05-detectron2-experiment-tracking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/mrdbourke/airbnb-object-detection/blob/wip/05-detectron2-experiment-tracking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHnVupBBn9eR"
   },
   "source": [
    "# Detectron2 Experiment Tracking Notebook\n",
    "\n",
    "This notebook builds upon the Detectron2 small model building notebook.\n",
    "\n",
    "The goal with this one is to create a way to track my Detectron2 model building experiments using [Weights & Biases](https://www.wandb.com).\n",
    "\n",
    "Previous notebook: https://drive.google.com/open?id=1bw5xIfyKGh-tT43PPoyePPME3TS3CYMY\n",
    "\n",
    "I'm going to work on merging the previous notebook with:\n",
    "- [Detectron2's plain_train_net.py](https://github.com/facebookresearch/detectron2/blob/master/tools/plain_train_net.py)\n",
    "- [Weights & Biases example PyTorch notebook](https://colab.research.google.com/drive/11wUb0v1_1CxcTYD6-LxRY51ozZyrdbk8#scrollTo=bZpt5W2NNl6S)\n",
    "\n",
    "Original Detectron2 tutorial: https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5\n",
    "\n",
    "<img src=\"https://dl.fbaipublicfiles.com/detectron2/Detectron2-Logo-Horz.png\" width=\"500\">\n",
    "\n",
    "+\n",
    "\n",
    "<img src=\"https://i.imgur.com/1oTyk79.png\" width=\"500\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vM54r6jlKTII"
   },
   "source": [
    "# Install detectron2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9_FzH13EjseR"
   },
   "outputs": [],
   "source": [
    "# # install dependencies: (use cu100 because colab is on CUDA 10.0)\n",
    "# !pip install -U torch==1.4+cu100 torchvision==0.5+cu100 -f https://download.pytorch.org/whl/torch_stable.html \n",
    "# !pip install cython pyyaml==5.1\n",
    "# !pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'\n",
    "\n",
    "# # install detectron2:\n",
    "# !pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu100/index.html\n",
    "\n",
    "# # WandB - Install the W&B library\n",
    "# !pip install wandb -q\n",
    "\n",
    "# print(\"#### Versions: ####\")\n",
    "# import torch, torchvision\n",
    "# torch.__version__\n",
    "# !gcc --version\n",
    "# # opencv is pre-installed on colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 120
    },
    "colab_type": "code",
    "id": "ZyAvNCJMmvFF",
    "outputId": "6fb32b0d-4bd2-4be4-a988-9313ad4bed76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4.0+cu100\n",
      "gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0\n",
      "Copyright (C) 2017 Free Software Foundation, Inc.\n",
      "This is free software; see the source for copying conditions.  There is NO\n",
      "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# You may need to restart your runtime prior to this, to let your installation take effect\n",
    "# Some basic setup:\n",
    "# Setup detectron2 logger\n",
    "import detectron2\n",
    "from detectron2.utils.logger import setup_logger\n",
    "setup_logger()\n",
    "\n",
    "# import some common libraries\n",
    "import numpy as np\n",
    "import cv2\n",
    "import random\n",
    "import os\n",
    "import pandas as pd\n",
    "from google.colab.patches import cv2_imshow\n",
    "\n",
    "# import some common detectron2 utilities\n",
    "from detectron2 import model_zoo\n",
    "from detectron2.engine import DefaultPredictor\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.utils.visualizer import Visualizer\n",
    "from detectron2.data import MetadataCatalog\n",
    "\n",
    "# wandb - Import weight's & biases\n",
    "import wandb\n",
    "\n",
    "import torch, torchvision\n",
    "print(torch.__version__)\n",
    "!gcc --version\n",
    "# opencv is pre-installed on colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "js46nlyluFAk"
   },
   "outputs": [],
   "source": [
    "# # wandb - Login to wandb account so all metrics can be logged\n",
    "# !wandb login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q5oAbVaqUc2a"
   },
   "source": [
    "# Setup Custom Dataset\n",
    "\n",
    "TODO - add annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "9Gve0ORiUqxy",
    "outputId": "66a0369e-9541-4111-afba-00bf4150ab18"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0NNQOwQ4VTyP"
   },
   "outputs": [],
   "source": [
    "# Setup training and validation data paths\n",
    "train_path = \"/content/drive/My Drive/Airbnb/coffeemaker-train\"\n",
    "valid_path = \"/content/drive/My Drive/Airbnb/coffeemaker-validation\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8_em6-NTWsco"
   },
   "source": [
    "**Note:** You only need to run the cell below once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CkY1u60WUjCb"
   },
   "outputs": [],
   "source": [
    "# # Download required meta-files for V5 - https://storage.googleapis.com/openimages/web/download.html\n",
    "\n",
    "# # Training bounding boxes (1.11G)\n",
    "# !wget https://storage.googleapis.com/openimages/2018_04/train/train-annotations-bbox.csv\n",
    "\n",
    "# # Validating bounding boxes (23.94M)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv\n",
    "    \n",
    "# # Testing bounding boxes (73.89M)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/test-annotations-bbox.csv\n",
    "\n",
    "# # Class Names (11.73K)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8KghB8rfU1lX"
   },
   "outputs": [],
   "source": [
    "# Classes of amenities Airbnb mostly cares about\n",
    "subset = [\"Toilet\",\n",
    "          \"Swimming_pool\",\n",
    "          \"Bed\",\n",
    "          \"Billiard_table\",\n",
    "          \"Sink\",\n",
    "          \"Fountain\",\n",
    "          \"Oven\",\n",
    "          \"Ceiling_fan\",\n",
    "          \"Television\",\n",
    "          \"Microwave_oven\",\n",
    "          \"Gas_stove\",\n",
    "          \"Refrigerator\",\n",
    "          \"Kitchen_&_dining_room_table\",\n",
    "          \"Washing_machine\",\n",
    "          \"Bathtub\",\n",
    "          \"Stairs\",\n",
    "          \"Fireplace\",\n",
    "          \"Pillow\",\n",
    "          \"Mirror\",\n",
    "          \"Shower\",\n",
    "          \"Couch\",\n",
    "          \"Countertop\",\n",
    "          \"Coffeemaker\",\n",
    "          \"Dishwasher\",\n",
    "          \"Sofa_bed\",\n",
    "          \"Tree_house\",\n",
    "          \"Towel\",\n",
    "          \"Porch\",\n",
    "          \"Wine_rack\",\n",
    "          \"Jacuzzi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8NWJt6PaU8wj"
   },
   "outputs": [],
   "source": [
    "def get_image_ids(image_folder=None):\n",
    "    \"\"\"\n",
    "    Explores a folder of images and gets their ID from their file name.\n",
    "    Returns a list of all image ID's in image_folder.\n",
    "    E.g. image_folder/608fda8c976e0ac.jpg -> [\"608fda8c976e0ac\"]\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): path to folder of images, e.g. \"../validation/\"\n",
    "    \"\"\"\n",
    "    return [os.path.splitext(img_name)[0] for img_name in os.listdir(image_folder)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pcOEmTBMVCIT"
   },
   "outputs": [],
   "source": [
    "# Make a function which formats a specific annotations csv based on what we're dealing with\n",
    "def format_annotations(image_folder, annotation_file, target_classes=None):\n",
    "    \"\"\"\n",
    "    TODO - NOTE: This function could (definitely can) be faster.\n",
    "    TODO - Some ideas: skip the use of pandas entirely and use CSV's\n",
    "    \n",
    "    Formats annotation_file based on images contained in image_folder.\n",
    "    Will get all unique image IDs and make sure annotation_file\n",
    "    only contains those (the target images).\n",
    "    Adds meta-data to annotation_file such as class names and categories.\n",
    "    If target_classes isn't None, the returned annotations will be filtered by this list.\n",
    "    Note: image_folder and annotation_file should both be validation if working on\n",
    "    validation set or both be training if working on training set.\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): path to folder of target images.\n",
    "    annotation_file (str): path to annotation file of target images.\n",
    "    target_classes (list), optional: a list of target classes you'd like to filter labels.\n",
    "    \"\"\"\n",
    "    # Get all image ids from target directory\n",
    "    image_ids = get_image_ids(image_folder)\n",
    "    \n",
    "    # Setup annotation file and classnames\n",
    "    # TODO - improve this, is pandas required? \n",
    "    annot_file = pd.read_csv(annotation_file)\n",
    "    classes = pd.read_csv(\"class-descriptions-boxable.csv\",\n",
    "                          names=[\"LabelName\", \"ClassName\"])\n",
    "    \n",
    "    # Create classname column on annotations which converts label codes to string labels\n",
    "    annot_file[\"ClassName\"] = annot_file[\"LabelName\"].map(classes.set_index(\"LabelName\")[\"ClassName\"])\n",
    "    \n",
    "    # TODO - fix this, Make sure we only get the images we're concerned about\n",
    "    if target_classes:\n",
    "        annot_file = annot_file[annot_file[\"ImageID\"].isin(image_ids) & annot_file[\"ClassName\"].isin(target_classes)]\n",
    "    else:\n",
    "        annot_file = annot_file[annot_file[\"ImageID\"].isin(image_ids)]\n",
    "   \n",
    "    # Add ClassID column, e.g. \"Bathtub, Toilet\" -> 1, 2\n",
    "    annot_file[\"ClassID\"] = pd.Categorical(annot_file[\"ClassName\"]).codes\n",
    "    \n",
    "    return annot_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "colab_type": "code",
    "id": "RjlsKsnvDZkU",
    "outputId": "d7e40c92-647c-43f1-a386-0390e06633e8"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001eeaf4aed83f9</td>\n",
       "      <td>xclick</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02xwb</td>\n",
       "      <td>1</td>\n",
       "      <td>0.141384</td>\n",
       "      <td>0.179676</td>\n",
       "      <td>0.676275</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.213549</td>\n",
       "      <td>0.253314</td>\n",
       "      <td>0.299335</td>\n",
       "      <td>0.354767</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000595fe6fee6369</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/02xwb</td>\n",
       "      <td>1</td>\n",
       "      <td>0.232695</td>\n",
       "      <td>0.288660</td>\n",
       "      <td>0.490022</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.178125</td>\n",
       "      <td>0.710938</td>\n",
       "      <td>0.412500</td>\n",
       "      <td>0.579167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Houseplant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303976</th>\n",
       "      <td>ffff21932da3ed01</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.345833</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Person</td>\n",
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       "    <tr>\n",
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       "      <td>0.085938</td>\n",
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       "    <tr>\n",
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       "      <td>ffff21932da3ed01</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0c9ph5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.540625</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.493750</td>\n",
       "      <td>0.577083</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Flower</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                 ImageID  Source  LabelName  ...  IsDepiction  IsInside   ClassName\n",
       "0       0001eeaf4aed83f9  xclick   /m/0cmf2  ...            0         0    Airplane\n",
       "1       000595fe6fee6369  xclick   /m/02wbm  ...            0         0        Food\n",
       "2       000595fe6fee6369  xclick   /m/02xwb  ...            0         0       Fruit\n",
       "3       000595fe6fee6369  xclick   /m/02xwb  ...            0         0       Fruit\n",
       "4       000595fe6fee6369  xclick   /m/02xwb  ...            0         0       Fruit\n",
       "...                  ...     ...        ...  ...          ...       ...         ...\n",
       "303975  ffff21932da3ed01  xclick  /m/03fp41  ...            0         0  Houseplant\n",
       "303976  ffff21932da3ed01  xclick  /m/01g317  ...            1         0      Person\n",
       "303977  ffff21932da3ed01  xclick  /m/01g317  ...            0         0      Person\n",
       "303978  ffff21932da3ed01  xclick  /m/0c9ph5  ...            0         0      Flower\n",
       "303979  ffff21932da3ed01  xclick  /m/0c9ph5  ...            0         0      Flower\n",
       "\n",
       "[303980 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_annots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ANTIXT3DVD52"
   },
   "outputs": [],
   "source": [
    "def rel_to_absolute(bbox, height, width):\n",
    "    \"\"\"\n",
    "    Converts bounding box dimensions from relative to absolute pixel values (Detectron2 style).\n",
    "    See: https://detectron2.readthedocs.io/modules/structures.html#detectron2.structures.BoxMode\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    bbox (array): relative dimensions of bounding box in format (x0, y0, x1, y1 or Xmin, Ymin, Xmax, Ymax)\n",
    "    height (int): height of image\n",
    "    width (int): width of image\n",
    "    \"\"\"\n",
    "    bbox[0] = np.round(np.multiply(bbox[0], width)) # x0\n",
    "    bbox[1] = np.round(np.multiply(bbox[1], height)) # y0\n",
    "    bbox[2] = np.round(np.multiply(bbox[2], width)) # x1\n",
    "    bbox[3] = np.round(np.multiply(bbox[3], height)) # y1\n",
    "    return [i.astype(\"object\") for i in bbox] # convert all to objects for JSON saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kOR6a4LvVI6I"
   },
   "outputs": [],
   "source": [
    "# Import CV2 for getting height & width of image\n",
    "import cv2\n",
    "\n",
    "# Import Detectron2 BoxMode for bounding boxes style\n",
    "from detectron2.structures import BoxMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UjyrDacTVLD-"
   },
   "outputs": [],
   "source": [
    "# Next: functionise the below code\n",
    "def get_image_dicts(image_folder):\n",
    "    \"\"\"\n",
    "    Returns Detectron2 style labels of images in image_folder based on data in annotations.\n",
    "    \n",
    "    TODO -- Maybe create some verbosity here? AKA, what are the outputs?\n",
    "    TODO -- what if annotations = None? Can we create a call to create an annotations CSV in 1 hit?\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): target folder containing images\n",
    "    annotations (DataFrame): DataFrame of image label data\n",
    "    \"\"\"\n",
    "    annot_file = \"validation\" if \"valid\" in image_folder else \"train\"\n",
    "    print(f\"Using {annot_file} for annotations...\")\n",
    "    # Get annotations (automatically picks annotations CSV based on image_folder)\n",
    "    annotations = format_annotations(image_folder, \n",
    "                                     annot_file+\"-annotations-bbox.csv\",  # TODO: change for train/val\n",
    "                                     target_classes=subset) # TODO: this uses a global variable\n",
    "    \n",
    "    # TODO: Make sure to only use \"Coffeemaker\" class - FIX THIS Janky code!\n",
    "    annotations = annotations[annotations[\"ClassName\"] == \"Coffeemaker\"]\n",
    "\n",
    "    print(f\"On dataset: {annot_file}\")\n",
    "    print(\"Classes we're using: {}\".format(annotations[\"ClassName\"].value_counts()))\n",
    "\n",
    "    # Get all unique image ids from target folder\n",
    "    img_ids = get_image_ids(image_folder)\n",
    "    \n",
    "    # Start creating image dictionaries (Detectron2 style labelling)\n",
    "    img_dicts = []\n",
    "    for idx, img in enumerate(img_ids):\n",
    "        record = {}\n",
    "        \n",
    "        # Get image metadata\n",
    "        file_name = image_folder + \"/\" + img + \".jpg\"\n",
    "        height, width = cv2.imread(file_name).shape[:2]\n",
    "        img_data = annotations[annotations[\"ImageID\"] == img].reset_index() # reset index important for images\n",
    "                                                                            # with multiple objects\n",
    "        # Update record dictionary\n",
    "        record[\"file_name\"] = file_name\n",
    "        record[\"image_id\"] = idx\n",
    "        record[\"height\"] = height\n",
    "        record[\"width\"] = width\n",
    "        \n",
    "        # Create list of image annotations (labels)\n",
    "        img_annotations = []\n",
    "        for i in range(len(img_data)): # this is where we loop through examples with multiple objects in an image\n",
    "            category_id = img_data.loc[i][\"ClassID\"].astype(\"object\") # JSON (for evalution) can't take int8 (NumPy type) must be native Python type\n",
    "            # Get bounding box coordinates in Detectron2 style (x0, y0, x1, y1)\n",
    "            bbox = np.float32(img_data.loc[i][[\"XMin\", \"YMin\", \"XMax\", \"YMax\"]].values) # needs to be float/int\n",
    "            # Convert bbox from relative to absolute pixel dimensions\n",
    "            bbox = rel_to_absolute(bbox=bbox, height=height, width=width)\n",
    "            # Setup annot (1 annot = 1 label, there might be more) dictionary\n",
    "            annot = {\n",
    "                \"bbox\": bbox, \n",
    "                \"bbox_mode\": BoxMode.XYXY_ABS, # See: https://detectron2.readthedocs.io/modules/structures.html#detectron2.structures.BoxMode.XYXY_ABS\n",
    "                \"category_id\": category_id\n",
    "            }\n",
    "            img_annotations.append(annot)\n",
    "            \n",
    "        # Update record dictionary with annotations\n",
    "        record[\"annotations\"] = img_annotations\n",
    "        \n",
    "        # Add record dictionary with image annotations to img_dicts list\n",
    "        img_dicts.append(record)\n",
    "    return img_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "RfK1nCeYFHic",
    "outputId": "24e827d8-c808-44cd-b9f7-ea3128463826"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/content/drive/My Drive/Airbnb/coffeemaker-train',\n",
       " '/content/drive/My Drive/Airbnb/coffeemaker-validation')"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_path, valid_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 188
    },
    "colab_type": "code",
    "id": "TyKf-5ZcVPOF",
    "outputId": "9818cee2-e151-4c79-c838-e2ea911d763c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using train for annotations...\n",
      "On dataset: train\n",
      "Classes we're using: Coffeemaker    323\n",
      "Name: ClassName, dtype: int64\n",
      "Using validation for annotations...\n",
      "On dataset: validation\n",
      "Classes we're using: Coffeemaker    21\n",
      "Name: ClassName, dtype: int64\n",
      "CPU times: user 20.3 s, sys: 3.09 s, total: 23.4 s\n",
      "Wall time: 4min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_img_dicts = get_image_dicts(train_path)\n",
    "val_img_dicts = get_image_dicts(valid_path)\n",
    "len(val_img_dicts), len(train_img_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OeBNVm-2oK6y"
   },
   "outputs": [],
   "source": [
    "# # Dump label files to JSON\n",
    "# import json\n",
    "\n",
    "# with open(\"train-coffeemaker-labels.json\", \"w\") as fp:\n",
    "#   json.dump(train_img_dicts, fp)\n",
    "\n",
    "# with open(\"val-coffeemaker-labels.json\", \"w\") as fp:\n",
    "#   json.dump(val_img_dicts, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "EkxeIw2RpvKM",
    "outputId": "1a8c5de1-7ba9-4926-cd25-da2e4ce8ddd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'annotations': [{'bbox': [185.0, 9.0, 800.0, 1024.0],\n",
       "    'bbox_mode': 0,\n",
       "    'category_id': 0}],\n",
       "  'file_name': '/content/drive/My Drive/Airbnb/coffeemaker-validation/aa9b35d1bee25627.jpg',\n",
       "  'height': 1024,\n",
       "  'image_id': 0,\n",
       "  'width': 1024},\n",
       " {'annotations': [{'bbox': [151.0, 32.0, 563.0, 743.0],\n",
       "    'bbox_mode': 0,\n",
       "    'category_id': 0}],\n",
       "  'file_name': '/content/drive/My Drive/Airbnb/coffeemaker-train/0e7724ecedc24ffa.jpg',\n",
       "  'height': 768,\n",
       "  'image_id': 0,\n",
       "  'width': 1024})"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load annotations from JSON\n",
    "import json\n",
    "with open(\"train-coffeemaker-labels.json\", \"r\") as fp:\n",
    "  train_img_dicts = json.load(fp)\n",
    "\n",
    "with open(\"val-coffeemaker-labels.json\", \"r\") as fp:\n",
    "  val_img_dicts = json.load(fp)\n",
    "\n",
    "val_img_dicts[0], train_img_dicts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N4olWM78WDkc"
   },
   "outputs": [],
   "source": [
    "# # TODO - turn this into function\n",
    "# val_annot = format_annotations(valid_path, annotation_file=\"/content/validation-annotations-bbox.csv\", target_classes=subset)\n",
    "# val_things = val_annot.set_index(\"ClassID\").to_dict()[\"ClassName\"]\n",
    "# val_thing_list = list(val_things.values())\n",
    "# val_thing_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sA1ZN5VklXBT"
   },
   "outputs": [],
   "source": [
    "# train_annot = format_annotations(train_path, annotation_file=\"/content/train-annotations-bbox.csv\", target_classes=subset)\n",
    "# train_things = train_annot.set_index(\"ClassID\").to_dict()[\"ClassName\"]\n",
    "# train_thing_list = list(train_things.values())\n",
    "# train_thing_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B9IsnhxzqUkw"
   },
   "outputs": [],
   "source": [
    "# # TODO - Look at this yucky code... time to fix things up!\n",
    "# unique_cats = []\n",
    "# for dicty in train_img_dicts:\n",
    "#   unique_cats.append(dicty[\"annotations\"][0][\"category_id\"])\n",
    "# print(f\"Unique categories in train_img_dicts: {set(unique_cats)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b2bjrfb2LDeo"
   },
   "source": [
    "# Train on a custom dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tjbUIhSxUdm_"
   },
   "source": [
    "TODO - we convert Open Images dataset to Detectron2 dataset\n",
    "\n",
    "## Prepare the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tVJoOm6LVJwW"
   },
   "source": [
    "Register our dataset to detectron2, following the [detectron2 custom dataset tutorial](https://detectron2.readthedocs.io/tutorials/datasets.html).\n",
    "\n",
    "Here, the dataset is in its custom format, therefore we write a function to parse it and prepare it into detectron2's standard format. See the tutorial for more details.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0e4vdDIOXyxF"
   },
   "source": [
    "## Inference & evaluation using the trained model\n",
    "Now, let's run inference with the trained model on the balloon validation dataset. First, let's create a predictor using the model we just trained:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qWq1XHfDWiXO"
   },
   "source": [
    "Then, we randomly select several samples to visualize the prediction results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ya5nEuMELeq8"
   },
   "outputs": [],
   "source": [
    "cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\n",
    "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7   # set the testing threshold for this model\n",
    "cfg.DATASETS.TEST = (\"coffeemaker-validation\", )\n",
    "predictor = DefaultPredictor(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "6QlbRhsiu6VZ",
    "outputId": "130f9ae1-91d6-43e6-d583-1a06d8a21bd1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instances': Instances(num_instances=1, image_height=1024, image_width=1024, fields=[pred_boxes: Boxes(tensor([[ 147.5955,    1.1270, 1001.1558, 1024.0000]], device='cuda:0')), scores: tensor([0.7839], device='cuda:0'), pred_classes: tensor([0], device='cuda:0')])}"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor(cv2.imread(\"/content/drive/My Drive/Airbnb/coffeemaker-validation/aa9b35d1bee25627.jpg\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PsbApLqsvvJ2"
   },
   "source": [
    "## Draw predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 566
    },
    "colab_type": "code",
    "id": "U5LhISJqWXgM",
    "outputId": "08724958-8800-48cf-a230-372754d0a682"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'instances': Instances(num_instances=1, image_height=1024, image_width=679, fields=[pred_boxes: Boxes(tensor([[  0.0000,   0.0000, 518.8336, 867.3401]], device='cuda:0')), scores: tensor([0.7698], device='cuda:0'), pred_classes: tensor([0], device='cuda:0')])}\n"
     ]
    },
    {
     "data": {
      "image/png": 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/fnZWVL47JpDkU51vjmdiHhMeJ88he99ueMYeHC8vEdRGReC0scDcz501z3hb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Ebv8+F3v/Llv/5/+85P/Pif+5mf+ZfOz7b/8O///G998w8iDj/xk//6pz/3A4//T4+/8vR3\n3nl4572Pn1ucaOhji17ErsGgS57u9xEExvJ/KLV5ys87mLAkpTQAUEF2WUJJuRIsHNutUWMZkM4c\nxBqRo1Ij07Br2ytNinV2ynlqCQ0OEw1pJ8aQApVYOqtndqMrARrKABOOZcG4Z1umWW3oQuVwy0JD\nLcgKhTgviAiXaNNalpgslag+aP5hVkrQPZmwIAIx9GBeSuPUMg+HI+lBpzlIyCbMWK1IkQnYMtyp\nFjmsSCWTC4mXh5dLH8qq9nK/U3A9SRndALHUaSroojK57Ogpyrm6eviJu/fubdaFUNCrVXAomCnS\ne4g8xnI87ufj8vDqte3Kz+69nvNc6p312u+/8db3f/4HU2jKmksDsxfRSkQJYwaD14cbLcdA33hd\nV61KKWVbNpt333rjm9/5w7PX3j27usfo27OyV+Nq25devKxWm53ZTK7c7l1uLzbrdVlPEy/Oyltv\nvfnWa3dff+vBn/oTP/LFT39hXW2zLmv3IkxQQe5SGbm2lezo7PP1CyAmWSoWhqqsWovIVOU0GtEt\nB6yR1YYIZMScQDkCw4MzAJFFGRxHwEyIhiWzsSgXeRtsPNAMpciaWU812uCmSQllIWpHTyLhxJTo\n4ECwFikHBDHVxTAT6GTXgvSSNEVRgaUxG+sm0B0loyMSOSVdSmJJL0I3SnbgKMZ5WpaMiqAFI4rQ\nkdkH7NkTJ4K1QhR7ctkfvv297/6jf/xz/96/9zd++qf/zE/+hZ/64lufOby8/c//zn+82Wz/J/+H\n/839fG27xsYtXaeiMnDSYRBjnZcpHzsk/FGM/nTETFlKIQF+UvQyyTH4TxA+dgMSNdb0Nv6MITMg\nIdqwtZ2ch+N8dtoNpmPs/mSKIudoU6gspkxvNNdwztrIEyYgximVP6Y5kEkGDxZkdhSoC3ni/A2p\neHdURfSlcGIiFIeSBtAGOS1TWCgzsqQcRWnUCmbAMeTuJWPNrsyRz0vZUDVXbu7ffX21WsstLNdW\nzOqKpQ5DW0DwAmxsJcXV9ur+u5+uuThttd5ebc8PSEhtPvZ2ekdiaWY89kZaspxdvnH/rc8/eOMT\nFysn4myzsWyZZbM6M+Nu6UaiHZd+fO/j92ZcXF1cPXjjbdgxUeZ2/PDjR/ev1hfrdSmr9WrDItj2\n81/8vs1Z1bo0TgUNwaWxbs/P7tzZbi4jI5bDdb989xNvP3r/Dzfl/Orq8uGbb22dYdPmfFNKMeH+\ntL13dvbJt996+43X3vjE259558Fbr11enW2MWpaDyZ15scV6qsf54MT2bMu69kJDvPXw/pJHW+zA\nNBZLHSMdfrXeViFb0rwBPSNTC2yPRtC9VK8SHAFmIHrGgELXCAz8I+Qj1w2a5KfeWBSsMlc67bds\n7CBN4xXRO6pyGHgK5bOrQItjAo/saVgAisPZFrDZhoMElKAscrclLYLWDZCnTVB0SgnZWBX7YmFu\n6waznGCrDJaYsuRJKxDNPGEmeGcaPUFqP6DqkhAjYLMoU7YgGfzuhx/+01//tf/X3/gbf+qHv/QT\nf/Gn76wuf/PLv//Lv/qP775+8S/+yz/z1sXq3TfvjNDboNGRBH2YbceFfoTaAYgKIsGAkgP6Pobn\nGYgkHYWyhJvc8gT54uDxDJk95AIxFtLQqUkQROqE1Ryb8QEkcgydMkqHiDDlkJ8OJ9NAeZgSSAcI\nl1aCA4wRKDxVekVxGDY45pOEEcbxvmsWlm1pKYZ7WKmcCGWijAYF4UEH6yvScICylPrauF67nDG0\nIdYT2QVQk3RAPnr6uEVDWk1bFFbq5uKsDZycSGUqDjmzlJfH59ePHs1iUMhz876NjBH2VYpYRTb2\n5Xb2aTW33WF/s7CUenbnzptvfeL7Hrz57sXZ3c3ZeVDLvIi5MZeWstperLmarup8+9HH31uO81v3\n7/K4mPD85bP7Dz6d/fawLLG/qVYybr/67Q+uj8Khk5mBwJzsu/3h+dMXyzKs1PDcHaL+8Pd99u7D\nh/fvXLLND+5dvXa5fu3Ondfunr/+5gOtV/Ry5+rq/r2z1y/Xq7PNnbM7281mW7ndrM9W2K7X91fb\nu5t67875ZrLNxu+cl81qenjn6jNf/NEn3/nesp1KT1vZZBw1rsmHWX2ks6R+QE7zvOrhtEnmmSnI\n4cbiKAZ2VaI1Ww+x0KBUjVwomch0DMhkzDYCrZ0j6qkeQxUlW3GmhTvESMrg48HryQLzDILpx2nU\nQcgahFUL7/R2uqEWsYCYAoVHAT6mCAFEkuxmBofYbEjvejOzXoi5swybXYFJDGpWHIHIoLEKQnSE\ncjw1UVIjy6hsLfPxow9+6zd/+Rd//R987nOf/8m/9Fdu9/qP/sP/cJ6PX/rnv3R1/wc/ffdYfBpo\niZNnCCPsQiDLSWjsY25XTp25IR1nntJxMJwkiM0yhyzFKFkfZ4fTHCrJHCd+O+nxBinF/0iDN8hc\nQXScbFQOiR1UqiR8EEPSsigEy1PdnxofRcboAI+f72nMhwHk98IuOYeGWlnyFNOOCJ/7TjgyV9Ub\n4QssLSPrcMOGLM0qSkd0qDIZ0eCBtOkcOHRVG30fyoUOJgBF60kE0mFC4Ki9bik6oDq4XtAKXld+\n2OP57vrcvfXjizZ3ZDGuErMB9JJqZWDRZ19Ar3HcL7ar1eTbqXpMm43z/OLO3d3L4zy/ePpojjRD\npsRasPjK7919eLXehJcHD69eHg53Lu89a/s2m6A9bNP3mZ6HZ8lSENVKMe+DezPPDiy+V2tdfXvv\n4ZPv/fIffHX9uR/64+d3Hrz2xru7ec7IzKWKKLZZX63dFLK6ZiwUxKUdFxhWmFe28rVt7pydn7+x\n3pw3q603r6tvffs7LJv3vv2HT54/2th5+OPDsRsZVtdV1dynSqkTl2Xzvf3LFze7A/xsfYZUgt1g\nIpRFEprok0R54yJWQ/SswayZMi3wikQuxpIsRX1KJadgFyijS2FZZMGSAGglRfVl2PdMmdlZjFmU\nRF0IUq4mTjXbnpwgKjtchINTlvDsLBJXJxSuJYF0Yyb6JJvNhpPPmI1L0USEXmm4QXPyqGOB91xG\no4VkGa+zHCWWwaFQh5Mxh3/jvQ9+4R/94jRdvvn6/c99+osfvPfNL3/lt378Sz/+k3/uv/1f/Se/\n9rnX49XnHQJMAYp0AIEgEioJESeuxKTR1u043dszUAqF0VhBCZ6uDCVLUAOjY0iXDX92Sk6+eh8I\nQA5Nl4uSs1hGs1KBRlYJ0oAKKQDvdgIEhIk0Ngk2GodMsJtcZuPjnr0QjTZJHXayx1PQYHIBAIOc\nWzIyV+Hw7nOYHCL6KhAlDTNkvZh6B1aBJcdTmSn1bZ1u5mVchzQAg9kNQw4nCMnUyEUoTbQRfXKG\nUEq93K6f3bzs8CnVMsNE0OCJFOHIoNGHFSs7i3ovCiFvdk+a496DumDaZPFiZSpX51dnxQ+feOfZ\n+9+9uenNS2v7Zb9cPXjLY//hId+4vLs6O9fN/t6d/N6j7/pU22Gucbh889Mvn363HwFXJ0s2eM3w\n2ptUOpbcL77eVOXh5W31ur3/9kZ+Pd9cnG3OVm5A4MxMbFiVKjtMVsww2WqznTalrldeVptQbLaX\nNzf7UleL8N6zl/3Y93P/vW/8wbNnuzXa02cfnde6YJpV727PnvTbgp5y1tV6mg4tXjx58bvPr2/6\ncu+dd19+/OzYBObCLKPxRTbGNOx6ls1QwieL5YREy4Usp48bEz6y6kVoQzvEYooYBXSxs01ppIpS\n0tHATLKaemdPo6cBmuEGUFpoVb3JN4xOS0xQUEpmKuRVmgrnyCrPqWMexkUoWWKgyjKd7MLEkiON\nTwwGVRkAjKFFRVbaQKENmwVpDoj91I0hM9MNGfraV77y6U996kd/7Md/8Me+9Oi//vCf/PzP/sgP\n/egXv/QD/9l/9OD773/NnznURqw14QQ7smBMwGAaPB56QmY5IvAjeyVIItWgIhtB9eQo1I3RXRLD\n1abOE7DXyc5MsEiWajaKqsYctZzMQUaljUoAAcCBAOmjZKA6ztdDMZBSx+B8sGQms4ALsgIdo4pI\nwDxJZgKkl1GtAeXIEvOcvu5A7QU+WS4KY9z66hwRqsbYAjMqbEYWx3Lic7SAa6R9RxsBgGsIME62\nlKIxjSJZlkwyRB9tRw6gCVlAoDd6kRcwlTGobZKNVAIbB8zrpCD2FNvNk+sMe/1NlmWV01RWpprA\n5fa1+1942Lks805dN8/3bzzYNh3s6dPbui5W6mZzfbxZ2/ag67o9+8I7n/3O975+8oGSZSDNBLfI\ndLFFmhfL+dbO35ybZh3ZblgffOH+G2+98+k7D+5++P6jyaUiKqxrjq7e0rkupc/tuCzLgptnT6Nd\n9/Bnjz7a7Xb7eX705PFhth527Eubby63V5+5F3/wbdx/o3re2c8ve5jJzs4ups3q97754e7FflX9\n6upii817Hz4RtDbsiSIYinMWZOmg3HqqMM0Zy6icqXTmpJ5uJgg2AaHezHoigDLgznCodVSHkfVA\nO1MLBowuBI1qzTBh7WpCSoXZm2slm9Jmx1n0Bm9gsTyluNCaFeRhwjrMi2VPNreaNjyCCw2UhY5O\n66jjPoFY0Weoig3ZmUQWaTGiF1GiABkZ0knKO5Jyo+YGZqRbuc749V//tTLVd978vjtX97786195\n8vjDO+cPfuD7fuqjb37lfJpEw8mLNZD2OC35wHSWZAdgKG4ZMYyxA/JnLORAVo6GzYnsAWjIbwjx\n1CszE5oDGtxMdgIcC294pA98FkI4OaBOk8ZBGD8tCD3hYhdk8hEMIigyxt+BSGHx9PARkktzUT4k\nm1ISY58vicqhxF7acuzrs5oGRMVYDtRVEu4lsySjAseR4OtrWCs1kNuNHdvoFjNEmTPkzGQ2t7UY\ng1U44JLJ4S/tkrIVY6pcv9yBILLAx3i0I9P+qM4EpssoViqHAhkpSwOPwLTbPcEjxP03cGZGK1YA\n1uzLaj2Vq8323P3s6p3FfVWxvX/55mL24ulHV9urzQr9xYunxvnw/OOnlyirphi26brythCMkgiL\nyEL27KKv5rbfv3x59/L+1vzq8vU/9qM/+MZbr7f55u7lnfefvPzu975z3B2O+2McX0Q73N7eVNKh\n2+tHd87P55ivnz5GrbE0GV/sOtFpuS4XZW07O9vdPH9Rzjfr6WbWvi/Wp/B+ZtM8929+8LQy719s\ntneurq/bs+untHpZNy/7fhVYsE4/hqbMHBbAbfew0zvf1WdaHWY4KxPQzQQ1MhAmI3wak194ItKt\nRi+IRiciYM3MlGvylmlZqpKIEAvc1dxLopsUdpyyHpwlzZkWHTBhBNrTOIWlZRWOoirWYejoSC7S\nRFZgyjnpfQjxtJrVjbbY4iqUCWygqSNGhThNTslAUZ1R4DHsDxg7Mg/JQh98/Pjl8+uH9+e3Pvm5\n3/qN7/zyP/1Hf/bP/gvf/6e/9Gu/dvFjr+2W/aj6YUxDajINRLgQKgKFLEL2JozBvJ2k7HiVwIeG\n0aaAQiZRTiSvcZiQi81Uk3Hq4obDKAbgOYQcGXKyiyryNnrDp92KQAieGaSKTMhkpxgcmO2R6vEY\nQeAT+QJyFcWJ1hWRp+ygyigPCGnmBbDolmglauIINoPJ3XrIZAlMTq059zSQnXOMmYWvN9PmVi2c\nNUFR1EL39DXRmBIq02EdNDMoxkMss5CROVLQoAX56ptj02CPUFNiGTmGHPnJIXHNBrlKRyRwvH3a\ncqnxhm/PO+iy3Xo99TA1s+Jq1TYr99W0NTNnbN5893A8QmdXd+6Xf/ab33nvOgx/6k//9PXT73zj\n6197fj0fjlnpgJMxAqwgpUXNRez3Lz/3pR9mxgcfvrhz9t5nPv/FffHf/u1f+4NvfSPmZb59UbIv\n/Zi3L1CwqEefLePDj18WzW3uy20u06YmMkwLJlu1Jev28t6D89c/k9/86ld35aruQnW9TyFjzvRY\n7m/K+Z3XdxnPHt1kHGupNXFoy8qtGc963HK10XEGjaUIx6IpxkzKiliIDhp9lbo1XnbcWBZxQunU\nhOzwETUrMGUI3gkSHkBx742E4BUuHjsmjI2WesozSjUs1jZZO7EJv6n08DRU4Aitk4vGlpsLurOa\nMnmyWSZyIwazQYlixCrLgl6tQe6Ym4ojkgkFqJSTfVioTxGE0WwzV8AMHbCT41SiPLIf++PHj+f5\nN7/v0z/0m4nf+PKXv/Rjf+7BOw8z7l5ubh7vnVSFOmCyMEhGOHAysPsATmaAOfD4RlMGARkaULOn\nigvdSAQxFLuRPLG8QXMsQjUMv5prJHtO6cEMg6knjeBxDAgyCEsyTwP87GAdMACDYIQJdIxTtNuQ\nb1IpS2EihzDFlGCG0TGi9CiAjXmspbL4cmx5Jx0yoNCE2i2RRd6OKWRUZxgzC9HDfFzEgyiTe2EJ\nBjEi/QPYGgQBM6aKD6EtmCx+Wr9mShiwjdAQfZJJLKJmOaCVsAcmnQqTApqxSCajOsxN6XAxjvvr\nD5b9+XSxvvfGcn553sVV2KZ3bOd1OWuNXmTFijknJ0sp7nfIuLq8+L7H3/ebv/Vbv/Frv4fp7PU3\nP3lz+/VUYbZGtywFLZnjrzKo6Mt2VfYv+r036xc+9/q917df+We/shyO19ePy3H38uXz3XH2OJQU\nfJ3L/rZzQjWW4qVn3a17X5i936oosV1vrK5WG99c3blz/vDtd999+MYbv/jzv/qiCz0z+qx+d312\ncXYH9GXZlRSmUrCtSi/raPNWeNTD0AsstSnsSYRUS3GGW+kaMHvVLMl+cKzMjp736vYmFkmmnInR\n3h5awshpQsqSyRhwexbyOMuSi6tWhBCQh9xMxCwHk91QZHPFJiOtIOFgoRZDkjWZQCG62G283Mta\nA1JhZEA+ISxzZo4vG6FU2UABK1BfPBiWIZ5MFaP2Ko2xl4+TMUnCR7Jb4AjJvff++59h9RXOL+8/\n+eDxo8ffvXP29oNP/bDy2yt6UAGZkmQKpPE0IR9/zGisDrd7Gkc3nEPC4aBwAtoDJ25EMgm6LAlZ\nSjlSc2O2Ny4Eo0SPV2sLcJT4ZZCPdBBNopHDt2sYuM0kChkpB7rJYzzjGXayekgQUt1U4MM9WVIj\nFTBmOXAhhi9bcViOCQzbpMCCsKChJVRkAa6VhZZ2lIY6vTUWN21YXLqlNuIR5hAUGlSD4SekhyKV\nggXGzlIaQ4ZMUm7sEA0lKef4XaOpA9OISztyIADGyIje5DJMWTpRBkC56yae37z//LlPm/M7m/N7\nV1d3z7YBPH9Z6r3Lq7ffMBbk4otNK0eYu5WLi9fr5uJLXm9uP3726OOyfedP/lh979vfXtq6LQdk\nMnXsNWg1YxHPVtOz54/6/vozn/nvfvNbf/hzv/CL/82f/NL91+699tobb77+8Gtf/e0Pn9yU+ewm\njhm2Wq0uW0nsLLKwsF6cqTpDrGW68BVXZ5OmrXjeQrslfusPP7q91U7T7uZD9M6sgfbi0J49XbpN\nYliXsfeUkV1eZTl5MQ8HZccylcRkVkppEsqqxrIGubK0mtFc62I7b2UufhsNZGFQ3odUGnKC8oUS\ncgJvLScYscw8L77yRIXP3ixMWBU1WDezIWRbMWYDc+xDtaCtR1mL8BwuK3bGGg6zqhhPApSDljvq\nbUkQEySYSjKgI2yLkEXP0nkswQVweqQSJN0VSR/kh5FqkhiEcTC20OmGuHlxfef7zhvyE+98+pvf\n+u0//PbXv/8Ldz/7xR98+Zv/5cUE25EoHTGJr4bufJVLkA0LzgnbP8Bbw1EyVHggmqGc3hU0YIQC\nLGljHCEL0whJcrxPxp/lYgIJFSEQfvrjrKM4+4B08QTY12lojopMng4RhWyuOo4/CXZmSSU5m8bN\nfYwkxqhMBhPL6ZkSxz+12oLsJaqz0JYlqxgdtgqDBZPdJllDUGZdmajMnuGqxYr7MTrT4GbK1BCD\nnKIL4yd52j680hAN9JDZlOx+sgI0Z0FPc2SHHIB0kp+7VMkUU3TvNZ0t0s2TMKtKoAUgOZb99fPl\n+vnjx98rdbWt63o2lY9qffrsi2++84nPf/6Ne+fTtgrzcdeON7fH3vPh2599wM999ouIkFM/8COh\nflj6vJ8Xzoeb/WE53B6XeV3Xi9LpGTocbx7ce3h5dvOdx8/v3rv/2hsPz7abhw/f+vJv/Mrz65vL\nkNFCWJVs0UyglbNStVrt53y2mx8/evrR+8+evnh0vD7Oh0fZS9VCqzlpDVtNxUuV+qR15BLhaIdu\nCtQMhWVPcx6XcB36ItxgDEpGohFe11OdNtv1antZ3LvmFaZYrQitsOYmqJwWzWgdm5JlY7loblGp\nMOvQNGaCl/KjteBmlQtUpRZkicnQFutdqGkZoNV1tCNRMeZZqHIUdI+SJGjg0bCWVoAl6mDayR1x\nmpqTUoUdlEXWC8ScZlNRXwlGb7AVrWUhI1NGs+xOgl1j0Q24ARrINpkYcCBNWZXBXJZlsf7R00ev\nvXbvN3/n9g+//tV33/7jD964t5/rG5fdnpfxWe48HaQxbuzjkRu8nVOxfzzPYRhl3ixSWvVEJ0e2\nyMfCW0OBAzGhAi6ZK7OFA1IlvIJ2jCswwt3jxOmzsRAzSkaFmYfSTpiQbnSyMZmIhIkdgtFH6Zg0\nQ/f0MJqGlg/DC1ekQJQ+8oqQt+we2eYWcKYKCQQDgCNmwglTAJpcx75a93ngDboM7JCzTt72ljkU\nbaJVIJiTEGCIPspJ4/QlkA7MRAGy0VKahI6TG8UQFdakzhNgLE0YVzrYWBM5EM6RZEpYBgahHQhw\nSnbCg5Hz9XLkjunVn1zffvSdr/+zX4XZ+Z2Hb3z2s59/55NvvPb61dvn21rMeVwZivrLly93u1u3\nc694+mj3fLe7c4xDQpFi76Ee8sws1b2n2Qff+/hTn/7E4Q+//dN/+a9eX7//Ez/+k//fX/gFq3U7\nrQ/Hl2U6K22+ud5/+OTx00fXjz/83rOXL9nmbeXqbPPW/Tvn96/W5++uS12fbZOaD0FjiHMu1nRo\noehPXzx6/MGjx89fQim4o6YC9KlawDX4hWIiR988l+Nung+3L57x8eWdO29e3Y/SSypQOhbBJvf1\nBuI0LYJxEqxsahM09ywXZPYUkBZTeGq4ulGLefrMrFkjMYmHcmBsHH159eilZQ7iPVhJmIrUHMAk\nRaYNr0VGxrgUky5z9eAU4mQJaYFNjI14MFtFP2IiImwKHcjuOQseZqePJynARyURNgJ5Y7Nm4wsD\nM6Jl3lw/i7CHn/+BKfOb3/jmn/rT1+vzi5kXm+l5wkUV2Yz003B/gKRQItNgYiOncX8d3toReHw1\nXm8mT8EUGhadHFeGV7cPNk30MTy0UaztUk0bm0MRU6oDztOwEWY4NQJ8tFK6bKDtBDE9TDkOGHJY\nT40JwLimDKbXCfsnmAggO01pxYdLfNxksgCLumxikEmuEx3ZYDXNoGZakSurK/RuE9FSsoCSTK7X\ndrg1whxsZJz+6U95hwImGUhSVQiDKYLuUqg4m1DihBnJsTrqpxzlaGvI8OpJl0o6xo5HDMJJF2FZ\nhJTNbqYWqDVTLMkE0gQtvbWX37t9nlZXbo8+/u63vvqr61o3K4NK57qxTmdXdy/Pt6vt5u7V/Qd3\nH9578+L8ansHV+u6Vos+T8XmRV//xjcfXF198hNvPH/+6Gvfeu/m+c3Tp/rM6+3Lv/rPfvLP/unr\nsw9//E98/y/80leP0V5+/PL9j7/+3re/fXt9Lc6X52dXVxeffP2zufWr1Z2zO5fTerNi6Z41sY/c\nH26PudfuwGm1HPfXu2Omjr31zgzL7BrzrBgw5ZqWK1lYsnhPmYgxPlEakelQPn/+fHdz+/D+gzKt\nklk9C4uUoWSpm5JRV1NPadms0jmlIYXItWNqykSbWj+i2AK3aemwOJJyK5lRtEovDKLYOtEVk5WI\nMImUzEtDEFNIzPHhO/3CgfV0/MsC77QpjwfUBMkc9+0DBWlHc/SVCMaKmVkD89iYjQadYB2aMCJ0\npxkSqFMbngyNnVnsd/s65Xp74eXq5np33B+8TNPm0uzGFGAJocCYvVOugcpnMxv43TJIPjDHAljA\nTno8MdjqaNgOYQHydCIg7YS1ipPfcpwLQBeLJI+RZjRgnCMGpIZkpuco/J5+6oUwoLuyWakJyV4l\nHqLBitwYgaHvtvFUn+hCGL29sbxUoRBmJoXXygRj32LieuD9j6cWg9x6sHS4KeRsdDAtB2JLiTzK\np1INfSEnGTkSxQTQ8Uf0L1UxwfZH4lFZY76CdjHZLb0yO0feuRE2EjtMT0rGDhQpLSn5K4GhQVIa\nGEQSNRPytCC1+DKFpaELOcoV5pJFb+ByXHJPTLBGC6Ky1mffuyk5lXUfXUJTLVNEW5HZpsF+lTLl\n6/Pyx3/wJ19/52z34smT5y9/9u/+3f/5/+pf+drv/5Mn18c//xf/ue++v/ziP/mFJ08fr3x79/7q\n+e5mPhwvLy5fe+3di4tNQFvXYiit9+U4O5l5LR51sBCViTged8vhcHvcz7fHJi7LYdhCaFIkaaZg\nekV2Q6a5qVLImGGjpZ+SoO1mczjuevaPnj1Zby8vt+e9CTialQmKQGvH2Y6FDMMEb2wrq1Xdaumy\nWhtV6jSl5arIvSB78CyP3bFkV0tPBqTBnQK9I9ycRJVF9I17c+4izhraaUKsOnAVQmMRenDoqKvT\noYDkZAcEFgFWp2gCXAdo4CiyDO0VCIw59ti/G06LdhB/NJoHxh2O5fnN4eEbBWyX9y+fPX3vxcsX\n9x483F7dW5b3qlvKOISC1EDhOdKFZfCrKcEpEMuAwBoA0uESVqfGDEv2JJNj9e+uTGMORsJp+HB6\nNQESB65zDK8HrRphYfAA3Rdm9VERGry7sbUAqCDcER0Ww4SLpfNU1xkHBp1GfSgJMhtzFOxXQAFB\nGlirmRkSHvNCrcupp4kOBhWozr5KZfpEgnPQzy33Yk9ryJKkr+Fu6bDOGIY8C2SOXB/Gtg9UViJI\nJop1ytrpTNXGTHNkKUGt0hZiwZSIyYThLQqLscqAgrZCZsqMobFeoUkiO7BKBazkEH9FIe3/Tzte\nCEgKmKSdxdR7cVsUcxC3Ib+BVMjmvmaRcZdFfjsJyTgGKEz77R/8wa+8//69jz7++HhcDrj5j//T\nn/vEm+tf/Pn/59/+W//34+7FfDwurd/ki48/zj17SfXny3qzMd0tqzp3r70d8+A1kJBC89xVmX3f\n+nF/OCzHnNGWTNg8Ny45I8yqYpiOLVkNvcFPv1Zj9jwY6QP+IDOPeVkIuimi7W6eqy+r84sNLKVG\n9aV3eQUHqmlGT1GcDwCWwT/o3Vy5VHPC3LpDw60O9w2RPm2wNGLjk/FoWj1elm5jS5VutpdZ9gJT\nFdKKQMtK7GlrIWmeJNJYlG1k2kEXspPr7J10NdFkuWRJNYKWGGJJRMIczMCrzvnpmD6Gf+O3H4E0\nTKZ22O8vtmeSHtx/58Wz7z1+/ujq6vW7l1fxIaaCOvrq0LiHj6D+8ICL8Mh0lIxk6RxoDSXQCSIb\n0lhDeTLmSIuZQa94NON8H2mp4QqHRqyGRCdPQV8oT+7pNFrkCuyvTs4ynQxCDZzEQAd93ATEhCYH\nxCbVU55hjCgNORQc5qkUuaifdB1lAG1kyYz5KF6S01La3IpZMIzKYQrrGaVME9DVA9ZAMNeRrXBD\nFLcIdTBJYASXWMeTDGsnLA8McORiNi5FkyQm4UY0RMIMZoo0B+gKG5tapSWqWsA75CCZM5Kj3EmE\nzDPDiWRV8tS9MhAdnMCwMCVhhljoFTKEYDUZDKaZIpwzFw+HgTAXusRIMS11AASHYlZfdvuP3ufh\nTR6ORxdsNS3v/87vPPK154aYtufPfcLtzX4/h7oHzKhsT54/WZ+t765LrVOgWYMY0ZgxR2rJ28jW\n+hTR2PrNcshjmEJLa+i9983k+/0yXCaGAYRGs5wSHbbJfmRl9s5uaabAWN2ALQeTz3aH28ju51fV\n2JCF3tVaVKcFWhonoEEe3hDFZ3RLbxXeO8A4RmKgaQiLXGBLn3fWtqovs1dbbcp0XvIm5w3YjXSc\nTcY+RXrRciw+RW8s6VrJ3GJi7Uv0UixsM20WNwulMpm+hFDLxLL0nZH0VJPB+kJ6DKAdSyoAp/XA\naegz+n8EpDAVmSbVxCza8XikeQS3l3d3h3777KN49wvrTd01XEwMU6aBTIykM/JVls8AISzRzcfu\n0yyQ5gNCBwJ11KKKHEDaqPEkYGYQNXQIY3BoGvcydpeJRSOMI0gORg5ZJcVuEGVJM2Sgiw6ZDW0f\n3DDaV2N/EaQlRrBvJGPH48ZMM+b4oxxM1QLBydOLR87M57G/JxC9QLRjTc4QrSqXoFNMdjIRTJtc\n+5Aa1zVnFUMxW7qAgoxTzlgIBg3QWjZMm4InbKUujGxngawbjmTNCoSYAYSC0jC6VY6vXDZMQ03s\nOp0Gh4E4NezUY6DTJe8YRcMxzwwCSDbaGghopRiBq5CJ2cwcyfApoqWIDBGAdW+WE5nULNTMVCSw\nSsrY+s3LpxtBd7b19curqzeu7lzdaSzLy+MHjz5qz26eEuqLD/YZBOJw2F0/fnQ2OZtWq1XTXJb1\ndbu1tESSOhyWOaIvc+77nCEdc2FmHKNbZJ4gr0aFhiZN3UYzJAe5Mccpbrz1ApLoxsgckS2HluPx\nZby4uLpTUBu1lu0dyIhhZKF1RA3WMkeb0gIsBh0zqmRgkc+GMK2DkXQLx5QuCj377jgbHFx1CGpE\n3bXchPXaK+sWS2dZrezMy7FhPi7h3W0KWFhfDIygbF01d5+JyiNtzVLXIPL4os+VJw+WEnT0Pqbp\nNrQ5wRb0YbMY17VkQmqAYSLUe1NGX46XZxMjnzzfkSrTNsLWa4OFxaj/xijKw8wTQATM6EIKdcwU\n47TzT0gBJ0GhSmNCbcKpTnQ6hIxbiWxUi04tXI7RwCu+AMclpZAatzUzJglWRQeJKihdnuokZYF8\nNUSgJx1sYIGZuk60vyH762O3mIaBDCyna5L7q5SyVstcmk6/PZoSMHaqpcnYpaq06nXmoZEhE2Fa\nEBCMtbxKdKYrSRZ5Yx+TkWZZhAIAOJ6OM3TVmTYhLDUR4GJwwWCZYh1KQQFKg4YxqgMEFmoK9kIq\nmUNhWsLGtcKSIRaOTu1ICUB0mzLCsqQHQqwcGVZaxaRMZ850wDwV9KCEgFmXkKywzjhVJWz5xNtf\naP2WZfPWvYefeufN1z7/ua1Nu+P++bPHN3ZbJpvn/W2bmVjGJThpEJnzcXlxfdieo7X9IjPslDMV\njc4I9shFDc240BBHHNALeoFmQ4EX+CjYJwQkbIJa4jQKNYbGmx05jChCZmQ1NgyFsWiIdrx9nud3\n704x7eFUegFsyk6FjGwGRaGTkC04lF7kBXnLcHJKWGgxOBfGJr0jNPSw3epaXGEBmLbq/eDwhZ5L\nXksyVsZ05IGzVuXsbCUv6FHN1Is0XORIVfDgEfLqyaQrulhqHhq8YXFTRCJpNDISKWaqQANROyZF\nbsowFSnhsA4xw4/tZj8vZ2dnSrt9+Txb8+LAtK5JpbgI6/EtFYzKJEJlJA4TDsCGZsrK4NxSsHGh\nBQAPmqQC2fiivprtAwOHLY1ODgsVA8/DkWoYHZZB+RtGD5DsJnaKKBrIjZO6i2Xk4nEyjYhyKgcG\nDdZNRFgWScZTkjmF8Q9dTkEi0kQqD0ajdqmLZAPdhC5AHYFcORSuwuhugZUFK2LJPv6PzzCz0m1B\nKshgyVQHV8MxSkJsMABJ1BBKdK3GHUgawOJRdhCQCW6IMX8utKAVdKiKFANJI5sLcuege3YDOqyA\nZJR0/f+I+vPn27bsqg8cY8619j7n+73f27w2MyVlqiVTQggUNMaADZgwBtHIYAcOwpSr7AhXRUVV\nRf015aqCKlO2S8aAwVAEuAojIckSEiAJJak+lalU9vnae++3OWfvNecc9cM6N/1Dvnzvvsi893vO\n3mvNZozPQAQdwoR+L0sfGhFqVWlMJGQdl1CVZrkjC96lAYRx0KwqjX0KEQo+bauojepYvvLOu3/2\nh/7db3nr5u2PfczYt/P28mFDYV2uHl1pvLxvB7cPtX+TVGMOCK4X2wPvXoKFdQ3PR2rZAXXsdTqH\nJVMRiqXWPU7TDQplGRkmSc1r3/2ChkFZCM1UDRXVBnOdEEZNryoKZiZBLnJ6b0XBos4vn3/49PFr\n7lYYtR/Obazi0HAiOeZgpGhl0QobWOCK5mUEwpVSrwWIDbYiRrVutBj3tE5Y8dTqkEswvJRGQ6oo\nGzlwat1O24uqdX1Y1aO1TsGsl6dBqBi+syhcGU+qNiWfgJfGJbbFZmacyi4eGVhSMzJQMEkzG7OY\nPh9Kd0hjy32/f/rskXl7ePHuyAET+oF9S4loxsGpzkNNVc2iSqNkNj/0KUu5wE5QdOP00XL+OzFj\nvlOv1HrzLQNQoMEJ1uQTFgCVGS/w72nBme/pPE+sTFZWry5wAUEsCTF9dtSAwQaUMp8pHpCLVl6W\nszMrldOMU+dPExBAqqA9IIdlRWxjun+BVpQJa3VXwColgCsmgCjKRhijtMORtfI4I0oJsGolnMli\nsBKXoI+AeVl4mdyRMznYaWOueFklTyiBMWuqC7ysgpxkCMraXBlOBxQ8IcpCciiRxZyxIpgOAcqo\njOEx+URmAtG7fMwB8gxIvgQP2qoq1HVVozUkUbLDwS9dRQpNJcV2fv/nf/43P/rxT7Z+A3rQUUZ6\ndTdjgFkp7LBm1Ex9vyQxZ9zf3+13D/vDjj3v97u7hzrd326n+0pt+eLFfh5b3I67ihxJZm0FhCDE\nbKGscj5zc6eDIG2nzzzm/QKWcGPBZHO4SicFjr52UtZAWyzj7va9EWdZ64erI4EI57KRgTlzqVWT\nBoPGyiKBsysAq1orwX2wjkrQHgGtYLSlfGmrdx5HCUi414xgYUNEcqeitlNF1YbzuN3i4eF8/3A6\n3++3++m0n1vE0N4Q3UZG9axAKhlMA5Qt52xIRvGCvpFNk+jCPv005CsBLDynNLlqyJ7f38UebVmW\n9dGLU446u+nQH5nTNDtoL2CApksK7OwM581tr4KieDmV54rBDH5R9zElM5nYTC1BMrvmKzqjS+bJ\ndPEXY0qGkcJlzyXJQFdKE9zpYjnQgIIZ1IpBAEq9ksGrHExkzvpDArCbBDRMNp5BaAmX5xw3upKJ\nmHqbKmXktgWtIOdwmVgbtwKSuUiW7EyDGkrqNkIVB46mNLSrYzdmK4kIzPJ9rt+0AGnVobACfLzy\nPNtUOxODNvX8AA02ZwCL6EVCrWSiS/MlGCYUzSpl02roU1kB9lwKFNNh65xjYMY/kKQmDhRZlj1d\nvRv24hB9VwK102bXnUqZO1sDk5RMM92HCmWwfeXrv/HX/vrf+/JXPjg9jByVOJ3H+f6Dl/cPz29P\n++3DKOsl1aUYnOdhmaTzdnt/+/z++fPb24dtP2+nGrmHzmOM3VqBoW071T40zhrByJFn5RkZrMvi\nZu6uatIWmGBrgArUNJHOUHPMdy8lIyNwdXV1vbbM7MLVsT17+jq2u0MydXYBjrKdFa0Mc74kDvOA\nucJNIbsqFBXIpAGNbIVy+MCFXS+vUZGFZkzuDp1NS4GqHd7YBT6yfsXu8pSXEMjKkJS5jz3u91iS\nxVbJrIc9s5Kbqs2t2yUdwy+YLNoscaampZSzOhBMZMJmPK2MBTnqdHu7bRvM1kfPSry/vVfpcDwg\n0W3MLG2Ka1ZxAgcvbRMpiAFJaoVAOrykdvmQCdnMIgMxo1+lcKjUdiu/oHUBqJiCGUJCUtLcVjXB\n9ao9AKfcrZIW6BfIz8UPLLv8jRmZhqRD1cR2wf6r6tWeGyxM57/CUQgCDYYgwdqBDixiKM9j23Bj\nDLDHNyVHJZaF26Kg21XLbdheYa1sVKkN1rmNBddjGwMDF4jA5H5RUE5En4FzBPgq35dVBBeWgCFz\nwiDmmAKMmWtk8Ol+glpHS24A3US5vEyVaBMCElA29ULJyIJoLEBpMBDSbqJ8KYOlWrVAcpXKsQWb\nZGm4QO1JaCsYVbu5mCozlNN2A1Pn7f5zv/mz//35/o/+od/z+ptPc+T9w3kn33se33j/a6fTniFz\n1tBUa1cVgDBVZezbrjoudZv9mfI5s5SlJWrfRq0Y58CifauyiqhhxV2aiI6LhmWWvJygexleIXQQ\nRVixlZddav1GDdXR/fZ0dh4eLZHMj33so9/1LW9/9+/5i++/98G/+Omf/fBUya4cRu+TdRHTmlgN\ndra1VxL7mK+6FiBwuXlNCl1AsWYqFALM0kIz2alpGdqcqxAsEPdVIMs4UAYnfLBKo0lJjtoCDYDJ\nTlxndkPLuDUcBi7C2ctCHJNZe1m/XTbCl08GmBXB9NxPrFve399l1chY1sPt87Ftd3Rr6zpu46bx\n/cAiO7PCIKAVk0xqBm+BnHTbMaf0DIAqJ2tW/sPUpmgXLFWZHGZgTgweLrL+RbUbupikY+odoVcW\nWkKlCHbTKC2m4WzDs03IPi54Dtpk9cppqKIVq5cGjJyeXYSpz2q/pQ0WqhpdyIapIhIWs1KW1BMP\n5/1jspCoNI0Bb9yL3TVCSi5k9uNj+kOcc5vDI2IpSI2I7raXeSHcegYvc8Q5dJ1kFTrKgZxlKRmQ\nS0k6hSwZC3Syqw2rllnz4cIcqRbIpRAGAxYowZUahTSD6iAm2xSHGJmVNFC1lO9mTQPEwOJwqMyt\nZSWjanGJVvPplEjBsQrlhaI5RqKnBoGO8uJL5A34lS9+5h/dfvgDP/h73377Zrx4/rV337u9fXm6\n3WPsUkEBYCaqA2ZGk4qRCUKL+5LthM0SA1njvGeeR5wlIqLK5Bsn2g0+KVkoc1raEAyBi8GrwWJR\nC5ahzzycuGjZ5SIxDL6Bhz2rnWnLd3zsre1891Df9l/+1b9R7fCJT33q2d39i/e+BEPCRsZM7Sjl\nIoRZr93A4jpknfvOtOpmZ6BJTowUi0uPUc6EqtBt2UCwelYYTW0gZwZvE3blwpYgK+pVgsjAXmXh\ndhwazYreFPP+mPyXVBBj7uyMKrTClPPh4tVFORi4MKwwFXVMCmAXeHv3PPYHVjw9Ht6n3Z0f7Mb7\n2m+HbD0cnEq1GMnmksgLIV6vGk+CmOpAFn1m4Dgvv69pgipcE28NlwCm0auKM5xe2M1dOdjslRRV\nhF2G/OS05YuD1qEkC2w1N1lRcNoFYDkzvrusDCiTjYlB1dxLXE5kJ0xMrxJtsJqsSQgCGil6SvTd\n0XNXBWEbQHGVQKUiiSZGVjMpR1/XBWbbOJmi1NxRJHLph9MIEh0Gs0CxYHTHDGchqgIksVFLOeZm\ngpfcHFglbYI9SrSyYSbRia46m/fiZI82wZF7NaJO5MKUrElBltRhJKEkBdkizylakAMqy4S1QpXS\n4DTQC+lo5kmFZM3ojmYNk42Mo/nZIpMt064OvpzjgVyUHz7/4s/9zP59v+uTy9LOGR/e3p5Pd3vu\nJVkAc7M6x6+z+L+YMPRw3rCNUclKB6ti7l1NPenNbJc6VHBUpMnVnAxF0hxDkuAmI5Plu5GCM4AW\nVlYBOtlgJTUH3H01pTxRX3rn5e/6ru/83Oc/8/SJv/v+u5/9Vx/oePPW07VOBmKi3jc4Z0lazTgK\nKFQDUjQWGYluM5OGaPQde3gn0rAYhmk0Mw5Ub7twlWNvaJDk4yIvT6BBidoMi1SzYu/FzcLSgUrI\naIUxjJbYRcE4PWBq4M5qu7PXbIEmCGN6WjFhMIIarZBpbGhjIG9vk/n6a6//xhfH7e0drm1p7fkt\nb08PsGsAML/AaWvmWc5cUk4Z/w64UGaWmJYYfZOEKauaXSVBceqJyssuMqPpsDWJ8EkOAigjy+jG\nSnGa9qee14sytEsiAOvSomeZTfhvFi0v8rcJFCjIurBbFdDlCSQFycwkOVpBbR4UZJ/KkBScqDyP\nsN6ygA7bWVa9EJVwMCw7ZKgtjuuKDuWwzN3gS21nrL4EzxylVplCo52sulSoXp4+fw4RcFhxYD4M\nFz0vwtoCDcHVjNMYYWZWKhKrqngJPwQqyEIAgFxwB2jM+f1IG9VRJmnCgoleKbCAY+k87VKsA1mg\nLA8oitmNacYsLV4+RRMFQGfICJbQzZPj0LENTurN/cM7n/n03eNnbzw6WMWWsbcsFZLwsgSAqolG\nmG0HyJEDKa/LkzExr2xAwozIIeOF4R5k74o5pu5q8iAOLkRtrcWBa7lMa9WIyt3LdtEOEpJw2lLa\nBCe+/Ts/9uJr37h+8uwTbz3+1a9+6Xu+4wd/6Vd+/tD9xSbev/jGHR+/+cwA59Wm6hgb4MCChxPb\nWvOHDZGH9L2ZV8I8gIJ1afVe2lVsFpuxVe1VixurVjHMvLRjTY6D+u7Vq6gKVGFpCKiZ+UD1JE0z\nzj4be0I6su5MSuQyt1x6VfpOXOZldfrK+W4UaoJVIN8UZGuCO6rGb37xN9qz166Ojz75bR99/vzF\n6Y0bFcoOBAmFsoCl12rYz0xmguUGoQE5m+IpGGex6CAUnPRTsE1zHmZXJsphcGTKRE3tronzeCqq\nzKkU4almqAsZCWXVqya0ACANqilagTNNleglN0ZyWaqmHQBKzHALkYbZp6CyaCi0aX8HpoFSyWy1\nhCUVxtoUI4Y3e5RI455mGYbeuG0ZjTaCY2A4ile2+KIthVKZOTDc2tXSb88jUM1QqiYDsIjp5Wos\n2PwDYTL9ZITgRCRgqF3eWdJlNTNBBQmmtck6T2rVPG4JliQHNWVWF6wCJLWUmUSbS1ADBkmShd1o\noDuqbLBfu95+7ebpGzeHfvPey6//+hfeiS0HqyOi2qy/0szlZdGiwnA+26ErrJhuBDT20927sT30\no3rbp4xqmiYuYmqU5DbPdRRgNOOetaDqMrUiB0TM88UWap+hbFoWpq1rs64m2HKYiznJ/fHV8Rnz\nfi8xttPgw66b1LnOqT1pXValnWauDP3mVz74xJO3P3z59ddf+8Tv+PZHv/Dpn914MGuvf+R4lXW3\nZ55eHm7ePMdmZkqYGsBidc2gt9k7WrK8JFTXnDIhDUPe2Bty6CIedwA11NqadWJz7U2npjrb0hMu\n3HkdSFaFUlwmmcVYULfOWW/svrNWHxVmPVPTPzL7MUxzIGemHBxZNcPlfMrgwDl2nOye4Yzghx+8\n/O1f/YXv/9T3fPJTv/PT//pzX8Q7H1vPY2Sh9jFAuCVL8kfr4z6ixv2dXnHzBaZN7rnRbP5ikq5p\neWdOiAdKlNAwdShl4Iy5sEngEUG44L1q0BqmHpmEDEzTWgUyCVcmbUrcxYvQIGEgrSJhRAwzg+pi\n0Zm4D8OFgDDBxJLPhwwk28zmsIQskbGZL9KCHOO8tMd33NaiY2x0Jh5EtzonFvrIfct2LfZD70ud\ngT0Ylg1r5sLD3s8xMJFG6BfrvbosWGmdqs2iFRI0SxOT000Fr7YbUHaxLIl2WWS6Zw1WGQ/izlzL\nk2VyUySqCQmkCKmhCKl35dbEnO5nu+DPy9Cny8q6ub7zo298x3d+7HB4ej/y6x++fDGOVWjX19xO\nA7bMCbpobGIJS804w27QQ7NWimk8ixqIOiOr+iK71/CSC5tjusicjqokp5mkhKxu3OELamaO1WQv\nQLPqsGM3X9erdW3mYnR2gcEEl4vmJmE15IfVlVT35WCJ88vlsMTeTUXsUApeqaRwd/6N81ffuH76\nW1/8jfXR61dPX7/72tfPYDvVW5/63X/wk2//5q999qvPH66PT+9vn4f5wiiGdCDk5cNgmhPTNCCs\nVeVaOFOu3nC2coJeO50Jp0+65L77suRZWAZFtKXKbTrbTdB6OC7pO+i82mvPiFC1ZJkfw3czIvcJ\n79YeXBLGuSCrSpt8d14Qs3M3xCl/D6EVRmdPpVTcXKZT50O0t77rk7/jO38X+fgrn/4f2M/7djrt\nLBEIuXtb7k9nG3F0b0TQkJebvZTkRV1kgmYSPKYwfZb0U7hbVIHGYs7ogtmCg0G6GJPySriUIMGL\nNml686bGVwCyGUOBC5nPQ6RNA3Lzi2M3OQXisxd6NagCJomQsjJlgYaWqmakQWHspTBbYaCNIqvO\neeri8frRerg539++uL1HkYm9R1Y5S9u7H3xor33ko8erdSVGnuHmBJHQ1d7uNCzMUrUqExVosvIy\n4wCty0AF5t0vuwSV2oCoeYgyYMbLsNKQ0+fgAlUNTCqEziyywVNhXIGiJdQK6aJqDnZtYZoUNMqB\nlM1E4Pbtb7/+7Z/6jm3Hr/zSr3393a+NbZz2uD62ON+v7qUKpcNaa5YMwEGnNsA1Nr/W6TbNnRk5\nNJPDAtC2FzubLEMT8NSANIaABR6WJTfOdXfvYjZ0ZBRMJNkanxzWw/Gwctlm/TrzEZPR66rWnTmx\nzWV2r71ZX2iLemPawWmPHk4nO5gyV+t7jgg9KNtAonr6e7cv29Kv64P94W6XMSORv/6rP/+1r37L\nf/qf/ce/8M/+0S/83K+cS1c3b3gv54GsM5orCK5aTh4HebB5CcQ99MiWXUksyVHygjQpfWWCda1n\nsawnsYbCFFORAmtkyxhjq4rM0Y5Pm3uM7EKKaDAbs2RzWNYeXFkxB+wUwx2Sw0DNfpDghf4yBbQ2\nHh9XwO7PBMtpRNWIL7/77l//r/7Oxz/+ryT7Iw+358MmEDKKTkYFhoHoefJldVRmn2oCIBx0MC/u\nL+3TM0S1KZW9RDxfbMq0ohC00oDMZlqXIJaVGUpACm6RaI4qvPq+q8pGhwNeyAZLODFwmQFOo7NE\na7KdRVm7RP0l1QEBnrLGIiSa11z0ZyMbAIgukWxCMHtJyAodm42R21aput3TiuQIelc1diWOzR8a\nts7H1uWka6aoT4L2crw+xZ2LnBYImks5x+lzcjL9yZSBM4lCaGTB2OefwOZms11CvWEF6ZsmRBpF\nswssKRRp1iqcQDExhGYak0S4W/S0yzyAaUSpxPbscPzWb/vo3e3+y7/xq+987Z1gMtWWTusZmbXQ\nz15Y1uOjQ+5nKSkpzS1rT5rV2tfbfWteMxmNkBSV1lwQiTYDCaEIlcEMPjBa9SAaS+rOahPcrN4w\njutxOXasx0MhqZ1zsM1iQQt9v7I1tJux1WKq3bdVrY2BfgXTQg9VHA5r83G/DSs/HA+t5RbrNobv\npxy1b0WPfWwIP9BvRzhcqBovXnzh//J/+3/92b/wZ/78t3/3j//j//Ebt+/s6+vHq1hhhxpFGis9\njBrlxhDLmAtbREYjC50E67r1h23PpVpYGVGjo890mjIY1Yn9gm+w3ZBjJwvWt/MDWu3Oq7CmNnV4\nk+xG1hzIJMwYJhSsB3IyrvlKI3+hbguvIizh/f7hoUSfbE5viP6s6eY6j9tXx+DhYEYarawMenJ1\n9EP78MUmjGZ50MODr9DmWHw6P+dKUaLBShMT47q09QCqNAcMpCnnbr4EwzTEilNXKjBEMI22o5kQ\nl8XfFGIYp9JegmZCHwImVJ9uPxqkqtq8Gqa6UAn4BO+hpvYBdZEjJCvJVrYbmiZIlIRkrCRL5rRT\nxJXIZiNPd6dCUbaY2QI1MzCi2YL12bNHS7mTbiW2jWNlj9pBHHp/IFGVcyoqJmWlzbBMWzJx6c8h\ngzUpTSZFVdCqda/0S0IssiiYK2duKZClslKpp+1MJV05N0Zz2gRpAwM4DPKYdKDMJt44UVJbmd/6\nie+4vb//3Be++u5772QmlKKV2imiWT/t92/cPDudX2wD3/ep77tZNB5evP/B3cuH+w+2EacRkWv3\npXwEGy25RTWnee2Vnb1mPFXMyanZqFqM4FKsAzy5NlJoUekLrtd16Qfvj1rt57k9BhLlBTcuZrJs\nXNHM28q6suXhzMN1OZqzqqmVVKpFi2da93qED7eGc/Lgh8MhlsNVPdjGcWo2thG5DT/0FR6WExpN\nlW0PX/q7f+O/+a7f8bv+5F/4K7/4Uz/6i5/9ErW2mycNOZE1wcaKxi2tWfUFDKkck/02pJU2cjTD\nQ6JZLLmQk9wooO9Trsr5QRRIt2ZEWO+sMTbb3X3dfe8aoA9vFeS2l5jhRAlbXTDzGA7THKRyIih4\niZNMKwtHZd3eP3jJeBHoNepwpb/4F//0G29+FDq8uBvf842fyq/9lk4vH3d/+6OvffwT3z6q/8ov\n/Or9+QPSlqpO2ylTDDMXl8LOckhg0BqYF3OtgXPrjzlqLJWbp+ZVPIEkdTEZwwwCjCVBTSL9Esor\neY4yM6BkJgzTSpPKplFncrwkmjwtBABlcKiVFXPigHRxsaBJg0XgIEtULzTOzYdix9pAL01x6P7w\n3F97htbkccg+MNiseQ8Bxps+wlbU6FxkU0LUVj6gDNgDHVZsXjbXiZjHHc1YeS1PgwEiXCk2AWHm\nkQmWyghgb+U1A+WlKhdlHJDWyZoRJ4LcKgroBXqOWXRdjAkArbSUxSIZuLk8WjiNVQKIAVU9/Prn\nPv/ei4eIpBRQ976YTw/E2pez4nC9nh/2z3/hq9//qU88+7bvevvj3E4vR+0/8VOfvg9loPmifCgV\nuXAeu2pTd7RYn+BlWiLlNBDNkGg274Gsw+Hq0eNnZnPIu4dYZo0pQwO9e2N3d5oaWzAOaQ+2rpZh\n10fsbB00GZW7G5jGUe52JrpdXfu239+f9tNV691EO9642aKXG8/n8xg63W9uS2EYGRMYh1Zx/vxn\nf/Hd9z74i3/2Tx4e/fQv/OIvvSw8vXkqxATbm1q17siFOhPNemXMe8RpQ95Sw3ANUj6sutwJUabo\nTIfvZe65kAkvd8eYKvze/QQ7Vllxm4mxWaa6r1GAEM5IcebP7FRLyHy6YaZwPgWDDJNCQRfSKl/N\nG9flydtPn468Pb75vaPr/nb/3Fef67e/8Zbu3nhy/N2vf+9ydb0Pvffhi6ASGTJIbtXgl9RLIKcx\n6DLWtGD1Utq0lGjqbQJzDipDpTXPotVM1AVpqJxiYFXQO7wwphWYBVHlLee/ZzaYi6/yey9Y0Zwh\n34UwrYU0NsGgMem2RUz+DupCUIcJjKlFE1vBnWiwgZ3qjqpph4j7d0/nt57cVGscoLlqEGyES97N\niV3T+Tc0Oi3gB9l9oS25pXKUL2ZQo3IzpWRIWc2h6fQtBxDIrvJog9VEyDfHMesMLbQUCCSrUS5A\ndppR0OIC7ZSJKqQZBVdtZkjQLVWtuyldVeYbrGfS0+CcqB2loX/2t7/w3ocfVqZXpdKsWevTC1hU\nGXNsB79er/bz6eXnfuvrHx/sh7a2ddC8GRKjwq3Re1Whwmh5MW7AU8ndDjdLjr0uN9FuOoJIbcjX\nrm+evvGsrYfT/T3TTx4G6wDgB7PurbqRPkwswTxlB3Hr6cW9alEFZxpVGbC4D4MVtiVa1RJW3I++\n+lE59nx4sMPx0Bqh6Mt1Ww/9FKfteW15ltHmJzInvjIq8/l7v/m3/rv3/q0/9UP/3usf/Ymf/In3\nP3z+5OkjA1S5Oh6SUy68GKPUHUzzir31kjaXwUspdCLENPpAa0JebHQB2IYSaFVssqiTHx+xXylS\nCtJkppDaKTaN82KHE+YaLzX9tHOJXcH5tb8ivs5ASwddsu5WJcOIiG3jHl88P7Dsr/5f/+9Xj59I\nfP7Bux/52C0fY12P798G71/eP9xvp9tkoVyolB08btXxKuMK0GVmzUt8TcEsKZ8mahHVZr6UrNhM\nmrEwlw8FSizUKBgsHJYl0qiYG8mpCgR9JtMkObWITa65VUASSSwUO3NwbpeQujzaM/anMN0rFL2o\nJtbkthANqgEXWpfAQlmad+3ZlnrxIZ6+cWxQ6YWNlktkdZsDCKoXY12Ut7UGtm3b93NUVe4nYYwR\ne5xRcaYWTZ+MTFVAenVhkCoa3RENrbF2WlWCbFlkdwzADRXwZgkpzEJaSxPAcGa1afMjpjTCzQbU\nnFWzeiiRwtQpTpMEIJPmzTbOxP7+C2WNi6TYDM1gc2dLYClL4cPTw9Xh0Fa8//zrX3/3KykztsVx\nv51qWDbDtsm6t5G7F+BuiKnNBjN9JI6HR+O0ZaBsqX3x5XDVeXVzXK+U4/TyviyaRcNC6to8va+N\njnVro2tx7W42KGAEW6qcOqrf+8Si7mIjefZcA9n6Ata+m3GS4Lkcb8wHx7bvIhtba7vJ/XjA0t7Y\nlpfYY9uROXETms93hGCn8+0//Yd/53t/3x/9E3/uj//cj/2Ldz94jmdPu3MDbGY5okoOzsgDwdo6\n4Q7CTjltlw6wjUmYIdzQpj9LiyraTIolh/ritqCiMmhH6N58qWrwof1bPvLau18fY5f2SJr4zUHY\n9G+7kGZTNzjpuHaxzKJotrQ+RkyadSoto2AP9+8/PHxAWRmrkvLTVi9e3lY+nAYt90omSOMZbK7V\neU4Bmma4lrb7tGHNbl5ls/cGBcIKNp1UumgNOEOjJqlBik4bTMNlKAlImHWA2aX45sT5uCaf80K3\nsmKgDJ4XsRA6yaqaJX7NgDTGFMcJMpUKZWGlCVVXNeNcOApUEIuDzKF+EFQPe5yXw9XDwmVgl6z2\nKGfVA6pJ9/HyfMayPGxhD6dxv0eDKmvtMI1GC8MhrKzmUNHBs/laI+iOli2h9Oxlk14mwIopeKIa\nWAUjyYtGuZcJygt5VC7mq8UyS4PoFzA6ndzh84BKiCgnJWZxuhtUliRim1CVnpaVIKw7nJvKyHms\nFGstO50233B16L7Y+fQQefdyUL5AW89DGdW05rLxnvTMcvNQrGKaTtvt4leAkfb0aB//5Pdt97y7\nvT1Jwrmy6E5aK8hqaauvZt6QFVAnqb2bDU37aWtWCbnh1LAkidq9k/sSxyiEsVWQHGu3rdz6QGQp\nIkZAFRx59egm6RE6pJe1uFpulkOez+fT+Xw+T3svSLgt8oGt9vqVn/sJjT/4p37oh/7h//d/+vqH\n7z25edqWNaXVd8AWtMQlDc1K6Whlxd7qvF+c19W17MyOLPYmL5UzZX36SVmV9JGD3mjOwu61RpG5\n8/qtN64+9rGPrG393G99hShXQTNzElMV4cJFAfCK+wYmgFJC5uigW6MjCeRsxK3lplSKkcXZ8Z63\nfTvfDphHpERugqqYpZvKO0vkhZFfQBr9wsuf89dhnEHSUegEXRV0YvKzBaBY1LSFW5eHVRd22jeN\nvIYmsYS4KPwvajfZ5Z1P0KFUNPawbCqQddkMUioHp2tpQi6mm6MEn42DIEahNbDNYE5ZDXAVopmX\nSAvq0eHqfD7t+96XZcTQnspTlJN5b3m1LEhJY8sbre4ua8WCCgRO3roq3VVVohNzSmdQaHXuiWJy\n2iFbpuCDYfZNs/LYp1sJ7LmHLV0qhsFmuIIkXCTTaAUBTTOubCbuzXniclzbiG1cvFvpgNCmz8xE\nFTv70u3l/QMdAJs5MYP7QKCkY/esUiFKd6cN9OPh0ep20HmQ7394twe1HK+FVLm3UTunQtvsTHW5\nUWM7p7nb8tYn/+Dddv7Kl37tuMCuGnEoc1eA1rrZ6tf+KNveE27r2cNi3fsw2XHUrXGyWDpTsTRI\naIZtkWX5uY8lkbSzeUM1GBZuYmfntuXiWmnL0dSaa8G6LudtH223OROLR48Oy1Jteb5vpwpGuWBK\nYy/mGONXP/2ztbc/88M//GP/4G994b0Pbp68tnYLra7aWS41X4TNQC+GKGqx3gVYzSTqg2HEkigw\n4Y7IYrrNPCsg5MZTREdWVrcFq/HEQ9/eeuMjNG/ro70KtFQKEst0UY8ZLoOjSz0tTQbGtFyVBKhZ\nC4xpvvdkMWvy4nUJukjVUGTszgUVAQMb6IbciaK6aKjh1spaVXg1Wc1xM2Gcip3pkBc0+XHCbMcw\nO/xLwI2Bxki1oC5h0/MIESb981UlKxNSMgByB1OpKUtkzlmUJHFxADBBRZPSp+J0OnxtTjaTSKGF\nqkGEGqfsQXJpQBYCuCtc7f2753rx/pOnH8PNYwkx0UTInF47OTCcQeYyfHTaTrZhkbI22b0L16gz\nDAW4MRVX2YLD5UGCWbBVlZzUOy+ZQ8ZBoRMACzJbZIyawdtGS+pVHw6RNdcC06TPmTxkdZCVSr7c\nPLpZrQy5Z3mcx9jutpnpXpto4ENMNbr74bobZtbChroyb1WZvs9kYveErOr+fPbCnXFls9ZHhI3T\n1o/ZCmVea+L86meZaDRvrDGGDJ/5Vz95XI5X14fDcnCMQS2QNb/qix0PqBzEwZqjFXOVVxstvSFO\nfVlriPuQucG1JRuRJScZMM8aTGUnzo2tpALXqQs+eNcz04Ax8hIwiVzXw7r2u22zXbVWZPP2aL2p\ntd2+fMhzGAJwYDLQtxi/9K9/3FN/+M/9pfH3//bnP/zgjauneNSpYJDOUroZL3EZqNJmWAlagyFT\nLTttSO2M8ah0Mi5RD6yrqJeOQ8UZdTQ79J4a5r62VX5+9vTp1eEmcz+4WOXQLnOkzQWamSvJi16b\nr4IXXznfTAoJZBfK4EUWstkU4HVqmC5CcghepYLs7NZZY+bQAxlVA66WytZrmoDkSk4lKjnRYFPB\n3FUFFC+0DGnO6XwaaCaZZPYhZnmhvahkNEDzL5AElxkNnB2CFTSmLtiwo9lFdK/BQKURl+ZHMnCA\nHQ22s3og51zWpy/HGDM7L6SscR5O0MghBWAOzz0UblCNhLoTVSLOTp8qU8vUsUgoUzxiPdlwMJqX\nbELWlUgnJcNgLAtbok1+EC7+4lmNc0AOzEBfl53pDXGhYhmZgo1ST2aHJUq0JFoVIS8FTGpTFTsw\nzZNpwO3L27v7O5Q1S+vr48PNzVsff2IV57t33n3xcH4uVAZsOSy+mpmARBr9IEEQrKpgc/0yTHOP\nM08eDG3GQ+ND1l6DjQeI4cPjUAybilFcKGtuJuyKtls8OxyK5Vp702GxR+0ai+1Ymsk52kxbNc9M\nWiMsWR3og3d+9DoBSluZQdYut/KmM7Sat9DmtLMLclYdQ2efYtgA+4PqyiG0phzECgTX/dCvdB6b\nReXMcnztyWt9ub2/f6ihHYQHkqgE2i/+yk997f2v//E/+af6T/7E57789Rvl4ea4NfM67bYgsImQ\nVgIsFUezY4E0MxWzRdsrF7ZznQcagEXnUB6ApS3Xy+HA2MoXQ0mm/eOf+Jarq6cbbNtPqS4pjCw5\nPKrmAl8lwRwoWqGM82qdTBsBnaKrQC+EqznsIgo1TiX45asGsiSMxDrpv2OS/IpLAR1H4F5TXj+b\n43YZ9UGTeZywhgHrplfYhFmFCK8gvTNvAxM5pUqxkk64A1LJZLIZbC8CnJrU6qDYDeHIAfdXvPlE\nGhzkdIU4OFG2TaTlhOYKdJqguhDbQcBl7ar7cW1PDj7kpjFGhoBiQFXu3TIKsKBN76FVGTr4UGps\n5SUDk5XubhRa015mpBnUvKXuA6sRpFIAzw0m2G7RVV7IiQ0WgrWKadilnopL3Tbdu0h4QVdzQgMr\nkUyHBzJNBlhENU7Ap0yCd2HD8OqpUEI6jXH//osPN1U3Url0hta+LE1RdJbKQMx8YqWwAtPkPQVp\nA+oEyejmEUU5mvXlNPaqUTJvvdW0tobNMQXgc98kNmubSnF6OJ0eXx87fT3w6nCUrTIeUQoGWrAc\nkKUqJ1aokcjYu1vSfBUjJbq5yriaJk8gRRiLBRtLeq3K+85WJevAA6tf0YABcbfR1cJqt/aILbWs\n4IkbTvDa09vx5nE/9O3F3T4G94amSlSx5/bON37j//MPXvzlP/fn8bP/6Iuf++KHhjefHuRPW4Wp\nHoXOKWHbyj2VwVtHKzdfOnrahiovhS09Tge266uDXfW3jsu6HLC626PIe4KjcPPk6c2jp7cvN9cw\nYCuEAoVFlbyE6gCaiepBEWwzAHT2gpcUnPnC2aX0t5wU2lamGRRDe/X2QsKOdQ2NNphOytEGYoCb\nuHqW6GxRlyhqzoYQAGfeO4kxd82clTYtZ8RcVbMZjMNXuXpTliqgjMr5j6WLnQcC5wzTiFYCNQxe\noCmTNqj2TW8yMuFOhiCTy4blgjQzIieWgIDRIMxY3rJqvbdjLk8eP87ZmuSozDFSiJEq1p6ngxLo\nV5CEnQlshjlVsHCivFFoWOhbwbopqpvtTMLkpIYld4pGqjU5mStm6M7MJGURKy7ig4S5ZdiSElVF\nWanLC5XmqTk5lYBBI+cNIDY0JcQyTPjnmdaqCQXDdLnKWjAdzos18NCpUelmORnHLJQn0YFCA3cv\nn7y0EpsxJZesahiPYellh8O69u32JUY6m4lpRS5VkakpPaIRylDvjop4+PC2LdfHJ/740TW4BGKB\nEgq3VRzmirJCM5dktqfW4qbanSZgqFZUsQfktY2pXCGDgVwN22SOhDcbvXFs1FbLle1nsqkJ5VgM\nyAQLOx9oi7W84gHH/Zz9HOORH1+I/bXD2+fz+y+fYwDssEgtpbq7/cZ/93f//l/6y3/x5/APf+ML\nX/5K1cfeeA22GNC8ruQB6zBXnZMSVDHqXsVRtu/n64p61D/+1tM3X3/j+umzm6urbOtaIbbKGP66\nw6xgziwz7bQVXB8e3m0aw16Rd3G5TBNyTuSWimawUoEo1TeX/DS20jZn2mQarTiMiWaXm1WACdmA\nwWo5Oe5GA7LCEMUrL9gkatms/Hgx0pnIQDmqsKqGG0VdqPkXVggkjClRB4yWM6WPnOp6YqbIFi91\n4iR3Ndflz0Zgem2TLtjMlLdXUZ1ENyQnFPyCESHh5N7A6QvFxQyAWYE0M0ezlQxfChXUoR3WQ4KE\nuVOo46OrY7Vly57n+65mEVXMCOvG4sBudVx72RwAAQAASURBVET2oJfgXANbNwOXbvsZ7pUbsYil\nao6MyvljoC6iJRSI1AzLlcvPoJUMZVLKDIRyNPhUZIg+iSQ5ytSoQS5CipdZKFKXmIQ5DjTNZYGU\nsIMqMHXW83JQFWlhaHEBpbEEt1R5AU1VNBMBLZihQDwWhxWsKSJiGgcQuZPmk/NYl5iAEoBqZqod\nbK0tW5yff/D+k0ffqtabNdFqi0NDg5Wqi2EMy0XU0Am2QlLd+5HZPM9H4M7aKoFVxqYOO0V5j3Vz\nIfs85FuZEDvh2B2HM3cToQG5FGmens4mHZBhbl7cvJu3ta2VG60tSlvXt15/4+XLD86bRjkqrVRV\nd6dv/Pf/77/57/8nf2H9qR/7zOe+8LWvxNsfeTt6qyIXtHy0x/0wCO3a0oTl+PT60eHGzDrW5XA4\nXNGP0Zv5MVi9bIjmRu8NQEqE2ZKVm3Uza6bxcJ/1yj0IFHjJU5gr2jmen55uTRnIzJyPxHJJmLw4\n3mjyQnTMpMYSpvdnjsvCNctbAVHmLDPEULMKXkTfhAC0QbXLbzx7/nkYOOb/IYWqZhpsDhZq6lPm\niG+O8Wbs5zAZ1XKy+DihiSCpkeZeZlBiLg7MOIiQfOJpnZencHC+Fyib2EJ37EALZruUrXOwZyWI\nagV3WbjPlVqjs/lCL5qBzhxm7O14OFzvD6Mfb0olCNFQrdW2+37aAmpKowKCzqdUYx+6k5yqVB6q\npWl+Y4GJykFiQilnPWoDI9FKSZXb9O0xTK00KLtwkWCyZcbv1Zzl8AxRrskwQu3GK7UoE6dR2ajs\n8qJEdOFs6Ir5wAgwWTQsVYNDao2wUrInzrBDr33uVBuwz+WxLK1yotAqIXnzCi/HIqQyCW+9cd33\nDVNUUQmsZk0FUevSt9OLr36lPX38+OYoVi3eSyEcy91yI0UilWp9jRlZhUNV8bz7SVyvMsTFagQs\nUU2La9+BVpVEQIvWYCSy1CgrapkYrMzkttiaGhwCq4FhGJXDJA3KDWytX19bnbdkZvZnTz76cHp+\nd3sftZSFJ0r77fbej/zXf/c/+g/+3OPl+id/7bNf/tqX3nzzrbWvj6r7Uk+ur5duh9Wvlt7XZVmu\n2nEFzLFICfcCmVEYW+S5dmS2JQ/rIauwS5MxoegZp/NWiDhdLM8lwsxUQKmmMD0vYdkzqkKcQzdK\nMDNWMyhVchXgEDKna3NOmaDifHfZm4+KBq+yBhR5hlrOjRyvGHdcNTN0oHYJtJBP/TznNqpcVnQA\naVOuNwE5KKbQxDKlzWwutSAa5o8yTYac6/9LAGHNw0GigTkUTR0IqESDWU7GNtVgYci5YfN5jBlB\nK9ZU7RsLznkslbU5eUi0hiDL0Qwmunv5JeGPMpixHdZ9z6bMns5lsbUteVgyD9ejXx2dT2q9O9/a\nfnzYd2x34X2t7blZjojpSM1cgsYa5lbulJUGzYms3WCFmMrkkoXUgVYolhWD7hUyOhQgZGWqunyo\nzpxQEp86B6JdzGDTL6ndqwtSFr3PqfAkVYEBelbOPTO3QDNKtlu6eyRQ5ibtfMV2ZvTyjeWgTxye\ntByP+3aSEEBmXbdeIPta+xkiaKpc+xqRmQl3X9bzi/c/+/kv/p5PfVd2DQxAZXcswZfKcMF5CKHa\nltndGqstVekHD4suGwmyqjeMhJmvreqw9Nj2RDNu0tLBIKEqRsmE4W6L+i4/UANr6qQJ8J1nqRZU\nmaPSZLTjcgjjaXuwuLq6WZbl+fP7MbYBU8Eg7S/+h7/5N/7In/hzf/iq/4tf+JX3vvH+7//BT33i\nzTdqsUoRbk5aY51tWUrs6HS59VKb+cecUTpjr4qoOG+b+eJpS6uXge32wVuc7l6u/fGL8y3AWZ7l\nDIqGDM4J4JmjGOCyrb8IqSQ4C5GGi7iWUhXMZWlWdMPMs4IBUVRcIoMbESoTO7ERS6nYOwcFm2i3\ny+5wPmWzojeqHFZEEwarFeVFWRopsBpZpEMmmSCyZrh1AQU0AMp5bs1fNE64UBC9qsOqZlIXAFA1\nTxbU9JGBRqupfoFPPhlZSaN8qemqdjJdMpqbc7Wky6yV296KlSgmUO60WNQXGGztS7fem63w3pTk\naour99XDvS2H41V/vF49eu3xk9efPv6Wm2c3r7/xkTfffPPZs9eeHl+7vn52vR6v+6OOg8mUKZ0l\nVGwxIjAqKyuVe2akKuIc8RBxGmOPUeOhxp57xr7tuW2ROSIyqsoKVeVCqHaqB0LpVMACVKFgXpjQ\nexastMstK+dYd2oIEEAC3uScYUizXqNNXD5VhhqAqpdlAwkGc8ANTPJRW2MOOcHT6RSEEa25LlZr\njBw0eeO8HWyxuw+++quf/4Kn99Z6rl4Hs4Wq5rYYjXuyGnpndZM5HzrWcGvluexUGM0iQOcIZCHu\ntu0BjVU7ZYWzZl6Ceak4RSyAHK6dVljkNUxeM1uDZ5WoyNNURRas0Nr1IzssbL2ty7M3Hl1dP+ot\nO9Z0F3SK/X/+0b+3PvmuP/5v/OBVH//807/22+/cO7oDdAIu5c6+76Q8nCmVG6c4E6AyI9AwMjCG\n7RmnczCG5xd//de/8I3PJw/R7GHf9nFWmYFUGpKX+xa4WN/MzG22w1Nab7yM3lmbwqYEBpm0ZAJh\nhGvKxObKnw4NZJQvNUVkh0IbMJWfoRDdwRm3dTECGnGJ0eYsISZFelqTaUlLNeoC2yCHoagQprpU\nCfVZNoAmE7zoCctJ9qQlmWRak0QOoEwTNGTzBeYlhoAGa7BilE3PYMyYnSkoWDiAvBzxZSVrhAnu\nPCTTHIlasOx979UMUISzReW+WKc0Gnoh0N1khMurufbUgYBZhw/mCUSuj308NLoObY3Ws2mRVR/I\n0uMrjMqmekgJWVXKLIgDGmqtKpVnq5ZNXj1ZXlnpsK6i21KClUF7pApEJrC9opQ8oLDzBKZidmEG\nJ5mqeTIPBgbOtILAiR+f5KJX14ZmBiuTqEwDEgKxg0ZszKvQoIghtVQYnaP2ufQzVVBSbQ/r1ZOE\nsx5Yxmk01WQDRs9FbVWOd77x1b6u3/3xj/XmZntGdrMHtWQc1JxBp2ULkYqjUE1JL6mzQRqWZJOi\noXZvJhyjBqvCi2qcbIHcab3C5cXY4VRIDbhbaklkrx7EsOBltXpQyTWMmpfPtXc/WIZtwOObR4e+\nfvDiucmJKMNZ+tF/8g//0B/+Y3/sD/7Rf/LPf/pffvrnt/1T3/UdH0fu00fgoBMjc42qVRWLY4wY\nnj3m/nfLpWiLWe+WEAy7XT1Z/S69TmZ8OV5k1iwJaWZV03cizsxPlSLBBqOioOIyy4MmiM1KY0lR\nLqOSkMz9opyxmUstlFSV0dwFmsZuMKO5T5+bgDZD0pgTAZJMuxA226D5ZEIYjTOgehStY7JbI6km\nxNTQs6b+IKWaaSCXYaCmJRdmVgGWiwW3KS5PYwM1pXigKKZg5Pyh5lzQShNUlZyewVlTqKWhlwYr\nCao117Dyk86HbGlcvBvTsaC1pkpkWnGMVYC1A86jjDYy1rJ0wSt3lpEw68awkpoK1C5vFpvDzxKt\nUQVnm4dmWbaSXV+XkbHDArrCTI8wg4ciSsYyNoGy5BnlnooEl8JeWUjNSz9yy4xExUZRUoA+EfdF\noqZmEhc06wBJTZuLoWqaq9x0EtzYMAQlLxMIgeNiVMDkgEEWGDsbCdVwcLNiaXoOmvMcGxAJ38e2\noLV+2PdTqgrpcKIoRIZ7N7al5Te++MWDX33rxx+bFnSDdMiqwlC7AraEYYgwLvuejQozc4ibcpGN\nPtTQNjTWQ5Pv3kLqYlqW2mD2YgMTrpasK9gZWpPbguWM6PBSVqoJzUQr0A1jr0m8qN3bdcYGWVsa\nURzN8Nb67L0PbkfCFBKGHn7mp/5/3/v9/+af/GP/zo//+D/9zK/90t05fuBT37nUVtO4iUvGr9QU\n1VqZtW1LWcQIafTDscAqyGjaRtRrN8/Oh4WC++HaBisuFF1d5uWTepGyPtu6yWifL/tFnGMX0rl4\nqEMwR22NazOfjKqpss+aB74g2dy0m7dEA0U2aBOAGlpWTjqTTZIiQSPywtkog8kMoFDGAg2FMDOM\nQpuUkMuIHVSBk51jkLLRZ0KeIM4EdJvyyrkYVqHm2twmgR+c4zywICaNYKBUw+CwuR9kQ5ow3a8u\nFdVmyC7Sil6mxZl+wXzsZVkdpUFDOzQ7Ahvpgqs3b2JfWssiLCtpwWoQNGHKnrTNHNmbLd6ufD0c\nW1/oqxnlNiU83jr7Ip2thppbW9rS/XHzY6cD5d6asckcraN3tFhnSqR61kA092Z97+ssqZdl6cuy\n9F400R02M+f0agZsYJtdJ6bB2y5xBZhC3RpVBqvUuWpUIYDImKPfKlaqSpGZFbWfxKpUJCoqB1Io\nARXas3RYzK1ft9UyVEG6LavoSFRV5twAV8QOKuWl0+c/96vvfuOFWXVlkqJ169580OgNrQtU9exi\nI0W3bDwYz2suavnQzJGkJd2KhjQOqhFaMKYeZKA8FXg4ZDWMLolqZqnavLkbvROzBmWwnNZFuntk\nKmmgs1k7HNbDenA/vvbGW1eP11XOVs0sYZ/5pX/2mS88/Lt/5s/eLI+/8MXP/ctf+M1aDutyXPvx\nuGrp7u2QaiXbB04DG3eog2Voe0RUgRzRHh5CpzxvZ+35W199/+tf/trdtodtZEx9G0BTlmruvkIF\nmNVs/sNQc9mFy39YCuuSoiIoTQ5qmpH+v7QHYkIBUmRpun8oNLQD2szS2ab/3+CXsaBYVM3Pxohp\n1IKgJFISXTU3LQKygPn3CVwwI/MkACeUBtS80acHVxdqxuxtrGBBKzHoBRRR8lIjJmBnbkSWohmy\nUTbndCI0M89RmD7jNtBaXCIIuyGpltijerNzSwHmiXA/5+Nj6dhGqGdjxd4mWwyw7qtukvvCxQQ6\nM9GUO/NAE1uTn31NbSylATADkm3GHB8KMEhhtZxsZMqqBERGyVbkxtG47CyLLEOrg3xMzZWLx9JZ\nLs3tNx6mrAtVFzLKpQu0CbUBOd3LABFOqpAzIIYT4iib7RbnflCNHNOVDI0GC5NpqQZeUrtlpHnB\nJjFzsXUgmx8W7E5uGYnypBnlbjEzhyx1kfdIWMjsh4r47K//Zm/f/+brK1xtPhwztEALLFe2rZ2v\nsw8raSCUrM6ZjMZDcNhGHJ1yDFULg1cWuMhO9AKAvqsccW/NUa1cdtFAJVppY9LNXJvAM3hECAxZ\n155QsUtsbKHRvNnR/Jzt8dO75U7PH4rKfW9uv/WbP/Py7vv++A/9iZ/+0X/ypXc/P/7Z6Q/8od/9\nuC+TVw3g+f3th197/73bh3zYHoyrL62DZgfnw7kytxenaqaPPLl++80rAd/4xjtkPMmboVdBYTAh\nTQCbzby42c6blRIwoxVkzNlLi8yoYpk7s4X2Yy3lOEinxgxeJHCXBySLBJ2yQ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JSKIacKUgNEhQpkQgNUvmQhC7eCgsMyhUd6ZsxSatyN3U2MT1aeFA3kE9rMy8b5IcDtSi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\np4YoddQoPgH3udtR/ATy9+B8WFGgr439Ap6TDqT0gS9Z7nB0IKz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27OJA7VCg3jPMrTUi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\nzNieJM1LvP4/H86NvhPL4eoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=339x512 at 0x7F18CF7D56D8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from detectron2.utils.visualizer import ColorMode\n",
    "#dataset_dicts = get_balloon_dicts(\"balloon/val\")\n",
    "for d in random.sample(val_img_dicts, 1):    \n",
    "    im = cv2.imread(d[\"file_name\"])\n",
    "    outputs = predictor(im)\n",
    "    print(outputs)\n",
    "    v = Visualizer(im[:, :, ::-1],\n",
    "                   metadata=coffee_metadata, \n",
    "                   scale=0.5 \n",
    "                   #instance_mode=ColorMode.IMAGE_BW   # remove the colors of unsegmented pixels\n",
    "    )\n",
    "    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
    "    cv2_imshow(v.get_image()[:, :, ::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kblA1IyFvWbT"
   },
   "source": [
    "## Evaluation\n",
    "\n",
    "We can also evaluate its performance using AP metric implemented in COCO API.\n",
    "This gives an AP of ~70%. Not bad!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "h9tECBQCvMv3",
    "outputId": "1968f509-31ff-435d-f014-cc23fc53b645"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/02 06:28:32 d2.evaluation.coco_evaluation]: \u001b[0mjson_file was not found in MetaDataCatalog for 'coffeemaker-validation'. Trying to convert it to COCO format ...\n",
      "\u001b[32m[03/02 06:28:32 d2.data.datasets.coco]: \u001b[0mConverting dataset annotations in 'coffeemaker-validation' to COCO format ...)\n",
      "Using validation for annotations...\n",
      "On dataset: validation\n",
      "Classes we're using: Coffeemaker    21\n",
      "Name: ClassName, dtype: int64\n",
      "\u001b[32m[03/02 06:28:33 d2.data.datasets.coco]: \u001b[0mConverting dataset dicts into COCO format\n",
      "\u001b[32m[03/02 06:28:33 d2.data.datasets.coco]: \u001b[0mConversion finished, num images: 20, num annotations: 21\n",
      "\u001b[32m[03/02 06:28:33 d2.data.datasets.coco]: \u001b[0mCaching annotations in COCO format: ./output/coffeemaker-validation_coco_format.json\n",
      "Using validation for annotations...\n",
      "On dataset: validation\n",
      "Classes we're using: Coffeemaker    21\n",
      "Name: ClassName, dtype: int64\n",
      "\u001b[32m[03/02 06:28:33 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
      "\u001b[36m|  category   | #instances   |\n",
      "|:-----------:|:-------------|\n",
      "| Coffeemaker | 21           |\n",
      "|             |              |\u001b[0m\n",
      "\u001b[32m[03/02 06:28:33 d2.data.common]: \u001b[0mSerializing 20 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[03/02 06:28:33 d2.data.common]: \u001b[0mSerialized dataset takes 0.01 MiB\n",
      "\u001b[32m[03/02 06:28:33 d2.evaluation.evaluator]: \u001b[0mStart inference on 20 images\n",
      "\u001b[32m[03/02 06:28:35 d2.evaluation.evaluator]: \u001b[0mInference done 11/20. 0.1447 s / img. ETA=0:00:01\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:02.218460 (0.147897 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:02 (0.140734 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./output/coco_instances_results.json\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions ...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\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 ] = 0.463\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.846\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.439\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 ] = 0.463\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.510\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.605\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.605\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 ] = 0.605\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n",
      "|   AP   |  AP50  |  AP75  |  APs  |  APm  |  APl   |\n",
      "|:------:|:------:|:------:|:-----:|:-----:|:------:|\n",
      "| 46.294 | 84.638 | 43.890 |  nan  |  nan  | 46.295 |\n",
      "\u001b[32m[03/02 06:28:37 d2.evaluation.coco_evaluation]: \u001b[0mNote that some metrics cannot be computed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('bbox',\n",
       "              {'AP': 46.294068564818126,\n",
       "               'AP50': 84.63833480122204,\n",
       "               'AP75': 43.8896475854482,\n",
       "               'APl': 46.294798726329844,\n",
       "               'APm': nan,\n",
       "               'APs': nan})])"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from detectron2.evaluation import COCOEvaluator, inference_on_dataset\n",
    "from detectron2.data import build_detection_test_loader\n",
    "evaluator = COCOEvaluator(\"coffeemaker-validation\", cfg, False, output_dir=\"./output/\")\n",
    "val_loader = build_detection_test_loader(cfg, \"coffeemaker-validation\")\n",
    "inference_on_dataset(trainer.model, val_loader, evaluator)\n",
    "# another equivalent way is to use trainer.test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gKhgPf7DyQHm"
   },
   "source": [
    "## plain_train_net.py \n",
    "\n",
    "I'm going to write out the plain_train_net.py script verbatim to see what it's doing.\n",
    "\n",
    "https://github.com/facebookresearch/detectron2/blob/master/tools/plain_train_net.py\n",
    "\n",
    "I'll leave notes and change it where I need to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KrnI50c2ZpsQ"
   },
   "outputs": [],
   "source": [
    "# Different models I'd like to try - https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md\n",
    "models_to_try = {\n",
    "    \"R50-FPN-1x\": \"COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml\",\n",
    "    \"R50-FPN-3x\": \"COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml\",\n",
    "    \"R101-FPN-3x\": \"COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml\",\n",
    "    \"X101-FPN-3x\": \"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\",\n",
    "    \"RN-R50-1x\": \"COCO-Detection/retinanet_R_50_FPN_1x.yaml\",\n",
    "    \"RN-R50-3x\": \"COCO-Detection/retinanet_R_50_FPN_3x.yaml\",\n",
    "    \"RN-R101-3x\": \"COCO-Detection/retinanet_R_101_FPN_3x.yaml\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pKejAERoyYA0"
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import argparse\n",
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.nn.parallel import DistributedDataParallel\n",
    "\n",
    "import detectron2.utils.comm as comm\n",
    "from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer\n",
    "from detectron2 import model_zoo\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.data import (\n",
    "    MetadataCatalog,\n",
    "    build_detection_test_loader,\n",
    "    build_detection_train_loader,\n",
    ")\n",
    "from detectron2.engine import default_argument_parser, default_setup, launch\n",
    "from detectron2.evaluation import ( ### Changes from original, I don't need all the different evaluators\n",
    "    COCOEvaluator,\n",
    "    DatasetEvaluators,\n",
    "    inference_on_dataset,\n",
    "    print_csv_format\n",
    ")\n",
    "from detectron2.modeling import build_model\n",
    "from detectron2.solver import build_lr_scheduler, build_optimizer\n",
    "# Not sure what these do but they may help to track experiments\n",
    "from detectron2.utils.events import (\n",
    "    CommonMetricPrinter,\n",
    "    EventStorage,\n",
    "    JSONWriter,\n",
    "    TensorboardXWriter\n",
    ")\n",
    "\n",
    "# Setup logger\n",
    "logger = logging.getLogger(\"detectron2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "D71LSVmsv9qt",
    "outputId": "506d00cf-e1b3-4dfd-b03f-d11d339ead94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Mar  6 04:09:26 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.59       Driver Version: 418.67       CUDA Version: 10.1     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   44C    P0    28W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n9xyqh9Y5pAC"
   },
   "outputs": [],
   "source": [
    "# Initialize Weights & Biases\n",
    "import wandb\n",
    "wandb.init(project=\"airbnb-object-detection\", \n",
    "           sync_tensorboard=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HaK6NbD_HkAt"
   },
   "source": [
    "## Register dataset\n",
    "Before I can train a model I've got to register a dataset in Detectron2 style: https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset\n",
    "\n",
    "This will leverage my preprocessing functions.\n",
    "\n",
    "**Note:** Be sure to update the config for new datasets: https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ces49AiO0rga"
   },
   "outputs": [],
   "source": [
    "# Setup training and validation data paths\n",
    "train_path = \"/content/drive/My Drive/Airbnb/coffeemaker-train\"\n",
    "valid_path = \"/content/drive/My Drive/Airbnb/coffeemaker-validation\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DmVsNBaLMQoo"
   },
   "outputs": [],
   "source": [
    "from detectron2.data import DatasetCatalog, MetadataCatalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qxur706AKMwW"
   },
   "outputs": [],
   "source": [
    "def register_datasets(train_path, valid_path=None):\n",
    "  \"\"\"\n",
    "  Registers a Detectron2 style dataset from training paths.\n",
    "\n",
    "  Params\n",
    "  ------\n",
    "  train_path (str) : pathname to training data containing training images\n",
    "  valid_path (str) : pathname to validation data containing validation images\n",
    "  \"\"\"\n",
    "  # TODO - update to accept any kind of path, e.g. not only coffeemaker, maybe could take a dict as input?\n",
    "  # E.g. {\"training\": \"path/to/training\",\n",
    "  #          \"valid\": \"path/to/valid\"}\n",
    "  for d in [train_path, valid_path]:\n",
    "    print(\"Registering: {}\".format(d.split(\"/\")[-1]))\n",
    "    DatasetCatalog.register(d.split(\"/\")[-1], lambda d=d: get_image_dicts(d))\n",
    "    MetadataCatalog.get(d.split(\"/\")[-1]).set(thing_classes=[\"Coffeemaker\"]) # TODO - this could be more robust than hard-coded\n",
    "  return MetadataCatalog.get(\"coffeemaker-validation\") # TODO - make this better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "yHrI-Ws8Mc81",
    "outputId": "ac112405-8d18-4ce4-833f-a12af53c7b88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering: coffeemaker-train\n",
      "Registering: coffeemaker-validation\n"
     ]
    }
   ],
   "source": [
    "coffee_metadata = register_datasets(train_path=train_path,\n",
    "                                    valid_path=valid_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 808
    },
    "colab_type": "code",
    "id": "ZT0Jl0T1O9Jw",
    "outputId": "a9dbff7f-8927-41b0-b939-464ffb2c6880"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cityscapes_fine_instance_seg_test': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'cityscapes_fine_instance_seg_train': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'cityscapes_fine_instance_seg_val': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'cityscapes_fine_sem_seg_test': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'cityscapes_fine_sem_seg_train': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'cityscapes_fine_sem_seg_val': <function detectron2.data.datasets.builtin.register_all_cityscapes.<locals>.<lambda>>,\n",
       " 'coco_2014_minival': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2014_minival_100': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2014_train': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2014_val': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2014_valminusminival': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_test': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_test-dev': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_train': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_train_panoptic_separated': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coco_2017_train_panoptic_stuffonly': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coco_2017_val': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_val_100': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'coco_2017_val_100_panoptic_separated': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coco_2017_val_100_panoptic_stuffonly': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coco_2017_val_panoptic_separated': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coco_2017_val_panoptic_stuffonly': <function detectron2.data.datasets.register_coco.register_coco_panoptic_separated.<locals>.<lambda>>,\n",
       " 'coffeemaker-train': <function __main__.register_datasets.<locals>.<lambda>>,\n",
       " 'coffeemaker-validation': <function __main__.register_datasets.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2014_minival': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2014_minival_100': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2014_train': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2014_val': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2014_valminusminival': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2017_train': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2017_val': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'keypoints_coco_2017_val_100': <function detectron2.data.datasets.register_coco.register_coco_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_test': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_train': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_train_cocofied': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_val': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_val_cocofied': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'lvis_v0.5_val_rand_100': <function detectron2.data.datasets.lvis.register_lvis_instances.<locals>.<lambda>>,\n",
       " 'voc_2007_test': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2007_train': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2007_trainval': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2007_val': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2012_train': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2012_trainval': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>,\n",
       " 'voc_2012_val': <function detectron2.data.datasets.pascal_voc.register_pascal_voc.<locals>.<lambda>>}"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check registered dataset\n",
    "DatasetCatalog._REGISTERED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BwffpokbHjeP"
   },
   "outputs": [],
   "source": [
    "# ### ORIGINAL REGISTER CODE\n",
    "\n",
    "# for d in [valid_path, train_path]:\n",
    "#     print(\"Registering: {}\".format(d.split(\"/\")[-1]))\n",
    "#     DatasetCatalog.register(d.split(\"/\")[-1], lambda d=d: get_image_dicts(d))\n",
    "#     MetadataCatalog.get(d.split(\"/\")[-1]).set(thing_classes=[\"Coffeemaker\"])\n",
    "# coffee_metadata = MetadataCatalog.get(\"coffeemaker-validation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kDqCqaU8H5-w"
   },
   "source": [
    "## TODO - Visualize dataset\n",
    "\n",
    "Once you've got a dataset registered with Detectron2 it's a good idea to be able to visualize an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "colab_type": "code",
    "id": "viQ7JRyoHnY6",
    "outputId": "33ea05f8-8811-47eb-87e2-bf55ce423450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'annotations': [{'bbox': [185.0, 9.0, 800.0, 1024.0],\n",
       "   'bbox_mode': <BoxMode.XYXY_ABS: 0>,\n",
       "   'category_id': 0}],\n",
       " 'file_name': '/content/drive/My Drive/Airbnb/coffeemaker-validation/aa9b35d1bee25627.jpg',\n",
       " 'height': 1024,\n",
       " 'image_id': 0,\n",
       " 'width': 1024}"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_img_dicts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "colab_type": "code",
    "id": "7vajEm3tIDW8",
    "outputId": "24d2c12a-c40a-48c3-df39-ca8b3be9360e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'file_name': '/content/drive/My Drive/Airbnb/coffeemaker-validation/d0dd806989582fe0.jpg', 'image_id': 10, 'height': 691, 'width': 1024, 'annotations': [{'bbox': [362.0, 8.0, 764.0, 656.0], 'bbox_mode': <BoxMode.XYXY_ABS: 0>, 'category_id': 0}]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ggw4V1bIBjdy++Whtf49jwMcBZ0+yrj8CpCoJBgwICGpmYtipgbWW7/vAfYrc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4sXvuveeFb7zGs0MGlJhWKVd19ev/6d89uuXe11++lBMhVadnq3msF1xTXUewKlZ7Gv75f/f/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\nvXmlSDuDZVWtlmcXF5cxxL7vIKfrJ1fD6IMP+/1+HP2h7DiW85eX5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wak4zbWgEfjF8Dj\nAxZB95RzPjA2mQGYSCGSMBdJUc5J+HbSgxUqlZAZ8kGngok/GRbNkCFT37Xd/q7v1gB0cfWsnM8D\nMmPWykCKmUEBcuJ+6I1JZVkQUYoJckRNifM0xXEYLy8uv3h6o5C0NUVRFHXhsj3CzZ4IT/CfdMyn\naWRCYzQzEJF4puXMTTOz1hljsvS0SDGz914ikXWuqUsZMrL2YFVKR8swsZFxzlnttDY5J60NAHTd\nRhuyRRnHWLhk1YzIc0rTmLUyzhYWC6OLYQxDN/3lX3yzeaDVz+dLmnh80CmUKlkL4BMAlIszNBVg\nXS8vyC6ycrv9nhmK5QVyBKMuvvxqeLICDtM4RQ/BgwdlXc3gh9H70W57aGaXf/vv/+O3v/h2mmYZ\nh+1ujTrm9FDPXF2xyQlpuL+9bUOnC9PtFtfLC0NVjFs+SmUYY1xZyP5EJGk25ZzpGIAeJbCHv57S\nTBLdZURr3G7caU0MqIyxlbNjIQQ4rXS37xY/n01Tu911RJM1nNKkEEIMXTddXz0pbdF1vTF2sVpc\nXF0AwH6/u729+81vfmOtXi5Xz549L8tqQzutddcPbdvGNNX1mTEGiZ7cXCsyWuuirH7+858rpZJI\nEWi1mM9DDPvdNobEnPw4phjGrkOEZlY7Z8chpJSm4Pf7jrSq6/rq5smTZ0/OF4u2bZeL+Xa73W33\nzGyUixBZJKc5I2FZlTHnfhiaWeO93+32bd8DqZwR0RpdhLCTaJAzMCuAdIIaEufMmQ9scjqadGQE\nUIcEMp8Q/MNuFVQWgaWJJ0HnszmwQzL+40FiOHnF86G3iCeoIR11bQ5/TQkYMDNmHqcWRFRbEx/1\nGFCmNY8TvHCQBT1EcPHjEz631jrFKGR8ydOPTilsy5KI+mHw3gNorXXKabvdqqObIxHtdrv1er06\nO6vLcjabscDT0nwaR8mjBeyV+K6UnqZRIYUYhq4HgHm9OFWr8kpjtGhR5JyNcVrblIFTkLEAOd7G\ncby9u5/P52dPnpWF+erLLxP7P/7+D33Xdd3g7L4qCoEZEI7mGcdbS0QpZiDpoR5uTmZGOSyNkUng\nYRzVcU6wrpsQvGAtRIRUW6tTZkIgokNPHoAxAys+nNcfH+UnBwDD6Yg4NPHl0+HxlAcAZEghKq05\nM4tuMh78PaRWyvmgYrjf71NK0r3hAy6cTukDHhtRhxKVYArj0O32u7XRuqpmIZFRJgOighByDOFw\niqREmhUgAceUD4vxkF+zIlrOF4VzZPQ0TX6aCHGcJq01AbZtLyzj02RgURTBT8IQPQ2LixKAQGwp\nJTENPoyBKKWIpK2stW6aRiZxYozDMPrgJcbJsI8b1Gw2U1prDSHGzCIXnImorioEDSYQyPRYHMeI\nRrfDsO3b7WZ6v2//0w+/cwuTg3fQpm7nMFjL59UZNaXO3u25oJhdWZompTymeH/3bmhv99vbadih\nySMMQ+xi8LHnOFECXTVWGfLe7DZqfR/+przoE+99SGzb/v7yclHWVC2U0gRJq8xGF7wLOquEoKgY\ne4iTAmZpd/Oh90MA4s0d4JRofMoB/ezC45SJmFQgYYwxpcxMWtu6bhTqoW+JVIyhH/aAvFg0Ic3e\nvt0wc0rBlY4hTtOQOe3bVmhwwzAIvud98e233+ac7+5u379///Dw6+XizBTF8vxsDP7yyZO6LhaL\nxf39PQDc3BhXFDmzFAohxHEYiHTw6fmzxfv37z58uHXOcU7BT1qpsiiWy4WxepqmorSb9Z4Zf/Wr\nX6YUjdH//J//s7IsunZf1yVwyjkSKe9DzIEUWmcFCemH/mw5k2XjnE0ZxskP42StJUKtjbW26z/B\n+uFTiOb0Lz++t59xQCVaQT4wu2X/HU7iT4/kP/ee/+3X488ph0HmbJ0VIk9RFMIgl+mqnLPkYSEE\nYzIzj9N4e3s7DENRFKvVarFYIFHOeRzH9XoNiIv5XDzC0rFPIPPAzGCNvbq6OrFLp2n67W9/27bt\n69evnz59+vTps+12M02TqAUfZKwQOWXZ8gAHhxmB9W9ubgCAtFKkfPBCexN+oIRaAIg+ycTAST5S\nHigAnJ2dXV1dVWUdw9As5l999bPFfPVffv2f+n27qBftbj+bz4zROTMRIWAm8Clylq0h7M+PGN1J\nW0cG3yQN7YlmsxkRFYWr6yalHGMcx1EbhVgYcxrq+rgw8L/SDAL9qAw8HAFyFCg+DnZlxswaSUlV\nceQqH7c6nsgJwzCEEKqqEi2dUwHyKRR1GApARVMIu3Z7+/ZNt9+9eP71+fkVKZcSkSHSWFZlGAiY\nCXHezIqi1FoBZKUwp+TDhETWuMuz8wOFQyZKmRl4u9//8Y9/nKbp2c1T+bwnTaUYU9/v+65DPNAJ\npAxUpIZh6IfBT5Nzrq7rEML9/T0inp2daa3X6/Vmu57PFnXdMIOsYzFqqOtaeMdd13V9h4Szph7C\nsG/botRGm5QSZEwpIwQgH3IuLBltnCar0RammRX39zsO2wU8qLEffE6VZ5URitKtbsOYtqGa0WLW\nYFF8oDGPt2jLFrrOToONcWFMvSDNRTRpABuMqjSiZTIh+HbYeQpcmgnvTN1PsBnSdoThyReXN0+a\n7eZDDG2KaEhPY4xEwFS4wudotAKIRDmHFLyfxlEVpXOOwUp7TTKjUzJz6gPzj6ZO8CjBnflg1AMA\nRIoQrXWkyI9BqPCKzHa7b9tWKUBQIuerNdV15QpDBMMw+MkLOVr6eHU1u7t94Iyz2awq65snz8uy\n2my22+2OGVarVd/3bduHmFbLs77v37170/W9nwIiOVc452LIZWmUUu/evjdW/+IXvwrRW62s0Qhs\njSXC3X7LnB8e7qty+Tf/5n949fqP6/Xum29+aa0KQSgGOXgv60ralQCMyNoQQ/J+VGop6YUgk5MP\nbd8ziTbkIUfOR8cbybBPQfZE1/lx/E0xBR9+8gBA/DjFzwc5lv8TQP8/cZ1+kfxV3MLkX45NMuLM\nJ9MuIhrHUablrbWSQgkmtd1suqG/vrqWjy29w6Io1ut113WC5ODRRV32Xd+3/X5fFIV0axGx67qu\n625ubowxDw8PKadh6He7HRKGo8ProQRhQEQZ+VRK13WV0sGZK+c8BR9ikANJGGt8JEc55yB7EbB7\nnCMCgHzNaRj7dlAajIHExIBnZ+eQ0n671pqqsiAUOXExKyLKlCAzM6ECODg+IIKQPulgX5FAvNJC\nDN7HEKR7UTgXyqLv+pySWFdprZQiEW84QkB8Apr+3PWoCfyI/oMnmv4R7NPCrmXGI/lHXoiEMYqx\nSu77XpGazWZa65wzITEf6k74kbBtznnbtrvdrm93WuHz58+ur5+ysYE5QxqnoNkW1k3Bj+PADMC5\nqmqpk6TJQ0pVpXXOVUWhCVOIIQRAsNYO4yAJvqwecbosijSOQ0osJh5KkSwsQStF79N7D8xyWkgn\nQzJfpZSzNibJFxQASiYyn82BgPCgjrlcLhHn2mhmNQ3p9vbDfFELwO19mKagdVA6aaNIIecUpqEp\nauLIybe79cOwnwPi6Gfz2uZC20QpLNRU186CslWRLCGXztagjLIm6TO1Ois0AcecImafhzZOXcDE\nzEwImtibiEMfx7cfdv/eqJcv/wCAu31/df302c25xun1/f3LD3fz+dIq5JgL15tKM8ddvxn8goFi\n7oLP+3272W7nSsmEvXDanLUIeLB1/vOpxjGpOSjYyDsIsyIzpphTTs4VYZpyAqMxRe67qW7KsqwR\nqeu7fuirqmnq6pZkbIoBUKKJCCie9LqtLfb7fr+/u7y8+vrn3wIAZ44pKYWucP0wbHbbnPl8dd7M\nZjGms7NzRXqafN/3wzCGEMuyKMsq7Pw0+cLZD+/fX11eKoVaq/Wm3262V5cvjHXjOBTOGK0PYEmO\nWmuAzJD3+y0gE8H79x/Kuso5MWeliJTu+z6mZK0bRx7HaRwno01OGYC11lob70M6aCx/Tt4+7J0f\nRe0D/+IY+eFEEjq8Hh+Hfv4zVdqhgfijHPInH+hjpuDHTyW/ntloAwwpBqUPA00pJZGxFOVdJBJa\nY/BhmvJ8sViuVkLMhSO7X2BkMUpk5hijpFnMj9cPWWNiTJOf5OC5vLx0zp2dnwOw0aYqSxD0iRRJ\nssIgW15wP7FSkdb0er3e7XbNrMnMwzAUzilSZVnpo8ixUqrvBnVEfpQipUjYgADAwJP3SpuUMyeu\n6rpv26+/+ropih9evvLD0O33zXxurGKJ71pppTgfbjgRxZgAspgTSQ+GmU/6EEVZ7Ha7ruvEU0HG\nTmOI0zSF4KdJO2eRUCGfQjccMR18BOV9dmlAAM4InOETCjAjJDjY7eKRW3qCkw7NOrGwYE6cp2Gc\nhrEsy7qs8GARRgAQMB1mSVhoSQfQZghT1/a73Xby/WreVK5BdBlyCFM7dClE8lC5xlgdU1KaUIFx\nxqBBRcRKaaO1RsreZ62ItUo5SecipqSsffHihXNutVgepdVzSinGDAB1XZvFPHMCPri7TNOkpqmu\n6/LoPZSZtdbL5fKU2FZ1ba05eqWRYI6ucJnZT36/32ltVqsVETIk731RFE+f3wDn7a6QWi14rsvR\nGONAibWgNjpOEaKijHVRcde/WHyN+vnVF19UM8w5hnENsTWYnAVkm8BCMugDUEI/MSGQYlIMlMMA\nGBgSGu0YEIAQiSEqUqa8nDXPF0++ffYXf/sfXu7Xb68ultvt/X/4P/6utH1d4osvrievhy4bMN1u\ntAmKZcl5AKDRs/fAnPZt2w9dnebWVqCsUjr4aEiTosz58el+Cj2nGhmOLDIBf5COryFFinLOKSZC\npY0B5LIs2v2u77uzsyVRBtbjELq2nzcRAKTnJjM7Xdd575ummcZ+v9teXF7lzCnkZzfPlLHTNM3n\nK2b247iYz4dhCFP0fixdYbXZbDbD6IuiGPoJYBLx9y+//Or8/Hy/3798+VIZZYzODD+8eUMET58+\nsaiLQoeQPtw/KGuNq7abD0AQk1eKZrOaSL17927oByJdVeXd3d2Hu9ufLb6JUZqfUFWz3f7BWctA\nSHEYBgIsTNmHIcaktEJQOYMI/EiKdcIhRZDxeNDmxKyAGAkIRQVIABjIzHzou3FmVAQMnIEFsMUD\nfsun3O7R4f256cejKH9AmX50DMgnzMycAgKkjIETKgKAbt8G6w5bSR2UJZVWohNHiohUzr7rRvkA\nk5+EOHTiyErgizFO08DMKVnmZIxZr++7rhPth7uHLkxeDgBm3mw2og4t0LGum8wZAAW09OMkpYAU\noE1TA2DOSdL59+/fr9fr+XwuKqF916cjCtR1HRk9m82UpmEYALgoCqVomrwidM4Mw4SKivqgMy8w\n6fnVpSVMMX549abdrJtZNcWgrJXYnnPWoEFhTAkJMgBqwsyajxMBzJQzISlAZi7L4kBGUEpOSmE/\ny5zEOA7WmlrVGVgdqfyMBCozAvLn47qPDoDj/jypVJ+aeFKNMrN6ZCyJhz7eYd1Ia1sgKgAQrU1C\nRkKRkcBjqUfSa2UGxMg4DGO7XW/u70bvry6vLs7PtFIpK1OSm9IQun3bKrSMoI0uq7KZ1QCQckLG\nw5ABIgAkSNM0hoA5hSxGaMYs5vNCm6IorDZHP4CUUioKp7WRiQkCYjiUnKcvTkqlnGXkRAzf5/N5\nTpmBich7T6SU0rKeJO2y1mp1oOKVRRFTRMQQMOcwm83GvjfWKKW9n5x2OfHYjRU2jrQFRZ4BsyoA\nQpoX1XC7P/vFVbms6+bcrBZTDOiu8tTm0E48EDHpguKUgaWuSjkjlsw1o9a2Bh45ppw9coTMiQGB\nAD2yxwAYVTHSExvfFb5Mu13+QLiPYN5v3iPX2jaozDik0s6ePHnah54yTm2RSwfsQkBXuNmsKVyh\njKJkASHndOgcARJ9hBrwSDmQPYlHFSCJZQgIjDGkGLPk78aYceqnaRJ7uJQSQ04pIdJu1yKadj+E\nmJmhaWZaaa1NURTb7e76+lpIB9EHwd+0NnVdx5xyjFUz++//zf/wd//+797c/QDAzrlxHHf73Wq5\ncs54P3kf+q7jjPPZvKqq3Xa7221n89nkR2N0Bp6mcTFvLi4unjx5IudZ4YpmOW/Hrht7WzhRi4ox\nAigkijFNU0gZAHJZVuv1uixLAPQ+IqLWVhvVtm1RVikBgtrt9sygDaUULWhFYieOp5v22c2UEH2i\ndRymrBkPQyakEUksOzISHNq/H7P40/7/yUzwFPQPdcSP2sI/+SPySTiLEhBk5pyhKOvZYjlfrqSE\nKbSevBelLKVUhuycI6SckwzlCN5itBHukHQLFKmYIgAURWGtBmaltcQZCYIywH93dzeN02w+WywW\nbduKC5DI1XDmEMNJ+H0ap/1uBwBVVQmNJYSgjkLN1toXL148ffp0vV5/+PBhHEdj7PWT6y+++AIB\nnXVktNXaKC0BTfK/h4f7h/s76wrnytX5WdM0hzUPmGM0DiGllLKP0Yew2WzmyoqgKAAmQkOgMrKE\nNQJNxAg5p8xJKBWZhTMBmVkpLbi095NzTu6VSN8PE3sfum4wxlmrZU8qIgChswBz+nMP8aPyOAqj\n+9Sc/pjHIQJJTaGUAiKl8OQ0ysw5c9d1IrEpXq8MUaI/s3QSDpMPSBkYWGk/xX3b7TcPw343m80W\n50/B2ACxHcd28BCn0hbFWUmktFHa6KIsUOS8FZ02xjFkAzApBbasM8A4TagIicBlGe7QWsvE1jR5\naeinFGPwWiuBqqTdL3+WfS7FpizEA8EAiYgQNQDK1JIcjfLOzjmpbduuQ+SYfAwm5T5jevvDDw/b\nD0XhZk1DiSiDRRUHDilrkxFtxOAsDv04n8/+8Ordu12twvisHuf9PINSWJABDBPlgNnnsAsYGDgz\nMHBCrXIgGglNzJGyV3nUaSKIyIigCVQcex/HTT/sduFhm+6GPITpV19+wVvY4xbyuS0u/LhDDRc3\nZ47s+sPw8vXbb779OWBt1JRi9iElAOfsYjEPye/aHUCMUdRFkIg4RnikccifeRYe/3qCKBRpoV0p\npYqidM6tN/G3v/3Hoe9Wy/m/+pf/QmvatztjjHMlZ2jbXU4eMBaFK6tyHAelVLvfn5+fz2azsizZ\nusVikY9sGVQ0ThMT/sM//Gc/haZpyrIMcdjtdjIS2cxqrZUcYEQ0TmMIITPPl4u6qZHwj3/6k4wx\nIsLZ2RkwlGVxd3cbU4QUOWPwUwjTSdsLAFJmYY7FlK1xOWfnSldURjtmds6llI1Rw9AtlysE0lrt\n961kWYCcOQGySN88PgCO46OitZkfEwePrxS4AKW/zgwJUMgRpy38SXH2aRP4J2P6f+N1enOFSEgZ\nWayAlYHNdrdvu9ViTog55xiCbEYZiAMA8S8rCpdz9/79+/Pz8/Pzc/nW0m6VppoxWuumsI6Zp2na\nbbaSaJ5dX4vk17yZydjwfD5fzhdEJA6UZVmCaDgwKDz4Mkq2ceLgii/NNHmJqlqbsizbdr9aLWVh\n11XFOWulrDHK6Bzz6EetlHVOuq/GmBDiD2/+dHFxtTxbSSUhxYfgPIistQVFt3d3qBWZ0mhjrEVN\ngCDm1JQz5CS+kXAo3VkMygUGQkJNFHNyzlZVudlshqGv60YdTSzyNjOD96Ft28VioTQJW5Mx0aEL\n9PmA92cHAAAcgT2BARlkwlxqdsqY4cACBMITBihpXYpB2Ehywktl8DgESJIAwDnlBISMvZ/a/b5v\nd4T56vIyU/HDhw/zC1j3bWSYV+W8niPQicyajwJYMivY9W1KWWlltCEiRRhjBs5FVasYAWEcx3dv\n3jJzU9XC7Wnbtu97a621ERH7rk3pgCoenCvkeAMw2ihNIcTZbCaPWc4eKRRijDIqPPbdbrsbx9FY\no5UuikI6CkQ4DF2M5uKqmeLUNE03Fpw5x+hsYxgVZCKVGWJCAGSGyft92z59fkHTNNz72w/vaDDl\ntyFBQJiS34f+vVO5dKwwW2pYID6ERAPxjnKCnEP2mCNNHmNkr/wU48Qp8RSKkdNDn95v/J8+9LSY\n2/OvujRfD94sOsX7PCG5TUh8u+lCp59dfWEq97vf/X4+X5RFqXUm4hjHYRxSTrd393f3DxcXZ9bY\nnFlpQkLODMT4qAP8uNg85bDHP6A+MgKdc2XpEGEcx+1203d74IiEs9ms67rMPGtmxqp9uxnHgTlV\nVaOV3m53Mh642WwWi8X5+VnwoR+GxfIsxnh7ezdfLvMY1us15+9++ctfbNYPssglG0gpjuPInCfv\nm7rR2hDhfr+fzWbnZ6vlaqE0MUdZD+JCMU0TQ2qaJm79fr87v3zWlNVtZqP1aTLGuSKmnHKqm8Yo\nq5WztvTBSyDTWgyluevaq6ubnJghirBXSgERUooijn3Cyh4nOjlnIIJDWBdaJx5TdU6ZY4whppRz\nyFmnRJS1Vo/L/Y8nwccN/3k0Px4ScPq9j4+E07sdZOOOFyJIuxJzZoHKDTlXKK0JUfKq+Xy+2Wze\nvHlT1ZXgM5wZkeqmJBpkwuv0G43WSGStNVojIeccQs457/f77XZLRIvFXFB7GSxYLlfyTEMIzh0U\noYlwHCfZ16dp0BCiHELMLCNUkkfnnLfbLQA4Vy6Xq/PzC6kM9vu9EO2FcqqV8jHmw+zFcHt7a619\n+vTpbD4vikoe9DgOxlhjbI558kNpzdnF+Yuvvry9ffv+3TtG5YqibGqt6ECjR1YIQAeyTgYkUuJH\nLTALMuacUSkAkI4mM6zXD1rpZjZjZudsWVYp5WEYptGPdlQlkjEi2yHk/sfV+Y8PAEQ8BFtWcNQV\nZzH5pGPRiABiAcVIDJgBMmDOSXq/yYeqKEW59NF6knoUiQ+BDjMAOc+w27cPdw/dbjNvysuzy/PV\nFVVOFXqharTFqjlTgIUh6denFFKMHr10I5ih6/rtdvvkyZNyXiFIpp8THBwV5DMsFoscU1EUIg4l\n7Ith6O/vH+bz+aypx3HIR7NfOjgZyViHBjRCTL69vZ2mqWma2Wwm0L/Wepy81qYoyvv7h7brwibk\nlIyxz58/u7y8jDGWZZ2SjjHGkC6fXGoT/8H+p32GnCEkBpWZQRMBpxiSUcwphxCBdK/oN93uvm3b\nd7d08dQqMjGrMW/fdSq3pY1KcWE+aK0ZgRHZgMqAzIk5Jg6AHAkT5pDimFNUiclPfvD+w96/+tC/\nWvuVwctnX0feZ3VfahM7D6Q1FajSBGhr/ebuzcXqi/PV+bu3r5G/1hqUopjGfpoyZNI4m1dl2Wht\nEShwEJ51ZuCfQhh/4h/xQADPKQNl5jT5vqqKb775KsYAORttSIMPAYmM09aVw4dhGEZk5bQunJuG\nrqxLIowxSDUwO5+9fP065ni3eXh3e5sAz88vFri4unjycH8PcCgElaJx7IvKTNM0TiPn7P1UFE6w\nKe/95mFjC/fDuzfGalDUj/3r1z9sNw9fffXF/cN9XTmF2NTF+dkqpRBDKIuCEFPMRVFkSDElV7iy\nrC4vn7x9905pxZ6bphmGwRiFCoe+AyStNBK2+3a9FoYrG0N93xfOaW1kpvfxwQkHCF72JQJIFBeU\njIkwpehjCByzRGckYJk2xR/HcQDFR+XIz46HU0A//e0xiCf/JR+IJYSYiShGL8r+yIhACpXSFjmt\nLs5L62QsaxiH1XJ1cX4h6ooCnMovyilXVXVzczObzYRZQETiTWu1SUgyhhSiTN0kIWUgkmh4COnc\nGNO2rQBuwvYWFql8eCLSWuXMIu6WM0viKo29GAMiOFdcXFxKL1drU5aFhC/mxnsvh2IMMeecYiyK\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04OLbCBBRKoOkOJP0AFWeMg8cptyfzZvVWWHdoFCV2viokjFMRQqW0JUEfsJU6qoo+35H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mUcx8YYRCDygRgeTppSSgwB00LUVbVcrEeTcVEUp2dnz549ZSLfW2RgpPl8fv/W3TRLHXEURdqY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\nx5/gPvli4650Bv5ruPLFjKLIaw4452++8cbNmzf9tSvLwr/pZ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XQ6TdM0iqLLy8tHjx7t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LS0tSykqlorWZTkYAQPcrQhTYkrn5+eXV1Xq9LqTUWmNCCCEGWISxscaVg1xJKi/yk97xhOWc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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x345 at 0x7FE7DCE29048>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for d in random.sample(val_img_dicts, 1):\n",
    "    print(d)\n",
    "    img = cv2.imread(d[\"file_name\"])\n",
    "    visualizer = Visualizer(img[:, :, ::-1], metadata=coffee_metadata, scale=0.5)\n",
    "    vis = visualizer.draw_dataset_dict(d)\n",
    "    cv2_imshow(vis.get_image()[:, :, ::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cf1rSuz3QdZ8"
   },
   "source": [
    "## Train\n",
    "\n",
    "The following code is original training code from the Detectron2 example notebook but I think we can make it better/suit our problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "FldONTtpX7D-",
    "outputId": "f16c403d-ef9c-4a6c-dbd7-8bbc62d23329"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl'"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example use case of model_zoo\n",
    "model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AJwu3GlwQk0A"
   },
   "outputs": [],
   "source": [
    "# ### Original Training Code ###\n",
    "# from detectron2.engine import DefaultTrainer\n",
    "# from detectron2.config import get_cfg\n",
    "\n",
    "# cfg = get_cfg()\n",
    "# cfg.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml\"))\n",
    "# cfg.DATASETS.TRAIN = (\"coffeemaker-train\",)\n",
    "# cfg.DATASETS.TEST = ()\n",
    "# cfg.DATALOADER.NUM_WORKERS = 2\n",
    "# cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml\")  # Let training initialize from model zoo\n",
    "# cfg.SOLVER.IMS_PER_BATCH = 2\n",
    "# cfg.SOLVER.BASE_LR = 0.00025  # pick a good LR\n",
    "# cfg.SOLVER.MAX_ITER = 300    # 300 iterations seems good enough for this toy dataset; you may need to train longer for a practical dataset\n",
    "# cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # faster, and good enough for this toy dataset (default: 512)\n",
    "# cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1  # only has one class (coffeemaker)\n",
    "\n",
    "# os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n",
    "# trainer = DefaultTrainer(cfg) \n",
    "# trainer.resume_or_load(resume=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "F_Bd7ILEJkc1",
    "outputId": "43de4b6b-3d6f-423e-da94-e8af193ed3ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CfgNode({'TRAIN': 'my-training-data', 'PROPOSAL_FILES_TRAIN': (), 'PRECOMPUTED_PROPOSAL_TOPK_TRAIN': 2000, 'TEST': (), 'PROPOSAL_FILES_TEST': (), 'PRECOMPUTED_PROPOSAL_TOPK_TEST': 1000})"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_config = get_cfg()\n",
    "default_config.DATASETS.TRAIN = (\"my-training-data\",)\n",
    "default_config.DATASETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VGRcwaM773Jo"
   },
   "outputs": [],
   "source": [
    "# Changed from original: Create evaluator for COCOEvaluator only \n",
    "# Since we are only using bounding boxes to begin with, our evaluator can be simple COCO style\n",
    "def get_evaluator(cfg, dataset_name, output_folder=None):\n",
    "  \"\"\"\n",
    "  Create a COCOEvaluator\n",
    "  \"\"\"\n",
    "  if output_folder is None:\n",
    "    output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n",
    "  evaluator = COCOEvaluator(\n",
    "      dataset_name=dataset_name,\n",
    "      cfg=cfg,\n",
    "      distributed=False,\n",
    "      output_dir=output_folder\n",
    "  )\n",
    "  return evaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kujKs5gN9u2-"
   },
   "outputs": [],
   "source": [
    "# Create testing function\n",
    "def do_test(cfg, model):\n",
    "  results = OrderedDict()\n",
    "  for dataset_name in cfg.DATASETS.TEST:\n",
    "    data_loader = build_detection_test_loader(cfg, dataset_name)\n",
    "    # Create the evaluator\n",
    "    evaluator = get_evaluator(\n",
    "        cfg, \n",
    "        dataset_name,\n",
    "        output_folder=os.path.join(cfg.OUTPUT_DIR, \"inference\", dataset_name)\n",
    "    )\n",
    "    # Make inference on dataset\n",
    "    results_i = inference_on_dataset(model,\n",
    "                                     data_loader,\n",
    "                                     evaluator)\n",
    "    # Update results dictionary\n",
    "    results[dataset_name] = results_i\n",
    "\n",
    "    print(\"### Returning results_i...\")\n",
    "    print(results_i)\n",
    "    #print(f\"### Average Precision: {results_i['AP']}\")\n",
    "    # Let's get some communication happening\n",
    "    if comm.is_main_process():\n",
    "      logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n",
    "      ## wandb.log()? TODO/NOTE: This may be something Weights & Biases can track\n",
    "      print(\"### Calculating results...\")\n",
    "      print_csv_format(results_i)\n",
    "    \n",
    "    # Check to see length of results\n",
    "    if len(results) == 1:\n",
    "      results = list(results.values())[0]\n",
    "    print(\"### Returning results...\")\n",
    "    print(results)\n",
    "\n",
    "    # TODO : log results_i dict with different parameters\n",
    "    print(\"### Saving results to Weights & Biases...\")\n",
    "    wandb.log(results_i)\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5QOmcDNIBO3T"
   },
   "source": [
    "Source code for Checkpointer: https://detectron2.readthedocs.io/_modules/fvcore/common/checkpoint.html\n",
    "\n",
    "Maybe information from the Checkpointers can be saved to Weights & Biases? Or at least used to track in my own tracking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XM5ocNdR_2R3"
   },
   "outputs": [],
   "source": [
    "# Create training function\n",
    "def do_train(cfg, model, resume=False):\n",
    "  # Set model to training mode\n",
    "  model.train()\n",
    "  # Create optimizer from config file (returns torch.nn.optimizer.Optimizer)\n",
    "  optimizer = build_optimizer(cfg, model)\n",
    "  # Create scheduler for learning rate (returns torch.optim.lr._LR_scheduler)\n",
    "  scheduler = build_lr_scheduler(cfg, optimizer)\n",
    "\n",
    "  # Create checkpointer\n",
    "  checkpointer = DetectionCheckpointer(\n",
    "      model, save_dir=cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler\n",
    "  )\n",
    "\n",
    "  # Create start iteration (refernces checkpointer) - https://detectron2.readthedocs.io/modules/checkpoint.html#detectron2.checkpoint.Checkpointer.resume_or_load\n",
    "  start_iter = (\n",
    "      # This can be 0\n",
    "      checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, # Use predefined model weights (pretrained model)\n",
    "                                  resume=resume).get(\"iteration\", -1) + 1\n",
    "  )\n",
    "  # Set max number of iterations\n",
    "  max_iter = cfg.SOLVER.MAX_ITER\n",
    "\n",
    "  # Create periodiccheckpoint\n",
    "  periodic_checkpointer = PeriodicCheckpointer(checkpointer=checkpointer,\n",
    "                                               # How often to make checkpoints?\n",
    "                                               period=cfg.SOLVER.CHECKPOINT_PERIOD,\n",
    "                                               max_iter=max_iter)\n",
    "  \n",
    "  # Create writers (for saving checkpoints?)\n",
    "  writers = (\n",
    "      [\n",
    "       # Print out common metrics such as iteration time, ETA, memory, all losses, learning rate\n",
    "       CommonMetricPrinter(max_iter=max_iter),\n",
    "       # Write scalars to a JSON file such as loss values, time and more\n",
    "       JSONWriter(os.path.join(cfg.OUTPUT_DIR, \"metrics.json\")),\n",
    "       # Write all scalars such as loss values to a TensorBoard file for easy visualization\n",
    "       TensorboardXWriter(cfg.OUTPUT_DIR),\n",
    "      ]\n",
    "      if comm.is_main_process()\n",
    "      else []\n",
    "  )\n",
    "\n",
    "  ### Original note from script: ###\n",
    "  # compared to \"train_net.py\", we do not support accurate timing and precise BN\n",
    "  # here, because they are not trivial to implement\n",
    "\n",
    "  # Build a training data loader based off the training dataset name in the config\n",
    "  data_loader = build_detection_train_loader(cfg)\n",
    "\n",
    "  # Start logging\n",
    "  logger.info(\"Starting training from iteration {}\".format(start_iter))\n",
    "\n",
    "  # Store events\n",
    "  with EventStorage(start_iter) as storage:\n",
    "    # Loop through zipped data loader and iteration\n",
    "    for data, iteration in zip(data_loader, range(start_iter, max_iter)):\n",
    "      iteration = iteration + 1\n",
    "      storage.step() # update stroage with step - https://detectron2.readthedocs.io/modules/utils.html#detectron2.utils.events.EventStorage.step\n",
    "\n",
    "      # Create loss dictionary by trying to model data\n",
    "      loss_dict = model(data)\n",
    "      losses = sum(loss_dict.values())\n",
    "      # Are losses infinite? If so, something is wrong\n",
    "      assert torch.isfinite(losses).all(), loss_dict\n",
    "\n",
    "      # TODO - Not quite sure what's happening here\n",
    "      loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}\n",
    "      # Sum up losses\n",
    "      losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n",
    "      \n",
    "      # # TODO: wandb.log()? log the losses\n",
    "      # wandb.log({\n",
    "      #     \"Total loss\": losses_reduced\n",
    "      # })\n",
    "\n",
    "      # Update storage\n",
    "      if comm.is_main_process():\n",
    "        # Store informate in storage - https://detectron2.readthedocs.io/modules/utils.html#detectron2.utils.events.EventStorage.put_scalars\n",
    "        storage.put_scalars(total_loss=losses_reduced,\n",
    "                            **loss_dict_reduced)\n",
    "      \n",
    "      # Start doing PyTorch things\n",
    "      optimizer.zero_grad()\n",
    "      losses.backward()\n",
    "      optimizer.step()\n",
    "      # Add learning rate to storage information\n",
    "      storage.put_scalar(\"lr\", optimizer.param_groups[0][\"lr\"], smoothing_hint=False)\n",
    "\n",
    "      # Perform evaluation?\n",
    "      if (\n",
    "          cfg.TEST.EVAL_PERIOD > 0\n",
    "          and iteration % cfg.TEST.EVAL_PERIOD == 0\n",
    "          and iteration != max_iter\n",
    "      ):\n",
    "          do_test(cfg, model)\n",
    "          # TODO - compared to \"train_net.py\", the test results are not dumped to EventStorage\n",
    "          comm.synchronize()\n",
    "      \n",
    "      # Log different metrics with writers\n",
    "      if iteration - start_iter > 5 and (iteration % 20 == 0 or iteration == max_iter):\n",
    "        for writer in writers:\n",
    "          writer.write()\n",
    "      \n",
    "      # Update the periodic_checkpointer\n",
    "      periodic_checkpointer.step(iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ePUueRP-MLTx"
   },
   "outputs": [],
   "source": [
    "# Create setup function\n",
    "def setup(args):\n",
    "  \"\"\"\n",
    "  Create configs and perform basic setups.\n",
    "  \"\"\"\n",
    "  cfg = get_cfg()\n",
    "  cfg.merge_from_file(args.config_file) # This will take some kind of model.yaml file\n",
    "  cfg.merge_from_list(args.opts)\n",
    "  cfg.freeze()\n",
    "  default_setup(\n",
    "      cfg, args\n",
    "  ) # this logs the config and arguments passed to the command line to the output file\n",
    "\n",
    "  # Load config YAML as dict\n",
    "  cfg_yaml = cfg.load_yaml_with_base(os.path.join(cfg.OUTPUT_DIR, \"config.yaml\"))\n",
    "\n",
    "  # default_config = get_cfg()\n",
    "  # default_config_loaded = default_config.load_yaml_with_base(\"output/config.yaml\")\n",
    "  # default_config_loaded\n",
    "\n",
    "  # TODO: turn config into YAML and save to weights & biases\n",
    "  # TODO: Init wandb and add configs\n",
    "  # Setup a new weights & biases run every time we run the setup() function\n",
    "  wandb.init(project=\"airbnb-object-detection\", \n",
    "            sync_tensorboard=True)\n",
    "  print(\"### Printing config_yaml file to go into Weights & Biases\")\n",
    "  print(cfg_yaml)\n",
    "  wandb.config.update(cfg_yaml)\n",
    "  return cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uj0bgT2uMueN"
   },
   "outputs": [],
   "source": [
    "# Create main function\n",
    "def main(args):\n",
    "  \n",
    "  # Create the config file\n",
    "  cfg = setup(args)\n",
    "\n",
    "  # Build the model\n",
    "  model = build_model(cfg)\n",
    "  \n",
    "  # Log what's going on\n",
    "  logger.info(\"Model:\\n{}\".format(model))\n",
    "\n",
    "  # TODO: Fix this (if it doesn't work)\n",
    "  #wandb.watch(model, log=\"all\")\n",
    "\n",
    "  # Only do evaluation if the args say so\n",
    "  if args.eval_only:\n",
    "    DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(\n",
    "        cfg.MODEL.WEIGHTS, resume=args.resume\n",
    "    )\n",
    "    return do_test(cfg, model)\n",
    "\n",
    "  # Do distributed training? (depends on number of GPUs available)\n",
    "  distributed = comm.get_world_size() > 1\n",
    "  if distributed:\n",
    "    # Put the model on multiple devices if available\n",
    "    model = DistributedDataParallel(\n",
    "        model, \n",
    "        device_ids=[comm.get_local_rank()], \n",
    "        broadcast_buffers=False\n",
    "    )\n",
    "\n",
    "  # Train the model\n",
    "  do_train(cfg, model)\n",
    "  # TODO - May want to evaluate in a different step?\n",
    "  return do_test(cfg, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "colab_type": "code",
    "id": "jlJBGMmld_8P",
    "outputId": "c9776f9a-a2ab-4a2a-950f-3d150fc712dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R101-FPN-3x': 'COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml',\n",
       " 'R50-FPN-1x': 'COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml',\n",
       " 'R50-FPN-3x': 'COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml',\n",
       " 'RN-R101-3x': 'COCO-Detection/retinanet_R_101_FPN_3x.yaml',\n",
       " 'RN-R50-1x': 'COCO-Detection/retinanet_R_50_FPN_1x.yaml',\n",
       " 'RN-R50-3x': 'COCO-Detection/retinanet_R_50_FPN_3x.yaml',\n",
       " 'X101-FPN-3x': 'COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml'}"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_to_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "2jBmeP8OeJ6L",
    "outputId": "b00ddf10-1479-4d64-ce89-c6197148bd3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml'"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_to_try[\"R50-FPN-1x\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rjyWVEnleSnO",
    "outputId": "9d208fc2-e628-48d3-8aae-48d1bafcabcb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl'"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_zoo.get_checkpoint_url('COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4a0Feq-6ePaz",
    "outputId": "8d9243a1-9cec-4c24-ed59-6b84cdc94b81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml'"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c4tzLJ-QxW-2"
   },
   "source": [
    "Let's use a MAX_ITER of ~30 whilst we're building a small model.\n",
    "\n",
    "We'll also change some defaults:\n",
    "* `SOLVER.IMS_PER_BATCH` = 16 -> 2 (lower for memory issues)\n",
    "* `DATALOADER.NUM_WORKERS` = 4 -> 2 (lower for memory issues)\n",
    "\n",
    "These lowering settings are based on the example Detectron2 tutorial notebook (which runs in Colab), we can update them later once we figure out what resources we're using.\n",
    "\n",
    "See this post for an example of resource use: https://lambdalabs.com/blog/weights-and-bias-gpu-cpu-utilization/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "6C2jupOidZmO",
    "outputId": "7316abed-131f-4407-bb54-7533b307528d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(config_file='/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl', 'DATASETS.TRAIN', \"('coffeemaker-train',)\", 'DATASETS.TEST', \"('coffeemaker-validation',)\", 'DATALOADER.NUM_WORKERS', '2', 'SOLVER.IMS_PER_BATCH', '2', 'SOLVER.MAX_ITER', '500', 'MODEL.ROI_HEADS.NUM_CLASSES', '1'], resume=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup arg parser\n",
    "parser = default_argument_parser() # default in Detectron2\n",
    "args = parser.parse_args(\"--config-file /usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml \\\n",
    "                          MODEL.WEIGHTS https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl \\\n",
    "                          DATASETS.TRAIN ('coffeemaker-train',) \\\n",
    "                          DATASETS.TEST ('coffeemaker-validation',) \\\n",
    "                          DATALOADER.NUM_WORKERS 2 \\\n",
    "                          SOLVER.IMS_PER_BATCH 2 \\\n",
    "                          SOLVER.MAX_ITER 500 \\\n",
    "                          MODEL.ROI_HEADS.NUM_CLASSES 1\".split())\n",
    "# TODO: Add args to Weights & Biases\n",
    "args"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FiAQDPnTCmz3"
   },
   "source": [
    "## Run custom training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "7PUh74wHd299",
    "outputId": "f33eb188-bdf0-4b63-c362-53712a0ca11b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[03/11 06:17:11 detectron2]: \u001b[0mRank of current process: 0. World size: 1\n",
      "\u001b[32m[03/11 06:17:11 detectron2]: \u001b[0mRank of current process: 0. World size: 1\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mEnvironment info:\n",
      "------------------------  ---------------------------------------------------------------\n",
      "sys.platform              linux\n",
      "Python                    3.6.9 (default, Nov  7 2019, 10:44:02) [GCC 8.3.0]\n",
      "numpy                     1.17.5\n",
      "detectron2                0.1.1 @/usr/local/lib/python3.6/dist-packages/detectron2\n",
      "detectron2 compiler       GCC 7.3\n",
      "detectron2 CUDA compiler  10.0\n",
      "detectron2 arch flags     sm_35, sm_37, sm_50, sm_52, sm_60, sm_61, sm_70, sm_75\n",
      "DETECTRON2_ENV_MODULE     <not set>\n",
      "PyTorch                   1.4.0+cu100 @/usr/local/lib/python3.6/dist-packages/torch\n",
      "PyTorch debug build       False\n",
      "CUDA available            True\n",
      "GPU 0                     Tesla T4\n",
      "CUDA_HOME                 /usr/local/cuda\n",
      "NVCC                      Cuda compilation tools, release 10.0, V10.0.130\n",
      "Pillow                    6.2.2\n",
      "torchvision               0.5.0+cu100 @/usr/local/lib/python3.6/dist-packages/torchvision\n",
      "torchvision arch flags    sm_35, sm_50, sm_60, sm_70, sm_75\n",
      "cv2                       4.1.2\n",
      "------------------------  ---------------------------------------------------------------\n",
      "PyTorch built with:\n",
      "  - GCC 7.3\n",
      "  - Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - NNPACK is enabled\n",
      "  - CUDA Runtime 10.0\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\n",
      "  - CuDNN 7.6.3\n",
      "  - Magma 2.5.1\n",
      "  - Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, \n",
      "\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mEnvironment info:\n",
      "------------------------  ---------------------------------------------------------------\n",
      "sys.platform              linux\n",
      "Python                    3.6.9 (default, Nov  7 2019, 10:44:02) [GCC 8.3.0]\n",
      "numpy                     1.17.5\n",
      "detectron2                0.1.1 @/usr/local/lib/python3.6/dist-packages/detectron2\n",
      "detectron2 compiler       GCC 7.3\n",
      "detectron2 CUDA compiler  10.0\n",
      "detectron2 arch flags     sm_35, sm_37, sm_50, sm_52, sm_60, sm_61, sm_70, sm_75\n",
      "DETECTRON2_ENV_MODULE     <not set>\n",
      "PyTorch                   1.4.0+cu100 @/usr/local/lib/python3.6/dist-packages/torch\n",
      "PyTorch debug build       False\n",
      "CUDA available            True\n",
      "GPU 0                     Tesla T4\n",
      "CUDA_HOME                 /usr/local/cuda\n",
      "NVCC                      Cuda compilation tools, release 10.0, V10.0.130\n",
      "Pillow                    6.2.2\n",
      "torchvision               0.5.0+cu100 @/usr/local/lib/python3.6/dist-packages/torchvision\n",
      "torchvision arch flags    sm_35, sm_50, sm_60, sm_70, sm_75\n",
      "cv2                       4.1.2\n",
      "------------------------  ---------------------------------------------------------------\n",
      "PyTorch built with:\n",
      "  - GCC 7.3\n",
      "  - Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - NNPACK is enabled\n",
      "  - CUDA Runtime 10.0\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\n",
      "  - CuDNN 7.6.3\n",
      "  - Magma 2.5.1\n",
      "  - Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, \n",
      "\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mCommand line arguments: Namespace(config_file='/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl', 'DATASETS.TRAIN', \"('coffeemaker-train',)\", 'DATASETS.TEST', \"('coffeemaker-validation',)\", 'DATALOADER.NUM_WORKERS', '2', 'SOLVER.IMS_PER_BATCH', '2', 'SOLVER.MAX_ITER', '500', 'MODEL.ROI_HEADS.NUM_CLASSES', '1'], resume=False)\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mCommand line arguments: Namespace(config_file='/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml', dist_url='tcp://127.0.0.1:49152', eval_only=False, machine_rank=0, num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl', 'DATASETS.TRAIN', \"('coffeemaker-train',)\", 'DATASETS.TEST', \"('coffeemaker-validation',)\", 'DATALOADER.NUM_WORKERS', '2', 'SOLVER.IMS_PER_BATCH', '2', 'SOLVER.MAX_ITER', '500', 'MODEL.ROI_HEADS.NUM_CLASSES', '1'], resume=False)\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mContents of args.config_file=/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml:\n",
      "_BASE_: \"../Base-RCNN-FPN.yaml\"\n",
      "MODEL:\n",
      "  WEIGHTS: \"detectron2://ImageNetPretrained/MSRA/R-50.pkl\"\n",
      "  MASK_ON: False\n",
      "  RESNETS:\n",
      "    DEPTH: 50\n",
      "\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mContents of args.config_file=/usr/local/lib/python3.6/dist-packages/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml:\n",
      "_BASE_: \"../Base-RCNN-FPN.yaml\"\n",
      "MODEL:\n",
      "  WEIGHTS: \"detectron2://ImageNetPretrained/MSRA/R-50.pkl\"\n",
      "  MASK_ON: False\n",
      "  RESNETS:\n",
      "    DEPTH: 50\n",
      "\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mRunning with full config:\n",
      "CUDNN_BENCHMARK: False\n",
      "DATALOADER:\n",
      "  ASPECT_RATIO_GROUPING: True\n",
      "  FILTER_EMPTY_ANNOTATIONS: True\n",
      "  NUM_WORKERS: 2\n",
      "  REPEAT_THRESHOLD: 0.0\n",
      "  SAMPLER_TRAIN: TrainingSampler\n",
      "DATASETS:\n",
      "  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n",
      "  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n",
      "  PROPOSAL_FILES_TEST: ()\n",
      "  PROPOSAL_FILES_TRAIN: ()\n",
      "  TEST: ('coffeemaker-validation',)\n",
      "  TRAIN: ('coffeemaker-train',)\n",
      "GLOBAL:\n",
      "  HACK: 1.0\n",
      "INPUT:\n",
      "  CROP:\n",
      "    ENABLED: False\n",
      "    SIZE: [0.9, 0.9]\n",
      "    TYPE: relative_range\n",
      "  FORMAT: BGR\n",
      "  MASK_FORMAT: polygon\n",
      "  MAX_SIZE_TEST: 1333\n",
      "  MAX_SIZE_TRAIN: 1333\n",
      "  MIN_SIZE_TEST: 800\n",
      "  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)\n",
      "  MIN_SIZE_TRAIN_SAMPLING: choice\n",
      "MODEL:\n",
      "  ANCHOR_GENERATOR:\n",
      "    ANGLES: [[-90, 0, 90]]\n",
      "    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]\n",
      "    NAME: DefaultAnchorGenerator\n",
      "    OFFSET: 0.0\n",
      "    SIZES: [[32], [64], [128], [256], [512]]\n",
      "  BACKBONE:\n",
      "    FREEZE_AT: 2\n",
      "    NAME: build_resnet_fpn_backbone\n",
      "  DEVICE: cuda\n",
      "  FPN:\n",
      "    FUSE_TYPE: sum\n",
      "    IN_FEATURES: ['res2', 'res3', 'res4', 'res5']\n",
      "    NORM: \n",
      "    OUT_CHANNELS: 256\n",
      "  KEYPOINT_ON: False\n",
      "  LOAD_PROPOSALS: False\n",
      "  MASK_ON: False\n",
      "  META_ARCHITECTURE: GeneralizedRCNN\n",
      "  PANOPTIC_FPN:\n",
      "    COMBINE:\n",
      "      ENABLED: True\n",
      "      INSTANCES_CONFIDENCE_THRESH: 0.5\n",
      "      OVERLAP_THRESH: 0.5\n",
      "      STUFF_AREA_LIMIT: 4096\n",
      "    INSTANCE_LOSS_WEIGHT: 1.0\n",
      "  PIXEL_MEAN: [103.53, 116.28, 123.675]\n",
      "  PIXEL_STD: [1.0, 1.0, 1.0]\n",
      "  PROPOSAL_GENERATOR:\n",
      "    MIN_SIZE: 0\n",
      "    NAME: RPN\n",
      "  RESNETS:\n",
      "    DEFORM_MODULATED: False\n",
      "    DEFORM_NUM_GROUPS: 1\n",
      "    DEFORM_ON_PER_STAGE: [False, False, False, False]\n",
      "    DEPTH: 50\n",
      "    NORM: FrozenBN\n",
      "    NUM_GROUPS: 1\n",
      "    OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']\n",
      "    RES2_OUT_CHANNELS: 256\n",
      "    RES5_DILATION: 1\n",
      "    STEM_OUT_CHANNELS: 64\n",
      "    STRIDE_IN_1X1: True\n",
      "    WIDTH_PER_GROUP: 64\n",
      "  RETINANET:\n",
      "    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n",
      "    FOCAL_LOSS_ALPHA: 0.25\n",
      "    FOCAL_LOSS_GAMMA: 2.0\n",
      "    IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']\n",
      "    IOU_LABELS: [0, -1, 1]\n",
      "    IOU_THRESHOLDS: [0.4, 0.5]\n",
      "    NMS_THRESH_TEST: 0.5\n",
      "    NUM_CLASSES: 80\n",
      "    NUM_CONVS: 4\n",
      "    PRIOR_PROB: 0.01\n",
      "    SCORE_THRESH_TEST: 0.05\n",
      "    SMOOTH_L1_LOSS_BETA: 0.1\n",
      "    TOPK_CANDIDATES_TEST: 1000\n",
      "  ROI_BOX_CASCADE_HEAD:\n",
      "    BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))\n",
      "    IOUS: (0.5, 0.6, 0.7)\n",
      "  ROI_BOX_HEAD:\n",
      "    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)\n",
      "    CLS_AGNOSTIC_BBOX_REG: False\n",
      "    CONV_DIM: 256\n",
      "    FC_DIM: 1024\n",
      "    NAME: FastRCNNConvFCHead\n",
      "    NORM: \n",
      "    NUM_CONV: 0\n",
      "    NUM_FC: 2\n",
      "    POOLER_RESOLUTION: 7\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "    SMOOTH_L1_BETA: 0.0\n",
      "    TRAIN_ON_PRED_BOXES: False\n",
      "  ROI_HEADS:\n",
      "    BATCH_SIZE_PER_IMAGE: 512\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n",
      "    IOU_LABELS: [0, 1]\n",
      "    IOU_THRESHOLDS: [0.5]\n",
      "    NAME: StandardROIHeads\n",
      "    NMS_THRESH_TEST: 0.5\n",
      "    NUM_CLASSES: 1\n",
      "    POSITIVE_FRACTION: 0.25\n",
      "    PROPOSAL_APPEND_GT: True\n",
      "    SCORE_THRESH_TEST: 0.05\n",
      "  ROI_KEYPOINT_HEAD:\n",
      "    CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    MIN_KEYPOINTS_PER_IMAGE: 1\n",
      "    NAME: KRCNNConvDeconvUpsampleHead\n",
      "    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: True\n",
      "    NUM_KEYPOINTS: 17\n",
      "    POOLER_RESOLUTION: 14\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "  ROI_MASK_HEAD:\n",
      "    CLS_AGNOSTIC_MASK: False\n",
      "    CONV_DIM: 256\n",
      "    NAME: MaskRCNNConvUpsampleHead\n",
      "    NORM: \n",
      "    NUM_CONV: 4\n",
      "    POOLER_RESOLUTION: 14\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "  RPN:\n",
      "    BATCH_SIZE_PER_IMAGE: 256\n",
      "    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n",
      "    BOUNDARY_THRESH: -1\n",
      "    HEAD_NAME: StandardRPNHead\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']\n",
      "    IOU_LABELS: [0, -1, 1]\n",
      "    IOU_THRESHOLDS: [0.3, 0.7]\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    NMS_THRESH: 0.7\n",
      "    POSITIVE_FRACTION: 0.5\n",
      "    POST_NMS_TOPK_TEST: 1000\n",
      "    POST_NMS_TOPK_TRAIN: 1000\n",
      "    PRE_NMS_TOPK_TEST: 1000\n",
      "    PRE_NMS_TOPK_TRAIN: 2000\n",
      "    SMOOTH_L1_BETA: 0.0\n",
      "  SEM_SEG_HEAD:\n",
      "    COMMON_STRIDE: 4\n",
      "    CONVS_DIM: 128\n",
      "    IGNORE_VALUE: 255\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    NAME: SemSegFPNHead\n",
      "    NORM: GN\n",
      "    NUM_CLASSES: 54\n",
      "  WEIGHTS: https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl\n",
      "OUTPUT_DIR: ./output\n",
      "SEED: -1\n",
      "SOLVER:\n",
      "  BASE_LR: 0.02\n",
      "  BIAS_LR_FACTOR: 1.0\n",
      "  CHECKPOINT_PERIOD: 5000\n",
      "  GAMMA: 0.1\n",
      "  IMS_PER_BATCH: 2\n",
      "  LR_SCHEDULER_NAME: WarmupMultiStepLR\n",
      "  MAX_ITER: 500\n",
      "  MOMENTUM: 0.9\n",
      "  STEPS: (60000, 80000)\n",
      "  WARMUP_FACTOR: 0.001\n",
      "  WARMUP_ITERS: 1000\n",
      "  WARMUP_METHOD: linear\n",
      "  WEIGHT_DECAY: 0.0001\n",
      "  WEIGHT_DECAY_BIAS: 0.0001\n",
      "  WEIGHT_DECAY_NORM: 0.0\n",
      "TEST:\n",
      "  AUG:\n",
      "    ENABLED: False\n",
      "    FLIP: True\n",
      "    MAX_SIZE: 4000\n",
      "    MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)\n",
      "  DETECTIONS_PER_IMAGE: 100\n",
      "  EVAL_PERIOD: 0\n",
      "  EXPECTED_RESULTS: []\n",
      "  KEYPOINT_OKS_SIGMAS: []\n",
      "  PRECISE_BN:\n",
      "    ENABLED: False\n",
      "    NUM_ITER: 200\n",
      "VERSION: 2\n",
      "VIS_PERIOD: 0\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mRunning with full config:\n",
      "CUDNN_BENCHMARK: False\n",
      "DATALOADER:\n",
      "  ASPECT_RATIO_GROUPING: True\n",
      "  FILTER_EMPTY_ANNOTATIONS: True\n",
      "  NUM_WORKERS: 2\n",
      "  REPEAT_THRESHOLD: 0.0\n",
      "  SAMPLER_TRAIN: TrainingSampler\n",
      "DATASETS:\n",
      "  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n",
      "  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n",
      "  PROPOSAL_FILES_TEST: ()\n",
      "  PROPOSAL_FILES_TRAIN: ()\n",
      "  TEST: ('coffeemaker-validation',)\n",
      "  TRAIN: ('coffeemaker-train',)\n",
      "GLOBAL:\n",
      "  HACK: 1.0\n",
      "INPUT:\n",
      "  CROP:\n",
      "    ENABLED: False\n",
      "    SIZE: [0.9, 0.9]\n",
      "    TYPE: relative_range\n",
      "  FORMAT: BGR\n",
      "  MASK_FORMAT: polygon\n",
      "  MAX_SIZE_TEST: 1333\n",
      "  MAX_SIZE_TRAIN: 1333\n",
      "  MIN_SIZE_TEST: 800\n",
      "  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)\n",
      "  MIN_SIZE_TRAIN_SAMPLING: choice\n",
      "MODEL:\n",
      "  ANCHOR_GENERATOR:\n",
      "    ANGLES: [[-90, 0, 90]]\n",
      "    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]\n",
      "    NAME: DefaultAnchorGenerator\n",
      "    OFFSET: 0.0\n",
      "    SIZES: [[32], [64], [128], [256], [512]]\n",
      "  BACKBONE:\n",
      "    FREEZE_AT: 2\n",
      "    NAME: build_resnet_fpn_backbone\n",
      "  DEVICE: cuda\n",
      "  FPN:\n",
      "    FUSE_TYPE: sum\n",
      "    IN_FEATURES: ['res2', 'res3', 'res4', 'res5']\n",
      "    NORM: \n",
      "    OUT_CHANNELS: 256\n",
      "  KEYPOINT_ON: False\n",
      "  LOAD_PROPOSALS: False\n",
      "  MASK_ON: False\n",
      "  META_ARCHITECTURE: GeneralizedRCNN\n",
      "  PANOPTIC_FPN:\n",
      "    COMBINE:\n",
      "      ENABLED: True\n",
      "      INSTANCES_CONFIDENCE_THRESH: 0.5\n",
      "      OVERLAP_THRESH: 0.5\n",
      "      STUFF_AREA_LIMIT: 4096\n",
      "    INSTANCE_LOSS_WEIGHT: 1.0\n",
      "  PIXEL_MEAN: [103.53, 116.28, 123.675]\n",
      "  PIXEL_STD: [1.0, 1.0, 1.0]\n",
      "  PROPOSAL_GENERATOR:\n",
      "    MIN_SIZE: 0\n",
      "    NAME: RPN\n",
      "  RESNETS:\n",
      "    DEFORM_MODULATED: False\n",
      "    DEFORM_NUM_GROUPS: 1\n",
      "    DEFORM_ON_PER_STAGE: [False, False, False, False]\n",
      "    DEPTH: 50\n",
      "    NORM: FrozenBN\n",
      "    NUM_GROUPS: 1\n",
      "    OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']\n",
      "    RES2_OUT_CHANNELS: 256\n",
      "    RES5_DILATION: 1\n",
      "    STEM_OUT_CHANNELS: 64\n",
      "    STRIDE_IN_1X1: True\n",
      "    WIDTH_PER_GROUP: 64\n",
      "  RETINANET:\n",
      "    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n",
      "    FOCAL_LOSS_ALPHA: 0.25\n",
      "    FOCAL_LOSS_GAMMA: 2.0\n",
      "    IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']\n",
      "    IOU_LABELS: [0, -1, 1]\n",
      "    IOU_THRESHOLDS: [0.4, 0.5]\n",
      "    NMS_THRESH_TEST: 0.5\n",
      "    NUM_CLASSES: 80\n",
      "    NUM_CONVS: 4\n",
      "    PRIOR_PROB: 0.01\n",
      "    SCORE_THRESH_TEST: 0.05\n",
      "    SMOOTH_L1_LOSS_BETA: 0.1\n",
      "    TOPK_CANDIDATES_TEST: 1000\n",
      "  ROI_BOX_CASCADE_HEAD:\n",
      "    BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))\n",
      "    IOUS: (0.5, 0.6, 0.7)\n",
      "  ROI_BOX_HEAD:\n",
      "    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)\n",
      "    CLS_AGNOSTIC_BBOX_REG: False\n",
      "    CONV_DIM: 256\n",
      "    FC_DIM: 1024\n",
      "    NAME: FastRCNNConvFCHead\n",
      "    NORM: \n",
      "    NUM_CONV: 0\n",
      "    NUM_FC: 2\n",
      "    POOLER_RESOLUTION: 7\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "    SMOOTH_L1_BETA: 0.0\n",
      "    TRAIN_ON_PRED_BOXES: False\n",
      "  ROI_HEADS:\n",
      "    BATCH_SIZE_PER_IMAGE: 512\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n",
      "    IOU_LABELS: [0, 1]\n",
      "    IOU_THRESHOLDS: [0.5]\n",
      "    NAME: StandardROIHeads\n",
      "    NMS_THRESH_TEST: 0.5\n",
      "    NUM_CLASSES: 1\n",
      "    POSITIVE_FRACTION: 0.25\n",
      "    PROPOSAL_APPEND_GT: True\n",
      "    SCORE_THRESH_TEST: 0.05\n",
      "  ROI_KEYPOINT_HEAD:\n",
      "    CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    MIN_KEYPOINTS_PER_IMAGE: 1\n",
      "    NAME: KRCNNConvDeconvUpsampleHead\n",
      "    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: True\n",
      "    NUM_KEYPOINTS: 17\n",
      "    POOLER_RESOLUTION: 14\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "  ROI_MASK_HEAD:\n",
      "    CLS_AGNOSTIC_MASK: False\n",
      "    CONV_DIM: 256\n",
      "    NAME: MaskRCNNConvUpsampleHead\n",
      "    NORM: \n",
      "    NUM_CONV: 4\n",
      "    POOLER_RESOLUTION: 14\n",
      "    POOLER_SAMPLING_RATIO: 0\n",
      "    POOLER_TYPE: ROIAlignV2\n",
      "  RPN:\n",
      "    BATCH_SIZE_PER_IMAGE: 256\n",
      "    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n",
      "    BOUNDARY_THRESH: -1\n",
      "    HEAD_NAME: StandardRPNHead\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']\n",
      "    IOU_LABELS: [0, -1, 1]\n",
      "    IOU_THRESHOLDS: [0.3, 0.7]\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    NMS_THRESH: 0.7\n",
      "    POSITIVE_FRACTION: 0.5\n",
      "    POST_NMS_TOPK_TEST: 1000\n",
      "    POST_NMS_TOPK_TRAIN: 1000\n",
      "    PRE_NMS_TOPK_TEST: 1000\n",
      "    PRE_NMS_TOPK_TRAIN: 2000\n",
      "    SMOOTH_L1_BETA: 0.0\n",
      "  SEM_SEG_HEAD:\n",
      "    COMMON_STRIDE: 4\n",
      "    CONVS_DIM: 128\n",
      "    IGNORE_VALUE: 255\n",
      "    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n",
      "    LOSS_WEIGHT: 1.0\n",
      "    NAME: SemSegFPNHead\n",
      "    NORM: GN\n",
      "    NUM_CLASSES: 54\n",
      "  WEIGHTS: https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl\n",
      "OUTPUT_DIR: ./output\n",
      "SEED: -1\n",
      "SOLVER:\n",
      "  BASE_LR: 0.02\n",
      "  BIAS_LR_FACTOR: 1.0\n",
      "  CHECKPOINT_PERIOD: 5000\n",
      "  GAMMA: 0.1\n",
      "  IMS_PER_BATCH: 2\n",
      "  LR_SCHEDULER_NAME: WarmupMultiStepLR\n",
      "  MAX_ITER: 500\n",
      "  MOMENTUM: 0.9\n",
      "  STEPS: (60000, 80000)\n",
      "  WARMUP_FACTOR: 0.001\n",
      "  WARMUP_ITERS: 1000\n",
      "  WARMUP_METHOD: linear\n",
      "  WEIGHT_DECAY: 0.0001\n",
      "  WEIGHT_DECAY_BIAS: 0.0001\n",
      "  WEIGHT_DECAY_NORM: 0.0\n",
      "TEST:\n",
      "  AUG:\n",
      "    ENABLED: False\n",
      "    FLIP: True\n",
      "    MAX_SIZE: 4000\n",
      "    MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)\n",
      "  DETECTIONS_PER_IMAGE: 100\n",
      "  EVAL_PERIOD: 0\n",
      "  EXPECTED_RESULTS: []\n",
      "  KEYPOINT_OKS_SIGMAS: []\n",
      "  PRECISE_BN:\n",
      "    ENABLED: False\n",
      "    NUM_ITER: 200\n",
      "VERSION: 2\n",
      "VIS_PERIOD: 0\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mFull config saved to /content/output/config.yaml\n",
      "\u001b[32m[03/11 06:17:12 detectron2]: \u001b[0mFull config saved to /content/output/config.yaml\n",
      "\u001b[32m[03/11 06:17:12 d2.utils.env]: \u001b[0mUsing a generated random seed 12828442\n",
      "\u001b[32m[03/11 06:17:12 d2.utils.env]: \u001b[0mUsing a generated random seed 12828442\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://app.wandb.ai/mrdbourke/airbnb-object-detection\" target=\"_blank\">https://app.wandb.ai/mrdbourke/airbnb-object-detection</a><br/>\n",
       "                Run page: <a href=\"https://app.wandb.ai/mrdbourke/airbnb-object-detection/runs/25juvofi\" target=\"_blank\">https://app.wandb.ai/mrdbourke/airbnb-object-detection/runs/25juvofi</a><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Printing config_yaml file to go into Weights & Biases\n",
      "{'CUDNN_BENCHMARK': False, 'DATALOADER': {'ASPECT_RATIO_GROUPING': True, 'FILTER_EMPTY_ANNOTATIONS': True, 'NUM_WORKERS': 2, 'REPEAT_THRESHOLD': 0.0, 'SAMPLER_TRAIN': 'TrainingSampler'}, 'DATASETS': {'PRECOMPUTED_PROPOSAL_TOPK_TEST': 1000, 'PRECOMPUTED_PROPOSAL_TOPK_TRAIN': 2000, 'PROPOSAL_FILES_TEST': [], 'PROPOSAL_FILES_TRAIN': [], 'TEST': ['coffeemaker-validation'], 'TRAIN': ['coffeemaker-train']}, 'GLOBAL': {'HACK': 1.0}, 'INPUT': {'CROP': {'ENABLED': False, 'SIZE': [0.9, 0.9], 'TYPE': 'relative_range'}, 'FORMAT': 'BGR', 'MASK_FORMAT': 'polygon', 'MAX_SIZE_TEST': 1333, 'MAX_SIZE_TRAIN': 1333, 'MIN_SIZE_TEST': 800, 'MIN_SIZE_TRAIN': [640, 672, 704, 736, 768, 800], 'MIN_SIZE_TRAIN_SAMPLING': 'choice'}, 'MODEL': {'ANCHOR_GENERATOR': {'ANGLES': [[-90, 0, 90]], 'ASPECT_RATIOS': [[0.5, 1.0, 2.0]], 'NAME': 'DefaultAnchorGenerator', 'OFFSET': 0.0, 'SIZES': [[32], [64], [128], [256], [512]]}, 'BACKBONE': {'FREEZE_AT': 2, 'NAME': 'build_resnet_fpn_backbone'}, 'DEVICE': 'cuda', 'FPN': {'FUSE_TYPE': 'sum', 'IN_FEATURES': ['res2', 'res3', 'res4', 'res5'], 'NORM': '', 'OUT_CHANNELS': 256}, 'KEYPOINT_ON': False, 'LOAD_PROPOSALS': False, 'MASK_ON': False, 'META_ARCHITECTURE': 'GeneralizedRCNN', 'PANOPTIC_FPN': {'COMBINE': {'ENABLED': True, 'INSTANCES_CONFIDENCE_THRESH': 0.5, 'OVERLAP_THRESH': 0.5, 'STUFF_AREA_LIMIT': 4096}, 'INSTANCE_LOSS_WEIGHT': 1.0}, 'PIXEL_MEAN': [103.53, 116.28, 123.675], 'PIXEL_STD': [1.0, 1.0, 1.0], 'PROPOSAL_GENERATOR': {'MIN_SIZE': 0, 'NAME': 'RPN'}, 'RESNETS': {'DEFORM_MODULATED': False, 'DEFORM_NUM_GROUPS': 1, 'DEFORM_ON_PER_STAGE': [False, False, False, False], 'DEPTH': 50, 'NORM': 'FrozenBN', 'NUM_GROUPS': 1, 'OUT_FEATURES': ['res2', 'res3', 'res4', 'res5'], 'RES2_OUT_CHANNELS': 256, 'RES5_DILATION': 1, 'STEM_OUT_CHANNELS': 64, 'STRIDE_IN_1X1': True, 'WIDTH_PER_GROUP': 64}, 'RETINANET': {'BBOX_REG_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'FOCAL_LOSS_ALPHA': 0.25, 'FOCAL_LOSS_GAMMA': 2.0, 'IN_FEATURES': ['p3', 'p4', 'p5', 'p6', 'p7'], 'IOU_LABELS': [0, -1, 1], 'IOU_THRESHOLDS': [0.4, 0.5], 'NMS_THRESH_TEST': 0.5, 'NUM_CLASSES': 80, 'NUM_CONVS': 4, 'PRIOR_PROB': 0.01, 'SCORE_THRESH_TEST': 0.05, 'SMOOTH_L1_LOSS_BETA': 0.1, 'TOPK_CANDIDATES_TEST': 1000}, 'ROI_BOX_CASCADE_HEAD': {'BBOX_REG_WEIGHTS': [[10.0, 10.0, 5.0, 5.0], [20.0, 20.0, 10.0, 10.0], [30.0, 30.0, 15.0, 15.0]], 'IOUS': [0.5, 0.6, 0.7]}, 'ROI_BOX_HEAD': {'BBOX_REG_WEIGHTS': [10.0, 10.0, 5.0, 5.0], 'CLS_AGNOSTIC_BBOX_REG': False, 'CONV_DIM': 256, 'FC_DIM': 1024, 'NAME': 'FastRCNNConvFCHead', 'NORM': '', 'NUM_CONV': 0, 'NUM_FC': 2, 'POOLER_RESOLUTION': 7, 'POOLER_SAMPLING_RATIO': 0, 'POOLER_TYPE': 'ROIAlignV2', 'SMOOTH_L1_BETA': 0.0, 'TRAIN_ON_PRED_BOXES': False}, 'ROI_HEADS': {'BATCH_SIZE_PER_IMAGE': 512, 'IN_FEATURES': ['p2', 'p3', 'p4', 'p5'], 'IOU_LABELS': [0, 1], 'IOU_THRESHOLDS': [0.5], 'NAME': 'StandardROIHeads', 'NMS_THRESH_TEST': 0.5, 'NUM_CLASSES': 1, 'POSITIVE_FRACTION': 0.25, 'PROPOSAL_APPEND_GT': True, 'SCORE_THRESH_TEST': 0.05}, 'ROI_KEYPOINT_HEAD': {'CONV_DIMS': [512, 512, 512, 512, 512, 512, 512, 512], 'LOSS_WEIGHT': 1.0, 'MIN_KEYPOINTS_PER_IMAGE': 1, 'NAME': 'KRCNNConvDeconvUpsampleHead', 'NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS': True, 'NUM_KEYPOINTS': 17, 'POOLER_RESOLUTION': 14, 'POOLER_SAMPLING_RATIO': 0, 'POOLER_TYPE': 'ROIAlignV2'}, 'ROI_MASK_HEAD': {'CLS_AGNOSTIC_MASK': False, 'CONV_DIM': 256, 'NAME': 'MaskRCNNConvUpsampleHead', 'NORM': '', 'NUM_CONV': 4, 'POOLER_RESOLUTION': 14, 'POOLER_SAMPLING_RATIO': 0, 'POOLER_TYPE': 'ROIAlignV2'}, 'RPN': {'BATCH_SIZE_PER_IMAGE': 256, 'BBOX_REG_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'BOUNDARY_THRESH': -1, 'HEAD_NAME': 'StandardRPNHead', 'IN_FEATURES': ['p2', 'p3', 'p4', 'p5', 'p6'], 'IOU_LABELS': [0, -1, 1], 'IOU_THRESHOLDS': [0.3, 0.7], 'LOSS_WEIGHT': 1.0, 'NMS_THRESH': 0.7, 'POSITIVE_FRACTION': 0.5, 'POST_NMS_TOPK_TEST': 1000, 'POST_NMS_TOPK_TRAIN': 1000, 'PRE_NMS_TOPK_TEST': 1000, 'PRE_NMS_TOPK_TRAIN': 2000, 'SMOOTH_L1_BETA': 0.0}, 'SEM_SEG_HEAD': {'COMMON_STRIDE': 4, 'CONVS_DIM': 128, 'IGNORE_VALUE': 255, 'IN_FEATURES': ['p2', 'p3', 'p4', 'p5'], 'LOSS_WEIGHT': 1.0, 'NAME': 'SemSegFPNHead', 'NORM': 'GN', 'NUM_CLASSES': 54}, 'WEIGHTS': 'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl'}, 'OUTPUT_DIR': './output', 'SEED': -1, 'SOLVER': {'BASE_LR': 0.02, 'BIAS_LR_FACTOR': 1.0, 'CHECKPOINT_PERIOD': 5000, 'GAMMA': 0.1, 'IMS_PER_BATCH': 2, 'LR_SCHEDULER_NAME': 'WarmupMultiStepLR', 'MAX_ITER': 500, 'MOMENTUM': 0.9, 'STEPS': [60000, 80000], 'WARMUP_FACTOR': 0.001, 'WARMUP_ITERS': 1000, 'WARMUP_METHOD': 'linear', 'WEIGHT_DECAY': 0.0001, 'WEIGHT_DECAY_BIAS': 0.0001, 'WEIGHT_DECAY_NORM': 0.0}, 'TEST': {'AUG': {'ENABLED': False, 'FLIP': True, 'MAX_SIZE': 4000, 'MIN_SIZES': [400, 500, 600, 700, 800, 900, 1000, 1100, 1200]}, 'DETECTIONS_PER_IMAGE': 100, 'EVAL_PERIOD': 0, 'EXPECTED_RESULTS': [], 'KEYPOINT_OKS_SIGMAS': [], 'PRECISE_BN': {'ENABLED': False, 'NUM_ITER': 200}}, 'VERSION': 2, 'VIS_PERIOD': 0}\n",
      "\u001b[32m[03/11 06:17:17 detectron2]: \u001b[0mModel:\n",
      "GeneralizedRCNN(\n",
      "  (backbone): FPN(\n",
      "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (top_block): LastLevelMaxPool()\n",
      "    (bottom_up): ResNet(\n",
      "      (stem): BasicStem(\n",
      "        (conv1): Conv2d(\n",
      "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (res2): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res3): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (3): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res4): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (3): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (4): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (5): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res5): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (proposal_generator): RPN(\n",
      "    (anchor_generator): DefaultAnchorGenerator(\n",
      "      (cell_anchors): BufferList()\n",
      "    )\n",
      "    (rpn_head): StandardRPNHead(\n",
      "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "  )\n",
      "  (roi_heads): StandardROIHeads(\n",
      "    (box_pooler): ROIPooler(\n",
      "      (level_poolers): ModuleList(\n",
      "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
      "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
      "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
      "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
      "      )\n",
      "    )\n",
      "    (box_head): FastRCNNConvFCHead(\n",
      "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
      "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "    )\n",
      "    (box_predictor): FastRCNNOutputLayers(\n",
      "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
      "      (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "\u001b[32m[03/11 06:17:17 detectron2]: \u001b[0mModel:\n",
      "GeneralizedRCNN(\n",
      "  (backbone): FPN(\n",
      "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (top_block): LastLevelMaxPool()\n",
      "    (bottom_up): ResNet(\n",
      "      (stem): BasicStem(\n",
      "        (conv1): Conv2d(\n",
      "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (res2): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res3): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (3): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res4): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (3): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (4): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (5): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (res5): Sequential(\n",
      "        (0): BottleneckBlock(\n",
      "          (shortcut): Conv2d(\n",
      "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "          (conv1): Conv2d(\n",
      "            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (1): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "        (2): BottleneckBlock(\n",
      "          (conv1): Conv2d(\n",
      "            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv2): Conv2d(\n",
      "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "          )\n",
      "          (conv3): Conv2d(\n",
      "            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (proposal_generator): RPN(\n",
      "    (anchor_generator): DefaultAnchorGenerator(\n",
      "      (cell_anchors): BufferList()\n",
      "    )\n",
      "    (rpn_head): StandardRPNHead(\n",
      "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "  )\n",
      "  (roi_heads): StandardROIHeads(\n",
      "    (box_pooler): ROIPooler(\n",
      "      (level_poolers): ModuleList(\n",
      "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
      "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
      "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
      "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
      "      )\n",
      "    )\n",
      "    (box_head): FastRCNNConvFCHead(\n",
      "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
      "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "    )\n",
      "    (box_predictor): FastRCNNOutputLayers(\n",
      "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
      "      (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "\u001b[32m[03/11 06:17:17 fvcore.common.checkpoint]: \u001b[0mLoading checkpoint from https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl\n",
      "\u001b[32m[03/11 06:17:17 fvcore.common.file_io]: \u001b[0mDownloading https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl ...\n",
      "\u001b[32m[03/11 06:17:17 fvcore.common.download]: \u001b[0mDownloading from https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "model_final_b275ba.pkl: 167MB [00:19, 8.69MB/s]                           "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[03/11 06:17:37 fvcore.common.download]: \u001b[0mSuccessfully downloaded /root/.torch/fvcore_cache/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl. 167266879 bytes.\n",
      "\u001b[32m[03/11 06:17:37 fvcore.common.file_io]: \u001b[0mURL https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl cached in /root/.torch/fvcore_cache/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl\n",
      "\u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0mReading a file from 'Detectron2 Model Zoo'\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0m'roi_heads.box_predictor.cls_score.weight' has shape (81, 1024) in the checkpoint but (2, 1024) in the model! Skipped.\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0m'roi_heads.box_predictor.cls_score.bias' has shape (81,) in the checkpoint but (2,) in the model! Skipped.\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0m'roi_heads.box_predictor.bbox_pred.weight' has shape (320, 1024) in the checkpoint but (4, 1024) in the model! Skipped.\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0m'roi_heads.box_predictor.bbox_pred.bias' has shape (320,) in the checkpoint but (4,) in the model! Skipped.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[03/11 06:17:37 fvcore.common.checkpoint]: \u001b[0mSome model parameters are not in the checkpoint:\n",
      "  \u001b[34mroi_heads.box_predictor.cls_score.{weight, bias}\u001b[0m\n",
      "  \u001b[34mroi_heads.box_predictor.bbox_pred.{weight, bias}\u001b[0m\n",
      "Using train for annotations...\n",
      "On dataset: train\n",
      "Classes we're using: Coffeemaker    323\n",
      "Name: ClassName, dtype: int64\n",
      "\u001b[32m[03/11 06:18:01 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 233 images left.\n",
      "\u001b[32m[03/11 06:18:01 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 233 images left.\n",
      "\u001b[32m[03/11 06:18:02 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
      "\u001b[36m|  category   | #instances   |\n",
      "|:-----------:|:-------------|\n",
      "| Coffeemaker | 323          |\n",
      "|             |              |\u001b[0m\n",
      "\u001b[32m[03/11 06:18:02 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
      "\u001b[36m|  category   | #instances   |\n",
      "|:-----------:|:-------------|\n",
      "| Coffeemaker | 323          |\n",
      "|             |              |\u001b[0m\n",
      "\u001b[32m[03/11 06:18:02 d2.data.common]: \u001b[0mSerializing 233 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[03/11 06:18:02 d2.data.common]: \u001b[0mSerializing 233 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[03/11 06:18:02 d2.data.common]: \u001b[0mSerialized dataset takes 0.07 MiB\n",
      "\u001b[32m[03/11 06:18:02 d2.data.common]: \u001b[0mSerialized dataset takes 0.07 MiB\n",
      "\u001b[32m[03/11 06:18:02 d2.data.detection_utils]: \u001b[0mTransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n",
      "\u001b[32m[03/11 06:18:02 d2.data.detection_utils]: \u001b[0mTransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n",
      "\u001b[32m[03/11 06:18:02 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n",
      "\u001b[32m[03/11 06:18:02 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n",
      "\u001b[32m[03/11 06:18:02 detectron2]: \u001b[0mStarting training from iteration 0\n",
      "\u001b[32m[03/11 06:18:02 detectron2]: \u001b[0mStarting training from iteration 0\n",
      "\u001b[32m[03/11 06:18:11 d2.utils.events]: \u001b[0m eta: N/A  iter: 20  total_loss: 0.889  loss_cls: 0.753  loss_box_reg: 0.123  loss_rpn_cls: 0.022  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:11 d2.utils.events]: \u001b[0m eta: N/A  iter: 20  total_loss: 0.889  loss_cls: 0.753  loss_box_reg: 0.123  loss_rpn_cls: 0.022  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:20 d2.utils.events]: \u001b[0m eta: N/A  iter: 40  total_loss: 0.641  loss_cls: 0.478  loss_box_reg: 0.112  loss_rpn_cls: 0.027  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:20 d2.utils.events]: \u001b[0m eta: N/A  iter: 40  total_loss: 0.641  loss_cls: 0.478  loss_box_reg: 0.112  loss_rpn_cls: 0.027  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:28 d2.utils.events]: \u001b[0m eta: N/A  iter: 60  total_loss: 0.497  loss_cls: 0.334  loss_box_reg: 0.106  loss_rpn_cls: 0.022  loss_rpn_loc: 0.009      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:28 d2.utils.events]: \u001b[0m eta: N/A  iter: 60  total_loss: 0.497  loss_cls: 0.334  loss_box_reg: 0.106  loss_rpn_cls: 0.022  loss_rpn_loc: 0.009      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:37 d2.utils.events]: \u001b[0m eta: N/A  iter: 80  total_loss: 0.436  loss_cls: 0.256  loss_box_reg: 0.103  loss_rpn_cls: 0.026  loss_rpn_loc: 0.012      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:37 d2.utils.events]: \u001b[0m eta: N/A  iter: 80  total_loss: 0.436  loss_cls: 0.256  loss_box_reg: 0.103  loss_rpn_cls: 0.026  loss_rpn_loc: 0.012      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:46 d2.utils.events]: \u001b[0m eta: N/A  iter: 100  total_loss: 0.372  loss_cls: 0.219  loss_box_reg: 0.126  loss_rpn_cls: 0.014  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:46 d2.utils.events]: \u001b[0m eta: N/A  iter: 100  total_loss: 0.372  loss_cls: 0.219  loss_box_reg: 0.126  loss_rpn_cls: 0.014  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:56 d2.utils.events]: \u001b[0m eta: N/A  iter: 120  total_loss: 0.379  loss_cls: 0.200  loss_box_reg: 0.125  loss_rpn_cls: 0.019  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:18:56 d2.utils.events]: \u001b[0m eta: N/A  iter: 120  total_loss: 0.379  loss_cls: 0.200  loss_box_reg: 0.125  loss_rpn_cls: 0.019  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2643M\n",
      "\u001b[32m[03/11 06:19:05 d2.utils.events]: \u001b[0m eta: N/A  iter: 140  total_loss: 0.330  loss_cls: 0.184  loss_box_reg: 0.129  loss_rpn_cls: 0.016  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:05 d2.utils.events]: \u001b[0m eta: N/A  iter: 140  total_loss: 0.330  loss_cls: 0.184  loss_box_reg: 0.129  loss_rpn_cls: 0.016  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:14 d2.utils.events]: \u001b[0m eta: N/A  iter: 160  total_loss: 0.319  loss_cls: 0.166  loss_box_reg: 0.101  loss_rpn_cls: 0.018  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:14 d2.utils.events]: \u001b[0m eta: N/A  iter: 160  total_loss: 0.319  loss_cls: 0.166  loss_box_reg: 0.101  loss_rpn_cls: 0.018  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:23 d2.utils.events]: \u001b[0m eta: N/A  iter: 180  total_loss: 0.292  loss_cls: 0.149  loss_box_reg: 0.120  loss_rpn_cls: 0.011  loss_rpn_loc: 0.006      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:23 d2.utils.events]: \u001b[0m eta: N/A  iter: 180  total_loss: 0.292  loss_cls: 0.149  loss_box_reg: 0.120  loss_rpn_cls: 0.011  loss_rpn_loc: 0.006      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:33 d2.utils.events]: \u001b[0m eta: N/A  iter: 200  total_loss: 0.343  loss_cls: 0.152  loss_box_reg: 0.135  loss_rpn_cls: 0.019  loss_rpn_loc: 0.017      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:33 d2.utils.events]: \u001b[0m eta: N/A  iter: 200  total_loss: 0.343  loss_cls: 0.152  loss_box_reg: 0.135  loss_rpn_cls: 0.019  loss_rpn_loc: 0.017      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:43 d2.utils.events]: \u001b[0m eta: N/A  iter: 220  total_loss: 0.305  loss_cls: 0.152  loss_box_reg: 0.119  loss_rpn_cls: 0.019  loss_rpn_loc: 0.013      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:43 d2.utils.events]: \u001b[0m eta: N/A  iter: 220  total_loss: 0.305  loss_cls: 0.152  loss_box_reg: 0.119  loss_rpn_cls: 0.019  loss_rpn_loc: 0.013      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:52 d2.utils.events]: \u001b[0m eta: N/A  iter: 240  total_loss: 0.269  loss_cls: 0.128  loss_box_reg: 0.109  loss_rpn_cls: 0.017  loss_rpn_loc: 0.006      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:19:52 d2.utils.events]: \u001b[0m eta: N/A  iter: 240  total_loss: 0.269  loss_cls: 0.128  loss_box_reg: 0.109  loss_rpn_cls: 0.017  loss_rpn_loc: 0.006      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:20:02 d2.utils.events]: \u001b[0m eta: N/A  iter: 260  total_loss: 0.285  loss_cls: 0.132  loss_box_reg: 0.111  loss_rpn_cls: 0.022  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:20:02 d2.utils.events]: \u001b[0m eta: N/A  iter: 260  total_loss: 0.285  loss_cls: 0.132  loss_box_reg: 0.111  loss_rpn_cls: 0.022  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2676M\n",
      "\u001b[32m[03/11 06:20:12 d2.utils.events]: \u001b[0m eta: N/A  iter: 280  total_loss: 0.282  loss_cls: 0.133  loss_box_reg: 0.116  loss_rpn_cls: 0.020  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:12 d2.utils.events]: \u001b[0m eta: N/A  iter: 280  total_loss: 0.282  loss_cls: 0.133  loss_box_reg: 0.116  loss_rpn_cls: 0.020  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:21 d2.utils.events]: \u001b[0m eta: N/A  iter: 300  total_loss: 0.256  loss_cls: 0.117  loss_box_reg: 0.113  loss_rpn_cls: 0.014  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:21 d2.utils.events]: \u001b[0m eta: N/A  iter: 300  total_loss: 0.256  loss_cls: 0.117  loss_box_reg: 0.113  loss_rpn_cls: 0.014  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:31 d2.utils.events]: \u001b[0m eta: N/A  iter: 320  total_loss: 0.310  loss_cls: 0.132  loss_box_reg: 0.118  loss_rpn_cls: 0.018  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:31 d2.utils.events]: \u001b[0m eta: N/A  iter: 320  total_loss: 0.310  loss_cls: 0.132  loss_box_reg: 0.118  loss_rpn_cls: 0.018  loss_rpn_loc: 0.015      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:40 d2.utils.events]: \u001b[0m eta: N/A  iter: 340  total_loss: 0.280  loss_cls: 0.125  loss_box_reg: 0.118  loss_rpn_cls: 0.011  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:40 d2.utils.events]: \u001b[0m eta: N/A  iter: 340  total_loss: 0.280  loss_cls: 0.125  loss_box_reg: 0.118  loss_rpn_cls: 0.011  loss_rpn_loc: 0.007      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:50 d2.utils.events]: \u001b[0m eta: N/A  iter: 360  total_loss: 0.333  loss_cls: 0.141  loss_box_reg: 0.145  loss_rpn_cls: 0.015  loss_rpn_loc: 0.012      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:50 d2.utils.events]: \u001b[0m eta: N/A  iter: 360  total_loss: 0.333  loss_cls: 0.141  loss_box_reg: 0.145  loss_rpn_cls: 0.015  loss_rpn_loc: 0.012      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:59 d2.utils.events]: \u001b[0m eta: N/A  iter: 380  total_loss: 0.343  loss_cls: 0.142  loss_box_reg: 0.144  loss_rpn_cls: 0.016  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:20:59 d2.utils.events]: \u001b[0m eta: N/A  iter: 380  total_loss: 0.343  loss_cls: 0.142  loss_box_reg: 0.144  loss_rpn_cls: 0.016  loss_rpn_loc: 0.008      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:09 d2.utils.events]: \u001b[0m eta: N/A  iter: 400  total_loss: 0.251  loss_cls: 0.112  loss_box_reg: 0.108  loss_rpn_cls: 0.017  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:09 d2.utils.events]: \u001b[0m eta: N/A  iter: 400  total_loss: 0.251  loss_cls: 0.112  loss_box_reg: 0.108  loss_rpn_cls: 0.017  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:18 d2.utils.events]: \u001b[0m eta: N/A  iter: 420  total_loss: 0.278  loss_cls: 0.117  loss_box_reg: 0.114  loss_rpn_cls: 0.017  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:18 d2.utils.events]: \u001b[0m eta: N/A  iter: 420  total_loss: 0.278  loss_cls: 0.117  loss_box_reg: 0.114  loss_rpn_cls: 0.017  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:27 d2.utils.events]: \u001b[0m eta: N/A  iter: 440  total_loss: 0.279  loss_cls: 0.115  loss_box_reg: 0.121  loss_rpn_cls: 0.012  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:27 d2.utils.events]: \u001b[0m eta: N/A  iter: 440  total_loss: 0.279  loss_cls: 0.115  loss_box_reg: 0.121  loss_rpn_cls: 0.012  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:37 d2.utils.events]: \u001b[0m eta: N/A  iter: 460  total_loss: 0.256  loss_cls: 0.110  loss_box_reg: 0.118  loss_rpn_cls: 0.017  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:37 d2.utils.events]: \u001b[0m eta: N/A  iter: 460  total_loss: 0.256  loss_cls: 0.110  loss_box_reg: 0.118  loss_rpn_cls: 0.017  loss_rpn_loc: 0.011      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:46 d2.utils.events]: \u001b[0m eta: N/A  iter: 480  total_loss: 0.252  loss_cls: 0.110  loss_box_reg: 0.125  loss_rpn_cls: 0.013  loss_rpn_loc: 0.014      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:46 d2.utils.events]: \u001b[0m eta: N/A  iter: 480  total_loss: 0.252  loss_cls: 0.110  loss_box_reg: 0.125  loss_rpn_cls: 0.013  loss_rpn_loc: 0.014      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:55 fvcore.common.checkpoint]: \u001b[0mSaving checkpoint to ./output/model_final.pth\n",
      "\u001b[32m[03/11 06:21:56 d2.utils.events]: \u001b[0m eta: N/A  iter: 500  total_loss: 0.266  loss_cls: 0.113  loss_box_reg: 0.115  loss_rpn_cls: 0.014  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:56 d2.utils.events]: \u001b[0m eta: N/A  iter: 500  total_loss: 0.266  loss_cls: 0.113  loss_box_reg: 0.115  loss_rpn_cls: 0.014  loss_rpn_loc: 0.010      lr: 0.000020  max_mem: 2730M\n",
      "\u001b[32m[03/11 06:21:57 fvcore.common.checkpoint]: \u001b[0mSaving checkpoint to ./output/model_final.pth\n",
      "Using validation for annotations...\n",
      "On dataset: validation\n",
      "Classes we're using: Coffeemaker    21\n",
      "Name: ClassName, dtype: int64\n",
      "\u001b[32m[03/11 06:21:59 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
      "\u001b[36m|  category   | #instances   |\n",
      "|:-----------:|:-------------|\n",
      "| Coffeemaker | 21           |\n",
      "|             |              |\u001b[0m\n",
      "\u001b[32m[03/11 06:21:59 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
      "\u001b[36m|  category   | #instances   |\n",
      "|:-----------:|:-------------|\n",
      "| Coffeemaker | 21           |\n",
      "|             |              |\u001b[0m\n",
      "\u001b[32m[03/11 06:21:59 d2.data.common]: \u001b[0mSerializing 20 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[03/11 06:21:59 d2.data.common]: \u001b[0mSerializing 20 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[03/11 06:21:59 d2.data.common]: \u001b[0mSerialized dataset takes 0.01 MiB\n",
      "\u001b[32m[03/11 06:21:59 d2.data.common]: \u001b[0mSerialized dataset takes 0.01 MiB\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:21:59 d2.evaluation.coco_evaluation]: \u001b[0mjson_file was not found in MetaDataCatalog for 'coffeemaker-validation'. Trying to convert it to COCO format ...\n",
      "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[03/11 06:21:59 d2.evaluation.coco_evaluation]: \u001b[0mjson_file was not found in MetaDataCatalog for 'coffeemaker-validation'. Trying to convert it to COCO format ...\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConverting dataset annotations in 'coffeemaker-validation' to COCO format ...)\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConverting dataset annotations in 'coffeemaker-validation' to COCO format ...)\n",
      "Using validation for annotations...\n",
      "On dataset: validation\n",
      "Classes we're using: Coffeemaker    21\n",
      "Name: ClassName, dtype: int64\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConverting dataset dicts into COCO format\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConverting dataset dicts into COCO format\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConversion finished, num images: 20, num annotations: 21\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mConversion finished, num images: 20, num annotations: 21\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mCaching annotations in COCO format: ./output/inference/coffeemaker-validation/coffeemaker-validation_coco_format.json\n",
      "\u001b[32m[03/11 06:21:59 d2.data.datasets.coco]: \u001b[0mCaching annotations in COCO format: ./output/inference/coffeemaker-validation/coffeemaker-validation_coco_format.json\n",
      "\u001b[32m[03/11 06:21:59 d2.evaluation.evaluator]: \u001b[0mStart inference on 20 images\n",
      "\u001b[32m[03/11 06:21:59 d2.evaluation.evaluator]: \u001b[0mStart inference on 20 images\n",
      "\u001b[32m[03/11 06:22:01 d2.evaluation.evaluator]: \u001b[0mInference done 11/20. 0.1157 s / img. ETA=0:00:01\n",
      "\u001b[32m[03/11 06:22:01 d2.evaluation.evaluator]: \u001b[0mInference done 11/20. 0.1157 s / img. ETA=0:00:01\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:01.825336 (0.121689 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:01.825336 (0.121689 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:01 (0.112885 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:01 (0.112885 s / img per device, on 1 devices)\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./output/inference/coffeemaker-validation/coco_instances_results.json\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./output/inference/coffeemaker-validation/coco_instances_results.json\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions ...\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions ...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.00s)\n",
      "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 ] = 0.261\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.596\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.108\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 ] = 0.262\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.338\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.557\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.557\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 ] = 0.557\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n",
      "|   AP   |  AP50  |  AP75  |  APs  |  APm  |  APl   |\n",
      "|:------:|:------:|:------:|:-----:|:-----:|:------:|\n",
      "| 26.126 | 59.571 | 10.814 |  nan  |  nan  | 26.208 |\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n",
      "|   AP   |  AP50  |  AP75  |  APs  |  APm  |  APl   |\n",
      "|:------:|:------:|:------:|:-----:|:-----:|:------:|\n",
      "| 26.126 | 59.571 | 10.814 |  nan  |  nan  | 26.208 |\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mNote that some metrics cannot be computed.\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.coco_evaluation]: \u001b[0mNote that some metrics cannot be computed.\n",
      "### Returning results_i...\n",
      "OrderedDict([('bbox', {'AP': 26.12616413450383, 'AP50': 59.57093759886336, 'AP75': 10.814081408140815, 'APs': nan, 'APm': nan, 'APl': 26.207569998144443})])\n",
      "\u001b[32m[03/11 06:22:02 detectron2]: \u001b[0mEvaluation results for coffeemaker-validation in csv format:\n",
      "\u001b[32m[03/11 06:22:02 detectron2]: \u001b[0mEvaluation results for coffeemaker-validation in csv format:\n",
      "### Calculating results...\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: Task: bbox\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: Task: bbox\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: AP,AP50,AP75,APs,APm,APl\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: AP,AP50,AP75,APs,APm,APl\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: 26.1262,59.5709,10.8141,nan,nan,26.2076\n",
      "\u001b[32m[03/11 06:22:02 d2.evaluation.testing]: \u001b[0mcopypaste: 26.1262,59.5709,10.8141,nan,nan,26.2076\n",
      "### Returning results...\n",
      "OrderedDict([('bbox', {'AP': 26.12616413450383, 'AP50': 59.57093759886336, 'AP75': 10.814081408140815, 'APs': nan, 'APm': nan, 'APl': 26.207569998144443})])\n",
      "### Saving results to Weights & Biases...\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "  main(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "KJdgF6lOIo8i",
    "outputId": "29fb5bd0-4963-434f-ac57-4b3454f05eff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.421105753554186"
      ]
     },
     "execution_count": 91,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict = OrderedDict([('bbox', \n",
    "                             {'AP': 0.421105753554186, \n",
    "                              'AP50': 2.570381670807977, \n",
    "                              'AP75': 0.0034985830738550883, \n",
    "                              'APs': None, \n",
    "                              'APm': None, \n",
    "                              'APl': 0.5861111021958075})])\n",
    "results_dict[\"bbox\"][\"AP\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WTk93hZP1Z2B"
   },
   "source": [
    "# SUCCESS!!!!!!!\n",
    "\n",
    "Model trained with a custom training loop! Now to figure out how to track experiments...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "9I6biP4gmqY9",
    "outputId": "c3e9b7d0-fa6c-4114-cf2d-c61e5f93a0dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Mar  6 04:01:43 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.59       Driver Version: 418.67       CUDA Version: 10.1     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   45C    P0    35W / 250W |   4075MiB / 16280MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "9_4mKVj9Pq5B",
    "outputId": "39ea9d58-653d-4785-f72c-4046b2eb0300"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--config-file FILE] [--resume]\n",
      "                             [--eval-only] [--num-gpus NUM_GPUS]\n",
      "                             [--num-machines NUM_MACHINES]\n",
      "                             [--machine-rank MACHINE_RANK]\n",
      "                             [--dist-url DIST_URL]\n",
      "                             ...\n",
      "ipykernel_launcher.py: error: unrecognized arguments: -f\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2890: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "# if __name__ == \"__main__\":\n",
    "#   # Creates an argument parser with common arguments used by Detectron2\n",
    "#   args = default_argument_parser().parse_args()\n",
    "#   print(\"Command Line Args:\", args)\n",
    "#   # Abstracted funciton for launching the main() function with a bunch of different things\n",
    "#   launch(\n",
    "#       main,\n",
    "#       args.num_gpus,\n",
    "#       num_machines=args.num_machines,\n",
    "#       machine_rank=args.machine_rank,\n",
    "#       dist_url=args.dist_url,\n",
    "#       args=(args,),\n",
    "#   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vEN9BUfPy_ya",
    "outputId": "6cf8dd8b-f6d1-44d7-9b2b-836cb30f8b6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 103,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_config = get_cfg()\n",
    "default_config.SOLVER.CHECKPOINT_PERIOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "x51Fh35tzFbS",
    "outputId": "11009f72-f8c0-4054-bf8f-0e727d15046f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"CUDNN_BENCHMARK: false\\nDATALOADER:\\n  ASPECT_RATIO_GROUPING: true\\n  FILTER_EMPTY_ANNOTATIONS: true\\n  NUM_WORKERS: 4\\n  REPEAT_THRESHOLD: 0.0\\n  SAMPLER_TRAIN: TrainingSampler\\nDATASETS:\\n  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\\n  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\\n  PROPOSAL_FILES_TEST: []\\n  PROPOSAL_FILES_TRAIN: []\\n  TEST: []\\n  TRAIN: []\\nGLOBAL:\\n  HACK: 1.0\\nINPUT:\\n  CROP:\\n    ENABLED: false\\n    SIZE:\\n    - 0.9\\n    - 0.9\\n    TYPE: relative_range\\n  FORMAT: BGR\\n  MASK_FORMAT: polygon\\n  MAX_SIZE_TEST: 1333\\n  MAX_SIZE_TRAIN: 1333\\n  MIN_SIZE_TEST: 800\\n  MIN_SIZE_TRAIN:\\n  - 800\\n  MIN_SIZE_TRAIN_SAMPLING: choice\\nMODEL:\\n  ANCHOR_GENERATOR:\\n    ANGLES:\\n    - - -90\\n      - 0\\n      - 90\\n    ASPECT_RATIOS:\\n    - - 0.5\\n      - 1.0\\n      - 2.0\\n    NAME: DefaultAnchorGenerator\\n    OFFSET: 0.0\\n    SIZES:\\n    - - 32\\n      - 64\\n      - 128\\n      - 256\\n      - 512\\n  BACKBONE:\\n    FREEZE_AT: 2\\n    NAME: build_resnet_backbone\\n  DEVICE: cuda\\n  FPN:\\n    FUSE_TYPE: sum\\n    IN_FEATURES: []\\n    NORM: ''\\n    OUT_CHANNELS: 256\\n  KEYPOINT_ON: false\\n  LOAD_PROPOSALS: false\\n  MASK_ON: false\\n  META_ARCHITECTURE: GeneralizedRCNN\\n  PANOPTIC_FPN:\\n    COMBINE:\\n      ENABLED: true\\n      INSTANCES_CONFIDENCE_THRESH: 0.5\\n      OVERLAP_THRESH: 0.5\\n      STUFF_AREA_LIMIT: 4096\\n    INSTANCE_LOSS_WEIGHT: 1.0\\n  PIXEL_MEAN:\\n  - 103.53\\n  - 116.28\\n  - 123.675\\n  PIXEL_STD:\\n  - 1.0\\n  - 1.0\\n  - 1.0\\n  PROPOSAL_GENERATOR:\\n    MIN_SIZE: 0\\n    NAME: RPN\\n  RESNETS:\\n    DEFORM_MODULATED: false\\n    DEFORM_NUM_GROUPS: 1\\n    DEFORM_ON_PER_STAGE:\\n    - false\\n    - false\\n    - false\\n    - false\\n    DEPTH: 50\\n    NORM: FrozenBN\\n    NUM_GROUPS: 1\\n    OUT_FEATURES:\\n    - res4\\n    RES2_OUT_CHANNELS: 256\\n    RES5_DILATION: 1\\n    STEM_OUT_CHANNELS: 64\\n    STRIDE_IN_1X1: true\\n    WIDTH_PER_GROUP: 64\\n  RETINANET:\\n    BBOX_REG_WEIGHTS:\\n    - 1.0\\n    - 1.0\\n    - 1.0\\n    - 1.0\\n    FOCAL_LOSS_ALPHA: 0.25\\n    FOCAL_LOSS_GAMMA: 2.0\\n    IN_FEATURES:\\n    - p3\\n    - p4\\n    - p5\\n    - p6\\n    - p7\\n    IOU_LABELS:\\n    - 0\\n    - -1\\n    - 1\\n    IOU_THRESHOLDS:\\n    - 0.4\\n    - 0.5\\n    NMS_THRESH_TEST: 0.5\\n    NUM_CLASSES: 80\\n    NUM_CONVS: 4\\n    PRIOR_PROB: 0.01\\n    SCORE_THRESH_TEST: 0.05\\n    SMOOTH_L1_LOSS_BETA: 0.1\\n    TOPK_CANDIDATES_TEST: 1000\\n  ROI_BOX_CASCADE_HEAD:\\n    BBOX_REG_WEIGHTS:\\n    - - 10.0\\n      - 10.0\\n      - 5.0\\n      - 5.0\\n    - - 20.0\\n      - 20.0\\n      - 10.0\\n      - 10.0\\n    - - 30.0\\n      - 30.0\\n      - 15.0\\n      - 15.0\\n    IOUS:\\n    - 0.5\\n    - 0.6\\n    - 0.7\\n  ROI_BOX_HEAD:\\n    BBOX_REG_WEIGHTS:\\n    - 10.0\\n    - 10.0\\n    - 5.0\\n    - 5.0\\n    CLS_AGNOSTIC_BBOX_REG: false\\n    CONV_DIM: 256\\n    FC_DIM: 1024\\n    NAME: ''\\n    NORM: ''\\n    NUM_CONV: 0\\n    NUM_FC: 0\\n    POOLER_RESOLUTION: 14\\n    POOLER_SAMPLING_RATIO: 0\\n    POOLER_TYPE: ROIAlignV2\\n    SMOOTH_L1_BETA: 0.0\\n    TRAIN_ON_PRED_BOXES: false\\n  ROI_HEADS:\\n    BATCH_SIZE_PER_IMAGE: 512\\n    IN_FEATURES:\\n    - res4\\n    IOU_LABELS:\\n    - 0\\n    - 1\\n    IOU_THRESHOLDS:\\n    - 0.5\\n    NAME: Res5ROIHeads\\n    NMS_THRESH_TEST: 0.5\\n    NUM_CLASSES: 80\\n    POSITIVE_FRACTION: 0.25\\n    PROPOSAL_APPEND_GT: true\\n    SCORE_THRESH_TEST: 0.05\\n  ROI_KEYPOINT_HEAD:\\n    CONV_DIMS:\\n    - 512\\n    - 512\\n    - 512\\n    - 512\\n    - 512\\n    - 512\\n    - 512\\n    - 512\\n    LOSS_WEIGHT: 1.0\\n    MIN_KEYPOINTS_PER_IMAGE: 1\\n    NAME: KRCNNConvDeconvUpsampleHead\\n    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true\\n    NUM_KEYPOINTS: 17\\n    POOLER_RESOLUTION: 14\\n    POOLER_SAMPLING_RATIO: 0\\n    POOLER_TYPE: ROIAlignV2\\n  ROI_MASK_HEAD:\\n    CLS_AGNOSTIC_MASK: false\\n    CONV_DIM: 256\\n    NAME: MaskRCNNConvUpsampleHead\\n    NORM: ''\\n    NUM_CONV: 0\\n    POOLER_RESOLUTION: 14\\n    POOLER_SAMPLING_RATIO: 0\\n    POOLER_TYPE: ROIAlignV2\\n  RPN:\\n    BATCH_SIZE_PER_IMAGE: 256\\n    BBOX_REG_WEIGHTS:\\n    - 1.0\\n    - 1.0\\n    - 1.0\\n    - 1.0\\n    BOUNDARY_THRESH: -1\\n    HEAD_NAME: StandardRPNHead\\n    IN_FEATURES:\\n    - res4\\n    IOU_LABELS:\\n    - 0\\n    - -1\\n    - 1\\n    IOU_THRESHOLDS:\\n    - 0.3\\n    - 0.7\\n    LOSS_WEIGHT: 1.0\\n    NMS_THRESH: 0.7\\n    POSITIVE_FRACTION: 0.5\\n    POST_NMS_TOPK_TEST: 1000\\n    POST_NMS_TOPK_TRAIN: 2000\\n    PRE_NMS_TOPK_TEST: 6000\\n    PRE_NMS_TOPK_TRAIN: 12000\\n    SMOOTH_L1_BETA: 0.0\\n  SEM_SEG_HEAD:\\n    COMMON_STRIDE: 4\\n    CONVS_DIM: 128\\n    IGNORE_VALUE: 255\\n    IN_FEATURES:\\n    - p2\\n    - p3\\n    - p4\\n    - p5\\n    LOSS_WEIGHT: 1.0\\n    NAME: SemSegFPNHead\\n    NORM: GN\\n    NUM_CLASSES: 54\\n  WEIGHTS: ''\\nOUTPUT_DIR: ./output\\nSEED: -1\\nSOLVER:\\n  BASE_LR: 0.001\\n  BIAS_LR_FACTOR: 1.0\\n  CHECKPOINT_PERIOD: 5000\\n  GAMMA: 0.1\\n  IMS_PER_BATCH: 16\\n  LR_SCHEDULER_NAME: WarmupMultiStepLR\\n  MAX_ITER: 40000\\n  MOMENTUM: 0.9\\n  STEPS:\\n  - 30000\\n  WARMUP_FACTOR: 0.001\\n  WARMUP_ITERS: 1000\\n  WARMUP_METHOD: linear\\n  WEIGHT_DECAY: 0.0001\\n  WEIGHT_DECAY_BIAS: 0.0001\\n  WEIGHT_DECAY_NORM: 0.0\\nTEST:\\n  AUG:\\n    ENABLED: false\\n    FLIP: true\\n    MAX_SIZE: 4000\\n    MIN_SIZES:\\n    - 400\\n    - 500\\n    - 600\\n    - 700\\n    - 800\\n    - 900\\n    - 1000\\n    - 1100\\n    - 1200\\n  DETECTIONS_PER_IMAGE: 100\\n  EVAL_PERIOD: 0\\n  EXPECTED_RESULTS: []\\n  KEYPOINT_OKS_SIGMAS: []\\n  PRECISE_BN:\\n    ENABLED: false\\n    NUM_ITER: 200\\nVERSION: 2\\nVIS_PERIOD: 0\\n\""
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkout the contents of a default config file\n",
    "default_config.dump()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Rdw1f9J_25gX",
    "outputId": "8e5ea132-4cd0-48be-fe40-ba3130b74774"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CUDNN_BENCHMARK': False,\n",
       " 'DATALOADER': {'ASPECT_RATIO_GROUPING': True,\n",
       "  'FILTER_EMPTY_ANNOTATIONS': True,\n",
       "  'NUM_WORKERS': 2,\n",
       "  'REPEAT_THRESHOLD': 0.0,\n",
       "  'SAMPLER_TRAIN': 'TrainingSampler'},\n",
       " 'DATASETS': {'PRECOMPUTED_PROPOSAL_TOPK_TEST': 1000,\n",
       "  'PRECOMPUTED_PROPOSAL_TOPK_TRAIN': 2000,\n",
       "  'PROPOSAL_FILES_TEST': [],\n",
       "  'PROPOSAL_FILES_TRAIN': [],\n",
       "  'TEST': ['coffeemaker-validation'],\n",
       "  'TRAIN': ['coffeemaker-train']},\n",
       " 'GLOBAL': {'HACK': 1.0},\n",
       " 'INPUT': {'CROP': {'ENABLED': False,\n",
       "   'SIZE': [0.9, 0.9],\n",
       "   'TYPE': 'relative_range'},\n",
       "  'FORMAT': 'BGR',\n",
       "  'MASK_FORMAT': 'polygon',\n",
       "  'MAX_SIZE_TEST': 1333,\n",
       "  'MAX_SIZE_TRAIN': 1333,\n",
       "  'MIN_SIZE_TEST': 800,\n",
       "  'MIN_SIZE_TRAIN': [640, 672, 704, 736, 768, 800],\n",
       "  'MIN_SIZE_TRAIN_SAMPLING': 'choice'},\n",
       " 'MODEL': {'ANCHOR_GENERATOR': {'ANGLES': [[-90, 0, 90]],\n",
       "   'ASPECT_RATIOS': [[0.5, 1.0, 2.0]],\n",
       "   'NAME': 'DefaultAnchorGenerator',\n",
       "   'OFFSET': 0.0,\n",
       "   'SIZES': [[32], [64], [128], [256], [512]]},\n",
       "  'BACKBONE': {'FREEZE_AT': 2, 'NAME': 'build_resnet_fpn_backbone'},\n",
       "  'DEVICE': 'cuda',\n",
       "  'FPN': {'FUSE_TYPE': 'sum',\n",
       "   'IN_FEATURES': ['res2', 'res3', 'res4', 'res5'],\n",
       "   'NORM': '',\n",
       "   'OUT_CHANNELS': 256},\n",
       "  'KEYPOINT_ON': False,\n",
       "  'LOAD_PROPOSALS': False,\n",
       "  'MASK_ON': False,\n",
       "  'META_ARCHITECTURE': 'GeneralizedRCNN',\n",
       "  'PANOPTIC_FPN': {'COMBINE': {'ENABLED': True,\n",
       "    'INSTANCES_CONFIDENCE_THRESH': 0.5,\n",
       "    'OVERLAP_THRESH': 0.5,\n",
       "    'STUFF_AREA_LIMIT': 4096},\n",
       "   'INSTANCE_LOSS_WEIGHT': 1.0},\n",
       "  'PIXEL_MEAN': [103.53, 116.28, 123.675],\n",
       "  'PIXEL_STD': [1.0, 1.0, 1.0],\n",
       "  'PROPOSAL_GENERATOR': {'MIN_SIZE': 0, 'NAME': 'RPN'},\n",
       "  'RESNETS': {'DEFORM_MODULATED': False,\n",
       "   'DEFORM_NUM_GROUPS': 1,\n",
       "   'DEFORM_ON_PER_STAGE': [False, False, False, False],\n",
       "   'DEPTH': 50,\n",
       "   'NORM': 'FrozenBN',\n",
       "   'NUM_GROUPS': 1,\n",
       "   'OUT_FEATURES': ['res2', 'res3', 'res4', 'res5'],\n",
       "   'RES2_OUT_CHANNELS': 256,\n",
       "   'RES5_DILATION': 1,\n",
       "   'STEM_OUT_CHANNELS': 64,\n",
       "   'STRIDE_IN_1X1': True,\n",
       "   'WIDTH_PER_GROUP': 64},\n",
       "  'RETINANET': {'BBOX_REG_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\n",
       "   'FOCAL_LOSS_ALPHA': 0.25,\n",
       "   'FOCAL_LOSS_GAMMA': 2.0,\n",
       "   'IN_FEATURES': ['p3', 'p4', 'p5', 'p6', 'p7'],\n",
       "   'IOU_LABELS': [0, -1, 1],\n",
       "   'IOU_THRESHOLDS': [0.4, 0.5],\n",
       "   'NMS_THRESH_TEST': 0.5,\n",
       "   'NUM_CLASSES': 80,\n",
       "   'NUM_CONVS': 4,\n",
       "   'PRIOR_PROB': 0.01,\n",
       "   'SCORE_THRESH_TEST': 0.05,\n",
       "   'SMOOTH_L1_LOSS_BETA': 0.1,\n",
       "   'TOPK_CANDIDATES_TEST': 1000},\n",
       "  'ROI_BOX_CASCADE_HEAD': {'BBOX_REG_WEIGHTS': [[10.0, 10.0, 5.0, 5.0],\n",
       "    [20.0, 20.0, 10.0, 10.0],\n",
       "    [30.0, 30.0, 15.0, 15.0]],\n",
       "   'IOUS': [0.5, 0.6, 0.7]},\n",
       "  'ROI_BOX_HEAD': {'BBOX_REG_WEIGHTS': [10.0, 10.0, 5.0, 5.0],\n",
       "   'CLS_AGNOSTIC_BBOX_REG': False,\n",
       "   'CONV_DIM': 256,\n",
       "   'FC_DIM': 1024,\n",
       "   'NAME': 'FastRCNNConvFCHead',\n",
       "   'NORM': '',\n",
       "   'NUM_CONV': 0,\n",
       "   'NUM_FC': 2,\n",
       "   'POOLER_RESOLUTION': 7,\n",
       "   'POOLER_SAMPLING_RATIO': 0,\n",
       "   'POOLER_TYPE': 'ROIAlignV2',\n",
       "   'SMOOTH_L1_BETA': 0.0,\n",
       "   'TRAIN_ON_PRED_BOXES': False},\n",
       "  'ROI_HEADS': {'BATCH_SIZE_PER_IMAGE': 512,\n",
       "   'IN_FEATURES': ['p2', 'p3', 'p4', 'p5'],\n",
       "   'IOU_LABELS': [0, 1],\n",
       "   'IOU_THRESHOLDS': [0.5],\n",
       "   'NAME': 'StandardROIHeads',\n",
       "   'NMS_THRESH_TEST': 0.5,\n",
       "   'NUM_CLASSES': 1,\n",
       "   'POSITIVE_FRACTION': 0.25,\n",
       "   'PROPOSAL_APPEND_GT': True,\n",
       "   'SCORE_THRESH_TEST': 0.05},\n",
       "  'ROI_KEYPOINT_HEAD': {'CONV_DIMS': [512, 512, 512, 512, 512, 512, 512, 512],\n",
       "   'LOSS_WEIGHT': 1.0,\n",
       "   'MIN_KEYPOINTS_PER_IMAGE': 1,\n",
       "   'NAME': 'KRCNNConvDeconvUpsampleHead',\n",
       "   'NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS': True,\n",
       "   'NUM_KEYPOINTS': 17,\n",
       "   'POOLER_RESOLUTION': 14,\n",
       "   'POOLER_SAMPLING_RATIO': 0,\n",
       "   'POOLER_TYPE': 'ROIAlignV2'},\n",
       "  'ROI_MASK_HEAD': {'CLS_AGNOSTIC_MASK': False,\n",
       "   'CONV_DIM': 256,\n",
       "   'NAME': 'MaskRCNNConvUpsampleHead',\n",
       "   'NORM': '',\n",
       "   'NUM_CONV': 4,\n",
       "   'POOLER_RESOLUTION': 14,\n",
       "   'POOLER_SAMPLING_RATIO': 0,\n",
       "   'POOLER_TYPE': 'ROIAlignV2'},\n",
       "  'RPN': {'BATCH_SIZE_PER_IMAGE': 256,\n",
       "   'BBOX_REG_WEIGHTS': [1.0, 1.0, 1.0, 1.0],\n",
       "   'BOUNDARY_THRESH': -1,\n",
       "   'HEAD_NAME': 'StandardRPNHead',\n",
       "   'IN_FEATURES': ['p2', 'p3', 'p4', 'p5', 'p6'],\n",
       "   'IOU_LABELS': [0, -1, 1],\n",
       "   'IOU_THRESHOLDS': [0.3, 0.7],\n",
       "   'LOSS_WEIGHT': 1.0,\n",
       "   'NMS_THRESH': 0.7,\n",
       "   'POSITIVE_FRACTION': 0.5,\n",
       "   'POST_NMS_TOPK_TEST': 1000,\n",
       "   'POST_NMS_TOPK_TRAIN': 1000,\n",
       "   'PRE_NMS_TOPK_TEST': 1000,\n",
       "   'PRE_NMS_TOPK_TRAIN': 2000,\n",
       "   'SMOOTH_L1_BETA': 0.0},\n",
       "  'SEM_SEG_HEAD': {'COMMON_STRIDE': 4,\n",
       "   'CONVS_DIM': 128,\n",
       "   'IGNORE_VALUE': 255,\n",
       "   'IN_FEATURES': ['p2', 'p3', 'p4', 'p5'],\n",
       "   'LOSS_WEIGHT': 1.0,\n",
       "   'NAME': 'SemSegFPNHead',\n",
       "   'NORM': 'GN',\n",
       "   'NUM_CLASSES': 54},\n",
       "  'WEIGHTS': 'https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl'},\n",
       " 'OUTPUT_DIR': './output',\n",
       " 'SEED': -1,\n",
       " 'SOLVER': {'BASE_LR': 0.02,\n",
       "  'BIAS_LR_FACTOR': 1.0,\n",
       "  'CHECKPOINT_PERIOD': 5000,\n",
       "  'GAMMA': 0.1,\n",
       "  'IMS_PER_BATCH': 2,\n",
       "  'LR_SCHEDULER_NAME': 'WarmupMultiStepLR',\n",
       "  'MAX_ITER': 30,\n",
       "  'MOMENTUM': 0.9,\n",
       "  'STEPS': [60000, 80000],\n",
       "  'WARMUP_FACTOR': 0.001,\n",
       "  'WARMUP_ITERS': 1000,\n",
       "  'WARMUP_METHOD': 'linear',\n",
       "  'WEIGHT_DECAY': 0.0001,\n",
       "  'WEIGHT_DECAY_BIAS': 0.0001,\n",
       "  'WEIGHT_DECAY_NORM': 0.0},\n",
       " 'TEST': {'AUG': {'ENABLED': False,\n",
       "   'FLIP': True,\n",
       "   'MAX_SIZE': 4000,\n",
       "   'MIN_SIZES': [400, 500, 600, 700, 800, 900, 1000, 1100, 1200]},\n",
       "  'DETECTIONS_PER_IMAGE': 100,\n",
       "  'EVAL_PERIOD': 0,\n",
       "  'EXPECTED_RESULTS': [],\n",
       "  'KEYPOINT_OKS_SIGMAS': [],\n",
       "  'PRECISE_BN': {'ENABLED': False, 'NUM_ITER': 200}},\n",
       " 'VERSION': 2,\n",
       " 'VIS_PERIOD': 0}"
      ]
     },
     "execution_count": 115,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_config = get_cfg()\n",
    "default_config_loaded = default_config.load_yaml_with_base(\"output/config.yaml\")\n",
    "default_config_loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p5csdbdvMU78"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "05-detectron2-experiment-tracking.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
 "nbformat": 4,
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