training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import os\n",
    "import sys\n",
    "import pdb\n",
    "import time\n",
    "import h5py\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import dask.dataframe as dd\n",
    "from sklearn import preprocessing\n",
    "from collections import OrderedDict\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dd.read_csv('/mnt/disks/open-images/metadata/bboxes/train-annotations-bbox.csv')\n",
    "df.to_parquet('data/parquet', engine='fastparquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = {}\n",
    "datasets[\"training\"] = {\"path\":\"/mnt/disks/open-images/metadata/bboxes/train-annotations-bbox.csv\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14610230\n"
     ]
    }
   ],
   "source": [
    "# Check number of lines\n",
    "train_num_lines = !wc -l /mnt/disks/open-images/metadata/bboxes/train-annotations-bbox.csv | awk '{ print $1 }'\n",
    "train_num_lines = int(train_num_lines[0])\n",
    "print(train_num_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_output(output):\n",
    "    sys.stdout.write('\\r'+output)\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Groups work like dictionaries, and datasets work like NumPy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_common_metadata  part.13.parquet  part.1.parquet  part.7.parquet\n",
      "_metadata\t  part.14.parquet  part.2.parquet  part.8.parquet\n",
      "part.0.parquet\t  part.15.parquet  part.3.parquet  part.9.parquet\n",
      "part.10.parquet   part.16.parquet  part.4.parquet\n",
      "part.11.parquet   part.17.parquet  part.5.parquet\n",
      "part.12.parquet   part.18.parquet  part.6.parquet\n"
     ]
    }
   ],
   "source": [
    "!ls data/parquet  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dd.read_parquet('data/parquet', engine='fastparquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.195312</td>\n",
       "      <td>0.148438</td>\n",
       "      <td>0.587500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.276563</td>\n",
       "      <td>0.714063</td>\n",
       "      <td>0.948438</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.151562</td>\n",
       "      <td>0.310937</td>\n",
       "      <td>0.198437</td>\n",
       "      <td>0.590625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.429688</td>\n",
       "      <td>0.651563</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.257812</td>\n",
       "      <td>0.346875</td>\n",
       "      <td>0.235938</td>\n",
       "      <td>0.385938</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.320312</td>\n",
       "      <td>0.368750</td>\n",
       "      <td>0.260938</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>1</td>\n",
       "      <td>0.412500</td>\n",
       "      <td>0.945312</td>\n",
       "      <td>0.120312</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0284d</td>\n",
       "      <td>1</td>\n",
       "      <td>0.528125</td>\n",
       "      <td>0.923437</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.964063</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/04bcr3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.064062</td>\n",
       "      <td>0.492188</td>\n",
       "      <td>0.889063</td>\n",
       "      <td>0.993750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/04bcr3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.301562</td>\n",
       "      <td>0.990625</td>\n",
       "      <td>0.462500</td>\n",
       "      <td>0.595312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/04bcr3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.464063</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0fszt</td>\n",
       "      <td>1</td>\n",
       "      <td>0.523438</td>\n",
       "      <td>0.928125</td>\n",
       "      <td>0.668750</td>\n",
       "      <td>0.981250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0hnyx</td>\n",
       "      <td>1</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.920313</td>\n",
       "      <td>0.664062</td>\n",
       "      <td>0.976562</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054409</td>\n",
       "      <td>0.903333</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105066</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.725833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.068168</td>\n",
       "      <td>0.151345</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.956667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.147592</td>\n",
       "      <td>0.261413</td>\n",
       "      <td>0.853333</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.626016</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.765000</td>\n",
       "      <td>0.999167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.806754</td>\n",
       "      <td>0.913071</td>\n",
       "      <td>0.677500</td>\n",
       "      <td>0.768333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0cgh4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.861163</td>\n",
       "      <td>0.999375</td>\n",
       "      <td>0.414167</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ImageID  Source  LabelName  Confidence      XMin      XMax  \\\n",
       "0   000002b66c9c498e  xclick  /m/01g317           1  0.012500  0.195312   \n",
       "1   000002b66c9c498e  xclick  /m/01g317           1  0.025000  0.276563   \n",
       "2   000002b66c9c498e  xclick  /m/01g317           1  0.151562  0.310937   \n",
       "3   000002b66c9c498e  xclick  /m/01g317           1  0.256250  0.429688   \n",
       "4   000002b66c9c498e  xclick  /m/01g317           1  0.257812  0.346875   \n",
       "5   000002b66c9c498e  xclick  /m/01g317           1  0.320312  0.368750   \n",
       "6   000002b66c9c498e  xclick  /m/01g317           1  0.412500  0.945312   \n",
       "7   000002b66c9c498e  xclick   /m/0284d           1  0.528125  0.923437   \n",
       "8   000002b66c9c498e  xclick  /m/04bcr3           1  0.064062  0.492188   \n",
       "9   000002b66c9c498e  xclick  /m/04bcr3           1  0.301562  0.990625   \n",
       "10  000002b66c9c498e  xclick  /m/04bcr3           1  0.312500  0.578125   \n",
       "11  000002b66c9c498e  xclick   /m/0fszt           1  0.523438  0.928125   \n",
       "12  000002b66c9c498e  xclick   /m/0hnyx           1  0.525000  0.920313   \n",
       "13  000002b97e5471a0  xclick   /m/0cgh4           1  0.000000  0.054409   \n",
       "14  000002b97e5471a0  xclick   /m/0cgh4           1  0.000000  0.105066   \n",
       "15  000002b97e5471a0  xclick   /m/0cgh4           1  0.068168  0.151345   \n",
       "16  000002b97e5471a0  xclick   /m/0cgh4           1  0.147592  0.261413   \n",
       "17  000002b97e5471a0  xclick   /m/0cgh4           1  0.626016  0.999375   \n",
       "18  000002b97e5471a0  xclick   /m/0cgh4           1  0.806754  0.913071   \n",
       "19  000002b97e5471a0  xclick   /m/0cgh4           1  0.861163  0.999375   \n",
       "\n",
       "        YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "0   0.148438  0.587500           0            1          0            0   \n",
       "1   0.714063  0.948438           0            1          0            0   \n",
       "2   0.198437  0.590625           1            0          0            0   \n",
       "3   0.651563  0.925000           1            0          0            0   \n",
       "4   0.235938  0.385938           1            0          0            0   \n",
       "5   0.260938  0.328125           1            0          0            0   \n",
       "6   0.120312  0.475000           1            0          1            0   \n",
       "7   0.675000  0.964063           0            0          0            0   \n",
       "8   0.889063  0.993750           0            0          0            0   \n",
       "9   0.462500  0.595312           0            0          0            0   \n",
       "10  0.351562  0.464063           0            0          0            0   \n",
       "11  0.668750  0.981250           0            1          0            1   \n",
       "12  0.664062  0.976562           0            0          0            0   \n",
       "13  0.903333  0.999167           1            1          0            0   \n",
       "14  0.190000  0.725833           0            1          0            0   \n",
       "15  0.900000  0.956667           1            0          0            0   \n",
       "16  0.853333  0.970000           1            0          0            0   \n",
       "17  0.765000  0.999167           1            0          0            0   \n",
       "18  0.677500  0.768333           1            0          0            0   \n",
       "19  0.414167  0.887500           1            1          0            0   \n",
       "\n",
       "    IsInside  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "5          0  \n",
       "6          0  \n",
       "7          0  \n",
       "8          0  \n",
       "9          0  \n",
       "10         0  \n",
       "11         0  \n",
       "12         0  \n",
       "13         0  \n",
       "14         0  \n",
       "15         0  \n",
       "16         0  \n",
       "17         0  \n",
       "18         0  \n",
       "19         0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = pd.DataFrame(df.groupby(df[\"ImageID\"]).size().compute())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts.columns = [\"ImageID\", \"Count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageID</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002b97e5471a0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000002c707c9895e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000048549557964</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000004f4400f6ec5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ImageID  Count\n",
       "0  000002b66c9c498e     13\n",
       "1  000002b97e5471a0     11\n",
       "2  000002c707c9895e      1\n",
       "3  0000048549557964     12\n",
       "4  000004f4400f6ec5     25"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts.to_csv('data/index.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1743043\n"
     ]
    }
   ],
   "source": [
    "# Check number of lines\n",
    "index_num_lines = !wc -l data/index.csv | awk '{ print $1 }'\n",
    "index_num_lines = int(index_num_lines[0])\n",
    "print(index_num_lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_hdf5(csv_path, chunksize, output, num_lines, num_features=4):\n",
    "\n",
    "    # this is your HDF5 database:\n",
    "    with h5py.File(output, 'w') as h5f:\n",
    "\n",
    "        string = h5py.special_dtype(vlen=str)\n",
    "        # use num_features-1 if the csv file has a column header\n",
    "        img_id_dataset = h5f.create_dataset('ids',\n",
    "                                   shape=(num_lines,),\n",
    "                                   compression=None,\n",
    "                                   dtype=string)\n",
    "        bbox_dataset = h5f.create_dataset('annotations',\n",
    "                                   shape=(num_lines, num_features),\n",
    "                                   compression=None,\n",
    "                                   dtype='float64')\n",
    "\n",
    "        # change range argument from 0 -> 1 if your csv file contains a column header\n",
    "        for i in range(1, num_lines, chunksize):  \n",
    "\n",
    "            df = pd.read_csv(csv_path,\n",
    "                         header=None,\n",
    "                         nrows=chunksize, # number of rows to read at each iteration\n",
    "                         skiprows=i)   # skip rows that were already read\n",
    "            \n",
    "            # use i-1 and i-1+10 if csv file has a column header\n",
    "            img_id_dataset[i:1+chunksize] = df.iloc[:, 0]\n",
    "            bbox_dataset[i:1+chunksize] = df.iloc[:, 4:8]\n",
    "            write_output(f\"Chunk: {i}/{num_lines}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_indexer_hdf5(csv_path, chunksize, output, num_lines, num_features=4):\n",
    "\n",
    "    # this is your HDF5 database:\n",
    "    with h5py.File(output, 'w') as h5f:\n",
    "\n",
    "        string = h5py.special_dtype(vlen=str)\n",
    "        # use num_features-1 if the csv file has a column header\n",
    "        img_id_dataset = h5f.create_dataset('ids',\n",
    "                                   shape=(num_lines,),\n",
    "                                   compression=None,\n",
    "                                   dtype=string)\n",
    "        counts_dataset = h5f.create_dataset('counts',\n",
    "                                   shape=(num_lines, num_features),\n",
    "                                   compression=None,\n",
    "                                   dtype=\"int32\")\n",
    "\n",
    "        # change range argument from 0 -> 1 if your csv file contains a column header\n",
    "        for i in range(1, num_lines, chunksize):  \n",
    "\n",
    "            df = pd.read_csv(csv_path,\n",
    "                         header=None,\n",
    "                         nrows=chunksize, # number of rows to read at each iteration\n",
    "                         skiprows=i)      # skip rows that were already read\n",
    "            # import pdb; pdb.set_trace()\n",
    "            # use i-1 and i-1+10 if csv file has a column header\n",
    "            img_id_dataset[i:1+chunksize] = df.iloc[:, 1]\n",
    "            counts_dataset[i:1+chunksize] = df.iloc[:, 2:3]\n",
    "            \n",
    "            write_output(f\"Chunk: {i}/{num_lines}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Already run\n",
    "# create_hdf5(\"/mnt/disks/open-images/metadata/bboxes/train-annotations-bbox.csv\", 10**6, \"training.h5\", train_num_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk: 1000001/1743043"
     ]
    }
   ],
   "source": [
    "create_indexer_hdf5(\"data/index.csv\", 10**6, \"index.h5\", index_num_lines, num_features=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Dataset class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OpenImagesDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "    def __init__(self, data_dir, data_path, index_path, transform=None):\n",
    "        \n",
    "        self.data_dir = data_dir\n",
    "        self.data = h5py.File(\"training.h5\", \"r\")\n",
    "        self.indexer = h5py.File(\"index.h5\", \"r\")\n",
    "        self.transform = transform\n",
    "        \n",
    "        self.current_index = 0\n",
    "        \n",
    "    def pad_to_square(self, img, pad_value=0):\n",
    "        x, y, _ = img.shape\n",
    "        dim_diff = np.abs(y - x)\n",
    "        # (upper / left) padding and (lower / right) padding\n",
    "        pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2\n",
    "        # Determine padding\n",
    "        pad = (0, 0, pad1, pad2) if y <= x else (pad1, pad2, 0, 0)\n",
    "        # Add padding\n",
    "        img = F.pad(img, pad, \"constant\", value=pad_value)\n",
    "\n",
    "        return img, pad \n",
    "\n",
    "    def resize_and_pad(self, img):\n",
    "        # https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/\n",
    "        desired_size = 416\n",
    "\n",
    "        old_size = img.shape[:2] # old_size is in (height, width) format\n",
    "\n",
    "        ratio = float(desired_size)/max(old_size)\n",
    "        new_size = tuple([int(x*ratio) for x in old_size])\n",
    "\n",
    "        # new_size should be in (width, height) format\n",
    "\n",
    "        img = cv2.resize(img, (new_size[1], new_size[0]))\n",
    "        before_padding = img.shape\n",
    "        delta_w = desired_size - new_size[1]\n",
    "        delta_h = desired_size - new_size[0]\n",
    "        top, bottom = delta_h//2, delta_h-(delta_h//2)\n",
    "        left, right = delta_w//2, delta_w-(delta_w//2)\n",
    "        color = [0, 0, 0]\n",
    "        new_img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT,\n",
    "            value=color)\n",
    "\n",
    "        return new_img, delta_w, delta_h, before_padding\n",
    "    \n",
    "    def convert_annotations(self, annotations):\n",
    "        x_min, x_max, y_min, y_max = annotations\n",
    "        width = x_max - x_min\n",
    "        height = y_max - y_min\n",
    "        x_center = (width / 2) + x_min\n",
    "        y_center = (height / 2) + y_min\n",
    "        return [width, height, x_center, y_center]       \n",
    "        \n",
    "    def preprocess(self, img, annotations, jitter=0):\n",
    "        \n",
    "         # Resize image and apply padding\n",
    "        img, padding_x, padding_y, without_padding = self.resize_and_pad(img)\n",
    "        unpad_h, unpad_w, _ = without_padding\n",
    "\n",
    "        # Convert annotations (bboxes)\n",
    "        mod_annotations = []\n",
    "        for a in annotations:\n",
    "            w, h, x_center, y_center = self.convert_annotations(a)\n",
    "            # Adjust x,y center for padding\n",
    "            x_center += (padding_x / 2) / unpad_w\n",
    "            y_center += (padding_y / 2) / unpad_h\n",
    "            mod_annotations.append([x_center, y_center, w, h])\n",
    "            \n",
    "        return img, mod_annotations\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        idx += 1\n",
    "        \n",
    "        # Get image id and number of corresponding bboxes\n",
    "        bbox_count = self.indexer[\"counts\"][idx][0]\n",
    "        \n",
    "        \n",
    "        # Load image and bbox annotations\n",
    "        img_name = self.data[\"ids\"][idx] + \".jpg\"\n",
    "        \n",
    "        annotations = self.data[\"annotations\"][idx:idx + bbox_count]\n",
    "        \n",
    "        img_url = os.path.join(self.data_dir, \"images/train\", img_name)\n",
    "        img = cv2.imread(img_url)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "        # img = Image.open(img_url)\n",
    "        # img = Image.fromarray(np.uint8(img)).convert(\"RGB\")\n",
    "        # if img.getbands()[0] == 'L':\n",
    "        #     img = img.convert('RGB')\n",
    "        \n",
    "        # annotations_tensor = torch.from_numpy(annotations.reshape(-1, 4))\n",
    "        \n",
    "        assert img is not None\n",
    "                \n",
    "        img, targets = self.preprocess(img, annotations)\n",
    "        \n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)        \n",
    "    \n",
    "        return img, targets\n",
    "\n",
    "    def __len__(self):\n",
    "         return self.data[\"annotations\"].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfs = transforms.Compose([\n",
    "    # transforms.Resize((128, 128)),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "training_data = OpenImagesDataset(\"/mnt/disks/open-images\", \"training.h5\", \"index.h5\", tfs)\n",
    "training_generator = torch.utils.data.DataLoader(\n",
    "    training_data,\n",
    "    batch_size=4,\n",
    "    shuffle=False,\n",
    "    num_workers=8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-168-566b5141017d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m# Training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mlocal_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_labels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtraining_generator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    574\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcvd_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    554\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_batch\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    512\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m                     \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeadline\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_poll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    415\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(object_list, timeout)\u001b[0m\n\u001b[1;32m    918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 920\u001b[0;31m                 \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    921\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    922\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "max_epochs = 1\n",
    "for epoch in range(max_epochs):\n",
    "    # Training\n",
    "    for local_batch, local_labels in training_generator:\n",
    "        print(f\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.256250</td>\n",
       "      <td>0.429688</td>\n",
       "      <td>0.651563</td>\n",
       "      <td>0.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000002b66c9c498e</td>\n",
       "      <td>/m/01g317</td>\n",
       "      <td>0.257812</td>\n",
       "      <td>0.346875</td>\n",
       "      <td>0.235938</td>\n",
       "      <td>0.385938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ImageID  LabelName      XMin      XMax      YMin      YMax\n",
       "0  000002b66c9c498e  /m/01g317  0.012500  0.195312  0.148438  0.587500\n",
       "1  000002b66c9c498e  /m/01g317  0.025000  0.276563  0.714063  0.948438\n",
       "2  000002b66c9c498e  /m/01g317  0.151562  0.310937  0.198437  0.590625\n",
       "3  000002b66c9c498e  /m/01g317  0.256250  0.429688  0.651563  0.925000\n",
       "4  000002b66c9c498e  /m/01g317  0.257812  0.346875  0.235938  0.385938"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_loader.dataset.image_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = cv2.imread(\"/mnt/disks/open-images/images/train/000002b66c9c498e.jpg\")\n",
    "img = cv2.cvtColor(img_path, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}