00.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a35967a3-4e62-47bf-97db-0ceb9c538127",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch,torchvision\n",
    "import detectron2\n",
    "from detectron2.utils.logger import setup_logger\n",
    "setup_logger()\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import wandb\n",
    "import os, json, cv2, random\n",
    "from detectron2 import model_zoo\n",
    "from detectron2.engine import DefaultPredictor,DefaultTrainer\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.structures import BoxMode\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from detectron2.utils.visualizer import ColorMode\n",
    "from detectron2.evaluation import COCOEvaluator, inference_on_dataset\n",
    "from detectron2.data import build_detection_test_loader\n",
    "from detectron2.utils.visualizer import Visualizer\n",
    "from detectron2.data import MetadataCatalog, DatasetCatalog\n",
    "PROJECT_NAME = 'Microcontroller-Detection-Learning-Detectron2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "edf48a93-2866-431c-a822-5e60bc7486da",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./train_labels.csv').append(pd.read_csv('./test_labels.csv')).sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0266624-f19b-4b59-a95d-7db527d6522c",
   "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",
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       "        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>filename</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>class</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>IMG_20190104_164125.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Arduino_Nano</td>\n",
       "      <td>356</td>\n",
       "      <td>225</td>\n",
       "      <td>514</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IMG_20181228_102317.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>389</td>\n",
       "      <td>271</td>\n",
       "      <td>543</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>IMG_20181228_103009.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>561</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>IMG_20181228_103828.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>100</td>\n",
       "      <td>129</td>\n",
       "      <td>590</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>IMG_20190104_164622.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Arduino_Nano</td>\n",
       "      <td>569</td>\n",
       "      <td>121</td>\n",
       "      <td>691</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>IMG_20190104_163951.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>449</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IMG_20181228_102048.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>457</td>\n",
       "      <td>198</td>\n",
       "      <td>553</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>IMG_20181228_104139.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>773</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>IMG_20190104_163951.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>ESP8266</td>\n",
       "      <td>358</td>\n",
       "      <td>336</td>\n",
       "      <td>656</td>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>IMG_20181228_104228.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>343</td>\n",
       "      <td>349</td>\n",
       "      <td>687</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    filename  width  height              class  xmin  ymin  \\\n",
       "121  IMG_20190104_164125.jpg    800     600       Arduino_Nano   356   225   \n",
       "15   IMG_20181228_102317.jpg    800     600  Heltec_ESP32_Lora   389   271   \n",
       "30   IMG_20181228_103009.jpg    800     600  Heltec_ESP32_Lora   203   210   \n",
       "55   IMG_20181228_103828.jpg    800     600     Raspberry_Pi_3   100   129   \n",
       "154  IMG_20190104_164622.jpg    800     600       Arduino_Nano   569   121   \n",
       "..                       ...    ...     ...                ...   ...   ...   \n",
       "110  IMG_20190104_163951.jpg    800     600     Raspberry_Pi_3     4    29   \n",
       "8    IMG_20181228_102048.jpg    800     600  Heltec_ESP32_Lora   457   198   \n",
       "61   IMG_20181228_104139.jpg    800     600     Raspberry_Pi_3     1    45   \n",
       "111  IMG_20190104_163951.jpg    800     600            ESP8266   358   336   \n",
       "66   IMG_20181228_104228.jpg    800     600  Heltec_ESP32_Lora   343   349   \n",
       "\n",
       "     xmax  ymax  \n",
       "121   514   356  \n",
       "15    543   398  \n",
       "30    561   401  \n",
       "55    590   511  \n",
       "154   691   267  \n",
       "..    ...   ...  \n",
       "110   449   467  \n",
       "8     553   287  \n",
       "61    773   579  \n",
       "111   656   537  \n",
       "66    687   474  \n",
       "\n",
       "[177 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eaa47508-044c-4a73-b0d5-359fc71ed6ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin,ymin,xmax,ymax = 317,265,556,342\n",
    "x = xmin\n",
    "y = ymin\n",
    "w = xmax - xmin\n",
    "h = ymax - ymin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b314892-a56c-4c3a-9d14-86059718b17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('./data/IMG_20181228_101826.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4177763f-2d29-4802-a4e7-56dbe0e9bfb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "crop = img[y:y+h,x:x+w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "086e8865-62c3-4512-8ef6-ee9a78e6ad6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fae985df130>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe5c6105-85c1-4313-ba2e-931d904c9b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fae9852ed30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JvygiilfytPPmuEKltL82K3LKpEjo9OopqSNMM0xjZ76ARsJCw01KKTGZTLBYzM3KnMmklWb8t8vy3aY+GPsCKAK0XhXNADVjt58rTdE9Acpm3XVLEauNc6wByhJSTsFawPlBhg0ci9+xc2oMmH2d0jAk1NqMItRpRNk2/v5BXpyr0pD+p8XdRsxjSpHu6yOaCFpp2ME8RjzpK3tKkZ8KF78LB28v54wuo+cAhqhZthqWL5Xvm4D6Lv2VCxOqMFoTSvQuFKFZa9S101GWqFV7qV+T725ZEMU5poyx/huwoqOQXjfpDDnTydS4CQUeHjFI38WE2Veu1rOojDeKG/065F1t68IYjnVYH5mi+wGU3IjGodIaMExiXde4vr4Gs9mKbQj0+mbqoUby31Aj9aa41jBNt9FBvESyuzC/oRSYMJuVD6In352qitQCYSfsCz+kbmink+6GfeFD3qIPYON7ovSATIlISamhJ0+pdFt5HeDQhiYBZnirduiWw8zGcGPXT5dlaZYt6mbZYvhH1PjsxXnqTJ5wk0rXYBG7s/hJOgBAx1FOJhMURWGcz6PlikOTaMzls3Uc3yUJDUkw7CpzoN5T9R/HNxSuj6kI3xkOvNuPdoH3vQBKsk6yHbHEc4mM1WoFkBUxdc8giUCkj5tMcZoArFHFdMZQ7O7tJNQdzKHInxK1kgN3h+4lfJ4Ew/Y/Oxt9iOvrn7OHaR/xOBT5sizrcGkuvn1oV72F1DdAWWvvYhM8jQCPrO+kyWdV1agqoxoqyzK5NZv3qUylmVCpDJfT7IiTHCvUiN+z2cwA5WTi96VEwl+z9bcDDF0+b0IG98ernO6Cu03FbTjK/km9j+4FUALGbUEzg7VfyAgQoEEoNXCxXKOsNArJLSNCCoDcIEvvKOIGQgROBK+3cPGE3yhlRURh9vIzPp02nwgb1im9BcyS2OY5ceO/5faWNMkGnERvDaEZECZjrXd9H8Y6HbLf93XcVPnbq4TaoOprl5p7Z1xrZ6CREMK8xBNWPNHEYmIfB9CuvdBPzqUXhCXqlBMwk7ARzNiqd7TfrYdsJFopExk7Q06FuqqgVQ1V19BKgaC99RpsLOBg9r6PqUkv7JuxKii+dhxrI0o6g4sJREJiMptD5AWySQGS0nP/KQahaQ8GwWxJxnBGDu7tk6l2aL0DdjIAu6gLmGFD2n+CcRXmK9mXhQBzWO92MtsBzPcDKJmNQzmFOkcBtkyiBrDaVKhqjWmRQXB71gasngZt9jvVIfx9NIBTFIopbiMNdoMIbs2wCSv8npdNh/cWagsiLo62eNUA5RBX1BIrbJ013ESrQM1gjLkVGk4n1MOF37n/2nVoBpLzFWwBWrRUMchKp1yt/KK9rC0FkkmO3MXn8w+foFYcPIRvo3BSbH9vNg1mB7YWKJ11m4ONXrUyonZdlajLLVRdQanKtA0YYAOYiNaKp8qQ5qBayo0OZxtOriTI/glkkwLT+QLZZAKZ58btzoYL3Ye6ElW4Ssyl387zGGpy3J6oh8TtVqmHRPQoG6m+FeY1pWKI3yP2OknQvQBKtrNtyAnCDlCzWwpju9mirhWYRbKDOc7HPdvlNxnWTNwwqT/3rm+2j9/H3+1qvDGUyudgePuNq4l4bW3nezZbUXXynuxGZP8fBt8+IiJUVeVn+VR+dk0e7bx3w/RNlmMoVt+E0ombpN12atvtFmVldJTspKIBEEyBRZ9uNuS8Uyu94r8sk5hNZ5jOZsiLwq7xbr7pbK12Q07vTdJgnpJdsR/ohibWfeheAKWjsCOaveIAI5YTym2NjdscA12fyPDazZqhGNPpFNToKmNOhahZsz0Uh1PSu+tQ0R4OLt9BBwZDUmfamflCbnR/YOobjOEzot26wSYv3Y6YEulS78bk5SbUN+nF78bG5drF6dD9JhiVWaaolEJZGa8Mc16O9iJuS+8a9oNEP4rTTfWPvvI14q3p99PpFJNigsI6qIfpxrRzso0mjLukXRxm6j0lpAEjUI1r41il5p7t+u7eAGWaAySrTyBoXaPcVhBCAlp1C8bcYvHCTt7S57BTrjezc2qDDPe7C5jixgyvQ0smsxPV29wpM6fl0p600nlpi/Ltp+3wu2breMIgcjrN1C42aa7aAUVfGVy5sywbzM/ogTkg1qefxUJhQgxP9KUY/NxZOWVZeuBkq48cC8qpgeueD6oZQq4fjjkwLWWMOIVd390jZkdSQx/FUtKu8PG3Q8/6wPe23F9fXuL0dgF1SPcCKB1nJkSwQSka5bexRBLWqzViX8EoJhufuYvDtS2q1AKGMC+tb7nLmYSUEhedWLbdbr0FMsvaVR0DUW/6aCzKTaOmGndYRE1xxUN6vlaZe+qzR95tLYXrvu7h8EdQsq1aZRgbp9Mbt/PUN2RSQOnAsipLY/muzUYVYDb7T8ZxBPH0pbHrWYo7Z2a/qkYIYc7IOThoLN6UFrdT9b9LmvlBUSsv1kaw73f++xtyxvcDKLUCVyuIyQyaJTQch8fQUAAINTMurzeoNSGT5GdtiUbnyCFnZdkdYveUG6U3WQdg8w+My4V1HA9maVi7HTMC61/YmWDiJ/LHkbqOmQmBXErUSsHCcguEPFCi3e5eXxc8i7nQFKXa3ov/geFraA9CCjtRUOauISe9ntzb01l3Bt0QxzaaHGC7y9hNLNSsDNWViyv4Y0anLVz9pdx9zB6UFapyi6rcQNUVwMrHEIOqexZTs1Vb20ndOeAPbVwCOE5SgiAgZYaimGI+W6DIJ8jzovf7FGCEIqy/TokNA7RPq/a1UQqwkyJ4QF79ED+LRHQhhN2xPWQ6wvs03Q+gZI3l9SUOpYTIpnA7zQthGkpYpma92UJpIMuCSmiBTcNRuoO7gPZ2Ys79qAEqY7nt0+O4dFKWXzOTB7sPRbN0lmUdZXqHIwjSikWbVKcz7/rF2qhiW7oxoBnoreWZPWmGZe7jPuIOGw62wbz10Hhxez8up09y8NfcFcdTnFcDkrXdBq1EXZXGbQj9GzrEaYcShVNBEJGP30klQHPSYdg/wr4qyOhQwcBkMsF0OkOeGx1lHyfZJ0ZTdM0Ut/BwvcZx7KqHMe3d1kcmQD90YWqLCum2YEbPm166F0AppERZlri4uMDigJFPpsZ5nhyAEaQAyu3G7y6E1IyY1H+0O4TnHHcsl4vjc52rDSxd3c3QLOneDek++zrOjTiwKF9hPEmjB9r97CbiMQOgLuZ00rsLGhtPSlwN33lOzqC8fx46j3f0k6pGWW6x3W79ihzmZhJ26fWJ1K4PhhvtOpAMv3N5CPMuoyWJsMAppcRisTDGnMnEq7J2/Y2lVBvu265ftBjfN0Hu2wfvBVBKIbCYzXC9WmMjJDIpIKTzPTOO3awUNqsVynKLySQzhyOpxsrsWe+ocxLaDeo6R3icQ+ebJCiie231nPHzVByp533xpqjhQEzKo4m65Qe6/oquRDH490fbDwKpT+8KHFPxjR18qTy39hbYoed1YGjWdddQtUK53aLclsHRxbpXHxlOlG5dtvOMqKqqI6rHG9CG/TZ0qXLxms2AZ8FGvcN7T/aVM7zu45Bvquu7KfWJ4GO+ceTzTM19+G6I7gVQggiz+Rx1rVBuV1gLxtHRkdnyzOu8GGW5wWa9xtHREeCcuRMFbG2WASA2AIWdK/bh29WpWu+oR/RIpBWD4xBIElFbt9J63zZExNSeJNDSMcVli/PSVQakDQqpuJo4u1xpXLa75i6HuPBe7jHxvZHAu+/Ddd5Op6iUtXhXJSq7IoetigZBHfk2TwB010hnvvHnhkd6S/c+lkzIxuc26y2KAkVRdIByDGDG/SNOO67Pu2q/IaAKQbKPk93F7fpwMG0Ui967ynE/gJIBKTMcHC5wvbzCZn0NKRmT2QIym9iT6swqh+1mDUHH0HbVA/kpL+AqXUUwA+6kxYBuM1gdV8cuzT06ixswzujji8/sdXuhroXC75rH/pvWO3cffxumH7wPv3WzrFmu1+XUXH2Fm3qE4nq7kE0ex4pi4+u/0UH75Hq+HRJ5ewflrvdodJRaKdSqEbmdb1+8U3YfQEl3tEcU1qXvdKBuMg/j6K5eMmJ3MZlgOpuimExQFLnf8Ne1Uyof7Yl/eNL3906CYzfGgn6Q6N9D8bly7Et3xc2OFcXvB1CCIQRjMs1Aco7VWmO9uQKxxsEhI5cCmSRIImzWGwhNECShWZlljsTmCE2nZgJbcG2vG3bX4UzfJyI7alh1YxhiaLuKSPYKwCFYhUDGzGaDAiIjqrVHfBfUmt4Xx97No02jk75Lgyg4Tpfh1dk2ITcBEHdnb0ehAz1s3EOcpc99D1fayvtY2mNMhW3cJ2a1AYiAcGMM+8fhvdYgpYFaQZVb1FUJcA2ya7oFmd36w/TtTau+WGsQ23OzE1Z1gjHSuEnUr65yR8wy2/5NdtOLHMVkhul8jmJSQOTG2Nja1BoDEyXaPas1Obtvgr5ECDYdDqSWsRROCrehWFJz1Due0WwMzGgzBkN0L4CSiJBlZhBPZYEsO8RKrFBttrg4r3Awn+LwYAGAsFourTUOcGuuPQg2yofezuDIdcjYvzHOlw3te5HBGmNgSopwwQwbph2KSZ6D7Et34F2f+B6eKR7mx+UpHAxjuuYg5zF4vzv2mJvuA670x/1JpESulC74JntdEpkd9VnZPSjtQV9uQwwPpDvidNl3XKN7Gy6RZGazd0HQZ1x8FO2z6jfrzTLM5nMUeQ6Z2b09ExtLuzygp57juope+ssUOMVNM2YS3CXe9+VtDPWK4IAVwIEGMr4MOkowAG1319HIZYajg0NsshKr1RKXl5fQqsZiPsdyuTL9kVzhuNENddq1vyP0DRgg0Qmo24E6onMjb/aKOWOpMYj0i5Vh/MzcceNIicZxmW6et+6E1K6DJmwnXbQ78Bjdkn9HlLSoh3lLpbMr/jFcTVVVOD87w2a1xna9RlXX/gxvpdqW8THU4naBlj4yBM1Uv+sadxr95GQ69QscjOYpUVmBJDUmn+1Pm/ETj4Fd38bl6Xs2hju8lVHHRODfjWm3ewGUzBpabWGOp/Wee2YLezA266XZj5I1Li8vUdc1ikkOwIrfFjCdSJJq3FhxHgNN+NttzMaAEnfkOK59qG/27tPbpTpmCrRa8STKYwZnt6zMvRjUyYMvf09e47g7+UpwxaPqkKz4tKNjD4Fkqh7D+kxtvhwvR9VKQ9lNes13/f0g7mtExttiu92aXmu5wlYfJYIINgpO9VcX3lnQF4sFijxHURT21MXGQu4nzoE6vk0fjjnBMeqVXUB7G0pNikOguKvsO4GSiP5jAL8PwAtm/o322QMA/ymArwD4DoDfz8xnZFL7UwB+L4AVgD/MzH93VxpK1VitLjEppsiyCQjSS7uT6RSTIsN2vUZZbnH2+jVWqyUmk1PTCVxnAIwoHs3sYedIzVSpGbLbocaB4m06Whc8+kEmld84zqE8OsvfTblKImqJmW3wcXkaGc+IZ01CI8P1fd47Efbnjdkspc2yDAcHByDN2K7XBhjhdswG4srcJUISNRsAE7obuYR/vj2DenfP/Bk5xcQfTZvnORiA7AHHXiljxH3qWUr/D6Cle43zMZTOLslgLO1iOFxex8Q/Zhvp/yOAfyV69tMA/gozfx3AX7H3APB7AHzd/n0DwJ8eET8AYL1cYXW9xHa9hq5qNHuiGJ+zxcECBwcHqJXG5dUSsLMy67Yl1LlJNEvAJMIlijGlGqVTcUTOlgM0KvEu7ZoVme0EkGg8+yt8507H51f/xGJrXIbgmc86kFTvkc2br/OEvq1TDkpb5V1q8YQVD/6dxNHfLSnmduLJ1JYEDo/cIHLrt913rTOR3IDr879MPHf6SQCQWeaPanA6y3Bz3xYXaOu7VRVkdJZCSkznM0ymU+RFgSzP/Psm6AAniaCvDLTPWMkpBPFwBVFqEti7X4woz85vwz/fhsO0k6Nk5r9BRF+JHv8EgN9lr38WwF8H8Cfs8z/LpsS/QEQnRPScmT8ZSkMKiUkxR1lWqOsVyrzCZDJBbkUIIqOHKSZTECa4XpbGv1IQSLP1qXQuEyZOIWSkIDfVYcZ42xdxZwdgNJ2OyeMIEeB2JXMWSpNKO24nAgPk130jUDIA3NIlOeDqxGUiNJbsYPAAhnvoy3qrGDZl4UGxCUOAXy4app2cTBDUyQ1pbGcfCtXH3cTXqXv3rBmkIahrCEHI8wwAQ6sakoAsE5ACyDOJTObIZI6NJrAGrAtGlADBbQUMIcwO6NRYo7mnHjTbXbctGIKotQs3k9VtEoGlQDabIZtOkE/N0Q9+soy4yli14NtxT1VGHw2ppMKVTp2J4AaUEq9HfBRmtsVMDNFNdZRPA/D7FMBTe/0ugA+DcB/ZZ4NASULg4PAIZVWhKkuUZYWqXqGoKi9KOA5RErDdlqYD2Q7nOtCQaO3ToshZN2L5+xut3Sjm2671OpylYq6ldUSCT9RHHaXdFqtbINzDKXTK31MSG9gO1HYZ+vSH43WC7UmoL2+7nscD96aqgn11mY5SO627NhRCIrfr+M1a/li0tW2G8Fv4DTZ83gby3Kcm8PcBN5nnOY6ODjGdTjGdzSDsuvDw4Lu4TVv9xsY3tm5itcJQHwzJjeFdKpAxwNmXfup5n+S1j6Bya2MOMzNRjx/CABHRN2DEc+TTA4gsxywrkBdT5FWJzWbjD2xyJ8rleQ5IgeVyGXBp8Ja/IZHB5tV2/GYchp1oHx1WTH1pe+BxcQdLJ82DdBxhB4+5pFRnj/PvDAJ95aLged+EEuclDBvqd0ZzhomO29tm0f1t2mZsvsK0wvqXUkLb9dhGJyiQZTmmkykmkykyKVGqJq4UB+4meQAdp/RxaoEI5ALRdjqb4ujoGIvFArPZLDDedB3ax4rOb4qG8rCv6D02fF/Y8PmuuG4KlJ85kZqIngN4YZ9/DOD9INx79lmHmPmbAL4JAPOTp0zC6GlkJpDlBYpigmq7xmazwXq9xna7RZHnmE0nuLq6MtuFWW4y1N7EAzlKM+hEwyJa3EEdh9Hu5E26Kep0iiBo23LeD1K7wD+MOuYMQ9/K1mBEf9lT3Ecq3b4O3zA8/YNt1EAcrtpRlJoEU+mngDs2QjjLdJ7ndleoDNNpcxxsWbbTTcYDanFV8aYbYXoOBF0crk+GE7zbUej09AEODw9xeHjodyJqDq8bzynuejeWUnGMAbWhdtkn7T5Q7It7TJlvCpQ/D+CnAPyM/f2LwfM/RkQ/B+C3A7jYpZ8EYPRk3NahZBOJaZFhOp1ivV6jLM2aWl4pvHr1CtvtFot5AVUzYBm2eJu0eMCHW1UFDFcnfPzMdepuvJxsmPC+ow9Ce/DEaHAbcSRMn4iaLeISoBZPKH5gj0hvrKh1k7w3GUxKgz6+sRPImG9aycZcub2W1kjY/OWYzedeNURCNI7nGGcUkVI2lu8ejj7U6YXvHJAuFnM8ffoUp6cnODw89EBsQ7Y441T8NwXNfdt03/6b+iY1qae+3ZVWM+mkx2mKxrgH/V9gDDePiOgjAP8uDED+OSL6owC+C+D32+B/CcY16Jdh3IP+yK74feZNamAGtNIQZEzyMssxX0hMpgpVVUHXFbZljYvLa8wXj0BS+gUIDLRXzDBanSQUawRR12oRl93XQTxod/gOclOi1oAnCoCoKbMrgIPNUK3QijdBQ7osJ357gO586/JmdGh9g7sPZOPrQYqjpbDa+3LYn4d9KFQTmGTa9R1eO68Czc2hbBDCAKH1V8zzAlm2xWw2xXwxw2w+w9X1pd1HmPzyUHsLze2SuToOd9yPpQjXNrFEQvZXkIAggadPnuPJoyc4PjlBURRe5A4BMpYCYuDcR4y9Db3pdG4a951wlMz8r/e8+pcSYRnAv7Ez1f60mhmZyK42IYAERJahkAUARkaM1aYCRAYme/4NMYgYGjCD0A0OTojSiK7D+nUGmhBkWpxfOLSaDtjSHYZpsBklZiekoKwuWhdb+8aKas27VCfoU1InAnbCsAfHYNAEO3S7+PcGKNqRFxcsKE6EX+OSGTnoYs7JTUIOBOPJgWH6noCxKLt2ICEgrc9ilmfIJjkmKsfx8RHOzuaQeQ7UNZi1WQTh4mLYHbWbPMSHzjmuMXYu75TPdkMiQJDE4cExfui9r+D05CEOD44gRQaCWbQh7OKNsB5ipuG+guWbzMu+Ireje7EyB2jrYbweMdA7AnbmBZBnhM1maytUwxwyT611343zbiIxp0fr0d/EXBoF33BigIdh3XUsZsdpJylIz+kch0SkPnEqdZ8WWzq6B1/AfQdSE3bHIBh82xfnzd4PfDgOzB242LqQUqIoCuvrqMFqi8VigaOjIxweHhr1UKWScTnvAreKZj6fG8dwZvtdhaqqgk2Ae8pp53WZZ3j/h34IDx4+xMnpg9b+k/tVxTg1ym04+r64xvSnPmDb99vb0r0Byi4gtEHSXbv3V1eXXi/pwnMQpqVzSxkskN6sNzXQHWiNLkuibATquNh10ol0JmIAnG4yIPp0qS1OeEDE74iwifd937r449pJGVJuMijH6K/8u/i+h8uK9VZuMcNkMoHWCqzmYGY8evQIy+US19fXUFpB68R2a2jaoK5r77nh9o10m7M06qGuTpzZrOmXUuLxk8d49/338fjpE8wW86RInSx70IZ3CX53SUMAd1vwuym3eu+AEmh0NAg6i58tybjWLJdL96X9xly2jCcWoMLuEOsr25lIc3GsNUimObu44k1nRkc/6jK5y1jiBytzWyOwawDsCDMEZM6YEPp5xvUT67nuktITVj+ldKZD7zvvBiaf2Prs4neSjgz0lDyZAgCePn2KsjQubZ988gnWmzqRL0Jd1yCi5nRRt7elrX+343mTdsIIIwQWBwf4kV/za/Hk2VMcnZygmEwgEj6TqTqK9ctD9fWmgXQsh+jCDunPx+jWY2qpy3aEvTdAOUSu8Z3oTUTY2p1bCin88aAeWnpYwEYX14jkYceiyFl67Mzbfd/Pme6isZzBbak7MXFHbxq+T3131/m4TfxjRUj3ngKQ6Btk7eON4cHNiOA5BCZwrfree+9Z4KvxyacVNptNZ7JxrkXvvvuu35j39evX2G632Gw2qOu6BcZxJyYhUEyn+NFf/+vwwVc+wJNnzzCdzYwqYQdAunKGm3uMqasfBL1pFUB6dA7T/QFK5pYBxXCP7XNBAPhNVcttjdVyi+nx3OttwAStmx1znBbO9VUjzph4zFnxzlgDf0ytS69FPW3TDyDpZmilgf70Usl50B7OUvxVkHbMQYR5t8em2mV2qUGXnPGpnY9A2eF/msnJ/aS51Fa0e6ocQuCz2eoifvS+T9wO048nrfCoX+Yc4AK51jDbnOWQMvMg99HHHxmph21bU2OwMVsFmvxMp1NorVGWFYRwnKv0RhsO6uLg4AC/7sd+E379b/yNeO+993FwcAgpBQS1J9i+v5uSG0dD5BkP/0/0HvuDU9wP2tJhEBu34+8bP466p67vpnsClMHMa8HSXDbiQnioEpGAUoTNpoQ4OYRmu5clgh2dLXpydPYz+U4FX6NE8MvNmnBBdZPJYx91Bl2PIp44um+BZVuX19cxQ7BM5jV+11qi2NRxzFGazb3Zg8HQ4GqBSytIetcmH67PQLGrDLsGOnNrd5d9ICGuh87gtO/D3X0A65OLwr6rILMMQkp8NctwenqKx4+f4MMPP8SLFy+wWq1g7ehQSuPFixc+vTA+5+fLzH7wExHyyQTPnz/Hb/ixH8NXfuTX4PHjJ5hMJjbN9GFjNwXKVDi5q1Y7k1o3rE48vomk0vVKBky/C5/FcbWNukxN3abykqJ7ApRdvy4/s6IphLaztxCGA7q+uoZ87wlUZbcOY3gR0lvR0fYrs8m1U6f2+u8OMfymFSl92hAHdFPaR++2L/VxZeExqLuMNjFXGsedGpyUCLdPHvcJ3/l6APTHpu9WvTjxVZMG7ATrdhU6PjrCYj7Ho0eP8bUf+Tpev36FFy8+x9nrV7i6ujCnNpal11M6HaWdKU2ejPMWZvM5Hj1+jB/+2tfww1/7Gh48foRiumg5vjfMw25wvC1X2f9yON67HRkpGBwuG3P8/stqzKH2kr6QQ/NBqNGxMAOKgKurK0ghIYSE1myl9xhg2p3IA6KgTjopq7BRAbA3zoT5S3GgHkC4Hccwx9SOo/s+7f8Wxp0e8LGInQbImBrL67ABKDR+7QZVdDiPOwPLBJe9T5xDk1IIjEDbWCgoA9m+6ww1tRCQeYGsmGAyX+DBw0f4yle/BlXXqKotqrIyOsntxuglq7p1SJkUEpPpBIv5HMcnJzg8PML8YIFiOoXMJCCk12O6/N0F1xjXxV1OzLehsG/3zM07OcPWe8dQ7ZmPewGU4WBrg1ZXHCZqgOV6ee2fOUt56Mxrvm00G4143yjmh46DaG4a3VrcIZNgGSvvUmWOuNu4Y8Yg0wc2g/FzeoVPLB6nxM/wfZyvPq7avd+XbvNtKp67+Cash3ASb3lViMz2R/IuPrVSUGw4zKyuoZSG1gpaK0DPvcTTGBTZrwgCuS35CFIKs1N5lgFSQmYZSLZ3Qh/DOfb11zFSkOlD4+rxJjSmvVqTdaI/D8XZW75gYh3b5+4FUALU2t3ZP6XG2t3h9qCxWq5QlqXtYOh8N9TQsYN6+G342wB2f+67bkZifIP2BEyCZE8c/R0CSaf6kHaVP85L2z9wePaOsrJTRHsTXMw+ccaTSHwd90/hm5CgSENKhpAZag1IydCZMtKPMkBJtr+5M8Dd9oAxCBOATGbI8szsRSml3ToNEJJa4yJ0Mo/7zND9rrq5H/xkm2KvlJBuOsn2MKkduidAGYqf7rfrGOs3CACgACw3JdbbEgfTDIqqAR/FxtZqZu7GeNPq+AlAtjHCL9wNzCNmA13dsma79FxXa4kyFIYIueUmfJRqkE5UQclyJnSoIK//at4RmHt20g7TTwzAUI/puhiBzDG3nexw66cvz0M0xj8uvLuJbndIrRGHc+5Bpq9oSBIgkUGwUQlJzZABqHp1kTZLbbU7DlezcVp36iJffQQhhTcOmboXZpNqAoRs8htylh3/4Z6J1hjuKDraINF/bb4I+7dZp37RPkqhaaLGaNX5hrr5aniL/vN5xgBmq+xuEtzxzb0BypR+Mr4O9WYahM22xuXVEieLB6iFgNJdP7GAxzY/UZoxWPbperzUHqgEwtXbrW8C3GvN4PafMY1JaHeT1nUElildrvsTlrvlwCpIaHeMPi4jVIOkAZWiPMbz891zkENiZoqG9Kx9ccffxOoI98tMYBYgCs7mFsZKHLZBBzQT12G8QghzNngAgr7ehQZRdxJLSQB97Rp7X3QrzYaL6qZv0hrbn8N4ObjvDRuB5U3gujM+uqZ3tEdymu4JUA6flxF2JkFkxBEjOOPi4hLyvSdJbjIVZ9sXrm0MCUE27Gh96gC7/38rrhT5uPbf33gnDVmnmRlKqxZ3bt96qIwNWeHAjMs9Js0m/K2L1kk3de+4HpfgXYjvqTRD8Ti+Zubu7uUJkATaxyE47jxmEhxQhn26AUqMAsq4HPGEPVhLA/14LI3RfzbyTW8ke6c9SoIIkxgV670Byob6rG7+ntkcDm87r7F8i0ZUGBhYfQaSECxT+UiJNf77hKjXB9YxZzaWxnBN8a+wu92E2Byui49BO9zBxrm6hPt3xpMI0DjQ78O13ZRSXLQvL7qD7S7zMyTOh30nPkXRfRu3y05R0TIEKa7fLKVtj5GhPpUEUGAUQuwDTnGZOvrcaAylvrsrSjE4t6V7B5SOYlDzXCVspZP5vbq6hJASUorW4UtAtwMppZLv3XX4PMwHgKR1PORkOu86XFxXB7qrI/ZxtCnqDUN95py2Yjz2oXT5jUHJvWc2xoi4js3v/nrCm4RtSnI36YwSIUe0Waq+wuv4fScfgD88rBO/YMRA6a6HmITWe6vyvgsgift5XL6hMRZ/96Zol5Q5hu4JULpZuYs7HCiWw2vA/K7XGwDm6E8V7doSc1rxc6Cfc+0OiISg0AtCUel8Z0kvwt/VkJ28JNSAu/PdZn6JOLofbyFNcUxeNRJwrfEg2C2ujwO6OL5GoRAV0gTqfE89eepLf1/OJwTLPm4rDh8Sw6xkSaYomjet7xjWKhz2z2ERuwnbqsFWXocoLFtKPRVej4nrpvSmpRng3gAl0LRqoEdjNqK2e03CHvdqdqCulcb1aoOr1QaSJJibg0tijhRIA1KsSI87t4kDALlwdkYP1qH3ligBcOGxtEHJPXUt6G3auVQv7o+tHXITwXuAuTOY2wHMvRQ+QTeRadZGQb6Du7kLCuOJd4F04ngqJUL/gfYd0OoB+SGwi3XfKVFzCDh8/jr9h8EcLuJLTNqtaNtubYFs07prrswigl0cb5OdLuccT7K34RT3maBumt7YNO4PUNqGbjjCxuDgOBVyncFtfAEBpTRW6zUeHh9is1kPJjGGe4kPbCdyAB5XZpf9HeJIBpVCEbAM6pq4PfiHeVp2gfz9Lg56DIi1VA6J71i1d+sORfiUSqI3nR3g5MK0Bmv7ZXp52w6OLvUuBYx9A2yvyXOfcD1p7u53dhz1+lEAzdTSjLmh8jl9dgz8IVD2id37UJ+0tU//6ftuH1C9H0BJqYFKMOe+R+IgYDbBsM2uFeP87BLPHz/00fXpPvrAqFeXg7Cz7aYvwqhhOG1ztQ/oxDTE8fmUXOc3AUbHLaS03E/z5wxEYb770h2b55uGAVGSc98XMHepEoazMJzPvvd76VH39bLwc9+4NglPk+wDylH5fEP0q9aY0zV+JEBLM4jtShwisNY4e32OIi+SLhr+u0T8e4lV1L733G2C9hr8o0O6vAXKiV0gSYS+I9dj8fBGlEqa/T+9gyWeyHadgNdJ9paAObbOb1I38Tf7gOm+5RrDYZnHUTmIEioa36tace7Kq5MY+nSUNzGQvUnDzk3pXgAlwbiixDoPpx8MOROjpRR+fQyzwutXZxBCttwzdlGsbE+BadPgfdzPzUWsRpTeX5xIhUp/a8SpruS5H9dDQ3kb8XhQlXAHdBcc9ZhwY7j3MWHuquzD8XRF6farlOjdPE/1kUG96qD4vx/tOxnsI5LHYcdKZPcCKENqQArm5EKGFeMAp7c0YazjOUms1hswzFb6dV37eFIzYqxw7s6GJh0HMA6s3dPxlAhNwQ93dZyDsXkxuKezII1ZQ6A6RjyyAQcy1iREYYL85kWr29BNrO1jaCiufSy/+3Ji3fAWJIP2ab9LRbg77TdVb18U3VSCujdAmWoAY0C1EGDBUgsGSEFYx1vFwMWmwuVmCypyqO0GYECMtCq6nbHdGvDGe810MinC5YBtYGnbDsMEErBFgEzo5vbtbNpNFCFAUb8Vt3dM7ADJlsoDuzsYRVzKl3AM7aTbANlN0xprvEmnFWwWk1yL34oAY1iBPt3+F0ljwW6M2mcs3Q+gpLYzdsqS5sXq2JlZCNS1xvnFBR6czCGWS7hDyXoNBoFIGf+m9CVDriD7iKQxm98nno7j8nYHcYGG1Zjj1Qft+8QAHpOjG4qlX4y4+mbi2UfEA/bj2nZxsESJKTTVN3v2LQhBKZ5c34Qrzhgdb+qbm+ZpbDr7adG/QIoBqtms1IrdTBAkzaYPJPDq5Sss5ubYTvTo8YBdHautD73toArjivPwxc3M3fXGY/J403C3pb507iL9m9RzmO5N005ZhPfJ59jvxuTjJuUI0/+i+sG+9KbzdD84SvRbqOND3d3BS8JtO0WAUhpnZ+f22E6Buu4eQj+ukzUAmQKy5Iza4Va7sYbfDXNp/ZQSFxpjS8/E4KSpIRVjH9cdhhmO4ktBfZz8D4pSksvQ+6HnfeL5XXBaN6EfVLo3obFi+P0ASu6CUSyuuF9BBGJh8cn6bzHw+uwMRMZ6rqgGEhzhvjPyLqt4TEQ9GxkkONwxeRvzrA0Acfg310mdca0vX2PorjnpfcS0sRLHeH3gbtpX77hPvLfVmZIJMPj9WClraKy8CdXHrnylLN370v0AykhZGG6DFv42RozGCq7BIDCur9ZYLrcQJI2BhjnY0i4htoxqsC4XSCZDrWwnyXtbUJOHoIF26X2G9FrjOxthH6fjMdzlYFp7hXGDKRHqhjrVMK50MPuwr0osB87Ro35VTncHzoSDYrgw6s4pNZH3hfPZSIYxJRnidH9QxpuYui6E6XzdJTe7U0dJRO8T0V8jon9MRP+IiP5N+/wBEf1lIvol+3tqnxMR/e+I6JeJ6B8S0W/dmQbcFvcwA5sYlFiVY8IyiLT9M2uLNYDVWuPyvEIhZyYKFxUI0oKUsAUWgLWix3oNgrNlMxOcPtTsSsYegMHNzs+dOJitU7zx9iSG+b7nMOFYB7ZLF5UEzqGKvQNBc5gPISQNBoOxtNUa3TJTIrxLhwb+mjhT73eOcw4C2HZL5aOTPw7/yMZj00wm1BPPHdCNweyO+sp9pLsA+DEcZQ3gf8rMf5eIDgH8HSL6ywD+MIC/wsw/Q0Q/DeCnAfwJAL8HwNft328H8Kftbz9FOrzBglHTnMxNeK00Xr9+jR965yGuEYJsnNRNOJ+EXnAP5XozXHqGzQ2U+y1KcmVBvvcE3jFppr/bv25j40Bb1RKD33Cex77fmb9YXxiVq3O/Z3IRz7Z/BHF8e4ih/bSbE+39cgQXuyvs2DRva9W+qRi+kxVg5k+Y+e/a6ysA/wTAuwB+AsDP2mA/C+C/a69/AsCfZUO/AOCEiJ6PKUhsuEmBESH9XrPGp59+iok16DT57/pOxSJ9nI+o/MkwQwATH/rkQaAnvpirjLnUlAX4B01vwhp8W7qreL7MlObQR33ZaCbuoG2H8nfbsO3JNE3hGL+LcuyloySirwD4LQD+cwBPmfkT++pTAE/t9bsAPgw++8g++yR4BiL6BoBvAMBkfpRE9xRoGkYpDSavX78GWZBSSrestfvoWvr0gh3g20M34hkkbsIMKb3DuO5y8N+m8911muH7XeqG2+SrO9n+ahUy2zS2fxK1T1HqG4v7cI59tEufOnZs7ta57o5/H0wYDZREdADg/wbgjzPzZZQgU9/uCz3EzN8E8E0AOHz4Dicz2lLO2woKZW+EBWSsV2vUVQUhpD0eFElrwS4jyV2DU4r69JH7iJg/SHpTefuiytxnzvivAvXqub/g/jamrcdIeLeJfyyNAkoiymFA8v/EzP93+/gzInrOzJ9Y0fqFff4xgPeDz9+zz/qJ7Tk4JrU290YAa1jgI3M6XdLfkXC1XuPs4gIik3YnV/bcQxOKrJRhNjUlq5JqDqH1Zd7ZKH36jrRuxqTlVAeNYSJNRJEmjBuGtKPjYnQ6eW9eOkWigNON3JvcJ5aLD/OWor4Zemhm71O19KkoUrRrQMQTn7fT2DsXbZ8eakivdZ8nsz5K17Frj0R9vQERvJ2qTWdHmDepcNpVvjFWbwLwZwD8E2b+3wSvfh7AT9nrnwLwF4Pn/30y9F8HcBGI6P0Z1Wz/zBnIRrx2u2UDUggI0YCkzZv/XhNQKoWXr88AIaDNftsgaqzPxgLNzq4NQcZhXZpjky1YmufEGgIMYZ/11M0osdC/d9ZQNi7iZK/d5AAEDWbzSwxAs19X7u2+1grvLfzoG7QEgvB/6Py5PATXHD4Tb6yHhvWX4qyHgHPM4O0NQ6a/aDLzqQ7++uK4rRFiLMV1su/fzdMKmIkd8e37vDdNwHuRwPbnnh7q+/zucryZiWsMR/k7APwhAP8lEf19++x/DuBnAPw5IvqjAL4L4Pfbd38JwO8F8MsAVgD+yD4ZSg2MeMCEe06GnAkB+Pzzl3jy6BCsLQuXqFsTdb8+NHU/Nt9D9CY4kZgzu88czk3yNsSN3rQ+O3F+ESxLX/ptTdK9pPvcp3bRkISyD+0ESmb+z9Dflv9SIjwD+Df2ywYlT/xLbZQBdMU6IgJIAMw4e32OqqxhT7Q1TFL8rVHIdOKNyjG4oWy/S8v4jtV8n1SltvO8J+37TYpLD/N3VxSLrikxr++71LN963pHqFY7pFUo/eqWoTTGDtS7MJjchNj928OguLx9UVbw23pS9MW9j5EqpPuxMicQp93Aabv4tMEyZrPNrwCzwnK5xma9RSYtyFLC0ZgBBKcQ9imM9zXquPBD3w3pL/vCj83DFzXz93XGVKfrA7ibphE/G4pr/8limLuLJ5Oxngs/SBoDFncZ/17A59pwTFj3SfDdUPixk+/YdrsnQNkGhD5wSHEjDScKEASqqsL19RInxwfmxENub83mOMqbDuC+BviiBswPcjCOAcEvIu03ma5Lhdz1Gyhzqywm0lvH6eO7YVxj8/FFtXkv4H4hqXfpfgAldUXv8MAiAC0nbq2111OaMARhzcJbRXh9ucbB4QFyaS3bsZN6z2qbYfGz23DugCqvaPZh7DpzouA6LK//J+nTR9YKn+q4bxqI0+APpHW6hHDZHzsDVROq/VmPzjjFx7Xi6Zr6x+Wdm7hboBf1iTCZEChd64jIzLPLj3YvuldMqDlipdtEzYmajsaqGW6rNupMzn1p7hH3kBdDH90LoPTwEhlqgIa7lFL6a3eIWAtICQAzSsV4fXGNZ08eIANbc7ZoA4wbKNROJxS5d+knTZrRdmz2n/CY3QZkKAjUxkAzHO/ViImoR3SJYd7OCL2DozNjNDGNzgLplsqkT+xqvAc6MxTijS9S+QjnhrCUN9VRxvm7y3D7UtrAQU37BU/7SrSvbjEZ/gYqgFSq7bl4mMmJ1SVj6V4AJaN/FkoZeTrfR+9WqxW22w1mWYGxU3Yq/RuLMUSdMvWl57qlOwuoT6e0T17GduI75YwG0gnT2DfNblx7Dq470MeleCwT9X5xp8u8W1f+pgFzmG4AZjvibfp++/4+070ASkJTuTEn58BjF1BqF48QKMsS27IEL3J4ISpsPAaYGrZiTMPGBqTB0gTM6i7QZTtQvggdZx/Xc5dp9sXV9zzmynZTozYZSisMeztufZyI2cnl2PoMhI0flHFoiBt2htKwznfF5WhXWGNTvf8gCdwToATaFRyLwXGnSbHSTjwSRFBaYblcgo/nPSXscla7BveuDuzDNQ96yxY/E9wtZ5+4MIZ+0BbYfcAyVLeMO987zRX3EfVo3fahMRxlmPd9xPBd5bltO95ezGevUxrTr8a45+xXP01cN+Xgd+kkx9TRvQHKmPoy73SUQFs0ac1MDGw2GyilAM4R+1L6QDvSCt+PAS7fMCZQA5Z912hz07FF/9ZgF6XVR70hAuNHcBmq+vyL8aK0yVI8EQKA1np3PMwDGXZBGo5S3MGEsa/oPQ7wm9gd7av3i+mNTY4+ecvNty1ee5GTB1oMwRfMVd4EdO8NUIYVt2tWCgdUS2QnAgkBzRqr1RbbssJsOk3aVJm7q3tS6Y0VhVriI1tre2g0CL+PBruzNwxNDruIomvfIXvi0szj+3lbWw6v+B/6JO6IvgwcjQuy79istveDkNGbiLPT+HXubV1vUOlQXPv0qRuFy2w3jZaqpqmrMGQIh7q1V0FT866fEUSgjzELbH2+Oa2CeNM6yvEUlNTX1UD79JCZZMMd373uq/ebMYD2RXCo9w4od73rA1HXNZkZmhmbbYmyrPdKLyX2jhULkyJFoKu0gVpgOQTCNxGZ+r5Iibuu046jNgzfROfn3LgSb1rX4cSyO84gV43Oo2cAxMCdftwKT93LGECbSwdqDM22llr1G+wZILSN1Bwn27sPes9g/mKBMy5v73RjQveNVSRafw99Zhx3SLt01UNGsrFgeU+AcveuMaGItktEYWKousZmszHfRfEIQXaDjRE5oyF3l9tqv5o0XP7i6z693r46yzCd+NvYJSuVt1R8+1DfFze3eMbLDWPQ6g7oXYxj+E2Yr07tOLzrPDQzo/DLZyl8bMOzzYgBSikA0Hj95hiJ6y3100372z0Bykah3zcw+0DSD34PWoZb0lpjs91AKwWRZe2wNwCe3lmpJ8+peMemEYLkKPErEGH24UTD+GOQHpPfdhZ2dcC2UWXIgyH1bRDC4kykvgjEbQAw29i5CdY9dyATi7hR3ECgR22UGORUBGzrK/gunFA9h0jmeGUhjA5dei2DNu8kddQjYyfL+0GubsaFvI80lqu8J0DZL1KnnrlO2QoX6cCU1thuNijLClLK1gYbJr1wMHRpX6vzWNXBTTm0VP0MiRS78tSXv5u878tn63kACWPKqzWSLGA8ofYbwHTAKTcuWO06Sq8CA+AXNWitk9uvAW2OkgCQ62NBXEJKZFJCCqOLllJASFipRnh1UVim+Dqk24DmGB37TeIcM4YAtMq6T/yO7sLqfVO6H0BJZuUNYJTfuzgaZ/nuiIyNlhhaaaw3JWpVI9ZbWd5ipxoszEfKgJQO28qIfzcWSFPv+gZQyNWmOML4m11g2zaEtPM/hjzHaJk29jf2fkfHDTlcsDsJEzDnt7swGtqBkdf3hnkP/VLbXGTDXdpJUrscMrRuA6afRNnsW+nyRtb4JKirWXSTsVaqtYBASoEizzDNJIhykLATAWmro2yIgjI1D+HLS+6dFedb1/ecuOf6TeX9LgDS0f0ASgDhbj7uutkNvAE6QWb7WWjtXYLaBg0zKDRlWFUC21JjobXXSZIwcYugg4YGFsYwcKWpv9nHiE+7lNEOpIUQISscJNnW8e7Ov9nUuGMabwFJCKBRXm2axqIbiP2u/Cy8eOqAisGAYA8+Li4vCLM5ethJ1KwboDRlckU2cXlQ5QYAmzRtuM59CMIRMNt0tJuoe8YYicaY1Rj4XL0pEAGZIAOAZMJIIcAAagagFEgKSDhHeAKbs5l9XYeb1bqHRGhEfXfdCFGAGH+m052Qn4jG0d1BVpf2y0mbvmTGHG44NlvqWCyOt13rF7ncoCJUtcJ2W7a4J3LTMHMa3kYo09v3qTA9pdyTs0xysAlOJvXtLvLh3Q9ZcBow7nujmv/MDnQA0IHRTLd1nx4QNTyotTm8trHOcGXBXGB/NWswVBNnAJLEwt87sGsbdYI25TaghPmMr3vrDWkVEIFQCwEhDScpYThpCbK7WQFCk0dCKZvFEo2OMwJKl/12kwUuUe383QQgx4i4sRW/6Tt7iMdhHAkJZ1QciTibuX74230s3SHdE6A0xBxsRmFYu04YtxFFKCa1Ck62kxNQK4XNdmsByh0f61byNHFrrY2+KOHIviPHCPVuu7i6XfH1pbtLF7k/pedgEgTi7lnbDHh9XQwOrT5XK4hXn0MreE6PXCAPeo4LNNfkWEE7eWl7HIhWba6OAWiloNkc2uDr2InHTD5NQY6LbcNkqKpw9ejA1DlBDIFksiaFMNKJbwoBiAxCCpAgsGCwlGBBQG5/iwzIyXCBUkC6vmlY9UasD7t1ngGPn/ox0Qbn/TiqPtXMr1ba1Z5j2vyeAGXCPYjIdsA2KGo7ECgATPed8GIRQykFgsZmvYZSClK216syNTNyaws3NLPy0My6S8/mAP82nXBIue+AYh8OosMRxO/s5EREZlUT+jtZI1Y3YeZ/46/i+Jt/CnoyjQKnBnJKR8zdsP03PU8GXnpEeQOCIPl/glsHaravOUbShiVK9Y+eDeDKEvXP/Enwr/+N3X5hI42f36WOLpGjTl3eiJO9qwztQUkGawfdC6BsDd9g1tectjf2cVSNLstwJQpmKWNZliiK3OBAyGa49EOQ7tHzpDtB24raFvF3d4HbWC/Dv1SDD1rXA/XGmLyFluZQX8pM3iACAOLVS6x//W/GZ//DPw7tdYBODAaUarbH06wNKGoNctZlZss1BqqRluiuA0WduXdxCyGTKpmwfbyxRSsfz74cZKqeXF0Z7woJsIaQAllmXIOyPEMuCUVGyDJCJgmCDGestcC0mGO+mENKCSkafaX/VQrZv/0/Aa4uR+Undb1vmVIU1xNbSWCXsXEgIfMzJuy+cffQTb+7F0AJGOOKGZDkB0nTEMLPxpaPAQAjpjkDgBWn/UwuBBiMzbbyK3T6dD7ho5R4aV4aZHEGBMOBGn1cChh3dbYQ4G6jUxoLkmH6nvnxdeD2zWxXCQUD1h0nHK6zZ609IAKmPVSWY7s4ts+1bSMjUiu20oEFRmaGDQitNLRWUHXtQdSkYyUGhCJ6KMmzbxuXVntAtctjQLoByjCAA2SEcdillM0c2gi6ZA0y7YnLbHKilEK9WaOsSpTlFnW5RbVdYr1aYrW6xHa9xHa7hlYCP/abfit+/Lf8Vrz7/vugorBpBxO0UoD1CklTF0DeuDjtq438+PPP9kyao9+dScbfuXYL7oeu/Xe2KRnpEzhDujdASRqQbu0CWXcN22EZDAGnY9RejAEzrA0cxMHO5SwAMlzLtmKs1xVYaUhpvtMsrPvGHvlDDLQGZVKWxpSVu0907nsfp526ToYdVahwAoiee2zscstxp9MgwwVa15paaQgNVJXxY3WTmA6uDWjq5s9yhFopsKrBujZhtdvTU5lMWqUdeU61rRPWISJ6tyIDwO3i2T5g0w7L4wGSNQSZJYfMGkIawFYWzDUz6rIE6o311S2xXq2w3mywWa+g1RZgRlmWWK3XyLMMi/kC8+kUq9U1VssrVJs1CBqSMnz+vV/B9VfeQ/XkIWQhAZIgNvuNm/pHI1pHfabRbY6noUl2fCQAIQXedjIbSX0g2VekVPhUamnu14IiNX3IToM783xvgLK7asNuLEHwMzXgROqufs4AZZuDAIC6Vlit10ZElMM9itBsFNzKi5Ht0t8kOt2+yvIYhELrZ5i3XfHtI3btWq8dfi8AQAivt3TE2oEee0s3ow2EAFr33jId3Gtm6FoBuoJWtQEj7U5Vd+ui3eYS8OK44+2auktw16yad9pyDlbsFWCfPjODtNFtV1UJVZWoyg226zXUdo31contdgOlNaQQ2G422K6voVSN6WSC2WyOsixxvVpCEePw8BCZACaSsN2scFWV2KxyaF2jKkuACHk+AZjw+auX+PzVSzy+vsbkYAFw//rvgRbbM/y4SbVXT59Mz4koPd+M1ZvuKZLvonYcdmIO9JRfImOOwTCvkwJDwxhtGpVNMxCBts7JPIAZT2FjMKDqGmtr0Mlz4YP29ZFYdNFaD3TBtNN6yO306SuHdZ+7KQWsd0EpnSuIQFYlESrCNTdO2ko3HU4pBRUCYeK3BZxaG4BUFaAVmBU0S8/5e+GIDHQaqZmtqsTkRwS6S6PbJhBrCNRW323ypZXCtlxjWy2x3W6xtdzgdruFYGBSFCg3a1xdXqDablAUOebTAnVVYbVaYzIp8ODBAxwtDvD6bIuryzXKSiHLARLAbJaj1gxoBSkEDg8WWMxmEFJAZhm01ijLEqqucXJ6grKssN2W+P6nn+L5B2c4evAAxTSL9PZ31ryj6E0Zgfq4vJvGc6PvA5VbCJJfGqAEtDe0GM6O4LRTjoNwejISsgVkWmtISMN9gvwgdvrKuqpQ1RVmZPemRKNPiqnt+tJ/PMO+5Boi3uA1lf4+tG/+xoaNVQPhtw3ANaJ0yERoboNmH0gqpUybcg2tFMAKJlINaDZSAlmxHdq0L2srEtv6hOP6lWnnqkJZblGWRjdYrpdYrZYot6URuZU2OsLtEoeHhzg4OEBVlbi4uMCkmOL42TMcHM+ht9e4WG1QrdcoswNACiBjrKs1LleXmEwnODw9wvHDEwBAXuSYTCYosgJcM+q6RlXVRixfr3B+eYnlem3KrTWUYnx+dmZW6AhCSYTTJ0+xODzG46fPIGTRMIlOqnlLnsaAZAqY205jXzaOkhla1YYjtCsMyBp14PQIgdLeDUCgscgqVt791livtV/JUlYVyrIE8ywQ2cdVtJTS+7WFwGlAIy1i38Y4sw/tK+LfJJ0hcSmlI3RW5WZJoNHtpThJw+HVgK7NKZowHB+BIey3pn0VVF2hqkpwvUG53WKz2WC73WK9WmO73WCaC2ilcHV1hc1mAyLCweEBtKpxdXUFEPDk8RPMj+cQagVsGJuLC0xhfHPnMoOqK1xenGM+n+L46BCZYKyW1zhZLLA4OIAQAqvlCtPZFJmU0GWNzWaNzWaDq+0WZVWhrhXKunGtYrBV+whUqkKtGJXS0EzIJ1MgI2hovPj8c/zCL/zn0Ez48d8s8OTxU+R5bvqcl5B+sGC5r9X5xlbqpNLR/OOwYKzRquMFYWOJuckvBVBW5QaffPsXwTCcpJQSUkpkeY5iMkVeTCCyAkQCGtJaQI3V0a4Qt0YfC2bQBmCVgpKEbVmjLCtoBoRV9BtDkUm/Y922z6QQHnzdLOT0p+GsdFuAYqQ30WWGEXvR9J1YV9mAM5pQCRAP02qfTNifP44GqOPQfchAhNHKcIgZszHk6IBDhJ3QtAa0ArFCXW2h6wqqqsy9qlBuN1ivV6irGqvlFTbLK6zWKxABWSZxeXmJ9fUFiizD0fERSAi8fvUaSis8f/oIh4sDsC5Qbq+x2WwhSGEyLZAXEtuywvnFOcq6xuLgCKSB5fUFys0az549R57n2FQlrpZLrK6XEESoSqOrvPj8c1y8+BxEhMr2I6MvN79SSuR5gTyTIElgKXB4cIAsy6HqGpcXF1iXW8NBwyxskDKDzAvjlM4adV1juVri7Ow1rq4u8eD0gd/MxbWl799vwKJ9p+I2N6Om1XM57JXsnyWjaJRsPowpf3cDE6CHe2Rj+vUgaxdTaHKAC6/Y2VX8ewGU2/U1vvVPfgEAvLuFAcwMxXSOYnqA+dEJDo9OMV0cYzI9QCYD8dvGQxDGYq7cGmRGBcK6LLEuK2jd+FkSpR22Kfhz96ZNh63kO11yesTYBtzij6yKwATekR5H33O/EtanGZYynedWejYfBBjXK2aoYGZ2ukIwjOValZBcQVUlym2F5WqDcr3CerXEcnmJ5fIC69U1VFVhkktcXV5geX2NPC/w6PFjgBXOXr/E9fU1DhYHeP78GehggnoFVNs1lteE2WKOYprj6nqLj7//fSwWC5ycnOD5e+/h7PVrzBcLHB4eQAO4Xm1AIoPIclSVMppLElivl/ju974NzQJK1xBCQGlCpRQkFCQpSJDZBSgrwFkOAmE6m6JWNebzOX7Nj/4o3v/gA8wXC3zy2af49re/g6vzC1NvooLMC+jtFiSkMYDZ/lCpEqjIc9vldovzs9e4vDzDZvsME++4352U37j7zw5Kc4tugnYzaM/HO0DJRNed7APzXRSeO79Gq01QdhcqBnnwZAq5SpOeUrfkKIloCuBvAJjY8H+emf9dIvoqgJ8D8BDA3wHwh5i5JKIJgD8L4J8D8ArAH2Dm7wylYWbUFQDjT2mqmiGYsF5dAiIDPsuRFRMcHJ/g6OgEB0fHmB8coZguQDIHUQ5Blr+kprJJK9SVwnazhlY18izzJzYGZQzy8uXUBb0p406K/ARj9Ybmmc0HNKArfPrRd/DL//Qf4Pr8FdbrNbabDVgpHB0fYTab4/NXL3G9WmI6yfH8yTPkmURdV6hVje3mGoeLBY6PDgGtsNmssFxe4+jwCPmzpzg7v8DDR48wP1hAaY2PPv4Yy8tL5JMJHjx8CCKB5WqFs7NzfPbiFQ4Pj/HsnXdwdHyK7bbEq5cvoVQNrWsQNFStUStAk8TB0SkePn6Gi4trXJy/RF1vUXGNaT7Bk/feRZZlqOsaRZFjfbXEtizxySef4eNPP8Nmu8Vmu4VSNbarNYqiwGwywWI+h2INvV6hqkqvN8/yAovFAQiwutUSH374IZ48eYIHp08wny0wnU7b4BhM7vezr0YTeDwZt0C1j/rC9Jc3Fp9DEAzfh14Ooe489uiIaQxHuQXwu5n5mohyAP8ZEf0/AfxbAP4kM/8cEf1HAP4ogD9tf8+Y+UeI6CcB/AcA/sCuRAgKpmK01wkKz1NpsK5RrVc425zj4pVAls9QLI5wdPoIh8cPcHRwikkxAwlpxEBrX1CsIJTGenUNZmU4SSvSDpHjAm+iB3ThUv6Uo4kCkeMGcewGzgFRO3GfqgchCFKw8UszrCTADAGNSU5YXb7C5etPUJcltFaQAM5eXGA9MwA3zQUENF6+eoGDo1O880MfQBAwywXACicnx8ikwKtXryAFoSw3WG02qOoKn774DA/UQ+STCU4fPoSqa5RVjc8+f4mjoyMcHh1jMl1guTSGmrN//Is4PDzEgwcP8c6TJ6gOp/j0o+/i8vwljPhMODx+iMWDJzh48BQPnn8VWtWQgvHq5Sf47NOP8Z1PXkASoOoKRMBUZMbCLiXe/+ADPHv+HIvFHJ999im+8yvfxvLqCqquvUtTXZZGByslJFn9+XYLKWWrjfM8h8xk03/2avl0ew3RXU+sY0Tjm8XV5SbDJcytsEZv1RnDIUA2XhtGjz5EO4GSTSrX9ja3fwzgdwP4g/b5zwL492CA8ifsNQD8eQD/IRER76gpstIicaNTULA7Cll3DymsVVsTqq3hNK7PXyOfzHB68gAHB0c4ODxGPplDZBMwCc9qr5ZL6LqCgAZImLXekXU7BqQ+sBnjx9j3frQ+cwSYR1/tF3wk9YElucmMyDhnO1WGCYmDgwOcnD7E2asXUJTB7XHhdIsPjo6QFwWul0swCXztR38MDx4+gVZbfP87v4SPv/MtXF9d+Y69Wl57cWoymeLRkycQUqLWCheXl7harlGWW7w+u8Bk8hIPHjzAbLbA7OAAxycn2GxWuLq8wCeffA+ffvRtSFYgtYWA0adKmePR44eYnjzEdH4AyqaY5jlUtcX8cIv8/AqKr/Hk0SnyjMw2f0rj6uoa680WH338MV6fX6CuK6xX11hdXSOTErPpFAfzORYHCxweHeFqucLF9RLbum4ZJpyR6/LyEv/gH/wDzGaHWMwP8OTJk972uEvaV6raFWassaUvzvR1dwVOSux2v5qN0SyuY60BpbTVpTceGEM0SkdJRBJGvP4RAP97AN8CcM7sjrjDRwDetdfvAvjQZrYmogsY8fxlFOc3AHzDXAsQm134OGC7tV2NY5aKNfv3gc1sLLi2hoE1Xq7OcZEXmMwWmC1OsDh6gMXhMfJ8ilIBy+srrFdLHB4cgFILvtEPZKHR56buOz9onVJIZh66vagubXNoQWYRgNWVkpDIJzM8fPouthXw9PlzgCRWV5f4zrd/BcvVFdavL5FnAkIQtlrg8/MlxKwGscbs6BRZXpgJUtuVWGw2uZ0vDvDg0SNMZjPUSuHV2RlevXoFrgxHUCsFpddYfvgRZJ5jdnCIo4MFDhYzPH320C4l3OL69TmqSgFQhktUCq9evURRAc9nh/jhr30dpw8e4fXLz/GpAF5+/hLXl5f4/MVLCCjU2w3AZus0BnB8+gBf+eoP4/HjRyBW+PA738Grly9xfXVl3IHqGtW2xHq9RrndmgPIJAEZYz6f+0FceRenEmVZmp2tGr3GF6pi2UU3lbb6vh8HlF3Or6WbbInY1AOUZrmrM0DeGVCyWUf240R0AuAvAPjRMd/tiPObAL4JAFJmRh4mtAcwHNfitl5zhg/jeGxkaOsIrRXqbY263GB1fYGL159hcXCI+ewAeZZDrY6x/nU/AmZlXIx8fCEnZpTFzKHS3L6JuM/wWVCmFmfaNeB0RYcwnlZ4WxfmUdudid2kEQbfd8yE7lHOYhXUP4fhvHK9IWYG/Oa0gMjYbC1GhFwQKMvx8NFTXF6tkE/nyIsJQAKL41PksymKPMN8NsF0OoXMp6B8hnK9Ausan338AhfXWyg2FmHWCiCB04cnODp5iKwosC23+PzVS1xcXqJWCuQW/FnggiCUqkJ5eYarqzNMJwUOF3M8ODlBud1CqdIq9SXIfr26vkI+P8L66gKb6yvMnr+DBw8f4/LiHAeHR1hdnSEXCg9Oj6BrBUFGZL5eLpEJic8+/RTf//gjlOUaq2vDVW5WK6i6BphR1QqV0siFQC6MV0dW5MhZoZhOkBcFsqLAw0cP8ZUP3sfJ8ZHZ9UoHbSLM7OQMEuaOvCdIp5lHdIy75FLvQsTuvQZBcXus+AUJfoms+dPWQOMWQmjWfiWZUu5XQ6naLJC4regdFeaciP4agP8GgBMiyixX+R6Aj22wjwG8D+AjIsoAHMMYdYbJLlFrDX4WRhRvngAECHcuMgBvkbZdhsGAqlCrCtfVCquLl5AiQ719hKvrSyitrMuF20c65C4b4GTWneWMrezuyyVS9LsrPmZraW+L1L7jUHcQjNefxk+s3oM5qAp21Y247zczuOm83sAAs3nGdrNEXVUQQuLg8AB1tUFVrVGWJZ49f4LZbGaWCpYlttsKVaWBaolldY6HDx9isTjC9vghrlQFVZnJ4vj0FIcnD5AVOTbbDT5/9QpX19eotQIJERwD3mRWWDDRzNiWJXStMJvOMS0KYFJgWVdm2z7rs1tXFbgusVleotpcY319jWI6x/HRIZ48eoDV+fdxdX6Jzz5dGd2WUsikgNaM7foaH8w+wNe++hWcPjhBVW7xK7/8Lbx++RLLqyts1lssV2vkmnF0cgyRZYYb1cbvtK62qHUNcI2zlwqvPv8MTx8/wcHBgeco2TZeDEVvymVoFw2leZf+uo4Ydt9RdwPbN9ktPGhAUivjDqSN2hx1HewvYJfdOm4yZIz6aIzV+zGAyoLkDMC/DGOg+WsA/lUYy/dPAfiL9pOft/d/077/q7v0k85U332R+qzRfXA4sF1+m3z7StBa4+rqEmdnxudOUt7judgq941F5pD7DO9/VZLjuIURQzfbDX7xF/8pVkvjEzibT6FZY7NZg2B2iNrazZQBIM8LHB4cQjPj7Pw1ZrMpjo+PcHwwxUdS4bOPvwuualycnYMhkRUTXFxc4Hq9AsPs1MPKiOVdC6vRT0shMMlzPH3yFO8+f46Hx4d4/cnH+P73v4/1cglWzRr0crvG+8eHOD1cgOoNlucrlFfnyHSJqQC2YMxmc8znCzBXyDKJsiyx2WzBqsSH3/kWvvXPtlitVthuNs3ySMv3SUlYLi89dyhlBikIs9kcX/v61/Hsned49eo1tpsNri4v8eDhI4h80hTpHoneXxTFonX4nC06ekt2YKSpFUNpRAAZLqfdfT6XozEc5XMAP2v1lALAn2Pm/wcR/WMAP0dE/z6Avwfgz9jwfwbAf0JEvwzgNYCf3KcyfKa5/c6Ls0CSK+vDYmPpJpTVFq/PXqGstsiKAux2HRoQlcM4Rc+mGD6/t6ShATAk7t8VpfRNYSdyr1sd1S4vhNCGPyfGdDKBtvo+EoDSZtu0vMhBLJBlGWazGdbrNc7OzpBlGa6Wl2CtIQSwXF8jLyTyTOD04UNcX7zE+rIGa43ri0s8f+890PExSAistxvUdkmgW//dLoQRS6eTCd55+gzvPHuGg8UCLz79DC++/31cX18bHagVbTMhUK2XeP3iE/y9ywu88+4HYBA+/+z7eP35JyhXV6jKDXRdY3W9BMMsl3QD7/Xrl0ZdAiDLcuR5bhdNTCCExOHBEabTCWYHCzARysqs89aqBpHAarXExx9+hMvLS+jsEqcPHuPRk2coTopWO8UTcYpSYW7bd/rH2Pjnu8L2gqELw+3VOW75bCheMxtusVZsd3zSfimtCad8nDKTfiPwIRpj9f6HAH5L4vmvAPhtiecbAP/arnh3Ukrc7LkHGgNFR5dodZ9aK7x69Tmqagshjqzap3EfSB9z0NWVDHGZLh5pneHHWMEHXvbyvLfp8EOTQfi+NSAB5/reUpYDztptd5cXBJlneOfdZ/jwo+9CkkCtKjgAe//dD3BycoqPPvoI5+fnODs7M6taisynd3F5jul0Ai5yTOczTKZTrC7OIUB49uQJHj14iNnBAuv1GucXFzi7vMDZ+TnqsvbcQjOpCCwWc7z3zrt4/PAhFvM5vv/xx3jxycfYXptNcMmK3kZxo1Fv1/jkw+8CQuDF9z+ClBnKaotyswRxZfqJUpD5FPP5BCSNX+Xp6Slm0ym0Ziyvlzg5PUUxmUATcH19je22AhVT1CLDi9eXuLi6RGZX5uRZhtXyEpvVGlVVIc8yzA5PcXFxgc1mDa0Ofdv09Yo3JX7flf6yT+8YhwmZohgkmds+kI2BhsHOQKO1sXYrjdqK1iEnCcCo76Q5QcGdbuAOH+yje7EyZ4g44hKM0rotfg/rSgBzjo7GxeUFlqsljo5PIWQGB5T93zbvUjNznDYRIcsyf93x4UJaRXlXHXyvweL6TByeE2G8fO10kW41FFn1pjuR0SxBffLkCeaLOV58/hnyXAJgTKdzCGE2LplMJnjy5AmkFFDa7PdIZHaDOj8/x9X1JeTxMdbXV6hrY3xTVY3vfvvb+M73vofj01O88+67ePjwIR48fIBtWeLy4gqvX7/GcrlEXRvQPDo6wvN338HjR4+hlcKvfOtbePHZC6hqg8zqZB34EOxZ3szIBKOY5lD1FtvNEovjI5yeLjApJC4uLpHlMzx4/AxSSLx+/RpXV1dgZJDFHJIZ9cUK3/voU0znc4gix3qzwXZbYrlRePjoEVhOMT2Qxgf09BQnp6eYT4w70mI2w/HRMRZHR5gfHGA2P/DGTLbcVI/TRtDBAonshl1rDLDt8+2+QBneu+twt6rQim2MM20RWymFuq4brhOmj5lz1cns5mSXiLq/IbrnQOkMPOEmFOkKly3up6FmQ19gvVxidX0Fc6yo+5btVpPOiOOSbANgH4cYzn7Nd3YzhJaSuMlZHPcQ3RWIxvH4LVfDwdVCSfIVaQaoGaTW6ApmWKf+xjNBWOvx8dER3nn2FFcX5zg8PMTh4SEmsym29RYvX79EzSVWm0uUao26UlC1MZxNJhOcHJ8gkxmODw5x+fJzXF5eQymGFARVb6CVwNnLF1henmMymeLJ0yd49Ogxnj5+hHeePzNAe3WFsixx+uABHj16iKrc4sMPP8T52TnyQuL48AFIa6yWS2R5hoPFARaLBS6vrwEyQH9weIjLy0u8ePE58qnxg6yqEgpbbDc1xPklHjx6AlHMQFmJVaUxh0Q+KXDy5BlmZYnT01Mcn57g4OAAWT7B7ODI10cxnWBSTJDlGYTdeEXA7S9AIHvELRGBU64rDKuXjZ/TjcHxLmmsOJ4Ws4OjQtxzraFYN1ZszVDKAqEiaEV2az9ljTTKGPqsGUMIA4yZlBDS7B2QZZkBSDL71g7RPQdKR11dWUwhB9firCxYEQPr9RoXFxfQWiOnCBYGrNFh/MncxQphavLceg5ude7xVur9LYjJfLn3MOXtinHpQeZ2BqLgHyt4g0lYXwN76JsgTIocP/T+D+G73/ku1qs1rq+u8eTpU1RcY7NZAawxn01xdPgMeZbjcHGE2WxmVqRIiYPFAkeLQ7z75BEeHh/il/7pP8J2dQmgQF1ryCw326Utl/jk4+/j008+xfzoGKenpzg4OMBmvcb52ZnZXHezQZ5lKMsKxWSCZ8+eoshzbFZrlEojz3OcPn6CoiiwrBWur1c4u1pBiQxXyw02lcKqugbL3HC9xRzzLMPxg4c4fvgIz97/IRSTCeazGQ4Pj3BwsMBicYAsz8ygFBJZJsEQ0PawvKGJVwfPvd8wmn4Vt1lSFGfbwCl/oRvQGJ3oTSmtx7Rb9Vmk1HYXKaUZipsd9Q0gMlgRVA3UdWX1j2avB+JGUsmzDEUAlMLqJuu6xvVyidevXw/m80sClIZ2Kq93mPnLbYmzszM7EzVi/V1wba3Ov2d0uyxvfXrEXfENB2qrNfZx9XDxu42T28Y20zGfP3+OH/7hH0ZVVTg9PcXR8RFqrrCYz5AJAlSNX/pnv4hPP/0EL7//EeqqhlI1aqXw/Nlz/Av/zd+Fr371K3j44Ajr1SW+9Uu/CEmEYlYgy3IcHh5iPp+hKAqcnZ3jernCJC8ghcS0mEAKicODQxwcHmM+n2NbKbx+/RoXl0s8fPgQcjKFyCdYVxUul2scyRzT2SGYckxmCzx9/h7ee9+AYz6f4fDoCIuDA8xnM8zmc8ONZIXXRwt7rpOTTEIg1LZfysQkmQLM2Jh5V7TLYLjrmzHxDr0bI4Y3ukgEW/Vxs48nw/o9No7iWsE4kasamhVI2F3IrA4ykxJFliO3QAnBqKoKl9fXODs7w9nZGa6vrjFEXyqgHCLN2m702tYNSuk2YgNqVePs7AxlWWE6syaKAXAK3/UeOtZDqTC7OuddK+RjHelt4tmVRlg/MstwcHCA3/k7fyeIzNG3l1cX+P/9zf8vDmYzvPvsCT549118Wyt8/tFHKKsSVVV7/e7nzPj7f/dvW3+4Gvlkil/3G38MUkpsN6XZFLcscXxyis12g21do9Iay80WlOc4PDnB1WqFq9Ua81PgdHGEYnaNfLaFLGZYHJ0iL3I8fv4eZrM5Tk6MeDyZznBwcIjJZOK3+pMyA2fCb6RCXulvOT3X36zqguwzpyRisNlWLTDOpVQvnTp2ulODuoDVpL4B/LwRjTKwRsC4y6Id6h5r3XCVWmtou2t+XRu/U6W1X1mjVLA5iwBkcAJmnuUorAuWYKCutri8usT5mTEmrtZrVGUZtGuavjRAuRNEgnZKzdAMWBeO11ivVjg81Gbbq1a8jV5xH7H4rvSMNwHJfdOmUBE7Mr60HrZNzjmfyOjZ2Io8ShkH/6PDIzx9/Ajf//B7qJeX+KX/8h/ikw+/B6oV3nnnPRRFgdVqhfXabIT72aefQOQ5NpsN6rrGbDZDUUywWq3w6uUr5FmGR0+f4cnpKcpaYfviJbYMHE/nKIoCXz04QjGZ4tnz93B6eopf9xt+DJPJBPP53KyAyXOfVyGldTMhX5bYy8EPxBDk3BLbRifRW+ehoJF+31WDtICSdve1N2H1HkNjOMT4OryPQVJpjTq0YmuNWtXW1YetsU4ZET0w1Ahpxew8Q15kyIQ5/lcAqDZbXF1e4uzsDJcXl1itVv7b2XTqJ+k+ujdAOaaRTSWP2/whZbFmzbi6usJqZZ2VOxxX2zUonu1joIg5zOaae4E9BTa36eBD4lRfvBRwN/tQqm68yB2oHdxuOMwciOcCX/ngh/AP/ou/hb//ne8AZQmpFZBPcPDwCY6ODyCzCT5/eYa6rjGZzjBfzLFeb3C1XAEiw+nDxzg6NtugzWYzPH78GKcnJ/iRX/vrkc/nmM5mmEwmyDOz+04mMxA3+5Y6Lg0EsDAnSMIaTSAIgmSzEsyt9iJ7Ho9XE1JwLZLg5e9cvdhrDpaNtlqAUnpky0V6jjId7k3RTThGd83tQINA2ZzEacXsAChrpf0573VVoa5rKG3OfgfYcvymDaQ0Fm2ZS+R5ZjhKBjarFS7PL/D61StcX12h3G7NaaEkUEwKZDIzvpRfBqs3IT1T9upNNAXGPTfj2oGLQP1NjajCzCCtsby8wvLyEqRrCCntEjfXKYXXLyXzmRgUMfA5C7Fh3LrOwXcFkGNoUPeZMAz0UTzpOO4yrI8WYErRcK1aQ9UKJAVOnzzCBz/yI5hNp7h++QpnLz4HZzm2tcZWMfLJDO9/9WsoihyPnz3Hw8dP8aO/SRhjyXyO+XyOTObIsszoBkPjk7DtZzIc/AZgZcsNULPrkX3PwTWCb1v2rbjOgj7XN9Ba8bkJtafew6cpi7YT611976a76V8h0A2plEzfN2dd+W+864QxsDDYLtu0Rhs4p3DlncM1a6xrq5uszfEgurbHBdulzpkASDAyKZBn5i/LcrM2Hozrywtcnp/j+vIKV1eXZuWUXZZc5LmRKmw/KooCi4PFYB3cC6AMlzCG66sda9wO7Da0CIx7jijRiaNZb7vd4uz1a79xaqM4vxvxmZuucWsgvGsg9fElxthYjj4Eyfh7IgKt15CffN9wEraRJBsd0gQ1fsPxKQ5fn2Py1ROc/KYfx3RxgNnRMWbzOaZTww0CMNZ0IeG8llgr4OoKwTQYpR9MZHa2bHH6MQcXWJL9lNvqPukJrQXOAYmUq05f3D1xtMsTBFAaVJaRl8bISS6Z0DDI7uIeU7rvRnxGGygdF8nh2e6wG0OxWVkVGGeMmK3MNnRagy1XCW05VemOixEo8gx5LpFnEoU0ap7l1TXOz8+9iq2uKgBGb1kUVsQWAlluDoNbLBY4PDzEbDYbrJN7AZRAU7HOKBACZqtTcHo5XXgfD+ZQB1LXNV69fo1tucVkNrcHkHXjS3GOo4w45uNBzvSLoJ26yz3Cjg3H77yH7C/8ORz+iT/ePIvS+y3M+HE09XQjiiMdoL2VDDfM0s2UGXuQEMDDR3caZd/4SQFlOKZSRhr3y5aB6egeg+WFrO3OPkpBMXugrJWCqmvUqjbGnNocOkdkwFEIAZkLZFYPWeQSUhhr99Xy2jj/Xxr9Y1lVxuKdm6WkeZ5DCnNkcF4UODw6wsHBAWZOXZPng3V1b4AS6Ip4DjTdO6evIe4CYWh1DcXAlusKmY0yzs7OsN1swYfacC6R28ZYA02ocxmjj3SGDW+JHwDmfSnM9y4x36pRb5xu3zf17/idWP7m39JEHjogMbzVlsFQlusLOfBWGsyGi2Bugxeny+TuCXaVkK2LvuOBk8AWcZqNyB1O1PDc6pCK5ibP+8RpIjLc9eFh8v0XRUm9f6SD1Gxce7pA2TiKa8WolTaLDdgYamorYrsVNQQJrhlwzuGSkGfSg54ghlYVLi8vcXVxiaurKyyXKzvGCJM8h8xzFPYoDSklZrOZd/ifTqeYTCa+f+waB/cGKFNcoKMQ7ExXFi1QCK2UIXUtlwYor6+vUZbm7JLOQEzka9f9PhyZ1sPbt43Nx9DzoTy3RNMR8e9FUgLHJ0DLaGGIA9AkAMK1DYDUqZDOIIYY9JNho3aIDUxBmNR1HM/YMPsC4q4wg195/eYXS7vE8BQgxkDpnMVru7UZa0DZs8/Luoayq2mc+A0AUjMymSHPBYpCYFJIZFJAkEC13eDq6hyXF+e4urwwJ2NqgwOTPMd0OjWYkEmIPPPi9Xw+x3w2R5Hne4/BewOUjtKzqrUBeqDscnNuZmi+b3fm8Pfq8tIcLaCNkcGmDK8Qa+mVYjkvfB/8tsKyVw+EE0DYOIOGljdEFAy2JDRFdb8vEJhyIilSE5zBxJD0VUVAQr/nFk3G7WlfdijkDPp0qLvyPyaMe5fS0w3FMVZ62KneGHy7B1m9vNMjh4+5FQbeJuAfc2NXCMGQLUiGVmzvGK5huMhaGbG7Vh4onQEH7HZeylBkOYrc7CI1nWQAzAGBV1dXuLy8wPXVhT+LKctyTKYTFEWBPMv8rk35bIrZwSEWBwtMJ+a9ECJYO6+D8ThcXfcKKNudxHKOIU/iz6XtdjpmeL2gu7Yykj072XAnWjNWqzUuL16DuEYmCyj7gWkmDZB2kQV5Co+rZb8OtwFXkx7bzDjuMcVxjgHJfeBzzKDuRL7HiNtXh9lnQKAUN9jHkbM5WoJTeXXS8QC3OIbT3ud9Xxv2vd91v2/6jnQ4X9wBamqPjC1TEZzNmr17iZ38qdFBalZwX2qG3cFHQ3OzasYBZVUDVW2OxFDKHHnhdJRubpUE5LlElglMc0KREwSA7fIa15eXBiC3RhpUSkFKiclkgslkapaMWpCcz+fGQDM/QF5MjaguhO832s8Nxh2sPSGn6V4BpSMvHrafdsJ1C9d1DnadT1ulsRACm+0GL1+doao1ZK4BSOvSo3fiR3cAtLMYCkh9gyX8vQl3sa+43XmWEL13xXF7VcFNuCjn5rPfd3cuEt8S5Papu6Gwvm8SdqqMdqRi4vNiTwO6scrLJdxYr9212aSCGXabM7OruNsQt1YKtQNDuzdkVTUAyVr7wZNJaa3XxpKdZRIEjeurK6yur3F9eYXNem3WcpOGyCTm83nDIQqBoihwcHBgAXKO6WQCmRWAPa7DFDOxO3xQH0N0L4ESQIszM5dti7YPlxBpwl9JRrmvnShHgGLGy7NLrLc1JjPRcDV7cFpjB8aggn7HoBxraR+btzC+XbHelQ51TNhUfd0FuLwJtcYXqSq5a9rFNaWs3vE34TZnxmLdnEOjamXB0ugfa6t/rKvavlN+A10iQibNDj6TIvMGG2iF5eUFri8vcX19bQGyNjv/FAVm0xx5nkFmhoMs8hyz2QwHBwdYHByY5acWPNkf93J7urdA6SjlwpDurG3rdtPoBFAOzTVYSrCUQF7garXFcl3i4AjIMoJb6smUXk+7i/vry9suo0IcdhftimdUHLeIf9c3+4a7iYi6Tx7uAthGu4bdEef4JmnIOOOuQzeg+LrRRTIqv9baAKbbMs9ZsCu7mqZWCtAK5PcsNRxgURSYFmYddl2XuLq8xvWVsR+440JImAUH5i9HVki/Jd/h4SEW8zkm0ykK60DuGaukVHpz2Lx3QNlwgwBASY4xFT713FjDC4iswHxeIJtNMF0sDGu+OMH1eounJBGzkvsO/NCwFL4bK2LHae6j77oRWDrVxA27zU1B6U2C2RcZ91jQHGsc+6Kpj2MM+3HIOYbvGuOMRqUZVVVD2VUzda1QVxrltmqdcKiVNpshS7OTT17kyDOzwkqVW1xeX+H62gBkXZeA1pBCIp9MIYsck+kUbHfPn87MKpr5bG7X/+d2Q2gjEZrNS6i1Qm+shDdE9w4oWwVx/nDNAxumDVLtQhqjDQkBkEA+PcLpo6c4efAAk/kclEkwgCLLcLVcm11rpF0NYo0/MQ2JsmGYMYCYotvoAsfkq/MejeXSBDL/uI5123R3AfuQCBirWlJx7KK70hfexXc3IY5+9/p2DwtPDJLGnGl9HnVg0Q6s27UyoFgphVIxqrJGXSnzVxtQLMvKLDvUZkWNzAQmucSsyFAUBWRmjs84P3+J5eUFVqslqqoEwSxJLKbmCJDc+kLKLEM+mWBxeIDFodFNOgONcMcTO6U7h/U3vC6JW3fDdO+Ask3GJNeoYh0lBr+wA91a8JgI2XSKxekTHD1+B7PFwg5Sg4WKGev1BkrVIJYwLiqihZMx8MWgHPtp9pYiASCpb1rvo+/HDtSxbih+P2dq0ro7jc54ui9c1n0jszL67ijuFx2ukhmCFAja9wFl3/kzsDVQ1Qq10tiWClvFqLY1VKWgqhqqrhuAhIYAQ0jCJJdYzDJMcoGq3uLq9StcX1+bFTRlCTB7i/XUAiTZVTXzxQKL+dxsgzeZQOSZ33SFAvHaLJtMlDt+EHW3sfPKvQHK5ABvFcoiXE/BDFNEYAhImYNkjun8CIfHx8jzAlVVt6x8mQSWSzOTAZN0pHF+eu77wo+d3fvif6MQEkRuXJ2cMXy8CHtTzm1Mfd6WK/xB6AvvSt/aRHC7z2Pq00+a5+5EQ+MLqdju3mMNNbUDyEqhqhU29jz2ujYAqaw1m7RZ159lZlfxyaRAkUkQzF6wDiCrqgKz2RW/sD6ORWE2Qp7NZ5gtFlgszG7xxcTqHwEjKfrMIykBDtZBsPJqH/+qewOUIQ1ZPmNruAvHRIYrZAkFgclkjtnBKfJ8grquW3EQAawZy+US280WOG6Who2xuoZGo6H8h/kbE1/r3mV0B/Vxm2PVBfeZ+sT0fcu8S9y/D9QxWlJ7HN+kDEPWa/cs/LNaK3/kQm0NM1WlUNcaZa2wKSuUVY2yrKy4XUGrGtDKrMuWDiBzc+wCFK6vrnB9dYHV8tpbvc2+kWY7PJllyIoC84MFDg4OcXhkXHzyotlBPi6HZ0b2rAcAad/cHXQvgXIMhatdDKrYNdskIfMpJvNjyOkCigFdVW0dIgEAY7vdYr1eA8EqEKJxxhdgGIz6LN2jRWh0GYrbcml3RV8kwA4p4n9QVuY3XX7v/32HycT1mAJOs6oGdqkho6xqVJVCWSqUVY1tVaMsa5RVZfSRVQ1mK66ThpSESZFhNptC1TWW1+fYrJbYrFcorRU7z40Rx/3NDszywoMDc8Cb26CCpGzyG/wK3K7+w7rdZ+K510CZ9JF0q22oWbZIsKtvSGA6mWJ2cIrZ4Qk0FagVQ1L3GAdBQF3XWK1XqGuFvJB3phMK07rvnExMSdeoLzwX/9WhtAP0D4a05SbrWhuArGtsNiW229qDZa00qlpDKwZpw1hIQSjyApNJBoBxdXWB68sLbDYrKGukIRiXnqIo/PZmR8fHODg+wnQ282K3IGucSXCMrh/+IOroXgOlow63BLM7udNZGNE7g5AFposjHJ48RDZZoGIRzMzOF8waczSjLGtcX69RVQrZZLy6Y5cryk11mjE1Waf2/Qi6iYjNgD8bpmNC6zFk7RP/XdF9VxmMJUYw6CNRuwOgAxPumMl4SAxvrNuwx8BqA5JljfW2RLk2/pBVbU891AwJIJewusUMBMZ6s8TV5TnW6yW2m43RVUqJyaRAnheYzeY4PDo0ZxQtDjCdzSDtcb1tcwQjsRNtq3/Gz4NP2+TGflyj7F5yy1LeR/caKFNcmQFIc+odCWnPPyZk2QSzgxMcHD1AVkwN12kVPcxuWaOrYjNrKQ0sVxtUtcYMAAsG6WEQjK/jvMZhhr4b5RYUW94xHizT0Q1/zWgPYCJAcFBzOyaJfd7tC3j7qC/uGkzfFDg3dU1Bw34xPFNcn2a7QbsDkPWRrKoaWtVmAxk2ekgpCUUmsZhkkFJgs97g4urSGke3EKQxn06RFwWKPMfBwQGOT09xeGR28HHbm/WquKjZob4D6uirnS6/ya1by2wkkLRPFRHSvQZKR2GDChIgGF0kyJyXnBdTzI8sSOYTeyhRDc0CQshWRTQNQ9CKsbxeWYu4MfAIam/hNiZfv6roBynf/FeZgq70RWlrQmdys70Zg4jtEkCCEGa/YGQCUhKADEIKc4QCAevrM7x6+Tk+//wllqslZtMp5osFZrMp5vM5jo6OcHpygqOjY0xnZvUMsL++/rblc9TnjXKnQElEEsB/AeBjZv59RPRVAD8H4CGAvwPgDzFzSUQTAH8WwD8H4BWAP8DM3xmZRjLDYcWaPwuUIkc+mWFxdILF4QmyfNpwRAy/ksBVRGvDXC3AxNhutlivNgCftGa4cNPgIR+0uLFDi2zSmn2DzjHmu3CpWV/e7oJuWoYvmr5M1n1gP8PCvuHHTPhCCLNRhSTkuQRzDjAjMy4ikHb1CxHh6uoKn3z6fXz2yffw8ccf4dWrVyjLEsfHx/jKV34Yjx4/wgcffICTkxN/9K8ImI94XN10TNzmG4cJLk93yVH+mwD+CYAje/8fAPiTzPxzRPQfAfijAP60/T1j5h8hop+04f7ArsiHZpnQOELWMVxmOYr5EaaLE0zmB1DIoGptDDvkdGwMzTLZIIIBBcZmXWK93kBr9tuxxcA8pkHjb9wSsNi9YR+6CxHzJiJx/I4o0FXekm6T37e0H42RisL3UgCChUUFI1ZPJgWIFUDAer3GZ59+hu9973v4+KMP8fmLT7BZr3F0fIyvf/0dfPDBB3jnnXfx8NFjLBaLZu21+8N+3GSfS9NdfjMWcEcBJRG9B+C/DeB/CeDfIlPK3w3gD9ogPwvg34MByp+w1wDw5wH8h0REfIMpoOHwGit3JjNk2RSzxTGK+RGQz6BYwmyM7Nx72JywR2bVJ3ObQwQMxykFoSwrrJYrsAYkSRB1gdJdh799792123X9V8PAb+ksg+uh8C6sux8j0Ydxf/lrbT/yZU+Zed9kumH/ZOMNkgkBkQvoDMg1UG6WePHiM3z729/Gxx9/jBcvXkAphedP38F777+P9957D48fPcLx8TFk0bj2uA1mKCrKzj7UY8wxr3a7iLXC9Ijb+9JYjvJ/C+B/BsB5Zj8EcM7MzpP7IwDv2ut3AXxoM1cT0YUN/3KfjBl9pAQ5nSEMNylEhnwyRVYUgDAnrxk+Ulj2XoC1aSQICad3YWbUWjn2CJmS0Ewoa+ByuUKlNHImuE2BU4aW1PbxfQ3Vq6i+JTWri2gUsAyBdUo8TXXE8FQ9N/nAGnnidDyoUs9gYA7sFqYEZjA1+4gCw8sp9/WnvAsxvG+gjeHa+r4DYM4Gcs+C9w4objPAx4jcYV9lbVqDyGyYKwCsVmt893vfw7e+9S28ePECAPAjv+bX4N133sWzp89wcnKC6dQZZ4xPs+60j3E98uUbM9smyxN85q794oxumTlx33wXxb1jVtoJlET0+wC8YOa/Q0S/a1f4sURE3wDwDXud5NQI0g8eZbd0qqsaZa2w2myQTeaQ+RRZMUMu7SadmgASYDKzmgBMJQqAtQCEsZxnQkCQOVf4enWN7bbEfDbpFf33pduA5c6BD/Su3Hlj1lnXEQNfCuN9kJgowp7bFx+i8RJ9s491/za6rtvSTYHM57nn/Q2xpFfHPwT0/jtqeD+y72azKb7y1a/i4aNH2G63yLIM87nZuaezasb+09Xboz1xppBqTNlcXInn7kUHKH0eQv2kKeE+TTeGo/wdAP47RPR7AUxhdJR/CsAJEWWWq3wPwMc2/McA3gfwERFlAI5hjDotYuZvAvgmAMgs467Bhjy/oS1IajYL9lVdQq+vIeQVZFZgMptjMjmAlAVknkPIHCBj8YYw12AyYjULSEiQZB/3crlEWW5A4vDOuZM3KX7fJO6bcFd94eKnzWBzfOJwXHGYzn1PR465X7ckbgzdFagOGR2Hwowm6i//rrz0tfEu/V0q/0IIv2LGqZOS5dpj3NyUmPt6VTod3fMc6B65u4t2AiUz/zsA/h0AsBzlv83M/z0i+r8C+FdhLN8/BeAv2k9+3t7/Tfv+r47RT8bcFzND6SoAyIDjBEMwwGqDWm2hyhXW8gpC5MinU0xnc+T5BEJkQDYByQxS5gAkSBvxUBMBMAv415sVVusltHpgwPUttWgfQDadmfpZpdFpjo/iV4MeOCTPOe1RrNtagXe9yzIDFe6UxJbI/gb9mZLAPHZCssAaTo7OGyb+9i6t3jH9CQA/R0T/PoC/B+DP2Od/BsB/QkS/DOA1gJ8cE1nIJWit/SFFjkJRTXmncIbTiulKoWZCXa2wWV0iLyaYTGYQWYHJZIYsn0DIAlIWIGSolTBGH0HYbtdYrpY2PdnKU5+xZqgMY8p5U+rVNwItFuSNW9qHtfHYFylvwundpi5vq7f8QYNzarAPhbuNO1E4Nu9KNbVvHoBQdO/noOPwKaCNg9+1exCY+a8D+Ov2+lcA/LZEmA2Af22feAF4f0fnVgO0WQp/LhwLaG4U4USAgDnsSFitseYaW1WiLjegTGK9maAopsizGbJ8hqyYQOYFpCAISNSqxvXVZccy7oxIw2PeVXCsVeo1RYwIkwrX9x37H5+TNwwgbBXoHVXTGN3kQIeMuRMOqzY0/+wYyLvKEL+/K45oKK690xgheu/rKrOvm8wY9YIJmLwcSal028850cfb4dMieQooHbfeCn/XQPmmiJlRq8reUWMVTalCgo1FKRxA9iEJ+LM5iCvoWoG1gq4rqKJCVm+R1TnyzB5jqXNsrxnLy2vo2ugww5nT80ap8R8+o4CLCgF+SFs/lnriblPDkZGvvDfH9TAAlRCP74q5UEjEjbQ1PGwvJxqmrPhvmgvcFwz7uOiU6D1W/XFXQL2bY7WGn4bN2yPuMHyPDpVSgz9COG6em3VF9r1re82tyYbZHW8hfB68aP5lAMo2tXcuTnaNISDy1kS7tT2bP6gaVbmB0gpK5dCyhqpy+1fi7PUrbLcbLOxO6DYr/scDcUuXGiFFT776+jfFjOJAOceUuUtvFiz7XID2BaRezjUq81BfDqURv6tUQpf1pty2bkspa21c3DepC+zNy2ClhzfDnPu452HEPaK3UyUE15oBRcHE455rBJ2I4fBQI9iDE29A9P6iqZcZ20uPxQDM+cPQNXRt9tuDZrCuwVyBucLF+WtstyswTv368J3+h0YOHG0pT1tFG5FyLO0rWn7RtEu8flNU1/UgEN5GzL5Ti3ZEsR6xDyj3je8m4e5KlzkU1+7nDecYKQ+gAzB1Ircd4R5oPZ+jzV8T3qShuM1JfsmB0i5FvEGHbDUUGXEdRGCtwVxDwzifayXAnIGgcH19ge1mDQeqZNk9sr5lyVxww1EOAVbKSb3VKe6C60typ3cDSj8okTX2gtg1pbhNF94EvQlgHKQbVnk86He5B/Xla7d+c3iCvglYtp8FLjwBaqqW7jIM0+RHawO0OhCpHUgya3OajwdIQCvV687m6F4BZduQghtPqR1fTGYw1wBJM1kphiays40CWOHi4jXOz17i/Q8+gJTCc4suM7c92jX1G3KUt6O2njZMpy8/vTH1AP4XYdHfRYZ76Dtbb8T3I8vxg6AOcOzoGmMBaCw43pz763LajOF8jOFkHbCFpJn9oXgN0LWZkfA5cyJvDNTBRjlOZbOL7g1QOs6t1Y9DfYQLNzIu9yeEiUcxjDLXsvVOj8WaoWqN5fUFzs9fg1mBRO650V6rUkRDltSWv1kwABpdaDBBhCUO+2IqCzeYTG4qtu+yFI9Z/RHHM0R98RkxKxE+CpN6TyIhGYRpJK12bYNAX5rjqNW6iG8CqRLc4nHSpRslOidFWBsrNeDhQqXKmlx5zbBA1E3PAWVYAtbD+XbPQwBuh7MbC7tYGYGHjAlvSxActds0bxsY0TqCd8wEem+A0oEFtzoOdXrjPjyYqxRHzsADmIbzVcuMarvF689foC7XmExyCBLQMGeD+4QH0gEaQHRuRuFgb/Gk3C5D2EbJDt1T6N7nPsI3wz19UUaFFGlq6nGUBOurgFIw1TpoKvW+EyZsq5tWA7cnjqaHBEESFuWYbejj0hruysTbfGbCaG7CcfDc10CE5BxZoD24crd/McK13oHekNEpD4BgH4FGh5hSHzCbDYWrAPh9OJuXEJyZGazt+vUgPgeuYVom/DBXeX+A0lIX2alzR0FljI43uG4pzS3rrZXC67PXKMsSh5QaQF3qSz3tnG5QLRVXn1K8L/+jno8UMfcRRVP5vGtRdoxhrCVhDIQPuaUhq7l3LRroT26wxkFSRp50v6T+DoNuuVLvAaAxX6Tft6/baYb9HsCgyNkqQ3hCrHYH8SHJ2pvHAVg5RiHIdgjOFiKtPrHhRAkUAZkFyiBNA4Yhh9kkwpqhtdksh1nbshI066YM2ugsv+TGnH5KiXLMzWwfit6OY4y/dey2qySlFF69fIXVconTBw/s1s63y19qVc+boL7y2Scto9gu6+0ucXxM2jcJM4b2Efd3fbtvHPvq8JLh9uBA+3V6RhwZlW4E7GEfSH2dStOAkd/LyEWbLIsrog6/ZQObxFaPxY2RxUXjRGQVLDYhMgDHuikvs/VY8XE3HCUztZ6ZfMNIhgFQGtB037F911OggO4dUMb6go7F2IRqvYu/aXWIntk9Hq9aa1xcXuDs7AzP3n0Xmdjdr41atZvXLwogb0t94Bm/fxNppmhf951dYW/joH0b6utz+6bVAa5I57A7vraBw8c1kEaau0pz0917I52pFjqH8Yacs9UXcrP+WqmQRQ0NLta9D+RBji1nRIA9+kU3ukmEANo23MR/WmsorbCL7h1QpqjNkaRF211iVfuZv/JhiAib9Rpn5+dBQzThkmOuByiT14mZPQyXzud4Y8ttKJ5o4nTitG6S7l0C0lifxlS5donqd0HJeLi/j+6Kw18zANLJMOlvjQ4pDsda++7YeZcEy8RxCZaL63xns9noApvnmmNOlaHtKpkWkDIHhia3kMBNpEaHYvaCYA+UzXsDrErpqCxh/DqI+0tm9QZCfRsj1OeFtjXH3sdidljJrTg7omgiNTugyrLE5y9eoCpLFMU0cm5txxfGYYylVtT36YTcGgeF6A7yuxRHw2dviqN9E2Dt4h3HBcZPwh7SDdvMd+k9CFsp3gArO59w6jnHKbXDcPcpR7/Nq36Osj0GGqAMAaoll7nnQANOYOuL6FLubq/GDECH4zLkGrUHNzd2NACtyes3GwBlD6Ba6XY9tYDS6C3dcbkhM6OVEZ9d7WpuzspynKvrIkmuknWiEdt0b4BSIF5f3e5Y/eYNo/uIx9c+xozGWs04e/0a1XYLWjCE7SAdg1LCTN0KYTXXAYzaf9NL68Zyk/vT3Rt0Ytqt5rhZfP2U1okQ9euU02Dkvgv1dTcvQ8yBdVNPc8EcTdhNvtoiZPtbC54ekLnlfhOm7QHFhQ1ANORUmRlaaJ9mG/jcX8QN+i3Xgn4AMkuy2XCuTl+pWJgVMSFA6eaU7pC7IwAUcJiIvkmK0QhdhdoTRpNW+10c3xDdD6CMMtoS88yD5GexOBgaZ9ouMulKSIlwZ2dnWK1WODnlVt8e48DdBksfqxe9+wbzrrhvw8GFnPfYdMekc1NA6fPBHB3nHYDxcPR3AZRtSpW5BTqJOELRM5VW/FwHQOmuTbejAEQdQFjOOgYbEYqmaH5DwHXpaxOPuXdhneRF9mxw7Q1BWrf3gmS7lSKj3fYuPGvlrdOhBVwHYnPIWfpJJagjD+QBYANoncz65QJKpDmT+Dr1TZ8Rp/ndo+Mz4+z1GS6vrvAOXGfdrUPsiuMJYEtk5U0aexqLf49+Fd3BPdZpvC+um5ZnNDh5sXNYd3tT2hfsxsQRTt6pcNqDSxzPeKBs7gMuzMahvY6SG1eaKP42CAJocZUAQzWWYsCn45YKOu5Waw1tZUHn0O3FZg2zv0IMzoHUq724DIAVQl1iqrxm31oD7C5/mtkuVXYAig4jFj7ra5+Y7g1QAjFYmlkq5BR3+e351TaJgu/yzwRMg69WS5yfn0MphSIrkPJDHeTEhFEFdLjRgUG+i27i63hTg8tN/C9DTiN8PwaI9wa41GqQW9CY9MN89yn+U1zxUB9sOKR0ml4XB4Yg4bkmo3dsQCAWTVviJsNvSsjeDSYNksxsXHislN56R83xy2Henf4z3PWcGahVoxdkK4JTkE+3MbeLQyvlAc4ZaQSUd+vxYEvNYg44UGSndjBjX4hm+TGxU+EBiKU5rwa13GhqW7eA7gdQ7gDAEPlN8OHBPM49J8WVAGVZGYNOVSHLJ3A60GZwxgN1X0AKwXKocd4ct/kmKAbJ1POYxlqvo0D7Z+6WNAR+u8IMfes5oFSUgduMDlxlwnNrHIiE8YVgpjSjDjlFdw20Vqx4oNQOhNEScXUrbptfBpRudJXG0qxMvpVGXddNXmBFcwt4DgDZOYG72aJVETWs0N+YdQXZk1ZhMMOK+RDGGh5OVoarbcA97GqdyW5En7ofQBlR06HaAy4egH3i0C7OswnnrwAAQhCUrvH61UuU2y1mM7s3JcFbHsk2nf+K0MW0vtnJrzkcaBiXqTvEAydqDNFtdZIc3XCq73fijO9HcIuxCGXpBvxz95FvUPt+RxmSYjEAaGeUDOIw9t0gWSfWpsGUdddazfYDL1LCApAHsFCkNfsbOExop2EsxY0u0oJu7dIwVmOlFJQVb5XSnptTSjVib5DvxhBjq5PIABpst7ZiOjGQkQALw8Fq4YArtjFEE66rS+24a1MWFyqeNMB2J/6I6/UuViH3vMND6H4A5Q25hBRYxtxk/wBvODvTnqZhWCu8fvUK6/USJycnMEff6mY2pgBUKFg/TEB40nYHeMiEGxrQjLAcAuH5PWOpU+6Wy1IiS3Ee9miLMGR7IDb1EEbXxeJUWru40Zv3lf58hGk36Oi/aOFbGxzcZ+7IEGbAHfXk+SkLEGaQWjBwRo4A6MKiabuXIsO5zTR5DMVgx5kpVVtgZiitoWqFutZQyuj+tPWd1GZHCM8FOnHZAZ/rxa5vO+D3KjCw4eogIYPu6VmACPT9vW4DPchx1A23HOrzW8sRW4yTU2O1t2EL12qn8hCGdSwxa+eD2cPVB3Q/gHJPcpU6ZC1u3gnsO7jOz89weXmJZ8/egXA447ExSje24+yiYaS8U0pxknfpcN0HlIg66k1orO5wV/i0LnUozXhwAoBo+MxO2SgYfIb8phPu3hkXgnXJ7IwrdhA7Ds7d15WGqmuzJZhm1KqGqo1RRWnlj3B2g7yJ34qqZNY5E4Tvs1JKkBAQJECiEWndXNqI9I5DtZmV7Z16CASGRmuVTE/9xEAZqs8MYHKQbrNUstlQLW6LRuIKuUokry3HGug4XVN5AIWdMHYMvnsNlOPEZ2pdC9F0jLDjD5GvRMstrtdrXF1dQSkNuuHptX06uyFK+WfemgIOaAxI9qkydn039HxITRKGHavbDMM0g3d32OaXOvUb5y+uj/B9M/jREl+NSKpQ15XxCWSz2UpZVQbkahjAc6KrUqgt+AHt834MwDUGivAvEzmEFNZwCJiNH8KdgLylotFFNjoAqxdt8u24Xe9k7plqGw7NwX+tNqL2Cpy47lr3bPMTcI+Os4wnJ2YNkO7EY4tk66i5b1QP4WTndg5CK03NGmZ5pPMZVQ23P0D3Ayh7BmKLHR/83OkYhQfKsRxJC9BsXrbbLV6/fo26rpDlBSDGg90uGsP53GW84S7QLYMYxmNxf9w970JRZwSQ7kpn17s4XGqgtgYnA8zhLteNZdU4PVvOTSkoVUPVGlVdW2CrUdeG+yMtoGoTJnSWdhMus+naQkhIIc2vlBBCIBcZRCZAUwkiARFM8mj5PjbgTtSI8A4QnJ5QBYDVWJTZgrkDksA4E4AjJ4we4bu+etU9AJPiKh0eduKhblg4fS7a/TbZrmjAPcQLM0mhZbTy8VBpRH4HpI1mrZfuB1AmKGShW7MYImmXwk16G6B0VrexFnKTpqn0qqrw+YvPUVUVpvNEoq2MuoiCa3ffKk9/S3TyOAITkrN2T/b2cugemZ6LO/lcD3fwvrhTVvDYQhmCQWhYAAcGCG9s0K175YCucvo7FYjFurWZK2AmXiklCEZczbLMP8tkhkk+gZxJCCmRSQnA9kHKWtbplpino/rw1m0gdAwnDq3bbpmdExWbOtBagwlQrLq+izBa7nDSYjZCrS9rBLwhd+eGYLyno2ksNPrWDjcZi8HUBspAHxsCYqN3dEAJtPLDOgJWG4NtvzAfWjNYkdH1Bv3I6WK1YqiawEqClfAGuD66H0AZVfZOCyyAcJM8IoCJwIJg1BLaGGB8I9ldRqzuxiQVHnsbgKVWYBZ49fIFVqtrHB0dAUJ4z/6WqA9Cs3lpsxSOqNH+E1FjenSZTVZBG0yMONUusQnXrQl/xQ2nEFpvQwOX6zAi5UEQ5ylKsMNRBCKa/54ZgsmDqF9pwU73ZUjVRuRxgzoGt7quYFxOalRlFbzTxlihlNfhqbpuFPNuwwdbBxkJCEEQUiLPcmR5hkzmKEgin8yQ5xnyPIeUmeX6BGSWwTnsCzIH5BKaMpmKJpilLA2IuHpnCGhlBqNbR2whCUZnFoKnOYC55STt4rHg0GzwYHnBCIC13cqsiRMeVMuAWwzzEQKcf08NaDUTFIG11e2Ffc7dh4yCaVkQVCsMQQRpuu+c6K08+DUcMyBYei6RwVb/qkx4mDHuwY8ZShk1A1iYVUAaqCuCrpudjxqwlAALKMWoSo26Kr8kVu8EkZVhkityyIIfOWAkkJBgEl7nIYSw25WYeBrOyjRaHKsX3y2wXVycY7W8tp3TsvOWi/Eikt1jz+JuE5cIVsMEYoHTmrcAyFvLm47olc7RGdWdeoi5SvfMpRGF80yvHzjsMuAHkeM0HDeYcjTWzGYVRaADMq4kxsigAxBzFlelNeoqAjeg4fisK4qf/VlDisYKKoUzREjkWYYsz1EUOabFFFmWIZMFisL8ec5PCBQia0kbACAgLdCZMhvXF2UnJ1d2QNvtt8zgbjZ5aFaEyC44MQxQBo7SMccVuqrUbLYlG7MW2cTTXo8dh28xq83BCXbSagAJPt8hlxhzgyaW3dfwsyaRE7NNpyI/OJqJwq0FB7MBZ5cuucpm1Lr0TIgzLmlNqP2hOcI41CtGrQTqSqAqjbhdVwxVM8qtBmDUHaYbEYQUgGTkRYaDwwNMp1PUdY3VaoUhurdA2U9kVhAIbbhIFhBZjiyfYjKZAqxRlSU0KgCNOAU2DSiEa6gEAAec0eXlJc7Pz1Gr2opVaRGWrE9I+Eypdgfs06/4OFKTga5789j3Gw+wUNxw4ZRSBoC9saFZ/aDsH+uAa3PWVm44v0acbYAyXF2BaIA3ddWoRhyY5XmO2WSCPM89yBVFgSLPMSkK5HlmRF0bXpAMfOzYWnft9lsIJiVTKfZYYvPccLF2agv8FB3HBriNYk2bKq1sEu1Jw4FirduuLR6omFrld644rp3CdzVze0dwNOJ2iETNu7BeG+7PZDkG5VYUEYPAbmZtAlDYVhSEUz4tF7414aJ5bThFYVMDgEZcFg6YSXvOVnNjWNOWodXM0KRM9JqgFUHVQF0D222BumaoSqOuBFTNqGpCWQnUlQazsMfUEpTWlmkhyExiPptDyhzgLcpNhVfrNYA1iASybNhq+6UCysaXi4xbAQFCFigmh3j4+DlOTx9Aqxrnr19hubzCdrsB15XhFkhDECCEGdQpEdiLqAA2mw1ev34NrbTz3gLt2PXcDwBqBqsHEMfpeeU5YGZJ0+EdQLMHLW7ps0Lw09qsfAjvWYX6OGW4JKXsiXPN4FLKrpBwgGZBDgh3mHYcSAR25JyHHVctLTeXocgk8jxHnuUoJhMr0hYoitxyehPkeeGNGTIQcR2n61uEyChWPOhZANR2EwULiprh69Mvp+Ogfe1A64qqZiA5DtwBPTPCXLS5aVMpgShtBrZbcgfbdszcOJyHQGk2GvN5hAVc1QJK2LK2OcyWTyHsteWGXR8ho4yMJIZGNdQAqyuf9n3dUQi+zeTNYNRewvNismufRr724RsR3pXT5Zfg1QcEA5gqs76ebOpTMeqaUZWZBUSgKhmqZJS1xrYCVA1jqFFWhNZmHY/RyDrgJbBggLURq5XGplohzzMUuXGVaoxzW+yiUUBJRN8BcAVAAaiZ+Z8nogcA/lMAXwHwHQC/n5nPyNTwnwLwewGsAPxhZv67OxJIcFVpsdsOIeP4LSVEnuPw+BEePn0fR6enEMSYHT/AxfkZzs9eY319iarcALoCcQ2CEekUd6JuUVVXePnyJaq6QoFZEljDjuXulVJQYFRVjaquoJRCVVWoygpsubnGwtos9XIgZkCuWfkQGiLC33jwc61aA8UPGKJm95SA+yFmA/z2PssySCGQywx5kaPICxSTArPZDJNiYgBvMsFkMmlEXFm03Fb8RCNEA0ItY0wzaMP8wOXVWnbBRr/HqjHaNJxYE1uzzA5g1a4PF2Xbut2oDpSiFtBoN6Dc4EcDUBxwXiG4qsAA5C3eNi9hnzDcoEJoiHGpuJMFXdxw5Qn6lVFFoEEnd0M+lmiHoKa2RQh4dhL34jEaFRecaNwByqbslt1rWtPuNqThxHpXTmUBWvgDvipNUIqgldPfmh2F6ppQVYy6YlRlbcVmYLvJoZ0xRguwAhQbDahTeZFVoXD75DJbT9rbLhy3rbXGdltiuzWqOTdBM0uvlumjfTjKf5GZXwb3Pw3grzDzzxDRT9v7PwHg9wD4uv377QD+tP0dRe2VNW2Dg7mwnKUEIAnzxSEePXkHs6NTcD4FpMBUFhDTBWZHp1idv8L15Rk2y0voag2oredOUsQWQJiBzz9/ifV6jcXhUSN5hA2SAMq6rrHelri6vsZyucR6vcZ2u8Vms4GqqhZQOmB136qQ+7NuH+T0h37ABfl0elwAEmYdrCCCzDJk0uhmHKgVReFBbjqdYjaZYFIUkFmGyWSCLMswKSbILcfXBj/nKhJzJY2S33DMcKo/b4l2eTVlalV0ox6wYB62P2u01AKeywrUEW3xtkkn1Pkq5biepgyaYZf3tUVVZwzSweFTnpuNy+IAwpXFAaA2xiyXARPWcJTOHzEspwKj3RMbMA+7mouPRANarpRMDBJBLC4NMMiJvcEkRckDf22lt7hb86uDSQg2ZsBZm5u6atQ3bABSZdC1hKqAdWm4wbpiqJKgKjLgqN3qIQL0BGDDFZa2X4DafV278U/W0Z9MfUvOAtWBaTetAwwJZ1jBNi7HOAgbtp9uI3r/BIDfZa9/FsBfhwHKnwDwZ9nU6i8Q0QkRPWfmT8ZEyhzOZo0hhUiAIE1HIQZQIMsO8eDR+zg4fYxsMgORddGQAsU0g8xnmM8PcfLgCVbX51henmF5dYZquwbqbSASNTO8gLQAJXBxeYmrq2s8fPQEJDKfwZZeCs01YPQr5XaL8/NzXJyfY7VcYr1aoSrLgHNpLIoAQ5Db+t48lVJACuNykmUZsizzOrxpUWA2nWI2n6PIc/t8gkkx9fdZZvR6woKld6FylUxkrbkJtpptjQf5YcB0Lg8WwVpZYqsTsp1PO97EgaP2AwqhJZNdZ2YwN6swdKCaMOqDtq6vAWb4SQcwA60LaIAKdvj2RhQyZXD6Wk6k7cppdIVtUdqTqoNqazi52vtrcjOJQMOvMQ503a0WoGYKEqhBCf/dlG9CwwnaicsH0WCuwO0ZCsTGkg9YfSC0GWnKia3s420mKVhLsgFBzTD6VeVWERlOsFLAVhFUxairGrpisDW2KC0MB2hVAgwj2dmqMuPXFkN7Z3rbx4K6MbXg6pthdKLCRsJBQMd5AoLcHVC7vZ+JIGH03qx6Jg9LY4GSAfx/yExP/wdm/iaApwH4fQrgqb1+F8CHwbcf2Wc7gLLdIRq+xR2xYLkbyzGJYo6TB8/w4OEzFLMFKFDyO4u4lBIiKzCdLbA4OsbDx8+wur7AxdlLXF+eYbVcQqkKmpUBKjtoGMZJ+Opqhdevz/H+DzEyQXGGO6KeW22h6sponusaGYCplJjPF8gyiSzPkecG/CZWlJ3OckynU8/ZFUVhDBkWJGXA5UkLcvGKJASdPx7QrcHdquH+Zmh/H4iMVtwLxVLWjlu0W/VHdePXEXP7OTsuL+BWY3WD4bitaKiD9EzGrM6QWhxl+FeHp/M5bklr6GBBtt/NJqqnph5t+g7wLYlgmZ2vzkBX5/IIW29ezScoMPA2+XI/Jpzd2ZERcJcMFS2383lMtHHDINo0GIazZWXFfYImA3rG71AZdYcvJ0EpjbrKUNcwYGhBsa6BSkkjHisBVgJKAbUGahamP2oJ1tKDYroXkskLwdgd4JYnN2HCQ54pAEGvNhBsjbewajxXN+1eTi4dbmIUgkBQINkzHiyNBcp/gZk/JqInAP4yEf3TVmGZmahvy5w0EdE3AHzD3iTDGLcfO1sARilCEiwmODx+hCfP3sXRyQOIbAL2hhjTI4WtFMMJMqQsILMpTmaHmB89wnZ1jaurS6zWS2y3G1RVCbCCsGy+FAJFkePs4hpVrZHlifxx233GDeyMCF95710svv51TCcTZFIilxKwPn0ULLMEAdoqvzvLMR13bcVvJ4ZH9WjDND597rnLY7Jue56nluq1rO8WqIw4phvgYljAYr9iwwFLIyJ3V4+wBnTdiNGa2S4BbMCynde272LjS5fYtdoMO68PjdvNgx/gRciwnA1yBVwbNc/N6X3t+gZMu4HZa/4cG+xrUQUO372rQtqA7z8l7bUPnoNmtu6FrszOcCSglV2pFqgyFFk5igU0SygFqDpwxK7Yrmoh1LXEdpMZna4OjS4EzYUBQeu/aOxcFhR9lq2OkLTt5xGFkk4PJW0VLSQ16genF214R8c0WXUDGc5eOB9ZoSGEApFqbfCRolFAycwf298XRPQXAPw2AJ85kZqIngN4YYN/DOD94PP37LM4zm8C+CYACNmFczZsHYyzKll9RQaRTbE4fIjHT9/H6YOnyLIZmGQwExHcGlkzCMhsPAoCSQmAkc8K5JMDHBw/hWYFrWvUyogoAgwBQBIbj0vOUJY1ptPueuX42u3kkmcS7z5/hkxmIHa7rYSAEwwcgrd4B8JGI0Gw9UVzVlw0YObFfa3trEut9zGFK2ViQA3LEevjlDZ/zmG9vYO0A00zsLTSFihDXawpc10HukY0m9MaoGS/FFBp0yZaG0u0siIu+cnAta3Lo9X/2ZchB+aecat8pl696OYKrx3HYZ97Rk916iQEMgAtY0Bo1Q43ZPAbS4fbhLk6047rJN8fPPAHm+7WrosEXC4YxszKrpsYwHCGIueMryoNpRhKC9RKQGlCVRruUCmgLjPomoyhRRFYGWNMbfWGzuPA9zubC20nSdd9CWSEeTI6UjtVIDz2wZUz7q9eLz7Ad3W+Y9GcG+4ywWylMPZAKSQjl4R8AhTTDPNFjsUix+HRFAcHU/ytv9Wb5G6gJKIFAMHMV/b6vwXgfwHg5wH8FICfsb9/0X7y8wD+GBH9HIwR52KMfrIzaxAZ7pEECAQpc2Qyw3RxhEdP38fDJ+9iOj8CRA7NwuygTO14yLLzCGDISBQSJDLITCC3ExBZkRIaEMSQ0CgygCnDtqx8Z4+5Pg8mwVZWVV1huV5hMZ+bLsvGyNLssGGBzmaPlPZMtf8FQwY7trtR60oDNA7wPkxEMTcSOjrHXGPfN04/pZXhtJ3PYegbaFyOHAditvvSQRgHTrETtrfsV+Fu1mwBUgUicaPTbe1YwxYK2XAKsYht2d9gcgjK6EZ5wIXHYMgcGMxsPQvrKWAmEDdJAcoaSBhozmdhti5XGmY9d3iaYSMBKQWjtyaz/odIeK7M/LnNbe2ETwJECHYFZ9TKOFsbzk4afaI2eXT6w7oi1BWga2mAUrE9LtaK3yo3FmZtdYiu21kMIhi1gZtg3GxObDk6Mh24bU5zfVZ4MBUtNZaFUGoDZ8hmhv3RG3KC8SgASAaItNkVSWgICUxmAtO5xOJghtksw+HhFEdHc5yczHF4OMfh4RTTmcR8nmNSZPhf/6/QS2M4yqcA/oItRAbg/8zM/y8i+tsA/hwR/VEA3wXw+234vwTjGvTLMO5Bf2REGlZIoqCS2qtpiATyyQyPnr6Dx8/fw8HhA5DMjJsPwzuWhrOTuXc6LgOSzoQhgu2nhLAuBuzcDwxbzmDUClguN3j4IAbzNHAKIaABLLcrTGYTCACayCzr08EAsVymYFix4P/f3tvF2pZl912/Medaa3+ffc69t7qqutpxu2krkAdIrBhiEUXIEYhEKE95cIREHoKQIA9EPCBbSEg8wgMCJERAfIgHCIHwFVlCISSWkBBycBI7tuM4btptl+12VVfV/Tgfe+/1MScPY4655tpnn3ur21V1TqMzqs49++y99lpzzTXnf47xHx9T79lO5xIo3ZKjAVRyaMcGyjF/lTWxAkxOAaWCRxn6IurJjAZkIceHgpptMWigfd/3amaZ97j4Ti71X3qyY8hm9uQzNLVt6PviXih+Rq3tGNjHv5NGE8flxfjcGMx0Ljgwo0KGMAnGJxH+qhH2uS0hWAgRyTmmWBCKMaHcqZqAzpVgaQ4xBazxFnRMDdE86WbupnuInhjTxltRc937EGmHoJkpvcue5r53hFARk4ZoQErwxCgpyiY5RgQN/k6IaKE/kWjRiamLRr7TRXPOhHxAiHr/klI/FU+Thuhimquq/OhzGYAxusJKxMVoLCrZVEZ0Xpi26X3EVxXzWtgsHav1jMW6Zn02Z3ux4uy8YnVWs9ksmM8rlsuaunbMqorae0TM/I4QR8fcKXkjUMYYvwn8Eyfe/xj44yfej8Cff9N5b32v0JqV7BacgHcB7zy+XrB9+h5P3vkqzfqcwdeA0A8aeG0qe1lmDdAV00DNpaweQFxUvlBMnYzEMOBRLUJx09EHeHl5wwCaUpcmqJNxcNt1y/qAu5sD3WrAi1A5z0DEMXptAXDqDpjS3MnELQjJTFoDZT5uaa6YOVM8B31PkSS/Z5qeaUUhBpy4iXZSanxDClofktbcd10GuX5IZH/SprsU/hQlObUmMZ9J84gxA9EIGAl8Y8gcs4EgScPK9yQWRjU1d4m+MK/LfVYMYMcICnDJvBwTD6zPy/x1dUwJTjzeVTDkrtTvxLSYCVjhDJ36MR8QghDCgMNRRZ9i0ZPGZpp3HPvDAGGIQfnCgWQOJ69yUM0x9ELfCqFXJ0obKjWXg8DgiUG5/Whxx/kfDQ4KYzcX/VKKZA03HHHU9o1xgRkjKJyQQpjGewEIkjTQqBqgWCZVcOO8zJYdhFjrs3cD3g/UtbBc1CwXMF/WrDdzzs6XbLZLnl6s2J7VrNYz5ktPs3A0c0/tHZXNeymsNdFeMFIg5nu4Wx5MZk7WzIpfXhxeHOJrNk+f8dZ7P8Bqe0FVN+MKW34Xsko+bjQmY8CuaXPONE8dKKMfSkOLJA0v03D3hwNd11HXXuPRzAwRJqA8Id0T7+TQGoQhxuycMa4PCk7mdX0yuTcdcLdNbV2ls/ZmE88yN0QY+h7bHL7ve01VLD3MIaTMiC45puz9gT6E/P1RU5XspY6JY1MtJGRu0BahSUsnRLx58aH21RhbmY+b8qqdFcywz9PM0gVRn5nypta/VQIEN84UJAU9Sy5ka2OjdFxF9L6CzV4z1e3owdH3CQAIjLhdrPpBwWuQnkEOqMdVzVt9Vrr3zDCkrRg0u5Subeg7SZ5mbeswQJcoDjWPPXnLCKcapkSPxiIqeOXwwFLrlnFsnR555fiTOw4Kky0cRgAtEamYo7GCwSelILl8RHBeuUORiPMR54Vm5qmXPavNnM1mw3a7ZHu+4vxswXbbsF4vmC8aZouapvHUtceLpidHGXSOuJAAmSPOvkD6OxeJ2/JwgLJcTtDJ4/GIa5hvnnLxpfdYXTxD6jqbMRYCBKNWN3JLyQw+ymJRk9fqCcWsmYio+Z2MggRIgDj2h1a1pTgDZyWjxlVqdCJpp3vnubm+4fLlJfOmISZHhS/5rRRo7k+420YOcMolZgBkuMU3WnhM9mxaaE3hObYsoMH2TI7GB5rWFbGSWqklJDzUvNmjBamyOoqifRFC1IXG+2wV6P/aUV3UNinPN2pt/TBqgAbcljlRXk81MOgHBWLvfNZSQnomRmEE49jCWAA3Nz8mB8qgmkxMpmIQc1jo/atGNygA+lKL1zEQYs8Q+0QbWd5y0pKiLdLax0OMymPGlGWSwm36XpKZDEPvtDTYAIfWE1JojRbrSHRBdPn6aSCme+pRXc1iCnU8W6XwbDlhGDEqGSVVdVpGy6a0oiiPz8qC4KJPls/oaRYi+AHvBpwPNLXQNI7lwqszZbNge7FkfbZQbfGiZrmomS/092xWUddCXcc854gp0N7MdBm97YJPCtCxUjFaapM7vKV4TOXBAKVpRJlrRMDVuGbN9ul7nD39MtVsiYjPoDFyjG7y+pj89d5PyuwrOArO+WJwSDaBY/pP54zQ9wO73Y7NZp0B1vSKcotckxhVE7i+uqGve7q2pe87iANt26b80v4Wx1mGw0wdHiVopiyPUrPL1y3DlcQoN2IwD/k42Y/DgLzVUxQFubLuYhToE8BVKT87Bl1UbC9mNb17nBP6CNMc0Yg4oQ+qnVr4jM12S0nWrwWGRIGMky8mTS45J1JITYi2K+EIpPbMlUpQwDyl+YcoDAEwUE4ZL2O2Bik+1JIEpv0cYyQ4dTgNuaQNCmb9mLqnjpRA1wtdV2ezeRhEucPgtSZi0HhEota/HFwoprMCeUzjUXUJM22VcnE02g+2vFhqn92zLVj2PI40qeOxOHl2iccUESSSqoQ73FCNZnamhAKVj0pt+YiroW4qFkthufScbTecX6w4v1iyXs24OF+xXs9ZrGqaucfXEe8dtav1IUer0xBS4Yqkz8fEgwZBi2yEZOmnwaRNP7kgmOVTPs83yQMBSru55NFD9Wg3W3D+9pd5672vslw/ofba3GMNzuSU97JMxSvNXe+rfA47PkTlw0RIBTQAdL+S6+tr4Bml00k11hF4DJC1knXFbneg3bdcXV6y3+/o2z27/e4WyN11H6dMejO9j79j2wJgWrPT6SMiVI2nqmrqqsJXCn4G8OKErkslBZxmzqi2N67SILi+m+y54rxDovLE2g+Rtm2pqopgnGfUaas1JHvodoS2zZRHGG47cqw021BQEqMDSjVEy7H2ZvUyNZlF1JwlRIRBn1fiBMs+Dmm7gSEMiTMrQDeOjqix3Nfo8Veu16UYRIiD8p59D13iDTXERpKZ7RlCpTRBsljVsVMRo8/LdHryaGt0/Ck0pJBtSzFJn43B3JI80omLNedJ4WGeAC9TuRsoR2pKDT4Nd/NAFQd8Ba4ecPVAPRNWy4azsyXrzaLgEVesV57NZsZqOWM+9zQzwTmovFcsl5BowhRWFFEwjFoBXmJEQ55KyErtDf5k28eIiPGZlz1xjBevkwcClCDOJ97P4SuPr2dsnn2Jd3/fD7G9eIa4OmtzZcl8/XIyCSYcUjFxnKMqnC0uZboYSGZnSDLBXCJ/Q3I09H3k8vKGoQ80Ppl74nIhBuckm50aUK7ZQ/vdjm4Y2O93vHj5gr7dTR9KavOx5zzfwWhf4ZzgfaWDq3JUdY2IUFc188Uc712utF1VVQ6tGboOBbMSdHTQO6fHzueB2WzOrJkpoDYaiuUrBVhxwuHQUtU60dVUhvYAh0NH27Uc9gf6eAWpLmjfdanCUeI4u5ah62gzUMaUCaV0gmltVt6trB+ZuyumghaFR91ZRtIkji4mkzdC7BKXaR1u2qelVo5jRj9Lym50uYZmCEXaXVDg7/tIt68Ig9NqNr16k0P0dCGx3MFD0NRbTcNTT75NYFVSzRkkKQc5afixIheAKVIQneT8uwzgUYQheal1wTRQLcHxGCbHuWO/cl+W1X8EnFeN0XuHrzQRY7moOd94zs9XrM9nbM4bLp6u2WxmrJeJP5w5mplHfKTCUYua5CIBjW00b35aFgLkxAkDTlMafLKMSFx01qhDDmca7zNO+nnEBXvQo+X1uvC6Uh4MUA5Bi1E454niWW+f8ezdr3N2/iXqqlJSOgK4VKB3GnSty52+ryBoZ04hFmSmJKcAngrtKfwDIFpKLCLsdwP9YUBqDz7lEAu4SgOuISAuID4itUNqx7DrCaHj0N3QDzti7PNUNvO2qjze2+8q/fYZvGczBUGxsJJgBW2Fvu/0HLXHp1RH733OC6+bBmLUCkCzeT631XY0MU1QnJt4ODWkZzT/AxGcIw4VhzbQdh2HrqNt9bfGm7YMotEIfd/RDwqYceihb2nbMff45AqvQZsKmCe0hMAYjxmi5Mntgnm1lbaJ0dG3A96B/gMk83wgEBhShSIt1aVl19QcHzroOxi6iqGrOISQKmh74lARewXHGCtyjnEmbtJ8T7wtLmK57FkzS5ytUSQ2yc3zC0Jwo9loM904yNwr2crWlc+lxUCTHEhadOpfa0/6oi2WyAgqNYJ3ToGtjsyWFYtFw9l5xeZswfnFmnXiE9dnNWdngeViTjOvqSqHq4QqaZvHj07vfSjf0Z64FVNpx49soi7uLkUHDNkKmFTTyqmzGtZ1aLUQTXto6XpbtKclCtu2fa2voJQHAZQiUHkBVxFcw/biS7z7+77OxVtv0zTzxCVqpoE+YJnwmQBE07imG4zlPO44am7KuTApTXW8KVk+D2oC9b2WTovMMOpHC6orgLlUlNaJw0vMhUAPhx1V7fnSW29RewXHtm0BEiBKBo7SWw/kij91XTObzVgsFjR1zTzliCuQzqibBpeKX7gjk7XrultUxbGTaAwvum3uF8wfIQyIGz3dfd/Tti2Hw4HD4ZC51z6q82oIPV2v/GwYeiTtQGjhPXf5DmzwS3L6lM/ZJoOZmDFGBRirNh8dQby2Marn3kULolaKoO89/dDQtQN9F1JYjXKLQ28ecbD8+QApl133w9GF0aX8YNV+I5HKe0LQsmHj2JbxRo/pEjnWhMexiZseO2LqoDykaZXp2eRLpPHqBDVJhzprcZFBg7HdgJOAr4S6cTTzmsWyZrt1bM/nnJ2tODtfcna+4uxsxXLpmS9qqtpT1Q7vJRXAjhlsJdFUujYca2c276Z/T19l5VFfJxqm6zr2+z273Y6bmxteXr7i1atXvHz5ksPhQNu2tK06Ww0AjWYzf0DZt3fRC4fD62tSPgygREOBom/YPHmHH/jaP8azt7/MbL7A+QpxHsQr+R5JPKKMJksSWxVKjs9LlQGwBAeB7BUH8K5K5vYw7cykqR76wG7fEXG6WpeqPSPIOe+oKjVLu3aPdzCbz6i8wzvBe8dyuWC1WrJYLJnNmlz6zIpiWLtms9mk5Jn1laR7MCqhqqo0b6b3WYYi1XU91cCP+kmByU9H7njgeEzSuhQkD+wPOw77PYf2QNcraOrnupJ33UHTQ0PIG10p4FraGZPnM1msxEgIXSFjMptImmeIScMFXOgJoSaEWusWhkjbahYKUfnDodPfXSu0vRBDpRxz2hOGpCGOpL9Ouso5fK3afjNrNJY0Dvi6YjafAzFVaXIMfeDVq5b20I4xlqYpHi1CI310m3qZFl8TNVVF0OK0MIJPspISy+mSWescOD/gmoBUkWYmNAvHcjXnbNNwljTEs/MFZxcrVusZZ2thvnA0TYWrzCEjVDkGsmeccU698tbmxPfaYnvKlL3rvfL4tm3Z7/e8fPmCTz75hI8++ohPPvmE58+fc31zzf5wyDzx2F/6u+97uhTnWyoHZWSMOSOHYcgLvC36r5MHAZQ6EGrWZ0947ytf4/zpO9Sz9Tg5QjIvogWV+wkIjKl0wy2tDMYHZB7SFASStBqXwltS/GTed2ck750IXoSX1zveQagcKeA8DdIEbPZQYiWslnO+/o/8EKv5jPViwXq5UBPZe7q+p66qpCmNRNKxJnhcEFd7aiol+B8PxLLo7SlHV9kvZZEI+275Y9/pu45hcHRdx26/Y7+/Yb/f0/c9h8OB3W6XtLYuxTuGZHqqZmrB1RLGUC0DpzEHPLXleHOu5NApnT6IaDxoD6GfM3Qrus7RDlrQoW/rHHcosSLvtYI57iyeNibngvLMCMzmtSY7ONHydd7ja0ffV+n+ew6HdoxKCAERDfweIzD0oU3CcyZazRQo02xAJm/bwiFISMWWXUR84vok4p3Q1I5Z45nPa9brBecXCw2zWc04O9fQm83ZksXcMZtpULavIuLVQ11Tay1N28cmc5XHvKj+eHMaWanCFKp1zP2VY9DGV9d12oe7HdfX17x8+ZJXr0Zt8erqklevXnF1dZWP7YeeoXDI2jy/i2+0KI2m0cr6NkaPnbu3n8lteSBA6Vhv3+Kd977Kl95+l6qZ6RYMTvQZuKRFFgA5NSc1uPY4VEimo21cyZ3DFZgiiZRMFOioHWYAEaIT9ocuk8/Gn1gHl9ps6APrszVf+fK7NN7j0VivEC0WUMGiEq1sRAaMsT3e+7sfZKGRlB7hUkrQLMHOPjseuEEjnyecURlyVVUe1wl4dU5U1aAOpGQWayV31QbiMKRiI4POrajEfBS99yH0CGmjp6gae9ke3aenw6W00pC00GEYaHutHK8mV5ecRgOx99TuKbWbEYY1Q5gRcISQ4g4zSDmcC9SaO8p8PgOgrmu69kDT1KxXK3a7A03TEGPgsG+53u9yPw1DgKi1FMPQqSc4peWF2GI5/fmadv2Tk3Eco5Nn7MYqQZlikMCsCdRNymNeOdbbOdvtkouzJRcXZ2y3S9brGYtlzXJRsVioA8b5VFLMTGQzlxOvCRExx0ra80Z/Y2r9iaarQ0YXMfOKTRc+4xFvbm64ubnhxYsXPH/+nOfPn2dwvLy85Orqiv1+nxWEqvIZzAzwqrpKkSmSLan5fJ6BMG8zkuaO0UGl9mlapXOO+Xye+fzZbMb//X/+zImbVHkQQCm+4t0f/Dpfeuc95otV2kg20g0BF1NGS0zmh0tFZzUMP5nQPnv7jAI2M01XHbPhxtCesircCKgJlZOWI5J4RNF0qP2h49C1zGczXHDkLBlSaFH6tnfQd63GGDpH2ukFGFdYzRyRDHplvFfWQNI5mQDhaIZGYtai44lJOAWfYWKCHJtHtiDY9fO+NCIMEpVyqGrCEBK9oP3VdS2Hw57dXifCbn+jhF6agHqNaCXFdaEIqZBET154DAiHQc/ZHvb0fZvSJvtMyNu+PqZZRHPihJroO3zlISyIcUZ0VsFmvOenTy8YhgOzxieqJVD5Sk0w8YROeP7xJTFGXr240T4YtD8sKiImy0YDqDxEPcYWjSABW/rUkg6AJ0SXnm2K00z8qscRzRnotECKq6CuPPXMsVg1rNYLttslbz2t2Z4vOTtfsD6bsVjVzOcVi1lFVQm+kpy/bDkTZmBlMMuB6cbT27YIaQ4dD6VSszVvc9IwbWRqvOlA1/W0h47LyyueP3/OixcvuLq65JNPnvPixXM++eQTrm9uaA8HdUCmyAsRYblcFlqgRl8452nqWp9raoxGfOi8t43omqahqjXtse97DvsDXUqwALJmuVwuWa9W1ClqxBfc/uvkQQBl3cy4ePsr1IslVjBM4/jSKiwAWlGGKIhT8NK12pMSXoiFJmiSs3WCAZEt7OMqno83zYaYApUTcETNmGi7Azf7HdvVDBc9Ilbi3wDSM3jNsB36nv1hT1PXpIJtEIeEIbZim1FVeIENu9K2BEMKTM8yUUxUKwhxwLlqck/5iLTy2vt3hUOY9uxsAUkT2UlK4RPBuQoXBnzlqRsYBjWJrq+vuby6VA9j16U0Mm3NYNpsCMgQJzngzjnlhF1kv99zdXWVifkQ+ly04pbYXBZyCMngerp4Q80BiYEgPfgeP4yV6b3z7G92VLVwvbuZ1GAMQ9Kg4lBoWakrxE0cSpIXgNQvYot08ryiZf+coAWfo0YMSNTA9piq3CADc/HM64rZ3LHY1JydzznbLnlytma7XbHZzlifN8xXjsXCs5hF6qoCMfPTxqg1R0OTjFqa3ElhYWWgLodWjKeYgMlYMoqYpEV2XcfhcOD58+d89NFHvHz5ku985zs8f/6cy0s1nw+HA5GIr1TbW60XPHvriW5NUs2zRjebzVgulzSzRkPRGPP5s4Y6xKw1zudzVgXo2Vy3faqMCgJYLBYsl0vVIOs6n9dA+vsCKKu6ZrZcaAyePSUnVK6aTG6TECMxBXY7Z4NDB+sxUMaQzC0/BYjRFFPgmnB8BQ+jPz7xhpGbmwPDRaS6oyKycw4XHcE5rq+vWc4X5NbEtAFYtEyWTgO3y8Fq7fKmdR4VfygmcWmiW5peeY7y71KjPJZjQv1YVLMWKu8g6iSvKodzsNtdc/nqOTc3N4XW1+Y4xzZtrNb3HfQDbdsBkadPn2bPZN3oc7a9hUIYNcHymd11fwg4CQzDDYEdXnokNhCrlLs/Hr/f74m7MIlYGF0h1iEwWhZCSPGP2ZBO4Wl5HbcCKZJomeCRlFEibsC5SON65vOK2dyzWM1Zbxecn6+5OF/w9EKLO6w2MxarirrxLOqGunYgA75GU/Uk5nCokpM7XvykAMPvSj7F8TZ29/s9r14pKH7729/mgw8+4OOPP9ZxkDzQFnpTVRXNvOHsbMNqtWI+n+ef9eqM1XLFdrtluVyyWq3UiVlsY1LSQ+bsOaT6CxZ1YX1Qmtll1Ehd19k8b5Jj0wA3xvj9ER7kvG1d6pK2JYmPtBzdkXuMZnpSmsxq9sQ0gEo+zommhJnJHTDvnBtL8mOrpaSK6mWKl13DATW7XUcwFZJyUI58oYvqIGpT6mLt1aOeizgwDmp1Hp0CqKl5PIJDvHWOEAI+xpMzwwaEDdrye/Y60wFJ+zsm32OEGDTUNww9EiRVWQq07Q2vLl9weXmZKsV3HA77HAbUD33aLyem7Aq9l5cvdQP67fYMZHTEjX1ZZgYVvXKkLdsT8h5i7BjiNY3vdbHSgya0hojgpMKYZo25LJ60K5+5amohjbPcrqgB/JUURUrcgDhwvqeeReaLivVqwdl2zsWTDRfnC966WLLZLlhtZsxXFbN5zaz2+MQkiQdxqo17icRoJeZCHp/jeB+988fP87ifPo2cWiRLALbP9/s9H374Ie+//z6//du/xQcffMCrV690AYpjmJ1piBrDO2O92bDZbDg/P+fiyRPOzs6YNQ3z2Tzv82TjTscitF1L13ZpXGlOfdse8oJqoW/lnlI5OiQpEfaegWEIITtT7bpd172xfx4EUAJIchKoBqgP6NiLnUMn4tEqmv6emEdi0yClQNnkDwAWUuEyF+iP4g9zu0QyeCMVu12vgesuFbCKtwek5g47wqCcTVPV6Tpjgd7j69ySArymx902me04KcyU8hpmjtjxJsfntmDcsr9F1OPvXMS7imFoceJ49fKKX/7FX+DXf/1bvHz5kv1+lzmhUGjOeqLiB9XwF6sZy+WM2ayh70fNIz8/Z3m9UyktjJJr1SDrQIjX4HZ42WB50+V3c7+ZOpjGgy26piGG9KxEUA4x9hpyUwVwAe9hOatYLjVPebWZsz3fsD1fsn1Wc7bVHOblomI+r2hqaGqUO3QR5wEJSHBa8Sc/2+QUDGlxToU2stfZlUWePz0YvlErP3FsuWAOw8Dl5SW/8zu/wze/+U3ef/99vvOdD3OsonOO9XrNs2fPePvtt3nrrbe4uLhgtVolLXGue125sSZnr1H7OVTH4nD3+wNdZyX6oG1VY1TOEuUZ1+uJ0+bm5obD4aBa6nrNbDbLGqRpprmWQqJ97DOjpl4nDwIoY1QuK4fppEDj49qSEw9vJPNcJJBEjjziWDkqnQROHHjTIqJqmzIS0i5VbzamaZxAWuswBmG/7zgcOpaLBH7hNLh65+jTAFjO5/p+cc/OuVTN50Rx3nS08Yam5aXeyicqga4fhhzoW06CaQTAFCiPwytsQNnAmYJoTCauMAw9L1885/33v8Unz7+jICwKHiGogyImLuuUhuMrz2q11LRF4sREG0OtRpi8iz+aeLKjw/tIDG0yvzuNzY1ucqy+Tv1IJDq1N3TspekgA84FfB2pamE5dyzmNauzhrOLOdunC7bnK55sVmzWc5brGbOFp2qEpvaJllCHYeVTxow5TRK5qmtJTN7txL2b5RJyZrcNhU9lFn/WYtbZ4XDg448/5jd/8zf5jd/4DT744AOur6+IMTKfaym0d955hy9/+cu89957XFxcZI+yaYhdN2iA/3Cg7zq6vqc9tBwOO9r2kM+VAc7ViLjs0Q4xpHCzYWI2iwjz+ZyLiwuAvBlfCY5lBo96z0cN1n5KK/SUPAigNK0tRo60QocVqbCRYlkJYxhLxLzZMRh3lDJvRJIGmSh2n+r0SQpNSVyTkfMBKSZRbhzeYi1jQ9e33Ox6zrezXCEaIeXyG+AqR4kIXd/lRSAn+1Noev2QCfkyzMfuTkNs5DSH4txYsT1oZkhStzOlABHxnhiq1A5t8ykQc87pamsLk7VzckkFMlvBwTy4yVtri81RIYljk9kGeN/1QMwB65bloSbybYDMxS+EkVeMFg0BQQYCN2gxDEfwijLReMeg2qR3EWLA1QFfBRZzz3zhWK5nnG0XbM7nbM7V0/z0fM5qqR7meuaoZ0JVCU0lqQA06qQhxTVaKSTGtMLI2C+CFMDYk7exNe+eGPVTaOSMh9zuk3j6g1tfPv7O660a++zy6pJv/vo3+bVv/BovX7ykqiq+/N57bLdbnj17xlvP3uLiyRPWa3WsqINP42u7Xs3aYQjqgC1uq5k1rNZPcqKFT5aYLlo+a5OHQ0t/SFW3Bt2WZbFYZA95GRZkypUFn5dZOwbG5vyxRA1z/rxOHgRQ6gproSs+ZcmUdQQLbaDg1MwU1/1SBA1ILwK4zS2abZVRQ7UADrHlOiNTch8WbZM8bB3D4Lm+6QhBw2ZEdOKp0qPu90E0b91XlWqVbatkMiGb1CZDCHhX5UD3ceCqBhe98StJ+0tAqDxr0X9RA+Mz0KYFQLVs3Ss8pF0DJfUFTCeKd1oI4riNMHJixum8fPmSru9uabxGi5RfP45jMzrFiHl7jnVd0bbuBN0wXj/nokfSlgIKQ1GMtw5Et8P5HRIXKX5Tq9z4GpqZZ72ccbHZsN4sOL9Ysj1fcL5dsT6bsVo1zBc1volUtRYgmVVeC0gXJr82qvDKh2QNncg2MqrHQmtuca+T8v5my4yhYSUGBjvuM5C7nXfT8z999owf/dEf5Yd/+IfZH/a6xfJ8Tl01mf/uuo6bmx0x3mSQMoeLAdtiOVevdtISp8VobjtthyHxvo68gM4qvXZZ8s/aHGMc02j7Pr/fNA2r1WqiYbZdR5vAsYy1vEseCFCSPNhCVdW5BJrJsVk7VqSOyQRXx4L3o9s/r5jJMTPJchHJlY8nIJz/mU5q3UtbEm8VuL7e0Q/nVLWbbI45fjeZi94TkvdvNpvhRSZDPEYNl7G2TTWvmIEzZwhlYJhOROe0/uXURC+uUQBVDmthGnBeHt8XIUkxpg2yGCeQTYxyK9mRiIfylMdms92rBRa3bZvDNvb7/a001PK5g4L5ZDGwiYJoDnLlmVWBiy1sVwvWyy3bJyvOn83YPKnTHipzVos5Ta3VsetK8E4dNN7KfgHmefc4JI7PJ0Z9Np+OI7SJ/OZjTv1lWv3k0y/YDBeE2WzO22+/k01xBcab7IEuAa+u62ySl+PaxvHhcFATuCjOctyXxpfHGKnqinW1njiLSlrOOFSzckyDhDEsqOTyDUgNxE9d/1geBFAK2rnWaX3fT1YcKL18ZqYXHGLU4N/SOM0DOnGOtpJksCyyfCBpPQnIQggTnkPBIm325OD6Zk/X98xjTQ7iNLNdBCFxp1EzdruuS2A62h3mlRv6PgPWCGSkmXU7LaucOOWxavJO08X0vnQbVxtMOtim+6CUx9vr0tSXApys/z755BPaIn1vPNf02R6b3fbeq1evMkCaM2A2m3FzczPJ1S3P4ZJrWMTlKkmr1Yrt2Zbz86c8fXbO+ZNzzs6est28w2Z1wXzmaOYVdePwFZqulygCQfDKiKhzJ2kuo3fZF3naFq5kAex5oL3WfC3llLPw037vmAL5NBKNzP+uvnM7VCxGNOi/67i8uuL66op+6KlrDeK+uLjIHmQLtzkWq9RjVoRppaZdmhiQ2fFmImuW1Oj4sblcKkbOKadZxg3bPLZjgIkTpyyi8Tp5EEAJqNmMmd/k51tqJAaKQsrHBdTBoJkGRtjasep9zvpefj9rlWaGn+gk08TEVKSYSpAFoesHDu3AZt2M57Vzo+Xc7PxO1JRUE/vEbR9dq/QUy5FGCTCc0AJNyhCg3C6nRTxKMHTOj7sGxiPNshhM+RzRdgQcQf4uc2XExNsAUgLm9fU119fXk3s+1i4svMO0gu12y3b7hCdPn7Ldbrm4uEjxdyuaapEAUbVKR63bVPhikYoeSZuQhWCaqObtC2ipLkcGSrGHQAoDukPu0kY+ncb5ehk19WPwOt2e2+9/ujZMHXdjzr1qjgct8hGGtKh50k5E2RseY8zFXQwwbTwauDnn8m9ThOy1geMxQJahQ+aUsZxtO4e1v1xQZ7NZDg0qK4MZdWTKmIHv9wlQpj1ynM8rp27VMF0FFLRcAlXycXrDnmEouUsVV3CUpXZz2xw1cJ1uJ2FaqR6gDqB+CNzsdsS4SO+Pky5PsQTGzieg7HtiPYJPBqeoBTBueZqT1nO7p8hhQNZm/Z5MNebxSlgu8mgqT8F2zMoRYjKLJyETabGwvrKwjGMdZ5zQI2De4vWO3rP25vSy9ZrtdqvxdhcXnJ+fc35+znq91iyMZs5sNs/f1cpIDc41eC+IDESGVAjcwbBM1mridq3VAiKJ20xFIDSDysKSYn6gYxm3z1c+vaZ52hFjXXxMVd117mPwteOstFl7aOmHAaEiRujafgQ975jNqwk4llEUpVXQNA11XedKPWXojj3Dw+HA9fU1IQTm8zmLxSIHiccYc2B5NsfT+LS/y6Byq7plWubxAmy/jT56U+UgeCBAKYJ6u5M5NAzKifUhbV7l3Ggqp5JZIYNe2ngJCISkRfiTVE7ZUbVLKZCS3RuJ55pynCGEVN8v8XShYmhrbq5bYtq3wwG54jLk3RaDUya6Dz2HrsP7VAEm6m8HWpTYwpxudUwyAF1FCOrFrVwRNsKY0y5eUvHjQh1nJP8j4NLKjW23cdQ3MUZwGqg9pLhMOf4cctUX3aCsNLe1PyahLem7zjuaumY2V0J/e7bl4skFF+fn+fVms2G5XGaTrPJaYaku+azCqZK1CDQUJ2uOFuZTgptATNsMqJHhEnjGse3WK4VJUyjIn6t8GpCUo9envlG+H9/Q6FIZUI2up2t72q5lv9uz3++TViiT0K2qqlitV6xWc003LBbV44XaFmcrG9g0aoWVHGVZ0f5wOGhYUdplMgxwOIwgaVsdO0keb9Gx771MwPKYTgIdhwaMk6QU507Pv0IeBFAiAs4xGFnrLSzIT24i5HGdp2/xQ94eQU858pf2+dTTOM4HA+O8c2EhFvSaq0jHmsrPubk50PeBujFNd8gntquYhiaiFb9xutIpJ2Zmn9AWITSnzDXT0vIx4yem7KWtHwaGoZ8+9FKTS+EXkTH3Habmtw3qLvGGJSjZ4DJnz4SewAacZ9ZoPJxlYmy3W54+fcpms8m/zftZVRVNMtVKDbN8huXPcb9kbT7xh6lXxuHhiwpMuc/GAPNj7Wu8hqlntx7H71nK+/hu+MrJ95jmaY/vHx83/e6Uvw5Ze7y6umJ3oxWZjKNX/rjNFfCNBjk7O6Npanw9OlOyxZeuZVqimdNm6lqmjlkllupo17SFUnB5R9G+S9XoA9S1jpm21TTZEEKKrir3rBodPuW4NW3ZuFIr1nvXvCvlYQAlYGE8WtjCouVvZ5FI4glvkf3+tjdUHSpF3ncBBBNlozjXsYnoUvC7CIgXKhS89/sDbdsxr2s99shRY2IPbeIhtmum12Ws4Smz6dQgP76nsp/y8TBZOU1zcKKAdkqOy1WV1zYANo5ns9H83PVmnUHx2dNnbC+e5NzdcmKUvFQ2jcSCvU8vEHeB5Kln9SbQedM5vij5bsDxLpP5u/2+9Y+Nw7Zt2e12mRO0grfHBVQM3GwcmTXRdgd85ahrLXV2WzttE5gpr1gGk4vIpASazbOmaSbWlXmmd7sdXddNHEVVVWXFpqorLTackzhGL3upaJXaZDnnLNTodfIggFJEqOtZCr+xzBnVlo45REFUMyomUIyROARwp7QOsjY2cRpEpn8XUqru5pBxXog+EqOkzaWE9jAg60J7KThQe1B2fhuMlRuLGpRyFwiWr03bLds23mjMptHI506PsXYIY3mpcoCXfHC5Ch8D1dnZGT/+4z/ObL5itVonD+YsEe81+KNwLJg8q1IDnZiTRwtWedxd/fI60Pg0msKbzvH/FxmGgf1+z/X19a0tEswDXPkZfT/ksJkYYw6tMXCxwhRzNy5+5nyxz8wrbWawgZEtjmOFqDGV0BwvNseHAbqk3dq4t/FTbvW8Wq2UX45Dft+0ytLZA2PWjhWahtHs/r4ASrB6ii6b4SHGyd4b4wTSdLXjSRByTGU4CgeIlKZ3nrC3AstHDfJ44kQRhmgFISKCZxgcNzcH3FuOtJ1UYQbKBGDsJ2+ElPwErmhB3/e3HlYJGqUX8WTsWdoqwAZt/u5RTzunJs3rtJoxjfA0gKxWK77+9a9T1QvdpVE0ZVPvPxKkWNhOmMsTMGSkPu76jr1fLpin5KRp/prjH5J8r+b4pzmP7TlzfX3Nfr/Pn5tWttvt0gJaoVWf4i2Pckm1hBBoGrWkTCO16lRNo1ub2Jgtz2HOHPvMQoNMCTAtU0Qg+By0btcwT7fNkwzEKTPKtOUM/EcOJrsPcxoaCJfAeZc8EKDULVDNmaLpS7qFQIwUk1BNXKvCM5q79kqdCyVYmFaqGlY5CNXDLcUkhVETnIKN6AbyzjJ/BPBcX+sAqyvJ3OnxnMwraRhrSwpRMz1QIKvrmrZtj0DaHDUjgI/ZBuT+KNaBiRZ7arKZ+dz3w2RBOb73HANaBLqXWqyIMJ8vQLTwauaJlSBDbAtWewaMXJlzKUUt9blLn9uzGvuwBEa9UXv/pIzdduL9E8Bz11vl2nPyArdP8CZYy/d/6ppH4H/Lk508emUfllZASbPoCjyOixACr1IB3avLK25ubohE6kqdagZowxBSweSOum6yx7l87gaEZVzi7uYmb4v86tUlAGebDYvlgsViQQyRbujyufu+m2Tl2MLed71myrSHNPccDq/fK7RHM+mNP3VO2O+1zmXdjOBYgrTdw/FYLy2v2Wz2/bG5mIavJGXSg4htyjROQqEwxdNEtJJr4lIJsKAeuhg0XAYEXI9uA2tbi6opHdN+NTb5LE5QJ62lE6L7oDBASFkaXqvUDNHz8ipw0/Zsaked9nGObsBKGuhA1nsbJKUfonGMdi39+4QWC1lLFNB79KmAh4xOHPPainm2xev+x2Ie+ZhPaB5qM3gNCEvu1AASIJpJNaRgfEn5+OLxHi2g7NMCVHiJYwGQkhe1E5xjWjQc432kkxmRy9gw65/pplvjx3ZszKeY9OXkW59GY5M7XpfyuqucutrrNeXJ+4ba0WpdGlhKUYTa5dfDkZf+cDhwfXXNy5dXfPzRJ1xdX7NaLrk4P8/72u/3HYekTamzpcK5IYNGnfj3MuysNJtFoK7U0/z0/BlWf/T6cgfBM4Qh01zOq5lsAGb51n07EAYh9C3dwTzkA308jBW9nOB9TdU0xGHg0O5xXhAfifQMQaALEyA/9sQfe8DNhLeYzO8LjdKJ5NXNJq5zkqv7TM0u/af0Tlt4CGhVcKJP5pxXTdBM4eKaOdYRUjzmbR4sa1SJfwQFzkGSKdH2qbRYpZxqdj6NbQ3BwFEIvfIovqpvXcu0ubxdQ5oZMdnpBn45X/uYegijo2pcLUdP9rEZerw1hF7zdqiE9z7BvkyeD3CcopzP5Ypr3QLHyTOY4mEpoVgESkVpKnH894Qz7C6JsXQTHp+t+CtZIa+VYmzccbXi1+2bKB2MNgb6vif0ga5whmT6JujeS6ZdmRPEshksLfTy8pL97sDVpRbSnTUzQDgc2vRMbSfNrtjfuqVpmklMY1nj0X7XdZ23TjZnj5nBllyx2+0YDgP7nfKideO5eHI+ceio97mjPbSApi9rjUkgpIpiybqx1MTaeXzltM7rzKUKRWRe1bjJ/HiKsVh6+8t88FOKyrE8CKCE29yUcHuQqqYTsVJLUx5Qh6HucZO2RYiC5OoxSeOMFi+I1jA86tgyTAZGUzE7Rgx8ZMw4iNGcJ7fv66TXsarHKZMXBjfJsQaIjFxi+ZDtfJO+KnjY45Cd4352TkvAlRkL47nGZzH2r+2pfndWUHlumD7PCTd8yhF1UuKd+PM6QPw0QFnooZ+rTNtyHLk6Pc7CdG5ubri6uuLj73zEYX/IwLXf71VLSuPE+nm32+n4qavJlghq6gYcPr9v1b67VBDi8vKSy8vLibPEUgs3mw11XRMS6AGZf7Rc7kWqxANj2cDjvbgttGi1WmZwtDaKiBbsTk4b80hnzrI9TPqIGNkdWiKB9XpFDBqTWWbsmKZo4Wd39bdxplYd/a5j89P7vRDHn5WIyCXwq/fdjkKeAR/ddyOO5KG16bE9r5eH1h54eG16aO35wRjjW6c+eCga5a/GGP/wfTfCRER+7iG1Bx5emx7b83p5aO2Bh9emh9ae18nr83Ye5VEe5VEe5REoH+VRHuVR3iQPBSj/s/tuwJE8tPbAw2vTY3teLw+tPfDw2vTQ2nOnPAhnzqM8yqM8ykOWh6JRPsqjPMqjPFi5d6AUkX9eRH5VRL4hIj/5BV3zvxSRD0Xkl4r3nojIXxeRX0u/L9L7IiL/UWrf3xORH/kc2vMDIvIzIvL3ReSXReRfv882ichcRP6WiPxCas+/k97/IRH52XTdvywiTXp/lv7+Rvr8q59le4p2eRH5uyLy0w+kPd8SkV8UkZ8XkZ9L793nODoXkb8iIv9ARH5FRH7sHsfQ70/9Yj+vROQv3Gf//J6kDLD+on/Q3d7/X+BrQAP8AvAHvoDr/jHgR4BfKt7794CfTK9/Evh30+s/CfxvaITyHwF+9nNoz7vAj6TXG+AfAn/gvtqUzrtOr2vgZ9N1/nvgJ9L7fxH4V9Prfw34i+n1TwB/+XN6bv8G8N8CP53+vu/2fAt4dvTefY6j/xr4l9PrBji/z/YU7fLA7wI/+BDa8z3dw71eHH4M+GvF3z8F/NQXdO2vHgHlrwLvptfvorGdAP8p8GdOHfc5tu1/Bf7Zh9AmYAn8HeCfQoODq+NnB/w14MfS6yodJ59xO74C/A3gx4GfThPq3tqTzn0KKO/lmQFb4NeP7/OBjKF/Dvi/Hkp7vpef+za93wPeL/7+rfTefcjbMcZvp9e/C7ydXn+hbUxm4h9Ctbh7a1Myc38e+BD466jm/yLGaNUDymvm9qTPXwJPP8v2AP8B8G8yVsV4es/tAU1a/d9F5G+LyL+S3ruvZ/ZDwHeA/yrRE/+5iKzusT2l/ATwl9Lrh9Ce71ruGygfpERd0r7wcAARWQP/I/AXYoyv7rNNMcYhxvgHUU3unwT+0S/q2sciIv8C8GGM8W/fVxvukD8aY/wR4E8Af15E/lj54Rf8zCqUTvpPYox/CLhGTdv7ag8AiTf+U8D/cPzZfc2z70XuGyh/G/iB4u+vpPfuQz4QkXcB0u8P0/tfSBtFpEZB8r+JMf5PD6FNADHGF8DPoKbtuYjt3DW5Zm5P+nwLfPwZNuOfBv6UiHwL+O9Q8/s/vMf2ABBj/O30+0Pgf0YXlPt6Zr8F/FaM8WfT338FBc77HkN/Avg7McYP0t/33Z7vSe4bKP8f4IeT97JBVfS/ek9t+avAn02v/yzKE9r7/1Lyyv0R4GVhOnwmIiIC/BfAr8QY//37bpOIvCUi5+n1AuVLfwUFzD99R3usnX8a+JtJW/hMJMb4UzHGr8QYv4qOkb8ZY/wX76s9ACKyEpGNvUZ5uF/inp5ZjPF3gfdF5Pent/448Pfvqz2F/BlGs9uue5/t+d7kvklS1Nv1D1EO7N/6gq75l4BvAx26Ev85lMP6G8CvAf8H8CQdK8B/nNr3i8Af/hza80dRE+TvAT+ffv7kfbUJ+MeBv5va80vAv53e/xrwt4BvoKbULL0/T39/I33+tc/x2f0zjF7ve2tPuvYvpJ9ftrF7z+PoDwI/l57b/wJc3HN7Vqgmvy3eu7f2/F5+HjNzHuVRHuVR3iD3bXo/yqM8yqM8eHkEykd5lEd5lDfII1A+yqM8yqO8QR6B8lEe5VEe5Q3yCJSP8iiP8ihvkEegfJRHeZRHeYM8AuWjPMqjPMob5BEoH+VRHuVR3iD/H+j1vwNjznVgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cv2.rectangle(img,(x,y),(x+w,y+h),(200,0,0),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35720e79-9c1b-461d-9557-ae62ef654d33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('./box.png',cv2.rectangle(img,(x,y),(x+w,y+h),(200,0,0),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f209b31f-c8ce-41fd-9071-e89de59f9563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('./crop.png',crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "520aba81-4f40-4ed0-98b0-4da8b674bf96",
   "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>filename</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>class</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>IMG_20190104_164125.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Arduino_Nano</td>\n",
       "      <td>356</td>\n",
       "      <td>225</td>\n",
       "      <td>514</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IMG_20181228_102317.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>389</td>\n",
       "      <td>271</td>\n",
       "      <td>543</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>IMG_20181228_103009.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>561</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>IMG_20181228_103828.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>100</td>\n",
       "      <td>129</td>\n",
       "      <td>590</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>IMG_20190104_164622.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Arduino_Nano</td>\n",
       "      <td>569</td>\n",
       "      <td>121</td>\n",
       "      <td>691</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>IMG_20190104_163951.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>449</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IMG_20181228_102048.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>457</td>\n",
       "      <td>198</td>\n",
       "      <td>553</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>IMG_20181228_104139.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Raspberry_Pi_3</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>773</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>IMG_20190104_163951.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>ESP8266</td>\n",
       "      <td>358</td>\n",
       "      <td>336</td>\n",
       "      <td>656</td>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>IMG_20181228_104228.jpg</td>\n",
       "      <td>800</td>\n",
       "      <td>600</td>\n",
       "      <td>Heltec_ESP32_Lora</td>\n",
       "      <td>343</td>\n",
       "      <td>349</td>\n",
       "      <td>687</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    filename  width  height              class  xmin  ymin  \\\n",
       "121  IMG_20190104_164125.jpg    800     600       Arduino_Nano   356   225   \n",
       "15   IMG_20181228_102317.jpg    800     600  Heltec_ESP32_Lora   389   271   \n",
       "30   IMG_20181228_103009.jpg    800     600  Heltec_ESP32_Lora   203   210   \n",
       "55   IMG_20181228_103828.jpg    800     600     Raspberry_Pi_3   100   129   \n",
       "154  IMG_20190104_164622.jpg    800     600       Arduino_Nano   569   121   \n",
       "..                       ...    ...     ...                ...   ...   ...   \n",
       "110  IMG_20190104_163951.jpg    800     600     Raspberry_Pi_3     4    29   \n",
       "8    IMG_20181228_102048.jpg    800     600  Heltec_ESP32_Lora   457   198   \n",
       "61   IMG_20181228_104139.jpg    800     600     Raspberry_Pi_3     1    45   \n",
       "111  IMG_20190104_163951.jpg    800     600            ESP8266   358   336   \n",
       "66   IMG_20181228_104228.jpg    800     600  Heltec_ESP32_Lora   343   349   \n",
       "\n",
       "     xmax  ymax  \n",
       "121   514   356  \n",
       "15    543   398  \n",
       "30    561   401  \n",
       "55    590   511  \n",
       "154   691   267  \n",
       "..    ...   ...  \n",
       "110   449   467  \n",
       "8     553   287  \n",
       "61    773   579  \n",
       "111   656   537  \n",
       "66    687   474  \n",
       "\n",
       "[177 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c723a8f-1da1-4392-8e77-4cc358100258",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = {}\n",
    "labels_r = {}\n",
    "labels_list = []\n",
    "i = -1\n",
    "for label in data['class']:\n",
    "    if label not in list(labels.keys()):\n",
    "        i += 1\n",
    "        labels[label] = i\n",
    "        labels_r[i] = label\n",
    "        labels_list.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ac2121c-c018-4143-be7c-7289b67d9632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Arduino_Nano': 0,\n",
       "  'Heltec_ESP32_Lora': 1,\n",
       "  'Raspberry_Pi_3': 2,\n",
       "  'ESP8266': 3},\n",
       " {0: 'Arduino_Nano',\n",
       "  1: 'Heltec_ESP32_Lora',\n",
       "  2: 'Raspberry_Pi_3',\n",
       "  3: 'ESP8266'},\n",
       " 3,\n",
       " ['Arduino_Nano', 'Heltec_ESP32_Lora', 'Raspberry_Pi_3', 'ESP8266'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels,labels_r,i,labels_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3a4aef4-b567-4b23-ab67-84243f95d02e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    new_data = []\n",
    "    idxs = len(data)\n",
    "    labels = {}\n",
    "    labels_r = {}\n",
    "    i = -1\n",
    "    print(labels)\n",
    "    for label in data['class']:\n",
    "        if label not in list(labels.keys()):\n",
    "            i += 1\n",
    "            labels[label] = i\n",
    "            labels_r[i] = label\n",
    "    for idx in tqdm(range(idxs)):\n",
    "        record = {}\n",
    "        info = data.iloc[idx]\n",
    "        record['file_name'] = f\"./data/{info['filename']}\"\n",
    "        record['height'] = info['height']\n",
    "        record['width'] = info['width']\n",
    "        record['image_id'] = idx\n",
    "        record['annotations'] = [{\n",
    "            'bbox':[info['xmin'],info['ymin'],info['xmax'],info['ymax']],\n",
    "            'bbox_mode':BoxMode.XYXY_ABS,\n",
    "            'category_id':labels[info['class']]\n",
    "        }]\n",
    "        new_data.append(record)\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f70323b9-eb71-48ab-bd8f-5224f0ee33d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "DatasetCatalog.register('data',lambda : load_data())\n",
    "MetadataCatalog.get('data').set(thing_classes=labels_list)\n",
    "metadata = MetadataCatalog.get('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "422103f5-e53a-4aa7-868c-f13565a448ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = \"COCO-Detection/faster_rcnn_R_101_C4_3x.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dc4bdc8-cfdd-4fb3-a523-c64ff50a6c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 20486\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Program ended successfully.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: - 0.00MB of 0.00MB uploaded (0.00MB deduped)\r"
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    },
    {
     "name": "stdout",
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     "text": [
      "\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find user logs for this run at: wandb/run-20211015_135107-20uay098/logs/debug.log\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find internal logs for this run at: wandb/run-20211015_135107-20uay098/logs/debug-internal.log\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mbaseline\u001b[0m: \u001b[34mhttps://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2/runs/20uay098\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.12.4 is available!  To upgrade, please run:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.10.0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in wandb/run-20211015_142409-c3w5l7u8\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mbaseline\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "\n",
       "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2\" target=\"_blank\">https://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2</a><br/>\n",
       "                Run page: <a href=\"https://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2/runs/c3w5l7u8\" target=\"_blank\">https://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2/runs/c3w5l7u8</a><br/>\n",
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     "text": [
      "\n",
      "\u001b[32m[10/15 14:24:17 d2.engine.defaults]: \u001b[0mModel:\n",
      "GeneralizedRCNN(\n",
      "  (backbone): ResNet(\n",
      "    (stem): BasicStem(\n",
      "      (conv1): Conv2d(\n",
      "        3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
      "        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "      )\n",
      "    )\n",
      "    (res2): Sequential(\n",
      "      (0): BottleneckBlock(\n",
      "        (shortcut): Conv2d(\n",
      "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv1): Conv2d(\n",
      "          64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (res3): Sequential(\n",
      "      (0): BottleneckBlock(\n",
      "        (shortcut): Conv2d(\n",
      "          256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv1): Conv2d(\n",
      "          256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (res4): Sequential(\n",
      "      (0): BottleneckBlock(\n",
      "        (shortcut): Conv2d(\n",
      "          512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "        (conv1): Conv2d(\n",
      "          512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (3): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (4): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (5): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (6): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (7): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (8): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (9): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (10): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (11): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (12): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (13): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (14): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (15): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (16): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (17): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (18): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (19): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (20): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (21): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (22): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (proposal_generator): RPN(\n",
      "    (rpn_head): StandardRPNHead(\n",
      "      (conv): Conv2d(\n",
      "        1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
      "        (activation): ReLU()\n",
      "      )\n",
      "      (objectness_logits): Conv2d(1024, 15, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (anchor_deltas): Conv2d(1024, 60, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (anchor_generator): DefaultAnchorGenerator(\n",
      "      (cell_anchors): BufferList()\n",
      "    )\n",
      "  )\n",
      "  (roi_heads): Res5ROIHeads(\n",
      "    (pooler): ROIPooler(\n",
      "      (level_poolers): ModuleList(\n",
      "        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
      "      )\n",
      "    )\n",
      "    (res5): Sequential(\n",
      "      (0): BottleneckBlock(\n",
      "        (shortcut): Conv2d(\n",
      "          1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "        )\n",
      "        (conv1): Conv2d(\n",
      "          1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (1): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "      (2): BottleneckBlock(\n",
      "        (conv1): Conv2d(\n",
      "          2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv2): Conv2d(\n",
      "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
      "        )\n",
      "        (conv3): Conv2d(\n",
      "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
      "          (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (box_predictor): FastRCNNOutputLayers(\n",
      "      (cls_score): Linear(in_features=2048, out_features=5, bias=True)\n",
      "      (bbox_pred): Linear(in_features=2048, out_features=16, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "{}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 177/177 [00:00<00:00, 9524.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[10/15 14:24:17 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 177 images left.\n",
      "\u001b[32m[10/15 14:24:17 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n",
      "\u001b[32m[10/15 14:24:17 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n",
      "\u001b[32m[10/15 14:24:17 d2.data.common]: \u001b[0mSerializing 177 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[10/15 14:24:17 d2.data.common]: \u001b[0mSerialized dataset takes 0.07 MiB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 2048) in the checkpoint but (5, 2048) in the model! You might want to double check if this is expected.\n",
      "Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (5,) in the model! You might want to double check if this is expected.\n",
      "Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 2048) in the checkpoint but (16, 2048) in the model! You might want to double check if this is expected.\n",
      "Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (16,) in the model! You might want to double check if this is expected.\n",
      "Some model parameters or buffers are not found in the checkpoint:\n",
      "\u001b[34mroi_heads.box_predictor.bbox_pred.{bias, weight}\u001b[0m\n",
      "\u001b[34mroi_heads.box_predictor.cls_score.{bias, weight}\u001b[0m\n",
      "The checkpoint state_dict contains keys that are not used by the model:\n",
      "  \u001b[35mproposal_generator.anchor_generator.cell_anchors.0\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[10/15 14:24:17 d2.engine.train_loop]: \u001b[0mStarting training from iteration 0\n",
      "\u001b[32m[10/15 14:24:26 d2.utils.events]: \u001b[0m eta: 0:17:12  iter: 19  total_loss: 2.368  loss_cls: 1.555  loss_box_reg: 0.7172  loss_rpn_cls: 0.03453  loss_rpn_loc: 0.008314  time: 0.4195  data_time: 0.0115  lr: 4.9953e-06  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:24:34 d2.utils.events]: \u001b[0m eta: 0:17:40  iter: 39  total_loss: 2.182  loss_cls: 1.428  loss_box_reg: 0.7134  loss_rpn_cls: 0.04073  loss_rpn_loc: 0.01029  time: 0.4230  data_time: 0.0025  lr: 9.9902e-06  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:24:43 d2.utils.events]: \u001b[0m eta: 0:17:31  iter: 59  total_loss: 1.98  loss_cls: 1.226  loss_box_reg: 0.6806  loss_rpn_cls: 0.05235  loss_rpn_loc: 0.01527  time: 0.4234  data_time: 0.0024  lr: 1.4985e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:24:51 d2.utils.events]: \u001b[0m eta: 0:16:38  iter: 79  total_loss: 1.695  loss_cls: 0.9342  loss_box_reg: 0.6753  loss_rpn_cls: 0.04628  loss_rpn_loc: 0.01252  time: 0.4171  data_time: 0.0026  lr: 1.998e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:24:59 d2.utils.events]: \u001b[0m eta: 0:16:24  iter: 99  total_loss: 1.536  loss_cls: 0.7152  loss_box_reg: 0.7123  loss_rpn_cls: 0.0557  loss_rpn_loc: 0.0201  time: 0.4154  data_time: 0.0022  lr: 2.4975e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:07 d2.utils.events]: \u001b[0m eta: 0:16:10  iter: 119  total_loss: 1.316  loss_cls: 0.5835  loss_box_reg: 0.6697  loss_rpn_cls: 0.0436  loss_rpn_loc: 0.00811  time: 0.4127  data_time: 0.0024  lr: 2.997e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:15 d2.utils.events]: \u001b[0m eta: 0:16:02  iter: 139  total_loss: 1.272  loss_cls: 0.513  loss_box_reg: 0.6789  loss_rpn_cls: 0.04713  loss_rpn_loc: 0.008271  time: 0.4129  data_time: 0.0023  lr: 3.4965e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:24 d2.utils.events]: \u001b[0m eta: 0:15:57  iter: 159  total_loss: 1.277  loss_cls: 0.4826  loss_box_reg: 0.7374  loss_rpn_cls: 0.03972  loss_rpn_loc: 0.01169  time: 0.4147  data_time: 0.0024  lr: 3.996e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:32 d2.utils.events]: \u001b[0m eta: 0:15:49  iter: 179  total_loss: 1.217  loss_cls: 0.4524  loss_box_reg: 0.6664  loss_rpn_cls: 0.03722  loss_rpn_loc: 0.01427  time: 0.4161  data_time: 0.0024  lr: 4.4955e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:41 d2.utils.events]: \u001b[0m eta: 0:15:41  iter: 199  total_loss: 1.187  loss_cls: 0.4201  loss_box_reg: 0.696  loss_rpn_cls: 0.03502  loss_rpn_loc: 0.004344  time: 0.4164  data_time: 0.0026  lr: 4.995e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:49 d2.utils.events]: \u001b[0m eta: 0:15:33  iter: 219  total_loss: 1.183  loss_cls: 0.4009  loss_box_reg: 0.7051  loss_rpn_cls: 0.03196  loss_rpn_loc: 0.01196  time: 0.4156  data_time: 0.0024  lr: 5.4945e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:25:57 d2.utils.events]: \u001b[0m eta: 0:15:24  iter: 239  total_loss: 1.129  loss_cls: 0.3694  loss_box_reg: 0.7098  loss_rpn_cls: 0.02965  loss_rpn_loc: 0.01348  time: 0.4155  data_time: 0.0025  lr: 5.994e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:05 d2.utils.events]: \u001b[0m eta: 0:15:16  iter: 259  total_loss: 1.093  loss_cls: 0.3537  loss_box_reg: 0.6824  loss_rpn_cls: 0.02949  loss_rpn_loc: 0.01611  time: 0.4149  data_time: 0.0024  lr: 6.4935e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:14 d2.utils.events]: \u001b[0m eta: 0:15:12  iter: 279  total_loss: 1.11  loss_cls: 0.3383  loss_box_reg: 0.7096  loss_rpn_cls: 0.02722  loss_rpn_loc: 0.00843  time: 0.4163  data_time: 0.0025  lr: 6.993e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:22 d2.utils.events]: \u001b[0m eta: 0:15:04  iter: 299  total_loss: 1.111  loss_cls: 0.3338  loss_box_reg: 0.7534  loss_rpn_cls: 0.01989  loss_rpn_loc: 0.01352  time: 0.4164  data_time: 0.0025  lr: 7.4925e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:31 d2.utils.events]: \u001b[0m eta: 0:14:57  iter: 319  total_loss: 1.019  loss_cls: 0.3205  loss_box_reg: 0.6744  loss_rpn_cls: 0.01982  loss_rpn_loc: 0.007804  time: 0.4171  data_time: 0.0023  lr: 7.992e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:39 d2.utils.events]: \u001b[0m eta: 0:14:48  iter: 339  total_loss: 1.017  loss_cls: 0.2905  loss_box_reg: 0.6935  loss_rpn_cls: 0.01067  loss_rpn_loc: 0.005333  time: 0.4165  data_time: 0.0024  lr: 8.4915e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:47 d2.utils.events]: \u001b[0m eta: 0:14:37  iter: 359  total_loss: 0.9952  loss_cls: 0.2789  loss_box_reg: 0.6809  loss_rpn_cls: 0.01708  loss_rpn_loc: 0.008545  time: 0.4159  data_time: 0.0024  lr: 8.991e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:26:56 d2.utils.events]: \u001b[0m eta: 0:14:31  iter: 379  total_loss: 0.9739  loss_cls: 0.2586  loss_box_reg: 0.6696  loss_rpn_cls: 0.01187  loss_rpn_loc: 0.01569  time: 0.4161  data_time: 0.0025  lr: 9.4905e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:04 d2.utils.events]: \u001b[0m eta: 0:14:20  iter: 399  total_loss: 0.9537  loss_cls: 0.238  loss_box_reg: 0.6978  loss_rpn_cls: 0.008587  loss_rpn_loc: 0.00842  time: 0.4160  data_time: 0.0024  lr: 9.99e-05  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:12 d2.utils.events]: \u001b[0m eta: 0:14:14  iter: 419  total_loss: 0.8481  loss_cls: 0.218  loss_box_reg: 0.587  loss_rpn_cls: 0.01801  loss_rpn_loc: 0.006843  time: 0.4164  data_time: 0.0024  lr: 0.0001049  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:21 d2.utils.events]: \u001b[0m eta: 0:14:04  iter: 439  total_loss: 0.8104  loss_cls: 0.1851  loss_box_reg: 0.595  loss_rpn_cls: 0.01633  loss_rpn_loc: 0.008546  time: 0.4159  data_time: 0.0024  lr: 0.00010989  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:29 d2.utils.events]: \u001b[0m eta: 0:13:56  iter: 459  total_loss: 0.7692  loss_cls: 0.2103  loss_box_reg: 0.5497  loss_rpn_cls: 0.01204  loss_rpn_loc: 0.006703  time: 0.4162  data_time: 0.0026  lr: 0.00011489  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:37 d2.utils.events]: \u001b[0m eta: 0:13:48  iter: 479  total_loss: 0.7958  loss_cls: 0.1529  loss_box_reg: 0.5851  loss_rpn_cls: 0.0196  loss_rpn_loc: 0.01269  time: 0.4161  data_time: 0.0025  lr: 0.00011988  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:46 d2.utils.events]: \u001b[0m eta: 0:13:43  iter: 499  total_loss: 0.6493  loss_cls: 0.1121  loss_box_reg: 0.5141  loss_rpn_cls: 0.007002  loss_rpn_loc: 0.008616  time: 0.4172  data_time: 0.0026  lr: 0.00012488  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:27:55 d2.utils.events]: \u001b[0m eta: 0:13:35  iter: 519  total_loss: 0.5569  loss_cls: 0.1163  loss_box_reg: 0.4171  loss_rpn_cls: 0.01049  loss_rpn_loc: 0.007574  time: 0.4177  data_time: 0.0027  lr: 0.00012987  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:04 d2.utils.events]: \u001b[0m eta: 0:13:28  iter: 539  total_loss: 0.4998  loss_cls: 0.09483  loss_box_reg: 0.3477  loss_rpn_cls: 0.008046  loss_rpn_loc: 0.008041  time: 0.4190  data_time: 0.0027  lr: 0.00013487  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:12 d2.utils.events]: \u001b[0m eta: 0:13:20  iter: 559  total_loss: 0.3674  loss_cls: 0.07174  loss_box_reg: 0.2602  loss_rpn_cls: 0.00811  loss_rpn_loc: 0.008516  time: 0.4192  data_time: 0.0026  lr: 0.00013986  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:21 d2.utils.events]: \u001b[0m eta: 0:13:12  iter: 579  total_loss: 0.4666  loss_cls: 0.1147  loss_box_reg: 0.2749  loss_rpn_cls: 0.008021  loss_rpn_loc: 0.009301  time: 0.4196  data_time: 0.0026  lr: 0.00014486  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:30 d2.utils.events]: \u001b[0m eta: 0:13:05  iter: 599  total_loss: 0.4235  loss_cls: 0.1156  loss_box_reg: 0.2519  loss_rpn_cls: 0.005479  loss_rpn_loc: 0.007838  time: 0.4203  data_time: 0.0025  lr: 0.00014985  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:38 d2.utils.events]: \u001b[0m eta: 0:12:57  iter: 619  total_loss: 0.3117  loss_cls: 0.09084  loss_box_reg: 0.1852  loss_rpn_cls: 0.004822  loss_rpn_loc: 0.007771  time: 0.4207  data_time: 0.0026  lr: 0.00015485  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:47 d2.utils.events]: \u001b[0m eta: 0:12:49  iter: 639  total_loss: 0.2874  loss_cls: 0.08246  loss_box_reg: 0.1785  loss_rpn_cls: 0.004378  loss_rpn_loc: 0.007482  time: 0.4208  data_time: 0.0025  lr: 0.00015984  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:28:56 d2.utils.events]: \u001b[0m eta: 0:12:40  iter: 659  total_loss: 0.2986  loss_cls: 0.08112  loss_box_reg: 0.1871  loss_rpn_cls: 0.004182  loss_rpn_loc: 0.007954  time: 0.4210  data_time: 0.0027  lr: 0.00016484  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:04 d2.utils.events]: \u001b[0m eta: 0:12:32  iter: 679  total_loss: 0.2663  loss_cls: 0.07632  loss_box_reg: 0.1754  loss_rpn_cls: 0.003526  loss_rpn_loc: 0.007466  time: 0.4210  data_time: 0.0025  lr: 0.00016983  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:13 d2.utils.events]: \u001b[0m eta: 0:12:24  iter: 699  total_loss: 0.2882  loss_cls: 0.08168  loss_box_reg: 0.1851  loss_rpn_cls: 0.009637  loss_rpn_loc: 0.006456  time: 0.4218  data_time: 0.0027  lr: 0.00017483  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:21 d2.utils.events]: \u001b[0m eta: 0:12:16  iter: 719  total_loss: 0.2346  loss_cls: 0.06748  loss_box_reg: 0.1587  loss_rpn_cls: 0.003087  loss_rpn_loc: 0.004007  time: 0.4216  data_time: 0.0026  lr: 0.00017982  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:30 d2.utils.events]: \u001b[0m eta: 0:12:09  iter: 739  total_loss: 0.2736  loss_cls: 0.06378  loss_box_reg: 0.1733  loss_rpn_cls: 0.008976  loss_rpn_loc: 0.006866  time: 0.4219  data_time: 0.0027  lr: 0.00018482  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:39 d2.utils.events]: \u001b[0m eta: 0:12:01  iter: 759  total_loss: 0.2515  loss_cls: 0.05358  loss_box_reg: 0.1858  loss_rpn_cls: 0.008253  loss_rpn_loc: 0.007218  time: 0.4224  data_time: 0.0027  lr: 0.00018981  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:47 d2.utils.events]: \u001b[0m eta: 0:11:53  iter: 779  total_loss: 0.2251  loss_cls: 0.05202  loss_box_reg: 0.1668  loss_rpn_cls: 0.004573  loss_rpn_loc: 0.007717  time: 0.4226  data_time: 0.0025  lr: 0.00019481  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:29:56 d2.utils.events]: \u001b[0m eta: 0:11:45  iter: 799  total_loss: 0.2518  loss_cls: 0.07371  loss_box_reg: 0.1629  loss_rpn_cls: 0.005159  loss_rpn_loc: 0.0122  time: 0.4227  data_time: 0.0025  lr: 0.0001998  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:05 d2.utils.events]: \u001b[0m eta: 0:11:37  iter: 819  total_loss: 0.2399  loss_cls: 0.05744  loss_box_reg: 0.1806  loss_rpn_cls: 0.00155  loss_rpn_loc: 0.006656  time: 0.4231  data_time: 0.0025  lr: 0.0002048  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:13 d2.utils.events]: \u001b[0m eta: 0:11:29  iter: 839  total_loss: 0.224  loss_cls: 0.06931  loss_box_reg: 0.1352  loss_rpn_cls: 0.003156  loss_rpn_loc: 0.004877  time: 0.4235  data_time: 0.0027  lr: 0.00020979  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:22 d2.utils.events]: \u001b[0m eta: 0:11:21  iter: 859  total_loss: 0.2373  loss_cls: 0.05571  loss_box_reg: 0.1483  loss_rpn_cls: 0.005055  loss_rpn_loc: 0.008335  time: 0.4238  data_time: 0.0027  lr: 0.00021479  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:31 d2.utils.events]: \u001b[0m eta: 0:11:12  iter: 879  total_loss: 0.2132  loss_cls: 0.05108  loss_box_reg: 0.1476  loss_rpn_cls: 0.006607  loss_rpn_loc: 0.00687  time: 0.4237  data_time: 0.0027  lr: 0.00021978  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:40 d2.utils.events]: \u001b[0m eta: 0:11:05  iter: 899  total_loss: 0.2271  loss_cls: 0.05044  loss_box_reg: 0.1674  loss_rpn_cls: 0.002107  loss_rpn_loc: 0.007236  time: 0.4242  data_time: 0.0026  lr: 0.00022478  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:48 d2.utils.events]: \u001b[0m eta: 0:10:57  iter: 919  total_loss: 0.2175  loss_cls: 0.04954  loss_box_reg: 0.135  loss_rpn_cls: 0.004293  loss_rpn_loc: 0.006231  time: 0.4244  data_time: 0.0026  lr: 0.00022977  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:30:57 d2.utils.events]: \u001b[0m eta: 0:10:50  iter: 939  total_loss: 0.2439  loss_cls: 0.05481  loss_box_reg: 0.1575  loss_rpn_cls: 0.007354  loss_rpn_loc: 0.006016  time: 0.4248  data_time: 0.0028  lr: 0.00023477  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:07 d2.utils.events]: \u001b[0m eta: 0:10:43  iter: 959  total_loss: 0.2146  loss_cls: 0.04073  loss_box_reg: 0.1414  loss_rpn_cls: 0.002735  loss_rpn_loc: 0.009962  time: 0.4259  data_time: 0.0026  lr: 0.00023976  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:16 d2.utils.events]: \u001b[0m eta: 0:10:38  iter: 979  total_loss: 0.1843  loss_cls: 0.04  loss_box_reg: 0.1218  loss_rpn_cls: 0.001763  loss_rpn_loc: 0.009742  time: 0.4269  data_time: 0.0031  lr: 0.00024476  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:25 d2.utils.events]: \u001b[0m eta: 0:10:31  iter: 999  total_loss: 0.2227  loss_cls: 0.03293  loss_box_reg: 0.1659  loss_rpn_cls: 0.001259  loss_rpn_loc: 0.003816  time: 0.4272  data_time: 0.0037  lr: 0.00024975  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:34 d2.utils.events]: \u001b[0m eta: 0:10:25  iter: 1019  total_loss: 0.2002  loss_cls: 0.05609  loss_box_reg: 0.1135  loss_rpn_cls: 0.004599  loss_rpn_loc: 0.00892  time: 0.4279  data_time: 0.0030  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:43 d2.utils.events]: \u001b[0m eta: 0:10:17  iter: 1039  total_loss: 0.2202  loss_cls: 0.0369  loss_box_reg: 0.1579  loss_rpn_cls: 0.002741  loss_rpn_loc: 0.006988  time: 0.4282  data_time: 0.0030  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:31:52 d2.utils.events]: \u001b[0m eta: 0:10:09  iter: 1059  total_loss: 0.2087  loss_cls: 0.04605  loss_box_reg: 0.1321  loss_rpn_cls: 0.004812  loss_rpn_loc: 0.004181  time: 0.4283  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:00 d2.utils.events]: \u001b[0m eta: 0:10:01  iter: 1079  total_loss: 0.1945  loss_cls: 0.04304  loss_box_reg: 0.1276  loss_rpn_cls: 0.002249  loss_rpn_loc: 0.005674  time: 0.4281  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:09 d2.utils.events]: \u001b[0m eta: 0:09:52  iter: 1099  total_loss: 0.1734  loss_cls: 0.04159  loss_box_reg: 0.1255  loss_rpn_cls: 0.002896  loss_rpn_loc: 0.004327  time: 0.4279  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:17 d2.utils.events]: \u001b[0m eta: 0:09:44  iter: 1119  total_loss: 0.1833  loss_cls: 0.03854  loss_box_reg: 0.1269  loss_rpn_cls: 0.003515  loss_rpn_loc: 0.00949  time: 0.4276  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:25 d2.utils.events]: \u001b[0m eta: 0:09:37  iter: 1139  total_loss: 0.1861  loss_cls: 0.0418  loss_box_reg: 0.1076  loss_rpn_cls: 0.001799  loss_rpn_loc: 0.00407  time: 0.4275  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:34 d2.utils.events]: \u001b[0m eta: 0:09:27  iter: 1159  total_loss: 0.1874  loss_cls: 0.04573  loss_box_reg: 0.1292  loss_rpn_cls: 0.002252  loss_rpn_loc: 0.004002  time: 0.4272  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:42 d2.utils.events]: \u001b[0m eta: 0:09:18  iter: 1179  total_loss: 0.1681  loss_cls: 0.02616  loss_box_reg: 0.1226  loss_rpn_cls: 0.00217  loss_rpn_loc: 0.009723  time: 0.4271  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:50 d2.utils.events]: \u001b[0m eta: 0:09:10  iter: 1199  total_loss: 0.1632  loss_cls: 0.02447  loss_box_reg: 0.1081  loss_rpn_cls: 0.0009807  loss_rpn_loc: 0.004833  time: 0.4268  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:32:58 d2.utils.events]: \u001b[0m eta: 0:09:01  iter: 1219  total_loss: 0.1848  loss_cls: 0.04139  loss_box_reg: 0.1175  loss_rpn_cls: 0.003314  loss_rpn_loc: 0.004683  time: 0.4265  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:07 d2.utils.events]: \u001b[0m eta: 0:08:52  iter: 1239  total_loss: 0.1785  loss_cls: 0.05498  loss_box_reg: 0.1048  loss_rpn_cls: 0.003941  loss_rpn_loc: 0.007778  time: 0.4262  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:15 d2.utils.events]: \u001b[0m eta: 0:08:44  iter: 1259  total_loss: 0.1711  loss_cls: 0.03852  loss_box_reg: 0.1076  loss_rpn_cls: 0.001113  loss_rpn_loc: 0.006433  time: 0.4260  data_time: 0.0028  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:23 d2.utils.events]: \u001b[0m eta: 0:08:35  iter: 1279  total_loss: 0.1774  loss_cls: 0.03205  loss_box_reg: 0.1167  loss_rpn_cls: 0.00266  loss_rpn_loc: 0.005145  time: 0.4257  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:31 d2.utils.events]: \u001b[0m eta: 0:08:26  iter: 1299  total_loss: 0.1917  loss_cls: 0.03871  loss_box_reg: 0.1284  loss_rpn_cls: 0.00127  loss_rpn_loc: 0.006135  time: 0.4256  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:40 d2.utils.events]: \u001b[0m eta: 0:08:17  iter: 1319  total_loss: 0.1703  loss_cls: 0.03698  loss_box_reg: 0.1125  loss_rpn_cls: 0.0009509  loss_rpn_loc: 0.007908  time: 0.4255  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:48 d2.utils.events]: \u001b[0m eta: 0:08:07  iter: 1339  total_loss: 0.1514  loss_cls: 0.03806  loss_box_reg: 0.09787  loss_rpn_cls: 0.001464  loss_rpn_loc: 0.004947  time: 0.4253  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:33:56 d2.utils.events]: \u001b[0m eta: 0:08:00  iter: 1359  total_loss: 0.175  loss_cls: 0.03586  loss_box_reg: 0.1117  loss_rpn_cls: 0.001309  loss_rpn_loc: 0.004402  time: 0.4252  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:05 d2.utils.events]: \u001b[0m eta: 0:07:52  iter: 1379  total_loss: 0.1616  loss_cls: 0.03676  loss_box_reg: 0.09832  loss_rpn_cls: 0.001276  loss_rpn_loc: 0.004451  time: 0.4251  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:13 d2.utils.events]: \u001b[0m eta: 0:07:43  iter: 1399  total_loss: 0.1629  loss_cls: 0.03417  loss_box_reg: 0.1061  loss_rpn_cls: 0.002142  loss_rpn_loc: 0.0058  time: 0.4249  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:21 d2.utils.events]: \u001b[0m eta: 0:07:34  iter: 1419  total_loss: 0.1861  loss_cls: 0.04874  loss_box_reg: 0.1098  loss_rpn_cls: 0.003856  loss_rpn_loc: 0.005462  time: 0.4248  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:30 d2.utils.events]: \u001b[0m eta: 0:07:26  iter: 1439  total_loss: 0.1632  loss_cls: 0.03087  loss_box_reg: 0.1044  loss_rpn_cls: 0.001212  loss_rpn_loc: 0.004416  time: 0.4247  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:38 d2.utils.events]: \u001b[0m eta: 0:07:19  iter: 1459  total_loss: 0.1678  loss_cls: 0.03643  loss_box_reg: 0.1104  loss_rpn_cls: 0.001875  loss_rpn_loc: 0.004749  time: 0.4248  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:47 d2.utils.events]: \u001b[0m eta: 0:07:10  iter: 1479  total_loss: 0.1562  loss_cls: 0.02725  loss_box_reg: 0.1073  loss_rpn_cls: 0.002591  loss_rpn_loc: 0.004551  time: 0.4246  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:34:55 d2.utils.events]: \u001b[0m eta: 0:07:00  iter: 1499  total_loss: 0.1762  loss_cls: 0.04102  loss_box_reg: 0.1159  loss_rpn_cls: 0.001086  loss_rpn_loc: 0.003977  time: 0.4245  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:03 d2.utils.events]: \u001b[0m eta: 0:06:51  iter: 1519  total_loss: 0.1383  loss_cls: 0.03006  loss_box_reg: 0.08599  loss_rpn_cls: 0.001617  loss_rpn_loc: 0.007053  time: 0.4245  data_time: 0.0023  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:12 d2.utils.events]: \u001b[0m eta: 0:06:42  iter: 1539  total_loss: 0.1451  loss_cls: 0.04001  loss_box_reg: 0.09118  loss_rpn_cls: 0.001824  loss_rpn_loc: 0.003863  time: 0.4245  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:20 d2.utils.events]: \u001b[0m eta: 0:06:33  iter: 1559  total_loss: 0.1484  loss_cls: 0.0299  loss_box_reg: 0.1013  loss_rpn_cls: 0.0006429  loss_rpn_loc: 0.006373  time: 0.4244  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:29 d2.utils.events]: \u001b[0m eta: 0:06:25  iter: 1579  total_loss: 0.1316  loss_cls: 0.02135  loss_box_reg: 0.08923  loss_rpn_cls: 0.001113  loss_rpn_loc: 0.007502  time: 0.4244  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:37 d2.utils.events]: \u001b[0m eta: 0:06:16  iter: 1599  total_loss: 0.1577  loss_cls: 0.02912  loss_box_reg: 0.09874  loss_rpn_cls: 0.001702  loss_rpn_loc: 0.006844  time: 0.4242  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:45 d2.utils.events]: \u001b[0m eta: 0:06:07  iter: 1619  total_loss: 0.16  loss_cls: 0.03197  loss_box_reg: 0.09695  loss_rpn_cls: 0.001567  loss_rpn_loc: 0.004694  time: 0.4242  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:35:54 d2.utils.events]: \u001b[0m eta: 0:05:58  iter: 1639  total_loss: 0.1424  loss_cls: 0.03285  loss_box_reg: 0.09526  loss_rpn_cls: 0.001711  loss_rpn_loc: 0.006353  time: 0.4242  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:02 d2.utils.events]: \u001b[0m eta: 0:05:50  iter: 1659  total_loss: 0.1625  loss_cls: 0.02968  loss_box_reg: 0.09029  loss_rpn_cls: 0.00254  loss_rpn_loc: 0.004198  time: 0.4239  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:10 d2.utils.events]: \u001b[0m eta: 0:05:42  iter: 1679  total_loss: 0.1529  loss_cls: 0.03197  loss_box_reg: 0.1028  loss_rpn_cls: 0.0009549  loss_rpn_loc: 0.008098  time: 0.4238  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:18 d2.utils.events]: \u001b[0m eta: 0:05:32  iter: 1699  total_loss: 0.1579  loss_cls: 0.03322  loss_box_reg: 0.09977  loss_rpn_cls: 0.002034  loss_rpn_loc: 0.007186  time: 0.4235  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:27 d2.utils.events]: \u001b[0m eta: 0:05:24  iter: 1719  total_loss: 0.1482  loss_cls: 0.02188  loss_box_reg: 0.1023  loss_rpn_cls: 0.001557  loss_rpn_loc: 0.004414  time: 0.4235  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:35 d2.utils.events]: \u001b[0m eta: 0:05:16  iter: 1739  total_loss: 0.1478  loss_cls: 0.04481  loss_box_reg: 0.08907  loss_rpn_cls: 0.002755  loss_rpn_loc: 0.004114  time: 0.4235  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:44 d2.utils.events]: \u001b[0m eta: 0:05:07  iter: 1759  total_loss: 0.1412  loss_cls: 0.02804  loss_box_reg: 0.08866  loss_rpn_cls: 0.0006636  loss_rpn_loc: 0.006817  time: 0.4239  data_time: 0.0030  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:36:54 d2.utils.events]: \u001b[0m eta: 0:04:59  iter: 1779  total_loss: 0.1217  loss_cls: 0.0241  loss_box_reg: 0.07913  loss_rpn_cls: 0.001535  loss_rpn_loc: 0.004051  time: 0.4243  data_time: 0.0032  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:02 d2.utils.events]: \u001b[0m eta: 0:04:51  iter: 1799  total_loss: 0.1351  loss_cls: 0.02582  loss_box_reg: 0.09184  loss_rpn_cls: 0.003893  loss_rpn_loc: 0.005512  time: 0.4245  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:11 d2.utils.events]: \u001b[0m eta: 0:04:43  iter: 1819  total_loss: 0.1416  loss_cls: 0.02873  loss_box_reg: 0.09244  loss_rpn_cls: 0.001533  loss_rpn_loc: 0.002983  time: 0.4247  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:20 d2.utils.events]: \u001b[0m eta: 0:04:34  iter: 1839  total_loss: 0.126  loss_cls: 0.01966  loss_box_reg: 0.08697  loss_rpn_cls: 0.0008989  loss_rpn_loc: 0.003288  time: 0.4246  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:28 d2.utils.events]: \u001b[0m eta: 0:04:26  iter: 1859  total_loss: 0.1303  loss_cls: 0.02549  loss_box_reg: 0.09211  loss_rpn_cls: 0.001613  loss_rpn_loc: 0.006807  time: 0.4248  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:38 d2.utils.events]: \u001b[0m eta: 0:04:18  iter: 1879  total_loss: 0.1482  loss_cls: 0.02873  loss_box_reg: 0.09509  loss_rpn_cls: 0.0006882  loss_rpn_loc: 0.004514  time: 0.4251  data_time: 0.0028  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:46 d2.utils.events]: \u001b[0m eta: 0:04:10  iter: 1899  total_loss: 0.136  loss_cls: 0.02778  loss_box_reg: 0.09636  loss_rpn_cls: 0.001131  loss_rpn_loc: 0.004038  time: 0.4252  data_time: 0.0031  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:37:55 d2.utils.events]: \u001b[0m eta: 0:04:01  iter: 1919  total_loss: 0.159  loss_cls: 0.03242  loss_box_reg: 0.09904  loss_rpn_cls: 0.001455  loss_rpn_loc: 0.005265  time: 0.4253  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:03 d2.utils.events]: \u001b[0m eta: 0:03:53  iter: 1939  total_loss: 0.1425  loss_cls: 0.0279  loss_box_reg: 0.08904  loss_rpn_cls: 0.001759  loss_rpn_loc: 0.003899  time: 0.4252  data_time: 0.0029  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:12 d2.utils.events]: \u001b[0m eta: 0:03:44  iter: 1959  total_loss: 0.1301  loss_cls: 0.02548  loss_box_reg: 0.0874  loss_rpn_cls: 0.0004059  loss_rpn_loc: 0.003912  time: 0.4252  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:20 d2.utils.events]: \u001b[0m eta: 0:03:36  iter: 1979  total_loss: 0.1539  loss_cls: 0.03225  loss_box_reg: 0.09198  loss_rpn_cls: 0.001022  loss_rpn_loc: 0.003769  time: 0.4252  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:29 d2.utils.events]: \u001b[0m eta: 0:03:27  iter: 1999  total_loss: 0.1367  loss_cls: 0.02272  loss_box_reg: 0.08905  loss_rpn_cls: 0.001264  loss_rpn_loc: 0.004884  time: 0.4251  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:37 d2.utils.events]: \u001b[0m eta: 0:03:19  iter: 2019  total_loss: 0.1303  loss_cls: 0.02354  loss_box_reg: 0.08028  loss_rpn_cls: 0.0007514  loss_rpn_loc: 0.005226  time: 0.4250  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:45 d2.utils.events]: \u001b[0m eta: 0:03:10  iter: 2039  total_loss: 0.1432  loss_cls: 0.02412  loss_box_reg: 0.08854  loss_rpn_cls: 0.001347  loss_rpn_loc: 0.003494  time: 0.4249  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:38:54 d2.utils.events]: \u001b[0m eta: 0:03:02  iter: 2059  total_loss: 0.1491  loss_cls: 0.03148  loss_box_reg: 0.09267  loss_rpn_cls: 0.001722  loss_rpn_loc: 0.007465  time: 0.4250  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:02 d2.utils.events]: \u001b[0m eta: 0:02:54  iter: 2079  total_loss: 0.1323  loss_cls: 0.02248  loss_box_reg: 0.09052  loss_rpn_cls: 0.001164  loss_rpn_loc: 0.007173  time: 0.4250  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:10 d2.utils.events]: \u001b[0m eta: 0:02:45  iter: 2099  total_loss: 0.1303  loss_cls: 0.02922  loss_box_reg: 0.08872  loss_rpn_cls: 0.0009981  loss_rpn_loc: 0.004457  time: 0.4247  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:19 d2.utils.events]: \u001b[0m eta: 0:02:37  iter: 2119  total_loss: 0.1383  loss_cls: 0.03073  loss_box_reg: 0.09107  loss_rpn_cls: 0.00124  loss_rpn_loc: 0.005416  time: 0.4246  data_time: 0.0024  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:27 d2.utils.events]: \u001b[0m eta: 0:02:29  iter: 2139  total_loss: 0.1391  loss_cls: 0.02564  loss_box_reg: 0.1012  loss_rpn_cls: 0.001496  loss_rpn_loc: 0.005267  time: 0.4247  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:35 d2.utils.events]: \u001b[0m eta: 0:02:20  iter: 2159  total_loss: 0.1688  loss_cls: 0.03235  loss_box_reg: 0.1135  loss_rpn_cls: 0.002458  loss_rpn_loc: 0.007597  time: 0.4245  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:44 d2.utils.events]: \u001b[0m eta: 0:02:12  iter: 2179  total_loss: 0.1549  loss_cls: 0.03941  loss_box_reg: 0.0882  loss_rpn_cls: 0.004104  loss_rpn_loc: 0.003771  time: 0.4248  data_time: 0.0028  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:39:53 d2.utils.events]: \u001b[0m eta: 0:02:04  iter: 2199  total_loss: 0.1184  loss_cls: 0.02439  loss_box_reg: 0.08386  loss_rpn_cls: 0.0006521  loss_rpn_loc: 0.006367  time: 0.4248  data_time: 0.0028  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:01 d2.utils.events]: \u001b[0m eta: 0:01:56  iter: 2219  total_loss: 0.121  loss_cls: 0.02056  loss_box_reg: 0.07306  loss_rpn_cls: 0.0007126  loss_rpn_loc: 0.004831  time: 0.4247  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:10 d2.utils.events]: \u001b[0m eta: 0:01:47  iter: 2239  total_loss: 0.1346  loss_cls: 0.03287  loss_box_reg: 0.09006  loss_rpn_cls: 0.001912  loss_rpn_loc: 0.004382  time: 0.4246  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:18 d2.utils.events]: \u001b[0m eta: 0:01:39  iter: 2259  total_loss: 0.1374  loss_cls: 0.02822  loss_box_reg: 0.086  loss_rpn_cls: 0.001198  loss_rpn_loc: 0.00355  time: 0.4246  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:27 d2.utils.events]: \u001b[0m eta: 0:01:31  iter: 2279  total_loss: 0.1379  loss_cls: 0.03717  loss_box_reg: 0.08261  loss_rpn_cls: 0.001176  loss_rpn_loc: 0.003637  time: 0.4249  data_time: 0.0030  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:37 d2.utils.events]: \u001b[0m eta: 0:01:23  iter: 2299  total_loss: 0.1218  loss_cls: 0.02164  loss_box_reg: 0.08979  loss_rpn_cls: 0.001137  loss_rpn_loc: 0.008042  time: 0.4253  data_time: 0.0034  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:46 d2.utils.events]: \u001b[0m eta: 0:01:15  iter: 2319  total_loss: 0.1348  loss_cls: 0.03885  loss_box_reg: 0.07481  loss_rpn_cls: 0.0008112  loss_rpn_loc: 0.006112  time: 0.4256  data_time: 0.0029  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:40:54 d2.utils.events]: \u001b[0m eta: 0:01:06  iter: 2339  total_loss: 0.1113  loss_cls: 0.02198  loss_box_reg: 0.06991  loss_rpn_cls: 0.0009094  loss_rpn_loc: 0.00385  time: 0.4255  data_time: 0.0027  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:03 d2.utils.events]: \u001b[0m eta: 0:00:58  iter: 2359  total_loss: 0.1184  loss_cls: 0.02131  loss_box_reg: 0.08721  loss_rpn_cls: 0.00176  loss_rpn_loc: 0.005463  time: 0.4255  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:11 d2.utils.events]: \u001b[0m eta: 0:00:50  iter: 2379  total_loss: 0.1442  loss_cls: 0.03067  loss_box_reg: 0.08485  loss_rpn_cls: 0.001247  loss_rpn_loc: 0.003065  time: 0.4254  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:19 d2.utils.events]: \u001b[0m eta: 0:00:41  iter: 2399  total_loss: 0.1235  loss_cls: 0.02224  loss_box_reg: 0.09198  loss_rpn_cls: 0.0007336  loss_rpn_loc: 0.005551  time: 0.4252  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:27 d2.utils.events]: \u001b[0m eta: 0:00:33  iter: 2419  total_loss: 0.1379  loss_cls: 0.04039  loss_box_reg: 0.08325  loss_rpn_cls: 0.0006176  loss_rpn_loc: 0.003523  time: 0.4251  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:36 d2.utils.events]: \u001b[0m eta: 0:00:25  iter: 2439  total_loss: 0.1246  loss_cls: 0.02358  loss_box_reg: 0.08121  loss_rpn_cls: 0.001071  loss_rpn_loc: 0.004531  time: 0.4252  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:44 d2.utils.events]: \u001b[0m eta: 0:00:16  iter: 2459  total_loss: 0.1306  loss_cls: 0.02478  loss_box_reg: 0.07791  loss_rpn_cls: 0.001597  loss_rpn_loc: 0.007061  time: 0.4251  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:41:53 d2.utils.events]: \u001b[0m eta: 0:00:08  iter: 2479  total_loss: 0.1272  loss_cls: 0.02025  loss_box_reg: 0.08935  loss_rpn_cls: 0.001012  loss_rpn_loc: 0.005099  time: 0.4250  data_time: 0.0026  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:42:02 d2.utils.events]: \u001b[0m eta: 0:00:00  iter: 2499  total_loss: 0.1403  loss_cls: 0.02134  loss_box_reg: 0.105  loss_rpn_cls: 0.0008825  loss_rpn_loc: 0.004636  time: 0.4249  data_time: 0.0025  lr: 0.00025  max_mem: 3129M\n",
      "\u001b[32m[10/15 14:42:02 d2.engine.hooks]: \u001b[0mOverall training speed: 2498 iterations in 0:17:41 (0.4249 s / it)\n",
      "\u001b[32m[10/15 14:42:02 d2.engine.hooks]: \u001b[0mTotal training time: 0:17:43 (0:00:01 on hooks)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'fiugre'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_20453/1662586660.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_instance_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'instances'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiugre\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./img.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'fiugre'"
     ]
    }
   ],
   "source": [
    "wandb.init(project=PROJECT_NAME,name='baseline',sync_tensorboard=True)\n",
    "cfg = get_cfg()\n",
    "cfg.merge_from_file(model_zoo.get_config_file(model))\n",
    "cfg.DATASETS.TRAIN = ('data')\n",
    "cfg.DATASETS.TEST = ('data')\n",
    "cfg.DATASETS.NUM_WORKERS = 2\n",
    "cfg.SOLVER.MAX_ITER = 2500\n",
    "cfg.SOLVER.STEPS = []\n",
    "cfg.TEST.EVAL_PERIOD = 50\n",
    "cfg.SOLVER.IMS_PER_BATCH = 2\n",
    "cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(labels)\n",
    "cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128\n",
    "cfg.SOLVER.BASE_LR = 0.00025\n",
    "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model)\n",
    "cfg.TEST.EVAL_PERIOD = 50\n",
    "trainer = DefaultTrainer(cfg)\n",
    "trainer.resume_or_load(resume=False)\n",
    "trainer.train()\n",
    "cfg.MODEL.WEIGHTS = './output/model_final.pth'\n",
    "predictor = DefaultPredictor(cfg)\n",
    "img = cv2.imread('./data/IMG_20181228_101826.jpg')\n",
    "preds = predictor(img)\n",
    "v = Visualizer(img[:,:,::-1],metadata=metadata)\n",
    "v = v.draw_instance_predictions(preds['instances'].to('cpu'))\n",
    "v = v.get_image()[:,:,::-1]\n",
    "plt.fiugre(figsize=(12,6))\n",
    "plt.imshow(v)\n",
    "plt.savefig('./img.png')\n",
    "plt.close()\n",
    "wandb.log({'Img':wandb.Image(cv2.imread('./img.png'))})\n",
    "evaluator = COCOEvaluator('data',output_dir='./output/')\n",
    "val_loader = build_detection_test_loader('data',cfg)\n",
    "metrics = inference_on_dataset(predictor.model,val_loader,evaluator)\n",
    "wandb.log(metrics)\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "52aeaba0-c8ba-41b1-9621-5ee82a66b320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 177/177 [00:00<00:00, 9631.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[10/15 14:43:42 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n",
      "\u001b[32m[10/15 14:43:42 d2.data.common]: \u001b[0mSerializing 177 elements to byte tensors and concatenating them all ...\n",
      "\u001b[32m[10/15 14:43:42 d2.data.common]: \u001b[0mSerialized dataset takes 0.07 MiB\n",
      "\u001b[32m[10/15 14:43:42 d2.evaluation.evaluator]: \u001b[0mStart inference on 177 batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[10/15 14:43:45 d2.evaluation.evaluator]: \u001b[0mInference done 11/177. Dataloading: 0.0006 s/iter. Inference: 0.2451 s/iter. Eval: 0.0001 s/iter. Total: 0.2459 s/iter. ETA=0:00:40\n",
      "\u001b[32m[10/15 14:43:50 d2.evaluation.evaluator]: \u001b[0mInference done 32/177. Dataloading: 0.0009 s/iter. Inference: 0.2461 s/iter. Eval: 0.0001 s/iter. Total: 0.2472 s/iter. ETA=0:00:35\n",
      "\u001b[32m[10/15 14:43:55 d2.evaluation.evaluator]: \u001b[0mInference done 52/177. Dataloading: 0.0009 s/iter. Inference: 0.2476 s/iter. Eval: 0.0001 s/iter. Total: 0.2487 s/iter. ETA=0:00:31\n",
      "\u001b[32m[10/15 14:44:00 d2.evaluation.evaluator]: \u001b[0mInference done 72/177. Dataloading: 0.0009 s/iter. Inference: 0.2506 s/iter. Eval: 0.0001 s/iter. Total: 0.2518 s/iter. ETA=0:00:26\n",
      "\u001b[32m[10/15 14:44:06 d2.evaluation.evaluator]: \u001b[0mInference done 92/177. Dataloading: 0.0009 s/iter. Inference: 0.2508 s/iter. Eval: 0.0001 s/iter. Total: 0.2519 s/iter. ETA=0:00:21\n",
      "\u001b[32m[10/15 14:44:11 d2.evaluation.evaluator]: \u001b[0mInference done 113/177. Dataloading: 0.0009 s/iter. Inference: 0.2501 s/iter. Eval: 0.0001 s/iter. Total: 0.2513 s/iter. ETA=0:00:16\n",
      "\u001b[32m[10/15 14:44:16 d2.evaluation.evaluator]: \u001b[0mInference done 134/177. Dataloading: 0.0009 s/iter. Inference: 0.2499 s/iter. Eval: 0.0001 s/iter. Total: 0.2510 s/iter. ETA=0:00:10\n",
      "\u001b[32m[10/15 14:44:21 d2.evaluation.evaluator]: \u001b[0mInference done 154/177. Dataloading: 0.0009 s/iter. Inference: 0.2499 s/iter. Eval: 0.0001 s/iter. Total: 0.2510 s/iter. ETA=0:00:05\n",
      "\u001b[32m[10/15 14:44:26 d2.evaluation.evaluator]: \u001b[0mInference done 175/177. Dataloading: 0.0009 s/iter. Inference: 0.2496 s/iter. Eval: 0.0001 s/iter. Total: 0.2508 s/iter. ETA=0:00:00\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:43.211325 (0.251229 s / iter per device, on 1 devices)\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:42 (0.249617 s / iter per device, on 1 devices)\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./output/coco_instances_results.json\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions with unofficial COCO API...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *bbox*\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.02 seconds.\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.01 seconds.\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.793\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.874\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.874\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.836\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.794\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.921\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.926\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.926\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.942\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.926\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n",
      "|   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |\n",
      "|:------:|:------:|:------:|:-----:|:------:|:------:|\n",
      "| 79.311 | 87.416 | 87.416 |  nan  | 83.604 | 79.422 |\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mSome metrics cannot be computed and is shown as NaN.\n",
      "\u001b[32m[10/15 14:44:27 d2.evaluation.coco_evaluation]: \u001b[0mPer-category bbox AP: \n",
      "| category     | AP     | category          | AP     | category       | AP     |\n",
      "|:-------------|:-------|:------------------|:-------|:---------------|:-------|\n",
      "| Arduino_Nano | 82.571 | Heltec_ESP32_Lora | 83.557 | Raspberry_Pi_3 | 83.606 |\n",
      "| ESP8266      | 67.512 |                   |        |                |        |\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 37534\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Program ended successfully.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: - 0.52MB of 0.52MB uploaded (0.00MB deduped)\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find user logs for this run at: wandb/run-20211015_142409-c3w5l7u8/logs/debug.log\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find internal logs for this run at: wandb/run-20211015_142409-c3w5l7u8/logs/debug-internal.log\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:        _step 6\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:     _runtime 1212\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   _timestamp 1634289267\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:        _step ▁▂▃▅▆▇█\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:     _runtime ▁▂▂▂▅▅█\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   _timestamp ▁▂▂▂▅▅█\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 6 media file(s), 0 artifact file(s) and 0 other file(s)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mbaseline\u001b[0m: \u001b[34mhttps://app.wandb.ai/ranuga-d/Microcontroller-Detection-Learning-Detectron2/runs/c3w5l7u8\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.imshow(v)\n",
    "plt.savefig('./img.png')\n",
    "plt.close()\n",
    "wandb.log({'Img':wandb.Image(cv2.imread('./img.png'))})\n",
    "evaluator = COCOEvaluator('data',output_dir='./output/')\n",
    "val_loader = build_detection_test_loader(cfg,'data')\n",
    "metrics = inference_on_dataset(predictor.model,val_loader,evaluator)\n",
    "wandb.log(metrics)\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5564d5fb-23dd-4086-8a40-0f63376684b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(cfg,'cfg.pt')\n",
    "torch.save(cfg,'cfg.pth')\n",
    "torch.save(predictor,'predictor.pt')\n",
    "torch.save(predictor,'predictor.pth')\n",
    "torch.save(evaluator,'evaluator.pt')\n",
    "torch.save(evaluator,'evaluator.pth')\n",
    "torch.save(v,'img.pt')\n",
    "torch.save(v,'img.pth')\n",
    "torch.save(model,'model.pt')\n",
    "torch.save(model,'model.pth')\n",
    "torch.save(labels,'labels.pt')\n",
    "torch.save(labels,'labels.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "baf3c346-37fb-4935-8dc4-46211299474d",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(metrics,'metrics.pt')\n",
    "torch.save(metrics,'metrics.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2f6909d-efa2-47fb-ab6b-b532f00d5e6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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