main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
    "from fashionai.data_util import extract_landmarks, scale_coordinates\n",
    "from fashionai.data_util import ImageData, DataSequence, train_test_split\n",
    "from fashionai.model import build_xception_model\n",
    "from fashionai.loss import NormalizedError, SquareNormalizedError\n",
    "from fashionai.utils import save_df_to_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = '/workspace/ssd/fashionai/'\n",
    "TRAIN_DATA_DIR = os.path.join(DATA_DIR, 'train')\n",
    "TRAIN_ANNO_FILE = os.path.join(TRAIN_DATA_DIR, 'Annotations/train.csv')\n",
    "\n",
    "# TEST_DATA_DIR = os.path.join(DATA_DIR, 'test')\n",
    "TEST_DATA_DIR = os.path.join(DATA_DIR, 'testb')\n",
    "TEST_ANNO_FILE = os.path.join(TEST_DATA_DIR, 'test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_anno = pd.read_csv(TRAIN_ANNO_FILE)\n",
    "test_anno = pd.read_csv(TEST_ANNO_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>image_id</th>\n",
       "      <th>image_category</th>\n",
       "      <th>neckline_left</th>\n",
       "      <th>neckline_right</th>\n",
       "      <th>center_front</th>\n",
       "      <th>shoulder_left</th>\n",
       "      <th>shoulder_right</th>\n",
       "      <th>armpit_left</th>\n",
       "      <th>armpit_right</th>\n",
       "      <th>waistline_left</th>\n",
       "      <th>...</th>\n",
       "      <th>top_hem_right</th>\n",
       "      <th>waistband_left</th>\n",
       "      <th>waistband_right</th>\n",
       "      <th>hemline_left</th>\n",
       "      <th>hemline_right</th>\n",
       "      <th>crotch</th>\n",
       "      <th>bottom_left_in</th>\n",
       "      <th>bottom_left_out</th>\n",
       "      <th>bottom_right_in</th>\n",
       "      <th>bottom_right_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Images/blouse/3578977274375a7ab75862852210b5f5...</td>\n",
       "      <td>blouse</td>\n",
       "      <td>209_130_1</td>\n",
       "      <td>291_141_0</td>\n",
       "      <td>238_164_1</td>\n",
       "      <td>176_135_1</td>\n",
       "      <td>329_160_1</td>\n",
       "      <td>170_216_0</td>\n",
       "      <td>296_269_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>...</td>\n",
       "      <td>280_380_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Images/blouse/524eb709a171eab14598860253023d62...</td>\n",
       "      <td>blouse</td>\n",
       "      <td>232_104_1</td>\n",
       "      <td>293_114_1</td>\n",
       "      <td>269_144_1</td>\n",
       "      <td>210_108_1</td>\n",
       "      <td>314_122_0</td>\n",
       "      <td>215_142_1</td>\n",
       "      <td>310_158_0</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>...</td>\n",
       "      <td>322_255_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Images/blouse/72e09104ad0d46d59379b32197a14d64...</td>\n",
       "      <td>blouse</td>\n",
       "      <td>215_161_1</td>\n",
       "      <td>267_157_1</td>\n",
       "      <td>241_165_1</td>\n",
       "      <td>173_182_1</td>\n",
       "      <td>310_182_1</td>\n",
       "      <td>180_251_1</td>\n",
       "      <td>306_252_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>...</td>\n",
       "      <td>307_398_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Images/blouse/ff068d86d60181edea224ffb16d1dbe8...</td>\n",
       "      <td>blouse</td>\n",
       "      <td>202_125_1</td>\n",
       "      <td>252_122_1</td>\n",
       "      <td>226_138_1</td>\n",
       "      <td>149_144_1</td>\n",
       "      <td>301_153_1</td>\n",
       "      <td>171_199_1</td>\n",
       "      <td>284_205_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>...</td>\n",
       "      <td>276_337_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Images/blouse/d5def3258cee180b9af54505ecfaa2e0...</td>\n",
       "      <td>blouse</td>\n",
       "      <td>243_169_0</td>\n",
       "      <td>341_171_0</td>\n",
       "      <td>270_193_0</td>\n",
       "      <td>209_181_0</td>\n",
       "      <td>384_202_1</td>\n",
       "      <td>188_266_1</td>\n",
       "      <td>333_271_0</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>...</td>\n",
       "      <td>384_464_1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "      <td>-1_-1_-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            image_id image_category  \\\n",
       "0  Images/blouse/3578977274375a7ab75862852210b5f5...         blouse   \n",
       "1  Images/blouse/524eb709a171eab14598860253023d62...         blouse   \n",
       "2  Images/blouse/72e09104ad0d46d59379b32197a14d64...         blouse   \n",
       "3  Images/blouse/ff068d86d60181edea224ffb16d1dbe8...         blouse   \n",
       "4  Images/blouse/d5def3258cee180b9af54505ecfaa2e0...         blouse   \n",
       "\n",
       "  neckline_left neckline_right center_front shoulder_left shoulder_right  \\\n",
       "0     209_130_1      291_141_0    238_164_1     176_135_1      329_160_1   \n",
       "1     232_104_1      293_114_1    269_144_1     210_108_1      314_122_0   \n",
       "2     215_161_1      267_157_1    241_165_1     173_182_1      310_182_1   \n",
       "3     202_125_1      252_122_1    226_138_1     149_144_1      301_153_1   \n",
       "4     243_169_0      341_171_0    270_193_0     209_181_0      384_202_1   \n",
       "\n",
       "  armpit_left armpit_right waistline_left       ...        top_hem_right  \\\n",
       "0   170_216_0    296_269_1       -1_-1_-1       ...            280_380_1   \n",
       "1   215_142_1    310_158_0       -1_-1_-1       ...            322_255_1   \n",
       "2   180_251_1    306_252_1       -1_-1_-1       ...            307_398_1   \n",
       "3   171_199_1    284_205_1       -1_-1_-1       ...            276_337_1   \n",
       "4   188_266_1    333_271_0       -1_-1_-1       ...            384_464_1   \n",
       "\n",
       "  waistband_left waistband_right hemline_left hemline_right    crotch  \\\n",
       "0       -1_-1_-1        -1_-1_-1     -1_-1_-1      -1_-1_-1  -1_-1_-1   \n",
       "1       -1_-1_-1        -1_-1_-1     -1_-1_-1      -1_-1_-1  -1_-1_-1   \n",
       "2       -1_-1_-1        -1_-1_-1     -1_-1_-1      -1_-1_-1  -1_-1_-1   \n",
       "3       -1_-1_-1        -1_-1_-1     -1_-1_-1      -1_-1_-1  -1_-1_-1   \n",
       "4       -1_-1_-1        -1_-1_-1     -1_-1_-1      -1_-1_-1  -1_-1_-1   \n",
       "\n",
       "  bottom_left_in bottom_left_out bottom_right_in bottom_right_out  \n",
       "0       -1_-1_-1        -1_-1_-1        -1_-1_-1         -1_-1_-1  \n",
       "1       -1_-1_-1        -1_-1_-1        -1_-1_-1         -1_-1_-1  \n",
       "2       -1_-1_-1        -1_-1_-1        -1_-1_-1         -1_-1_-1  \n",
       "3       -1_-1_-1        -1_-1_-1        -1_-1_-1         -1_-1_-1  \n",
       "4       -1_-1_-1        -1_-1_-1        -1_-1_-1         -1_-1_-1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_anno.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>image_id</th>\n",
       "      <th>image_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Images/blouse/e21708ac483bc37679b26d064650bd40...</td>\n",
       "      <td>blouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Images/blouse/6089d5c425308edaaf418a74c684a8fc...</td>\n",
       "      <td>blouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Images/blouse/e6b066c5022da7666e1a0245b160f7a3...</td>\n",
       "      <td>blouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Images/blouse/17eff8648aeceea2006d8ef9923e4722...</td>\n",
       "      <td>blouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Images/blouse/e802cc5029e33753680e2937f8293a93...</td>\n",
       "      <td>blouse</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            image_id image_category\n",
       "0  Images/blouse/e21708ac483bc37679b26d064650bd40...         blouse\n",
       "1  Images/blouse/6089d5c425308edaaf418a74c684a8fc...         blouse\n",
       "2  Images/blouse/e6b066c5022da7666e1a0245b160f7a3...         blouse\n",
       "3  Images/blouse/17eff8648aeceea2006d8ef9923e4722...         blouse\n",
       "4  Images/blouse/e802cc5029e33753680e2937f8293a93...         blouse"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_anno.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把不存在归一化参数的样本丢弃，即不存在腋窝点的上衣、外套、连衣裙，不存在裤头的裤子和半身裙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NO_LANDMARK_SYM = '-1_-1_-1'\n",
    "\n",
    "is_blouse = (train_anno.image_category == 'blouse')\n",
    "is_outwear = (train_anno.image_category == 'outwear')\n",
    "is_dress = (train_anno.image_category == 'dress')\n",
    "is_trousers = (train_anno.image_category == 'trousers')\n",
    "is_skirt = (train_anno.image_category == 'skirt')\n",
    "\n",
    "no_armpit = ((train_anno.armpit_left == NO_LANDMARK_SYM) | (train_anno.armpit_right == NO_LANDMARK_SYM))\n",
    "no_waistband = ((train_anno.waistband_left == NO_LANDMARK_SYM) | (train_anno.waistband_right == NO_LANDMARK_SYM))\n",
    "\n",
    "bad_sample = ((is_blouse & no_armpit) | (is_dress & no_armpit) | (is_outwear & no_armpit) |\n",
    "              (is_trousers & no_waistband) | (is_skirt & no_waistband))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136 samples are without armpit or waistband.\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(bad_sample), 'samples are without armpit or waistband.')\n",
    "train_anno = train_anno.loc[~bad_sample]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "landmark 对应的编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'neckline_left'),\n",
       " (1, 'neckline_right'),\n",
       " (2, 'center_front'),\n",
       " (3, 'shoulder_left'),\n",
       " (4, 'shoulder_right'),\n",
       " (5, 'armpit_left'),\n",
       " (6, 'armpit_right'),\n",
       " (7, 'waistline_left'),\n",
       " (8, 'waistline_right'),\n",
       " (9, 'cuff_left_in'),\n",
       " (10, 'cuff_left_out'),\n",
       " (11, 'cuff_right_in'),\n",
       " (12, 'cuff_right_out'),\n",
       " (13, 'top_hem_left'),\n",
       " (14, 'top_hem_right'),\n",
       " (15, 'waistband_left'),\n",
       " (16, 'waistband_right'),\n",
       " (17, 'hemline_left'),\n",
       " (18, 'hemline_right'),\n",
       " (19, 'crotch'),\n",
       " (20, 'bottom_left_in'),\n",
       " (21, 'bottom_left_out'),\n",
       " (22, 'bottom_right_in'),\n",
       " (23, 'bottom_right_out')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmark_types = train_anno.columns.values[2:].tolist()\n",
    "list(zip(range(len(landmark_types)), landmark_types))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将 category 信息变成整数，从 dataframe 中抽取 landmark 的坐标和可见性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_cate_map = dict(zip(train_anno.image_category.unique(),  \n",
    "                        range(train_anno.image_category.nunique())))\n",
    "rev_img_cate_map = {v: k for k, v in img_cate_map.items()}\n",
    "\n",
    "train_img_ids = train_anno.image_id.values\n",
    "train_img_cats = train_anno.image_category.map(img_cate_map.get).values\n",
    "train_landmark_pos, train_landmark_vis = extract_landmarks(train_anno.iloc[:, 2:].values)\n",
    "\n",
    "test_img_ids = test_anno.image_id.values\n",
    "test_img_cats = test_anno.image_category.map(img_cate_map.get).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把不可见的点坐标全部设置为0，并转换到 `[0, 1]` 的区间中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "SOURCE_SHAPE = (512, 512)\n",
    "TARGET_SHAPE = (224, 224)\n",
    "\n",
    "AS_GREY = False\n",
    "\n",
    "train_landmark_pos[train_landmark_pos < 0] = 0\n",
    "train_landmark_pos = scale_coordinates(train_landmark_pos, SOURCE_SHAPE, (1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imgs = ImageData(train_img_ids, TRAIN_DATA_DIR, normalize=False,\n",
    "                       source_shape=SOURCE_SHAPE,\n",
    "                       target_shape=TARGET_SHAPE, \n",
    "                       as_grey=AS_GREY)\n",
    "\n",
    "test_imgs = ImageData(test_img_ids, TEST_DATA_DIR, normalize=False,\n",
    "                      source_shape=SOURCE_SHAPE,\n",
    "                      target_shape=TARGET_SHAPE, \n",
    "                      as_grey=AS_GREY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(img, landmarks=None, landmark_coords=(1, 1)):\n",
    "    cmap = 'gray' if img.shape[-1] == 1 else None\n",
    "    img = img.squeeze()\n",
    "    plt.imshow(img, cmap=cmap)\n",
    "    if isinstance(landmarks, np.ndarray):\n",
    "        xys = landmarks.reshape(-1, 2).copy()\n",
    "        xys[:, 0] *= img.shape[0] / landmark_coords[0]\n",
    "        xys[:, 1] *= img.shape[1] / landmark_coords[1]\n",
    "        for p in xys:\n",
    "            plt.scatter(p[0], p[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拿一个样本出来看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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nxII2fGfE5sfaoH/C5rDNCUK2wvkNsJGrcHa77PZBlhgkMmh/PQYdlPKQbN9V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\nW93LUrp3ABqru1uLDZGOz0YIgoB2u00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8VkrW96KvL5k/54dv6+5BzYPjvjQIdzs+fDdr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ivJknTIyQibzS0aW1sgLSVanmf0el1cz0FIgfQdNIZOt0u71WLQ6zNRq2OEA0gcYZ1ANibbbnkP\nBFpBv5dglKBeqxPHljkp8HzLoyAdVJZRr9XxpAdakCY5vh8QxzGNRsM2EzkC40py9HA1Nxw9dWpb\nWm67UsRoAlnVakcAaY9ssAX5ACkUWtg+DSOMhfAKW40QRqK1GJLbavyCw933nWByukx70KLR2SIo\nhXT7XbTOSBObP/nUZz5NrVajXK2QGU2j0+HIqbtYPHYSg4uvHRxlwVcJN662du7byoHrOhZheYux\ntN+43e/1o9/Ht7s3S6KPO4A7zTXs+l/u+B1/g23v/n5kbyahOB7G7X3sd9wezBFa4eoc4j6q16Hg\nW9YlrTLc4ep77uxZ/vwvn6E7UHhaEhiXM+/5LrS/OymZCMm/6O+oJPXaHbIkYTAY0O91SNOUTqfL\n8vIyL770EhJBFseQKwqej+9YDQa02U4ettttNtbXrVBIHOFKCxjK0tSGy93udihsmZGtwAuA73n0\n+z2iyEYmcqg/kaUZUjp0mu1tQE2n3yNJU5LY9ioI6QwTnRrfs2zNnV6PwWCwvUVRWtltirFwXAeF\nMDnpYBOdtIAYI3OL65cGhUKgkCg8I3BzBwtBN0iHYfVBc/c9p6jUp9lsdfELZZrtNq1OizzPeenl\nlzlz+gzT09O4jsNrr1/g7vsf4NCpkxw8cgTHdTHK5mS0NtsVgp3JNpqsdnHYIajdmaBvBjew93E7\nY3WvI7mVE7mZvaFDEEL8hhBiXQjx0tix/14IsSyEeH74+MjYc/9ECHFeCHFOCPGh276Sr4Pt3YPt\nlwR8o/ff7p5t3EZfvEuOUAkyT0k6HULfs9gax0WQsr5xndMvn6ZYqXC92cap1Km4VQLjk07O7Hvu\nNW3x/P1+3+o6ui5xFLHVbGKMIRoMmJyc5KmnniKLBjhG4wFFz6Pb3MIVYFRGu9PBcRympqaYn5+n\nPjVJoWC7JZXakX4boQ2zLMOgSJKYq1cuU6tVLZOz3kHojQBNlUqFRqPBxsYmxcBqPPpD4hEEVMpl\nXMfFdVx818UTksD1GAyi7a2C1npbyWI7h2FSdB6h0y6uTHEcCxHPdI5SKWmSINA4RuNog6ckUoB0\nxHaytVAoojLNgYMnue++RzHCZxCnbDW3uHTpEs8991eUy2UOLC6QJAlTMzPc/eD9zB8+jBg2LBlh\noxCBQYwlqHcm3Y0TUWu1a0LvHL9xst9q8o+Px1slx0dR1vj7x993u3Y7OYTfBP4F8Mk9x/83Y8z/\nuuei7gW+H7gPWAT+WAhxytj2wG+43eyLGv18oxDqth3BEEWnhdXhdY0kN5oICHVCtHyVkshwXTtY\n8lwRD1KkUZx//RwbzQ7/358/y8D4oFJe+erT/EgW0fKLN3zUDDl5nhF4LteWrzBZmyARijQzBKFN\n0M1MFFEmZ6On+Zmf/d955ukXOH78JJOTFRzfcOjIEgcOzCDjmPuOHmJhapKi49NsNFEmp1YpoLG9\nCP1+nyvXrjExUUMlGUJKJibqHDtyzCYhA9sSnSubYS8WQjbXN+n3etTqNbbaLcJCAfLcTnYMYaWE\nMQpXerYByPWYm5rj3OVrHDhxN8ViGW0ExgiUyEEoVJ4Q97qoqEVB5Gjj2DKkVjS2MpxwgpmDR8jz\nlLjbgayJ5/bs1Bw1EEmN0QmOa5Wzlo7fzdKpuzipXuXk+qeprPxb3nkC/mjmJNfCOZxShYefehfH\n7rkfIwSRkQjjILD67trBSu7tGmt7m+RGuqHsWpDeOC91c0j+3gjgZufZu73Nh1oaI9av27E3fKUx\n5gtCiCO3eb7vBn7HGJMAF4UQ54EngL+87Sv6Gm3vpN9v/zaOCR9P1uznSffHM9iss8qHN8/Els1O\nhGBi0ArpGDtUtETnGVnc4/QLz/Doow/x2pVNOpmDKAWoGA6fPMWPnr/CL5qQROwMsADDD9PD9YpE\ng4Q41xjHQWFITIJJtvCLOXGeUags8uu/93m+eO46hbmjrKqA5bUByuR86fUXmCzB3/72D5LJEu1+\nTKHbBAdKTsDGRotcp+DA3OwSBkmr2UB6Aa4nmZycIIoHhMUinvDJVIZKM6rlIv1Ol9cvvEYQFAiG\nkm4Y2w69trpKUCjgFwq2nKpBYiHAnufS3lpHxwmZ6yP9IrlWSK3wHEN30CGPt5B5hJKKPNcYx0Up\nQ5QbDs0dQLghWhu8UplGe43AU4SuRJrRPR4BmTLSOAPpUb70GU5s/XscaRuzFsrwd0qv89vrAcUH\n/gEHT91Nbmw+Ysi0t+9kvtkCs0PUa3MN481Kt1qURsf3AxLtdQT75QfG3zca36OS5V9X2fEnhBA/\nBDwL/GNjTBNYAr489pprw2N/Y+1OtgejgSGF7WYQwxZULTTaaISyFFeuNDYZpkDlGWmakAwiHnrw\nQf7w839KffFegrDIIBkQaMn6tXXeN2hRroX8Ul5i3TjMCsWPeG3mz5/m5z//F2xsNDl88DDPvHyZ\n3qDN6Vev4BfrSA21Ug3tlJi//zHe9+EfIEoi0jTGGIlSBi8sIJKMFy5JIhUzWy9RbF0lDB1CFKEM\nMXmbqSmXxmpGGE7gCM0g7lF0igipSNI+W90tjBS4fsD89Axxt0fcj7h2+QoPP/YYcZYRej46z3GF\n5NrVaxw4fIhEZaT9lDAs4jgeruOjVMbs9CS99ha1yWlLhiI06JRBf5O408QzCSNOICUUEiuVhyfA\nUSgV47kgnJCgNoeiD+kWGksEa3McNskYhkWMEcy89Os4e3QaPaH4+NIy5x94iNwIAulZxhPYVVLc\nbzzcOI6EhZsPCWfHx803wvaWLd9MInHc3qxD+CXgp7Gbp58Gfg74kTs5gRDiE8AnAA4dOvQmL+PN\n2Zu9OdseWhsrAMKIa0mgtCEkR6YxLkMyEW1vWNTrs7qyzrMvPMPZK1f4R9/zo6TRgEro4EQD5KBP\na32Z71qs8q7BJk4wzf/zO5/l9575UxqtCC08Pv5938/kzAwbGw0+8we/w9VmTN2vcOTwXTz1vvdx\n5MRRvvryq3h+yMraOoXhuNBG4wY+1YLtMoxcWPc9/vTTv0tzY5mss0na6jARZpTcHtMTFT72se9l\nZnaWONUUywGlWoVc5dQrdXJt0AaazSbtzS2ibo/p6WmSOKFQLqEzy9DU6XTo93tMTk9ZYRad43sB\nSRSRpCndTpdSGNLpNFkSyk5gE6PiFll3C19nOGKof2D0cB8vQEhybencMAKJRBufcmURAkW63MAT\nEiM1SmmElCBdpOthjMQbrO97bwt5C+G4lrZN212hjRj3U/W6cZXeCfclUt5I43e7W9b9nIwxbwxB\nHr+ucZTjCOB0u/amHIIxZm30uxDi14BPDf9cBg6OvfTA8Nh+5/hV4FcBHnvssa9/a+ItbL+s7BvZ\n6IaMynEKkFIP+/V9HOkguw2SVoMiNiOtlETpnEIhoN1pMTk1y8OzB1k8foLnz5zjc5/6fe5dXODI\ngQWOHDvOQPtcXG3zX/+3P0GShRw/dR/3veMg9ak6Jpikm0jCap3llZx+O6E4p7ged/nCy2doADrw\nyKWkPDNHvzcgzzPCgovrO2ipiEQfB4dOEvGOb/pW1q5dZn35Gml3QNzaIBqsk/oJf/Xyaa5++nW+\n88Pfy8lTdxPldh+9vLJJoRAghGBuapqo2+ML/+FZjhw+bKXT8pwwDMnSlHa3Y9WghbSgJ4GlWcPS\nt01M1CmEJdbjiCiNEI6LVAOS3iaujhFDsRsBZNqWXJUyqCxn0OkjpY8yHrkGoQVSZegoQkoXo7Lt\nyZXkGYMopTp/lCRTZMU5/MHqDfc3K84iheWrMMayXdmxsnvy36ykN4oktsunYwnA0dgZbR9ulisY\n2e1sM242hkefO+5AvuFbBiHEgjFmZfjn3wJGFYjfB35bCPHz2KTiSeDpN/MZf6Nt+0aqYUgb4AiX\nuNnAMynoHG2kZSs2mlZzi8X5WSbnZvm3f/wF/qef+3k++0dfpOZ6PPZjP8zpc+eYWzxEX8ek3iQ/\n+T/+LFEa0Y0Srl5eJk5TS4UmXF49+xqt9oCHHnsnlZklNlKP61spW185zXd86zfT7XdZ27iKMIZy\nsYRjJGqg2EozXMeASAlCHy09ZmYP44UVep0BKrmHQWOZPD7HgbtO4AcOzc0Wn/xXv8073vt+KuUy\nM5MTaAWVcsBgMLC9AWkCwzZnKYRViXYc+oPBtiq1g21ZdhxnyCtpewySKMdIgxAarRP67U1E0iOU\nVnFKCUthJ6VHkg4IgpCzL5/j4KkHyPOUTFp+Rk9kePTIB6vkeixnJARSSLq9PrOFIsYNWHvwJ1h6\n5qeRaoeIVjsBqw9+AulI2/uBQItbl+D2bgPGncReuPGNw2d/R7DfJN8vMhkd36s+dqvru117Q4cg\nhPi/gfcD00KIa8BPAe8XQjyM3TJcAn5seBEvCyH+DXAGyIF/+NdVYfhGmw3BhvtKgRUoES45Epkl\nhCojI6Zgy/70ySDPIUt4+fVXSeOEdGuA24755d/8RSLh8uP3nOLAL/4aR9o9onKFM+/5EBeO3k/o\ngyDFzwTHj00xVa2xenWFjastWpsN3vHw/Rw6cS/Kn+CehaP0M1jf3ORTf/pnTE9Pcu9dJ2hurNNo\ntK0QjDFDnoQiFVcSmR6l2TJSBhT9Ejo0lKYC0mmPdDDNq5spCwtPEoucuZl5fvJnfhXhSArlkPtm\nivzDH/t7FDyXrc1NHNcSyAbVMkkS0em0kEbS6rSZP7CEG7ioOEKGBVwHlONiNDRaLa6vXEfOHMTL\nMrLOJk7UxoghOYpWGGH7IATg4aNzzYmTR5BSoTuXbU5hGEukWYLJMxyZIYUmU4rMeDT7UJ46BW4F\nlce0D3wASc7Mi7+MN1gjK86x8eCPMzjyIdA2M2SMsXkigy217irnjcp+lp9hZCMnMO4MRqv0zaoM\ne1d3KzAzTvFvcyDbZS32n+jjzmjUo/JmkYria01CfD3sscceM88+++xtv77b7X4Dr+ZWNvYlC8iy\nHMdx8bWivXKFSt4jVApjMgY6p9ft4jmGF145zUunn0d3M5RT5ve+/DzH2hH/uFwhGINBK8/n2Y/+\nIF+qLVi0YJ4zUasxWS4zWytz9uUXKJZCet0YLVxi46KdkK12l2qtRpoKBnGfjUaDpaV5XNfj9UuX\n8Qohnms5ESWgVYrjW37GcuAzUaljTEKxJKlXJpkoBsTtK8zPTuKZlM984TRKQRp1OVH3eOfjd3Pm\n9AuUQ59qucTDDz+IJEcZ8LyA559/nmJQoFQqUa/XwZGEQ7ZjKSQrKytUazWSPMNbWGJxsoSM2whp\nUAKLKyADdtii7aAfOmapMcJuY2yVZ9glaeljwSi0kcS5y6sX1nniXR8Av4QxO8xE42bnwLiq9SgI\nNMCOarZ9To29Zzx34Oyb4LtdbMvebYT92xkeuzGHAbt7FMYdgjFmu2lqZAcPHnzOGPPYLS+Ct3sZ\n7siG6wICjTTgkeMbxeb1ZXwd42lDmlvcQJLFxHHM6VfPMoh7vPupd/LsF55GhCVOHjvC3z1zaZcz\nAHCylAc+//s0f+oX6HViBqnmypUNXr18nbnpSQrVgxQmikzPOyAEURLT7XQpFCbQScrsTA2vME27\nP8vGZhMpfd79rvdx/vJFPC2sVLtjyPKYPBqQ+S6r7R5rmwPKRUGpKIgjRQMHRE4WaE4tzFOeaZNE\nmko5o5dt8urVJpW5Y/xfn/wNKsWABJcPvu8ptjY3uXbtIrNzs0yUq7x+/oKlea9UACiXy0SDCMdx\nKRQKXDp3mYXpKZQJSHRGwXNBa4zQVn3zhpq/GYKjDFqnFqKsLa28rfxoNC5COmg8rq5sct9Dj+H6\nJXLYBkHtuqe3WBDtU7uTdCMbTxrac+wwce933pt9zn6OYsexjDRIdycXx4F3o3MYYxvcXNe1HJve\n7es5jtvbDuE2bPsGCVthEK7ApAmhUPQ2NinoGE9npElElCSoLGXQ63Ft+QpnTr/IyWPHuXzpEo8+\n8givXV5lZmqKeffavp9VaG2xWPdRpRCd5pRUTjI3QZJrrl5fRklBoeQjhMEPXEq1KkU/4OLZ80zN\nCS5fe52J6XlOnTxOu92ntblB0XFwHBdcQ6RTXOlTD4usxX2KQYGql7G1eo2V89dYS3scmJ+hWJui\n46zB2EQAACAASURBVDn0KxUGaUYQ1hBxl/LkAdxqgS89/WWc6gFKM3V+8//9HF/8ygv853//4xw6\ndAQVR/R6HcJiQBD4OFqTD3kMsyyjPllHSkm/28PDoT57mLVVSZp1KQ31dXPp2JV/7wMFWmK0QDoS\nxxlOEGPASBwJvUFCqzfA8ctU6tPkwyhilKwbZ9zawQ3cuvlofCzsh22x3I83Mh7fLCl4sz3+6Pr2\nyxWMJy1H590uhUu5rTMRDiny34y97RBuw/YCQgwaVyh6m+uIaICbZzha0c8iBnGM1NBttXn6L77E\n4088xpWrV/jmD38Lg802tXZMdnWNhhDsB1bOp6aY8Cu0uj2ifp+7Ds/QzzIuLa9ycHESx7HMPoVy\nAaMl2nPpakFp6RCvXLjE5PQ0pVKFzfXrlMKQ8qSPFi5BpUa32yfKc7SBpJNTL5Q5WMwossbAL+Af\nPMkjJ+uk/U3KMyf485fXyLKYIPAplkK8ggEfGpGhtnAMZ6NDeeYABHWubqxxvdmjaDRenoCT44Yu\nWZbgIzCeh9Y27C9VS5b7wfMoFGoMEpfZxbvorlwkbl0nDEA7EkfYVu7RHlophZAGLQxGatvhOBbm\nGyCLNZ5fYnXzKu/5wEfJjARpEYbjk/RGCPCopjF+33c7hL2JwnFQ2622BONoxb3H9zvvfuXMcXrA\n8dfeDNX4Zu1th3CbZtvgBTiSIE3YuHyRuoSo2cAvlpCBh0wU/ZVNvvrii6xsrkKe0et2ePTb3s/0\n7DxXW32iJGFtY5P/5foa/2x+hsI4wswPGPzAD6PyhIHukBFx/rUVBnlOWK1wdHEWB0EnmeT62hq9\ndoI2Dsa4JHFOJ3PpLm9y7fI1Di/MEbU7bEYDKpN16KVUi0W8/oDjx49wJj3HNx27h2sb15gtzLGR\nJXz4W99D3WzgqjnOXN3i0ZPT/OhP/8/c/cC3oPVlPBK0KBFnXXyT47k+izPziLklegeO8iuf/DQH\nl6Z41zvuYbbiQZZSLBXJMcTdhMnpOlpEtDptsiRl6dABylNLBK5AxQlB7RCmdoDu1gV0dI3AA4UB\n4w0TfRKtFPkwoSeNGqIJJWiB5/p85qtf5Tu+53t5fOF+Ul3AlQ5GZRZ6vCfs390hCCPh3nGk334R\ngtajSWtxB6OWcSHkrm3EKAK5GXZhv/zCqI/Dfu4IHKWAHcKU8RzIeA/DeLQwiobulELtbYdwm2ap\nxyRSC3Lh4iIQeYzWmkxIQuGRRSkbUcyzZy9iSPjIQw9wrbHGtxw+Qtwd0G33WGtucfHyVZ7p9imX\nS/x3Bw4Qdlpk9Sniv/ujuN/+ncygmF+cIUly8tzl1fPnWV5d4eL5SxRLFQqVKrOTUziyx9pmk1yl\ngGR6ssKFS5c5Ob/E9c1NpMh44NhJGr0+F9evIXLNIw89xLnXX+HI0iKNjTWy9hZUJilPLXLm3Arv\nfuIIL77wJc69skwi68zXFjl06iT1Yh2TKQZ5l0ajRa/ZoBulJMLj9fOvUqpUuN4D05Jc++xzPPnA\nKT70/kdpDdZRqkfVC6lOlLj44kUq5TKOWyBLc0JPIKVHphRKW2LX4tQSrW6JtHmJ0BWE9DEYlJFo\nIXC0QpoMR2VIJLhFEqfIKxev88GPfow0c3EckDIF4TJa/feWCWEvtflONt/aTpVgexwIHyGG3Zlj\nuILhWcded+vI4VYruVUX38t/cPPy5d6fX0uh4C3tEPaHjt75e2/n/WYUkwqDUIo8TxHDjl6VZ8gg\nIE1yLly8yr/7wz/hrhNLFPtdyot1pBRI5RJFhq1uyvlLyxgMn9c5d/3gJ7j77rspFAoYYyg1NgmL\nIb7KEEPSkoMH5qnVq7S7fa5ev86l8xeYXVgi9FxmJupcu75CrzsgcIsEbki73WV+dpqNzWt85rOf\n4YFHH+Whx5/gymsX6PS7fOXpZ3jRD6mXpnj/B96J8BVZLycRAb/3h3/O3Xcd5tB9B/mZn/sV3v+t\nH0JOzZOnKZNTU+RrCQcXplAzdTbWSswePMrEzAy1SokXn/8qxXKVPCny0vlNWluf5wc+9gEGPYXj\nF7h6+RKuyPE9By0kYbG0rRsh5U7GXClDqVTBc4+yev0Kjudj8gTQCMdFGwekQ5RAWCjRiRSf/twf\n8cGPfDe+V7TYBeEOM/+MVQ12O4LxisNIUEfK3d2Fo1V2Z5XfqTLsFz2Mj6e9K/f48yOx3JvhFWxO\nYufvcRj0fmQpe53dm3UOb2mH8GbtTpI8N7xeawTD2rjnUamUiVKFNJqoG/G5P/8LtpThzIVlfvz7\nvpcj9xxEIoh7Ma1+zJ99+TkGqYXhvus97+S9730v1WqVUqkEMJT4NtvltDzPCH2PIKhTq9VwXY8s\nNSxfvkK5VkM6IbVSEV86tJsptUqFiu/R7/UoFoo8+s4nEb5HrDIWDy8RIFg6dBjPq7C+0uS3fvez\nGBkziAa88/HHOHFskcttyJMArzKBK2Hl4utIN6e/JTg4v8TqyjL9KGZmcoo0TsAIwsDnxIljPPGu\nd2KMoNvcYu3yOX7lN3+bb33Pw9zz1H10mqt85Qtf5P6H3sHFzR7f/pHvHLYJW+0HpTQ6U0hXII2D\nkjUWDt7L1XPPUC0V8V2rQ6GlJM2hFSU886WnWTx0nI//0I8h3ACjXaR0t0t2YLiZr987gS0Xg0QI\nuV1hGM/s744Ubow2bvUZbzQxbwdINO5kxrc0t3r9ndp/lA7hZvZGN0WgyfMMVxgcx1KYe4Uig7SH\nEA4Sl4tXlskcQWVigudePM2H/9aHiHp9Vq5dYq3V5OVLF9GAdDye++ppzp49x6FDB1lYWKBYLFoO\nATnkBFAatCDux2TakGqD7/ocO3oUIV3OvvYahVIF3y9Qq5bp95ukKLTISPp9CqHPi2df5aGHHqGz\n1cRVilIhoNnqkLuC8vQcS6fuI1N9SqWALI34yxfPs7mxyvpGh0fve5BqOcT3XFIRUylKnLzH4QOL\n9DtdWlsNBhsDyuUijirRa/U4d/YlZg4sEKmI4lSVzLSYmplna32LV19sM+3+EKsvFSl6GVtXA2Zq\neriKm+1BrowF6DhuiDEZ00snaGyu0W60iKIBea5RxlCfnee7/pMfQbo+xghbSdESrc126/kOdmTv\nfR05Cus0tB6HC++8ZvfP0TjRYxEHexzOeJVh/PmblyRvZrtxDeN5COtEb9YZ+bUkFt+SDuFmK/zX\n/9w7mecROs1zJHmcEhZChE4Q0jbbJEmCdF1UnOE7LncfO47rW+rx5sYGm+srXLx+ldYgo1Cs0MkU\nH/v+v8eDDz1oCUSGLMpxnJEp2yGpMoMgxPcL5HFMHEVkSpHkGcVKmUNHDnH12nWiJKVYLBEWLG35\nIOoxXS2htSAsT6C0hsgqRV1ZXabRbDN/13EKxSoDbSiWJ1htrFIvl6hPH6JQrnP8npBqsUwSd5le\nnCXRMYOoQ5wktLfW8I3Cl4qLZ14mTVPuffAhsjRmenKaifIUSdImqBSRcY+zFy7Q4jCqeTcYuzUw\nmc9L/6HJSUos3F0cC8sFwkiMAK1zjNH41SkWKpPM6mGPgQYhBUgXY6TtNDUgjcaIUSJwBJAddiqZ\nvZNnyOrMjav93shBjajh9c7WYvScPb5TFh3fYuycd9j6uk23N1rt9zqTnePj59+5rnECFRtVjYsU\nj0cMe/+P27W3pEP467PtJmdLJqqGTkFrCuUyeTtCOB4CTRIPyJWiUihQG8QM1tb5xE//E1w3oLPR\nQeeSv/jKVxGBh+O4nDpxD9/20e+xwip5jsECZ6TjIDIH1/GhIFhbbZJkCo3B9z3SgZUjKxaLhAMr\n6LqytkoS2W7CzElBaBytaTfbtHstJIcohx4qN/zSL/823/Y930elVsPzfBxH0m636XQGtLfaNtGY\nJQjX4HgBS3PTHMKl37PCsrlKKAYFwmKB5vIVTj7wMMePHOLMuTMcP3mM5eUrtLoRW52E6alJfLfM\nwVP3MHh6Gml2d93pHC59OWL2ZLBN4iEEQ2JVu1LbFicbjVlYsW1tNtoCl7E87WMTbe8kkHsX+F1h\n/Di8+Ia7P7b33w8KPF4K3FsWHM8NWEThneWsxp8eTxqOnI3jjBzfzv/+9bC3HcJtmA0ODI4U9Lp9\nVHMD4ypkEoHOSePBkC4deltbmChCDQqEoU9zY4tes09jq8/FKytIpwBa8MCDD5Fp6McRlWIJrTVJ\nkmKMIY5jizRzJKVqBT/L6CcxcZriBR4l1+Pq8gqu6zI3N4fRLutrmxyYP4giR0hB2htw6uhRls88\nh1AJxjgcPLSI0DkgmKpP4gCDuE3oSRbmZ0n6CbXSFDiKzCT4vgQE0peExTJS+sRZm263g04Vsj5P\nOFFlpRMRliucOrCI5wDSpZ85lCuTNFd8Gj1NUe8Plkm6ertsJx1AjK1y29+//W0IVsbC+w0YNVxl\nh0lAI4bNZuM2qh7ciFAcr+/vZ0Luv/+/WV5g5GD2nnd3FWL8vTcHLe13rSPbj2dhLyDqzdrbDuEW\npoXEU5JcavSQU6/gerhhAUe36UU9KroGcUwvHTBZmuD7vv/j/PNf/1dMzc2C77H1lTMkX3qZC+0G\nV5KEmWqN1X6b933T+wnDEM9x0EbjS2+4OxG4rsQIgyfAly4ajUoUnVYXjcQLSniOxyC2isjVySq9\nKKLTi/HKDkXXZdBbI0oKTNcLLM5OYAZN+v0OYb1KlERUwpB2p0naHWByje8XKE4WUSrD9SAIPBCC\nSinEFVAp19jqtJFulVxnqDgjiyI2Gw3Wry+TJ30KhWUq5Sq+47K0uISXdui3m1zOEu52FxH5jXBa\nv2SpvhzHTndber/JJBEC4Tg2Lth2EtZunlU32P377uN725X3bhuklPZ6xrYCO+e7UUd03MGMn29n\nazDaRuy6Cmzz0u7/YSffoG+Y4OPVkhHbtz3u7Puet6sMX2cbfZ3DhLVVOEJhVEacRLjSR7uCMy++\nwpP3PsJ73/sE//rTn+ZPXjnDH/7Jn/Exf45XPv8051SOUBJtHA4uHOLAyVMUgxLChTAIEcJOjDTP\nUNoMCT4Dmp0uUdwnzhO0o8hVTr+X0hv0yLOUxvomnl9gamKSq69fJ1Quc0fmWDh6nGvXrzExXSVL\nYiqVKi+cOcsD73icYrVGFHVApQTFEN/Y1bXV2qRYCgm9EMeFSiGkELpMlsvEScbSVI1Wt0WxVCTx\nUspz03jHjhBF9+IHLi986Susrq6wuLhAu9VjY/UaRqXUipLz6WmO8oClah99p1Jz/D21PQCgrw1p\ndyd2M1gx7DiFnYm3PRK2f9+7Eud5vk0rP7JRRWDvJL1ThOFeh2SPjb9Cjz3/5r/Dt2S3Y6fT+QZe\nzY4ZAWKInLVVKI3Qmgt/9RXKWZtp1/DC839FbbZKrValuRZzYG6BRhTzc7/zu/zWp/+YinI47JdZ\nps+qzlicPYBW8E9/6ZP0dUBgLENxWCyilCLJEqRrpdfTPCcZ5Gil6XW7gKRYLLPe6xH1B2xtbhH1\nYyYm6uQq5+qVNbJsgJAKP4148pH7UAx47bmnOXbvg/zrf/MpnvjmjzB14AhKpfhS4gQB1VrNCrgk\nCZ4vqZYLBJ7LVK1CuRRQkDZUTxOLBfCDwraMXJYr+oM+jucyUBG5EvSjnF4vJun32VxZprG2Qtxa\noabrPD75XspulUJFcvzdJaZOWOr5vavtrvtg9u8fuOF+7RNC299vToFmJ/6o0rBbVXyUBNwdPWhG\niMbR9i4IArRmiGvYcRg3ljXt+R3HGbtO+znj1783mTi+RbhZqXE/BzP+XRw9evztbsev2YbOdnug\n2j5HjjzyDtZffZG1tRWWjp1kbfMymcnY3Ozg5gPCeoUf/Og3c+XKMn/80itEWUR1oo5oNeh022it\n6Fy6QHFmkYm5OYIwoFqt0u32CHOP/iAmi2O6sSSKrPBKpxuxudkg15BmikqxRKFQQeVw+cplonhA\npTJLr9skzSKmygEr6+sI0+fQgSUkhmOHD3Ng8QC9LMMvFhDKWKk3pTHSJqp86ZBFMZ4Ih2Uz28Hp\nCW+42gkypcnSHN930XlK6DmUSiFJOyHNcnSa4yLoZzlGGzzPR/sTXNi4xtX+b+GrmE/+Hz9HlBsM\n6Q1h++3ug/eDAd+JjVcBxv3MznnF9newc227G4tG+Q/X9ce2IDZncSdRwK1AdvttTfZ773gZ9c1G\nCW87hFvY9lc7vkcUgtQJmbnrXlZzw/UzrzAVTrP6+gUW5+dYOXueYlhgeu4AP/XxH+SlV/8HVgcJ\nshUjtByWEzOKYQETp6jWKj2tUVsFjBEopZkqVygHLnOewp2bYbPT43I+oLA4R5IZ8iSlsdVi0O1S\nrtWoTUxgthTNzVWqfkBbRQziiFZvQGNzmSfvu5fFI0c53jI4rodjcjAarQW5ysjSFGlAogiKPmkU\nUw5roBTJYIBxHYwjLGGrtKrOjuOg8twi+7QkT3JU35DHKWlvyKYUD+h32/S6HXIFOUWqpQKh7loW\nKOEixG5HsLulmBt+H7dbTaIby8f73N89TmS0NdjPuYzKjTZC2DlmV2y7Jchzva3aNAI27We7m6Ik\nQuyULcdbrccjo73bkL3/q40ixLZT2HmvvKG0eSt72yG8kQ29gr0hEm0UYe6ghM/CfQ/y4z/xX/Ho\nsSXmKiWqzSZVEbJ1fpnKZsxSdZ5vf+Qp/uVffoGYFOkL8jyjIDzk9AKF0jS+7iGFReBlaQZ4rG32\nCHyfdq+BbrTJ8pSlyQoYF50LNnodpibKXF5eodtps766hiMkKulzffkKtflJNhtbeK4LXpkvv3CW\n+tomx44/QGpsMXVt9TquE6C1IggctEqplQqUCgGha/AcQbkQIqSmWilbpuQ0x3EEaZoQlkvooTZD\nrzdAuC7Sg4Lwaba2gJxWq8lWs02WK3qDFmGpSLPb4eRiAeOkaJMh9O5GHdiZCDutyXe++t+O3bja\n3hjuW52F0TXuUJqPwv7RuNjbK3FjX8Tu8P92bARs2hsd3GqXv/M/jR53Zm87hFuaRJhxb5zhyJwM\ngVaSZJDx8sU1vnrmAr4DwjHUZcDcZI1qpcpMscZGp0NgIC25uFlGvTxDagLWOwmeahEGk7iOR2/Q\nRYoCxiiyXKNjQ05IRQiCQsDrFy9SLpZAK1QGOS4HZ2aIajEFV3NtdZ2JmUlcT9JqNlmcX2B1fYNS\nrcZT7/0gzeYm4cQkSbtLMfCJghJRnNHqtun3+tTKJcTcNKHjUCqGKGXD4TTLcV2HwJVo4RF6AV2j\nqFbKxHGMNFCv1qzaU57SbneZmZpgZmqSmalJOp0uq6urtDtFVtZWCQM4ePCgjbykQeeWrgxh2ZKU\n1HgIdKIQUYz2DbglXKnB+BiZgnGGE2M0Ee3KjdDbwCcYX1VvTidm5dn226rs5BF2oo3dWX0p92by\ndzuPvba/UzPDSW8/a/d7rVMZvW133mHXmXflH3beq7hTx/C2Q7hjE8jhl58kCXGeguOSu4Y4yWmK\nAReu9gi4joskE6DdkJJKEdLQ77c4cPJBQq2oCsOkt8X83BJnzzYYpDlbgwFudYKeSin4ZRpJjEpy\nmqJEpouUwyL0V6lUAl6/dJ5iyWe6AEcfvJvnTl9ipR0TVOtcXFtnemqCzuYGL375i0wvLBL6ko14\nQJKmBJ5L6BeYKAW0Wi3SqE+zAeQpE9UyxWCBLAetBBhBkkTkaUboSxwXDBlCKjqtNlmWWaRlEoNK\n8AREUR9XwES5SFqt4gqBFIJ+t0G9VscxIw5CB7BqTWgxlEqL6LZWyNMWrShl/ujjNsFrJMIYpMi3\nuQ93Vkx7nftVAN4oWTkC/OysrDeHGY+rIN0qwbl7K7L7WsbtRhzE+ATeH22437FRJWN3FHLnpce3\nHcIb2niCZhi+Ym9kmqZoJEmeUvB8Qg19R4JJ8YwgQyODgKLjW7BPnoF0ePe7nyLsLdNd2WLl+mm2\n5ubp9bvUZxc4snicwtQsW10XSYnr5Ky3ujgqo9/rsLm+DqJA0mmgXQ8KHvWKR6O9Sm0CHqwd4Pr1\nNcJqiWuvX+TY8WOsrKwwP7fI+pU1rl68ihYOymSUSmWq1TqT07OkeYYX+BCEdCPFtdUGG40mGEWj\nUSBwBYXQp7W1gXAdokGZxsYm7XZ7Wwuy3WzT6XTIc4u1355YRuJ7Aa4jyNOEcqEAGqR0yHSGEBaO\nLAWQReTJOipaxpcl1q9scPCIgzDaytNrDy0jRpBke3ccbHfg0LmwewKOYOd7KwY7ttsJ7DsKbpLU\n+1qBQPs5jvGw/3bPPb6V2emUlNwqOtrP3nYIb2gjh2C1B8ejL20Uge/jSAlaEbu2p1+6giT7/9t7\n81jJ8uu+73N+v3treWu/fr1OT0/37As1Q3LIoRVtlpzIMonAjP6R7T9skTYsBGIQOUqQKJYBGwEC\nOAHiRLECC3JsQAosL7IUiBDswLKUICZiajHFVTSHnNEMZ3q6+/Xrt1W9qrr3t5z88bu36la9et3v\nzcLuJt8BClV16y6/+i3nd873bBEv0GorMYvYbJHB1m0E2Lpxnf/+f/95fvRHvp+/8GP/MVeuPs6Z\nM+cSSJXlBDX0+kN2i9u8b3GdV990vHVb2CuUG1vQdx4GHhdzbt0csXFjyP7+gIfPneX6tTfIRIk6\nYMmOePWLv8vK8go7t7Y4feoCly49Smthge5ii9FwSOED3cVFVjo5YoSlxUUyVRZaOZ3M0W23eOTh\nc6gvOXd2HUKsJAJL9JEzZ86SZRbvA7u7u+zv7xNC5I03rrG7s8drr32TEAJl6Rm5EaKe9TNnQOr+\njMSYYbDg+uB2KXq3wAfe2t7gkYuP0vE5wTqiKZP3eMwqNSGlfq/Hqfb1b5rpJjQB2urjNRCnyhRe\nMW9HrR2Zmr/PwzamGcfk2NyZdQRMoZaAVA8u7GnT4uQ/HuYncRQ6YQh3pOYgVAU0Q13Ew5BllrIc\nQagG30NLc7yJlDaCgvEQYkAY0rKgIfJbv/mbXFrp8tGP/jDv/8CzdBZPY1vLkFnyDNxoj9WVFg91\nVyiGhsurF9jb87x6/RY3VpVR3uar33id6xsjJFryfJmVU6cYjQa4IGzu7GHxtBeWKErH1vY2+Y1X\nWFjucm1zj6y7xOraMpcuXuTxq4+xunaKUTkCiaytrtDKDaeWl1E3IESPD55Oq0Ur7zByA0ajUYpr\n8J7hcMjCwkJyvXYlJrOYrMX62XMsLZ9CbM7GxiY33nqL21tboEPOXzxfxSZUfawZEiGXyPbuTfZ7\nPaxdYDs4nnz8SRRPRAhEPAarEZHaY5EkYUxFNsJ8b0fmMITkaJLqP8xfnIepHHc6V2pPtjvQRAo4\nzGJymPShY/PnYW2YVhve3erP3/E04bqKqhBDElcXF5fodrsM9vvEkHIVpmGwpCIEEe8gyyBXKGOk\n8Ioa2N4tuXLlMZayQMuOUBPx0RBcRp51QXMYRhYMLEjBUqvg3NVFRtphY6fH5WyR184Iu26B1zeG\n3NgbsHjmDOXWdUpv6N3Y5spDZ9gvbzDauc7rwy2uPPYoFy6ssTcKlK7klVe+ztatTRYWuqysLPD0\nk0/QNoEWkRyHI3L+7FlcOaK3s83XtjZxztFttVldXWVpaQlVpdVqsb+/j4aANZZeb4/MGLJuh9Xl\nZdZWT7F+ao1z2+fo797i7Po66ABNegP4VBm7v/sWGkuWltb5wh99k6de+mGKsIDJSsS0MERyD94M\n0ZhhTZ6kjKqgHtJcQPOcd+aNbr3DJokDDLOqQb3AvfdzHYcm9387qsOkvsNxrp+HSzTBx7drbThh\nCEektLNErDFYyVGU9kqbp55+gi988Qt4lKAeVLAKlKkGYmxFyiD4vUCIhmABlHZmWMkNyDLRtchI\nJdJMbvAo2EDLRZwGxESyliGWjkwjF9pduusrXDm9wO2dAU/kgdtLnlc3X2ZhDf6/Nzydy5e5td9j\nIBbaHcJoxKmlFVy+glNHCJGVlVWyxUUWV1dYWlrEedjbG9JqCWqFPMLeTo9O17K8vIhdWyK4EvWw\nvb3F9vY2rVZrnEqslVuMKCvLy7jS431keWmBjY1Ntm9vMRoO2Lhxg9FwQDf3YCwhgjWBYrSFK/cI\nZclWb8SVx59hrZtjJaPQDD8ckuk3GWy+xm5+nosXruBDxEiLebb2+X4IkwU+e85EnQhVdOLB2g1N\n4K7pnHQQmGTqOen6ZjsOtvHorsvMZSBNBjDfnHo0+o5iCPOcPO5OtR97jQbbsYk5txk/9z//L/zQ\nD/0QuVjy3OKcw4eGiBaTo0lQBygSBTU5y0vCykoXMZ4oBjUZSorvtygSA1iwEaKk0mIYQ+YihXra\nCNY7bBZYXoWLWc45VV4vb9G95Pjqjbd45k/+SX79N29AZxWxOT/9U3+Zv/rT/zWar3H5sWd43/s+\nkKIHY2RhsUskUCj4IrBQZhTFHhs3bzPoDcBGzq6fopWRzIpnznBmfX1cB0BV2dvbI4TA5uYmg8GQ\n/v6Q25vbya23lbEgCzz26KOEEIhtIRpQiZTDbYa712iZwFe+cY0P/8BHie0zuGFBaRx5vMFg81WC\nBryD3u0tLp1/kkAGAkY9iBCYlHB/u2SMoSxLjDHkeXvqt8M8BWualioO5l9IDkyTQmaHgZNN68n0\no2qT5+SeB+e0Hnj2ccKjv+MYwtsR62bBo9pltSxLrl69ysWLF7l16yYxxqpYhiVUuRNqMCqEgEYF\nI4jC6uqpFIdnUtGVsRnagMbp0lzaKBwaqxoHDsVHkuuxRto2cqqd41cWaCMstVr83mc+zZm2UsqQ\nH/yR/4BHHl7jx/7sx3n5zYJX3nydz/zOb/H4089y6eIF+ns7dLo5rUxT0pfRkKWVRc6un0Fihs0A\nSva2N9i+vYUKFEXBUpXcpXSOzFq63S4XLlygKB2jUcFCd4nt7V1CUDY3++zv7aX8kTKs+gjCYB/j\nRuwOh5x7+HECLdSVtIwStGDv5uu0NTIsha1+zpPPfhc+BiK2qsWYMkvNogiT8arHcXZHPig5szLz\n4AAAIABJREFUqOrYtFhLPjCJb5jFGZoqxGHelUedc02h5iA4OC1dJGllkuAleThOJJXp+36Hmh0P\nW/BNLjr7OU2S43p11XhCpN1u8fM//3f5yZ/8Sfb2djHGsr8/ACaFQLIsSwsaMAgR6PX6KedBncnH\nzLeTi06uUVW8RoJ6gkacQqlQqqIoIzLUtsnNkNNduLLe5tpXXqZjDX/zb/zn+P4WH3n+g/zL/+sf\n88xLH6RtlDdu3uCtN64hRjlz5hTnzq9z5dJFBqMei8tnUwVnM8IYWFpss7JwAcEQYiSEgHOOvV4P\n5xz9fp92u00K+im4cWODne09NjY22drZI0YPqpgq+amIIBrQ0YC2Cte3Cq6+8CI2byOhRLSk7G+S\nERm4nOu3S554+kWiiVibJyyHFGNi3pbvviU57xxOs3jBnRb3YQDgtOn6Ts+auXJqOsxXReapP++E\nvq0YQo0Yp900HanNMfUOu7+/z9LSUqXvTXS7iQvqNM3v6Mozrnp/6aUP89u//Tt84hOf4JVXXiHL\ncgaDQRKNxwwhUIZAnmWEaOh2uxhr0EosiFESU6h3AgVBiCHU8Hj6P4ANERsU8RETUyyBc+A1EKoc\nQ6inlQvdbpsn3/cCl1bWIF/m1lsv82c++r38q9/7MkuLXdbXz/Pk098FErm9s0Gv8Pzrz/xbcCXP\nP/0cj125xPmzbdp5G+/K5L6ctSjLkrIsabfarC6vMmoXxBAonWNnZ5c337zGzl6f27e32B8MyFo5\n3kU6+XKKDiz6RCDafUoGGAJqLLbdxZUlLePY73+TcucmSItXN/o89dxL2E6XKMnvwZikMmiUSjK4\nc0HVmgmlhd3MpHzQG3AeHWWnn44zqEOf08gdv+7x4apJ/X7nGI7j07cVQ5gGfOoAkZSSqyxLrLU4\nV2LtwYwziZqup/X3w23I9bsxhtXVVT796U/jnONXfuVX+IVf+AVeffVVVldX6Xa79PsRda6qPmQr\nhB5UMwxZslA0OX/dPlU0pFqS6qvS0jFVIqL0UDhwnug80e0TY4nznmKUrAF5O+dn/8bPQnudL33t\nBp/53Be4+PijfPSHv5+Xv/F1drc2uR1HlMHx6FNPYDLh6tWrlPsDytE+n/vSl3jk0jrnL5zlzOop\nyuGITrukKIoKfBNcWTIajRgOhxRFQdbqsLx8is7iKkXpaXe69Hp9trc2WTy9iobkTASKhsoHwAQu\nnl+GYgsJke2dW2jcwRUlN7dHPPvC9yBZG0cJUcZ1n4mmLuwIpICtg9S01UOdVbl23pn4C6Rzjxpv\ncCcsgQPZnufNo+M7DjWfcdBdedKut8sYvq0YQhNsqQEXEcFaQ7uTSqvarAYFpwfsuPhCkyGk9zSx\nW60Wn/zkJ/nkJz/JH/7hH/Krv/qr3L59m1OnVviNX/t1hsMSNTlra2vp2sogL02f+Vq6aewQvpI2\nYowUIgSEMpT4WBBCgdcSXwUb+RDRGAjliIunlnjyobN87o1b/It/80ecffRDDItNuqNbvO/hdS6+\n9AK7e3u89uYb7Lz1KmSW1ZVVhr0Bvi2cOneO7tJZdvYcezs3aVvL8lJODIFOu02/t09ZlI0kqYb9\n/oAYleA91uZ0FpewNmdvdweNSlEWtERBoO1WcGEB4/bJdcj+zX9PCNAx4PNF9krl2edfhKxDEFCj\nyWNRA7W2p5VcJUzvkDWznrbzJ7DOvFP0kcPnzGRDav727oj08577LmkLwAPKEOYPRPI6m1BT9wM0\nDcnK8kqK/x/fo+l1dngijdlnTwe8ME56WZ8TY+SFF17g/e9///jcn/4vfop/9s//OYOR8p/+uY8R\nhtfRamGoVBMJUEmSgZAyNM16qZkQcc4nG7wafACDRSIEjeQaCKpEk/Gh93+YIIbPfOYPeHVrn6xX\nspYV5Oc6uGC5ee01XAg89tBFQuFpdxbY6O9wO+yzlq9jnePVV76OZjm4EbkGut2cPG/TarXw0THc\n69HJM9pZh8WlJfZ6fXwI9Ab77I/2McWIrc1NYnQUvsDHgk7uIQTc3jVc2EfyNsVwRLsVEAw7u/vc\nHioPX3mcYBcq5lhiiBjNkqNSlX5MYuqrNNCCSkBDKrHmvQepC8FkCAErILFNsH4q8ethnoezAOF8\nH4D52FUIoZI4GnOIeqZOJ0htqrnaiMtIc+5g0JYxKct2rSolqccemK/HoQeSIUx3fu3YMamoc/dr\nDqP54M/sBJi1cdeDeqdAFNXI6TPrfOpTn2LohMXiNqNyY/IMrV51MxoUVaesDEQlqsf79KrBvVID\nPgZC1BQOHQI/8Kf+FF4N3/P846yt9fjGrQG3h4ZYGNZswdJD51kE8J6Nm9eqyMY2z6yfJ7gh6xfX\nsY9dYbc/5PbeFr1Bj61bO1y/+XJqW4icOXuW4DwrayuErQ12dnawRtjv9RgO94k+sNcrQB3f+8Pf\nn2ovRHA+4Ie3QOD6rX1UOuSuS+kCFy5e4cL6Q5Q+YPKQoiKxEC0qPkl3FUNM/WsSM9UyWXMkSVCt\nVgcfcjSUGGuJ6jGWlCOzFsgOEb3nzpAjS5KzlgGZ86nxa7U5NZ2hDs61ozxnfpuPSg8kQ6ipBmmS\nH3rSSY+qP83fCWYxhLsPiEh9Xm3WqqWE2Dindk9NizvLc3zfo019twYOx7sdU+NcT44YI1E9MYRU\n7Sh6QnS4UI79H2KMxNJRjEqeff79BLG88OxTPPGI57P//lX+4K1Ntncj2/sDbn3+j2mhrK60WTp1\nCo0FNiht7xiGwO7uNmZvl1AoC6KsX7xAPHee97/wPlQjvV6fxaUlNjZu0xv26e/3ybMWg94e/Z1d\ntjY3ISpZK+PcudPYUNA2AdyAQa+HuB2idtl38OzzH8J2lhgVBZiIo42TERZXJWIRRDOiaKVmRSAm\nviCRIAo+Aw3kxhFdn41br7HQXmFl8UzaMnJLEI/RgNUGiMv8hTOL4h82vw4ylekdfpYNTHCL2VwH\nY+VnDIjfmZpS7vT/aDKXo9IDzBB0rFMnW/90Prq7L+SDol+KGAsYY8co9FxT4IEOnsYTZs+vQarM\nJvAQqVJvSV7hCBPwUCszY40fuHHsREQ1EHyJL0ti8FWuBh23PfqQwMcQyGISkFvdBYbRIDrALHm+\n5wNn+NCz63x5s+Dal15ls58zDCUDjbzx1m22tzdZaBmeuHoV2+2QZcL5pUVMcGxu3KK/OaQYDnDF\niHI0oiyGLC8vc3PjFnnWZRFlNByxasF22yyfP5sK0MRAOdxnf28Lg0NQyuGApWyJr7z8Gn/i+34E\nb3JcjGTtHNWA+kBmTNrxq14WCSgZUumAWgO/AmhENceEgmJ4k1H/BnlZ4Aa3iPkS0s5S6IQVfAxY\nqWtBHDTd3cmbcbr2QzJZz86nsRPboWbH+ns8MM9SbkaLaqoofZhj0eR5trExHWQG3yEMQSrA0M6t\nvXfHKw/ppAmnhtozcXaRH/6M6R3i4MBUIIY03WwbyTYRiClZyKRsV3VnrQFFnxaKxlTmrcrg471P\nTNGDd4qGgBsOcKOCICAqBGmx5HJKAlnb8Xt/979DOutceu4ZTj/2BHvS5htvRM6fPk0RPVtBWXE5\ne7dH7LDBQtfSMm10FMgX1+gX26yeP8fGzRt0Vs/yUHeVYW+f7uIi2zs7mCyHhR5F6ekN9lkUy7mz\nq2SLLZzkeG/JFk+z8eoOTzzx3UTtggpWkuVAgiXgSElQqgrOJiajqvrUvyIVGJuqchMMWdyit73B\noH+D3I7QGEBztjb+mHOXnybEDA2REJQ4J3pxdozn/T4tos9XMxsz5tDfa70fph2eJsVj5mNa86i2\nkNzNm/Ju9IAyhGSuaebAO3BGIwR23i7frOzbJBGZsSUfne6k8wlJlA8oQQJalmSmhdZ+A1P3AdDx\nzghJJE4+CQFCrFSGOMYRvPfoMBI1sO8Kyuj5/v/oB+lrQUtaCJFSIlEyTFSeuhjpssPqtc8S3/g3\nLBBZsXBjf4CPLbJsgc7581z6rhd5/bay3Gpxe7fHwrl1rt+4zcrqKluDwMCu8vWNIRpLXBBcb5vo\nHNH1CK5g2O8xGPS5ePkqagRMKqrabbcxWZu17/oQkLwvjUja8SUQrRJjBZqpR2MyF8bk74zikSyg\nscQGj3iH7w/o77+G0RFd8Ug0KIagJY4Nbt0wdE5dIl/o0rJtfIyITgqn1nS3UGgkVNhFk0HM3whm\n50d9fFLXYTLXmnUi5rkyz7tfTc32vxPfhAeUITRz0M+nO+l5hx2b1efeTYpaMbHK10E15XCeNV9O\n2lWpMbGSDkKtHgGaqhzVQKNW6kbwI7x6uhoZieG5Fz5CbhcJeGzMAEs7RtRZls0qaytLkEVogWSw\n5OCCWnyIlBoY9AfsffZ3cLd7XHOeMoBbWGRUDsjaXdY6i5xbXCW2uzz3gY/whT/8fRBDrz+g1e2w\n0xvhl1dYW3uCixfXWGy36GYRGwKWgq4oQ2Mb6UlS2TVRWw1tqH3NwOqEKVAiGtHSg3qiDujtvEk5\nvE1LSFmqTIaoITNC1EAoBmxuvsbFpTXyfAkNAaSNRo8SQAKqqY+ShN7c+WurFOnYuE6kTsZSar+X\nw0X0O3kVToOJRxPz78Z03g7dlSGIyGXgl4HzpB74RVX9ORE5DfxT4CrwGvBjqrotqTU/B3wMGACf\nUNXPve0Wvk2aZypsRqs1f5t1Mnq3aZwmS3VaepwxezbbPW03bwKKStIWzPglalCvlCHgnKcsPB7D\n6QsXyTuLuNEQg1J6hzUJY2h1WuQdS7uzAFmkvdBFxGDJcKGKlzg9JHhlYSmn9J69Xo/RqM+i36W3\nc42iDGwPhuz0R7z+O/+UYTlJi6aZpbO0jKfNX/7rf5O9TguD0kogB8V4UU1Mw7XtPmU2rv9/bUVK\n/achohiMUazxjIa7DPsbSLlHG0fQLGEy0WNtRlk6RqMR2/sLPPfsS+jCEr4saGGJeTv1f43jECvz\nZC1FNgrGjodMGtJnfcwQgq/OOLxWZJPuBPg15+RxrB/vxkZ2FAnBA/+lqn5ORJaBfycivwV8Avht\nVf3bIvIzwM8A/w3wUeDJ6vUngL9XvX/LqLmomh3VfNW/G5PMc++FVDDbpmlecJh5syF+1tdKKnqq\nMo9hJDs8UXExIGqIEfKlZc5evsK+12RrF4cRxbsSidDu5LS7nVR5umUx7RTyq9HQzSwuemLsQlQ6\n7RxXFhTDFZxzjMpL9Ps9BoMBw9GIXm+f/nDAbn80+b+ZxXQznvrAh9GWYcEmNcdUKrWLFkWq4KT6\n36aFWTOGCbtMsQ8aPZaI6j6+2KcYbOFHfQgFrSiY2MJoJAopo0NocX1rxMraOd739AeRsEAURd0O\nPiRQNxOqfp2g9PUYTDs01RuLGe/gzbLwzfJrd0vHdhB8nE/v9ZycR3dlCKp6Hbhefe6JyFeBS8DH\ngR+sTvsl4P8hMYSPA7+sqSc/KyKnRORidZ93jY5io51ny60xgtp+X4Ny7XabTmd+QdJ56PNx2zq+\nTg3M+C3c7Z4HzpNJSVOpftcYUQkE5wga6a6tMgwO7zytJGtjTIrCNAqtdptOt0un08FkgrGGKAbE\nIsZgJcOqMByNWFxaTVWdslSxKR8NsUawRsisRVBsJoQgCcRDIM8J7Q7v/9BLOMmqxLSmEvtJVoIU\nfM1BJD59HktGKmj0iJYQHVpcY7B3GygxmhyVRHNEc7I4JBjFxYxrmz2uPPUB2itrCYPxAwa9reT/\nURrWHzuFJ/lsTBzbZuNgJjUSUv8fJvZPEp3UNRuPEnY8X32o3w9Xb2evfbcYx7EwBBG5CnwQ+F3g\nfGOR3yCpFJCYxRuNy96sjk0xBBH5CeAnAB555JFjNbrWne+GIdTSQDMXXlNiUFWGwyF5nrNQlVI7\nFBSUOsT07QGO89qXzEp2fP/ms+p2pPDn6ZRZtcQwPl8hhmSSLFzJSAPPPvU4rU4bFwJIJHglxEhm\nLBqSOctmrbErd2YyYpahxpBlaQFrTHr1ACWjlcKLRWhHXwUytQmqdDQy8pFOp8SVICajFKG7usL5\ny5fZ8YZ2JAGGlfOUQIp0xDTMeLV5UaFWH0hh49aACSWD3nUYbWHLIcbWXgSBoBEVIWYmXUPgoYun\n0XKPwdY++7HAesWXA3LryCTDhAFi2mnxVvEtMXqmsyYdHLd51EzJXkt6Y0nnLlJAcz42y9PPW/yH\nAZ6zONh7bmUQkSXg14C/pqp7M41VmTbO3pVU9ReBX4RU2/E411bt4ZCxGf9+yHPHqHKWZSwsLADT\nse/1ec3vdeTicTGG8aBJnOJfh4GJzWPCZLcwkAKbGmRMcs/FCGIM0ceUhSjLePixq0TvyU1G1ECt\nsKhQ1WWszJvWkFtLu90i2oxoTXpW8KgBm1vyGJJ5z4HkgveCN0q3ZQilUmhIsQeSJ4aTtVAsLzz/\nEs4JhmQtEEmJaWvNSAA1TTu8VJv0LGKvlMM+o95bxOEGrahYkyEhVqXhBTWOEDwBixIQo5gYiPt9\njMkIxmLVY0SRYLB2SH/3Np21C8kDsrLsJPfFCOSIzPd+nZ0bIppyEZjYAByr+6Ekf4T5G8k8q8Jh\n5x22aczSO5EWjjS7RSQnMYN/pKq/Xh2+KSIXq98vArUf7jXgcuPyh6tj7xodRV2oab7DyIS891Mi\n+WGfrbVVdN/xaC5oBCBpV4f5/2cKWGoej4pFsFVUnlSMDcCo4FyELOPpZ58nukgsEwgnxpDwsuTp\nmBhChZObCbZiREBSQJixHQwtMpOTiSUXg/GRMmbp5QXvQWPFkMQR8EQD0sp44tmnGboB0QSS74AH\nqRlDrDwOa1/8gwBv7VtsDGh0DPtb5LZErE/empHkuehJFaCcx2ogE6WV5RCVdt5CQqAVFUOBVcVg\niL5gMNilLIqETwSS2tVwFDrQnjnjEysr0LTKUJmYq02kBknnveZhW9P3jzOl3ybSwSy+0Xz2vP9w\nFLorQ6isBv8A+Kqq/p3GT58Gfrz6/OPAbzSO/yVJ9N3A7ruNH6S4hVp0sgdekwo76VUfNybDmGxq\nALJs8r1WB+rBDGF6hzhOxzYpWRiSz4QRSTqvRDAFU7OoFiGipokbwcSUo1GqqsMIBJM2UbGpzmKd\nyy/EEjKDba/RsWtEEYqsIAuhAiVz1GaI7yXcwCiZtVibY2yOMUJmUxoyMRmZUTRXjBXyGuSzBqOO\nTAImOgwRKwmXqBlPFMvph69gl0/T7S7Q8QVqPWpCxQwrDCUmU6JqqqA0QROSmpNqMUR8UEzeYWnx\nFCa0iFiiMTiruEwosHz91Vu4eBpDB4ltxCt5DkELyDJUIspC4i52hDeKHe2xffMNTFUbIoytG5EY\nw1iFrNXTpBbUC3yy0EOdMk8nC3rCJKZV29lFOjtv6+CkmhncaUHPk2jrudC8/jh0FJXhe4G/CHxJ\nRD5fHfvrwN8G/pmI/BXgdeDHqt/+Bcnk+A2S2fGTx2rRPaTDTEHvHdI7DxSq9NZGtFtTkR3v5MZg\njMWImUgvEgmF4/ITVyhM5XwV0kKNAplGFGUUPEYMWDPegTRWGZCbrZP6eYkBpck1mXDjhU1aAE5B\nxYLNef6DH8QHDyVYZGJEiEnXn0dNSBFIICfpOYphYfUsPTekXewSULJWm9dv9eiV8MwHP4KGQNh/\nE0MgKki0GBGiOoxEhCzhBaaFZC36feX0uXWwGbGOg6mCnmorcbN1ze8H/0JjvJhImLXkc5w5NIsH\nvNNrjvPso1gZPsO01Nqk/3DO+Qp86sgteBuk83bVd0izKsJ7be6p7+9DQBr5+hRS6HOE+r/VRkgR\nJozAGJLZK7U7z3M0Jj+Ec5cfoszThLYRggR8BAnJrBdicvgJjXiNGJP+O0H2qygBlRSCXe3gIjKO\nvgxVnEXtVh0weFJdhouXr+KzLMVhiCQJonpFnSSe0cnfHJNqJS1otdOFgJgc7DLnH32eGy//PoKy\nvV1gumd5+rkn8eUA/DYGRYlYsXgPeSvDx0HKLJUSueNKoV84rj71Apq38SEBGkqorheM6NRuC81s\nSFJ9r+dL3e70Z5pesM2FOktN0PIw68E8mmedmlVBmnQcIPwB9VQ8Hh3swNmsSMz8frf71aWyjsKM\nalPV9KDEmMRsIRUpFWMqMRmkcs1ucvt6jiQg0RAzi80N1kmSgq0gJid2c37w43+WnVLJYwquCRrI\ngxCNgI/ocJjUj6gpjKpSlzR4wKYlEZVASB59ZYkPJcGXxKIkFAPKYshoOGQwGDAYjAghSQhmYYU/\n/aN/Dp+1USOYKJWVIBkYk1FyOspwum8bgKomVSeXgBHLyEXcyNB65CXUFZy7AHkujAZbxJ1v0maf\nLFigjZeAk8i+F06deZYitMiyFp18gSWbsxoFWp1kVTCgmoBTqZiQmpD6Ys7ia06TCTM4anTifJov\nDSQTaDPHwVHm6GHYwlHoO4Ih1DSfg74doDC9T8yU8UBO/lq0blw11jEFIcRI9J5WlkFMzi5URUds\nMtBNrqysIinyrdZXAdUU2FQh93nWYa+3xzBEMtPGIDjxRA+dKOyLpmKqwxEYRXwC50IQRDKU5M8A\nVQi1xhSX4Euid5gYwDuCK/CjIW40pBglZyTn0n8ZhcCZCw9RmApZr4BBV+ExmIohjEO/78AYVJMp\nUZUYUz6DEANWDdg2MRSM9jbw+xtkOiRISOZSyYnaoj90XHjsGTRfpKvVohLBxzQKzjlMJpVxoepX\nyYAmdlCP+WSnn95xJx6wMSaLw7w4mllpY/I/J05x0yqJVJhFnYuxLjt3Z2/a+l6Hxercjd59P937\nmN49NaAGK+v7zTIZmZFGZrp5TjPGyVEqZfugEXfmgMQD2IILkVPnzuNVIaQ9OBiQzJBhkSzFDRS7\n/VRkNfqGihAJIaZsymNcIVSBSo7oCrwvKYshbjSiGA0SblD6SmVIov2TTz6JUoNaYbJ4JvzwwL9q\nvqZ+E4gYYmUSEakZzIiIw/sB5f42bR2SRY+JGdEoUQJlMIS4jJgVfGhX0aGhksg8ik9MsSmliVQS\n0+EJbw6Mm9S1Fyff551zt3vMJwWZMJ/jYhFvhx5QCaHZKRNX0VmT4WznzXLPt9+5E++12TDp2ill\n0q56QKtyY6TJk2UtXANxrttkJbn0ahUuXfvypZ1IxklEo1b5mpu7UfCcP3+B0kfaBlJOZwFJd7HG\nIFYYFEOMpKIwGiLRVJWVk50gpV+r0HGvmjIbRQhBKQpH4SMhpt0ta7fYHxV4ETJp89j7XmCEYGNa\naMGAMzaFJ9ft1Dno9yGWHFu5YMVqkYpWsQxKqqodlCwGogawAbWWYcjZGlkeuvIMPkIrTxWhVbPE\nWOpxipJyNMokibsy7cY+2b2n2ze9UzdFdKauq8HJ2flYXz+P6dTPC6Ee36OrC817AMe2NHzbSAiH\neW41vzcH+e2aEA8+9/hdWC/k5mRTbcY6HNQBmxNwnD4lTiaVj8nr7/SZdbLMVo7BVXZmJgCiYOj3\n+4gq6MQ8VQOHMcaxE6/GhCV47ymLlFG5dA4fCpI/Q7JOBB9TJmWbce7iBVQSUwkkxjLWclQP9H2N\nsNSWikm/zg8/Tru4TXGjJqe1dJoi5gQyQhACC+z0PCun1+kudSofhukdfN5caB6r2zGri0+bC+uW\nc+D7tLlwUpF53n+5GzXH/yib2MHN6Xj0bcAQmkkq726vbdI7ZQpNxP/gvQ+7qsm0GoclMqk91JiI\nMrFpQ6r6hFZ2/xjREJIbcVmiIlx6+BGQlB5dKxndqKCZVAvY0+/1IEZCcJPJW+8kddWoGFEfkv46\ntsmHSf6F4JOaECMaHKqwdvYCEYOLJYFIUCEqaNAxIn8YNbuiVh8OLoB6UYKQU0ZDe2Wd2Foh2CWi\nLHBzu2Th1EMsnz5D6QuUmDALrZ+SXsL0uB0UWA6fS9OMYv45h32vzbx3esasNHI3mmVcTWZ6XBzh\ngVQZ5lsLGHsSzttZmjRbtPOdtkO1BvumRcRmqa3qRCASiRAdVHb+zEoVYFMNYBVRl+6R6hYaY9Ho\nAMjEECpTnneOGKpgLRXWz5wlKGO0vM5IXMQSI0qmgaLoYxbbiCQG4YMmk1u0Yy5V524M3uNcCUBZ\nlsn1QA1RLaOyIPj0P1wouXj1cWy7QygcNhMQMFWOA6VWQyY0T1Ibg4mV2jLZeRteg6LJtyCzFBpZ\nOH2BrRuvk1tLa2GRMxeuEgwYCzkZ6pTKTbPx3DDOlF2refOmzOxuO7tTN60MCbOU8fmTeTjf5DjP\nFX7amtFkJL5hxZhuU41jSDVPaiZfP/84HrYPqIQwB6hj3o4yn2YR4ybXfmdtksY7HITMJhMjVgvV\nECc67RTFRlsnKLSpwEdTlaWPfoKI+xBZWV1LTxKpt1lAiCYgwVP09yA4jAbqEugxeELwqTJTSBaF\n6MsUOelLvPdVEdtURt6XSvDgveJdSvaqmeXRZ56jjD6ZQKsF7H0Vu3AXE+3Y92Dm1FkRfSJ+J2/V\ngBBsh+Uzl9na81x+9GlCFIxkKexbBbEWIxki1fdaSpCjgIAHf3+31M3x/z6yiJ82lNpLsj6mmjwr\nZ7GZsiwZDofHau8DKSEcl+7U4XcbjDvpffM6+vBjMjW1jDGE5q7XFPsq+3MtadRWiyR9VDsUpEUX\n6vyK4H1gcWEZV6kMjN1sFDEWcQE/LJEQsdW9QwhkIuNkIFKDlpVKQYgEX6V5d2XCEFxJ4TwhRlwI\neB/xonRPrVEYISMiVRHaECI2M0ykp8PF4TEbleneml00MWpV5Sr1UyQnb5/iyWc+RN+71D9RESxY\nSRpUTJJXckev/UgOjtlBIDql6psnybzbiP9hc7E+3oxpmLRt1hw6OdZut48Nnj/wDOHozkQHLRDH\nuXbW/jsL9jSp1vknh9OiHreZVGG4CJM08nVQUcohODFlaaUDBq+TiEtNPhBGkuuxc67KPJ2l0OUs\nG2cfg0qPj0qmwtbNzaRORKEsPHbMYMz4P9bh1DGm/I3Ol4QKpxiNRoRYEKpiN94HVAyvgPt0AAAI\nnElEQVSYNsFaxAqZGEwV4kxmmlLuAfT7bjv07GJVrZgblYRlLWLS/w0RWu0q72QUjGmBKIpDpI64\nFOqK3M1n1Lp2c5zntXF63igTJ7X6XvV59bg3qfn9IKh58P53ZqB1W8dRr43r6/seN0L3gWYIxwEL\n321ufphps9GKKZ1UjScPCibp/y1AaTXvWL031aGYiqDWxyvXYq/J9u+8H8OQMSoma2OMQqwWgCQx\nvAoIJgO+9vnP03YhtS0EggoueCx2PHnSQvcJsFSP94HgItGn44X3hCLiiqRGWMmxNmdYeURmmgys\nik3p5Su1qP6fTYY8u+uN+6uhAze/V3cYYwxpkZeYHIIqlg6IRY0SpUgxGwqHhR83x7L+fDdqnjvv\n9ONMtfS/IG0gs85s022c/XyQWUzUyyaD+bZ3XW5ywKOIbkc1N94JWZ4dhMPaBLXtuOmDAGhGmUds\nVDpEXJ5j/XRdAIAQPCIpP59WunLUibQhIjifkq0GX7+UsgiYU2v01RCDkKmhrlmQovsdGY7dzeuc\nawVcmRFwtGkTRNBqp/ENTEVVKcuCYa/PqHAMhkOKwjFwIwoPo9IhJqcg46kXP4xkOaopxbkaqUrZ\nK82AqOCn7f7WHhSPm0zCVHUZNAUbYKTygKzM88okIUmtktS75Xh+MI24z+6Y83Jt1tS8V/P85v2b\nYzg91+r/VI9dc74A47bXizZMebweBjDOHqv7LGVtTmpajY80gc6j0APJEOrAmjoT0v1LjdTYaggS\nsSgmRtwsqj1e8M2DOnZintoFtA6TTZMtVGa9xdU1MJZcsnEylUyrxMUq7G3t4MsSFx0aF5NTUiUV\nNMHVsTNLTBiF957olVDWGaBTDckogqqlMDnPfODFSvWhqsZceVtWr+YCuRs1kf9krpy48dKIC0ls\nIlZ9N82wZ/0KZr0PZ13N77Yzz1uks+e9E6zxMJXhKHSnTU7keIVtH1iGICJ47xGRuzKGdxMVftsk\nkw9CavPIe7xPCU7q3AswPYAJr58whRgDYiYZnEKI49qO3W4XVYMJEcWCKFaTRcKI5fbuLgTFZgbv\nPHnWwoXk/1/fXxqWF61wgxh13D6LRcgYFvt4LB6lc2qdpbNn6ZGuCUrl12+mojLR6VRjd6Jp7KD2\n1pOKQdS/6QEjQFN6bKYjm8UwJp/r/z41SOPdPO2wd1ZPm2J5jHJX5jF9ffofTUl33NczOEe6/yRu\nZlalSg9Vah/x5jVHpQeSIdQDXecBqLMe3c/SghKr+ARJDsJRCdGj0aYxjHUKcEVUQHRc9q2ezBPz\nqBKCq14erY6vnz6N2HRfY4RAkhAMoCHQ29mFGKoaDgEbNeEENXYQk64/lhQ0Jg/Isc5vibFIebhN\nSsgatcXFxx4jGIuJjhTQVE3I8X+vSJKnJNxZ9aoncFNSmCzMGqBVpgM+Do59E1CbB9TN7upJ5Zjd\nUe/MuGbn3PHn4MSK1LzH7IJv/jZvro+/zwkY+bY3OzZ1QO+Tvn0/MwMwqPjkc6ApwsAKRInEKGkB\nVyrA2MRVzfvJWE706jpoKIRQFXtNn1fXVnAxENUQUYKBPE5yCmzvbAORGBwhepx3GDEYSbZ6qCT8\nFKiQajPEBCoWRVm9HGXh8D6wHz354hIvfOhFRj6lSDOiKUZAhfle3c0dmYatcVrRnsVs6uIpaehT\nItVpJnBQ8jgKQDe3he9wc6mZ1+w6nGU+1SeaDG9Wsnnn5s6jZX8en30/iNMicouUdemodAbYfI+a\n817SSbu/dfQgthneu3ZfUdWzdzvpvmAIxyUR+QNV/fC9bsdx6aTd3zp6ENsM977dD6jr8gmd0Am9\nF3TCEE7ohE5oTA8qQ/jFe92At0kn7f7W0YPYZrjH7X4gMYQTOqETem/oQZUQTuiETug9oAeOIYjI\nnxGRr4nINySVob9vSUReE5EvicjnReQPqmOnReS3ROTr1fvaPW7jPxSRDRH5cuPY3DZKov+16vsv\nisiL91m7/5aIXKv6+/Mi8rHGb/9t1e6viciP3KM2XxaR/1tE/khEviIiP1Udv3/6uxkkdL+/SDnT\nXwEeA1rAF4Dn7nW77tDe14AzM8f+R+Bnqs8/A/wP97iNPwC8CHz5bm0kVeT6lyRvmu8Gfvc+a/ff\nAv6rOec+V82VNvBoNYfsPWjzReDF6vMy8HLVtvumvx80CeEjwDdU9VVVLYF/Anz8HrfpuPRx4Jeq\nz78E/Cf3sC2o6v8LbM0cPqyNHwd+WRN9FjglVcHfbzUd0u7D6OPAP1HVQlX/mFRm8CPvWeMOIVW9\nrqqfqz73gK8Cl7iP+vtBYwiXgDca39+sjt2vpMC/EpF/JyI/UR07r5PitzeA8/emaXekw9r4IPT/\nf1aJ1/+woY7dd+0WkavAB4Hf5T7q7weNITxo9H2q+iLwUeBTIvIDzR81yYX3tZnnQWhjg/4e8Djw\nAeA68D/d2+bMJxFZAn4N+Guqutf87V7394PGEK4BlxvfH66O3Zekqteq9w3g/ySJqTdrsa9637h3\nLTyUDmvjfd3/qnpTVYOmDKR/n4lacN+0W0RyEjP4R6r669Xh+6a/HzSG8PvAkyLyqIi0gD8PfPoe\nt2kuiciiiCzXn4E/DXyZ1N4fr077ceA37k0L70iHtfHTwF+q0O/vBnYbou49pxn9+kdJ/Q2p3X9e\nRNoi8ijwJPB796B9AvwD4Kuq+ncaP90//f2tRlrfBaT2YyR09hXgZ+91e+7QzsdIyPYXgK/UbQXW\ngd8Gvg78a+D0PW7nPyaJ146ko/6Vw9pIQrv/t6rvvwR8+D5r9/9RteuLpMV0sXH+z1bt/hrw0XvU\n5u8jqQNfBD5fvT52P/X3iafiCZ3QCY3pQVMZTuiETug9pBOGcEIndEJjOmEIJ3RCJzSmE4ZwQid0\nQmM6YQgndEInNKYThnBCJ3RCYzphCCd0Qic0phOGcEIndEJj+v8Bx0xgAMVugYAAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f663ac88eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(train_imgs[0], train_landmark_pos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1, 24)        120         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 24)           0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "xception (Model)                (None, 7, 7, 2048)   20861480    input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "softmax_1 (Softmax)             (None, 24)           0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           xception[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "repeat_vector_1 (RepeatVector)  (None, 2, 24)        0           softmax_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 48)           98352       global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 48)           0           repeat_vector_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 48)           0           dense_1[0][0]                    \n",
      "                                                                 reshape_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 20,959,952\n",
      "Trainable params: 20,905,424\n",
      "Non-trainable params: 54,528\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "num_cat = len(img_cate_map)\n",
    "num_lm = train_landmark_vis.shape[1]\n",
    "input_shape = train_imgs.shape[1:]\n",
    "\n",
    "model = build_xception_model(input_shape, num_cat, num_lm)\n",
    "model.name = 'xception'\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用来归一化的 landmark 坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 15, 16]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmark_normalizer_indices = [\n",
    "    landmark_types.index('armpit_left'),\n",
    "    landmark_types.index('armpit_right'),\n",
    "    landmark_types.index('waistband_left'),\n",
    "    landmark_types.index('waistband_right')\n",
    "]\n",
    "landmark_normalizer_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用这些坐标构建 loss 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "square_ne = SquareNormalizedError(landmark_normalizer_indices)\n",
    "ne = NormalizedError(square_ne)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compile 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss=square_ne, metrics=[ne])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "切分训练测试集，配置好 callbacks\n",
    "\n",
    "- `TensorBoard` 可以记录训练的过程中 loss 的变化，方便你进行不同模型之间的对比\n",
    "- `ModelCheckpoint` 可以把当前最好的模型给保存下来，如果中途中断训练，也方便后续把模型 load 回来\n",
    "- `ReduceLROnPlateau` 当 loss 不再下降的时候，降低学习率\n",
    "- `EarlyStopping` 当 loss 不再下降时，中断训练\n",
    "\n",
    "因为有了 EarlyStopping 这个 callback，可以设置一个很大的 `epochs` 然后关掉显示器去干其他事情。实际情况下，\n",
    "训练30-40个epochs左右模型会停下来，我用的是 `1080 Ti`，大约需要3-4个小时。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_seq, val_seq = train_test_split([train_imgs, train_img_cats], train_landmark_pos,\n",
    "                                      batch_size=16,\n",
    "                                      num_samples=len(train_landmark_pos), \n",
    "                                      validation_split=0.1)\n",
    "\n",
    "base_log_dir = './logs'\n",
    "os.makedirs(base_log_dir, exist_ok=True)\n",
    "\n",
    "log_dir = os.path.join(base_log_dir, model.name)\n",
    "shutil.rmtree(log_dir, ignore_errors=True)\n",
    "os.makedirs(log_dir, exist_ok=True)\n",
    "\n",
    "model_save_path = os.path.join(log_dir, model.name + '.hdf5')\n",
    "\n",
    "callbacks = [\n",
    "    TensorBoard(log_dir, batch_size=train_seq.batch_size),\n",
    "    ModelCheckpoint(model_save_path, monitor='val_ne', verbose=1, save_best_only=True),\n",
    "    ReduceLROnPlateau('val_ne', factor=0.1, patience=3, verbose=1, mode='min'),\n",
    "    EarlyStopping('val_ne', patience=5, mode='min')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit_generator(train_seq, \n",
    "                    epochs=100, \n",
    "                    callbacks=callbacks,\n",
    "                    validation_data=val_seq,\n",
    "                    workers=4, \n",
    "                    use_multiprocessing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(model_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "16/16 [==============================] - 0s 27ms/step\n"
     ]
    }
   ],
   "source": [
    "samples = val_seq[0][0]\n",
    "preds = model.predict(samples, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大概看一下在验证集下的表现，这里是把所有点都显示出来了，因此会发现有一些点很飘，并不是预测错误，而是该图片中不存在这个 landmark，比如裤子的图片上是没有领口的landmark的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f66aa0fab70>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m1pTQp6Opu2umexuiJwZPYSy/Pvz93vE6a/D85vgPKMqq//vSWs5ub18VHc+5\n8z1NSzjINNNqm4Zf3P4PF5xcdaz5mfveTWEtx48f54H7P4N10LQ1Ig7IDZE8OwshMBmP8m0pmind\nqGE6a+vb/sbkRkfosZeqii0K1AjGWNqQ0DUrKyssLS1SliWDhWWMdXlJSRIy6qKbEhMTvp9Npq5M\nR7jtBKC6Y+4iYOcsRjrcaYqKHbTtYmukAbbac71WqTEGMYb93Z30yys78D2ztFNEHgD2gQB4Vb1F\nRI4BvwKcAh4Avk1VL/7OP4+tp6EGj6qwHS7+Es/Umzz2yEPsnj3HwLk8B7MURcmkOcDgiFlSIWpS\nPTNGCG2KgEaSE/WYT5nO31Q6fGVMeMnOAWyR7psdpCxLtsbbtPUBAK6smLQNUQRbWKwtaZoM/4qa\nay3TN3aMGIqi6gHd0h/D9MIBWafTWkzm/EECehtj2LBrbF7kPToSljAxXXiUVF/u7e0dOv4r1Z6N\nyPd6Vf1yVb0l//wjwD2qehNwT/75CrTkMMPhARo8x+3aRe+1Ua6zVC6ysrQMVqiKEmcMoWlSV884\nyqrCOJPmcuMxVpO0PKSmgzGSv5ssvGuTKFHUvnsZmjoRXjEYV+CqAYPFZYgGo4aidCwvLLKwuIyr\nSowxDKoKayrEFKha6klDOx7mwXgGc2tSF+ujnth+1BAj/cC/G4VEFHEGawssivqWGD13HPtmKikO\nvTeVlHzfC+9ga2ub8d4WljTu2NnZBIQrXdTsUqSd3wq8N///vcCbLsFzXH5TEhKlTU50x9q3UJny\n0F0GpuK7r/02DkZDBoNBkvrLf9qla85ayrJMo4GOVxdjqrlyM0VEUoOFLMLbdTu7Omv2UxShcCVg\nMMYlWcCcGo/H47TPAUnppCQ5d9VACA0x1D1nsJ/b5ehj7eFRw6zsw+zPxqT6MDVhUmLlnOO2Y6/m\nbRvfyYZbQ4ANd4zvXfs2bj36VViXIl/btlnr0+S3+Mqu+55pt1OB3xcRBX5OVe8CTqpqp4L6OHDy\nYn8oIncCdwLccMMNz/AwLpdF2jaBhb925RYGy8u874nf4PHxGTaqde489RbeePJW6tGI8XiEsYbQ\nJuhWE3yanRUlKiZRemJunGQHhAQpizNgZiChS3J9p2ienWWqD4pGwRU2MwoUjZGmrVkoHAuLy2mk\nkZdy2iI996Qe0tRJQpDMSeyimTF50YKeT1tKhN3uIpCwpUk5W0mjEd8GjIJFeP3qV3Pb0VcTCdhy\ngRACo9EBoa2xxVra526UqipTpL+CU0545s73GlV9REROAB8UkU/N/lJVNTvmBZYd9S6AW2655fl3\niZOEbQy+JQSP956vW3sV/+goNQS1AAAgAElEQVSLvx3v04ZX1cD+3h4+Bsoq1VWSOXjGQAhpf7lz\nBUZMRsFoovN0bXy6usf0zZTZOkgkCRt5Y8gkhjRYNynFC9ZSlgUb6+uYGKnbmCMTLCwucrB3jqZp\niG1C4GhUjJ2VqZhKQHT/P78TGdGkeJYjYBJzmg75Nde0HfFWpaAoKmKcMBgsUS0vJ2hdjIgRrDX5\nIqA9o+NKtGeUdqrqI/n7GeC3gK8CnhCRawHy9zPP9CCfkyaws3OO8cE+hsjK8ipeoSgy7tJCUabN\nsLYoiQrqUzMjxuSszjkGi0u4osSIYTQcXtA9VBSTgc1FUVAYl6BlCBrIUK2IGJsxoIaiLDDOJhkI\nY2h9YLCwSBTD/kEastdtQ+OFm1725exvn+Ng+wzaNHSU2k66sK/fbJob9pSi85jsCbua5n2ucFjj\nUvMmO0/M1KZg0qtqJnUqeW0BboEYtJcqHJRZw+YKn7I/becTkSURWen+D3wD8HHgd4Dvynf7LuC3\nn+lBPhdNFfb29jCZ07a4uMhgsAB0mMmO9Z27lKp9Ghj7PexpJmatzfqcYRo9Mhsi4S2lJ7r288AZ\n4qwPniYzFmJMQrTGmKTDKYbRaExVDSiKioWlAU07YXX1CC+8/gbObJ5lb2eb0NbEWNPVgFPH6lGd\nfTTuXs9hub/OUQRIYwtr0msXOUxRCnECZOwoyWmraqGfRaZ6NlzxMLNnknaeBH4rX9kc8Iuq+nsi\n8hfAr4rI9wKngW975of53DMFmnpEjEpRLbF45AhNpuZoFJJvCGICFqGJmoftEYxNOpaGxCLIQ/Do\n25nZVid7a5LCGAYjFrKwbfARDGjM+p5AEMUah1GLl4DP6d+57dO08QaaZsjykVVcuUzbHLB5ZpO2\nrvG+ToAdCqxI70dqQDIx1+Z0kCxRSI5gqSkqGRiQdEPRkGUFE/NBYndBAYkBguK1SfNK47AqIIHG\n7xP8EUQsbdNQ5IvZlWpP2/lU9TPAl13k9m3gtmdyUM8HSxfn0EeHsiwzYoR+Jtc1K6LGrDQdsMag\nMUOy8pZZVKnrCU1dYwRCTNGni55d92/Kp5MetQL034MPFIOqB2KXpaGpa45vXMPq+knadoJzjrNn\nd6jHI0JoqetxX2t187muhhNmmi1mmmami4JcEAm7uWCbB/KoAcIFUdIULl9I0rYijFCWFbsHu6yu\npu7u/Z/5DDd98Svmc765XWhN00xP+hAZj2sWFhNtx7kyNRxikkUPbYtvk05KDNq34ctigCmKRLMZ\njdGYdvh1exhiTCBja23qdIqkzT5xBtMZA8ZMU76yLJHFAoxBQqRta45uXM84BCY+sLOzw97mE+xt\nPc5ofxuNY5wrscbhY4TcRY0qFNUA68rEoJcs2qR5DKAJwgYQQwZah+SMg6pKnEZXJCZGVtruHFA0\npbChbYk+weKGoz0KNwACxlo++cm/vuLTzrnzPU2bjEfU9aSPQPt7B70oLNDXZCEkQqvGkLh5ABjE\nWYpyAJlh3tYTTM73egSLag/X6nVbOo5Rtg7T2UVIV1QIhryfk+F4COJo24ZmUqfnaqeLLFUVMih6\nFpkiPZ4zy9HPKJZBV/N1jZkkDdFHaGdBEh5VxaTXKzOd0uiREIitT2Rd31DXE4yxvdRGSh7mzje3\ni9h4PGY8GSMibGxscHBwgIjQBE/djR+CzxtZG2TmxDXGULiCsigJqrRNTT6TD3U6E4g5t+1nGhpx\n5n7doB6mw3AreTcCKYWcTBqibzAkZnxUxdgCjWDFJn0VUrosCK4TaRKXoW2uh5kdagjl+V6SmnD9\nBcB7n0YxdCn4dGYIOa0WiATECBilbmpi1JRui+Rh/9z55nYRC7FlPDrAGKGua+q6nqHYhP572zZo\nhxQhw7FsSk3LakDwnqZpIO8ziHE6R+tO+g403aNMchnUOV+bB3w9210Fza17U9gEVYsR9UlI15UF\nKmlppRGXVMs0ixrl891m9rwV27+u7vhh+lzTNFR7Am7nkIjp68bZbmeUjF6xhqBpl0PwnsKlCwa5\nObO/u/cF+Swvl82d72mYqrK7u0s7GeMGS8TocRJRbEZz2H4PgvqYduWJIMZRLSwzWDrC4vIRtCxp\nhkOa8TjFqCwb0TuR6WZsiSdnrU00pDhtYgRVXB6JubKiHCwQoqI0jEcjjq0cYzLewYjFVSW2rCiK\nkqQiHZIT9HJ9ZEVsiysriqLC2QItlCiHQc4xa3F2+W3btvgY+p1+iUHvECepgdOtHzMmc28NThyh\nVYxalhdXqAYOsASUslzg/vv/9gv+2X4hbe58T9GS4panqSeIKDEExBg2Tl4DTDuSMe8iiDFikR4B\nYqzDWAfWYo2hrms0p1jdcpPZ50qDxBQFu7SwAzqLCL5tUe32NRT5GJTGtwnkLEI9GmfurVIUHbg5\nO1Lq/yMas2B8zN3UxF4wzvZd2Rin6eZs97KbW5IbQZYOlpYlJmxqAHXUoUSfUiRr0sRcX1prU9MH\nIMS0WPQKtrnzPQ3zPm0OQknQMOtYWztKR2qdDqlDD3ruhurJ+Sw2p1ihbTKWcqYVn0WKOiEkYzsq\nT+4uZt2W5KxTBIkrE040xCTU5Kxld/cc9XiMn9SHmiYdeTY1XSIhU5m6ujGNFswFY47zKUQigjMG\niYqTFNV658y1YBq3dA7dNV6Sbk3awJsib0pblZilKybjUf+eXIk2d76naF13M4RO61IYTyZUgwE+\nxszHm6JBRASvER8V45JOS1GVqcnhk4SeYQo7g85R8zRtptuZ3WYqHS+C0UjrkzxgOtFt0pVBObGx\nnmpRTcs6014FJU3nQUUAxeXvMcY0v+udbvak747nsHJZ/mGqUZozg+R8GSEmqTZUmT5G12kV0rw0\nxsjBQeoYd4+T1qxduZy+ufM9BeuuwDs7O9TjYa8Adnb7LE3b9F3IWW2TEAJiCowrEVswWBhgraVu\nxoyGQ4iJQNttqnUuSfeZrMXiyhKxFuvK3Migr6li3kSkEjHOYgvHYHHAwc4u11/7Aj796U+zsJhW\nTA+HB+mCgaRBPwkWB1MZ+s7BuvHGLLQtSUrM6LbEmdQTUNNx/NLrF2cPvWfGutycEdRm4i2Kqk+L\nWmJkd3e3H28kzdGa0Wh0aJh/Jdnc+Z60xQwdU+rJCPGpwVCYSPSetg1IlvsLCMY6YkzCXtZZXFlQ\nlCXY6cnl20la6WWmV/agmmdjgnTCRdbmes7kTmpaKx1Cm1AiPmLFYoukhbK4vMjf3ff3rKys5uWX\nSqTB1w2SlcGMJE0Yn1PjQOIQCoqIA7VJrcwmUVxriuxInW5ngtGpkHc2GHzfsY04MUSbFc06ZE6u\n70y0KdXMqW0EMGXagJsvYGINRoR6cjAlNmjH9LgybO58T8E6AHHbNskpMGh+C03HbaNbg5XwjtY5\nymrQk0w7a8YT2rrp139NmximT7tSJCxn0tBkaSTQjSNsHq4n+T9VZXExgZQ70HbbtoSg/TjBGptw\nmkDSn0jfuyZL14txzqVNujMRvQNDQwcGSJExxOkev/R6EofPkmrCnnQbY//3yuzj5sfJxN/8YIzH\n42fr43vO2dz5noJJx+ELnhDpQc6DhUFeA5buF6PvZ31lWaXdDS7JsFugHo2ZTDpWOXQitYdmaH3a\nWfRNjy6tTQ0M7S8G3XjAGEM9nvDQ6dMcWV7qFalNJ4TrA6HNC1VckbqYklLZzplnO5rB+7xBd2au\nJ9P3Ag5jPI0x+BAwxuF9mB7rTP1rrcsyFYlN3ymqddG/q/G6zm89mfTR+ko7Xa+sV3PJbFpztG1C\nrLRN2zcXOohYyHMuiQnnWJYlrqwSQzsPmM9ub7K/c5bYjCEm2FnMTYbQ6WRKmrNV1UJanpL3ps9G\noC73ij5gbMHKkVVCiKysrLC2dpyFxeXEI8zOEUKLbxta32RGfGYi5MjVa8bItCvZOV1nmmFks9b9\nvtst2KlmJxhZFxndjOJagsd1aTTYfmFnWZYMDw769y6q8tjDD05heXLo2/Pe5s73pC29VQcH+3if\n6q3QtrRNQ+mKHsXfwatizPOyGQ5eCJ56PCb6Bg3+AuCwMUmuvWORG2Nwxk01TbrUTRXN9VXT1kkD\nxhWsrKzwsY99DFeUaYEm3V4HBR/wbdOnszB9rF78qIO2HWpyyhSZY8zMzXmeqbHnvDoxmVqUJDE6\nJI4xWeg37//qHVxz1zWrr0WUyXhCDDHPRC27O1ec8F1vc+d7ira/vweSTv5U/9VT2FevdRkRM02f\npmDrDswc+yZC1w3sAopIx14oM9/NZMmKdIeullOmVKKyLHGF47777uPYsWOASY9npzWmasjHkgb6\nXbSZPYYu3eyiVAcLOywfkeaNnROL6Mz/s85LJ7qr2jsagLFZUBcLYUpNkizw2120JL9+EZhMJlfs\nnG8uF/95LJ1602tU2zRo65MDIQQfsIMyOZj3lGWJ90oMYCShPbwmBIlva0wMCUON5gqwG8yn2wok\nN0QspkhrxjrqDqRhuvqAweJ9xErAFovsnN2hMo7VlSOMm5rBYAA+baKFNGoTIHiwTkBTxxQjiGoe\njgPO4WNLYWx/RegaI9aYtG12pjaNISurhYTZDDHy/279Ce9+5Bc502yxUa7zPS/4Dl6/9jVYcQlB\ngweblsKI2BTpYl4qYwWx+YWKkHa7pw/iShv3zZ3vKVqIgclkgm/aPGJo+fP2Xt73md9gs9nmRLXB\n277ordy28Tp8RqJIVHzHXQPEaBYIS8gSJa8BQ5CipBhU2KLCFSWuqHqH64bgiBDyINt7pfWBtfUN\ntra2aEK6AEwFbmfwmKHFhzaLE01lCLWdRkGTJemTsllC4/SQMqbpZpoNTh/fWstkMuFD23/Cj53+\nGeqYOpZnmk3edfpnUQ28/tiraeoWsRYNqX5MgtiWkDVtOg6kyevKyqIg+gZnqy/YZ/yFsnna+RRN\nlf7E16j85+FH+alHf54zTdrR8ER9hv/9kz/O7z/+oXx/PZTWdQXS7Enb/T6gGcNZIsbmE/D8y30H\nG0k1YdN6BktLQNpvV9f1tJbrBt894z6ieajfQeDOrzu7HuYsbag7ztkRyCw/L2UGaVnY//3Q3b3j\ndVZrw3se/ZVpnZjT887ZVDWtvs6H0iFk0gUkLda8YrosMzZ3vqdobdPkHQnpBP/V/d+l1vNOtlhz\n1/3vnRE8mtZ8aQfD7HahxGoXY3A2bRcqijItOOkjS3ffkLCPMSApAFJVFY888iiaH7+7MHT1F9A3\nOWIMxOgR0kIW0RmVtO6YoD/Rez1QZmQBu4E5U3lDk5djIobNZuui79tmu90vcElON20q9QCG/Php\nbJO27Koq57bPXom+N3e+p2pp1pbgX2VZsvU5djRMF1em4fL5b3Zqx+u0SSEO280DsyOCQeNUumEW\n4KwaKazjyJEjqZmiSlWUF0TL7qdOaLdbUkkXDfvo5vpUtGsidezy/vUfIvumX3QMfiHJ41/MNorj\nU+xnbOnK2A7LGbOEhqomzRtSt1OAxx575PN/MM9Dmzvf5zE5D8wUfUtsEyC6bVvW7dGL/t2Jah1r\nBYipla6RNgSiCpoFI7p9CDbTbYqyoCgKyqrElg6xiQ+ntKhm0DYQvOJD+kIMa8eOsb21hcl4y7pu\n8T5melCiCakRIoKS1o+JzVtsM2vJGIPOylBIwmsGjRmnkvCcRg7v5VMCIl0XNXLnjW+mMofrs0pK\nvucF/xjIoPFoU/NFpx3VaEyC1UWlntTEtoU2IGJ48PTpvubMn8Kz8dFedps731M0VaVp2j4CfPuR\nf0Alh3c0VKbiB17yVkajSY5+TR8Zkk0RH/2QOs/z0iA6rXW2HbJlJhfs0r8OOhYyuLqrHQeDQZ/S\ndrpKIgKhI+vmGjR0qV7sRxmzamn9/8XQ7QmcnfP1j0tHwE2/e+PJN/A/3/gDnCjWEYQT5Tr/4/Xf\nxxuOfd3M/Wfhafl9AKxJNKzxaMhoNEydZGtp2sNp/ZVi827n57ULqw1jukZJ4NbVV1EtLPD+zX/P\nZrvNiWqdO1/03bz+2GtSKuhjjnM5AhH7Hodg+rmeStJdcWWBcS6Dlact/VTTTdO9bn7oyqQmvbi4\nmNaPOYOMk3N1+x+MCAGQ3F2E0NeARkgwL2sOPfa0bsw9/q75onpBzZcABZndANy2/lpef+zV/fsV\nfCerMW3eMIOUiVlZO6XmiohS2LyME2FQVv178Nk+k+ejzZ3vKZpquhp7063Egtcd+WreePJ2bFFS\nLVaopqZBGg9MF5/E6KcdxER0y+373Nm0rmeR21zvIJaQ0TBTX8h4yM4xrMGqY29nF1ss9WgVk2s8\n0Q4vatICEiRz66YWgcLamdepfURNz9MNzWeH50K3VajHZIZIURRMJu2h+rBLGjU/Voghyd6LQTWm\n/QytB5MiIJKlJ8iKhRo5fyvu892urFdzyU3zbK3NXcfcUMmwqRgjmjGTMcS0gCS0RN8iTNvz3W51\nawyIxbjpPA1rcpcvN2Fm0s3Z9n4HehbAmTRj0xhp64aiKIgxPf8sm2AKc5tKHMIUz9kdW28h5sn2\nlOE+2/DpjmU2os0Co/tZ43nHL1mfZgp1kwzozl1YlLIse2e3580rrxSbR76nYGmGFnPzI8G8iqLs\nneEP9/4z77n3lzhTb7FRHOc7jn0rr1380oR/jOcBlDuUixNs4SiKASIWm9PIbtjdO4pAG6Yq1k2T\n6sjClmiWD7zuuut49NGHk7CuJtB31LQLQYxBOqibndKCYsbwWEksg67+Qukhc1EjFosPAbRbAzY7\najCE1ie6UFdviiF0kR6lsEWWqki3p/vM4EWzvmeMTdK2aRusKSkELgh4KldE5jmPfJ/H7r73bk69\n6xTmRw03/fRL+d2Hfo/ofWKR58UiAB8692HedfpneaLeRFHOtFv8zJn38wc7fzaNPn2LHqw1RJMQ\n/6JJGMlYwem06dF9twjEQJGdseMG2kxWhcQW2NnfYTgcIs5SVUUirGoacXQDbDEgMWljJnJwut1a\nl51d+517U6Jvnk2eh/GalQ6MKIjNqDBBmb3YkISeQkxbbGN6XiVL0JicBZhEthVrsRmkHWOnhB37\nmrLb9/7ZPqdT7zrF3ffe/WydApfM5s73Oezue+/mzg/cyend0yjKQ3sP8a/+9if40NkP5xRuOm97\n7+O/dsGwvdGGX977vUNYyK7D1zVP0tA54RoLl5amTKOBMBMw+w7mZDKhrus+FewkIMbjMevr64c4\ndN6HQ8OSWaRKr4Sd9Vp63t2M6NOhdDE3QDrrXruqYjutmZyidkplh+rD/NoScHq618LO1pp0w3yT\nN/a6QyTkJ/M5nd49zZ0fuPM574Bz5/sc9o573sGoHR26rY4179/6TTrOmzEJIrXZXhzZsR3OHTrx\n0kjBYW1aEGmtS+pkWZtTMpA6Cn1T5LD7AHnUMAu4tnnov7q6SmxbxuMxMeNBNc5ELD08skg/TB2t\nW9Aye8zTud7UOaYmfeeybdvUWc0D/ET8mD5fr7qmIWFa8+PMYkSlZ9antLkNvt+K+1Q+p1E74h33\nvOOz/9FzwObO9znswd0HL3r7tj+b0zTTX/nXi+MXve+6WyNJrhcZteL6LzEub5q1SU/TJon2oIm6\nM3vGxW6DUEjdU2cszth+57q1DlcUnH74obyLoduSJBnmlurMBBkjdxkzl886QuhoRud1QeNUlwWm\n3L9DKBcjvSCu79Tb0D6itW3bDVmSNktOm7uI1jWBejjbzIgloFRVRea+01F3n8zn9Nluf67Y3Pk+\nh92wevFd8esuO5p2cCt488b/cOGwXUruOPbfpp3qLikxq1GiRKLE/opvs/JY52qmW83F4a5ifsrU\nOMmOIsZNnSMzFbqtt2VZ9o8hImnrrBGipC816UsQ/mj3I/zA/W/nWz/xVv7Jx36YD2798UU6moc7\nr7PfTYaliTGJBjWDf52uhu7mnEkj9GKqZN34Ih17ejfavPjzs4W/z/Y5fbbbnyv2eZ1PRN4tImdE\n5OMztx0TkQ+KyN/n72v5dhGRnxSR+0Tkr0XkKy/lwV9qe+dt72SxWDx0WyUldxx/U7pSZ4CzauA1\ny/81P3TtWzKyI2EZ33byDl67+qo+Asymen2qJVPhIDNTE3X3n/2eUsh0wnbRTlGCZke2ac9d2tcw\nJaJ2TRDgvMdOH/8f7X2En37i/Wz6syhwpt3ix07/LB984g/6+rMDeM8CwrvHkZxqds2grkPb4UOn\nWNQpibcjAs++J306KxyKrsDnVK++2Oe0WCzyztve+bk+3stuTybyvQf4xvNu+xHgHlW9Cbgn/wzw\nTcBN+etO4GeencO8PHbHzXdw1zffxY2rNyII1y5eyw9d8xZuXb4FDTWhTSeEIXXkXrfyKt73ip/k\nP37Z3fz8S3+MW4++ZqaWsdi8y8GoAXWQSbNdg6JDmagmycHuQp/2G8TeWbEOWxZg6bcfiRiEpDY2\naia0pAG5NSbN6wCJqdMos5GLgl/Y+q2LMDMa7nrgFxCdka+I2WF1qp6dIpVBo1DkTUWJpTBNJdPw\n3Sc+o4/EmBpOnbMCEJNKtZUCUQfS4H2b5Bg1MhpPa7rDNWf+nL5l+jnduHojd33LXdxx8x3P1qlw\nSezzzvlU9Y9F5NR5N38rcGv+/3uBPwT+Rb79fZrenT8TkaMicq2qPvZsHfAX2r7z5u/kji+9g7at\n+eQnP85nPvnXiAacwHhUp5Z+1rRUMbS+SauZrcGJpamTF/l2gjEOVZ/kHTJio5dQN9OKposyRkzS\n8ZypiSBFWoAQlELL1O2cqcFWj6zS1OnCEHPnECLWSiLyimRRIoMS2PJnL/raz9SbhBgoXGLqG2tR\njbm7OhtFyd+7CCY9OmZ2cUr3GF3kLIr0vnXDeY3pdSd+YEq7o8IfnvtT3vrz/5zHRo9zw+oNvPO2\nd17gWHfcfMdz3tnOt6db852ccajHSfvZAa4DHpq538P5tgtMRO4UkY+KyEc3Nzef5mFceuu4bF19\nkpZ5BJQkqx5zZw5IEDBjUDKaJAQ0BMhX/16vsoNpxVxH2dSAOTxNPtzS7xAj3Yk8izQRmerDOOco\nnGM0HFI39fRxVPNOvATVApA8/Ft3xy762k9UG4eOgxl4WX+UM2OE85EzU77itLnT/dzVqT1aRgVn\nXb+Bt1PR/uO9j/DjD97Fo6PHnldjhCdjz7jhop+rEv7cf3eXqt6iqrdsbGw808O4pDYLNu6wkjFv\nYp3d5kpuxfc6m7kJMvu3OgPNSnXatPbrnmsWSjV7kvZ/M/MYUwfUPprEkHb+pW7ilMBr8lggHpq9\nwXce+5aLMzNe/JYkLT8DEph93mkXdHqswKG5XO94Jmmcxg55M+N41lrECL6rJY2wv7+PqvJvH/qF\nC1Li58MY4cnY03W+J0TkWoD8/Uy+/RHghTP3uz7f9vy181jdvm0wIj1+UjNzgKhJvp0LCa+dpfGC\n9E5o0tR6xtm6Rob2iJKLnfTdqCKdwDZhInODo2katre3s5Nm3RXoHxNVrEquPRNY5LVHvoof2LiD\nDXccAU6U6/zzF72NWzMzA50yK7qarut+dq9ztgnTHSfMMOQ7WJpcOOiX3CWdPoYwHI6w1rLZbF/0\nY3mujxGejD1dbOfvAN8F/Kv8/bdnbv9hEfll4FXA7vO53kuWnOdgfzeLwyvBTzDGUGYpvBhj3q+Q\nYFHee0LbzLC0TW6t+zzbS/M9DGm9lnTRKTPXlX7xSAgBiQExSohp8C4xYTvLQUEkImoxRQltg0jq\nMN5888387Sc+ldS/SJWU5hRZNTELEqQ5YCh57eqr+PoTr0WsoyyqqYRFLkVj6Dqqs+/LdN/6FESQ\nI6WxxDxvVBRCJlW1EVPYDCnLcz5JdWJqAHV0KaUqSzbK45y5iDTFc32M8GTsyYwafgn4U+BlIvKw\niHwvyem+XkT+//bePMi27Crz+6299zk3M99Qg1QaUGmCwG2LoEOtLgM2UzeiGRRBCOzAhlBjBWAX\nEQjcHYH/AIsIYwei3Q66aQYbt2RoRKOQjBs6UAfYGGTcHe4OhoIADQgkgSZKNbxX9epNmffes/de\n/mPtvc+++fINUg358tVd8V5k5s17z9335Flnrb3W933ro8DXlp8BfgP4S+BjwDuA731WVv0cWr0o\n9g9K2T6X/pqYo/TN57qHqs3qyhbYLPMXaUDnmxpZjXY9dMtR9nF5jg4xRcQJqwKqrvPXe1HdnI3S\n8/jjjxNLJK4mMh/LqEzO9pla01ulqVe3NVfKT22X9GnxZgoqZa+2LrMW+tRWHBuFl8GHOY0XQVNE\nc0QEhmEg+MC4WPDml/7n16TEJ6GNcCt2K9XOb7/Or15/xHMVeMvTXdTtZ471eiKlTMbhxZd0ryOg\nGl21KDZbla6meSJVZMguVtsTCd7PJXvzgPJuzhzCWcDYKPbU11dp+cOpXk3pHnvkEUQVLYMnW69Q\ni1itC3Pq6LpGfM5Fa7cj03bNddcVhapjAY3eVDOB+jlimqhDOPFGrK3RTVMR6FWsFZHnczjuLMgi\n/O17vxwy/LNH3s0T8QIvv+sV/NjX/OiJq2weZVtK0U2s7k1iiiTNjMMAuUwhUvB1z1P2LykmE5ct\n+xffVfpyccJaYCmSsHZBy2axxVLDDlHijHlu2Ee9poFdCxjTZIMyV6sVL3/Z5/HUhSdbShhrlbOi\nXuhaBgLStRD64zrnbc9WPldrDWgV+53nBdbXe2xevC+v7acR5WQJb0Xf2E2qqnlbcSYDcUqg8Pp7\nv5IvGf863/BN38qps/fwOdT3bkvbwstuydRSIs1M64k0ZWKKDONMLq2wKC0qXK5MB7IokNvFPowj\nWSkkWWkSC+YSNu8O75p4UUJxIbQen4iwniydrE5dvwcIw0AIgRfe9yJcKG0R58l0VKWccZpBJ0Rs\n72azHeiONe/h+j1dC3Xt/S0SVyVrVW2joUUdZNBobQ7xhvmsiBycfd4pRlJppZhe6cBiDMT1AavV\nsul2HuxfKedgK6D0vAtmaasAACAASURBVLDWOM5zFbIWHlSV1M1iAEcYXBM2yjl2+7GM866V+aGU\n4ZnxoTnPY8D6faLB2GzsMwKhpJY1xRMtqWy7CTiWqyWPPvoYYRhKoCiN69S1BgDSzC6okK++1VF/\nnvtxc9RpymMdlKz2+rSMGqt0qdqnbG0bmbOKhuyR2lg39sPly5dwIeCDxzm4dOnSNX3Gk2xb57uJ\nSU2PUBzWl5tQSKX/VqBUQRyOTIo6j8jCkVRQGQjjblOgFikRDsEFQbyUKa2yeaFjF5pHwAUbJZbV\nlK1DaGmjEzWnqiV/MsE5gvOcWuxAjgSX8RjOs/43oPdMlq3OkZIWOQyLUkm79BC7SUjZ9wJoNvGn\nXnjJh4FMImlCnbEwptWaNEV8LUKJUlm+XgRJRuhFrIWCZMZxx0ShNHD50kUDMOC5E2zrfDcxpRQT\nnGOa1sYs93OBYhMILRt35T5li8Uhe+JorYjmvHkn752wPl9EGPw8Lqw/jkUVc9Qqwb5Y7CAijIvF\nob7jjDipx63wtRqFm3hT+T3dwMz+9bWVUqucBwcHbW9XZzn0e71KJQJIU5z3uNkgZb7QjTSnjirl\ni95L5tzjj7XK7J1g24LLTU154olzoMr+wT5pMgHbJmUgZdIQpWhCqQo6xTkhZmEYQxnr5RBc477N\n6JWKZSzveAhRoqptyEqcpgaY7sdpVaeJMeLCgjytcd7xxIUncUVqAqyCWlNAyyJL5JWZkweYQ3ib\nSzEMdpn0NxMRi4AVPSMd8CBIdbpZut4k4N08miwraV1mMniPhICqUaWcc3gXEBzjsGDKCXHChaee\nBLLtJe8A/9tGvluw5XJJyrGAkWd6T+gIqX2lUpxVKmOOZc6cTWjNmts45ybn16JQ3jgWbDqhdBG2\nKnv1zzf9lbn3BzYPb7k8QFPeiFz2PbQWQu6lIubiihcT7603ifr7ukftjwez8lkfafsI3hdwtLQ8\nNM/9zVy+TymxXq+tCCTOAN1GhSh/ke2e73ljq9WKGCM7OwuCcwxBDHUi2toGWWOLhtWZfCgl+VLF\ntGJITZvm1A26VFM3nY7S+MYV0dhyoccOxJySkrMSgo1aFmy/JypFig9yiTxzulq1VmSW5uvYCVWC\notrhqmpNP2taOe8XU4vIh9PDlur6+XXt/bSTgy/nYLFYtP6hYvMBpes7nnTbOt9NTERJedq40HLq\nlJop8ClmhEm95nKpUnIoMvTHNoeYESqVUDvvJasj2t5wmhLgybmMkWZzDl+NpNMUQVIh3xYEjetT\nx9oOmKUifBe56tf6mfuCS/1dH/mqAw7DcA2B9vBrU5quKS7V/WA9B2BprLU8PILig2+ghTvBts53\nM6sXV4osD/ZRJtRloubWPwOPDTsud3v1OBlB/VxFrC2HxpDV7qIuGE43sxPMycEqrSA5oTnhRFGN\njGNgGHwZxgJ4Z4K7ImRR8BZx9/b2GtPCu7AhyWDvZcM5VbXNgk9TpE4NmqZpbqLnXCqdFtmrdqlS\n1l4+45RTc0YTepJDjus7h9RSS84GQdMM4oylr8rB1UuAJ/jREDttvMzJt63z3czK/PXKDMDZdJ0Z\n5WEXcCvd61wZHPw4c9rqHV66/Y14u5tT005PbbnnVFEuhgOt7+X9tUyCw4iUWQLQNTRLSvMaa2rY\nmuH4ljKmNO9ra2Wy38uZAvUmP89uOJt6o/V7O2uWduesdCSPttaK+azV2mohDEzTuji7nbflwUHT\nID3ptnW+m5ilexQAssPJQPA7QMetK8+taWT5qe3h7Dhan9ScIwMxWpldMdZ6Pwxl5gXOqZvzjphs\nNHVT+6qOrZbqVsA22RxIM01luq67X1OfttYU1TlHnOKGM9E9o993HXUjsMhohSfNc/Sv0fFwcSan\nhCZL6asTxpzsxifSIGpXr1z9HP+St59tne8mNk2JnAyjCFLK61YwqdYXFubvS6rlrMjSVwvtqx3B\nFxaBFUjslX0FcS5KmAwEJUKZw1hEnHI3U935snei7R9rStn3DYEZZUJfNe0qlYca/5uV2HnfZ5ou\n8x5vHvecN0oj3snGzIj6WStnsKahqd5AnPVuvPdW8US5fPnids/3fLH9q5dxoqzX++wfXELTEvI0\nRwNVnCoetRFcprFORbH4wS5ER6lkxmTqzswRwhVWqwH/DdGiJJCML/uyrNbrCiEwDAOLrnneF3JE\nxFoEMheIFouFRaGUiVO0fmTFj2I3lhpFmzPA3JNj7g221BVlypYW1xkNAqT1RFpP84CXlAnePm9K\nCeelK/rMhRrTpTGHdU6Y8kTOkcXuDn4cTOZQ4OHPfIaeoHySbet8N7HVeo0KpHVGsmMcd8AVNbGc\ncJKKFqezAoxkUpqMPBuTbd5ETCDJRsDa8dQgXhYdTf0LH8CNZEw8VzCtTxFPTuDEUsh5X2cwLM9c\n4ldNuCGYQmbO4APDzo6pYOeMqBJwuKRIzDYzxRkXURQkG1VKhSbyC3UCUR/h7YYDU2umG/rE442T\n1NomiEMRYsrEdATj3dXWS2nFaIHUAX7YJa4PEDdAUg4uX7xTOg1b57uZNX1JrIGdDqWPqtrAytUp\nDFidy0W12WI43JS2Xl4ubO/YnldJuvX7BsvKZehlbyU188GXlM8ipa0Tm++Xo+E4vSOhRM2G8XSz\nRkyPXgE2enb9Z66foxZyYoyoFsEoTaQUW+W0FnLsOT0+NF/zs5QWA+W13gd2d3c4OFiaYzujSt0p\ntnW+m1jFKdbSNyUtS0ij/qg4JOvcamBzYKQrE3dqg71+7ynID7Gq5DXoFjZ7bFUMV9h06JitShlL\nK0Cz8eAqVvPikxfQnEmawEmTbTeKj7QiB8w0qLr/queg7dEO7dc0m6wFJUWtMyOkRPiMtulEwdlE\n2t6hoUhUhMr7U1JWJHgo0orL5UEjGKeY7hSAy9b5bmYGc3IsDw7KUMyZpd1SJbHiR9VPUYU5Q3OF\nMGvPgwqK7qqeragCKU+mrclmJbNqXLpK3SkKYMZcD2V2hNhFC4X8GtAsTNPKemnrCWIkqCAJyDZ8\n08m8T41qUSsXfc7mhJJbU141tSpvzhkJA1FBneIGZ2k4h4WVZiC2q0x21Tb5tu+HDqHMssD6q5/5\nq4et+INhTe8U2zrfTSzlaIWHUhWkFjl6BTC1MctVFrAWFCpSJWfDdNZ0KufCTJcCbC77KqQKLV2L\nKJkj3Yz1bFGqsAKqmG2MESfeaEfekbKlhOtpRc7JhlwWOJngS89xboTXm0GM681eXgk5dT8mmN4K\nIoRhwJfhJ+4QI972i1Z97bdrGxXdbIrYuQxycQop2mfzIdhNorZEtnu+O9f6iz63fU+5WFovy4if\nQFOVNseaS/g5Z6YUNyIY2GDMYRhaY7rCxyrHr19HTdHm42tjB9SKat9kV1XCOHROY+9ViyDOl4JP\nzmSEmBMim9Qfw6DOLZF53DPQ4Uva6DKZxZXq8oMb5mOVKUZ2vE1uYH+ugSZHUQHoNWuAWYAJvTPy\nzq3zHWEbUaBU8bw4nHik9JsMdWHIC5v8OqdODmsae+8bF06cs7t7YS+s18tSsDdTKXCvbg02o2GO\netXhSlnSnEHmY4LtvzRnnBc02WCVs2fvBTewXk5WHMoRTWtGp4ySQSIFy2ZRfKMQkshJ0CzkLHP6\nme0/GJl3dlorNq3jqh0nTtFmUQRHzLFNo617wkxCJYPr2BVYCh6TMoyL0hQV1Ok28j0frO5VhhBY\nLS1lU7VWQtZaYi99KqlygnPVs/awDJI1Q8hEbEhmCKE5uq/Il40oWtJVamO/9NskMIRFSxedm4s0\nKSVSzKT1PNshO7i6WuKDR7zJ0lfFspizFU1K8z11+9BZg6adkRat6/tVp6tjySoDpE+76/FqpVJV\n575nygVcUHRknGvp6+7OLg7h7JkzdpxccKcxcSfY1vluYj2y3352LQ2aB0/mrgeWqYNMDrcY6r5H\nlabz0iM+6sXcX/C1ZF+PN6dtxmqw59a009LGEIay97JqoWa1iT8lXTVpCIu7mnPBXdprU47Nmfvq\n62a6aIWmfrhl/TyxK4jUzzKn22UvyCb1aCP1FAOfpxRZL5fEaWIxLvC+IHdyZlpvysefVNs633Ws\nlfxz7V+Vypxku/M6j2rZ9xWERl8JlXJB0128ViypjjK3IyorvTLA+1q6qumsVDGm1kvsIop0z60X\ne0qpwMwyaCJN6wZittfMLQUj9rp23MqC6J1n3rf2QzE3YXMbI7+YI2NjN5RIWOFsvSRG306p52Ca\nJss2S4aQst2sVsvrz+o7SbZ1vutY3wy3Er+QYkQbydTZdFed7/xyzdnUhuucSbPdcWv6JJuzz2tL\nojpQNdNFsYhTZ5qbbF+ey/iFH6hiz9ecGZxn/8qVjQGW3tf95Ny/LEsG5v4mbMpH9KlkLUJVp+8j\nc2/181Y6Uy0UbSiY1eJVSkWjtCiBO2ck2nKjct6z3DrfnW3tIhIhaiLliWk6aFEFDOycsOm0ubQh\nGgtChcEP7W5u/TjfItmkkaimfJbzBGVMNNDQIKgxFUixc6xQHM2a2YMbEHyLHlEzMaYWaQD2dnf5\nyIc/2vZmFrG0YDQTWRIpVbwqxoJwxg/05TOYg8UCdxNSmicNgckZ1t6jxoTGWbOlprW9PkydsNv0\nPLOiSa2ohUedx4UFEcV7I9Vm9WSduHjpwnN5KTxrtnW+m5hd6BRhn9Dd9bs+ntYBlJvo/9jm8hXr\n0qqaNm5KO2zC1o7qvdUo21I4VZy3yiBYmoxUwPJ8rPPnz0FWTp061fas9f2rE/UFn/qeR0WzXvqi\nOZRsAqZrGjp/9E7vxQm+2y/2qWu/d/XeWxTXsjct64l3SKN963zXsb6S1ypzpezvChDaiqEmMkTu\n6UKzGGwuOK+G8i9l+hTTXNEs+6o+PYVNzluLbB2ypZJ3m2PkjBQaT734a5VxtbIixepgac3szuFc\noTXVnlyd91DXuwEn686LveUc/er75e75m5jRzQKMapHKz7WHOLcRbI832cSnnPHOM5S933bP9zyx\neiFXvFhPPK0MA5gdrs1BcD12MbUUtjpOUxg7RGydm+6b+yZVA1+LE2NWeNdEc7XMBxTdXF/ONhdv\nf3+f9XrNem2IlXDEe9ZoOE3T3CbRo9fT32T6Br81yHPbj9bPdzh6bt6kDjmjE1ypnJr0hSd43wSW\ncs5tCtJJt63zXcf6Zm9OiYQyDKNBwly9q8/yEJq19tzL67tqohjyo+pi9siV2iro08z2e+aLOuUI\nojjZTPe0EAEqakVVbfZB25cqT5x/gpQTl5+62G4C0DuGb7A5oBWODvPu+tS3ft3sA7IBwYO559lH\ny3p+7bxIE05qY6W1G5qpNNHcGpHvFHzn1vmuYw2dkRMxQ/AjqaSSKoo4pUwWwKpyWhlEBW9pLQkv\nrogCyUzjKenlzMHT1i80Xm1htovhP1fRhHJDmezqhmDNbgHvR9N7yYJWFnqusxNsQZ/4xCfY293l\nz/7sz0rKaxfvfLFfC2Wr5+DwfgzY2G/OA0AzqUtBa3rctz76gkvPbsgdewJobI5pmphiJK4nxnEs\n2FAxZbY7wG5lOObPi8jjIvLB7rEfEZGHReSPy/83dL/7IRH5mIj8uYh8/bO18Gfb2l1aFVFTDUvT\nZCBmLUNNcDb9Rzy59PtinqiNdhumkua0TBWXab8PBelidXe32ZKo751zcRjFyYgLO4ZIKXtRxSqx\nohO0CmRRA/MOcQNTnPBOuXrlIlM0yJaIbwRfQ8opWiFxgJfAHNS6fh12E4mqJISs8z7O4W0PjLHp\n2yiGup/E2WcttKbKaCBLq3qmlKzimRXJMHpQ5wgOxJvAlE7Pn7TzF4BvOOLxn1DV15b/vwEgIq8B\nvg34ovKa/0Xm6Y8n0mo6OPeoahSAxjDQOTrUwshhaYf+WPV/lXcHNtK3GZKVyDkVjtxOc4CqrVKP\n7UqLY+7FGZu8Ci9dePJi0QYVhjC0WYI5FTZB1wwH20OmzhFrM/1wpBOZtT77Xl//mXt+YIuWMRP8\nQE4GOuDQ9N6NhruqZR0pMQzjxjk76XZT51PVfwM8eYvHeyPwHlVdqerHsfHQX/I01nfspjqDnYch\ntJRMxNIju9hsVruqognqyGjnAplNx8u6eSFWnl5/8eVsfbecZ5haXyXsccWapdNfkcI/DEzrialE\nwvV6jQ+BMQycP3cO14onsnGjUOp+TjrHOywlr0hOBAGn8zyG+vv6NVZcac7ts7aCj3eGky1KaRVR\nUy1rblo1oc1qKNSovHmjOsn2dPZ83yci7y9p6T3lsZcBn+6e81flsWtMRB4UkYdE5KFz5849jWU8\nuyZiFJaUJmJcl5K9a5HLIsDcjM45Nqc5ar9UES99QaKlet6Grog4QhgJYWQcdxiGRcNqGjwr01rk\nXS9xdgLXkCHOOc6c3mNnHPF+4A/+4A9MnHa9QnOyMWCV31dT7Y4SVRvlG8z52kzvHHPokDi1zbJR\nPOr6gDlHUp7QJrGfSGUf12Q7tIARSvV1PU0GuF4MLKeZMXGS7XN1vp8FvgB4LfAI8I8+2wOo6ttV\n9QFVfeC+++77HJfxHFj5A09lOtGMBOmJrjYj3dgKFgmrhkqt0NWKZ84W7frUraZzKSUkBGsjBN/U\nxXJLJ91GFDSnmauldX8Zp8kqpYX7t7NYsBiGIrhra1+nqRRlKHMgKvfON1Jwk293vXanww2jiT35\nyjjfBAEc5RR1PysiiA+Y0JLNlqiXoeh8vhLaeH3m/La/1ZTawJS+enoS7XNyPlV9TFWTmobbO5hT\ny4eBl3dPvb88dmLNeV/6SpYkNcAyfXWOzR6fSGE8zG2F+nuElibWiNeX8WEzzdQshaXuNhSk+4qk\nPUbRmvGEITT4F6qMPuBEWCwWLBYLw6l2vbg+Qh+OyvX9emvN9CMa6oeb6PUY9TObfo2z9DEZA1/0\nUGO/7ENz6T8uFgvbn6YZGN5/9pNqn5PzichLux+/BaiV0PcC3yYiCxF5NfCFwO8/vSUej9kfNpPq\nHg3BSyDGDGqSe7UJHafJBGr9nP7llDs2eHm8E6etVntdlTJTWw0kq3J6sf9VKzOlBMlaEkFc3W0y\nFfxnSsmKKDEaaiUEYjpAh9B0ZUQdO4tdUlK8+o3CiigEncv/DTzQtSAG53FaBKBqVMwlTRXXIqcK\nuDKpqceaHjYRIepkFU9MKEnUJAFcNgoUCEkcozNZRLkDNONvOhxTRN4N/C3ghSLyV8B/B/wtEXkt\ndtP/BPA9AKr6IRH5ZeBPgQi8RSu57QRavehs878gp1qZtEijAsGXSUF+Fp6t6Zs03GdVdgZ12sim\nqgLeMYzjTKsRS/soM9/F2WhppFP96iKWCcymVrW0lDIyTRMIjGHAKSzCiHcUpTIrXPgw2P5OCybU\n2efIUojjfgZs1z0uuI3o2ApSTkgxU0QQN4HUxfp93waqRWZtl1pJzikhPuG8kWs1RYYxQAwsD+qw\nl5Oddt7U+VT12494+Odu8Py3AW97Oou6HcwuEKg9LtuTzRdUFpAiTDuMoz2W6yxxacRV8fMAkf5i\naW0BmXl5M9mU5rQww67afqpLtyohF8xZ1us1ThzDYiSv1229wXuSCuMwsDw4YOf0aQ4O9jm9exot\nRaO2L8UABDVt9GGu8qZOW7SmkaramBSqildHZNZc6Ys2h6un7asNJyqf0c7H4Fzbt66WERVHSvMI\ntrqGk7r32yJcbmaFfpPz4R4f1EJHymnjQquOUntw80XWVyTLvkpn2YbqXP1epn+srxxukFw7J6jH\nMgUzhw/e5ON9pTUpF5+6WIZMFhEkN8+8s+a9adbAvP90zhU6kd10jHlgmNNqOdugk6x5Y1/az25v\nqTGz41TGPS1Nn/e3dW+ZcnHeFO2P0p3Dk2pb57uZlQtMRK+5aMDUwOaUDNBU7uCJTCIVrc9WnaRj\nPXSg41kWT6kiRc3ZNmb5bbIM8hRbEciOF9CU8eUCzrkiXWzP5YPn4oULVBW0OnKsYuOaLhTzDUHE\n0uP2vt1nGIbBIqcaZKyfxddH7sOMDZiLMNM0WaumDKGxG1ouNzxXIqvp5eSc242hf4+TaFvnu5mp\nKTqLZshrwuihssBVbBSD0oSAFEeMln6KGmj5sBDR7IwTecrkFPGuRB0txRbp5qBnI9Wm9dSk6etx\nJHjcENpkn5wjmlbkHIk4njp/nnD6LkJwhDKk5LFHHyEAO8NYLmqLOiGEMrfBCi6tOimCxoRkveZz\nTKtIinOq6KhapoAosbQ0wDRvsiaUTEwTxiZKqJjGDNBaIRlhCCPDuINHWS/XBBRcgAZdONm2db5b\nMM21jLDJy6MJF+lG62C+2Db3OLWc3kevvvhQj9u3J/qfQwjXIE9EDEjtKosiZ1yJeDs7OyyXBxaV\nhK4PSesrpmShLoT5JtFXN49aR99OGIahRWZLKadDiJg5zRTXD+CcP2+thPZpqms9v7If7mBsInfG\nZXtnfIpnyexis0Z2VYi2tsPsOM77NvsgZxuOUtnp1xQZdBPdAiZBWyNN/779fqj11Erq1vfUcmFP\n1EKMrdm+TuuJg4NlAzC7wpRwTjg4OGj7KgFyNOUxYb5ZVHznBoJGDo/2KufCKT7IRsQ20vEskFTl\nMQ4jcmoxqzql8w4wWFxVRKtIF++9CVid4HSz2tb5bmKKsru7a3u70GM7y6wC7cDEfh54clTxpJ8u\n1F+4G+93uCBzKBLV57Toki1yaK6ygYGUM8O4wzAOHFy9YpLr3f7S+8C5c+c6R57T4pwzlNSvInE2\n1qQzPagHB6SUWK/XTWk6ow0ccBiU3R+vVX39XGSh+5z1+NYHHRoWliPO3UmzrfPdxEQcqU6jLTQb\nj5A0E4sUfB+V6oz1jBpsS2dGt/FMpTmuSrnLCxvHkS669KpktUhTIwlQZAWtuplSMsmIaTLF6jjx\nxLnHQWdspY0Sg49/4i8IITAWzKcfRoqUtKFODjn/4ZJ+X1lNKeGdyd+Ld4y7xsCojHObLFvhcdey\nO8CQRD54g6vVG4vOmURC0Sp34e+My/bO+BTPotmFncjKPCMvaav40X2t3+fDEa9GiL7tQCkq6rXR\nrlrfkD5cKazO2gSYurU45xiGwDgOJkuv2tjpNjfeoG+LnYUdC0UzLQLV4xyOzIcjcHs8d7A0tZ5e\nlvkYh4s09WvfHrF+nm+tC0t5HeM4QrendMMwS+yf8Oi3db7rmP1hje6CsxmsDsGrVTVbRS+nttfS\nUm4nZVwGlzfZAZX/V53RuYCS6pHLhVkKCzEhOTWCbN+WaNHCOVKaWho4xUhWYfQLkhtBE4M4VCIp\n2TSkMOziGPDiWR4sUecNBqfWu8uaLMr7edyYYFNp63v3kdduKhkEwjiUibhqmqSFse4KHMhSUDbU\n17z3DGHElVKWk1oACmWyk+DVW5V3KmlyouEVbB0nk2K0db4bmBYHyzESfGjRKmktKJTn5SL62pXi\nVZWkcxpZbcOR2ESo2MU9R8H6Wpsy5Dcet/ff/PMNw8AYhuJAyvJgycHBQee4FUtq3MQLTz5BilNx\nOoOyGdrECk0tYknGHXGl9JG5rtuJvU9NsXtybb9n7dd/uNprzy/9T3GslyvrS5YCV7X+RnQSbet8\n1zEpV5BD2F3sEGNkPU1FQMm0VWJV3tLcJtPW6a8qsyZn1Zqse7a6j6oDP5xzTNPUeHLGaav7It+q\noa6oeCE22iujbbqtiBRlZyO7Zo1cvnyRcRxQzUVNzSYC7eyMjCHwlx/5KLsu4IJnGAeyzmDwfl+W\nC4Knb/5Dp0CdSyUzZVSjDTPJkVgIs33RpL+J9Clp2wtLGV+WbbxanCZLP3cWpoGjSuqmhJ3k1HPr\nfDc0aQiM3FFwYJO3phjxU53gxNAtItKcqYeewSZToI9wm5XBuCHvDr5JrCfNuE6ePufcHKGW5eN6\nzcWnnmKxGAjBSLpgg1qGYWAYLZoOg6lBG0nXPksPoLbPOuM4q9V1+tJq8W4oJ6aMna69zRAaU/0w\nAbfvjfbFKaA13W3/bDeoxWJkWq9NH4aT63TVbgqsfr5ada6U08xeEGW91NZOgJkS1C6eOryyG5PV\nV+dq2tkuzirlwAyutspmLcuXgSSqtm/yUlgRBmYOEnBOWYxjmeXgGIJj5/RpnrpwgWma2PM7ODyS\nwTlPKlLufliYIK1qAY3P0adP6WZnMaW2vsVge8DRzpfQSLm5kIu1pLM5bTIheiB5tTqnwTlHGAYD\nFgwBEIYYuHrlKoAVYbobGCfUEbeR7wibq3q5iQ31kUm6qIcTwhBsz+Pm5rRqwSBqZSVcK8fQX4yb\nBNm5kV7nt3NE1fBwJbSmrynbJJ/LVy6ZOrXOk3VztrQ1lH3kcrVivVy1/l3OuY3s9G4gRS0NemOc\nH654tptJQa80jZoOidM7WX3dRrvkUGW49QlF8MPQ0DmXL11mmibbe+drX3vSbOt811i9qLFhjllt\nBFhwllZ6YcoTucxhd9QL0aTypmliGBY4b+TVXBrWlcPX9/Fgpg5VaFh1vOqMOdssPSXhPNY7SxnJ\ngqitScV4gzaVSBh39zi9cxdhWkEYmi6KDZ10nNrb5fTps3jvOffoZ0ia8cHZTQRnWFLnyBoZRuMl\nGvpFcC6QpoyXYLwqQMSqwXb/sUGfHs/gQylYdSx2mUHo3ntjXng/O5NxlWcMa0xcXS4JPpBzZMc5\nkmrlP7T2xkm0k7nq58BEYL2euugkhbFe7uJiKZg53mbVLReR3DYWukszqXIUlIu2K5lXPc6+olmj\naY/vrNHTNyZEmYqrDh8EPxjbezWt7fne5sCLCOM4tkriYrFgvVqyGBeFDqRNriKIVS57NMnhfmbf\n/O9xq/1zaoTrGR0wC+/GAmur7ZqK0ikfmhhXOGdsDx+8zZAvwISTblvnu46pwhSn2RmuO4p45qHV\n1BKZUzMRnWc1UDpSbqYQ1SqoqjbibY18UosgXXGmh2gdRp+0go6zHtnOzk77DLWt74OV6733+LJe\nVRqqZSbHRrTTfE6SIAAAIABJREFUS6ltkT6C14KSc51wbofkwUlTsZ4n+M6ObHu8er61vU+7qZRC\nyxgGFuMCcdZ0r0Wik7rXq7Z1vhtYnCIu+FKxnIqbaZkhPveq6t2/8upEDCpVWwUw73F86aOpzr26\nPkLM8oC6UTXsI8zG89QEklRNMW1arZqmTErJJvuUBn7WyiiY8acpRnxwrNbLtpaGPinHcBKIaRPc\nXW8a1iJJraDSg8DtPXPDtLZ0WzdHX/dygVm1OWxlktR5FDkmxtEEdE+225ltne8GtlwuCcH2PiJi\n4uyt31XHehk0yzvftF5ijDasRHPhAkJcT22fotn6glHj3KjGoF8xZ+tnsTkirEXDrgKZc2YsEus5\nJpb7+6S4ZnDCpz7+cYKnRGEHElolNYRhTi/L2C1LX7UUTDL4wHpao1lYx4hFmvlyObyWVuxJiVTS\n435fW1kJrnf8ElldkY+va6oVYgWmCLiAZnjyyScZfCCIFATEyc49t853jdVTYr021YAmIKcChhYE\n35rfKtlIoaJF6cwkGCTZrIHaZLa79zwiDB9sqmy5gOI0VWEYTJTXk1KNfBknASd2QWcp8g/ekbTo\nXOaJTDKZeDdw9fJjtjYZGPyA89aLC36kKmqbQ5s0YdKMJvAEm4eQhXHYKTedTSB13d/NbA5KRVQY\nvSlNT1B0ObGiVQEdVLIuKuWcOMR7PPP+0SrK1oZxmhi84WXrvEPxNigGxeB5Yn+vk2Zb57uO2Z4k\nQ0zkOBkhNSVTeaCHS21Sh+peJqMbMxXqHd85ByV17Ptl3s/FF5F5n1dTt1bsUBso2Tftq7xCbZaL\nCE9duMA4DCwWC1OTPgQQmCPX3Aqw6GvwkaqGXd+/f12fMs5r1EbozWlm1c8apa5Fw/qzd7MyWq36\n1raF94NVculqK6VglDYADyc3+m2d7zo2FwAyab0iOGeFk2xthRmf6DcdAVpTvbUQdBY4qg7bX8D1\n/VRTmcGe8Z3mZ71gtVBue2eIaV0itF2mU1Ziily+fNl6fFVOXvpBKpXdYEyNVMR21SovZR6eFtD3\nJoOiR6i0G0LWlga2m00nwGTRU9qshnbjKi0DzV2ByhcZxhLJckosl2U/ihWoDPDwDP6xj8m2zncD\nyzkzaWaVJiKZ5BQNjuRo46/6qh/Qolev+tUiXrZGfK0YrtMa7wabrcdc7KhX1uDmRrXNrEtWBJI5\nKlp1cXaOFJUhOPavXJ0JrwnEzfLs42ipYa0cjuOIc451imXAJtjMiUxWc+xKYu3/p6SNf1cjZNRs\nQrkFGleLKc55QhFJSim1Jrx3vhF917FgRUt7xTt7zzBY+nr27N34MKDMso4n2bbOdx0zh7E9WOXu\nuVL+roMr5+e5LqpsKpJtRA02L95WfocGvq7zEWr6WpWhG0JFZ2SLiODUMcVOtoFUhHHLXrP0GW2t\nvqytT1ulMdZ9IbRqzgSxOXvDMNiAz2keZVZhbzXat9TSQiq5fLaeCNyvubYRLCLb+Va1gaApJVAh\nFMhaXTvAYrEAhJwS5QDPwZXw7NnW+Y6wfv+Wc0H6ZzVJPoAUSXHugfU4x7q3q4+L2MASayTb8RuI\nufboSqXQ+252XkdFqheuaXFKGR4ytfc1WpJ9XS6Xc3pbWwMF82lrmuk69tWxXq82PnPdi6maNH1t\n7muapxb1bYVqVY+m3lRq5bJ3xPo+7ecyOKbvE9bKqd1AYnPYsQCrK9h6G/nuQGt7LLW0qF6sMcZG\ndxFnVc9KNdCoeHGou3Y/h5eZZkR1aBosSst8BS8OJ+XCd0JSytRbB+qs4ukHjNjqS9ob0RzJWsWa\nEpIi3ikqARcyMS0RF3CSCKHsSZ2gZd/31JOPE1wwZ00TmYipjqpVVlOPNa1VSmXW0ZyFdh3e8KNY\nW6EKM204HdZIr/C0HmjgEFKKiFZETCBHS3EzjoP1CskTyLr4Xr2ET14U3DrfEdanR1bGn5EkcwSZ\n+W3tbp825SPanHWkaXvmktLVgoa4AqkaB8TPEuuS68Rbe45ijHGNU9PxFMq+dJ0IfiClxM7OHhfO\nn2NwHi82P4FUHKTj09WIFMLIpz/9sKFJpKaHYoUf5Rqh3xyNuQHMqm1Y1GsFlZwLt29zMEzb+4oU\n9M4MskZKGq42stp1g0hjXLf1tpkWBSCuJzj4bZ3vCJsj31xxg03Zvyq3Xmk4/X5so6LHXBUMIbSp\nPk5nlEsmt+/7i9WOX0r7OZu0RPe7WY7CWZGkIFquXr2EHxwpRYOQ5Vlrs9f9dBKoUhQbTfFc+5Vi\nYsBSYWcyN8Fbb80mJFXy8OHJu3MRaTbTaRJc8LiK1yxtD+dc24M6b+djGIYGCJimCRFn7P8Tvu3b\nOt9NrP5tXadfWSUgenpMBVP3ka9nsler0KkMzeG8+HIHnx3PGBG0PSBOGuql7zFCTeVsT6ops16t\n2Nvdsf1elXBvs9d925uBtRVQZSiM98NtBTQ17CbMDt+qqyk25oItfFMG/3BBqucs1nOUYxXwzQ2W\nVy3nzDiOTJNFvyqLcSfYlkx7E/uNh3+Tn/zwT3N+Os8L/D1866mv4ytP/01DbOTUGsviHU7EpOJb\nCc/QI5lZPBcpHOxS8Egpg5Y+W1KS18Ymn53AohPFIa3yOEcTq1CmNhwzpYnFYmS1WnN69zR5fbWk\nxkNDmqjYVNqcM7u7i3bRW3R3aMobjApVI8tmCmm27M9gbnnknI32tJyg8Bm99yyXy3bsWs2tldis\nXTQX29sNg2sQtZRSiXb2nPV6je6dKiFPy/73uboanlnbOt81Zgpi0zTx65/6dX70T/4By7QE4Hy6\nwM9d+lUkBL5873WkFMka8WEsDlck+graY1qv8EOwcWFTLPqVBs62aDIjZLz34DqQdM6grtCMlJwg\nhJFZosKKIDllPIbfjJoNW7p/CecHfFDSdEBSAHtd8AF1c4kftXQ4HhxYKjeMSJzIo/XWRKyA4odg\nhSelDU2J5TOFEKxN4iAm6/OlbAihXCqtDVrmhzZMpeJXVDM+lNRyHFARBm80J0T47fO/wzs++Yuc\nW53nXn8336V/l684+FqG8VS9P5xIu2n8FpGXi8jviMifisiHROTvlcfvFZHfEpGPlq/3lMdFRH5K\nRD4mIu8Xkdc92x/i2bCUEj/xgZ9sjldtzcT/fvHX7YfCsK5EUKv8uZaGuuBtSKbY/kbEelSpjDiu\nd/Naiq9O16YhdX1B5yGmddt31oZ5nFKLkrUtUGXeY4wsdnbaa2r03RxbZnuqCxcu4EqfTUtBpy+Y\n9L3HGRDtumhZjlk+W07Joic9ssaTokXtHCcgl5uJtnuBl5mr55zjX1/4d/z4x36Gx1fnUJQn0gV+\n6lP/lF/6wLufvT/+c2S3kjxH4AdU9TXAlwFvEZHXAD8IvE9VvxB4X/kZ4BuxcdBfCDwI/Owzvurn\nwGKMPLr/6JG/ezI9VS5Gt1FYEHEb6l8NeIxr2E3fKTLDXAix/dOMb7T/tGOpJttflZ5hjIkc1RAo\nMvMCfWFeVPDz/v4+TubxzdXm7+29DpYHiFhlsUHIUm6yhr2zVbJrPyO+nrP2ebQWrrqq5xEFmPr8\n4AOqJSXPdg7GceTnP/1LrPJq4/yvdM2P/t4/2NgbnkS7qfOp6iOq+kfl+8vAh4GXAW8E3lme9k7g\nm8v3bwR+Uc1+F7hbNme43/amahSYl+y+5Mjfv8Dfbc/LljbNAOfa/C34yHptiKCZts85zBCor62O\n3FdJ+9l8IobrXK8n4nrmwMVYZyfMZNdqOzs7XYWyFIy8Qc1CGIuUg2EvxQk7i8VGk1+0a4iXNVbI\nWAMRFDyocwaWJuei/RlbFB+GoUDhDknXN/Wy+nMw9kJx0MdX5478G3zm6iMbN5OTaJ9V2UhEXgX8\nDeD3gBer6iPlV48CLy7fvwz4dPeyvyqPHT7WgyLykIg8dO7c0Sf4uKymV//1F30/O26x8buRgW89\n9Q3liTZXbnaW0jAuTlIdycDNGEqmg1PVr32Fsa80NuZ30YEp2tItMtZIU0dmjePIOMwyE7ApTFv3\nXdo9LqX/p6rs7e5BTU/T3PqoZhE3tqpuO6YaKdfWW//Pok4iUqq1M4D6cD9UxOGCx4cBHzzDYsR5\nz4sW9x35N3rJ7ovv/MhXTUROA78C/H1VvdT/Tvu69y2aqr5dVR9Q1Qfuu+/oE3yctr+/zze87Ov5\noS/+Qe4bXogAL3B3811nvpmvOPU3jRnghJSjgZ7V8IYqELMi3psYUDJFoArxUrK1EHzflpj7b32E\nmnGgGY1W2VuvJ3I0UdpafMnZoqGotRtckXfPCC4MLMZd4w76ooMpZSSZOJwPbWCKiDBNppEyTZNh\nOruJRDXitfaLc6xWqwafizGyXB203l1KyRxqXJArpK3QhXC2xvpfgieEBTjBh1Awtcp/9ao3szh0\nA1zIyIOv/k5Oqkx8tVuqdorIgDneu1T1V8vDj4nIS1X1kZJWPl4efxh4effy+8tjJ8RqpLC7/hte\n9vU84P4D9i89yf6lJwGDdEk2mFcoGErxHgNOpab+VTVZEG1iRN6FJoOgFSSNTT0anTPEVZfyQdHs\nLNITtek9pUjKicEFY77HNUkEKSlmzFbwOLi6z2IxoKErfpRJRLXQg3PgbAKQk4CQDUSO/SprRtSX\nKmXGdyRg5xyObJXOOLcSnDeUTM4ZN2DRUgacB9VSJNIJ1UQYdywFDlbZtaBsoO+vvverOLh/n198\n5D2cn57kvuEF/N37/hP+9t1faZXgE9zyu6nziZ3lnwM+rKr/uPvVe4E3A/9j+fpr3ePfJyLvAb4U\nuNilpyfGGv6SWmwo1UFoRYcqjVAvaieCunmE15yazZEMitCS2lQe73w31ajs+Qr9KKMMzpFWK8i5\nCAfZcVIpjPhhtxFRtTa+84QXi2g7O3ZR25rKBzqEwOnZCTAXSlarFYvdnXInyqgadpRUHDdu8v3M\n8eYBKVrOU91Xxmys9FRmCsZ+/1coRyEM5XgJY/Mnvuber+b19361UYskc/mpCyxXS2w+xMlNPW/l\nvvHlwHcAXyMif1z+vwFzur8jIh8Fvrb8DPAbwF8CHwPeAXzvM7/sZ99mwdyK3reeWmsJpESwoeIN\nLCx5LsfbMXSjLJ+zybpPsSL1bWpPTTVrYQNoZFITMLLUrKFL4rrMhDexoWFYoKqM48KkCsVkJVTn\nBnU7dukNAg3KVccvr9dr6IHUpWeZcgVG59bXq1zGnKNFXqEdqxZ9QhhLYcXGVIdhLqTEnAnBbiYV\n8ZI7knH9rA08EAZjt2er7C6XqxPOabiFyKeq/x/Xv728/ojnK/CWp7muY7cYbQ/XpO9cQJxHc0Sz\n9erGEOwC0HoH3pTx01Tms4qBrl3RfamRx8lQBJkEYYZdrVareYwWsN5fEsLIarVif3+fwdt8PVXH\n7t4ely5eYrVaMYpjf3WwcQPw3iKKiBjroMzykxDAGTPCe4dK4MrVy9zzwhexvHy5qbaJeRlKNIZF\nuZHU1kbWmQW/nJak9YSiDC5ACI0IW3uQOdt89WHcQXPC+cCws2Cxs0cVDM7ZVOCW+wdoingHirCO\nE8E51qsVuINjuS6eSdsiXI4w1YK7LJQdisPY70qK5aRFp1a5dJs4xlTZ7YdK4u1O5qTsCxNBRqPg\npHmEWK1kOmC9Xjaco3SprDm6EIJjtTxAkwnlHhwcdM17CH60tWTBDw5XWfLOmAHiYL1as7e3x5UL\nFyyFLCx4AzzTOIg1mhvEDKIqITjQgSyO9XrNsBjBh3LDcaib1aqlySI6nOtZ+a2Uw7Syeew5GQ/S\n9s62luCD9TxlBgKcRDvB29Vnz2qKJs61uXUpRXJllncCSIJBtbLOYON6jA2AMpuMhZkIOxELTE1J\nrffXGtFlP1jnQ+RS3FnHjAujKaepvS54z/7+1RZdQwjs7Z2emRHUQ87pXWW211SzkmU3zwVt/U2a\nIuf2vpTmv+icygJoKZrUz219PJt+O447hQVRgdjdBN9Kn9JsgHFNpUpqk5nCUIfJ5BPreLB1viMt\npsSkxizwakRRnBgp1mOTZzXb5FhRcplNN2AcNbuoEhKMp1emnG9UCH3h+OWoiLpCzLUop9EY5BqV\nGE1HRjvnDs7hRXGDAZ2dOtbrhOaJaXm1XejOBSP9euuhGZfOiMDWM7T3RASXk1VFJdt6HFal9VKu\nkowXhbwux7DoW0ej4cvc9O5GM4grtKRkeFAnJAQXSoopgA9onilXdo4cOSlTXBdCr2MMHoeSp4i4\nAaR2PQE9mS2Hbdp5A1NVYlZyYbR7H6zfVoZjNsxjTTe9w+WiwVkuwriecN4GTCoQfAUs22srd61G\nnlEgFxRMGAaWBytyTKXQ4QjDAk2RYbHL3u4ZpjwxTasWEQ72r7I3mvaZOMgpEhajseyh6H9accNL\nHT9WoiDCpz/xSQYJc2EkRsZSMBGZUTV98z6IpbTOOZJTPOD8aGTelGzfl+q4MEPqxLWNKRtDaOdg\nvV53PUUrKvlhZBxHA3anBIwM40ieFNrcDLcpMXhCbOt8R9gmAmNuAdDd1f/t/h/xLy7/JufTU7ww\n3MObX/Kf8foXfJVNGioisaoU0dzacghtH1Z1XGqaV+FXJgcR2igs63s5xA0EbynuOCyYcrJGdbJh\nmK7MBwwdusUiidvQ5YQCawsVLF3aG9n0aUQtmvZtB8M6u9npCiysNt6ds8sopcQ42FQkAyAo487O\nRhpbWy/jMIDTjTS97ifrmG3nHP/vhX/LOz/zbh5fnedFi/v4L1/xHfzHp76U1VR1Z7aUojvWfvOR\n3+Z//eg/5dz6PPe6u/lPd1+Pc8LPX/411tj01PPxAj/z8D/DieOrznxZafxKk2Uw25RUADZGZdXC\nQU4JXGanXLQxRhaLBao2fUg1sVpP+DI0EurNIjMtVxhTwDHFyLjYBUyzxSYlzUrTh9eSUHypaair\no86GdtPJWnGo9XUlbWUWOHKFfzcMQ4Gx5Y0IGXt6UbZGvoogxqbCueq49vx/ffHf8ZOffjurvAbg\nsdXj/Phf/Azff/+KL937Uire9eTFPLPtnu8IqxfM//Xwb/IPP/zjPL4+jwJP5Kf4havv5Zeu/Hpz\nvGorXfPOR/8P6oTaGiUqgLhKIYD1v4ZhsTHuC0pU8K6BqWuPLpTUzIKOJ+dO5DZDXK+Z1hPL1QE5\nRZKpTrC3t0cYh0KirRhLA3xXqYhWZIkRwWQRQ+v9GThamW8Q7TVF6Mmk76GGzcbIQPBBiGndPts4\njt28CHeo6CPtc9WZfb/wmfc0x2vnOa/4hc+8u6SqWk/cM38RPAe2jXyHrKaCkpWf/uBPX0NnWTOx\n1unI156bzheibEHoFwRLvTT8YKO7/BCKExl8S7UiUKRgNsEPtsczZB9osPl5vuzXdvd2SJqY0sR6\nOmBn8FzZj+ztnWXv7FmDmQVPXi8Zdk7j/EjWhCKIKwpoLpA1s7d3Bh92TNDIee6//2U8/PAnjE5U\nYGriHXib2xfKkM0cM7iBcTFrjQJtLxtLlLToPbSBlyJCEg/Os7PYtf3kMDIOtu8jeMLOLuemJ448\nz0/EJ7j73nvnv1mF4p2wALh1viOs3uUfPTiaz3c9u298oSlDF1THEAYEA1qP40gWCKWF4bwnxhUh\njKVhXfZso6E4Us7gvKlTO4eo9cE0lf2QCHFKpFUkJ4VxF3/qLhyZ3VNniQysV0t2zuwSZcT7kZ1x\nFxc8L7//ldx1z91MceKJ8xe4+NQFht0FVw/2cThWWcAFLj71JA5lZ+8uVjEyLgbCzg4p2TSisLPL\n2bNnuXDpggk1qdGsnPeoCzgVdnZ3ip7oPntnz3DxyQvcc889qLcm+t13381qiizXdpM7c+Ysq9WK\n5D0v3nkRjy4fu+Y8vzC8gM///C/gxHnbIds633VsZ2eHl+y+hEcOroWlnpFTrJlY6ZwSLdyC737F\nd7B7+kwRgS26limzu9hhmibj1g0DSa1QsXtmwZUrVyy1HEYGHwwWhrJaTYyjtQtinAgiDMPIlNdN\ntmGaJqb1CvED3/hN38Kpu+4mpsT5xx5nd3fBmdOnGRYjqgISkOLguA40kDNOFG0IHfjQhz5YmOYB\nNLHYO8Xd97yAKwdLdnZ2Sdka3FeuHpCdZ7FzqtCpckmPS2M9ZdaryOmzp9GsLA/WrGPmytUlZ+6+\ni/39A+4+cxcf+chHeOnn3W+Fo9PBRm4rfP9rvpf/4Y9/bCP7WMiC737ld3L//a+kIG3NBU9g6Ns6\n3xEWo0nu/cBrf4Af/v23skz9H3/kwZe9GRz880d/mXPrJ3jR4j6+5wu+m6//vL/D7u5umY2euXTp\nEnfddQ8xZU6duYvVes06JrJmFotdQ5GUKuKpU6cAOLh6lav7l9nd3QOM+b57eo8pre24znH2nnuJ\nKZFwJI2cOXuWs3ffg8rA4OHz7n/lXIwoDfeKYpGinNK2Sa5QemrbROCv/fuv4ZOLgZ290zz+2KNc\n2V+xeyYXSQrl6pUDFjsLUkzs7x+U5rmReTUL6ykRgiPFhA+ljyc2Anq9nkjpCmfuuoeUlUtX93Eu\n4Jzn7rtPM44LLl26xHpa8rUv/joO/tqad3z8f+Px5eO89NRL+eEv+2G+6z/8TgR3SDfw5Dmf3A5s\n4AceeEAfeuih415GsYyqcPHiZc4/8QT/z7nf4m2/+zY+ffHTvPyul/NjX/NjvOmvv+kayJiJSM4l\n+fqYoTDcxvNmRkFRmG4XkaMKOAFcuXKF8+fP8+ST5xvqZh0nFosFy/0DTp06xXJ1QEB44Ev/Iyuq\n3PAaPNyMvn69rRaNbG3zQUWhCLxgM/aMBmQfuZuopJv6L/1pAgMwSDme3qLPVDTR7Q4pE5E/VNUH\nbvq8rfMdNrv4c7L0zDX8JgVLSLkOZf65Pkfs5T0cq14o/debmWrFbbZHrrlINZdE0TlM0dMcAfrx\nnmUdN3yzay9+YROPetP113J/xbgyU6/aOvrjUeaAKm1yUz3vfdFmfn75vUqTM5w/YXu3G33K59Ru\n1fm2aec1VsRcff1jbjrC4Rtu32eS+eXd7+XIrzeya58i9d/8iO/eE9d9v/Gqm5sc/bzD67yl9Uu/\npkPrOPQ6V3/RzrO/4fuDO2Kht5fTfba27fNtbWvHZFvn29qJt3d94F286p+8CvffO171T17Fuz7w\nruNe0i3ZNu3c2om2d33gXTz4rx5kf9oH4JMXP8mD/+pBAN70xW86zqXd1LaRb2sn2t76vrc2x6u2\nP+3z1ve99ZhWdOu2db6tnWj71MVPfVaP3062db6tnWh7xV2v+Kwev51s63xbO9H2tte/jb1hb+Ox\nvWGPt73+bce0olu3rfNt7UTbm774Tbz9m97OK+96JYLwyrteydu/6e23fbEFtgiX55W96wPv4q3v\neyufuvgpXnHXK3jb6992Ii7Sk2a3inDZRr7nidWS/CcvfhJFW0m+9sROaq/sJNu2z/c8sZuV5E9q\nr+wk2zbyPU/sRiX5k9wrO8m2db7nid2oJH+Se2Un2bbO9zyxG5XkT3Kv7CTb1vmeJ3ajkvxJ7pWd\nZNu2GrYGbNsQz6Rtmexb29ox2TPW5xORl4vI74jIn4rIh0Tk75XHf0REHj40MLO+5odE5GMi8uci\n8vVP76NsbWt3pt1Kny8CP6CqfyQiZ4A/FJHfKr/7CVX98f7JIvIa4NuALwI+D/htEfn3VDWxta1t\nrdlNI5+qPqKqf1S+vwx8GHjZDV7yRuA9qrpS1Y9j46G/5JlY7Na2difZZ1XtFJFXAX8D+L3y0PeJ\nyPtF5OdF5J7y2MuAT3cv+ytu7Kxb29rz0m7Z+UTkNPArwN9X1UvAzwJfALwWeAT4R5/NG4vIgyLy\nkIg8dO7cuc/mpVvb2h1ht+R8YtM6fgV4l6r+KoCqPqaqSVUz8A7m1PJh4OXdy+8vj22Yqr5dVR9Q\n1Qfuu+++p/MZtra1E2m3Uu0U4OeAD6vqP+4ef2n3tG8BPli+fy/wbSKyEJFXA18I/P4zt+Stbe3O\nsFupdn458B3AB0Tkj8tj/y3w7SLyWkwy+BPA9wCo6odE5JeBP8UqpW/ZVjq3trVr7bZosovIOeAq\ncP6413IDeyHb9T0dez6t75WqetO91G3hfAAi8tCtoAKOy7bre3q2Xd+1tgVWb21rx2Rb59va1o7J\nbifne/txL+Amtl3f07Pt+g7ZbbPn29rWnm92O0W+rW3teWXH7nwi8g2FevQxEfnB414PgIh8QkQ+\nUKhSD5XH7hWR3xKRj5av99zsOM/gen5eRB4XkQ92jx25HjH7qXI+3y8irzum9d02lLMb0OKO9xzW\n+dnH8R8bR/oXwOcDI/AnwGuOc01lXZ8AXnjosf8J+MHy/Q8C//A5XM9XAa8DPniz9QBvAP5PbGTr\nlwG/d0zr+xHgvzniua8pf+cF8Ory9/fP8vpeCryufH8G+EhZx7Gew+OOfF8CfExV/1JV18B7MErS\n7WhvBN5Zvn8n8M3P1Rur6r8BnrzF9bwR+EU1+13g7kNQwOdqfdez55xyptenxR3rOTxu57td6UcK\n/N8i8oci8mB57MWq+kj5/lHgxceztGbXW8/tdE5vO8rZIVrcsZ7D43a+29W+QlVfB3wj8BYR+ar+\nl2q5yW1TJr7d1lPsaVHOng07ghbX7DjO4XE73y3Rj55rU9WHy9fHgX+JpUWP1dSjfH38+FYIN1jP\nbXFO9WlSzp5pO4oWxzGfw+N2vj8AvlBEXi0iI6b98t7jXJCInCpaNYjIKeDrMLrUe4E3l6e9Gfi1\n41lhs+ut573Af1Eqdl8GXOxSq+fMbifK2fVocRz3OXy2K2G3UIl6A1Z9+gvgrbfBej4fq8b9CfCh\nuibgBcD7gI8Cvw3c+xyu6d1Y6jZh+4/vvt56sArd/1zO5weAB45pff+8vP/7y8X80u75by3r+3Pg\nG5+D9X0FllK+H/jj8v8Nx30OtwiXrW3tmOy4086tbe15a1vn29rWjsm2zre1rR2TbZ1va1s7Jts6\n39a2dky4XxmXAAAAHElEQVS2db6tbe2YbOt8W9vaMdnW+ba2tWOy/x9nK3agkZZXtwAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f630f497860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_id = np.random.randint(0, len(samples[0]))\n",
    "sample_img = samples[0][sample_id].squeeze()\n",
    "plt.imshow(sample_img, cmap='gray')\n",
    "xys = preds[sample_id].reshape(-1, 2) * TARGET_SHAPE\n",
    "plt.scatter(xys[:, 0], xys[:, 1], c='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对 test 数据进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = DataSequence([test_imgs, test_img_cats],\n",
    "                         num_samples=len(test_imgs), \n",
    "                         batch_size=16,\n",
    "                         shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624/624 [==============================] - 54s 87ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict_generator(test_data, verbose=True, workers=4, use_multiprocessing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "肉眼看一下有没有严重 bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0LuVBLNN0btiibfIs1483Cfv9WzDdUhvMzJS8z10w/3rjIfuMx8oAtp1MpzxT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Td43+1f5PyX/oDfbGTDBqPA+TzQapLU88QnXsE1Vz+N6z/0N0zGCzhnMOKopcXpDHQL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\nKEMNUGhQgxqUoQYoNKhBDcrQ/wOVoE6Hd5TWOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f631431acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_id = np.random.randint(0, len(preds))\n",
    "sample_img = test_imgs[sample_id]\n",
    "imshow(sample_img, preds[sample_id])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出答案，注意要把坐标点的范围从 [0, 1] 还原成 [0, 512]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = preds * 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_submission(predictions, column_names=None):\n",
    "    predictions = np.round(predictions).astype('int')\n",
    "    predictions = np.clip(predictions, 0, 512)\n",
    "    predictions = predictions.reshape(-1, 24, 2)\n",
    "    res = []\n",
    "    for lms in predictions:\n",
    "        lms = ['{}_{}_1'.format(*lm) for lm in lms]\n",
    "        res.append(lms)\n",
    "    res = pd.DataFrame.from_records(res)\n",
    "    res.columns = column_names\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df = make_submission(preds, train_anno.columns[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit_df = pd.concat([test_anno, submit_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 26 columns</p>\n",
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      ],
      "text/plain": [
       "                                            image_id image_category  \\\n",
       "0  Images/blouse/e21708ac483bc37679b26d064650bd40...         blouse   \n",
       "1  Images/blouse/6089d5c425308edaaf418a74c684a8fc...         blouse   \n",
       "2  Images/blouse/e6b066c5022da7666e1a0245b160f7a3...         blouse   \n",
       "3  Images/blouse/17eff8648aeceea2006d8ef9923e4722...         blouse   \n",
       "4  Images/blouse/e802cc5029e33753680e2937f8293a93...         blouse   \n",
       "\n",
       "  neckline_left neckline_right center_front shoulder_left shoulder_right  \\\n",
       "0      148_85_1       271_83_1    210_138_1     117_104_1      301_101_1   \n",
       "1     237_148_1      302_144_1    265_172_1     203_166_1      336_163_1   \n",
       "2     208_166_1      252_164_1    229_169_1     174_185_1      287_178_1   \n",
       "3      140_77_1       256_41_1     218_67_1      95_130_1       322_54_1   \n",
       "4     191_179_1      269_168_1    235_205_1     161_191_1      300_172_1   \n",
       "\n",
       "  armpit_left armpit_right waistline_left       ...        top_hem_right  \\\n",
       "0   126_195_1    297_190_1      131_268_1       ...            319_391_1   \n",
       "1   201_234_1    331_236_1      192_291_1       ...            334_372_1   \n",
       "2   180_238_1    283_236_1      177_281_1       ...            281_296_1   \n",
       "3   145_231_1    348_163_1      179_322_1       ...            393_315_1   \n",
       "4   177_246_1    305_231_1      184_307_1       ...            340_440_1   \n",
       "\n",
       "  waistband_left waistband_right hemline_left hemline_right     crotch  \\\n",
       "0      140_115_1       330_118_1    117_405_1     352_432_1  240_321_1   \n",
       "1      227_264_1       346_267_1    182_440_1     332_461_1  284_421_1   \n",
       "2      212_311_1       293_311_1    183_450_1     304_434_1  263_407_1   \n",
       "3      176_157_1       369_103_1    192_476_1     446_425_1  326_408_1   \n",
       "4      213_328_1       328_319_1    184_495_1     340_497_1  285_459_1   \n",
       "\n",
       "  bottom_left_in bottom_left_out bottom_right_in bottom_right_out  \n",
       "0      255_433_1       135_409_1       265_462_1        362_427_1  \n",
       "1      285_462_1       213_453_1       285_475_1        334_457_1  \n",
       "2      274_473_1       224_472_1       270_459_1        295_440_1  \n",
       "3      352_480_1       229_487_1       373_461_1        445_416_1  \n",
       "4      296_492_1       212_493_1       283_501_1        342_476_1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_df_to_zip(submit_df, 'result.csv', index=False)"
   ]
  }
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