # an iterator which loops over the test set and sample submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31e2a444",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-11T23:47:53.501993Z",
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     "start_time": "2022-01-11T23:47:53.230483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This version of the API is not optimized and should not be used to estimate the runtime of your code on the hidden test set.\n",
      "Prediction: 0.04 72 496 38 39 0.02 65 264 57 35\n",
      "Prediction: 0.06 60 512 46 38\n",
      "Prediction: 0.02 48 518 37 36\n"
     ]
    }
   ],
   "source": [
    "submission_dict = {\n",
    "    'id': [],\n",
    "    'prediction_string': [],\n",
    "}\n",
    "\n",
    "for (image_np, sample_prediction_df) in iter_test:\n",
    " \n",
    "    bboxes, bbclasses, scores = yolox_inference(image_np, model, test_size)\n",
    "    \n",
    "    predictions = []\n",
    "    for i in range(len(bboxes)):\n",
    "        box = bboxes[i]\n",
    "        cls_id = int(bbclasses[i])\n",
    "        score = scores[i]\n",
    "        if score < confthre:\n",
    "            continue\n",
    "        x_min = int(box[0])\n",
    "        y_min = int(box[1])\n",
    "        x_max = int(box[2])\n",
    "        y_max = int(box[3])\n",
    "        \n",
    "        bbox_width = x_max - x_min\n",
    "        bbox_height = y_max - y_min\n",
    "        \n",
    "        predictions.append('{:.2f} {} {} {} {}'.format(score, x_min, y_min, bbox_width, bbox_height))\n",
    "    \n",
    "    prediction_str = ' '.join(predictions)\n",
    "    sample_prediction_df['annotations'] = prediction_str\n",
    "    env.predict(sample_prediction_df)\n",
    "\n",
    "    print('Prediction:', prediction_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86ad8db6",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>annotations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   index                          annotations\n",
       "0      0  0.04 72 496 38 39 0.02 65 264 57 35\n",
       "1      1                    0.06 60 512 46 38\n",
       "2      2                    0.02 48 518 37 36"
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     "metadata": {},
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   ],
   "source": [
    "sub_df = pd.read_csv('submission.csv')\n",
    "sub_df.head()"
   ]
  }
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