paddleX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# PaddleX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.客户端图像分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.1数据选择\n",
    "\n",
    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/1c1dc0d6f757404cb461299bc4061c77e7851379511a400684b427d13237d206\" width=\"600\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.2参数配置\n",
    "\n",
    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/67f20c83adfc48ea99b8d4e2967ece607584b6e794c24d46aa5df9d0fcaca363\" width=\"600\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.3模型训练\n",
    "\n",
    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/28c5987924b84a4fac7c5af8076cc936ab8fb5b9270e41f3be5dfb86477ecf0b\" width=\"600\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.4测试图片\n",
    "\n",
    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/54c5a20ea19e4f7eb36360694bbf4397454a72e3f2ad4b5d81bcfdb67e74baad\" width=\"600\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.客户端图像分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!unzip -oq /home/aistudio/data/data81942/00.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
      "Collecting paddlex\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/02/a4/90f3f1f997b65952d9517c74a3f672ba83721c124a481bc0cdc295e8adbc/paddlex-1.3.7-py3-none-any.whl (514kB)\n",
      "\u001b[K     |████████████████████████████████| 522kB 14.1MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: visualdl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (2.1.1)\n",
      "Requirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (4.1.1.26)\n",
      "Collecting xlwt (from paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/44/48/def306413b25c3d01753603b1a222a011b8621aed27cd7f89cbc27e6b0f4/xlwt-1.3.0-py2.py3-none-any.whl (99kB)\n",
      "\u001b[K     |████████████████████████████████| 102kB 19.7MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting pycocotools; platform_system != \"Windows\" (from paddlex)\n",
      "  Downloading https://mirror.baidu.com/pypi/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz\n",
      "Requirement already satisfied: flask-cors in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (3.0.8)\n",
      "Requirement already satisfied: psutil in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (5.7.2)\n",
      "Requirement already satisfied: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (5.1.2)\n",
      "Requirement already satisfied: colorama in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (0.4.4)\n",
      "Requirement already satisfied: sklearn in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (0.0)\n",
      "Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (4.36.1)\n",
      "Collecting shapely>=1.7.0 (from paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/98/f8/db4d3426a1aba9d5dfcc83ed5a3e2935d2b1deb73d350642931791a61c37/Shapely-1.7.1-cp37-cp37m-manylinux1_x86_64.whl (1.0MB)\n",
      "\u001b[K     |████████████████████████████████| 1.0MB 13.1MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting paddleslim==1.1.1 (from paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/d1/77/e257227bed9a70ff0d35a4a3c4e70ac2d2362c803834c4c52018f7c4b762/paddleslim-1.1.1-py2.py3-none-any.whl (145kB)\n",
      "\u001b[K     |████████████████████████████████| 153kB 70.5MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six>=1.14.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (1.15.0)\n",
      "Requirement already satisfied: bce-python-sdk in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (0.8.53)\n",
      "Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (2.22.0)\n",
      "Requirement already satisfied: shellcheck-py in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (0.7.1.1)\n",
      "Requirement already satisfied: Flask-Babel>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (1.0.0)\n",
      "Requirement already satisfied: Pillow>=7.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (7.1.2)\n",
      "Requirement already satisfied: pre-commit in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (1.21.0)\n",
      "Requirement already satisfied: flake8>=3.7.9 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (3.8.2)\n",
      "Requirement already satisfied: flask>=1.1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (1.1.1)\n",
      "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (1.16.4)\n",
      "Requirement already satisfied: protobuf>=3.11.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlex) (3.14.0)\n",
      "Requirement already satisfied: setuptools>=18.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pycocotools; platform_system != \"Windows\"->paddlex) (41.4.0)\n",
      "Requirement already satisfied: cython>=0.27.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pycocotools; platform_system != \"Windows\"->paddlex) (0.29)\n",
      "Requirement already satisfied: matplotlib>=2.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pycocotools; platform_system != \"Windows\"->paddlex) (2.2.3)\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from sklearn->paddlex) (0.22.1)\n",
      "Requirement already satisfied: pyzmq in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddleslim==1.1.1->paddlex) (18.1.1)\n",
      "Requirement already satisfied: future>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlex) (0.18.0)\n",
      "Requirement already satisfied: pycryptodome>=3.8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlex) (3.9.9)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlex) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlex) (2019.9.11)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlex) (1.25.6)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlex) (2.8)\n",
      "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddlex) (2019.3)\n",
      "Requirement already satisfied: Babel>=2.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddlex) (2.8.0)\n",
      "Requirement already satisfied: Jinja2>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddlex) (2.10.1)\n",
      "Requirement already satisfied: nodeenv>=0.11.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (1.3.4)\n",
      "Requirement already satisfied: identify>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (1.4.10)\n",
      "Requirement already satisfied: virtualenv>=15.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (16.7.9)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (0.23)\n",
      "Requirement already satisfied: cfgv>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (2.0.1)\n",
      "Requirement already satisfied: aspy.yaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (1.3.0)\n",
      "Requirement already satisfied: toml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlex) (0.10.0)\n",
      "Requirement already satisfied: pyflakes<2.3.0,>=2.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlex) (2.2.0)\n",
      "Requirement already satisfied: mccabe<0.7.0,>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlex) (0.6.1)\n",
      "Requirement already satisfied: pycodestyle<2.7.0,>=2.6.0a1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlex) (2.6.0)\n",
      "Requirement already satisfied: itsdangerous>=0.24 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl>=2.0.0->paddlex) (1.1.0)\n",
      "Requirement already satisfied: Werkzeug>=0.15 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl>=2.0.0->paddlex) (0.16.0)\n",
      "Requirement already satisfied: click>=5.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.1->visualdl>=2.0.0->paddlex) (7.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools; platform_system != \"Windows\"->paddlex) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools; platform_system != \"Windows\"->paddlex) (2.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools; platform_system != \"Windows\"->paddlex) (2.8.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools; platform_system != \"Windows\"->paddlex) (0.10.0)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->sklearn->paddlex) (1.3.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->sklearn->paddlex) (0.14.1)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.5->Flask-Babel>=1.0.0->visualdl>=2.0.0->paddlex) (1.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->pre-commit->visualdl>=2.0.0->paddlex) (0.6.0)\n",
      "Requirement already satisfied: more-itertools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from zipp>=0.5->importlib-metadata; python_version < \"3.8\"->pre-commit->visualdl>=2.0.0->paddlex) (7.2.0)\n",
      "Building wheels for collected packages: pycocotools\n",
      "  Building wheel for pycocotools (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=278369 sha256=1bbca2ad58aaf47537f2eaacd24043f0049fab5e950e1d2f2f564b2c7537864b\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/fb/44/67/8baa69040569b1edbd7776ec6f82c387663e724908aaa60963\n",
      "Successfully built pycocotools\n",
      "Installing collected packages: xlwt, pycocotools, shapely, paddleslim, paddlex\n",
      "Successfully installed paddleslim-1.1.1 paddlex-1.3.7 pycocotools-2.0.2 shapely-1.7.1 xlwt-1.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install paddlex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  import imp\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!paddlex --data_conversion --source labelme --to MSCOCO \\\n",
    "        --pics 00 \\\n",
    "        --annotations 00 \\\n",
    "        --save_dir dataset_coco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  import imp\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "\u001b[0m2021-04-21 19:08:53,694 - INFO - font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']\n",
      "2021-04-21 19:08:54,064 - INFO - generated new fontManager\n",
      "loading annotations into memory...\u001b[0m\n",
      "\u001b[0mDone (t=0.02s)\u001b[0m\n",
      "\u001b[0mcreating index...\u001b[0m\n",
      "\u001b[0mindex created!\u001b[0m\n",
      "\u001b[0mDataset Split Done.\u001b[0m\n",
      "\u001b[0mTrain samples: 293\u001b[0m\n",
      "\u001b[0mEval samples: 83\u001b[0m\n",
      "\u001b[0mTest samples: 41\u001b[0m\n",
      "\u001b[0mSplit files saved in dataset_coco\u001b[0m\n",
      "\u001b[0m\u001b[0m"
     ]
    }
   ],
   "source": [
    "!paddlex --split_dataset --format COCO --dataset_dir dataset_coco --val_value 0.2 --test_value 0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from paddlex.det import transforms\n",
    "\n",
    "train_transforms = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(), transforms.Normalize(),\n",
    "    transforms.ResizeByShort(\n",
    "        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)\n",
    "])\n",
    "\n",
    "eval_transforms = transforms.Compose([\n",
    "    transforms.Normalize(), transforms.ResizeByShort(\n",
    "        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "2021-04-21 19:10:40 [INFO]\tStarting to read file list from dataset...\n",
      "2021-04-21 19:10:40 [INFO]\t293 samples in file dataset_coco/train.json\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "2021-04-21 19:10:40 [INFO]\tStarting to read file list from dataset...\n",
      "2021-04-21 19:10:40 [INFO]\t83 samples in file dataset_coco/val.json\n"
     ]
    }
   ],
   "source": [
    "import paddlex as pdx\n",
    "\n",
    "train_dataset = pdx.datasets.CocoDetection(\n",
    "    data_dir='dataset_coco/JPEGImages',\n",
    "    ann_file='dataset_coco/train.json',\n",
    "    transforms=train_transforms,\n",
    "    shuffle=True)\n",
    "eval_dataset = pdx.datasets.CocoDetection(\n",
    "    data_dir='dataset_coco/JPEGImages',\n",
    "    ann_file='dataset_coco/val.json',\n",
    "    transforms=eval_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/nets/detection/fpn.py:172\n",
      "The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "2021-04-21 19:11:13,012 - INFO - If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. The Regularization[L2Decay, regularization_coeff=0.000100] in Optimizer will not take effect, and it will only be applied to other Parameters!\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/nets/detection/bbox_head.py:278\n",
      "The behavior of expression A / B has been unified with elementwise_div(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_div(X, Y, axis=0) instead of A / B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/nets/detection/mask_rcnn.py:349\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-21 19:11:13 [INFO]\tDownloading ResNet50_cos_pretrained.tar from https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100310/100310 [00:06<00:00, 15145.95KB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-21 19:11:20 [INFO]\tDecompressing output/mask_rcnn_r50_fpn/pretrain/ResNet50_cos_pretrained.tar...\n",
      "2021-04-21 19:11:25 [INFO]\tLoad pretrain weights from output/mask_rcnn_r50_fpn/pretrain/ResNet50_cos_pretrained.\n",
      "2021-04-21 19:11:26 [INFO]\tThere are 265 varaibles in output/mask_rcnn_r50_fpn/pretrain/ResNet50_cos_pretrained are loaded.\n",
      "2021-04-21 19:11:34 [INFO]\t[TRAIN] Epoch=1/100, Step=2/73, loss=3.117201, loss_cls=0.713111, loss_bbox=0.0, loss_mask=1.336037, loss_rpn_cls=0.692118, loss_rpn_bbox=0.375935, lr=0.0005, time_each_step=3.78s, eta=9:52:24\n",
      "2021-04-21 19:11:35 [INFO]\t[TRAIN] Epoch=1/100, Step=4/73, loss=2.03852, loss_cls=0.56796, loss_bbox=0.0, loss_mask=0.548011, loss_rpn_cls=0.694677, loss_rpn_bbox=0.227872, lr=0.000667, time_each_step=2.24s, eta=5:50:49\n",
      "2021-04-21 19:11:36 [INFO]\t[TRAIN] Epoch=1/100, Step=6/73, loss=1.908709, loss_cls=0.478807, loss_bbox=0.0, loss_mask=0.548671, loss_rpn_cls=0.694603, loss_rpn_bbox=0.186628, lr=0.000833, time_each_step=1.69s, eta=4:25:5\n",
      "2021-04-21 19:11:37 [INFO]\t[TRAIN] Epoch=1/100, Step=8/73, loss=1.654609, loss_cls=0.371165, loss_bbox=0.0, loss_mask=0.465646, loss_rpn_cls=0.691886, loss_rpn_bbox=0.125912, lr=0.001, time_each_step=1.41s, eta=3:40:17\n",
      "2021-04-21 19:11:38 [INFO]\t[TRAIN] Epoch=1/100, Step=10/73, loss=1.710644, loss_cls=0.220723, loss_bbox=0.0, loss_mask=0.533999, loss_rpn_cls=0.689527, loss_rpn_bbox=0.266394, lr=0.001167, time_each_step=1.23s, eta=3:12:39\n",
      "2021-04-21 19:11:39 [INFO]\t[TRAIN] Epoch=1/100, Step=12/73, loss=1.694834, loss_cls=0.093619, loss_bbox=0.0, loss_mask=0.488023, loss_rpn_cls=0.686227, loss_rpn_bbox=0.426966, lr=0.00125, time_each_step=1.11s, eta=2:53:13\n",
      "2021-04-21 19:11:40 [INFO]\t[TRAIN] Epoch=1/100, Step=14/73, loss=1.577713, loss_cls=0.02853, loss_bbox=0.0, loss_mask=0.512959, loss_rpn_cls=0.681416, loss_rpn_bbox=0.354808, lr=0.00125, time_each_step=1.02s, eta=2:38:48\n",
      "2021-04-21 19:11:41 [INFO]\t[TRAIN] Epoch=1/100, Step=16/73, loss=1.139136, loss_cls=0.011666, loss_bbox=0.0, loss_mask=0.352712, loss_rpn_cls=0.673994, loss_rpn_bbox=0.100764, lr=0.00125, time_each_step=0.95s, eta=2:28:44\n",
      "2021-04-21 19:11:42 [INFO]\t[TRAIN] Epoch=1/100, Step=18/73, loss=2.035163, loss_cls=0.066557, loss_bbox=0.0, loss_mask=1.033072, loss_rpn_cls=0.666622, loss_rpn_bbox=0.268912, lr=0.00125, time_each_step=0.9s, eta=2:20:21\n",
      "2021-04-21 19:11:43 [INFO]\t[TRAIN] Epoch=1/100, Step=20/73, loss=1.449488, loss_cls=0.100868, loss_bbox=0.0, loss_mask=0.42245, loss_rpn_cls=0.664528, loss_rpn_bbox=0.261642, lr=0.00125, time_each_step=0.86s, eta=2:14:28\n",
      "2021-04-21 19:11:44 [INFO]\t[TRAIN] Epoch=1/100, Step=22/73, loss=2.38989, loss_cls=0.080711, loss_bbox=0.0, loss_mask=0.908808, loss_rpn_cls=0.666079, loss_rpn_bbox=0.734291, lr=0.00125, time_each_step=0.53s, eta=1:23:2\n",
      "2021-04-21 19:11:45 [INFO]\t[TRAIN] Epoch=1/100, Step=24/73, loss=1.636004, loss_cls=0.067278, loss_bbox=0.0, loss_mask=0.5495, loss_rpn_cls=0.651664, loss_rpn_bbox=0.367562, lr=0.00125, time_each_step=0.51s, eta=1:19:1\n",
      "2021-04-21 19:11:46 [INFO]\t[TRAIN] Epoch=1/100, Step=26/73, loss=1.574502, loss_cls=0.021979, loss_bbox=0.0, loss_mask=0.558275, loss_rpn_cls=0.638479, loss_rpn_bbox=0.355768, lr=0.00125, time_each_step=0.49s, eta=1:16:32\n",
      "2021-04-21 19:11:47 [INFO]\t[TRAIN] Epoch=1/100, Step=28/73, loss=1.327915, loss_cls=0.007823, loss_bbox=0.0, loss_mask=0.446902, loss_rpn_cls=0.607782, loss_rpn_bbox=0.265409, lr=0.00125, time_each_step=0.47s, eta=1:13:54\n",
      "2021-04-21 19:11:48 [INFO]\t[TRAIN] Epoch=1/100, Step=30/73, loss=1.450873, loss_cls=0.006825, loss_bbox=0.0, loss_mask=0.456049, loss_rpn_cls=0.591738, loss_rpn_bbox=0.39626, lr=0.00125, time_each_step=0.46s, eta=1:12:32\n",
      "2021-04-21 19:11:49 [INFO]\t[TRAIN] Epoch=1/100, Step=32/73, loss=1.121831, loss_cls=0.005446, loss_bbox=0.0, loss_mask=0.45406, loss_rpn_cls=0.547134, loss_rpn_bbox=0.115191, lr=0.00125, time_each_step=0.46s, eta=1:12:11\n",
      "2021-04-21 19:11:49 [INFO]\t[TRAIN] Epoch=1/100, Step=34/73, loss=0.952773, loss_cls=0.007182, loss_bbox=0.0, loss_mask=0.358993, loss_rpn_cls=0.490748, loss_rpn_bbox=0.09585, lr=0.00125, time_each_step=0.46s, eta=1:12:13\n",
      "2021-04-21 19:11:50 [INFO]\t[TRAIN] Epoch=1/100, Step=36/73, loss=1.304763, loss_cls=0.014125, loss_bbox=1e-06, loss_mask=0.483886, loss_rpn_cls=0.443457, loss_rpn_bbox=0.363294, lr=0.00125, time_each_step=0.45s, eta=1:10:41\n",
      "2021-04-21 19:11:51 [INFO]\t[TRAIN] Epoch=1/100, Step=38/73, loss=1.066413, loss_cls=0.012497, loss_bbox=1e-06, loss_mask=0.446388, loss_rpn_cls=0.34562, loss_rpn_bbox=0.261907, lr=0.00125, time_each_step=0.45s, eta=1:10:29\n",
      "2021-04-21 19:11:52 [INFO]\t[TRAIN] Epoch=1/100, Step=40/73, loss=0.894237, loss_cls=0.013955, loss_bbox=1e-06, loss_mask=0.410653, loss_rpn_cls=0.277098, loss_rpn_bbox=0.192531, lr=0.00125, time_each_step=0.44s, eta=1:7:56\n",
      "2021-04-21 19:11:53 [INFO]\t[TRAIN] Epoch=1/100, Step=42/73, loss=0.593499, loss_cls=0.027165, loss_bbox=0.004895, loss_mask=0.338549, loss_rpn_cls=0.170364, loss_rpn_bbox=0.052526, lr=0.00125, time_each_step=0.42s, eta=1:5:53\n",
      "2021-04-21 19:11:53 [INFO]\t[TRAIN] Epoch=1/100, Step=44/73, loss=0.552946, loss_cls=0.015458, loss_bbox=0.004236, loss_mask=0.305457, loss_rpn_cls=0.175278, loss_rpn_bbox=0.052516, lr=0.00125, time_each_step=0.42s, eta=1:4:44\n",
      "2021-04-21 19:11:54 [INFO]\t[TRAIN] Epoch=1/100, Step=46/73, loss=0.785878, loss_cls=0.023203, loss_bbox=0.002921, loss_mask=0.41272, loss_rpn_cls=0.194211, loss_rpn_bbox=0.152823, lr=0.00125, time_each_step=0.41s, eta=1:3:26\n",
      "2021-04-21 19:11:55 [INFO]\t[TRAIN] Epoch=1/100, Step=48/73, loss=1.098801, loss_cls=0.03915, loss_bbox=0.013857, loss_mask=0.359218, loss_rpn_cls=0.245898, loss_rpn_bbox=0.440679, lr=0.00125, time_each_step=0.41s, eta=1:3:9\n",
      "2021-04-21 19:11:56 [INFO]\t[TRAIN] Epoch=1/100, Step=50/73, loss=0.995116, loss_cls=0.018896, loss_bbox=0.005603, loss_mask=0.359766, loss_rpn_cls=0.248026, loss_rpn_bbox=0.362825, lr=0.00125, time_each_step=0.4s, eta=1:1:56\n",
      "2021-04-21 19:11:56 [INFO]\t[TRAIN] Epoch=1/100, Step=52/73, loss=0.75729, loss_cls=0.07249, loss_bbox=0.018334, loss_mask=0.393425, loss_rpn_cls=0.148535, loss_rpn_bbox=0.124507, lr=0.00125, time_each_step=0.39s, eta=1:0:20\n",
      "2021-04-21 19:11:57 [INFO]\t[TRAIN] Epoch=1/100, Step=54/73, loss=0.790145, loss_cls=0.032169, loss_bbox=0.008009, loss_mask=0.385945, loss_rpn_cls=0.166722, loss_rpn_bbox=0.1973, lr=0.00125, time_each_step=0.38s, eta=0:58:44\n",
      "2021-04-21 19:11:58 [INFO]\t[TRAIN] Epoch=1/100, Step=56/73, loss=0.79022, loss_cls=0.040216, loss_bbox=0.018625, loss_mask=0.467073, loss_rpn_cls=0.156457, loss_rpn_bbox=0.10785, lr=0.00125, time_each_step=0.37s, eta=0:58:7\n",
      "2021-04-21 19:11:58 [INFO]\t[TRAIN] Epoch=1/100, Step=58/73, loss=0.76708, loss_cls=0.049423, loss_bbox=0.026305, loss_mask=0.353994, loss_rpn_cls=0.174791, loss_rpn_bbox=0.162567, lr=0.00125, time_each_step=0.36s, eta=0:56:38\n",
      "2021-04-21 19:11:59 [INFO]\t[TRAIN] Epoch=1/100, Step=60/73, loss=0.498955, loss_cls=0.054899, loss_bbox=0.025296, loss_mask=0.248168, loss_rpn_cls=0.108379, loss_rpn_bbox=0.062213, lr=0.00125, time_each_step=0.36s, eta=0:56:43\n",
      "2021-04-21 19:12:00 [INFO]\t[TRAIN] Epoch=1/100, Step=62/73, loss=0.651555, loss_cls=0.060398, loss_bbox=0.028884, loss_mask=0.286328, loss_rpn_cls=0.146182, loss_rpn_bbox=0.129762, lr=0.00125, time_each_step=0.36s, eta=0:56:47\n",
      "2021-04-21 19:12:01 [INFO]\t[TRAIN] Epoch=1/100, Step=64/73, loss=0.963121, loss_cls=0.064021, loss_bbox=0.018428, loss_mask=0.451157, loss_rpn_cls=0.192687, loss_rpn_bbox=0.236828, lr=0.00125, time_each_step=0.36s, eta=0:56:44\n",
      "2021-04-21 19:12:01 [INFO]\t[TRAIN] Epoch=1/100, Step=66/73, loss=0.654595, loss_cls=0.081392, loss_bbox=0.033798, loss_mask=0.306002, loss_rpn_cls=0.126193, loss_rpn_bbox=0.10721, lr=0.00125, time_each_step=0.37s, eta=0:56:50\n",
      "2021-04-21 19:12:02 [INFO]\t[TRAIN] Epoch=1/100, Step=68/73, loss=0.639708, loss_cls=0.05702, loss_bbox=0.021691, loss_mask=0.286353, loss_rpn_cls=0.146895, loss_rpn_bbox=0.127749, lr=0.00125, time_each_step=0.37s, eta=0:56:47\n",
      "2021-04-21 19:12:03 [INFO]\t[TRAIN] Epoch=1/100, Step=70/73, loss=0.457782, loss_cls=0.053756, loss_bbox=0.033917, loss_mask=0.256361, loss_rpn_cls=0.076238, loss_rpn_bbox=0.037511, lr=0.00125, time_each_step=0.37s, eta=0:56:51\n",
      "2021-04-21 19:12:04 [INFO]\t[TRAIN] Epoch=1/100, Step=72/73, loss=0.801837, loss_cls=0.058135, loss_bbox=0.02126, loss_mask=0.536132, loss_rpn_cls=0.106764, loss_rpn_bbox=0.079546, lr=0.00125, time_each_step=0.37s, eta=0:56:50\n",
      "2021-04-21 19:12:04 [INFO]\t[TRAIN] Epoch 1 finished, loss=1.194744, loss_cls=0.105859, loss_bbox=0.00893, loss_mask=0.473854, loss_rpn_cls=0.400438, loss_rpn_bbox=0.205663, lr=0.001187 .\n",
      "2021-04-21 19:12:04 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:12:04 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:17<00:00,  4.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.05s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.074\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.325\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.074\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.128\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.233\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.233\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.233\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.10s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.022\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.006\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n",
      "2021-04-21 19:12:23 [INFO]\t[EVAL] Finished, Epoch=1, bbox_mmap=0.074487, segm_mmap=0.002192 .\n",
      "2021-04-21 19:12:27 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:12:31 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_1.\n",
      "2021-04-21 19:12:31 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_1, bbox_mmap=0.07448673160912538\n",
      "2021-04-21 19:12:37 [INFO]\t[TRAIN] Epoch=2/100, Step=1/73, loss=0.725125, loss_cls=0.045561, loss_bbox=0.01951, loss_mask=0.32877, loss_rpn_cls=0.148322, loss_rpn_bbox=0.182961, lr=0.00125, time_each_step=0.62s, eta=1:48:7\n",
      "2021-04-21 19:12:38 [INFO]\t[TRAIN] Epoch=2/100, Step=3/73, loss=0.730044, loss_cls=0.050032, loss_bbox=0.022656, loss_mask=0.381644, loss_rpn_cls=0.146882, loss_rpn_bbox=0.128831, lr=0.00125, time_each_step=0.66s, eta=1:48:9\n",
      "2021-04-21 19:12:40 [INFO]\t[TRAIN] Epoch=2/100, Step=5/73, loss=0.606821, loss_cls=0.063679, loss_bbox=0.025646, loss_mask=0.307176, loss_rpn_cls=0.094033, loss_rpn_bbox=0.116289, lr=0.00125, time_each_step=0.69s, eta=1:48:9\n",
      "2021-04-21 19:12:41 [INFO]\t[TRAIN] Epoch=2/100, Step=7/73, loss=0.794368, loss_cls=0.056001, loss_bbox=0.022325, loss_mask=0.348254, loss_rpn_cls=0.154245, loss_rpn_bbox=0.213544, lr=0.00125, time_each_step=0.72s, eta=1:48:10\n",
      "2021-04-21 19:12:42 [INFO]\t[TRAIN] Epoch=2/100, Step=9/73, loss=1.064239, loss_cls=0.050829, loss_bbox=0.023399, loss_mask=0.403662, loss_rpn_cls=0.202895, loss_rpn_bbox=0.383455, lr=0.00125, time_each_step=0.74s, eta=1:48:10\n",
      "2021-04-21 19:12:43 [INFO]\t[TRAIN] Epoch=2/100, Step=11/73, loss=0.656052, loss_cls=0.046851, loss_bbox=0.024733, loss_mask=0.341009, loss_rpn_cls=0.123682, loss_rpn_bbox=0.119776, lr=0.00125, time_each_step=0.76s, eta=1:48:9\n",
      "2021-04-21 19:12:44 [INFO]\t[TRAIN] Epoch=2/100, Step=13/73, loss=0.642099, loss_cls=0.048969, loss_bbox=0.027276, loss_mask=0.295398, loss_rpn_cls=0.109586, loss_rpn_bbox=0.160869, lr=0.00125, time_each_step=0.77s, eta=1:48:9\n",
      "2021-04-21 19:12:45 [INFO]\t[TRAIN] Epoch=2/100, Step=15/73, loss=0.50728, loss_cls=0.051622, loss_bbox=0.04584, loss_mask=0.263318, loss_rpn_cls=0.08133, loss_rpn_bbox=0.06517, lr=0.00125, time_each_step=0.78s, eta=1:48:7\n",
      "2021-04-21 19:12:46 [INFO]\t[TRAIN] Epoch=2/100, Step=17/73, loss=0.394717, loss_cls=0.046211, loss_bbox=0.028857, loss_mask=0.225047, loss_rpn_cls=0.064367, loss_rpn_bbox=0.030235, lr=0.00125, time_each_step=0.79s, eta=1:48:7\n",
      "2021-04-21 19:12:47 [INFO]\t[TRAIN] Epoch=2/100, Step=19/73, loss=0.548258, loss_cls=0.071387, loss_bbox=0.047262, loss_mask=0.292008, loss_rpn_cls=0.086709, loss_rpn_bbox=0.050892, lr=0.00125, time_each_step=0.81s, eta=1:48:6\n",
      "2021-04-21 19:12:48 [INFO]\t[TRAIN] Epoch=2/100, Step=21/73, loss=0.556303, loss_cls=0.064376, loss_bbox=0.022553, loss_mask=0.29093, loss_rpn_cls=0.084664, loss_rpn_bbox=0.09378, lr=0.00125, time_each_step=0.57s, eta=1:47:52\n",
      "2021-04-21 19:12:49 [INFO]\t[TRAIN] Epoch=2/100, Step=23/73, loss=0.359361, loss_cls=0.052683, loss_bbox=0.035742, loss_mask=0.176729, loss_rpn_cls=0.057147, loss_rpn_bbox=0.037061, lr=0.00125, time_each_step=0.55s, eta=1:47:50\n",
      "2021-04-21 19:12:50 [INFO]\t[TRAIN] Epoch=2/100, Step=25/73, loss=0.576113, loss_cls=0.047235, loss_bbox=0.033904, loss_mask=0.297766, loss_rpn_cls=0.094011, loss_rpn_bbox=0.103197, lr=0.00125, time_each_step=0.53s, eta=1:47:48\n",
      "2021-04-21 19:12:51 [INFO]\t[TRAIN] Epoch=2/100, Step=27/73, loss=0.676772, loss_cls=0.051222, loss_bbox=0.021396, loss_mask=0.317863, loss_rpn_cls=0.132307, loss_rpn_bbox=0.153982, lr=0.00125, time_each_step=0.51s, eta=1:47:46\n",
      "2021-04-21 19:12:52 [INFO]\t[TRAIN] Epoch=2/100, Step=29/73, loss=0.536164, loss_cls=0.076206, loss_bbox=0.039008, loss_mask=0.25266, loss_rpn_cls=0.084193, loss_rpn_bbox=0.084097, lr=0.00125, time_each_step=0.49s, eta=1:47:44\n",
      "2021-04-21 19:12:53 [INFO]\t[TRAIN] Epoch=2/100, Step=31/73, loss=0.402495, loss_cls=0.036279, loss_bbox=0.023683, loss_mask=0.20042, loss_rpn_cls=0.078046, loss_rpn_bbox=0.064067, lr=0.00125, time_each_step=0.48s, eta=1:47:42\n",
      "2021-04-21 19:12:54 [INFO]\t[TRAIN] Epoch=2/100, Step=33/73, loss=0.487078, loss_cls=0.058901, loss_bbox=0.045445, loss_mask=0.205719, loss_rpn_cls=0.077051, loss_rpn_bbox=0.099962, lr=0.00125, time_each_step=0.46s, eta=1:47:41\n",
      "2021-04-21 19:12:54 [INFO]\t[TRAIN] Epoch=2/100, Step=35/73, loss=0.441326, loss_cls=0.043279, loss_bbox=0.030877, loss_mask=0.231113, loss_rpn_cls=0.063999, loss_rpn_bbox=0.072058, lr=0.00125, time_each_step=0.46s, eta=1:47:40\n",
      "2021-04-21 19:12:55 [INFO]\t[TRAIN] Epoch=2/100, Step=37/73, loss=0.455616, loss_cls=0.052878, loss_bbox=0.034531, loss_mask=0.215192, loss_rpn_cls=0.064953, loss_rpn_bbox=0.088062, lr=0.00125, time_each_step=0.44s, eta=1:47:38\n",
      "2021-04-21 19:12:56 [INFO]\t[TRAIN] Epoch=2/100, Step=39/73, loss=0.498469, loss_cls=0.051547, loss_bbox=0.034772, loss_mask=0.203809, loss_rpn_cls=0.092089, loss_rpn_bbox=0.11625, lr=0.00125, time_each_step=0.43s, eta=1:47:37\n",
      "2021-04-21 19:12:57 [INFO]\t[TRAIN] Epoch=2/100, Step=41/73, loss=0.425331, loss_cls=0.054662, loss_bbox=0.042925, loss_mask=0.195143, loss_rpn_cls=0.043152, loss_rpn_bbox=0.08945, lr=0.00125, time_each_step=0.42s, eta=1:47:36\n",
      "2021-04-21 19:12:57 [INFO]\t[TRAIN] Epoch=2/100, Step=43/73, loss=0.403795, loss_cls=0.052994, loss_bbox=0.031432, loss_mask=0.192451, loss_rpn_cls=0.063953, loss_rpn_bbox=0.062965, lr=0.00125, time_each_step=0.4s, eta=1:47:34\n",
      "2021-04-21 19:12:58 [INFO]\t[TRAIN] Epoch=2/100, Step=45/73, loss=0.422932, loss_cls=0.039032, loss_bbox=0.026648, loss_mask=0.247695, loss_rpn_cls=0.044022, loss_rpn_bbox=0.065534, lr=0.00125, time_each_step=0.38s, eta=1:47:33\n",
      "2021-04-21 19:12:59 [INFO]\t[TRAIN] Epoch=2/100, Step=47/73, loss=0.461952, loss_cls=0.045803, loss_bbox=0.040071, loss_mask=0.207743, loss_rpn_cls=0.061795, loss_rpn_bbox=0.106541, lr=0.00125, time_each_step=0.38s, eta=1:47:32\n",
      "2021-04-21 19:13:00 [INFO]\t[TRAIN] Epoch=2/100, Step=49/73, loss=0.323772, loss_cls=0.049731, loss_bbox=0.043207, loss_mask=0.169646, loss_rpn_cls=0.028448, loss_rpn_bbox=0.03274, lr=0.00125, time_each_step=0.38s, eta=1:47:31\n",
      "2021-04-21 19:13:00 [INFO]\t[TRAIN] Epoch=2/100, Step=51/73, loss=0.421857, loss_cls=0.04148, loss_bbox=0.031899, loss_mask=0.182409, loss_rpn_cls=0.054322, loss_rpn_bbox=0.111746, lr=0.00125, time_each_step=0.37s, eta=1:47:31\n",
      "2021-04-21 19:13:01 [INFO]\t[TRAIN] Epoch=2/100, Step=53/73, loss=0.310335, loss_cls=0.038777, loss_bbox=0.02431, loss_mask=0.15881, loss_rpn_cls=0.038051, loss_rpn_bbox=0.050387, lr=0.00125, time_each_step=0.37s, eta=1:47:30\n",
      "2021-04-21 19:13:02 [INFO]\t[TRAIN] Epoch=2/100, Step=55/73, loss=0.564813, loss_cls=0.037404, loss_bbox=0.019331, loss_mask=0.266481, loss_rpn_cls=0.075508, loss_rpn_bbox=0.166089, lr=0.00125, time_each_step=0.38s, eta=1:47:29\n",
      "2021-04-21 19:13:03 [INFO]\t[TRAIN] Epoch=2/100, Step=57/73, loss=0.444353, loss_cls=0.040474, loss_bbox=0.032266, loss_mask=0.235314, loss_rpn_cls=0.039695, loss_rpn_bbox=0.096604, lr=0.00125, time_each_step=0.37s, eta=1:47:28\n",
      "2021-04-21 19:13:03 [INFO]\t[TRAIN] Epoch=2/100, Step=59/73, loss=0.449457, loss_cls=0.049204, loss_bbox=0.034638, loss_mask=0.223798, loss_rpn_cls=0.042427, loss_rpn_bbox=0.09939, lr=0.00125, time_each_step=0.37s, eta=1:47:27\n",
      "2021-04-21 19:13:04 [INFO]\t[TRAIN] Epoch=2/100, Step=61/73, loss=0.384434, loss_cls=0.052203, loss_bbox=0.03743, loss_mask=0.210287, loss_rpn_cls=0.025129, loss_rpn_bbox=0.059384, lr=0.00125, time_each_step=0.37s, eta=1:47:27\n",
      "2021-04-21 19:13:05 [INFO]\t[TRAIN] Epoch=2/100, Step=63/73, loss=0.39076, loss_cls=0.053278, loss_bbox=0.041281, loss_mask=0.197828, loss_rpn_cls=0.029468, loss_rpn_bbox=0.068905, lr=0.00125, time_each_step=0.37s, eta=1:47:26\n",
      "2021-04-21 19:13:06 [INFO]\t[TRAIN] Epoch=2/100, Step=65/73, loss=0.419989, loss_cls=0.042233, loss_bbox=0.031421, loss_mask=0.250127, loss_rpn_cls=0.033471, loss_rpn_bbox=0.062737, lr=0.00125, time_each_step=0.37s, eta=1:47:25\n",
      "2021-04-21 19:13:06 [INFO]\t[TRAIN] Epoch=2/100, Step=67/73, loss=0.41445, loss_cls=0.042088, loss_bbox=0.034353, loss_mask=0.206608, loss_rpn_cls=0.042501, loss_rpn_bbox=0.088899, lr=0.00125, time_each_step=0.37s, eta=1:47:25\n",
      "2021-04-21 19:13:07 [INFO]\t[TRAIN] Epoch=2/100, Step=69/73, loss=0.486532, loss_cls=0.039849, loss_bbox=0.031638, loss_mask=0.277412, loss_rpn_cls=0.045068, loss_rpn_bbox=0.092565, lr=0.00125, time_each_step=0.37s, eta=1:47:24\n",
      "2021-04-21 19:13:08 [INFO]\t[TRAIN] Epoch=2/100, Step=71/73, loss=0.489538, loss_cls=0.043987, loss_bbox=0.03876, loss_mask=0.270102, loss_rpn_cls=0.049306, loss_rpn_bbox=0.087383, lr=0.00125, time_each_step=0.37s, eta=1:47:23\n",
      "2021-04-21 19:13:09 [INFO]\t[TRAIN] Epoch=2/100, Step=73/73, loss=0.32162, loss_cls=0.037055, loss_bbox=0.022678, loss_mask=0.186358, loss_rpn_cls=0.023292, loss_rpn_bbox=0.052238, lr=0.00125, time_each_step=0.37s, eta=1:47:22\n",
      "2021-04-21 19:13:09 [INFO]\t[TRAIN] Epoch 2 finished, loss=0.485958, loss_cls=0.047885, loss_bbox=0.030739, loss_mask=0.248233, loss_rpn_cls=0.068535, loss_rpn_bbox=0.090565, lr=0.00125 .\n",
      "2021-04-21 19:13:09 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:13:09 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:20<00:00,  4.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.06s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.078\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.341\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.005\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.078\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.094\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.276\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.276\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.276\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.11s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.052\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.305\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.070\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.102\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.193\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.193\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.193\n",
      "2021-04-21 19:13:31 [INFO]\t[EVAL] Finished, Epoch=2, bbox_mmap=0.077967, segm_mmap=0.05158 .\n",
      "2021-04-21 19:13:35 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:13:45 [INFO]\t[TRAIN] Epoch=3/100, Step=2/73, loss=0.367177, loss_cls=0.034835, loss_bbox=0.017495, loss_mask=0.222595, loss_rpn_cls=0.026142, loss_rpn_bbox=0.066109, lr=0.00125, time_each_step=0.6s, eta=1:51:55\n",
      "2021-04-21 19:13:47 [INFO]\t[TRAIN] Epoch=3/100, Step=4/73, loss=0.416572, loss_cls=0.046791, loss_bbox=0.03573, loss_mask=0.194931, loss_rpn_cls=0.028632, loss_rpn_bbox=0.110488, lr=0.00125, time_each_step=0.63s, eta=1:51:56\n",
      "2021-04-21 19:13:48 [INFO]\t[TRAIN] Epoch=3/100, Step=6/73, loss=0.359071, loss_cls=0.052596, loss_bbox=0.040688, loss_mask=0.160611, loss_rpn_cls=0.020257, loss_rpn_bbox=0.084919, lr=0.00125, time_each_step=0.65s, eta=1:51:56\n",
      "2021-04-21 19:13:49 [INFO]\t[TRAIN] Epoch=3/100, Step=8/73, loss=0.293714, loss_cls=0.037789, loss_bbox=0.035862, loss_mask=0.163996, loss_rpn_cls=0.017086, loss_rpn_bbox=0.038981, lr=0.00125, time_each_step=0.68s, eta=1:51:57\n",
      "2021-04-21 19:13:50 [INFO]\t[TRAIN] Epoch=3/100, Step=10/73, loss=0.351561, loss_cls=0.045592, loss_bbox=0.036495, loss_mask=0.176892, loss_rpn_cls=0.040358, loss_rpn_bbox=0.052224, lr=0.00125, time_each_step=0.71s, eta=1:51:58\n",
      "2021-04-21 19:13:52 [INFO]\t[TRAIN] Epoch=3/100, Step=12/73, loss=0.326484, loss_cls=0.045543, loss_bbox=0.03741, loss_mask=0.154314, loss_rpn_cls=0.021322, loss_rpn_bbox=0.067894, lr=0.00125, time_each_step=0.74s, eta=1:51:58\n",
      "2021-04-21 19:13:53 [INFO]\t[TRAIN] Epoch=3/100, Step=14/73, loss=0.339028, loss_cls=0.034906, loss_bbox=0.037712, loss_mask=0.182099, loss_rpn_cls=0.031742, loss_rpn_bbox=0.05257, lr=0.00125, time_each_step=0.76s, eta=1:51:57\n",
      "2021-04-21 19:13:54 [INFO]\t[TRAIN] Epoch=3/100, Step=16/73, loss=0.450334, loss_cls=0.046256, loss_bbox=0.030326, loss_mask=0.180219, loss_rpn_cls=0.040025, loss_rpn_bbox=0.153507, lr=0.00125, time_each_step=0.77s, eta=1:51:57\n",
      "2021-04-21 19:13:55 [INFO]\t[TRAIN] Epoch=3/100, Step=18/73, loss=0.424676, loss_cls=0.044529, loss_bbox=0.041693, loss_mask=0.202986, loss_rpn_cls=0.024476, loss_rpn_bbox=0.110992, lr=0.00125, time_each_step=0.79s, eta=1:51:56\n",
      "2021-04-21 19:13:56 [INFO]\t[TRAIN] Epoch=3/100, Step=20/73, loss=0.346298, loss_cls=0.042467, loss_bbox=0.03634, loss_mask=0.175689, loss_rpn_cls=0.037519, loss_rpn_bbox=0.054282, lr=0.00125, time_each_step=0.8s, eta=1:51:55\n",
      "2021-04-21 19:13:57 [INFO]\t[TRAIN] Epoch=3/100, Step=22/73, loss=0.293629, loss_cls=0.040509, loss_bbox=0.031788, loss_mask=0.152723, loss_rpn_cls=0.017026, loss_rpn_bbox=0.051583, lr=0.00125, time_each_step=0.61s, eta=1:51:44\n",
      "2021-04-21 19:13:58 [INFO]\t[TRAIN] Epoch=3/100, Step=24/73, loss=0.424819, loss_cls=0.038184, loss_bbox=0.028634, loss_mask=0.225531, loss_rpn_cls=0.034944, loss_rpn_bbox=0.097527, lr=0.00125, time_each_step=0.59s, eta=1:51:42\n",
      "2021-04-21 19:13:59 [INFO]\t[TRAIN] Epoch=3/100, Step=26/73, loss=0.415853, loss_cls=0.036014, loss_bbox=0.03067, loss_mask=0.208225, loss_rpn_cls=0.036222, loss_rpn_bbox=0.104722, lr=0.00125, time_each_step=0.58s, eta=1:51:40\n",
      "2021-04-21 19:14:00 [INFO]\t[TRAIN] Epoch=3/100, Step=28/73, loss=0.426778, loss_cls=0.036183, loss_bbox=0.038521, loss_mask=0.22022, loss_rpn_cls=0.04066, loss_rpn_bbox=0.091194, lr=0.00125, time_each_step=0.56s, eta=1:51:38\n",
      "2021-04-21 19:14:01 [INFO]\t[TRAIN] Epoch=3/100, Step=30/73, loss=0.407361, loss_cls=0.041846, loss_bbox=0.044998, loss_mask=0.219274, loss_rpn_cls=0.020926, loss_rpn_bbox=0.080317, lr=0.00125, time_each_step=0.53s, eta=1:51:35\n",
      "2021-04-21 19:14:02 [INFO]\t[TRAIN] Epoch=3/100, Step=32/73, loss=0.352662, loss_cls=0.034019, loss_bbox=0.035522, loss_mask=0.215875, loss_rpn_cls=0.022695, loss_rpn_bbox=0.044551, lr=0.00125, time_each_step=0.5s, eta=1:51:33\n",
      "2021-04-21 19:14:03 [INFO]\t[TRAIN] Epoch=3/100, Step=34/73, loss=0.419907, loss_cls=0.039792, loss_bbox=0.029315, loss_mask=0.199882, loss_rpn_cls=0.031556, loss_rpn_bbox=0.119362, lr=0.00125, time_each_step=0.48s, eta=1:51:32\n",
      "2021-04-21 19:14:03 [INFO]\t[TRAIN] Epoch=3/100, Step=36/73, loss=0.318122, loss_cls=0.043163, loss_bbox=0.037346, loss_mask=0.156827, loss_rpn_cls=0.016814, loss_rpn_bbox=0.063972, lr=0.00125, time_each_step=0.47s, eta=1:51:30\n",
      "2021-04-21 19:14:04 [INFO]\t[TRAIN] Epoch=3/100, Step=38/73, loss=0.433087, loss_cls=0.033661, loss_bbox=0.020036, loss_mask=0.245557, loss_rpn_cls=0.031899, loss_rpn_bbox=0.101934, lr=0.00125, time_each_step=0.45s, eta=1:51:28\n",
      "2021-04-21 19:14:05 [INFO]\t[TRAIN] Epoch=3/100, Step=40/73, loss=0.428148, loss_cls=0.041184, loss_bbox=0.035234, loss_mask=0.164276, loss_rpn_cls=0.033579, loss_rpn_bbox=0.153875, lr=0.00125, time_each_step=0.44s, eta=1:51:27\n",
      "2021-04-21 19:14:05 [INFO]\t[TRAIN] Epoch=3/100, Step=42/73, loss=0.333716, loss_cls=0.049567, loss_bbox=0.023084, loss_mask=0.176641, loss_rpn_cls=0.026474, loss_rpn_bbox=0.05795, lr=0.00125, time_each_step=0.4s, eta=1:51:25\n",
      "2021-04-21 19:14:06 [INFO]\t[TRAIN] Epoch=3/100, Step=44/73, loss=0.273875, loss_cls=0.043522, loss_bbox=0.039176, loss_mask=0.149548, loss_rpn_cls=0.009259, loss_rpn_bbox=0.032371, lr=0.00125, time_each_step=0.39s, eta=1:51:24\n",
      "2021-04-21 19:14:07 [INFO]\t[TRAIN] Epoch=3/100, Step=46/73, loss=0.292099, loss_cls=0.03251, loss_bbox=0.02941, loss_mask=0.117218, loss_rpn_cls=0.015342, loss_rpn_bbox=0.097618, lr=0.00125, time_each_step=0.38s, eta=1:51:23\n",
      "2021-04-21 19:14:08 [INFO]\t[TRAIN] Epoch=3/100, Step=48/73, loss=0.457256, loss_cls=0.045246, loss_bbox=0.043751, loss_mask=0.27751, loss_rpn_cls=0.012664, loss_rpn_bbox=0.078085, lr=0.00125, time_each_step=0.37s, eta=1:51:22\n",
      "2021-04-21 19:14:08 [INFO]\t[TRAIN] Epoch=3/100, Step=50/73, loss=0.306597, loss_cls=0.044222, loss_bbox=0.034871, loss_mask=0.121499, loss_rpn_cls=0.025304, loss_rpn_bbox=0.080702, lr=0.00125, time_each_step=0.37s, eta=1:51:21\n",
      "2021-04-21 19:14:09 [INFO]\t[TRAIN] Epoch=3/100, Step=52/73, loss=0.312287, loss_cls=0.036129, loss_bbox=0.032581, loss_mask=0.140942, loss_rpn_cls=0.026617, loss_rpn_bbox=0.076018, lr=0.00125, time_each_step=0.37s, eta=1:51:20\n",
      "2021-04-21 19:14:10 [INFO]\t[TRAIN] Epoch=3/100, Step=54/73, loss=0.384428, loss_cls=0.038877, loss_bbox=0.035038, loss_mask=0.194721, loss_rpn_cls=0.02247, loss_rpn_bbox=0.093321, lr=0.00125, time_each_step=0.37s, eta=1:51:20\n",
      "2021-04-21 19:14:11 [INFO]\t[TRAIN] Epoch=3/100, Step=56/73, loss=0.40651, loss_cls=0.048893, loss_bbox=0.040027, loss_mask=0.200884, loss_rpn_cls=0.022976, loss_rpn_bbox=0.093729, lr=0.00125, time_each_step=0.37s, eta=1:51:19\n",
      "2021-04-21 19:14:11 [INFO]\t[TRAIN] Epoch=3/100, Step=58/73, loss=0.407129, loss_cls=0.043163, loss_bbox=0.033432, loss_mask=0.17639, loss_rpn_cls=0.033079, loss_rpn_bbox=0.121066, lr=0.00125, time_each_step=0.37s, eta=1:51:18\n",
      "2021-04-21 19:14:12 [INFO]\t[TRAIN] Epoch=3/100, Step=60/73, loss=0.344127, loss_cls=0.039074, loss_bbox=0.027951, loss_mask=0.144175, loss_rpn_cls=0.039437, loss_rpn_bbox=0.09349, lr=0.00125, time_each_step=0.37s, eta=1:51:17\n",
      "2021-04-21 19:14:13 [INFO]\t[TRAIN] Epoch=3/100, Step=62/73, loss=0.41048, loss_cls=0.036638, loss_bbox=0.036533, loss_mask=0.175843, loss_rpn_cls=0.021963, loss_rpn_bbox=0.139504, lr=0.00125, time_each_step=0.37s, eta=1:51:17\n",
      "2021-04-21 19:14:14 [INFO]\t[TRAIN] Epoch=3/100, Step=64/73, loss=0.311463, loss_cls=0.039056, loss_bbox=0.036501, loss_mask=0.148182, loss_rpn_cls=0.019024, loss_rpn_bbox=0.0687, lr=0.00125, time_each_step=0.37s, eta=1:51:16\n",
      "2021-04-21 19:14:14 [INFO]\t[TRAIN] Epoch=3/100, Step=66/73, loss=0.251414, loss_cls=0.035656, loss_bbox=0.029826, loss_mask=0.13131, loss_rpn_cls=0.007376, loss_rpn_bbox=0.047247, lr=0.00125, time_each_step=0.37s, eta=1:51:15\n",
      "2021-04-21 19:14:15 [INFO]\t[TRAIN] Epoch=3/100, Step=68/73, loss=0.291464, loss_cls=0.033555, loss_bbox=0.02621, loss_mask=0.136766, loss_rpn_cls=0.036583, loss_rpn_bbox=0.05835, lr=0.00125, time_each_step=0.37s, eta=1:51:14\n",
      "2021-04-21 19:14:16 [INFO]\t[TRAIN] Epoch=3/100, Step=70/73, loss=0.33506, loss_cls=0.034437, loss_bbox=0.031662, loss_mask=0.168197, loss_rpn_cls=0.027718, loss_rpn_bbox=0.073045, lr=0.00125, time_each_step=0.37s, eta=1:51:14\n",
      "2021-04-21 19:14:16 [INFO]\t[TRAIN] Epoch=3/100, Step=72/73, loss=0.361302, loss_cls=0.041997, loss_bbox=0.035391, loss_mask=0.180003, loss_rpn_cls=0.02739, loss_rpn_bbox=0.076522, lr=0.00125, time_each_step=0.37s, eta=1:51:13\n",
      "2021-04-21 19:14:17 [INFO]\t[TRAIN] Epoch 3 finished, loss=0.366007, loss_cls=0.040526, loss_bbox=0.031274, loss_mask=0.179132, loss_rpn_cls=0.030387, loss_rpn_bbox=0.084688, lr=0.00125 .\n",
      "2021-04-21 19:14:17 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:14:17 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.10s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.142\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.555\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.007\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.142\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.151\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.357\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.370\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.18s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.118\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.568\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.120\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.161\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.257\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.261\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.261\n",
      "2021-04-21 19:14:37 [INFO]\t[EVAL] Finished, Epoch=3, bbox_mmap=0.141544, segm_mmap=0.117555 .\n",
      "2021-04-21 19:14:42 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:14:46 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_3.\n",
      "2021-04-21 19:14:46 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_3, bbox_mmap=0.14154406309784684\n",
      "2021-04-21 19:14:56 [INFO]\t[TRAIN] Epoch=4/100, Step=1/73, loss=0.281286, loss_cls=0.037608, loss_bbox=0.018431, loss_mask=0.155723, loss_rpn_cls=0.021393, loss_rpn_bbox=0.048132, lr=0.00125, time_each_step=0.82s, eta=1:47:53\n",
      "2021-04-21 19:14:57 [INFO]\t[TRAIN] Epoch=4/100, Step=3/73, loss=0.360549, loss_cls=0.035689, loss_bbox=0.030877, loss_mask=0.170165, loss_rpn_cls=0.043097, loss_rpn_bbox=0.080721, lr=0.00125, time_each_step=0.85s, eta=1:47:53\n",
      "2021-04-21 19:14:58 [INFO]\t[TRAIN] Epoch=4/100, Step=5/73, loss=0.273503, loss_cls=0.04192, loss_bbox=0.029217, loss_mask=0.11961, loss_rpn_cls=0.021134, loss_rpn_bbox=0.061622, lr=0.00125, time_each_step=0.87s, eta=1:47:53\n",
      "2021-04-21 19:14:59 [INFO]\t[TRAIN] Epoch=4/100, Step=7/73, loss=0.452874, loss_cls=0.032869, loss_bbox=0.020165, loss_mask=0.32159, loss_rpn_cls=0.016367, loss_rpn_bbox=0.061883, lr=0.00125, time_each_step=0.89s, eta=1:47:52\n",
      "2021-04-21 19:15:00 [INFO]\t[TRAIN] Epoch=4/100, Step=9/73, loss=0.347331, loss_cls=0.038293, loss_bbox=0.033731, loss_mask=0.178283, loss_rpn_cls=0.018804, loss_rpn_bbox=0.078221, lr=0.00125, time_each_step=0.9s, eta=1:47:51\n",
      "2021-04-21 19:15:01 [INFO]\t[TRAIN] Epoch=4/100, Step=11/73, loss=0.326899, loss_cls=0.041864, loss_bbox=0.037915, loss_mask=0.162984, loss_rpn_cls=0.02472, loss_rpn_bbox=0.059416, lr=0.00125, time_each_step=0.91s, eta=1:47:50\n",
      "2021-04-21 19:15:02 [INFO]\t[TRAIN] Epoch=4/100, Step=13/73, loss=0.267323, loss_cls=0.031818, loss_bbox=0.027227, loss_mask=0.150123, loss_rpn_cls=0.021056, loss_rpn_bbox=0.037098, lr=0.00125, time_each_step=0.93s, eta=1:47:49\n",
      "2021-04-21 19:15:03 [INFO]\t[TRAIN] Epoch=4/100, Step=15/73, loss=0.294453, loss_cls=0.038667, loss_bbox=0.03577, loss_mask=0.140617, loss_rpn_cls=0.025264, loss_rpn_bbox=0.054135, lr=0.00125, time_each_step=0.94s, eta=1:47:48\n",
      "2021-04-21 19:15:04 [INFO]\t[TRAIN] Epoch=4/100, Step=17/73, loss=0.335878, loss_cls=0.037029, loss_bbox=0.027387, loss_mask=0.171357, loss_rpn_cls=0.030018, loss_rpn_bbox=0.070087, lr=0.00125, time_each_step=0.95s, eta=1:47:47\n",
      "2021-04-21 19:15:05 [INFO]\t[TRAIN] Epoch=4/100, Step=19/73, loss=0.342555, loss_cls=0.040912, loss_bbox=0.0391, loss_mask=0.16033, loss_rpn_cls=0.013893, loss_rpn_bbox=0.088321, lr=0.00125, time_each_step=0.97s, eta=1:47:46\n",
      "2021-04-21 19:15:06 [INFO]\t[TRAIN] Epoch=4/100, Step=21/73, loss=0.360881, loss_cls=0.034435, loss_bbox=0.032051, loss_mask=0.184901, loss_rpn_cls=0.029695, loss_rpn_bbox=0.0798, lr=0.00125, time_each_step=0.52s, eta=1:47:21\n",
      "2021-04-21 19:15:07 [INFO]\t[TRAIN] Epoch=4/100, Step=23/73, loss=0.259995, loss_cls=0.03111, loss_bbox=0.019934, loss_mask=0.121834, loss_rpn_cls=0.011901, loss_rpn_bbox=0.075215, lr=0.00125, time_each_step=0.51s, eta=1:47:19\n",
      "2021-04-21 19:15:08 [INFO]\t[TRAIN] Epoch=4/100, Step=25/73, loss=0.266972, loss_cls=0.04051, loss_bbox=0.029443, loss_mask=0.11477, loss_rpn_cls=0.019178, loss_rpn_bbox=0.06307, lr=0.00125, time_each_step=0.5s, eta=1:47:18\n",
      "2021-04-21 19:15:09 [INFO]\t[TRAIN] Epoch=4/100, Step=27/73, loss=0.273114, loss_cls=0.037533, loss_bbox=0.023724, loss_mask=0.134109, loss_rpn_cls=0.014945, loss_rpn_bbox=0.062803, lr=0.00125, time_each_step=0.48s, eta=1:47:16\n",
      "2021-04-21 19:15:10 [INFO]\t[TRAIN] Epoch=4/100, Step=29/73, loss=0.245579, loss_cls=0.04852, loss_bbox=0.031072, loss_mask=0.110695, loss_rpn_cls=0.022105, loss_rpn_bbox=0.033187, lr=0.00125, time_each_step=0.47s, eta=1:47:14\n",
      "2021-04-21 19:15:11 [INFO]\t[TRAIN] Epoch=4/100, Step=31/73, loss=0.332755, loss_cls=0.043457, loss_bbox=0.023502, loss_mask=0.133013, loss_rpn_cls=0.020944, loss_rpn_bbox=0.111838, lr=0.00125, time_each_step=0.46s, eta=1:47:13\n",
      "2021-04-21 19:15:11 [INFO]\t[TRAIN] Epoch=4/100, Step=33/73, loss=0.328959, loss_cls=0.042908, loss_bbox=0.043702, loss_mask=0.141816, loss_rpn_cls=0.010483, loss_rpn_bbox=0.090051, lr=0.00125, time_each_step=0.45s, eta=1:47:12\n",
      "2021-04-21 19:15:12 [INFO]\t[TRAIN] Epoch=4/100, Step=35/73, loss=0.643005, loss_cls=0.03855, loss_bbox=0.032625, loss_mask=0.170168, loss_rpn_cls=0.128939, loss_rpn_bbox=0.272723, lr=0.00125, time_each_step=0.44s, eta=1:47:10\n",
      "2021-04-21 19:15:13 [INFO]\t[TRAIN] Epoch=4/100, Step=37/73, loss=0.26713, loss_cls=0.033298, loss_bbox=0.023522, loss_mask=0.15295, loss_rpn_cls=0.011782, loss_rpn_bbox=0.045577, lr=0.00125, time_each_step=0.42s, eta=1:47:9\n",
      "2021-04-21 19:15:14 [INFO]\t[TRAIN] Epoch=4/100, Step=39/73, loss=0.296827, loss_cls=0.04166, loss_bbox=0.048518, loss_mask=0.149249, loss_rpn_cls=0.019393, loss_rpn_bbox=0.038006, lr=0.00125, time_each_step=0.41s, eta=1:47:7\n",
      "2021-04-21 19:15:14 [INFO]\t[TRAIN] Epoch=4/100, Step=41/73, loss=0.255477, loss_cls=0.02904, loss_bbox=0.02931, loss_mask=0.144317, loss_rpn_cls=0.012092, loss_rpn_bbox=0.040718, lr=0.00125, time_each_step=0.4s, eta=1:47:6\n",
      "2021-04-21 19:15:15 [INFO]\t[TRAIN] Epoch=4/100, Step=43/73, loss=0.243358, loss_cls=0.031914, loss_bbox=0.025001, loss_mask=0.123201, loss_rpn_cls=0.02623, loss_rpn_bbox=0.037013, lr=0.00125, time_each_step=0.39s, eta=1:47:5\n",
      "2021-04-21 19:15:16 [INFO]\t[TRAIN] Epoch=4/100, Step=45/73, loss=0.264804, loss_cls=0.039557, loss_bbox=0.030082, loss_mask=0.114381, loss_rpn_cls=0.020084, loss_rpn_bbox=0.0607, lr=0.00125, time_each_step=0.38s, eta=1:47:4\n",
      "2021-04-21 19:15:17 [INFO]\t[TRAIN] Epoch=4/100, Step=47/73, loss=0.297268, loss_cls=0.041086, loss_bbox=0.028432, loss_mask=0.176408, loss_rpn_cls=0.015718, loss_rpn_bbox=0.035623, lr=0.00125, time_each_step=0.37s, eta=1:47:3\n",
      "2021-04-21 19:15:17 [INFO]\t[TRAIN] Epoch=4/100, Step=49/73, loss=0.231061, loss_cls=0.044167, loss_bbox=0.027427, loss_mask=0.111136, loss_rpn_cls=0.014915, loss_rpn_bbox=0.033416, lr=0.00125, time_each_step=0.37s, eta=1:47:3\n",
      "2021-04-21 19:15:18 [INFO]\t[TRAIN] Epoch=4/100, Step=51/73, loss=0.299026, loss_cls=0.034034, loss_bbox=0.034215, loss_mask=0.141492, loss_rpn_cls=0.011948, loss_rpn_bbox=0.077337, lr=0.00125, time_each_step=0.37s, eta=1:47:2\n",
      "2021-04-21 19:15:19 [INFO]\t[TRAIN] Epoch=4/100, Step=53/73, loss=0.287348, loss_cls=0.041181, loss_bbox=0.031505, loss_mask=0.12764, loss_rpn_cls=0.024753, loss_rpn_bbox=0.062269, lr=0.00125, time_each_step=0.37s, eta=1:47:1\n",
      "2021-04-21 19:15:19 [INFO]\t[TRAIN] Epoch=4/100, Step=55/73, loss=0.378862, loss_cls=0.029256, loss_bbox=0.018262, loss_mask=0.147189, loss_rpn_cls=0.019853, loss_rpn_bbox=0.164303, lr=0.00125, time_each_step=0.37s, eta=1:47:0\n",
      "2021-04-21 19:15:20 [INFO]\t[TRAIN] Epoch=4/100, Step=57/73, loss=0.220951, loss_cls=0.026738, loss_bbox=0.02192, loss_mask=0.111592, loss_rpn_cls=0.012617, loss_rpn_bbox=0.048084, lr=0.00125, time_each_step=0.37s, eta=1:47:0\n",
      "2021-04-21 19:15:21 [INFO]\t[TRAIN] Epoch=4/100, Step=59/73, loss=0.272124, loss_cls=0.039165, loss_bbox=0.029223, loss_mask=0.139529, loss_rpn_cls=0.026723, loss_rpn_bbox=0.037483, lr=0.00125, time_each_step=0.37s, eta=1:46:59\n",
      "2021-04-21 19:15:22 [INFO]\t[TRAIN] Epoch=4/100, Step=61/73, loss=0.215529, loss_cls=0.031078, loss_bbox=0.019192, loss_mask=0.107222, loss_rpn_cls=0.014505, loss_rpn_bbox=0.043532, lr=0.00125, time_each_step=0.37s, eta=1:46:58\n",
      "2021-04-21 19:15:22 [INFO]\t[TRAIN] Epoch=4/100, Step=63/73, loss=0.374418, loss_cls=0.021418, loss_bbox=0.016631, loss_mask=0.121276, loss_rpn_cls=0.012745, loss_rpn_bbox=0.202347, lr=0.00125, time_each_step=0.37s, eta=1:46:57\n",
      "2021-04-21 19:15:23 [INFO]\t[TRAIN] Epoch=4/100, Step=65/73, loss=0.36382, loss_cls=0.040417, loss_bbox=0.041237, loss_mask=0.122422, loss_rpn_cls=0.015875, loss_rpn_bbox=0.14387, lr=0.00125, time_each_step=0.37s, eta=1:46:57\n",
      "2021-04-21 19:15:24 [INFO]\t[TRAIN] Epoch=4/100, Step=67/73, loss=0.402382, loss_cls=0.034327, loss_bbox=0.031005, loss_mask=0.146937, loss_rpn_cls=0.019065, loss_rpn_bbox=0.171048, lr=0.00125, time_each_step=0.37s, eta=1:46:56\n",
      "2021-04-21 19:15:25 [INFO]\t[TRAIN] Epoch=4/100, Step=69/73, loss=0.311619, loss_cls=0.035769, loss_bbox=0.031641, loss_mask=0.13454, loss_rpn_cls=0.020797, loss_rpn_bbox=0.088872, lr=0.00125, time_each_step=0.37s, eta=1:46:55\n",
      "2021-04-21 19:15:25 [INFO]\t[TRAIN] Epoch=4/100, Step=71/73, loss=0.374006, loss_cls=0.035859, loss_bbox=0.020989, loss_mask=0.158123, loss_rpn_cls=0.015413, loss_rpn_bbox=0.143623, lr=0.00125, time_each_step=0.37s, eta=1:46:54\n",
      "2021-04-21 19:15:26 [INFO]\t[TRAIN] Epoch=4/100, Step=73/73, loss=0.333019, loss_cls=0.045659, loss_bbox=0.028833, loss_mask=0.127987, loss_rpn_cls=0.012914, loss_rpn_bbox=0.117626, lr=0.00125, time_each_step=0.37s, eta=1:46:54\n",
      "2021-04-21 19:15:26 [INFO]\t[TRAIN] Epoch 4 finished, loss=0.323569, loss_cls=0.03795, loss_bbox=0.029972, loss_mask=0.150078, loss_rpn_cls=0.022217, loss_rpn_bbox=0.083352, lr=0.00125 .\n",
      "2021-04-21 19:15:26 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:15:26 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:15<00:00,  5.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.06s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.151\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.555\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.011\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.151\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.161\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.383\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.384\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.12s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.194\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.746\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.207\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.240\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.302\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.304\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.304\n",
      "2021-04-21 19:15:44 [INFO]\t[EVAL] Finished, Epoch=4, bbox_mmap=0.151457, segm_mmap=0.193988 .\n",
      "2021-04-21 19:15:49 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:15:53 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_4.\n",
      "2021-04-21 19:15:53 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_4, bbox_mmap=0.15145701953304755\n",
      "2021-04-21 19:15:58 [INFO]\t[TRAIN] Epoch=5/100, Step=2/73, loss=0.247255, loss_cls=0.033744, loss_bbox=0.025471, loss_mask=0.113853, loss_rpn_cls=0.006652, loss_rpn_bbox=0.067536, lr=0.00125, time_each_step=0.59s, eta=1:46:33\n",
      "2021-04-21 19:15:59 [INFO]\t[TRAIN] Epoch=5/100, Step=4/73, loss=0.284968, loss_cls=0.038677, loss_bbox=0.019622, loss_mask=0.129158, loss_rpn_cls=0.020654, loss_rpn_bbox=0.076857, lr=0.00125, time_each_step=0.61s, eta=1:46:34\n",
      "2021-04-21 19:16:00 [INFO]\t[TRAIN] Epoch=5/100, Step=6/73, loss=0.269694, loss_cls=0.033583, loss_bbox=0.022363, loss_mask=0.168099, loss_rpn_cls=0.010861, loss_rpn_bbox=0.034788, lr=0.00125, time_each_step=0.63s, eta=1:46:34\n",
      "2021-04-21 19:16:02 [INFO]\t[TRAIN] Epoch=5/100, Step=8/73, loss=0.30722, loss_cls=0.02825, loss_bbox=0.021468, loss_mask=0.163373, loss_rpn_cls=0.020078, loss_rpn_bbox=0.074051, lr=0.00125, time_each_step=0.66s, eta=1:46:34\n",
      "2021-04-21 19:16:03 [INFO]\t[TRAIN] Epoch=5/100, Step=10/73, loss=0.313183, loss_cls=0.046044, loss_bbox=0.029131, loss_mask=0.122941, loss_rpn_cls=0.013913, loss_rpn_bbox=0.101155, lr=0.00125, time_each_step=0.68s, eta=1:46:34\n",
      "2021-04-21 19:16:04 [INFO]\t[TRAIN] Epoch=5/100, Step=12/73, loss=0.32171, loss_cls=0.032079, loss_bbox=0.02187, loss_mask=0.142402, loss_rpn_cls=0.012568, loss_rpn_bbox=0.112791, lr=0.00125, time_each_step=0.69s, eta=1:46:33\n",
      "2021-04-21 19:16:05 [INFO]\t[TRAIN] Epoch=5/100, Step=14/73, loss=0.290111, loss_cls=0.039244, loss_bbox=0.023987, loss_mask=0.123899, loss_rpn_cls=0.018327, loss_rpn_bbox=0.084654, lr=0.00125, time_each_step=0.69s, eta=1:46:32\n",
      "2021-04-21 19:16:06 [INFO]\t[TRAIN] Epoch=5/100, Step=16/73, loss=0.299585, loss_cls=0.042542, loss_bbox=0.039211, loss_mask=0.145512, loss_rpn_cls=0.01424, loss_rpn_bbox=0.05808, lr=0.00125, time_each_step=0.71s, eta=1:46:32\n",
      "2021-04-21 19:16:07 [INFO]\t[TRAIN] Epoch=5/100, Step=18/73, loss=0.317108, loss_cls=0.045197, loss_bbox=0.032575, loss_mask=0.131153, loss_rpn_cls=0.013849, loss_rpn_bbox=0.094335, lr=0.00125, time_each_step=0.73s, eta=1:46:31\n",
      "2021-04-21 19:16:08 [INFO]\t[TRAIN] Epoch=5/100, Step=20/73, loss=0.38498, loss_cls=0.0414, loss_bbox=0.030528, loss_mask=0.124866, loss_rpn_cls=0.028723, loss_rpn_bbox=0.159463, lr=0.00125, time_each_step=0.75s, eta=1:46:31\n",
      "2021-04-21 19:16:09 [INFO]\t[TRAIN] Epoch=5/100, Step=22/73, loss=0.209203, loss_cls=0.033985, loss_bbox=0.027235, loss_mask=0.084096, loss_rpn_cls=0.029076, loss_rpn_bbox=0.034811, lr=0.00125, time_each_step=0.53s, eta=1:46:19\n",
      "2021-04-21 19:16:10 [INFO]\t[TRAIN] Epoch=5/100, Step=24/73, loss=0.345268, loss_cls=0.04341, loss_bbox=0.032132, loss_mask=0.205272, loss_rpn_cls=0.01194, loss_rpn_bbox=0.052513, lr=0.00125, time_each_step=0.53s, eta=1:46:17\n",
      "2021-04-21 19:16:11 [INFO]\t[TRAIN] Epoch=5/100, Step=26/73, loss=0.310536, loss_cls=0.034456, loss_bbox=0.029414, loss_mask=0.121579, loss_rpn_cls=0.018334, loss_rpn_bbox=0.106753, lr=0.00125, time_each_step=0.52s, eta=1:46:16\n",
      "2021-04-21 19:16:12 [INFO]\t[TRAIN] Epoch=5/100, Step=28/73, loss=0.306355, loss_cls=0.053445, loss_bbox=0.03028, loss_mask=0.125987, loss_rpn_cls=0.018312, loss_rpn_bbox=0.07833, lr=0.00125, time_each_step=0.5s, eta=1:46:14\n",
      "2021-04-21 19:16:13 [INFO]\t[TRAIN] Epoch=5/100, Step=30/73, loss=0.256968, loss_cls=0.035032, loss_bbox=0.023402, loss_mask=0.133869, loss_rpn_cls=0.00897, loss_rpn_bbox=0.055695, lr=0.00125, time_each_step=0.49s, eta=1:46:12\n",
      "2021-04-21 19:16:13 [INFO]\t[TRAIN] Epoch=5/100, Step=32/73, loss=0.307116, loss_cls=0.03884, loss_bbox=0.02135, loss_mask=0.200404, loss_rpn_cls=0.012553, loss_rpn_bbox=0.033969, lr=0.00125, time_each_step=0.48s, eta=1:46:11\n",
      "2021-04-21 19:16:14 [INFO]\t[TRAIN] Epoch=5/100, Step=34/73, loss=0.39714, loss_cls=0.061793, loss_bbox=0.030705, loss_mask=0.1961, loss_rpn_cls=0.013323, loss_rpn_bbox=0.09522, lr=0.00125, time_each_step=0.47s, eta=1:46:10\n",
      "2021-04-21 19:16:15 [INFO]\t[TRAIN] Epoch=5/100, Step=36/73, loss=0.238209, loss_cls=0.03564, loss_bbox=0.026914, loss_mask=0.107593, loss_rpn_cls=0.00799, loss_rpn_bbox=0.060071, lr=0.00125, time_each_step=0.46s, eta=1:46:8\n",
      "2021-04-21 19:16:16 [INFO]\t[TRAIN] Epoch=5/100, Step=38/73, loss=0.266258, loss_cls=0.04553, loss_bbox=0.033228, loss_mask=0.139789, loss_rpn_cls=0.008564, loss_rpn_bbox=0.039146, lr=0.00125, time_each_step=0.44s, eta=1:46:7\n",
      "2021-04-21 19:16:16 [INFO]\t[TRAIN] Epoch=5/100, Step=40/73, loss=0.317998, loss_cls=0.038866, loss_bbox=0.023388, loss_mask=0.149991, loss_rpn_cls=0.016717, loss_rpn_bbox=0.089036, lr=0.00125, time_each_step=0.42s, eta=1:46:5\n",
      "2021-04-21 19:16:17 [INFO]\t[TRAIN] Epoch=5/100, Step=42/73, loss=0.274952, loss_cls=0.038493, loss_bbox=0.030434, loss_mask=0.143057, loss_rpn_cls=0.018165, loss_rpn_bbox=0.044804, lr=0.00125, time_each_step=0.41s, eta=1:46:4\n",
      "2021-04-21 19:16:18 [INFO]\t[TRAIN] Epoch=5/100, Step=44/73, loss=0.27053, loss_cls=0.041905, loss_bbox=0.035678, loss_mask=0.110868, loss_rpn_cls=0.010616, loss_rpn_bbox=0.071464, lr=0.00125, time_each_step=0.4s, eta=1:46:3\n",
      "2021-04-21 19:16:19 [INFO]\t[TRAIN] Epoch=5/100, Step=46/73, loss=0.340815, loss_cls=0.041555, loss_bbox=0.042919, loss_mask=0.126455, loss_rpn_cls=0.015724, loss_rpn_bbox=0.114163, lr=0.00125, time_each_step=0.38s, eta=1:46:2\n",
      "2021-04-21 19:16:19 [INFO]\t[TRAIN] Epoch=5/100, Step=48/73, loss=0.764186, loss_cls=0.075333, loss_bbox=0.038807, loss_mask=0.22711, loss_rpn_cls=0.177703, loss_rpn_bbox=0.245233, lr=0.00125, time_each_step=0.38s, eta=1:46:1\n",
      "2021-04-21 19:16:20 [INFO]\t[TRAIN] Epoch=5/100, Step=50/73, loss=0.406571, loss_cls=0.037191, loss_bbox=0.029972, loss_mask=0.17087, loss_rpn_cls=0.014391, loss_rpn_bbox=0.154147, lr=0.00125, time_each_step=0.37s, eta=1:46:0\n",
      "2021-04-21 19:16:21 [INFO]\t[TRAIN] Epoch=5/100, Step=52/73, loss=0.186685, loss_cls=0.026357, loss_bbox=0.024094, loss_mask=0.108541, loss_rpn_cls=0.005597, loss_rpn_bbox=0.022095, lr=0.00125, time_each_step=0.37s, eta=1:45:59\n",
      "2021-04-21 19:16:22 [INFO]\t[TRAIN] Epoch=5/100, Step=54/73, loss=0.28797, loss_cls=0.031963, loss_bbox=0.027732, loss_mask=0.106402, loss_rpn_cls=0.03391, loss_rpn_bbox=0.087964, lr=0.00125, time_each_step=0.37s, eta=1:45:58\n",
      "2021-04-21 19:16:22 [INFO]\t[TRAIN] Epoch=5/100, Step=56/73, loss=0.245366, loss_cls=0.033718, loss_bbox=0.025248, loss_mask=0.133791, loss_rpn_cls=0.009971, loss_rpn_bbox=0.042638, lr=0.00125, time_each_step=0.37s, eta=1:45:58\n",
      "2021-04-21 19:16:23 [INFO]\t[TRAIN] Epoch=5/100, Step=58/73, loss=0.258695, loss_cls=0.03833, loss_bbox=0.024703, loss_mask=0.1298, loss_rpn_cls=0.008484, loss_rpn_bbox=0.057377, lr=0.00125, time_each_step=0.37s, eta=1:45:57\n",
      "2021-04-21 19:16:24 [INFO]\t[TRAIN] Epoch=5/100, Step=60/73, loss=0.304174, loss_cls=0.02914, loss_bbox=0.026474, loss_mask=0.125405, loss_rpn_cls=0.012741, loss_rpn_bbox=0.110414, lr=0.00125, time_each_step=0.37s, eta=1:45:56\n",
      "2021-04-21 19:16:25 [INFO]\t[TRAIN] Epoch=5/100, Step=62/73, loss=0.291295, loss_cls=0.042112, loss_bbox=0.034508, loss_mask=0.121841, loss_rpn_cls=0.013377, loss_rpn_bbox=0.079458, lr=0.00125, time_each_step=0.37s, eta=1:45:55\n",
      "2021-04-21 19:16:25 [INFO]\t[TRAIN] Epoch=5/100, Step=64/73, loss=0.548687, loss_cls=0.063771, loss_bbox=0.037871, loss_mask=0.171054, loss_rpn_cls=0.112743, loss_rpn_bbox=0.163247, lr=0.00125, time_each_step=0.37s, eta=1:45:55\n",
      "2021-04-21 19:16:26 [INFO]\t[TRAIN] Epoch=5/100, Step=66/73, loss=0.285698, loss_cls=0.031689, loss_bbox=0.027574, loss_mask=0.150541, loss_rpn_cls=0.017906, loss_rpn_bbox=0.057988, lr=0.00125, time_each_step=0.37s, eta=1:45:54\n",
      "2021-04-21 19:16:27 [INFO]\t[TRAIN] Epoch=5/100, Step=68/73, loss=0.305114, loss_cls=0.03353, loss_bbox=0.030667, loss_mask=0.116464, loss_rpn_cls=0.007448, loss_rpn_bbox=0.117005, lr=0.00125, time_each_step=0.37s, eta=1:45:53\n",
      "2021-04-21 19:16:28 [INFO]\t[TRAIN] Epoch=5/100, Step=70/73, loss=0.311972, loss_cls=0.040142, loss_bbox=0.036958, loss_mask=0.105978, loss_rpn_cls=0.012937, loss_rpn_bbox=0.115956, lr=0.00125, time_each_step=0.37s, eta=1:45:52\n",
      "2021-04-21 19:16:28 [INFO]\t[TRAIN] Epoch=5/100, Step=72/73, loss=0.22332, loss_cls=0.036862, loss_bbox=0.026545, loss_mask=0.100962, loss_rpn_cls=0.02573, loss_rpn_bbox=0.033222, lr=0.00125, time_each_step=0.37s, eta=1:45:52\n",
      "2021-04-21 19:16:29 [INFO]\t[TRAIN] Epoch 5 finished, loss=0.309481, loss_cls=0.038712, loss_bbox=0.029246, loss_mask=0.142029, loss_rpn_cls=0.018588, loss_rpn_bbox=0.080906, lr=0.00125 .\n",
      "2021-04-21 19:16:29 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:16:29 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.07s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.248\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.707\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.084\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.248\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.275\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.457\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.457\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.457\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.13s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.309\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.832\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.058\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.373\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.406\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.408\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.408\n",
      "2021-04-21 19:16:46 [INFO]\t[EVAL] Finished, Epoch=5, bbox_mmap=0.248056, segm_mmap=0.308552 .\n",
      "2021-04-21 19:16:51 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:16:56 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_5.\n",
      "2021-04-21 19:16:56 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, bbox_mmap=0.24805560207847518\n",
      "2021-04-21 19:17:00 [INFO]\t[TRAIN] Epoch=6/100, Step=1/73, loss=0.431087, loss_cls=0.062288, loss_bbox=0.032355, loss_mask=0.194767, loss_rpn_cls=0.019112, loss_rpn_bbox=0.122565, lr=0.00125, time_each_step=0.59s, eta=1:39:3\n",
      "2021-04-21 19:17:01 [INFO]\t[TRAIN] Epoch=6/100, Step=3/73, loss=0.281141, loss_cls=0.042367, loss_bbox=0.035132, loss_mask=0.130202, loss_rpn_cls=0.010369, loss_rpn_bbox=0.063072, lr=0.00125, time_each_step=0.6s, eta=1:39:3\n",
      "2021-04-21 19:17:02 [INFO]\t[TRAIN] Epoch=6/100, Step=5/73, loss=0.348093, loss_cls=0.040489, loss_bbox=0.034359, loss_mask=0.119836, loss_rpn_cls=0.012485, loss_rpn_bbox=0.140924, lr=0.00125, time_each_step=0.62s, eta=1:39:3\n",
      "2021-04-21 19:17:04 [INFO]\t[TRAIN] Epoch=6/100, Step=7/73, loss=0.314547, loss_cls=0.028438, loss_bbox=0.020768, loss_mask=0.144221, loss_rpn_cls=0.015101, loss_rpn_bbox=0.106018, lr=0.00125, time_each_step=0.65s, eta=1:39:4\n",
      "2021-04-21 19:17:05 [INFO]\t[TRAIN] Epoch=6/100, Step=9/73, loss=0.225897, loss_cls=0.034334, loss_bbox=0.032061, loss_mask=0.098852, loss_rpn_cls=0.009606, loss_rpn_bbox=0.051043, lr=0.00125, time_each_step=0.68s, eta=1:39:5\n",
      "2021-04-21 19:17:06 [INFO]\t[TRAIN] Epoch=6/100, Step=11/73, loss=0.23955, loss_cls=0.03015, loss_bbox=0.023916, loss_mask=0.103091, loss_rpn_cls=0.008395, loss_rpn_bbox=0.073998, lr=0.00125, time_each_step=0.69s, eta=1:39:4\n",
      "2021-04-21 19:17:07 [INFO]\t[TRAIN] Epoch=6/100, Step=13/73, loss=0.434616, loss_cls=0.078039, loss_bbox=0.033686, loss_mask=0.204061, loss_rpn_cls=0.010617, loss_rpn_bbox=0.108213, lr=0.00125, time_each_step=0.71s, eta=1:39:3\n",
      "2021-04-21 19:17:08 [INFO]\t[TRAIN] Epoch=6/100, Step=15/73, loss=0.548825, loss_cls=0.072659, loss_bbox=0.036018, loss_mask=0.181033, loss_rpn_cls=0.115896, loss_rpn_bbox=0.14322, lr=0.00125, time_each_step=0.72s, eta=1:39:3\n",
      "2021-04-21 19:17:09 [INFO]\t[TRAIN] Epoch=6/100, Step=17/73, loss=0.236143, loss_cls=0.039893, loss_bbox=0.02807, loss_mask=0.110408, loss_rpn_cls=0.013727, loss_rpn_bbox=0.044045, lr=0.00125, time_each_step=0.72s, eta=1:39:2\n",
      "2021-04-21 19:17:10 [INFO]\t[TRAIN] Epoch=6/100, Step=19/73, loss=0.323949, loss_cls=0.040508, loss_bbox=0.032652, loss_mask=0.141305, loss_rpn_cls=0.015515, loss_rpn_bbox=0.093969, lr=0.00125, time_each_step=0.74s, eta=1:39:1\n",
      "2021-04-21 19:17:11 [INFO]\t[TRAIN] Epoch=6/100, Step=21/73, loss=0.274103, loss_cls=0.03375, loss_bbox=0.028468, loss_mask=0.129, loss_rpn_cls=0.015591, loss_rpn_bbox=0.067293, lr=0.00125, time_each_step=0.54s, eta=1:38:49\n",
      "2021-04-21 19:17:12 [INFO]\t[TRAIN] Epoch=6/100, Step=23/73, loss=0.267637, loss_cls=0.045172, loss_bbox=0.031493, loss_mask=0.118476, loss_rpn_cls=0.01401, loss_rpn_bbox=0.058486, lr=0.00125, time_each_step=0.54s, eta=1:38:48\n",
      "2021-04-21 19:17:13 [INFO]\t[TRAIN] Epoch=6/100, Step=25/73, loss=0.297332, loss_cls=0.043222, loss_bbox=0.027163, loss_mask=0.1261, loss_rpn_cls=0.017891, loss_rpn_bbox=0.082957, lr=0.00125, time_each_step=0.53s, eta=1:38:47\n",
      "2021-04-21 19:17:14 [INFO]\t[TRAIN] Epoch=6/100, Step=27/73, loss=0.28703, loss_cls=0.03188, loss_bbox=0.02328, loss_mask=0.159633, loss_rpn_cls=0.012144, loss_rpn_bbox=0.060093, lr=0.00125, time_each_step=0.5s, eta=1:38:44\n",
      "2021-04-21 19:17:15 [INFO]\t[TRAIN] Epoch=6/100, Step=29/73, loss=0.326368, loss_cls=0.045925, loss_bbox=0.036308, loss_mask=0.175962, loss_rpn_cls=0.007899, loss_rpn_bbox=0.060275, lr=0.00125, time_each_step=0.48s, eta=1:38:42\n",
      "2021-04-21 19:17:15 [INFO]\t[TRAIN] Epoch=6/100, Step=31/73, loss=0.200031, loss_cls=0.03173, loss_bbox=0.025127, loss_mask=0.109047, loss_rpn_cls=0.005308, loss_rpn_bbox=0.028819, lr=0.00125, time_each_step=0.47s, eta=1:38:41\n",
      "2021-04-21 19:17:16 [INFO]\t[TRAIN] Epoch=6/100, Step=33/73, loss=0.23913, loss_cls=0.03752, loss_bbox=0.03916, loss_mask=0.113708, loss_rpn_cls=0.007494, loss_rpn_bbox=0.041248, lr=0.00125, time_each_step=0.46s, eta=1:38:39\n",
      "2021-04-21 19:17:17 [INFO]\t[TRAIN] Epoch=6/100, Step=35/73, loss=0.262183, loss_cls=0.031952, loss_bbox=0.021108, loss_mask=0.14342, loss_rpn_cls=0.009713, loss_rpn_bbox=0.055989, lr=0.00125, time_each_step=0.45s, eta=1:38:38\n",
      "2021-04-21 19:17:18 [INFO]\t[TRAIN] Epoch=6/100, Step=37/73, loss=0.26029, loss_cls=0.029597, loss_bbox=0.031969, loss_mask=0.093821, loss_rpn_cls=0.013806, loss_rpn_bbox=0.091097, lr=0.00125, time_each_step=0.44s, eta=1:38:37\n",
      "2021-04-21 19:17:18 [INFO]\t[TRAIN] Epoch=6/100, Step=39/73, loss=0.273829, loss_cls=0.037122, loss_bbox=0.043776, loss_mask=0.110944, loss_rpn_cls=0.009066, loss_rpn_bbox=0.072921, lr=0.00125, time_each_step=0.43s, eta=1:38:36\n",
      "2021-04-21 19:17:19 [INFO]\t[TRAIN] Epoch=6/100, Step=41/73, loss=0.174847, loss_cls=0.034238, loss_bbox=0.023996, loss_mask=0.084744, loss_rpn_cls=0.006806, loss_rpn_bbox=0.025063, lr=0.00125, time_each_step=0.41s, eta=1:38:34\n",
      "2021-04-21 19:17:20 [INFO]\t[TRAIN] Epoch=6/100, Step=43/73, loss=0.284686, loss_cls=0.038137, loss_bbox=0.030533, loss_mask=0.132557, loss_rpn_cls=0.012148, loss_rpn_bbox=0.071311, lr=0.00125, time_each_step=0.4s, eta=1:38:33\n",
      "2021-04-21 19:17:21 [INFO]\t[TRAIN] Epoch=6/100, Step=45/73, loss=0.194398, loss_cls=0.031544, loss_bbox=0.024852, loss_mask=0.083279, loss_rpn_cls=0.011788, loss_rpn_bbox=0.042936, lr=0.00125, time_each_step=0.39s, eta=1:38:32\n",
      "2021-04-21 19:17:21 [INFO]\t[TRAIN] Epoch=6/100, Step=47/73, loss=0.208901, loss_cls=0.027487, loss_bbox=0.023502, loss_mask=0.098512, loss_rpn_cls=0.014068, loss_rpn_bbox=0.045333, lr=0.00125, time_each_step=0.38s, eta=1:38:31\n",
      "2021-04-21 19:17:22 [INFO]\t[TRAIN] Epoch=6/100, Step=49/73, loss=0.216238, loss_cls=0.039541, loss_bbox=0.031308, loss_mask=0.090369, loss_rpn_cls=0.012507, loss_rpn_bbox=0.042514, lr=0.00125, time_each_step=0.38s, eta=1:38:30\n",
      "2021-04-21 19:17:23 [INFO]\t[TRAIN] Epoch=6/100, Step=51/73, loss=0.225627, loss_cls=0.037104, loss_bbox=0.037697, loss_mask=0.09975, loss_rpn_cls=0.011245, loss_rpn_bbox=0.03983, lr=0.00125, time_each_step=0.37s, eta=1:38:29\n",
      "2021-04-21 19:17:24 [INFO]\t[TRAIN] Epoch=6/100, Step=53/73, loss=0.24056, loss_cls=0.038518, loss_bbox=0.025167, loss_mask=0.086919, loss_rpn_cls=0.01965, loss_rpn_bbox=0.070306, lr=0.00125, time_each_step=0.37s, eta=1:38:28\n",
      "2021-04-21 19:17:24 [INFO]\t[TRAIN] Epoch=6/100, Step=55/73, loss=0.361439, loss_cls=0.026023, loss_bbox=0.01969, loss_mask=0.115658, loss_rpn_cls=0.028708, loss_rpn_bbox=0.17136, lr=0.00125, time_each_step=0.37s, eta=1:38:28\n",
      "2021-04-21 19:17:25 [INFO]\t[TRAIN] Epoch=6/100, Step=57/73, loss=0.302969, loss_cls=0.034203, loss_bbox=0.032391, loss_mask=0.128622, loss_rpn_cls=0.01086, loss_rpn_bbox=0.096892, lr=0.00125, time_each_step=0.37s, eta=1:38:27\n",
      "2021-04-21 19:17:26 [INFO]\t[TRAIN] Epoch=6/100, Step=59/73, loss=0.241572, loss_cls=0.034554, loss_bbox=0.028979, loss_mask=0.098897, loss_rpn_cls=0.007496, loss_rpn_bbox=0.071647, lr=0.00125, time_each_step=0.37s, eta=1:38:26\n",
      "2021-04-21 19:17:26 [INFO]\t[TRAIN] Epoch=6/100, Step=61/73, loss=0.230504, loss_cls=0.033075, loss_bbox=0.032833, loss_mask=0.094499, loss_rpn_cls=0.005587, loss_rpn_bbox=0.064511, lr=0.00125, time_each_step=0.37s, eta=1:38:25\n",
      "2021-04-21 19:17:27 [INFO]\t[TRAIN] Epoch=6/100, Step=63/73, loss=0.26939, loss_cls=0.032665, loss_bbox=0.022031, loss_mask=0.10106, loss_rpn_cls=0.016208, loss_rpn_bbox=0.097427, lr=0.00125, time_each_step=0.36s, eta=1:38:25\n",
      "2021-04-21 19:17:28 [INFO]\t[TRAIN] Epoch=6/100, Step=65/73, loss=0.179638, loss_cls=0.030492, loss_bbox=0.0219, loss_mask=0.08903, loss_rpn_cls=0.01062, loss_rpn_bbox=0.027595, lr=0.00125, time_each_step=0.36s, eta=1:38:24\n",
      "2021-04-21 19:17:29 [INFO]\t[TRAIN] Epoch=6/100, Step=67/73, loss=0.245272, loss_cls=0.032438, loss_bbox=0.029679, loss_mask=0.120831, loss_rpn_cls=0.00938, loss_rpn_bbox=0.052944, lr=0.00125, time_each_step=0.36s, eta=1:38:23\n",
      "2021-04-21 19:17:29 [INFO]\t[TRAIN] Epoch=6/100, Step=69/73, loss=0.191022, loss_cls=0.029742, loss_bbox=0.028655, loss_mask=0.086951, loss_rpn_cls=0.005261, loss_rpn_bbox=0.040413, lr=0.00125, time_each_step=0.36s, eta=1:38:23\n",
      "2021-04-21 19:17:30 [INFO]\t[TRAIN] Epoch=6/100, Step=71/73, loss=0.260086, loss_cls=0.030177, loss_bbox=0.022656, loss_mask=0.105519, loss_rpn_cls=0.011023, loss_rpn_bbox=0.090711, lr=0.00125, time_each_step=0.36s, eta=1:38:22\n",
      "2021-04-21 19:17:31 [INFO]\t[TRAIN] Epoch=6/100, Step=73/73, loss=0.186784, loss_cls=0.036341, loss_bbox=0.022435, loss_mask=0.082286, loss_rpn_cls=0.012948, loss_rpn_bbox=0.032774, lr=0.00125, time_each_step=0.36s, eta=1:38:21\n",
      "2021-04-21 19:17:31 [INFO]\t[TRAIN] Epoch 6 finished, loss=0.280046, loss_cls=0.038589, loss_bbox=0.029652, loss_mask=0.120216, loss_rpn_cls=0.015335, loss_rpn_bbox=0.076253, lr=0.00125 .\n",
      "2021-04-21 19:17:31 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:17:31 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.05s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.275\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.722\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.128\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.275\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.264\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.465\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.465\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.287\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.823\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.049\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.341\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.371\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.371\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371\n",
      "2021-04-21 19:17:47 [INFO]\t[EVAL] Finished, Epoch=6, bbox_mmap=0.274692, segm_mmap=0.287339 .\n",
      "2021-04-21 19:17:52 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:17:56 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_6.\n",
      "2021-04-21 19:17:56 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_6, bbox_mmap=0.274691885822062\n",
      "2021-04-21 19:18:02 [INFO]\t[TRAIN] Epoch=7/100, Step=2/73, loss=0.223199, loss_cls=0.038948, loss_bbox=0.033731, loss_mask=0.07423, loss_rpn_cls=0.008682, loss_rpn_bbox=0.067608, lr=0.00125, time_each_step=0.62s, eta=1:35:4\n",
      "2021-04-21 19:18:03 [INFO]\t[TRAIN] Epoch=7/100, Step=4/73, loss=0.172114, loss_cls=0.030148, loss_bbox=0.028531, loss_mask=0.069625, loss_rpn_cls=0.010792, loss_rpn_bbox=0.033018, lr=0.00125, time_each_step=0.64s, eta=1:35:5\n",
      "2021-04-21 19:18:04 [INFO]\t[TRAIN] Epoch=7/100, Step=6/73, loss=0.219032, loss_cls=0.040551, loss_bbox=0.034183, loss_mask=0.087623, loss_rpn_cls=0.006185, loss_rpn_bbox=0.050489, lr=0.00125, time_each_step=0.67s, eta=1:35:5\n",
      "2021-04-21 19:18:06 [INFO]\t[TRAIN] Epoch=7/100, Step=8/73, loss=0.23758, loss_cls=0.032297, loss_bbox=0.029333, loss_mask=0.107789, loss_rpn_cls=0.01254, loss_rpn_bbox=0.055621, lr=0.00125, time_each_step=0.7s, eta=1:35:6\n",
      "2021-04-21 19:18:07 [INFO]\t[TRAIN] Epoch=7/100, Step=10/73, loss=0.228691, loss_cls=0.035089, loss_bbox=0.036951, loss_mask=0.088315, loss_rpn_cls=0.009127, loss_rpn_bbox=0.059209, lr=0.00125, time_each_step=0.71s, eta=1:35:5\n",
      "2021-04-21 19:18:08 [INFO]\t[TRAIN] Epoch=7/100, Step=12/73, loss=0.216872, loss_cls=0.030977, loss_bbox=0.031918, loss_mask=0.092286, loss_rpn_cls=0.006053, loss_rpn_bbox=0.055638, lr=0.00125, time_each_step=0.72s, eta=1:35:4\n",
      "2021-04-21 19:18:09 [INFO]\t[TRAIN] Epoch=7/100, Step=14/73, loss=0.236558, loss_cls=0.039906, loss_bbox=0.035554, loss_mask=0.071875, loss_rpn_cls=0.009692, loss_rpn_bbox=0.079532, lr=0.00125, time_each_step=0.73s, eta=1:35:3\n",
      "2021-04-21 19:18:09 [INFO]\t[TRAIN] Epoch=7/100, Step=16/73, loss=0.239377, loss_cls=0.032857, loss_bbox=0.029568, loss_mask=0.074385, loss_rpn_cls=0.021176, loss_rpn_bbox=0.081391, lr=0.00125, time_each_step=0.73s, eta=1:35:2\n",
      "2021-04-21 19:18:10 [INFO]\t[TRAIN] Epoch=7/100, Step=18/73, loss=0.236475, loss_cls=0.031377, loss_bbox=0.029063, loss_mask=0.090572, loss_rpn_cls=0.014151, loss_rpn_bbox=0.071312, lr=0.00125, time_each_step=0.74s, eta=1:35:1\n",
      "2021-04-21 19:18:11 [INFO]\t[TRAIN] Epoch=7/100, Step=20/73, loss=0.235326, loss_cls=0.036497, loss_bbox=0.029035, loss_mask=0.098729, loss_rpn_cls=0.006819, loss_rpn_bbox=0.064247, lr=0.00125, time_each_step=0.76s, eta=1:35:0\n",
      "2021-04-21 19:18:12 [INFO]\t[TRAIN] Epoch=7/100, Step=22/73, loss=0.227208, loss_cls=0.03556, loss_bbox=0.030538, loss_mask=0.099797, loss_rpn_cls=0.012424, loss_rpn_bbox=0.048889, lr=0.00125, time_each_step=0.52s, eta=1:34:47\n",
      "2021-04-21 19:18:13 [INFO]\t[TRAIN] Epoch=7/100, Step=24/73, loss=0.243894, loss_cls=0.03265, loss_bbox=0.030642, loss_mask=0.09444, loss_rpn_cls=0.006028, loss_rpn_bbox=0.080135, lr=0.00125, time_each_step=0.51s, eta=1:34:45\n",
      "2021-04-21 19:18:14 [INFO]\t[TRAIN] Epoch=7/100, Step=26/73, loss=0.257839, loss_cls=0.035941, loss_bbox=0.039923, loss_mask=0.104117, loss_rpn_cls=0.008125, loss_rpn_bbox=0.069732, lr=0.00125, time_each_step=0.48s, eta=1:34:43\n",
      "2021-04-21 19:18:15 [INFO]\t[TRAIN] Epoch=7/100, Step=28/73, loss=0.330457, loss_cls=0.042008, loss_bbox=0.030537, loss_mask=0.128993, loss_rpn_cls=0.012355, loss_rpn_bbox=0.116564, lr=0.00125, time_each_step=0.47s, eta=1:34:41\n",
      "2021-04-21 19:18:16 [INFO]\t[TRAIN] Epoch=7/100, Step=30/73, loss=0.270319, loss_cls=0.032492, loss_bbox=0.027682, loss_mask=0.114786, loss_rpn_cls=0.008922, loss_rpn_bbox=0.086436, lr=0.00125, time_each_step=0.46s, eta=1:34:40\n",
      "2021-04-21 19:18:17 [INFO]\t[TRAIN] Epoch=7/100, Step=32/73, loss=0.255205, loss_cls=0.03317, loss_bbox=0.034306, loss_mask=0.107695, loss_rpn_cls=0.004402, loss_rpn_bbox=0.075632, lr=0.00125, time_each_step=0.45s, eta=1:34:39\n",
      "2021-04-21 19:18:17 [INFO]\t[TRAIN] Epoch=7/100, Step=34/73, loss=0.257847, loss_cls=0.031054, loss_bbox=0.025659, loss_mask=0.102476, loss_rpn_cls=0.007102, loss_rpn_bbox=0.091556, lr=0.00125, time_each_step=0.44s, eta=1:34:38\n",
      "2021-04-21 19:18:18 [INFO]\t[TRAIN] Epoch=7/100, Step=36/73, loss=0.241609, loss_cls=0.036152, loss_bbox=0.03105, loss_mask=0.102523, loss_rpn_cls=0.005361, loss_rpn_bbox=0.066522, lr=0.00125, time_each_step=0.44s, eta=1:34:37\n",
      "2021-04-21 19:18:19 [INFO]\t[TRAIN] Epoch=7/100, Step=38/73, loss=0.254847, loss_cls=0.033109, loss_bbox=0.023383, loss_mask=0.10689, loss_rpn_cls=0.006299, loss_rpn_bbox=0.085165, lr=0.00125, time_each_step=0.43s, eta=1:34:35\n",
      "2021-04-21 19:18:20 [INFO]\t[TRAIN] Epoch=7/100, Step=40/73, loss=0.604932, loss_cls=0.056976, loss_bbox=0.032557, loss_mask=0.245006, loss_rpn_cls=0.108427, loss_rpn_bbox=0.161965, lr=0.00125, time_each_step=0.42s, eta=1:34:34\n",
      "2021-04-21 19:18:20 [INFO]\t[TRAIN] Epoch=7/100, Step=42/73, loss=0.174703, loss_cls=0.034949, loss_bbox=0.028504, loss_mask=0.082194, loss_rpn_cls=0.005164, loss_rpn_bbox=0.023892, lr=0.00125, time_each_step=0.4s, eta=1:34:33\n",
      "2021-04-21 19:18:21 [INFO]\t[TRAIN] Epoch=7/100, Step=44/73, loss=0.24363, loss_cls=0.038994, loss_bbox=0.031745, loss_mask=0.109225, loss_rpn_cls=0.017427, loss_rpn_bbox=0.046239, lr=0.00125, time_each_step=0.39s, eta=1:34:32\n",
      "2021-04-21 19:18:22 [INFO]\t[TRAIN] Epoch=7/100, Step=46/73, loss=0.222294, loss_cls=0.035604, loss_bbox=0.023415, loss_mask=0.095631, loss_rpn_cls=0.003941, loss_rpn_bbox=0.063704, lr=0.00125, time_each_step=0.38s, eta=1:34:31\n",
      "2021-04-21 19:18:23 [INFO]\t[TRAIN] Epoch=7/100, Step=48/73, loss=0.27784, loss_cls=0.034062, loss_bbox=0.026527, loss_mask=0.129316, loss_rpn_cls=0.010417, loss_rpn_bbox=0.077518, lr=0.00125, time_each_step=0.38s, eta=1:34:30\n",
      "2021-04-21 19:18:23 [INFO]\t[TRAIN] Epoch=7/100, Step=50/73, loss=0.200775, loss_cls=0.028354, loss_bbox=0.022312, loss_mask=0.100794, loss_rpn_cls=0.015739, loss_rpn_bbox=0.033575, lr=0.00125, time_each_step=0.37s, eta=1:34:29\n",
      "2021-04-21 19:18:24 [INFO]\t[TRAIN] Epoch=7/100, Step=52/73, loss=0.416422, loss_cls=0.057353, loss_bbox=0.025499, loss_mask=0.211388, loss_rpn_cls=0.012643, loss_rpn_bbox=0.109539, lr=0.00125, time_each_step=0.37s, eta=1:34:28\n",
      "2021-04-21 19:18:25 [INFO]\t[TRAIN] Epoch=7/100, Step=54/73, loss=0.471281, loss_cls=0.038187, loss_bbox=0.05146, loss_mask=0.172302, loss_rpn_cls=0.014279, loss_rpn_bbox=0.195054, lr=0.00125, time_each_step=0.37s, eta=1:34:27\n",
      "2021-04-21 19:18:25 [INFO]\t[TRAIN] Epoch=7/100, Step=56/73, loss=0.231261, loss_cls=0.038454, loss_bbox=0.043505, loss_mask=0.101672, loss_rpn_cls=0.011362, loss_rpn_bbox=0.036267, lr=0.00125, time_each_step=0.37s, eta=1:34:27\n",
      "2021-04-21 19:18:26 [INFO]\t[TRAIN] Epoch=7/100, Step=58/73, loss=0.257008, loss_cls=0.033109, loss_bbox=0.024209, loss_mask=0.094042, loss_rpn_cls=0.018066, loss_rpn_bbox=0.087583, lr=0.00125, time_each_step=0.37s, eta=1:34:26\n",
      "2021-04-21 19:18:27 [INFO]\t[TRAIN] Epoch=7/100, Step=60/73, loss=0.198256, loss_cls=0.034604, loss_bbox=0.033328, loss_mask=0.091872, loss_rpn_cls=0.009042, loss_rpn_bbox=0.02941, lr=0.00125, time_each_step=0.37s, eta=1:34:25\n",
      "2021-04-21 19:18:28 [INFO]\t[TRAIN] Epoch=7/100, Step=62/73, loss=0.244637, loss_cls=0.03954, loss_bbox=0.031468, loss_mask=0.104877, loss_rpn_cls=0.014785, loss_rpn_bbox=0.053967, lr=0.00125, time_each_step=0.37s, eta=1:34:24\n",
      "2021-04-21 19:18:28 [INFO]\t[TRAIN] Epoch=7/100, Step=64/73, loss=0.238827, loss_cls=0.037377, loss_bbox=0.04066, loss_mask=0.113995, loss_rpn_cls=0.01152, loss_rpn_bbox=0.035275, lr=0.00125, time_each_step=0.36s, eta=1:34:24\n",
      "2021-04-21 19:18:29 [INFO]\t[TRAIN] Epoch=7/100, Step=66/73, loss=0.25949, loss_cls=0.046384, loss_bbox=0.039521, loss_mask=0.101696, loss_rpn_cls=0.005401, loss_rpn_bbox=0.066488, lr=0.00125, time_each_step=0.36s, eta=1:34:23\n",
      "2021-04-21 19:18:30 [INFO]\t[TRAIN] Epoch=7/100, Step=68/73, loss=0.198375, loss_cls=0.030999, loss_bbox=0.02125, loss_mask=0.092444, loss_rpn_cls=0.012214, loss_rpn_bbox=0.041466, lr=0.00125, time_each_step=0.36s, eta=1:34:22\n",
      "2021-04-21 19:18:31 [INFO]\t[TRAIN] Epoch=7/100, Step=70/73, loss=0.225078, loss_cls=0.027813, loss_bbox=0.018876, loss_mask=0.08381, loss_rpn_cls=0.011703, loss_rpn_bbox=0.082876, lr=0.00125, time_each_step=0.36s, eta=1:34:22\n",
      "2021-04-21 19:18:31 [INFO]\t[TRAIN] Epoch=7/100, Step=72/73, loss=0.273332, loss_cls=0.034243, loss_bbox=0.03976, loss_mask=0.135417, loss_rpn_cls=0.019878, loss_rpn_bbox=0.044034, lr=0.00125, time_each_step=0.36s, eta=1:34:21\n",
      "2021-04-21 19:18:32 [INFO]\t[TRAIN] Epoch 7 finished, loss=0.272211, loss_cls=0.037803, loss_bbox=0.031316, loss_mask=0.115646, loss_rpn_cls=0.012695, loss_rpn_bbox=0.074751, lr=0.00125 .\n",
      "2021-04-21 19:18:32 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:18:32 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:15<00:00,  5.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.06s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.219\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.705\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.047\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.219\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.243\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.445\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.445\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.10s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.306\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.869\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.037\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.360\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.440\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.440\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.440\n",
      "2021-04-21 19:18:48 [INFO]\t[EVAL] Finished, Epoch=7, bbox_mmap=0.219348, segm_mmap=0.30579 .\n",
      "2021-04-21 19:18:53 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_7.\n",
      "2021-04-21 19:18:53 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_6, bbox_mmap=0.274691885822062\n",
      "2021-04-21 19:18:57 [INFO]\t[TRAIN] Epoch=8/100, Step=1/73, loss=0.386337, loss_cls=0.05613, loss_bbox=0.043817, loss_mask=0.162809, loss_rpn_cls=0.006009, loss_rpn_bbox=0.117572, lr=0.00125, time_each_step=0.55s, eta=1:27:45\n",
      "2021-04-21 19:18:58 [INFO]\t[TRAIN] Epoch=8/100, Step=3/73, loss=0.306614, loss_cls=0.048416, loss_bbox=0.051382, loss_mask=0.109626, loss_rpn_cls=0.014532, loss_rpn_bbox=0.082658, lr=0.00125, time_each_step=0.58s, eta=1:27:46\n",
      "2021-04-21 19:18:59 [INFO]\t[TRAIN] Epoch=8/100, Step=5/73, loss=0.222969, loss_cls=0.040857, loss_bbox=0.034514, loss_mask=0.096216, loss_rpn_cls=0.014828, loss_rpn_bbox=0.036554, lr=0.00125, time_each_step=0.6s, eta=1:27:46\n",
      "2021-04-21 19:19:00 [INFO]\t[TRAIN] Epoch=8/100, Step=7/73, loss=0.228678, loss_cls=0.03098, loss_bbox=0.033827, loss_mask=0.092273, loss_rpn_cls=0.006038, loss_rpn_bbox=0.065561, lr=0.00125, time_each_step=0.62s, eta=1:27:46\n",
      "2021-04-21 19:19:02 [INFO]\t[TRAIN] Epoch=8/100, Step=9/73, loss=0.15897, loss_cls=0.031687, loss_bbox=0.029915, loss_mask=0.067304, loss_rpn_cls=0.006013, loss_rpn_bbox=0.024051, lr=0.00125, time_each_step=0.64s, eta=1:27:46\n",
      "2021-04-21 19:19:03 [INFO]\t[TRAIN] Epoch=8/100, Step=11/73, loss=0.255706, loss_cls=0.041087, loss_bbox=0.031887, loss_mask=0.12259, loss_rpn_cls=0.003688, loss_rpn_bbox=0.056453, lr=0.00125, time_each_step=0.66s, eta=1:27:46\n",
      "2021-04-21 19:19:03 [INFO]\t[TRAIN] Epoch=8/100, Step=13/73, loss=0.231491, loss_cls=0.038178, loss_bbox=0.034364, loss_mask=0.079479, loss_rpn_cls=0.017363, loss_rpn_bbox=0.062107, lr=0.00125, time_each_step=0.67s, eta=1:27:45\n",
      "2021-04-21 19:19:04 [INFO]\t[TRAIN] Epoch=8/100, Step=15/73, loss=0.220426, loss_cls=0.030091, loss_bbox=0.029338, loss_mask=0.093997, loss_rpn_cls=0.005411, loss_rpn_bbox=0.061588, lr=0.00125, time_each_step=0.67s, eta=1:27:44\n",
      "2021-04-21 19:19:05 [INFO]\t[TRAIN] Epoch=8/100, Step=17/73, loss=0.283779, loss_cls=0.038396, loss_bbox=0.040369, loss_mask=0.099937, loss_rpn_cls=0.009217, loss_rpn_bbox=0.095859, lr=0.00125, time_each_step=0.68s, eta=1:27:44\n",
      "2021-04-21 19:19:06 [INFO]\t[TRAIN] Epoch=8/100, Step=19/73, loss=0.258299, loss_cls=0.04416, loss_bbox=0.032982, loss_mask=0.08692, loss_rpn_cls=0.009738, loss_rpn_bbox=0.084499, lr=0.00125, time_each_step=0.69s, eta=1:27:42\n",
      "2021-04-21 19:19:07 [INFO]\t[TRAIN] Epoch=8/100, Step=21/73, loss=0.257829, loss_cls=0.02781, loss_bbox=0.023915, loss_mask=0.093225, loss_rpn_cls=0.015595, loss_rpn_bbox=0.097285, lr=0.00125, time_each_step=0.5s, eta=1:27:31\n",
      "2021-04-21 19:19:08 [INFO]\t[TRAIN] Epoch=8/100, Step=23/73, loss=0.17569, loss_cls=0.029518, loss_bbox=0.025603, loss_mask=0.087962, loss_rpn_cls=0.010226, loss_rpn_bbox=0.022382, lr=0.00125, time_each_step=0.48s, eta=1:27:29\n",
      "2021-04-21 19:19:09 [INFO]\t[TRAIN] Epoch=8/100, Step=25/73, loss=0.173808, loss_cls=0.027214, loss_bbox=0.01952, loss_mask=0.087103, loss_rpn_cls=0.004088, loss_rpn_bbox=0.035882, lr=0.00125, time_each_step=0.47s, eta=1:27:28\n",
      "2021-04-21 19:19:10 [INFO]\t[TRAIN] Epoch=8/100, Step=27/73, loss=0.221404, loss_cls=0.032067, loss_bbox=0.027738, loss_mask=0.093192, loss_rpn_cls=0.00326, loss_rpn_bbox=0.065147, lr=0.00125, time_each_step=0.46s, eta=1:27:26\n",
      "2021-04-21 19:19:11 [INFO]\t[TRAIN] Epoch=8/100, Step=29/73, loss=0.174086, loss_cls=0.035409, loss_bbox=0.029315, loss_mask=0.074677, loss_rpn_cls=0.014129, loss_rpn_bbox=0.020556, lr=0.00125, time_each_step=0.45s, eta=1:27:25\n",
      "2021-04-21 19:19:11 [INFO]\t[TRAIN] Epoch=8/100, Step=31/73, loss=0.468015, loss_cls=0.075084, loss_bbox=0.039818, loss_mask=0.175436, loss_rpn_cls=0.012884, loss_rpn_bbox=0.164793, lr=0.00125, time_each_step=0.44s, eta=1:27:24\n",
      "2021-04-21 19:19:12 [INFO]\t[TRAIN] Epoch=8/100, Step=33/73, loss=0.230477, loss_cls=0.03698, loss_bbox=0.036935, loss_mask=0.088103, loss_rpn_cls=0.003375, loss_rpn_bbox=0.065084, lr=0.00125, time_each_step=0.44s, eta=1:27:23\n",
      "2021-04-21 19:19:13 [INFO]\t[TRAIN] Epoch=8/100, Step=35/73, loss=0.238883, loss_cls=0.038509, loss_bbox=0.029745, loss_mask=0.106232, loss_rpn_cls=0.005079, loss_rpn_bbox=0.059317, lr=0.00125, time_each_step=0.43s, eta=1:27:21\n",
      "2021-04-21 19:19:14 [INFO]\t[TRAIN] Epoch=8/100, Step=37/73, loss=0.232197, loss_cls=0.031518, loss_bbox=0.029521, loss_mask=0.081814, loss_rpn_cls=0.010224, loss_rpn_bbox=0.079119, lr=0.00125, time_each_step=0.42s, eta=1:27:20\n",
      "2021-04-21 19:19:14 [INFO]\t[TRAIN] Epoch=8/100, Step=39/73, loss=0.362669, loss_cls=0.038032, loss_bbox=0.026213, loss_mask=0.168506, loss_rpn_cls=0.022197, loss_rpn_bbox=0.107721, lr=0.00125, time_each_step=0.41s, eta=1:27:19\n",
      "2021-04-21 19:19:15 [INFO]\t[TRAIN] Epoch=8/100, Step=41/73, loss=0.207869, loss_cls=0.034709, loss_bbox=0.020726, loss_mask=0.089366, loss_rpn_cls=0.004307, loss_rpn_bbox=0.058761, lr=0.00125, time_each_step=0.41s, eta=1:27:18\n",
      "2021-04-21 19:19:16 [INFO]\t[TRAIN] Epoch=8/100, Step=43/73, loss=0.236187, loss_cls=0.041093, loss_bbox=0.030543, loss_mask=0.079111, loss_rpn_cls=0.009568, loss_rpn_bbox=0.075872, lr=0.00125, time_each_step=0.41s, eta=1:27:17\n",
      "2021-04-21 19:19:17 [INFO]\t[TRAIN] Epoch=8/100, Step=45/73, loss=0.262612, loss_cls=0.038, loss_bbox=0.035687, loss_mask=0.122936, loss_rpn_cls=0.003492, loss_rpn_bbox=0.062497, lr=0.00125, time_each_step=0.4s, eta=1:27:16\n",
      "2021-04-21 19:19:17 [INFO]\t[TRAIN] Epoch=8/100, Step=47/73, loss=0.428248, loss_cls=0.040083, loss_bbox=0.039306, loss_mask=0.239423, loss_rpn_cls=0.007281, loss_rpn_bbox=0.102154, lr=0.00125, time_each_step=0.38s, eta=1:27:15\n",
      "2021-04-21 19:19:18 [INFO]\t[TRAIN] Epoch=8/100, Step=49/73, loss=0.272347, loss_cls=0.029888, loss_bbox=0.023273, loss_mask=0.097513, loss_rpn_cls=0.015822, loss_rpn_bbox=0.105851, lr=0.00125, time_each_step=0.38s, eta=1:27:14\n",
      "2021-04-21 19:19:19 [INFO]\t[TRAIN] Epoch=8/100, Step=51/73, loss=0.177823, loss_cls=0.031097, loss_bbox=0.028724, loss_mask=0.079834, loss_rpn_cls=0.01557, loss_rpn_bbox=0.022597, lr=0.00125, time_each_step=0.37s, eta=1:27:13\n",
      "2021-04-21 19:19:20 [INFO]\t[TRAIN] Epoch=8/100, Step=53/73, loss=0.298888, loss_cls=0.049768, loss_bbox=0.047844, loss_mask=0.102715, loss_rpn_cls=0.011926, loss_rpn_bbox=0.086635, lr=0.00125, time_each_step=0.37s, eta=1:27:13\n",
      "2021-04-21 19:19:20 [INFO]\t[TRAIN] Epoch=8/100, Step=55/73, loss=0.244616, loss_cls=0.039273, loss_bbox=0.042394, loss_mask=0.10241, loss_rpn_cls=0.006491, loss_rpn_bbox=0.054047, lr=0.00125, time_each_step=0.37s, eta=1:27:12\n",
      "2021-04-21 19:19:21 [INFO]\t[TRAIN] Epoch=8/100, Step=57/73, loss=0.213026, loss_cls=0.034003, loss_bbox=0.028364, loss_mask=0.104302, loss_rpn_cls=0.003137, loss_rpn_bbox=0.043221, lr=0.00125, time_each_step=0.37s, eta=1:27:11\n",
      "2021-04-21 19:19:22 [INFO]\t[TRAIN] Epoch=8/100, Step=59/73, loss=0.213185, loss_cls=0.037181, loss_bbox=0.028762, loss_mask=0.114516, loss_rpn_cls=0.005511, loss_rpn_bbox=0.027215, lr=0.00125, time_each_step=0.37s, eta=1:27:10\n",
      "2021-04-21 19:19:23 [INFO]\t[TRAIN] Epoch=8/100, Step=61/73, loss=0.275989, loss_cls=0.0326, loss_bbox=0.034256, loss_mask=0.100237, loss_rpn_cls=0.011029, loss_rpn_bbox=0.097867, lr=0.00125, time_each_step=0.37s, eta=1:27:10\n",
      "2021-04-21 19:19:23 [INFO]\t[TRAIN] Epoch=8/100, Step=63/73, loss=0.226547, loss_cls=0.030231, loss_bbox=0.021105, loss_mask=0.100894, loss_rpn_cls=0.007084, loss_rpn_bbox=0.067233, lr=0.00125, time_each_step=0.37s, eta=1:27:9\n",
      "2021-04-21 19:19:24 [INFO]\t[TRAIN] Epoch=8/100, Step=65/73, loss=0.237251, loss_cls=0.030866, loss_bbox=0.031803, loss_mask=0.083657, loss_rpn_cls=0.017675, loss_rpn_bbox=0.07325, lr=0.00125, time_each_step=0.37s, eta=1:27:8\n",
      "2021-04-21 19:19:25 [INFO]\t[TRAIN] Epoch=8/100, Step=67/73, loss=0.310971, loss_cls=0.039416, loss_bbox=0.037449, loss_mask=0.165036, loss_rpn_cls=0.014622, loss_rpn_bbox=0.054449, lr=0.00125, time_each_step=0.37s, eta=1:27:7\n",
      "2021-04-21 19:19:26 [INFO]\t[TRAIN] Epoch=8/100, Step=69/73, loss=0.262988, loss_cls=0.034285, loss_bbox=0.032842, loss_mask=0.097929, loss_rpn_cls=0.010722, loss_rpn_bbox=0.08721, lr=0.00125, time_each_step=0.37s, eta=1:27:7\n",
      "2021-04-21 19:19:26 [INFO]\t[TRAIN] Epoch=8/100, Step=71/73, loss=0.195623, loss_cls=0.03271, loss_bbox=0.026339, loss_mask=0.085313, loss_rpn_cls=0.006148, loss_rpn_bbox=0.045113, lr=0.00125, time_each_step=0.37s, eta=1:27:6\n",
      "2021-04-21 19:19:27 [INFO]\t[TRAIN] Epoch=8/100, Step=73/73, loss=0.213016, loss_cls=0.02673, loss_bbox=0.021119, loss_mask=0.108778, loss_rpn_cls=0.016737, loss_rpn_bbox=0.039652, lr=0.00125, time_each_step=0.37s, eta=1:27:5\n",
      "2021-04-21 19:19:27 [INFO]\t[TRAIN] Epoch 8 finished, loss=0.260402, loss_cls=0.03713, loss_bbox=0.031509, loss_mask=0.107139, loss_rpn_cls=0.012778, loss_rpn_bbox=0.071845, lr=0.00125 .\n",
      "2021-04-21 19:19:27 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:19:27 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:18<00:00,  4.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.06s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.263\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.760\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.111\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.263\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.323\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.436\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.436\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.436\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.255\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.777\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.340\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.371\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.372\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372\n",
      "2021-04-21 19:19:47 [INFO]\t[EVAL] Finished, Epoch=8, bbox_mmap=0.262713, segm_mmap=0.254848 .\n",
      "2021-04-21 19:19:52 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_8.\n",
      "2021-04-21 19:19:52 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_6, bbox_mmap=0.274691885822062\n",
      "2021-04-21 19:20:00 [INFO]\t[TRAIN] Epoch=9/100, Step=2/73, loss=0.215233, loss_cls=0.034894, loss_bbox=0.027516, loss_mask=0.08451, loss_rpn_cls=0.009883, loss_rpn_bbox=0.05843, lr=0.000125, time_each_step=0.76s, eta=1:30:42\n",
      "2021-04-21 19:20:01 [INFO]\t[TRAIN] Epoch=9/100, Step=4/73, loss=0.164582, loss_cls=0.03652, loss_bbox=0.022617, loss_mask=0.084908, loss_rpn_cls=0.008061, loss_rpn_bbox=0.012475, lr=0.000125, time_each_step=0.78s, eta=1:30:42\n",
      "2021-04-21 19:20:02 [INFO]\t[TRAIN] Epoch=9/100, Step=6/73, loss=0.323787, loss_cls=0.039044, loss_bbox=0.04273, loss_mask=0.137057, loss_rpn_cls=0.010464, loss_rpn_bbox=0.094492, lr=0.000125, time_each_step=0.8s, eta=1:30:42\n",
      "2021-04-21 19:20:03 [INFO]\t[TRAIN] Epoch=9/100, Step=8/73, loss=0.405196, loss_cls=0.067159, loss_bbox=0.034189, loss_mask=0.176685, loss_rpn_cls=0.017666, loss_rpn_bbox=0.109497, lr=0.000125, time_each_step=0.81s, eta=1:30:41\n",
      "2021-04-21 19:20:04 [INFO]\t[TRAIN] Epoch=9/100, Step=10/73, loss=0.214742, loss_cls=0.032919, loss_bbox=0.021576, loss_mask=0.107415, loss_rpn_cls=0.007554, loss_rpn_bbox=0.045279, lr=0.000125, time_each_step=0.83s, eta=1:30:40\n",
      "2021-04-21 19:20:06 [INFO]\t[TRAIN] Epoch=9/100, Step=12/73, loss=0.214347, loss_cls=0.029797, loss_bbox=0.021098, loss_mask=0.078108, loss_rpn_cls=0.00553, loss_rpn_bbox=0.079814, lr=0.000125, time_each_step=0.84s, eta=1:30:40\n",
      "2021-04-21 19:20:07 [INFO]\t[TRAIN] Epoch=9/100, Step=14/73, loss=0.198523, loss_cls=0.029618, loss_bbox=0.030919, loss_mask=0.079015, loss_rpn_cls=0.009612, loss_rpn_bbox=0.049359, lr=0.000125, time_each_step=0.85s, eta=1:30:39\n",
      "2021-04-21 19:20:07 [INFO]\t[TRAIN] Epoch=9/100, Step=16/73, loss=0.365865, loss_cls=0.029025, loss_bbox=0.021616, loss_mask=0.247218, loss_rpn_cls=0.008978, loss_rpn_bbox=0.059028, lr=0.000125, time_each_step=0.86s, eta=1:30:37\n",
      "2021-04-21 19:20:08 [INFO]\t[TRAIN] Epoch=9/100, Step=18/73, loss=0.241265, loss_cls=0.042883, loss_bbox=0.039324, loss_mask=0.081817, loss_rpn_cls=0.013755, loss_rpn_bbox=0.063486, lr=0.000125, time_each_step=0.87s, eta=1:30:36\n",
      "2021-04-21 19:20:09 [INFO]\t[TRAIN] Epoch=9/100, Step=20/73, loss=0.246994, loss_cls=0.031052, loss_bbox=0.02552, loss_mask=0.097085, loss_rpn_cls=0.007782, loss_rpn_bbox=0.085554, lr=0.000125, time_each_step=0.89s, eta=1:30:35\n",
      "2021-04-21 19:20:10 [INFO]\t[TRAIN] Epoch=9/100, Step=22/73, loss=0.260281, loss_cls=0.036727, loss_bbox=0.029083, loss_mask=0.092425, loss_rpn_cls=0.0187, loss_rpn_bbox=0.083345, lr=0.000125, time_each_step=0.51s, eta=1:30:14\n",
      "2021-04-21 19:20:11 [INFO]\t[TRAIN] Epoch=9/100, Step=24/73, loss=0.187409, loss_cls=0.03872, loss_bbox=0.029365, loss_mask=0.074383, loss_rpn_cls=0.012445, loss_rpn_bbox=0.032497, lr=0.000125, time_each_step=0.5s, eta=1:30:13\n",
      "2021-04-21 19:20:12 [INFO]\t[TRAIN] Epoch=9/100, Step=26/73, loss=0.259761, loss_cls=0.035539, loss_bbox=0.030804, loss_mask=0.076741, loss_rpn_cls=0.006292, loss_rpn_bbox=0.110386, lr=0.000125, time_each_step=0.49s, eta=1:30:11\n",
      "2021-04-21 19:20:13 [INFO]\t[TRAIN] Epoch=9/100, Step=28/73, loss=0.204872, loss_cls=0.035414, loss_bbox=0.029237, loss_mask=0.080566, loss_rpn_cls=0.013995, loss_rpn_bbox=0.04566, lr=0.000125, time_each_step=0.49s, eta=1:30:10\n",
      "2021-04-21 19:20:14 [INFO]\t[TRAIN] Epoch=9/100, Step=30/73, loss=0.225928, loss_cls=0.032862, loss_bbox=0.026353, loss_mask=0.080903, loss_rpn_cls=0.009636, loss_rpn_bbox=0.076174, lr=0.000125, time_each_step=0.47s, eta=1:30:9\n",
      "2021-04-21 19:20:15 [INFO]\t[TRAIN] Epoch=9/100, Step=32/73, loss=0.261494, loss_cls=0.040301, loss_bbox=0.0327, loss_mask=0.10148, loss_rpn_cls=0.017421, loss_rpn_bbox=0.069593, lr=0.000125, time_each_step=0.45s, eta=1:30:7\n",
      "2021-04-21 19:20:15 [INFO]\t[TRAIN] Epoch=9/100, Step=34/73, loss=0.456628, loss_cls=0.068971, loss_bbox=0.02063, loss_mask=0.18064, loss_rpn_cls=0.059732, loss_rpn_bbox=0.126656, lr=0.000125, time_each_step=0.44s, eta=1:30:6\n",
      "2021-04-21 19:20:16 [INFO]\t[TRAIN] Epoch=9/100, Step=36/73, loss=0.238154, loss_cls=0.03451, loss_bbox=0.028208, loss_mask=0.101243, loss_rpn_cls=0.010227, loss_rpn_bbox=0.063966, lr=0.000125, time_each_step=0.44s, eta=1:30:5\n",
      "2021-04-21 19:20:17 [INFO]\t[TRAIN] Epoch=9/100, Step=38/73, loss=0.18962, loss_cls=0.025608, loss_bbox=0.030444, loss_mask=0.071163, loss_rpn_cls=0.004295, loss_rpn_bbox=0.058111, lr=0.000125, time_each_step=0.43s, eta=1:30:3\n",
      "2021-04-21 19:20:18 [INFO]\t[TRAIN] Epoch=9/100, Step=40/73, loss=0.204261, loss_cls=0.041143, loss_bbox=0.027449, loss_mask=0.073774, loss_rpn_cls=0.018635, loss_rpn_bbox=0.04326, lr=0.000125, time_each_step=0.41s, eta=1:30:2\n",
      "2021-04-21 19:20:18 [INFO]\t[TRAIN] Epoch=9/100, Step=42/73, loss=0.206138, loss_cls=0.028861, loss_bbox=0.027504, loss_mask=0.078798, loss_rpn_cls=0.012116, loss_rpn_bbox=0.05886, lr=0.000125, time_each_step=0.4s, eta=1:30:1\n",
      "2021-04-21 19:20:19 [INFO]\t[TRAIN] Epoch=9/100, Step=44/73, loss=0.246606, loss_cls=0.023737, loss_bbox=0.033532, loss_mask=0.084401, loss_rpn_cls=0.004215, loss_rpn_bbox=0.100722, lr=0.000125, time_each_step=0.4s, eta=1:30:0\n",
      "2021-04-21 19:20:20 [INFO]\t[TRAIN] Epoch=9/100, Step=46/73, loss=0.185688, loss_cls=0.034613, loss_bbox=0.025795, loss_mask=0.086112, loss_rpn_cls=0.009333, loss_rpn_bbox=0.029834, lr=0.000125, time_each_step=0.38s, eta=1:29:59\n",
      "2021-04-21 19:20:21 [INFO]\t[TRAIN] Epoch=9/100, Step=48/73, loss=0.20623, loss_cls=0.043602, loss_bbox=0.040754, loss_mask=0.074774, loss_rpn_cls=0.016206, loss_rpn_bbox=0.030893, lr=0.000125, time_each_step=0.38s, eta=1:29:58\n",
      "2021-04-21 19:20:21 [INFO]\t[TRAIN] Epoch=9/100, Step=50/73, loss=0.184574, loss_cls=0.031574, loss_bbox=0.028628, loss_mask=0.082244, loss_rpn_cls=0.007868, loss_rpn_bbox=0.03426, lr=0.000125, time_each_step=0.37s, eta=1:29:57\n",
      "2021-04-21 19:20:22 [INFO]\t[TRAIN] Epoch=9/100, Step=52/73, loss=0.186399, loss_cls=0.037546, loss_bbox=0.039935, loss_mask=0.068146, loss_rpn_cls=0.007962, loss_rpn_bbox=0.032809, lr=0.000125, time_each_step=0.37s, eta=1:29:56\n",
      "2021-04-21 19:20:23 [INFO]\t[TRAIN] Epoch=9/100, Step=54/73, loss=0.284026, loss_cls=0.032218, loss_bbox=0.02474, loss_mask=0.086036, loss_rpn_cls=0.033357, loss_rpn_bbox=0.107674, lr=0.000125, time_each_step=0.37s, eta=1:29:55\n",
      "2021-04-21 19:20:24 [INFO]\t[TRAIN] Epoch=9/100, Step=56/73, loss=0.16964, loss_cls=0.029066, loss_bbox=0.024624, loss_mask=0.080774, loss_rpn_cls=0.006092, loss_rpn_bbox=0.029085, lr=0.000125, time_each_step=0.37s, eta=1:29:55\n",
      "2021-04-21 19:20:24 [INFO]\t[TRAIN] Epoch=9/100, Step=58/73, loss=0.38492, loss_cls=0.075104, loss_bbox=0.031837, loss_mask=0.151278, loss_rpn_cls=0.014649, loss_rpn_bbox=0.112052, lr=0.000125, time_each_step=0.37s, eta=1:29:54\n",
      "2021-04-21 19:20:25 [INFO]\t[TRAIN] Epoch=9/100, Step=60/73, loss=0.334908, loss_cls=0.072474, loss_bbox=0.03275, loss_mask=0.162986, loss_rpn_cls=0.014413, loss_rpn_bbox=0.052286, lr=0.000125, time_each_step=0.37s, eta=1:29:53\n",
      "2021-04-21 19:20:26 [INFO]\t[TRAIN] Epoch=9/100, Step=62/73, loss=0.28432, loss_cls=0.030828, loss_bbox=0.027201, loss_mask=0.093709, loss_rpn_cls=0.014265, loss_rpn_bbox=0.118318, lr=0.000125, time_each_step=0.37s, eta=1:29:52\n",
      "2021-04-21 19:20:27 [INFO]\t[TRAIN] Epoch=9/100, Step=64/73, loss=0.228013, loss_cls=0.042347, loss_bbox=0.035418, loss_mask=0.100969, loss_rpn_cls=0.005024, loss_rpn_bbox=0.044255, lr=0.000125, time_each_step=0.37s, eta=1:29:52\n",
      "2021-04-21 19:20:27 [INFO]\t[TRAIN] Epoch=9/100, Step=66/73, loss=0.206194, loss_cls=0.034291, loss_bbox=0.033735, loss_mask=0.081947, loss_rpn_cls=0.012364, loss_rpn_bbox=0.043856, lr=0.000125, time_each_step=0.37s, eta=1:29:51\n",
      "2021-04-21 19:20:28 [INFO]\t[TRAIN] Epoch=9/100, Step=68/73, loss=0.189601, loss_cls=0.033324, loss_bbox=0.027407, loss_mask=0.09134, loss_rpn_cls=0.003928, loss_rpn_bbox=0.033602, lr=0.000125, time_each_step=0.37s, eta=1:29:50\n",
      "2021-04-21 19:20:29 [INFO]\t[TRAIN] Epoch=9/100, Step=70/73, loss=0.180718, loss_cls=0.029763, loss_bbox=0.02645, loss_mask=0.085153, loss_rpn_cls=0.00912, loss_rpn_bbox=0.030233, lr=0.000125, time_each_step=0.37s, eta=1:29:49\n",
      "2021-04-21 19:20:30 [INFO]\t[TRAIN] Epoch=9/100, Step=72/73, loss=0.231579, loss_cls=0.035829, loss_bbox=0.036701, loss_mask=0.094364, loss_rpn_cls=0.011818, loss_rpn_bbox=0.052866, lr=0.000125, time_each_step=0.37s, eta=1:29:49\n",
      "2021-04-21 19:20:30 [INFO]\t[TRAIN] Epoch 9 finished, loss=0.237596, loss_cls=0.036004, loss_bbox=0.029302, loss_mask=0.093363, loss_rpn_cls=0.01247, loss_rpn_bbox=0.066457, lr=0.000125 .\n",
      "2021-04-21 19:20:30 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:20:30 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:15<00:00,  5.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.05s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.288\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.777\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.144\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.314\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.505\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.409\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.888\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.222\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.446\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.472\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.517\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.517\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517\n",
      "2021-04-21 19:20:47 [INFO]\t[EVAL] Finished, Epoch=9, bbox_mmap=0.288483, segm_mmap=0.408649 .\n",
      "2021-04-21 19:20:51 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/best_model.\n",
      "2021-04-21 19:20:56 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_9.\n",
      "2021-04-21 19:20:56 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:21:00 [INFO]\t[TRAIN] Epoch=10/100, Step=1/73, loss=0.213021, loss_cls=0.032656, loss_bbox=0.028284, loss_mask=0.094256, loss_rpn_cls=0.014592, loss_rpn_bbox=0.043233, lr=0.000125, time_each_step=0.57s, eta=1:37:18\n",
      "2021-04-21 19:21:02 [INFO]\t[TRAIN] Epoch=10/100, Step=3/73, loss=0.162178, loss_cls=0.026767, loss_bbox=0.022749, loss_mask=0.069041, loss_rpn_cls=0.010329, loss_rpn_bbox=0.033292, lr=0.000125, time_each_step=0.64s, eta=1:37:22\n",
      "2021-04-21 19:21:04 [INFO]\t[TRAIN] Epoch=10/100, Step=5/73, loss=0.325201, loss_cls=0.031901, loss_bbox=0.02016, loss_mask=0.240593, loss_rpn_cls=0.008305, loss_rpn_bbox=0.024241, lr=0.000125, time_each_step=0.67s, eta=1:37:23\n",
      "2021-04-21 19:21:05 [INFO]\t[TRAIN] Epoch=10/100, Step=7/73, loss=0.198379, loss_cls=0.037688, loss_bbox=0.029708, loss_mask=0.072214, loss_rpn_cls=0.006286, loss_rpn_bbox=0.052483, lr=0.000125, time_each_step=0.69s, eta=1:37:22\n",
      "2021-04-21 19:21:06 [INFO]\t[TRAIN] Epoch=10/100, Step=9/73, loss=0.547148, loss_cls=0.077613, loss_bbox=0.03643, loss_mask=0.146856, loss_rpn_cls=0.062212, loss_rpn_bbox=0.224037, lr=0.000125, time_each_step=0.7s, eta=1:37:22\n",
      "2021-04-21 19:21:07 [INFO]\t[TRAIN] Epoch=10/100, Step=11/73, loss=0.142835, loss_cls=0.034673, loss_bbox=0.029742, loss_mask=0.059703, loss_rpn_cls=0.003437, loss_rpn_bbox=0.015281, lr=0.000125, time_each_step=0.72s, eta=1:37:22\n",
      "2021-04-21 19:21:08 [INFO]\t[TRAIN] Epoch=10/100, Step=13/73, loss=0.257706, loss_cls=0.039853, loss_bbox=0.032057, loss_mask=0.116975, loss_rpn_cls=0.009243, loss_rpn_bbox=0.059577, lr=0.000125, time_each_step=0.73s, eta=1:37:21\n",
      "2021-04-21 19:21:09 [INFO]\t[TRAIN] Epoch=10/100, Step=15/73, loss=0.190768, loss_cls=0.035192, loss_bbox=0.023355, loss_mask=0.066343, loss_rpn_cls=0.014196, loss_rpn_bbox=0.051681, lr=0.000125, time_each_step=0.74s, eta=1:37:20\n",
      "2021-04-21 19:21:10 [INFO]\t[TRAIN] Epoch=10/100, Step=17/73, loss=0.183224, loss_cls=0.034912, loss_bbox=0.02575, loss_mask=0.082198, loss_rpn_cls=0.013807, loss_rpn_bbox=0.026558, lr=0.000125, time_each_step=0.75s, eta=1:37:19\n",
      "2021-04-21 19:21:11 [INFO]\t[TRAIN] Epoch=10/100, Step=19/73, loss=0.179785, loss_cls=0.024527, loss_bbox=0.01908, loss_mask=0.073145, loss_rpn_cls=0.007669, loss_rpn_bbox=0.055364, lr=0.000125, time_each_step=0.76s, eta=1:37:18\n",
      "2021-04-21 19:21:12 [INFO]\t[TRAIN] Epoch=10/100, Step=21/73, loss=0.209479, loss_cls=0.028274, loss_bbox=0.02728, loss_mask=0.096649, loss_rpn_cls=0.020494, loss_rpn_bbox=0.036782, lr=0.000125, time_each_step=0.57s, eta=1:37:7\n",
      "2021-04-21 19:21:12 [INFO]\t[TRAIN] Epoch=10/100, Step=23/73, loss=0.237884, loss_cls=0.035542, loss_bbox=0.034742, loss_mask=0.091835, loss_rpn_cls=0.022524, loss_rpn_bbox=0.053241, lr=0.000125, time_each_step=0.5s, eta=1:37:2\n",
      "2021-04-21 19:21:13 [INFO]\t[TRAIN] Epoch=10/100, Step=25/73, loss=0.225118, loss_cls=0.034131, loss_bbox=0.030535, loss_mask=0.071633, loss_rpn_cls=0.004847, loss_rpn_bbox=0.083973, lr=0.000125, time_each_step=0.49s, eta=1:37:0\n",
      "2021-04-21 19:21:14 [INFO]\t[TRAIN] Epoch=10/100, Step=27/73, loss=0.214014, loss_cls=0.035143, loss_bbox=0.030319, loss_mask=0.096106, loss_rpn_cls=0.022126, loss_rpn_bbox=0.030319, lr=0.000125, time_each_step=0.47s, eta=1:36:59\n",
      "2021-04-21 19:21:15 [INFO]\t[TRAIN] Epoch=10/100, Step=29/73, loss=0.254732, loss_cls=0.033659, loss_bbox=0.028496, loss_mask=0.087383, loss_rpn_cls=0.013741, loss_rpn_bbox=0.091452, lr=0.000125, time_each_step=0.47s, eta=1:36:58\n",
      "2021-04-21 19:21:16 [INFO]\t[TRAIN] Epoch=10/100, Step=31/73, loss=0.29036, loss_cls=0.053199, loss_bbox=0.030935, loss_mask=0.11728, loss_rpn_cls=0.01193, loss_rpn_bbox=0.077016, lr=0.000125, time_each_step=0.47s, eta=1:36:57\n",
      "2021-04-21 19:21:17 [INFO]\t[TRAIN] Epoch=10/100, Step=33/73, loss=0.235189, loss_cls=0.032337, loss_bbox=0.026555, loss_mask=0.126808, loss_rpn_cls=0.018515, loss_rpn_bbox=0.030975, lr=0.000125, time_each_step=0.46s, eta=1:36:56\n",
      "2021-04-21 19:21:18 [INFO]\t[TRAIN] Epoch=10/100, Step=35/73, loss=0.191584, loss_cls=0.036944, loss_bbox=0.030913, loss_mask=0.091309, loss_rpn_cls=0.004007, loss_rpn_bbox=0.028411, lr=0.000125, time_each_step=0.45s, eta=1:36:54\n",
      "2021-04-21 19:21:19 [INFO]\t[TRAIN] Epoch=10/100, Step=37/73, loss=0.163524, loss_cls=0.03263, loss_bbox=0.019437, loss_mask=0.077776, loss_rpn_cls=0.007734, loss_rpn_bbox=0.025947, lr=0.000125, time_each_step=0.45s, eta=1:36:53\n",
      "2021-04-21 19:21:19 [INFO]\t[TRAIN] Epoch=10/100, Step=39/73, loss=0.166129, loss_cls=0.028805, loss_bbox=0.021331, loss_mask=0.073281, loss_rpn_cls=0.017016, loss_rpn_bbox=0.025695, lr=0.000125, time_each_step=0.44s, eta=1:36:52\n",
      "2021-04-21 19:21:20 [INFO]\t[TRAIN] Epoch=10/100, Step=41/73, loss=0.212704, loss_cls=0.031476, loss_bbox=0.027172, loss_mask=0.078195, loss_rpn_cls=0.003165, loss_rpn_bbox=0.072695, lr=0.000125, time_each_step=0.43s, eta=1:36:51\n",
      "2021-04-21 19:21:21 [INFO]\t[TRAIN] Epoch=10/100, Step=43/73, loss=0.22317, loss_cls=0.031526, loss_bbox=0.030353, loss_mask=0.071601, loss_rpn_cls=0.027947, loss_rpn_bbox=0.061743, lr=0.000125, time_each_step=0.42s, eta=1:36:50\n",
      "2021-04-21 19:21:22 [INFO]\t[TRAIN] Epoch=10/100, Step=45/73, loss=0.206908, loss_cls=0.036405, loss_bbox=0.030988, loss_mask=0.081696, loss_rpn_cls=0.003922, loss_rpn_bbox=0.053898, lr=0.000125, time_each_step=0.41s, eta=1:36:49\n",
      "2021-04-21 19:21:23 [INFO]\t[TRAIN] Epoch=10/100, Step=47/73, loss=0.193173, loss_cls=0.032583, loss_bbox=0.031359, loss_mask=0.067557, loss_rpn_cls=0.024417, loss_rpn_bbox=0.037258, lr=0.000125, time_each_step=0.42s, eta=1:36:48\n",
      "2021-04-21 19:21:23 [INFO]\t[TRAIN] Epoch=10/100, Step=49/73, loss=0.287607, loss_cls=0.027704, loss_bbox=0.023207, loss_mask=0.093673, loss_rpn_cls=0.009343, loss_rpn_bbox=0.133679, lr=0.000125, time_each_step=0.42s, eta=1:36:47\n",
      "2021-04-21 19:21:24 [INFO]\t[TRAIN] Epoch=10/100, Step=51/73, loss=0.183321, loss_cls=0.028254, loss_bbox=0.023324, loss_mask=0.077547, loss_rpn_cls=0.012876, loss_rpn_bbox=0.041319, lr=0.000125, time_each_step=0.39s, eta=1:36:46\n",
      "2021-04-21 19:21:25 [INFO]\t[TRAIN] Epoch=10/100, Step=53/73, loss=0.34829, loss_cls=0.032799, loss_bbox=0.036859, loss_mask=0.121814, loss_rpn_cls=0.010716, loss_rpn_bbox=0.146102, lr=0.000125, time_each_step=0.39s, eta=1:36:45\n",
      "2021-04-21 19:21:25 [INFO]\t[TRAIN] Epoch=10/100, Step=55/73, loss=0.26754, loss_cls=0.035133, loss_bbox=0.026876, loss_mask=0.080441, loss_rpn_cls=0.016708, loss_rpn_bbox=0.108382, lr=0.000125, time_each_step=0.39s, eta=1:36:44\n",
      "2021-04-21 19:21:26 [INFO]\t[TRAIN] Epoch=10/100, Step=57/73, loss=0.1901, loss_cls=0.026782, loss_bbox=0.027841, loss_mask=0.068533, loss_rpn_cls=0.004746, loss_rpn_bbox=0.062198, lr=0.000125, time_each_step=0.39s, eta=1:36:43\n",
      "2021-04-21 19:21:27 [INFO]\t[TRAIN] Epoch=10/100, Step=59/73, loss=0.238773, loss_cls=0.033374, loss_bbox=0.0297, loss_mask=0.072219, loss_rpn_cls=0.009485, loss_rpn_bbox=0.093995, lr=0.000125, time_each_step=0.38s, eta=1:36:42\n",
      "2021-04-21 19:21:28 [INFO]\t[TRAIN] Epoch=10/100, Step=61/73, loss=0.157912, loss_cls=0.024414, loss_bbox=0.024283, loss_mask=0.071209, loss_rpn_cls=0.009094, loss_rpn_bbox=0.028913, lr=0.000125, time_each_step=0.38s, eta=1:36:42\n",
      "2021-04-21 19:21:28 [INFO]\t[TRAIN] Epoch=10/100, Step=63/73, loss=0.240294, loss_cls=0.042897, loss_bbox=0.032841, loss_mask=0.077835, loss_rpn_cls=0.009405, loss_rpn_bbox=0.077315, lr=0.000125, time_each_step=0.38s, eta=1:36:41\n",
      "2021-04-21 19:21:29 [INFO]\t[TRAIN] Epoch=10/100, Step=65/73, loss=0.203351, loss_cls=0.035675, loss_bbox=0.029632, loss_mask=0.080202, loss_rpn_cls=0.0144, loss_rpn_bbox=0.043442, lr=0.000125, time_each_step=0.38s, eta=1:36:40\n",
      "2021-04-21 19:21:30 [INFO]\t[TRAIN] Epoch=10/100, Step=67/73, loss=0.307894, loss_cls=0.036285, loss_bbox=0.040297, loss_mask=0.091369, loss_rpn_cls=0.007142, loss_rpn_bbox=0.1328, lr=0.000125, time_each_step=0.37s, eta=1:36:39\n",
      "2021-04-21 19:21:31 [INFO]\t[TRAIN] Epoch=10/100, Step=69/73, loss=0.210208, loss_cls=0.031544, loss_bbox=0.028082, loss_mask=0.07506, loss_rpn_cls=0.007456, loss_rpn_bbox=0.068066, lr=0.000125, time_each_step=0.37s, eta=1:36:39\n",
      "2021-04-21 19:21:31 [INFO]\t[TRAIN] Epoch=10/100, Step=71/73, loss=0.452752, loss_cls=0.069956, loss_bbox=0.029157, loss_mask=0.159438, loss_rpn_cls=0.00939, loss_rpn_bbox=0.18481, lr=0.000125, time_each_step=0.37s, eta=1:36:38\n",
      "2021-04-21 19:21:32 [INFO]\t[TRAIN] Epoch=10/100, Step=73/73, loss=0.24424, loss_cls=0.027606, loss_bbox=0.027728, loss_mask=0.090134, loss_rpn_cls=0.012103, loss_rpn_bbox=0.086669, lr=0.000125, time_each_step=0.37s, eta=1:36:37\n",
      "2021-04-21 19:21:32 [INFO]\t[TRAIN] Epoch 10 finished, loss=0.231972, loss_cls=0.035403, loss_bbox=0.028775, loss_mask=0.089195, loss_rpn_cls=0.012827, loss_rpn_bbox=0.065772, lr=0.000125 .\n",
      "2021-04-21 19:21:32 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:21:32 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.06s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.264\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.739\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.122\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.304\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.472\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.472\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.380\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.872\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.154\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.416\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.447\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.507\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.510\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510\n",
      "2021-04-21 19:21:49 [INFO]\t[EVAL] Finished, Epoch=10, bbox_mmap=0.263832, segm_mmap=0.379976 .\n",
      "2021-04-21 19:21:53 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_10.\n",
      "2021-04-21 19:21:53 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:21:58 [INFO]\t[TRAIN] Epoch=11/100, Step=2/73, loss=0.341962, loss_cls=0.034214, loss_bbox=0.021517, loss_mask=0.268491, loss_rpn_cls=0.002678, loss_rpn_bbox=0.015061, lr=0.000125, time_each_step=0.57s, eta=1:25:54\n",
      "2021-04-21 19:21:59 [INFO]\t[TRAIN] Epoch=11/100, Step=4/73, loss=0.228167, loss_cls=0.036395, loss_bbox=0.030662, loss_mask=0.077902, loss_rpn_cls=0.008029, loss_rpn_bbox=0.07518, lr=0.000125, time_each_step=0.61s, eta=1:25:55\n",
      "2021-04-21 19:22:00 [INFO]\t[TRAIN] Epoch=11/100, Step=6/73, loss=0.5373, loss_cls=0.056843, loss_bbox=0.028861, loss_mask=0.15959, loss_rpn_cls=0.076711, loss_rpn_bbox=0.215294, lr=0.000125, time_each_step=0.62s, eta=1:25:55\n",
      "2021-04-21 19:22:01 [INFO]\t[TRAIN] Epoch=11/100, Step=8/73, loss=0.188752, loss_cls=0.031849, loss_bbox=0.021956, loss_mask=0.081983, loss_rpn_cls=0.012486, loss_rpn_bbox=0.040479, lr=0.000125, time_each_step=0.64s, eta=1:25:55\n",
      "2021-04-21 19:22:02 [INFO]\t[TRAIN] Epoch=11/100, Step=10/73, loss=0.164684, loss_cls=0.032079, loss_bbox=0.027649, loss_mask=0.070344, loss_rpn_cls=0.004414, loss_rpn_bbox=0.030197, lr=0.000125, time_each_step=0.65s, eta=1:25:54\n",
      "2021-04-21 19:22:03 [INFO]\t[TRAIN] Epoch=11/100, Step=12/73, loss=0.185129, loss_cls=0.032474, loss_bbox=0.021918, loss_mask=0.070366, loss_rpn_cls=0.020211, loss_rpn_bbox=0.04016, lr=0.000125, time_each_step=0.66s, eta=1:25:53\n",
      "2021-04-21 19:22:04 [INFO]\t[TRAIN] Epoch=11/100, Step=14/73, loss=0.194496, loss_cls=0.03065, loss_bbox=0.017319, loss_mask=0.07637, loss_rpn_cls=0.019041, loss_rpn_bbox=0.051117, lr=0.000125, time_each_step=0.67s, eta=1:25:53\n",
      "2021-04-21 19:22:05 [INFO]\t[TRAIN] Epoch=11/100, Step=16/73, loss=0.224089, loss_cls=0.027144, loss_bbox=0.025813, loss_mask=0.066773, loss_rpn_cls=0.013775, loss_rpn_bbox=0.090585, lr=0.000125, time_each_step=0.68s, eta=1:25:52\n",
      "2021-04-21 19:22:06 [INFO]\t[TRAIN] Epoch=11/100, Step=18/73, loss=0.27751, loss_cls=0.038765, loss_bbox=0.046785, loss_mask=0.100161, loss_rpn_cls=0.004703, loss_rpn_bbox=0.087097, lr=0.000125, time_each_step=0.69s, eta=1:25:51\n",
      "2021-04-21 19:22:07 [INFO]\t[TRAIN] Epoch=11/100, Step=20/73, loss=0.233468, loss_cls=0.023693, loss_bbox=0.025896, loss_mask=0.081951, loss_rpn_cls=0.016069, loss_rpn_bbox=0.085859, lr=0.000125, time_each_step=0.71s, eta=1:25:51\n",
      "2021-04-21 19:22:08 [INFO]\t[TRAIN] Epoch=11/100, Step=22/73, loss=0.151097, loss_cls=0.035185, loss_bbox=0.021066, loss_mask=0.062699, loss_rpn_cls=0.011881, loss_rpn_bbox=0.020267, lr=0.000125, time_each_step=0.51s, eta=1:25:39\n",
      "2021-04-21 19:22:09 [INFO]\t[TRAIN] Epoch=11/100, Step=24/73, loss=0.19076, loss_cls=0.037005, loss_bbox=0.034349, loss_mask=0.068154, loss_rpn_cls=0.011913, loss_rpn_bbox=0.03934, lr=0.000125, time_each_step=0.49s, eta=1:25:37\n",
      "2021-04-21 19:22:10 [INFO]\t[TRAIN] Epoch=11/100, Step=26/73, loss=0.575007, loss_cls=0.106501, loss_bbox=0.032375, loss_mask=0.195631, loss_rpn_cls=0.060645, loss_rpn_bbox=0.179854, lr=0.000125, time_each_step=0.48s, eta=1:25:36\n",
      "2021-04-21 19:22:11 [INFO]\t[TRAIN] Epoch=11/100, Step=28/73, loss=0.338793, loss_cls=0.059877, loss_bbox=0.031905, loss_mask=0.137692, loss_rpn_cls=0.005666, loss_rpn_bbox=0.103653, lr=0.000125, time_each_step=0.47s, eta=1:25:34\n",
      "2021-04-21 19:22:12 [INFO]\t[TRAIN] Epoch=11/100, Step=30/73, loss=0.199629, loss_cls=0.029669, loss_bbox=0.026217, loss_mask=0.067711, loss_rpn_cls=0.011165, loss_rpn_bbox=0.064867, lr=0.000125, time_each_step=0.46s, eta=1:25:33\n",
      "2021-04-21 19:22:12 [INFO]\t[TRAIN] Epoch=11/100, Step=32/73, loss=0.214587, loss_cls=0.034558, loss_bbox=0.026486, loss_mask=0.069404, loss_rpn_cls=0.00838, loss_rpn_bbox=0.075761, lr=0.000125, time_each_step=0.45s, eta=1:25:32\n",
      "2021-04-21 19:22:13 [INFO]\t[TRAIN] Epoch=11/100, Step=34/73, loss=0.217364, loss_cls=0.027737, loss_bbox=0.026181, loss_mask=0.083488, loss_rpn_cls=0.01065, loss_rpn_bbox=0.069308, lr=0.000125, time_each_step=0.44s, eta=1:25:31\n",
      "2021-04-21 19:22:14 [INFO]\t[TRAIN] Epoch=11/100, Step=36/73, loss=0.286254, loss_cls=0.038108, loss_bbox=0.032453, loss_mask=0.106196, loss_rpn_cls=0.013868, loss_rpn_bbox=0.095628, lr=0.000125, time_each_step=0.43s, eta=1:25:29\n",
      "2021-04-21 19:22:15 [INFO]\t[TRAIN] Epoch=11/100, Step=38/73, loss=0.202911, loss_cls=0.031889, loss_bbox=0.030682, loss_mask=0.081507, loss_rpn_cls=0.005356, loss_rpn_bbox=0.053476, lr=0.000125, time_each_step=0.42s, eta=1:25:28\n",
      "2021-04-21 19:22:15 [INFO]\t[TRAIN] Epoch=11/100, Step=40/73, loss=0.154291, loss_cls=0.035398, loss_bbox=0.022182, loss_mask=0.058911, loss_rpn_cls=0.008255, loss_rpn_bbox=0.029545, lr=0.000125, time_each_step=0.41s, eta=1:25:27\n",
      "2021-04-21 19:22:16 [INFO]\t[TRAIN] Epoch=11/100, Step=42/73, loss=0.20019, loss_cls=0.033808, loss_bbox=0.030437, loss_mask=0.092395, loss_rpn_cls=0.001429, loss_rpn_bbox=0.042121, lr=0.000125, time_each_step=0.4s, eta=1:25:26\n",
      "2021-04-21 19:22:17 [INFO]\t[TRAIN] Epoch=11/100, Step=44/73, loss=0.181246, loss_cls=0.033501, loss_bbox=0.02402, loss_mask=0.094622, loss_rpn_cls=0.004452, loss_rpn_bbox=0.024651, lr=0.000125, time_each_step=0.38s, eta=1:25:24\n",
      "2021-04-21 19:22:18 [INFO]\t[TRAIN] Epoch=11/100, Step=46/73, loss=0.362545, loss_cls=0.070879, loss_bbox=0.044546, loss_mask=0.145077, loss_rpn_cls=0.029696, loss_rpn_bbox=0.072349, lr=0.000125, time_each_step=0.38s, eta=1:25:24\n",
      "2021-04-21 19:22:18 [INFO]\t[TRAIN] Epoch=11/100, Step=48/73, loss=0.156911, loss_cls=0.031217, loss_bbox=0.026616, loss_mask=0.06987, loss_rpn_cls=0.00163, loss_rpn_bbox=0.027579, lr=0.000125, time_each_step=0.37s, eta=1:25:23\n",
      "2021-04-21 19:22:19 [INFO]\t[TRAIN] Epoch=11/100, Step=50/73, loss=0.151891, loss_cls=0.031793, loss_bbox=0.018099, loss_mask=0.073892, loss_rpn_cls=0.004846, loss_rpn_bbox=0.023261, lr=0.000125, time_each_step=0.37s, eta=1:25:22\n",
      "2021-04-21 19:22:20 [INFO]\t[TRAIN] Epoch=11/100, Step=52/73, loss=0.262596, loss_cls=0.026546, loss_bbox=0.030977, loss_mask=0.079751, loss_rpn_cls=0.010948, loss_rpn_bbox=0.114373, lr=0.000125, time_each_step=0.37s, eta=1:25:21\n",
      "2021-04-21 19:22:20 [INFO]\t[TRAIN] Epoch=11/100, Step=54/73, loss=0.277413, loss_cls=0.047875, loss_bbox=0.044354, loss_mask=0.076609, loss_rpn_cls=0.0101, loss_rpn_bbox=0.098475, lr=0.000125, time_each_step=0.37s, eta=1:25:20\n",
      "2021-04-21 19:22:21 [INFO]\t[TRAIN] Epoch=11/100, Step=56/73, loss=0.281881, loss_cls=0.053177, loss_bbox=0.028879, loss_mask=0.112898, loss_rpn_cls=0.013109, loss_rpn_bbox=0.073819, lr=0.000125, time_each_step=0.37s, eta=1:25:20\n",
      "2021-04-21 19:22:22 [INFO]\t[TRAIN] Epoch=11/100, Step=58/73, loss=0.179076, loss_cls=0.034277, loss_bbox=0.027482, loss_mask=0.082842, loss_rpn_cls=0.00399, loss_rpn_bbox=0.030486, lr=0.000125, time_each_step=0.37s, eta=1:25:19\n",
      "2021-04-21 19:22:23 [INFO]\t[TRAIN] Epoch=11/100, Step=60/73, loss=0.275398, loss_cls=0.037916, loss_bbox=0.032481, loss_mask=0.093032, loss_rpn_cls=0.004797, loss_rpn_bbox=0.107173, lr=0.000125, time_each_step=0.37s, eta=1:25:18\n",
      "2021-04-21 19:22:23 [INFO]\t[TRAIN] Epoch=11/100, Step=62/73, loss=0.285112, loss_cls=0.034792, loss_bbox=0.036611, loss_mask=0.083322, loss_rpn_cls=0.015403, loss_rpn_bbox=0.114985, lr=0.000125, time_each_step=0.37s, eta=1:25:17\n",
      "2021-04-21 19:22:24 [INFO]\t[TRAIN] Epoch=11/100, Step=64/73, loss=0.192332, loss_cls=0.026183, loss_bbox=0.016873, loss_mask=0.096973, loss_rpn_cls=0.016239, loss_rpn_bbox=0.036065, lr=0.000125, time_each_step=0.37s, eta=1:25:17\n",
      "2021-04-21 19:22:25 [INFO]\t[TRAIN] Epoch=11/100, Step=66/73, loss=0.250527, loss_cls=0.038245, loss_bbox=0.026653, loss_mask=0.101785, loss_rpn_cls=0.011861, loss_rpn_bbox=0.071984, lr=0.000125, time_each_step=0.37s, eta=1:25:16\n",
      "2021-04-21 19:22:26 [INFO]\t[TRAIN] Epoch=11/100, Step=68/73, loss=0.207516, loss_cls=0.026761, loss_bbox=0.025797, loss_mask=0.073001, loss_rpn_cls=0.004687, loss_rpn_bbox=0.07727, lr=0.000125, time_each_step=0.37s, eta=1:25:15\n",
      "2021-04-21 19:22:26 [INFO]\t[TRAIN] Epoch=11/100, Step=70/73, loss=0.300701, loss_cls=0.032733, loss_bbox=0.026703, loss_mask=0.091788, loss_rpn_cls=0.014261, loss_rpn_bbox=0.135217, lr=0.000125, time_each_step=0.37s, eta=1:25:14\n",
      "2021-04-21 19:22:27 [INFO]\t[TRAIN] Epoch=11/100, Step=72/73, loss=0.224248, loss_cls=0.030897, loss_bbox=0.033984, loss_mask=0.0853, loss_rpn_cls=0.004474, loss_rpn_bbox=0.069594, lr=0.000125, time_each_step=0.37s, eta=1:25:14\n",
      "2021-04-21 19:22:28 [INFO]\t[TRAIN] Epoch 11 finished, loss=0.229493, loss_cls=0.035451, loss_bbox=0.028602, loss_mask=0.089173, loss_rpn_cls=0.01098, loss_rpn_bbox=0.065286, lr=0.000125 .\n",
      "2021-04-21 19:22:28 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:22:28 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.21s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.272\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.759\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.145\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.272\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.316\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.488\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.488\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.488\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.385\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.878\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.211\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.432\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.465\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.519\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.519\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519\n",
      "2021-04-21 19:22:44 [INFO]\t[EVAL] Finished, Epoch=11, bbox_mmap=0.272462, segm_mmap=0.385015 .\n",
      "2021-04-21 19:22:48 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_11.\n",
      "2021-04-21 19:22:48 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:22:52 [INFO]\t[TRAIN] Epoch=12/100, Step=1/73, loss=0.239797, loss_cls=0.038457, loss_bbox=0.024723, loss_mask=0.095344, loss_rpn_cls=0.01184, loss_rpn_bbox=0.069433, lr=1.2e-05, time_each_step=0.56s, eta=1:21:44\n",
      "2021-04-21 19:22:53 [INFO]\t[TRAIN] Epoch=12/100, Step=3/73, loss=0.218306, loss_cls=0.033741, loss_bbox=0.026574, loss_mask=0.087104, loss_rpn_cls=0.004331, loss_rpn_bbox=0.066556, lr=1.2e-05, time_each_step=0.58s, eta=1:21:45\n",
      "2021-04-21 19:22:54 [INFO]\t[TRAIN] Epoch=12/100, Step=5/73, loss=0.222407, loss_cls=0.034768, loss_bbox=0.021277, loss_mask=0.083525, loss_rpn_cls=0.007227, loss_rpn_bbox=0.075611, lr=1.2e-05, time_each_step=0.6s, eta=1:21:45\n",
      "2021-04-21 19:22:55 [INFO]\t[TRAIN] Epoch=12/100, Step=7/73, loss=0.229282, loss_cls=0.034156, loss_bbox=0.029893, loss_mask=0.078937, loss_rpn_cls=0.011695, loss_rpn_bbox=0.074601, lr=1.2e-05, time_each_step=0.61s, eta=1:21:44\n",
      "2021-04-21 19:22:57 [INFO]\t[TRAIN] Epoch=12/100, Step=9/73, loss=0.188241, loss_cls=0.028813, loss_bbox=0.026624, loss_mask=0.085906, loss_rpn_cls=0.009188, loss_rpn_bbox=0.03771, lr=1.2e-05, time_each_step=0.63s, eta=1:21:44\n",
      "2021-04-21 19:22:58 [INFO]\t[TRAIN] Epoch=12/100, Step=11/73, loss=0.180241, loss_cls=0.034258, loss_bbox=0.032234, loss_mask=0.066779, loss_rpn_cls=0.003081, loss_rpn_bbox=0.043889, lr=1.2e-05, time_each_step=0.64s, eta=1:21:44\n",
      "2021-04-21 19:22:59 [INFO]\t[TRAIN] Epoch=12/100, Step=13/73, loss=0.281311, loss_cls=0.029362, loss_bbox=0.036484, loss_mask=0.075666, loss_rpn_cls=0.018892, loss_rpn_bbox=0.120907, lr=1.2e-05, time_each_step=0.65s, eta=1:21:43\n",
      "2021-04-21 19:23:00 [INFO]\t[TRAIN] Epoch=12/100, Step=15/73, loss=0.228267, loss_cls=0.033366, loss_bbox=0.037653, loss_mask=0.091978, loss_rpn_cls=0.005198, loss_rpn_bbox=0.060072, lr=1.2e-05, time_each_step=0.67s, eta=1:21:43\n",
      "2021-04-21 19:23:00 [INFO]\t[TRAIN] Epoch=12/100, Step=17/73, loss=0.211333, loss_cls=0.031778, loss_bbox=0.036995, loss_mask=0.070465, loss_rpn_cls=0.009361, loss_rpn_bbox=0.062733, lr=1.2e-05, time_each_step=0.68s, eta=1:21:42\n",
      "2021-04-21 19:23:01 [INFO]\t[TRAIN] Epoch=12/100, Step=19/73, loss=0.163377, loss_cls=0.032099, loss_bbox=0.027124, loss_mask=0.070479, loss_rpn_cls=0.008029, loss_rpn_bbox=0.025645, lr=1.2e-05, time_each_step=0.69s, eta=1:21:41\n",
      "2021-04-21 19:23:02 [INFO]\t[TRAIN] Epoch=12/100, Step=21/73, loss=0.207903, loss_cls=0.026391, loss_bbox=0.032485, loss_mask=0.082638, loss_rpn_cls=0.009075, loss_rpn_bbox=0.057314, lr=1.2e-05, time_each_step=0.51s, eta=1:21:30\n",
      "2021-04-21 19:23:03 [INFO]\t[TRAIN] Epoch=12/100, Step=23/73, loss=0.185128, loss_cls=0.029015, loss_bbox=0.026415, loss_mask=0.077388, loss_rpn_cls=0.003854, loss_rpn_bbox=0.048456, lr=1.2e-05, time_each_step=0.49s, eta=1:21:28\n",
      "2021-04-21 19:23:04 [INFO]\t[TRAIN] Epoch=12/100, Step=25/73, loss=0.422127, loss_cls=0.048247, loss_bbox=0.033802, loss_mask=0.145779, loss_rpn_cls=0.009958, loss_rpn_bbox=0.18434, lr=1.2e-05, time_each_step=0.49s, eta=1:21:27\n",
      "2021-04-21 19:23:05 [INFO]\t[TRAIN] Epoch=12/100, Step=27/73, loss=0.136762, loss_cls=0.026193, loss_bbox=0.013064, loss_mask=0.069891, loss_rpn_cls=0.011953, loss_rpn_bbox=0.01566, lr=1.2e-05, time_each_step=0.48s, eta=1:21:26\n",
      "2021-04-21 19:23:06 [INFO]\t[TRAIN] Epoch=12/100, Step=29/73, loss=0.18608, loss_cls=0.033257, loss_bbox=0.02682, loss_mask=0.076367, loss_rpn_cls=0.004882, loss_rpn_bbox=0.044754, lr=1.2e-05, time_each_step=0.47s, eta=1:21:24\n",
      "2021-04-21 19:23:07 [INFO]\t[TRAIN] Epoch=12/100, Step=31/73, loss=0.165886, loss_cls=0.034431, loss_bbox=0.027125, loss_mask=0.065402, loss_rpn_cls=0.00776, loss_rpn_bbox=0.031168, lr=1.2e-05, time_each_step=0.46s, eta=1:21:23\n",
      "2021-04-21 19:23:07 [INFO]\t[TRAIN] Epoch=12/100, Step=33/73, loss=0.342181, loss_cls=0.061218, loss_bbox=0.025718, loss_mask=0.14651, loss_rpn_cls=0.008188, loss_rpn_bbox=0.100548, lr=1.2e-05, time_each_step=0.45s, eta=1:21:22\n",
      "2021-04-21 19:23:08 [INFO]\t[TRAIN] Epoch=12/100, Step=35/73, loss=0.230088, loss_cls=0.027765, loss_bbox=0.029513, loss_mask=0.093415, loss_rpn_cls=0.005321, loss_rpn_bbox=0.074073, lr=1.2e-05, time_each_step=0.44s, eta=1:21:20\n",
      "2021-04-21 19:23:09 [INFO]\t[TRAIN] Epoch=12/100, Step=37/73, loss=0.214037, loss_cls=0.028418, loss_bbox=0.020278, loss_mask=0.076454, loss_rpn_cls=0.015169, loss_rpn_bbox=0.073718, lr=1.2e-05, time_each_step=0.43s, eta=1:21:19\n",
      "2021-04-21 19:23:10 [INFO]\t[TRAIN] Epoch=12/100, Step=39/73, loss=0.161188, loss_cls=0.034154, loss_bbox=0.032167, loss_mask=0.067614, loss_rpn_cls=0.002701, loss_rpn_bbox=0.024553, lr=1.2e-05, time_each_step=0.42s, eta=1:21:18\n",
      "2021-04-21 19:23:10 [INFO]\t[TRAIN] Epoch=12/100, Step=41/73, loss=0.18731, loss_cls=0.030506, loss_bbox=0.033101, loss_mask=0.080524, loss_rpn_cls=0.008414, loss_rpn_bbox=0.034765, lr=1.2e-05, time_each_step=0.41s, eta=1:21:17\n",
      "2021-04-21 19:23:11 [INFO]\t[TRAIN] Epoch=12/100, Step=43/73, loss=0.231394, loss_cls=0.031446, loss_bbox=0.026605, loss_mask=0.079077, loss_rpn_cls=0.008002, loss_rpn_bbox=0.086264, lr=1.2e-05, time_each_step=0.4s, eta=1:21:16\n",
      "2021-04-21 19:23:12 [INFO]\t[TRAIN] Epoch=12/100, Step=45/73, loss=0.19406, loss_cls=0.028691, loss_bbox=0.026226, loss_mask=0.084662, loss_rpn_cls=0.007237, loss_rpn_bbox=0.047245, lr=1.2e-05, time_each_step=0.39s, eta=1:21:15\n",
      "2021-04-21 19:23:13 [INFO]\t[TRAIN] Epoch=12/100, Step=47/73, loss=0.251407, loss_cls=0.033998, loss_bbox=0.033054, loss_mask=0.067961, loss_rpn_cls=0.017921, loss_rpn_bbox=0.098473, lr=1.2e-05, time_each_step=0.38s, eta=1:21:14\n",
      "2021-04-21 19:23:13 [INFO]\t[TRAIN] Epoch=12/100, Step=49/73, loss=0.195143, loss_cls=0.029155, loss_bbox=0.029222, loss_mask=0.084058, loss_rpn_cls=0.015081, loss_rpn_bbox=0.037627, lr=1.2e-05, time_each_step=0.38s, eta=1:21:13\n",
      "2021-04-21 19:23:14 [INFO]\t[TRAIN] Epoch=12/100, Step=51/73, loss=0.500691, loss_cls=0.081815, loss_bbox=0.040244, loss_mask=0.137549, loss_rpn_cls=0.075277, loss_rpn_bbox=0.165806, lr=1.2e-05, time_each_step=0.37s, eta=1:21:12\n",
      "2021-04-21 19:23:15 [INFO]\t[TRAIN] Epoch=12/100, Step=53/73, loss=0.509581, loss_cls=0.081031, loss_bbox=0.027105, loss_mask=0.150882, loss_rpn_cls=0.059636, loss_rpn_bbox=0.190928, lr=1.2e-05, time_each_step=0.37s, eta=1:21:11\n",
      "2021-04-21 19:23:16 [INFO]\t[TRAIN] Epoch=12/100, Step=55/73, loss=0.211589, loss_cls=0.033944, loss_bbox=0.024998, loss_mask=0.083774, loss_rpn_cls=0.029628, loss_rpn_bbox=0.039244, lr=1.2e-05, time_each_step=0.37s, eta=1:21:11\n",
      "2021-04-21 19:23:16 [INFO]\t[TRAIN] Epoch=12/100, Step=57/73, loss=0.17887, loss_cls=0.031292, loss_bbox=0.019677, loss_mask=0.067581, loss_rpn_cls=0.008737, loss_rpn_bbox=0.051583, lr=1.2e-05, time_each_step=0.37s, eta=1:21:10\n",
      "2021-04-21 19:23:17 [INFO]\t[TRAIN] Epoch=12/100, Step=59/73, loss=0.315988, loss_cls=0.033852, loss_bbox=0.032706, loss_mask=0.083719, loss_rpn_cls=0.005056, loss_rpn_bbox=0.160656, lr=1.2e-05, time_each_step=0.37s, eta=1:21:9\n",
      "2021-04-21 19:23:18 [INFO]\t[TRAIN] Epoch=12/100, Step=61/73, loss=0.173393, loss_cls=0.032154, loss_bbox=0.026729, loss_mask=0.075769, loss_rpn_cls=0.003231, loss_rpn_bbox=0.035509, lr=1.2e-05, time_each_step=0.37s, eta=1:21:8\n",
      "2021-04-21 19:23:19 [INFO]\t[TRAIN] Epoch=12/100, Step=63/73, loss=0.153442, loss_cls=0.025483, loss_bbox=0.021491, loss_mask=0.079321, loss_rpn_cls=0.00592, loss_rpn_bbox=0.021227, lr=1.2e-05, time_each_step=0.37s, eta=1:21:8\n",
      "2021-04-21 19:23:19 [INFO]\t[TRAIN] Epoch=12/100, Step=65/73, loss=0.236191, loss_cls=0.030157, loss_bbox=0.02018, loss_mask=0.087841, loss_rpn_cls=0.01701, loss_rpn_bbox=0.081004, lr=1.2e-05, time_each_step=0.37s, eta=1:21:7\n",
      "2021-04-21 19:23:20 [INFO]\t[TRAIN] Epoch=12/100, Step=67/73, loss=0.136517, loss_cls=0.025598, loss_bbox=0.016471, loss_mask=0.071096, loss_rpn_cls=0.002841, loss_rpn_bbox=0.020511, lr=1.2e-05, time_each_step=0.37s, eta=1:21:6\n",
      "2021-04-21 19:23:21 [INFO]\t[TRAIN] Epoch=12/100, Step=69/73, loss=0.349589, loss_cls=0.02938, loss_bbox=0.024865, loss_mask=0.237753, loss_rpn_cls=0.013639, loss_rpn_bbox=0.043952, lr=1.2e-05, time_each_step=0.37s, eta=1:21:5\n",
      "2021-04-21 19:23:22 [INFO]\t[TRAIN] Epoch=12/100, Step=71/73, loss=0.169529, loss_cls=0.036595, loss_bbox=0.026507, loss_mask=0.073194, loss_rpn_cls=0.005302, loss_rpn_bbox=0.027931, lr=1.2e-05, time_each_step=0.37s, eta=1:21:5\n",
      "2021-04-21 19:23:22 [INFO]\t[TRAIN] Epoch=12/100, Step=73/73, loss=0.166488, loss_cls=0.040299, loss_bbox=0.023913, loss_mask=0.066103, loss_rpn_cls=0.009139, loss_rpn_bbox=0.027035, lr=1.2e-05, time_each_step=0.37s, eta=1:21:4\n",
      "2021-04-21 19:23:22 [INFO]\t[TRAIN] Epoch 12 finished, loss=0.225178, loss_cls=0.034628, loss_bbox=0.027924, loss_mask=0.087921, loss_rpn_cls=0.010232, loss_rpn_bbox=0.064474, lr=1.2e-05 .\n",
      "2021-04-21 19:23:22 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:23:22 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:15<00:00,  5.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.07s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.275\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.758\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.140\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.275\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.324\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.481\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.481\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.10s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.393\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.877\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.168\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.439\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.469\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.523\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.525\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525\n",
      "2021-04-21 19:23:40 [INFO]\t[EVAL] Finished, Epoch=12, bbox_mmap=0.274688, segm_mmap=0.392868 .\n",
      "2021-04-21 19:23:45 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_12.\n",
      "2021-04-21 19:23:45 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:23:51 [INFO]\t[TRAIN] Epoch=13/100, Step=2/73, loss=0.224967, loss_cls=0.025653, loss_bbox=0.031088, loss_mask=0.076079, loss_rpn_cls=0.003686, loss_rpn_bbox=0.088461, lr=1.2e-05, time_each_step=0.66s, eta=1:23:25\n",
      "2021-04-21 19:23:53 [INFO]\t[TRAIN] Epoch=13/100, Step=4/73, loss=0.227803, loss_cls=0.033854, loss_bbox=0.038831, loss_mask=0.073508, loss_rpn_cls=0.007145, loss_rpn_bbox=0.074466, lr=1.2e-05, time_each_step=0.69s, eta=1:23:26\n",
      "2021-04-21 19:23:54 [INFO]\t[TRAIN] Epoch=13/100, Step=6/73, loss=0.287638, loss_cls=0.039521, loss_bbox=0.027961, loss_mask=0.107788, loss_rpn_cls=0.007447, loss_rpn_bbox=0.104922, lr=1.2e-05, time_each_step=0.71s, eta=1:23:26\n",
      "2021-04-21 19:23:55 [INFO]\t[TRAIN] Epoch=13/100, Step=8/73, loss=0.215663, loss_cls=0.03268, loss_bbox=0.029217, loss_mask=0.079837, loss_rpn_cls=0.012752, loss_rpn_bbox=0.061179, lr=1.2e-05, time_each_step=0.73s, eta=1:23:26\n",
      "2021-04-21 19:23:56 [INFO]\t[TRAIN] Epoch=13/100, Step=10/73, loss=0.171335, loss_cls=0.031856, loss_bbox=0.031803, loss_mask=0.069561, loss_rpn_cls=0.003385, loss_rpn_bbox=0.03473, lr=1.2e-05, time_each_step=0.75s, eta=1:23:25\n",
      "2021-04-21 19:23:57 [INFO]\t[TRAIN] Epoch=13/100, Step=12/73, loss=0.30963, loss_cls=0.059952, loss_bbox=0.027948, loss_mask=0.172513, loss_rpn_cls=0.002879, loss_rpn_bbox=0.046338, lr=1.2e-05, time_each_step=0.76s, eta=1:23:24\n",
      "2021-04-21 19:23:58 [INFO]\t[TRAIN] Epoch=13/100, Step=14/73, loss=0.148597, loss_cls=0.034193, loss_bbox=0.020485, loss_mask=0.057648, loss_rpn_cls=0.004986, loss_rpn_bbox=0.031286, lr=1.2e-05, time_each_step=0.77s, eta=1:23:23\n",
      "2021-04-21 19:23:59 [INFO]\t[TRAIN] Epoch=13/100, Step=16/73, loss=0.210057, loss_cls=0.026369, loss_bbox=0.024576, loss_mask=0.085088, loss_rpn_cls=0.00554, loss_rpn_bbox=0.068483, lr=1.2e-05, time_each_step=0.79s, eta=1:23:23\n",
      "2021-04-21 19:24:00 [INFO]\t[TRAIN] Epoch=13/100, Step=18/73, loss=0.244475, loss_cls=0.045717, loss_bbox=0.031309, loss_mask=0.108734, loss_rpn_cls=0.00257, loss_rpn_bbox=0.056144, lr=1.2e-05, time_each_step=0.79s, eta=1:23:22\n",
      "2021-04-21 19:24:01 [INFO]\t[TRAIN] Epoch=13/100, Step=20/73, loss=0.180121, loss_cls=0.028403, loss_bbox=0.023154, loss_mask=0.065196, loss_rpn_cls=0.002148, loss_rpn_bbox=0.061221, lr=1.2e-05, time_each_step=0.8s, eta=1:23:21\n",
      "2021-04-21 19:24:02 [INFO]\t[TRAIN] Epoch=13/100, Step=22/73, loss=0.258571, loss_cls=0.034827, loss_bbox=0.031812, loss_mask=0.081006, loss_rpn_cls=0.009112, loss_rpn_bbox=0.101813, lr=1.2e-05, time_each_step=0.52s, eta=1:23:4\n",
      "2021-04-21 19:24:03 [INFO]\t[TRAIN] Epoch=13/100, Step=24/73, loss=0.167877, loss_cls=0.032209, loss_bbox=0.02923, loss_mask=0.067343, loss_rpn_cls=0.005776, loss_rpn_bbox=0.033319, lr=1.2e-05, time_each_step=0.5s, eta=1:23:3\n",
      "2021-04-21 19:24:04 [INFO]\t[TRAIN] Epoch=13/100, Step=26/73, loss=0.210054, loss_cls=0.031406, loss_bbox=0.030128, loss_mask=0.084608, loss_rpn_cls=0.004171, loss_rpn_bbox=0.05974, lr=1.2e-05, time_each_step=0.49s, eta=1:23:1\n",
      "2021-04-21 19:24:04 [INFO]\t[TRAIN] Epoch=13/100, Step=28/73, loss=0.174491, loss_cls=0.029084, loss_bbox=0.026945, loss_mask=0.076888, loss_rpn_cls=0.010188, loss_rpn_bbox=0.031387, lr=1.2e-05, time_each_step=0.47s, eta=1:22:59\n",
      "2021-04-21 19:24:05 [INFO]\t[TRAIN] Epoch=13/100, Step=30/73, loss=0.237174, loss_cls=0.038284, loss_bbox=0.038923, loss_mask=0.08007, loss_rpn_cls=0.0049, loss_rpn_bbox=0.074997, lr=1.2e-05, time_each_step=0.46s, eta=1:22:58\n",
      "2021-04-21 19:24:06 [INFO]\t[TRAIN] Epoch=13/100, Step=32/73, loss=0.294962, loss_cls=0.028048, loss_bbox=0.026931, loss_mask=0.084668, loss_rpn_cls=0.009914, loss_rpn_bbox=0.145401, lr=1.2e-05, time_each_step=0.45s, eta=1:22:56\n",
      "2021-04-21 19:24:07 [INFO]\t[TRAIN] Epoch=13/100, Step=34/73, loss=0.188081, loss_cls=0.030172, loss_bbox=0.025509, loss_mask=0.087886, loss_rpn_cls=0.015031, loss_rpn_bbox=0.029484, lr=1.2e-05, time_each_step=0.44s, eta=1:22:55\n",
      "2021-04-21 19:24:08 [INFO]\t[TRAIN] Epoch=13/100, Step=36/73, loss=0.149315, loss_cls=0.02706, loss_bbox=0.019787, loss_mask=0.072581, loss_rpn_cls=0.005783, loss_rpn_bbox=0.024104, lr=1.2e-05, time_each_step=0.43s, eta=1:22:54\n",
      "2021-04-21 19:24:08 [INFO]\t[TRAIN] Epoch=13/100, Step=38/73, loss=0.177532, loss_cls=0.031826, loss_bbox=0.024252, loss_mask=0.059086, loss_rpn_cls=0.013711, loss_rpn_bbox=0.048658, lr=1.2e-05, time_each_step=0.42s, eta=1:22:53\n",
      "2021-04-21 19:24:09 [INFO]\t[TRAIN] Epoch=13/100, Step=40/73, loss=0.229737, loss_cls=0.032806, loss_bbox=0.034997, loss_mask=0.084589, loss_rpn_cls=0.026611, loss_rpn_bbox=0.050734, lr=1.2e-05, time_each_step=0.41s, eta=1:22:51\n",
      "2021-04-21 19:24:10 [INFO]\t[TRAIN] Epoch=13/100, Step=42/73, loss=0.145292, loss_cls=0.031567, loss_bbox=0.025227, loss_mask=0.064507, loss_rpn_cls=0.002071, loss_rpn_bbox=0.021919, lr=1.2e-05, time_each_step=0.4s, eta=1:22:50\n",
      "2021-04-21 19:24:11 [INFO]\t[TRAIN] Epoch=13/100, Step=44/73, loss=0.235802, loss_cls=0.040402, loss_bbox=0.035974, loss_mask=0.070793, loss_rpn_cls=0.006103, loss_rpn_bbox=0.082529, lr=1.2e-05, time_each_step=0.39s, eta=1:22:49\n",
      "2021-04-21 19:24:11 [INFO]\t[TRAIN] Epoch=13/100, Step=46/73, loss=0.210645, loss_cls=0.040191, loss_bbox=0.029809, loss_mask=0.093836, loss_rpn_cls=0.016089, loss_rpn_bbox=0.030721, lr=1.2e-05, time_each_step=0.38s, eta=1:22:48\n",
      "2021-04-21 19:24:12 [INFO]\t[TRAIN] Epoch=13/100, Step=48/73, loss=0.196306, loss_cls=0.031609, loss_bbox=0.024146, loss_mask=0.085127, loss_rpn_cls=0.003942, loss_rpn_bbox=0.051481, lr=1.2e-05, time_each_step=0.38s, eta=1:22:47\n",
      "2021-04-21 19:24:13 [INFO]\t[TRAIN] Epoch=13/100, Step=50/73, loss=0.170012, loss_cls=0.037616, loss_bbox=0.029341, loss_mask=0.066352, loss_rpn_cls=0.003067, loss_rpn_bbox=0.033637, lr=1.2e-05, time_each_step=0.38s, eta=1:22:47\n",
      "2021-04-21 19:24:13 [INFO]\t[TRAIN] Epoch=13/100, Step=52/73, loss=0.175739, loss_cls=0.033695, loss_bbox=0.023807, loss_mask=0.091587, loss_rpn_cls=0.005431, loss_rpn_bbox=0.02122, lr=1.2e-05, time_each_step=0.37s, eta=1:22:46\n",
      "2021-04-21 19:24:14 [INFO]\t[TRAIN] Epoch=13/100, Step=54/73, loss=0.195081, loss_cls=0.042787, loss_bbox=0.03454, loss_mask=0.071162, loss_rpn_cls=0.003913, loss_rpn_bbox=0.042679, lr=1.2e-05, time_each_step=0.37s, eta=1:22:45\n",
      "2021-04-21 19:24:15 [INFO]\t[TRAIN] Epoch=13/100, Step=56/73, loss=0.210314, loss_cls=0.036053, loss_bbox=0.032979, loss_mask=0.085184, loss_rpn_cls=0.007715, loss_rpn_bbox=0.048383, lr=1.2e-05, time_each_step=0.37s, eta=1:22:44\n",
      "2021-04-21 19:24:16 [INFO]\t[TRAIN] Epoch=13/100, Step=58/73, loss=0.190232, loss_cls=0.031761, loss_bbox=0.018925, loss_mask=0.071828, loss_rpn_cls=0.003188, loss_rpn_bbox=0.06453, lr=1.2e-05, time_each_step=0.37s, eta=1:22:43\n",
      "2021-04-21 19:24:16 [INFO]\t[TRAIN] Epoch=13/100, Step=60/73, loss=0.20313, loss_cls=0.031848, loss_bbox=0.029303, loss_mask=0.06619, loss_rpn_cls=0.011551, loss_rpn_bbox=0.064237, lr=1.2e-05, time_each_step=0.37s, eta=1:22:43\n",
      "2021-04-21 19:24:17 [INFO]\t[TRAIN] Epoch=13/100, Step=62/73, loss=0.213458, loss_cls=0.036625, loss_bbox=0.030443, loss_mask=0.095424, loss_rpn_cls=0.002303, loss_rpn_bbox=0.048664, lr=1.2e-05, time_each_step=0.37s, eta=1:22:42\n",
      "2021-04-21 19:24:18 [INFO]\t[TRAIN] Epoch=13/100, Step=64/73, loss=0.210077, loss_cls=0.032403, loss_bbox=0.030751, loss_mask=0.064065, loss_rpn_cls=0.006303, loss_rpn_bbox=0.076556, lr=1.2e-05, time_each_step=0.37s, eta=1:22:41\n",
      "2021-04-21 19:24:19 [INFO]\t[TRAIN] Epoch=13/100, Step=66/73, loss=0.507434, loss_cls=0.061261, loss_bbox=0.024236, loss_mask=0.178293, loss_rpn_cls=0.074716, loss_rpn_bbox=0.168927, lr=1.2e-05, time_each_step=0.37s, eta=1:22:40\n",
      "2021-04-21 19:24:19 [INFO]\t[TRAIN] Epoch=13/100, Step=68/73, loss=0.177649, loss_cls=0.043869, loss_bbox=0.028446, loss_mask=0.072815, loss_rpn_cls=0.003305, loss_rpn_bbox=0.029213, lr=1.2e-05, time_each_step=0.37s, eta=1:22:40\n",
      "2021-04-21 19:24:20 [INFO]\t[TRAIN] Epoch=13/100, Step=70/73, loss=0.173939, loss_cls=0.024313, loss_bbox=0.03034, loss_mask=0.070731, loss_rpn_cls=0.005819, loss_rpn_bbox=0.042736, lr=1.2e-05, time_each_step=0.37s, eta=1:22:39\n",
      "2021-04-21 19:24:21 [INFO]\t[TRAIN] Epoch=13/100, Step=72/73, loss=0.247373, loss_cls=0.038412, loss_bbox=0.03588, loss_mask=0.081915, loss_rpn_cls=0.021833, loss_rpn_bbox=0.069334, lr=1.2e-05, time_each_step=0.37s, eta=1:22:38\n",
      "2021-04-21 19:24:21 [INFO]\t[TRAIN] Epoch 13 finished, loss=0.225514, loss_cls=0.034992, loss_bbox=0.028575, loss_mask=0.087734, loss_rpn_cls=0.009987, loss_rpn_bbox=0.064227, lr=1.2e-05 .\n",
      "2021-04-21 19:24:21 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:24:21 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:14<00:00,  5.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.05s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.288\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.782\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.151\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.330\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.506\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.506\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.394\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.875\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.191\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.470\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.520\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.520\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.520\n",
      "2021-04-21 19:24:37 [INFO]\t[EVAL] Finished, Epoch=13, bbox_mmap=0.287685, segm_mmap=0.394374 .\n",
      "2021-04-21 19:24:42 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_13.\n",
      "2021-04-21 19:24:42 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:24:46 [INFO]\t[TRAIN] Epoch=14/100, Step=1/73, loss=0.199117, loss_cls=0.041532, loss_bbox=0.032078, loss_mask=0.072923, loss_rpn_cls=0.003989, loss_rpn_bbox=0.048595, lr=1.2e-05, time_each_step=0.58s, eta=1:22:38\n",
      "2021-04-21 19:24:48 [INFO]\t[TRAIN] Epoch=14/100, Step=3/73, loss=0.198275, loss_cls=0.031417, loss_bbox=0.02639, loss_mask=0.101315, loss_rpn_cls=0.005456, loss_rpn_bbox=0.033696, lr=1.2e-05, time_each_step=0.62s, eta=1:22:39\n",
      "2021-04-21 19:24:49 [INFO]\t[TRAIN] Epoch=14/100, Step=5/73, loss=0.2173, loss_cls=0.038588, loss_bbox=0.042908, loss_mask=0.061511, loss_rpn_cls=0.007994, loss_rpn_bbox=0.066299, lr=1.2e-05, time_each_step=0.64s, eta=1:22:40\n",
      "2021-04-21 19:24:50 [INFO]\t[TRAIN] Epoch=14/100, Step=7/73, loss=0.211427, loss_cls=0.030475, loss_bbox=0.024351, loss_mask=0.076637, loss_rpn_cls=0.011824, loss_rpn_bbox=0.068139, lr=1.2e-05, time_each_step=0.66s, eta=1:22:40\n",
      "2021-04-21 19:24:51 [INFO]\t[TRAIN] Epoch=14/100, Step=9/73, loss=0.17217, loss_cls=0.029185, loss_bbox=0.023944, loss_mask=0.068473, loss_rpn_cls=0.008085, loss_rpn_bbox=0.042483, lr=1.2e-05, time_each_step=0.68s, eta=1:22:40\n",
      "2021-04-21 19:24:52 [INFO]\t[TRAIN] Epoch=14/100, Step=11/73, loss=0.20578, loss_cls=0.038457, loss_bbox=0.025215, loss_mask=0.088206, loss_rpn_cls=0.015688, loss_rpn_bbox=0.038213, lr=1.2e-05, time_each_step=0.69s, eta=1:22:39\n",
      "2021-04-21 19:24:53 [INFO]\t[TRAIN] Epoch=14/100, Step=13/73, loss=0.215894, loss_cls=0.028752, loss_bbox=0.030237, loss_mask=0.075305, loss_rpn_cls=0.012546, loss_rpn_bbox=0.069054, lr=1.2e-05, time_each_step=0.7s, eta=1:22:38\n",
      "2021-04-21 19:24:54 [INFO]\t[TRAIN] Epoch=14/100, Step=15/73, loss=0.192059, loss_cls=0.032905, loss_bbox=0.026504, loss_mask=0.092249, loss_rpn_cls=0.003591, loss_rpn_bbox=0.03681, lr=1.2e-05, time_each_step=0.7s, eta=1:22:37\n",
      "2021-04-21 19:24:55 [INFO]\t[TRAIN] Epoch=14/100, Step=17/73, loss=0.166391, loss_cls=0.028593, loss_bbox=0.024857, loss_mask=0.069176, loss_rpn_cls=0.006305, loss_rpn_bbox=0.037461, lr=1.2e-05, time_each_step=0.71s, eta=1:22:36\n",
      "2021-04-21 19:24:56 [INFO]\t[TRAIN] Epoch=14/100, Step=19/73, loss=0.247219, loss_cls=0.031604, loss_bbox=0.03133, loss_mask=0.082444, loss_rpn_cls=0.018085, loss_rpn_bbox=0.083756, lr=1.2e-05, time_each_step=0.72s, eta=1:22:35\n",
      "2021-04-21 19:24:57 [INFO]\t[TRAIN] Epoch=14/100, Step=21/73, loss=0.245886, loss_cls=0.031424, loss_bbox=0.02203, loss_mask=0.084136, loss_rpn_cls=0.01243, loss_rpn_bbox=0.095867, lr=1.2e-05, time_each_step=0.52s, eta=1:22:24\n",
      "2021-04-21 19:24:58 [INFO]\t[TRAIN] Epoch=14/100, Step=23/73, loss=0.226392, loss_cls=0.031963, loss_bbox=0.03144, loss_mask=0.083667, loss_rpn_cls=0.011908, loss_rpn_bbox=0.067414, lr=1.2e-05, time_each_step=0.5s, eta=1:22:21\n",
      "2021-04-21 19:24:59 [INFO]\t[TRAIN] Epoch=14/100, Step=25/73, loss=0.221937, loss_cls=0.029347, loss_bbox=0.027521, loss_mask=0.08191, loss_rpn_cls=0.014436, loss_rpn_bbox=0.068722, lr=1.2e-05, time_each_step=0.48s, eta=1:22:19\n",
      "2021-04-21 19:24:59 [INFO]\t[TRAIN] Epoch=14/100, Step=27/73, loss=0.200245, loss_cls=0.029024, loss_bbox=0.023744, loss_mask=0.076596, loss_rpn_cls=0.008874, loss_rpn_bbox=0.062007, lr=1.2e-05, time_each_step=0.46s, eta=1:22:18\n",
      "2021-04-21 19:25:00 [INFO]\t[TRAIN] Epoch=14/100, Step=29/73, loss=0.215742, loss_cls=0.030896, loss_bbox=0.030194, loss_mask=0.071163, loss_rpn_cls=0.013251, loss_rpn_bbox=0.070238, lr=1.2e-05, time_each_step=0.45s, eta=1:22:16\n",
      "2021-04-21 19:25:01 [INFO]\t[TRAIN] Epoch=14/100, Step=31/73, loss=0.197301, loss_cls=0.032257, loss_bbox=0.035846, loss_mask=0.071474, loss_rpn_cls=0.014907, loss_rpn_bbox=0.042817, lr=1.2e-05, time_each_step=0.44s, eta=1:22:15\n",
      "2021-04-21 19:25:02 [INFO]\t[TRAIN] Epoch=14/100, Step=33/73, loss=0.167661, loss_cls=0.027842, loss_bbox=0.0219, loss_mask=0.065421, loss_rpn_cls=0.010634, loss_rpn_bbox=0.041865, lr=1.2e-05, time_each_step=0.44s, eta=1:22:14\n",
      "2021-04-21 19:25:03 [INFO]\t[TRAIN] Epoch=14/100, Step=35/73, loss=0.187623, loss_cls=0.029688, loss_bbox=0.025066, loss_mask=0.074481, loss_rpn_cls=0.004769, loss_rpn_bbox=0.053618, lr=1.2e-05, time_each_step=0.43s, eta=1:22:13\n",
      "2021-04-21 19:25:03 [INFO]\t[TRAIN] Epoch=14/100, Step=37/73, loss=0.238074, loss_cls=0.034035, loss_bbox=0.036154, loss_mask=0.098681, loss_rpn_cls=0.003785, loss_rpn_bbox=0.065419, lr=1.2e-05, time_each_step=0.42s, eta=1:22:12\n",
      "2021-04-21 19:25:04 [INFO]\t[TRAIN] Epoch=14/100, Step=39/73, loss=0.183921, loss_cls=0.027207, loss_bbox=0.020402, loss_mask=0.095331, loss_rpn_cls=0.006185, loss_rpn_bbox=0.034796, lr=1.2e-05, time_each_step=0.42s, eta=1:22:11\n",
      "2021-04-21 19:25:05 [INFO]\t[TRAIN] Epoch=14/100, Step=41/73, loss=0.300558, loss_cls=0.062913, loss_bbox=0.041548, loss_mask=0.109435, loss_rpn_cls=0.01727, loss_rpn_bbox=0.069393, lr=1.2e-05, time_each_step=0.4s, eta=1:22:9\n",
      "2021-04-21 19:25:06 [INFO]\t[TRAIN] Epoch=14/100, Step=43/73, loss=0.269547, loss_cls=0.042271, loss_bbox=0.027263, loss_mask=0.110713, loss_rpn_cls=0.006109, loss_rpn_bbox=0.083192, lr=1.2e-05, time_each_step=0.39s, eta=1:22:8\n",
      "2021-04-21 19:25:06 [INFO]\t[TRAIN] Epoch=14/100, Step=45/73, loss=0.194697, loss_cls=0.03665, loss_bbox=0.02958, loss_mask=0.075342, loss_rpn_cls=0.017374, loss_rpn_bbox=0.035751, lr=1.2e-05, time_each_step=0.38s, eta=1:22:7\n",
      "2021-04-21 19:25:07 [INFO]\t[TRAIN] Epoch=14/100, Step=47/73, loss=0.153616, loss_cls=0.033478, loss_bbox=0.025961, loss_mask=0.072729, loss_rpn_cls=0.004025, loss_rpn_bbox=0.017422, lr=1.2e-05, time_each_step=0.38s, eta=1:22:6\n",
      "2021-04-21 19:25:08 [INFO]\t[TRAIN] Epoch=14/100, Step=49/73, loss=0.218861, loss_cls=0.029479, loss_bbox=0.028177, loss_mask=0.083746, loss_rpn_cls=0.012068, loss_rpn_bbox=0.065392, lr=1.2e-05, time_each_step=0.38s, eta=1:22:5\n",
      "2021-04-21 19:25:09 [INFO]\t[TRAIN] Epoch=14/100, Step=51/73, loss=0.22236, loss_cls=0.030733, loss_bbox=0.026667, loss_mask=0.09559, loss_rpn_cls=0.003333, loss_rpn_bbox=0.066037, lr=1.2e-05, time_each_step=0.37s, eta=1:22:5\n",
      "2021-04-21 19:25:09 [INFO]\t[TRAIN] Epoch=14/100, Step=53/73, loss=0.225515, loss_cls=0.033671, loss_bbox=0.03036, loss_mask=0.07773, loss_rpn_cls=0.010712, loss_rpn_bbox=0.073042, lr=1.2e-05, time_each_step=0.37s, eta=1:22:4\n",
      "2021-04-21 19:25:10 [INFO]\t[TRAIN] Epoch=14/100, Step=55/73, loss=0.226325, loss_cls=0.02878, loss_bbox=0.027605, loss_mask=0.09704, loss_rpn_cls=0.009419, loss_rpn_bbox=0.063481, lr=1.2e-05, time_each_step=0.37s, eta=1:22:3\n",
      "2021-04-21 19:25:11 [INFO]\t[TRAIN] Epoch=14/100, Step=57/73, loss=0.224367, loss_cls=0.034994, loss_bbox=0.02936, loss_mask=0.066262, loss_rpn_cls=0.027832, loss_rpn_bbox=0.065919, lr=1.2e-05, time_each_step=0.37s, eta=1:22:2\n",
      "2021-04-21 19:25:11 [INFO]\t[TRAIN] Epoch=14/100, Step=59/73, loss=0.368956, loss_cls=0.060959, loss_bbox=0.033592, loss_mask=0.160951, loss_rpn_cls=0.023825, loss_rpn_bbox=0.089629, lr=1.2e-05, time_each_step=0.37s, eta=1:22:2\n",
      "2021-04-21 19:25:12 [INFO]\t[TRAIN] Epoch=14/100, Step=61/73, loss=0.188452, loss_cls=0.037542, loss_bbox=0.030242, loss_mask=0.079602, loss_rpn_cls=0.012019, loss_rpn_bbox=0.029047, lr=1.2e-05, time_each_step=0.37s, eta=1:22:1\n",
      "2021-04-21 19:25:13 [INFO]\t[TRAIN] Epoch=14/100, Step=63/73, loss=0.249642, loss_cls=0.039193, loss_bbox=0.030336, loss_mask=0.085274, loss_rpn_cls=0.007454, loss_rpn_bbox=0.087385, lr=1.2e-05, time_each_step=0.37s, eta=1:22:0\n",
      "2021-04-21 19:25:14 [INFO]\t[TRAIN] Epoch=14/100, Step=65/73, loss=0.209717, loss_cls=0.031361, loss_bbox=0.041567, loss_mask=0.066534, loss_rpn_cls=0.010755, loss_rpn_bbox=0.0595, lr=1.2e-05, time_each_step=0.37s, eta=1:21:59\n",
      "2021-04-21 19:25:14 [INFO]\t[TRAIN] Epoch=14/100, Step=67/73, loss=0.204896, loss_cls=0.033674, loss_bbox=0.031935, loss_mask=0.080261, loss_rpn_cls=0.011872, loss_rpn_bbox=0.047155, lr=1.2e-05, time_each_step=0.37s, eta=1:21:59\n",
      "2021-04-21 19:25:15 [INFO]\t[TRAIN] Epoch=14/100, Step=69/73, loss=0.23189, loss_cls=0.032726, loss_bbox=0.026931, loss_mask=0.090665, loss_rpn_cls=0.013976, loss_rpn_bbox=0.067592, lr=1.2e-05, time_each_step=0.37s, eta=1:21:58\n",
      "2021-04-21 19:25:16 [INFO]\t[TRAIN] Epoch=14/100, Step=71/73, loss=0.467057, loss_cls=0.061868, loss_bbox=0.034066, loss_mask=0.133771, loss_rpn_cls=0.077036, loss_rpn_bbox=0.160316, lr=1.2e-05, time_each_step=0.37s, eta=1:21:57\n",
      "2021-04-21 19:25:17 [INFO]\t[TRAIN] Epoch=14/100, Step=73/73, loss=0.202898, loss_cls=0.03151, loss_bbox=0.033552, loss_mask=0.080515, loss_rpn_cls=0.004624, loss_rpn_bbox=0.052697, lr=1.2e-05, time_each_step=0.37s, eta=1:21:56\n",
      "2021-04-21 19:25:17 [INFO]\t[TRAIN] Epoch 14 finished, loss=0.227693, loss_cls=0.035325, loss_bbox=0.028804, loss_mask=0.088412, loss_rpn_cls=0.010881, loss_rpn_bbox=0.064271, lr=1.2e-05 .\n",
      "2021-04-21 19:25:17 [WARNING]\tMask RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1.\n",
      "2021-04-21 19:25:17 [INFO]\tStart to evaluating(total_samples=83, total_steps=83)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 83/83 [00:15<00:00,  5.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.05s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.282\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.765\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.152\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.282\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.327\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.510\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.510\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *segm*\n",
      "DONE (t=0.09s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.398\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.877\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.219\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.478\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.525\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.525\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525\n",
      "2021-04-21 19:25:33 [INFO]\t[EVAL] Finished, Epoch=14, bbox_mmap=0.282326, segm_mmap=0.398493 .\n",
      "2021-04-21 19:25:38 [INFO]\tModel saved in output/mask_rcnn_r50_fpn/epoch_14.\n",
      "2021-04-21 19:25:38 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_9, bbox_mmap=0.2884830094212573\n",
      "2021-04-21 19:25:42 [INFO]\t[TRAIN] Epoch=15/100, Step=2/73, loss=0.161322, loss_cls=0.026786, loss_bbox=0.021679, loss_mask=0.075269, loss_rpn_cls=0.004729, loss_rpn_bbox=0.032859, lr=1.2e-05, time_each_step=0.57s, eta=1:20:16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-230:\n",
      "Process Process-228:\n",
      "Process Process-231:\n",
      "Process Process-232:\n",
      "Process Process-226:\n",
      "Traceback (most recent call last):\n",
      "Process Process-227:\n",
      "Process Process-225:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 113, in decode_image\n",
      "    im = cv2.imread(im_file).astype('float32')\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 113, in decode_image\n",
      "    im = cv2.imread(im_file).astype('float32')\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 162, in _read_into_queue\n",
      "    queue.put(result)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "Process Process-229:\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/shared_queue/queue.py\", line 66, in put\n",
      "    obj = pickle.dumps(obj, -1)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 162, in _read_into_queue\n",
      "    queue.put(result)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 145, in __call__\n",
      "    outputs = decode_image(im, im_info, label_info, input_channel)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 154, in __call__\n",
      "    outputs = op(im, im_info, label_info)\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 154, in __call__\n",
      "    outputs = op(im, im_info, label_info)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/shared_queue/queue.py\", line 70, in put\n",
      "    buff.put(obj)\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 122, in decode_image\n",
      "    im = im.astype('float32')\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 252, in __call__\n",
      "    interpolation=cv2.INTER_LINEAR)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 252, in __call__\n",
      "    interpolation=cv2.INTER_LINEAR)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/shared_queue/sharedmemory.py\", line 133, in put\n",
      "    self.owner().put_data(self, data)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/shared_queue/sharedmemory.py\", line 517, in put_data\n",
      "    self._base[start:end] = np.frombuffer(data, 'uint8', len(data))\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 166, in _read_into_queue\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 145, in __call__\n",
      "    outputs = decode_image(im, im_info, label_info, input_channel)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/datasets/dataset.py\", line 158, in _read_into_queue\n",
      "    result = mapper(sample[0], sample[1], sample[2])\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 145, in __call__\n",
      "    outputs = decode_image(im, im_info, label_info, input_channel)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 121, in decode_image\n",
      "    im_file))\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 154, in __call__\n",
      "    outputs = op(im, im_info, label_info)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 121, in decode_image\n",
      "    im_file))\n",
      "TypeError: Can't read The image file dataset_coco/JPEGImages/lhgd41.jpg!\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/det_transforms.py\", line 554, in __call__\n",
      "    im = normalize(im, mean, std, min_val, max_val)\n",
      "TypeError: Can't read The image file dataset_coco/JPEGImages/lhgd412.jpg!\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/transforms/ops.py\", line 24, in normalize\n",
      "    im = (im - min_value) / range_value\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-17831a55f820>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mlr_decay_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0msave_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'output/mask_rcnn_r50_fpn'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     use_vdl=True)\n\u001b[0m",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/models/mask_rcnn.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, num_epochs, train_dataset, train_batch_size, eval_dataset, save_interval_epochs, log_interval_steps, save_dir, pretrain_weights, optimizer, learning_rate, warmup_steps, warmup_start_lr, lr_decay_epochs, lr_decay_gamma, metric, use_vdl, early_stop, early_stop_patience, resume_checkpoint)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0muse_vdl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_vdl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0mearly_stop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mearly_stop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             early_stop_patience=early_stop_patience)\n\u001b[0m\u001b[1;32m    321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m     def evaluate(self,\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlex/cv/models/base.py\u001b[0m in \u001b[0;36mtrain_loop\u001b[0;34m(self, num_epochs, train_dataset, train_batch_size, eval_dataset, save_interval_epochs, log_interval_steps, save_dir, use_vdl, early_stop, early_stop_patience)\u001b[0m\n\u001b[1;32m    499\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel_train_prog\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m                     \u001b[0mfeed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m                     fetch_list=list(self.train_outputs.values()))\n\u001b[0m\u001b[1;32m    502\u001b[0m                 \u001b[0moutputs_avg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mrecords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs_avg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache, return_merged, use_prune)\u001b[0m\n\u001b[1;32m   1106\u001b[0m                 \u001b[0muse_program_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_program_cache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m                 \u001b[0muse_prune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_prune\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1108\u001b[0;31m                 return_merged=return_merged)\n\u001b[0m\u001b[1;32m   1109\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1110\u001b[0m             \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36m_run_impl\u001b[0;34m(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache, return_merged, use_prune)\u001b[0m\n\u001b[1;32m   1249\u001b[0m                 \u001b[0mfetch_var_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfetch_var_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1250\u001b[0m                 \u001b[0mreturn_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_numpy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1251\u001b[0;31m                 return_merged=return_merged)\n\u001b[0m\u001b[1;32m   1252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m     def _run_program(self, program, feed, fetch_list, feed_var_name,\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36m_run_parallel\u001b[0;34m(self, program, scope, feed, fetch_list, fetch_var_name, return_numpy, return_merged)\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0mfetch_var_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_name_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 913\u001b[0;31m         \u001b[0mtensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch_var_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_merged\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_move_to_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    914\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mas_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_numpy\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "num_classes = len(train_dataset.labels) + 1\n",
    "\n",
    "model = pdx.det.MaskRCNN(num_classes=num_classes, backbone='ResNet50')\n",
    "\n",
    "model.train(\n",
    "    num_epochs=100,\n",
    "    train_dataset=train_dataset,\n",
    "    train_batch_size=4,\n",
    "    eval_dataset=eval_dataset,\n",
    "    learning_rate=0.00125,\n",
    "    warmup_steps=10,\n",
    "    lr_decay_epochs=[8, 11],\n",
    "    save_dir='output/mask_rcnn_r50_fpn',\n",
    "    use_vdl=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-21 19:26:30 [INFO]\tModel[MaskRCNN] loaded.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-21 19:26:31 [INFO]\tThe visualized result is saved as ./visualize_lhgd295.jpg\n"
     ]
    }
   ],
   "source": [
    "model = pdx.load_model('output/mask_rcnn_r50_fpn/best_model')\n",
    "image_name = 'dataset_coco/JPEGImages/lhgd295.jpg'\n",
    "result = model.predict(image_name)\n",
    "pdx.det.visualize(image_name, result, threshold=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "![](https://ai-studio-static-online.cdn.bcebos.com/29090b6ea14f4f2e931f0d4abfe613452d3a47f8a1f3477bb652657bc2a75b9c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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