765 lines (764 with data), 254.6 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hs_dataset import HSDataset\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "import albumentations as A\n",
    "IMG_DIR = '../2d_data/images/'\n",
    "MASK_DIR = '../2d_data/masks/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_start = 50\n",
    "x_end = 462\n",
    "y_start = 50\n",
    "y_end = 462 \n",
    "train_transforms = A.Compose(\n",
    "                                [\n",
    "                                A.Crop(x_start, y_start, x_end, y_end, always_apply= True),\n",
    "                                #A.CLAHE(p=1),\n",
    "                                #A.GaussNoise(p=1),\n",
    "                                A.Resize(height = 256, width = 256),\n",
    "                                A.Rotate(limit = 30, p = 0.2), \n",
    "                                A.HorizontalFlip(p = 0.2), \n",
    "                                A.VerticalFlip(p = 0.2), \n",
    "                                A.Normalize(\n",
    "                                            mean = [0.0, 0.0, 0.0],\n",
    "                                            std = [1.0, 1.0, 1.0], \n",
    "                                            max_pixel_value = 255.0\n",
    "                                            ),\n",
    "                                ToTensorV2(), # conversión a tensor de Pytorch\n",
    "                                ],\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def dilate(image, kernel_size):\n",
    "    kernel = np.ones(kernel_size, np.uint8)\n",
    "    dilated_image = cv2.dilate(image, kernel, iterations=1)\n",
    "    return dilated_image\n",
    "\n",
    "def erode(image, kernel_size):\n",
    "    kernel = np.ones(kernel_size, np.uint8)\n",
    "    eroded_image = cv2.erode(image, kernel, iterations=1)\n",
    "    return eroded_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = HSDataset(IMG_DIR, MASK_DIR, train_transforms, 40, 350, normalized=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, mask =  dataset.__getitem__(33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image = image.numpy().transpose(1, 2, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2cdc4e890>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2cd9012d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(erode(image,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id = 0\n",
    "id +=1\n",
    "id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/danielcrovo/anaconda3/envs/DL/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'collections.OrderedDict' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:354\u001b[0m, in \u001b[0;36m_check_seekable\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 354\u001b[0m     f\u001b[39m.\u001b[39;49mseek(f\u001b[39m.\u001b[39mtell())\n\u001b[1;32m    355\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'collections.OrderedDict' object has no attribute 'seek'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mUnet\u001b[39;00m \u001b[39mimport\u001b[39;00m UNET\n\u001b[1;32m      3\u001b[0m model \u001b[39m=\u001b[39m UNET(\u001b[39m3\u001b[39m,\u001b[39m1\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m torch\u001b[39m.\u001b[39;49mload(model\u001b[39m.\u001b[39;49mstate_dict(torch\u001b[39m.\u001b[39;49mload(\u001b[39m'\u001b[39;49m\u001b[39m../checkpoints/Unet_my_checkpoint.pth [conflicted].tar\u001b[39;49m\u001b[39m'\u001b[39;49m)[\u001b[39m'\u001b[39;49m\u001b[39mstate_dict\u001b[39;49m\u001b[39m'\u001b[39;49m]))\n",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:791\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mencoding\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m pickle_load_args\u001b[39m.\u001b[39mkeys():\n\u001b[1;32m    789\u001b[0m     pickle_load_args[\u001b[39m'\u001b[39m\u001b[39mencoding\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m--> 791\u001b[0m \u001b[39mwith\u001b[39;00m _open_file_like(f, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m opened_file:\n\u001b[1;32m    792\u001b[0m     \u001b[39mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[1;32m    793\u001b[0m         \u001b[39m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[1;32m    794\u001b[0m         \u001b[39m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[1;32m    795\u001b[0m         \u001b[39m# reset back to the original position.\u001b[39;00m\n\u001b[1;32m    796\u001b[0m         orig_position \u001b[39m=\u001b[39m opened_file\u001b[39m.\u001b[39mtell()\n",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:276\u001b[0m, in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[39mreturn\u001b[39;00m _open_buffer_writer(name_or_buffer)\n\u001b[1;32m    275\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mr\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m mode:\n\u001b[0;32m--> 276\u001b[0m     \u001b[39mreturn\u001b[39;00m _open_buffer_reader(name_or_buffer)\n\u001b[1;32m    277\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    278\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExpected \u001b[39m\u001b[39m'\u001b[39m\u001b[39mr\u001b[39m\u001b[39m'\u001b[39m\u001b[39m or \u001b[39m\u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m\u001b[39m in mode but got \u001b[39m\u001b[39m{\u001b[39;00mmode\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:261\u001b[0m, in \u001b[0;36m_open_buffer_reader.__init__\u001b[0;34m(self, buffer)\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, buffer):\n\u001b[1;32m    260\u001b[0m     \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(buffer)\n\u001b[0;32m--> 261\u001b[0m     _check_seekable(buffer)\n",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:357\u001b[0m, in \u001b[0;36m_check_seekable\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    356\u001b[0m \u001b[39mexcept\u001b[39;00m (io\u001b[39m.\u001b[39mUnsupportedOperation, \u001b[39mAttributeError\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m--> 357\u001b[0m     raise_err_msg([\u001b[39m\"\u001b[39;49m\u001b[39mseek\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mtell\u001b[39;49m\u001b[39m\"\u001b[39;49m], e)\n\u001b[1;32m    358\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/DL/lib/python3.10/site-packages/torch/serialization.py:350\u001b[0m, in \u001b[0;36m_check_seekable.<locals>.raise_err_msg\u001b[0;34m(patterns, e)\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[39mif\u001b[39;00m p \u001b[39min\u001b[39;00m \u001b[39mstr\u001b[39m(e):\n\u001b[1;32m    347\u001b[0m         msg \u001b[39m=\u001b[39m (\u001b[39mstr\u001b[39m(e) \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m. You can only torch.load from a file that is seekable.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    348\u001b[0m                         \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m Please pre-load the data into a buffer like io.BytesIO and\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    349\u001b[0m                         \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m try to load from it instead.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 350\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mtype\u001b[39m(e)(msg)\n\u001b[1;32m    351\u001b[0m \u001b[39mraise\u001b[39;00m e\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'collections.OrderedDict' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead."
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from Unet import UNET\n",
    "model = UNET(3,1)\n",
    "\n",
    "torch.load(model.state_dict(torch.load('../checkpoints/Unet_my_checkpoint.pth [conflicted].tar')['state_dict']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../scores/MANET_.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_dice</th>\n",
       "      <th>train_jaccard</th>\n",
       "      <th>dice_score</th>\n",
       "      <th>jaccard_score</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.087677</td>\n",
       "      <td>0.108417</td>\n",
       "      <td>0.063022</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.600743</td>\n",
       "      <td>0.282656</td>\n",
       "      <td>0.172786</td>\n",
       "      <td>0.142250</td>\n",
       "      <td>0.085746</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.485984</td>\n",
       "      <td>0.362717</td>\n",
       "      <td>0.232265</td>\n",
       "      <td>0.176420</td>\n",
       "      <td>0.111765</td>\n",
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       "      <th>...</th>\n",
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       "      <th>256</th>\n",
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       "      <th>257</th>\n",
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       "      <th>258</th>\n",
       "      <td>97</td>\n",
       "      <td>0.092229</td>\n",
       "      <td>0.938405</td>\n",
       "      <td>0.886313</td>\n",
       "      <td>0.859641</td>\n",
       "      <td>0.825389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>98</td>\n",
       "      <td>0.115307</td>\n",
       "      <td>0.922139</td>\n",
       "      <td>0.871133</td>\n",
       "      <td>0.758947</td>\n",
       "      <td>0.722365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>99</td>\n",
       "      <td>0.079929</td>\n",
       "      <td>0.944857</td>\n",
       "      <td>0.895942</td>\n",
       "      <td>0.858001</td>\n",
       "      <td>0.822592</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>261 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     epoch  train_loss  train_dice  train_jaccard  dice_score  jaccard_score\n",
       "0        0    3.200016    0.039759       0.020428    0.037158       0.019634\n",
       "1        1    2.921519    0.065598       0.034340    0.062902       0.034481\n",
       "2        2    2.846555    0.156394       0.087677    0.108417       0.063022\n",
       "3        3    2.600743    0.282656       0.172786    0.142250       0.085746\n",
       "4        4    2.485984    0.362717       0.232265    0.176420       0.111765\n",
       "..     ...         ...         ...            ...         ...            ...\n",
       "256     95    0.080821    0.937158       0.890050    0.861564       0.828710\n",
       "257     96    0.079362    0.939575       0.894031    0.860349       0.826620\n",
       "258     97    0.092229    0.938405       0.886313    0.859641       0.825389\n",
       "259     98    0.115307    0.922139       0.871133    0.758947       0.722365\n",
       "260     99    0.079929    0.944857       0.895942    0.858001       0.822592\n",
       "\n",
       "[261 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_dice</th>\n",
       "      <th>train_jaccard</th>\n",
       "      <th>dice_score</th>\n",
       "      <th>jaccard_score</th>\n",
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       "      <td>0.062916</td>\n",
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       "      <td>0.043568</td>\n",
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       "      <td>0.758260</td>\n",
       "      <td>0.723098</td>\n",
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       "      <th>196</th>\n",
       "      <td>256</td>\n",
       "      <td>95</td>\n",
       "      <td>0.080821</td>\n",
       "      <td>0.937158</td>\n",
       "      <td>0.890050</td>\n",
       "      <td>0.861564</td>\n",
       "      <td>0.828710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>257</td>\n",
       "      <td>96</td>\n",
       "      <td>0.079362</td>\n",
       "      <td>0.939575</td>\n",
       "      <td>0.894031</td>\n",
       "      <td>0.860349</td>\n",
       "      <td>0.826620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>258</td>\n",
       "      <td>97</td>\n",
       "      <td>0.092229</td>\n",
       "      <td>0.938405</td>\n",
       "      <td>0.886313</td>\n",
       "      <td>0.859641</td>\n",
       "      <td>0.825389</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
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       "      <td>0.115307</td>\n",
       "      <td>0.922139</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  epoch  train_loss  train_dice  train_jaccard  dice_score  \\\n",
       "0       60      0    3.356456    0.038992       0.019954    0.035672   \n",
       "1       61      1    3.028554    0.055432       0.028650    0.046448   \n",
       "2       62      2    2.933625    0.075920       0.039747    0.062916   \n",
       "3       63      3    2.871678    0.103241       0.055107    0.080106   \n",
       "4       64      4    2.805723    0.141347       0.076901    0.097725   \n",
       "..     ...    ...         ...         ...            ...         ...   \n",
       "195    255     94    0.083382    0.936606       0.888928    0.758260   \n",
       "196    256     95    0.080821    0.937158       0.890050    0.861564   \n",
       "197    257     96    0.079362    0.939575       0.894031    0.860349   \n",
       "198    258     97    0.092229    0.938405       0.886313    0.859641   \n",
       "199    259     98    0.115307    0.922139       0.871133    0.758947   \n",
       "\n",
       "     jaccard_score  \n",
       "0         0.018527  \n",
       "1         0.024397  \n",
       "2         0.033608  \n",
       "3         0.043568  \n",
       "4         0.054073  \n",
       "..             ...  \n",
       "195       0.723098  \n",
       "196       0.828710  \n",
       "197       0.826620  \n",
       "198       0.825389  \n",
       "199       0.722365  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "new_df =df[60:-1].reset_index()\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.drop(['index'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['epoch'] = new_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.drop(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='train_dice'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KYdqiDSRlly2eFuzryaU9wvh40wmig71JzSmisMTBw1f25PZRnSpcu/5wOre9u5GcolKCfT2dY0Oig705nlHAkA6t2HY8i2K7A19PN+fYl8/uHsHAdr90KbwXf5S5X+7Cy8NK93B/9iTmOMeeAHi6WXn6mr5cPbCsq+mZpXtZujOJf9wwgH5tgwDYdjyT9q19CPL5ZSBrQzGMslYvf1vjWg9l0uvxrD9S1o1jtcC6OZc6191pqo6k5ZGcXVjldPH6tvNkFr/952o83a38/FDsWQc/10VtPr+1t5SINHv5xaVk5v8y4LTU7iD+UDr5xWVdNWsOpvH8sv18sz2R99cdq3Dt6bxibngjnqTsQrqE+dE7KoCMvGI+3lQ2LfqRcb2cTf9/+3YPf/1qFwVnAsqq/alMe3sDOUWlDG7fiqV/uoibRnQA4HhGAT6ebvxz8kAev6o3AHnFdsobMz7e+Mu0a8MweC++rK45Y3vy5YwL+ezuEUQHl40L6RTqy+fTRziDDcDsK3qwYtZvnMEGoH90kCnBBspacQK8PBpVsAGc69OU/7mpBxuAjiG+pgQbKBvMPf3izrx781D8vczrHFK3lIg0a8fS87jhjXWk5xZzX2xXxvaJYNbH29ickEmPCH/+b9pgHvlip/P81388xJRh7ZzL7L+1+gin80voGubHx3cOJ7eolAkvryEjr5gL2gVx+ZmVWw8k5/LO2qO8s/YoX287ha/NnVOZBZQ6DC7pEcarUwbi5eHGg2N7sO5wOnuTcrjv0q5EBHoxeWg7QvxsuLtZ8LBa+cNb6/lm2ykeG98LLw834g+lczAlF19PN64eWNbV0KdNIEvuvYj4Q+lc1DXEWa/Uzuhuoc4F/64Z2PYcZ8u5WCwW/jzG9TOlal2HuqVEpLHbn5zDloTTXD2wbZWb/Z3NycwCrl8YX2Gcyv/y9nCjoMROmL8Nm4eV4xkFzu6lzPxiLnxmBblFpSz8wyCu6FM2Y2XTsdO8tvIgs8Z0p0fEL79HVu1PZfYn253dV1A2s+iF6wfg6f5L3Rl5xWw6drrKKbgOh8Go51Zw4nQB/7hhAFcNaMMf/7WR73clc+Ow9jwxsU+N71/OzeEwuOHNdeQWlvLZ3eeePi7m0To31VC4EWlasgtL+M1zZQulje0TwUuTLzjrbsblS9sfTcvjSHo+6w+nk5JTRKcQX265sCN//+8+MvNLGNy+Ffdf1o2/fLLdGXxe/v0F5Bfb+csn2wnx8+SHWb/h/346wktxB+gR4c+3915UowGw+cWl7DqVjdViIdDbnc6hfrXuinlh2X7+EXeAi7qG8MDl3bn61TU4DFh2/6hq9x4Sac4UbqqhcCPSuO0+lc3URRuY0D+KR3/bk+e+38erKw85X7+ybwTPXtsfP5s7O09m8VLcAX4+msHp/JIq369tK28+vnM4kYHeZOQVs+NkFiM7t8bdzcrJzAIe/HQ7XcL8mPvbXpQ6DC59fhUJGWXrlTgMg6JSB69OGVjl/kj1JSE9n1HPrQB+2ULgwi4hvH9bTIPVINLYKNxUQ+FGpHG78a31/HQgDYCpw9uz+OfjFJU6uPXCjvwr/hjFdkeFXYt/LSLAiw4hPnQM8aVDa186hPgysksIfraaj0dZfzidBz7exonTZS063cL9WHrfqDpNWz4fv57FM6F/FI/8tidh/k1/sKtIXSncVEPhRqR+HEjO4dEvdzK6Wxi3XtixwhiTmoo/lM7kN9dhtYDjV7+ZhnUK5sPbh7FqfyqPf73buauyxVL2wX/LyI50Dfdz2aBawzDYeTKbdYfTie0VTseQ6pe8rw+HU3P5cEMCE/q3qdPqxyLNjcJNNRRuROrH3f/exLc7kgDoGubH09f0cy7XX5X84lJs7m7OJdoNw+DahfFsOnaaG4e1J9Dbg5dXlC2s9vWMCyt8wB/PyGfr8bLZThqDItIyaIViEWlQp/OKWb47BYBAbw8OpOQy5f/W8c7NQxnWqTX5xaWsPpDG7sRs9iRmszsxm+MZBfRrG8jHdw7H5u7GD3tT2HTsNF4eVu65pAuh/jaCfT0J9Pao1HIRHexDdLCPGbcqIk2Awo2I1Eh6btFZl5f/atspiu0OekcF8O/bYvjT4q2s3JfKre/8zE0jO/DRhuPO3aV/bfuJLP4Zd5CbRnZw7qw8bXgH50Jqt1xY+w0IRUQUbkTknMo3iLzv0q7cf1k3oGx9EMuZHYw/ObNa77WD2hLk48nCPwzi1nd/Zs3BdF5ZUTbTqU2QN8M6taZXVAA9I/1JzCzkgY+38dqqQ6zan0pydhGdQ32599Kupt2niDQPCjciUq1TmQU8v6xsBdd/xB2gV1QA7Vv7MOODLeQWlnL94LbsOJmFh5uFq85s1Ofl4cabUwcz/d+bOX66gDsu6sTvBraptD7ND/tSWLI9kR0ns/D2cGPhHwbhW4uZTSIiVdFvERGp1lPf7qGwxIGPpxv5xXZmLt6K3TAoLCnbtPGlH8oG/V7SI4xg31/2LfLxdOftm4dW+97zJvRm3aF00vOKmX91Xw0OFhGX0MaZIlKJYRhk5hfzzfZTfLM9EasFPrpjGEM7BpNXbKewxMFFXUN4ZFxPgn09sVhg6vAOtf4+rf1sfHb3CD66YxgTL2jj+hsRkRZJLTciLcCRtDye+W4vsb3CuXZQ9ZsDnsws4I73NrLrVLbz2OSh7ejXNohXfj+Qx77aSe+oQO4c3Rk3q4XJQ9uRlltE+9Z1WwumfWvfOl8rIlIVhRuRZuh4Rj5puUUMiA4it6iU2979mUOpeSzdlcSOE5k88tteVe7PdCQtjylvruNUVtnGj2H+NoZ0COYvZ3b5DfW38eqUQRWu8bW5a5yMiDQq+o0k0swUldq5duFakrOLuKxXOA6HwaHUPAK83MkuLOXd+GMkZRfy+o2DK1x3LD2P6xbGk5ZbRKdQX/51awxtgrxNugsRkbrTmBuRJiozv5hV+1PJLy6tcHzF3hSSs4sAWLY7mbi9KXi6WXnv1hhev3EQHm4Wvt+VzLbjmc5rikrtTP9gM2m5RfSI8Oc/fxyuYCMiTZbCjUgTlJRVyMRX1jBt0QYGP7mcez/cwvGMfADnmjMT+kcxIDoIqwWe/F0fBkQHMaZ3BOP7RwHw9pojzvd7askedp7MppWPB2/fPISQsyzWJyLSFKhbSqQJMAyD73clY7GU7Xx9/+KtHE3Px91qIb/YzlfbTrE3KZtFNw1hxb5UAO69tCudQ33JLiwl0NvD+V63jOzIZ5tP8s32ROZc2ZM1B9N4N/4YAAsmDSAyUC02ItK0KdyINHKGYfDYV7t470wAKdcmyJuP7hhGam4Rd7y3kf3JuUx+cx12h8GA6CC6hPkBVAg2AH3aBDKkQyt+Pnqau/+9mc0JpwG4c3RnLu4e1jA3JSJSj9QtJdKIORwGj365k/fij2GxQI8If2zuVtoF+/Dh7cOIDvZhYLtWPHttPwCOZxQAnHO6980jy/Zs2nTsNIYBfxjWjr+M6V6/NyMi0kDUciPSSO1NyuavX+1i3eEMLBZ49pp+XDc4usKeTuUu6RHOlJh2/Ht9Ap7uVsb3i6r2vS/vFU50sDfHMwq455IuzLysW4X3ExFpyhRuRBqhd9ce5fGvd+EwwOZuZf7Vfbl6YFlrjNVadQh5eFxP7A6Dfm2DCPTxqPKccu5uVj68fRinMgsZ2jHY5fWLiJhJ4UakkSkssfPc9/twGHBF7wge+W1P2rbyOed1Pp7uPH1Nvxp/n7atfGr0viIiTY3CjUgjYBiGs1vov7uTyS0qpW0rb16dMvCsLTUiIlI1hRsRk2w8msGCZfs5mpZHck4RNw5rz18n9ObTM+vUXH1BGwUbEZE6ULgRcaF31x7l8y0nSc4upLjUwfj+Udx6YUeig32wOwysZwYCbzueybRFG8grtjuvfWftUbqF+/PTgbJ1asrH2IiISO1YDMMwzC6iIWVnZxMYGEhWVhYBAQFmlyPNSEZeMUP/tpxSR8V/UlZL2QDe4lIHUYFexPYK5+ttpzidX8KIzq2ZNaY7X209xTtrjzqvGdS+FZ/eNaKB70BEpPGqzee3Wm5EXGTJjkRKHQY9Ivx5+pp+nM4v5q2fjrD6YBrFpQ4ATmUVOhfj6982kDemDsbP5k6vyABWH0zjYEouAFcPbGPafYiINHUKNyIu8uWWk0DZAnoDooMAuLh7GMnZhZQ6DDzdrGw7nsm3OxMpLLHzt4l98bOV/RP08nBjwfX9ufrVtdjcrfy2b/Xr1IiIyNkp3IjU0fGMfJ5cspspMe3pGOLLxmOnsVhwbkxZLjzAy/nn2F7hxPYKr/L9+rUN4ssZI3G3Ws+5To2IiJydwo1INUrsDl5dcYjDablkF5TQMzKAP4/pjsVi4f11x/h+VzIr9qUyulsoAMM7ta4QZmqrd1Sgq0oXEWmxFG5EqvHu2qO8sHy/8+8r9qUyrl8kvaMCWX8kA4DiUgfLdicDMHGAxsqIiJhNG2eKnEV6bhH/iDsAwI3D2tOvbVmrypqDaeQXl7LzZBaA87inu5Ur+kaYU6yIiDgp3IicxQvL95NTWErvqAD+OqE3V51plVl9MJ2tCZmUOgwiArz48PZh/GFYO564qjcBXhorIyJiNnVLiVRh2/FMPlifAMDc3/bCzWrhwi4hAGw4kk6fqLI1FoZ0DMbX5s6TE/uaVquIiFSklhuRXzEMg/9sPM6kN+JxGHBl3whiOrUGoFu4H6H+NgpLHPz7TPAZ2qGVmeWKiEgVFG6kxTIMg5X7UkjKKnQee+KbPfzlk+0UljgY1S2Up373S4uMxfJL601WQQlQ1nIjIiKNi7qlpMVatOYoT3yzm9a+nnx0xzA2HM1g0ZojWCww6/Lu3DW6c6WNKy/sEsLnZxbrC/T2oFuYvxmli4hINRRupEVKSM/n79/vAyA9r5gb3lhHdmFZa8ysy7sz/eIuVV438kzLDcDg9q20a7eISCPUKLqlXnnlFTp06ICXlxcxMTFs2LCh2vNffPFFunfvjre3N9HR0dx///0UFhZWe41IOcMweOjzHRSU2BnSoRU9IwNIzyumxG4wrl8kd/+m81mvjQj0omuYHwBD1SUlItIomd5ys3jxYmbOnMnChQuJiYnhxRdfZMyYMezbt4+wsLBK53/wwQc8+OCDLFq0iBEjRrB//35uuukmLBYLCxYsMOEOpCkxDINXVx5i9cE0bO5Wnru2PwHeHtz30RbcrBaeu7YfFkv1rTGzr+jBfzYe57rB0Q1UtYiI1IbFMAzDzAJiYmIYMmQIL7/8MgAOh4Po6GjuueceHnzwwUrnz5gxgz179hAXF+c89sADD7B+/XpWr159zu9Xmy3TpXkpKrXz8Oc7+WTTCQAevrInt4/qZHJVIiJSE7X5/Da1W6q4uJhNmzYRGxvrPGa1WomNjSU+Pr7Ka0aMGMGmTZucXVeHDx/m22+/5corr6zy/KKiIrKzsyt8Sct074db+GTTCawWePS3vbjtoo5mlyQiIvXA1G6ptLQ07HY74eEVd0kODw9n7969VV7z+9//nrS0NC688EIMw6C0tJQ777yThx56qMrz58+fz+OPP+7y2qXxO5VZQHiAF25WC3sSs/l+VzJWC7xz81BGndnoUkREmp9GMaC4NlauXMlTTz3Fq6++yubNm/nss89YsmQJTzzxRJXnz5kzh6ysLOfX8ePHG7hiaWhH0/K47d2fGfH0D8z4YDOGYfD2miMAjO0TqWAjItLMmdpyExISgpubG8nJyRWOJycnExFR9QaEjz76KDfeeCO33XYbAH379iUvL4877riDhx9+GKu1Yl6z2WzYbLb6uQFpVHKLSvnnDwdYtPoIJfayoWTf7Uzi9R8P88XWUwDccmEHEysUEZGGYGrLjaenJ4MGDaowONjhcBAXF8fw4cOrvCY/P79SgHFzcwPKZsJI85ZbVMqi1Ue4buFaPtt8wnn8m+2nuPjvK3l91WFK7AajuoVy88gOADz93V6KSx30bxvIwHbaLkFEpLkzfSr4zJkzmTZtGoMHD2bo0KG8+OKL5OXlcfPNNwMwdepU2rRpw/z58wEYP348CxYs4IILLiAmJoaDBw/y6KOPMn78eGfIkebpq22neOTzHWQXlgLw89HTJGTkk55bzL/WHQOgfWsfHh3Xi0t7huEwYPuJLDYdOw3ALRd2POc0bxERafpMDzeTJk0iNTWVuXPnkpSUxIABA1i6dKlzkHFCQkKFlppHHnkEi8XCI488wsmTJwkNDWX8+PH87W9/M+sWpAHsS8rhzx9vo6jUQccQX/q3DeSLrad4cfkB5zkzLu7CPZd2weZeFnLdLPD8df2Z8PJqQvxsXNk30qzyRUSkAZm+zk1D0zo3TU9hiZ2rXl7DvuQcLu4eylvThmC1Wngv/iiPfbULf5s7/7jhAi7uUXnRR4Cs/BLc3Sz42kzP8iIiUke1+fzWb3tp9J76dg/7knMI8fPkuev6O/dzmjq8A6O7hRLg5UErX8+zXh/o49FQpYqISCOgcCON2sJVh3gvvmw8zXPX9SfEr+LMt/atfc0oS0REGjGFG2lUDMNg6/FM8orsbD+ZybNLy3bufnBsDy7uXnW3k4iIyK8p3EijUWp38KfFW/lme2KF49Mv7sydo8++U7eIiMivKdxIo2B3GPz5k+18sz0RDzcLnUL8cBgGE/pHMeOSLmaXJyIiTYjCjTQKzy7dy+dbTuJutfDy7wcypnfVK1SLiIicS5PbW0qan7yiUucifM9f31/BRkREzovCjZhu6c4k8ovtdAzxZUL/KLPLERGRJk7hRkz36Zk9oq6+oI22RxARkfOmcCOmOplZQPzhdAB+N7CNydWIiEhzoHAjpvpiy0kMA4Z3ak3bVj5mlyMiIs2Awo2Yxu4w+HTTmS4ptdqIiIiLKNyIad5ec4TDaXn429wZqx27RUTERRRuxBSHU3N57vuyrRUeHtcTP+3YLSIiLqJPFGlQDofBodRcZn+6naJSBxd1DWHSkGizyxIRkWZE4UYazI/7U/nT4q1k5BUD4Ovpxvyr+2r6t4iIuJTCjdSbwhI77lYL7m5lvZ//WneMjLxivDysDIgO4t5LumqGlIiIuJzCjdSLgyk53PDGekL9bSy550IANh7NAODftw1jUPtWZpYnIiLNmMKNuFxydiHTFv1MWm4RablF7DqVjc3Dyun8Erw93OjbJtDsEkVEpBlTuBGXyisq5ea3f+ZkZoHzWNzeZFr72QAY2D4IT3dN0hMRkfqjTxlxqQ/WJ7A7MZsQP09mXNwFgB/2pvDzkbIuqSEdgs0sT0REWgCFG3Gp8k0w74vtxtQR7QHYfiKLVftTARjaUeFGRETql8KNuMzuU9nsTcrB083K+H6RhPl70b9t2fiarIISPNwsXBCtgcQiIlK/FG7EZT4702pzac8wgnw8AbikR7jz9X5tg/D2dDOlNhERaTkUbuS8PPCfbVz07A/8sDeZL7aeAuDqgW2dr1/aM8z5Z423ERGRhqDZUlJnWfklfLblBIYBt7yzEYBgX09Gdwt1ntM7KoCIAC+SsgsZ1knhRkRE6p/CjdTZuiPpGAZ4eVgpLHEAMKF/VIWp3haLhVemDGTnyawKoUdERKS+KNxIncUfSgfgukHRXNYrnJX7Urnnki6VzhvUvpVWJBYRkQajcCN1tuZgGgAjOrdmVLdQRqllRkREGgENKJY6Sckp5EBKLhYLDOvU2uxyREREnBRupE7Ku6R6RQbQytfT5GpERER+oXAjdVIebkZ0VquNiIg0LhpzIzVWXOrgpwOplNgd/HSgfLxNiMlViYiIVKRwIzWy61QWsz7ezp7EbOcxN6uFIdorSkREGhmFGzmnTzad4MFPt1PqMAjy8aBzqB95RaWM7ROJn03/CYmISOOiTyap1onT+Tz6xU5KHQZjeofz5MS+hPrbzC5LRETkrBRu5KwMw+CxL3dRUGJnaMdgFv5hEBaLxeyyREREqqXZUnJW3+9KIm5vCh5uFp76XR8FGxERaRIUbqRKdofBE9/sAeDO0Z3pEuZvckUiIiI1o3AjVfr5aAYnMwsI8HJn+sWV94sSERFprBRupEpLticCMKZ3BF4ebiZXIyIiUnMKN1KJ3WHw3c4kAMb1izS5GhERkdpRuJFKNhzJIC23iEBvD0Z20QrEIiLStCjcSCXf7ijvkgrHw03/iYiISNOiTy6poGKXVJTJ1YiIiNSewo1UsGRHImm5RQT5eGjHbxERaZIUblowwzBYfzidf607RmGJndyiUv62ZDcAt4zsqC4pERFpkrT9Qgu18WgGT327h80JmQB8svE43cL9Sc4uon1rH+4Y1cncAkVEROpI4aYFyi0q5aa3fya3qBRPdys2dyvbTmSx7UQWAH+d0Ftr24iISJOlfocWaPOx0+QWlRIZ6MXq2Rez5J6L6BFRtr3C5b3Cubh7mMkVioiI1J1ablqgDUcyABjeuTVh/l4AfHb3CNYeTOfCrlrXRkREmjaFmxaoPNwM6/jLbCgfT3die4WbVZKIiIjLqFuqhSkssbP1eCYAQzsGm1uMiIhIPVC4aWG2Hc+k2O4gzN9G+9Y+ZpcjIiLicgo3LUx5l9TQjsFYLBaTqxEREXE9hZsWZsPRsnAToy4pERFpphRuWpASu4NNx04DMLSjtlYQEZHmSeGmBdl5Mov8YjtBPh50DfMzuxwREZF6oXDTgqzYmwLA8E6tsVo13kZERJonhZsWZNmesnBzmdazERGRZkzhpoU4cTqfPYnZWC1oewUREWnWFG5aiLgzrTaDOwTTytfT5GpERETqj8JNC7F8TzIAl/VUl5SIiDRvjSLcvPLKK3To0AEvLy9iYmLYsGFDtednZmYyffp0IiMjsdlsdOvWjW+//baBqm16sgtLWHc4HUD7R4mISLNn+saZixcvZubMmSxcuJCYmBhefPFFxowZw759+wgLqzw2pLi4mMsuu4ywsDA++eQT2rRpw7FjxwgKCmr44puIVftSKbEbdA71pWOIr9nliIiI1CvTw82CBQu4/fbbufnmmwFYuHAhS5YsYdGiRTz44IOVzl+0aBEZGRmsXbsWDw8PADp06HDW9y8qKqKoqMj59+zsbNfeQCOXkVfM09/tBWBM7wiTqxEREal/pnZLFRcXs2nTJmJjY53HrFYrsbGxxMfHV3nNV199xfDhw5k+fTrh4eH06dOHp556CrvdXuX58+fPJzAw0PkVHR1dL/fSGJXaHcz4YDMnMwvo0NqHP47ubHZJIiIi9c7UcJOWlobdbic8vOI4kPDwcJKSkqq85vDhw3zyySfY7Xa+/fZbHn30UZ5//nmefPLJKs+fM2cOWVlZzq/jx4+7/D4aI8MwmPfNbtYeSsfH0403pg4m0NvD7LJERETqnendUrXlcDgICwvjjTfewM3NjUGDBnHy5Emee+45HnvssUrn22w2bDabCZWax+EweOTLnXywPgGA56/rT7dwf5OrEhERaRimhpuQkBDc3NxITk6ucDw5OZmIiKrHh0RGRuLh4YGbm5vzWM+ePUlKSqK4uBhPT63h8uBn2/nPxhNYLPD01X0Z2zfS7JJEREQajKndUp6engwaNIi4uDjnMYfDQVxcHMOHD6/ympEjR3Lw4EEcDofz2P79+4mMjFSwAU5mFvCfjSewWuClGy5g0pB2ZpckIiLSoExf52bmzJm8+eabvPvuu+zZs4e77rqLvLw85+ypqVOnMmfOHOf5d911FxkZGdx3333s37+fJUuW8NRTTzF9+nSzbqFROZyaC0CnUD/G948yuRoREZGGZ/qYm0mTJpGamsrcuXNJSkpiwIABLF261DnIOCEhAav1lwwWHR3N999/z/3330+/fv1o06YN9913H7NnzzbrFhqVo2l5AHRorfVsRESkZbIYhmGYXURDys7OJjAwkKysLAICAswux+Xmfb2bRWuOcPtFHXl4XC+zyxEREXGJ2nx+m94tJa51JK2sW6qDViIWEZEWqs7h5tChQzzyyCNMnjyZlJSyHae/++47du3a5bLipPaOpucD0FHdUiIi0kLVKdysWrWKvn37sn79ej777DNyc8taC7Zt21blWjPSMErtDo5nnAk3oQo3IiLSMtUp3Dz44IM8+eSTLFu2rML060suuYR169a5rDipnROnCyh1GHh5WAn39zK7HBEREVPUKdzs2LGD3/3ud5WOh4WFkZaWdt5FSd0c+dVMKavVYnI1IiIi5qhTuAkKCiIxMbHS8S1bttCmTZvzLkrq5oimgYuIiNQt3Nxwww3Mnj2bpKQkLBYLDoeDNWvWMGvWLKZOnerqGqWGjqaXhRuNtxERkZasTuHmqaeeokePHkRHR5Obm0uvXr0YNWoUI0aM4JFHHnF1jVJD5S03miklIiItWZ1WKPb09OTNN99k7ty57Nixg9zcXC644AK6du3q6vqkFpzdUlrjRkREWrDz2n4hOjqa6OhoV9Ui56Go1M6pzAIAOoT4mFyNiIiIeerULXXNNdfwzDPPVDr+7LPPct111513UVJ7xzPycRjgZ3Mn1M9mdjkiIiKmqVO4+fHHH7nyyisrHR87diw//vjjeRcltbfhyGmgrNXGYtE0cBERabnqFG5yc3MrLN5XzsPDg+zs7PMuSmpn58ks5n1Ttu1FbM9wk6sRERExV53CTd++fVm8eHGl4x999BG9emkn6oaUkl3Ibe9upLDEwehuocy4uIvZJYmIiJiqTgOKH330Ua6++moOHTrEJZdcAkBcXBwffvghH3/8sUsLlOq9GHeApOxCuoT58c/fX4C7mzZ6FxGRlq1O4Wb8+PF88cUXPPXUU3zyySd4e3vTr18/li9fzujRo11do1Rj3eF0AOaM7UGAl4fJ1YiIiJivzlPBx40bx7hx41xZi9RSRl4xh1PL1rYZ1L6VydWIiIg0DurDaMK2JJTNkOoc6kuQT+UB3iIiIi1RjVtugoOD2b9/PyEhIbRq1ara6cYZGRkuKU6qt+lYWbhRq42IiMgvahxuXnjhBfz9/QF48cUX66seqQWFGxERkcpqHG6mTZtW5Z/FHCV2B9tOZAIKNyIiIr9W43BTm8X5AgIC6lSM1NyexGwKSxwEenvQKcTP7HJEREQajRqHm6CgoBov62+32+tckNRMeZfUwHZBWK3abkFERKRcjcPNihUrnH8+evQoDz74IDfddBPDhw8HID4+nnfffZf58+e7vkqpZKPG24iIiFSpxuHm14vzzZs3jwULFjB58mTnsQkTJtC3b1/eeOMNjclpAFsTMgEY2E7hRkRE5NfqtM5NfHw8gwcPrnR88ODBbNiw4byLkupl5BVzMrMAgL5tA02uRkREpHGpU7iJjo7mzTffrHT8//7v/4iOjj7voqR6u05lAdC+tQ/+2nJBRESkgjptv/DCCy9wzTXX8N133xETEwPAhg0bOHDgAJ9++qlLC5TKdp0qm7nWO0qz0kRERP5XnVpurrzySg4cOMCECRPIyMggIyOD8ePHs3//fq688kpX1yj/45dwoy4pERGR/1XnjTPbtm3L3/72t2rPufvuu5k3bx4hISF1/TZShfJuqV5quREREamkXjfOfP/992u1+J+cW15RKUfSynYCV7eUiIhIZfUabgzDqM+3b5H2JmVjGBDqbyPM38vsckRERBqdeg034nrl4236qNVGRESkSgo3TcyukxpMLCIiUh2FmyZmV2LZYGKNtxEREamawk0TUmJ3sD8pF1DLjYiIyNnUa7j5wx/+QECAWhhcZV9SDsV2B/5e7kQHe5tdjoiISKNU53VuMjMz2bBhAykpKTgcjgqvTZ06FYDXXnvt/KqTCrYezwSgf9sgLBaLucWIiIg0UnUKN19//TVTpkwhNzeXgICACh+0FovFGW7EtcrDzYDoIFPrEBERaczq1C31wAMPcMstt5Cbm0tmZianT592fmVkZLi6RjlD4UZEROTc6hRuTp48yb333ouPj4+r65GzyC4s4VBq2WDiAe2CzC1GRESkEatTuBkzZgwbN250dS1Sje3HszAMaNvKmxA/m9nliIiINFp1GnMzbtw4/vznP7N792769u2Lh4dHhdcnTJjgkuLkF1uPnwbUJSUiInIudQo3t99+OwDz5s2r9JrFYsFut59fVVKJxtuIiIjUTJ3Czf9O/Zb6ZRiGM9xcoPE2IiIi1dIKxU3AidMFpOUW4261aGViERGRc6hxy81LL73EHXfcgZeXFy+99FK15957773nXZj8YtuJTAB6Rgbg5eFmbjEiIiKNXI3DzQsvvMCUKVPw8vLihRdeOOt5FotF4cbF9ibmANCnjbayEBEROZcah5sjR45U+WepfwdSysJN1zB/kysRERFp/DTmpgk4kFK2eF+XMD+TKxEREWn86rxx5okTJ/jqq69ISEiguLi4wmsLFiw478KkTHGpg2Pp+QB0DVe4EREROZc6hZu4uDgmTJhAp06d2Lt3L3369OHo0aMYhsHAgQNdXWOLdjQ9D7vDwM/mTkSAl9nliIiINHp16paaM2cOs2bNYseOHXh5efHpp59y/PhxRo8ezXXXXefqGlu0g2e6pDqH+VXYfV1ERESqVqdws2fPHqZOnQqAu7s7BQUF+Pn5MW/ePJ555hmXFtjSHUguCzddNd5GRESkRuoUbnx9fZ3jbCIjIzl06JDztbS0NNdUJsAvM6U0mFhERKRm6jTmZtiwYaxevZqePXty5ZVX8sADD7Bjxw4+++wzhg0b5uoaW7Tybim13IiIiNRMncLNggULyM0t+9B9/PHHyc3NZfHixXTt2lUzpVzI7jA4nJYHqOVGRESkpmodbux2OydOnKBfv35AWRfVwoULXV6YwPGMfIpLHdjcrbRt5WN2OSIiIk1CrcfcuLm5cfnll3P69On6qEd+pXzxvs6hfrhZNVNKRESkJuo0oLhPnz4cPnzY1bXI/ziolYlFRERqrU7h5sknn2TWrFl88803JCYmkp2dXeFLXOOXPaUUbkRERGqqTgOKr7zySgAmTJhQYWE5wzCwWCzY7XbXVNfCncgoAKB9iK/JlYiIiDQddQo3b7/9NtHR0bi5uVU47nA4SEhIcElhAiczy8JNmyBtuyAiIlJTdeqWuuWWW+jTpw+jR4+u8NWvXz9uueWWWr/fK6+8QocOHfDy8iImJoYNGzbU6LqPPvoIi8XCxIkTa/09Gzu7wyA5uxCAqCBvk6sRERFpOuoUbsq7n/5Xbm4uXl61a2VYvHgxM2fO5LHHHmPz5s3079+fMWPGkJKSUu11R48eZdasWVx00UW1+n5NRWpOEaUOAzerhTB/tdyIiIjUVK26pWbOnAmAxWLh0Ucfxcfnl7VX7HY769evZ8CAAbUqYMGCBdx+++3cfPPNACxcuJAlS5awaNEiHnzwwSqvsdvtTJkyhccff5yffvqJzMzMs75/UVERRUVFzr83lQHPp7LKuqQiArw0DVxERKQWahVutmzZApS13OzYsQNPT0/na56envTv359Zs2bV+P2Ki4vZtGkTc+bMcR6zWq3ExsYSHx9/1uvmzZtHWFgYt956Kz/99FO132P+/Pk8/vjjNa6psTh1ZrxNZKBabURERGqjVuFmxYoVANx888384x//ICAg4Ly+eVpaGna7nfDw8ArHw8PD2bt3b5XXrF69mrfeeoutW7fW6HvMmTPH2eIEZS030dHRda65oZSHG423ERERqZ06z5YyQ05ODjfeeCNvvvkmISEhNbrGZrNhs9nquTLXO5VZNpg4UjOlREREaqVO4cZVQkJCcHNzIzk5ucLx5ORkIiIiKp1/6NAhjh49yvjx453HHA4HAO7u7uzbt4/OnTvXb9EN5JRzGrhabkRERGqjTrOlXMXT05NBgwYRFxfnPOZwOIiLi2P48OGVzu/Rowc7duxg69atzq8JEyZw8cUXs3Xr1ibR3VRTiVlnWm4CFW5ERERqw9SWGyibgTVt2jQGDx7M0KFDefHFF8nLy3POnpo6dSpt2rRh/vz5eHl50adPnwrXBwUFAVQ63tT9MuZG3VIiIiK1YXq4mTRpEqmpqcydO5ekpCQGDBjA0qVLnYOMExISsFpNbWBqcIUldtLzigF1S4mIiNSWxTAMw+wiGlJ2djaBgYFkZWWd92yv+nIkLY+L/74Sbw83ds8bU+WCiSIiIi1JbT6/W1aTSBOR+KsuKQUbERGR2lG4aYROao0bERGROlO4aYTKZ0pFaaaUiIhIrSncNELOrRc0U0pERKTWFG4aoVPlLTfqlhIREak1hZtGSKsTi4iI1J3CTSNjGIZ2BBcRETkPCjeNTHZBKfnFdkDdUiIiInWhcNPIlE8DD/b1xMvDzeRqREREmh6Fm0YmMUt7SomIiJwPhZtG5pfxNuqSEhERqQuFm0amfBq4ZkqJiIjUjcJNI6OZUiIiIudH4aaRSczUAn4iIiLnQ+GmkdGmmSIiIudH4aYRsTsMkrPLW27ULSUiIlIXCjeNSGpOEaUOAzerhTB/hRsREZG6ULhpRMq7pCICvHCzWkyuRkREpGlSuGlEtICfiIjI+VO4aUS0gJ+IiMj5U7hpRE5pGriIiMh5U7hpRMpbbtqoW0pERKTOFG4akcQzWy+oW0pERKTuFG4akVNawE9EROS8Kdw0EoUldtLzigHNlhIRETkfCjeNRHmXlI+nG4HeHiZXIyIi0nQp3DQS5WvcRAR6YbFoAT8REZG6UrhpJFJzigAI17YLIiIi50XhppFIyS4LN2EBNpMrERERadoUbhqJlJyyMTdh/go3IiIi50PhppFIOdMtpd3ARUREzo/CTSORnH2m5UbdUiIiIudF4aaRKG+5CVW3lIiIyHlRuGkkUs8MKA4PULeUiIjI+VC4aQQKiu3kFJUCGlAsIiJyvhRuGoHymVLeHm742dxNrkZERKRpU7hpBJwzpQJsWp1YRETkPCncNALOmVLqkhIRETlvCjeNgHN1Yq1xIyIict4UbhoBTQMXERFxHYWbRqB8QLGmgYuIiJw/hZtGINW59YJabkRERM6Xwk0joB3BRUREXEfhphFIdu4Irm4pERGR86VwY7KiUjuZ+SWAuqVERERcQeHGZOXjbTzdrAT5eJhcjYiISNOncGOyX08D1+rEIiIi50/hxmQaTCwiIuJaCjcmS83R1gsiIiKupHBjsqRsLeAnIiLiSgo3JjuVWRZuooK8Ta5ERESkeVC4MdmpzAJA4UZERMRVFG5MdiqrLNy0CVK3lIiIiCso3JjI7jBIylK3lIiIiCsp3JgoLbeIEruBm9WirRdERERcROHGRCfPjLeJCPDCzaoF/ERERFxB4cZEvwwmVquNiIiIqyjcmEgzpURERFxP4cZEWuNGRETE9RRuTHRSLTciIiIup3BjovJuKa1xIyIi4joKNyZK1Bo3IiIiLqdwY5KCYjsZecWAwo2IiIgrNYpw88orr9ChQwe8vLyIiYlhw4YNZz33zTff5KKLLqJVq1a0atWK2NjYas9vrMq3XfCzuRPg5WFyNSIiIs2H6eFm8eLFzJw5k8cee4zNmzfTv39/xowZQ0pKSpXnr1y5ksmTJ7NixQri4+OJjo7m8ssv5+TJkw1c+fnRGjciIiL1w2IYhmFmATExMQwZMoSXX34ZAIfDQXR0NPfccw8PPvjgOa+32+20atWKl19+malTp1Z6vaioiKKiIuffs7OziY6OJisri4CAANfdSC0t/jmB2Z/u4DfdQ3nn5qGm1SEiItIUZGdnExgYWKPPb1NbboqLi9m0aROxsbHOY1arldjYWOLj42v0Hvn5+ZSUlBAcHFzl6/PnzycwMND5FR0d7ZLaz9dJrXEjIiJSL0wNN2lpadjtdsLDwyscDw8PJykpqUbvMXv2bKKioioEpF+bM2cOWVlZzq/jx4+fd92u8Ms0cIUbERERV3I3u4Dz8fTTT/PRRx+xcuVKvLyqHrtis9mw2WwNXNm5acyNiIhI/TA13ISEhODm5kZycnKF48nJyURERFR77d///neefvppli9fTr9+/eqzzHrhDDeBarkRERFxJVO7pTw9PRk0aBBxcXHOYw6Hg7i4OIYPH37W65599lmeeOIJli5dyuDBgxuiVJcyDINTWsBPRESkXpjeLTVz5kymTZvG4MGDGTp0KC+++CJ5eXncfPPNAEydOpU2bdowf/58AJ555hnmzp3LBx98QIcOHZxjc/z8/PDz8zPtPmojPa+Y4lIHFgtEBKpbSkRExJVMDzeTJk0iNTWVuXPnkpSUxIABA1i6dKlzkHFCQgJW6y8NTK+99hrFxcVce+21Fd7nscce469//WtDll5n5V1S4f5eeLiZvtSQiIhIs2J6uAGYMWMGM2bMqPK1lStXVvj70aNH67+gelYebiI1mFhERMTl1GxgAq1xIyIiUn8UbkygNW5ERETqj8KNCX6ZBq5uKREREVdTuDHBLwv4qeVGRETE1RRuTKAxNyIiIvVH4aaBFZXaScst26VcY25ERERcT+GmgSWdWZnY28ONIB8Pk6sRERFpfhRuGtjJX22YabFYTK5GRESk+VG4aWCnNN5GRESkXincNDDtBi4iIlK/FG4amKaBi4iI1C+Fmwb26zE3IiIi4noKNw1MWy+IiIjUL4WbBmQYhgYUi4iI1DOFmwaUmlNEQYkdqwUi1S0lIiJSLxRuGtDBlFwA2rf2xebuZnI1IiIizZPCTQM6cCbcdA71M7kSERGR5kvhpgGVt9x0CVO4ERERqS8KNw1I4UZERKT+Kdw0oIOpCjciIiL1TeGmgWTll5CaUwQo3IiIiNQnhZsGcjA1B4DIQC/8bO4mVyMiItJ8Kdw0EI23ERERaRgKNw3koKaBi4iINAiFmwailhsREZGGoXDTQDRTSkREpGEo3DSAgmI7J06X7QaucCMiIlK/FG4awKHUXAwDWvl40NrX0+xyREREmjWFmwaw+1Q2AN3C/bFYLCZXIyIi0rwp3DSATcdOAzCwfSuTKxEREWn+FG4awOaEM+GmncKNiIhIfVO4qWdZBSUcODMN/IJ2QeYWIyIi0gIo3NSzLWdabTq09iHEz2ZyNSIiIs2fwk0925yQCahLSkREpKEo3NSzzRpMLCIi0qAUbuqR3WGw9XgmoJYbERGRhqJwU48OpOSQW1SKr6cb3SP8zS5HRESkRVC4qUfl69sMaBeEm1WL94mIiDQEhZt6tPZgOgCD2gebXImIiEjLoXBTT4pLHfy4PxWAi7uHmlyNiIhIy6FwU09+PppBTlEpIX6e9G8bZHY5IiIiLYbCTT1ZvicZgIu7h2HVeBsREZEGo3BTDwzDIG5PCgCX9gw3uRoREZGWReGmHhxKzSUhIx9PNysXdQ0xuxwREZEWReGmHpS32gzr3Bpfm7vJ1YiIiLQsCjf1YNnusvE2sT3DTK5ERESk5VG4cbGE9Hw2HjuNxQKX9dJ4GxERkYamcONin24+AcCFXUKIDPQ2uRoREZGWR+HGhRwOwxlurh3U1uRqREREWiaFGxfacDSDE6cL8Le5c3mvCLPLERERaZEUblzok01lrTbj+kXi7elmcjUiIiItk8KNi+QXl/LdjkRAXVIiIiJm0iIsLpKQkU+Iv41QYFD7VmaXIyIi0mIp3LhIj4gAVs76DSk5RVgs2ktKRETELOqWciGLxUJ4gJfZZYiIiLRoCjciIiLSrCjciIiISLOicCMiIiLNisKNiIiINCsKNyIiItKsKNyIiIhIs6JwIyIiIs2Kwo2IiIg0K40i3Lzyyit06NABLy8vYmJi2LBhQ7Xnf/zxx/To0QMvLy/69u3Lt99+20CVioiISGNnerhZvHgxM2fO5LHHHmPz5s3079+fMWPGkJKSUuX5a9euZfLkydx6661s2bKFiRMnMnHiRHbu3NnAlYuIiEhjZDEMwzCzgJiYGIYMGcLLL78MgMPhIDo6mnvuuYcHH3yw0vmTJk0iLy+Pb775xnls2LBhDBgwgIULF1Y6v6ioiKKiIuffs7OziY6OJisri4CAgHq4IxEREXG17OxsAgMDa/T5bWrLTXFxMZs2bSI2NtZ5zGq1EhsbS3x8fJXXxMfHVzgfYMyYMWc9f/78+QQGBjq/oqOjXXcDIiIi0uiYGm7S0tKw2+2Eh4dXOB4eHk5SUlKV1yQlJdXq/Dlz5pCVleX8On78uGuKFxERkUbJ3ewC6pvNZsNmszn/Xt4Ll52dbVZJIiIiUkvln9s1GU1jargJCQnBzc2N5OTkCseTk5OJiIio8pqIiIhanf+/cnJyANQ9JSIi0gTl5OQQGBhY7TmmhhtPT08GDRpEXFwcEydOBMoGFMfFxTFjxowqrxk+fDhxcXH86U9/ch5btmwZw4cPr9H3jIqK4vjx4/j7+2OxWM73FiooH6x8/PjxZjtYubnfY3O/P9A9NgfN/f6g+d9jc78/cP09GoZBTk4OUVFR5zzX9G6pmTNnMm3aNAYPHszQoUN58cUXycvL4+abbwZg6tSptGnThvnz5wNw3333MXr0aJ5//nnGjRvHRx99xMaNG3njjTdq9P2sVitt27att/sBCAgIaLb/sZZr7vfY3O8PdI/NQXO/P2j+99jc7w9ce4/narEpZ3q4mTRpEqmpqcydO5ekpCQGDBjA0qVLnYOGExISsFp/Gfc8YsQIPvjgAx555BEeeughunbtyhdffEGfPn3MugURERFpREwPNwAzZsw4azfUypUrKx277rrruO666+q5KhEREWmKTF+huDmx2Ww89thjFWZnNTfN/R6b+/2B7rE5aO73B83/Hpv7/YG592j6CsUiIiIirqSWGxEREWlWFG5ERESkWVG4ERERkWZF4UZERESaFYUbF3nllVfo0KEDXl5exMTEsGHDBrNLqrP58+czZMgQ/P39CQsLY+LEiezbt6/COb/5zW+wWCwVvu68806TKq69v/71r5Xq79Gjh/P1wsJCpk+fTuvWrfHz8+Oaa66ptO1HY9ahQ4dK92exWJg+fTrQNJ/fjz/+yPjx44mKisJisfDFF19UeN0wDObOnUtkZCTe3t7ExsZy4MCBCudkZGQwZcoUAgICCAoK4tZbbyU3N7cB76J61d1jSUkJs2fPpm/fvvj6+hIVFcXUqVM5depUhfeo6tk//fTTDXwnVTvXM7zpppsq1X7FFVdUOKcpP0Ogyn+XFouF5557znlOY36GNfl8qMnvz4SEBMaNG4ePjw9hYWH8+c9/prS01GV1Kty4wOLFi5k5cyaPPfYYmzdvpn///owZM4aUlBSzS6uTVatWMX36dNatW8eyZcsoKSnh8ssvJy8vr8J5t99+O4mJic6vZ5991qSK66Z3794V6l+9erXztfvvv5+vv/6ajz/+mFWrVnHq1CmuvvpqE6utnZ9//rnCvS1btgygwvpQTe355eXl0b9/f1555ZUqX3/22Wd56aWXWLhwIevXr8fX15cxY8ZQWFjoPGfKlCns2rWLZcuW8c033/Djjz9yxx13NNQtnFN195ifn8/mzZt59NFH2bx5M5999hn79u1jwoQJlc6dN29ehWd7zz33NET553SuZwhwxRVXVKj9ww8/rPB6U36GQIV7S0xMZNGiRVgsFq655poK5zXWZ1iTz4dz/f602+2MGzeO4uJi1q5dy7vvvss777zD3LlzXVeoIedt6NChxvTp051/t9vtRlRUlDF//nwTq3KdlJQUAzBWrVrlPDZ69GjjvvvuM6+o8/TYY48Z/fv3r/K1zMxMw8PDw/j444+dx/bs2WMARnx8fANV6Fr33Xef0blzZ8PhcBiG0fSfH2B8/vnnzr87HA4jIiLCeO6555zHMjMzDZvNZnz44YeGYRjG7t27DcD4+eefned89913hsViMU6ePNlgtdfU/95jVTZs2GAAxrFjx5zH2rdvb7zwwgv1W5wLVHV/06ZNM6666qqzXtMcn+FVV11lXHLJJRWONZVnaBiVPx9q8vvz22+/NaxWq5GUlOQ857XXXjMCAgKMoqIil9SllpvzVFxczKZNm4iNjXUes1qtxMbGEh8fb2JlrpOVlQVAcHBwheP//ve/CQkJoU+fPsyZM4f8/HwzyquzAwcOEBUVRadOnZgyZQoJCQkAbNq0iZKSkgrPtEePHrRr165JPtPi4mLef/99brnllgqbxTb15/drR44cISkpqcIzCwwMJCYmxvnM4uPjCQoKYvDgwc5zYmNjsVqtrF+/vsFrdoWsrCwsFgtBQUEVjj/99NO0bt2aCy64gOeee86lzf31beXKlYSFhdG9e3fuuusu0tPTna81t2eYnJzMkiVLuPXWWyu91lSe4f9+PtTk92d8fDx9+/Z1brMEMGbMGLKzs9m1a5dL6moU2y80ZWlpadjt9goPCSA8PJy9e/eaVJXrOBwO/vSnPzFy5MgK+3f9/ve/p3379kRFRbF9+3Zmz57Nvn37+Oyzz0ystuZiYmJ455136N69O4mJiTz++ONcdNFF7Ny5k6SkJDw9PSt9YISHh5OUlGROwefhiy++IDMzk5tuusl5rKk/v/9V/lyq+ndY/lpSUhJhYWEVXnd3dyc4OLhJPtfCwkJmz57N5MmTK2xKeO+99zJw4ECCg4NZu3Ytc+bMITExkQULFphYbc1cccUVXH311XTs2JFDhw7x0EMPMXbsWOLj43Fzc2t2z/Ddd9/F39+/Upd3U3mGVX0+1OT3Z1JSUpX/VstfcwWFG6nW9OnT2blzZ4XxKECFPu6+ffsSGRnJpZdeyqFDh+jcuXNDl1lrY8eOdf65X79+xMTE0L59e/7zn//g7e1tYmWu99ZbbzF27FiioqKcx5r682vpSkpKuP766zEMg9dee63CazNnznT+uV+/fnh6evLHP/6R+fPnN/ql/m+44Qbnn/v27Uu/fv3o3LkzK1eu5NJLLzWxsvqxaNEipkyZgpeXV4XjTeUZnu3zoTFQt9R5CgkJwc3NrdJI8OTkZCIiIkyqyjVmzJjBN998w4oVK2jbtm2158bExABw8ODBhijN5YKCgujWrRsHDx4kIiKC4uJiMjMzK5zTFJ/psWPHWL58Obfddlu15zX151f+XKr7dxgREVFpkH9paSkZGRlN6rmWB5tjx46xbNmyCq02VYmJiaG0tJSjR482TIEu1KlTJ0JCQpz/XTaXZwjw008/sW/fvnP+24TG+QzP9vlQk9+fERERVf5bLX/NFRRuzpOnpyeDBg0iLi7OeczhcBAXF8fw4cNNrKzuDMNgxowZfP755/zwww907NjxnNds3boVgMjIyHqurn7k5uZy6NAhIiMjGTRoEB4eHhWe6b59+0hISGhyz/Ttt98mLCyMcePGVXteU39+HTt2JCIiosIzy87OZv369c5nNnz4cDIzM9m0aZPznB9++AGHw+EMd41debA5cOAAy5cvp3Xr1ue8ZuvWrVit1krdOU3BiRMnSE9Pd/532RyeYbm33nqLQYMG0b9//3Oe25ie4bk+H2ry+3P48OHs2LGjQlAtD+q9evVyWaFynj766CPDZrMZ77zzjrF7927jjjvuMIKCgiqMBG9K7rrrLiMwMNBYuXKlkZiY6PzKz883DMMwDh48aMybN8/YuHGjceTIEePLL780OnXqZIwaNcrkymvugQceMFauXGkcOXLEWLNmjREbG2uEhIQYKSkphmEYxp133mm0a9fO+OGHH4yNGzcaw4cPN4YPH25y1bVjt9uNdu3aGbNnz65wvKk+v5ycHGPLli3Gli1bDMBYsGCBsWXLFudMoaefftoICgoyvvzyS2P79u3GVVddZXTs2NEoKChwvscVV1xhXHDBBcb69euN1atXG127djUmT55s1i1VUt09FhcXGxMmTDDatm1rbN26tcK/zfIZJmvXrjVeeOEFY+vWrcahQ4eM999/3wgNDTWmTp1q8p2Vqe7+cnJyjFmzZhnx8fHGkSNHjOXLlxsDBw40unbtahQWFjrfoyk/w3JZWVmGj4+P8dprr1W6vrE/w3N9PhjGuX9/lpaWGn369DEuv/xyY+vWrcbSpUuN0NBQY86cOS6rU+HGRf75z38a7dq1Mzw9PY2hQ4ca69atM7ukOgOq/Hr77bcNwzCMhIQEY9SoUUZwcLBhs9mMLl26GH/+85+NrKwscwuvhUmTJhmRkZGGp6en0aZNG2PSpEnGwYMHna8XFBQYd999t9GqVSvDx8fH+N3vfmckJiaaWHHtff/99wZg7Nu3r8Lxpvr8VqxYUeV/l9OmTTMMo2w6+KOPPmqEh4cbNpvNuPTSSyvde3p6ujF58mTDz8/PCAgIMG6++WYjJyfHhLupWnX3eOTIkbP+21yxYoVhGIaxadMmIyYmxggMDDS8vLyMnj17Gk899VSFcGCm6u4vPz/fuPzyy43Q0FDDw8PDaN++vXH77bdX+p/EpvwMy73++uuGt7e3kZmZWen6xv4Mz/X5YBg1+/159OhRY+zYsYa3t7cREhJiPPDAA0ZJSYnL6rScKVZERESkWdCYGxEREWlWFG5ERESkWVG4ERERkWZF4UZERESaFYUbERERaVYUbkRERKRZUbgRERGRZkXhRkRERJoVhRsRafFWrlyJxWKptNmfiDRNCjciIiLSrCjciIiISLOicCMipnM4HMyfP5+OHTvi7e1N//79+eSTT4BfuoyWLFlCv3798PLyYtiwYezcubPCe3z66af07t0bm81Ghw4deP755yu8XlRUxOzZs4mOjsZms9GlSxfeeuutCuds2rSJwYMH4+Pjw4gRI9i3b1/93riI1AuFGxEx3fz583nvvfdYuHAhu3bt4v777+cPf/gDq1atcp7z5z//meeff56ff/6Z0NBQxo8fT0lJCVAWSq6//npuuOEGduzYwV//+lceffRR3nnnHef1U6dO5cMPP+Sll15iz549vP766/j5+VWo4+GHH+b5559n48aNuLu7c8sttzTI/YuIa2lXcBExVVFREcHBwSxfvpzhw4c7j992223k5+dzxx13cPHFF/PRRx8xadIkADIyMmjbti3vvPMO119/PVOmTCE1NZX//ve/zuv/8pe/sGTJEnbt2sX+/fvp3r07y5YtIzY2tlINK1eu5OKLL2b58uVceumlAHz77beMGzeOgoICvLy86vmnICKupJYbETHVwYMHyc/P57LLLsPPz8/59d5773Ho0CHneb8OPsHBwXTv3p09e/YAsGfPHkaOHFnhfUeOHMmBAwew2+1s3boVNzc3Ro8eXW0t/fr1c/45MjISgJSUlPO+RxFpWO5mFyAiLVtubi4AS5YsoU2bNhVes9lsFQJOXXl7e9foPA8PD+efLRYLUDYeSESaFrXciIipevXqhc1mIyEhgS5dulT4io6Odp63bt06559Pnz7N/v376dmzJwA9e/ZkzZo1Fd53zZo1dOvWDTc3N/r27YvD4agwhkdEmi+13IiIqfz9/Zk1axb3338/DoeDCy+8kKysLNasWUNAQADt27cHYN68ebRu3Zrw8HAefvhhQkJCmDhxIgAPPPAAQ4YM4YknnmDSpEnEx8fz8ssv8+qrrwLQoUMHpk2bxi233MJLL71E//79OXbsGCkpKVx//fVm3bqI1BOFGxEx3RNPPEFoaCjz58/n8OHDBAUFMXDgQB566CFnt9DTTz/Nfffdx4EDBxgwYABff/01np6eAAwcOJD//Oc/zJ07lyeeeILIyEjmzZvHTTfd5Pwer732Gg899BB333036enptGvXjoceesiM2xWReqbZUiLSqJXPZDp9+jRBQUFmlyMiTYDG3IiIiEizonAjIiIizYq6pURERKRZUcuNiIiINCsKNyIiItKsKNyIiIhIs6JwIyIiIs2Kwo2IiIg0Kwo3IiIi0qwo3IiIiEizonAjIiIizcr/A78JHCCtSf+CAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(new_df, x='epoch', y='train_dice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0e412224a0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from  PIL  import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "path_X = '/home/danielcrovo/Documents/01.Study/01.MSc/02.MSc AI/Deep Learning/Heart_Segmentation/saved_images_test/x_4.png'\n",
    "path_y = '/home/danielcrovo/Documents/01.Study/01.MSc/02.MSc AI/Deep Learning/Heart_Segmentation/saved_images_test/y_4.png'\n",
    "path_yh = '/home/danielcrovo/Documents/01.Study/01.MSc/02.MSc AI/Deep Learning/Heart_Segmentation/saved_images_test/y_hat_4.png'\n",
    "x = Image.open(path_X).convert('RGBA')\n",
    "y = Image.open(path_y).convert('RGBA')\n",
    "y_hat = Image.open(path_yh).convert('RGBA')\n",
    "\n",
    "plt.imshow(y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_mask_to_red(img):\n",
    "    img = np.array(img)   # \"data\" is a height x width x 4 numpy array\n",
    "    red, green, blue, alpha = img.T # Temporarily unpack the bands for readability\n",
    "\n",
    "    # Replace white with red... (leaves alpha values alone...)\n",
    "    white_areas = (red == 255) & (blue == 255) & (green == 255)\n",
    "    img[..., :-1][white_areas.T] = (255, 0, 0) # Transpose back needed\n",
    "    im2 = Image.fromarray(img)\n",
    "    return im2\n",
    "def convert_mask_to_green(img):\n",
    "    img = np.array(img)   # \"data\" is a height x width x 4 numpy array\n",
    "    red, green, blue, alpha = img.T # Temporarily unpack the bands for readability\n",
    "\n",
    "    # Replace white with red... (leaves alpha values alone...)\n",
    "    white_areas = (red == 255) & (blue == 255) & (green == 255)\n",
    "    img[..., :-1][white_areas.T] = (0, 255, 0) # Transpose back needed\n",
    "    im2 = Image.fromarray(img)\n",
    "    return im2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_n = convert_mask_to_green(y)\n",
    "y_hat_n = convert_mask_to_red(y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "blended = Image.blend(x, y_hat_n, 0.2)\n",
    "blended.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DL",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}